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8th International Conference on Advances in Energy Resources and Environment engineering, ICAESEE 2022

作者： Peng, ShiJie Li, LinSen Wang, WenZhuo Ni, YueQi Zhang, ManLin Wang, JingWen Ke, CuiLing He, Kun School of Mechanical and Electrical Engineering Wuhan Institute of Technology Wuhan China School of Optical Information and Energy Engineering Wuhan Institute of Technology Wuhan China Polytechnic Institute of Zhejiang University Zhejiang Hangzhou China School of Computer Science & Engineering Wuhan Institute of Technology Wuhan China School of Chemical Engineering & Pharmacy Wuhan Institute of Technology Wuhan China Hefei Institutes of Physical Science Institute of Plasma Physics Chinese Academy of Sciences Hefei China Science Island Branch of Graduate School University of Science and Technology of China Hefei China

ISBN: (纸本)9783031425622

With the development of technology and the improvement of people’s living standards, the requirements for wastewater treatment are becoming higher and higher. In view of the current problems of environmental pollutant reduction and energy utilization, we propose to combine the semiconductor photocatalytic reaction with activated carbon membrane modules and RO membrane modules to generate strong oxidizing radicals through sunlight excitation of semiconductor photocatalytic materials at room temperature in order to achieve the treatment of harmful and refractory organic pollutants when photocatalytic materials are excited by sunlight in ordinary temperature, solving the problems of traditional wastewater treatment such as high energy consumption, incomplete treatment and easy generation of secondary pollution. The wastewater treatment apparatus designed in this paper and related technology can not only be used for the pretreatment or advanced treatment of refractory organic wastewater in pharmaceutical, breeding, and medical industries, but also for the purification of micro-polluted water sources. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Wastewater treatment

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AgeSynthGAN: Advanced Facial Age Synthesis with StyleGAN2

AgeSynthGAN: Advanced Facial Age Synthesis with StyleGAN2

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IEEE Visual Communications and Image Processing (VCIP)

作者： Tung-Ke Hsieh Tsung-Jung Liu Kuan-Hsien Liu Dept. of Electrical Engineering and Graduate Institute of Communication Engineering National Chung Hsing University Taiwan Dept. of Computer Science and Information Engineering National Taichung University of Science and Technology Taiwan

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Facial age synthesis is an important research area that aims to synthesize facial images from the past (age progression) or the future (age regression) by reflecting the age factors of a given face. Ideally, this task should be able to synthesize natural faces of different ages while maintaining identity consistency. However, existing methods suffer from background blur and inconsistency issues when processing the generated images. In this study, we propose a new method that utilizes semantic segmentation technology and attention mechanisms to solve these problems. Through this method, we can control the background of the generated image, making it clearer and more consistent, and we added an attention mechanism to better extract the features of faces. Additionally, we introduce a shape loss to simulate the shape changes of faces across different ages. We evaluate our approach through both qualitative and quantitative assessments. The results show that, compared with state-of-the-art methods, our approach is visually superior, achieves higher age accuracy, and provides reasonable identity confidence. Overall, our research presents a new method for facial age synthesis with promising applications and theoretical significance. The source code and model are available at https://***/34QX5O.

关键词： Attention mechanisms Shape Visual communication Source coding Semantic segmentation Transforms Aging Feature extraction Faces Facial features

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Digitally-defined ultrathin transparent wireless sensor network for room-scale imperceptible ambient intelligence

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npj Flexible Electronics 2024年第1期8卷 869-878页

作者： Yunxia Jin Mengxia Yu Dat T.Nguyen Xin Yang Zhipeng Li Ze Xiong Chenhui Li Yuxin Liu Yong Lin Kong John S.Ho Institute for Health Innovation and Technology National University of SingaporeSingapore 117599Singapore Department of Biomedical Engineering National University of SingaporeSingapore 119276Singapore Department of Electrical and Computer Engineering National University of SingaporeSingapore 117583Singapore Integrative Sciences and Engineering Programme National University of Singapore Graduate SchoolSingapore 119077Singapore The N.1 Institute for Health National University of SingaporeSingapore 117456Singapore Institute of Materials Research and Engineering Agency for Science Technology and ResearchSingapore 138634Singapore Department of Mechanical Engineering University of UtahSalt Lake CityUT 84112USA

