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2024 28th International Conference on Information Technology, IT 2024

作者： Javidi, Kamran Martini, Maria G. Kara, Peter A. School of Computer Science and Mathematics Faculty of Engineering Computing and the Environment Kingston University Wireless Multimedia and Networking Research Group Department of Computer Science Penrhyn Road Campus Kingston upon Thames LondonKT1 2EE United Kingdom Budapest University of Technology and Economics Faculty of Electrical Engineering and Informatics Department of Networked Systems and Services Budapest1111 Hungary

ISBN: (纸本)9798350369618

Light field visualization commonly provides a single content over the entire field of view. However, the angularly-selective nature of the technology enables the simultaneous visualization of different contents at different viewing angles. Yet segmenting the valid viewing area comes with content interference as well in forms of separation zones, resulting invalid viewing domains. In this paper, we introduce a study on the separation zone of split-domain light field visualization. We created a static-content-based scenario in which we split the valid viewing area into two distinct domains, with a separation zone in between them. This was achieved by merging two light field contents in the middle with an instantaneous switch. The resulting visualization on a light field display was captured by a DSLR camera from two viewing distances, and it was compared to the crosstalk effect caused by insufficient angular resolution. © 2024 IEEE.

关键词： Visualization

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Empowering Smart Homes with Fog Computing for IoT Connectivity

Empowering Smart Homes with Fog Computing for IoT Connectivi...

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2024 International Conference on Decision Aid Sciences and Applications, DASA 2024

作者： Ahmed, Amjed Abbas Najim, Ali Hamza Mahdi, Ali Jafer Ali Alheeti, Khattab M. Mohd Satar, Nurhizam Safie Abdallah Hashim, Aisha Hassan Center for Cyber Security Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Bangi Malaysia Department of Computer Techniques Engineering Baghdad Iraq Department of Computer Technical Engineering Al-Diwaniyah Iraq AI-Zahraa University for Women Department of Scientific Affairs Bangi43600 Malaysia College of Computer and Information Technology University of Anbar Computer Networking Systems Department Anbar Iraq Cyber Security Faculty of Information Science and Technology Bangi43600 Malaysia International Islamic University Malaysia Faculty of Engineering Department of Electrical and Computer Engineering Selangor Malaysia

ISBN: (纸本)9798350369106

Innovative grid technology leverages Information and Communication Technology (ICT) to enhance energy efficiency and mitigate losses. This paper introduces a 'novel three-tier hierarchical framework for smart homes within the Internet of Things (IoT)' ecosystem, addressing the current shortage of intelligent solutions that underutilize IoT advantages. Our framework's primary objective is to elevate smart homes to the microgrid level, enabling the seamless integration from the microgrid's renewable distributed energy sources and attaining maximum energy efficiency. In addition to traditional data processing, we propose the application of fog computing in smart homes. Through a rigorous simulation using real smart meter datasets, our study demonstrates that fog computing, mainly when using predictive filtering, may drastically cut down on the amount of data being sent and the load on the home's network. This innovation holds immense potential for revolutionizing energy management and resource allocation in the context of smart homes, thus contributing to a sustainable and efficient future. This research represents a critical step towards harnessing the power of IoT and fog computing in smart grids and homes, offering a promising path towards more intelligent, eco-friendly, and energy-efficient living spaces. © 2024 IEEE.

关键词： Smart homes

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Pushing AI to wireless network edge: an overview on integrated sensing, communication, and computation towards 6G

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Science China(Information Sciences) 2023年第3期66卷 7-25页

作者： Guangxu ZHU Zhonghao LYU Xiang JIAO Peixi LIU Mingzhe CHEN Jie XU Shuguang CUI Ping ZHANG Shenzhen Research Institute of Big Data Future Network of Intelligence Institute (FNii) The Chinese University of Hong Kong (Shenzhen) School of Science and Engineering (SSE) The Chinese University of Hong Kong (Shenzhen) State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Peking University Department of Electrical and Computer Engineering and Institute for Data Science and Computing University of Miami Peng Cheng Laboratory State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications

