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2021 3rd International Conference on Signal Processing and Communication (ICPSC)最新文献

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An Improved Bleeding Detection Method for Wireless Capsule Endoscopy (WCE) Images Based on AlexNet 基于 AlexNet 的无线胶囊内窥镜 (WCE) 图像出血检测改进方法
2021 3rd International Conference on Signal Processing and Communication (ICPSC) Pub Date : 2021-05-13 DOI: 10.1109/ICSPC51351.2021.9451699
S. Sunitha, S. Sujatha
{"title":"An Improved Bleeding Detection Method for Wireless Capsule Endoscopy (WCE) Images Based on AlexNet","authors":"S. Sunitha, S. Sujatha","doi":"10.1109/ICSPC51351.2021.9451699","DOIUrl":"https://doi.org/10.1109/ICSPC51351.2021.9451699","url":null,"abstract":"Bleeding is a common symptom of many gastrointestinal (GI) diseases. Hence, early detection of bleeding is a very important thing in the medical field. Wireless capsule endoscopy (WCE) is a major breakthrough in the medical field. This makes it very easy to diagnose various gastrointestinal (GI) related ailments. The primary benefit of WCE is painless diagnostic. As a result, it has received favorable reviews from physicians and patients. The WCE generates several images of the small intestine during diagnosis. However, only a highly trained doctor can make an effective diagnosis of bleeding images from this. Accuracy is an unavoidable factor in medical imaging. Even a small flaw inaccuracy can have huge repercussions. Therefore developing a high accurate WCE bleeding detection system is a very urgent research problem. This article proposes an improved AlexNet architecture to address the limitation of bleeding detection in the WCE procedure. The average accuracy of the proposed AlexNet is 94.5 % and its sensitivity and specificity are 95.24 % and 96.72 % respectively.","PeriodicalId":182885,"journal":{"name":"2021 3rd International Conference on Signal Processing and Communication (ICPSC)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133078926","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
Design of high gain antennas using patch and ground deformations for satellite applications 利用贴片和地面变形为卫星应用设计高增益天线
2021 3rd International Conference on Signal Processing and Communication (ICPSC) Pub Date : 2021-05-13 DOI: 10.1109/ICSPC51351.2021.9451817
V. Roy, T. Mary Neebha, B. Abshiek, G. Sivaganesh, M. M. Praveen, A. Diana Andrushia
{"title":"Design of high gain antennas using patch and ground deformations for satellite applications","authors":"V. Roy, T. Mary Neebha, B. Abshiek, G. Sivaganesh, M. M. Praveen, A. Diana Andrushia","doi":"10.1109/ICSPC51351.2021.9451817","DOIUrl":"https://doi.org/10.1109/ICSPC51351.2021.9451817","url":null,"abstract":"In this work, high gain rectangular slotted array antenna with compact size is proposed. This radiator has nearly broad pattern of radiation and high gain, thus suitable for satellite applications. Multiband of operation is achieved by adding rectangular slots in the array structure. Meanwhile, more slots in the radiator improves the radiation effectively. Consequently, the side lobes formed due to the array structure can be eliminated by using the slots in the unit cell structure. Different slot shapes are also analyzed for effective gain improvement. The antenna operates between 17 and 19GHz and a superstrate is added to improve the gain up to 17dB.","PeriodicalId":182885,"journal":{"name":"2021 3rd International Conference on Signal Processing and Communication (ICPSC)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128304352","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
Sample and Hold Circuit with Clock Boosting 带时钟增强的采样和保持电路
2021 3rd International Conference on Signal Processing and Communication (ICPSC) Pub Date : 2021-05-13 DOI: 10.1109/ICSPC51351.2021.9451640
K. Aneesh, G. Manoj
