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14th International Conference on Cloud Computing, Data Science and engineering, Confluence 2024

作者： Saxena, Virat Raj Yadav, Rajeev Parveen, Azra Sadiq, Mohd. Mangalayatan University Department of Computer Science and Engineering Uttar Pradesh Aligarh202001 India Indraprastha Institute of Information Sciences Private Limited Indraprastha Research Laboratory New Delhi110025 India Upfet Jamia Millia Islamia A Central University Software Engineering Laboratory Computer Engineering Section New Delhi110025 India

ISBN: (纸本)9798350344837

Various techniques have been developed for the identification of different types of requirements like interview, questionnaire, group elicitation techniques, attributed goal-oriented requirements analysis, fuzzy based goal-oriented requirements analysis method, non-functional requirements framework, etc. Based on the critical analysis of requirements elicitation methods, we found that single elicitation technique is not suitable to understand the need of the stakeholders. In real life applications, multiple elicitation techniques are required to elicit and select the requirements of an information system because each technique has its strength and limitations. Therefore, to address this issue, this paper presents a mathematical model for the selection of requirements elicitation techniques so that complete set of requirements along with its priority can be identified before the development of an information system. The institute examination system is considered to show the applicability of the proposed mathematical model. © 2024 IEEE.

关键词： Information systems

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VLC-enabled UAV Network for IoT with Co-channel Interference: Joint Spatial Deployment and Resource Allocation

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IEEE Internet of Things Journal 2025年第12期12卷 20707-20721页

作者： Liu, Jiaji Yang, Fang An, Nan Song, Jian Han, Zhu State Key Laboratory of Widegap Semiconductor Optoelectronic Materials and Technologies Beijing100084 China Tsinghua University Beijing National Research Center for Information Science and Technology Department of Electronic Engineering Beijing100084 China Research Institute of Tsinghua University Shenzhen518057 China Tsinghua University Shenzhen International Graduate School Shenzhen518055 China University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

Unmanned aerial vehicles (UAVs) have gained significant attention due to their low cost and efficient deployment. Specifically, UAV-based communications hold promise to supplement the coverage limitations of terrestrial networks and achieve superior connectivity for the Internet of Things (IoT). Moreover, UAVs with visible light communication (VLC) capability do not interfere with existing radio frequency (RF) communication infrastructure, thereby providing flexible, high-throughput on-demand wireless communications. In this paper, considering the limited battery capacity of UAVs, we aim to maximize the energy efficiency (EE) of a multi-UAV downlink VLC system for IoT. To this end, the system model with co-channel interference is first established, followed by the formulation of the joint spatial deployment and resource allocation problem. Besides, the original problem is decomposed into three subproblems, focusing on spatial deployment, subchannel assignment, and power allocation. By employing the block coordinate descent method, the three subproblems are solved alternately. Moreover, simulation results demonstrate that the proposed method achieves superior improvements in EE compared to other baselines. In addition, the convergence and computational complexity of the proposed algorithm, as well as the impacts of key parameters on the overall system performance are also investigated. © 2025 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Optimizing Acoustic Echo Cancellation with Variable Step Size in Adaptive Filtering

Optimizing Acoustic Echo Cancellation with Variable Step Siz...

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2024 International Conference on Advanced Technologies for Communications, ATC 2024

作者： Chen, Yen-Yi Wang, Jian-Hong Chan, Po-Cheng Liang, Kai-Wen Wang, Ze-Yu Wang, Jia-Ching School of Computer Science and Technology Shandong University of Technology Shandong Zibo China National Central University Department of Computer Science and Information Engineering Taoyuan Taiwan Advanced Technology Laboratory Chunghwa Telecom Laboratories Taiwan

ISBN: (纸本)9798350353983

This paper presents an innovative approach to acoustic echo cancellation (AEC) by applying Variable Step Size (VSS) separately to each adaptive filtering technique: Normalized Least Mean Square (NLMS), and Proportionate Normalized Least Mean Square (PNLMS). This application is tailored to overcome the challenges of step size adjustment and signal sparsity, significantly enhancing system performance. We demonstrate that utilizing VSS with each algorithm independently allows for optimized control over convergence rates-larger step sizes quicken convergence while smaller sizes ensure smoother transitions. This method improves the robustness and efficiency of echo cancellation systems, removing the need for manual step size adjustments and proving highly effective in real-time applications. © 2024 IEEE.

