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15th IEEE International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management, HNICEM 2023

作者： Bolaybolay, John Mel A. Aleluya, Earl Ryan M. Clar, Steve E. Pao, Jeanette C. Salaan, Carl John O. Alagon, Prancis Jann A. Villame, Cherry Mae G. Guirnaldo, Sherwin A. Department of Electrical Engineering Philippines Department of Computer Engineering and Mechatronics Philippines Graduate School of Engineering Philippines Mindanao State University - Iligan Institute of Technology Department of Mechanical Engineering and Technology Iligan City9200 Philippines

ISBN: (纸本)9798350381177

The development of collaborative aerial and ground robot technology offers a promising solution for volcano-exploring robotic systems. However, certain components are missing, particularly concerning the concept of ground robot retrieval. While the use of visual servo control is not new, it can be applied for aligning drones and rovers to facilitate ground robot retrieval. Traditional visual servo control methods often rely on fiducial markers, which may lack robustness in practical implementations. Within this paper, the authors propose an alternative visual servo control marker suitable for real-world scenarios. We present a comparative analysis of existing instance seg-mentation models from different YOLO versions, specifically, YOLOvS, YOLOv7, and YOLOv8, trained for drone detection. Additionally, we introduce a novel dataset, DroneCVS, comprising drone images captured from the ground robot's perspective under varying weather and altitude conditions. Our experimental findings demonstrate the instance segmentation model's capacity to acquire and learn drone segmentation from the DroneCVS dataset, achieving outstanding performance in different in evaluation metrics. In most evaluation scenarios, YOLOvS outperforms other models, leading to the conclusion that it provides the best balance between speed, weight, and performance in real-time applications. © 2023 IEEE.

关键词： Visual servoing

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An FM-Index Based High-Throughput Memory-Efficient FPGA Accelerator for Paired-End Short-Read Mapping

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IEEE Transactions on Biomedical Circuits and Systems 2023年第6期17卷 1331-1341页

作者： Yang, Chung-Hsuan Wu, Yi-Chung Chen, Yen-Lung Lee, Chao-Hsi Hung, Jui-Hung Yang, Chia-Hsiang Graduate Institute of Electronics Engineering National Taiwan University Taipei10617 Taiwan GeneASIC Technologies Corporation Hsinchu302 Taiwan Department of Computer Science National Yang Ming Chiao Tung University Hsinchu30010 Taiwan Department of Electrical Engineering Graduate Institute of Electronics Engineering National Taiwan University Taipei10617 Taiwan

This article presents an Ferragina-Manzini index (FM-index) based paired-end short-read mapping hardware accelerator. Four techniques are proposed to significantly reduce the number of memory accesses and operations to improve the throughput. First, an interleaved data structure is proposed to reduce the processing time by 51.8% by leveraging the data locality. Second, the boundaries of possible mapping location candidates can be retrieved within only one memory access by constructing a lookup table along with the FM-index. This reduces the number of DRAM accesses by 60% with only a 64 MB memory overhead. Third, an additional step is added to skip the time-consuming repetitive location candidates filtering conditionally, avoiding unnecessary operations. Lastly, an early termination method is proposed to terminate the mapping process if any location candidate with a high enough alignment score is detected, greatly decreasing the execution time. Overall, the computation time is reduced by 92.6% with only a 2% memory overhead in DRAM. The proposed methods are realized on a Xilinx Alveo U250 FPGA. The proposed FPGA accelerator processes 1,085,812,766 short-reads from the U.S. Food and Drug Administration (FDA) dataset within 35.4 minutes at 200 MHz. It achieves a 1.7-to-18.6× higher throughput and the highest 99.3% accuracy by exploiting the paired-end short-read mapping, compared to state-of-the-art FPGA-based designs. © 2023 IEEE.

关键词： Genome

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White rice stem borer pest detection system using image-based convolution neural network 9th

White rice stem borer pest detection system using image-base...

