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arXiv 2023年

作者： Zhou, Zishun Ma, Liping Liu, Xilong Cao, Zhiqiang Yu, Junzhi The Institute of Automation Chinese Academy of Sciences Beijing100190 China The College of Electrical Engineering and Computer Science Australian National University Canberra2601 Australia The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China The School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China The State Key Laboratory for Turbulence and Complex System Department of Advanced Manufacturing and Robotics BIC-ESAT College of Engineering Peking University Beijing100871 China

Precise calibration is the basis for the vision-guided robot system to achieve high-precision operations. systems with multiple eyes (cameras) and multiple hands (robots) are particularly sensitive to calibration errors, such as micro-assembly systems. Most existing methods focus on the calibration of a single unit of the whole system, such as poses between hand and eye, or between two hands. These methods can be used to determine the relative pose between each unit, but the serialized incremental calibration strategy cannot avoid the problem of error accumulation in a large-scale system. Instead of focusing on a single unit, this paper models the multi-eye and multi-hand system calibration problem as a graph and proposes a method based on the minimum spanning tree and graph optimization. This method can automatically plan the serialized optimal calibration strategy in accordance with the system settings to get coarse calibration results initially. Then, with these initial values, the closed-loop constraints are introduced to carry out global optimization. Simulation experiments demonstrate the performance of the proposed algorithm under different noises and various hand-eye configurations. In addition, experiments on real robot systems are presented to further verify the proposed method. © 2023, CC BY-SA.

关键词： Multipurpose robots

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Lunar Asset Messaging and on Orbit Navigation (LA MOON)

Lunar Asset Messaging and on Orbit Navigation (LA MOON)

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IAF Space Exploration Symposium 2021 at the 72nd International Astronautical Congress, IAC 2021

作者： Derewa, Chrishma Holt, Samuel Lally, Michael Trybula, Robert Yuan, Christine Coble, Kyle Huang, Calvin Jet Propulsion Laboratory California Institute of Technology United States University of Colorado Boulder Aerospace Engineering United States New York University Computer Science United States University of Southern California Computer Science United States Cornell University Mechanical Engineering United States Kth Royal Institute of Technology Systems Control and Robotics Pasadena City College Computer Science United States

ISBN: (纸本)9781713842965

NASA has titled its 2020 thrust for the Moon, Artemis. The increased focus on the Moon as a destination for future human and robotic expeditions necessitates general purpose navigational and communications infrastructure reducing their complexity to help establish a sustained presence. A framework through which Lunar missions can relay communications and localize their positions shifts the burden from the individual mission and enables resource allocation tailored to mission-specific goals. During the summer of 2020, student interns under the Innovation to Flight (i2F) program at the National Aeronautics and Space Administration s (NASA) Jet Propulsion laboratory (JPL) in collaboration with the University of Colorado Boulder designed, built, and tested a prototype framework capable of providing surface assets with communication and positioning services. The team utilized the existing i2F CubeSat bus in addition to developing several CubeSat engineering development units (EDUs), a ground vehicle, and a ground station to simulate a scenario in which a lunar surface mission is supported by these services. A primary goal of the summer was to develop a method for localizing the ground vehicle through trilateration. Distances are inferred from the round-Trip time of flight (ToF) of radio signals between an asset and several elements. Signals were sent and received using LimeSDR software defined radios on-board both the ground vehicle and the EDUs;ToF and trilateration were calculated on a Qualcomm Snapdragon development board located within the LA MOON payload data system. The ModalAI chipset on the Qualcomm was instrumental in executing visual based position estimation. Communications was facilitated through a bent-pipe approach addressing the NASA requirement to provide solutions for in communication denied locations. The ground vehicle relayed information to other surface assets in addition to its ground station through the supporting constellation. This project demons

关键词： Orbits

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Design of A Highly Reliable and Low-Power SRAM With Double-Node Upset Recovery for Safety-critical Applications

Design of A Highly Reliable and Low-Power SRAM With Double-N...

