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2022 IEEE International Conference on Consumer Electronics, ICCE 2022

作者： Yachi, Yuta Mukasa, Yousuke Tawada, Masashi Togawa, Nozomu Waseda University Department of Computer Science and Communications Engineering Japan Green Computing System Research Organization Waseda University Japan

ISBN: (纸本)9781665441544

Ising machines such as quantum annealing machines and semiconductor-based annealing machines can solve various combinatorial optimization problems very efficiently by transforming it into a data structure called an Ising model. At that time, the bit-widths of the coefficients of the Ising model have to be kept within the range that an Ising machine can deal with. However, by reducing the Ising-model bit-widths, its minimum energy state, or ground state, may become different from that of the original one and hence the targeted combinatorial optimization problem cannot be well solved. This paper proposes an effective method for reducing Ising-model bit-widths. The proposed method is composed of the two processes: First, given an Ising model with large coefficient bit-widths, the shift method is applied to reduce its bit-widths roughly. Second, the spin-adding method is applied to further reduce its bit-widths to those that Ising machines can deal with. Without adding too many extra spins, we efficiently reduce the coefficient bit-widths of the original Ising model. Furthermore, the ground state before and after reducing the coefficient bit-widths is not much changed in most of the practical cases. Experimental evaluations demonstrate the effectiveness of the proposed method, compared to existing methods. © 2022 IEEE.

关键词： Ising model

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Real Time Adaptive Routing for Efficient Urban Mobility

Real Time Adaptive Routing for Efficient Urban Mobility

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Visual Analytics and Data Visualization (ICVADV), International Conference on

作者： I. Karthika D. Pushpalatha Infant Hilda D Dheenadhayalan S Mukilan K Hariprasaath S Department of Computer Science and Engineering M Kumarasamy college of engineering Karur India Department of Electronics and Communications Engineering M Kumarasamy college of engineering Karur India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

Urban mobility faces growing challenges due to the inefficiencies of traditional public transportation systems, which rely on fixed schedules and routes. These systems struggle to meet dynamic passenger demands, resulting in overcrowding during peak times and underutilization during off-peak periods. Variable passenger demand frequently results in inefficiencies in public transportation networks, which create crowding during rush hours and underutilization during off-peak hours. The proposed system offers a Smart Ticket Scheduling System that uses real-time crowd data to dynamically schedule and distribute tickets in order to overcome these difficulties. Using sensors, cameras, and mobile network data, the system tracks the number of passengers at different locations by utilizing technologies like IoT, AI, and advanced analytics. By controlling ticket availability and managing passenger flow, this data ensures that commuters are distributed among buses and other public transportation options as efficiently as possible. Additionally, the technology modifies bus routes and schedules in real-time to meet changes in demand, which eases traffic and enhances the entire travel experience.

关键词： Schedules Data integration Pricing Dynamic scheduling Real-time systems Complexity theory Resource management Internet of Things Artificial intelligence Public transportation

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Investigation of network architecture for single-channel end-to-end denoising 28

Investigation of network architecture for single-channel end...

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28th European Signal Processing Conference, EUSIPCO 2020

作者： Hasumi, Takuya Kobayashi, Tetsunori Ogawa, Tetsuji Department of Communications and Computer Engineering Waseda University Tokyo Japan

ISBN: (纸本)9789082797053

This paper examines the effectiveness of a fully convolutional time-domain audio separation network (Conv-TasNet) on single-channel denoising. Conv-TasNet, which has a structure to explicitly estimate a mask for encoded features, has shown to be effective in single-channel sound source separation in noise-free environments, but it has not been applied to denoising. Therefore, the present study investigates a method of learning Conv-TasNet for denoising and clarifies the optimal structure for single-channel end-to-end modeling. Experimental comparisons conducted using the CHiME-3 dataset demonstrate that Conv-TasNet performs well in denoising and yields improvements in single-channel end-to-end denoising over existing denoising autoencoder-based modeling. © 2021 European Signal Processing Conference, EUSIPCO. All rights reserved.

