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S0045790615003006 | The druse, an abnormal yellow/white deposit on retina, is a dominant characteristic of age-related macular degeneration (AMD) which is a retinal disorder associated with age. The early detection of drusen is useful for ophthalmologists to diagnose the patients that suffer from AMD. An automated method has been proposed in this work to detect and segment drusen using retinal fundus images by gradient based segmentation to find true edges of drusen, connected component labeling to remove suspicious pixels from drusen region and edge linking to connect all labeled pixels into a meaningful boundary. The proposed method outperforms other existing methods in detection of drusen with an accuracy/sensitivity/specificity of 96.17/89.81/99.00 on two publicly available retinal image databases. In order to grade the severity of AMD, the detected drusen by the proposed method are further quantified into small, intermediate and large with an accuracy of 88.46, 98.55, and 88.37%, respectively. | Automated detection and segmentation of drusen in retinal fundus images |
S0045790615003018 | A honeypot system can be deployed to decoy and record malicious intrusions over the Internet. However, events logged by a honeypot can rapidly accumulate an enormous amount of data, which an administrator will be unable to handle. The proposed system combines episode mining and pruning, and allows an administrator to identify suspected intrusions, and thus focus his energy on addressing them, instead of reading enormous amounts of raw data. An attack episode is composed of a series of events, and represents an Internet intrusion as a series of relevant events occurring to a victim host in a specific sequence. Due to the variety of internet attacks, this paper focuses on discovering attack episodes for the Server Message Block (SMB) protocol, which provides Microsoft Windows Network services. Experiments show that the proposed approach can locate suspicious episodes that are very likely novel attacks, from an immense amount of logged data. | Applying episode mining and pruning to identify malicious online attacks |
S0045790615003031 | Finding new techniques to accelerate electromagnetic (EM) simulations has become a necessity nowadays due to its frequent usage in industry. As they are mainly based on domain discretization, EM simulations require solving enormous systems of linear equations simultaneously. Available software-based solutions do not scale well with the increasing number of equations to be solved. As a result, hardware accelerators have been utilized to speed up the process. We introduce using hardware emulation as an efficient solution for EM simulation core solvers. Two different scalable architectures are implemented to accelerate the solver part of an EM simulator based on the Gaussian Elimination and the Jacobi iterative methods. Results show that the performance gap between presented solutions and software-based ones increases as the number of equations increases. For example, solving 2,002,000 equations using our Clustered Jacobi design in single floating-point precision achieved a speed-up of 100.88x and 35.24x over pure software implementations represented by MATLAB and the ALGLIB C++ package, respectively. | On kernel acceleration of electromagnetic solvers via hardware emulation |
S0045790615003043 | This paper introduces a new scheme for encrypting images with a few details based on wavelet fusion. In this scheme, the image with a few details to be encrypted is fused with another image that is rich in details utilizing the Discrete Wavelet Transform (DWT) prior to encryption. The fusion is a pre-processing step to change the homogeneity of flat areas in the images having a few details. RC6 or chaotic Baker map encryption are then performed on the fused image. Encryption with chaotic Baker map is just a permutation algorithm that cannot perform well on flat areas of the images, because the permutation yields approximately the same intensities. So, circular shifts on pixels are performed on the fused image prior to chaotic encryption to remove flat areas or reduce the degree of homogeneity. Chaotic encryption is then performed in the wavelet domain to increase the degree of diffusion. Several metrics are used in this paper for performance evaluation of the suggested ciphering schemes like visual inspection, histogram test, encryption quality analysis, and diffusion analysis. The robustness of the suggested image ciphering schemes is tested in the presence of noise before decryption. Simulation results demonstrated that the suggested image ciphering schemes provide a secure and effective way for encrypting images with few details. | Wavelet fusion for encrypting images with a few details |
S0045790615003055 | Signals associated with eye blinks (230–350 micro-volts) are orders of magnitude larger than electric potentials (7–20 micro-volts) generated on the scalp because of cortical activity. These and other such non-cortical biological artifacts spread across the scalp and contaminate the Electroencephalogram (EEG). We present here a novel approach for efficient detection and effective suppression of these artifacts using single channel EEG data by combining Ensemble Empirical Mode Decomposition (EEMD) along with Principal Component Analysis (PCA). We present a methodology for ocular artifact suppression, by performing EEMD on the contaminated EEG data segment to get the intrinsic mode functions (IMFs) and subsequent elimination of artifacts by automatic selection of particular principal components, which capture ocular artifact features after using PCA on IMFs. | Ocular artifact suppression from EEG using ensemble empirical mode decomposition with principal component analysis |
S0045790615003079 | The paper introduces a new automated seizure detection model that integrates Weighted Permutation Entropy (WPE) and a Support Vector Machine (SVM) classifier model to enhance the sensitivity and precision of the detection process. The proposed system utilizes the fact that entropy based measures for the EEG segments during epileptic seizure are lower than in normal EEG. The new suggested model better tracks abrupt changes in the signal and assigns less complexity to segments that exhibit regularity or are subjected to noise effects. The Weighted Permutation Entropy algorithm relies on the ordinal pattern of the time series along with the amplitudes of its sample points. The proposed technique is implemented and tested on hundreds real EEG signals and the performance is compared based on sensitivity, specificity and accuracy. Various experiments have been applied in different scenarios including healthy with eyes open, healthy with eyes closed, epileptic patients during no-seizure state from two different location of the brain. Other scenarios have been applied accompanied by background simulated noise resulting from physiological and environmental artifacts. Results showed outstanding performance and revealed promising results in terms of discrimination of seizure and seizure-free segments. It also manifests high robustness against noise sources. | A hybrid automated detection of epileptic seizures in EEG records |
S0045790615003080 | A carbonating tank is continuously operated and filled with water supplied by an immersed flow injecting nozzle. At the same time, CO2 gas is introduced from the gas tank. CO2 gas can be dissolved in water and can form carbonated water with minimum production of gas bubbles depending on various process conditions. Therefore, the flow mixing phenomenon of CO2 and water can be affected by the placement of the injecting nozzle and the flow rate in consideration of the interfacial surface. In this study, the gas–liquid flow mixing with the mass transport inside the carbonation process tank is numerically predicted. The Euler–Euler methodology is used to observe the effects of the design of the injecting nozzle, the gas bubble size, as well as the velocities of liquid and gas on the gas volume fraction, liquid velocity, gas concentration, interfacial area, and mass transfer coefficient. | Numerical investigation on the flow mixing feature inside a continuously carbonating process tank |
S0045790615003110 | We propose a novel technique for automatic classification of modulation formats/bit-rates of digitally modulated signals as well as non-data-aided (NDA) estimation of signal-to-noise ratio (SNR) in wireless networks. The proposed technique exploits modulation format, bit-rate, and SNR sensitive features of asynchronous delay-tap plots (ADTPs) for the joint estimation of these parameters. Simulation results validate successful classification of three commonly-used modulation formats at two different bit-rates with an overall accuracy of 99.12%. Similarly, in-service estimation of SNR in the range of 0−30 dB is demonstrated with mean estimation error of 0.88 dB. The proposed technique requires low-speed asynchronous sampling of signal envelope and hence, it can enable simple and cost-effective joint modulation format/bit-rate classification and NDA SNR estimation in future wireless networks. | Automatic modulation format/bit-rate classification and signal-to-noise ratio estimation using asynchronous delay-tap sampling |
S0045790615003122 | Designing a lightweight, secure communication protocol for Wireless Sensor Networks (WSNs) remains a challenging issue since sensor networks are resource limited and are left unattended. Sensor nodes in WSNs are subjected to varying forms of attacks. An adversary may destroy or damage the communications done in multi-hop WSNs by means of packet dropping and modification. Hence it is essential to have an efficient cryptographic scheme to protect the communications done in WSNs. This study introduces a Reliable Anonymous Secure Packet forwarding (RASP) scheme that can prevent not only traffic analysis attack but also the attacks done through compromised forwarding nodes. The mechanisms followed here are effective with low computation and communication overhead. The performance of the proposed scheme is evaluated over NS2 with a series of simulation. The simulated results show that the proposed scheme performs better than other comparable schemes. | Reliable anonymous secure packet forwarding scheme for wireless sensor networks |
S0045790615003134 | Nonlocal means (NLM)-based denoising is an efficient and simple method for image sequence denoising which has been applied successfully in many image sequence denoising applications. We extend the method for image sequence denoising using Zernike moments (ZMs) called NLM–ZMs which provides better denoising performance as compared to NLM-based approach. In addition, the proposed method is faster because the number of computations needed for block matching and weight computation are significantly reduced. The NLM approach uses the photometric distance between two image patches for determining the similarity distance. In the proposed approach, low order ZMs are used for the block matching process, resulting in better denoising performance at a much lower computation cost. Detailed experimental results are provided to demonstrate better performance and higher speed of the proposed approach as compared to the NLM approach. The results are also compared with the state-of-the-art NLM-based image sequence denoising methods and the denoising results are observed to be competitive with higher speed. | An efficient approach for image sequence denoising using Zernike moments-based nonlocal means approach |
S0045790615003146 | Vehicular Cyber-Physical Systems (VCPS) are the most popular systems of the modern era due to their abilities to disseminate the safety related information to the moving vehicles on time. For efficient data dissemination, vehicles form a cluster with other vehicles in VCPS environment. But, due to high velocity and constant topological changes, cluster maintenance is one of the most difficult tasks to be performed in this environment. To address this issue, in this paper, we propose a novel Learning Automata (LA)-based hybrid clustering scheme for vehicles in VCPS environment. We have improved our existing solution Energy Efficient Predictive Clustering (EEPC) approach, by incorporating the future mobility prediction computed by LA stationed on the vehicles. For this purpose, a Predictive Clustering Algorithm using Learning Automata (PCALA) is proposed. Extensive simulations are performed to evaluate the performance of the proposed scheme with respect to various metrics. Results obtained confirm the effectiveness of the proposed scheme in comparison to the existing EEPC scheme. | Learning Automata-assisted Predictive Clustering approach for Vehicular Cyber-Physical System |
S0045790615003158 | In ubiquitous high-performance computing (UHPC), performing concurrent services is an important task of middleware. Because a major development effort is not easily achieved, isolating a virtual machine (VM) may be a helpful solution but will likely suffer from additional overhead costs. This challenge can also be resolved by reusing the device driver. However, these solutions are difficult to implement without the VM technique. In this study, we present an alternative approach to minimizing overhead and develop a middleware called userspace virtualized middleware (Uware). Instead of bypassing the VM, the proposed approach depends on userspace transparency and contention management to shift the VM concept into middleware. We introduce two strategies to enhance the system adaptively: comprehensive restructuring by simplifying the VM memory mechanism and implementing zero-copy buffers to reuse devices. The result demonstrates that Uware is feasible and could be applied in a broad variety of UHPC. | A virtualization approach to develop middleware for ubiquitous high performance computing |
