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Assessment of Pain using Optimized Feature Set from Corrugator EMG 基于瓦楞纸肌电图优化特征集的疼痛评估
2020 IEEE Applied Signal Processing Conference (ASPCON) Pub Date : 2020-10-07 DOI: 10.1109/ASPCON49795.2020.9276691
P. Das, Jhilik Bhattacharyya, Kausik Sen, S. Pal
{"title":"Assessment of Pain using Optimized Feature Set from Corrugator EMG","authors":"P. Das, Jhilik Bhattacharyya, Kausik Sen, S. Pal","doi":"10.1109/ASPCON49795.2020.9276691","DOIUrl":"https://doi.org/10.1109/ASPCON49795.2020.9276691","url":null,"abstract":"Pain is one of the most complex sensation of human physiology. Till now, physicians use subjective scores for measuring pain of any individual and doctors need to completely depend on patient's response for assessment of pain. Although, these methods are not always effective in the medical field, when the subjects are non-cooperative or unable to response. Hence, subject's response independent pain recognition systems are utmost important. Noxious stimulus excites Sympathetic Nervous System (SNS), which is related to changes in neuro-somatic biosignals and facial expression. In this present work, EMG of corrugator muscle which is pertaining to pain sensitiveness is analyzed. Considering non-linear & non-stationary nature of the EMG signal stimulated through pain, Empirical Mode Decomposition technique is applied on EMG for its data adaptive nature. Taguchi Method of feature optimization is applied onthe IMFs for ranking of features according to their significance. Classification of different nociception levels with ‘no pain' was performed employing linear SVM algorithm, using all extracted features as well as the most significant features. Appreciable increase in classification accuracy is noticed with optimized set of features.","PeriodicalId":193814,"journal":{"name":"2020 IEEE Applied Signal Processing Conference (ASPCON)","volume":"31 Spec No 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116337121","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}
引用次数: 1
Classification of Tea Samples using Learning Vector Quantization Neural Network 基于学习向量量化神经网络的茶叶样本分类
2020 IEEE Applied Signal Processing Conference (ASPCON) Pub Date : 2020-10-07 DOI: 10.1109/ASPCON49795.2020.9276662
S. Damarla, M. Kundu
{"title":"Classification of Tea Samples using Learning Vector Quantization Neural Network","authors":"S. Damarla, M. Kundu","doi":"10.1109/ASPCON49795.2020.9276662","DOIUrl":"https://doi.org/10.1109/ASPCON49795.2020.9276662","url":null,"abstract":"A supervised multi-class classification method based on learning vector quantization (LVQ) neural network was proposed to classify tea samples of five commercial brands; Brook bond, Double-Diamond, Lipton, Lipton-Darjeeling and Marvel. Data required for classifier design were obtained by performing laboratory experiments with electronic tongue. Multi-class classifiers based on multilayer perceptron, weighted k-nearest neighbors and Mahalanobis distance were developed to compare the results of LVQ neural network classifier. The LVQ neural network classifier showed superior performance with classification rate of 97.9%.","PeriodicalId":193814,"journal":{"name":"2020 IEEE Applied Signal Processing Conference (ASPCON)","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116383152","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}
引用次数: 0
Design of a Low Power High Speed CML-Based Divide-by-5 Pre-Scaler in 180 nm Process Technology 180nm制程低功耗高速cmos / 5预缩放器设计
2020 IEEE Applied Signal Processing Conference (ASPCON) Pub Date : 2020-10-07 DOI: 10.1109/ASPCON49795.2020.9276689
S. Maity, S. Kumar Jana
{"title":"Design of a Low Power High Speed CML-Based Divide-by-5 Pre-Scaler in 180 nm Process Technology","authors":"S. Maity, S. Kumar Jana","doi":"10.1109/ASPCON49795.2020.9276689","DOIUrl":"https://doi.org/10.1109/ASPCON49795.2020.9276689","url":null,"abstract":"A power-efficient high speed MOS current mode logic (MCML)-based divide-by-5 pre-scaler is proposed in this paper. Optimized latches and XOR gates are used in order to design the proposed divide-by-5 pre-scaler. The pre-scaler is realized in 180 nm CMOS process technology and simulation results show that proposed divide-by-5 pre-scaler can faithfully work up to an operating frequency of 12.12 GHz in worst case process corner with an excellent power head performance. The maximum power dissipation of the core circuit is 1.39 mW under 1.8 V supply. The performance corresponds to figure of merit: FoM of 9.4 dB which compares favorably with the state of the art.","PeriodicalId":193814,"journal":{"name":"2020 IEEE Applied Signal Processing Conference (ASPCON)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130458563","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}
引用次数: 0
A Pencil Drawn Capacitive Sensor used for Liquid Drug Volume Measurement in Syringe Pump 一种用于注射泵液体药物体积测量的铅笔画电容式传感器
2020 IEEE Applied Signal Processing Conference (ASPCON) Pub Date : 2020-10-07 DOI: 10.1109/ASPCON49795.2020.9276687
Subir Das, B. Chakraborty
