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An Overview of Facial Micro-Expression Analysis: Data, Methodology and Challenge

IEEE Transactions on Affective Computing 2023年第3期14卷 1857-1875页

作者： Xie, Hong-Xia Lo, Ling Shuai, Hong-Han Cheng, Wen-Huang National Yang Ming Chiao Tung Univresity Institute of Electronics Hsinchu300093 Taiwan National Yang Ming Chiao Tung Univresity Department of Electrical and Computer Engineering Hsinchu300093 Taiwan National Chung Hsing University Artificial Intelligence and Data Science Program Taichung40227 Taiwan

Facial micro-expressions indicate brief and subtle facial movements that appear during emotional communication. In comparison to macro-expressions, micro-expressions are more challenging to be analyzed due to the short span of time and the fine-grained changes. In recent years, micro-expression recognition (MER) has drawn much attention because it can benefit a wide range of applications, e.g., police interrogation, clinical diagnosis, depression analysis, and business negotiation. In this survey, we offer a fresh overview to discuss new research directions and challenges these days for MER tasks. For example, we review MER approaches from three novel aspects: macro-to-micro adaptation, recognition based on key apex frames, and recognition based on facial action units. Moreover, to mitigate the problem of limited and biased ME data, synthetic data generation is surveyed for the diversity enrichment of micro-expression data. Since micro-expression spotting can boost micro-expression analysis, the state-of-the-art spotting works are also introduced in this paper. At last, we discuss the challenges in MER research and provide potential solutions as well as possible directions for further investigation. © 2010-2012 IEEE.

关键词： Surveys

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NFT-Based Hydroponic Automated Control Using Adaptive Network-Based Fuzzy Inference System 2

NFT-Based Hydroponic Automated Control Using Adaptive Networ...

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2nd International Conference on Robotics, Automation and Artificial Intelligence, RAAI 2022

作者： Surantha, Nico Vincentdo, Vito Bina Nusantara University Binus Graduate Program - Computer Science Jakarta11480 Indonesia Tokyo City University Faculty of Scienc and Engineering Department of Electrical Electronics and Communication Engineering Japan

ISBN: (纸本)9781665459440

The rapid population growth and industrial development in developing countries harm the agricultural sector because many agricultural lands are converted into residential or industrial areas. Applying modern agriculture technologies such as hydroponic could help to overcome the problem. However, hydroponics requires special attention in adjusting the pH and nutrient levels to maximize plant growth, so an automated system is needed to manage the process. In this research, a smart hydroponic system is proposed by applying Adaptive Networkbased Fuzzy Inference System (ANFIS) and Internet-of-Things. The IoT system consists of sensor, actuator, and data processing layer is designed to monitor and control the condition of pH and nutrition of the observed plants. Then, the ANFIS algorithm is designed to control the level of pH and nutrition. The experiment results show that the system can automatically adjust the pH and nutrient levels to the expected range for growing plants, and the fuzzy controller made using ANFIS are more accurate and stable than the fuzzy controller made using Sugeno. This study shows that ANFIS has excellent performance when controlling multiple actuators, as long as the data set has great granularity and well defined. © 2022 IEEE.

关键词： Hydroponics

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Improving electrically Evoked Compound Action Potential Based on electrical Field Imaging and Electrode Interfaces

Improving Electrically Evoked Compound Action Potential Base...

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IEEE Conference on Electromagnetic Field Computation

作者： Charles T.M. Choi Chun Ting Ke Jelani Lawrence Alexander C. C. Wang Department of Electronics and Electrical Engineering College of Electrical & Computer Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan Institute of Electrical and Control Engineering College of Electrical & Computer Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan Institute of Biomedical Engineering College of Electrical & Computer Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan Electrical Engineering & Computer Science International Graduate Program College of Electrical & Computer Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan

ISBN: (数字)9798350348958

ISBN: (纸本)9798350348965

The electrically evoked compound action potential (ECAP) has been used in various clinical studies and has become a key physiological signal for cochlear implants (CI). This study used four sensing electrodes to record ECAP signals based on the alternating polarity approach. An electrical field imaging (EFI) result based on the finite element method was used to obtain the interface impedance, then ECAP simulation results were computed and compared with a patient's clinical ECAP measurements. Preliminary modeling results show that the interface impedance obtained by this EFI-based technique can improve the simulation accuracy of the ECAP model. The ECAP modeling result will be compared with clinical ECAP measurements to validate the model in the full paper.

关键词： Electrodes Action potentials Impedance measurement Computational modeling Simulation Imaging Finite element analysis

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Performance Enhancement of Stream-Based Decompression Process by Notifying Compression Buffer Size

Performance Enhancement of Stream-Based Decompression Proces...

