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Perceptive self-supervised learning network for noisy image watermark removal

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arXiv 2024年

作者： Tian, Chunwei Zheng, Menghua Li, Bo Zhang, Yanning Zhang, Shichao Zhang, David School of Software Northwestern Polytechnical University Xi’an710129 China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Xi’an710129 China School of Electronics and Information Northwestern Polytechnical University Xi’an710129 China School of Computer Science Northwestern Polytechnical University National Engineering Laboratory for Integrated AeroSpace-Ground-Ocean Big Data Application Technology Xi’an710129 China Guangxi Key Lab of Multisource Information Mining & Security College of Computer Science & Engineering Guangxi Normal University Guilin541004 China Shenzhen518172 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518172 China

Popular methods usually use a degradation model in a supervised way to learn a watermark removal model. However, it is true that reference images are difficult to obtain in the real world, as well as collected images by cameras suffer from noise. To overcome these drawbacks, we propose a perceptive self-supervised learning network for noisy image watermark removal (PSLNet) in this paper. PSLNet depends on a parallel network to remove noise and watermarks. The upper network uses task decomposition ideas to remove noise and watermarks in sequence. The lower network utilizes the degradation model idea to simultaneously remove noise and watermarks. Specifically, mentioned paired watermark images are obtained in a self-supervised way, and paired noisy images (i.e., noisy and reference images) are obtained in a supervised way. To enhance the clarity of obtained images, interacting two sub-networks and fusing obtained clean images are used to improve the effects of image watermark removal in terms of structural information and pixel enhancement. Taking into texture information account, a mixed loss uses obtained images and features to achieve a robust model of noisy image watermark removal. Comprehensive experiments show that our proposed method is very effective in comparison with popular convolutional neural networks (CNNs) for noisy image watermark removal. Codes can be obtained at https://***/hellloxiaotian/PSLNet. Copyright © 2024, The Authors. All rights reserved.

关键词： Image denoising

Two Adaptive Control Structures of Robot Manipulators

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IFAC Proceedings Volumes 1989年第10期22卷 371-377页

作者： B.J. Oh M. Jamshidi H. Seraji Automation Technologies Department Korea Electronics and Telecomm Research Institute Daedog Danji. P.O. Box 8. Daejeon. Korea CAD Laboratory for Systems/Robotics. Department of Electrical and Computer Engineering University of New Mexico. Albuquerque. New Mexico 87131. USA Jet Propulsion Laboratory. California Institute of Technology. 4800 Oak Grove Drive. Pasadena. California 91109. USA

This paper proposes two simple schemes for adaptive control of robot manipulator, to achieve trajectory tracking. The state feedback controller consists of feedforward from the reference position trajectory, feedback from the actual trajectory, and an auxiliary input. The feedforward/feedback controller is different from the state feedback controller in that it consists of feedforward from the reference position, velocity, and acceleration trajectory based on “inverse” dynamics of robot manipulator. The feedforward and feedback gains and the auxiliary input are adapted using adaptive control theory based on Lyapunov's direct method, and using only the local information of the corresponding joint. The proposed control schemes are computationally fast and do not require a priori knowledge of the parameter of the manipulator or the payload. Simulation results are presented in support of the proposed schemes.

关键词： Adaptive Control Decentralized Adaptive Control Robot Manipulator Feedforward Feedback

Genetic algorithm based optimization for EMG pattern recognition system

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IFAC Proceedings Volumes 2009年第13期42卷 53-58页

作者： P. Fischer T.G. Amaral O.P. Dias A.R. Wołczowski M. Lichtenstein Institute of Computer Engineering Control and Robotics Faculty of Electronics Wroclaw University of Technology Wybrzeze Wyspianskiego 27 50-370 Wroclaw Poland Escola Superior de Tecnologia de Setúbal/IPS CESET ISR – Pólo Coimbra 2910-761 Setúbal Portugal (Tel: +351-265-79000) Escola Superior de Tecnologia de Setúbal/IPS CESET INESC – Lisboa 2910-761 Setúbal Portugal (Tel: +351-265-79000)

Abstract The paper presents the concept of genetic algorithm based optimization for the EMG pattern recognition system controlling the hand prosthesis. The recognition of EMG signals for determining the hand movements is made by a linear neural network discriminating between five predefined grasps. The input feature vector for the classification was established using AR model, and its coefficients became the features. The genetic algorithms are used to optimize the number of elements in the input feature vector and simultaneously maintain the recognition efficiency at the same level. Experimental results show a good efficiency in the optimization method maintaining the performance of the recognition system for the studied grasp movements’ repertoire.

