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1st Workshop on Advanced Multimedia Computing for Smart Manufacturing and engineering, AMC-SME 2023

作者： Liu, Jun Wu, Chao Yuan, Geng Niu, Wei Zhang, Wenbin Song, Houbing Herbert Robotics Institute of School of Computer Science Carnegie Mellon University Pittsburgh United States Department of Electrical & Computer Engineering Northeastern University Boston United States School of Computing University of Georgia Athens United States Department of Computer Science Michigan Technological University Houghton United States Department of Information Systems University of Maryland Baltimore County Baltimore United States

ISBN: (纸本)9798400702730

Computational resources are limited on real-time embedded devices, so the available computing cost of deployment on the target platform must be considered. We develop a feature extraction module based on the MobileNet backbone that can be adjusted in terms of computational complexity and capacity using the depth multiplier parameter, classifier depth, and kernel depth. These three parameters allow us to control the count of channels within the network, effectively managing the model's capacity and computational requirements. To achieve semantic segmentation, we incorporate additional components, such as an extension module. This extension module typically includes 1x1 pointwise convolutional layers for pixel-level classification and a transposed convolutional layer for upsampling the output to the original input image size. By combining the feature extraction module with this extension module, we can create a complete architecture capable of performing semantic segmentation tasks. The feature extraction module provides the initial feature extraction and the extension module adds the necessary components for accurate pixel-wise classification and upsampling. Compared to Hardware-aware Neural Architecture Search (NAS), pruning, runtime pruning, and knowledge distillation methods, our model has several advantages in terms of modular design, structural controllability, ease of implementation, and cost-effectiveness. Our computational efficiency, as measured by FLOPS, is highly competitive. Our method is distinguished by solving the problem of MobileNet's inability to adjust the size and number of convolution kernels. It achieves this through adaptable parameter tuning, including MobileNet's depth multiplier, the kernel size in the FCN head's Separable Convolution layer, and the depth of the first Point-wise Convolution layer. These adjustments are customized to match hardware's max multiply-accumulates (MACs), optimizing network capacity and maximizing resource utilizat

关键词： Feature extraction

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Tiny-PULP-Dronets: Squeezing Neural Networks for Faster and Lighter Inference on Multi-Tasking Autonomous Nano-Drones

arXiv

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arXiv 2024年

作者： Lamberti, Lorenzo Niculescu, Vlad Barcis, Michal Bellone, Lorenzo Natalizio, Enrico Benini, Luca Palossi, Daniele Department of Electrical Electronic and Information Engineering University of Bologna Italy Integrated Systems Laboratory ETH Zürich Switzerland Autonomous Robotics Research Center Technology Innovation Institute United Arab Emirates Dalle Molle Institute for Artificial Intelligence USI SUPSI Switzerland

Pocket-sized autonomous nano-drones can revolutionize many robotic use cases, such as visual inspection in narrow, constrained spaces and ensure safer human-robot interaction due to their tiny form factor and weight - i.e., tens of grams. This compelling vision is challenged by the high level of intelligence needed aboard, which clashes against the limited computational and storage resources available on PULP (parallel-ultra-low-power) MCU class navigation and mission controllers that can be hosted aboard. This work moves from PULP-Dronet, a State-of-the-Art convolutional neural network for autonomous navigation on nano-drones. We introduce Tiny-PULP-Dronet: a novel methodology to squeeze by more than one order of magnitude model size (50× fewer parameters), and number of operations (27× less multiply-and-accumulate) required to run inference with similar flight performance as PULP-Dronet. This massive reduction paves the way towards affordable multitasking on nano-drones, a fundamental requirement for achieving high-level intelligence. Copyright © 2024, The Authors. All rights reserved.

关键词： Drones

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Fast Realization of Robot 3D Simulation Based on WebGL

Fast Realization of Robot 3D Simulation Based on WebGL

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第35届中国控制与决策会议

作者： Tao Zhang Xintong Wang Yun Teng Jingzhe Fang Yiqiang Zhang Xiyou Chen School of Electrical Engineering Dalian University of Technology Shenyang Institute of Automation Chinese Academy of Sciences Key Laboratory of Networked Control Systems Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences School of Electrical Engineering Shenyang University of Technology

As science and technology have advanced,an increasing number of people are focusing their study on 3 D robot *** 3 D quick simulation of the mechanical arm can be achieved using 3 D modeling software and *** the third-party WebGL library *** to construct the simulation scenario,then interact with and render the 3 D scene and 3 D robot model directly on the Web,which is easier and faster than downloading *** essay briefly explains the benefits and applicability of *** in the creation of 3 D virtual simulations,then goes on to discuss the quick implementation process of 3 D robot simulations before summarizing the building technique.

