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Fundamental Limits of Two-Hop MIMO Channels: An Asymptotic Approach

IEEE Transactions on Information Theory 2025年第6期71卷 4069-4111页

作者： Zhuang, Zeyan Zhang, Xin Xu, Dongfang Song, Shenghui The Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong The Hong Kong University of Science and Technology Division of Integrative Systems and Design Hong Kong

Multi-antenna relays and intelligent reflecting surfaces (IRSs) have been utilized to construct favorable channels to improve the performance of wireless systems. A common feature between relay systems and IRS-aided systems is the two-hop multiple-input multiple-output (MIMO) channel. As a result, the mutual information (MI) of two-hop MIMO channels has been widely investigated with very engaging results. However, a rigorous investigation on the fundamental limits of two-hop MIMO channels, i.e., the first and second-order analysis, is not yet available in the literature, due to the difficulties caused by the two-hop (product) channel and the noise introduced by the relay (active IRS). In this paper, we employ large random matrix theory, specifically Gaussian tools, to derive the closed-form deterministic approximation for the mean and variance of the MI. Additionally, we determine the convergence rate for the mean, variance and the characteristic function of the MI, and prove the asymptotic Gaussianity. Furthermore, we also investigate the analytical properties of the fundamental equations that describe the closed-form approximation and prove the existence and uniqueness of the solution. An iterative algorithm is then proposed to obtain the solutions for the fundamental equations. Numerical results validate the accuracy of the theoretical analysis. © 1963-2012 IEEE.

关键词： Iterative methods

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TECMH:Transformer-Based Cross-Modal Hashing For Fine-Grained Image-Text Retrieval

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computers, Materials & Continua 2023年第5期75卷 3713-3728页

作者： Qiqi Li Longfei Ma Zheng Jiang Mingyong Li Bo Jin College of Computer and Information Science Chongqing Normal UniversityChongqing401331China Institute of Systems and Robotics(ISR) Department of Electrical and Computer Engineering(DEEC)University of CoimbraCoimbraPortugal

In recent years,cross-modal hash retrieval has become a popular research field because of its advantages of high efficiency and low ***-modal retrieval technology can be applied to search engines,crossmodalmedical processing,*** existing main method is to use amulti-label matching paradigm to finish the retrieval ***,such methods do not use fine-grained information in the multi-modal data,which may lead to suboptimal *** avoid cross-modal matching turning into label matching,this paper proposes an end-to-end fine-grained cross-modal hash retrieval method,which can focus more on the fine-grained semantic information of multi-modal ***,the method refines the image features and no longer uses multiple labels to represent text features but uses BERT for ***,this method uses the inference capabilities of the transformer encoder to generate global fine-grained ***,in order to better judge the effect of the fine-grained model,this paper uses the datasets in the image text matching field instead of the traditional label-matching *** article experiment on Microsoft COCO(MS-COCO)and Flickr30K datasets and compare it with the previous *** experimental results show that this method can obtain more advanced results in the cross-modal hash retrieval field.

关键词： Deep learning cross-modal retrieval hash learning transformer

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Parameter Tuning in Automatic Detection Algorithm for Detecting Artificial Brain Shift 24

Parameter Tuning in Automatic Detection Algorithm for Detect...

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6th International Conference on Intelligent Medicine and Image Processing, IMIP 2024

作者： Watanabe, Kaoru Noborio, Hiroshi Koeda, Masanao Department of Computer Science Osaka Electro-Communication University Japan Faculty of Computer Science and Systems Engineering Okayama Prefectural University Japan

ISBN: (纸本)9798400710032

In neurosurgery to remove brain tumors, DICOM data, a medical imaging standard, is generated preoperatively using CT and MRI. This data is used for surgical planning. However, brain deformation problems, known as brain shift, occur during surgery, resulting in discrepancies from the preoperative surgical plan. Deviations from the surgical plan due to brain shift are a life-threatening problem as they reduce the success rate of surgery. Brain shifts have not yet been elucidated, and DICOM data acquired from MRIs and CTs for surgical planning remain stored and archived in hospital information centers. Here, it is planned to extract the physical parameters of the brain model from a large amount of brain deformation data using pre- and post-operative brain DICOM pairs, which are stored in large quantities in hospitals and discarded after a particular time. This study investigates the optimization of parameters in the brain shift detection algorithm. © 2024 ACM.

