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arXiv 2024年

作者： Qiang, Xianke Chang, Zheng Ye, Chaoxiong Hämäläinen, Timo Min, Geyong School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China Faculty of Information Technology University of Jyväskylä P. O. Box 35 JyväskyläFIN-40014 Finland Department of Computer Science University of Exeter ExeterEX4 4QF United Kingdom

To realize ubiquitous intelligence of future vehicular networks, artificial intelligence (AI) is critical since it can mine knowledge from vehicular data to improve the quality of many AI driven vehicular services. By combining AI techniques with vehicular networks, Vehicular Edge Intelligence (VEI) can utilize the computing, storage, and communication resources of vehicles to train the AI models. Nevertheless, when executing the model training, the traditional centralized learning paradigm requires vehicles to upload their raw data to a central server, which results in significant communication overheads and the risk of privacy leakage. In this article, we first overview the system architectures, performance metrics and challenges ahead of VEI design. Then we propose to utilize distribute machine learning scheme, namely split federated learning (SFL), to boost the development of VEI. We present a novel adaptive and parellel SFL scheme and conduct corresponding analysis on its performance. Future research directions are highlighted to shed light on the efficient design of SFL. Copyright © 2024, The Authors. All rights reserved.

关键词： Digital storage

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Natural Language Processing: Bridging the Gap between Human Language and Machine Understanding

Natural Language Processing: Bridging the Gap between Human ...

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Trends in Quantum Computing and Emerging Business Technologies (TQCEBT), International Conference on

作者： Keerthipati Kumar Mohammad Haider Syed Saumya Bhargava Lalit Lalitav Mohakud Mohammad Serajuddin Melanie Lourens Computer Science and Engineering Department S.V. College of Engineering Tirupati Andhra Pradesh Department of Computer Science Saudi Electronic University Indira School Of Business Studies PGDM Pune Department of Education Jadavpur University Kolkata College of Dentistry King Saud University Faculty of Management Sciences Durban University of Technology South Africa

ISBN: (数字)9798350384277

ISBN: (纸本)9798350384284

NLP in relation to deep learning models is focused on the review of NLP evolution from the middle of the 1950s up to present days. Today, NLP remains the pillar on which rests our digital age, connecting speech nuances of a human language and the computational power of machines. It is tracked along with the historical evolution from rule based systems to the paradigm shift to the machine learning decade since the nineties and constellations of the NLP components. The methodology involves using a conventional linguistic model alongside ultra-modern machine learning models to overcome ambiguity as well as contexts associated with other languages. The technical specifics and continuity loops under the Human-in-the-loop (HITL) framework are discussed. For example, they look at words like "uncommon" words, which have cultural implications in showing how involved human speech is. For instance, the strength of NPL can be depicted by being used in diverse sectors like health and social media. Finally, this article illustrates an advanced time when smart NPL units give topnotch solutions proving that interconnection between linguistics, computing and artificial intelligence is ongoing.

关键词： Deep learning Quantum computing Social networking (online) Reviews Computational modeling Knowledge based systems Semantics

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Distributed Information Bottleneck for a Primitive Gaussian Diamond MIMO Channel

arXiv

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arXiv 2023年

作者： Song, Yi Xu, Hao Wong, Kai-Kit Caire, Giuseppe Shamai, Shlomo Faculty of Electrical Engineering and Computer Science Technical University of Berlin Berlin10587 Germany Department of Electronic and Electrical Engineering University College London LondonWC1E7JE United Kingdom Department of Electrical and Computer Engineering Technion-Israel Institute of Technology Haifa3200003 Israel

This paper considers the distributed information bottleneck (D-IB) problem for a primitive Gaussian diamond channel with two relays and MIMO Rayleigh fading. The channel state is an independent and identically distributed (i.i.d.) process known at the relays but unknown to the destination. The relays are oblivious, i.e., they are unaware of the codebook and treat the transmitted signal as a random process with known statistics. The bottleneck constraints prevent the relays to communicate the channel state information (CSI) perfectly to the destination. To evaluate the bottleneck rate, we provide an upper bound by assuming that the destination node knows the CSI and the relays can cooperate with each other, and also two achievable schemes with simple symbol-by-symbol relay processing and compression. Numerical results show that the lower bounds obtained by the proposed achievable schemes can come close to the upper bound on a wide range of relevant system parameters. © 2023, CC BY.

关键词： Channel state information

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Expanding Horizon: The Role of Reconfigurable Intelligent Surfaces in Enabling Massive Connectivity

Expanding Horizon: The Role of Reconfigurable Intelligent Su...