Pushing artificial intelligence(AI) from central cloud to network edge has reached board consensus in both industry and academia for materializing the vision of artificial intelligence of things(AIoT) in the sixth-generation(6G) era. This gives rise to an emerging research area known as edge intelligence, which concerns the distillation of human-like intelligence from the vast amount of data scattered at the wireless network edge. Typically, realizing edge intelligence corresponds to the processes of sensing, communication,and computation, which are coupled ingredients for data generation, exchanging, and processing, ***, conventional wireless networks design the three mentioned ingredients separately in a task-agnostic manner, which leads to difficulties in accommodating the stringent demands of ultra-low latency, ultra-high reliability, and high capacity in emerging AI applications like auto-driving and metaverse. This thus prompts a new design paradigm of seamlessly integrated sensing, communication, and computation(ISCC) in a taskoriented manner, which comprehensively accounts for the use of the data in downstream AI tasks. In view of its growing interest, this study provides a timely overview of ISCC for edge intelligence by introducing its basic concept, design challenges, and enabling techniques, surveying the state-of-the-art advancements, and shedding light on the road ahead.

关键词： sixth-generation (6G) edge intelligence artificial intelligence of things (AIoT) integrated sensing communication and computation (ISCC)

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Key-Value Database Access Optimization for PMIx Standard Implementation

Key-Value Database Access Optimization for PMIx Standard Imp...

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2021 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology, USBEREIT 2021

作者： Polyakov, Artem Y. Efimov, Alexandr V. Kramarenko, Konstantin E. Pavsky, Kirill V. Networking SW Sys Arch NVIDIA Corporation Santa ClaraCA United States Computer Systems Laboratory of ISP SB RAS Computer Systems Department of SibSUTIS Novosibirsk Russia

ISBN: (纸本)9781728176918

This paper outlines our results in the field of distributed computing systems software stacks based on Process Management Interface eXascale (PMIx). PMIx standard represents the runtime environment information as a Key-Value Database (KVDb) thatis typical lylocatedin the shared memory of the compute nodes. An important specific of PMIx is the parallel and intensive read accesses to the KVDb with relatiwly infrequent updates. Locking schemes for synchronizing access to shared memory database with a predominance of reading operations are considered. In this paper, we propose two modifications of the reviewed locking scheme aimed at optimizing the throughput in the concurrent read-write access mode. The reduction in overhead is achieved by improving the signaling method, which reduces the number of atomic operations required to access the KVDb. A micro-benchmark has been developed that allows evaluating the performance of locking schemes. The results of the experiments showed an increase in the throughput of the synchronization scheme for both read and write operations. © 2021 IEEE.

关键词： Database systems

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Statistical Delay and Error-Rate Bounded QoS Provisioning for RSMA Based 6G Mobile Wireless Networks in the Non-Asymptotic Regime

Statistical Delay and Error-Rate Bounded QoS Provisioning fo...

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IEEE International Conference on Communications (ICC)

作者： Xi Zhang Qixuan Zhu H. Vincent Poor Department of Electrical and Computer Engineering Networking and Information Systems Laboratory Texas A&M University College Station TX USA Department of Electrical and Computer Engineering Princeton University Princeton NJ USA

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

The upcoming 6G mobile wireless networks are expected to support massive ultra-reliable and low-latency communications (mURLLC), which is an emerging service that demands more stringent requirements than the fifth generation (5G) wireless networks on delay and error-rate bounded quality-of-services (QoS) with massive connectivity. Finite blocklength coding (FBC) techniques based short packets communication techniques have been shown to be able to support both the statistical delay and error-rate bounded QoS provisioning. Rate splitting (RS) multiple access schemes have been proposed to address the sum degree-of-freedom loss problem over massive multiple-input and multiple-output channels when massive mobile users request to access the network. However, how to support the statistical delay and error-rate bounded QoS provisioning using FBC among massive mobile users to enable the massive access has not been sufficiently studied. In this paper, we propose to integrate FBC with RS techniques to achieve mURLLC transmissions. First, we define the ∊-effective capacity to measure the performance of statistical delay and error-rate bounded provisioning, and obtain a closed-form ∊-effective capacity under the RS scheme. Then, we maximize the aggregate ∊-effective capacity over all mobile users by deriving an optimal transmit power allocation strategy for the RS scheme. Finally, using numerical analyses, we validate and evaluate our proposed RS schemes to support statistical delay and error-rate bounded QoS provisioning over 6G communication networks in the non-asymptotic regime.