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IEEE Symposium on computers and Communications (ISCC)

作者： Taiki Kato Shinichi Yamagiwa Koichi Marumo Master's Program in Computer Science Degree Program in Systems and Information Engineering University of Tsukuba Tsukuba Ibalaki Japan Faculty of Engineering Information and Systems University of Tsukuba Tsukuba Ibalaki Japan

This paper focuses on a performance enhancement of communication performance by compressing data stream. ASE coding is an effective lossless data compression method for data stream. The software implementation of the coding/decoding method inevitably meets a performance mismatch in memory and storage devices. In the compressor side, it is predictable to decide the size of an original data block and is available to process a flexible buffer memory. However, the decompressor is not able to predict the buffer size because the original data size is not obvious before the decompression. This causes a performance mismatch in the filesystem level. This paper proposes a novel method to address the performance mismatch by applying a notification mechanism of compression size from the compressor. This paper describes the mechanism focusing on the system call usage. Through experimental evaluations, we show the performance improvement of the decompression performance for handling data stream.

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Toward Parallelization Technique for Stream-based Lossless Data Compression

Toward Parallelization Technique for Stream-based Lossless D...

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IEEE International Conference on Big Data

作者： Taiki Kato Shinichi Yamagiwa Koichi Wada Master’s Program in Computer Science Degree Program in Systems and Information Engineering University of Tsukuba Tsukuba Ibalaki Japan Faculty of Engineering Information and Systems University of Tsukuba Tsukuba Ibalaki Japan

IoT edge platform has become popular in various distributed environments. The edge devices need to communicate BigData among them or with the cloud servers by collaborating with AI technologies for finding events from the applications. Those devices exchange data streams from such as distributed sensors and remote image/video devices. We focus on an acceleration technique for the communication performance using a stream-based lossless data compression technology. This paper proposes a parallelization technique for the compression process in a software environment running on a multicore processor. The technique invokes concurrent compression processes assigned to multiple threads with splitting a data stream to chunks. The paper exposes three scheduling methods for assigning the chunks to the threads: in-order, hybrid and out-of-order. As an original data order of chunks must be obtained in decompression side, the proposed technique introduces packeting mechanisms in each chunk by adding headers to support the scheduling methods. Through experimental performance evaluations, we discuss the packeting overhead focusing on compression ratio and speedup by the parallelization with three scheduling methods.

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ADMM-SRNet: Alternating Direction Method of Multipliers Based Sparse Representation Network for One-Class Classification

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IEEE Transactions on Image Processing 2023年 32卷 2843-2856页

作者： Chiou, Chien-Yu Lee, Kuang-Ting Huang, Chun-Rong Chung, Pau-Choo National Cheng Kung University Department of Electrical Engineering Tainan 701 Taiwan National Chung Hsing University Department of Computer Science and Engineering Taichung 402 Taiwan National Cheng Kung University Cross College Elite Program and Academy of Innovative Semiconductor and Sustainable Manufacturing Tainan 701 Taiwan

One-class classification aims to learn one-class models from only in-class training samples. Because of lacking out-of-class samples during training, most conventional deep learning based methods suffer from the feature collapse problem. In contrast, contrastive learning based methods can learn features from only in-class samples but are hard to be end-to-end trained with one-class models. To address the aforementioned problems, we propose alternating direction method of multipliers based sparse representation network (ADMM-SRNet). ADMM-SRNet contains the heterogeneous contrastive feature (HCF) network and the sparse dictionary (SD) network. The HCF network learns in-class heterogeneous contrastive features by using contrastive learning with heterogeneous augmentations. Then, the SD network models the distributions of the in-class training samples by using dictionaries computed based on ADMM. By coupling the HCF network, SD network and the proposed loss functions, our method can effectively learn discriminative features and one-class models of the in-class training samples in an end-to-end trainable manner. Experimental results show that the proposed method outperforms state-of-the-art methods on CIFAR-10, CIFAR-100 and ImageNet-30 datasets under one-class classification settings. Code is available at https://***/nchucvml/ADMM-SRNet. © 1992-2012 IEEE.

关键词： contrastive learning deep learning dictionary learning One-class classification sparse representation

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Simulation of Photonic Metasurfaces using a Surface Integral Equation Solver with Generalized Sheet Transition Conditions

Simulation of Photonic Metasurfaces using a Surface Integral...

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Antennas and Propagation (LACAP), IEEE Latin American Conference on

作者： Sebastian Celis Sierra Partha Mondal Ran Zhao Hakan Bagci Electrical and Computer Engineering (ECE) Program King Abdullah University of Science and Technology Thuwal Saudi Arabia School of Electronic Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798331527587

ISBN: (纸本)9798331527594

Photonic metasurfaces are efficiently and accurately simulated using a surface integral equation (SIE) solver. This solver models the metasurface as an infinitesimally thin sheet on which generalized sheet transition conditions (GSTCs) are enforced. GSTCs connect the electromagnetic fields on both sides of the metasurface using equivalent susceptibility tensors and avoid modelling of geometrically intricate metasurface unit cells by the SIE solver. Susceptibility tensors are obtained from the transmission and the reflection coefficients associated with a unit cell. A numerical example, involving a metasurface that supports high-quality factor resonances and excited by a Gaussian beam, is presented to demonstrate the accuracy of the proposed approach.