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Information technology and engineering (HITE), Horizons of

作者： Muhammad Shoaib Ayub Pablo Adasme Demostenes Zegarra Rodriguez Muhammad Saadi Thokozani Shongwe Ainur Zhumadillayeva Kanadilovna Department of Electrical Engineering Universidad de Santiago de Chile Santiago Chile Department of Computer Science Federal University of Lavras Brazil Lavras Brazil Department of Computer Science Nottingham Trent University England Department of Electrical & Electronic Engineering Technology University of Johannesburg Johannesburg South Africa Faculty of Information Technology L. N. Gumilev Eurasian National University Astana Kazakhstan

ISBN: (数字)9798331516055

ISBN: (纸本)9798331516062

This comprehensive article explores the integration of reconfigurable intelligent surfaces (RIS) with massive multiple input and multiple output (mMIMO) systems for future wireless networks, focusing on millimeter-wave (mmWave), beyond 5G (B5G), and sixth-generation (6G) technologies. It provides an in-depth understanding of the theoretical foundations of RIS and mMIMO, discussing their advantages, limitations, and unique capabilities in achieving massive connectivity. This article also highlights role of RIS in enhancing the performance of various wireless systems, including IoT, 5G, B5G, and 6G communications. The paper concludes by identifying open challenges and opportunities in RIS for massive connectivity, offering a roadmap for future research in this rapidly evolving field.

关键词： 6G mobile communication Uncertainty Array signal processing 5G mobile communication Reviews Wireless networks Millimeter wave technology Reconfigurable intelligent surfaces Reliability Millimeter wave communication

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Miniaturized Quad-band Implantable Antenna for Wireless Capsule Endoscopy and Deep Tissue Monitoring Applications

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IEEE Transactions on Antennas and Propagation 2025年

作者： Nasir, Jamal Zada, Muhammad Shah, Syed Manaf Ali Basir, Abdul Shah, Izaz Ali Khan, Shahid Koziel, Slawomir Gdansk University of Technology Faculty of Electronics Telecommunication and Informatics Gdansk80-233 Poland Department of Health Sciences and Technology ETH Zurich Biomedical and Mobile Health Technology Lab Lengghalde 5 Zurich8008 Switzerland COMSATS University Department of Electrical and Computer Engineering Abbottabad Campus Islamabad22060 Pakistan Tampere University Faculty of Information Technology and Communication Sciences Tampere33720 Finland Hanyang University Department of Electronic Engineering Seoul04763 Korea Republic of Reykjavik University Department of Engineering Reykjavik102 Iceland

Miniaturized multiband antennas are essential components of implantable biomedical devices (IMDs), enabling them to perform multiple tasks simultaneously in different biomedical applications. This study proposes a highly miniaturized implantable antenna with quad-band characteristics. The implantable antenna’s volume is 6 mm × 6 mm × 0.6 mm. The proposed antenna operates in the Medical Implant Communication System (MICS) band (401–406 MHz), Wireless Medical Telemetry Service (WMTS) (1427—1432 MHz) and Industrial, Scientific, and Medical (ISM) bands (433.1–434.8 MHz, 902–928 MHz, 2400–2483.5 MHz) for multi-tasking, such as data telemetry, wireless power transfer, and control signalling. This design achieves miniaturization using meandered lines, eliminating the need for shortening vias or ground plane slots, typically employed in implantable antennas for size reduction. This arrangement simplifies the structure and minimizes back radiation, enhancing overall performance. The proposed antenna was thoroughly analyzed through simulations integrated into a wireless capsule endoscopy device. A fabricated prototype embedded in minced pork was experimentally validated, demonstrating excellent agreement with the simulated parameters. The presented antenna exhibits a measured -10 dB bandwidth of 76.8, 72, 125, and 215.4 MHz at 402/433, 915, 1410, and 2450 MHz, respectively. Furthermore, it maintains stable measured gains of -44.7, -34, -30.6, and -29.7 dBi across the respective bands of interest. Specific absorption rates in heterogeneous stomach tissues were evaluated at the four resonance frequencies to ensure human safety, showing compliance with the IEEE standards. Furthermore, to evaluate the range of effective communication, a link budget analysis was performed at data rates of 1, 12, and 78 Mbps, demonstrating a minimum effective transmission range of 4.7 m. These features make the proposed miniaturized implantable antenna suitable for various biomedical applications.

