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5th Experiment at International Conference, exp.at 2019

作者： Barreto, Ricardo Lobo, Jorge Menezes, Paulo ISR-Institute of Systems and Robotics Department of Electrical and Computer Engineering University of Coimbra Portugal

ISBN: (纸本)9781728136370

This demonstration showcases the use of reconfigurable logic to implement edge computing for IoT devices able to provide specific information from raw data produced from some sensor, e.g. a camera or microphone, instead of the raw data itself. In what concerns the embedded processing capabilities, the focus is image processing using convolutional neuronal networks (CNN). This approach is clearly distinct from the current trends in IoT devices of using cloud computing to process the collected data. We intend a twist on the established paradigm and pursue an edge computing approach. Since we are targeting small and simple devices, we need some low power solution for the CNN computation. The demonstration will be made on a Terasic DE1 (SoC) reconfigurable system, with a field programmable gate array (FPGA), and hardwired ARM processor to build the IoT device. The collected data from the CNN computation, is transmitted using an IoT protocol to a broker. © 2019 IEEE.

关键词： Edge computing

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Graphene-Assisted Chemical Stabilization of Liquid Metal Nano Droplets for Liquid Metal Based Energy Storage

arXiv

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arXiv 2025年

作者： Sanati, Afsaneh L. Nikitin, Timur Fausto, Rui Majidi, Carmel Tavakoli, Mahmoud Institute of Systems and Robotics Department of Electrical and Computer Engineering University of Coimbra Coimbra3030–290 Portugal Dept. de Química Universidade de Coimbra Portugal Faculty of Sciences and Letters Dept. of Physics Istanbul Kultur University Ataköy Campus Bakirköy Istanbul34156 Turkey Soft Machines Lab Department of Mechanical Engineering Carnegie Mellon University PittsburghPA15213 United States

Energy storage devices with liquid metal electrodes have attracted interest in recent years due to their potential for mechanical resilience, self-healing, dendrite-free operation, and fast reaction kinetics. Gallium alloys such as Eutectic Gallium Indium (EGaIn) are particularly interesting due to their low melting point and high theoretical specific capacity. However, when exposed to a highly alkaline electrolyte, thin-film electrodes composed of EGaIn droplets are not stable due to the dissolution of Gallium oxide. In this letter, we address this bottleneck by introducing chemically stable films in which nanoscale droplets of EGaIn are coated with trace amounts of graphene oxide (GO). We demonstrated that a GO to EGaIn weight ratio as low as 0.01 provides enough protection for a thin film formed by GO@EGaIn nanocomposite against significantly acidic or alkaline environments (pH 1-14). We show that the GO coating significantly enhances the surface stability in such environments, and thus improves the energy storage capacity by over 10 times. We perform detailed microstructural analysis of various GO@EGaIn composites and analyze their electrochemical cycling performance under various conditions. We then use this technique to fabricate a thin-film supercapacitor. The results indicate that when coating the EGaIn by a GO to EGaIn ratio of 0.001, the areal capacitance is improved by 10x, reaching 20.02 mF/cm2. This discovery can lay the foundation for the development of the next generation of high-performance liquid metal based thin-film electrodes for energy storage applications. © 2025, CC BY-NC-ND.

关键词： Gallium alloys

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Robotic wheelchair control considering user comfort - Modeling and experimental evaluation

Robotic wheelchair control considering user comfort - Modeli...

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ICINCO 2008 - 5th International Conference on Informatics in Control, Automation and robotics

作者： Solea, Razvan Nunes, Urbano ISR - Institute of Systems and Robotics Department of Electrical and Computer Engineering University of Coimbra 3030-290 Coimbra Portugal

ISBN: (纸本)9789898111319

This paper analyzes the comfort of wheelchair users when a sliding-mode trajectory-tracking controller is used. The transmission of the horizontal (fore-and-aft) vibration to the head-neck complex (HNC) in the seated human body may cause unacceptable discomfort and motion sickness. A double-inverted pendulum model with two degrees of freedom is considered as a model for the HNC. The user comfort is examined not only in the time domain (using the fourth power vibration dose value), but also in the frequency domain (using the cross-spectral density method). For measuring the acceleration of the wheelchair, along the trajectory, an inertial measurement unit was used.