{"title":"A Pencil Drawn Capacitive Sensor used for Liquid Drug Volume Measurement in Syringe Pump","authors":"Subir Das, B. Chakraborty","doi":"10.1109/ASPCON49795.2020.9276687","DOIUrl":"https://doi.org/10.1109/ASPCON49795.2020.9276687","url":null,"abstract":"In the field of medical applications, tracking and controlling of liquid drug delivery to the patient body is an important task at the critical stage of treatment. In this paper, a low cost, noninvasive continuous drug volume measurement in a syringe pump has been studied using a pencil-drawn capacitive (PDC) sensor. The drug volume variation inside the syringe injection pump has been measured using the fringing field capacitive method. In this preliminary investigation, the design optimization of the PDC sensor has been studied and observed its performance during the dispense of drugs through a syringe. Experimentally obtained the sensitivity, accuracy, and resolution of the sensor are 0.033pF/mm, ±0.2ml, and 0.1ml respectively.","PeriodicalId":193814,"journal":{"name":"2020 IEEE Applied Signal Processing Conference (ASPCON)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121404088","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}
引用次数: 1
Occlusion Robust Object Tracking with Modified Particle Filter Framework 基于改进粒子滤波框架的遮挡鲁棒目标跟踪
2020 IEEE Applied Signal Processing Conference (ASPCON) Pub Date : 2020-10-07 DOI: 10.1109/ASPCON49795.2020.9276725
Shaswata Gupta, M. Bhuyan, Pradipta Sasmal
{"title":"Occlusion Robust Object Tracking with Modified Particle Filter Framework","authors":"Shaswata Gupta, M. Bhuyan, Pradipta Sasmal","doi":"10.1109/ASPCON49795.2020.9276725","DOIUrl":"https://doi.org/10.1109/ASPCON49795.2020.9276725","url":null,"abstract":"Object tracking is a classical problem of computer vision and is ubiquitous in many applications. Multiple tracking frameworks have been proposed in the past, and still attracting many researchers due to its high applicability in various fields. Major challenges in object tracking are because of constraints like illumination, occlusions, changing background, etc. This work proposes a modified Particle Filtering framework that is robust to partial and complete occlusions. In achieving so, this work suggests the use of a forward prediction filter that is fused with the proposed framework. It works irrespective of the measurement model. Also, our proposed work proposes an Uncertainty Factor for every prediction that controls the amount of uncertainty during particle update and adjusts the search area accordingly. This Uncertainty Factor also acts as a measure of tracking performance. Extensive experiments prove the better performance of the proposed work in comparison with the existing ones in presence of occlusion.","PeriodicalId":193814,"journal":{"name":"2020 IEEE Applied Signal Processing Conference (ASPCON)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125405017","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}
引用次数: 3
Feature selection for attention demanding task induced EEG detection 注意要求任务诱发脑电检测的特征选择
2020 IEEE Applied Signal Processing Conference (ASPCON) Pub Date : 2020-10-07 DOI: 10.1109/ASPCON49795.2020.9276710
V. Raj, Jupitara Hazarika, Ranjay Hazra
{"title":"Feature selection for attention demanding task induced EEG detection","authors":"V. Raj, Jupitara Hazarika, Ranjay Hazra","doi":"10.1109/ASPCON49795.2020.9276710","DOIUrl":"https://doi.org/10.1109/ASPCON49795.2020.9276710","url":null,"abstract":"Electroencephalography (EEG) is a popular noninvasive method used to record and analyse the electrical activity of the brain. Despite the poor spatial resolution, this tool provides a very high temporal resolution. With the use of a large number of channels, it is necessary to select the relevant features in EEG analysis. Hence, this research paper aims to identify the features that are capable of differentiating an attention-demanding task- induced brain activity from the resting state condition. Eleven different features including mean, root mean square, band power, skewness, mode, data range, interquartile range (IQR) and three Hjorth parameters are extracted from alpha, beta and gamma frequency bands of EEG. Each feature is tested using the statistical tool called paired t-test. Results demonstrate the importance of feature selection step for the recognition process. Hjorth parameters have shown significant statistical difference (p<0.05) between the datasets of attention task and resting-state and thus, can be a biomarker in this particular case.","PeriodicalId":193814,"journal":{"name":"2020 IEEE Applied Signal Processing Conference (ASPCON)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125488649","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}
引用次数: 1
FPGA Implementation of Phase Disposition PWM (PD-PWM) Strategy for Cascaded H-Bridge Multilevel Inverter (CHB-MLI) 级联h桥多电平逆变器相位配置PWM (PD-PWM)策略的FPGA实现
2020 IEEE Applied Signal Processing Conference (ASPCON) Pub Date : 2020-10-07 DOI: 10.1109/ASPCON49795.2020.9276676
R. Sarker, A. Datta, S. Debnath
