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arXiv 2025年

作者： Oikonomou, Katerina Maria Kansizoglou, Ioannis Gasteratos, Antonios Department of Production and Management Engineering Laboratory of Robotics and Automation Democritus University of Thrace Xanthi Greece

Feature discrimination is a crucial aspect in neural network design, as it directly impacts the network’s ability to distinguish between classes and generalize across diverse datasets. The accomplishment of achieving high-quality feature representations ensures high intra-class separability and poses one of the most challenging research directions. While convetional deep neural networks (DNNs) rely on complex transformations and very deep networks to come up with meaningful feature representations, they usually require days of training and consume significant energy amounts. To this end, spiking neural networks (SNNs) offer a promising alternative. SNN’s ability to capture temporal and spatial dependencies renders them particularly suitable for complex tasks, where multi-modal data are required. In this paper, we propose a feature discrimination approach for multi-modal learning with SNNs, focusing on audio-visual data. We employ deep spiking residual learning for visual modality processing and a simpler yet efficient spiking network for auditory modality processing. Lastly, we deploy a spiking multilayer perceptron for modality fusion. We present our findings and evaluate our approach against similar works in the field in classification challenges. To the best of our knowledge, this is the first work investigating feature discrimination in SNNs. © 2025, CC BY-NC-SA.

关键词： Deep neural networks

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Comprehensive Performance Optimization of a Novel Anthropomorphic Dexterous Hand

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IEEE robotics and automation Letters 2025年第7期10卷 6816-6823页

作者： Ma, Yifan Shang, Weiwei Zhang, Fei Pang, Shunxiang Dai, Shuqing Yi, Zhengkun Li, Guotao Cong, Shuang University of Science and Technology of China Department of Automation Jinzhai Road 96 Hefei 230027 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen 518055 China Institute of Automation Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing 100190 China

Grasp and in-hand manipulation of dexterous hands are essential for daily operational tasks. In this study, an optimized design method for a novel anthropomorphic dexterous hand is proposed, which improves the comprehensive performance of grasp and manipulability of the dexterous hand. The constraints of multi-fingered contact force and kinematics are established with full consideration of the applicability to the objects of different sizes and the shape characteristics of finger contact surface. The performance index of in-hand manipulation ability is proposed, and the optimization problem is constructed through the technique for order preference by similarity to ideal solution (TOPSIS) to achieve multi-objective optimization of precision grasp and in-hand manipulation. Finally, simulation and experiment verify the feasibility of our prototype and optimization method. The results show that the in-hand translation space and in-hand rotation range are increased by 20% and 8.5%, respectively. Our dexterous hand has excellent abilities of multi-fingered interaction and in-hand manipulation, which can perform 33 grasp gestures and two/three-fingered in-hand manipulation. This study provides a valuable reference for the design of dexterous hands with anthropomorphic functions. © 2016 IEEE.

关键词： design optimization Dexterous manipulation multifingered hands precision grasp

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Augmenting Attention and Focus Using Brainwave Monitoring and Haptic Feedback

Augmenting Attention and Focus Using Brainwave Monitoring an...

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International Carpathian Control Conference (ICCC)

作者： Andrei-Costin Trasculescu Ionut-Cristian Resceanu Horatiu Roibu Florina-Luminita Petcu Stefan-Irinel Cismaru Constantin Patrascu Nicu George Bizdoaca Mechatronics and Robotics Department Faculty of Automation Computers and Electronics University of Craiova Craiova Romania

ISBN: (数字)9798350350708

ISBN: (纸本)9798350350715

The emerging technologies are focusing on monitoring and augmenting people in doing various tasks. One of the newest haptic technologies uses ultrasonic soundwaves to create the feeling of touching an object or sensations linked to a specific action. This paper aims to analyze and demonstrate that brainwave monitoring and haptic feedback via ultrasonic technology can increase an individual's focus and attention levels using the system. The applications in which this technology can prove its value are various, from learning and training to the operation of very sensitive and high-precision equipment that requires a lot of attention and focus. From the point of view of the fields in which this technology can be applied, they can also be varied, from the didactic field, machine operation and even medicine. A non-invasive technology, it can be easily implemented and applied to various environments. The equipment can also be calibrated and used according to the specific needs of each activity so that the feedback provided by the system is not too aggressive in applications where the level of attention does not need to be very high. Still, it can be calibrated to be very sensitive in cases where even a small loss of attention or focus can affect that specific activity. In the following paper, the authors combined ultrasonic feedback technology with brainwave monitoring technology to create an application to demonstrate all the benefits of this kind of augmentation of attention and focus, which is very effective in various domains, including education and training.

