2018 International Conference on Circuits and Systems in Digital Enterprise Technology (ICCSDET)最新文献_第7页

Cardiac Monitoring & Resuscitation System for Ventricular Fibrillation Detection 用于心室颤动检测的心脏监测与复苏系统

2018 International Conference on Circuits and Systems in Digital Enterprise Technology (ICCSDET) Pub Date : 2018-12-01 DOI: 10.1109/ICCSDET.2018.8821201

Naveena Pai G, Usha Desai

{"title":"Cardiac Monitoring & Resuscitation System for Ventricular Fibrillation Detection","authors":"Naveena Pai G, Usha Desai","doi":"10.1109/ICCSDET.2018.8821201","DOIUrl":"https://doi.org/10.1109/ICCSDET.2018.8821201","url":null,"abstract":"Ventricular fibrillation is considered to be the most serious cardiac rhythm disturbance, which can cause sudden cardiac arrest which requires immediate medical attention. This paper proposes a cost-effective and efficient Cardiac Monitoring & Resuscitation System (CMRS) to save the life of the subjects during ventricular fibrillation. The ARM7 microcontroller used to compare the fibrillation cycles with sinus cardiac Electrocardiogram (ECG) signal to generate resuscitating output across its terminals connected to the patient chest, resulting shock signal helps to reverse the effect of arrhythmia. However, an arrhythmic output is an erratic cardiac rhythm needs to be simulated using MATLAB. Further, a Global Positioning System (GPS) supported for periodic updates of the cardiac condition of the patient. Healthcare service and delivery may never be finalised. However, they can always be improved to a marked extent. Again, our proposed system may not completely solve all the problems with healthcare delivery, but it can improve it to a certain extent. In a country like India, which bears a heavy burden of cardiac ailments, an initiative like this which uses widespread technology such as Global System for Mobile (GSM), this system could be a ray of hope for the urban and the rural folk alike.","PeriodicalId":157362,"journal":{"name":"2018 International Conference on Circuits and Systems in Digital Enterprise Technology (ICCSDET)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114430126","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

Hand-over-Face Gesture based Facial Emotion Recognition using Deep Learning 基于手势的深度学习面部情感识别

2018 International Conference on Circuits and Systems in Digital Enterprise Technology (ICCSDET) Pub Date : 2018-12-01 DOI: 10.1109/ICCSDET.2018.8821186

Niti Naik, M. Mehta

{"title":"Hand-over-Face Gesture based Facial Emotion Recognition using Deep Learning","authors":"Niti Naik, M. Mehta","doi":"10.1109/ICCSDET.2018.8821186","DOIUrl":"https://doi.org/10.1109/ICCSDET.2018.8821186","url":null,"abstract":"Facial emotion recognition is significant for several applications such as human-computer interaction, video surveillance and robotics. Occlusion covering the face is a barrier in clearly recognizing facial emotion. Hence, in early researches, the hand-over-face occlusion is either removed or ignored while recognizing emotion. However, hand-over-face (hand gesture) along with facial expression represents an emotion. The existing hand-over-face gesture based emotion recognition methods identify merely basic emotions due to coding schema with limited hand gestures. In this paper, we propose a new hand-over-face gesture based emotion recognition method that includes coding schema with more hand gestures and uses deep learning. Specifically, we use Convolutional Neural Network (CNN) that eliminates the need for manual feature extraction and extracts more class specific features automatically. Moreover, we use Recurrent Neural Network (RNN) to recursively learn the features and classify them into more advanced emotion categories. Hence, our proposed method identifies more advanced emotions such as confident, making decision, scared, ashamed and ok sign along with basic emotions.","PeriodicalId":157362,"journal":{"name":"2018 International Conference on Circuits and Systems in Digital Enterprise Technology (ICCSDET)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125353329","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

Block Level Incremental Backup With Brotli Compression 块级增量备份与Brotli压缩

2018 International Conference on Circuits and Systems in Digital Enterprise Technology (ICCSDET) Pub Date : 2018-12-01 DOI: 10.1109/ICCSDET.2018.8821114

Sminu Paul, Sumayya K.A, J. P. Abraham

{"title":"Block Level Incremental Backup With Brotli Compression","authors":"Sminu Paul, Sumayya K.A, J. P. Abraham","doi":"10.1109/ICCSDET.2018.8821114","DOIUrl":"https://doi.org/10.1109/ICCSDET.2018.8821114","url":null,"abstract":"Block level incremental backup is one in which successive copies of the data contain only the portion that has been changed since the preceding backup. It backs up only the blocks within the file that was changed. Most of the existing backup system perform file level backup which takes up lot of storage space. Existing block level incremental backup algorithm including ‘rsync’ works with MD5 hash function. Hash functions are difficult to calculate and also take memory for storing hash table. The proposed method uses bit map to identify block that was modified. Bit map takes less space than that of hash table moreover it does not need excessive computation. Furthermore ‘brotli’ compression can be used to reuse the storage space taken for each backup. Brotli is a data format specification for data streams compressed with a specific combination of the general-purpose LZ77 lossless compression algorithm and Huffman coding. Brotli is independent of CPU type, operating system, file system, and character set. It has higher compression ratio and decompresses quickly when com-pared with other existing compression techniques like gzip, LZMA, etc.","PeriodicalId":157362,"journal":{"name":"2018 International Conference on Circuits and Systems in Digital Enterprise Technology (ICCSDET)","volume":"2008 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125625276","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

