2020 IEEE Applied Signal Processing Conference (ASPCON)最新文献_第4页

Prediction of Andrographolide Content in Andrographis paniculata Using NIR Spectroscopy 近红外光谱法预测穿心莲中穿心莲内酯的含量

2020 IEEE Applied Signal Processing Conference (ASPCON) Pub Date : 2020-10-07 DOI: 10.1109/ASPCON49795.2020.9276668

Dilip Sing, Ranajoy Mallik, Sudarshana Ghosh Dastidar, R. Bandyopadhyay, Subhadip Banerjee, S. N. Jana, P. Mukherjee

引用次数: 0

Neuromuscular Disease Detection Employing 1D-Local Binary Pattern of Electromyography Signals 利用肌电信号的一维局部二值模式检测神经肌肉疾病

2020 IEEE Applied Signal Processing Conference (ASPCON) Pub Date : 2020-10-07 DOI: 10.1109/ASPCON49795.2020.9276657

Pranabendra Prasad Chandra, Sayanjit Singha Roy, S. Chatterjee

{"title":"Neuromuscular Disease Detection Employing 1D-Local Binary Pattern of Electromyography Signals","authors":"Pranabendra Prasad Chandra, Sayanjit Singha Roy, S. Chatterjee","doi":"10.1109/ASPCON49795.2020.9276657","DOIUrl":"https://doi.org/10.1109/ASPCON49795.2020.9276657","url":null,"abstract":"In this contribution, a novel technique for the detection of neuromuscular disorders is proposed employing a 1D-local binary pattern of electromyography signals. 1D-LBP is a local feature descriptor that is capable of identifying localized and sudden fluctuations present in EMG signals due to irregular firing patterns of motor neurons which is rooted in the physiology of the neuromuscular diseases. In the present contribution, initially, the 1D-LBP technique was applied on healthy, myopathy and amyotrophic lateral sclerosis EMG signals to obtain their respective LBP codes. The histogram of occurrence of LBP codes of different types of EMG signals was subsequently used as features to classify EMG signals using support vector machines (SVM) classifier. To reduce the size of the feature dimension, the performance of the proposed method was further evaluated using uniform 1D-LBP. Two binary classification problems were performed and investigations revealed that both conventional and uniform 1D-LBP returned very high detection accuracies for both problems, which can be potentially implemented for real-time neuromuscular disease detection.","PeriodicalId":193814,"journal":{"name":"2020 IEEE Applied Signal Processing Conference (ASPCON)","volume":"17 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":"115010799","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

Hardware Implementation of Image Dehazing Mechanism using Verilog HDL and Parallel DCP 基于Verilog HDL和并行DCP的图像去雾机制硬件实现

2020 IEEE Applied Signal Processing Conference (ASPCON) Pub Date : 2020-10-07 DOI: 10.1109/ASPCON49795.2020.9276702

Avrajit Ghosh, Sangita Roy, Sheli Sinha Chaudhuri

引用次数: 1

[Copyright notice] (版权)

2020 IEEE Applied Signal Processing Conference (ASPCON) Pub Date : 2020-10-07 DOI: 10.1109/aspcon49795.2020.9276682

引用次数: 0

Optical Flow guided Motion Template for Hand Gesture Recognition 用于手势识别的光流引导运动模板

2020 IEEE Applied Signal Processing Conference (ASPCON) Pub Date : 2020-10-07 DOI: 10.1109/ASPCON49795.2020.9276654

Debajit Sarma, M. Bhuyan

{"title":"Optical Flow guided Motion Template for Hand Gesture Recognition","authors":"Debajit Sarma, M. Bhuyan","doi":"10.1109/ASPCON49795.2020.9276654","DOIUrl":"https://doi.org/10.1109/ASPCON49795.2020.9276654","url":null,"abstract":"Gesture representation specially hand gesture has a special role in the computer and human interaction community. Model-based and appearance-based methods are two primary techniques for hand gesture representation. Apart from these two, space-time features and motion-based approaches have gained quite impressive performance in various applications of action and gesture recognition. In space-time features, actions/gestures are considered as local spatiotemporal neighbourhood. But most space-time features are computationally expensive. Motion-based approaches mainly constitute optical flow and motion templates. Motion estimation of the image pixels is the key factor in optical flow, whereas, in motion-templates, video-wide temporal evaluation and their representations are widely used for action/gesture recognition. Both these methods have their own advantages and accordingly applied in the analysis of motion and related applications. In this paper, we tried to combine both and proposed a new method to get the advantages of individual methods in representing the temporal templates of a video by fusing the video dynamics into a single image. The main benefits of the technique are basically its simplicity, ease of implementation, competitive performance and efficiency.","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":"129970631","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