S0045790615003171 | This paper proposes an accelerating correlation-based image alignment using CPUs for time-critical applications in automatic optical inspection (AOI). In order to improve computation efficiency, the image pyramid search scheme is combined with the parallel computation. The image pyramid search scheme is employed first to quickly find certain objects in both monochrome and color images with rotation, translation and scaling. Sub-pixel accuracy is then used to attain the more accurate results at the sub-pixel level. In our experimental results, rotation accuracy is smaller than 0.218°, and the speed is increased between 277 and 20,841 times. According to translation, rotation and scaling tests, the errors of rotation, translation and scaling are 0.2°, 2.07pixel and 0.55%, respectively. These results show that the proposed method is suitable for dealing with correlation-based image alignment for time-critical applications in automatic optical inspection. | An accelerating CPU based correlation-based image alignment for real-time automatic optical inspection |
S0045790615003183 | Improving image resolution by refining hardware is usually expensive and/or time consuming. A critical challenge is to optimally balance the trade-off among image resolution, Signal-to-Noise Ratio (SNR), and acquisition time. Super-resolution (SR), an off-line approach for improving image resolution, is free from these trade-offs. Numerous methodologies such as interpolation, frequency domain, regularization, and learning-based approaches have been developed for SR of natural images. In this paper we provide a survey of the existing SR techniques. Various approaches for obtaining a high resolution image from a single and/or multiple low resolution images are discussed. We also compare the performance of various SR methods in terms of Peak SNR (PSNR) and Structural Similarity (SSIM) index between the super-resolved image and the ground truth image. For each method, the computational time is also reported. | A performance comparison among different super-resolution techniques |
S0045790615003195 | Enhancing the throughput of cognitive radio network in the presence of malicious cognitive radio user (MCRU) is addressed in this paper. We have considered the impact of MCRU on the sensing performance and achievable throughput of secondary network during two phases, namely, sensing phase (SP) and secondary user transmission phase (SUTP), respectively. This impact is mitigated by employing cognitive radio (CR) users equipped with multiple receiving antennas. We have performed diversity combining and have formulated the optimization problems with solutions during SP and SUTP. Moreover, the method of estimating the channel between MCRU and CR receiver is integrated into the solution. Simulation results show that this method achieves 89% detection and 3.5 bits/s/Hz of achievable throughput, while maximal ratio combining technique offers only 22% and 0.4 bits/s/Hz, respectively, when MCRU is active with ten times stronger power than the primary user. | Enhancing the throughput of cognitive radio networks through malevolent presence |
S0045790615003201 | Interactive image segmentation aims to extract user-specified regions from the background. In this paper, an efficient two-stage region merging based method is proposed for interactive image segmentation. An image is first over-segmented into many super-pixels using a bottom-up method. The color histogram is exploited to represent each super-pixel, and the Bhattacharyya coefficient is computed to measure the similarity of two adjacent super-pixels. Then some strokes, denoting the desired object and background, are manually labeled by the user on the over-segmented image. With the labeled seed super-pixels, a merging strategy is designed to realize adaptive region merging. The whole merging process is divided into two stages, which are repeatedly executed until no new merging occurs. In the first stage, some unlabelled super-pixels are merged into the labeled foreground or background super-pixels if the labeled ones are their nearest neighbors. In the second stage, any two unlabelled super-pixels are merged together if one super-pixel is the nearest neighbor of the other. Extensive experiments are conducted to evaluate the performance of the proposed method. The results show that the proposed method can extract the object reliably and quickly from the background. | An efficient two-stage region merging method for interactive image segmentation |
S0045790615003213 | The wireless sensor networks deployed in hostile environments suffer from a high rate of node failures. Such failures may convert a fully-connected sensor network into multiple disjoint sub-networks, leading to the network partition problem. The placement of relay nodes is the only way to restore the lost connectivity because these devices, compared to the sensor nodes, have a higher energy backup, with a longer communication range. In this paper, a new solution is proposed to heal the network partition problem in the wireless sensor network. The solution is based on a zero gradient point inside the convex hull polygon. The proposed solution is compared with various naive approaches, along with existing state-of-the-art solutions, that is, the Spider Web-1C heuristic and Steiner-minimum-tree based optimal relay node placement algorithm. The simulation experiment results confirm the effectiveness of our proposed approach. | Relay node placement to heal partitioned wireless sensor networks |
S0045790615003225 | Cellular Automata (CA) are of interest in several research areas and there are many available serial implementations of CA. However, there are relatively few studies analyzing in detail High Performance Computing (HPC) implementations of CA which allow research on large systems. Here, we present a parallel implementation of a CA with distributed memory based on MPI. As a first step to insure fast performance, we study several possible serial implementations of the CA. The simulations are performed in three infrastructures, comparing two different microarchitectures. The parallel code is tested with both Strong and Weak scaling, and we obtain parallel efficiencies of ∼ 75%–85%, for 64 cores, comparable to efficiencies for other mature parallel codes in similar architectures. We report communication time and multiple hardware counters, which reveal that performance losses are related to cache references with misses, branches and memory access. | Performance analysis of Cellular Automata HPC implementations |
S0045790615003237 | This paper presents a method to predict the milling cutting force and cutting coefficient for aluminum 6060-T6 which is a general commercial alloy with 170–190 MPa of tensile strength, and is the most commonly used for anodizing and providing extra protection if needed. We introduce two cutting force prediction methods—Altintas and recursive least square (RLS)—and compare their results with experimental values. The influence of the feed per tooth and the tool diameter on the cutting force and cutting coefficient was investigated. After accurately determining the cutting coefficients, the cutting parameters, including the friction angle and shear stress, were estimated using the oblique cutting theory. The forces simulated by the RLS method are in good agreement with the experimentally determined forces. An increase in the feed per tooth is shown to increase the cutting force and reduce the cutting coefficient for shearing forces in the tangential direction. The shear stress in the model is close to the actual shear strength of the material. | Investigation of milling cutting forces and cutting coefficient for aluminum 6060-T6 |
S0045790615003389 | A nonlinear pull-in behavior analysis of a cantilever nano-actuator was carried out and an Euler–Bernoulli beam model was used in the examination of the fringing field and the surface and Casimir force effects in this study. In general, the analysis of an electrostatic device is difficult and usually complicated by nonlinear electrostatic forces and the Casimir force at the nanoscale. The nonlinear governing equation of a cantilever nano-beam can be solved using a hybrid computational scheme comprising differential transformation and finite difference to overcome the nonlinear electrostatic coupling phenomenon. The feasibility of the method presented here, as applied to the nonlinear electrostatic behavior of a cantilever nano-actuator, was analyzed. The numerical results for the pull-in voltage were found to be in good agreement with previously published results. The analysis showed that the surface effects had significant influence on the dynamic characteristics of the cantilever nano-actuator. | Surface effect on dynamic characteristics of the electrostatically nano-beam actuator |
S0045790615003420 | With lot of hype surrounding policy-based computing, XACML (eXtensible Access Control Markup Language) has become the widely used de facto standard for managing access to open and distributed service-based environments like Web services. However, like any other policy language, XACML has complex syntax, which makes the policies specification process both time consuming and error prone, especially with large size policies that govern complex systems. Moreover, with the diversity of rules and conditions, hidden conflicts, redundancies and access flaws are more likely to arise, which expose Web services to security breaches at runtime. This paper proposes a UML profile that allows systematic model-driven specification of XACML policies to resolve the complexity of policies designation. Based on mathematical sets that explore the rules meanings, the paper provides also a design-level analysis to detect anomalies in the specified policies, prior to their enforcement in the system. A real life case study demonstrates the feasibility and efficiency of the proposition. | From model-driven specification to design-level set-based analysis of XACML policies |
S0045790615003444 | This paper presents a novel methodology for improving efficiency and power consumption of networks-on-chip (NoCs). The proposed approach applies queue length considerations of a modified version of RED algorithm. Moreover, a stochastic learning-automata-based algorithm has been used to optimize the threshold values required in RED algorithm. Furthermore, a new architecture has been provided for dynamic flow control of virtual channels. The proposed method contributes to reduction in queue blockages and power consumption in addition to determining an appropriate size for virtual channels. The proposed algorithm was evaluated under various synthetic traffic patterns for different injection rates and trace-driven SPLASH-2 benchmark suite. The experimental results demonstrate that the algorithm reduces latency and power consumption by 23% and 52%, respectively, compared to the conventional NoC. Further, compared to Express Virtual Channels (EVC) scheme, it showed 13% and 36% improvement in latency and power consumption, respectively. | A novel power efficient adaptive RED-based flow control mechanism for networks-on-chip |
S0045790615003468 | Set Partitioning In Hierarchical Tree (SPIHT) is considered one of the most important algorithms for reducing the size of the vision data collected by the sensor node within wireless multimedia sensor network (WMSN). The traditional SPIHT algorithm suffers from image coders complexity due to large memory requirement. This is an essential problem for the implementation on limited resource environments such as WMSN. The main objective of this paper is to introduce a listless pipelined strip based SPIHT for WMSN to reduce system complexity and minimize processing time and memory usage. The proposed algorithm is implemented using discrete wavelet transform (DWT) lifting-based instead of DWT convolution-based filter. The experimental results show the superiority of the proposed algorithm in terms of peak signal-to-noise ratio (PSNR) which reaches 1 dB for all bit rates. In addition, the memory requirement is reduced to 71% with 27% of energy saving. | A modified listless strip based SPIHT for wireless multimedia sensor networks |
S0045790615003481 | In order to reduce aquaculture risks and optimize the operation of water quality management in prawn engineering culture ponds, this paper proposes a novel water temperature forecasting model based on empirical mode decomposition (EMD) and back-propagation neural network (BPNN). First, the original water temperature datasets are decomposed into a collection of intrinsic mode functions (IMFs) and a residue by EMD yields relatively stationary sub-series that can be readily modeled by BPNN. Second, both IMF components and residue is applied to establish the corresponding BPNN models. Then, each sub-series is predicted using the corresponding BPNN. Finally, the prediction values of the original water temperature datasets are calculated by the sum of the forecasting values of every sub-series. The proposed hybrid model was applied to predict water temperature in prawn culture ponds. Compared with traditional models, the simulation results of the hybrid EMD–BPNN model demonstrate that de-noising and capturing non-stationary characteristics of water temperature signals after EMD comprise a very powerful and reliable method for predicting water temperature in intensive aquaculture accurately and quickly. | Multi-scale prediction of water temperature using empirical mode decomposition with back-propagation neural networks |