关键词： Endoscopy

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A Lightweight and Enhanced Semantic Segmentation Network for Mapping of Retrogressive Thaw Slumps from Sentinel-2 Images

A Lightweight and Enhanced Semantic Segmentation Network for...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Yi Yuan Guiyun Zhou Zhonghua Su Ziyin Liu Weiwei Sun Xiangchao Meng Jie Wen Zhiqiang Wu School of Resources and Environment University of Electronic Science and Technology of China Chengdu China School of Computer and Software Engineering Xihua University Chengdu China Department of Geography and Spatial Information Techniques Ningbo University Ningbo China Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China Sichuan Provincial Public 7 Security Department Communications and Information Technology Headquarters Chengdu China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Fine mapping of retrogressive thaw slumps (RTSs) holds paramount significance in the study of permafrost degradation and carbon exchange. We propose a lightweight and enhanced semantic segmentation network (LessNet) for automatically mapping the RTSs from Sentinel-2 images. LessNet is constructed on the encoder-decoder framework with innovative incorporation of attention mechanism and dual-level semantic features fusion. The lightweight architecture of LessNet eliminates the need for pre-training, and the network hyperparameters are automatically updated based on the training dataset, which allows for fast convergence of supervised learning. Experiments conducted in the Beiluhe region of the Tibetan Plateau highlight the robustness and competitive performance of the model.

关键词： Training Semantic segmentation Supervised learning Transfer learning Semantics Robustness Permafrost

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The effects of Tx./Rx. pointing errors on the performance efficiency of local area optical wireless communication networks

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Journal of Optical Communications 2024年第s1期45卷 s253-s259页

作者： Eid, Mahmoud M. A. Zaki Rashed, Ahmed Nabih Ahammad, Mohd. Sultan Paul, Bikash Kumar Ahmed, Kawsar Department of Electrical Engineering College of Engineering Taif University Taif Saudi Arabia Electronics and Electrical Communications Engineering Department Faculty of Electronic Engineering Menoufia University Menouf32951 Egypt Department of Computer Science and Engineering Mawlana Bhashani Science and Technology University Santosh Tangail1902 Bangladesh Department of Information and Communication Technology Mawlana Bhashani Science and Technology University Santosh Tangail1902 Bangladesh Group of Bio-photomatiχ Mawlana Bhashani Science and Technology University Santosh Tangail1902 Bangladesh Department of Software Engineering Daffodil International University Dhanmondi Sukrabad Dhaka1207 Bangladesh

This study presents the effects of Tx./Rx. pointing errors on the performance efficiency of local area optical wireless communication networks. The received signal power and max Q factor are measured in the presence of vertical-cavity surface-emitting lasers (VCSELs) bias current and modulation current at maximum propagation distance for the wireless network. The signal is enhanced with increasing of bias and modulation peak currents of the VCSEL device. The optimum received power and max. Q factor is also examined at Tx./Rx. the pointing error of 0.1 mrad and propagation reach of 5 km at available bit rates transmission of 10 Gb/s. © 2021 Walter de Gruyter GmbH, Berlin/Boston.

关键词： Surface emitting lasers

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Hysteresis Current Control of Buck-Boost Non-Isolated Onboard Charger for Electric Vehicles

Hysteresis Current Control of Buck-Boost Non-Isolated Onboar...

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Annual Conference of Industrial electronics Society

作者： Hasan Komurcugil Naki Guler Sertac Bayhan Ozan Gulbudak Department of Computer Engineering Eastern Mediterranean University Famagusta Turkey Department of Electrical-Electronic Engineering Technology Faculty Gazi University Ankara Turkey Qatar Environment and Energy Research Institute Doha Qatar Department of Electrical & Electronics Engineering Karabuk University Turkey

This paper proposes a hysteresis current control (HCC) method for a single-inductor buck-boost non-isolated onboard charger for electric vehicles. The charger is capable of working both in the boost and buck modes. The proposed HCC relies on the buck-boost inductor current and its reference which is generated using a proportional-resonant (PR) controller using grid current error. The reference current generated by PR controller is modified to suppress the oscillations in the inductor currents. An active damping by using a virtual resistor connected in series to filter inductor is used which does not require an additional sensor. A proportional-integral (PI) controller is used to generate the amplitude of grid current reference, which is utilized in constant current (CC) and constant voltage (CV) modes. The effectiveness of the proposed control strategy as well as the control method, is investigated by simulation studies by considering two different battery voltage levels (48V and 350V). The results show that the proposed method is able to charge the battery in CV and CC modes. Moreover, the grid current is maintained in unity power factor at a reasonably low total harmonic distortion (THD) which is smaller than the limits recognized by international standards.

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INSWF DNA signal analysis tool: Intelligent noise suppression window filter

INSWF DNA signal analysis tool: Intelligent noise suppressio...