Wireless and battery-free radio-frequency(RF)sensors can be used to create physical spaces that ambiently sense and respond to human *** such sensors ultra-flexible and transparent is important to preserve the aesthetics of living environments,accommodate daily activities,and functionally integrate with ***,existing RF sensors are unable to simultaneously achieve high transparency,flexibility,and the electrical conductivity required for remote room-scale ***,we report 4.5μm RF tag sensors achieving transparency exceeding 90%that provide capabilities in room-scale ambient wireless *** develop a laser-assisted water-based adhesion-reversion process to digitally realize computer-aided RF design at *** individually tagging multiple objects and regions of the human body,we demonstrate multiplexed wireless tracking of humanenvironment interactions and physiological signals at a range of up to *** radio-frequency identification sensors open opportunities for non-intrusive wireless sensing of daily living spaces for applications in health monitoring and elderly care.

关键词： network ambient transparent

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LIRSRN: A Lightweight Infrared Image Super-Resolution Network

LIRSRN: A Lightweight Infrared Image Super-Resolution Networ...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Chun-An Lin Tsung-Jung Liu Kuan-Hsien Liu Department of Electrical Engineering and Graduate Institute of Communication Engineering National Chung Hsing University Taichung Taiwan Department of Computer Science and Information Engineering National Taichung University of Science and Technology Taichung Taiwan

ISBN: (数字)9798350330991

ISBN: (纸本)9798350331004

Infrared imaging is crucial for many applications, such as night vision and environmental monitoring. However, it often suffers from lower spatial resolution compared to visible light imaging, which hinders its effectiveness in scenes demanding fine detail discernment. This paper introduces a Lightweight Infrared Image Super-Resolution Network (LIRSRN), which is a novel architecture designed to enhance the resolution of IR images with minimal computation overhead. The core of LIRSRN is the Attention Enhancement Module, which synergizes various attention mechanisms to emphasize important features across both channel and spatial domains. It is further enhanced by spatial frequency processing, which helps to extract key image attributes. The training is conducted on the DIV2K dataset, and the test results from "results-A" and "results-C" demonstrate the superior performance of LIRSRN in terms of PSNR and SSIM. Ablation studies compare the trade-offs between performance and computational cost using different attention mechanisms. Overall, the proposed model strikes a balance between performance and complexity. The source code and trained model are available at https://***/yYDnA6.

关键词： Training Computational modeling Source coding Scalability Superresolution Refining computer architecture

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Comments on "An Efficient and Provably Secure Authenticated Key Agreement Protocol for Fog-Based Vehicular Ad-Hoc Networks" 6

Comments on "An Efficient and Provably Secure Authenticated ...

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6th IEEE International Conference on Electronics and Communication engineering, ICECE 2023

作者： Awais, Syed Muhammad Yucheng, Wu Mahmood, Khalid Kharel, Rupak Chongqing University School of Microelectronics and Communication Engineering Chongqing China University of Central Lancashire National Yunlin University of Science and Technology Preston United Kingdom School of Psychology and Computer Science Graduate School of Intelligent Data Science Yunlin64002 Taiwan University of Central Lancashire Ton Duc Thang University Preston United Kingdom School of Psychology and Computer Science Faculty of Electrical and Electronics Engineering Ho Chi Minh City Viet Nam

ISBN: (纸本)9798350328721

Vehicle ad-hoc networks (VANETs) have experienced rapid growth due to the advancement of cloud computing, IoT technologies, and intelligent transportation systems (ITS). Vehicles are required to have enhanced storage capacity, onboard computing capabilities, improved sensing power, and communication systems. To address real-world demands like low latency, affordable storage, and mobility in VANET deployments, There have been efforts to integrate fog computing with VANETs in a practical implementation. "An Efficient and Provably Secure Authenticated Key Agreement Protocol for Fog-Based Vehicular Ad-Hoc Networks"was proposed by Ma et al. (IEEE Internet of Things Journal, pp 8065-8075, 10.1109/JIOT.2019.2902840). According to their claims, the use of their secure authentication technique can help prevent security threats. However, after careful investigation, we discovered that their authentication protocol is susceptible to vehicle user impersonation attacks and also does not provide vehicle anonymity. In light of this, we have provided some recommendations to address the current flaws in the protocol developed by Ma et al. © 2023 IEEE.