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9th International Conference on computer Science and Computational Intelligence, ICCSCI 2024

作者： Saufi, Akhmad Suharjito Computer Science Department BINUS Online Learning Bina Nusantara University Jakarta11480 Indonesia Industrial Engineering Department BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia

Preventing agricultural resource loss caused by pests remains a crucial issue. While technological advancements are being achieved, the current agricultural management methods and equipment have yet to meet the required level for precise pest control, a huge portion of the pest population analysis process is still conducted manually. As a solution to this issue, the development of a White Rice Stem Borer pest detection system has been conducted by applying Convolutional Neural Network (CNN) technology to calculate the pest population count at the research location. This system has been specifically designed to detect the White Rice Stem Borer using available traps. The method involves training data from a direct dataset obtained from the field, categorized into two positive and negative classes of the White Stem Borer pests. Six models have been trained from this dataset, utilizing two different architectures. Out of the six trained models, four showed potential overfitting, one exhibited underfitting, and one model demonstrated optimal results. The highest accuracy in image detection achieved by the most optimal CNN model was 97.35%, with a training accuracy of 98.54%. This best-performing model utilized an architecture with three Convolution layers, 50 Epochs, and an automatic data split with an 80:20 training-validation data ratio. From the research findings, it is concluded that this study can assist in automatically analyzing the quantity of White Stem Borer pests in a specific area without directly counting the number of pests from existing traps. However, the study still encounters a limitation-the detection process still requires substantial server resources and cannot be directly processed on the Raspberry PI device installed in the trap. Consequently, the detection relies on transmitting image data from the field device to the server before the detection process can occur. © 2024 The Authors.

关键词： Convolutional neural networks

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Joint Design of Spirograph Method and Reconfigurable Intelligent Surface for Future Communication Systems

Joint Design of Spirograph Method and Reconfigurable Intelli...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Yafis, Balqis Huang, Jane-Hwa Yu, Chih-Min Wang, Li-Chun National Yang Ming Chiao Tung University Eecs Int'l Graduate Program Hsinchu Taiwan National Chi Nan University Dept. of Electrical Engineering Nantou County Taiwan Chung Yuan Christian University Department of Information and Computer Engineering Taoyuan City Taiwan

ISBN: (纸本)9798350351255

Reconfigurable Intelligent Surface (RIS) is a transformative technology using the passive beamforming capabilities to create the programmable wireless environments. However, the control overhead is a challenging issue. The system needs to deliver the high-precision reflection coefficients to the RIS controller, for adjusting the phase shifts of the numerous RIS elements. Therefore, we propose a Spirograph-based discrete beamdirection strategy, which selects a set of non-uniformly distributed beam directions based on the generic patterns generated by the Spirograph (SG). We evaluate the achievable data rate and the number of controlling bits of SG-based method. We compare the SG-based method, the ideal continuous phase-shift method, and the SISO method. Simulation results show that with the properly-selected discrete beam directions, the SG-based method can achieve a comparable data rate as the ideal method, while having a lower control overhead. © 2024 IEEE.

关键词： Beamforming

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Innovative Colormap for Emphatic Imaging of Human Voice for UAV-Based Disaster Victim Search

Innovative Colormap for Emphatic Imaging of Human Voice for ...

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2023 IEEE Region 10 Symposium, TENSYMP 2023

作者： Furusawa, Tomoki Premachandra, Chinthaka Graduate School of Engineering and Science Shibaura Institute of Technology Electrical Engineering and Computer Science Tokyo Japan School of Engineering Shibaura Institute of Technology Department of Electronic Engineering Tokyo Japan

ISBN: (纸本)9781665482585

Unmanned aerial vehicles (UAVs) are being utilized for damage assessment in natural disasters and for search and rescue operations. Currently, the search for victims primarily relies on analyzing images captured by cameras mounted on UAVs. However, this approach has limitations when it comes to locating victims who are not within the camera's field of view. As a result, sound-based search methods are being considered. In this method, a voice message is transmitted to the disaster area through a speaker mounted on a UAV, and the presence of victims is confirmed by detecting their response using the onboard microphone of the UAV. Nevertheless, the UAV's microphone captures both the sound of the victim and the propeller rotation, posing a significant challenge in extracting the victim's voice from this combined audio. To address this issue, we propose a solution that involves generating spectrogram images of the sound mixture and the propeller sound, and extracting the human sound by subtracting them. We found that the conventional colormap was ineffective in emphasizing the human sound in the spectrogram images. To overcome this limitation, this paper introduces a new colormap based on the normal distribution. This colormap enhances human voices while attenuating propeller sounds by adjusting the mean and variance. Through the results of our experiments, we confirm that the proposed colormap effectively reduces propeller sound interference in the sound mixing and simultaneously emphasizes the voice of a disaster victim. By utilizing the proposed colormap, it becomes possible to visualize the victim's voice from the audio mixture acquired by the UAV's onboard microphone, enabling the identification of the victim. © 2023 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Random Permutation Codes: Lossless Source Coding of Non-Sequential Data

arXiv

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arXiv 2024年

作者： Severo, Daniel Graduate Department of Electrical and Computer Engineering University of Toronto Canada

