WSEAS Transactions on Signal Processing archive最新文献

{"title":"Deep Learning Based on CNN for Emotion Recognition Using EEG Signal","authors":"Isah Salim Ahmad, Shuai Zhang, S. Saminu, Lingyue Wang, A. E. K. Isselmou, Z. Cai, Imran Javaid, Souha Kamhi, U. Kulsum","doi":"10.37394/232014.2021.17.4","DOIUrl":"https://doi.org/10.37394/232014.2021.17.4","url":null,"abstract":"Emotion recognition based on brain-computer interface (BCI) has attracted important research attention despite its difficulty. It plays a vital role in human cognition and helps in making the decision. Many researchers use electroencephalograms (EEG) signals to study emotion because of its easy and convenient. Deep learning has been employed for the emotion recognition system. It recognizes emotion into single or multi-models, with visual or music stimuli shown on a screen. In this article, the convolutional neural network (CNN) model is introduced to simultaneously learn the feature and recognize the emotion of positive, neutral, and negative states of pure EEG signals single model based on the SJTU emotion EEG dataset (SEED) with ResNet50 and Adam optimizer. The dataset is shuffle, divided into training and testing, and then fed to the CNN model. The negative emotion has the highest accuracy of 94.86% fellow by neutral emotion with 94.29% and positive emotion with 93.25% respectively. With average accuracy of 94.13%. The results showed excellent classification ability of the model and can improve emotion recognition.","PeriodicalId":151897,"journal":{"name":"WSEAS Transactions on Signal Processing archive","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122259981","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interference Cancellation for MIMO Systems Employing the Generalized Receiver with High Spectral Efficiency","authors":"V. Tuzlukov","doi":"10.37394/232014.2021.17.1","DOIUrl":"https://doi.org/10.37394/232014.2021.17.1","url":null,"abstract":"In this paper, we investigate the performan-ce in terms of symbol error probability (SEP) of multipleinput multiple-output (MIMO) systems employing the ge-neralized receiver with high spectral efficiency. In particular, we consider the coherent detection of M-PSK signals in a flat Rayleigh fading environment. We focus on spectrally efficient MIMO systems where after serial-to-parallel con-version, several sub-streams of symbols are simultaneously transmitted by using an antenna array, thereby increasing the spectral efficiency. The reception is based on linear mi-nimum mean-square-error (MMSE) combining, eventually followed by successive interference cancellation. Exact and approximate expressions are derived for an arbitrary nu-mber of transmitting and receiving antenna elements. Sim-ulation results confirm the validity of our analytical meth-odology. Keywords—Error propagation, minimum mean-square-error (MMSE) methods, multiple-input multiple-output (MIMO) systems, generalized receiver, Rayleigh channels, interference cancellation. Received: January 18, 2021. Revised: March 5, 2021. Accepted: March 12, 2021. Published: March 31, 2021.","PeriodicalId":151897,"journal":{"name":"WSEAS Transactions on Signal Processing archive","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128509550","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Three-Wheeled Omnidirectional Robot Localization in RFID-Tag Environments using UFIR Filtering","authors":"Jorge A. Ortega-Contreras, Y. Shmaliy, J. Andrade-Lucio","doi":"10.37394/232014.2021.17.2","DOIUrl":"https://doi.org/10.37394/232014.2021.17.2","url":null,"abstract":"cation (RFID) tag-based systems have attracted the interest of many consumers due to low cost and low (or zero) energy consumption and a wide distance range that made them standard for indoor object navigation and tracking [1]–[8]. In practical designs of mobile robot navigation systems, one finds various efficient hybrid solutions, such as the localization scheme combining information available from the RFID tag-based networks and other sensors. In [9], a novel localization method is proposed to combine the RFID tag-based data with laserbased measurements. In [10], a variable power RFID model is proposed for the localization over passive ultra-high frequency (UHF) RFID tag nets in complex environments. In [11], a location system is designed to combines two types of the RFID tag-generated signals with a logical classification strategy and the integration is provided using the Bayesian filter-based algorithms (BFA). The objective of the BFA is to compute the posterior distributions of the states of a dynamic system, given an observation function with noise. This method has many advantages, but the more remarkable is the capability to represent a complex distribution without requiring information about the state-space model or the state distributions, although with a high computational cost. The state estimation problem [12], [13] can be solved for linear Gaussian processes and observations using the Kalman Filter (KF) and for non-linear models using the Extended KF (EKF) or unscented KF (UKF). Another approach is the unbiased finite impulse response (UFIR) filter [14], which can also be applied to linear models and nonlinear models [15] as described in [16]. An advantage is that the UFIR algorithm does not require the noise statistics and has a better robustness. Navigation over the RFID tag nets is typically provided in the presence of the colored measurement noise (CMN) [17]–[19]. To estimate the robot state under CMN, there can be used two well-known approaches developed by Bryson et al. in [20], [21] and Petovello et al. in [22]. In the Bryson algorithm, the CMN is filtered out in two phases: smoothing and filtering. In the Petovello algorithm, only one stage (filtering) is needed. Another solutions were found by Shmaliy et al. to deal with the colored process noise using state differencing [23], Zhou et al. by using the second moment of information [24], and Ding et al. by applying the least squaresbased iterative parameter estimation to dynamical systems with the autoregressive moving average (ARMA) noise model [25]. In this paper, we apply the KF and UFIR filter modified in [26] under CMN to provide an accurate robot navigation over RFID tag networks.","PeriodicalId":151897,"journal":{"name":"WSEAS Transactions on Signal Processing archive","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128995562","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Power Quality Disturbances Detection and Classification Rule-Based Decision Tree","authors":"F. Zaro","doi":"10.37394/232014.2021.17.3","DOIUrl":"https://doi.org/10.37394/232014.2021.17.3","url":null,"abstract":"In this paper, the power quality (PQ) disturbances have been detected and classified using Stockwell’s transform (S-transform) and rule-based decision tree (DT) according to IEEE standards. The proposed technique based on the extracted features of the PQ events signals, which are extracted from the time-frequency analysis. Several PQ disturbances are considered with simple and complex disturbances to include spike, flicker, oscillatory transient, impulsive transient, and notch. The performance and robustness of the proposed technique for the recognition of PQ disturbances have been demonstrated through the results of the various disturbances. By comparing the performance of the proposed technique with other reported studies it was distinguished results under noiseless and noisy conditions.","PeriodicalId":151897,"journal":{"name":"WSEAS Transactions on Signal Processing archive","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129953292","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predication and Analysis of Epileptic Seizure Neurological Disorder using Intracranial Electroencephalography (iEEG)","authors":"S. S. Pawar, S. Chougule","doi":"10.37394/232014.2020.16.22","DOIUrl":"https://doi.org/10.37394/232014.2020.16.22","url":null,"abstract":"Epileptic seizure is one of the neurological brain disorder approximately 50 million of world’s population is affected. Diagnosis of seizure is done using medical test Electroencephalography. Electroencephalography is a technique to record brain signal by placing electrodes on scalp. Electroencephalography suffers from disadvantage such as low spatial resolution and presence of artifact. Intracranial Electroencephalography is used to record brain electrical activity by mounting strip, grid and depth electrodes on surface of brain by surgery. Online standard Intracranial Electroencephalography data is analyzed by our system for predication and analysis of Epileptic seizure. The pre-processing of Intracranial Electroencephalography signal is done and is further analyzed in wavelet domain by implementation of Daubechies Discrete Wavelet Transform. Features were extracted to classify as preictal and ictal state. Analysis of preictal state was carried out for predication of seizure. Intracranial Electroencephalography signals provide better result and accuracy in seizure detection and predication. Earlier warning can also be issued to control seizure with antiepileptic drugs. Keywords—Artifact, Daubechies Discrete Wavelet transform, Epileptic Seizure, Intracranial Electroencephalography, Seizure Classification, Seizure Predication.","PeriodicalId":151897,"journal":{"name":"WSEAS Transactions on Signal Processing archive","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121215925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"UFIR Filtering Under Uncertain One-Step Delayed and Missing Data","authors":"Karen J. Uribe-Murcia, Y. Shmaliy","doi":"10.37394/232014.2020.16.21","DOIUrl":"https://doi.org/10.37394/232014.2020.16.21","url":null,"abstract":"This paper develops the unbiased finite impulse response (UFIR) filter for wireless sensor network (WSN) systems whose measurements are affected by random delays and packet dropout due to inescapable failures in the transmission and sensors. The Bernoulli distribution is used to model delays in arrived measurement data with known transmission probability. The effectiveness of the UFIR filter is compared experimentally to the KF and game theory recursive H1 filter in terms of accuracy and robustness employing the GPS-measured vehicle coordinates transmitted with latency over WSN.","PeriodicalId":151897,"journal":{"name":"WSEAS Transactions on Signal Processing archive","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122195669","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optical