2020 IEEE International Students' Conference on Electrical,Electronics and Computer Science (SCEECS)最新文献_第6页

Automated E-circuit Designing and Characterization using Prominent Neural Network 基于突出神经网络的自动化电子电路设计与表征

2020 IEEE International Students' Conference on Electrical,Electronics and Computer Science (SCEECS) Pub Date : 2020-02-01 DOI: 10.1109/SCEECS48394.2020.85

Diksha, Yash Agrawal, R. Parekh, Vinay S. Palaparthy

{"title":"Automated E-circuit Designing and Characterization using Prominent Neural Network","authors":"Diksha, Yash Agrawal, R. Parekh, Vinay S. Palaparthy","doi":"10.1109/SCEECS48394.2020.85","DOIUrl":"https://doi.org/10.1109/SCEECS48394.2020.85","url":null,"abstract":"Designing, characterization and modelling of circuits comprising of several active and passive devices play a vital role in the field of electronics and various e-product development. In the present paper, neural network (NN) based model is developed. The developed model can be used for automation of any electronic circuit design. As a test case, amplifier design using common base (CB) configuration of bipolar junction transistor (BJT) is considered. The CB configuration is highly significant and predominantly used in several applications such as preamplifiers, UHF and VHF RF amplifiers, and current buffer circuits. Henceforth, designing and characterization of CB configuration is considered and performed using prospective NN technique. A set of performance parameters are considered to frame the amplifier that incorporates voltage gain, input impedance, output impedance and collector emitter voltage. The design parameters considered are base, emitter and collector resistors. In the present work, Levenberg Marquardt (LM) method is utilized as fitting and training algorithm for developing NN based model. The developed neural network based model comprises of two hidden layers with respective count of neurons as 10 and 8. It is envisaged that neural network based model is highly accurate and can be incredibly beneficial in the field of VLSI design.","PeriodicalId":167175,"journal":{"name":"2020 IEEE International Students' Conference on Electrical,Electronics and Computer Science (SCEECS)","volume":"196 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131923497","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 Comparative Analysis of Cloud Based Watson System and CNN for Gesture Recognition Systems 基于云的Watson系统与CNN手势识别系统的比较分析

2020 IEEE International Students' Conference on Electrical,Electronics and Computer Science (SCEECS) Pub Date : 2020-02-01 DOI: 10.1109/SCEECS48394.2020.66

Srikar Gullapalli, K. P., S. P

{"title":"A Comparative Analysis of Cloud Based Watson System and CNN for Gesture Recognition Systems","authors":"Srikar Gullapalli, K. P., S. P","doi":"10.1109/SCEECS48394.2020.66","DOIUrl":"https://doi.org/10.1109/SCEECS48394.2020.66","url":null,"abstract":"According to census 2011, the number of disabled people in India is 2.68 crores. Out of those, about 19 percent have a problem in hearing. With the advent of Convolutional Neural Networks (CNN) deployed on the host system and multi cloud platforms like IBM Watson, an important challenge faced by the developers is selection of suitable architecture for deployment. The ability of CNN to model non-linear relationships enables it to be used widely in biomedical domain and thus for the problem of disabled people. Recognition models deployed on Cloud offer on-demand secure storage, analysis and rapid scalability of services. This paper aims at providing a comparative study between the two architectures. For the first type of architecture the gesture of a mute person is recognized using image processing and CNN. Whereas second architecture uses cloud based visual recognizer to recognize the gestures. The prominent parameters such as recognition accuracy, angled detection and response time that play an important role when deploying the two architectures are measured and provide a perspective over the selection of architecture. The accuracy obtained for the CNN model is 98% and 97% for the cloud-based Watson model for the trained tested classes.","PeriodicalId":167175,"journal":{"name":"2020 IEEE International Students' Conference on Electrical,Electronics and Computer Science (SCEECS)","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128411100","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 and Synthesis of Multi-Segmented Robot to Mimic Serpentine Motion. 仿蛇形运动多节段机器人的设计与合成。

2020 IEEE International Students' Conference on Electrical,Electronics and Computer Science (SCEECS) Pub Date : 2020-02-01 DOI: 10.1109/SCEECS48394.2020.69

Nikhil V. Taiwade, Shashwat M. Majumdar, Neema Ukani, S. Chakole

引用次数: 0

Disease Symptom Analysis Based Department Selection Using Machine Learning for Medical Treatment 基于疾病症状分析的科室选择，使用机器学习进行医疗

2020 IEEE International Students' Conference on Electrical,Electronics and Computer Science (SCEECS) Pub Date : 2020-02-01 DOI: 10.1109/SCEECS48394.2020.139

