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arXiv 2023年

作者： De Boi, Ivan Pathak, Stuti Oliveira, Marina Penne, Rudi InViLab University of Antwerp Groenenborgerlaan 171 Antwerp2020 Belgium Institute of Systems and Robotics Department of Electrical and Computer Engineering University of Coimbra Coimbra3004-531 Portugal

Camera calibration is a first and fundamental step in various computer vision applications. Despite being an active field of research, Zhang's method remains widely used for camera calibration due to its implementation in popular toolboxes like MATLAB and OpenCV. However, this method initially assumes a pinhole model with oversimplified distortion models. In this work, we propose a novel approach that involves a pre-processing step to remove distortions from images by means of Gaussian processes. Our method does not need to assume any distortion model and can be applied to severely warped images, even in the case of multiple distortion sources, e.g., a fisheye image of a curved mirror reflection. The Gaussian processes capture all distortions and camera imperfections, resulting in virtual images as though taken by an ideal pinhole camera with square pixels. Furthermore, this ideal GP-camera only needs one image of a square grid calibration pattern. This model allows for a serious upgrade of many algorithms and applications that are designed in a pure projective geometry setting but with a performance that is very sensitive to nonlinear lens distortions. We demonstrate the effectiveness of our method by simplifying Zhang's calibration method, reducing the number of parameters and getting rid of the distortion parameters and iterative optimization. We validate by means of synthetic data and real world images. The contributions of this work include the construction of a virtual ideal pinhole camera using Gaussian processes, a simplified calibration method and lens distortion removal. Copyright © 2023, The Authors. All rights reserved.

关键词： Pinhole cameras

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Copper Losses Studying Using a New Real Time V/f Scalar Control Approach 24

Copper Losses Studying Using a New Real Time V/f Scalar Cont...

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24th International Conference on Soft Computing and Measurements, SCM 2021

作者： Hasan, Mohammed S. Albakhait, Mohammed D. Sharaf, Al Mahturi F. Jaber, Ahmed I. University of Diyala Department of Power and Electrical Machine College of Engineering Diyala Iraq Techniques Electrical Engineering Technical College Middle Technical University Department of Electrical Power Engineering Baghdad Iraq Saint-Petersburg Electrotechnical University 'LETI' Department of Robotics and Automation of Industrial Systems Saint Petersburg Russia

ISBN: (纸本)9781665439749

The paper presents an investigation of copper losses studying of Brushless Doubly-Fed Reluctance Machine supported with Real time results based on dSPACE 1103 hardware application and its features to prove a theoretical approach of driver capabilities of Brushless Doubly Fed Reluctance machine at multi-speed in induction and synchronous modes. To overcome these challenges, we need to modernize the control algorithms strategy. Scalar control is one of control theories with certain Boost voltage that brings a significant improvement to the performances of control algorithm supported with BDFRM features. This strategy can be used to confirm BDFRM's ability with FOC to manipulate multiple control algorithms that offer the machine an optimal solution instead of induction machine, synchronous machine and permanent magnate synchronous machine. © 2021 IEEE.

关键词： Induction machine

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Revisiting the K-nn algorithm for obstetric image segmentation

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International Journal of Hybrid Intelligent systems 2021年第1-2期17卷 1-13页

作者： Salgado, P. Azevedo Perdicoúlis, T.-P. CITAB-Department de Engenharias Ect Utad Quinta de Prados Vila Real Portugal Department of Electrical and Computer Engineering Ect Utad of Institute of Systems and Robotics University of Coimbra Coimbra Portugal

Medical image techniques are used to examine and determine the well-being of the foetus during pregnancy. Digital image processing (DIP) is essential to extract valuable information embedded in most biomedical signals. Afterwords, intelligent segmentation methods, based on classifier algorithms, must be applied to identify structures and relevant features from previous data. The success of both is essential for helping doctors to identify adverse health conditions from the medical images. To obtain easy and reliable DIP methods for foetus images in real-time, at different gestational ages, aware pre-processing needs to be applied to the images. Thence, some data features are extracted that are meant to be used as input to the segmentation algorithms presented in this work. Due to the high dimension of the problems in question, assemblage of the data is also desired. The segmentation of the images is done by revisiting the K-nn algorithm that is a conventional nonparametric classifier. Besides its simplicity, its power to accomplish high classification results in medical applications has been demonstrated. In this work two versions of this algorithm are presented (i) an enhancement of the standard version by aggregating the data apriori and (ii) an iterative version of the same method where the training set (TS) is not static. The procedure is demonstrated in two experiments, where two images of different technologies were selected: a magnetic resonance image and an ultrasound image, respectively. The results were assessed by comparison with the K-means clustering algorithm, a well-known and robust method for this type of task. Both described versions showed results close to 100% matching with the ones obtained by the validation method, although the iterative version displays much higher reliability in the classification. © 2021 - IOS Press. All rights reserved.