Fiber Cables Networks Defects Detection Using Thermal Images Enhancement Techniques","authors":"H. Elbehiery","doi":"10.26438/IJSRCSE/V6I1.2229","DOIUrl":"https://doi.org/10.26438/IJSRCSE/V6I1.2229","url":null,"abstract":"Image enhancement is a process to output an image which is more suitable and useful than \u0000original image for specific application. Thermal image enhancement includes many techniques used in \u0000Quality Control, Problem Diagnostics, and Insurance Risk Assessment. Various enhancement schemes are \u0000used for enhancing an image which includes gray scale manipulation, filtering and Histogram Equalization \u0000(HE), Fast Fourier Transform which results in Highlighting interesting detail in images, removing noise from \u0000images, making images more visually appealing, edge enhancement and increase the contrast of the image. \u0000This research article explains how could the various stated techniques and operations will be useful in the \u0000detection of the defects for the optical fiber cables and their connectors and most of optical devices to be \u0000more effective in Optical fiber based communication systems","PeriodicalId":151897,"journal":{"name":"WSEAS Transactions on Signal Processing archive","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133382188","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An adaptive stochastic-resonance-based detector and its application in watermark extraction","authors":"GuoGencheng, MandalMrinal","doi":"10.5555/2064871.2064873","DOIUrl":"https://doi.org/10.5555/2064871.2064873","url":null,"abstract":"In this paper, we explore a stochastic resonance (SR) based detector using bistable system (BS) to detect a binary pulse amplitude modulated (PAM) signal embedded in non-Gaussian noise. Through the...","PeriodicalId":151897,"journal":{"name":"WSEAS Transactions on Signal Processing archive","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122339972","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-class support vector machine classifier in EMG diagnosis","authors":"Gurmanik Kaur, A. Arora, V. K. Jain","doi":"10.5555/1853847.1853848","DOIUrl":"https://doi.org/10.5555/1853847.1853848","url":null,"abstract":"The shapes of motor unit action potentials (MUAPs) in an electromyographic (EMG) signal provide an important source of information for the diagnosis of neuromuscular disorders. In order to extract this information from the EMG signals recorded at low to moderate force levels, it is required to: i) identify the MUAPs composed by the EMG signal, ii) cluster the MUAPs with similar shapes, iii) extract the features of the MUAP clusters and iv) classify the MUAPs according to pathology. In this work, three techniques for segmentation of EMG signal are presented: i) segmentation by identifying the peaks of the MUAPs, ii) by finding the beginning extraction point (BEP) and ending extraction point (EEP) of MUAPs and iii) by using discrete wavelet transform (DWT). For the clustering of MUAPs, statistical pattern recognition technique based on euclidian distance is used. The autoregressive (AR) features of the clusters are computed and are given to a multi-class support vector machine (SVM) classifier for their classification. A total of 12 EMG signals obtained from 3 normal (NOR), 5 myopathic (MYO) and 4 motor neuron diseased (MND) subjects were analyzed. The success rate for the segmentation technique used peaks to extract MUAPs was highest (95.90%) and for the statistical pattern recognition technique was 93.13%. The classification accuracy of multi-class SVM with AR features was 100%.","PeriodicalId":151897,"journal":{"name":"WSEAS Transactions on Signal Processing archive","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121998970","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel blind digital watermarking technique for Stegano-encrypting information using nine-ac-coefficient prediction algorithm with an innovative security strategy","authors":"C. E. Moucary, Bachar El Hassan","doi":"10.5555/1853844.1853845","DOIUrl":"https://doi.org/10.5555/1853844.1853845","url":null,"abstract":"This paper presents a new methodology for data hiding using digital watermarking in the DCT Domain. The methodology relies on a new scheme for encrypting the data prior to the embedding stage. The key used for ciphering is almost of arbitrary length, type and format; this endows the watermark with a powerful, 3-level reinforced security structure. It is a blind-detector watermarking technique and the amount of the hidden data is increased by 60% compared with the traditional AC-Coefficients Prediction algorithm while sustaining a high level of transparency. Simulation results were carried out which demonstrated a promising PSNR, limited blocking artifacts, and a satisfactory level of the overall performance. The paper also presents an extensive survey of prominent digital-watermarking research outcomes in the WSEAS Transactions.","PeriodicalId":151897,"journal":{"name":"WSEAS Transactions on Signal Processing archive","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134466918","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}