This thesis deals with the problem of communicating and storing non-sequential data. We investigate this problem through the lens of lossless source coding, also sometimes referred to as lossless compression, from both an algorithmic and information-theoretic perspective. Lossless compression algorithms typically preserve the ordering in which data points are compressed. However, there are data types where order is not meaningful, such as collections of files, rows in a database, nodes in a graph, and, notably, datasets in machine learning applications. Compressing with traditional algorithms is possible if we pick an order for the elements and communicate the corresponding ordered sequence. However, unless the order information is somehow removed during the encoding process, this procedure will be sub-optimal, because the order contains information and therefore more bits are used to represent the source than are truly necessary. In this work we give a formal definition for non-sequential objects as random sets of equivalent sequences, which we refer to as Combinatorial Random Variables (CRVs). The definition of equivalence, formalized as an equivalence relation, establishes the non-sequential data type represented by the CRV. The achievable rates of CRVs is fully characterized as a function of the equivalence relation as well as the data distribution. The optimal rates of CRVs are achieved within the family of Random Permutation Codes (RPCs) developed in later chapters. RPCs randomly select one-of-many possible sequences that can represent the instance of the CRV. The selection is done through sampling with bits-back coding [72, 27] and asymmetric numeral systems [21], and guarantees the achievability of the optimal rate. Specialized RPCs are given for the case of multisets, graphs, and partitions/clusterings, providing new algorithms for compression of databases, social networks, and web data in the JSON file format. The computational and memory complexity of RPC

关键词： Graph algorithms

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Future Trends in Fog/Edge Computing and Networking

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IEEE Communications Magazine 2023年第12期61卷 38-39页

作者： Wei, Hung-Yu Zhang, Tao Hsing, T. Russell Zuckerman, Doug Institute of Communications Engineering Department of Electrical Engineering and Graduate National Taiwan University Taiwan United States British Computer Society and SPIE United States IEEE Serves on the Future Directions Committee and Industry Engagement Committee United States

Fog/edge computing and networking offers flexible operational capabilities across the thing-to-cloud continuum, catering to a variety of applications with low-latency requirements and enhanced data privacy. These fog/edge technologies are set to play pivotal roles in future computing systems and 6G wireless communications. They have also demonstrated substantial potential for integration with Machine Learning (ML) technologies. ML can enable smart fogedge computing, while fog/edge infrastructures can also support low-latency ML-based applications.

关键词： Special issues and sections Edge computing Cloud computing Machine learning Low latency communication

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Deep Learning Model for Classifying Public Opinions on Energy Sector IHSG Stocks on the Twitter Social Media Platform

Deep Learning Model for Classifying Public Opinions on Energ...

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2023 International Conference on Artificial Intelligence Robotics, Signal and Image Processing, AIRoSIP 2023

作者： Sulistio, Bambang Heryadi, Yaya Lukas Suparta, Wayan Sonata, Ilvico Binus Graduate Program - Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia Universitas Katolik Indonesia Atma Jaya Cognitive Engineering Research Group Jakarta Indonesia Institut Teknologi Nasional Yogyakarta Faculty of Industrial Technology Department of Electrical Engineering Yogyakarta Indonesia

ISBN: (纸本)9798350327472

Capital market transactions provide an opportunity for investors to acquire ownership of company shares and capital gains, as well as dividends. However, alongside the benefits, there are risks of capital loss and liquidation, leading to stress and depression due to profit targets and decision-making errors. To mitigate the risk of decision-making errors in investment, data analysis is needed, including sentiment analysis, which influences stock prices. This study aims to develop a new deep learning model to classify Indonesian public opinion on JCI stocks, especially the Energy sector, obtained from the Twitter social media platform. The model will perform sentiment analysis and categorize opinions as negative, neutral, or positive. We created a dataset that was trained using Bidirectional Encoder Representations from Transformers (BERT) to summarize the analysis of public sentiment above so that it can assist investors in studying public sentiment as a reference for investing with a yield precision of 76%, Recall of 77%, and F1-score on 76%. © 2023 IEEE.

