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IEEE International Conference on Flexible and Printable Sensors and Systems (FLEPS)

作者： Chanho Shin Ning Li Tse Nga Ng Material Science and Engineering Program University of California San Diego La Jolla USA Department of Electrical and Computer Engineering University of California San Diego La Jolla USA

In this work, we demonstrated upconversion imagers integrated with shortwave infrared photodetectors paired with an electron blocking layer. The use of electron blocking layer screened charge injection to prevent recombination in photosensitive layer. The characteristics of each electron blocking layer were analyzed in aspects of noise and detectivity. For the optimized device, the parasitic luminance in the dark was efficiently suppressed, and the photon-to-photon efficiency was increased. The electron blocking layer used in this work is generally applicable for upconversion imagers using different absorption and emitting materials.

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Proactive Opinion-Driven Robot Navigation Around Human Movers

Proactive Opinion-Driven Robot Navigation Around Human Mover...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Charlotte Cathcart María Santos Shinkyu Park Naomi Ehrich Leonard Department of Mechanical and Aerospace Engineering Princeton University Princeton NJ USA Park is with the Electrical and Computer Engineering program King Abdullah University of Science and Technology Thuwal Saudi Arabia

We propose, analyze, and experimentally verify a new proactive approach for robot social navigation driven by the robot's “opinion” for which way and by how much to pass human movers crossing its path. The robot forms an opinion over time according to nonlinear dynamics that depend on the robot's observations of human movers and its level of attention to these social cues. For these dynamics, it is guaranteed that when the robot's attention is greater than a critical value, deadlock in decision making is broken, and the robot rapidly forms a strong opinion, passing each human mover even if the robot has no bias nor evidence for which way to pass. We enable proactive rapid and reliable social navigation by having the robot grow its attention across the critical value when a human mover approaches. With human-robot experiments we demonstrate the flexibility of our approach and validate our analytical results on deadlock-breaking. We also show that a single design parameter can tune the trade-off between efficiency and reliability in human-robot passing. The new approach has the additional advantage that it does not rely on a predictive model of human behavior.

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A Genetic Algorithm to Optimize Cargo Stability in Container Transportation 11

A Genetic Algorithm to Optimize Cargo Stability in Container...

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11th IEEE Global Conference on Consumer Electronics, GCCE 2022

作者： Hsieh, Chi-Jung Wu, Kuang-Sung Wu, Tin-Yu Tey, Fu Jie Chen, Yi-Kai Ho, Jye Yi National Ilan University Department of Computer Science and Information Engineering Ilan County Taiwan National Ilan University Master Program of E-Learning for Multimedia and Network Communications Yilan Taiwan National Pingtung University of Science and Technology Department of Management Information Systems Pingtung County Taiwan National Taiwan University of Science and Technology Department of Electrical Engineering Taipei Taiwan

ISBN: (纸本)9781665492324

In the era of the global village, frequent cross-border trade in goods has made container transportation a significant part in delivery of cargo. However, rollover accidents of container trucks often occur because of tall truck height, improper cargo stowage or crooked roads. Therefore, how to find the optimal center of gravity (CG) of the packed cargo remains an issue that the transportation industry should not neglect. To optimize cargo stability and reduce the incidence of rollovers, this paper proposes a genetic algorithm that scores the cargo stacking process to find the best CG of the cargo based on the CG of the vehicle. According to the deviation angle and the point closest to the best CG, we can Figure out the optimal cargo stacking method. © 2022 IEEE.

关键词： Genetic algorithms

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Exploring Approaches to Answer Biomedical Questions: From Pre-processing to GPT-4 Notebook for the BioASQ Lab at CLEF 2023 24

Exploring Approaches to Answer Biomedical Questions: From Pr...

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24th Working Notes of the Conference and Labs of the Evaluation Forum, CLEF-WN 2023

作者： Kim, Hyunjae Hwang, Hyeon Lee, Chaeeun Seo, Minju Yoon, Wonjin Kang, Jaewoo Department of Computer Science and Engineering Korea University Seoul02841 Korea Republic of Computational Health Informatics Program Boston Children’s Hospital BostonMA02115 United States Harvard Medical School BostonMA02115 United States AIGEN Sciences Seoul04778 Korea Republic of

Biomedical question answering (QA) plays a crucial role in assisting researchers, healthcare professionals, and even patients in accessing and retrieving accurate and up-to-date information from the vast amount of biomedical knowledge available in literature. To enhance the efficiency of knowledge discovery and information retrieval, we investigate the efficacy of various pre-processing, model training, data augmentation, and ensemble methods and evaluate a range of advanced pre-trained models such as BioLinkBERT and GPT-4. Additionally, we explore data augmentation and ensemble methods to further improve system performance. In our participation in BioASQ Task 11b-Phase B, our systems achieved a top ranking in all four batches for the yes/no type of questions, in one out of four batches for factoid questions, and in two out of four batches for list-type questions. © 2023 Copyright for this paper by its authors.

