2020 International Conference on Computational Performance Evaluation (ComPE)最新文献_第2页

Object Detection and Tracking Turret based on Cascade Classifiers and Single Shot Detectors 基于级联分类器和单发探测器的炮塔目标检测与跟踪

2020 International Conference on Computational Performance Evaluation (ComPE) Pub Date : 2020-07-01 DOI: 10.1109/ComPE49325.2020.9200139

Pritom Gogoi, Manpa Barman, Mahendra Deka, Upasana Rajkonwar, Rhittwikraj Moudgollya

{"title":"Object Detection and Tracking Turret based on Cascade Classifiers and Single Shot Detectors","authors":"Pritom Gogoi, Manpa Barman, Mahendra Deka, Upasana Rajkonwar, Rhittwikraj Moudgollya","doi":"10.1109/ComPE49325.2020.9200139","DOIUrl":"https://doi.org/10.1109/ComPE49325.2020.9200139","url":null,"abstract":"The involvement of embedded systems and computer vision is increasing day by day in various segments of consumer market like industrial automation, traffic monitoring, medical imaging, modern appliance market, augmented reality systems, etc. These technologies are bound to make new developments in the domain of commercial and home security surveillance. Our project aims to make contributions to the domain of video surveillance by making use of embedded computer vision systems. Our implementation, built around the Raspberry Pi 4 SBC aims to utilize computer vision techniques like motion detection, face recognition, object detection, etc to segment the region of interest from the captured video footage. This technique is superior as compared to traditional surveillance systems as it requires minimum human interaction and intervention at the control room of such security systems. The proposed system is capable of sensing suspicious events like detection of an unknown face in the captured video or motion detection/object detection in a closed section of a building. Moreover, with the help of the turret mechanism built using servo motors, the camera integrated in the system is capable of having 360◦ rotation and can track a detected face or object of interest within its range. Apart from automated tracking, the system can also be manually controlled by the operator.","PeriodicalId":6804,"journal":{"name":"2020 International Conference on Computational Performance Evaluation (ComPE)","volume":"98 1","pages":"792-796"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79223635","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: 2

A Linear Model Predictive Control design for Magnetic Levitation System 磁悬浮系统线性模型预测控制设计

2020 International Conference on Computational Performance Evaluation (ComPE) Pub Date : 2020-07-01 DOI: 10.1109/ComPE49325.2020.9200143

Lakshmi Dutta, Dushmanta Kumar Das

引用次数: 2

A Mobile Robot for Hazardous Gas Sensing 一种用于危险气体传感的移动机器人

2020 International Conference on Computational Performance Evaluation (ComPE) Pub Date : 2020-07-01 DOI: 10.1109/ComPE49325.2020.9200082

Tanaya Das, Dhruba Jyoti Sut, Vishal Gupta, Lakhyajit Gohain, Priyanka Kakoty, N. M. Kakoty

引用次数: 4

A Gm-C Biquad Programmable Band Pass Filter for Wireless Applications 用于无线应用的Gm-C双路可编程带通滤波器

2020 International Conference on Computational Performance Evaluation (ComPE) Pub Date : 2020-07-01 DOI: 10.1109/ComPE49325.2020.9200008

Hara Gobinda Naskar, A. Maity

引用次数: 0

Comparative Assessment of AR, MA and ARMA for the Time Series Forecasting of Assam and Meghalaya Rainfall Division 阿萨姆邦和梅加拉亚邦降雨分区时间序列预报的AR、MA和ARMA比较评估

2020 International Conference on Computational Performance Evaluation (ComPE) Pub Date : 2020-07-01 DOI: 10.1109/ComPE49325.2020.9200014

Utpal Barman, Ridip Dev Choudhury, Asif Ekbal Hussain, Mridul Jyoti Dahal, Puja Barman, M. Hazarika

引用次数: 2

Using an Un-Balanced AC Wheatstone Bridge to Measure Capacitance and Inductance 用不平衡交流惠斯通电桥测量电容和电感

2020 International Conference on Computational Performance Evaluation (ComPE) Pub Date : 2020-07-01 DOI: 10.1109/ComPE49325.2020.9200172

Navarun Gupta, S. Patel, Lawrence V. Hmurick

引用次数: 0

Photonic Crystal based Micro Ring Resonator Sensor Design for Urinanalysis 基于光子晶体的微环谐振器尿液分析传感器设计

