2023 57th Annual Conference on Information Sciences and Systems (CISS)最新文献_第3页

Real-time Fitness Activity Recognition and Correction using Deep Neural Networks 基于深度神经网络的实时健身活动识别与校正

2023 57th Annual Conference on Information Sciences and Systems (CISS) Pub Date : 2023-03-22 DOI: 10.1109/CISS56502.2023.10089773

Michelle Mary Varghese, Sahana Ramesh, Sonali Kadham, V. M. Dhruthi, P. Kanwal

{"title":"Real-time Fitness Activity Recognition and Correction using Deep Neural Networks","authors":"Michelle Mary Varghese, Sahana Ramesh, Sonali Kadham, V. M. Dhruthi, P. Kanwal","doi":"10.1109/CISS56502.2023.10089773","DOIUrl":"https://doi.org/10.1109/CISS56502.2023.10089773","url":null,"abstract":"Fitness activities are beneficial to one's health and well-being. During the Covid-19 pandemic, demand for virtual trainers increased. There are current systems that can classify different exercises, and there are other systems that provide feedback on a specific exercise. We propose a system that can simultaneously recognize a pose as well as provide real-time corrective feedback on the performed exercise with the least latency between recognition and correction. In all computer vision techniques implemented so far, occlusion and a lack of labeled data are the most significant problems in correctly detecting and providing helpful feedback. Vector geometry is employed to calculate the angles between key points detected on the body to provide the user with corrective feedback and count the repetitions of each exercise. Three different architectures-GAN, Conv-LSTM, and LSTM-RNN are experimented with, for exercise recognition. A custom dataset of Jumping Jacks, Squats, and Lunges is used to train the models. GAN achieved a 92% testing accuracy but struggled in real-time performance. The LSTM-RNN architecture yielded a 95% testing accuracy and ConvLSTM obtained an accuracy of 97% on real-time sequences.","PeriodicalId":243775,"journal":{"name":"2023 57th Annual Conference on Information Sciences and Systems (CISS)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121427056","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

Active-IRS-Enabled Energy-Efficiency Optimizations for UAV-Based 6G Mobile Wireless Networks 基于无人机的6G移动无线网络的主动irs能效优化

2023 57th Annual Conference on Information Sciences and Systems (CISS) Pub Date : 2023-03-22 DOI: 10.1109/CISS56502.2023.10089767

Fei Wang, Xi Sheryl Zhang

{"title":"Active-IRS-Enabled Energy-Efficiency Optimizations for UAV-Based 6G Mobile Wireless Networks","authors":"Fei Wang, Xi Sheryl Zhang","doi":"10.1109/CISS56502.2023.10089767","DOIUrl":"https://doi.org/10.1109/CISS56502.2023.10089767","url":null,"abstract":"We propose the novel scheme to solve a multi-objective optimization problem over an unmanned aerial vehicle (UAV) communications system to jointly minimize the energy consumption of the UAV and ground users (GUs). In particular, the UAV communicates with multiple GUs using active intelligent reflecting surfaces (IRSs), which can actively amplify and thus significantly enhance the strengths of reflected signals. We develop an energy minimization scheme based on the multi-objective hierarchical deep reinforcement learning (DRL), by decomposing the formulated optimization problem into two-layered subproblems. By solving the upper-level subproblem, we derive the optimal UAV trajectory and GUs scheduling strategies to minimize the UAV's energy consumption. By solving the lower-level subproblem, we obtain the IRS's phase shifts and amplification factors and GUs' transmit/receive beamforming to minimize the GUs' energy consumption. Finally, we validate and evaluate the proposed schemes through simulations, which show that the UAV's and GUs' energy consumption can be significantly reduced by using the active IRS, when the thermal noise powers at the IRS are much smaller than those at the UAV and GUs.","PeriodicalId":243775,"journal":{"name":"2023 57th Annual Conference on Information Sciences and Systems (CISS)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122668430","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

