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14th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2023

作者： Mathias, Chris Xavier Lin, Meng Assadi, Arsh Imtiyaz Bhavish, B.S. Shenoy, Daksha D’Souza, Rolston Aravinda, C.V. NMAM Institute of Technology Department of Computer Science & Engineering Karkala Karnataka Udupi India Ritsumeikan University 1-1-1 Noji-higashi Shiga Kusatsu525-8577 Japan

Dispensing medicine to elderly residents is a crucial task in nursing homes, requiring specialized medical staff to prevent errors and manage inventory. With rapid advancements in deep learning technology, high-performance computers can replace some supervision tasks. In this paper, we have proposed a system consisting of a Convolutional Neural Network-YOLOV8 custom-trained image recognition model, Optical Character Recognition (OCR), and RoBERTa, a pre-trained Large Language Model (LLM) that uses the architecture of the transformer, and it has attained cutting-edge performance on a variety of natural language processing tasks. The RoBERTa model is particularly effective in handling unstructured text data, making it an ideal choice for this project. The model is hosted on a virtual machine instance on the Google Cloud Platform, a cloud computing service that provides a range of tools and services for building, deploying, and managing applications and services. The use of the Google Cloud Platform ensures that the system is scalable, reliable, and accessible from anywhere at any time, making it an ideal choice for a method for recognising pharmaceutical packets in real time. © Grenze Scientific Society, 2023.

关键词： Optical character recognition

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Deep Learning Aided LLR Correction Improves the Performance of Iterative MIMO Receivers

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IEEE Transactions on Vehicular Technology 2024年第2期73卷 2045-2060页

作者： Chen, Jue Wang, Tsang-Yi Wu, Jwo-Yuh Li, Chih-Peng Ng, Soon Xin Maunder, Robert G. Hanzo, Lajos University of Southampton School of Electronics and Computer Science SouthamptonSO17 1BJ United Kingdom National Yang Ming Chiao Tung University Institute of Communications Engineering Hsinchu300 Taiwan National Sun Yat-sen University Institute of Communications Engineering Kaohsiung804 Taiwan National Science and Technology Council Department of Engineering and Technologies Taipei10622 Taiwan

A Deep Learning (DL) aided Logarithmic Likelihood Ratio (LLR) correction method is proposed for improving the performance of Multiple-Input Multiple-Output (MIMO) receivers, where it is typical to adopt reduced-complexity algorithms for avoiding the excessive complexity of optimal full-search algorithms. These sub-optimal techniques typically express the probabilities of the detected bits using LLRs that often have values that are not consistent with their true reliability, either expressing too much confidence or not enough confidence in the value of the corresponding bits, leading to performance degradation. To circumvent this problem, a Deep Neural Network (DNN) is trained for detecting and correcting both over-confident and under-confident LLRs. We demonstrate that the complexity of employing the DL-aided technique is relatively low compared to the popular reduced-complexity receiver detector techniques since it only depends on a small number of real-valued inputs. Furthermore, the proposed approach is applicable to a wild variety of iterative receivers as demonstrated in the context of an iterative detection and decoding aided MIMO system, which uses a low-complexity Smart Ordering and Candidate Adding (SOCA) scheme for MIMO detection and Low-Density Parity Check (LDPC) codes for channel coding. We adopt Extrinsic Information Transfer (EXIT) charts for quantifying the Mutual Information (MI) and show that our DL method significantly improves the BLock Error Rate (BLER). Explicitly, we demonstrate that about 0.9 dB gain can be achieved at a BLER of 10-3 by employing the proposed DL-aided LLR correction method, at the modest cost of increasing the complexity by 16% compared to a benchmarker dispensing with LLR correction. © 1967-2012 IEEE.