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Intelligent upper-limb exoskeleton integrated with soft wearable bioelectronics and deep-learning for human intention-driven strength augmentation based on sensory feedback

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arXiv 2023年

作者： Lee, Jinwoo Kwon, Kangkyu Soltis, Ira Matthews, Jared Lee, Yoon-Jae Kim, Hojoong Romero, Lissette Zavanelli, Nathan Kwon, Youngjin Kwon, Shinjae Lee, Jimin Na, Yewon Lee, Sung Hoon Yu, Ki Jun Shinohara, Minoru Hammond, Frank L. Yeo, Woon-Hong Department of Mechanical Robotics and Energy Engineering Dongguk University Seoul04620 Korea Republic of IEN Center for Human-Centric Interfaces and Engineering Institute for Electronics and Nanotechnology Georgia Institute of Technology AtlantaGA30332 United States School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA30332 United States George W. Woodruff School of Mechanical Engineering Georgia Institute of Technology AtlantaGA30332 United States School of Industrial Design Georgia Institute of Technology AtlantaGA30332 United States School of Electrical and Electronic Engineering Yonsei University Seoul03722 Korea Republic of School of Biological Sciences Georgia Institute of Technology AtlantaGA30332 United States Wallace H. Coulter Department of Biomedical Engineering Georgia Institute of Technology and Emory University School of Medicine AtlantaGA30332 United States Institute for Materials Parker H. Petit Institute for Bioengineering and Biosciences Neural Engineering Center Institute for Robotics and Intelligent Machines Georgia Institute of Technology AtlantaGA30332 United States

The age and stroke-associated decline in musculoskeletal strength degrades the ability to perform daily human tasks using the upper extremities. Here, we introduce an intelligent upper-limb exoskeleton system that uses cloud-based deep learning to predict human intention for strength augmentation. The embedded soft wearable sensors provide sensory feedback by collecting real-time muscle signals, which are simultaneously computed to determine the user's intended movement. The cloud-based deep-learning predicts four upper-limb joint motions with an average accuracy of 96.2% at a 500-550 millisecond response rate, suggesting that the exoskeleton operates just by human intention. In addition, an array of soft pneumatics assists the intended movements by providing 897 newton of force while generating displacement of 87 millimeter at maximum. Collectively, the intent-driven exoskeleton can reduce human muscle activities by 3.7 times on average compared to the unassisted *** Codes 68T40 (Primary) 92C55, 68T99 (Secondary) Copyright © 2023, The Authors. All rights reserved.

关键词： Sensory feedback

Aesthetically Relevant Image Captioning

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arXiv 2022年

作者： Zhong, Zhipeng Zhou, Fei Qiu, Guoping College of Electronics and Information Engineering Shenzhen University China Peng Cheng National Laboratory Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen China Shenzhen Institute for Artificial Intelligence and Robotics for Society China Guangdong-Hong Kong Joint Laboratory for Big Data Imaging and Communication Shenzhen China School of Computer Science The University of Nottingham United Kingdom

Image aesthetic quality assessment (AQA) aims to assign numerical aesthetic ratings to images whilst image aesthetic captioning (IAC) aims to generate textual descriptions of the aesthetic aspects of images. In this paper, we study image AQA and IAC together and present a new IAC method termed Aesthetically Relevant Image Captioning (ARIC). Based on the observation that most textual comments of an image are about objects and their interactions rather than aspects of aesthetics, we first introduce the concept of Aesthetic Relevance Score (ARS) of a sentence and have developed a model to automatically label a sentence with its ARS. We then use the ARS to design the ARIC model which includes an ARS weighted IAC loss function and an ARS based diverse aesthetic caption selector (DACS). We present extensive experimental results to show the soundness of the ARS concept and the effectiveness of the ARIC model by demonstrating that texts with higher ARS’s can predict the aesthetic ratings more accurately and that the new ARIC model can generate more accurate, aesthetically more relevant and more diverse image captions. Furthermore, a large new research database containing 510K images with over 5 million comments and 350K aesthetic scores, and code for implementing ARIC are available at https://***/PengZai/ARIC. © 2022, CC BY-NC-ND.

