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Simultaneous Wireless Information and Power Transfer for STAR-RIS Assisted UAV Networks

IEEE Internet of Things Journal 2025年第12期12卷 19915-19928页

作者： Xie, Wenwu Qin, Lijuan Wang, Ji Wu, Weimin Li, Xingwang Xu, Hongbo Yang, Liang Hunan Institute of Science and Technology School of Information Science and Engineering Yueyang414006 China Central China Normal University Department of Electronics and Information Engineering Wuhan430079 China Huazhong University of Science and Technology School of Electronic Information and Communications Wuhan430074 China Henan Polytechnic University School of Physics and Electronic Information Engineering Jiaozuo454003 China Hunan University College of Computer Science and Electronic Engineering Changsha410082 China

This paper explores the benefits of deploying simultaneously transmitting and reflecting reconfigurable intelligence surfaces (STAR-RIS) in unmanned aerial vehicle (UAV) networks with simultaneous wireless information and power transfer. In the proposed system, the UAV utilizes STAR-RIS to radiate energy-carrying information signals (ECISs) to multiple outdoor energy receivers and multiple indoor information receivers without flying over indoor no-fly zone. Based on the UAV propulsion power formula, we successively introduce fly-hover-broadcast (FHB) and path discretization (PD) protocols to minimize the total UAV energy consumption by jointly using the extended penalty function and optimization algorithm based on the expected value of channel status information, where the UAV flight constraints, no-fly constraint, minimized energy or information threshold constraints, and STAR-RIS phase-shift constraints are met. The FHB protocol, which requires the UAV to radiate the ECISs to the users at only a few hovering positions, provides a lower-bound performance of UAV energy consumption, while the PD protocol is used to discuss the general situation of the ECISs during UAV flight. Simulation results demonstrate that the utilization of the STAR-RIS in UAV networks outperforms the traditional RIS in improving energy efficiency and extending UAV flight time. © 2014 IEEE.

关键词： Energy utilization

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Automatic Face Mask Detection Using Deep Learning-Based Mobile-Net Architecture 6

Automatic Face Mask Detection Using Deep Learning-Based Mobi...

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Kumar, Sandeep Rani, Shilpa Jain, Arpit Kumar, Munish Jaglan, Poonam Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vaddeswaram India Neil Gogte Institute of Technology Department of Computer Science and Engineering Hyderabad India MaaSaraswati Institute of Engineering and Technology Department of Electronics &computer Engineering Kalanaur India Panipat Institute of Engineering & Technology Department of Electronics and Communication Engineering Haryana India

ISBN: (纸本)9798350304480

COVID-19 affects public health and economies around the world. Proper wearing of masks plays a vital role in daily life to overcome this epidemic situation. To reduce human contamination, adequate people monitoring is required in public places, i.e., malls, railway stations, bus stands, markets, airports, universities, etc. In highly populated areas, manual inspection of the face mask is not easy, so an automatic face mask detection model helps detect the face mask. This paper proposed mobile Net architecture for face mask detection. This advanced architecture considers the colour image from the standard and its own database for extracting the face feature in-depth to identify the face mask. To validate our work, we used five-fold cross-validation techniques. Finally, the accuracy achieved by the proposed work on two standard databases is 96.4% and 95.7%, showing the outperforming face mask detection results. © 2023 IEEE.

关键词： Covid-19 Deep Learning Face Mask Machine Learning Mask Detection

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Towards Perpetual Wireless Rechargeable Sensor Networks with Path Optimization of Mobile Chargers

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SN computer Science 2024年第8期5卷 1-16页

作者： Kumari, Binita Yadav, Ajay Kumar Kumar, Rakesh Ranjan Department of Electronic and Communication Engineering C. V. Raman Global University Mahura Odisha Bhubaneswar 752054 India Department of Computer Science and Engineering C V Raman Global University Bhubaneswar 752054 India