S0045790615003493 | Code Division Multiple Access, which allows multiple users to access and transmit data over wireless channel simultaneously, is implemented using different code assignment schemes including Pair-wise Code Assignment. In this paper, the existing pair-wise code assignment procedure is enhanced and a new such scheme is presented with at most ? codes, where ? is the maximum degree of a network. In addition, a co-channel interference called secondary interference that exists in the existing schemes has been addressed and an interference free pair-wise code assignment procedure is proposed with at most ?2- c codes, where c is a constant. On simulation over different synthesized, actual and random networks, it is found that our scheme improves successful transmission rate and blocking probability by 17% and 12.5% respectively. Although the code requirement of the proposed interference free scheme is increased by 40%, it would improve the network performance. | Analysis and enhancement of pair-wise code assignment scheme used in Code Division Multiple Access protocol |
S0045790615003511 | Spectrum allocation scheme in cognitive radio networks (CRNs) becomes complex when multiple CR users concomitantly need to be allocated new and suitable bands once the primary user returns. Most existing schemes focus on the gain of individual users, ignoring the effect of an allocation on other users and rely on the ‘periodic sensing and transmission’ cycle which reduces spectrum utilization. This paper introduces a scheme that exploits collaboration among users to detect PU’s return which relieves active CR users from the sensing task, and thereby improves spectrum utilization. It defines a Capacity of Service (CoS) metric based on the optimal sensing parameters which measures the suitability of a band for each contending user and takes into consideration the impact of allocating a particular band on other band seeking users. The proposed scheme significantly improves capacity of service, reduces interference loss and collision, and hence, enhances dynamic spectrum access capabilities. | Modeling multiuser spectrum allocation for cognitive radio networks |
S0045790615003547 | This study examines a multicore processor based on a system-on-chip (SoC) and configured by a Tensilica Xtensa® LX2. The multicore processor is a heterogeneous, configurable dual-core processor. In this study, one core was used as the host to control the processor chip, and the other was used as a slave to extend digital signal processing applications. Each core not only owned its local memory, but also shared common data memory. In addition, the proposed multicore processors had a virtual memory. This additional memory supported the processor by enabling it to easily manage complex programs; it also allowed the two cores to access data from the unified data memory of different tasks. For bus management, a bus arbitration mechanism was added to handle the cores and to distribute the priority of asynchronous access requests. The benefits of the proposed structure include avoiding hardwired memory and reducing interface handshaking. To verify the proposed processor, it was simulated on the model level using a Petri net graph, and on the system level using ARM SoC designer tools. In the performance simulation, we found that the lowest latency-to-cost ratios were achieved using a 32-bit bus interface and a 4-entry data queue. | A novel memory management method for multi-core processors |
S0045790615003559 | In this paper, we propose a client-based solution to detect “evil twin” attacks in wireless local area networks (WLANs). An evil twin is a kind of rogue Wi-Fi access point (AP) which has the same SSID name as a legitimate one and is set up by an attacker. After a victim associates his device with an evil twin, an attacker can eavesdrop sensitive data forwarded through the evil twin. Most existing detection solutions are administrator-based, which are used by wireless network administrators to verify whether a given AP is in an authorized list or not. Such administrator-based solutions are limited, hardly maintained, and difficult to protect users 24–7. Hence, we propose a client-based detection mechanism, called evil twin detector, to detect this type of attacks. An evil twin detector changes its wireless network interface card (WNIC) to monitor mode to capture wireless TCP/IP packets. Through analyzing captured packets, our detector allows client users to easily and precisely detect an evil twin, thus avoids threats created by evil twins. Our method does not need to know any authorized AP list, and does not rely on data training or machine learning technique. Finally, we implement a detecting system on Windows 7. | A client-side detection mechanism for evil twins |
S0045790615003560 | To improve the precision of low-cost, vehicle-mounted global position system (GPS), this paper presents the multi-source information fusion algorithm of vehicle navigation, which is based on the interacting multiple model (IMM). Considering vehicle kinematics and dynamic characteristics, as well as its braking capacity in extreme accelerating situations, this study establishes the clothoid model, which shows longitudinal and lateral constant speed motion, and the Adaptive Current Statistics (ACS) model, which shows the acceleration of a vehicle. Through the parametric estimation of interactivity, filtering, and updating of probability, the vehicle trajectory is predicted within a period of time, and high-precision dead-reckoning is therefore achieved. Comparative analysis shows that the above algorithm can improve the precision of vehicle-mounted GPS/inertial navigation system. | Interacting multiple model for improving the precision of vehicle-mounted global position system |
S0045790615003572 | The problem of Support Vector Machines (SVM) tuning parameters (i.e., model selection) has been paramount in the last years, mainly because of the high computational burden for SVM training step. In this paper, we address this problem by introducing a recently developed evolutionary-based algorithm called Social-Spider Optimization (SSO), as well as we introduce SSO for feature selection purposes. The model selection task has been handled in three distinct scenarios: (i) feature selection, (ii) tuning parameters and (iii) feature selection+tuning parameters. Such extensive set of experiments against with some state-of-the-art evolutionary optimization techniques (i.e., Particle Swarm Optimization and Novel Global-best Harmony Search) demonstrated SSO is a suitable approach for SVM model selection, since it obtained the top results in 8 out 10 datasets employed in this work (considering all three scenarios). Notice the best scenario seemed to be the combination of both feature selection and SVM tuning parameters. In addition, we validated the proposed approach in the context of theft detection in power distribution systems. | Social-Spider Optimization-based Support Vector Machines applied for energy theft detection |
S0045790615003584 | We consider turbo coded multi-carrier double space–time transmit diversity (DSTTD) system that employs orthogonal frequency division multiplexing (OFDM) for the transmission of acoustic signals in underwater communication, where acoustic interference and ambient noise are the major channel deficiencies. DSTTD employs two space–time block codes at the transmitter. At each receiver, we implement space–time Block-Nulling detection technique to increase throughput. We consider the iterative decoding algorithm at the receiver to alleviate the effects of ambient noise and acoustic interference. Further, we implement multi-carrier modulation technique to mitigate the effects of multipath propagation. We investigate the effects of eleven tap delay pertaining to shallow water channel model for the DSTTD-OFDM system. Our simulation results reveal that our considered system with Block-Nulling technique provides better bit error rates for lower signal-to-noise ratio when compared to a minimum mean square error detector-based system. Further, it achieves higher throughput with fewer computations at the receivers. | Mitigating ambient noise and multi-path propagation in underwater communication using the DSTTD-OFDM system |
S0045790615003596 | We present a watermarking algorithm to synchronously transmit Gong-Che notation musical scores and their musical instrument digital interface (MIDI) information. Our algorithm transforms the MIDI information into a binary sequence, which is used to form watermarking data. This watermark is embedded into the Gong-Che notation musical score image, using an inward clockwise rotation. The algorithm then uses optical music recognition to judge whether the musical information in the image is loss. Experiments have validated the effectiveness of our watermarking and expansion algorithms. The expansion process increases transmission accuracy, and the added processing time is minimal. Furthermore, our methodology can be applied to other musical notations such as conventional musical notation or numbered musical notation. | Synchronous transmission of a Gong-Che notation musical score and its MIDI information |
S0045790615003602 | This paper presents multi-quantized local binary patterns for facial gender classification. For encoding the gray level difference (GLD) between a reference pixel and its neighbors, local binary pattern employs a binary quantization which retains the sign of GLD but discards the magnitude information. To improve the discrimination capability, the proposed method utilizes both the sign and magnitude components by performing multi-level vector quantization of GLD. Each quantized level is then separately encoded to generate multiple local binary patterns. The proposed method is evaluated on four publicly available datasets (FERET, PAL, CASIA and FEI) through extensive experiments. Comparison of performance with various existing methods clearly demonstrated that the proposed method has advantages such as higher discrimination power, improved noise robustness and better generalization capability. | Multi-quantized local binary patterns for facial gender classification |
S0045790615003626 | In this paper, a novel architecture of Vedic multiplier with ‘Urdhava-tiryakbhyam’ methodology for 16 bit multiplier and multiplicand is proposed with the use of compressor adders. Equations for each bit of 32 bit resultant are calculated distinctly and compressor adders are used to implement these equations. They are chosen as they decrease vertical critical delay in comparison to the conventional architectures of compressors implemented using half and full adders only and so make the multiplier fast. The designs are coded in VHDL (Very High-speed Integrated Circuits Hardware Description Language) and synthesized with Xilinx ISE 13.1 using Spartan 3e series of FPGA (Field Programmable Gate Array). The combinational delay calculated for proposed 16 × 16 bit multiplier is 32 ns. Further speed comparisons of compressor adders with traditional ones and proposed multiplier with popular methods for multiplication are shown. Results clearly indicate the better speed performance of our proposed Vedic multiplier. | A novel high-speed approach for 16 × 16 Vedic multiplication with compressor adders |
S0045790615003638 | In view of the fact that objects with different natures usually respond differently to the same external stimulus, this paper proposes a no-reference image quality assessment based on gradient histogram response (GHR). GHR is the gradient histogram variation of an image object under a local transform. In the metric, through preprocessing, a test image is transformed to a noise image and a blur image, which are taken as two image objects. Each image object is exerted with a local transform as an object input, and its GHR as an object output is extracted in multiscale space. The two GHRs compose a global feature vector and are mapped to an image quality score. Experiments show that GHR outperforms state-of-the-art no-reference metrics statistically in the condition that test images are degraded by different types of distortions. Especially, the metric is feasible for the quality assessment of the images degraded by mixed distortions though the types of these images are not included in the training database. | No-reference image quality assessment based on gradient histogram response |
S0045790615003663 | This paper proposes a novel unambiguous correlation function for composite binary offset carrier (CBOC) signal tracking based on partial correlations. In the proposed scheme, first, we partition sub-carriers of the CBOC signal into partial sub-carriers, and subsequently, we obtain partial correlations by correlating the partial sub-carriers with the received CBOC signal. Finally, a novel unambiguous correlation function with no side-peak is constructed by combining the partial correlations in a specially designed way. Unlike the conventional schemes, the proposed scheme does not require any auxiliary signal and from numerical results, it is found to offer a better tracking performance than those of the conventional schemes in terms of the tracking error standard deviation (TESD) and multipath error envelope (MEE). | A novel unambiguous composite binary offset carrier(6,1,1/11) tracking based on partial correlations |
S0045790615003675 | Hierarchical clustering technique is an effectual topology control methodology in Wireless Sensor Networks (WSNs). This technique is used to increase the life time of the network by reducing the number of transmissions towards the base station. We propose and validate a new routing protocol termed as Sleep-awake Energy Efficient Distributed (SEED) clustering algorithm. We divide the network sensing field into three energy regions because in SEED cluster heads are communicating directly with the base station. The cluster heads of the high energy region are communicating with the base station through a longer distance and paying extra energy cost as compared to the cluster heads of the low energy region. Same application base sensor nodes form sub-clusters to decrease the number of transmissions towards the base station. In every round, one node from these sub-clusters nodes awake and transmit the data and the rest of them sleep to save available resources. We select six criteria to check the performance of our algorithm. The simulation results show that SEED achieves longer network life time and high throughput as compared to the existing clustering protocols. | Sleep-awake energy efficient distributed clustering algorithm for wireless sensor networks |
