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The support vector machine (SVM) is a very important machine learning algorithm with state-of-the-art performance on many classification problems. However, on large datasets it is very slow and requires much memory. To solve this defficiency, we propose the fast support vector classifier (FSVC) that includes: 1) an efficient closed-form training free of any numerical iterative procedure; 2) a small collection of class prototypes that avoids to store in memory an excessive number of support vectors; and 3) a fast method that selects the spread of the radial basis function kernel directly from data, without classifier execution nor iterative hyper-parameter tuning. The memory requirements of FSVC are very low, spending in average only 6 $\cdot 10^{-7}$ · 10 - 7 sec. per pattern, input and class, and processing datasets up to 31 millions of patterns, 30,000 inputs and 131 classes in less than 1.5 hours (less than 3 hours with only 2GB of RAM). In average, the FSVC is 10 times faster, requires 12 times less memory and achieves 4.7 percent more performance than Liblinear, that fails on the 4 largest datasets by lack of memory, being 100 times faster and achieving only 6.7 percent less performance than Libsvm. The time spent by FSVC only depends on the dataset size and thus it can be accurately estimated for new datasets, while Libsvm or Liblinear are much slower on “difficult” datasets, even if they are small. The FSVC adjusts its requirements to the available memory, classifying large datasets in computers with limited memory. Code for the proposed algorithm in the Octave scientific programming language is provided.1 [ABSTRACT FROM AUTHOR]

As a common incipient fault for a compensation capacitor, the internal capacitor element breakdown fault (CEBF) causes slight changes in the steady-state voltage and current, which are difficult to accurately capture under the influence of measurement error. Therefore, to meet the demand for intelligent equipment maintenance by a smart grid, transient disturbances induced by a CEBF are used in this study to acquire element breakdown information for online monitoring of capacitor health. A transient response analysis of the CEBF in the time and frequency domains is used to establish the typical transient characteristics. Then, a multiple-kernel support vector machine algorithm is adopted to identify the transient disturbances. A method for determining the faulty capacitor unit is proposed based on the position of the waveform monitor. Under the assumption and statistics of the number of failed elements in a series group, a scheme for assessing the health condition is investigated. The proposed method can effectively obtain breakdown information to assess capacitor health with little influence of measurement error and background noise. The results of a theoretical analysis, computer simulation, lab experiment and field monitoring verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Series arc fault is one of the main causes of electrical fire. In a three-phase motor with inverter circuit, it is difficult to accurately identify series arc fault occurred in the lines at the back of the inverter, especially when the power supply contains complicated harmonics. To solve this problem, a new feature extraction method of the series arc fault based on the current signal measured at the front of the inverter (CSMFI) was proposed. The series arc fault experiments under different harmonic power supply conditions were carried out in the three-phase motor with inverter circuit. Five-layer decomposition was performed on the CSMFI by using empirical wavelet transform. The attractor track matrix (ATM) of each decomposed signal was established. The fault features of the series arc fault were obtained by calculating and selecting the singular values of the ATM. The series arc fault was identified by using an optimized support vector machine. The effectiveness of the method and its applicability under different experimental conditions were tested. The method can simultaneously identify the series arc fault occurred in the lines at the front or back of the inverter by analyzing the CSMFI. It also has strong anti-interference ability. [ABSTRACT FROM AUTHOR]

This paper proposes a data-driven small-signal stability constrained optimal power flow (SSSC-OPF) method with high computational efficiency. Instead of repeating the computational expense small-signal stability analysis via differential and algebraic equations during the iterative OPF process, a computationally cheap surrogate constraint for small-signal stability is developed. To reduce the learning difficulty for small-signal stability boundaries, an efficient sample generation strategy is proposed with sampling space compression. This allows us to use the support vector machine (SVM) with a kernel function to derive the explicit data-driven surrogate constraint for small-signal stability. Penalty factor optimization is proposed to compensate for the error caused by SVM. The learned small-signal stability constraint is embedded into the OPF model for generator control. An examination strategy is also developed to avoid the small-signal instability of re-dispatch caused by the error of the data-driven surrogate model. Comparison results with other model-based and data-driven methods on the IEEE 39-bus and 118-bus systems demonstrate the high computational efficiency and economic benefits of the proposed method. [ABSTRACT FROM AUTHOR]

The transient overvoltages in offshore wind farms caused by faults or frequent operations of electrical equipment are particularly severe. In order to classify the internal transient overvoltages in offshore wind farms, this research firstly proposes a feature extraction method based on sparse decomposition using alternating direction method of multipliers, which extracts the harmonic and impulsive components from transient overvoltages. Then based on the impulsive components and transient overvoltages, two identification features are constructed. One is an impulsive feature and the other one is a ratio feature between impulsive energy values and original energy values. Finally, based on the constructed features, a support vector machine is employed to identify different types of internal transient overvoltages. Extensive simulations and experiments are performed to verify the effectiveness of the proposed feature extraction method, which is also compared with the widely used conventional wavelet algorithms and the mathematical morphology. Results show the proposed feature extraction method is capable of classifying and discriminating among various types of internal transient overvoltages with a strong self-adaptive ability and an improved accuracy. [ABSTRACT FROM AUTHOR]

Semiconductors are essential components in many electronic devices. Because wafers are produced quickly and in large quantities, defects occur that adversely affect semiconductor properties. This makes it necessary to install powerful and robust inspection systems which use artificial intelligence techniques in the early stages of the manufacturing chain in order to detect and classify those defects. This paper proposes a method for defect detection and classification on images of semiconductor wafer materials obtained by means of a scanning electron microscope based in the following stages: (i) use of computer vision techniques to isolate the defect from the background; (ii) use of several descriptors based on shape, size, texture, histogram, and key-points to create a feature vector for the characterization of the defect; (iii) application of an exhaustive search as a feature selection method to determine the optimal subset of feature descriptors; and (iv) evaluation of the feature descriptors by using a support vector machine classifier providing the optimal set with highest F1-score metrics. Finally, the effectiveness of the proposed approach is compared with five popular feature selection methods, reporting better classification results than the latter. [ABSTRACT FROM AUTHOR]