Wireless Rechargeable Sensor Networks (WRSNs) are pivotal to providing sustainable power to an extensive array of recent technologies. Herein, energy replenishment of nodes takes place via Mobile chargers (MCs). However, optimizing their trajectories within the network is challenging, and finding a solution is hard. Most of the existing works have focused on the scheduling of the MC. Nonetheless, due to ignoring some important factors, such as charging time, energy consumption, and network coverage for optimized paths in dynamic environments, there is still room to improve network lifetimes. Optimization algorithms such as Ant colony optimization have proven to offer potential solutions. In this regard, we explore the Quantum Ant Colonization Optimization (QACO) algorithm as a sophisticated system merging quantum computing and ant behavior principles to determine optimal charging paths for the MC. The proposed scheme considers energy requirements, network topology, and communication protocols to enhance energy replenishment effectiveness. MCs can move around the network to boost their energy, but figuring out the best way for them to travel is crucial and objective of this paper. The experimental results show that QACO outperforms the competing algorithms in terms of various parameters. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Energy efficiency Mobile chargers Path optimization Quantum ant colony optimization Wireless rechargeable sensor networks

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Causal Digital Twins for Physical System Control 59

Causal Digital Twins for Physical System Control

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59th Annual Conference on Information Sciences and Systems, CISS 2025

作者： Mohseni-Sehdeh, Saeed Saad, Walid Sakaguchi, Kei Yu, Tao Virginia Tech Bradley Department of Electrical and Computer Engineering AlexandriaVA22305 United States Institute of Science Tokyo Department of Electrical and Electronic Engineering Tokyo Japan

ISBN: (纸本)9798331513269

Digital twins (DTs) are virtual representations of physical systems, replicating their behavior, dynamics, and interactions with the environment. Hosted on computational platforms, DTs have access to more extensive information and computational power than their physical counterparts (PTs), making them suitable for control applications. This paper proposes a novel DT-based control architecture that leverages causal learning to achieve autonomous control. In this framework, the DT employs causal inference to determine the desired system behavior and, subsequently, instructs the PT accordingly. By incorporating causal learning, a DT can gain self-training capabilities, enabling it to generate and analyze hypothetical scenarios based on cause-and-effect relationships. Simulation results show that DT-based control outperforms the traditional local control method by 49% in terms of the tracking performance measured through the mean squared error. Additionally, causal learning demonstrates 16% better tracking performance over statistical learning in a self-training scenario, as measured by the mean squared error. © 2025 IEEE.

关键词： Digital elevation model

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PULSE: Progressive Utilization of Log-Structured Techniques to Ease SSD Write Amplification in B-epsilon-tree 25

PULSE: Progressive Utilization of Log-Structured Techniques ...

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30th Asia and South Pacific Design Automation Conference, ASP-DAC 2025

作者： Peng, Huai-De Chen, Yi-Shen Chen, Tseng-Yi Chang, Yuan-Hao Department of Computer Science and Information Engineering National Central University Taiwan Department of Electronic and Computer Engineering National Taiwan University of Science and Technology Taiwan Institute of Information Science Academia Sinica Taiwan

ISBN: (纸本)9798400706356

During the unprecedented expansion of global data, efficient storage solutions are essential for processing massive datasets stored on modern storage devices. B-epsilon-tree (Bϵ-tree) is one of the most well-known techniques that provides a write-optimized structure for database file systems. With the excellent access performance and high energy efficiency of solid-state drives (SSDs), they are expected to yield promising outcomes for large-scale data computation. However, their integration into storage systems has the challenges of write amplification, which impacts SSD endurance and reliability. This work identifies significant write amplification issues with Bϵ-tree implementations on SSDs due to the complicated management of key-value pairs. To mitigate the impact of write amplification, we propose PULSE, a novel scheme that rethinks Bϵ-tree designs by leveraging log-structured techniques optimized for SSDs. Moreover, PULSE integrates auxiliary indexing and a dual flush selector to minimize write amplification. Experimental results demonstrate that PULSE significantly mitigates write amplification by more than 62.6% on SSDs for the representative benchmarks compared to the Bϵ-tree. © 2025 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Flash-based SSDs