关键词： Training Power demand Ultrasonic transducer arrays Mixed reality Focusing Virtual reality Haptic interfaces

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VibNet: Vibration-Boosted Needle Detection in Ultrasound Images

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IEEE Transactions on Medical Imaging 2025年第6期44卷 2696-2708页

作者： Huang, Dianye Li, Chenyang Karlas, Angelos Chu, Xiangyu Samuel Au, K.W. Navab, Nassir Jiang, Zhongliang Munich80333 Germany Munich80333 Germany Technical University of Munich CAMP Munich80333 Germany Technical University of Munich School of Medicine Munich80333 Germany The Chinese University of Hong Kong Department of Mechanical and Automation Engineering New Territories Hong Kong Multi-Scale Medical Robotics Centre Hong Kong

Precise percutaneous needle detection is crucial for ultrasound (US)-guided interventions. However, inherent limitations such as speckles, needle-like artifacts, and low resolution make it challenging to robustly detect needles, especially when their visibility is reduced or imperceptible. To address this challenge, we propose VibNet, a learning-based framework designed to enhance the robustness and accuracy of needle detection in US images by leveraging periodic vibration applied externally to the needle shafts. VibNet integrates neural Short-Time Fourier Transform and Hough Transform modules to achieve successive sub-goals, including motion feature extraction in the spatiotemporal space, frequency feature aggregation, and needle detection in the Hough space. Due to the periodic subtle vibration, the features are more robust in the frequency domain than in the image intensity domain, making VibNet more effective than traditional intensity-based methods. To demonstrate the effectiveness of VibNet, we conducted experiments on distinct ex vivo porcine and bovine tissue samples. The results obtained on porcine samples demonstrate that VibNet effectively detects needles even when their visibility is severely reduced, with a tip error of 1.61 ± 1.56 mm compared to 8.15 ± 9.98 mm for UNet and 6.63 ± 7.58 mm for WNet, and a needle direction error of 1.64 ± 1.86° compared to 9.29 ± 15.30° for UNet and 8.54 ± 17.92° for WNet. © 1982-2012 IEEE.

关键词： Needles

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Domain-Invariant Similarity Activation Map Contrastive Learning for Retrieval-Based Long-Term Visual Localization

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IEEE/CAA Journal of Automatica Sinica 2022年第2期9卷 313-328页

作者： Hanjiang Hu Hesheng Wang Zhe Liu Weidong Chen Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China IEEE Department of Automation Institute of Medical RoboticsKey Laboratory of System Control and Information Processing of Ministry of EducationKey Laboratory of Marine Intelligent Equipment and System of Ministry of EducationShanghai Jiao Tong UniversityShanghai 200240China Department of Computer Science and Technology University of CambridgeCambridge CB30FDUnited Kingdom

Visual localization is a crucial component in the application of mobile robot and autonomous *** retrieval is an efficient and effective technique in image-based localization *** to the drastic variability of environmental conditions,e.g.,illumination changes,retrievalbased visual localization is severely affected and becomes a challenging *** this work,a general architecture is first formulated probabilistically to extract domain-invariant features through multi-domain image ***,a novel gradientweighted similarity activation mapping loss(Grad-SAM)is incorporated for finer localization with high *** also propose a new adaptive triplet loss to boost the contrastive learning of the embedding in a self-supervised *** final coarse-to-fine image retrieval pipeline is implemented as the sequential combination of models with and without Grad-SAM *** experiments have been conducted to validate the effectiveness of the proposed approach on the CMU-Seasons *** strong generalization ability of our approach is verified with the RobotCar dataset using models pre-trained on urban parts of the CMU-Seasons *** performance is on par with or even outperforms the state-of-the-art image-based localization baselines in medium or high precision,especially under challenging environments with illumination variance,vegetation,and night-time ***,real-site experiments have been conducted to validate the efficiency and effectiveness of the coarse-to-fine strategy for localization.