关键词： Digital Imaging and Communications in Medicine (DICOM)

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Fuzzy Decision-Based Clustering for Efficient Data Aggregation in Mobile UWSNs

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computers, Materials & Continua 2025年第4期83卷 259-279页

作者： Aadil Mushtaq Pandith Manni Kumar Naveen Kumar Nitin Goyal Sachin Ahuja Yonis Gulzar Rashi Rastogi Rupesh Gupta Department of Computer Science and Engineering Lovely Professional UniversityJalandhar144402PunjabIndia Department of Computer Science and Engineering Chandigarh UniversityMohali140413PunjabIndia Salesforce Inc. DallasTX 75201USA Department of Computer Science and Engineering School of Engineering and TechnologyCentral University of HaryanaMahendragarh123031HaryanaIndia Department of Management Information Systems College of Business AdministrationKing Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Computer Application Sir Chhotu Ram Institute of Engineering and TechnologyCh.Charan Singh UniversityMeerut250001Uttar PradeshIndia Chitkara University Institute of Engineering and Technology Chitkara UniversityRajpura140401PunjabIndia

Underwater wireless sensor networks(UWSNs)rely on data aggregation to streamline routing operations by merging information at intermediate nodes before transmitting it to the ***,many existing data aggregation techniques are designed exclusively for static networks and fail to reflect the dynamic nature of underwater ***,conventional multi-hop data gathering techniques often lead to energy depletion problems near the sink,commonly known as the energy hole ***,cluster-based aggregation methods face significant challenges such as cluster head(CH)failures and collisions within clusters that degrade overall network *** address these limitations,this paper introduces an innovative framework,the Cluster-based Data Aggregation using Fuzzy Decision Model(CDAFDM),tailored for mobile *** proposed method has four main phases:clustering,CH selection,data aggregation,and *** CH selection,a fuzzy decision model is utilized to ensure efficient cluster head selection based on parameters such as residual energy,distance to the sink,and data delivery likelihood,enhancing network stability and energy *** the aggregation phase,CHs transmit a single,consolidated set of non-redundant data to the base station(BS),thereby reducing data duplication and saving *** adapt to the changing network topology,the re-clustering phase periodically updates cluster formations and reselects *** results show that CDAFDM outperforms current protocols such as CAPTAIN(Collection Algorithm for underwater oPTical-AcoustIc sensor Networks),EDDG(Event-Driven Data Gathering),and DCBMEC(Data Collection Based on Mobile Edge Computing)with a packet delivery ratio increase of up to 4%,an energy consumption reduction of 18%,and a data collection latency reduction of 52%.These findings highlight the framework’s potential for reliable and energy-efficient data aggregation mobile UWSNs.

关键词： Clustering data aggregation data collection fuzzy model monitoring UWSN

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Mango Disease Detection Using Fused Vision Transformer with ConvNeXt Architecture

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computers, Materials & Continua 2025年第4期83卷 1023-1039页

作者： Faten S.Alamri Tariq Sadad Ahmed S.Almasoud Raja Atif Aurangzeb Amjad Khan Department of Mathematical Sciences College of SciencePrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science University of Engineering&TechnologyMardan23200Pakistan AIDA Lab Department of Information SystemsCollege of Computer&Information SciencesPrince Sultan UniversityRiyadh12435Saudi Arabia Department of Computer Science International Islamic UniversityIslamabad44000Pakistan

Mango farming significantly contributes to the economy,particularly in developing ***,mango trees are susceptible to various diseases caused by fungi,viruses,and bacteria,and diagnosing these diseases at an early stage is crucial to prevent their spread,which can lead to substantial *** development of deep learning models for detecting crop diseases is an active area of research in smart *** study focuses on mango plant diseases and employs the ConvNeXt and Vision Transformer(ViT)*** datasets were *** first,MangoLeafBD,contains data for mango leaf diseases such as anthracnose,bacterial canker,gall midge,and powdery *** second,SenMangoFruitDDS,includes data for mango fruit diseases such as Alternaria,Anthracnose,Black Mould Rot,Healthy,and Stem and *** datasets were obtained from publicly available *** proposed model achieved an accuracy of 99.87%on the MangoLeafBD dataset and 98.40%on the MangoFruitDDS *** results demonstrate that ConvNeXt and ViT models can effectively diagnose mango diseases,enabling farmers to identify these conditions more *** system contributes to increased mango production and minimizes economic losses by reducing the time and effort needed for manual ***,the proposed system is integrated into a mobile application that utilizes the model as a backend to detect mango diseases instantly.