Machine learning approaches have exhibited high potential in power system transient stability assessment (TSA), yet their initial preparation stages of stability knowledge base generation (SKBG) based on time-domain simulations often undergo high computational costs. In fact, how to ease the heavy computational burden of SKBG without sacrificing the reliability is still a significant challenge. To address this problem, this paper develops a semi-supervised ensemble learning (SSEL) framework for reliable SKBG acceleration, without additional need for computing hardware upgrading. In particular, it performs detailed simulations for a minority of cases and fast simulations for the majority ones to reduce the total computation time. Considering the absence of stability status information of those fast simulated cases, an SSEL scheme is systematically designed to reliably label their stability status. Before implementing SSEL, two concise feature descriptors are first introduced to efficiently extract transient features from multiplex system trajectories. Then, all the cases are characterized in a unified feature space, whereby a series of semi-supervised support vector machines are trained in randomly formed subspaces. Afterward, these single machines are systematically combined to derive an enhanced SSEL model, which is able to make reliable and robust labeling decisions. Further, a backtrace strategy is carefully devised for SSEL, so as to maintain the high reliability of SKBG. Test results on the IEEE 39-bus system and the realistic GD Power Grid in South China illustrate the excellent performances of the proposed framework on SKBG acceleration. [ABSTRACT FROM AUTHOR]

Objective: Atrial Flutter (AFL) is a supraventricular tachyarrhythmia typically arising from a macroreentry circuit that can have variable atrial anatomy. It is often treated by catheter ablation, the success of which depends upon the correct determination of the electroanatomic circuit, generally through invasive electrophysiological (EP) study. We hypothesized that machine learning (ML) methods applied to the diagnostic 12-lead surface electrocardiogram (ECG) could determine the specific circuit prior to any invasive EP study. Methods: The 12-lead ECGs were reduced to eight independent leads: I, II, V1 – V6. Through an algorithm using ventricular complex cancellation methods, windows of atrial activity in the ECG were uncovered and spectra were generated. Three ML classifier approaches were applied: Support Vector Machine (SVM), Random Forest (RF) and k-Nearest Neighbors (KNN), and their outputs combined using soft voting. Results: Ten-second surface ECGs taken from 419 AFL patients prior to invasive EP study and ablation were analyzed retrospectively. Of the 419 patients, 285 had typical cavotricuspid isthmus (CTI)-dependent AFL, 41 had atypical right-atrial AFL and 93 had left-atrial AFL, as determined during the subsequent EP study. Lead V5 was found to be most useful giving a test accuracy of 98% and f1 score of 0.97. Conclusion: We conclude that ML methods have the potential to automatically determine the AFL macroreentry circuit from the surface ECG. Significance: The AFL classification method presented in this investigation achieves 95+% accuracy on an unbalanced inter-patient dataset which has important clinical applications. [ABSTRACT FROM AUTHOR]

This study presents a method for predicting the insulation status of power cable joints based on partial discharges (PDs). First, PD data are collected from aging experiments with 13 power cable joints involving artificial defects. Second, two PD features with a high identification of aging status are extracted from 104 PD features. Additionally, the initial and final stages of insulation aging are defined, and the labeled PD data are trained using a support vector machine (SVM). Finally, a method to detect insulation failure is proposed by estimating the minimum distance from the PD to the hyperplane of the SVM using nonlinear programming. The alert is defined as the turning of the curve in the final stage. The proposed method verifies that the warning and danger alerts are issued before insulation failure occurs for each sample. [ABSTRACT FROM AUTHOR]

To improve the comfort and user acceptance of the intelligent vehicle braking system, this paper proposes a control strategy for the electronic brake booster (Ebooster) that considers differences in driving behaviors. First, a real-vehicle driving data collection platform is built to collect the driving behavior data of 84 drivers under typical vehicle-following braking conditions. Subsequently, the k-means clustering algorithm optimized by particle swarm optimization (PSO) is used to realize the cluster analysis of driving behaviors, and the support vector machine (SVM) algorithm optimized by the genetic algorithm (GA) is used to complete the identification of driving behaviors. Next, through the analysis of the boost characteristics of the self-designed Ebooster, driving behaviors are matched with boost characteristics, and a personalized basic boosting control strategy based on the driver behavior is designed. Finally, a hardware-in-the-loop (HiL) bench and a real-vehicle test platform are designed and built to verify the effectiveness of the personalized control strategy of the Ebooster. The results show that this method can effectively identify the type of driver and make personalized adjustments to the basic boosting strategy of the Ebooster, which effectively improves the personalization of the braking system, resulting in a higher pedal response and user acceptance. [ABSTRACT FROM AUTHOR]

Dissolved gas analysis (DGA) is widely used for oil-immersed power transformers as a conventional fault diagnosis tool. However, interpretation criteria from DGA assessment often depends on empirical discrimination from a specialist, which can render unreliable or ambiguous diagnoses. Intelligent fault classification algorithms can be implemented to conquer uncertainty in conventional methods, and which require feature learning of transformer condition information data rather than expert experience. In this paper, a Gaussian process multi-classification (GPMC) method is proposed, which uses multiclass recognition with a Gaussian process (GP) and renders an output with a probabilistic interpretation rather than a deterministic guess. The method is investigated using large-scale DGA field datasets to improve diagnostic accuracy and presents reliable incipient fault diagnosis ability. A kernel-based learning algorithm and versatile artificial intelligence (AI) methods, (support vector machine (SVM), artificial neural network (ANN), K-nearest neighbors (KNN), decision tree and logistic regression (LR)) have been used to obtain comparative classification accuracy in comparison to the proposed method. Additional comparison is demonstrated between conventional DGA and AI methods. The effectiveness and robustness of the proposed GPMC method are confirmed by experimental accuracy >95%, which illustrates that the proposed method is able to provide superior and reliable diagnoses for operational transformer faults. [ABSTRACT FROM AUTHOR]

During the running of electric locomotives, a large amount of operating data will be stored through the Analog-Digital Converter (ADC) and the recording oscillograph of the on-board Traction Control Unit (TCU). Analysis based on mass data to detect slipping is of great significance for saving energy, improving the performance and safety of the traction control system of electric locomotives. Therefore, after analyzing and summarizing the advantages of Empirical Wavelet Transform (EWT) and Fuzzy Entropy (FE) algorithm in data processing and the classification theory of Support Vector Machine (SVM), this paper proposed a SVM slipping detection method based on EWT and FE algorithm. Firstly, feature extraction is carried out on the locomotive wheelset velocity data collected by the on-board velocity sensor, that is, the feature vector of data is extracted by calculating fuzzy entropy value after EWT, and the velocity data is converted into feature matrix for slipping-detection of SVM. Then considering the defects of traditional optimization algorithms such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) in the parameter optimization of SVM, Genetic Particle Swarm Optimization Algorithm (GAPSO) is proposed to optimize the parameters of SVM classification model, and the slipping detection model of SVM is established through training samples, which realizes high-precision slipping detection. Finally, combined with a large number of locomotive data, the comparative analysis test is carried out, the results show that the feature extraction method based on EWT and FE algorithm has excellent ability of mode decomposition and feature extraction, GAPSO has efficient optimization ability, and the proposed slipping detection method can accurately detect slipping. [ABSTRACT FROM AUTHOR]