关键词： Tensors Accuracy Integral equations Metasurfaces Mathematical models Numerical models Reflection coefficient Electromagnetic fields Dispersion Photonics

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AI-based computer-aided System on Long Term Care

AI-based Computer-aided System on Long Term Care

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IEEE International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS)

作者： Yi-Jui Huang Hong-Yao Ciou Yuan-Hsun Yang Chin-Yin Lin Chia-Wei Lee Yu-Hsien Chen Computer Science and Information Engineering National Taitung University Taitung Taiwan Computer Science University of Taipei Taitung Taiwan Bachelor’s Degree Program for Indigenous Peoples in Senior Health and Care Management National Taitung University Taitung Taiwan

ISBN: (数字)9798350389210

ISBN: (纸本)9798350389227

According to data from Taiwan’s National Development Council in 2024, Taiwan is expected to enter a super-aged society by 2025, indicating a great acceleration in population aging. With the aging population and the increasing prevalence of chronic diseases, the importance of rehabilitation for the elderly has become crucial. Rehabilitation aims not only to aid in recovering from illness or surgery but also to maintain health in daily life. For the elderly, appropriate rehabilitation exercises can improve physical strength, cardiopulmonary function, and overall quality of life. This not only helps restore physical function but also reduces the occurrence and progression of chronic diseases. The system includes contextual rehabilitation games, general rehabilitation training, accessible remote instruction, and physical fitness assessment. The contextual rehabilitation games utilize gamified interactive designs to enhance engagement and motivation. General rehabilitation training provides standardized guidance on rehabilitation movements. Accessible remote instruction allows patients who are unable to move due to various factors to receive professional guidance in hospitals or care centers, reducing the inconvenience of movement. Finally, physical fitness assessment provides accurate data support for medical personnel, while also reducing manual calculation errors and labor costs. Through this comprehensive assistive system, smart healthcare can not only improve the effectiveness and efficiency of rehabilitation but also provide a new approach for health management and rehabilitation of the elderly, helping to address the challenges brought by an aging society in the future.

关键词： Training Hospitals Surgery Games Manuals Signal processing Health and safety Personnel Older adults Diseases

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Comparative Analysis of Real-Time Multi-Person Pose Detection in electrical Industrial Safety Scenarios Using YOLOv8-Pose and OpenPose 4

Comparative Analysis of Real-Time Multi-Person Pose Detectio...

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4th IEEE International Conference on Electronic Communications, Internet of Things and Big Data, ICEIB 2024

作者： Tsai, Shang-Te Wu, Zong-Rong Lin, Pin-Hsuan Chen, Chin-Hsiung Chien, Wei Chang, Yu-Cheng School of Art and Design Sanming University Fujian365004 China National Yunlin University of Science and Technology Department of Technical and Vocational Program in Artificial Intelligence Yunlin Taiwan National Central University Department of Business Administration Taoyuan Taiwan Commercial Industrial Vocational Senior High School Department of Electrical Engineering New Taipei Municipal Tamsui New Taipei Taiwan Lunghwa University of Science and Technology Department of Computer Information and Network Engineering Taoyuan Taiwan National Taipei University of Technology Department of Computer Science and Information Engineering Taipei Taiwan

ISBN: (纸本)9798350360721

We reviewed the application of modern technology for rapid and accurate multi-person real-time pose detection in the hazardous field of electrical engineering. We focused on two leading pose detection technologies: YOLOv8 and OpenPose. To optimize performance, we integrated these two techniques into the LSTM model for training and investigated frame rates and accuracy. © 2024 IEEE.

关键词： Accident prevention

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Solution Processable Mos2 Based Memristive Synapse for Brain Inspired Computing

Solution Processable Mos2 Based Memristive Synapse for Brain...

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IEEE Conference on Nanotechnology

作者： Hanrui Li Dayanand Kumar Nazek El-Atab Smart Advanced Memory Devices and Applications (SAMA) labs Electrical and Computer Engineering Program Computer Electrical Mathematical Science and Engineering (CEMSE) Division KAUST Thuwal Saudi Arabia

As a novel 2D material, MoS 2 has shown excellent electrical properties and resistive switching characteristics to work as a switching layer for non-volatile memory. In this work, we drop cast the MoS 2 solution to prepare the thin film and deposit an interfacial layer of Al 2 O 3 . We demonstrate the proposed memristive device with Cu/Al 2 O 3 /MoS 2 /Pt structure to work as an artificial synapse. The device shows a steady resistive switching behavior with the SET and RESET voltages of 1.3 V and -0.5 V, respectively. We further demonstrate the synapse behavior via a Hopfield Neural Network (HNN) and achieve image recognition and reconstruction with a high accuracy of 96% after 15 training epochs.

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