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检索条件"任意字段=Conference on Photonic Devices and Algorithms for Computing V"

共 1067 条记录，以下是251-260 订阅

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Noma Based Full Duplex 6G Communication Systems 6

Noma Based Full Duplex 6G Communication Systems

引用

6th International conference on Contemporary computing and Informatics, IC3I 2023

作者： Sohal, Asha Sharma, Sandeep Agarwal, Sweta Balaji, N.v. Bhanu, D. Mokashi, Mandar Krishnarao School of Engineering & Technology Department of Computer Science & Engineering K. R. Mangalam University Gurugram India School of Electronic and Electrical Engineering Dev Bhoomi Uttarakhand University Uttarakhand India Invertis University Department of Electronics Bareilly India Karpagam Academy of Higher Education Department of Computer Science Coimbatore India Karpagam Institute of Technology Department of Information Technology Coimbatore India Vishwakarma Institute of Information Technology Pune India

ISBN: (纸本)9798350304480

Current wireless communications use the microwave band as its medium. Present-day wireless network makes use of the majority of the available spectrum. The word innovative Full-Duplex (FD) as well as the use Getting NOMA sharing scheme called Reflecting In-Band Full-Duplex (R-IBFD) is suggested in which piece in order to advance technology and meet the requirements of better utilizing these natural assets. In order to meet the needs of smart cities for 6th Generations (6thG or 6G), the suggested R-IBFD offers efsfective use of airwaves with superior system characteristics, including Concealment Outage Potential (SOP), efficiency, communication percentage, and secrets capacity. The suggested method aims to address the need for innovative algorithms which foster better transformation and usher with a digital shift in business specifications for 6G. Addressing suggested R-IBFD within this research primarily responds to the phenomenon of interference between channels related security issues. Most interference from co-channels experienced by In-Band Full-Duplex type devices experience among their individual communicating and reception antennas is greater. The use of R-IBFD lessens the impact of this disturbance and helps to secure a necessary transmission infrastructure. Using the aid of SOP creation, formulas, and modeling outcome, simulation, the rise of concealment capability and interaction using R-IBFD are explored for an improved comprehension of its contributions. Among of the many evaluation technologies being utilized to increase the concealing capabilities is an artificial algorithm based on genes. © 2023 IEEE.

关键词： 5G in-band full-duplex 6G machine learning genetic algorithm non-orthogonal multiple access reflective-in-band full-duplex secrecy capacity

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Emerging Applications of FinFET's in Artificial Intelligence and Machine Learning Hardwares

Emerging Applications of FinFET's in Artificial Intelligence...

引用

Communication, computing and Signal Processing (IICCCS), IEEE International conference on

作者： Jami venkata Suman v. Lavanya Babburu Kiranmai Department of ECE GMR Institute of Technology Rajam Andhra Pradesh India Department of ECE MVGR College of Engineering Vizianagaram Andhra Pradesh India Department of ECE Baba Institute of Technology and Sciences Visakhapatnam Andhra Pradesh India

ISBN: (数字)9798350390759

ISBN: (纸本)9798350390766

FinFET technology, a groundbreaking leap in semiconductor design, has substantially affected the buildup of hardware for the use of artificial intelligence (AI) and machine learning (ML) applications. This study investigates the developing uses of FinFETs in AI and ML hardware, explaining their high-quality fulfillment, power efficiency, and scalability. As AI and ML algorithms demand greater computing capacity, FinFETs offer a solid answer by raising processing speed and accuracy as they lowering power consumption. This study explores numerous applications among them neuromorphic computer technology, edge AI devices, and computing performance for ML training. It also includes FinFET-based methodology case studies, demonstrating their distinct benefits over antiquated technology. Although FinFETs may have manufacturing issues and constraints, they will advance technology for ML and AI. This study emphasizes FinFET technology's crucial role in satisfying AI and ML demands, enabling further along efficient and effective hardware solutions.

关键词： Training Accuracy Scalability Machine learning FinFETs Solids Hardware Manufacturing Telecommunication computing Artificial intelligence

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The Robustness of Spiking Neural Networks in Communication and its Application towards Network Efficiency in Federated Learning

The Robustness of Spiking Neural Networks in Communication a...