关键词： Vehicular ad hoc networks

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An Optimal Framework for SDN Based on Deep Neural Network

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computers, Materials & Continua 2022年第10期73卷 1125-1140页

作者： Abdallah Abdallah Mohamad Khairi Ishak Nor Samsiah Sani Imran Khan Fahad RAlbogamy Hirofumi Amano Samih M.Mostafa Department of Industrial Engineering School of Applied Technical Sciences German Jordanian UniversityAmman35247Jordan School of Electrical and Electronic Engineering Universiti Sains MalaysiaNibong Tebal14300Malaysia Center for Artificial Intelligence Technology Faculty of Information Science and TechnologyThe National University of Malaysia(UKM)Bangi43600SelangorMalaysia Department of Electrical Engineering University of Engineering and TechnologyPeshawar814Pakistan Turabah University College Computer Sciences ProgramTaif UniversityTaif21944Saudi Arabia Research Institute for Information Technology Kyushu UniversityFukuoka819-0395Japan Computer Science-Mathematics Department Faculty of ScienceSouth Valley UniversityQena83523Egypt

Software-defined networking(SDN)is a new paradigm that promises to change by breaking vertical integration,decoupling network control logic from the underlying routers and switches,promoting(logical)network control centralization,and introducing network ***,the controller is similarly vulnerable to a“single point of failure”,an attacker can execute a distributed denial of service(DDoS)attack that invalidates the controller and compromises the network security in *** address the problem of DDoS traffic detection in SDN,a novel detection approach based on information entropy and deep neural network(DNN)is *** approach contains a DNN-based DDoS traffic detection module and an information-based entropy initial inspection *** initial inspection module detects the suspicious network traffic by computing the information entropy value of the data packet’s source and destination Internet Protocol(IP)addresses,and then identifies it using the DDoS detection module based on *** assaults were found when suspected irregular traffic was *** reveal that the algorithm recognizes DDoS activity at a rate of more than 99%,with a much better accuracy *** false alarm rate(FAR)is much lower than that of the information entropy-based detection ***,the proposed framework can shorten the detection time and improve the resource utilization efficiency.

关键词： Deep neural network computer networks data security optimization

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Micro-Expression Recognition Based On 3DCNN Combined With GRU and New Attention Mechanism

Micro-Expression Recognition Based On 3DCNN Combined With GR...

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IEEE International Conference on Image Processing

作者： Chun-Ting Fang Tsung-Jung Liu Kuan-Hsien Liu Department of Electrical Engineering and Graduate Institute of Communication Engineering National Chung Hsing University Taichung Taiwan Department of Computer Science and Information Engineering National Taichung University of Science and Technology Taichung Taiwan

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

Micro-expressions, as a form of non-verbal emotional expressions, play a key role in interpersonal interaction. However, they are also quite challenging and not easy to analyze. In this paper, we propose a dual-branch shallow 3DCNN architecture that combines Gated Recurrent Unit (GRU) and enhances the Channel Attention Module (CAM) in Convolutional Block Attention Module (CBAM) to make it more suitable for recognizing micro facial expressions. Experiments show that the proposed method can achieve good results with a relatively simple architecture. The source code and pre-trained models are available at https://***/dannyFan-0201/ICIP_2024.

关键词： Convolutional codes Gold Attention mechanisms Image recognition Source coding Face recognition Logic gates

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A Novel Nighttime Close-up and Far-away Reflection Dataset with Absorption Effects

A Novel Nighttime Close-up and Far-away Reflection Dataset w...

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IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： U-In Chan Tsung-Jung Liu Kuan-Hsien Liu Chun-An Lin Dept. of Electrical Engineering and Graduate Institute of Communication Engineering National Chung Hsing University Taiwan Dept. of Computer Science and Information Engineering National Taichung University of Science and Technology Taiwan

ISBN: (数字)9798350386844

ISBN: (纸本)9798350386851

Existing methods for synthesizing reflection removal datasets meticulously account for various factors, including diverse glass properties (e.g., thickness, color, direction), the processing of reflection images (e.g., Gaussian noise, smoothness, intensity), and the weighting between transmission (T) and reflection (R) images. However, these approaches overlook the distinction between day and night reflections and the significant influence of the distance between the photographer and the glass on the reflection image. Therefore, we propose to incorporate considerations of distance, lighting conditions (day and night), and combined absorption effects into the synthetic dataset generation process to produce a reflection dataset that closely resembles real-world scenarios. Moreover, creating a nighttime reflection dataset poses greater challenges than its daytime counterpart. We aim to demonstrate the applicability and address the challenges of our proposed synthetic dataset through an approach that integrates absorption effects.