关键词： 6G mobile communication Numerical analysis Error probability Wireless networks Aggregates Measurement uncertainty Quality of service

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Methods for Predictive Performance Improvement of Deep Learning systems for Aerial Building Roof Detection with Multispectral Images

Methods for Predictive Performance Improvement of Deep Learn...

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IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement systems and Applications

作者： A. Churi D. B. Megherbi Department of Electrical and Computer Engineering Center for Human/Machine Intelligence Networking and Distributed Systems University of Massachusetts Lowell MA USA

In this paper, we present a dilated convolutional neural network-based deep learning system for the challenging problem of building accurate roof pixel detection from city aerial multispectral images in the visible and infrared spectral bands. The proposed system uses an automatic spectral band selection strategy and an automatic histogram-based threshold-finding method to improve the network's prediction accuracy performance. Current existing systems train a single model on all the cities of interest data at once. These systems also usually use only RGB bands and avoid post-processing of the predicted images for increased prediction accuracies. In this paper, we present the following solutions to these object detection challenges, 1) we propose and implement a separate model for each city dataset, 2) we propose an optimal band selection strategy to choose the most informative visible-infrared bands from multispectral images, 3) we propose an adaptive and automatic histogram-based optimal threshold finding technique for building roof object segmentation. Our results show that having a separate model for each city rather than only one model for all the combined cities increases the system's performance by 20.77 % in building roof pixel prediction in any of the cities considered. We show that our system also has a 35.6% increase in F1 score compared to its state-of-the-art counterpart, the winning implementation of the SpaceNet building detection national challenge, which took place in 2017.

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Lung Cancer Detection Using Machine Learning and Deep Learning Models

Lung Cancer Detection Using Machine Learning and Deep Learni...

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IEEE SSD International Multi-Conference on systems, Signals and Devices

作者： Khattab M. Ali Alheeti Tabreer T. Al-Shouka Saleem Hamad Majeed Amjed Abbas Ahmed Computer Networking Systems Department College of Computer and Information Technology University of Anbar Anbar Iraq Computer Sciences Department College of Computer and Information Technology University of Anbar Anbar Iraq Department of Computer Techniques Engineering Imam Al-Kadhum College (IKC) Baghdad Iraq

ISBN: (数字)9798350374131

ISBN: (纸本)9798350374148

Technology and artificial intelligence play a significant role in improving healthcare and enable tasks to be automated. In addition, the diseases can be better understood and diagnosed faster, saving time and reducing costs. This study examines the impact of transfer learning models on the effectiveness of deep learning models in classifying lung cancer through the analysis of CT scan images. Additionally, it investigates the relative performance of various machine learning and deep learning models, encompassing Support Vector Machine (SVM) and convolutional neural net-works (CNN) such as InceptionV3, VGG16, Xception, ResNet50, and MobileNetV2, in the early detection of lung cancer based on CT scan images. The SVM model achieved an overall accuracy of 89% after preprocessing, the proposed approach was applied to five pre-trained models (ResNet50, In-ceptionV3, VGG16, Xception, MobileNetV2) using the dataset: Chest CT-Scan; Among the pre-trained CNN models, the Mo-bileNetV2 model achieved the highest accuracy of 98% and the lowest test loss, indicating it performed the best. The Xception model achieved the second-highest accuracy of 97%. The image pre-processing phase plays a significant role in im-proving system performance in terms of improving image contrast and increasing processing speed.