{"title":"Sample and Hold Circuit with Clock Boosting","authors":"K. Aneesh, G. Manoj","doi":"10.1109/ICSPC51351.2021.9451640","DOIUrl":"https://doi.org/10.1109/ICSPC51351.2021.9451640","url":null,"abstract":"Sample and hold circuit is an integral part of analog to digital convertors. In this work different sample and hold circuits are simulated using LTSPICE XVII, in 180nm TSMC technology and their performances are analyzed. The input signal of 250mVP-P and a frequency of 100Hz is used for simulation purpose. It is found that the sample switch with a clock boosting circuit outperforms the other designs. A rail to rail sampling of the input voltage is achieved. Sampling frequency of 2KHz is used. An SNDR of 45.01dB and an average power consumption of 1.036nW are achieved. The sampling switch with clock boosted network can be used as a potential candidate in analog to digital convertor design for low frequency physiological signal.","PeriodicalId":182885,"journal":{"name":"2021 3rd International Conference on Signal Processing and Communication (ICPSC)","volume":"242 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121812284","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
CNN-based Mask Detection System Using OpenCV and MobileNetV2 基于OpenCV和MobileNetV2的cnn掩码检测系统
2021 3rd International Conference on Signal Processing and Communication (ICPSC) Pub Date : 2021-05-13 DOI: 10.1109/ICSPC51351.2021.9451688
H. G., Jesica. J, A. K., K. Sagayam
{"title":"CNN-based Mask Detection System Using OpenCV and MobileNetV2","authors":"H. G., Jesica. J, A. K., K. Sagayam","doi":"10.1109/ICSPC51351.2021.9451688","DOIUrl":"https://doi.org/10.1109/ICSPC51351.2021.9451688","url":null,"abstract":"this paper establishes a ‘Safety system for mask detection during this COVID-19 pandemic’. Face mask detection has seen an overwhelming growth in the realm of Computer vision and deep learning, since the unprecedented COVID-19 global pandemic that has mandated wearing masks in public places. To tackle the situation, machine learning engineers have come up with several algorithms and techniques to identify unmasked individuals using various mask detection models. The proposed approach in this paper adopts frameworks of deep learning, TensorFlow, Keras, and OpenCV libraries to detect face masks in real time. The trained MobileNet model, presented in this paper, yielded an accuracy score of 0.99 and an F1 score of 0.99 in the training data. This user-friendly model can be incorporated with several existing technologies such as face detection, biometric authentication and facial expression detection for further advancements in the future.","PeriodicalId":182885,"journal":{"name":"2021 3rd International Conference on Signal Processing and Communication (ICPSC)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121994159","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: 30
Design and Performance Analysis of Transformerless Z Source Inverter In PV Systems 光伏系统中无变压器Z源逆变器的设计与性能分析
2021 3rd International Conference on Signal Processing and Communication (ICPSC) Pub Date : 2021-05-13 DOI: 10.1109/ICSPC51351.2021.9451741
D. Gayathri, P. Thirumalai, T. Prashanth
{"title":"Design and Performance Analysis of Transformerless Z Source Inverter In PV Systems","authors":"D. Gayathri, P. Thirumalai, T. Prashanth","doi":"10.1109/ICSPC51351.2021.9451741","DOIUrl":"https://doi.org/10.1109/ICSPC51351.2021.9451741","url":null,"abstract":"This paper describes the utilization of z converter in power conversion systems. The Z-source inverter has ideal features in resources and control units. This paper represents the advanced operating conditions and control strategies of Z-source inverter for conversion purposes in renewable energy systems (photo voltaic Applications). The switching loss and system cost is reduced by using transformer less Z inverter in conversion systems. There are 4 levels resonant systems are used in Z source inverter instead of 6 level traditional inverters in PV system. As a result, the cost of the system is reduced and overall efficiency of PV system is improved.","PeriodicalId":182885,"journal":{"name":"2021 3rd International Conference on Signal Processing and Communication (ICPSC)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130410811","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 Survey on Pest and Disease Monitoring of Crops 作物病虫害监测研究进展
2021 3rd International Conference on Signal Processing and Communication (ICPSC) Pub Date : 2021-05-13 DOI: 10.1109/ICSPC51351.2021.9451787
P. Deepika, S. Kaliraj
