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International Conference on Display Technology, ICDT 2022

作者： Yuan, Zheng-Nan Sun, Zhi-Bo Vashchenko, Valerii. Kwok, Hoi-Sing. Srivastava, Abhishek. K. State Key Laboratory of Advanced Displays and Optoelectronics Technologies and Center for Display research Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Clear Water Bay Road Kowloon Hong Kong

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Fast Fringe-field-effect Free Continuous 2. 22ɧ Phase Modulation Based on Non-linear Kerr Effect of Vertical Aligned Deformed Helix Ferroelectric Liquid Crystal

Fast Fringe-field-effect Free Continuous 2. 22ɧ Phase Modul...

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International Conference on Display Technology, ICDT 2022

作者： Yuan, Zheng-Nan Sun, Zhi-Bo Yu, Xin-Yi Cheng, Ming Cheng, Yue-Chu Li, An-Ran Vashchenko, Valerii Kwok, Hoi-Sing. Srivastava, Abhishek K. State Key Laboratory of Advanced Displays and Optoelectronics Technologies and Center for Display research Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Clear Water Bay Road Kowloon Hong Kong

A fast (Relaxation ~25μs and response ~50μs at 55℃) high-contrast continuous 5 phase modulation without fringe field effect (FFE) based upon Kerr effect of vertical aligned deformed helix ferroelectric liquid crystal (VADHFLC) is proposed. Specifically, FFE is a long-term inevitable barrier for nematic liquid crystal (NLC). Surprisingly, it is the 1st-time found that no FFE exists for VADHFLC under 1μm pitch, which is an arresting feature for ultra-high-resolution applications. Moreover, the Kerr constant is 55nm/V2 and the contrast ratio is > 1000:1. It is a promising candidate for AR/VR, wavelength selective switch (WSS), hologram, ultra-high-PPI display, adaptive optics and LiDAR applications. © 2022, John Wiley and Sons Inc. All rights reserved.

关键词： Phase modulation

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Multiresource allocation in dynamic MEC systems: a continual learning-based approach 3

Multiresource allocation in dynamic MEC systems: a continual...

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3rd International Conference on Electronic Information engineering, Big Data, and computer Technology, EIBDCT 2024

作者： Zhan, Shutao Mao, Jin Chu, Zhipeng Xiong, Ke Fan, Pingyi Letaief, Khaled B. Eng. Res. Center of Network Management Technology for High Speed Railway of Ministry of Education School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China National Engineering Research Center of Advanced Network Technologies Beijing Jiaotong University Beijing100044 China Collaborative Innovation Center of Railway Traffic Safety Beijing Jiaotong University Beijing100044 China Department of Electronic Engineering Tsinghua University Beijing100084 China Department of Electrical and Computer Engineering Hong Kong University of Science and Technology Hong Kong Hong Kong

ISBN: (纸本)9781510680449

Recently, mobile edge computing (MEC) has been considered as a promising solution to address the challenges arising from the growing computationally intensive applications and limited computation capability of mobile devices. Thus, the resource allocation is crucial for ensuring the system performance of MEC. Recent deep learning-based resource allocation (DLRA) methods achieve preferable performance in real-time. However, in practical scenarios, the number of users often changes, which leads to a decrease in the performance of DLRA methods. Thus, once the number of users changes, the traditional DLRA methods need to retrain their neural networks, which consumes a lot of computing resources. To address this issue, this paper proposes a continual learning-based resource allocation (CLRA) method to jointly optimize multiple resource including offloading decision, power control, time slot assignment and computation resource distribution in MEC system. Our CLRA method is able to avoid forgetting previous knowledge when the number of users in the MEC system changes by selecting a small number of the most representative samples from the past and relearning. Experimental results show that, our proposed CLRA method achieves a better performance than the traditional DLRA method in terms of the higher quality of service's (QoS's) satisfaction rate and the less energy consumption. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Resource allocation

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A Convolutional Neural Network for Ultra-Wideband Radar-Based Hand Gesture Recognition

A Convolutional Neural Network for Ultra-Wideband Radar-Base...

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2023 International Technical Conference on Circuits/Systems, computers, and Communications, ITC-CSCC 2023

作者： Mekruksavanich, Sakorn Jantawong, Ponnipa Tancharoen, Datchakorn Jitpattanakul, Anuchit School of Information and Communication Technology University of Phayao Department of Computer Engineering Phayao Thailand Panyapiwat Institute of Management Faculty of Engineering and Technology Nonthaburi Thailand Intelligent and Nonlinear Dynamic Innovations Research Center King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Mathematics Bangkok Thailand

ISBN: (纸本)9798350326413

New developments in sensing technology have enabled the creation of improved assistive devices that enhance daily eldercare routines and offer personalized care to users. Wearable or ambient sensors can now detect a person's behavior, but the need for constant recharging due to their high energy consumption makes wearing such devices around the clock difficult. In this paper, a new sensing technique based on deep learning (DL) approaches for hand gesture recognition (HGR) using an ultra-wideband (UWB) radar sensor is introduced. The study examines the recognition performance of a convolutional neural network (CNN) for HGR and utilizes the UWB-gestures dataset to train and test the DL networks in various experiments with different scenarios. The experimental results demonstrate that the CNN achieved the highest F1-score of 97.19%. © 2023 IEEE.