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An Intelligent Age of Information Based Self-Energized Uav-Assisted Wireless Communication System

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SSRN

SSRN 2022年

作者： Jeganathan, Anandpushparaj Dhayabaran, Balaji Jayakody, Dushantha Nalin K. Ranchagodage Don, Sanjaya Arunapriya Muthuchidambaranathan, P. Department of Electronics and Communication Engineering National Institute of Technology Tiruchirappalli620015 India Senior Member Technical Staff Baseband Algorithm Development Team Saankhya Labs Private Limited Bengaluru560001 India Centro de Investigaçä em Tecnologias Autónoma TechLab Universidade Autónoma Lisboa Portugal School of Computer Science and Robotics National Research Tomsk Polytechnic University Tomsk Russia Centre for Telecommunication Research School of Engineering Sri Lanka Technological Campus Padukka 10500 Sri Lanka

Internet-of-things (IoT) is an enabling technology in the fourth generation industrial revolution. An unified performance metric named age of information (AoI) is introduced to quantify the freshness of data and its applications. Thus, this paper study the combination of AoI and full-duplex enabled unmanned aerial vehicle (FD-UAV) as a promising solution to improve latency and spectrum efficiency of a wireless powered sensor network. A sensor node (SN), located in a transport infrastructure, harvests energy from radio frequency signals transmitted by the FD-UAV. This uses to transmit real time sensor observations to the data sink via FD-UAV. The SN generates an update after replenished the battery and transmits by using the harvested energy. A closed-form expression for average AoI (AAoI) is derived as a function of time allocated for energy harvesting (EH). The optimal time allocation for EH that maximizes the freshness of data update is identified. Deep learning algorithms such as Long-Short Term Memory (LSTM) is used to predict the AAoI. Simulation results demonstrate the usefulness performance bounds in-terms of freshness of data updates. © 2022, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

MetaSleepLearner: A pilot study on fast adaptation of bio-signals-based sleep stage classifier to new individual subject using Meta-Learning

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arXiv 2020年

作者： Banluesombatkul, Nannapas Ouppaphan, Pichayoot Leelaarporn, Pitshaporn Lakhan, Payongkit Chaitusaney, Busarakum Jaimchariyatam, Nattapong Chuangsuwanich, Ekapol Chen, Wei Phan, Huy Dilokthanakul, Nat Wilaiprasitporn, Theerawit Bio-inspired Robotics and Neural Engineering Lab School of Information Science and Technology Vidyasirimedhi Institute of Science & Technology Rayong Thailand Department of Otolaryngology Faculty of Medicine Chulalongkorn University Thailand Excellence Center for Sleep Disorders King Chulalongkorn Memorial Hospital Thai Red Cross Society Bangkok Thailand Division of Pulmonary and Critical Care Medicine Faculty of Medicine Chulalongkorn University Thailand Head of Excellence Center for Sleep Disorders King Chulalongkorn Memorial Hospital Thai Red Cross Society Bangkok Thailand The Computer Engineering Department Chulalongkorn University Bangkok Thailand The Center for Intelligent Medical Electronics Department of Electronic Engineering School of Information Science and Technology Fudan University Shanghai200433 China The School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom

Identifying bio-signals based-sleep stages requires time-consuming and tedious labor of skilled clinicians. Deep learning approaches have been introduced in order to challenge the automatic sleep stage classification conundrum. However, the difficulties can be posed in replacing the clinicians with the automatic system due to the differences in many aspects found in individual bio-signals, causing the inconsistency in the performance of the model on every incoming individual. Thus, we aim to explore the feasibility of using a novel approach, capable of assisting the clinicians and lessening the workload. We propose the transfer learning framework, entitled MetaSleepLearner, based on Model Agnostic Meta-Learning (MAML), in order to transfer the acquired sleep staging knowledge from a large dataset to new individual subjects (source code is available at https://***/IoBTVISTEC/MetaSleepLearner). The framework was demonstrated to require the labelling of only a few sleep epochs by the clinicians and allow the remainder to be handled by the system. Layer-wise Relevance Propagation (LRP) was also applied to understand the learning course of our approach. In all acquired datasets, in comparison to the conventional approach, MetaSleepLearner achieved a range of 5.4% to 17.7% improvement with statistical difference in the mean of both approaches. The illustration of the model interpretation after the adaptation to each subject also confirmed that the performance was directed towards reasonable learning. MetaSleepLearner outperformed the conventional approaches as a result from the fine-tuning using the recordings of both healthy subjects and patients. This is the first work that investigated a non-conventional pre-training method, MAML, resulting in a possibility for human-machine collaboration in sleep stage classification and easing the burden of the clinicians in labelling the sleep stages through only several epochs rather than an entire recording. © 2020, CC BY-N