Offline Reinforcement Learning for Price-Based Demand Response Program Design 基于价格的需求响应方案设计的离线强化学习

2023 57th Annual Conference on Information Sciences and Systems (CISS) Pub Date : 2023-03-22 DOI: 10.1109/CISS56502.2023.10089681

Ce Xu, Bo Liu, Yue Zhao

引用次数: 0

Maximum Zero-Outage Secrecy Capacity of Fading Wiretap Channels with Finite Alphabets 有限字母衰落窃听信道的最大零中断保密能力

2023 57th Annual Conference on Information Sciences and Systems (CISS) Pub Date : 2023-03-22 DOI: 10.1109/CISS56502.2023.10089618

Xiaoyang Xu, Karl-Ludwig Bessert, Pin-Hsun Lin, Eduard Axel Jorswieck

{"title":"Maximum Zero-Outage Secrecy Capacity of Fading Wiretap Channels with Finite Alphabets","authors":"Xiaoyang Xu, Karl-Ludwig Bessert, Pin-Hsun Lin, Eduard Axel Jorswieck","doi":"10.1109/CISS56502.2023.10089618","DOIUrl":"https://doi.org/10.1109/CISS56502.2023.10089618","url":null,"abstract":"When a transmitted signal encounters slow fading and the support of the fading gain contains zero, an outage event will happen inevitably, if the transmitter has no knowledge of the channel state. This will deteriorate the system performance metrics such as latency due to re-transmissions caused by the outages. For scenarios like this, we use the outage probability or the outage capacity to measure the system performance. In this work, we investigate the possibility of achieving a positive zero-outage secrecy capacity (ZOSC) for a wiretap channel with fading gains from finite alphabet sets. In particular, we investigate the minimum secrecy outage probability with respect to all possible joint distributions of the fading gains, given their marginal distributions. We propose a systematic scheme to calculate the minimum secrecy outage probability by solving linear programming problems. From this, the maximum ZOSC of the considered wiretap channel model directly follows. Interestingly, we find that scenarios with a ZOSC of zero for independent fading can support a positive ZOSC when the fading links are dependent.","PeriodicalId":243775,"journal":{"name":"2023 57th Annual Conference on Information Sciences and Systems (CISS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129751551","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

Entropy-based scheduling performance in real-time multiprocessor systems 实时多处理器系统中基于熵的调度性能

2023 57th Annual Conference on Information Sciences and Systems (CISS) Pub Date : 2023-03-22 DOI: 10.1109/CISS56502.2023.10089704

C. CarlosA.Rincon, Daniel Rivas, A. Cheng

引用次数: 0

Generative Versus Discriminative Data-Driven Graph Filtering of Random Graph Signals 随机图信号的生成与判别数据驱动的图滤波

2023 57th Annual Conference on Information Sciences and Systems (CISS) Pub Date : 2023-03-22 DOI: 10.1109/CISS56502.2023.10089638

Lital Dabush, Nir Shlezinger, T. Routtenberg

{"title":"Generative Versus Discriminative Data-Driven Graph Filtering of Random Graph Signals","authors":"Lital Dabush, Nir Shlezinger, T. Routtenberg","doi":"10.1109/CISS56502.2023.10089638","DOIUrl":"https://doi.org/10.1109/CISS56502.2023.10089638","url":null,"abstract":"In this paper we consider the problem of recovering random graph signals by using graph signal processing (GSP) tools. We focus on partially-known linear settings, where one has access to data in order to cope with the missing domain knowledge in designing a graph filter for signal recovery. In this work, we formulate two main approaches for leveraging both the available domain knowledge and data for such graph filter design: 1) the GSP-generative approach, where data is used to fit the underlying linear model that determines the graph filter; and 2) the GSP-discriminative approach, where data is used to directly learn the graph filter for graph signal recovery, bypassing the need to estimate the underlying model. Then, we compare qualitatively and quantitatively these two approaches of graph filter design. Our results provide an understanding with regard to which approach is preferable in which regime. In particular, it is shown that GSP-discriminative learning reliably copes with mismatches in the available domain knowledge, since it bypasses the need to fit the underlying model. On the other hand, the model awareness of the GSP-generative approach results in its achieving a lower mean-squared error (MSE) when data is scarce. In the asymptotic region where the number of training data points approaches infinity, both approaches achieve the oracle minimum MSE estimator under the considered setting.","PeriodicalId":243775,"journal":{"name":"2023 57th Annual Conference on Information Sciences and Systems (CISS)","volume":"177 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120895213","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

