2021 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER)最新文献

Deep Neural Network Architectures for Spectrum Sensing Using Signal Processing Features 基于信号处理特征的频谱感知深度神经网络架构

2021 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER) Pub Date : 2021-11-19 DOI: 10.1109/DISCOVER52564.2021.9663583

Shreeram Suresh Chandra, Akshay Upadhye, Purushothaman Saravanan, Sanjeev Gurugopinath, R. Muralishankar

引用次数: 1

Design and Analysis of Self-write-terminated Hybrid STT-MTJ/CMOS Logic Gates using LIM Architecture 基于LIM结构的自写端STT-MTJ/CMOS混合逻辑门设计与分析

2021 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER) Pub Date : 2021-11-19 DOI: 10.1109/DISCOVER52564.2021.9663697

Prashanth Barla, Vinod Kumar Joshi, S. Bhat

{"title":"Design and Analysis of Self-write-terminated Hybrid STT-MTJ/CMOS Logic Gates using LIM Architecture","authors":"Prashanth Barla, Vinod Kumar Joshi, S. Bhat","doi":"10.1109/DISCOVER52564.2021.9663697","DOIUrl":"https://doi.org/10.1109/DISCOVER52564.2021.9663697","url":null,"abstract":"Among all spintronics devices, spin transfer torque (STT) magnetic tunnel junction (MTJ) is the most promising candidate for logic-in-memory (LIM) architecture. It alleviates the performance degradation observed in the present CMOS circuits which are built using standard von-Neumann architecture. However STT-MTJ suffers the issues such as switching delay due to stochasticity as well as wastage of write power. Hence, in this work continuous monitoring and self-write-termination (SWT) process is adopted for STT-MTJs and studied the performance of all the logic gates; AND/NAND, OR/NOR and XOR/XNOR developed using LIM architecture. Investigation of the read/write power, read/write delay, read/write power delay product and transistor count of SWT-STT-MTJ/CMOS logic gates are performed and compared them with its conventional counterparts. Further, Monte-Carlo simulations are also conducted to study the behavior of hybrid logic gates for variations that could occur during fabrication. The simulation results reveal that SWT-STT-MTJ/CMOS logic gates dissipates lower power, PDP and produce quicker output response.","PeriodicalId":413789,"journal":{"name":"2021 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115049574","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

Implementation of Radar Digital Receiver based on Xeon-Processor using Intel IPP 基于Intel IPP的至强处理器雷达数字接收机的实现

2021 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER) Pub Date : 2021-11-19 DOI: 10.1109/DISCOVER52564.2021.9663579

Nune Divya, B. H. Chandana, D. Harika

引用次数: 0

Performance Measurements of different Classification techniques for the Alzheimer’s Disease Neuroimaging Initiative 不同分类技术对阿尔茨海默病神经影像学倡议的性能测量

2021 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER) Pub Date : 2021-11-19 DOI: 10.1109/DISCOVER52564.2021.9663705

Archana Yashodhar, Shashidhar Kini

引用次数: 1

Heart Attack Probability Analysis Using Machine Learning 利用机器学习进行心脏病发作概率分析

2021 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER) Pub Date : 2021-11-19 DOI: 10.1109/DISCOVER52564.2021.9663631

Annapurna Anant Shanbhag, Chinmai Shetty, A. Ananth, Anjali Shridhar Shetty, K. Kavanashree Nayak, B. R. Rakshitha

{"title":"Heart Attack Probability Analysis Using Machine Learning","authors":"Annapurna Anant Shanbhag, Chinmai Shetty, A. Ananth, Anjali Shridhar Shetty, K. Kavanashree Nayak, B. R. Rakshitha","doi":"10.1109/DISCOVER52564.2021.9663631","DOIUrl":"https://doi.org/10.1109/DISCOVER52564.2021.9663631","url":null,"abstract":"Heart Attack is one of the most common diseases observed in people of middle age as well as old age in the present day scenario. This may be due to unhealthy food habits and negligence of health in most people. Detecting the risk of heart attack and taking timely medication, can prevent serious illness. In this paper we explain about the different machine learning approaches and techniques used for predicting the probability of heart-attack risk. Different models are applied for heart-attack risk prediction. The probability of heart attack risk is displayed through a website. If a person is found having risk, suitable precautions are displayed under the guidance of the cardiologist. The proposed work analyses whether the person has a normal range of values for some highly contributing attributes which lead to heart attack like Cholesterol, Blood pressure, Blood sugar. The proposed work has better results compared to the previous work in terms of accuracy of prediction with highest value of accuracy as 85.7% for SVM model.","PeriodicalId":413789,"journal":{"name":"2021 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131622971","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

Prototyping of Intelligent Office Monitoring and Control System Using IoT 基于物联网的智能办公监控系统原型设计

2021 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER) Pub Date : 2021-11-19 DOI: 10.1109/DISCOVER52564.2021.9663722

