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arXiv 2024年

作者： Ji, Zelin Qin, Zhijin School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom Department of Electronic Engineering Tsinghua University Beijing China The Beijing National Research Center for Information Science and Technology Beijing China The State Key Laboratory of Space Network and Communications Beijing China

The trend of massive connectivity pushes forward the explosive growth of end devices. The emergence of various applications has prompted a demand for pervasive connectivity and more efficient computing paradigms. On the other hand, the lack of computational capacity of the end devices restricts the implementation of the intelligent applications, and becomes a bottleneck of the multiple access for supporting massive connectivity. Mobile cloud computing (MCC) and mobile edge computing (MEC) techniques enable end devices to offload local computation-intensive tasks to servers by networks. In this paper, we consider the cloud-edge-end collaborative networks to utilize distributed computing resources. Furthermore, we apply task-oriented semantic communications to tackle the fast-varying channel between the end devices and MEC servers and reduce the communication cost. To minimize long-term energy consumption on constraints queue stability and computational delay, a Lyapunov-guided deep reinforcement learning hybrid (DRLH) framework is proposed to solve the mixed integer nonlinear programming (MINLP) problem. The long-term energy consumption minimization problem is transformed into the deterministic problem in each time frame. The DRLH framework integrates a model-free deep reinforcement learning algorithm with a model-based mathematical optimization algorithm to mitigate computational complexity and leverage the scenario information, so that improving the convergence performance. Numerical results demonstrate that the proposed DRLH framework achieves near-optimal performance on energy consumption while stabilizing all queues. © 2024, CC BY-NC-ND.

关键词： Reinforcement learning

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Modelling distillate output of a solar distiller with eco-friendly wood-based steam generation basin using machine learning model and starling murmuration optimizer

International Journal of Thermofluids

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International Journal of Thermofluids 2025年 27卷

作者： Al-Betar, Mohammed Azmi Muskens, Otto L. Hammoodi, Karrar A. Abd Elaziz, Mohamed Shambour, Qusai Yousef Fraihat, Salam Elsheikh, Ammar H. College of Engineering and Information Technology Ajman University Ajman United Arab Emirates Faculty of Engineering and Physical Sciences University of Southampton Southampton SO17 1BJ United Kingdom College of Engineering University of Al Maarif Al Anbar31001 Iraq Department of Mathematics Faculty of Science Zagazig University Zagazig44519 Egypt Faculty of Computer Science and Engineering Galala University Suez435611 Egypt Software Engineering Department Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman Jordan Department of Production Engineering and Mechanical Design Faculty of Engineering Tanta University Tanta31527 Egypt Faculty of Engineering Pharos University in Alexandria Egypt

Yield augmentation of solar distiller (SD) using low-cost modifications with minimum environmental impacts is highly desirable. In this study, we developed an eco-friendly steam generation device made of carbonized Ficus sycomorus wood to boost the yield of the conventional single-basin solar distiller. Moreover, a hybrid model composed of a long short-term memory (LSTM) network and a Starling murmuration optimizer (SMO) was developed to predict the yield of the established SD. To verify the accuracy of the model, three other models were employed and the distillate yield was predicted. The predicted data of all models was evaluated using different error measures. These models were standalone LSTM and LSTM optimized by an artificial hummingbird algorithm (AHA) or manta ray foraging optimizer (MRFO). Real field data were employed during the training and testing of all models. The thermal performance of the distiller with the steam generation device was compared with that of a traditional distiller based on exergy output, exergy efficiency, and energy efficiency. The distillate yield, exergy efficiency, and energy efficiency of the modified SD were enhanced by 34 %, 49 %, and 40 %, respectively, compared with that of the traditional distiller. Moreover, the total amount of produced drinkable water using the modified SD could reach a high value of 6.1 L/day with a low cost of 0.014 $/L. © 2025

关键词： Exergy

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Pre-Chirp-Domain Index Modulation for Full-Diversity Affine Frequency Division Multiplexing towards 6G

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IEEE Transactions on Wireless Communications 2025年

