Current Issue : April - June Volume : 2019 Issue Number : 2 Articles : 5 Articles
For the purpose of reducing noise from grain flow signal, this paper proposes a filtering\nmethod that is on the basis of empirical mode decomposition (EMD) and artificial bee colony (ABC)\nalgorithm. At first, decomposing noise signal is performed adaptively into intrinsic mode functions\n(IMFs). Then, ABC algorithm is utilized to determine a proper threshold shrinking IMF coefficients\ninstead of traditional threshold function. Furthermore, a neighborhood search strategy is introduced\ninto ABC algorithm to balance its exploration and exploitation ability. Simulation experiments\nare conducted on four benchmark signals, and a comparative study for the proposed method and\nstate-of-the-art methods are carried out. The compared results demonstrate that signal to noise ratio\n(SNR) and root mean square error (RMSE) are obtained by the proposed method. The conduction of\nwhich is finished on actual grain flow signal that is with noise for the demonstration of the effect in\nactual practice....
The precise estimation of the frequency of the signal is of great significance in\nthe Radar system, the electronic warfare system and many other systems. In\nthis paper, we propose a development and verification platform for the frequency\nestimation system in the Matlab and Simulink environment. Its\nopen-extensibility architecture enables the performance evaluation of different\nfrequency estimation algorithms and its graphic interface can greatly\npromote the system design, simulation and verification efficiency....
Cardiac disorders are critical and must be diagnosed in the early stage using routine\nauscultation examination with high precision. Cardiac auscultation is a technique to analyze and\nlisten to heart sound using electronic stethoscope, an electronic stethoscope is a device which provides\nthe digital recording of the heart sound called phonocardiogram (PCG). This PCG signal carries\nuseful information about the functionality and status of the heart and hence several signal processing\nand machine learning technique can be applied to study and diagnose heart disorders. Based on\nPCG signal, the heart sound signal can be classified to two main categories i.e., normal and abnormal\ncategories. We have created database of 5 categories of heart sound signal (PCG signals) from various\nsources which contains one normal and 4 are abnormal categories. This study proposes an improved,\nautomatic classification algorithm for cardiac disorder by heart sound signal. We extract features\nfrom phonocardiogram signal and then process those features using machine learning techniques\nfor classification. In features extraction, we have used Mel Frequency Cepstral Coefficient (MFCCs)\nand Discrete Wavelets Transform (DWT) features from the heart sound signal, and for learning and\nclassification we have used support vector machine (SVM), deep neural network (DNN) and centroid\ndisplacement based k nearest neighbor. To improve the results and classification accuracy, we have\ncombined MFCCs and DWT features for training and classification using SVM and DWT. From our\nexperiments it has been clear that results can be greatly improved when Mel Frequency Cepstral\nCoefficient and DiscreteWavelets Transform features are fused together and used for classification\nvia support vector machine, deep neural network and k-neareast neighbor(KNN). The methodology\ndiscussed in this paper can be used to diagnose heart disorders in patients up to 97% accuracy.\nThe code and dataset can be accessed at â??https://github.com/yaseen21khan/Classification-of-Heart-\nSound-Signal-Using-Multiple-Features-/blob/master/README.mdâ?....
The most significant barrier to success in human activity recognition is extracting and\nselecting the right features. In traditional methods, the features are chosen by humans, which requires\nthe user to have expert knowledge or to do a large amount of empirical study. Newly developed deep\nlearning technology can automatically extract and select features. Among the various deep learning\nmethods, convolutional neural networks (CNNs) have the advantages of local dependency and scale\ninvariance and are suitable for temporal data such as accelerometer (ACC) signals. In this paper,\nwe propose an efficient human activity recognition method, namely Iss2Image (Inertial sensor signal\nto Image), a novel encoding technique for transforming an inertial sensor signal into an image with\nminimum distortion and a CNN model for image-based activity classification. Iss2Image converts\nreal number values from the X, Y, and Z axes into three color channels to precisely infer correlations\namong successive sensor signal values in three different dimensions. We experimentally evaluated\nour method using several well-known datasets and our own dataset collected from a smartphone\nand smartwatch. The proposed method shows higher accuracy than other state-of-the-art approaches\non the tested datasets....
In this paper, we propose a low-cost posture recognition scheme using a single\nwebcam for the signaling hand with nature sways and possible occlusions.\nIt goes for developing the untouchable low-complexity utility based on\nfriendly hand-posture signaling. The scheme integrates the dominant temporal-\ndifference detection, skin color detection and morphological filtering\nfor efficient cooperation in constructing the hand profile molds. Those molds\nprovide representative hand profiles for more stable posture recognition than\naccurate hand shapes with in effect trivial details. The resultant bounding box\nof tracking the signaling molds can be treated as a regular-type object-matched\nROI to facilitate the stable extraction of robust HOG features. With such\ncommonly applied features on hand, the prototype SVM is adequately capable\nof obtaining fast and stable hand postures recognition under natural hand\nmovement and non-hand object occlusion. Experimental results demonstrate\nthat our scheme can achieve hand-posture recognition with enough accuracy\nunder background clutters that the targeted hand can be allowed with medium\nmovement and palm-grasped object. Hence, the proposed method can\nbe easily embedded in the mobile phone as application software....
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