2020 International Conference on Computational Performance Evaluation (ComPE) Pub Date : 2020-07-01 DOI: 10.1109/ComPE49325.2020.9200026

Uttara Biswas, J. K. Rakshit, M. Singh

引用次数: 2

Detection of Parkinson’s Disease Using Rating Scale 用评定量表检测帕金森病

2020 International Conference on Computational Performance Evaluation (ComPE) Pub Date : 2020-07-01 DOI: 10.1109/ComPE49325.2020.9200071

Banita

{"title":"Detection of Parkinson’s Disease Using Rating Scale","authors":"Banita","doi":"10.1109/ComPE49325.2020.9200071","DOIUrl":"https://doi.org/10.1109/ComPE49325.2020.9200071","url":null,"abstract":"Parkinson disease (PD) is a neurodegenerative disorder. Many common symptoms which may or may not indicate that patient is suffering from Parkinson Disease. In this study a novel rating scale has been introduced which helps to examine the level of Parkinson Disease but is not mandatory that a person having similar symptoms may surely suffering from Parkinson Disease. PD is an unsolved problem till date hence the study focuses on relevant features, drugs and common techniques used to detect or analyze PD. To overcome such problem different techniques will be used to study and analyze the early detection of PD. It can be analyzed with the help of deep understanding of Parkinson Disease. However presence of some common symptoms has not yet been described up to the mark to analyze the level of Parkinson Disease. Hence it is very challenging to detect early stage of Parkinson Disease. In study, work focuses on only confirmed symptoms of Parkinson Disease which doesn’t deals to any other disease completely. Study also focus on the Unified Parkinson’s Disease Rating scale (UPDRS) for Parkinson Disease along with respective symptoms. It includes the analysis in terms of medical science and computer applications for analyzing PD. Medication for PD has also been discussed in the study including wide literature survey which provide the clearance of the goal for treating PD. Proposed rating scale in the study is time efficient as compared with UPDRS.","PeriodicalId":6804,"journal":{"name":"2020 International Conference on Computational Performance Evaluation (ComPE)","volume":"14 1","pages":"121-125"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74345719","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

Design of Fruit Segregation and Packaging Machine 水果分离包装机的设计

2020 International Conference on Computational Performance Evaluation (ComPE) Pub Date : 2020-07-01 DOI: 10.1109/ComPE49325.2020.9199986

G. Khekare, Shrutika Verma, Samay Sur, R. Haldkar, Pratik Moon

引用次数: 2

Transfer Learning Code Vectorizer based Machine Learning Models for Software Defect Prediction 基于迁移学习代码矢量器的软件缺陷预测机器学习模型

2020 International Conference on Computational Performance Evaluation (ComPE) Pub Date : 2020-07-01 DOI: 10.1109/ComPE49325.2020.9200076

Rituraj Singh, Jasmeet Singh, M. S. Gill, R. Malhotra, Garima

{"title":"Transfer Learning Code Vectorizer based Machine Learning Models for Software Defect Prediction","authors":"Rituraj Singh, Jasmeet Singh, M. S. Gill, R. Malhotra, Garima","doi":"10.1109/ComPE49325.2020.9200076","DOIUrl":"https://doi.org/10.1109/ComPE49325.2020.9200076","url":null,"abstract":"Software development life cycle comprises of planning, design, implementation, testing and eventually, deployment. Software defect prediction can be used in the initial stages of the development life cycle for identifying defective modules. Researchers have devised various methods that can be used for effective software defect prediction. The prediction of the presence of defects or bugs in a software module can facilitate the testing process as it would enable developers and testers to allocate their time and resources on modules that are prone to defects. Transfer learning can be used for transferring knowledge obtained from one domain into the other. In this paper, we propose Transfer Learning Code Vectorizer, a novel method that derives features from the text of the software source code itself and uses those features for defect prediction. We focus on the software code and convert it into vectors using a pre-trained deep learning language model. These code vectors are subsequently passed through machine and deep learning models. Further, we compare the results of using deep learning on the text of the software code versus the usage of software metrics for prediction of defects. In terms of weighted F1 scores, the experiments show that applying the proposed TLCV method outperforms the other machine learning techniques by 9.052%.","PeriodicalId":6804,"journal":{"name":"2020 International Conference on Computational Performance Evaluation (ComPE)","volume":"74 1","pages":"497-502"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74141468","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