关键词： Iterative decoding

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Optimized Vision Transformer for Dementia Diagnosis Using Micro-Doppler Radar

Optimized Vision Transformer for Dementia Diagnosis Using Mi...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Ishibashi, Ryuto Nojiri, Naoto Kaneko, Hayata Kenshi, Saho Meng, Lin Graduate School of Science and Engineering Ritsumeikan University 1-1-1 Nojihigashi Shiga Kusatsu525-8577 Japan Ritsumeikan University Department of Electronic and Computer Engineering 1-1-1 Nojihigashi Shiga Kusatsu525-8577 Japan

ISBN: (纸本)9798350337020

In the aging society, the number of dementia patients continues to increase, and early detection of dementia is required. However, going to a hospital and being diagnosed by a doctor is burdensome for elderly people. This paper designs a Vision Transformer(ViT)-based gait diagnosis with micro-Doppler radar to diagnose dementia without burden for elderly people. The ViT is optimized by proposed Vertical Rectangle Patching and Adaptive Thresholding, which improve the Attention of Vi'I. The micro-Doppler radar collects the signal of elderly people, and the signal is transformed into two kinds of signal-analyzed images by two signal-analysis methods (Short-term Fourier Transform: STFT, and Continuous Wavelet Transform: CWT) for diagnosing by optimized ViI. Experiments compare eight kinds of CNN models, and current ViTs with the optimized ViT to evaluate the proposal's performance. The experimental results show that STFT is suitable for analyzing micro-Doppler radar signals, and ViT-56x4s+th, which uses Vertical Rectangle Patching and Adaptive Thresholding, achieves high scores such as an accuracy of 88.9%. Another proposed model ViT-224xl allows for faster learning convergence for time series data and improved accuracy without fine-tuning. In summary, the possibility of diagnosing dementia by optimized ViT has been provided. The experimentation of Adaptive thresholding also proves there are some unimportant patches and brings an idea for reducing the unimportant computations to achieve a compact ViT as future work. © 2023 IEEE.

关键词： Dementia Diagnosis MicroDoppler Radar Vision Transformer

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Dual Polarized Stack Antenna for Ku Band Satellite Communication with Capacitively Coupled Feeding

Dual Polarized Stack Antenna for Ku Band Satellite Communica...

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Asia-Pacific Conference on Microwave

作者： Mon-Li Chang Sung-Nien Hsieh Hsiang-En Hsieh Ding-Bing Lin Hsi-Tseng Chou 1Department of Electronic and Computer Engineering National Taiwan University of Science and Technology Taipei Taiwan Graduate Institute of Communication Engineering National Taiwan University Taipei Taiwan

ISBN: (数字)9798350363548

ISBN: (纸本)9798350363555

This study proposes a dual-polarization array antenna design applied to the Ku band low-orbit satellite communication system. The application scenario of this design can be used in the uplink and downlink frequency bands of satellite communication. The downlink frequency band is designed from 11 GHz to 12 GHz, and the uplink frequency band is designed from 13.75 GHz to 14.6 GHz. In addition, this paper also adds a dual-polarization antenna architecture. In satellite communication, the antenna polarization of the ground equipment uses a dual polarization antenna architecture. In the design of the receiver, the signals of two different polarizations can be processed separately. Use signal processing to get the correct signal.

关键词： Microwave antennas Satellite antennas Receivers Microwave devices Signal processing Microwave communication Downlink Antenna feeds Ku-band Uplink

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DIDOR: A Decentralized Identifier Based Onion Routing Protocol 1st

DIDOR: A Decentralized Identifier Based Onion Routing Protoc...

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1st EAI International Conference on Safe, Secure, Ethical, Responsible Technologies and Emerging Applications, SAFER-TEA 2023

作者： Pierre, Saha Fobougong Mejri, Mohamed Department of computer science and software engineering Laval University 2325 rue de l’université QuébecQCG1V 0A6 Canada

ISBN: (纸本)9783031563959

We propose a new communication protocol, called DIDOR, that provides strong anonymity and is based on a decentralized identifier (DID). The proposed protocol benefits from the persistence, anonymity, and resolvability properties of the DID and integrates a forward secrecy algorithm such as Ephemeral Diffie-Hellman. It is designed to protect the anonymity of communication between parties by combining DID properties and onion routing techniques. The chosen forward secrecy algorithm establishes a shared secret between the sender and the different nodes and strengthens security. The shared secret is then used at each onion layer to encrypt the message. Likewise, DID allows us to offer a protocol that overcomes some of the weaknesses of using digital certificates. Given the prior existence of the decentralized identity management system, our protocol requires less computation at each node, maintains low communication overhead and latency, and does not require directory servers on which most existing protocols rely. We finally analyze the security of our protocol and demonstrate how it can withstand the denial of service attack on DID documents. Overall, this protocol represents a promising approach to providing secure and anonymous communication in a wide range of applications. © ICST Institute for computer sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Denial-of-service attack