A Novel Framework for Enhancement of Diagnostic Information in MRI using Deep Super-Resolution 一种利用深度超分辨率增强MRI诊断信息的新框架

2020 IEEE Applied Signal Processing Conference (ASPCON) Pub Date : 2020-10-07 DOI: 10.1109/ASPCON49795.2020.9276697

S. Datta, S. Dandapat, B. Deka

{"title":"A Novel Framework for Enhancement of Diagnostic Information in MRI using Deep Super-Resolution","authors":"S. Datta, S. Dandapat, B. Deka","doi":"10.1109/ASPCON49795.2020.9276697","DOIUrl":"https://doi.org/10.1109/ASPCON49795.2020.9276697","url":null,"abstract":"Magnetic resonance imaging (MRI) is one of the preferred medical imaging modality for soft tissue imaging. Besides its several advantages, it has a fundamental limitation i.e., slow imaging, which restrict its high resolution (HR) applications. One alternative solution is the super-resolution (SR) to obtain the HR image from the acquired low resolution (LR) image. HR image reconstruction from an LR image with low computational time without sacrificing the quality of the HR image is the main challenging task in MRI for clinical applications. Most of the well-known SR methods are not designed for clinical applications and also requires a significant amount of computational time, which is not clinically feasible. In this paper, we have proposed a region-of-interest based framework using deep learning, which not only enhances the resolution but also improves the diagnostic quality of MR images. Several experiments have been carried out with a set of pathological MR images to check the performance of the proposed technique. From the experimental results, it is observed that the proposed method might be a good candidate for clinical implementation to enhance the diagnostic information in MR images.","PeriodicalId":193814,"journal":{"name":"2020 IEEE Applied Signal Processing Conference (ASPCON)","volume":"48 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":"122775558","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

Deep Network-based Hand Gesture Recognition using Optical Flow guided Trajectory Images 基于光流引导轨迹图像的深度网络手势识别

2020 IEEE Applied Signal Processing Conference (ASPCON) Pub Date : 2020-10-07 DOI: 10.1109/ASPCON49795.2020.9276714

V. Kavyasree, Debajit Sarma, Priyanka Gupta, M. Bhuyan

{"title":"Deep Network-based Hand Gesture Recognition using Optical Flow guided Trajectory Images","authors":"V. Kavyasree, Debajit Sarma, Priyanka Gupta, M. Bhuyan","doi":"10.1109/ASPCON49795.2020.9276714","DOIUrl":"https://doi.org/10.1109/ASPCON49795.2020.9276714","url":null,"abstract":"The use of body gestures and specially hand ges-tures can be a convenient and useful alternative tool for many utilizations in the human-computer interaction community. A typical hand gesture recognition system comprises different stages like detection, representation and recognition. In this process of hand gesture recognition, proper detection and tracking of the moving hand in a cluttered background play an important role due to the varied shape and size of the hand. In this work, we propose a framework for the recognition of isolated gestures where the moving hand with different shapes, size and colours is detected through optical flow, and the proper hand gesture is recognized using a VGG16 architecture. This paper utilizes the optical flow to track points of interest in video and store the tracked motion as images that we call trajectory-based images. These images are then fed to a VGG16 network for classification. For feature learning and recognition, a deep learning based method is used due to its inherent ability to extract robust and effective features for classification purposes. The main benefits of the proposed method is its simplicity and ease of implementation. This method has offered higher multi-class classification accuracy with a limited amount of continuous isolated hand gesture video dataset.","PeriodicalId":193814,"journal":{"name":"2020 IEEE Applied Signal Processing Conference (ASPCON)","volume":"57 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":"132646742","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: 6

Epileptic Seizure Classification Based on Gramian Angular Field Transformation and Deep Learning 基于Gramian角场变换和深度学习的癫痫发作分类

2020 IEEE Applied Signal Processing Conference (ASPCON) Pub Date : 2020-10-07 DOI: 10.1109/ASPCON49795.2020.9276717

Anand Shankar, H. Khaing, S. Dandapat, S. Barma

{"title":"Epileptic Seizure Classification Based on Gramian Angular Field Transformation and Deep Learning","authors":"Anand Shankar, H. Khaing, S. Dandapat, S. Barma","doi":"10.1109/ASPCON49795.2020.9276717","DOIUrl":"https://doi.org/10.1109/ASPCON49795.2020.9276717","url":null,"abstract":"This work proposes a new method to classify epileptic seizures based on a well-known deep learning technique named convolutional neural network (CNN), where the input images are generated by Gramian angular field (GAF) transformation. For this purpose, the EEG signals have been assumed as time series data. Certainly, two different signals such as the EEG signal and its instantaneous power have been used for image generation by two different ways — Gramian angular summation field (GASF) and Gramian angular difference field (GADF). The generated images are directly fed into multilayer CNN having multiple hidden layers. For experimental validation, EEG dataset from Bonn University has been considered. The experimental results exhibit the classification accuracy up to 98%. The efficiency of the proposed method has been evaluated by measuring sensitivity and specificity of 99% and 98.9% respectively. In a comparative study, the proposed idea displays significant improvement in seizure classification. Thus, the proposed idea reveals the usefulness of GAF in deep learning framework for epileptic seizure classification.","PeriodicalId":193814,"journal":{"name":"2020 IEEE Applied Signal Processing Conference (ASPCON)","volume":"353 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":"132342978","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: 11