关键词： Echo suppression

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Predictive modeling of MXene-based solar absorbers using a deep neural network

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Journal of the Optical Society of America B: Optical Physics 2025年第4期42卷 763-772页

作者： Waseer, Waleed Iqbal Baqir, Muhammad Abuzar Saqlain, Muhammad Mughal, Muhammad Junaid Khan, Shujaat School of Physics State Key Laboratory of Electronic Thin Films and Integrated Devices University of Electronic Science and Technology of China Chengdu China Department of Electrical Engineering COMSATS University Islamabad Sahiwal57000 Pakistan Department of Electrical and Computer Engineering COMSATS University Islamabad Islamabad45550 Pakistan Department of Computer Engineering College of Computing and Mathematics King Fahd University of Petroleum & Minerals Dhahran31261 Saudi Arabia SDAIA-KFUPM Joint Research Center for Artificial Intelligence King Fahd University of Petroleum & Minerals Dhahran31261 Saudi Arabia

Nanophotonic structures, such as photonic crystals, plasmonic nanostructures, and metamaterials, present transformative potential in advancing optical devices through innovative design capabilities. Among these, metamaterials—specifically metal–insulator–metal (MIM)-based absorbers—stand out for their ability to achieve high electromagnetic wave absorption across designated frequencies, proving valuable in applications ranging from solar cells to sensors. This study focuses on designing a high-efficiency MXene–SiO2–silver MIM metasurface absorber, leveraging deep learning techniques to streamline and enhance the simulation process traditionally reliant on finite-difference time-domain (FDTD) or finite-element methods (FEMs). By utilizing a deformable convolutional neural network (CNN), the proposed model predicts absorption spectra from metasurface geometries with superior accuracy and reduced computational demand while achieving dynamic spatial transformation handling. The model’s accuracy is further elucidated through Gradient-weighted Class Activation Mapping (Grad-CAM), which provides insights into the absorber’s most influential regions, supporting targeted optimization for specific applications. This deep learning approach demonstrates significant advantages over conventional methods in efficiency and adaptability, advancing the development of high-performance nanophotonic devices. © 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.

关键词： Time domain analysis

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Massive-MIMO Based RSMA Under Nakagami-m Channel Over 6G mURLLC Wireless Networks 57

Massive-MIMO Based RSMA Under Nakagami-m Channel Over 6G mUR...

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57th Annual Conference on Information Sciences and Systems, CISS 2023

作者： Zhang, Xi Zhu, Qixuan Poor, H. Vincent Texas AandM University Networking and Information Systems Laboratory Department of Electrical and Computer Engineering College StationTX77843 United States Princeton University Department of Electrical and Computer Engineering PrincetonNJ08544 United States

ISBN: (纸本)9781665451819

The sixth generation (6G) mobile wireless networks are expected to provide massive ultra-reliable and low-latency communications (mURLLC) for data services, which require extremely stringent quality-of-services (QoS) with massive connectivity. Rate splitting (RS) multiple access schemes have been proposed recently for tackling the difficulty of the massive access yielded by a large number of mobile users (MUs) requesting to connect to a single base station. Massive multiple-input and multiple-output (massive-MIMO) communication is considered a promising solution to serve massive MUs in 6G wireless networks. However, how to apply RS schemes into massive-MIMO techniques to support the massive access has not been sufficiently studied. In this paper, we propose to integrate RS schemes with massive-MIMO techniques to achieve mURLLC transmissions under a Nakagamir $m$ fading wireless channel. First, we establish the system model for the integrated RS and massive-MIMO schemes. Then, we derive closed-form expressions for the signal-to-interference-plus-noise ratios (SINRs) under a Nakagami- $-m$ fading channel and obtain the channel capacity. Finally, we use numerical analyses to validate and evaluate our proposed RS and massive-MIMO integrated schemes. © 2023 IEEE.