In this paper, we propose an accurate pedestrian detection scheme with privacy preservation (PPPD) based on pedestrian images. By utilizing the vector homomorphic encryption (VHE), private linear-transforming matrices are ingeniously designed to enable arbitrary permutation operations for the encrypted vectors. In this way, the feature vector extraction of the histogram of oriented gradient (HOG) can be efficiently performed over the encrypted pedestrian images. Furthermore, due to the encrypted inner product calculations supported by VHE, an encrypted kernel matrix is constructed to generate multiple encrypted kernels (i.e., linear, polynomial, and Gaussian kernels). The pedestrian detection model based on the supported vector machine (SVM) can be securely trained over the encrypted kernels. With the proposed scheme, the extracted features of pedestrian images are not necessary to be returned to the image owner for decryption, such that the communication costs can be significantly reduced. In addition, the privacy of the whole process in pedestrian detection can also be guaranteed. Extensive experiments are conducted over multiple pedestrian datasets, and it is demonstrated that PPPD can achieve high accuracy of pedestrian detection with lower computation and communication overhead compared with the existing schemes. [ABSTRACT FROM AUTHOR]

With the development of millimeter-wave (mmWave) technology and vehicle-to-vehicle (V2V) communications, the mmWave vehicular ad hoc networks (VANETs) is envisioned to support rapidly growing number of vehicles. Against this background, each V2V user (VUE) is expected to employ large-scale array to form directional analog beams for improving spatial spectrum reuse, and it is capable of achieving concurrent transmissions from multiple other VUEs simultaneously. However, due to the high dynamics of V2V links, it can be challenging for each VUE to quickly select an effective analog beam. In this paper, we propose a machine learning (ML) approach to achieve an efficient and fast analog beam selection for mmWave V2V communications. Specifically, we first derive the probabilities that multiple V2V transmitters (TXs) serve one VUE to obtain the average sum rate (ASR) for mmWave V2V communications. On that basis, we develop an ML approach to maximize the ASR, whereby the support vector machine (SVM) classifier is utilized for optimizing the analog beam selection. Besides, we further proposed an iteration sequential minimal optimization training algorithm to train data samples of all V2V links, and convergence of the proposed solution is also discussed. Finally, an extensive sample training and simulations are evaluated by Google TensorFlow. The results verified that our proposed ML approach is capable of achieving a higher ASR yet substantially lower computational complexity than traditional solutions based on explicitly estimated channels. [ABSTRACT FROM AUTHOR]

In the vehicle type classification area, the necessity to improve classification performance across traffic surveillance cameras has garnered attention in research especially on high level feature extraction and classification. The backpropagation (BP) training approach of traditional deep Convolutional Neural Network (CNN) approach is time-consuming without using a Graphics Processing Unit (GPU). In this paper, we propose a semisupervised strategy for the end-to-end Principal Component Analysis Convolutional Network (PCN) in the area of vehicle type classification. Even without using a GPU, the proposed model eliminates the time-consuming training procedure of convolutional filter bank. In particular, the convolutional filters of the network are generated using unsupervised learning by Principal Component Analysis (PCA) which has tremendously reduced training cost and also reinforced the robustness of extracted features against various distortions. In order to further improve the training procedure while still preserving the discriminative characteristic of the system, only the fully-connected layer is fine-tuned in the supervised classification stage. The PCN is tested using a public BIT-Vehicle dataset which comprises 9850 surveillance-nature vehicle frontal-view images. The PCN can be easily implemented and readily compatible with many effective classifiers. Two classifiers, namely softmax classifier and Support Vector Machine (SVM) are employed in this network and their classification performances are then compared. Both classifiers take less than 100 seconds in the training process and are able to produce an average accuracy of above 88.35%, even under various inferior imaging conditions. [ABSTRACT FROM AUTHOR]

Video services have hold a surprising proportion of the whole network traffic in wireless communication networks. Accurate prediction of video traffic can endow networks with intelligence in resource management, especially for the forthcoming beyond the fifth-generation (B5G) networks. However, the existing approaches fail to accurately predict video traffic with all types of frames, due to the natures of strong long-range dependence, self-similarity and burstiness. Obviously, it is unable to meet the QoS and QoE requirements of dynamic bandwidth allocation. In this paper, we propose the feasibility of advanced machine learning methodology applied in nonstationary video traffic prediction, i.e., smoothing-aided support vector machine (SSVM) model. The model utilizes classical smoothing methods to preprocess video traffic by relieving the drastic fluctuation of video stream. It can provide an effective association for the subsequent support vector regression, as the preprocessed data becomes more smooth and continuous than the original unprocessed one. Experimental results show that our proposed model significantly outperforms the state of the art model, i.e., logistic smooth transition autoregressive, in prediction performance. The superior nonlinear approximation capacity is further demonstrated by visualized statistical analysis. [ABSTRACT FROM AUTHOR]

A support vector machine (SVM) is an important method in the detection and classification of the color difference on a polysilicon wafer. However, the accuracy of a SVM is affected by its feature vector and parameters. Owing to the complex color information and random texture features on the wafer surface, the feature design is extremely complicated. Meanwhile, a SVM optimized using a popular intelligent algorithm easily falls into a local optimum, and the convergence of the algorithm needs to be improved. Therefore, a classification method is proposed for detecting the color difference from multi-scale features in polysilicon wafer images. First, to extract the features, an image segmentation method is devised based on the maximum region contrast, which effectively applies a threshold segmentation of the wafer images. Second, the multi-scale features and color representations in different color spaces are used to construct a nine-dimensional feature vector that sufficiently describes the surface characteristics of the wafer. An approach to optimize the SVM is finally proposed using a magnetic bacteria optimization algorithm based on an elitist strategy for parameter optimization. The optimum individual of each generation is used to adjust the magnetic moment such that the solution approaches the optimal direction and enhances the global search ability. A fitness function is also introduced to improve the diversity of the solutions through a cross-validation method. The experiment results show that the proposed algorithm achieves an accuracy of 98.3% with a better classification performance than the other methods and that the color difference of polysilicon wafers can be effectively detected. [ABSTRACT FROM AUTHOR]