关键词： Support vector machines Deep learning Analytical models Computed tomography Transfer learning Lung cancer Convolutional neural networks

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An Efficient Symbol Timing Scheme for OFDM systems Using Optimal Correlation-Based Circular-Shifted Preamble

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IEEE Wireless Communications Letters 2024年 1-1页

作者： Yang, Fan Zhang, Xi University of Electronic Science and Technology of China Chengdu 611731 China Networking and Information Systems Laboratory Department of Electrical and Computer Engineering Texas A&M University College Station TX 77843 USA

We propose a coarse symbol timing scheme for wireless orthogonal frequency division multiplexing (OFDM) systems using an optimal correlation-based circular-shifted preamble (CSP). Our proposed scheme is implemented by off-line correlation-ranking and on-line processing. The on-line processing derives an optimal CSP for transmission according to the off-line correlation-ranking, and employs a delayed multiplication operation for symbol timing. The conducted simulation results show that our proposed scheme outperforms the other existing representative schemes over typical multi-path fading channels in terms of mean square error (MSE) while keeping the computational complexity low. IEEE

关键词： Orthogonal frequency division multiplexing

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Evaluating Machine Learning Algorithms to Detect and Classify Attacks in IoT

Evaluating Machine Learning Algorithms to Detect and Classif...

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2021 International Conference on Communication and Information Technology, ICICT 2021

作者： Mohammed, Maha M. Alheeti, Khattab M. A. University of Anbar College of Computer and Information Technology Computer Sciences Department Anbar Iraq University of Anbar College of Computer Sciences and Information Technology Computer Networking Systems Department Ramadi Iraq

ISBN: (纸本)9781665439145

The advent of the Internet of Things (IoT) and its applications have piqued the interest of many researchers around the world due to modern technological improvements. IoT enables a considerable number of applications for the benefit of humanity and is one of the fastest emerging network models. It makes devices connected over the Internet so that they can share information automatically. However, devices connected to the Internet face many problems related to security risks and may be exposed to different types of attacks, which endangers the security and privacy of users. In this research, we performed a comparative analysis of Machine Learning (ML) algorithms to detect and classify attacks in IoT devices. To discover the classifier type with better classification accuracy at less execution time, we evaluated the performance of the Naïve Bayes, Bayes Net, and ZeroR algorithms by the UNSW-NB15 dataset. WEKA tool has been used to implement ML algorithms. © 2021 IEEE.

关键词： Internet of things

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Comprehensive Study of Side-Channel Analysis (CyberSecurity)

Comprehensive Study of Side-Channel Analysis (CyberSecurity)

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International Conference on Developments in esystems Engineering (DESE)

作者： Zaid M Obaid Khattab M Ali Alheeti Dept.Computer science College of Computer Sciences and Information Technology University of Anbar Anbar Iraq Computer Networking Systems Department College of Computer Sciences and Information Technology University of Anbar Anbar Iraq

In the ever-evolving environment of digital security, the value of cryptographic systems in preserving data and communications cannot be emphasized. However, the introduction of side-channel attacks has presented a substantial hazard, leveraging minor information breaches from the physical implementations of cryptographic algorithms. This research, via a thorough examination of literature spanning several years, tries to consolidate and classify the diverse approaches employed in side-channel assaults. This research dives into the fundamental weaknesses that these assaults exploit, spanning elements such as power usage, electromagnetic radiation, timing problems, and auditory emissions. To demonstrate the real-world ramifications of side-channel vulnerabilities, the paper presents realistic examples and case studies, providing light on the possible repercussions of these assaults. By classifying side-channel assaults depending on their strategy and identifying important instances within each category, this research elucidates the broad landscape of side-channel threats. It also investigates the severe effect of these attacks on cryptographic systems and its implications for general security, highlighting the critical need for comprehensive countermeasures. This thorough research gives a full evaluation of side-channel assaults, concentrating on their methodology, effect, and the increasing realm of response tactics. Understanding the subtleties of side-channel attacks is crucial to strengthening the security of cryptographic systems. As such, this study offers as a helpful resource for academics, practitioners, and policymakers contending with the evolving environment of side-channel assaults. It emphasizes the continual necessity for study and joint attempts to reinforce cryptography solutions in a more linked culture.

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