作者： Liu, Guangyao Mao, Tianqi Xiao, Zhenyu Wen, Miaowen Liu, Ruiqi Zhao, Jingjing Basar, Ertugrul Wang, Zhaocheng Chen, Sheng Beihang University School of Electronic and Information Engineering State Key Laboratory of CNS/ATM Beijing100191 China Greater Bay Area Innovation Research Institute of BIT Zhuhai519000 China Zhuhai519088 China South China University of Technology School of Electronic and Information Engineering Guangzhou510640 China ZTE Corporation Wireless and Computing Research Institute Beijing100029 China State Key Laboratory of Mobile Network and Mobile Multimedia Technology Shenzhen518055 China Tampere University Department of Electrical Engineering Tampere33720 Finland Koc University Department of Electrical and Electronics Engineering Sariyer Istanbul34450 Turkey Tsinghua University Department of Electronic Engineering Beijing100084 China University of Southampton School of Electronics and Computer Science SouthamptonSO17 1BJ United Kingdom

As a superior multicarrier technique utilizing chirp signals for high-mobility communications, affine frequency division multiplexing (AFDM) is envisioned to be a promising candidate for sixth-generation (6G) wireless networks. AFDM is based on the discrete affine Fourier transform (DAFT) with two adjustable parameters of the chirp signals, termed the pre-chirp and post-chirp parameters, respectively. Whilst the post-chirp parameter complies with stringent constraints to combat the time-frequency doubly selective channel fading, we show that the pre-chirp counterpart can be flexibly manipulated for an additional degree of freedom. Therefore, this paper proposes a novel AFDM scheme with the pre-chirp index modulation (PIM) philosophy (AFDM-PIM), which can implicitly convey extra information bits through dynamic pre-chirp parameter assignment, thus enhancing both spectral and energy efficiency. Specifically, we first demonstrate that the subcarrier orthogonality is still maintained by applying distinct pre-chirp parameters to various subcarriers in the AFDM modulation process. Inspired by this property, we allow each AFDM subcarrier to carry a unique pre-chirp signal according to the incoming bits. By such an arrangement, extra bits can be embedded into the index patterns of pre-chirp parameter assignment without additional energy consumption. We derive asymptotically tight upper bounds on the average bit error probability (BEP) of the proposed schemes with the maximum-likelihood detection, and validate that the proposed AFDM-PIM can achieve full diversity under doubly dispersive channels. Based on the derived result, we further propose an optimal pre-chirp alphabet design to enhance the bit error rate (BER) performance via intelligent optimization algorithms. Simulation results demonstrate that the proposed AFDM-PIM outperforms the classical benchmarks. © 2002-2012 IEEE.

关键词： Chirp modulation

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Digital Twin-based 3D Map Management for Edge-assisted Device Pose Tracking in Mobile AR

arXiv

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arXiv 2023年

作者： Zhou, Conghao Gao, Jie Li, Mushu Cheng, Nan Shen, Xuemin Zhuang, Weihua The Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada The School of Information Technology Carleton University OttawaONK1S 5B6 Canada The Department of Electrical Computer and Biomedical Engineering Toronto Metropolitan University TorontoONM5B 2K3 Canada The School of Telecommunications Engineering Xidian University Xi’an710071 China

Edge-device collaboration has the potential to facilitate compute-intensive device pose tracking for resource-constrained mobile augmented reality (MAR) devices. In this paper, we devise a 3D map management scheme for edge-assisted MAR, wherein an edge server constructs and updates a 3D map of the physical environment by using the camera frames uploaded from an MAR device, to support local device pose tracking. Our objective is to minimize the uncertainty of device pose tracking by periodically selecting a proper set of uploaded camera frames and updating the 3D map. To cope with the dynamics of the uplink data rate and the user’s pose, we formulate a Bayes-adaptive Markov decision process problem and propose a digital twin (DT)-based approach to solve the problem. First, a DT is designed as a data model to capture the time-varying uplink data rate, thereby supporting 3D map management. Second, utilizing extensive generated data provided by the DT, a model-based reinforcement learning algorithm is developed to manage the 3D map while adapting to these dynamics. Numerical results demonstrate that the designed DT outperforms Markov models in accurately capturing the time-varying uplink data rate, and our devised DT-based 3D map management scheme surpasses benchmark schemes in reducing device pose tracking uncertainty. Copyright © 2023, The Authors. All rights reserved.

关键词： Cameras

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Comparison of Classification Algorithms for Alzheimer’s Disease Prediction

Comparison of Classification Algorithms for Alzheimer’s Dis...