关键词： Wireless networks

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Fire and Smoke Detection using an Enhanced YOLOv8 Model

Fire and Smoke Detection using an Enhanced YOLOv8 Model

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computer vision and Machine Intelligence (CVMI), International Conference on

作者： Bhavesh Kumar Bohra Bulla Rajesh Mohammed Javed David Doermann Department of IT Computer Vision and Biometrics Lab IIIT Allahabad India Department of Computer Science and Engineering IIIT Sricity India Department of Computer Science and Engineering University at Buffalo Buffalo NY USA

ISBN: (数字)9798350376876

ISBN: (纸本)9798350376883

Due in part to global warming, the frequency of wildfires has increased in recent years, significantly impacting humans, wildlife, and forest ecosystems. To address this challenge, using technology for early fire detection through smoke can help firefighters and other stakeholders limit their spread and reduce the impact. In this research, we adopt the latest object detection model - YOLOv8 and use a fine-tuned version for detecting smoke and fire using the latest datasets. YOLOv8 backbone begins with two convolutional (Conv) layers, followed by a series of alternating Conv layers and C2F modules, and ends with a Spatial Pyramid Pooling - Fast (SPPF) module. Our improvement involves adding two extra Conv layers at the beginning of the backbone, between the alternating sequence of Conv and C2F layers. As the smoke and fire detection problem involves searching for smaller objects in the image, increasing the feature extraction layers improves the performance of smoke and fire detection. The fine-tuned model is tested with the FASDD v4 dataset, which depicts fire, smoke, non-fire, and non-smoke scenarios.

关键词： Climate change Fire safety Smoke detectors YOLO Global warming Wildfires Fires

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Adaptive Online Model Update Algorithm for Predictive Control in Networked Systems 60

Adaptive Online Model Update Algorithm for Predictive Contro...

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60th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2024

作者： Khatana, Vivek Chang, Chin-Yao Wang, Wenbo University of Minnesota Department of Electrical and Computer Engineering Minneapolis United States National Renewable Energy Laboratory GoldenCO80401 United States

ISBN: (纸本)9798331541033

In this article, we introduce an adaptive on-line model update algorithm designed for predictive control applications in networked systems, particularly focusing on power distribution systems. Unlike traditional methods that depend on historical data for offline model identification, our approach utilizes real-time data for continuous model updates. This method integrates seamlessly with existing online control and optimization algorithms and provides timely updates in response to real-time changes. This methodology offers significant advantages, including a reduction in the communication network bandwidth requirements by minimizing the data exchanged at each iteration and enabling the model to adapt after disturbances. Furthermore, our algorithm is tailored for non-linear convex models, enhancing its applicability to practical scenarios. The efficacy of the proposed method is validated through a numerical study, demonstrating improved control performance using a synthetic IEEE test case. © 2024 IEEE.

关键词： Predictive control systems

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Chemical environment adaptive learning for optical band gap prediction of doped graphitic carbon nitride nanosheets

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Neural Computing and Applications 2025年第5期37卷 3287-3301页

作者： Chen, Chen Xu, Enze Yang, Defu Yan, Chenggang Wei, Tao Chen, Hanning Wei, Yong Chen, Minghan Intelligent Information Processing Laboratory Hangzhou Dianzi University Hangzhou China Department of Computer Science Wake Forest University Winston-SalemNC United States Department of Chemical Engineering Howard University WashingtonDC United States Texas Advanced Computing Center University of Texas at Austin AustinTX United States Department of Computer Science & Information Systems University of North Georgia DahlonegaGA United States

This study presents a new machine learning algorithm, named Chemical Environment Graph Neural Network (ChemGNN), designed to accelerate materials property prediction and advance new materials discovery. Graphitic carbon nitride (g-C3N4) and its doped variants have gained significant interest for their potential as optical materials. Accurate prediction of their band gaps is crucial for practical applications;however, traditional quantum simulation methods are computationally expensive and challenging to explore the vast space of possible doped molecular structures. The proposed ChemGNN leverages the learning ability of current graph neural networks (GNNs) to satisfactorily capture the characteristics of atoms' chemical environment underlying complex molecular structures. Our experimental results demonstrate more than 100% improvement in band gap prediction accuracy over existing GNNs on g-C3N4. Furthermore, the general ChemGNN model can precisely foresee band gaps of various doped g-C3N4 structures, making it a valuable tool for performing high-throughput prediction in materials design and development. © The Author(s) 2024.