关键词： ConvNeXt model fusion mango disease smart agriculture vision transformer

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A decision-making model for self-driving vehicles based on GPT-4V, federated reinforcement learning, and blockchain

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Neural Computing and Applications 2024年第34期36卷 21545-21560页

作者： Alam, Tanweer Gupta, Ruchi Ahamed, N. Nasurudeen Ullah, Arif Department of Computer Science Faculty of Computer and Information Systems Islamic University of Madinah P.O.Box. 170 Madinah42351 Saudi Arabia Department of Information Technology Ajay Kumar Garg Engineering College Ghaziabad Abdul Kalam Technical University Lucknow India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Department of Computer Science Faculty of Computing and Artificial Intelligence Air University Islamabad Pakistan

Decision-making is crucial in fully autonomous vehicle operations and is expected to greatly influence future transportation systems. Observing the current driving status of autonomous vehicles is vital for its decision-making process. The autonomous connected vehicles on the road send significant data about their movements to the server to maintain continuous training. With the Proof of Authority (PoA) consensus process, blockchain technology provides a valid, decentralised and secure option to improve transactions throughput and minimise delay. The limited computational capacity of vehicles poses a challenge in achieving high accuracy and low latency while training self-driving algorithms. GPT-4V surpassed challenging autonomous systems in scene interpretation and causal thinking. GPT-4V has ability to navigate circumstances without access to database, interpret intentions, and make sound decisions in real-world driving scenarios. The reward function and different driving conditions are organised to allow an optimal search to find the most efficient driving style while ensuring safety. The consequences of the Blockchain-enabled decision-making model (DMM) for Self-Driving Vehicles (SDV) primarily based on GPT-4V and Federated Reinforcement Learning (FRL) would, likely, upgrades in decision-making accuracy, operational performance, statistics integrity, and potentially enhanced learning skills in SDV. Integrating blockchain technology, superior language modelling GPT-4V and FRL may lead to multiplied safety, reliability, and decision-making ability in SDV. This study utilised the Simulation of Urban MObility (SUMO) simulator to assess the ability of SDV to maintain its desired speed consistently and securely in a highway setting using proposed DMM. This study indicates that the suggested DMM, utilising the driving state evaluation approach for SDV, can help these vehicles operate safely and effectively. The performance of the proposed model, such as CPU utilisation

关键词： Reinforcement learning

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Cross-Layer Management Framework for Enhancing XR-Based System Security in Zero-Trust Wireless Communications

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IEEE Journal on Selected Areas in Communications 2025年第6期43卷 2011-2024页

作者： Ghourab, Esraa M. Azab, Mohamed Gracanin, Denis Al-Qutayri, Mahmoud Muhaidat, Sami Khalifa University 6G Center Department of Computer Science Abu Dhabi127788 United Arab Emirates Virginia Military Institute Department of Computer and Information Sciences VA United States Virginia Tech Department of Computer Science BlacksburgVA24060 United States Center Department of Computer and Information Engineering Abu Dhabi127788 United Arab Emirates Khalifa University 6G Center Department of Computer and Information Engineering Abu Dhabi127788 United Arab Emirates Carleton University Department of Systems and Computer Engineering OttawaONK1S 5B6 Canada

Extended reality (XR) and 6G networks are set to transform mobile immersive experiences, with privacy and security being paramount in XR communications. Achieving secure and reliable XR experiences while meeting high-resolution and low-latency requirements is challenging for wireless networks. A novel security-aware cross-layer communication management framework is proposed, employing zero-trust spatiotemporal physical layer level manipulations for moving-target defense. Driven by deep reinforcement learning and real-time monitoring, the proposed framework adaptively reprograms the network configuration to maximize the user’s quality of experience (QoE), reduce the overall latency, and minimize the attacker’s intercept probability. The framework was evaluated in a simulated scenario featuring an indirect multi-hop communication setup. The results show that the proposed framework effectively and efficiently secures XR user communications while maintaining QoE, outperforming conventional Q-learning algorithms. © 1983-2012 IEEE.

关键词： Resource allocation

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Resource Based Automatic Calibration System (RBACS) Using Kubernetes Framework

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Intelligent Automation & Soft Computing 2023年第1期35卷 1165-1179页

作者： Tahir Alyas Nadia Tabassum Muhammad Waseem Iqbal Abdullah S.Alshahrani Ahmed Alghamdi Syed Khuram Shahzad Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan Department of Computer Science&IT Virtual University of PakistanLahore54000Pakistan Department of Software Engineering The Superior UniversityLahore54000Pakistan Department of Computer Science&Artificial Intelligence College of Computer Science&EngineeringUniversity of Jeddah21493Saudi Arabia Department of Software Engineering College of Computer Science and EngineeringUniversity of Jeddah21493Saudi Arabia Department of Informatics&Systems University of Management&TechnologyLahore54000Pakistan