N. Sreenivasa, B. A. Mohan, E. G. Satish, Roshan Fernandes, P. Ramesh Naidu, T. Vinay

{"title":"Prototyping of Intelligent Office Monitoring and Control System Using IoT","authors":"N. Sreenivasa, B. A. Mohan, E. G. Satish, Roshan Fernandes, P. Ramesh Naidu, T. Vinay","doi":"10.1109/DISCOVER52564.2021.9663722","DOIUrl":"https://doi.org/10.1109/DISCOVER52564.2021.9663722","url":null,"abstract":"Automation plays key role in our day today lives making our life easier, simpler and easy living. In this research paper a prototype for smart office automation is designed and implemented. This project includes subsystems like managing energy saving, security and alarming systems. The sensors and actuators are used to mine the real time data from the indoor office environment. All the sensors and actuators are connected to the embedded system link microcontroller unit. It further processes the data, analyze and control the subsystems in office environment. The electrical/electronic devices such as bulbs, fans, buzzer, sensors like temperature, humidity and motion sensors are connected to the microcontroller, which will generate values/data when they cross the certain threshold values. This office system facilitate control of electrical/electronic devices like door-access, illuminating, lighting and ventilating system in order to save energy and uplifts employees’ and customers satisfactions. This work promotes indoor office automation along with saving the cost for the employer and providing comforts to the employees.","PeriodicalId":413789,"journal":{"name":"2021 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133285633","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

How Does Deep Brain Stimulation Affect Magnetoencephalography Data? 深部脑刺激如何影响脑磁图数据?

2021 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER) Pub Date : 2021-11-19 DOI: 10.1109/DISCOVER52564.2021.9663715

Vamsi Vijay Mohan Dattada, Sreedevi Sasidharan, A. Højlund, K. S. Sridharan

{"title":"How Does Deep Brain Stimulation Affect Magnetoencephalography Data?","authors":"Vamsi Vijay Mohan Dattada, Sreedevi Sasidharan, A. Højlund, K. S. Sridharan","doi":"10.1109/DISCOVER52564.2021.9663715","DOIUrl":"https://doi.org/10.1109/DISCOVER52564.2021.9663715","url":null,"abstract":"Deep Brain Stimulation (DBS) is an established and effective neuromodulation technique preferred in treating several neurological and neuropsychiatric disorders such as Parkinson’s Disease(PD), epilepsy, obsessive compulsive disorder, depression and several such disorders. Magnetoencephalography (MEG) is a widely used neuroimaging strategy to understand the pathology and the therapeutic effects of DBS in clinical cohorts. One of the significant limitations is the inability to differentiate the DBS stimulation artefact from actual neuronal excitations, especially in lower frequency bands of interest where sub-harmonics of DBS artefacts may obscure the biological response and is a confounder. The primary objective of this study is to understand how DBS stimulation artefacts affect MEG signals and to this end, we employ a phantom based on a water melon. Using this phantom, we record the spectral signature of the DBS stimulation artefact at various DBS frequencies and stimulation voltages, the effect of standard artefact rejection approaches like spatiotemporal signal space separation (tSSS). We present in this paper the results of the initial analysis.","PeriodicalId":413789,"journal":{"name":"2021 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER)","volume":"117 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129589399","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

Novel Approach to Harvest Energy from Salinity Gradient 从盐度梯度中获取能量的新方法

2021 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER) Pub Date : 2021-11-19 DOI: 10.1109/DISCOVER52564.2021.9663704

Rumana Ali, Vinayambika S. Bhat, C. Abhishek, Akash Joseph, Mohammed Arfadh, Akarsh Manoj

引用次数: 0

A Study on the Application of One Dimension Convolutional Neural Network for Classification of Gestures from Surface Electromyography Data 一维卷积神经网络在体表肌电数据手势分类中的应用研究

2021 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER) Pub Date : 2021-11-19 DOI: 10.1109/DISCOVER52564.2021.9663596

Praahas Amin, A. Khan

引用次数: 0

Neural Network based Biometric Attendance System 基于神经网络的生物考勤系统

2021 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER) Pub Date : 2021-11-19 DOI: 10.1109/DISCOVER52564.2021.9663661

R. Vandana, P. S. Venugopala, B. Ashwini

{"title":"Neural Network based Biometric Attendance System","authors":"R. Vandana, P. S. Venugopala, B. Ashwini","doi":"10.1109/DISCOVER52564.2021.9663661","DOIUrl":"https://doi.org/10.1109/DISCOVER52564.2021.9663661","url":null,"abstract":"In the modern world, education system has reached a new destination due to the introduction of concept called “smart classroom”. However, when we are speaking about any classroom the attendance system still remains primitive. The traditional attendance system where the teacher/lecturer calls the name of students to mark their attendance in an attendance register is a manual method which is found to be not suitable for a smart class due to a list of disadvantages. The automatic attendance management will replace the manual method. This dynamic attendance management system will consider the physiological features of the human beings for uniquely identifying them. Hence we are using a biometric based attendance system. There are many biometric processes, among which face recognition is the best method. In the proposed project, we are going to describe the attendance without human interference. In this method a camera, fixed within the classroom will capture the image, the faces are detected and then they are compared with the faces in the database and finally the attendance is marked. It also proposes a single image-based face liveness detection method for discriminating 2-D paper masks from the live faces. Freely available machine learning and deep learning tools like dlib, Keras are used for making the face recognition faster and accurate one. This makes the system suitable in a real life scenario.","PeriodicalId":413789,"journal":{"name":"2021 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126693648","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