关键词： Network architecture

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Multiple plant leaf disease detection using efficient Machine learning classification techniques

Multiple plant leaf disease detection using efficient Machin...

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2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Kalaiselvi, K. Deepa Thilak, K. Lalitha Devi, K. Kumaresan, K. Department of Networking and communications Faculty of Engineering and technology SRM Institute of science and technology Kattankulathur Chennai India Department of Computer science and Engineering Sathyabama Institute of Science and Technology Chennai India Department of CSE K.S.R College of Engineering Tiruchengode Namakkal India

ISBN: (纸本)9798350305708

Accurate diagnosis of plant diseases is essential for reversing declines in crop output. To study plant illnesses, one must examine the outward symptoms produced by the plant in question. It is also essential for long-term husbandry that plant health be monitored and complaints about diseases made. Disease auditing in plants is tedious work that requires special equipment. It takes a huge commitment of time, courage when dealing with plant illnesses, and fortitude to endure the devilishly long processing time. As a result, landing the photos of the leaves and comparing it with the data sets is how image processing is employed for the detection of plant conditions. To learn to distinguish between photos of unhealthy and healthy leaves, a group of computers is trained on datasets of both types. In conclusion, we now have a straightforward method to descry the complaint existent in crops at a massive scale by employing machine learning to train on the vast data sets available privately. To aid greenhouse growers, this report has been written. © 2023 IEEE.

关键词： ANN Crop Disease SVM

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An Intelligent Forwarding Strategy in SDN-Enabled Named-Data IoV

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computers, Materials & Continua 2021年第12期69卷 2949-2966页

作者： Asadullah Tariq Irfan ud din Rana Asif Rehman Byung-Seo Kim Department of Computer Science The Superior UniversityLahore54000Pakistan National University of Computer and Emerging Sciences Chiniot-Faisalabad Campus35400Pakistan Department of Software and Communications Engineering Hongik UniversitySejong City30016Korea

Internet of Vehicles(IoV),a rapidly growing technology for efficient vehicular communication and it is shifting the trend of traditional Vehicular Ad Hoc Networking(VANET)towards *** centralized management of IoV endorses its uniqueness and suitability for the Intelligent Transportation System(ITS)safety *** Data Networking(NDN)is an emerging internet paradigm that fulfills most of the expectations of *** of the current IP internet architecture are the main motivation behind ***-Defined Networking(SDN)is another emerging networking paradigm of technology that is highly capable of efficient management of overall networks and transforming complex networking architectures into simple and manageable *** combination of the SDN controller,NDN,and IoV can be revolutionary in the overall performance of the *** storm,due to the broadcasting nature of NDN,is a critical issue in NDN based on *** speed and rapidly changing topology of vehicles in IoV creates disconnected link problem and add unnecessary transmission *** order to cop-up with the above-discussed problems,we proposed an efficient SDN-enabled forwarding mechanism in NDN-based IoV,which supports the mobility of the vehicle and explores the cellular network for the low latency control *** IoV environment,the concept of Edge Controller(EC)to maintain and manage the in-time and real-time vehicular topology is being introduced.A mathematical estimation model is also proposed in our work that assists the centralized EC and SDN to find not only the shortest and best path but also the most reliable and durable *** naming scheme and in-network caching property of the NDN nodes reduce the *** used ndnSIM and NS-3 for the simulation experiment along with SUMO for the environment *** results of NDSDoV illustrate significant performance in terms of availability with limited routing overhead,minimized delay,retransmissions,and in

关键词： Software-defined networking named-data networking internet of vehicles routing

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Ultra-High Rate-Reliability Fairness in Grant-Free Massive URLLC NOMA System: Joint Power and Channel Allocation Using Meta-Heuristic Search

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IEEE Transactions on Vehicular Technology 2023年第12期72卷 15899-15915页

作者： Katwe, Mayur Singh, Keshav Li, Chih-Peng Ding, Zhiguo National Sun Yat-Sen University Department of Electrical Engineering Kaohsiung804 Taiwan Nanyang Technological University 639798 Singapore National Sun Yat-sen University Institute of Communications Engineering Kaohsiung80424 Taiwan Khalifa University Department of Electrical Engineering and Computer Science Abu Dhabi United Arab Emirates University of Manchester Department of Electrical and Electronic Engineering ManchesterM13 9PL United Kingdom