Wireless fidelity (WiFi) and long term evolution (LTE) coexistence system has attracted significant attention recently. However, most studies on coexistence only consider the issues about downlink performance and fairness between WiFi network and LTE network. In this paper, we take a comprehensive consideration of both the performance of WiFi and LTE, to propose a downlink/uplink decoupling (DUDe) scheme for the fairness of the coexistence system. Through the process of modeling construction, problem formulation with reformulation, solution and simulations, we illustrate that DUDe can achieve proportional fairness in terms of sum throughput and load balancing between uplink and downlink. In our decoupling model for coexistence between WiFi and LTE, resource allocation is under adaptive spectrum partitioning and users can access each network of WiFi and LTE. Moreover, to make our proposal more realistic, we adopt support vector machine (SVM) to predict the total number of users in WiFi and LTE coexistence system, where the predictive simulation shows its high prediction accuracy with low complexity. We further apply the prediction results to the optimization problem of DUDe. Simulation results manifest that the proposed DUDe scheme has better performance in terms of sum throughput. The sum throughput of DUDe scheme can come up to almost twice more than that of coupled association scheme. The result shows that DUDe can also achieve load balance between uplink and downlink in the coexistence system. [ABSTRACT FROM AUTHOR]

In this paper, a means of generating residuals based on a fault isolation observer (FIO) and evaluating them using a support vector machine (SVM) is proposed. The proposed FIO generates the isolated residual signals and they shows robust performance regardless of unknown road surface conditions. This FIO is designed using a linear time-invariant quarter-car model. While quarter-car models have the form of a bilinear system, in this study the authors convert this bilinear model to a linear model with model uncertainty based on the assumption that the control input is limited. Therefore, the proposed FIO can be used regardless of the type of damper or controller. Furthermore, the SVM based residual evaluator without empirically set thresholds is used to evaluate the generated residuals. The proposed fault diagnosis algorithm is expected to reduce the effort required in the design procedure and it can also detect a small amount of sensor fault that cannot be detected by traditional limit-checking method. The proposed fault diagnosis algorithm is verified using low cost production accelerometers and a quarter-car test rig. Consequently, the fault diagnosis algorithm proposed in this paper can detect the faults of a sprung mass accelerometer and an unsprung mass accelerometer independently, and this algorithm can reduce the effort required in designing the diagnosis algorithm greatly. [ABSTRACT FROM AUTHOR]

In order to accurately and effectively diagnose the transmission line faults of the power system, a genetic-algorithm support vector machine and the D-S evidence theory based fault diagnostic model is proposed. Two genetic-algorithm support vector machine based diagnosis boxes separately identify the fault types to get two kinds of preliminary diagnostic results according to the voltage and current phasors data from the phasor measurement units. Then, a deviation coefficient is derived to represent the diagnostic conflicts existing in the two kinds of preliminary diagnostic results. The mass function of the D-S evidence theory is updated by using the deviation coefficient to eliminate the diagnostic conflicts and improve the diagnosis accuracy. Finally, the proposed diagnostic model was applied in an actual power system in Hubei province in China. The simulation results and practical results indicated the feasibility and the effectiveness of the diagnostic model. [ABSTRACT FROM AUTHOR]

Voltage sags are often manifested as the permanent or incipient faults occurred in the power system because of equipment malfunctions or failures. The incipient faults that are originally self-cleaning faults may repeatedly occur and gradually develop to a permanent fault after its first occurrence. The incipient fault detection is considered as a crucial task in predictive maintenance for power equipment, such as transformers, circuit breakers, and underground cables. This paper proposes a hybrid method for incipient faults detection and classification. The proposed method firstladopts two extraction methods and two feature measures to obtain seven peculiar features from voltage waveforms of abnormal phases recorded by power quality monitors at substations in a transmission network. Then, a feature selection method and the support vector machine combined with particle swarm optimization are applied to classify various types of incipient faults. Test results show that the proposed method contributes relatively accurate classification of incipient faults and can be employed as a useful tool for condition monitoring of major power equipment in the transmission network. [ABSTRACT FROM AUTHOR]

In future 5G user-centric ultra-dense networks (UUDN), demands of high data rate and high spectrum efficiency are effectively met by dual connectivity (DC) technology. However, due to huge increase of base stations (BSs) and mobile users (MUs), it becomes difficult for BSs to quickly and precisely select the codeword and provide DC to MUs. Hence, different from some traditional methods, this correspondence paper aims to improve the network performance using the method of machine learning. First, we model the random distribution of BSs by homogeneous Poisson point processes, where each MU is served by millimeter-wave channel. Second, the probabilities that macro cell BS or small cell BS serves the MU are further derived to get the average sum rate (ASR) in UUDN. Third, inspired by ML, we utilize an iterative support vector machine (SVM) classifier to select the codewords of BSs, with sequential minimal optimization (SMO) algorithm used for training all link samples in UUDN. Then, an iterative SVM-SMO classification algorithm is proposed to achieve a highly efficient performance of DC, where the convergence and complexity are also discussed. The sample training and simulation at last are evaluated by Google TensorFlow. The simulation verified that our proposed algorithm gets a higher ASR than the traditional channel estimation based algorithm. In addition, the results also show a lower computational complexity can be achieved by the proposed algorithm as well. [ABSTRACT FROM AUTHOR]

For visual tracking methods based on kernel support vector machines (SVMs), data sampling is usually adopted to reduce the computational cost in training. In addition, budgeting of support vectors is required for computational efficiency. Instead of sampling and budgeting, recently the circulant matrix formed by dense sampling of translated image patches has been utilized in kernel correlation filters for fast tracking. In this paper, we derive an equivalent formulation of a SVM model with the circulant matrix expression and present an efficient alternating optimization method for visual tracking. We incorporate the discrete Fourier transform with the proposed alternating optimization process, and pose the tracking problem as an iterative learning of support correlation filters (SCFs). In the fully-supervision setting, our SCF can find the globally optimal solution with real-time performance. For a given circulant data matrix with $n^2$ n 2 samples of $n \times n$ n × n pixels, the computational complexity of the proposed algorithm is $O(n^2\; \log n)$ O (n 2 log n) whereas that of the standard SVM-based approaches is at least $O(n^4)$ O (n 4) . In addition, we extend the SCF-based tracking algorithm with multi-channel features, kernel functions, and scale-adaptive approaches to further improve the tracking performance. Experimental results on a large benchmark dataset show that the proposed SCF-based algorithms perform favorably against the state-of-the-art tracking methods in terms of accuracy and speed. [ABSTRACT FROM AUTHOR]