关键词： Feature extraction image segmentation K-nn algorithm magnetic resonance image ultrasound image

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Control Strategies for Pursuit-Evasion Under Occlusion Using Visibility and Safety Barrier Functions

arXiv

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arXiv 2024年

作者： Zhou, Minnan Shaikh, Mustafa Chaubey, Vatsalya Haggerty, Patrick Koga, Shumon Panagou, Dimitra Atanasov, Nikolay Department of Electrical and Computer Engineering University of California La Jolla San DiegoCA92093 United States General Dynamics Mission Systems BloomingtonMN55431 United States Honda R&D Co. Ltd. Tokyo Japan Department of Robotics Department of Aerospace Engineering University of Michigan Ann ArborMI48109 United States

This paper develops a control strategy for pursuit-evasion problems in environments with occlusions. We address the challenge of a mobile pursuer keeping a mobile evader within its field of view (FoV) despite line-of-sight obstructions. The signed distance function (SDF) of the FoV is used to formulate visibility as a control barrier function (CBF) constraint on the pursuer’s control inputs. Similarly, obstacle avoidance is formulated as a CBF constraint based on the SDF of the obstacle set. While the visibility and safety CBFs are Lipschitz continuous, they are not differentiable everywhere, necessitating the use of generalized gradients. To achieve non-myopic pursuit, we generate reference control trajectories leading to evader visibility using a sampling-based kinodynamic planner. The pursuer then tracks this reference via convex optimization under the CBF constraints. We validate our approach in CARLA simulations and real-world robot experiments, demonstrating successful visibility maintenance using only onboard sensing, even under severe occlusions and dynamic evader movements. © 2024, CC BY.

关键词： Convex optimization

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The Validity of a Machine Learning-Based Video Game in the Objective Screening of Attention Deficit Hyperactivity Disorder in Children Aged 5 to 12 Years

arXiv

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arXiv 2023年

作者： Zakani, Zeinab Moradi, Hadi Ghasemzadeh, Sogand Riazi, Maryam Mortazavi, Fatemeh Department of Psychology University of Tehran Iran Department of Robotics and Artificial Intelligence University of Tehran Iran Intelligent Systems Research Institute SKKU Iran Department of Electrical and Computer Engineering Boston University United States Department of Robotics and Artificial Intelligence University of Tehran Tehran Iran

Objective: Early identification of ADHD is necessary to provide the opportunity for timely treatment. However, screening the symptoms of ADHD on a large scale is not easy. This study aimed to validate a video game (FishFinder) for the screening of ADHD using objective measurement of the core symptoms of this disorder. Method: The FishFinder measures attention and impulsivity through in-game performance and evaluates the child's hyperactivity using smartphone motion sensors. This game was tested on 26 children with ADHD and 26 healthy children aged 5 to 12 years. A Support Vector Machine was employed to detect children with ADHD. results: This system showed 92.3% accuracy, 90% sensitivity, and 93.7% specificity using a combination of in-game and movement features. Conclusions: The FishFinder demonstrated a strong ability to identify ADHD in children. So, this game can be used as an affordable, accessible, and enjoyable method for the objective screening of ADHD. © 2023, CC BY-NC-ND.

关键词： Serious games

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AXI-REALM: A Lightweight and Modular Interconnect Extension for Traffic Regulation and Monitoring of Heterogeneous Real-Time SoCs

AXI-REALM: A Lightweight and Modular Interconnect Extension ...