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International Test Conference in Asia (ITC-Asia)

作者： Aibin Yan Jing Xiang Zhengfeng Huang Tianming Ni Jie Cui Patrick Girard Xiaoqing Wen School of Computer Science and Technology Anhui University Hefei China School of Microelectronics Hefei University of Technology Hefei China School of Integrated Circuits Anhui Polytechnic University Wuhu China Laboratory of Informatics Robotics and Microelectronics of Montpellier University of Montpellier / CNRS Montpellier France Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Fukuoka Japan

For high-speed operations, low power consumption and small silicon area, transistors are being scaled aggressively. Meanwhile, circuit reliability is facing greater challenges in advanced technologies. In this paper, a highly reliable and low-power SRAM with double-node-upset (DNU) recovery, namely HRLP16T, is proposed for safety-critical fields. HRLP16T can recover from single-node-upset (SNU) at all the sensitive nodes, and it has eight node pairs recoverable from DNUs. Simulation results demonstrate its advantages in terms of delay and power consumption over typical existing SRAM cell designs.

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DDCM: Cracking Anonymized Facial Images Using Denoising Diffusion Cryptanalytic Model

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IEEE Transactions on Consumer Electronics 2025年

作者： Jiang, Donghua Ni, Jiangqun Alasbali, Nada Nafea, Ibtehal Liu, Qingliang Ahmad, Jawad Boulila, Wadii Sun Yat-sen University School of Computer Science and Engineering Guangzhou510006 China Sun Yat-sen University School of Cyber Science and Technology Shenzhen518107 China Peng Cheng Laboratory Department of New Networks Shenzhen518052 China King Khalid University College of Computer Science Department of Informatics and Computing Systems Abha61421 Saudi Arabia Taibah University College of Computer Science and Engineering Medina41477 Saudi Arabia Qingdao University of Science and Technology School of Mathematics and Physics Qingdao266061 China Prince Mohammad Bin Fahd University Cybersecurity Center Alkhobar Saudi Arabia Prince Sultan University Robotics and Internet-of-Things Laboratory Riyadh12435 Saudi Arabia

Currently, in consumer electronics industry, quite a number of E-healthcare products highly depend on consumers' facial biometrics for both disease diagnosis and identity authen-tication. In light of this, various facial privacy-preserving approa-ches oriented towards the internet of medical things have been proposed, including visual cryptography and desensitization tech-nique. Nevertheless, few attentions have been paid to investigating their countermeasure, i.e., facial privacy analysis, owning to significant distribution discrepancy between the original and anony-mized facial images. To this end, a general Denoising Diffusion Cryptanalytic Model (DDCM) is proposed in this paper to recon-struct the original facial images in a coarse-to-fine manner from their encrypted ones with diverse anonymous approaches. Spec-ifically, to accurately reconstruct the original facial image without any prior knowledge, a customized initial prediction module with an encoder-decoder structure is introduced by taking advantage of its leaked visual cues to generate the coarse face. Furthermore, the residual map between the original and coarse facial images is explicitly modeled with the output of the customized prediction module as a conditional prior, thereby obtaining a refined facial image. Finally, extensive experiments demonstrate that the propo-sed DDCM could not only effectively crack multiple handcrafted facial anonymization approaches, such as blurring, pixelation and thumbnail-preserving encryption, but also outperform the prev-ious works (e.g., Pix2Pix) in terms of image fidelity and attack success rate. © 1975-2011 IEEE.

关键词： Anonymity

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EndoMamba: An Efficient Foundation Model for Endoscopic Videos via Hierarchical Pre-training

arXiv

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arXiv 2025年

作者： Tian, Qingyao Liao, Huai Huang, Xinyan Yang, Bingyu Lei, Dongdong Ourselin, Sebastien Liu, Hongbin State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Dept. of Radiology The First Affiliated Hospital Sun Yat-sen University Guangzhou China Centre for Artificial Intelligence and Robotics Chinese Academy of Sciences Hong Kong School of Engineering and Imaging Sciences King’s College London United Kingdom

Endoscopic video-based tasks, such as visual navigation and surgical phase recognition, play a crucial role in minimally invasive surgeries by providing real-time assistance. While recent video foundation models have shown promise, their applications are hindered by (1) computational inefficiencies and (2) suboptimal performance caused by limited data for pre-training in endoscopy. To address these issues, we present EndoMamba, a foundation model designed for real-time inference while learning generalized spatiotemporal representations. First, to mitigate computational inefficiencies, we propose the EndoMamba backbone, optimized for real-time inference. Inspired by recent advancements in state space models, EndoMamba integrates Bidirectional Mamba blocks for spatial modeling within individual frames and vanilla Mamba blocks for past-to-present reasoning across the temporal domain. This design enables both strong spatiotemporal modeling and efficient inference in online video streams. Second, we propose a self-supervised hierarchical pre-training diagram to enhance EndoMamba’s representation learning using endoscopic videos and incorporating general video domain knowledge. Specifically, our approach combines masked reconstruction with auxiliary supervision, leveraging low-level reconstruction to capture spatial-temporal structures and high-level alignment to transfer broader knowledge from a pretrained general-video domain foundation model. Extensive experiments on four downstream tasks—classification, segmentation, surgical phase recognition, and localization—demonstrate that EndoMamba outperforms existing foundation models and task-specific methods while maintaining real-time inference speed. The source code will be released upon acceptance. Copyright © 2025, The Authors. All rights reserved.