Next-generation wireless applications are envisaged to cater extended ultra-reliability low-latency communication (URLLC) services involving high data rates and improved user fairness for massive users by synergizing multiple driving technologies. Nevertheless, effective resource management with this heterogeneous quality of services (QoS) requirements under limited spectral resources becomes extremely imminent and challenging which is beyond the capabilities of existing architectures. In this article, we investigate a grant-free non-orthogonal multiple access-based uplink transmission to prompt a new service called massive URLLC (mURLLC) for high rate-reliability fairness for massive users under strict latency constraints. To enable spectral-efficient and reliable resource allocation policy, we formulate distinct resource optimization problems for the considered GF-NOMA scheme which involves worst-case rate and reliability maximization and latency minimization under joint power and channel allocation. To tackle the NP-hardness and mixed integer non-linear programming of formulated problems, we propose a general low-cost differential evolution-based meta-heuristic framework which jointly allocates the available resources among multiple URLLC users. Simulations demonstrate the effectiveness of the considered GF-NOMA-based mURLLC system over state-of-art approaches. Moreover, it is shown that the proposed DE algorithm exhibits efficient channel utilization and a high degree of user fairness when compared to random channel contention. © 1967-2012 IEEE.

关键词： Reliability

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Coded Multi-User Information Retrieval with a Multi-Antenna Helper Node

arXiv

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arXiv 2024年

作者： Abolpour, Milad Salehi, MohammadJavad Mohajer, Soheil Shariatpanahi, Seyed Pooya Tölli, Antti Centre for Wireless Communications University of Oulu Finland Department of Electrical and Computer Engineering University of Minnesota United States School of Electrical and Computer Engineering University of Tehran Iran

A novel coding design is proposed to enhance information retrieval in a wireless network of users with partial access to the data, in the sense of observation, measurement, computation, or storage. Information exchange in the network is assisted by a multi-antenna base station (BS), with no direct access to the data. Accordingly, the missing parts of data are exchanged among users through an uplink (UL) step followed by a downlink (DL) step. In this paper, new coding strategies, inspired by coded caching (CC) techniques, are devised to enhance both UL and DL steps. In the UL step, users transmit encoded and properly combined parts of their accessible data to the BS. Then, during the DL step, the BS carries out the required processing on its received signals and forwards a proper combination of the resulting signal terms back to the users, enabling each user to retrieve the desired information. Using the devised coded data retrieval strategy, the data exchange in both UL and DL steps requires the same communication delay, measured by normalized delivery time (NDT). Furthermore, the NDT of the UL/DL step is shown to coincide with the optimal NDT of the original DL multi-input single-output CC scheme, in which the BS is connected to a centralized data library. © 2024, CC BY.

关键词： Information retrieval

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Application of machine learning algorithm for prediction of heart disease 4

Application of machine learning algorithm for prediction of ...

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4th International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2022

作者： Upadhyay, Ankit Kumar Saleem, Sharzeel Anubhav, Adarsh Sagar, Shrddha Galgotias University Department of Computer Science and Engineering Greater Noida India Department of Electrical Electronics and Communications Galgotias University Greater Noida India

ISBN: (纸本)9781665474368

Healthcare is the one the domain in which every organization is working on it globally. It need to make it better and better via different researches and the innovations in this sector. Machine learning is the most used algorithm in every sector and organization. It not merely transforming the field of healthcare but also able to emulate the various human intelligence from the surrounding in order to solve various problems. Machine learning in health care sector has shown its various application in it. Disease prediction is one of the applications of it. This study helps to predict heart diseases with the help of its algorithm to determine the risk and treat the patient in the healthcare system. In this paper we have taken out the data from the kaggle and implemented it using the Jupiter notebook tool. The three algorithm we have implemented (naïve bayes, decision tree and random forest) out of which random forest is giving. Hence, with the help of the following algorithms it is possible for us to predict the heart disease with more accuracy. This research will able to predict various disease over its various symptoms more easily and accurately. © 2022 IEEE.