引用

IEEE International conference on Performance, computing and Communications (IPCCC)

作者： Manh v. Nguyen Liang Zhao Bobin Deng William Severa Honghui Xu Shaoen Wu College of Computing and Software Engineering Kennesaw State University Georgia USA Department of Cognitive & Emerging Computing Sandia National Laboratories New Mexico USA

ISBN: (数字)9798350367942

ISBN: (纸本)9798350367959

Spiking Neural Networks (SNNs) have recently gained significant interest in on-chip learning in embedded devices and emerged as an energy-efficient alternative to conventional Artificial Neural Networks (ANNs). However, to extend SNNs to a Federated Learning (FL) setting involving collaborative model training, the communication between the local devices and the remote server remains the bottleneck, which is often restricted and costly. In this paper, we first explore the inherent robustness of SNNs under noisy communication in FL. Building upon this foundation, we propose a novel Federated Learning with Top-κ Sparsification (FLTS) algorithm to reduce the bandwidth usage for FL training. We discover that the proposed scheme with SNNs allows more bandwidth savings compared to ANNs without impacting the model’s accuracy. Additionally, the number of parameters to be communicated can be reduced to as low as 6% of the size of the original model. We further improve the communication efficiency by enabling dynamic parameter compression during model training. Extensive experiment results demonstrate that our proposed algorithms significantly outperform the baselines in terms of communication cost and model accuracy and are promising for practical network-efficient FL with SNNs.

关键词： Training Accuracy Costs Federated learning Noise Spiking neural networks Bandwidth Robustness System-on-chip Noise measurement

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AI-Driven HR Optimization Strategies in Finance and Marketing: Methodological Framework and Applications

AI-Driven HR Optimization Strategies in Finance and Marketin...

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Innovative computing, Intelligent Communication and Smart Electrical Systems (ICSES), International conference on

作者： R.S. Lekshmi v. Sheela Mary G. Arasuraja v. Krishnamoorthy S. Kaliappan R. Selvameena Department of Management Studies St.Joseph's College of Engineering Chennai Tamil Nadu India Department of Management Studies Aarupadai Veedu Institute of Technology Vinayaka Mission's Research Foundation Chennai Tamil Nadu India Department of Management Studies Kongu Engineering College Erode Tamil Nadu India Division of Research and Development Lovely Professional University Punjab India Department of Computer Science and Engineering Dr.M.G.R Educational and ResearchChennai Tamil Nadu India

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

The proposed systems that include AI and the IoT present innovative financial and marketing opportunities. AI analytics capabilities and networked devices with the capability to provide real-time data mean the possibility to significantly improve recruitment, employee engagement, performance management, and retention. It combines natural language processing and machine learning algorithms, offering personalized recommendations, and in the vision insights, and in the case of environmental sensors, smart devices, and wearable health monitors, it facilitates accumulation of significant data. This framework will integrate IoT with AI to enhance HR procedures: more engaging, efficient, and enjoyable work environment. Apart from better decision-making and automated occupations, it also advocates for a data-driven approach to HRM. Further, it can result in the formation of intelligent and responsive HR systems that have the potential of improving organizational effectiveness and work productivity.

关键词： Performance evaluation Productivity Decision making Real-time systems Internet of Things Artificial intelligence Smart devices Intelligent sensors Wearable sensors Recruitment

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A Mobile App for Intersectional Traffic Optimization through Real-Time vehicle-to-Infrastructure (v2I) Communication and Cyber-Physical Control 19

A Mobile App for Intersectional Traffic Optimization through...

引用

19th IEEE International conference on Mobile Ad Hoc and Smart Systems (MASS)

作者： Xu, Haowen Yuan, Jinghui Wang, Chieh (Ross) Shao, Yunli Berres, Andy Laclair, Tim Oak Ridge Natl Lab Computat Sci & Engn Div Oak Ridge TN 37830 USA Oak Ridge Natl Lab Bldg & Transportat Sci Div Oak Ridge TN USA

ISBN: (数字)9781665471800

ISBN: (纸本)9781665471800

This extended abstract presents a novel mobile app that enables two-way vehicle-to-Infrastructure (v2I) Communication to reduce traffic congestion and excessive fuel consumption at signalized intersections. The app is developed using intelligent speed control algorithms powered with real-time signal timing information collected through the Internet of Things (IoT)-connected transportation infrastructure. Based on the nearby traffic condition and traffic signal green window, our app can advise drivers to maintain an optimal vehicle speed for individual vehicles to avoid stop-and-go traffic patterns. We demonstrate the capability of our app as an on-board speed optimization unit through a real-vehicle experiment conducted in a Connected and Automated vehicle Environment (CAvE) Laboratory. Our mobile app is implemented using cross-platform technologies and based on the commonly accepted communication protocol, allowing it to support multiple types of mobile devices and connect to various models of signal light controller.

