2021 8th International Conference on Signal Processing and Integrated Networks (SPIN)最新文献_第2页

Intelligent Traffic Control using Double Deep Q Networks for time-varying Traffic Flows 基于双深Q网络的时变交通流智能交通控制

2021 8th International Conference on Signal Processing and Integrated Networks (SPIN) Pub Date : 2021-08-26 DOI: 10.1109/SPIN52536.2021.9565961

Priyadharshini Shanmugasundaram, Aakash Sinha

{"title":"Intelligent Traffic Control using Double Deep Q Networks for time-varying Traffic Flows","authors":"Priyadharshini Shanmugasundaram, Aakash Sinha","doi":"10.1109/SPIN52536.2021.9565961","DOIUrl":"https://doi.org/10.1109/SPIN52536.2021.9565961","url":null,"abstract":"Reinforcement learning, a sub-field of Machine Learning has been garnering lot of research attention lately. It helps create intelligent agents that can incrementally learn optimal strategies for challenging environments by interacting with it. Such agents are best suited for solving problems like traffic congestion, which demand solutions that eater to dynamic changes in the traffic throughput. Intelligent transportation systems which use deep reinforcement learning can adapt to varying traffic demands and learn to maintain reduced congestion. In this paper, we propose a solution approach to use Double Deep Q Networks for traffic signal control of varied traffic flows in an isolated intersection. To improve the stability of our proposed method we have used target networks, delayed updates and experience replay mechanisms. We evaluate the performance of our method on different time-varying traffic flows and find that our method learns a robust and optimal strategy which reduces vehicle waiting time and queue length significantly. Our method achieved superior performance compared to traditional traffic signal control strategies. The method has been trained and evaluated through simulations of road networks created on Simulation of Urban Mobility (SUMO).","PeriodicalId":343177,"journal":{"name":"2021 8th International Conference on Signal Processing and Integrated Networks (SPIN)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123456000","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

Detection of COVID from Chest X-Ray Images using Pivot Distribution Count Method 基于枢轴分布计数法的胸部x线图像COVID检测

2021 8th International Conference on Signal Processing and Integrated Networks (SPIN) Pub Date : 2021-08-26 DOI: 10.1109/SPIN52536.2021.9566114

Abadhan Ranganath, P. Sahu, M. Senapati

引用次数: 1

In Orbit Single Event Upset Detection and Configuration Memory Scrubbing of Virtex-5QV FPGA Virtex-5QV FPGA在轨单事件干扰检测与组态内存清除

2021 8th International Conference on Signal Processing and Integrated Networks (SPIN) Pub Date : 2021-08-26 DOI: 10.1109/SPIN52536.2021.9566069

Yamuna Shanker Kumawat, Rajat Arora, Sanjay. D. Mehta

引用次数: 0

Heart Disease Diagnosis: Performance Evaluation of Supervised Machine Learning and Feature Selection Techniques 心脏病诊断:监督机器学习和特征选择技术的性能评估

2021 8th International Conference on Signal Processing and Integrated Networks (SPIN) Pub Date : 2021-08-26 DOI: 10.1109/SPIN52536.2021.9565963

Palak Khurana, Shakshi Sharma, Anjali Goyal

{"title":"Heart Disease Diagnosis: Performance Evaluation of Supervised Machine Learning and Feature Selection Techniques","authors":"Palak Khurana, Shakshi Sharma, Anjali Goyal","doi":"10.1109/SPIN52536.2021.9565963","DOIUrl":"https://doi.org/10.1109/SPIN52536.2021.9565963","url":null,"abstract":"Heart diseases are the leading cause of deaths nowadays. Due to the high severity of the problem, it has attracted several researchers around the globe. Researchers have considered the heart diagnosis as a classification problem where meaningful patterns are detected using data mining techniques. This paper presents an evaluation of various supervised learning algorithms and feature selection techniques for heart disease prediction. The performance of six machine learning classifiers (Naïve Bayes, Decision Tree, Logistic Regression, Random Forest, Support Vector Machine, k-Nearest Neighbour) and five feature selection techniques (Chi-Square, Gain Ratio, Information Gain, One-R and RELIEF) have been investigated on the benchmark dataset obtained from UCI Machine Learning Repository, Cleveland. The experimental results show that machine learning classifiers can achieve prediction accuracy up to 82.81% for heart disease prediction. The feature selection techniques further improve the classification performance and achieve prediction accuracy up to 83.41%.","PeriodicalId":343177,"journal":{"name":"2021 8th International Conference on Signal Processing and Integrated Networks (SPIN)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127688091","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: 4

A Study on an IBIS-like Model to Ensure Signal/Power Integrity for I/O Drivers I/O驱动信号/电源完整性的类ibis模型研究

2021 8th International Conference on Signal Processing and Integrated Networks (SPIN) Pub Date : 2021-08-26 DOI: 10.1109/SPIN52536.2021.9566125