关键词： Convolutional neural networks

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A Location-Aware WebAssembly-Based Software Update Framework for IoT End Devices

A Location-Aware WebAssembly-Based Software Update Framework...

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Consumer Communications and Networking Conference, CCNC IEEE

作者： Ivan Zyrianoff Federico Montori Angelo Trotta Luca Sciullo Lorenzo Gigli Carlos Kamienski Marco Di Felice Department of Computer Science and Engineering University of Bologna Italy Advanced Research Center on Electronic Systems “Ercole De Castro” University of Bologna Italy Federal University of ABC (UFABC) Brazil

ISBN: (数字)9798331508050

ISBN: (纸本)9798331508067

The increasing computational capabilities of IoT end devices push the deployment of application logic tasks directly on the extreme edge rather than the cloud or edge nodes. However, there are still unresolved issues on the Over-The-Air (OTA) software update operations for IoT end devices: (i) the hardware heterogeneity in IoT settings requires custom code for each different device type; (ii) the growing complexity of microcontroller code couples the development of high-level processing tasks with low-level operations; (iii) efficient methods for updating target IoT devices in a specific geographical area are absent. To address these issues, we propose an OTA firmware update framework that utilizes WebAssembly (WASM) and incorporates location-aware features. We split the application logic in WASM from the rest of the firmware written in native code, in order to create a greater separation of concerns. WASM's platform independence creates an abstraction layer for the underlying hardware, allowing the same application logic to be deployed virtually to any IoT device. We integrate a location-aware extension of the MQTT protocol in our framework to enable software updates targeting devices in specific geographical areas. Finally, our experiments demonstrate that location awareness does not add significant overhead to the system and that the performance of WASM in a microcontroller is comparable to native code and superior to Micropython.

关键词： Performance evaluation Codes Protocols Microcontrollers Wireless networks Software Hardware Internet of Things Logic Microprogramming

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A Hybrid Deep Neural Network with Attention Mechanism for Human Activity Recognition Based on Smartphone Sensors 7

A Hybrid Deep Neural Network with Attention Mechanism for Hu...

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7th International Conference on Information Technology, InCIT 2023

作者： Mekruksavanich, Sakorn Tancharoen, Datchakorn Jitpattanakul, Anuchit University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao Thailand Panyapiwat Institute of Management Faculty of Engineering and Technology Nonthaburi Thailand Faculty of Applied Science King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Bangkok Thailand

ISBN: (纸本)9798350358698

Human activity recognition (HAR) is an area of study that seeks to automatically and precisely detect an individual's behavior by analyzing bio-signal data. Bio-signal data can be acquired using sensing technology integrated into smartphones and other worn intelligent gadgets. Nevertheless, collecting sensor data tagged with activity information employing individual smartphones can result in data being analyzed in varying contexts, potentially compromising the accuracy of machine learning prediction methodologies. This paper introduces a novel HAR approach via smartphone sensors. The proposed method incorporates a hybrid deep neural network architecture enhanced with an attention mechanism. The deep learning model under consideration is referred to as the Att-CNN-BLSTM network. This particular network can autonomously extract significant features from smartphone sensor data. This feature extraction aims to effectively classify different human actions with a high degree of accuracy. In order to assess the efficacy of the hybrid model, a series of investigations were undertaken utilizing a publically accessible HAR dataset. This dataset was employed for training and testing purposes, employing a 5-fold cross-validation approach. We additionally carried out an analytical comparison utilizing state-of-the-art deep learning models from prior research. The findings from our experimentation demonstrate superior performance of the Att-CNN-BLSTM model over other advanced AI algorithms, with the highest accuracy rate of 90.54% and top F1-score of 86.88% attained. © 2023 IEEE.

关键词： Smartphones

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Human Activity Recognition in Logistics Using Wearable Sensors and Deep Residual Network 38

Human Activity Recognition in Logistics Using Wearable Senso...

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38th IEEE Region 10 Conference, TENCON 2023

作者： Mekruksavanich, Sakorn Tancharoen, Datchakorn Jitpattanakul, Anuchit School of Information and Communication Technology University of Phayao Department of Computer Engineering Phayao Thailand Panyapiwat Institute of Management Faculty of Engineering and Technology Nonthaburi Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Faculty of Applied Science Department of Mathematics Bangkok Thailand

ISBN: (纸本)9798350302196

Human action identification is a practical area of study with broad applicability in various domains, such as medical care, sport science, and manufacturing management. In logistics, it is essential to identify and examine individual actions, enabling machines to perceive and comprehend human motions for non-verbal interaction. This study specifically fo-cuses on efficiently classifying working activities in the logistics industry using wearable sensors, particularly in the context of human activity recognition. To achieve the research objective, a deep residual neural network was introduced, integrating convolutional layers, shortcut connections, and aggregated transformation for human activity recognition in logistics. The authors evaluated the effectiveness of their proposed deep learning model using the publicly accessible LARa dataset. The LARa dataset comprises a diverse range of human actions in the logistics domain, including standing, walking, cart handling, and synchronization. The details of activity were captured using wearable sensors affixed to different anatomical sites of the study participants. The experimental findings indicate that the model achieved a maximum F-measure of 85.30%. © 2023 IEEE.