S0045790615003705 | The present study integrates information system engineering and management methodologies to solve a real-life case problem. We develop an agricultural product recommendation service on a mobile platform, and then to understand users’ acceptance to confirm that it can be used to solve the problem. For consumers, making the decision to purchase is complex and can be full of contradictions and conflicts. This raises the need to design a product recommendation service that uses multiple criteria to assist consumers. This study employs a modified Elimination Et Choice Translating Reality (ELECTRE) method determine a ranking order which will assist consumers in deciding which agricultural product to buy. Two major findings are proposed. First, we identified five criteria that assist consumers making buying decisions regarding agricultural products. Second, we find that when the system is established on a mobile platform, perceived ease of use does not play a critical role in user acceptance. | Using a modified ELECTRE method for an agricultural product recommendation service on a mobile device |
S0045790615003924 | This work studies a digital hardware implementation of a radial basis function neural network (RBF NN) Firstly, the architecture of the RBF NN, which consists of an input layer, a hidden layer of nonlinear processing neurons with Gaussian function, an output layer and a learning mechanism, is presented. The supervising learning mechanism based on the stochastic gradient descent (SGD) method is applied to update the parameters of RBF NN. Secondly, a very high-speed IC hardware description language (VHDL) is adopted to describe the behavior of the RBF NN. The finite state machine (FSM) is applied for reducing the hardware resource usage. Thirdly, based on the electronic design automation (EDA) simulator link, a co-simulation work by Simulink and ModelSim is applied to verify the VHDL code of RBF NN. Finally, some simulation cases are tested to validate the effectiveness of the proposed digital hardware implementation of the RBF NN. | Digital hardware implementation of a radial basis function neural network |
S0045790615003936 | In underwater acoustic sensor networks, long baseline localization for autonomous underwater vehicles (AUVs) requires distance estimation that always encounters severe problems: (a) Time-synchronization is hard to achieve in underwater environment, which baffles ranging methods based on the synchronized time. (b) Long propagation delay of acoustic signals and the impact of AUVs’ mobility make it rash to use the round trip ranging (RTR) technology. (c) Sound speed uncertainty enlarges the inaccuracy of distance estimation. This work addresses those problems above and proposes an AUVs self-localization algorithm with accurate sound travel time solution (SL-STTS), which is time-synchronization free and ranging optimization based. Simulation results show that under the measurement noise of time and angles, the root mean square error of SL-STTS is decreased by about 8–79% compared with the counterparts. In addition the average distance estimation error of SL-STTS is declined by 42% compared with RTR. | Self-localization of autonomous underwater vehicles with accurate sound travel time solution |
S0045790615004115 | This paper aims to develop a location-based services supported Dr.What-Info system, i.e. a master multi-agent system on what the information is, using Google maps and an image recognition technology as a tourism information provider and as a route planner for tourists. Users can have great fun during vacation travels through an easy-to-use interface, integrating smartphone GPS function, a QR/Bar code reader and easy access to a cloud database, to find all of the required web services. In particular, given an archeological site in New Taipei City, Taiwan, for testing purposes, the presented system is demonstrated not only as a provider of information on popular tourist attractions, but also as a high performance GPS navigation device to guide users toward their desired destinations. The complete system developments, displays, and corresponding experiments and comparisons show that the research results demonstrate performance superiority over a number of previous studies. | A location-based services and Google maps-based information master system for tour guiding |
S0045790615004127 | The ever growing needs of Big Data applications are demanding challenging capabilities which cannot be handled easily by traditional systems, and thus more and more organizations are adopting High Performance Computing (HPC) to improve scalability and efficiency. Moreover, Big Data frameworks like Hadoop need to be adapted to leverage the available resources in HPC environments. This situation has caused the emergence of several HPC-oriented MapReduce frameworks, which benefit from different technologies traditionally oriented to supercomputing, such as high-performance interconnects or the message-passing interface. This work aims to establish a taxonomy of these frameworks together with a thorough evaluation, which has been carried out in terms of performance and energy efficiency metrics. Furthermore, the adaptability to emerging disks technologies, such as solid state drives, has been assessed. The results have shown that new frameworks like DataMPI can outperform Hadoop, although using IP over InfiniBand also provides significant benefits without code modifications. | Analysis and evaluation of MapReduce solutions on an HPC cluster |
S0045790615004139 | A new recurrent wavelet fuzzy neural network (RWFNN) with adaptive learning rates is proposed to control the rotor position on the axial direction of a thrust magnetic bearing (TMB) mechanism in this study. First, the dynamic analysis of the TMB with differential driving mode (DDM) is derived. Because the dynamic characteristics and system parameters of the TMB mechanism are high nonlinear and time-varying, the RWFNN, which integrates wavelet transforms with fuzzy rules, is proposed to achieve precise positioning control of the TMB. For the designed RWFNN controller, the online learning algorithm is derived using back-propagation method. Moreover, since the improper selection of learning rates for the RWFNN will deteriorate the control performance, an improved particle swarm optimization (IPSO) is adopted to adapt the learning rates of the RWFNN on-line. Numerical simulations show the validity of TMB system using the proposed RWFNN controller with IPSO under the occurrence of uncertainties. | Application of a recurrent wavelet fuzzy-neural network in the positioning control of a magnetic-bearing mechanism |
S0045790615004140 | This paper proposes a robust particle filter to deal with incomplete sensor data to predict the user’s routes and represents users’ movements using a dynamic Bayesian network model that patterns the user’s spatiotemporal routine. The proposed particle filter includes robust particle generation to supplement any incorrect and incomplete sensor information, efficient switching/weight functions to reduce computation complexity while considering uncertainty, and resampling to enhance the accuracy of the particles by solving the degeneracy problem. The robust particle filter enhances the accuracy and efficiency with which a user’s routes and destinations are determined. | Robust route inference and representation for uncertain sensor data |
S0045790615004152 | Various signaling techniques have been considered for I/O interconnects at 25 Gb/s or higher. However, they either suffer from high baud rate, as in the case of NRZ, or an excess signal-to-noise ratio penalty, as in the case of PAM-4. To overcome these problems, a four-phase shifted sinusoid symbol (PSS-4) signaling method is proposed that can be readily applied to high-speed I/O transceivers. By using PSS-4, the SNR penalty can be reduced from PAM-4; our theoretical analysis has revealed that the SNR performance of the PSS-4 is 6.5 dB better than that of PAM-4 with comparable bandwidth. The transistor-level simulation results have shown that PSS-4 has an average of twice larger vertical eye opening than PAM-4. In addition, the supply voltage sensitivity of PSS-4 is 55% and 20% less than that of PAM-4 and NRZ, respectively, and the power consumption of PSS-4 is 13.5% lower than that of PAM-4. | PSS4: Four-Phase Shifted Sinusoid Symbol Signaling for High Speed I/O interconnects |
S0045790615004176 | With the development of the Internet of Things (IoT), more and more devices that have diverse computing and communication capabilities are interconnected. For multimedia multicast in the IoT, a fixed flow rate cannot meet the quality-of-service requirements of heterogeneous devices and each device may not get the information from all other devices. In order to satisfy the heterogeneous requirements, we develop a distributed algorithm to solve the inter-layer flow optimization problem based on network coding for multimedia multicast in the IoT, by using primal decomposition and primal-dual analysis. We also apply Lyapunov theory to prove the convergence and global stability of the proposed iterative algorithm. Numerical results demonstrate that the proposed algorithm has better flexibility, stability, and implementation advantages than the previous intra-layer ones. | A distributed algorithm for inter-layer network coding-based multimedia multicast in Internet of Things |
S0045790615004231 | This paper presents an important step towards the standardization of research works on Optical Character Recognition in Persian language. It describes the formations of a standard handwritten database, including isolated digits, isolated signs, multi-digit numbers, numerical strings, courtesy amounts, and postal codes. In this regard, binary images of 72,180 samples were extracted from the designed forms. These forms were filled by 180 writers selected from different ages, genders, and jobs. Then these forms were scanned at 300 dpi with a high-speed scanner. Finally, forms are segmented into samples and are stored in bitmap format. This database is named PHOND, Persian Handwritten Optical Numbers & Digits, and it is available to the research community. Comparisons with the previous related databases illustrate the advantages of PHOND against other databases. Different experiments are done using PHOND database and the results are compared with other research works in handwritten recognition. | Handwriting recognition of digits, signs, and numerical strings in Persian |
S0045790615004243 | As a typical media access protocol, S-MAC has drawn much attention in the past decades. However, most of the attention focuses on energy efficiency, but fails to guarantee QoS performance. Firstly, we develop one state machines model and elaborate on the states’ transition processes of S-MAC. Secondly, combining our state machines with the specific parameters of S-MAC, we propose an analytical probabilistic model to investigate the latency and stability performances of S-MAC employing three different mechanisms: without sleeping mechanism, with sleeping mechanism, and with adaptive-listening mechanism. Also we analyze the latency and stability performances of S-MAC with different dutyCycle values. This research shows two interesting results: 1) Sleeping mechanism inside S-MAC does have negative impacts on its latency and stability; fortunately, adaptive listening mechanism can optimize them to some extent. 2) Latency and stability performances of S-MAC can be further improved through manipulating dutyCycle values. The simulations in NS2 exhibit consistent results with those obtained from our analytical probabilistic model. | Performance analysis and simulation verification of S-MAC for wireless sensor networks |
S0045790615004267 | An economical and portable on-line motor condition monitoring system based on an advanced microprocessor with a graphic user interface for abnormality detection is described. Both electrical and vibrational signal analysis for fault detection of motors were applied. A Cortex-M4 microcontroller and its built-in high speed analog-to-digital converter were used to process the on-line voltage, current, and vibration signals of the motors during operation. The acquired signals were digitally filtered through an infinite impulse filter and then, fast Fourier transform for spectral analysis was applied for abnormal pattern recognition. The analytic results were displayed in real time on an embedded touch-screen monitor to instantly provide users with the motor's operational conditions. Finally, a prototype was devised and verified through onsite experimentation. Comparing the results to commercial tools showed similar spectral characteristics; the difference for electrical and vibration signals was less than 10%. Thus, the system proposed shows promising ability and feasibility for online use. | On-line motor condition monitoring system for abnormality detection |
S0045790615004279 | Embedding secret information into a cover media and extracting the information and the image without any distortion is known as reversible watermarking (RW). This paper analyzes the performance of hybrid local prediction error-based RW using difference expansion (DE). The cover medical image is split into non-overlapping blocks. The border pixels in each block are predicted using median edge detection (MED) prediction. The other pixels in the block are predicted using least square prediction, and the prediction error is expanded. The secret data are embedded into the cover medical image corresponding to the prediction error using the DE method. The predictor is also embedded into the cover medical image to recover the data at detection without any additional information. The simulation results show that, this method achieves better watermarked image quality and high embedding capacity when compared to other classical prediction methods: Median, MED, Rhombus and Gradient Adjusted Prediction. | Hybrid local prediction error-based difference expansion reversible watermarking for medical images |