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Multi-Modal Transformer and Reinforcement Learning-Based Beam Management

IEEE Networking Letters

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IEEE Networking Letters 2024年第4期6卷 222-226页

作者： Ghassemi, Mohammad Zhang, Han Afana, Ali Sediq, Akram Bin Erol-Kantarci, Melike University of Ottawa School of Electrical Engineering and Computer Science OttawaONK1N 6N5 Canada Ericsson Inc Research and Development Department OttawaONK2K 2V6 Canada

Beam management is an important technique to improve signal strength and reduce interference in wireless communication systems. Recently, there has been increasing interest in using diverse sensing modalities for beam management. However, it remains a big challenge to process multi-modal data efficiently and extract useful information. On the other hand, the recently emerging multi-modal transformer (MMT) is a promising technique that can process multi-modal data by capturing long-range dependencies. While MMT is highly effective in handling multi-modal data and providing robust beam management, integrating reinforcement learning (RL) further enhances their adaptability in dynamic environments. In this letter, we propose a two-step beam management method by combining MMT with RL for dynamic beam index prediction. In the first step, we divide available beam indices into several groups and leverage MMT to process diverse data modalities to predict the optimal beam group. In the second step, we employ RL for fast beam decision-making within each group, which in return maximizes throughput. Our proposed framework is tested on a 6G dataset. In this testing scenario, it achieves higher beam prediction accuracy and system throughput compared to both the MMT-only based method and the RL-only based method. © 2019 IEEE.

关键词： Decision making

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Generalized Group Testing

Generalized Group Testing

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作者： Cheng, Xiwei Jaggi, Sidharth Zhou, Qiaoqiao The Chinese University of Hong Kong Department of Computer Science and Engineering Shatin Hong Kong University of Bristol School of Mathematics BristolBS8 1TH United Kingdom National University of Singapore Department of Computer Science Queenstown119077 Singapore

In the problem of classical group testing one aims to identify a small subset (of size d ) of diseased individuals/defective items in a large population (of size n ). This process is based on a minimal number of suitably-designed group tests on subsets of items, where the test outcome is positive iff the given test contains at least one defective item. Motivated by physical considerations, such as scenarios with imperfect test apparatus, we consider a generalized setting that includes as special cases multiple other group-testing-like models in the literature. In our setting the test outcome is governed by an arbitrary monotonically increasing (stochastic) test function f , with the test outcome being positive with probability f(x) , where x is the number of defectives tested in that pool. This formulation subsumes as special cases a variety of noiseless and noisy group-testing models in the literature. Our main contributions are as follows. Firstly, for any monotone test function f(c) we present a non-adaptive scheme that with probability 1- identifies all defective items. Our scheme requires at most O} ((f)} d({n}) tests, where {(f) is a suitably defined 'sensitivity parameter' of f, and is never larger than Od1+o(1) , but indeed can be substantially smaller for a variety of f. Secondly, we argue that any non-adaptive group testing scheme needs at least ((1- (f)} d ({n} d}) tests to ensure high reliability recovery. Here f is a suitably defined 'concentration parameter' of f, and 1. Thirdly, we prove that our sample-complexity bounds for generalized group testing are information-theoretically near-optimal for a variety of sparse-recovery group-testing models in the literature. That is, for any 'noisy' test function F (i.e., 0 © 1963-2012 IEEE.

关键词： Adaptive algorithms

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A review of federated learning in renewable energy applications:Potential,challenges,and future directions

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Energy and AI 2024年第3期17卷 444-457页

作者： Albin Grataloup Stefan Jonas Angela Meyer Bern University of Applied Sciences School of Engineering and Computer ScienceQuellgasse 21Biel2501Switzerland Universitàdella Svizzera italiana Faculty of InformaticsVia la Santa 1Lugano-Viganello6962Switzerland Delft University of Technology Department of Geoscience and Remote SensingStevinweg 1Delft2628The Netherlands

Federated learning has recently emerged as a privacy-preserving distributed machine learning *** learning enables collaborative training of multiple clients and entire fleets without sharing the involved training *** preserving data privacy,federated learning has the potential to overcome the lack of data sharing in the renewable energy sector which is inhibiting innovation,research and *** paper provides an overview of federated learning in renewable energy *** discuss federated learning algorithms and survey their applications and case studies in renewable energy generation and *** also evaluate the potential and the challenges associated with federated learning applied in power and energy ***,we outline promising future research directions in federated learning for applications in renewable energy.