关键词： Mobile Edge computing Cyberinfrastructure vehicle-to-Infrastructure Communication Cyber-Physical Control Mobile Sensing Connected and Autonomous vehicles

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SiEPICfab: the Canadian silicon photonics rapid-prototyping foundry for integrated optics and quantum computing 16

SiEPICfab: the Canadian silicon photonics rapid-prototyping ...

引用

conference on Silicon photonics XvI

作者： Darcie, Adam Mitchell, Matthew Awan, Kashif Abdolahi, Mahssa Hammood, Mustafa Pfenning, Andreas Yan, Xiruo Afifi, Abdelrahman Witt, Donald Lin, Becky Gou, Steven Jhoja, Jaspreet Wu, Jingda Taghavi, Iman Weekes, David M. Jaeger, Nicolas A. F. Young, Jeff Chrostowski, Lukas Univ British Columbia Dept Elect & Comp Engn Vancouver BC V6T 1Z4 Canada Univ British Columbia Dept Phys & Astron Vancouver BC V6T 1Z1 Canada Stewart Blusson Quantum Matter Inst Vancouver BC V6T 1Z4 Canada

ISBN: (数字)9781510642188

ISBN: (纸本)9781510642188;9781510642171

The goal of SiEPICfab is to conduct research in the fabrication of silicon photonic devices and photonic integrated circuits, and to make leading-edge silicon photonic manufacturing accessible to Canadian and international academics and industry. SiEPICfab builds on the success of the Silicon Electronic photonic Integrated Circuits (SiEPIC) program, which has been offering research training workshops since 2008, by adding a fabrication facility "fab". We have developed a rapid prototyping facility to support a complete ecosystem of companies involved in silicon photonics product development, including modelling, design, library development, fabrication, test, and packaging of silicon photonics. SiEPICfab allows designers to rapidly complete design-fabricate-test cycles, with technologies such as sub-wavelength sensors, PN junction ring modulators, silicon defect-based detectors, single photon detectors, single photon sources, and photonic wire bond integration of lasers and optical fibres.

关键词： silicon photonics SOI integrated optics rapid prototyping photonic wirebonds

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Impact of Machine Learning algorithms in Intrusion Detection Systems for Internet of Things 10

Impact of Machine Learning Algorithms in Intrusion Detection...

引用

10th International conference on Advances in computing and Communications, ICACC 2021

作者： Jose, Jinsi Jose, Deepa v Rao, Karna Srinivasa Janz, Justin CHRIST University Department of Computer Science Bangalore India

ISBN: (纸本)9781665439190

The importance of security aspects is increased recently due to the enormous usage of IoT devices. Securing the system from all sorts of vulnerabilities is inevitable to use IoT applications. Intrusion detection systems are power mechanism which provides this service. The introduction of artificial intelligence into intrusion detection systems can further enhance its power. This paper is an attempt to understand the impact of machine learning algorithms in attack detection. Using the UNSW-NB 15 dataset, the impact of different machine learning algorithms is assessed. © 2021 IEEE.

关键词： Computer crime

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The Possibilities of Using Digital Twins to Predict the Bifurcation of Traffic Flow

The Possibilities of Using Digital Twins to Predict the Bifu...

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International Youth conference on Radio Electronics, Electrical and Power Engineering (REEPE)

作者： Alexandra. v volosova Bauman Moscow State Technical University Moscow Russian Federation

ISBN: (数字)9798331531836

ISBN: (纸本)9798331531843

The article discusses the possibilities of using digital twins to predict the bifurcation of the traffic flow. The use of digital twins at different levels of a nonlinear dynamic system makes it possible to stabilize its lifecycle. The system's abnormal states can be predicted by digital twins based on AI algorithms. But the possibilities of such analytics are limited by the large amount of data and calculations based on a traditional element base. A possible solution of the problem is to use neuromorphic computing devices in combination with a conventional element base. The analogue calculations nature allows for additional capabilities for real-time data analysis. That is especially important for making predictions of the behavior of quantities, which can lead to undesirable consequences for the state of the system. The purpose of this article is to explore the possibilities of preventing critical states of the system, such as bifurcation, using digital twins.