S0045790615004292 | Decimal arithmetic circuits, based on IEEE-754-2008 standard, commonly use 10-bit densely-packed-decimal (DPD) encoding of three binary-coded-decimal (BCD) digits. Binary-coded-chiliad (BCC) encoding, as storage (arithmetic) efficient as DPD (BCD), equivalently packs three BCD digits. No unpacking/packing to/from BCD (entailing extra delay/power) per each arithmetic operation (required in case of DPD), are necessary for BCC. Therefore, while abiding to DPD standard, we are motivated to design decimal arithmetic operators that accept BCC operands and produce BCC results. As such, DPD data from memory or input devices are converted to BCC, manipulated in BCC and stored in the BCC register file, during multi-operation decimal computations, and converted back to DPD only on reporting results to memory or output devices. In this paper, following a previous simple mixed BCC/binary adder, we design and synthesize more efficient ones, and compare them with previous relevant BCD and BCC adders to show advantages in area, and power. | Conditional speculative mixed decimal/binary adders via binary-coded-chiliad encoding |
S0045790615004309 | Sample variations are one of the main problems associated with speaker recognition. Most approaches use multiple templates in the gallery database. But, this requires enormous memory space. In order to minimize classification errors and intra-class variations, adaptive online and offline template update methods using vector quantization (VQ) and Gaussian mixture model (GMM) are proposed. Online and offline feature update as well as model update techniques are considered here. Feature update utilizes the vector quantization approach, while Gaussian mixture model approach is considered for model updating. The proposed methods automatically update the feature (model) in accordance with the biometric sample variations over time and they continually adapt the templates (user model) based on semi-supervised learning strategies. Experiments with 50 subjects reveal that the proposed template update strategies, improve the recognition accuracy and reduce the classification errors for voice recognition systems, even under sample variations. | Efficient online and offline template update mechanisms for speaker recognition |
S0045790615004310 | In this paper, a distributed base station cooperation-based handover management method is proposed for WiMAX Point-to-Multi-Point networks to provide quality of service to handover nodes. Moreover, a delay reduction method is proposed to reduce the packet delivery delays during handover. A Call Admission Control (CAC) algorithm is proposed to handle handover calls of various service classes fairly, according to their priorities. A bandwidth borrowing scheme is proposed to reduce the handover call dropping probabilities of various service classes while not starving the ongoing calls of lower priority service classes. A Markov model is developed to analyze the proposed CAC method and to obtain the approximate handover call dropping probabilities of various service classes. Simulation experiments are conducted to establish the performance advantages of the proposed handover management and CAC methods. | Handover management framework for WiMAX Point-to-Multi-Point networks |
S0045790615004322 | This paper proposes an alternative for building a data hiding algorithm into digital images. The method is based on chaos theory and the least significant bit technique for embedding a secret message in a image. Specifically the Bernoulli’s chaotic maps are used, to perform the following processes: (i) encrypt the bits of the message before embedding them into the cover image, (ii) a random selection of the image’s compositions (R,G or B) must be performed and the insertion of the secret message in a random way to (iii) rows and (iv) columns of the image. Several experimental results are shown under different evaluation criteria, such as entropy, autocorrelation, homogeneity, contrast, energy, peak signal-to-noise ratio, mean squared error and maximum absolute squared deviation. Experimental results show a good improvement in the peak-signal-to-noise-ratio and Image Fidelity value of the proposed algorithm in comparison to the results obtained from similar algorithms. | A steganographic method using Bernoulli’s chaotic maps |
S0045790615004334 | Graphical abstract The system bifurcation diagrams for x 1 are shown for harmonic excitation force F ˜ = (a) 1.14 × 10−3, (b) 0.1, (c) 1.0 and (d) 2.0, respectively. There are two intervals of nonlinear motion at 0 < η < 0.006 and 1.248 ≦ η ≦ 2.0, respectively when harmonic excitation force ( F ˜ ) increases to 2.0. Image, graphical abstract | Numerical computation and nonlinear dynamic analysis of ultrasonic cutting system |
S0045790615004346 | In this paper, we present a set of server selection schemes that includes all crucial components of resources available at each relevant node of the network to reach at the server selection decision and at the same time attempts to maximise the system-wide resource utilisation. First in the series, a server-centric scheme is proposed that formulates the problem as distributed constraint optimisation problem (DCOP) and solves it using Fast-max-sum (FMS) algorithm. Though it does not scale well in large network scenario yet it provides an estimate of the optimal values of performance metrics achievable under a specific scenario. Next, a client-centric scheme is proposed that outperforms the existing ones but does not guaranty to attain optimal Nash Equilibrium (NE). This motivates us to design another client-centric scheme that can avoid the price of anarchy by attaining superior NE of mix strategies. | Server selection schemes for service-oriented computing in mobile pervasive environment |
S0045790615004371 | The clarity and accuracy of echocardiography images are greatly reduced by speckle noise. Noise suppression, however, is difficult to achieve without also obscuring both rapidly moving structures and object edges. This research seeks to address these challenges by introducing a novel filtering framework based on temporal information and sparse representation. The proposed method involves smoothing intensity variation time curves (IVTCs) assessed in each pixel. Using an over-complete dictionary that contains prototype signal-atoms, IVTCs can be reconstructed as linear combinations of a few of these atoms. After a comprehensive comparison of sparse recovery algorithms, three were selected for our method: Bayesian Compressive Sensing (BCS), the Bregman iterative algorithm, and Orthogonal Matching Pursuit (OMP). The performance of the proposed method was then evaluated and compared with other speckle reduction filters. The experimental results demonstrate that the proposed algorithm can be used to achieve better-preserved edges and reduce blurring. | Echocardiography noise reduction using sparse representation |
S0045790615004395 | This study developed a Kinect microphone array-based method for the voice-based control of humanoid robot exhibitions through speech and speaker recognition. A support vector machine (SVM), a Gaussian mixture model (GMM), and dynamic time warping (DTW) were used for speaker verification, speaker identification, and speech recognition, respectively; they were effectively combined for realizing advanced voice-based control of humanoid robot exhibitions. Speech recognition capability was enhanced by using the Kinect microphone array and by combining the DTW-based recognition decisions associated with all the microphones through a fuzzy control scheme. A humanoid robot with the proposed voice-based control can be controlled through voice commands by authenticated users. The robot first verifies the authenticity of the personal operator, following which it identifies the operator and validates the command. Subsequently, it executes the command if both the user and command are valid. Experimental results demonstrated the effectiveness and accuracy of the proposed method. | Kinect microphone array-based speech and speaker recognition for the exhibition control of humanoid robots |
S0045790615004401 | Removal of noise and restoration of images has been one of the most interesting researches in the field of image processing in the past few years. Existing filter-based methods can remove image noise; however, they cannot preserve image quality and information such as lines and edges. The proposed noise reduction image restoration technique initially performs noise removal by employing a hybrid denoising filter with an adaptive genetic algorithm. The performance of the proposed technique is evaluated by comparing the result for the proposed technique with existing denoising filters, and genetic algorithm and particle swarm optimisation methods. The comparison results show that the proposed method achieves higher-quality denoising and a high restoration ratio for noisy images than the existing methods. | Noise free image restoration using hybrid filter with adaptive genetic algorithm |
S0045790615004413 | In cognitive wireless network, throughput scheduling optimization under interference temperature constraints has attracted more attentions in recent years. A lot of works have been investigated on it with different scenarios. However, these solutions have either high computational complexity or relatively poor performance. Throughput scheduling is a constraint optimization problem with NP(Non-deterministic Polynomial) hard features. In this paper, we proposed an immune-clone based suboptimal algorithm to solve the problem. Suitable immune clone operators are designed such as encoding, clone, mutation and selection. The simulation results show that our proposed algorithm obtains near-optimal performance and operates with much lower computational complexity. It is suitable for slowly varying spectral environments. | Throughput optimization in cognitive wireless network based on clone selection algorithm |
S0045790615004449 | This paper analyzes the performance of spectrum-sensing-based energy detection (ED) in cognitive radio networks (CRNs) over generalized fading channels. The fading channel is modeled by the extended generalized-K (EGK) distribution. Exact and accurate analytical expressions for the average detection probability with different detection schemes, such as, single channel, diversity reception, and cooperative spectrum sensing, are derived and evaluated. Obtained expressions can be reduced to other well-known fading channels such as, Weibull, Nakagami-m, and Rayleigh. It is shown that the analytical framework can be used for both integer and non-integer values of the fading/shadowing parameters. The impact of key fading/shadowing parameters on the performance of energy detectors is discussed with receiver operating characteristics (ROC) curves. The accuracy of the derived analytical expressions is corroborated via Monte-Carlo simulation results. | Spectrum-sensing in cognitive radio networks over composite multipath/shadowed fading channels |
S0045790615004450 | The analysis of the surface Electrocardiogram (ECG) is the most extended non-invasive technique in cardiological diagnosis. The ectopic beats are heart beats remarkably different to the normal beat morphology that provoke serious disturbances in electrocardiographic analysis. These beats are very common in atrial fibrillation (AF), causing important residua when ventricular activity has to be removed for atrial activity (AA) analysis. These beats may occur in both normal subjects and patients with heart disease, and their presence represents an important source of error which must be handled before any other analysis. In this work, a method is proposed to cancel out ectopics by classification between normal and abnormal beats. The systems is based on Radial Basis Function Neural Network (RBFNN). This new approach is compared to state-of-the-art techniques for the ectopic classification and cancellation in the MIT database. The results clearly demonstrated the improved ECG beats classification accuracy compared with other alternatives and a very accurate reduction of ectopic beats together with low distortion of the QRST complex. | An efficient method for ECG beat classification and correction of ectopic beats |
S0045790615004474 | This study developed an air supply station for air-powered scooters. The station comprised mechanical and electrical systems. The key components of the mechanical system were a high-power air compressor, low-pressure cylinder, pneumatic boosting cylinder, high-pressure accumulator, and target tank. The electrical system comprised pressure sensors, air flow sensors, and control circuits, which were equipped adequately for the air charge. An air-powered scooter was used to evaluate the design specifications and charging performance of the station, and the scooter was tested on a chassis dynamometer to assess performance during a modified standard driving cycle. The experimental results confirmed that the air supply station can produce high-pressure air for air-powered vehicles. The station design can guide the development of similar technology by companies in the transportation and green energy industries. Future research will conduct a theoretical analysis by modeling and simulating the performance of the air station and air scooter. | System design and mechatronics of an air supply station for air-powered scooters |