{"title":"A Survey on Pest and Disease Monitoring of Crops","authors":"P. Deepika, S. Kaliraj","doi":"10.1109/ICSPC51351.2021.9451787","DOIUrl":"https://doi.org/10.1109/ICSPC51351.2021.9451787","url":null,"abstract":"Maintenance of Crop health is essential for the successful farming for both yield and product quality. Pest and disease in crops are serious problem to be monitored. pest and disease occur in different stages or phases of crop development. Due to introduction of genetically modified seeds the natural resistance of crops to prevent them from pest and disease is less. Major crop loss is due to pest and disease attack in crops. It damages the leaves, buds, flowers and fruits of the crops. Affected areas and damage levels of pest and diseases attacks are growing rapidly based on global climate change. Weather Conditions plays a major role in pest and disease attacks in crops. Naked eye inspection of pest and disease is complex and difficult for wide range of field. And at the same time taking lab samples to detect disease is also inefficient and time-consuming process. Early identification of diseases is important to take necessary actions for preventing crop loss and to avoid disease spreads. So, Timely and effective monitoring of crop health is important. Several technologies have been developed to detect pest and disease in crops. In this paper we discuss the various technologies implemented by using AI and Deep Learning for pest and disease detection. And also, briefly discusses their Advantages and limitations on using certain technology for monitoring of crops.","PeriodicalId":182885,"journal":{"name":"2021 3rd International Conference on Signal Processing and Communication (ICPSC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128819995","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: 7
Real-time Sign Language Recognition using Computer Vision 基于计算机视觉的实时手语识别
2021 3rd International Conference on Signal Processing and Communication (ICPSC) Pub Date : 2021-05-13 DOI: 10.1109/ICSPC51351.2021.9451709
Jinalee Jayeshkumar Raval, Ruchi Gajjar
{"title":"Real-time Sign Language Recognition using Computer Vision","authors":"Jinalee Jayeshkumar Raval, Ruchi Gajjar","doi":"10.1109/ICSPC51351.2021.9451709","DOIUrl":"https://doi.org/10.1109/ICSPC51351.2021.9451709","url":null,"abstract":"Speech impairment is a disability that affects an individual’s ability to verbal communication. To overcome this issue sign language is used which is one of the most organised languages. There is definitely a need for a method or an application that can recognize sign language gestures so that communication is possible even if someone does not understand sign language. My paper is an effort towards filling the gap between differently-abled people like deaf and dumb and the other people. Image processing combined with machine learning helped in forming a real-time system. Image processing is used for pre-processing the images and extracting different hand from the background. These images obtained after extracting background were used for forming data that contained 24 alphabets of the English language. The Convolutional Neural Network proposed here is tested on both a custom-made dataset and also with real-time hand gestures performed by people of different skin tones. The accuracy obtained by the proposed algorithm is 83%.","PeriodicalId":182885,"journal":{"name":"2021 3rd International Conference on Signal Processing and Communication (ICPSC)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129885167","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
Kannada Confusing Character Recognition and Classification Using Random Forest and SVM 基于随机森林和支持向量机的卡纳达语混淆字符识别与分类
2021 3rd International Conference on Signal Processing and Communication (ICPSC) Pub Date : 2021-05-13 DOI: 10.1109/ICSPC51351.2021.9451798
Shobha Rani N, B. Nair B J, Athira M R, Prajwal M L
{"title":"Kannada Confusing Character Recognition and Classification Using Random Forest and SVM","authors":"Shobha Rani N, B. Nair B J, Athira M R, Prajwal M L","doi":"10.1109/ICSPC51351.2021.9451798","DOIUrl":"https://doi.org/10.1109/ICSPC51351.2021.9451798","url":null,"abstract":"Dravidian scripts have a lot of confusing characters because of the complexity of the characters and curve nature so recognizing those confusing characters are a tedious process. Kannada have many confusing characters which cause difficulties in extraction from a kannada document. The proposed work deals with recognition and classification of confusing characters. In method uses Random Forest and SVM as the classifiers to classify the confusing characters. The proposed system achieved a classifier accuracy of 78%. Finally the system will recognize the confusing character using template matching and feature value outcome based out of the classifiers. In proposed work deals with ten classes that are used to classify and recognize the confusing characters.","PeriodicalId":182885,"journal":{"name":"2021 3rd International Conference on Signal Processing and Communication (ICPSC)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117276287","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