关键词： Forecasting

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Energy-Efficient Distributed Learning for NOMA-Based Unmanned Aerial Agent-assisted MEC Networks

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IEEE Internet of Things Journal 2025年

作者： Consul, Prakhar Budhiraja, Ishan Garg, Deepak Kumar, Neeraj Rodrigues, Joel J. P. C. Almuhaideb, Abdullah IILM University School of Computer Science and Engineering Department of Computing and Security Greater Noida India Bennett University School of Computer Science Engineering and Technology Greater Noida India SR University School of Artificial Intelligence and Computer Science Telangana Warangal India Thapar Institute of Engineering and Technology Department of Computer Science and Engineering Punjab Patiala India Imam Abdulrahman Bin Faisal University Department of Networks and Communications College of Computer Science and Information Technology Saudi Arabia PI Teresina Brazil

The Internet of Things (IoT) has become a revolutionary concept that connects various devices and systems to enable smooth communication and data exchange. In this vast network, unmanned aerial agents (UAA)-assisted mobile edge computing (MEC) communication plays a crucial role in facilitating direct interaction between edge devices. This aspect of IoT goes beyond traditional interactions between humans and machines. It creates a dynamic environment where devices collaborate autonomously, share information, and perform tasks. UAA-assisted MEC network offers several benefits such as supports short range communication, reduced delay, improved scalability, and enhanced energy efficiency. Furthermore, for the purpose of enhancing the widespread interconnection and exceptionally dependable minimal delay in the fifth generation (5G) and beyond network, the utilization of non-orthogonal multiple access (NOMA) can be considered. Within this context, the impact of federated learning (FL) on NOMA-based UAV-assisted MEC network in wirelesspowered communication networks is examined. Initially, the transmitters extract energy from the radio frequency signals emitted by the MEC server. Subsequently, the transmitters utilize NOMA to establish communication with the receivers by utilizing the stored harvested energy. The formulation of a stochastic optimization problem is proposed with the aim of improving energy consumption (EC) and minimizing delay. Results indicate that the proposed scheme exhibit superior accuracy compared to baseline schemes, achieving an accuracy 98.37% after 59 communication rounds. The FL is employed to attain the objective and accelerate the local training data across the UAA-assisted MEC network. © 2025 IEEE.

关键词： Resource allocation

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A Comprehensive Technique Based on Machine Learning for Device and Circuit Modeling of Gate-All-Around Nanosheet Transistors

IEEE Open Journal of Nanotechnology

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IEEE Open Journal of Nanotechnology 2023年 4卷 181-194页

作者： Butola, Rajat Li, Yiming Kola, Sekhar Reddy National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Institute of Communications Engineering the Institute of Biomedical Engineering the Department of Electronics and Electrical Engineering the Institute of Pioneer Semiconductor Innovation and the Institute of Artificial Intelligence Innovation Hsinchu300093 Taiwan

Machine learning (ML) is poised to play an important part in advancing the predicting capability in semiconductor device compact modeling domain. One major advantage of ML-based compact modeling is its ability to capture complex relationships and patterns in large datasets. Therefore, in this paper a novel design scheme based on dynamically adaptive neural network (DANN) is proposed to develop fast and accurate compact model (CM). This framework constitutes a powerful yet computationally efficient methodology and exhibits emergent dynamic behaviors. This paper demonstrates that the compact model based on ML can be designed to replicate the performance of conventional compact model for nanodevices. For this work, gate-all-around (GAA) nanosheet (NS) device characteristics are comprehensively analyzed for process variability sources using the proposed model. The device geometry parameters such as channel length, nanosheet width and nanosheet thickness are fed as input features to the DANN model. The adaptive neural network learns dynamically by updating weights of the model in accordance with the input features and achieves accurate neural weight convergence. The proposed model predicted the electrical characteristics of NS devices with less than 1% error rate. The model is also implemented and validated for the simulations of digital circuit designs such as inverter, and logic gates. ©;2023 The Authors.

关键词： Circuit simulation

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