Circular Polarized Square Micrsostrip Antenna Using Modified Ground Plane 基于改进地平面的圆极化方形微带天线

2018 International Conference on Circuits and Systems in Digital Enterprise Technology (ICCSDET) Pub Date : 2018-12-01 DOI: 10.1109/ICCSDET.2018.8821084

A. Deshmukh, Amita D. Mhatre, M. Shah, Chinmay S. Kudoo, Vivek M. Chaudhary

引用次数: 0

An Adaptive Enhancement method for Low Contrast Color Retinal Images based on Strucural Similarity 基于结构相似度的低对比度彩色视网膜图像自适应增强方法

2018 International Conference on Circuits and Systems in Digital Enterprise Technology (ICCSDET) Pub Date : 2018-12-01 DOI: 10.1109/ICCSDET.2018.8821194

Gopinath Palanisamy, P. Ponnusamy, V. Gopi

引用次数: 4

Hybrid approach for image segmentation using region splitting and clustering techniques 基于区域分割和聚类技术的混合图像分割方法

2018 International Conference on Circuits and Systems in Digital Enterprise Technology (ICCSDET) Pub Date : 2018-12-01 DOI: 10.1109/ICCSDET.2018.8821144

Mariena A. A, J. Sathiaseelan, John T. Abraham

引用次数: 3

Path Planning of an Autonomous Robotic Vehicle for Outdoor Cleaning 自主机器人车辆室外清扫路径规划

2018 International Conference on Circuits and Systems in Digital Enterprise Technology (ICCSDET) Pub Date : 2018-12-01 DOI: 10.1109/ICCSDET.2018.8821133

A.S. Darsana, P. S. Lal Priya

引用次数: 0

Web Based Smart Energy Metering System 基于网络的智能能源计量系统

2018 International Conference on Circuits and Systems in Digital Enterprise Technology (ICCSDET) Pub Date : 2018-12-01 DOI: 10.1109/ICCSDET.2018.8821099

S. Hallur, Roopa R Kulkarni, P. Patavardhan, S. Santaji

引用次数: 0

Deep Learning for Financial Prediction 金融预测的深度学习

2018 International Conference on Circuits and Systems in Digital Enterprise Technology (ICCSDET) Pub Date : 2018-12-01 DOI: 10.1109/ICCSDET.2018.8821178

V. Unadkat, Parth Sayani, Pratik Kanani, Prachi Doshi

{"title":"Deep Learning for Financial Prediction","authors":"V. Unadkat, Parth Sayani, Pratik Kanani, Prachi Doshi","doi":"10.1109/ICCSDET.2018.8821178","DOIUrl":"https://doi.org/10.1109/ICCSDET.2018.8821178","url":null,"abstract":"Finance is one of the main pillars on which an economy grows. Having a precise understanding of how the financial market works corroborates greater monetary returns. Stock Markets are established for the general public to own a piece of a company and state an ownership whereas the company gains funds from the public which are used for the expansion of the business and return profits to the investor based on the amount of investment. However choosing which company’s stock to buy is the most challenging task. Exhaustive research and study about the company, their way of working, history, their place in the market, their holdings, projects, etc is required to get an idea about their present performance and future. However no one can predict how a company will perform in the future but an estimation can be achieved by studying various parameters. Machine Learning and Deep Learning techniques have proven their mettle in analysing and prediction data with state of the art results. Though without any intention to predict the exact future of a company, these techniques can be extremely helpful in getting an estimation which bolster our choice rather than just trusting our intuition. Recurrent Neural Network and Long-Shot Term Memory (LSTM) are used to get an approximate estimation of the future of a stock. This would help us in choosing the right stock and thus improve the return of investment.","PeriodicalId":157362,"journal":{"name":"2018 International Conference on Circuits and Systems in Digital Enterprise Technology (ICCSDET)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131469514","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

Design of PWM Controlled Three Phase Induction Motor Drive with Soft-start and Redundancy 具有软启动和冗余的PWM控制三相异步电动机驱动器设计

2018 International Conference on Circuits and Systems in Digital Enterprise Technology (ICCSDET) Pub Date : 2018-12-01 DOI: 10.1109/ICCSDET.2018.8821167

B. N. Jamadar, K. V. Madhale, D. S. Sutrave

引用次数: 2