Interference Mitigation in Blind Source Separation by Hidden State Filtering 基于隐藏状态滤波的盲源分离干扰抑制

2023 57th Annual Conference on Information Sciences and Systems (CISS) Pub Date : 2023-03-22 DOI: 10.1109/CISS56502.2023.10089636

A. Ghosh, A. Haimovich, J. Dabin

引用次数: 1

A Deep Transfer Learning based approach for forecasting spatio-temporal features to maximize yield in cotton crops 基于深度迁移学习的棉花作物时空特征预测方法

2023 57th Annual Conference on Information Sciences and Systems (CISS) Pub Date : 2023-03-22 DOI: 10.1109/CISS56502.2023.10089748

Krishna Chaitanya Gadepally, S. Dhal, Mahendra Bhandari, J. Landivar, Stavros Kalafatis, K. Nowka

引用次数: 0

Near-optimal Sampling to Optimize Communication Over Discrete Memoryless Channels 离散无内存信道上的近最优采样优化通信

2023 57th Annual Conference on Information Sciences and Systems (CISS) Pub Date : 2023-03-22 DOI: 10.1109/CISS56502.2023.10089651

M. A. Tope, J.M. Morris

引用次数: 0

A Novel Feature Selection Technique for Intrusion Detection System Using RF-RFE and Bio-inspired Optimization 基于RF-RFE和仿生优化的入侵检测系统特征选择新技术

2023 57th Annual Conference on Information Sciences and Systems (CISS) Pub Date : 2023-03-22 DOI: 10.1109/CISS56502.2023.10089745

Zinia Anzum Tonni, M. Rashed

{"title":"A Novel Feature Selection Technique for Intrusion Detection System Using RF-RFE and Bio-inspired Optimization","authors":"Zinia Anzum Tonni, M. Rashed","doi":"10.1109/CISS56502.2023.10089745","DOIUrl":"https://doi.org/10.1109/CISS56502.2023.10089745","url":null,"abstract":"A massive change has been brought about by the constantly expanding usage of technology and the internet, which has substantially enhanced the accessibility to services that might drastically alter people's lives. However, this every time-on connection has also enabled malicious actors to exploit vulnerabilities in hardware and software, leading to potential damage to network infrastructure. Due to the large amount of data flowing through networks, it can be difficult for cyber security experts to quickly identify and respond to potential security breaches. To maintain the security and protection of the network infrastructure and digital assets, the implementation of Intrusion Detection Systems is necessary. These systems aid in preserving the availability, confidentiality, and reliability of the network. Intrusion Detection Systems (IDS) is a key component in securing networks, but the complexity of large datasets used to build them can lead to time-consuming computations. To address this issue, a two-layer feature selection technique is proposed in this study. Results show the usefulness of the suggested feature selection strategy in lowering the complexity of IDS while enhancing accuracy. This approach is tested on a significant dataset called CSE-CIC-IDS-2018. Finally, Random Forest Classification is used to verify the model. The outcomes of this strategy show how the suggested feature selection technique works to make IDS less difficult while improving accuracy.","PeriodicalId":243775,"journal":{"name":"2023 57th Annual Conference on Information Sciences and Systems (CISS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126717020","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