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A novel convolution neural network architecture with fully connected network for efficient speech emotion recognition system

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International Journal of Information Technology (Singapore) 2025年 1-9页

作者： Singh, Vandana Prasad, Swati Electronics and Communication Engineering Department Birla Institute of Technology Mesra Jharkhand Ranchi 835215 India Computer Science & Engineering Birla Institute of Technology Mesra (Noida Off Campus) Uttar Pradesh Noida 201301 India

Speech emotion recognition determines the emotion of a speaker utilizing only his/her spoken utterance. This research faces difficulty in achieving high speech emotion recognition accuracy due to differences in speaker’s emotional intensity. This study has proposed a novel 1D CNN architecture integrated with fully connected layers which is able to capture the emotion characteristics in speech efficiently for addressing the above mentioned issue. Experiments conducted on RAVDESS dataset which consists of two levels of speaker emotion intensity, namely, ‘strong’ and ‘normal’ have shown better overall performance for the proposed architecture over the Baseline. The proposed method has achieved 4.1% improvement in average speech emotion recognition accuracy compared to the Baseline across 8 different emotions. Further the proposed method has demonstrated superior performance for recognizing neutral & sad emotions. It has achieved 14 percent and 9 percent higher speech emotion recognition accuracy over the Basline method for neutral and sad emotions, respectively. © Bharati Vidyapeeth's institute of computer Applications and Management 2025.

关键词： Convolutional neural network Data augmentation Fully connected network Human–computer interaction RAVDESS Speech emotion recognition

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Implementing RNN and LSTM Models to Electrical Load Predictions 2

Implementing RNN and LSTM Models to Electrical Load Predicti...

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2nd International Conference Computational and Characterization Techniques in engineering and Sciences, IC3TES 2024

作者： Ramesh, T.R. Kumar, Kirana Asha, V. Kumar, S. Naveen Kumar, Manish Kareem, Ali School of Sciences and Computer Studies Bengaluru India Institute of Aeronautical Engineering Department of Electronics and Communication Engineering Hyderabad India New Horizon College of Engineering Department of Master of Computer Application Bangalore India Mlr Institute of Technology Department of Electronics and Communication Engineering Hyderabad India Lovely Professional University Phagwara India Hilla University College Babylon Iraq

ISBN: (纸本)9798350364699

Electrical utilities attach significant emphasis to power load forecasting to design the generating, transmission, and distribution systems. Additional uses of load forecasting in the power network include demand and supply management, determination of power plant operational resource needs, planning of spinning reserve, and scheduling power unit activities. Additionally, there is the option to use a plethora of other applications. The researcher used two distinct machine learning approaches, including Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM), also known to forecast the expected future demand for electrical power in this study. Historical load information's are used to assess the efficiency of implemented measures. The Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE) are analytical metrics used to assess the accuracy of the methods being used. © 2024 IEEE.

关键词： Electric load forecasting

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An Active Reconfigurable Mixer in GaN Technology for 5G Power Amplifier Wi-Fi communications 6

An Active Reconfigurable Mixer in GaN Technology for 5G Powe...

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6th Parul International Conference on engineering and Technology, PICET 2024

作者： Kumar, Rajesh Babu, Tosif Abdul Gaffar Kanaujia, Binod Kumar Tiwari, Devesh Department of Computer Science & Engineering Galgotias College of Engineering &Technology Greater Noida India Department of Electronics & Communication of Engineering PIET Gujarat Vadodara India Department of Electronics & Communication of Engineering National Institute of Technology Jalandhar Jawaharlal Nehru University New Delhi110067 India Department of Computer Science & Engineering Graphic Science and Engineering Graphic Era Era Hill University Uttarakhand Dehradun India

ISBN: (纸本)9798350369748

The mixer's key attributes in this article are its high power output, high conversion gain, high efficiency, and good isolation between board positions. We have designed and implemented a mixer that can be reconfigured dynamically. Using a voltage gain of 12.3 dB, the local oscillator (LO) signal is amplified. The purpose of the active reconfigurable mixer is to enhance the cross-driver platform's performance. Alongside, with the occurrence of a mixer a high-power level is attained. It is noteworthy to highlight that the third-order intermodulation (IIP3) of the new configuration is -9 dBm, which is regarded as negligible. The mixer runs at 6 GHz and is powered by 0.15-µm GaN technology. © 2024 IEEE.