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Design, Automation and Test in Europe Conference and Exhibition

作者： Thomas Benz Alessandro Ottaviano Robert Balas Angelo Garofalo Francesco Restuccia Alessandro Biondi Luca Benini Integrated Systems Laboratory ETH Zurich Switzerland Department of Electrical Electronic and Information Engineering University of Bologna Italy Department of Computer Science and Engineering UC San Diego San Diego CA USA Department of Excellence in Robotics & AI Scuola Superiore Sant'Anna Pisa Italy

ISBN: (数字)9783981926385

ISBN: (纸本)9798350348606

The increasing demand for heterogeneous functionality in the automotive industry and the evolution of chip manufac-turing processes have led to the transition from federated to integrated critical real-time embedded systems (CRTESs). This leads to higher integration challenges of conventional timing predictability techniques due to access contention on shared resources, which can be resolved by providing system-level observability and controllability in hardware. We focus on the interconnect as a shared resource and propose AXI-REALM, a lightweight, modular, and technology - independent real-time extension to industry-standard AXI4 interconnects, available open-source. AXI-REALM uses a credit-based mechanism to distribute and control the bandwidth in a multi-subordinate system on periodic time windows, proactively prevents denial of service from malicious actors in the system, and tracks each manager's access and interference statistics for optimal budget and period selection. We provide detailed performance and implementation cost assessment in a 12nm node and an end-to-end functional case study implementing AXI-REALM into an open-source Linux-capable RISC-V SoC. In a system with a general-purpose core and a hardware accelerator's DMA engine causing interference on the interconnect, AXI-REALM achieves fair bandwidth distribution among managers, allowing the core to recover 68.2 % of its performance compared to the case without contention. Moreover, near-ideal performance (above 95 %) can be achieved by distributing the available bandwidth in favor of the core, improving the worst-case memory access latency from 264 to below eight cycles. Our approach minimizes buffering compared to other solutions and introduces only 2.45 % area overhead compared to the original SoC.

关键词： Bandwidth Interference Real-time systems Hardware Regulation Timing System-on-chip

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Biodegradable Gliding Paper Flyers Fabricated Through Inkjet Printing

Biodegradable Gliding Paper Flyers Fabricated Through Inkjet...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Luca Girardi Rui Wu Yuki Fukatsu Hiroki Shigemune Stefano Mintchev Department of Environmental Systems Science Environmental Robotics Laboratory ETH Zurich Zurich Switzerland Swiss Federal Institute for Forest Snow and Landscape Research (WSL) Birmensdorf Switzerland Department of Electrical Engineering Shibaura Institute of Technology Tokyo Japan

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Seed-inspired minimalist microflyers are showing potential as dispersal platforms for sensor networks and seeds. Their function relies on the sheer number of low-cost flyers, inevitably raising concerns about the post-operation environmental impact. We propose a biodegradable paper glider platform fabricated through origami inkjet printing. This method can fold origami along printing patterns on different paper varieties. We created a printing pattern that allows 2D paper sheets to self-fold into a 3D flying wing glider with designable wing geometry and center of gravity (CG). The design allows stable and repeatable gliding behavior, proven in our gliding tests. It successfully achieves the dispersal of multiple gliders from a hovering drone, covering an average horizontal distance larger than the release height. We also tested the biodegradation of different paper types compatible with the printing method, showing near-complete degradation after 15 weeks in moist soil. Our study presents a novel, potentially scalable approach for fabricating environmentally friendly microflyers, offering new avenues for remote environmental sensing and automated forest restoration programs.

关键词： Geometry Atmospheric modeling Forestry Aerodynamics Three-dimensional printing Stability analysis Ink jet printing Seeds (agriculture) Thermal stability Paints

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Enhancing Perception for Autonomous Driving systems with Dual-Vision Input Cycle GAN for Night-to-Day Image Translation

Enhancing Perception for Autonomous Driving Systems with Dua...

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Artificial Intelligence (SLAAI-ICAI), SLAAI International Conference on

作者： K.J.P. Fernando H.K.I.S. Lakmal M.B. Dissanayake L.P. Kalansooriya Department of Electrical Electronic & Systems Engineering NSBM Green University Homagama Sri Lanka Department of Mechatronic & Industrial Engineering NSBM Green University Homagama Sri Lanka Department of Electrical and Electronic Engineering University of Peradeniya Peradeniya Sri Lanka Faculty of Computing General Sir John Kotelawala Defence University Ratmalana Sri Lanka