Food Calorie Estimation using Convolutional Neural Network 利用卷积神经网络估算食物卡路里
2021 3rd International Conference on Signal Processing and Communication (ICPSC) Pub Date : 2021-05-13 DOI: 10.1109/ICSPC51351.2021.9451812
V. Kasyap, N. Jayapandian
{"title":"Food Calorie Estimation using Convolutional Neural Network","authors":"V. Kasyap, N. Jayapandian","doi":"10.1109/ICSPC51351.2021.9451812","DOIUrl":"https://doi.org/10.1109/ICSPC51351.2021.9451812","url":null,"abstract":"The modern world healthy body depends on the number of calories consumed, hence monitoring calorie intake is necessary to maintain good health. At the point when your Body Mass Index is somewhere in between from 25 to 29. It implies that you are conveying overabundance weight. Assuming your BMI is more than 30, it implies you have obesity. To get in shape or keep up the solid weight individuals needs to monitor the calorie they take. The existing system calorie estimation is to be happened manually. The proposed model is to provide unique solution for measuring calorie by using deep learning algorithm. The food calorie calculation is very important in medical field. Because this food calorie is provide good health condition. This measurement is taken from food image in different objects that is fruits and vegetables. This measurement is taken with the help of neural network. The tensor flow is one of the best methods to classify the machine learning method. This method is implementing to calculate the food calorie with the help of Convolutional Neural Network. The input of this calculated model is taken an image of food. The food calorie value is calculated the proposed CNN model with the help of food object detection. The primary parameter of the result is taken by volume error estimation and secondary parameter is calorie error estimation. The volume error estimation is gradually reduced by 20%. That indicates the proposed CNN model is providing higher accuracy level compare to existing model.","PeriodicalId":182885,"journal":{"name":"2021 3rd International Conference on Signal Processing and Communication (ICPSC)","volume":"318 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121493808","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: 10
Computer Vision System for Railway Track Crack Detection using Deep Learning Neural Network 基于深度学习神经网络的铁路轨道裂纹检测计算机视觉系统
2021 3rd International Conference on Signal Processing and Communication (ICPSC) Pub Date : 2021-05-13 DOI: 10.1109/ICSPC51351.2021.9451771
R. Thendral, A. Ranjeeth
{"title":"Computer Vision System for Railway Track Crack Detection using Deep Learning Neural Network","authors":"R. Thendral, A. Ranjeeth","doi":"10.1109/ICSPC51351.2021.9451771","DOIUrl":"https://doi.org/10.1109/ICSPC51351.2021.9451771","url":null,"abstract":"For better inspections and security, we need an efficient railway track crack detection system. In this research, we present a computer vision-based technique to detect the railway track cracks automatically. This system uses images captured by a rolling camera attached just below a self-moving vehicle in the railway department. The source images considered are the cracked and crack-free images. The first step is pre-processing scheme and then apply Gabor transform. In this paper, first order statistical features are extracted from the Gabor magnitude image. These extracted features are given as input to the deep learning neural network for differentiate the cracked track image from the non-cracked track image. Accuracy of the proposed algorithm on the procured images is 94.9 % and an overall error rate of 1.5%.","PeriodicalId":182885,"journal":{"name":"2021 3rd International Conference on Signal Processing and Communication (ICPSC)","volume":"252 10","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120977322","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
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