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International Conference on Parallel Distributed and Grid Computing (PDGC)

作者： Harshit Saxena Divyansh Joshi Hari Singh Rohit Anand Computer Science & Engineering Department Jaypee University of Information Technology Solan Himachal Pradesh India ECE Department G.B.Pant DSEU Okhla-1 Campus New Delhi India

ISBN: (纸本)9781665454025

The application of machine learning to investigate metabolic disorders like diabetes and Alzheimer's Disease (AD), which impact significant populations of people worldwide, is currently receiving a lot of attention. Early on, AD was challenging to predict. The method of identifying the parameters that were most effective for predicting Alzheimer's disease involved the use of decision trees, support vector machines, XGBoost, random forest, extra trees, AdaBoost, gradient boosting, and voting classifiers. In this research, the Extra Trees classifier has been used which, from our research, has rarely been explored in the past. This classifier gives the best results in terms of performance among all the implemented techniques. The performance is evaluated using evaluation parameters such as precision, recall, F1-Score and accuracy. Using these machine learning methods, especially the Extra Trees algorithm, for early identification of Alzheimer’s disease will greatly benefit in lowering the annual mortality rate caused by AD.

关键词： Support vector machines Machine learning algorithms Sociology Prediction algorithms Feature extraction Classification algorithms Reliability

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Quantum Parameterized Complexity

arXiv

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arXiv 2022年

作者： Bremner, Michael J. Ji, Zhengfeng Mann, Ryan L. Mathieson, Luke Morales, Mauro E.S. Shaw, Alexis T.E. Centre for Quantum Computation and Communication Technology Centre for Quantum Software and Information School of Computer Science Faculty of Engineering & Information Technology University of Technology SydneyNSW2007 Australia Department of Computer Science and Technology Tsinghua University Beijing China School of Mathematics University of Bristol BristolBS8 1UG United Kingdom

Parameterized complexity theory was developed in the 1990s to enrich the complexity-theoretic analysis of problems that depend on a range of parameters. In this paper we establish a quantum equivalent of classical parameterized complexity theory, motivated by the need for new tools for the classifications of the complexity of real-world problems. We introduce the quantum analogues of a range of parameterized complexity classes and examine the relationship between these classes, their classical counterparts, and well-studied problems. This framework exposes a rich classification of the complexity of parameterized versions of QMA-hard problems, demonstrating, for example, a clear separation between the Quantum Circuit Satisfiability problem and the Local Hamiltonian problem. Copyright © 2022, The Authors. All rights reserved.

关键词： Computational complexity

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Integrated Sensing and Communications: Recent Advances and Ten Open Challenges

arXiv

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arXiv 2023年

作者： Lu, Shihang Liu, Fan Li, Yunxin Zhang, Kecheng Huang, Hongjia Zou, Jiaqi Li, Xinyu Dong, Yuxiang Dong, Fuwang Zhu, Jia Xiong, Yifeng Yuan, Weijie Cui, Yuanhao Hanzo, Lajos Southern University of Science and Technology Shenzhen518055 China The Department of Electronic and Information Engineering The Hong Kong Polytechnic University 999077 Hong Kong The School of Information Science and Engineering Southeast University Nanjing210096 China The School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China The School of Electronics and Computer Science University of Southampton SouthamptonSO17 1BJ United Kingdom

It is anticipated that integrated sensing and communications (ISAC) would be one of the key enablers of next-generation wireless networks (such as beyond 5G (B5G) and 6G) for supporting a variety of emerging applications. In this paper, we provide a comprehensive review of the recent advances in ISAC systems, with a particular focus on their foundations, physical-layer system design, networking aspects and ISAC applications. Furthermore, we discuss the corresponding open questions of the above that emerged in each issue. Hence, we commence with the information theory of sensing and communications (S&C), followed by the information-theoretic limits of ISAC systems by shedding light on the fundamental performance metrics. Next, we discuss their clock synchronization and phase offset problems, the associated Pareto-optimal signaling strategies, as well as the associated super-resolution physical-layer ISAC system design. Moreover, we envision that ISAC ushers in a paradigm shift for the future cellular networks relying on network sensing, transforming the classic cellular architecture, cross-layer resource management methods, and transmission protocols. In ISAC applications, we further highlight the security and privacy issues of wireless sensing. Finally, we close by studying the recent advances in a representative ISAC use case, namely the multi-object multi-task (MOMT) recognition problem using wireless signals. © 2023, CC BY-SA.