ISBN: (数字)9798331509170

ISBN: (纸本)9798331509187

Autonomous Driving systems (ADS) and Advanced Driver Assistance systems (ADAS) heavily rely on vision-based data for critical decision-making. However, these systems face significant performance limitations in low-light conditions where conventional RGB vision inputs struggle to capture information due to low light. Current solutions, including high-gain cameras or LiDAR-based approaches, are expensive and increase system complexity. This study presents a novel approach to address these challenges by employing a night-to-day image translation model with modified CycleGAN. The proposed model translates RGB and infrared (IR) inputs into a daytime representation, enhancing overall perception and object detection in low-light scenarios. Unlike existing methods, the model operates in an unsupervised learning framework, avoiding the need for large, paired datasets, which are often difficult to obtain. The study also introduces architectural modifications to the CycleGAN, including a dualinput framework and refined architecture, to optimize performance across varied conditions. The model’s performance is demonstrated through reference-less evaluation metrics, achieving average scores of FID 8.04, NIQE 4.92, and BRISQUE 36.3. These results illustrate the potential of the model to enhance the safety and operational stability of autonomous driving systems in low-light environments.

关键词： Solid modeling Visualization Translation Computer architecture Virtual reality Generative adversarial networks Safety Advanced driver assistance systems Autonomous vehicles Unsupervised learning

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Harvesting Curvatures for Communication-Efficient Distributed Optimization

Harvesting Curvatures for Communication-Efficient Distribute...

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Asilomar Conference on Signals, systems & Computers

作者： Diogo Cardoso Boyue Li Yuejie Chi João Xavier Department of Electrical and Computer Engineering Carnegie Mellon University USA Institute for Systems and Robotics IST-University of Lisbon Portugal

This paper considers the design of distributed optimization algorithms in the server-client setting, with the goal of achieving better communication efficiency, a crucial desiderata in modern distributed machine learning applications. One popular approach to reduce communication is to impose more computation between communication rounds, by letting each client optimize an approximation of the global objective function-constructed using the local objective function and aggregated history information-and exchange the updates with the parameter server for global consensus. In particular, judiciously incorporating second-order information in the local approximation often leads to better communication efficiency when the problem is ill-conditioned. However, existing methods construct the second-order correction term in an isotropic manner, without taking into account the curvature information of the global objective function accumulated over the course of the algorithm. This paper proposes a novel algorithm that refines this idea by constructing a second-order correction term using a BFGS-style update formula, where the kernel matrix is updated recursively using only history gradients to harvest curvature information for accelerating convergence. Numerical experiments demonstrate improved communication efficiency over competitive baseline algorithms on both synthetic and real datasets.

关键词： Machine learning algorithms Memory management Machine learning Approximation algorithms Linear programming Servers History

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Strength in Diversity: Multi-Branch Representation Learning for Vehicle Re-Identification

arXiv

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arXiv 2023年

作者： Almeida, Eurico Silva, Bruno Batista, Jorge The Institute of Systems and Robotics University of Coimbra Coimbra3030-219 Portugal The Department of Electrical and Computers Engineering Faculty of Science and Technology University of Coimbra Coimbra3030-290 Portugal

This paper presents an efficient and lightweight multi-branch deep architecture to improve vehicle re-identification (V-ReID). While most V-ReID work uses a combination of complex multi-branch architectures to extract robust and diversified embeddings towards re-identification, we advocate that simple and lightweight architectures can be designed to fulfill the Re-ID task without compromising performance. We propose a combination of Grouped-convolution and Loss-Branch-Split strategies to design a multi-branch architecture that improve feature diversity and feature discriminability. We combine a ResNet50 global branch architecture with a BotNet self-attention branch architecture, both designed within a Loss-Branch-Split (LBS) strategy. We argue that specialized loss-branch-splitting helps to improve re-identification tasks by generating specialized re-identification features. A lightweight solution using grouped convolution is also proposed to mimic the learning of loss-splitting into multiple embeddings while significantly reducing the model size. In addition, we designed an improved solution to leverage additional metadata, such as camera ID and pose information, that uses 97% less parameters, further improving re-identification performance. In comparison to state-of-the-art (SoTA) methods, our approach outperforms competing solutions in Veri-776 by achieving 85.6% mAP and 97.7% CMC1 and obtains competitive results in Veri-Wild with 88.1% mAP and 96.3% CMC1. Overall, our work provides important insights into improving vehicle re-identification and presents a strong basis for other retrieval tasks. Our code is available at the link. Copyright © 2023, The Authors. All rights reserved.

关键词： Vehicles

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