Shubham Saxena, Malek Souilem, W. Dghais, J. N. Tripathi, H. Shrimali

引用次数: 0

Automatic Colorization of images using Auto-encoders 使用自动编码器的图像自动着色

2021 8th International Conference on Signal Processing and Integrated Networks (SPIN) Pub Date : 2021-08-26 DOI: 10.1109/SPIN52536.2021.9566101

Naman Sood, Naveen Nandakumar, R. S

引用次数: 0

Electricity from Air: An efficient way of Wireless Power Tapping 空气发电:一种高效的无线供电方式

2021 8th International Conference on Signal Processing and Integrated Networks (SPIN) Pub Date : 2021-08-26 DOI: 10.1109/SPIN52536.2021.9566144

A. P., Jayalakshmi N. S., V. K. Jadoun

{"title":"Electricity from Air: An efficient way of Wireless Power Tapping","authors":"A. P., Jayalakshmi N. S., V. K. Jadoun","doi":"10.1109/SPIN52536.2021.9566144","DOIUrl":"https://doi.org/10.1109/SPIN52536.2021.9566144","url":null,"abstract":"Electromagnetic (EM) radiations are emitted by almost all electrically energized devices such as laptops, smartphones, wireless routers, Wi-Fi, etc. The radiations emitted from the antennas of these electrically charged bodies, when exposed to human tissue without any electromagnetic compatibility protection, can cause severe damage. The presence of electrically charged components is nevertheless increasing day by day and, in turn, increasing the presence of electromagnetic radiation. In present days, electromagnetic radiations consist of radio waves in higher percentages and are present abundantly everywhere. These electromagnetic radiations to be a source of renewable source of energy. The primary aim of this work is to fabricate an electromagnetic collector to energize a battery by absorbing surrounding natural radiation. The gatherer should assemble free power from pretty much anything, including PCs, cell phones, overhead electrical cables, iceboxes, or even the outflows from Wi-Fi or cell phone. This is achieved by the process of wireless power transfer (WPT). One harvester might not be useful, but several such harvesters put together with power electronic converters can generate high output power. A case study is carried out, and the same is demonstrated using a simulation study on the MATLAB/Simulink platform.","PeriodicalId":343177,"journal":{"name":"2021 8th International Conference on Signal Processing and Integrated Networks (SPIN)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125798570","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 analysis of EMG controlled anthropomorphic Prosthetic hand 肌电控制拟人假手的设计与分析

2021 8th International Conference on Signal Processing and Integrated Networks (SPIN) Pub Date : 2021-08-26 DOI: 10.1109/SPIN52536.2021.9565947

Divyam Dalal, U. Keshwala

引用次数: 1

Photoplethysmogram Based Mean Arterial Pressure Estimation Using LSTM 基于光电容积图的LSTM平均动脉压估算

2021 8th International Conference on Signal Processing and Integrated Networks (SPIN) Pub Date : 2021-08-26 DOI: 10.1109/SPIN52536.2021.9566027

Shresth Gupta, Anurag Singh, Abhishek Sharma

{"title":"Photoplethysmogram Based Mean Arterial Pressure Estimation Using LSTM","authors":"Shresth Gupta, Anurag Singh, Abhishek Sharma","doi":"10.1109/SPIN52536.2021.9566027","DOIUrl":"https://doi.org/10.1109/SPIN52536.2021.9566027","url":null,"abstract":"Mean Arterial Pressure (MAP) is defined as central pressure in the arteries of a person during a single cardiac cycle. It is regarded as an important bio-marker of blood perfusion in vital organs as compared to systolic blood pressure (SBP). The actual MAP can be determined by manual monitoring and complex calculations limited to occasional monitoring status. Growing personalized health care monitoring devices have already evinced a variety of health parameters to track on a daily basis with the additional advantage of continuous, noninvasive, and unobstructed measurement. This work proposes a direct strategy for the estimation of mean arterial pressure without using the systolic and diastolic BP values. By exploring 13 significant morphological features from a single PPG signal which are most related to the target MAP are derived such as Pulse Interval, Inflection Ratio etc. The estimation is performed using LSTM network with an architecture having 2- LSTM layers followed by a dropout and dense layer. With 942 subjects of UCI repository dataset our model achieves a remarkable mean absolute error of 1.48, standard deviation of 2.36 and pearson correlation coefficient of 0.96 which is better as compared to the existing works and even chalked up the British Hypertension Society (BHS) benchmark with grade A.","PeriodicalId":343177,"journal":{"name":"2021 8th International Conference on Signal Processing and Integrated Networks (SPIN)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130071198","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

A Robust Approach Using Fuzzy Logic for the Calories Evaluation of Fruits 模糊逻辑在水果热量评价中的鲁棒性研究

2021 8th International Conference on Signal Processing and Integrated Networks (SPIN) Pub Date : 2021-08-26 DOI: 10.1109/SPIN52536.2021.9566022

S. Veni, A. Krishna Sameera, V. Samuktha, R. Anand

引用次数: 3