The performance of the source enumeration with the uniform circular array (UCA) is deteriorated considerably by the colored noise. This effect becomes more obvious especially when the total number of the sources is lower and closed to that of the array sensors. To address this problem, this paper proposes a source enumeration modeling based on a UCA having M antennas (M-UCA). The M-UCA is operated in a narrow band far field environment with the arbitrary azimuth and elevation angles. First, the Hilbert Huang transformation is employed to derive the phase sequence of each selected intrinsic mode function (IMF) of the signal acquired by each antenna of the M-UCA. Then, the phase sequences of the selected IMFs of the signal acquired by all the antennas of the M-UCA are employed to construct an instantaneous phase covariance matrix (IPCM) among the antennas in the UCA. An IPCM is constructed for each order of the selected IMFs. After performing the eigenvalue decomposition of the all IPCMs, a set of eigenvalues carrying useful information for the estimation of the total number of the sources is acquired. This set is served as the training set. Finally, the training set is used to derive the mathematical model via the support vector machine. The trained model can be used to perform the sources number detection. Both the computer numerical simulations and the radio frequency anechoic chamber experiments validate that the source estimation accuracy of the constructed M-UCA mathematical model based on our proposed method has a high robustness to the incident angles variations, the SNR variations, and the snapshot number variations in the colored noise environment in the determined case, especially in the case of that the total number of the signal sources is only one less than the number of the antennas in the UCA. [ABSTRACT FROM AUTHOR]

This paper proposes a parameter identification technique for composite ZIP and electronic loads by leveraging the support vector machine (SVM) approach. Since the active power and the reactive power of electronic loads are piecewise functions of the voltage magnitude, the operating modes of electronic loads are determined by the voltage magnitude. To improve the accuracy of parameter identification, two filters (Hampel and Savitzky–Golay) are employed to preprocess measurements to reduce noise. The data after noise reduction serve as training data for the regression model that is solved by the SVM approach. Numerical results show that the SVM approach with filters can identify the parameters of the composite ZIP and electronic load model with high accuracy. [ABSTRACT FROM AUTHOR]

Vascular ageing is known to be accompanied by arterial stiffening and vascular endothelial dysfunction, and represents an independent factor contributing to the development of cardiovascular disease. The microvascular pulse is affected by the biomechanical alterations of the circulatory system, and has been the focus of studies aiming at the development of non-invasive methods able to extract physiologically relevant features.Objective: proposing an approach for the assessment of vascular ageing based on a support vector machine (SVM) learning from features of the pulse contour.Methods: the supervised classifier was trained and validated over 20935 models of pulse wave, obtained with a multi-Gaussian decomposition algorithm, applied to laser Doppler flowmetry signals of 54 healthy, non-smoker subjects.Results: the multi-Gaussian model showed a mean R2of 0.98 and an average normalized root mean square error of 0.90, demonstrating the ability to reconstruct the pulse shape. Over 30 training and validation experiments, the SVM showed a mean Pearson's r of 0.808 between the rate of waves classified asoldand the age of the subjects, along with an average area under the ROC curve of 0.953.Conclusion: the SVM showed the capability to discriminate differently aged individuals.Significance: the proposed method might detect the ageing-related modifications of the vascular tree; furthermore, since diabetes promotes vascular alterations comparable to ageing, this approach may be also suitable for the screening of diabetic angiopathy. [ABSTRACT FROM AUTHOR]

Objective: This study was performed to evaluate how well states of deep sedation in ICU patients can be detected from the frontal electroencephalogram (EEG) using features based on the method of atomic decomposition (AD).Methods: We analyzed a clinical dataset of 20 min of EEG recordings per patient from 44 mechanically ventilated adult patients receiving sedatives in an intensive care unit (ICU) setting. Several features derived from AD of the EEG signal were used to discriminate between awake and sedated states. We trained support vector machine (SVM) classifiers using AD features and compared the classification performance with SVM classifiers trained using standard spectral and entropy features using leave-one-subject-out validation. The potential of each feature to discriminate between awake and sedated states was quantified using area under the receiver operating characteristic curve (AUC).Results: The sedation level classification system using AD was able to reliably discriminate between sedated and awake states achieving an average AUC of 0.90, which was significantly better ($p <0.05$) than performance achieved using spectral (AUC = 0.86) and entropy (AUC = 0.81) domain features. A combined feature set consisting of AD, entropy, and spectral features provided better discrimination (AUC = 0.91, $p <0.05$) than any individual feature set.Conclusions: Features derived from the atomic decomposition of EEG signals provide useful discriminative information about the depth of sedation in ICU patients.Significance: With further refinement and external validation, the proposed system may be able to assist clinical staff with continuous surveillance of sedation levels in mechanically ventilated critically ill ICU patients. [ABSTRACT FROM AUTHOR]

In this paper, we present an efficient allocation of power and channel so that power-drained primary users can harvest energy from secondary systems through wireless power transfer, and in return, the secondary system can have full access to the licensed spectrum for a certain length of time. Considering partial channel state information between primary and secondary systems, we formulate an optimization problem consisting of probabilistic constraint. By introducing a confidence level and using support vector machine, we convert the probabilistic constraint to deterministic form. We also implement a particle swarm optimization technique to solve the resource allocation optimization problem, while guaranteeing the required data rate for both primary and secondary systems. The results verify that the proposed scheme enables a higher number of primary users to efficiently harvest energy, and it also increases total primary capacity. [ABSTRACT FROM AUTHOR]

The electric field calculations cannot effectively guide the insulation design of high voltage equipment since the insulation strengths of dielectrics cannot be predicted according to the electromagnetic analysis. In this paper, some features related to the electric field distribution such as the electric field strength, electric field gradient, path length, and the electric field integral are defined and extracted from finite element calculations of air gaps. The electric field features are taken as the input parameters of a grey correlative model established by support vector machine (SVM), so as to establish the multi-dimensional nonlinear relationships between these features and the discharge voltage of an air gap. The grid search (GS) method was used to search the optimal parameters of the SVM. This model was applied to predict the 50% discharge voltages of large sphere-plane air gaps in UHVDC valve halls. The predicted results were obtained after multiple iterative calculations, using the golden section search method. Compared to experimental data, the relative errors of the predicted results are within an acceptable range in view of engineering applications, which indicates the validity of the proposed method. This paper achieves the discharge voltage of air gaps by numerical computations instead of costly experiments. [ABSTRACT FROM AUTHOR]

