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Video surveillance systems have enabled the monitoring of complex events in several places, such as airports, banks, streets, schools, industries, among others. Due to the massive amount of multimedia data acquired by video cameras, traditional visual inspection by human operators is a very tedious and time consuming task, whose performance is affected by fatigue and stress. A challenge is to develop...
In this study, we proposed an analysis method of ElectroMyoGraphic (EMG) signals in order to diagnose and to identify neuromuscular pathologies (i.e.; myopathy and neuropathy). Analysis is performed fully automatically without expert assistance and without prior segmentation of muscle contractions. The method is based on Huang-Hilbert transform (HHT) which is a data-driven algorithm that decomposes...
Recognition of epileptic seizures is an important issue and in certain circumstances it is desirable to have portable equipment implementing the algorithm in order to better monitor the patients. This work considers a widely used EEG database from University of Bonn as reference for comparing our recognition method with other previously reported. In order to perform epileptic seizures we combine a...
Interest on palmprint biometrics has experimented a strong growth in the last decades due to its useful characteristics as uniqueness, permanence, reliability, user-friendliness, acceptability, non-intrusiveness, and low cost of the acquisition devices, which make it attractive for civil and commercial applications. Accordingly, a wide research has been developed in this field. Nevertheless, there...
It is necessary to monitor the operation state of the High voltage circuit breaker (HVCB) and analyze the fault by the related signal, the fault of HVCB operating mechanism is closely associated with coil current, mechanical vibration and other factors. The method of fault diagnosis for HVCB based on Hilbert-Huang transform (HHT) and support vector machine is proposed to analyze faults accurately...
This paper presents improvements in terms of accuracy for shape object classification using a new low complexity method compared to previous implementation [1]. The method is using echoes generated by a JAVA platform capable of emulate sound propagation in a controlled 2D virtual environment [2][3]. Echoes originate from the ultrasonic waves generated inside a virtual environment which contains geometrical...
In this work, the Curvelet transform is proposed as a fairly new feature extraction method for palmprint recognition. Particularly, a multiscale analysis has been performed at four levels, assessing and combining the features extracted at each level in order to find those which better represent the palmprint. Feature matching has been conducted by means of Euclidean distance and Support Vector Machines...
The problem of the wind turbine gearbox fault diagnosis was investigated by employing the support vector machine (SVM), which is optimized by the improved fruit fly intelligent algorithm with decreasing steps. First of all, the fault characteristic value extracted by Hilbert transform envelope is presented. Secondly, the general SVM solution to the wind turbine gearbox fault diagnosis problem is presented,...
A novel projection twin support vector machine (PTSVM), termed as NPTSVM, is presented in this paper for binary classification. Although this method determines two projection vectors using the same way as PTSVM, it has more advantages than existing PTSVMs. First, NPTSVM does not have to calculate inverse matrices during the learning process, which makes the training speed of NPTSVM be much faster...
The paper deals with the problem of stability during the solving of pattern recognition tasks from the point of view of transformation groups. It shows the possibility to avoid the necessity of regularization by using the geometric equaffine Lorentz transformation, exploiting as example the alpha-procedure.
This paper presents a novel method for fault classification based on Multiple Measurement Vector Compressive Sampling (MMV-CS), Fisher Score (FS), and Support Vector Machine (SVM). In this method, the original vibration signal passes through MMV-CS framework to obtain compressed samples that possess the quality of the original vibration signals. Afterwards FS algorithm is applied to select the most...
The automatic recognition of hidden defects plays a key role in the structure integrity and healthy monitoring (SIHM) system. The article proposes a novel method that combines the Hilbert Huang transform (HHT) and hybrid support vector machine-particle swarm optimization (Hybrid SVM-PSO) model for recognizing hidden defects during using pulsed eddy current (PEC) testing. The proposed approach uses...
In this paper, a novel spectral-spatial very high resolution images shadow detection algorithm based on random walker is proposed. First, a set of training samples is obtained by an improved Otsu based thresholding method automatically. Then, a widely used pixel-wise classifier, i.e., the Support Vector Machine (SVM), is applied to obtain an initial binary classification map. Finally, the initial...
Classification of objects, materials or terrain in hyperspectral imagery requires the definition of an appropriate measure of spectral similarity, typically expressed in terms of spectral reflectance. For many objects, absolute reflectance varies due to bidirectional reflectance distribution function (BRDF) effects or uneven illumination. Here, an appropriate similarity measure is spectral angle;...
The paper presents an automatic traffic sign recognition system using the videos recorded from an on-board dashcam. It is based on image processing, bilateral Chinese transform, and vertex and bisector transform techniques. The images captured from the dashcam are processed with the histogram of oriented gradients to form feature vectors, followed by support vector machines to detect the traffic signs...
Traffic Light Detection(TLD) and understanding their state semantics at intersections plays a pivotal role in driver assistance systems and, by extension, autonomous vehicles. Despite of several reliable traffic light state detection approaches in literature, traffic light state recognition still remains an open problem due to outdoor perception challenge which includes occlusions, illumination and...
Edge preserving image decomposition is proposed as a preprocessing technique to increase the accuracy in automatic radar target classification. The radar images are decomposed trough edge preserving image decomposition methods with parameters optimized for a better classification rate. By appropriate choice of the parameters, it is possible to keep the necessary information in the residual images...
A novel scheme is presented for real-time prediction of rotor angle stability status. The scheme is activated following a large disturbance and operates by obtaining a set of two rotor angles for each generator. Each set of two rotor angles sampled is decomposed using the fast Walsh-Hadamard transform resulting in two Walsh coefficients. The absolute value of each coefficient is obtained and the maximum...
Growth transform neuron models provide a neuromorphic approach for implementing well established machine learning algorithms while producing neural and population dynamics similar to what have been observed in biology, for example, spiking, bursting and noise-shaping [1]. In this demonstration, we will show some of these dynamics in real-time using an FPGA based acceleration platform that implements...
Road sign recognition (RSR) systems are one of the main tasks of intelligent transportation systems (ITS). These systems employ vehicle mounted cameras to identify traffic signs while driving on the road. Their primary function is to inform the driver of recent traffic signs that may have been missed due to distraction or inattentiveness. In this work, a new method for road sign detection and recognition...
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