S0045790615004498 | The scalability of Network-on-Chip (NoC) designs has become a rising concern as we enter the manycore era. Multicast support represents a particular yet relevant case within this context, mainly due to the poor performance of NoCs in the presence of this type of traffic. Multicast techniques are typically evaluated using synthetic traffic or within a full system, which is either simplistic or costly, given the lack of realistic traffic models that distinguish between unicast and multicast flows. To bridge this gap, this paper presents a trace-based multicast traffic characterization, which explores the scaling trends of aspects such as the multicast intensity or the spatiotemporal injection distribution for different coherence schemes. This analysis is the basis upon which the concept of multicast source prediction is proposed, and upon which a multicast traffic model is built. Both aspects pave the way for the development and accurate evaluation of advanced NoCs in the context of manycore computing. | Characterization and modeling of multicast communication in cache-coherent manycore processors |
S0045790615004735 | This paper proposes two architectures, including an Opportunistic Large Array Concentric Routing Algorithm with Geographic Relay Nodes (OLACRA-GRN) architecture, and an Opportunistic Large Array Concentric Routing Algorithm with Relay Nodes (OLACRA-RN) architecture. First, the OLACRA-GRN architecture with the geographic information of relay nodes reduces node energy consumption and finds the optimum number of relay nodes to forward the data; an analysis of the characteristics of the energy model is also presented. Besides, the OLACRA-RN architecture without the geographic information of relay nodes is proposed, which can find the number layer of concentric circles in the sensing field. The optimal number layer of concentric circles is calculated according to the distance between the sink and field boundary. Simulation results show that our proposed OLACRA-GRN and OLACRA-RN architectures can effectively reduce node energy consumption more than Opportunistic Large Array Concentric Routing Algorithm (OLACRA) architecture. | Opportunistic large array concentric routing algorithms with relay nodes for wireless sensor networks |
S0045790615004747 | Wireless sensor networks comprise nodes with limited power supply. The dense deployment of nodes on a terrain for monitoring the environment causes contention in wireless channels that leads to interference and high energy consumption. The topology of the network changes frequently because of failure of nodes, addition of new nodes, and channel fading. Topology control is an important technique for reducing energy consumption, interference, and maintaining connectivity in the network. This paper provides a survey on state scheduling-based topology control techniques for unattended wireless sensor networks. Topology control algorithms for both flat and hierarchical networks are discussed. The algorithms are further categorized according to the key parameters used for state scheduling. The advantages, disadvantages, and results of the algorithms are tabulated. In addition, we highlight future research directions in designing state scheduling-based topology control algorithms. | Survey on state scheduling-based topology control in unattended wireless sensor networks |
S0045790615004760 | The joint multipath routing and Network Coding (NC) method has been shown to improve the reliability and energy efficiency of multi-hop-relay wireless sensor networks (WSNs). However, NC is a kind of “all-or-nothing” code: the destination node cannot decode any information unless it successfully receives as many NC coded packets as the raw ones. Observed that WSNs often measure physical signals which show a high degree of correlation, in this paper, we propose a more deliberated scheme combined with Compressed Sensing (CS). Depending on the proposed measurement and recovery method, we prove that the original data can be recovered progressively while preserving the advantage of NC-based scheme. Detailed mathematical analysis of the performance of our proposed scheme along with other existing schemes is given. The experimental results also show the efficiency and robustness of the proposed scheme. | A reliable data transmission scheme based on compressed sensing and network coding for multi-hop-relay wireless sensor networks |
S0045790615004772 | Extracting river information from remote sensing images is of great importance in the investigation and monitoring of water resources and navigation of ships. In order to extract the river target from remote sensing images more accurately, a method based on image decomposition and distance regularized CV (Chan–Vese) model is proposed. Firstly, the remote sensing image is decomposed based on tensor diffusion. The original image is decomposed into a cartoon image and a texture image and the river is contained in the cartoon part. Secondly, the cartoon image is segmented based on the distance regularized CV model. Experimental results show that, the method proposed is more accurate in extracting the river target comparing with 6 other methods including image segmentation based on CV model, region-scalable fitting energy level set model, bias field correction level set model and some methods based on image decomposition and active contour model. | Automatic river target detection from remote sensing images based on image decomposition and distance regularized CV model |
S0045790616000021 | A graph grammar is a formal tool for providing rigorous but intuitive ways to define visual languages. Based on an existing graph grammar, this paper proposes new context-sensitive graph grammar formalism called the Extension of Edge-based Graph Grammar, or E-EGG. The E-EGG introduces new mechanisms into grammatical specifications, productions, operations and so on in order to conveniently treat the bidirectional transformation between the Business Process Modeling Notation (BPMN) and the Business Process Execution Language (BPEL). Besides formal definitions of the E-EGG are provided, steps and algorithms to achieve the bidirectional transformation and to check the correctness of BPMN models’ structure are presented. Finally, a case study on transformation from BPMN models to BPEL codes is provided to show how the parsing algorithm of the E-EGG works. | Bidirectional transformation between BPMN and BPEL with graph grammar |
S0045790616000033 | Recognition of age-separated face images is a challenging and open research problem. In this paper we propose a facial asymmetry based matching-score space (MSS) approach for recognition of age-separated face images. Motivated by its discriminatory information, we evaluate facial asymmetry across small and large temporal variations and use asymmetric facial features to recognize age-separated face images. We extract three different facial features including holistic feature descriptors using Principal Component Analysis (PCA), local feature descriptors using Local Binary Patterns (LBP), and Densely Sampled Asymmetric Features (DSAF) to represent face images. Then we develop MSS to discriminate genuine and imposter classes using support vector machine (SVM) as a classifier. Experimental results on three widely used face aging databases, the FERET, MORPH and FG-NET, show that proposed approach has superior performance compared to some existing state-of-the-art approaches. | The role of facial asymmetry in recognizing age-separated face images |
S0045790616000057 | The scale-free topology is robust when confronted with random faults, but it is fragile when confronted with selective remove attacks. In this paper, we propose a new scale-free topology model which has both fault-tolerance against random faults and intrusion-tolerance against selective remove attacks at the same time. Then the mathematical expression of the topological degree distribution is derived. Through analyzing the effect of topological degree distribution on these properties of topological fault-tolerance and topological intrusion-tolerance, the optimal scale-free topology which can keep the fault-tolerance and maximize intrusion-tolerance is obtained. We performed extensive experiments on the proposed model and compared it with other existing models. The simulation results show that the new scale-free topology model can keep the character that the scale-free topology has a stronger robustness to random faults. And it also can reduce their fragility for selective remove attacks and further prolong its lifetime. The topology structure entropy achieves its maximum when the proper scaling exponent is found. | A scale-free topology model with fault-tolerance and intrusion-tolerance in wireless sensor networks |
S0045790616000069 | In this paper, the performance of a turbo coded, triply-Polarized multiple-input multiple-output (MIMO) aided Code Division Multiple Access (CDMA) is investigated for Stanford University Interim (SUI) and Long-term Evolution (LTE) channel model specifications. The minimum mean square error algorithm based on ordered successive interference cancellation (OSIC) Multi-user detection (MUD) technique is implemented at each mobile station for diminishing the effects of multi-stream interference (MSI). We observe from the simulation results that a better bit error rate(BER) performance with less signal to noise ratio (SNR), is exhibited by our considered coded system in comparison with an un-coded system. The simulation results reveal that the MIMO–CDMA system with triply-Polarized antenna structure requires a higher SNR than a uni-Polarized antenna system for achievement of the same BER. However, it provides the advantage of replacement of three uni-polarized antennas by a single triply-Polarized antenna thereby achieves a higher data rate with reduced size of MS. | Performance of turbo coded triply-Polarized MIMO–CDMA system for downlink communication |
S0045790616000070 | In wireless LANs (WLANs), handover is usually performed based on either signal power or congestion level. However, considering only the congestion level could be insufficient for handover since it may cause traffic loss. Therefore, besides the load of access points (APs), it is necessary to consider the physical conditions of different WLANs for performing a seamless handover. This article introduces a novel scheme for seamless handover of IPTV streams in WLAN carrying IPTV traffic, called Physical Constraint and Load Aware (PCLA) handover. The PCLA can compute the load of APs for congestion detection purposes. In PCLA, a mobile node chooses the best network considering signal strength, bit error rate in the relevant environment, and the congestion of APs for making a seamless handover. The simulation results show the appropriateness of PCLA in improving handover performance. | Physical constraint and load aware seamless handover for IPTV in wireless LANs |
S0045790616000094 | Due to intra-flow and inter-flow interference problems, the throughput performance decreases dramatically in a multi-hop wireless network. These two kinds of bandwidth unfair sharing problems could cause serious collisions and congestion, hence affecting the performance of multi-hop wireless networks. That is to say, data packets that need to traverse more hops to arrive at the destination will get lower throughput and result in the inter-flow fairness problem. Furthermore, the quality of video transmission is especially poor in traditional multi-hop wireless network environments. Therefore, in this paper, we propose a virtual queue management scheme that does not require the modification of any communication protocol. According to the number of flows, it adjusts the queue management scheme to achieve each flow's fair sharing of channel resource. It also improves the quality of video transmission. Through NS2 simulations, the results show that our proposed scheme can mitigate the inter-flow fairness problem and effectively improves the quality of video transmission. | Inter-flow fairness support and enhanced video quality delivery over multi-hop wireless networks |
S0045790616000100 | This paper proposes a face recognition system based on a steerable pyramid transform (SPT) and local binary pattern (LBP) for e-Health secured login. In an e-Health framework, patients are sometimes unable to identify themselves by traditional login modalities such as username and password. Automatic face recognition can replace the conventional login modalities if the recognition system is robust. In the proposed system, SPT can decompose a face image into several subbands of different scales and orientations, and LBP can encode the subbands in binary texture pattern. Therefore, SPT-LBP scheme represents a face image in a robust way that includes multiple information sources from different scales and orientations. The proposed system is evaluated on the facial recognition technology (FERET) database. According to the results, the proposed system achieves 99.28% recognition in fb set, 80.17% in dup I set, and 79.54% in dup II set. | Steerable pyramid transform and local binary pattern based robust face recognition for e-health secured login |
S0045790616000112 | High concentration photovoltaic (HCPV) modules employing high-efficiency III–V solar cells promise greater system-level efficiency than conventional photovoltaic (PV) systems. Nevertheless, the output power of an HCPV system is very sensitive to rapidly fluctuating tracking errors and weather patterns. The fractional open circuit voltage (FOCV) based maximum power point (MPP) tracking technique benefits from simplified processing circuits with speed response. To investigate the feasibility of using the FOCV technique for MPP estimation on HCPV modules, a theoretical model and simulation are presented in this study. A MATLAB-based MJSC circuit model of an HCPV module with buck-type converter and load is proposed and validated. In addition, the magnitude of the optical loss caused by Fresnel lens shape deformation and air mass (AM) ratio is modeled and quantized. The FOCV technique is then employed and compared with the conventional perturb and observe (P&O) method on the HCPV module under varying irradiance and temperature conditions to study its effectiveness. The results suggest that the FOCV technique could help an HCPV module to attain greater power efficiency. | A performance evaluation model of a high concentration photovoltaic module with a fractional open circuit voltage-based maximum power point tracking algorithm |