关键词： Endoscopy

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Hybrid Metaheuristic and Artificial Neural Network Approach for Solving Inverse Kinematics of a SCARA Manipulator Robot

Hybrid Metaheuristic and Artificial Neural Network Approach ...

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International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)

作者： Rania Bouzid Jyotindra Narayan Hassène Gritli Polytechnic School of Tunisia University of Carthage La Marsa Tunisia RISC Lab (LR16ES07) ENIT University of Tunis El Manar Tunis Tunisia Department of Mechanical Engineering Thapar Institute of Engineering and Technology Patiala Punjab India Department of Computing Imperial College London UK Laboratory of Robotics Informatics and Complex Systems (RISC Lab - LR16ES07) ENIT University of Tunis El Manar Tunis Tunisia ISTIC University of Carthage Tunis Tunisia

ISBN: (数字)9798331533137

ISBN: (纸本)9798331533144

This paper presents a hybrid approach that inte-grates metaheuristic algorithms and Artificial Neural Networks (ANNs) to address the Inverse Kinematics (IK) problem of a SCARA (Selective Compliant Assembly Robot Arm) manipulator robot with four degrees of freedom. The method combines Particle Swarm Optimization (PSO) with ANNs and Genetic Algorithm (GA) with ANNs to optimize training key hyperpa-rameters, such as activation functions and hidden layer sizes using MATLAB's Neural Network Toolbox. Experimental results achieved on a random step size dataset obtained after training show that the PSO-ANN method achieves a Mean Squared Error (MSE) of 0.12846, with a hidden layer size of 90. The GA-ANN method results in an MSE of 0.13785, with a hidden layer size of 91. This hybrid approach significantly reduces MSE in computed joint configurations and demonstrates promise for real-time control applications.

关键词： Training Robotic assembly Technological innovation Metaheuristics Kinematics Artificial neural networks Manipulators Real-time systems Particle swarm optimization Genetic algorithms

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A Low Overhead and Double-Node-Upset Self-Recoverable Latch

A Low Overhead and Double-Node-Upset Self-Recoverable Latch

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International Test Conference in Asia (ITC-Asia)

作者： Aibin Yan Fan Xia Tianming Ni Jie Cui Zhengfeng Huang Patrick Girard Xiaoqing Wen School of Computer Science and Technology Anhui University Hefei China School of Microelectronics Hefei University of Technology Hefei China School of Integrated Circuits Anhui Polytechnic University Wuhu China Laboratory of Informatics Robotics and Microelectronics of Montpellier University of Montpellier / CNRS Montpellier France Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Fukuoka Japan

With the rapid advancement of semiconductor technologies, integrated circuits, especially storage elements (e.g., latches) have become increasingly vulnerable to soft errors. In order to effectively tolerate double-node-upsets (DNUs) caused by radiation and reduce the power and area of latches, this paper proposes a DNU self-recoverable latch with low overhead in terms of power and area. The proposed latch mainly comprises seven 2-input C-elements and two inverters to achieve DNU self-recovery. Simulation results show that the proposed latch can recover from all possible DNUs and that it can reduce delay by 45.7%, power by 29.1%, area by 65.9%, and area-power-delay-product by 87.4%, on average, compared to typical existing DNU self-recoverable latches.

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Modeling Spoof Noise by De-spoofing Diffusion and its Application in Face Anti-spoofing

Modeling Spoof Noise by De-spoofing Diffusion and its Applic...