关键词： webgl three.js 3D simulation robot

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Modified Metaheuristics with Transfer Learning Based Insect Pest Classification for Agricultural Crops

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Computer systems Science & engineering 2023年第9期46卷 3847-3864页

作者： Saud Yonbawi Sultan Alahmari T.Satyanarayana murthy Ravuri Daniel E.Laxmi Lydia Mohamad Khairi Ishak Hend Khalid Alkahtani Ayman Aljarbouh Samih M.Mostafa Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia King Abdul Aziz City for Science and Technology RiyadhKingdom of Saudi Arabia Chaitanya Bharathi Institute of Technology HyderabadTelanganaIndia Department of Computer Science and Engineering Prasad V.Potluri Siddhartha Institute of TechnologyVijayawadaIndia Department of Computer Science and Engineering GMR Institute of TechnologyAndhra PradeshRajamIndia School of Electrical and Electronic Engineering Engineering CampusUniversiti Sains Malaysia(USM)Nibong TebalPenangMalaysia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversitySaudi Arabia Department of Computer Science University of Central AsiaNarynKyrgyzstan Faculty of Computers and Information South Valley UniversityQenaEgypt

Crop insect detection becomes a tedious process for agronomists because a substantial part of the crops is damaged,and due to the pest attacks,the quality is *** are the major reason behind crop quality degradation and diminished crop ***,accurate pest detection is essential to guarantee safety and crop *** identification of insects necessitates highly trained taxonomists to detect insects precisely based on morphological ***,some progress has been made in agriculture by employing machine learning(ML)to classify and detect *** study introduces a Modified Metaheuristics with Transfer Learning based Insect Pest Classification for Agricultural Crops(MMTL-IPCAC)*** presented MMTL-IPCAC technique applies contrast limited adaptive histogram equalization(CLAHE)approach for image *** neural architectural search network(NASNet)model is applied for feature extraction,and a modified grey wolf optimization(MGWO)algorithm is employed for the hyperparameter tuning process,showing the novelty of the *** last,the extreme gradient boosting(XGBoost)model is utilized to carry out the insect classification *** simulation analysis stated the enhanced performance of the MMTL-IPCAC technique in the insect classification process with maximum accuracy of 98.73%.

关键词： Sustainable agriculture crop monitoring pest management insect classification computer vision

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Path-tracking of Mobile Robot Using PD-type Iterative Learning Control with Forgetting Factor

Path-tracking of Mobile Robot Using PD-type Iterative Learni...

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第35届中国控制与决策会议

作者： Tao Zhang Jingzhe Fang Yun Teng Xintong Wang Yiqiang Zhang Xiyou Chen School of Electrical Engineering Dalian University of Technology Shenyang Institute of Automation Chinese Academy of Sciences Key Laboratory of Networked Control Systems Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences School of Electrical Engineering Shenyang University of Technology

In this paper,a discrete iterative learning control strategy is proposed for the path-tracking problem of mobile robot trajectory tracking with repeated *** algorithm adds the forgetting factor to the PD-type discrete iterative learning control and considers the correction of the initial state of the *** convergence of the algorithm is proved rigorously by using the paradigm theory,and the sufficient conditions of the paradigm form to make the algorithm converge are ***,the effectiveness of the proposed algorithm is verified by simulation.

关键词： Mobile robot discrete iterative learning control forgetting factor path-tracking

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Privacy-preserving Biometric-driven Data for Student Identity Management: Challenges and Approaches 21

Privacy-preserving Biometric-driven Data for Student Identit...

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29th ACM Conference on User Modeling, Adaptation and Personalization, UMAP 2021

作者： Fidas, Christos Belk, Marios Portugal, David Pitsillides, Andreas University of Patras Department of Electrical and Computer Engineering Greece Cognitive Ux GmbH Germany University of Coimbra Institute of Systems and Robotics Portugal University of Cyprus Department of Computer Science Cyprus

ISBN: (纸本)9781450383677

Biometric technologies are being considered lately for student identity management in Higher Education Institutions, as they provide several advantages over the traditional knowledge-based and token-based authentication methods, i.e., biometrics provide high security entropies, convenience and a sense of technological modernity to the end-users. While biometric technologies have many benefits from both a security and usability point of view, still there is a need for innovative user identity management solutions that continuously identify and authenticate students during academic and teaching activities. In addition, biometrics entail several threats and weaknesses with regards to the privacy of data stored about the user, which negatively affect the user acceptance and the wider adoption of biometrics due to regulatory and legal issues. In this paper, we refer to our ongoing research on intelligent and continuous online student identity management for improving security and trust in European Higher Education Institutions. We further highlight based on the literature, existing challenges, threats and state-of-the-art approaches with regards to preserving the privacy of biometric-driven data. © 2021 Owner/Author.