关键词： Sliding mode control

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Vehicle communications: A short survey

Vehicle communications: A short survey

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2007 IADIS International Conference on Telecommunications, Networks and systems, TNS 2007, part of the 1st IADIS Multi Conference on computer Science and Information systems, MCCSIS 2007

作者： Fernandes, Pedro Nunes, Urbano ISR - Institute of Systems and Robotics Department of Electrical and Computer Engineering University of Coimbra Polo II Coimbra Portugal

ISBN: (纸本)9789728924409

Inter-vehicle communications (IVC) consists in a real application of vehicular ad-hoc networks (VANET) and is part of Intelligent Transportation systems (ITS). An overview of IVC is presented. To guarantee IVC effectiveness, attention must be paid to one of the most important component of the system: the driver and the way he/her receives crucial information. A brief summary of the communication issues is presented. © MCCSIS 2007 .All right reserved.

关键词： Automobile drivers

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Robot Force Control on a Beating Heart (22, pg 1736, 2017)

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IEEE-ASME TRANSACTIONS ON MECHATRONICS 2017年第6期22卷 2802-2802页

作者： Cortesao, Rui Dominici, Michel Department of Electrical and Computer Engineering Institute of Systems and Robotics University of Coimbra Coimbra Portugal Institute of Systems and Robotics University of Coimbra Coimbra Portugal

Cardiac interventions have to deal with several difficulties, where heart motion is a major one. This paper addresses the heart motion compensation problem under contact, relying on robot force control techniques. A double active observer (AOB) architecture is proposed to tackle precise force control in the presence of heart motion. One AOB controls the desired interaction force, and the other one is responsible for compensating heart motion autonomously. Three-degrees of freedom (DOF) force tracking on top of a beating heart is presented, showing that with the proposed control architecture, it is possible to have force controlled interventions with root mean square errors below 0.56 N (based on raw data). The 4-DOF whole arm manipulator (WAM) from Barrett Technologies is the medical arm, while another 3-DOF robot carrying an ex vivo heart generates heart motion based on recorded biosignals.

关键词： Heart Force Force control Robot sensing systems computer architecture Motion compensation

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Joint Design of Radar Receive Filter and Unimodular ISAC Waveform with Sidelobe Level Control

arXiv

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arXiv 2025年

作者： Zhang, Kecheng Liu, Ya-Feng Wang, Zhongbin Yuan, Weijie Keskin, Musa Furkan Wymeersch, Henk Xia, Shuqiang School of System Design and Intelligent Manufacturing The Shenzhen Key Laboratory of Robotics and Computer Vision Southern University of Science and Technology Shenzhen518055 China State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China Department of Electrical Engineering Chalmers University of Technology Gothenburg41296 Sweden ZTE Corporation The State Key Laboratory of Mobile Network and Mobile Multimedia Technology Shenzhen518055 China

Integrated sensing and communication (ISAC) has been considered a key feature of next-generation wireless networks. This paper investigates the joint design of the radar receive filter and dual-functional transmit waveform for the multiple-input multiple-output (MIMO) ISAC system. While optimizing the mean square error (MSE) of the radar receive spatial response and maximizing the achievable rate at the communication receiver, besides the constraints of full-power radar receiving filter and unimodular transmit sequence, we control the maximum range sidelobe level, which is often overlooked in existing ISAC waveform design literature, for better radar imaging performance. To solve the formulated optimization problem with convex and nonconvex constraints, we propose an inexact augmented Lagrangian method (ALM) algorithm. For each subproblem in the proposed inexact ALM algorithm, we custom-design a block successive upper-bound minimization (BSUM) scheme with closed-form solutions for all blocks of variable to enhance the computational efficiency. Convergence analysis shows that the proposed algorithm is guaranteed to provide a stationary and feasible solution. Extensive simulations are performed to investigate the impact of different system parameters on communication and radar imaging performance. Comparison with the existing works shows the superiority of the proposed algorithm. © 2025, CC BY-NC-SA.

关键词： Mean square error

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Improving the generalization properties of neural networks: An application to vehicle detection

Improving the generalization properties of neural networks: ...

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11th International IEEE Conference on Intelligent Transportation systems, ITSC 2008

作者： Ludwig Jr., Oswaldo Nunes, Urbano ISR-Institute of Systems and Robotics Department of Electrical and Computer Engineering University of Coimbra Polo II 3030-290 Coimbra Portugal

ISBN: (纸本)9781424421121

In this paper a Multilayer Feedforward Neural Network based approach for vehicle detection is proposed. The main idea is to use such network to perform both feature extraction and classification. This simplicity enables real time applications. In order to achieve such capabilities, the network is trained by a new algorithm, proposed in this paper, named Minimization of Inter- Class Interference (MCI). Such algorithm aims to create a hidden space (i.e. feature space) where the patterns have a desirable statistical distribution. Regarding the neural architecture, the linear output layer is replaced by the Mahalanobis kernel, in order to improve generalization. Experiments are performed by means of a dataset that includes two standard datasets from Caltech car rear. Finally, disturbed images are used, in order to evaluate the robustness of the neural-network based vehicle detection. The proposed method reveals low miss rate, low false alarm rate and high area under ROC curve. In Matlab environment, the algorithm spends only 3.280e-4 seconds per image. These facts encourage this research line. © 2008 IEEE.