Objective: An adaptable lower limb prosthesis with variable stiffness in the transverse plane requires a control method to effect changes in real time during amputee turning. This study aimed to identify classification algorithms that can accurately predict turning using inertial measurement unit (IMU) signals from the shank with adequate time to enact a change in stiffness during the swing phase of gait when the prosthesis is unloaded. Methods: To identify if a turning step is imminent, classification models were developed around activities of daily living including 90° spin turns, 90° step turns, 180° turns, and straight walking using simulated IMU data from the prosthesis shank. Three classifiers were tested: support vector machine (SVM), K nearest neighbors (KNN), and a bagged decision tree ensemble (Ensemble). Results: Individual training gave superior results over training on a pooled set of users. Coupled with a simple control scheme, the SVM, KNN, and Ensemble classifiers achieved 96%, 93%, and 91% accuracy (no significant difference), respectively, predicting an upcoming turn 400 ± 70 ms prior to the heel strike of the turn. However, classification of straight walking transition steps varied between classifiers at 85%, 82%, 97% (Ensemble significantly different, $p\,= \,0.002$ ), respectively. Conclusion: The Ensemble model produced the best result overall; however, depending on the priority of identifying turning versus transition steps and processor performance, the SVM or KNN might still be considered. Significance: This research would be useful to help determine a classifier strategy for any lower limb device seeking to predict turn intent. [ABSTRACT FROM PUBLISHER]

The carried out investigations deal with the application of machine learning algorithms to Duval Pentagon 1 graphical method for the diagnosis of transformer oil. In fact, combined to graphical methods, pattern recognition aims to may complement. For this purpose, we have used the Support Vector Machine (SVM) and the K-Nearest Neighbor (KNN) algorithms combined to the Duval method. The SVM parameters have been optimized with the Particle Swarm Optimization (PSO). Inspired from IEC and IEEE, five classes namely PD, D1, D2, T1&T2, and T3 have been adopted. The combined algorithms were verified using 155 samples from IEC TC 10 and related databases. We found that KNN, SVM may complement the Duval Pentagon 1 diagnosis method. [ABSTRACT FROM AUTHOR]

This paper proposes an automatic classification method based on machine learning in contrast-enhanced ultrasonography (CEUS) of focal liver lesions using the contrast agent Sonazoid. This method yields spatial and temporal features in the arterial phase, portal phase, and post-vascular phase, as well as max-hold images. The lesions are classified as benign or malignant and again as benign, hepatocellular carcinoma (HCC), or metastatic liver tumor using support vector machines (SVM) with a combination of selected optimal features. Experimental results using 98 subjects indicated that the benign and malignant classification has 94.0% sensitivity, 87.1% specificity, and 91.8% accuracy, and the accuracy of the benign, HCC, and metastatic liver tumor classifications are 84.4%, 87.7%, and 85.7%, respectively. The selected features in the SVM indicate that combining features from the three phases are important for classifying FLLs, especially, for the benign and malignant classifications. The experimental results are consistent with CEUS guidelines for diagnosing FLLs. This research can be considered to be a validation study, that confirms the importance of using features from these phases of the examination in a quantitative manner. In addition, the experimental results indicate that for the benign and malignant classifications, the specificity without the post-vascular phase features is significantly lower than the specificity with the post-vascular phase features. We also conducted an experiment on the operator dependency of setting regions of interest and observed that the intra-operator and inter-operator kappa coefficients were 0.45 and 0.77, respectively. [ABSTRACT FROM PUBLISHER]

Variations of target appearances due to illumination changes, heavy occlusions, and target deformations are the major factors for tracking drift. In this paper, we show that the tracking drift can be effectively corrected by exploiting the relationship between the current tracker and its historical tracker snapshots. Here, a multi-expert framework is established by the current tracker and its historical trained tracker snapshots. The proposed scheme is formulated into a unified discrete graph optimization framework, whose nodes are modeled by the hypotheses of the multiple experts. Furthermore, an exact solution of the discrete graph exists giving the object state estimation at each time step. With the unary and binary compatibility graph scores defined properly, the proposed framework corrects the tracker drift via selecting the best expert hypothesis, which implicitly analyzes the recent performance of the multi-expert by only evaluating graph scores at the current frame. Three base trackers are integrated into the proposed framework to validate its effectiveness. We first integrate the online SVM on a budget algorithm into the framework with significant improvement. Then, the regression correlation filters with hand-crafted features and deep convolutional neural network features are introduced, respectively, to further boost the tracking performance. The proposed three trackers are extensively evaluated on three data sets: TB-50, TB-100, and VOT2015. The experimental results demonstrate the excellent performance of the proposed approaches against the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

In modern industrial environment, the demand for condition monitoring and maintenance management for the induction motor has increased. Among all the components of the induction motor, bearing is the critical component and the fault occurring in it has to be considered as a major issue. Usually, the bearing fault can be detected by the vibrational analysis. However, this method has a disadvantage that location of the equipment is not always easily accessible, and also it is quite costly. Thus, in this paper, an experiment for detecting the fault in the bearing of a three phase induction motor is achieved by the frequency selection in the stator-current spectrum. Their feature was evaluated by the fast Fourier transform and the diagnosis was performed by a support vector machine. Experimental results were obtained considering two types of outer raceway bearing faults at different load conditions and promising results were obtained. [ABSTRACT FROM PUBLISHER]

Cable joints are the weakest point in cross-linked polyethylene (XLPE) cables and are susceptible to insulation failures. Partial discharge (PD) analysis is a vital tool for assessing the insulation quality in cable joints. Although many works have been done on PD pattern classification, it is usually performed in a noise-free environment. Also, works on PD pattern classification are mostly done on lab fabricated insulators, where works on actual cable joint defects are less likely to be found in literature. Therefore, in this work, classifications of cable joint defect types from partial discharge measurement under noisy environment were performed. Five XLPE cable joints with artificially created defects were prepared based on the defects commonly encountered on-site. A high noise tolerance principal component analysis (PCA)-based feature extraction was proposed and compared against conventional input features such as statistical and fractal features. These input features were used to train the classifiers to classify each defect type in the cable joint samples. Classifications were performed using Artificial Neural Networks (ANN) and Support Vector Machine (SVM). It was found that the proposed PCA features displayed the highest noise tolerance with the least performance degradation compared to other input features under noisy environment. [ABSTRACT FROM AUTHOR]

Active learning (AL) methods that select unlabeled samples only querying by informative measures (i.e., uncertainty and/or diversity criteria) have been extensively investigated. However, these methods usually do not exploit the manifold structure of the unlabeled data from the geometrical point of view, a choice that might lead to a sample bias and consequently undesirable performances. To control and possibly overcome such drawbacks, this paper explores AL methods based on joint informative and manifold structure representative sampling (JI-MSRS). In JI-MSRS, a portion of the unlabeled samples that are added at each iteration is selected according to the informative measures, whereas another portion is selected according to their capability to represent the data cluster structure. Four popular manifold learning methods, namely, principle component analysis (PCA), linear discriminant analysis, kernel PCA, and neighborhood preserving embedding, are used to model the data structure. Then, Delaunay triangulation nets are used to build a discrete approximation of the geometrical structure of the unlabeled data cloud in a low-dimensional space. To show the effectiveness of this novel sampling strategy, results on three real multi-/hyperspectral data sets are presented, adding a thorough comparison with other state-of-the-art AL techniques. In comparison to conventional AL heuristics, the proposed techniques are able to obtain competitive or even better classification accuracy values. [ABSTRACT FROM PUBLISHER]