{"title":"FPGA Implementation of Phase Disposition PWM (PD-PWM) Strategy for Cascaded H-Bridge Multilevel Inverter (CHB-MLI)","authors":"R. Sarker, A. Datta, S. Debnath","doi":"10.1109/ASPCON49795.2020.9276676","DOIUrl":"https://doi.org/10.1109/ASPCON49795.2020.9276676","url":null,"abstract":"Field-programmable gate array (FPGA)-based multi-carrier pulse-width modulation (MCPWM) generation technique is desirable for high-frequency dc/ac converter application where a fast-switching response is the primary concern. This paper offers an FPGA-based high-frequency, multi-carrier phase disposition pulse-width modulation (PDPWM) generation strategy that can support the requirements of modern fast-switching semiconductors. The FPGA architecture employs several pre-formulated VHDL-coded algorithms to develop a set of high-speed PD-PWM gating signals for the multilevel dc/ac converter. The proposed technique is verified through a Xilinx Spartan-6 FPGA-triggered cascaded H-bridge multilevel inverter (CHB-MLI) to quantify its merits among all the recently-reported similar architectures under study.","PeriodicalId":193814,"journal":{"name":"2020 IEEE Applied Signal Processing Conference (ASPCON)","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116681410","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}
引用次数: 5
Spatially Constrained Likeliness-based Fuzzy Entropy Clustering Algorithm and its Application to Noisy 3D Brain MR Image Segmentation 基于空间约束似然的模糊熵聚类算法及其在含噪三维脑磁共振图像分割中的应用
2020 IEEE Applied Signal Processing Conference (ASPCON) Pub Date : 2020-10-07 DOI: 10.1109/ASPCON49795.2020.9276685
Nabanita Mahata, J. Sing
{"title":"Spatially Constrained Likeliness-based Fuzzy Entropy Clustering Algorithm and its Application to Noisy 3D Brain MR Image Segmentation","authors":"Nabanita Mahata, J. Sing","doi":"10.1109/ASPCON49795.2020.9276685","DOIUrl":"https://doi.org/10.1109/ASPCON49795.2020.9276685","url":null,"abstract":"This paper proposes a spatially constrained likeliness-based fuzzy entropy clustering algorithm for noisy 3D brain MR image segmentation. It introduces a likeliness measure with respect to a voxel under consideration by using intensity distribution surrounding its local neighborhood. We use this measure as an additional membership function and named as fuzzy likeliness measures. We integrate these two fuzzy membership functions into a objective function by means of a regularizing parameter. Further, we introduce a fuzzy entropy using the fuzzifier weighted fuzzy likeliness measures to define the information uncertainty associated with a voxel in order to finding its cluster. By integrating weighted fuzzy membership function and fuzzy likeliness measure we generate the final membership function. The experiments on noisy 3D brain MR image volumes that include simulated and clinical data suggest that the proposed algorithm is superior while comparing with several state-of-the-art algorithms in terms of Dice coefficient, partition coefficient and partition entropy.","PeriodicalId":193814,"journal":{"name":"2020 IEEE Applied Signal Processing Conference (ASPCON)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116764032","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}
引用次数: 0
Enhanced Normalized Mutual Information for Localization in Noisy Environments 增强归一化互信息在噪声环境下的定位
2020 IEEE Applied Signal Processing Conference (ASPCON) Pub Date : 2020-08-24 DOI: 10.1109/ASPCON49795.2020.9276658
Samuel Todd Flanagan, Drupad K. Khublani, J. Chamberland, Siddharth Agarwal, Ankit Vora
{"title":"Enhanced Normalized Mutual Information for Localization in Noisy Environments","authors":"Samuel Todd Flanagan, Drupad K. Khublani, J. Chamberland, Siddharth Agarwal, Ankit Vora","doi":"10.1109/ASPCON49795.2020.9276658","DOIUrl":"https://doi.org/10.1109/ASPCON49795.2020.9276658","url":null,"abstract":"Fine localization is a crucial task for autonomous vehicles. Although many algorithms have been explored in the literature for this specific task, the goal of getting accurate results from commodity sensors remains a challenge. As autonomous vehicles make the transition from expensive prototypes to production items, the need for inexpensive, yet reliable solutions is increasing rapidly. This article considers scenarios where images are captured with inexpensive cameras and localization takes place using pre-loaded fine maps of local roads as side information. The techniques proposed herein extend schemes based on normalized mutual information by leveraging the likelihood of shades rather than exact sensor readings for localization in noisy environments. This algorithmic enhancement, rooted in statistical signal processing, offers substantial gains in performance. Numerical simulations are used to highlight the benefits of the proposed techniques in representative application scenarios. Analysis of a Ford image set is performed to validate the core findings of this work.","PeriodicalId":193814,"journal":{"name":"2020 IEEE Applied Signal Processing Conference (ASPCON)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116831432","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}
引用次数: 0
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