关键词： Graphitic Carbon Nitride

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ANUBIS: Hybrid FPAA-FPGA Architecture for Entropy-Based True Random Number Generation in Secure UAV Communication

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IEEE Embedded Systems Letters 2024年第3期17卷 164-167页

作者： El-Hadedy, Mohamed Abelian, Andrea Lee, Kenny Cheng, Benny Hwu, Wen-Mei California State Polytechnic University Department of Electrical and Computer Engineering Pomona United States University of Illinois at Urbana-Champaign Coordinated Science Laboratory United States Naval Surface Warfare Center United States

Field-Programmable Gate Arrays (FPGAs) and Field-Programmable Analog Arrays (FPAAs) are reconfigurable circuits that enable flexible digital and analog implementations post-manufacturing. FPGAs are widely used in telecommunications, mixed-signal, and embedded systems due to their parallel processing and reconfigurability. Meanwhile, FPAAs provide flexibility for analog systems, which is crucial for modern mixed-signal processing. This study introduces ANUBIS, a hybrid system combining FPGA and FPAA technologies to generate true random numbers (TRNGs) for secure UAV communication. Due to its reliability and cost efficiency, ANUBIS leverages a thermistor circuit as an entropy source. The FPAA amplifies the analog noise generated by the thermistor, while the FPGA digitizes and processes the signal using Von Neumann Whitening (VNW) to remove bias. The ASCON hash function is applied to the whitened bitstream to generate cryptographically secure keys. These keys are utilized in a DHKE to enable secure communication via Bluetooth Low Energy (BLE), an ideal protocol for energy-constrained UAV applications. ANUBIS demonstrates reconfigurability, power efficiency, and ease of implementation, showcasing its potential for secure communication applications. It achieves robust randomization, setting a new standard for UAV communication security and addressing applications requiring reliable TRNG solutions. The system consumes 1.615 W in total, with 1.54 W consumed by the FPGA and 75 mW by the FPAA. Resource utilization on the PYNQ-Z1 board includes 5,186 LUTs (9.75%), 549 units of memory (3.15%), and 5.5 units of BRAM (3.93%), indicating moderate resource usage with room for future enhancements. By integrating reliable analog noise harvesting with efficient digital post-processing, ANUBIS offers a novel approach to TRNG design, demonstrating the potential for broader cryptographic applications in resource-constrained environments. © 2009-2012 IEEE.

关键词： Random number generation

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Evolution of Neuromorphic Computing 4

Evolution of Neuromorphic Computing

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4th International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Sai Sree Vaishnavi, Vakada G. Bhowmik, Biswajit National Institute of Technology Karnataka Ishwarchandra Vidyasagar Ait Lab Brics Laboratory Department of Computer Science and Engineering Surathkal Mangalore575025 India

ISBN: (纸本)9798350343670

With the advancement of artificial intelligence (AI) technologies, novel and inventive approaches for addressing complex problems are coming to the forefront. Neuromorphic computing based on AI technologies stands as an exemplar, endeavoring to mimic the human brain's intricate neural architecture and computational principles within electronic devices. Contrary to conventional Von Neumann architecture, neuromorphic computing architecture offers a promising solution for building intelligent and efficient computational systems that excel in tasks requiring low power consumption, real-time processing, and adaptability. Subsequently, it is employed in various applications such as robotics, sensory processing, neuromorphic vision, edge computing, etc. This paper explores the conventional Von Neumann architecture and outlines its shortcomings. Next, neuromorphic architecture as an alternative and its evolution are described. Next, the characteristics of neuromorphic computing and its diverse applications are illustrated. The paper also addresses the key challenges hindering neuromorphic computing development. © 2024 IEEE.

关键词： Neuromorphic Computing Neuromorphic Computing Architecture Neuromorphic Computing Challenges SpiNNaker Von Neumann Architecture

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