Insulators are the most common equipment in power systems, and the failure of insulators would be the direct threat to the stability and safety of the system. With the advantages of being non-contact and non-destructive, infrared imaging technology is efficient for monitoring and evaluating the thermal condition of insulators. Detecting the position of the insulator string in an infrared image is a crucial step for automatic diagnosis, thus an efficient and robust representation of insulator string is necessary. This paper proposes a novel method for insulator strings detection, and presents an appearance representation for insulator strings in infrared image based on Binary Robust Invariant Scalable Keypoints (BRISK) and Vector of Locally Aggregated Descriptors (VLAD). We name this feature generation method Binary Feature Pooling. A classification model based on Support Vector Machine (SVM) is integrated into multi-scale sliding window framework for locating insulator string in infrared image. Then redundant regions are merged by non-maximum suppression and shape prior knowledge constraints. The classification accuracy of the proposed method is 89.1026% on our standard infrared insulator dataset, and insulator strings under various conditions can be successfully detected. The results show that this method can detect multiple insulator strings in infrared images with low resolution and complex background. [ABSTRACT FROM PUBLISHER]

Milling is one of the most popular forms of hard tissue removal process, it is sometimes high-risk because there may be very small distance between the milling trajectory and vital anatomy. This paper introduces a vibration signal acquiring and processing method to identify different types of milling states in robot-assisted orthopedics. During milling process, the tissue vibration signal measured by a laser displacement sensor, and the acceleration signal of the operation power device recorded by an accelerometer, are decomposed by the lifting wavelet packet transform, so the harmonic component whose frequency is an integer times of the spindle frequency of the operation power device is extracted. In consideration of that the tissue vibration amplitude varies with the milling force, the relative magnitude of the force is estimated from the acceleration signal, and then the wavelet energy of the tissue vibration signal is divided by that of the acceleration signal to reduce the influence of the force disturbance. The compensated wavelet energy is subsequently used as input vector to a support vector machine for discriminating different milling states. Experimental results on porcine spines demonstrate that the proposed method can successfully discriminate the tissues in the operation field: the success rate achieves 100% for the vertebra being milled and the spinal cord, for the adjacent bony structure and the muscle the success rate is about 90%. [ABSTRACT FROM PUBLISHER]

This paper proposes a combined modeling method for switched reluctance machine (SRM) with few samples of flux linkage characteristics measured without rotor clamping devices and position sensors. The proposed method mainly consists of two steps, namely data reconstruction and characteristic description. In data reconstruction, the entire flux linkage characteristics are obtained by training support vector machine (SVM) with the measured few samples. The characteristics from the trained SVM are consistent well with those from the rotor-clamping measurement. In characteristic description, back-propagation neural network (BPNN) is adopted to describe the reconstructed flux linkage characteristics and calculated static torque characteristics. On this basis, the simulation model of the SRM prototype is built in MATLAB. The results from simulation under both motoring and generating mode are compared with those from experiments, and good agreements can be found, which prove the effectiveness of the proposed modeling method. To further demonstrate the accuracy and application of the entire flux linkage characteristics obtained from data reconstruction, BPNN is used again to build the mapping from flux linkage and phase current to rotor position for rotor position estimation, and some results are presented. The applicability of the proposed method to different SRM topologies is discussed as well. [ABSTRACT FROM PUBLISHER]

The human category is among the most commonly captured and therefore important subject in digital cameras. As a result, human segmentation also plays an important role in the camera system. This paper presents a framework of non-parametric learning that achieves human segmentation using support vector machine (SVM). In the training stage, the training human windows are warped to a normalized size and oversegmented into regular superpixels. A two-dimensional (2D) array of SVM models is then trained by extracting various edge and color features from each superpixel. In this process of SVM training, the 2D array of SVM models automatically and effectively learns the characteristics of the human shape. Given an affinely warped human window for testing, the proposed method calculates superpixels' initial scores of belonging to a human (foreground) using the trained SVM models. Finally, the initial prediction scores are effectively propagated by optimizing a well-defined energy function using an estimated confidence map. In experiments on a publicly available challenging dataset, the proposed framework rapidly yields excellent results in human segmentation both qualitatively and quantitatively1. [ABSTRACT FROM PUBLISHER]

Dissolved gas analysis (DGA) of oil is used to detect the incipient fault of power transformers. This paper presents a new approach for transformer fault diagnosis based on selected gas ratios concentrated in oil and support vector machine (SVM). Firstly, based on IEC TC 10 database, the optimal dissolved gas ratios (ODGR) are obtained by genetic algorithm (GA) that is designed for simultaneous DGA ratios selection and SVM parameters optimization. Three traditional methods, namely, DGA data with SVM and back propagation neural network (BPNN), IEC criteria, and IEC three-key gas ratios with SVM and BPNN are employed for effectiveness comparison. The fault diagnosis results of IEC TC 10 database show that the proposed ODGR with SVM may be used as an alternative tool for transformer fault diagnosis. In addition, the robustness and generalization ability of ODGR is confirmed by the diagnosis accuracy of 87.18% of China DGA samples. The obtained results illustrate that it is preferable to apply the proposed ODGR to transformer fault diagnosis with the assistance of SVM. [ABSTRACT FROM AUTHOR]

Goal: The purpose of this paper is to develop a classification method that combines both spectral and spatial information for distinguishing cancer from healthy tissue on hyperspectral images in an animal model. Methods: An automated algorithm based on a minimum spanning forest (MSF) and optimal band selection has been proposed to classify healthy and cancerous tissue on hyperspectral images. A support vector machine classifier is trained to create a pixel-wise classification probability map of cancerous and healthy tissue. This map is then used to identify markers that are used to compute mutual information for a range of bands in the hyperspectral image and thus select the optimal bands. An MSF is finally grown to segment the image using spatial and spectral information. Conclusion: The MSF based method with automatically selected bands proved to be accurate in determining the tumor boundary on hyperspectral images. Significance: Hyperspectral imaging combined with the proposed classification technique has the potential to provide a noninvasive tool for cancer detection. [ABSTRACT FROM PUBLISHER]