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作者： Ahmad, Muneer Ahmad, Iftikhar Bilal, Muhammad Jolfaei, Alireza Mehmood, Raja Majid Department of Information Systems Faculty of Computer Sciences and Information Technology Universiti Malaya Kuala Lumpur Malaysia Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Department of Computer and Electronic Systems Engineering Hankuk University of Foreign Studies Yongin Korea Republic of Department of Computing Macquarie University SydneyNSW Australia Information and Communication Technology Department School of Electrical and Computer Engineering Xiamen University Malaysia Sepang Malaysia

DNA signals mainly differ from standard digital signals due to their biological data contents. Owing to unique properties of DNA signals the conventional signal processing techniques, such as digital filters, suffers with spectral leakage and results in insignificant noise suppression in DNA sequence analysis. This article presents an intelligent noise suppression window filter (INSWF) for DNA signal analysis. The filter demises the signal by separating high-level frequency contents and by identifying nucleotides with high fuzzy membership contribution at particular locations. The nucleotide contents of signals are later filtered by application of median filtering employing a combination of s-shaped and z-shaped filters. The fundamental characteristic of codons usage that causes uneven nucleotides segmentation has been tackled by finding the best fit of the curve in biological contents of filter. One of the fuzzy correlations existing between codons and median that nucleotides incorporated to reduce the signal noise to a larger magnitude. The INSWF filter outperformed the existing fixed-length digital filters tested over 250 benchmarked and random datasets of various species. A notable enhancement of 45% to 130% was achieved by significantly suppressing signal noise as compared with conventional digital filters in DNA sequence analysis. © 2020 John Wiley & Sons, Ltd.

关键词： Adaptive filtering

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Comparison of Epilepsy Induced by Ischemic Hypoxic Brain Injury and Hypoglycemic Brain Injury using Multilevel Fusion of Data Features

arXiv

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arXiv 2024年

作者： Kadem, Sameer Sami, Noor Elaraby, Ahmed Alyousif, Shahad Jalil, Mohammed Altaee, M. Almusawi, Muntather Ghany, Ismaeel A. Kareem, Ali Kamil Kamalrudin, Massila Ftaiet, Adnan Allwi Dijlah University College Baghdad Iraq Department of Computer Engineering Techniques Mazaya University College Th Qar Iraq Department of Computer Science Faculty of Computer Science and Information South Valley University Qena Egypt Department Of Cybersecurity College of Engineering and Information Technology Buraydah Private Colleges Buraydah Saudi Arabia Department of Electrical and Electronic Engineering College of Engineering Gulf University Sanad26489 Bahrain Research centre Northampton University Faculty of Engineering Department of Electrical and Electronics Engineering University Drive Northampton NN1 5PH United Kingdom Department of Computer Science Alturath University College Baghdad Iraq Department of Medical Engineering Techniques Alfarahidi University Baghdad Iraq College of Technical Engineering The Islamic University Najaf Iraq Computer Technology Engineering College of Engineering Technology Al-Kitab University Iraq Department of Biomedical Engineering Al-Mustaqbal University College Hillah51001 Iraq Faculty of Information & Communication Technology Universiti Teknikal Malaysia Melaka Durian Tunggal Melaka75450 Malaysia Medical Instruments Engineering Techniques National University of Science and Technology Thi Qar Iraq

The study aims to investigate the similarities and differences in the brain damage caused by Hypoxia-Ischemia (HI), Hypoglycemia, and Epilepsy. Hypoglycemia poses a significant challenge in improving glycemic regulation for insulin-treated patients, while HI brain disease in neonates is associated with low oxygen levels. The study examines the possibility of using a combination of medical data and Electroencephalography (EEG) measurements to predict outcomes over a two-year period. The study employs a multilevel fusion of data features to enhance the accuracy of the predictions. Therefore this paper suggests a hybridized classification model for Hypoxia-Ischemia and Hypoglycemia, Epilepsy brain injury (HCM-BI). A Support Vector Machine is applied with clinical details to define the Hypoxia-Ischemia outcomes of each infant. The newborn babies are assessed every two years again to know the neural development results. A selection of four attributes is derived from the Electroencephalography records, and SVM does not get conclusions regarding the classification of diseases. The final feature extraction of the EEG signal is optimized by the Bayesian Neural Network (BNN) to get the clear health condition of Hypoglycemia and Epilepsy patients. Through monitoring and assessing physical effects resulting from Electroencephalography, The Bayesian Neural Network (BNN) is used to extract the test samples with the most log data and to report hypoglycemia and epilepsy patients non-invasively. The experimental findings demonstrate that the suggested strategy improves accuracy by 95.05% and reduces the error rate to 0.41 when comparing diseases. © 2024, CC0.

关键词： Electroencephalography

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