Kubernetes,a container orchestrator for cloud-deployed applications,allows the application provider to scale automatically to match thefluctuating intensity of processing *** cluster technology is used to encapsulate,isolate,and deploy applications,addressing the issue of low system reliability due to interlocking ***-based platforms usually entail users define application resource supplies for eco container *** is a constant problem of over-service in data centers for cloud service *** operating costs and incompetent resource utilization can occur in a waste of *** revolutionized the orchestration of the container in the cloud-native *** can adaptively manage resources and schedule containers,which provide real-time status of the cluster at runtime without the user’s *** clusters face unpredictable traffic,and the cluster performs manual expansion configuration by the *** to operational delays,the system will become unstable,and the service will be *** work proposed an RBACS that vigorously amended the distribution of containers operating in the entire Kubernetes *** allocation pattern is analyzed with the Kubernetes *** estimate the overall cost of RBACS,we use several scientific benchmarks comparing the accomplishment of container to remote node migration and on-site *** experiments ran on the simulations to show the method’s effectiveness yielded high precision in the real-time deployment of resources in eco *** to the default baseline,Kubernetes results in much fewer dropped requests with only slightly more supplied resources.

关键词： Docker container virtualization cloud resource kubernetes

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A Hybrid Attention-Based Residual Unet for Semantic Segmentation of Brain Tumor

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computers, Materials & Continua 2023年第7期76卷 647-664页

作者： Wajiha Rahim Khan Tahir Mustafa Madni Uzair Iqbal Janjua Umer Javed Muhammad Attique Khan Majed Alhaisoni Usman Tariq Jae-Hyuk Cha Department of Computer Science COMSATS University IslamabadIslamabad45550Pakistan Department of Electrical and Computer Engineering COMSATS University IslamabadWah CampusPakistan Department of Computer Science HITEC UniversityTaxilaPakistan Department of Computer Science College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Management Information Systems CoBAPrince Sattam Bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia Department of Computer Science Hanyang UniversitySeoul04763Korea

Segmenting brain tumors in Magnetic Resonance Imaging(MRI)volumes is challenging due to their diffuse and irregular ***,2D and 3D deep neural networks have become famous for medical image segmentation because of the availability of labelled ***,3D networks can be computationally expensive and require significant training *** research proposes a 3D deep learning model for brain tumor segmentation that uses lightweight feature extraction modules to improve performance without compromising contextual information or *** proposed model,called Hybrid Attention-Based Residual Unet(HA-RUnet),is based on the Unet architecture and utilizes residual blocks to extract low-and high-level features from MRI *** and Squeeze-Excitation(SE)modules are also integrated at different levels to learn attention-aware features adaptively within local and global receptive *** proposed model was trained on the BraTS-2020 dataset and achieved a dice score of 0.867,0.813,and 0.787,as well as a sensitivity of 0.93,0.88,and 0.83 for Whole Tumor,Tumor Core,and Enhancing Tumor,on test dataset *** results show that the proposed HA-RUnet model outperforms the ResUnet and AResUnet base models while having a smaller number of parameters than other state-of-the-art ***,the proposed HA-RUnet model can improve brain tumor segmentation accuracy and facilitate appropriate diagnosis and treatment planning for medical practitioners.

关键词： MRI volumes residual Unet BraTs-2020 squeeze-excitation(SE)

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Energy Efficiency in Sustainable Software Development: Clean Code Approaches 4

Energy Efficiency in Sustainable Software Development: Clean...

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4th International Conference on Electrical engineering and computer science, ICECOS 2024

作者： Inayatullah Widhiarso, Wijang Stiawan, Deris Universitas Multi Data Palembang Faculty of Computer Science Department Program in Engineering Science Palembang Indonesia Universitas Sriwijaya Faculty Af Computer Science Department Program in Engineering Science Palembang Indonesia Universitas Multi Data Palembang Faculty of Computer Science Palembang Indonesia Universitas Sriwijaya Faculty Af Computer Science Palembang Indonesia

ISBN: (纸本)9798350368253

The use of technology and information devices contributes to global warming. This issue has also become a concern for UN institutions, as stated in international environmental agreements, which aim to stabilize greenhouse gas emissions in the atmosphere at a level that prevents anthropogenic and dangerous disturbances to the environmental system. This research uses a line of code derived from the tanjak dataset, a student project from 2003 to 2020, numbering 500. This research aims to apply energy consumption efficiency to reduce CO2 emissions produced using computer devices, especially in developing software. The approach used in this research is clean code, which can provide benefits in the form of a line of code that is easy to read, understand, test, and maintain so that it can create savings in energy use. In the test, the line of code used was 1,072,089, producing an energy of 82,152.18 Joules, minus the line of code comments, which was 76,750, producing an energy of 2,971.25 Joules. Initial research results show that the clean code approach can be used to save energy usage, with the result of the line of code used being 995,339 and producing 79,180 Joules of energy, so it can affect reducing CO2 emissions when implementing software development. © 2024 IEEE.

关键词： Greenhouse gas emissions

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