Md Latifur Rahman, Rahad Arman Nabid, Md. Farhad Hossain

{"title":"Disease Symptom Analysis Based Department Selection Using Machine Learning for Medical Treatment","authors":"Md Latifur Rahman, Rahad Arman Nabid, Md. Farhad Hossain","doi":"10.1109/SCEECS48394.2020.139","DOIUrl":"https://doi.org/10.1109/SCEECS48394.2020.139","url":null,"abstract":"Most of the patients today who face health problems, initially take advice from unprofessional or people with no knowledge that makes them more vulnerable. In many occasions, doctors also get confused with identifying actual disease. This might happen as they usually identify disease based on their limited experience. Moreover, general patient selects doctor according to their will and with no knowledge about the disease that may need specialist doctor. But some disease cannot be confirmed without a specialized doctor. Therefore, this paper proposes a Machine Learning based disease symptom analysis technique for assisting the patients seeking proper treatment by selecting accurate medical department using the symptom that they can easily recognize. Proposed framework will use machine learning technique to select a medical department based on the joint consideration of various disease symptoms of the patient. We investigate our proposed framework by using 9 different supervised machine learning techniques. Performance of framework for identifying appropriate medical department under the machine learning techniques is thoroughly investigated and compared. This framework can be used for telemedicine platform or in automated hospital management sector. This may create a path of enormous development in health care sector.","PeriodicalId":167175,"journal":{"name":"2020 IEEE International Students' Conference on Electrical,Electronics and Computer Science (SCEECS)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134238904","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

Performance Analysis of a Novel Dual Metal Strip Charge Plasma Tunnel FET 新型双金属带电荷等离子体隧道场效应管的性能分析

2020 IEEE International Students' Conference on Electrical,Electronics and Computer Science (SCEECS) Pub Date : 2020-02-01 DOI: 10.1109/SCEECS48394.2020.112

Nitish Parmar, D. Yadav, Sachin Kumar, Ritwik Sharma, Somya Saraswat, Atul Kumar

引用次数: 4

A Miniaturized On-Body Textile Antenna Based on Substrate Integrated Waveguide Technology 基于衬底集成波导技术的小型体上织物天线

2020 IEEE International Students' Conference on Electrical,Electronics and Computer Science (SCEECS) Pub Date : 2020-02-01 DOI: 10.1109/SCEECS48394.2020.159

Cherukumudi Sai Sri Vidya, Barigala Bhavya Sree, Chundru Sravanthi

{"title":"A Miniaturized On-Body Textile Antenna Based on Substrate Integrated Waveguide Technology","authors":"Cherukumudi Sai Sri Vidya, Barigala Bhavya Sree, Chundru Sravanthi","doi":"10.1109/SCEECS48394.2020.159","DOIUrl":"https://doi.org/10.1109/SCEECS48394.2020.159","url":null,"abstract":"A compact and simple on-body semi closed textile antenna based on SIW technology is proposed in this communication. The motto of the designed antenna is to make it resonate at the ISM band frequencies. Initially, a basic SIW antenna with cavity is designed which resulted a single band at 8.2 GHz. To obtain an ISM band and make it suitable for on-body application a semi closed textile antenna based on SIW technology was designed. At 5.48 GHz a single band got resonated. This proposed antenna is simulated using felt as a substrate with a compact area of 0.25λ02 (λ0 is the lower order resonant frequency) and co-axial feed of 50 ohm is used as an excitation. A full wave simulator is used to simulate this semi closed SIW antenna. After the simulations a single band resonated at 5.48 GHz within an impedance bandwidth (S11<-10 dB) of 120 MHz and a unidirectional radiation pattern obtained at 5.48 GHz. A gain of 3.2 dBi is obtained. On observing the results, a single band is achieved at the ISM band frequencies and suitable for on- body applications. This proposed antenna is compact in size compared to the existing single band antennas. The secured single band at 5.48 GHz is responsible for implementing this designed antenna in Industrial, scientific and Medical applications.","PeriodicalId":167175,"journal":{"name":"2020 IEEE International Students' Conference on Electrical,Electronics and Computer Science (SCEECS)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132964404","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

Comparison of Various Learning Rate Scheduling Techniques on Convolutional Neural Network 卷积神经网络上各种学习率调度技术的比较

2020 IEEE International Students' Conference on Electrical,Electronics and Computer Science (SCEECS) Pub Date : 2020-02-01 DOI: 10.1109/SCEECS48394.2020.94

Jinia Konar, Prerit Khandelwal, Rishabh Tripathi

引用次数: 20

Detection of Memory Leaks in C/C++ C/ c++中内存泄漏的检测

2020 IEEE International Students' Conference on Electrical,Electronics and Computer Science (SCEECS) Pub Date : 2020-02-01 DOI: 10.1109/SCEECS48394.2020.32

Rahul Jain, Raksha Agrawal, Riyanshi Gupta, Rajat Kumar Jain, Neha Kapil, Ankit Saxena

引用次数: 1

Testing MapReduce program using Induction Method 使用归纳法测试MapReduce程序

2020 IEEE International Students' Conference on Electrical,Electronics and Computer Science (SCEECS) Pub Date : 2020-02-01 DOI: 10.1109/SCEECS48394.2020.178

A. Rai, A. Malviya

引用次数: 3

A Survey on Machine Learning Approaches and Its Techniques: 机器学习方法及其技术综述

2020 IEEE International Students' Conference on Electrical,Electronics and Computer Science (SCEECS) Pub Date : 2020-02-01 DOI: 10.1109/SCEECS48394.2020.190

T. N, Roopam K. Gupta

引用次数: 9