关键词： Absorption Image color analysis Gaussian noise Lighting Glass Reflection Colored noise

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Nitrogen doping effect on InGaZnO-based artificial synapse for implementing reservoir computing and SVHN dataset pattern recognition

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Nano Energy 2024年 129卷

作者： Mahata, Chandreswar So, Hyojin Ju, Dongyeol Ismail, Muhammad Kim, Sungjoon Hsu, Chih-Chieh Park, Kyungchul Kim, Sungjun Division of Electronics and Electrical Engineering Dongguk University Seoul04620 Korea Republic of Department of AI Semiconductor Engineering Korea University Sejong30019 Korea Republic of Graduate School of Engineering Science and Technology and the Graduate School of Electronic Engineering National Yunlin University of Science and Technology Douliu64002 Taiwan Department of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of

This work focused on plasma-induced nitrogen-doped indium gallium zinc oxide (InGaZnO:N) based resistive switching devices. Nitrogen atoms in the InGaZnO:N can reduce the randomness of oxygen vacancy formation and conducting filaments, resulting in stable and uniform resistive switching (RS) and artificial synaptic performance. Optimized nitrogen and oxygen ion concentration improves the redox reaction depending on their reactivity with InGaZnO due to optimal VO concentration in the switching layer confirmed by X-ray photoelectron spectroscopic (XPS) analysis. Device-to-device promising RS variability with ION/IOFF ratio >10 confirmed with multiple devices with low average SET/RESET voltages of 1.35 /[sbnd]1.5 V. Pulse-induced short-term memory learning characteristics with linear weight-update, including synaptic function of short-term potentiation (STP), paired-pulse facilitation (PPF), spike rate-dependent plasticity (SRDP), and experience dependent synaptic weight modification are successfully emulated and demonstrated. The optimization of InGaZnO:N-based memristors provides efficient temporal information processing capability, serving as the physical reservoir computing system, demonstrated through experimental pattern recognition. The potentiaiton/depression of conductance was used for on-chip learning with the street view house numbers (SVHN) dataset. This study suggests that moderate nitrogen-doped ITO/InGaZnO:N/ITO memristors possess potential as synaptic devices for neuromorphic systems. © 2024 Elsevier Ltd

关键词： Memristors

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Impact of CLAHE-based image enhancement for diabetic retinopathy classification through deep learning 7

Impact of CLAHE-based image enhancement for diabetic retinop...

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7th International Conference on computer science and Computational Intelligence, CSCI 2022

作者： Hayati, Mira Muchtar, Kahlil Roslidar Maulina, Novi Syamsuddin, Irfan Elwirehardja, Gregorius Natanael Pardamean, Bens Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh23111 Indonesia Universitas Syiah Kuala Banda Aceh23111 Indonesia Faculty of Medicine Universitas Syiah Kuala Banda Aceh23111 Indonesia Department of Computer and Networking Engineering Politeknik Negeri Ujung Pandang Makassar90245 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program-Master of Computer Science Program Bina Nusantara University Jakarta 11480 Indonesia

Diabetic retinopathy (DR) is a type of diabetes mellitus that attacks the retina of the eye. DR will cause patients to experience blindness slowly. Generally, DR can be detected by using a special instrument called an ophthalmoscope to view the inside of the eyeball. However, in conditions where there is a very small difference between the normal image and the DR image, computer-based assistance is needed for maximizing image reading value. In this research, a method of image quality improvement will be carried out which will then be integrated with a classification algorithm based on deep learning. The results of image improvement using Contrast Limited Adaptive Histogram Equalization (CLAHE) shows that the average accuracy of the method on several models is very competitive, 91% for the VGG16 model, 95% for InceptionV3, and 97% for EfficientNet compared to the results original image which only has an accuracy of 87% for VGG16 model, 90% for InceptionV3 model, and 95% for EfficientNet. However, in ResNet34 better accuracy is obtained in the original image with an accuracy of 95% while in the CLAHE image the accuracy value is only 84%. The results of this comprehensive evaluation and recommendation of famous backbone networks can be useful in the computer-Aided diagnosis of diabetic retinopathy. © 2022 Elsevier B.V.. All rights reserved.

关键词： Image enhancement

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