关键词： Sufficient conditions Tensors Uncertainty Neuromorphic engineering Bifurcation Markov processes Prediction algorithms Mathematical models Real-time systems Digital twins

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Human Activity Recognition using EfficientNet for Wearable Sensor Data

Human Activity Recognition using EfficientNet for Wearable S...

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International conference on Communication, computing and Internet of Things (IC3IoT)

作者： v Rahul Chiranjeevi B S Murugan S Dhanasekaran S Senthil Pandi Department of CSE Rajalakshmi Engineering College Chennai India Department of CSE M Kumarasamy College of Engneering Karur India Department of IT Kalasalingam Academy of Research and Education Krishnankoil India

ISBN: (数字)9798350352689

ISBN: (纸本)9798350352696

Human Activity Recognition (HAR) has attracted considerable attention from researchers due to its broad implications in healthcare, smart environments, and entertainment. HAR involves the usage of sensor devices and the Internet of Things (IoT). Human Activity Recognition (HAR) has emerged as a crucial application in health monitoring, necessitating the ongoing utilization of Smart phones, smart watches, and wearable devices to document and track patients’ everyday activities. Deep learning (DL)-based algorithms have shown effective in predicting various human actions using time-series data collected from cell phones and wearable sensors. Deep learning-based methods have faced challenges when used to time-series data in activity recognition. The proposed methodology has the potential to effectively address those concerns. In this paper a novel approach is proposed using EfficientNet by adding various layers during the classification phase. Proposed method is evaluated by using UCI-HAR dataset and PAMAPS2 dataset, Class imbalance during training and testing phase is reduced by using various data augmentation methods. Proposed method achieved an accuracy of 99.98 on PAMAPS dataset thereby achieving significant results than state of art methods.

关键词： Training Watches Telecommunication computing Human activity recognition Internet of Things Reliability Wearable devices

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Improved Political Optimizer and Deep Neural Network-based Resource Management Strategy for fog Enabled Cloud computing 2

Improved Political Optimizer and Deep Neural Network-based R...

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2nd IEEE Mysore Sub Section International conference, MysuruCon 2022

作者： Prakash, M. vijayaganth, v. Shadrach, Finney Daniel Menaha, R. Daniya, T. Guha, Tapas Vit University School of Computer Science and Engineering Vellore India Kpr Institute of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Coimbatore India Kpr Institute of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India Sri Eshwar College of Engineering Department of Information Technology Tamil Nadu Coimbatore India Gmr Institute of Technology Department of Information Technology Andhra Pradesh Rajam India Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India

ISBN: (数字)9781665497909

ISBN: (纸本)9781665497909

Cloud-based processing of Internet of Things (IoT) applications isn't always the best option, especially when it comes to time-sensitive applications. For managing the massive amounts of data bandwidth required by end devices, fog and edge computing offer a viable alternative. Large volumes of newly generated data must be processed locally, rather than in the cloud, according to these paradigms. Allocating resources, balancing workloads, providing resources, scheduling tasks, and ensuring quality of service are all aspects of resource management that play an important role in cloud-based IoT systems with the goal of improving system performance. In this paper, we discuss different approaches to resource management in the context of cloud, fog, and edge computing. We introduce a methodology Improved Political optimizer (IPO) with Deep Neural Networks (DNN) for ranking resource management algorithms in the context of cloud, fog, and edge systems. Therefore, we start with the challenges of resource management in that industry. Current research contributions can be better categorised, which will aid in the creation of an evaluation scheme. There is a substantial addition made by reviewing and analysing research papers on methods for resource management. Existing Fog resource managers only account for a subset of Fog resource management criteria, such as system network bandwidth and Response Time. The administration of computing assets in the cloud, fog, and at the network's periphery are the primary topics of our studies. The purpose of this analysis is to develop a set of criteria for judging algorithms used for managing resources in cloud, fog, and edge environments. We employed Deep Neural Networks and the Improved Political Optimizer in our search for better methods of managing available resources (DNN). © 2022 IEEE.

关键词： Deep neural networks

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