关键词： Wearable sensors

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Sensor-Based Cattle Behavior Classification Using Deep Learning Approaches

Sensor-Based Cattle Behavior Classification Using Deep Learn...

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2023 International Technical Conference on Circuits/Systems, computers, and Communications, ITC-CSCC 2023

作者： Mekruksavanich, Sakorn Jantawong, Ponnipa Tancharoen, Datchakorn Jitpattanakul, Anuchit School of Information and Communication Technology University of Phayao Department of Computer Engineering Phayao Thailand Panyapiwat Institute of Management Faculty of Engineering and Technology Nonthaburi Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Faculty of Applied Science Department of Mathematics Bangkok Thailand

ISBN: (纸本)9798350326413

The usage of precision livestock has grown due to the need for higher efficiency and productivity in response to the high demand for food. To ensure sustainable development and quality control of the inputs required by the industry, it is essential to monitor and classify the behavior of cattle. Sensor-based monitoring systems provide accurate information by capturing raw data and identifying behavior through machine learning and deep learning algorithms. This approach has allowed farmers to better understand the individual needs of their animals. This study presents a deep residual neural network for the classification of cattle behavior. The performance of the ResNeXt model was evaluated using a public real-world dataset collected from sensors attached to the neck of six different Japanese black beef cows. The experimental results showed that the presented ResNeXt model achieved the highest average accuracy of 94.96% and the highest average F1-score of 93.66%. Compared to other baseline deep learning models and the current state-of-the-art model for cattle behavior classification, the presented model outperformed them and achieved better performance. © 2023 IEEE.

关键词： Quality control

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ON THE MAXIMUM SIZE OF VARIABLE-LENGTH NON-OVERLAPPING CODES

arXiv

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arXiv 2024年

作者： Wang, Geyang Wang, Qi Department of Electrical and Computer Engineering Institute for Systems Research University of Maryland College ParkMD20742 United States Department of Computer Science and Engineering National Center for Applied Mathematics Shenzhen Southern University of Science and Technology Nanshan District Guangdong Shenzhen518055 China

Non-overlapping codes are a set of codewords such that the prefix of each codeword is not a suffix of any codeword in the set, including itself. If the lengths of the codewords are variable, it is additionally required that every codeword is not contained in any other codeword as a subword. Let C(n, q) be the maximum size of q-ary fixed-length non-overlapping codes of length n. The upper bound on C(n, q) has been well studied. However, the nontrivial upper bound on the maximum size of variable-length non-overlapping codes of length at most n remains open. In this paper, by establishing a link between variable-length non-overlapping codes and fixed-length ones, we are able to show that the size of a q-ary variable-length non-overlapping code is upper bounded by C(n, q). Furthermore, we prove that the average length of the codewords in a q-ary variable-length non-overlapping codes is lower bounded by ⌈logq C˜⌉, and is asymptotically no shorter than n−2 as q approaches ∞, where C˜ denotes the cardinality of q-ary variable-length non-overlapping codes of length up to *** Codes 94A45, 94B25 © 2024, CC BY.

关键词： C (programming language)

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PID Tuning Using Neural Network Classification of Self-Excited Oscillations

PID Tuning Using Neural Network Classification of Self-Excit...

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International Workshop on Variable Structure Systems, VSS

作者： Ahmed Rehan Igor Boiko Yahya Zweiri Electrical Engineering and Computer Science Department Khalifa University Abu Dhabi UAE Department of Aerospace Engineering and Director of Advanced Research & Innovation Center (ARIC) Khalifa University Abu Dhabi UAE

ISBN: (数字)9798350353686

ISBN: (纸本)9798350353693

This paper proposes a method for tuning PID controllers based on the shape of self-excited oscillations in a system. The approach utilizes a Modified Relay Feedback Test (MRFT) to excite oscillations and a Neural Network (NN) classifier to identify their shapes. The novelty of this work lies in the application of shape-based tuning, categorized into triangular, sinusoidal, wavy, and curved triangular waveforms, each with distinct Tuning Rules (TR)s for integrating and non-integrating systems. A feedforward neural network is developed to classify the MRFT-induced oscillation shapes, enabling the application of appropriate TRs. This classifier shows remarkable accuracy with noise-free signals, and through retraining with noisy data, maintains high performance, outperforming traditional linear discriminant analysis. The study concludes that the NN-based classification significantly enhances the precision of PID tuning by accurately identifying the oscillation shape, thereby ensuring the application of the most effective TR.

关键词： Variable structure systems Training Shape Artificial neural networks Linear discriminant analysis Feedforward neural networks Noise measurement Relays Tuning Oscillators

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