关键词： Deep representation learning place recognition visual localization

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Synthesys of Adaptive Control System for Thrusters of Underwater Robots Using Technical Diagnostics

Synthesys of Adaptive Control System for Thrusters of Underw...

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2023 International Conference on Ocean Studies, ICOS 2023

作者： Zuev, Alexander Filaretov, Vladimir Zhirabok, Alexey Protsenko, Alexander Institute of Marine Technology Problems Vladivostok Russia Institute of Automation and Control Processes Vladivostok Russia Far Eastern Federal University Department of Automatics and Robotics Vladivostok Russia

ISBN: (纸本)9798350382457

The paper describes the development of adaptive control system for thrusters of underwater robots that allows you to compensate for the consequences of single and paired faults. This system., due to the additional control signals formed., make it possible to maintain operability and ensure stabilization of the most important characteristics of thrusters in the event of unexpected faults. Control signals are formed taking into account estimates of the values of deviations of the parameters of the thrusters from their nominal values and errors in the readings of sensors of angular velocity of thrusters. The operability and high efficiency of the developed system was confirmed by simulation. © 2023 IEEE.

关键词： Adaptive control systems

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Step-by-step Linearized Kalman Filter-Based Fault Detection of Workpiece Machining

Step-by-step Linearized Kalman Filter-Based Fault Detection ...

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2023 CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes, SAFEPROCESS 2023

作者： Sun, Xiaohui Wu, Xinyu Wen, Chenglin Ma, Xue Tsinghua University Department of Automation Beijing China Chinese Academy of Sciences Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Shenzhen China Guangdong University of Petrochemical Technology School of Automation Maoming China

ISBN: (纸本)9798350337754

This paper is concerned with step-by-step linearized Kalman filter to deal with multiplicative faults diagnosis problem for class of nonlinear systems. Multiplicative fault variables are defined as parameter variables to be estimated, and linear dynamic models between fault variables and all variables are established. The indirect linear state and measurement models of fault variables and the corresponding step-by-step linearized Kalman filters are established based on current estimated values of all variable and some obtained estimated values of future variables;further, combined with fault variables, a filter is designed for state variables in the linearized system. On basis of EKF, the redisual generation is obtained. The effectiveness of the novel filter is verified in workpiece machining process. © 2023 IEEE.

关键词： Fault detection

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A Manta Ray-Inspired Biosyncretic Robot with Stable Controllability by Dynamic Electric Stimulation

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Cyborg and Bionic Systems 2022年第1期2022卷 314-326页

作者： Chuang Zhang Yiwei Zhang Wenxue Wang Ning Xi Lianqing Liu State Key Laboratory of Robotics Shenyang Inst itute of AutomationChinese Academy of SciencesShenyang 110016China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of SciencesShenyang 110169China School of Automation and Electrical Engineering Shenyang Ligong University Shenyang 110159China Emerging Technologies Institute Department of Industrial Manufacturing Systems Engineering University of Hong KongPokfulamChina

Biosyncretic robots,which are new nature-based robots in addition to bionic robots,that utilize biological materials to realize their core function,have been supposed to further promote the progress in *** as the main operation mechanism relates to the robotic overall ***,biosyncretic robots actuated by living biological actuators have attracted increasing ***,innovative propelling modes and control methods are still necessary for the further development of controllable motion performance of biosyncretic *** this work,a muscle tissue-based biosyncretic swimmer with a manta *** propelling mode has been *** is more,to improve the stable controllability of the biosyncretic swimmer,a dynamic control method based on circularly distributed multiple electrodes(CDME)has been *** this method,the direction of the electric field generated by the CDME could be real-time controlled to be parallel with the adtuation tissue of the dynamic ***,the instability of the tissue actuation induced by the dynamic included angle between the tissue axis and electric field direction could be ***,the biosyncretic robot has demonstrated stable,controllable,and efective swimming,by adjusting the electric stimulation pulse direction,amplitude,and *** work may be beneficial for not only the development of biosyncretic robots but also other related studies including bionic design of soft robots and muscle tissue engineering.