Turn-to-turn short-circuit of stator winding due to deterioration of electrical insulation is one of the most probable faults in motor drive systems. This paper proposes two diagnostic methods to detect the fault based on Support Vector Machine. In the first method, magnitude and phase of load currents at fixed rotating speeds are considered as features reflecting winding condition. It is found that the method is effective particularly in slight insulation failure like one turn-to-turn short-circuit. Classification of winding condition, i.e. specification of number of short-circuit turns, is also achieved by this method. In the second method, only magnitude of load currents is treated as feature aiming at diagnosis available when load current varies. The method also has the advantage of low cost and short data processing time. It is found that two turn-to-turn short-circuit can be diagnosed with practically acceptable accuracy. An appropriate choice of the two methods should be recommended depending on the goal as well as circumstance of diagnosis. [ABSTRACT FROM AUTHOR]

This paper proposes a robust environmental sound recognition system using a fast noise suppression approach for home automation applications. The system comprises a fast subspace-based noise suppression module and a sound classification module. For the noise suppression module, we propose a noise suppression method that applies fast subspace approximations in the wavelet domain. We show that this method offers a lower computational cost than conventional methods. In the sound classification module, we use a feature extraction method that is also based on the wavelet subspace, derived from seventeen critical bands in a signal's wavelet packet transform. Furthermore, we create a multiclass support vector machine by employing probability product kernels. The experimental results for ten classes of various environmental sounds show that the proposed system offers robust performance in environmental sound recognition tasks. [ABSTRACT FROM AUTHOR]

Leveraging machine learning has been proven as a promising avenue for addressing many practical circuit design and verification challenges. We demonstrate a novel active learning guided machine learning approach for characterizing circuit performance. When employed under the context of support vector machines (SVMs), the proposed probabilistically weighted active learning approach is able to dramatically reduce the size of the training data, leading to significant reduction of the overall training cost. The proposed active learning approach is extended to the training of asymmetric SVM classifiers, which is further sped up by a global acceleration scheme. We demonstrate the excellent performance of the proposed techniques using four case studies: 1) dc/dc converter ripple noise analysis; 2) phase-locked loop lock-time verification; 3) reliability analysis of a ring oscillator with respect to process variations and initial conditions; and 4) prediction of chip peak temperature using a limited number of on-chip temperature sensors. [ABSTRACT FROM PUBLISHER]

In this paper, a novel technique on a multiscale energy and eigenspace (MEES) approach is proposed for the detection and localization of myocardial infarction (MI) from multilead electrocardiogram (ECG). Wavelet decomposition of multilead ECG signals grossly segments the clinical components at different subbands. In MI, pathological characteristics such as hypercute T-wave, inversion of T-wave, changes in ST elevation, or pathological Q-wave are seen in ECG signals. This pathological information alters the covariance structures of multiscale multivariate matrices at different scales and the corresponding eigenvalues. The clinically relevant components can be captured by eigenvalues. In this study, multiscale wavelet energies and eigenvalues of multiscale covariance matrices are used as diagnostic features. Support vector machines (SVMs) with both linear and radial basis function (RBF) kernel and K-nearest neighbor are used as classifiers. Datasets, which include healthy control, and various types of MI, such as anterior, anteriolateral, anterioseptal, inferior, inferiolateral, and inferioposterio-lateral, from the PTB diagnostic ECG database are used for evaluation. The results show that the proposed technique can successfully detect the MI pathologies. The MEES approach also helps localize different types of MIs. For MI detection, the accuracy, the sensitivity, and the specificity values are 96%, 93%, and 99% respectively. The localization accuracy is 99.58%, using a multiclass SVM classifier with RBF kernel. [ABSTRACT FROM PUBLISHER]

In addition to providing charging service, an electric vehicle charging station equipped with a distributed energy storage system can also participate in the deregulated market to optimize the cost of operation. To support this function, it is necessary to achieve sufficient accuracy on the forecasting of energy resources and market prices. The deregulated market price prediction presents challenges since the occurrence and magnitude of the price spikes are difficult to estimate. This paper proposes a hybrid method for very short term market price forecasting to improve prediction accuracy on both nonspike and spike wholesale market prices. First, support vector classification is carried out to predict spike price occurrence, and support vector regression is used to forecast the magnitude for both nonspike and spike market prices. Additionally, three clustering techniques including classification and regression trees, K-means, and stratification methods are introduced to mitigate high error spike magnitude estimation. The performance of the proposed hybrid method is validated with the Electric Reliability Commission of Texas wholesale market price. The results from the proposed method show a significant improvement over typical approaches. [ABSTRACT FROM AUTHOR]

The demand for online fault diagnosis has recently increased in order to prevent severe unexpected failures in machinery. To address this issue, this paper first proposes a comprehensive bearing fault diagnosis algorithm, which consists of fault signature extraction through time–frequency analysis and one-against-all multiclass support vector machines in order to make reliable decisions. In addition, acoustic emission (AE) signals sampled at 1 MHz are used for the early identification of bearing failures. Despite the fact that the proposed fault diagnosis methodology shows satisfactory classification accuracy, its computation complexity limits its use in real-time applications. Therefore, this paper also presents a high-performance multicore architecture, including 64 processing elements operating at 50 MHz in a Xilinx Virtex-7 field-programmable gate array device to support online fault diagnosis. The experimental results indicate that the multicore approach executes 1339.3x and 1293.1x faster than the high-performance Texas Instrument (TI) TMS320C6713 and TMS320C6748 digital signal processors (DSPs), respectively, by exploiting the massive parallelism inherent in the bearing fault diagnosis algorithm. In addition, the multicore approach outperforms the equivalent sequential approach that runs on the TI DSPs by substantially reducing the energy consumption. [ABSTRACT FROM PUBLISHER]

Device-free passive (DFP) localization techniques have received increasing attention for location-based services due to their ability to realize localization without holding any wireless device. Most of the existing DFP localization systems are based on the measurement of received signal strength (RSS) only. However, the localization accuracy is easily affected by the spatial and temporal variance of RSS due to multipath fading and noise, even in a static environment. In this paper, we propose a novel localization system for DFP localization using an array sensor, which uses an antenna array at a receiver and is mainly based on the signal eigenvector. We use a fingerprinting technique with multiclass support vector machines (SVMs) based on a combination of array signal features with spatial and temporal averaging. We evaluate the localization performance of our proposed system in different propagation environments, i.e., line-of-sight (LOS) and non-line-of-sight (NLOS). In addition, we analyze two types of receive antenna placement, i.e., centralized and distributed antennas. The experimental results show that the localization accuracy can be improved by the proposed system, particularly in the centralized antenna case. Moreover, they show that the proposed system can improve localization accuracy compared with the conventional RSS-only-based system. [ABSTRACT FROM PUBLISHER]