关键词： BioASQ 11b BioLinkBERT Data Augmentation Ensemble GPT-4

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Metaverse through Blockchain and Intelligent Networking: A Comprehensive Survey 5

Metaverse through Blockchain and Intelligent Networking: A C...

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5th International Conference on Blockchain Computing and Applications, BCCA 2023

作者： Alja'Afreh, Mohammad Alouneh, Sahel Obaidat, Muath Karime, Ali Elsaddik, Abdulmotaleb United Arab Emirates Princess Sumaya University for Technology Amman Jordan Cybersecurity Program College of Engineering Al Ain University United Arab Emirates German Jordanian University Amman Jordan Computer Science-City University of New York United States Royal Military College of Canada Canada

ISBN: (纸本)9798350339239

As technology has advanced, people's lives have been transformed by the virtual world, which has been created by technologies such as the Internet, computers, artificial intelligence, and hardware. The metaverse, a new social ecology connecting the physical and virtual worlds, is rapidly expanding as the demand for virtual reality grows. Privacy, security, high synchronization, and low latency have all been challenged as data volumes and value have grown and the metaverse continues to evolve. As blockchain and intelligent networking technologies continue to evolve, these challenges can be addressed and the metaverse's needs for trusted construction, continuous data interaction, and computing can be satisfied. In order to provide immersive experiences in the metaverse, a comprehensive review of blockchain's role and benefits is essential. The purpose of this survey is to discuss the metaverse's development trend, architecture, and characteristics. This review paper is novel in providing an overview of existing blockchain research, including overviews, applications, and challenges. Furthermore, we summarized the metaverse's applications, emphasizing their significance and areas of development. Using the survey, we are able to discuss open issues, challenges, and future research directions. © 2023 IEEE.

关键词： Blockchain

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A WEARABLE GRAPHENE TRANSISTOR-BASED BIOSENSOR FOR MONITORING IL-6 BIOMARKER 25

A WEARABLE GRAPHENE TRANSISTOR-BASED BIOSENSOR FOR MONITORIN...

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25th International Conference on Miniaturized Systems for Chemistry and Life sciences, MicroTAS 2021

作者： Laliberte, Kaitlyn E. Scott, Patrick Khan, Niazul I. Mahmud, Md Shaad Song, Edward Department of Electrical and Computer Engineering University of New Hampshire United States Materials Science Program University of New Hampshire United States

ISBN: (纸本)9781733419031

Graphene field-effect transistor (GFET) is becoming an increasingly popular biosensing platform for monitoring health conditions through biomarker detection. Moreover, the graphene's 2-dimensional geometry makes it ideal for implementing flexible wearable devices. In this work, we present a printable graphene transistor-based biosensor that can be used as a wearable sensor to detect a protein biomarker Interleukin-6 (IL-6). Our device contains a voltage control, a current meter, and a power source all integrated into a single miniaturized platform making it a truly skin-mounted wearable device. © 2021 MicroTAS 2021 - 25th International Conference on Miniaturized Systems for Chemistry and Life sciences. All rights reserved.

关键词： Graphene transistors

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Real-time multilingual speech recognition and speaker diarization system based on Whisper segmentation

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PeerJ computer science 2024年 10卷 e1973页

作者： Lyu, Ke-Ming Lyu, Ren-yuan Chang, Hsien-Tsung Computer Science and Information Engineering Chang Gung University Taoyuan Taiwan Physical Medicine and Rehabilitation Chang Gung Memorial Hospital Taoyuan Taiwan Bachelor Program in Artificial Intelligence Chang Gung University Taoyuan Taiwan

This research presents the development of a cutting-edge real-time multilingual speech recognition and speaker diarization system that leverages OpenAI’s Whisper model. The system specifically addresses the challenges of automatic speech recognition (ASR) and speaker diarization (SD) in dynamic, multispeaker environments, with a focus on accurately processing Mandarin speech with Taiwanese accents and managing frequent speaker switches. Traditional speech recognition systems often fall short in such complex multilingual and multispeaker contexts, particularly in SD. This study, therefore, integrates advanced speech recognition with speaker diarization techniques optimized for real-time applications. These optimizations include handling model outputs efficiently and incorporating speaker embedding technology. The system was evaluated using data from Taiwanese talk shows and political commentary programs, featuring 46 diverse speakers. The results showed a promising word diarization error rate (WDER) of 2.68% in twospeaker scenarios and 11.65% in three-speaker scenarios, with an overall WDER of 6.96%. This performance is comparable to that of non-real-time baseline models, highlighting the system’s ability to adapt to various complex conversational dynamics, a significant advancement in the field of real-time multilingual speech processing. © 2024 Lyu et al.