S0045790616000136 | This paper proposes a face recognition system based on a steerable pyramid transform (SPT) and local binary pattern (LBP) for e-Health secured login. In an e-Health framework, patients are sometimes unable to identify themselves by traditional login modalities such as username and password. Automatic face recognition can replace the conventional login modalities if the recognition system is robust. In the proposed system, SPT can decompose a face image into several subbands of different scales and orientations, and LBP can encode the subbands in binary texture pattern. Therefore, SPT-LBP scheme represents a face image in a robust way that includes multiple information sources from different scales and orientations. The proposed system is evaluated on the facial recognition technology (FERET) database. According to the results, the proposed system achieves 99.28% recognition in fb set, 80.17% in dup I set, and 79.54% in dup II set. | Image and medical annotations using non-homogeneous 2D ruler learning models |
S0045790616000148 | Botnet is one of the most serious threats to cyber security as it provides a distributed platform for several illegal activities. Regardless of the availability of numerous methods proposed to detect botnets, still it is a challenging issue as botmasters are continuously improving bots to make them stealthier and evade detection. Most of the existing detection techniques cannot detect modern botnets in an early stage, or they are specific to command and control protocol and structures. In this paper, we propose a novel approach to detect botnets irrespective of their structures, based on network traffic flow behavior analysis and machine learning techniques. The experimental evaluation of the proposed method with real-world benchmark datasets shows the efficiency of the method. Also, the system is able to identify the new botnets with high detection accuracy and low false positive rate. | Botnet detection via mining of traffic flow characteristics |
S0045790616300015 | Switching Devices such as IGBT used in Pulse Width Modulation (PWM) Inverter feeding an induction motor often suffer from different types of incipient faults like improper contact points, poor connections and problematic solder joints. These are due to ageing or prolonged operation in unfriendly environments. These faults need to be detected at their initial stages to prevent subsequent spreading of faults. In the present work, different variations of the above mentioned faulty cases in a PWM-Inverter have been studied by recording three phase inverter output current profiles and converting them to Concordia patterns. It has been observed that the Concordia patterns are quite different in shapes for different types of faults. A suitable image based shape descriptor has been applied to extract relevant information from these Concordia patterns. Finally, Nearest Neighbor Algorithm is employed on this information to identify the nature and location of faults. Performance of the algorithm is found to be quite satisfactory when its results are compared with two more related algorithms. | A combined image processing and Nearest Neighbor Algorithm tool for classification of incipient faults in induction motor drives |
S0045790616300027 | In this paper, we consider the performance of a cellular uplink (UL) multi-user feedback multiple-input multiple-output (MIMO) system assisted by joint transmitter-receiver (Tx-Rx) design and polarization-multiplexing (PM). PM is realized with the aid of dual and triply-polarized antennas. At the transmitter, we take advantage of only the individual user’s channel impulse responses (CIRs) obtained through feedback channels that endure noise, fading and delay to construct the preprocessing matrix, while at the receiver the post-processing matrix construction relies on the perfect CIRs of all the users. In multi-user UL-MIMO transmissions, multiple-access interference (MAI) and inter-antenna interference (IAI) can severely degrade the system’s performance. Our study shows that the joint Tx-Rx is capable of completely eliminating the IAI as well as the MAI when it employs perfect CIRs based preprocessing and post-processing. On the other hand, noise, fading and delay tainted quantized-CIRs based preprocessing results in noticeable performance degradation due to imperfect removal of IAI. Nevertheless, our results demonstrate that, when the preprocessing is based on the quantized-CIRs obtained through ideal feedback channels, the resultant achievable symbol-error-rate and sum-capacity remain close to that obtained with the perfect CIRs based design. | Performance of uplink feedback MIMO system using joint transmitter-receiver and polarization-multiplexing |
S0045790616300039 | The Binary Offset Carrier (BOC) modulated signals have been introduced in global navigation satellite systems (GNSS). They possess good frequency compatibility with existing Binary Phase Shift Keying (BPSK) signal. However, this type of modulation creates multiple side peaks in auto correlation function. Therefore it is hard to make full use of its ranging capability. To alleviate this problem, this paper analyzes the Double Estimator Technique (DET) thoroughly from a general formulation of two-dimension correlation and proposes a new subcarrier aided code tracking approach. In addition, based on the fact that the pseudo random noise (PRN) code can reflect the absolute delay, an adaptive code monitoring scheme is investigated to tap the potential tracking accuracy. Utilizing BOCsin(1,1) signal, Monte Carlo simulations demonstrate that the proposed method improves the tracking performance with respect to thermal noise significantly. | Improving tracking accuracy with subcarrier assistance and code monitoring for BOC modulated signal |
S0045790616300040 | Modeling traffic flow and gathering accurate traffic congestion information are two challenging problems in smart transportation systems. Most of the traffic flow models and velocity estimation methodologies that have been proposed so far gather the data from GPS-equipped smart phones and extract the flow model based on GPS sampling. However, these approaches tend to fail in real life scenarios due to the insufficient vehicle data and unpredictable dynamics of the flow. Furthermore, utilization of GPS sensor leads to a battery drainage and hence reduces the overall system performance. In this paper, we propose a new battery-friendly data acquisition model to obtain the raw data. We then evaluate our model under various traffic conditions to determine its feasibility in vehicle speed estimation. The proposed model results in 88% location accuracy whereas it reduces the battery consumption by half. | A battery-friendly data acquisition model for vehicular speed estimation |
S0045790616300052 | In recent years, exchanging data has become far easier with the rapidly growing popularity and increased mobility of mobile devices. However, data exchange between mobile devices is performed as a peer-to-peer communication. Each mobile device that serves as a candidate has a whole range of hardware statuses which may influence data exchange performance. Thus, the selection of a mobile device with sufficient computing resources to facilitate round-robin data exchange is an interesting process worth exploring. This study proposes an optimal mobile device selection approach for round-robin data exchange via the monitoring of the hardware status of each candidate device's system profile. A case study demonstrates the proposed approach, step by step. The experimental results show that the proposed approach can be used to improve round-robin data exchange performance. The contribution of this study is to provide an approach which selects an optimal candidate mobile device for round-robin data exchange in a local wireless communication network. | Optimal mobile device selection for round-robin data exchange via adaptive multi-criteria decision analysis |
S0045790616300064 | Next-generation wireless technology supports notably high data rates and smart hand-held devices together with data processing, which creates a great challenge for moving these contents from server to client. The classical issue of mobile data synchronization for high-speed data networks can be addressed through Software Defined Networking (SDN) Control. The proposed IDBSync (Improved Database Synchronization) mechanism expedites data synchronization between the server-side and mobile database in a SDN setup. The tables of the synchronization server are maintained in the control plane, including replicas of the data tables at the server and client devices collected from the data planes for application of the synchronization policy. The IDBSync uses BaSyM (Batch Level Synchronization Methodology) to group similar cases for synchronization to considerably reduce network usage and energy consumption in a heterogeneous environment. Comparative performance analysis of the proposed mechanism is performed with reference to commercial approaches. | An improved database synchronization mechanism for mobile data using software-defined networking control |
S0045790616300088 | Network-on-Chips (NoCs) have become the mainstream for Chip Multi-Processors (CMPs) design. Multicast, a one-to-many communication pattern, is widely used in barrier/clock synchronization, multithreading programs and cache coherence protocols for CMPs. Even though several multicast routing algorithms have been proposed for CMPs, few can adaptively deal with heavy traffic loads. With the increase of multicast traffic load, deterministic routing schemes suffer from long latency and low throughput, whereas adaptive routing algorithms can improve the routing performance by providing multiple redundant paths. In this paper, we proposed a novel multicast routing algorithm based on partition to reduce the latency of multicast packets, by finding multiple routing paths and adaptively choosing available output ports based on the size of buffer space in downstream routers. We evaluate our scheme through simulations, and results show that, under various configurations, both latency and energy consumption have been significantly reduced in comparison with recent multicast routing schemes. | An adaptive partition-based multicast routing scheme for mesh-based Networks-on-Chip |
S0045790616300106 | In this paper, an improved particle swarm optimization guidance (IPSOG) is proposed. The particle swarm optimization guidance (PSOG) is presented to solve nonlinear and dynamic missile guidance problems. However, the miss distance (MD) tends to be large. The objective function of the relative distance in the PSOG leads the missile to the current position of a target. Therefore, the PSOG is similar to pursuit guidance. In the IPSOG, a new objective function for the PSOG is introduced to improve the guidance performance. The line-of-sight (LOS) rate is taken as the objective function. The fitness function is then evaluated according to the defined objective function. Numerical simulation results show that the guidance performance of the IPSOG is better than the PSOG. | The design of particle swarm optimization guidance using a line-of-sight evaluation method |
S0045790616300131 | Digital watermarking based biometric images protecting has been an active research focus. In this paper, we propose a face image protection scheme based on semi-fragile self-recoverable watermarking. Authentication watermark is generated from the singular value decomposition (SVD) coefficients for each image block, and information watermark is generated from the principal component analysis (PCA) coefficients. Both of them are embedded into wavelet medium frequency coefficients by using the proposed group-based wavelet quantization method. On the authentication side, after identifying the tampered regions, the proposed method can recover the tampered face images by using the hidden information watermark. Experimental results demonstrate that the proposed watermark scheme has high localization accuracy and robustness comparing with the existing techniques, and the recovered PCA coefficients can be used to reconstruct the Eigen-face image or be directly used for recognition system. | Semi-fragile self-recoverable watermarking scheme for face image protection |
S0045790616300143 | Computational complexity and power consumption are prominent issues in wireless telemonitoring applications involving physiological signals. Because of its energy-efficient data reduction procedure, compressed sensing (CS) emerged as a promising framework to address these challenges. In this work, a multi-channel CS framework is explored for multi-channel electrocardiogram (MECG) signals. The work focuses on the successful joint recovery of the MECG signals using a low number of measurements by exploiting the correlated information across the channels. A CS recovery algorithm based on weighted mixed-norm minimization (WMNM) is proposed that exploits the joint sparsity of MECG signals in the wavelet domain and recovers signals from all the channels simultaneously. The proposed WMNM algorithm follows a weighting strategy to emphasize the diagnostically important MECG features. Experimental results on various MECG databases show that the proposed method can achieve superior reconstruction quality with high compression efficiency as compared to its non-weighted counterpart and other existing CS-based ECG compression techniques. | Weighted mixed-norm minimization based joint compressed sensing recovery of multi-channel electrocardiogram signals |