关键词： Tweets

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Mechanism of Threshold Voltage Instability in Double Gate α-IGZO Nanosheet TFT under Bias and Temperature Stress

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IEEE Journal of the Electron Devices Society 2024年 12卷 464-471页

作者： Aslam, Muhammad Chang, Shu-Wei Chen, Yi-Ho Lee, Yao-Jen Li, Yiming Lee, Wen-Hsi Parallel and Scientific Computing Laboratory National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Electrical Engineering and Computer Science International Graduate Program National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Department of Electrical Engineering National Cheng Kung University Tainan City701401 Taiwan Institute of Pioneer Semiconductor Innovation Industry Academic Innovation School National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Department of Electronics and Electrical Engineering National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Institute of Communications Engineering National Yang Ming Chiao Tung University Hsinchu300093 Taiwan

Amorphous indium gallium zinc oxide (a-IGZO)-based thin film transistors (TFTs) are increasingly becoming popular because of their potential in futuristic applications, including CMOS technology. Given the demand for CMOS-compatible, ultra-scaled, reliable, and high-performing devices, we fabricate and analyze scaled-channel a-IGZO-TFTs with an optimal double-gate structure, a thin nanosheet-based channel, and an effective high- κ dielectric namely HfO2. The reliably reported double gate IGZO nanosheet TFTs (DG-IGZO-NS-TFTs) are tested under positive and negative bias stress at variant temperatures, and the resulting modulations are analyzed critically. The reported DG-IGZO-NSTFTs exhibit a negative side threshold voltage shift (Δ Vth) accompanied by an increase in Ion/Ion(0) under negative bias temperature stress (NBTS) at elevated temperatures, which indicates the presence of additional charges. An anomalous negative side shifting of the Vth is observed under positive bias temperature stress (PBTS), where diffused hydrogen atoms are identified as introducing excess n-type carriers into the channel and causing the observed Δ Vth. The spectroscopic analysis is performed to establish evidence for the assumed mechanisms, and the role of individual gates is investigated in the context of performance variance under temperature-bias stress. Moreover, the partial reversibility of the stress-induced degradation is experimentally established and methodically discussed. Overall, the reported results offer a comprehensive understanding of scaled-channel DG-NS-IGZO-TFTs, which help shape performance-enhancement strategies, control degradation mechanisms, and define appropriate application scenarios. © 2013 IEEE.

关键词： Thin film transistors

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An Ensemble-Based Classifier to Determine the Harmonic Contribution Responsibility Between Utility’s Grid and Microgrids

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IEEE Access 2025年 13卷 85873-85881页

作者： Mella, Matheus C. Fernandes, Ricardo A.S Barbosa, Daniel Federal University of São Carlos Graduate Program in Electrical Engineering São Carlos Brazil University of São Paulo São Carlos School of Engineering Department of Electrical and Computer Engineering São Carlos Brazil Federal University of Bahia Department of Electrical Engineering Salvador Brazil

The integration of nonlinear loads and distributed energy resources in microgrids (MG) has increased the levels of harmonic distortions in distribution feeders. Identifying the responsibility for the harmonic distortions measured at the MG’s point of common coupling (PCC) is essential for managing the power quality. For this purpose, the present paper aims to determine whether the responsibility predominates from the utility’s grid or the MG itself. Thus, an ensemble-based approach was proposed, comparing the behaviour of Random Forest and XGBoost classifiers to identify the side with the predominant harmonic contribution. In order to validate the proposed approach, scenarios were simulated in the IEEE 34-node test feeder using the ATP/ATPDraw software, considering the harmonic sources located at different nodes of the feeder. Three-phase voltage and current signals were acquired at MG’s PCC to train and validate the ensembles. However, a feature extraction stage was performed to obtain time and frequency features used as inputs to the predictive models. The results demonstrated that the proposed approach effectively identified the contribution side, achieving an average F1-score above 99% for both ensembles, representing a feasible solution for determining the harmonic distortion responsibilities at the MG’s PCC. © 2013 IEEE.

关键词： Software testing

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