关键词： Federated learning Renewable energy Dis tributed learning Data heterogeneity Privacy Data sharing Wind energy Solar energy Buildings Review

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2024. The Distribution of 137 Cs Concentration in the Coppiced Trees of 10-year-old Konara Oak (Querucus serrata) Growned after the Fukushima Dai-ichi Nuclear Power Plant Accident.

Nihon Ringakkai Shi/Journal of the Japanese Forestry Society

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Nihon Ringakkai Shi/Journal of the Japanese Forestry Society 2024年第1期106卷 1-6页

作者： Ogawa, Hideki Kumata, Atsushi Saitou, Naohiko Sakurai, Satoshi Fukushima Prefectural Forestry Research Centre 1 Nishishimasaka Narita Asaka Koriyama Fukushima963-0112 Japan Department of Computer Science College of Engineering Nihon University 1 Nakagawara Tokusada Tamuramachi Koriyama Fukushima963-8642 Japan Japan Overseas Cooperation Volunteer: Bhutan National Mushroom Centre） Japan

J Jpn For Soc. 106(1): 1–6. The 137Cs contamination of the ground part of Quercus serrata, used as mushroom logs, was investigated, 10 years after coppicing in February 2011 just before the Fukushima nuclear accident. In May 2021, nine sprouting coppiced trees were felled from the three stumps in a deciduous broad-leaved forest in Fukushima Prefecture. Stem, branch, and leaf samples were collected, and the 137Cs concentrations in each parts and the 137Cs stock in the stems and the thicker branches were measured. As a result, the concentration of 137Cs in the branch tips and leaves was significantly higher than compared to the thicker branches and the trunk, and there was a significant variation in concentration. The differences in 137Cs concentration between the thicker branches and the trunk and was small. It was suggested that there was a relatively stable distribution of 137Cs concentration in the vertical direction in the coppiced trees, except for the leaves and the branch tips. Moreover, the concentration of the stem showed a positive correlation with the leaves and the branches classified by the diameter, especially in the thicker branches. Furthermore, because collecting thicker branches is easier and their concentration variability is lower, compared to leaves or branch tips, it was more suitable to use thicker branches for estimating trunk concentrations for use of mushroom logs. © 2024 Japanese Forestry Society. All rights reserved.

关键词： Forestry

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Simultaneous visualization method for mixed HDLSS data and its application for education 28th

Simultaneous visualization method for mixed HDLSS data and i...

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28th International Conference on Knowledge Based and Intelligent information and engineering Systems, KES 2024

作者： Sato-Ilic, Mika Ilic, Peter Institute of Systems and Information Engineering University of Tsukuba 1-1-1 Tennodai Ibaraki Tsukuba305-8573 Japan Center for Language Research Department of Computer Science and Engineering University of Aizu 90 Kamiiawase Tsuruga Itsukimachi Aizuwakamatsu Fukushima965-8580 Japan

This paper proposes a visualization method for the mixed type of high-dimension and low sample-size (HDLSS) data. The data consists of both numerical and categorical data with respect to quantitative and qualitative variables, and the number of variables is much larger than the number of objects. The proposed method can treat both types of data simultaneously in a lower dimensional space. From this, we can compare the two kinds of features obtained from the two types of data in the lower dimensional space. In addition, since this visualization can be obtained based on the mathematical theory of metric multidimensional scaling, the comparability is mathematically guaranteed. As a numerical example, we show how the devices used by students to perform activities of reading, writing, listening, and speaking English texts of different lengths are related to their English test scores. In this case, the choice of device is obtained as qualitative data, and the test scores are obtained as quantitative data. In order to visually obtain these relationships, the two must be mathematically comparable in the same low-dimensional space. With the proposed method, we have achieved this and shown that it is possible to interpret the relationship between the two. © 2024 The Authors.

关键词： Fuzzy clustering HDLSS data Mixed data Multidimensional scaling

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