S0045790616300155 | This study analyzes the vibration signals of fault induction motors for establishing an intelligent motor fault diagnosis system by using an extension neural network (ENN). Extension theory and a neural network (NN) are combined to construct the motor fault diagnosis system, which identifies the most likely fault types in motors. First, the vibration signal spectra of the 10 most common fault types are measured and organized into individual motor fault models. Subsequently, according to the motor fault data, representative characteristic frequency spectra are identified, and the correlation between the motor fault types and their corresponding characteristic frequency spectra are established to develop the motor fault diagnosis system. Finally, the test results confirm that the proposed motor fault diagnosis system is fast, requires less training data, and demonstrates first-rate identification capability. | Fault analysis and diagnosis system for induction motors |
S0045790616300167 | Energy efficiency (EE) maximization with limited interference to the primary user (PU) is one of the primary concerns in cognitive radio networks (CRNs). To achieve this objective, we first propose an algorithm to select less spatially-correlated secondary users (SUs) to lessen the shadowing effect in wireless environment. Further, the aid of parametric transformation with the Lagrangian duality theorem in our proposed algorithm called Novel Iterative Dinkelbach method (NIDM) is used to optimise both sensing time and transmission power allocation of the SUs for maximising EE under the constraints of maximum transmission power, interference to the PU, overall outage of secondary transmission and minimum data rate requirement. Extensive simulation results demonstrate the effectiveness of our proposed algorithm. It is also observed that our proposed scheme outperforms the other existing schemes in enhancing the EE with the same system parameters. | Optimal resource allocation for soft decision fusion-based cooperative spectrum sensing in cognitive radio networks |
S0045790616300179 | In this paper, we propose a Parzen window entropy based spectrum sensing algorithm for enhancing the signal-to-noise ratio (SNR) wall of cognitive radio primary user detection. We compute the information entropy using a non-parametric Kernel Density Estimation (KDE) method. Single node sensing is extended to cooperative sensing using the weighted gain combining (WGC) fusion method. The weights of WGC technique are computed using a Differential Evolution(DE) algorithm and compared with the log-likelihood ratio (LLR) method. In addition, the detection performance of the proposed Parzen window entropy is compared with Shannon entropy and energy detection techniques. We consider a DVB-T signal with Additive White Gaussian Noise (AWGN) subjected to Rayleigh fading under noise uncertainty as a primary user signal for simulation. The simulation result reveals that in the case of a single node and cooperative sensing, the proposed method achieves SNR wall of − 19 dB and − 24 dB respectively at the probability of false alarm 0.1. | Parzen window entropy based spectrum sensing in cognitive radio |
S0045790616300180 | Wireless body area networks (WBANs) consist of tiny sensors that enable monitoring the health status of a person. quality of service (QoS) is a major challenge for WBANs due to the importance of vital sign information. Therefore, many QoS-based medium access control (MAC) protocols and technologies have been developed to overcome this problem. Standardization of various technologies and protocols must be addressed. ISO/IEEE 11073 personal health data standards aim to provide interoperability between healthcare devices and technologies. This paper presents a new QoS-aware cross-layer MAC protocol based on the ISO/IEEE 11073 standards that employs a slot allocation scheme, multi-channel architecture, priority mechanism, admission control, and cross-layer solution. The proposed MAC protocol has been modeled and simulated by OPNET Modeler. In addition, the proposed MAC protocol is compared with standard technologies and recent protocols in the literature, and it achieves better results for end-to-end delay, packet loss ratio, and throughput parameters. | Design and implementation of a new quality of service-aware cross-layer medium access protocol for wireless body area networks |
S0045790616300192 | To solve the problems of long time delay and low reliability in existing systems, a Two-level Load-balance Monitoring Strategy (TLLBMS) is proposed in this paper. In monitoring terminal level, based on customizable System-on-Chip A2F500, monitoring terminals implement parallel Principal Component Analysis and Independent Component Analysis (PCA-ICA) algorithm to fulfil quickly preprocessing and feature extraction of electrocardiogram (ECG) signals, and give a pre-classification and alarming mechanism. Depending on the results of pre-classification, monitoring terminals transmit only eigenvectors to monitoring center at a Dynamically Variable Time Interval (DVTI). This will reduce the quantity of data transmission dramatically, which means it will lead to high robustness of communication and short time delay. In monitoring center level, monitoring server processes eigenvectors directly, and realizes quick classification, diagnosis and rescuing. To ensure the correctness of communication and protect data from being falsified, Message Digest Algorithm MD5 is realized to verify the integrity of data. | A remote electrocardiogram monitoring system with good swiftness and high reliablility |
S0045790616300222 | Extreme Learning Machine (ELM) proposes a non-iterative training method for Single Layer Feedforward Neural Networks that provides an effective solution for classification and prediction problems. Its hardware implementation is an important step towards fast, accurate and reconfigurable embedded systems based on neural networks, allowing to extend the range of applications where neural networks can be used, especially where frequent and fast training, or even real-time training, is required. This work proposes three hardware architectures for on-chip ELM training computation and implementation, a sequential and two parallel. All three are implemented parameterizably on FPGA as an IP (Intellectual Property) core. Results describe performance, accuracy, resources and power consumption. The analysis is conducted parametrically varying the number of hidden neurons, number of training patterns and internal bit-length, providing a guideline on required resources and level of performance that an FPGA based ELM training can provide. | Hardware implementation of real-time Extreme Learning Machine in FPGA: Analysis of precision, resource occupation and performance |
S0045790616300246 | Nowadays intelligent and pervasive environments are characterized by a great number of devices and sensors that develop continuously and capture enormous amounts of data. Designing a context-aware system able to provide the most tailored services to users according to their behaviors, preferences and needs is still a research challenge. In such environments, although the context is very complex, dynamic and full of data captured and produced, users aspire to automatically receive contextualized services. The Cultural Heritage domain represents a domain where exchanged and produced data can be opportunely exploited by a set of applications and services in order to transform a static space into a smart environment. In this perspective, this paper presents a context-aware system named Context Evolution System (CES) able to represent and manage the evolution of the context through its instances; such an evolution is driven by occurring events and opportunely modeled by a graph structure. To assess the proposed solution, a Cultural Heritage case study of a real temporary art exhibition named the Beauty or the Truth and located in Naples (Italy) is presented and discussed. | A smart system to manage the context evolution in the Cultural Heritage domain |
S0045790616300258 | Feature selection is a well-studied problem in the areas of pattern recognition and artificial intelligence. Apart from reducing computational cost and time, a good feature subset is also imperative in improving the classification accuracy of automated classifiers. In this work, a wrapper-based feature selection approach is proposed using the evolutionary harmony search algorithm, whereas the classifiers used are the wavelet neural networks. The metaheuristic algorithm is able to find near-optimal solutions within a reasonable amount of iterations. The modifications are accomplished in two ways—initialization of harmony memory and improvisation of solutions. The proposed algorithm is tested and verified using UCI benchmark data sets, as well as two real life binary classification problems, namely epileptic seizure detection and prediction. The simulation results show that the standard harmony search algorithm and other similar metaheuristic algorithms give comparable performance. In addition, the enhanced harmony search algorithm outperforms the standard harmony search algorithm. | An enhanced harmony search based algorithm for feature selection: Applications in epileptic seizure detection and prediction |
S0045790616300271 | In this paper, we propose a cross-layer framework for joint routing and rate adaptation in multi-rate, Multi-Channel, Multi-Radio (MCMR) infrastructure Wireless Mesh Networks (WMNs). These networks use MCMR capabilities of mesh routers to achieve high performance. However, the MCMR nodes introduce interference in the multi-hop mesh networks and can degrade QoS. Thus, the design of routing metrics to improve the QoS has become an important research issue. Furthermore, as the routing metric and rate adaptation decisions are closely related, the joint approach is needed to improve the performance of the network. Towards this, we analytically derive our routing metric using IEEE 802.11 Distributed Coordination Function (DCF) basic access mechanism. Using this model, we propose Passive Interference and Delay Aware (P-IDA) routing metric which estimates the delay and interference by exploiting cross-layer information. We extend the work by performing joint routing and rate adaptation. The simulation results using NS2 reveal that proposed framework improves throughput and delay compared to existing approaches with reduced routing overhead. | A cross-layer framework for joint routing and rate adaptation in infrastructure Wireless Mesh Networks |
S0045790616300283 | Homeland security represents one of the most relevant application contexts for smart cities, attracting the interest of both authorities and the research community. In case of a crisis event occurring in the urban area, authorities are responsible for effectively managing response operations. A critical challenge in emergency management is the lack of real-time coordinated reaction capabilities driven by integrated decision making facilities based on the information obtained by first responders acting on the crisis site. This work aims at supporting coordinated emergency management in smart cities based on the localization of first responders during crisis events. We present a hybrid cloud architecture for managing computing and storage resources needed by command & control activities in emergency scenarios, complemented by a first responder localization service relying on a novel positioning approach which combines the strength of signals received from landmarks placed by first responders on the crisis site with information obtained from motion sensors. | A cloud-based architecture for emergency management and first responders localization in smart city environments |
S0045790616300295 | This paper presents a reconfigurable fault tolerant routing for Networks-on-Chip organized into hierarchical units. In case of link faults or failure of switches, the proposed approach enables the online adaptation of routing locally within each unit while deadlock freedom is globally ensured in the network. Experimental results of our approach for a 16 × 16 network show a speedup by a factor of almost four for routing reconfiguration compared to the state-of-the-art approach. Evaluation with transient faults shows that a dedicated reconfiguration unit enables successful reconfiguration of routing tables even in case of high error probabilities. | Reconfigurable fault tolerant routing for networks-on-chip with logical hierarchy |
S0045790616300301 | X-ray angiography images are widely used to identify irregularities in the vascular system. Because of their high spatial resolution and the large amount of images generated daily, coding of X-ray angiography images is becoming essential. This paper proposes a diagnostically lossless coding method based on automatic segmentation of the focal area using ray-casting and α-shapes. The diagnostically relevant Region of Interest is first identified by exploiting the inherent symmetrical features of the image. The background is then suppressed and the resulting images are encoded using lossless and progressive lossy-to-lossless methods, including JPEG-LS, JPEG2000, H.264 and HEVC. Experiments on a large set of X-ray angiography images suggest that our method correctly identifies the Region of Interest. When compared to the case of coding with no background suppression, the method achieves average bit-stream reductions of nearly 34% and improvements on the reconstruction quality of up to 20 dB-SNR for progressive decoding. | Diagnostically lossless coding of X-ray angiography images based on background suppression |
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