关键词： Feedforward neural networks

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Trainable classifier-fusion schemes: An application to pedestrian detection

Trainable classifier-fusion schemes: An application to pedes...

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2009 12th International IEEE Conference on Intelligent Transportation systems, ITSC '09

作者： Junior, Oswaldo Ludwig Delgado, David Gonçalves, Valter Nunes, Urbano ISR-Institute of Systems and Robotics Department of Electrical and Computer Engineering University of Coimbra Polo II 3030-290 Coimbra Portugal

ISBN: (纸本)9781424455218

This work proposes a novel classifier-fusion scheme using learning algorithms, i.e. syntactic models, instead of the usual Bayesian or heuristic rules. Moreover, this paper complements the previous comparative studies on DaimlerChrysler Automotive Dataset, offering a set of complementary experiments using feature extractor and classifier combinations. The experimental results provide evidence of the effectiveness of our methods regarding false positive rate, AUC, and accuracy, which reached 96.67%. © 2009 IEEE.

关键词： Classification (of information)

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A dual-stage robust vehicle detection and tracking for real-time traffic monitoring

A dual-stage robust vehicle detection and tracking for real-...

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ITSC 2006: 2006 IEEE Intelligent Transportation systems Conference

作者： Batista, Jorge Peixoto, Paulo Fernandes, Catarina Ribeiro, Miguel ISR-Institute of Systems and Robotics Department of Electrical Engineering and Computers University of Coimbra Coimbra Portugal

ISBN: (纸本)1424400945

This paper addresses the important problem of detecting and tracking vehicles in outdoor dynamic scenes as part of a real-time traffic surveillance system. The proposed solution is based on a dual-stage approach, using a pixel-level stage to extract foreground object from background scenes and a block-level stage to detect and track vehicles. The pixel-level stage combines a multi-background modelling with a dynamic thresholding, using a low-scale quasi-connected-components as a first stage for image object grouping/cleaning. The block-level performs a 8×8 block-region analysis defining a block energy function that is used to label the blocks belonging to different vehicles and track them over a stack of images. This approach has proven to be very helpful for occlusion reasoning. The proposed solution has the ability to overcame some of the most difficult problem that arise in outdoor scenes such as illumination variations, shadow-casts and waving movement resulting from trees and camera vibration. The performance and robustness of the proposed algorithm is shown using real highway traffic monitoring situations. © 2006 IEEE.

关键词： Vehicle locating systems

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Time-varying EEG spectral power predicts evoked and spontaneous fMRI motor brain activity

arXiv

引用

arXiv 2025年

作者： Mehta, Neil Gonçalves, Inês Montagna, Alberto Fleury, Mathis Caetano, Gustavo Esteves, Inês Vourvopoulos, Athanasios Grover, Pulkit Figueiredo, Patrícia Institute for Systems and Robotics Department of Bioengineering Instituto Superior Técnico Universidade de Lisboa Lisboa Portugal Electrical and Computer Engineering Department Carnegie Mellon University United States

Simultaneous EEG-fMRI recordings are increasingly used to investigate brain activity by leveraging the complementary high spatial and high temporal resolution of fMRI and EEG signals respectively. It remains unclear, however, to what degree these two imaging modalities capture shared information about neural activity. Here, we investigate whether it is possible to predict both task-evoked and spontaneous fMRI signals of motor brain networks from EEG time-varying spectral power using interpretable models trained for individual subjects with Sparse Group Lasso regularization. Critically, we test the trained models on data acquired from each subject on a different day and obtain statistical validation by comparison with appropriate null models as well as the conventional EEG sensorimotor rhythm. We find significant prediction results in most subjects, although less frequently for resting-state compared to task-based conditions. Furthermore, we interpret the model learned parameters to understand representations of EEG-fMRI coupling in terms of predictive EEG channels, frequencies, and haemodynamic delays. In conclusion, our work provides evidence of the ability to predict fMRI motor brain activity from EEG recordings alone across different days, in both task-evoked and spontaneous conditions, with statistical significance in individual subjects. These results present great potential for translation to EEG neurofeedback applications. © 2025, CC BY.

关键词： Image coding

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