A Depthwise Separable Convolution Architecture for CNN Accelerator CNN加速器的深度可分离卷积结构

2020 IEEE Applied Signal Processing Conference (ASPCON) Pub Date : 2020-10-07 DOI: 10.1109/ASPCON49795.2020.9276672

Harsh Srivastava, K. Sarawadekar

{"title":"A Depthwise Separable Convolution Architecture for CNN Accelerator","authors":"Harsh Srivastava, K. Sarawadekar","doi":"10.1109/ASPCON49795.2020.9276672","DOIUrl":"https://doi.org/10.1109/ASPCON49795.2020.9276672","url":null,"abstract":"Convolutional Neural Network (CNN) give an unmatched performance in image classification, object detection and object tracking. As many of the modern embedded systems for portable devices deals with similar tasks, they often deploy CNN based algorithms. The intensive computational workload associated with CNN inference demands powerful computing platforms like Graphics Processing Units. However, deploying CNN on mobile devices demands low power, application specific computing platforms like Field-Programmable Gate Array (FPGA) and Application-Specific Integrated Circuit (ASIC) which can work as computation accelerator units. Moreover, using certain algorithmic optimizations like using Depthwise Separable Convolution instead of standard convolution, significantly reduces the computational burden of CNN inference. This paper discusses a pipelined architecture of Depthwise Separable Convolution followed by activation and pooling operations for a single layer of CNN. The architecture is implemented on Xilinx 7 series FPGA and works at a clock period of 40ns. It can be used as a building block for an integrated system of CNN accelerator for implementation on FPGAs of different sizes. This work focuses on speeding up the convolution process, instead of implementing large design of an integrated system of CNN accelerator which makes it difficult to focus on performance of the subsystems. To the best of the knowledge of the authors, earlier works have implemented an integrated system of CNN accelerator but the blueprint for architecture of a single layer of CNN is not discussed individually, which can be a great support for the beginners in understanding FPGA based computing accelerators for CNN.","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":"131321323","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: 9

Dynamic Range Improvement of Backscattered Optical Signals using Signal Processing Techniques 利用信号处理技术提高后向散射光信号的动态范围

2020 IEEE Applied Signal Processing Conference (ASPCON) Pub Date : 2020-10-07 DOI: 10.1109/ASPCON49795.2020.9276692

Ramji Tangudu, P. Sahu

{"title":"Dynamic Range Improvement of Backscattered Optical Signals using Signal Processing Techniques","authors":"Ramji Tangudu, P. Sahu","doi":"10.1109/ASPCON49795.2020.9276692","DOIUrl":"https://doi.org/10.1109/ASPCON49795.2020.9276692","url":null,"abstract":"Optical fiber-based instruments like optical time domain reflectometry (OTDR) and distributed fiber optic sensing (DFOS) systems are widely in use over the last two decades. OTDR is critical for optical fiber testing and troubleshooting in an optical network. Besides integrity testing, one can also measure the splice losses, length of the fiber under test and the fault identification over an optical network. Similarly, the DFOS is used to detect the surrounding environmental parameters, such as temperature, strain, and vibration, etc. Both the systems as mentioned above can be designed based on the backscattering signals. However, as the signals are very weak, it poses major design challenges for designing such systems. To improve the signal to noise ratio (SNR) as well as the dynamic range of backscattering based systems, we have proposed and implemented two signal processing techniques (translation invariant wavelet thresholding (TIWT) and lifting wavelet transform-modified particle swarm optimization (LWT-MPSO)). With the TIWT signal processing technique, we have achieved a L68 dB of dynamic range improvement for Rayleigh and Brillouin backscattered signals respectively and with the LWT-MPSO signal processing technique, we have achieved a 4.03 dB of dynamic range improvement for Rayleigh and Brillouin backscattered signals respectively. For this work, a single-mode fiber (SMF) is used along with a 13 dBm of laser source power for experimenting. The signal processing techniques were implemented using MATLAB 15.0 platform.","PeriodicalId":193814,"journal":{"name":"2020 IEEE Applied Signal Processing Conference (ASPCON)","volume":"38 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":"116945682","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