关键词： Convolutional neural networks

Harmonic insertion in PWM inverter drive schemes

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European Transactions on Electrical Power 1992年第3期2卷 143-151页

作者： Theocharis, J. Petridis, V. Dr.-Ing. John Theocharis (1956) graduated as an Electrical Engineer from Aristotelian University of Thessaloniki.Greece in 1980. From 1980 to 1985 he has been with the scientific staff of the Department of Electrical Engineering at the Aristotelian University where he received the Ph.D. degree in 1985. Since 1986 he is working as a lecturer and in 1990 he became assistant professor at the Department of Electronics and Computer Engineering in the m e university. His research activities include control power electronics and electrical motor drives. Recently he is working with the Neural Network Systems with applications to field oriented control problems. Aristotelian University of Thessaloniki School of Engineering Faculty of Electrical Engineering Dept. of Electronics & Computer Engineering P.O. Box 438 GR-Thessalonikil/Greece.T+3131/219784Fax + 3031/274868) Prof. Dr.-Ing. Vasilis Petridis (1946) graduated from National Technical University of Athens Greece in 1969.He obtained the M.Sc. and the Ph.D. in electronics and systems from the University of London in 1970 and 1974. respectively. H i s interests include applied automatic control neural networks drives dynamic systems robotics etc. He is currently professor in the Department of Electronics and Computer Engineering of the University of Thessaloniki. (Aristotelian University of Thessaloniki. School of EngineeringFaculty of Electrical Engineering Dept. of Electronics & Computer Engineering P.O. Box 438. GR-ThessaloniW Greece T+3031/219784.Fax+3031/274868)

The procedure of harmonic insertion is generalized in this paper. Analytical expressions of the voltage spectra are derived. The insertion of the 3rd harmonic to the modulating signal, which is of particular interest, is considered as a special case. It is demonstrated in this case that the fundamental harmonic component of the phase and the line‐to‐line voltage is not affected by the insertion of the 3rd harmonic for sufficiently high values of the chopping ratio. However it has been shown that the higher harmonic spectrum is significantly improved as compared to the case of pure sinusoidal modulation. For different values of the fundamental component the optimal values of the amplitude of the third harmonic to be inserted have been calculated so that the distortion factor of the higher harmonic spectrum is minimized. Other cases of harmonic insertion are also considered which provide spectrum efficiency improvement. Experimental results are given which have been obtained by means of a microcomputer controlled PWM generator constructed for this purpose. Copyright © 1992 John Wiley & Sons, Ltd.

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Capsule Vision 2024 Challenge: Multi-Class Abnormality Classification for Video Capsule Endoscopy

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arXiv 2024年

作者： Handa, Palak Mahbod, Amirreza Schwarzhans, Florian Woitek, Ramona Goel, Nidhi Dhir, Manas Chhabra, Deepti Jha, Shreshtha Sharma, Pallavi Thakur, Vijay Chawla, Simarpreet Singh Gunjan, Deepak Kakarla, Jagadeesh Raman, Balasubramanian Research Center for Medical Image Analysis and Artificial Intelligence Department of Medicine Danube Private University Krems Austria Department of Electronics and Communication Engineering Indira Gandhi Delhi Technical University for Women Delhi India Department of Artificial Intelligence and Data Sciences Indira Gandhi Delhi Technical University for Women Delhi India Department of Artificial Intelligence and Machine Learning University School of Automation and Robotics Guru Gobind Singh Indraprastha University Delhi India Department of Electronics and Communication Engineering Delhi Technological University Delhi India Columbia University New YorkNY United States Department of Gastroenterology and HNU All India Institute of Medical Sciences Delhi India Chennai Kancheepuram India Department of Computer Science and Engineering Indian Institute of Technology Roorkee India

We present the Capsule Vision 2024 Challenge: Multi-Class Abnormality Classification for Video Capsule Endoscopy. It was virtually organized by the Research Center for Medical Image Analysis and Artificial Intelligence (MIAAI), Department of Medicine, Danube Private University, Krems, Austria in collaboration with the 9th International Conference on computer Vision & Image Processing (CVIP 2024) being organized by the Indian Institute of Information Technology, Design and Manufacturing (IIITDM) Kancheepuram, Chennai, India. This document provides an overview of the challenge, including the registration process, rules, submission format, description of the datasets used, qualified team rankings, all team descriptions, and the benchmarking results reported by the organizers. © 2024, CC BY.

关键词： Endoscopy