关键词： Gallium nitride

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CardioNet: An Integrative AI-Machine Learning Framework for Enhanced Prediction and Management of Cardiovascular Diseases Using Deep Data Analytics and Clinical Insights 4

CardioNet: An Integrative AI-Machine Learning Framework for ...

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4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Senthil, G.A. Prabha, R. Thamarai, I. Roopa, D. Rajesh Kanna, R. Srimathi, C. Agni College Of Technology Department of Information Technology Chennai India Sri Sairam Institute of Technology Department of Electronics and Communication Engineering Chennai India Panimalar Engineering College Department of Computer Science and Engineering Chennai City Campus India Sri Sairam Institute of Technology Department of Computer Science and Engineering Chennai India Srm Institute of Science and Technology Department of Computer Science and Engineering Ramapuram Campus Chennai India

ISBN: (纸本)9798331508845

Cardio Vascular Disease (CVD) causes dysfunction in our heart and blood arteries, which frequently results in death or paralysis. Therefore, many lives can be prevented by the early and automatic recognition of CVD. Although this goal has been the subject of numerous studies, performance and dependability can yet be enhanced. This work represents another advancement in that direction. Given that cardiovascular diseases (CVDs) remain the primary cause of death worldwide, effective strategies for early detection and management are necessary. To help with CVD prediction and management, we introduce CardioNet, an integrated machine learning framework with strong data analytics and clinical insights. To accurately predict the risk and progression of CVD, CardioNet integrates state-of-the-art machine learning algorithms with a variety of medical data sources, including genetic profiles, imaging data, and electronic health records. CardioNet integrates clinical information and feedback mechanisms to offer customized risk assessment and therapy suggestions. This, in turn, improves patient outcomes and lowers the cost of healthcare. The potential of CardioNet as a comprehensive strategy to counter the widespread threat of CVDs is highlighted. © 2024 IEEE.

关键词： electronic health record

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Optimizing Restroom Resources: A Smart Toilet Paper Dispensing System using Reinforcement Learning Algorithm 10

Optimizing Restroom Resources: A Smart Toilet Paper Dispensi...

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10th International Conference on communication and Signal Processing, ICCSP 2024

作者： Sasikala, D. Lakshmi, V. Vidya Jayanthi, L.N. Muthukumarasamy, S. Joy Winnie Wise, D.C. Thirumaraiselvan, P. Department of Biomedical Engineering Tamil Nadu Namakkal India Velammal Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Department of Commerce Tamil Nadu Chennai India S.A.Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Rajalakshmi Institute of Technology Department of Computer Science and Engineering Chennai Tamil Nadu Thandalam India Adhiparasakthi Engineering College Department of Electronics and Communication Engineering Tamil Nadu Melmaruvathur India

ISBN: (纸本)9798350353068

Public restroom resource management may be enhanced with the help of the study's innovative proposal. Using IoT and RL-based technology, a system is put in place to deal with the ongoing issue of insufficient toilet paper supply in public restrooms. Through the use of Internet of Things (IoT) sensors embedded in the dispensers, the system continuously assesses the stock of toilet paper and transmits that data to a central control platform. An automatic reordering mechanism is initiated to streamline the process of distributing extra supplies when the quantity of toilet paper reaches a certain level. This article delves into the smart bathroom system's design, implementation, and benefits. It addresses the potential operational efficiency, savings, and user convenience that may result from reducing the number of human checks required and the number of disruptions caused by empty dispensers. It also highlights the system's commitment to sustainability and resource conservation initiatives. By suggesting an achievable approach to the management of bathroom resources based on the Internet of Things, this research adds to the optimization of public facilities. This method decreases operating costs while also improving user experiences. © 2024 IEEE.

关键词： Internet of things

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