关键词： Robot robot Dynamic

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Advanced 5 GHz CPW-Fed Microstrip Antenna with RF MEMS Switches for Enhanced RF Performance 4

Advanced 5 GHz CPW-Fed Microstrip Antenna with RF MEMS Switc...

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4th International Conference on Advancement in Electronics and Communication Engineering, AECE 2024

作者： Murali Narayanan, R. Ramkumar, G. Anitha, G. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Automation and Robotics Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Ece Chennai India

ISBN: (纸本)9798350364729

The coplanar waveguide (CPW) feeding method is used in this work to develop a microstrip antenna with better performance metrics appropriate for present day communication applications. The design incorporates a dual-ground plane arrangement, whereby the substrate is positioned beneath the first ground plane, and the patch dimensions are exactly aligned with the second ground plane, which is positioned above the substrate. 35.011 mm × 22 mm × 1 mm3 are the total antenna dimensions. The radiating patch, which measures 18.03 × 19.18 mm2, is positioned above the second substrate, while the feed line is below the first. A thorough parametric study was carried out, concentrating on a number of crucial dimensions, in order to maximize the antenna's performance. In order to attain the intended results, the design parameters were refined through this iterative approach. With an outstanding impedance match of 49.98 ohms and a remarkable gain value of 5.8 dBi, the completed antenna design provided effective signal transmission and reception. Because it operates in the 5 GHz frequency range, the antenna can be used for a variety of purposes. These comprise wireless networks, industrial automation systems, radar systems, point-to-point communication links, and some medical imaging equipment. A variety of high-frequency applications find the antenna desirable because to its inclusion of the CPW feeding method, which improves the antenna's gain and impedance matching while also streamlining the fabrication process. © 2024 IEEE.

关键词： Coplanar waveguides

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Trash Detection in Water Bodies Using Yolo With Explainable AI Insight

Trash Detection in Water Bodies Using Yolo With Explainable ...

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International Conference on robotics and Artificial Intelligence (ICRAI)

作者： Shafaq Rasheed Amal Mirza Muhammad Saad Saeed Muhammad Haroon Yousaf Department of Computer Engineering University of Engineering and Technology Taxila Pakistan Swarm Robotics Lab National Center of Robotics and Automation Taxila Pakistan

ISBN: (数字)9798350355703

ISBN: (纸本)9798350355710

Waste disposal into water bodies is a serious concern for environmental engineers, often resulting in urban flooding, soil degradation in agricultural areas, and freshwater pollution. Additionally, trash accumulation in water bodies threatens ma-rine life by introducing contaminants through storm drains, rivers, and other channels. Furthermore, the unique character-istics of underwater environments, including high noise levels, low visibility, and color variation, pose challenges for trash detection. Thus, addressing this issue is crucial for environmental sustainability. The work focuses on both floating and submerged trash in water bodies. Typically, the detection of trash on water bodies is impacted by environmental variability, diverse appearance, and background clutter. These conditions also make usual object detection techniques less effective. Deep learning methods have shown promise in handling challenging visual tasks, therefore researchers have turned to them to get over these challenges. In this work, we focus on applying different versions of YOLO to the problem of water trash detection. To assess these models' performance, we use four different datasets: Trash Flow rate Dataset, DeepTrash, FloW, and TrashICRA. Each dataset represents various trash types under different conditions such as underwater trash and floating trash. Our study attempts to identify the best method suitable for the detection of aquatic waste. Our results highlight the effectiveness of YOLO variants in identifying the presence of trash in water ecosystems. By leveraging Grad-CAM we gain insights into the interpretability and transparency of deep learning models thus enabling Explainable AI. This approach will also contribute to the understanding and trustworthiness of artificial systems deployed for trash detection applications by providing a transparent view of decision making process of deep learning models.

关键词： Deep learning YOLO Visualization Explainable AI Storms Biological system modeling Soil Water pollution Waste disposal robotics and automation

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