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IEEE International Joint Conference on Biometrics (IJCB)

作者： Bin Zhang Xiangyu Zhu Xiaoyu Zhang Zhen Lei Institute of Information Engineering Chinese Academy of Sciences School of Cyber Security University of Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences Centre for Artificial Intelligence and Robotics Hong Kong Institute of Science & Innovation Chinese Academy of Sciences

Face anti-spoofing is crucial for ensuring the security and reliability of face recognition systems. Several existing face anti-spoofing methods utilize GAN-like networks to detect presentation attacks by estimating the noise pattern of a spoof image and recovering the corresponding genuine image. But GAN’s limited face appearance space results in the denoised faces cannot cover the full data distribution of genuine faces, thereby undermining the generalization performance of such methods. In this work, we present a pioneering attempt to employ diffusion models to denoise a spoof image and restore the genuine image. The difference between these two images is considered as the spoof noise, which can serve as a discriminative cue for face anti-spoofing. We evaluate our proposed method on several intra-testing and inter-testing protocols, where the experimental results showcase the effectiveness of our method in achieving competitive performance in terms of both accuracy and generalization.

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Hair Direction Detection*

Hair Direction Detection*

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WRC Symposium on Advanced robotics and Automation (WRC SARA)

作者： Peng Ba Pengyi Wang Hongde Wu Qian Yang Yongqiang Feng Junchen Wang Yida David Hu Changsheng Li Wenyong Liu Shaolong Kuang Bai-Quan Su Medical Robotics Laboratory School of Intelligent Engineering and Automation Beijing University of Posts and Telecommunications Beijing China Plastic Surgery Hospital Chinese Academy of Medical Sciences and Peking Union Medical College Beijing China School of Mechanical Engineering and Automation Beihang University Beijing China Brigham and Women’s Hospital Harvard Medical School Boston MA USA School of Mechatronical Engineering Beijing Institute of Technology Beijing China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology Beijing China Key Laboratory for Biomechanics and Mechanobiology of Chinese Education Ministry Beijing Advanced Innovation Centre for Biomedical Engineering School of Biological Science and Medical Engineering Beihang University Beijing China School of Health Science and Environmental Engineering Shenzhen University of Technology Shenzhen China

ISBN: (数字)9798331506100

ISBN: (纸本)9798331506117

Hair direction is an important external feature of hair, and recognising hair direction is a prerequisite for processing hair. In this paper, a new algorithm is proposed and systematically verified experimentally for the problem of recognising hair direction. The main goal of this paper is to develop an algorithm that can identify hair direction in a complex image environment. A curve segment analysis method based on image skeletonisation is adopted, which is based on skeleton extraction, intersection identification, curve segmentation and direction prediction. In addition, this paper combines the technique of non-maximal value suppression and PCA analysis to improve the accuracy and stability of the estimation. In the experimental design, this paper chooses a representative image dataset to verify the effectiveness of this paper's algorithm. The experimental process includes the steps of image preprocessing, skeletonisation processing, intersection detection and merging, and direction prediction. The experimental results show that the method in this paper can accurately and effectively identify the hair direction. The main contribution of this paper is to propose a new hair direction recognition method and experimentally verify its effectiveness and accuracy in complex backgrounds.

关键词： Hair Image segmentation Accuracy Three-dimensional displays Merging Prediction algorithms Stability analysis

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Design and preliminary testing of a piezoelectric-based sole energy harvester

Design and preliminary testing of a piezoelectric-based sole...

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IEEE International Conference on robotics and Biomimetics

作者： Yidi Wang Xuan Zhao Qiuyu Shi Haisu Liao Xinyu Wu Fei Gao Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen China Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Shatin Hong Kong China

ISBN: (纸本)9781665481106

Harvesting energy from the human body to replenish existing energy sources is receiving substantial attention. Recently scientists have been focusing on harvesting biomechanical energy from human feet during walking. This paper develops a piezoelectric-based sole energy harvester that is designed to be worn on the bottom of shoes to harvest energy, thus powering wearable electronic devices. Specifically, a cam with piezoelectric(PZT) stacks is mounted on the heel to scavenge energy during the heel strike. The cam can change the input's direction from the vertical direction to the horizontal direction. In addition, the output power of this part can be optimized or improved by generating a specified cam profile. A bimorph piezoelectric plate is installed on the forefoot to convert the toes' bending deformation into electricity. Herein, piezoelectric Macro Fiber Composites (MFC) are employed to capture the large deformation. Preliminary testing on a healthy subject is conducted to validate this design. The average power output of the harvester is 8.83 m W.

关键词： Legged locomotion Biomechanics Wearable computers Bending Optical fiber devices Piezoelectric devices Optical fiber testing

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