关键词： Systems analysis

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Long-Term Rate-Fairness-Aware Beamforming Based Massive MIMO Systems

arXiv

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arXiv 2023年

作者： Zhu, W. Tuan, H.D. Dutkiewicz, E. Fang, Y. Poor, H.V. Hanzo, L. School of Communication and Information Engineering Shanghai University Shanghai200444 China School of Electrical and Data Engineering University of Technology Sydney BroadwayNSW2007 Australia Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States School of Electronics and Computer Science University of Southampton SouthamptonSO17 1BJ United Kingdom

This is the first treatise on multi-user (MU) beamforming designed for achieving long-term rate-fairness in full-dimensional MU massive multi-input multi-output (m-MIMO) systems. Explicitly, based on the channel covariances, which can be assumed to be known beforehand, we address this problem by optimizing the following objective functions: the users’ signal-to-leakage-noise ratios (SLNRs) using SLNR max-min optimization, geometric mean of SLNRs (GM-SLNR) based optimization, and SLNR soft max-min optimization. We develop a convex-solver based algorithm, which invokes a convex subproblem of cubic time-complexity at each iteration for solving the SLNR max-min problem. We then develop closed-form expression based algorithms of scalable complexity for the solution of the GM-SLNR and of the SLNR soft max-min problem. The simulations provided confirm the users’ improved-fairness ergodic rate distributions. Copyright © 2023, The Authors. All rights reserved.

关键词： MIMO systems

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Max-min Rate Optimization of Low-Complexity Hybrid Multi-User Beamforming Maintaining Rate-Fairness

arXiv

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arXiv 2023年

作者： Zhu, W. Tuan, H.D. Dutkiewicz, E. Poor, H.V. Hanzo, L. School of Communication and Information Engineering Shanghai University Shanghai200444 China School of Electrical and Data Engineering University of Technology Sydney BroadwayNSW2007 Australia Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States School of Electronics and Computer Science University of Southampton SouthamptonSO17 1BJ United Kingdom

A wireless network serving multiple users in the millimeter-wave or the sub-terahertz band by a base station is considered. High-throughput multi-user hybrid-transmit beamforming is conceived by maximizing the minimum rate of the users. For the sake of energy-efficient signal transmission, the array-of-subarrays structure is used for analog beamforming relying on low-resolution phase shifters. We develop a convex-solver based algorithm, which iteratively invokes a convex problem of the same beamformer size for its solution. We then introduce the soft max-min rate objective function and develop a scalable algorithm for its optimization. Our simulation results demonstrate the striking fact that soft max-min rate optimization not only approaches the minimum user rate obtained by max-min rate optimization but it also achieves a sum rate similar to that of sum-rate maximization. Thus, the soft max-min rate optimization based beamforming design conceived offers a new technique of simultaneously achieving a high individual quality-of-service for all users and a high total network throughput. Copyright © 2023, The Authors. All rights reserved.

关键词： Beamforming

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Field extraction and logo recognition on indian bank cheques using convolution neural networks 8th

Field extraction and logo recognition on indian bank cheques...

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8th International Conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2020

作者： Vishnuvardhan, Gopireddy Ravi, Vadlamani Mallik, Amiya Ranjan Center of Excellence in Analytics Institute for Development and Research in Banking Technology Castle Hills Road 1 Masab Tank Hyderabad500057 India School of Computer and Information Sciences University of Hyderabad Hyderabad500046 India Department of Computer Science Engineering IIIT Bhubaneswar Bhubaneswar751003 India

ISBN: (纸本)9789811556784

A large number of bank checks are processed manually every day, across the world. In a developing nation like India, cheques are significant instruments for achieving cashless transactions. Cheque processing is a tedious task that can be automated with advanced deep learning architectures. Cheque automation involves selecting the Regions Of Interest (ROI) and then analyzing the contents in the ROI. In this paper, we propose a novel approach to extract ROI (fields) on the cheque using a Convolutional Neural Network (CNN)-based object detection algorithms like YOLO. By virtue of employing a CNN-based model, our approach turns out to be scale, skew, and shift invariant. We achieved a mean average precision (mAP) score of 86.6% across all the fields on a publicly available database of cheques. On the extracted logo field from YOLO, we performed logo recognition using VGGnet as a feature extractor and achieved an accuracy of 99.01%. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021.

关键词： Extraction

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