关键词： Real time systems

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Tracking Players in a Badminton Court by Two Cameras

arXiv

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arXiv 2023年

作者： Chou, Young-Ching Zhang, Shen-Ru Chen, Bo-Wei Chen, Hong-Qi Lin, Cheng-Kuan Tseng, Yu-Chee Department of Computer Science National Yang Ming Chiao Tung University Taiwan Undergraduate Degree Program of Systems Engineering and Technology National Yang Ming Chiao Tung University Taiwan Computer Science and Information Engineering Chung Cheng Institute of Technology NDU Taiwan

This study proposes a simple method for multi-object tracking (MOT) of players in a badminton court. We leverage two off-the-shelf cameras, one on the top of the court and the other on the side of the court. The one on the top is to track players’ trajectories, while the one on the side is to analyze the pixel features of players. By computing the correlations between adjacent frames and engaging the information of the two cameras, MOT of badminton players is obtained. This two-camera approach addresses the challenge of player occlusion and overlapping in a badminton court, providing player trajectory tracking and multi-angle analysis. The presented system offers insights into the positions and movements of badminton players, thus serving as a coaching or self-training tool for badminton players to improve their gaming strategies. Copyright © 2023, The Authors. All rights reserved.

关键词： Cameras

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A Graph Neural Network Multi-Task Learning-Based Approach for Detection and Localization of Cyberattacks in Smart Grids

A Graph Neural Network Multi-Task Learning-Based Approach fo...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Abdulrahman Takiddin Rachad Atat Muhammad Ismail Katherine Davis Erchin Serpedin Department of Electrical & Computer Engineering Texas A&M University College Station TX USA Electrical & Computer Engineering Program Texas A&M University at Qatar Doha Qatar Department of Computer Science Tennessee Technological University Cookeville TN USA

False data injection attacks (FDIAs) on smart power grids’ measurement data present a threat to system stability. When malicious entities launch cyberattacks to manipulate the measurement data, different grid components will be affected, which leads to failures. For effective attack mitigation, two tasks are required: determining the status of the system (normal operation/under attack) and localizing the attacked bus/power substation. Existing mitigation techniques carry out these tasks separately and offer limited detection performance. In this paper, we propose a multi-task learning-based approach that performs both tasks simultaneously using a graph neural network (GNN) with stacked convolutional Chebyshev graph layers. Our results show that the proposed model presents superior system status identification and attack localization abilities with detection rates of 98.5−100% and 99 − 100%, respectively, presenting improvements of 5 − 30% compared to benchmarks.

关键词： Location awareness Convolution Power system stability Chebyshev approximation Benchmark testing Multitasking Graph neural networks

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A Comparison of Multi Class Support Vector Machine vs Deep Convolutional Neural Network for Brain Tumor Classification

A Comparison of Multi Class Support Vector Machine vs Deep C...

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International Seminar on Application for Technology of Information and Communication (ISemantic)

作者： Nur Ryan Dwi Cahyo Christy Atika Sari Eko Hari Rachmawanto Cahaya Jatmoko Rabei Raad Ali Al-Jawry Mohamed Ayad Alkhafaji Department of Informatics Engineering University of Dian Nuswantoro Semarang Indonesia Study Program in Informatics Engineering University of Dian Nuswantoro Semarang Indonesia Department Computer Science Northern Technical University Semarang Indonesia Department of Computer Engineering Technology Northern Technical University Mosul Iraq Department of Computer Sciece National University of Science and Technology DhiQar Iraq

Multi SVM has long been one of the popular methods in classification, while DCNN has recently gained significant attention in image processing and pattern recognition. This research evaluates the effectiveness of Multi Class Support Vector Machine (M-SVM) and Deep Convolutional Neural Network (DCNN) techniques in classifying brain tumors. A dataset of 2660 3D medical images with dimensions 227 x 227 x 3; including Glioma, Meningioma, and Pituitary tumors, has been partitioned into distinct sets for both training and testing purposes. DCNN approach achieves excellent accuracy in identifying tumor names, with a training accuracy of 97.8% and 100% success rate in 9 experiments. The Multi SVM method demonstrates relatively good accuracy, with training accuracies ranging from 70% to 90% based on different kernel functions. These findings provide valuable insights for selecting appropriate methods in brain tumor classification and encourage further exploration of hybrid Multi SVM-DCNN approaches to enhance accuracy and reliability.

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