关键词： Blockchain

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An Isomerism Learning Model to Solve Time-Varying Problems Through Intelligent Collaboration

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IEEE/CAA Journal of Automatica Sinica 2023年第8期10卷 1772-1774页

作者： Zhihao Hao Guancheng Wang Bob Zhang Leyuan Fang Haisheng Li the Department of Computer and Information Science University of MacaoMacao 999078China the School of Data Science the Chinese University of Hong KongShenzhen 518172 Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen 518000 China Industrial Control Systems Cyber Emergency Response Team Beijing 100040China IEEE the College of Electrical and Information Engineering Hunan UniversityChangsha 410082 the Peng Cheng Laboratory Shenzhen 518000China the Beijing Key Laboratory of Big Data Technology for Food Safety Beijing Technology and Business UniversityBeijing 100048 the School of Computer Science and Engineering Beijing Technology and Business UniversityBeijing 100048China

Dear Editor,This letter deals with a solution for time-varying problems using an intelligent computational(IC)algorithm driven by a novel decentralized machine learning approach called isomerism *** order to meet the challenges of the model’s privacy and security brought by traditional centralized learning models,a private permissioned blockchain is utilized to decentralize the model in order to achieve an effective coordination,thereby ensuring the credibility of the overall model without exposing the specific parameters and solution process.

关键词： ism thereby letter

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Multi-Quadrotor Cooperative Encirclement and Capture Approach in Obstacle Environments

Multi-Quadrotor Cooperative Encirclement and Capture Approac...

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International Conference on Mechatronics (ICM)

作者： Zhenyu Li Ruixin Wu Jin Wang Chuanqi Hu Zhi Zheng Jun Meng Guodong Lu School of Mechanical Engineering Zhejiang University Hangzhou China State Key Laboratory of Fluid Power and Mechatronic Systems Zhejiang University Hangzhou China Zhejiang Key Laboratory of Industrial Big Data and Robot Intelligent Systems Zhejiang University Hangzhou China Robotics Research Center of Yuyao City Ningbo China Polytechnic Institute Zhejiang University Hangzhou China College of Electrical Engineering Zhejiang University Hangzhou China Center for Data Mining and Systems Biology College of Electrical Engineering Zhejiang University Hangzhou China

ISBN: (数字)9798331533892

ISBN: (纸本)9798331533908

This article proposes an algorithm designed for autonomous quadrotors to capture target quadrotors in complex obstacle environments, enabling multiple pursuers to perform distributed capture while simultaneously avoiding obstacles and collisions. We first introduce a 3D obstacle space representation method based on safety-priority reachable region (SPRR) and implement real-time construction for general point cloud maps. Subsequently, leveraging SPRR, we employ an area minimization approach to ensure the effective capture of the evader within a limited time. The outcomes are then integrated into a MINCO-based trajectory planning framework to ensure their practical execution by real quadrotors. Relying on SPRR, the optimization problem is simplified and the solution efficiency is improved. In the scenario of simulating the dynamics of real quadrotors, we compare the proposed algorithm with state-of-the-art evasion and pursuit methods, validating its effectiveness and advancements. This work presents a practical solution for multi-quadrotor collaborative capture.

关键词： Point cloud compression Visualization Three-dimensional displays Trajectory planning Heuristic algorithms Collaboration Real-time systems Safety Optimization Quadrotors

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Towards spiking analog hardware implementation of a trajectory interpolation mechanism for smooth closed-loop control of a spiking robot arm

Towards spiking analog hardware implementation of a trajecto...

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IEEE International Symposium on Circuits and systems (ISCAS)

作者： Daniel Casanueva-Morato Chenxi Wu Giacomo Indiveri Juan P. Dominguez-Morales Alejandro Linares-Barranco Robotics and Technology of Computers Lab. ETSII-EPS Universidad de Sevilla Sevilla Spain Smart Computer Systems Research and Engineering Lab (SCORE) I3US Universidad de Sevilla Spain SynSense AG Corporation Zurich Switzerland Institute of Neuroinformatics University of Zurich and ETH Zurich Switzerland Spain

ISBN: (数字)9798350356830

ISBN: (纸本)9798350356847

Neuromorphic engineering aims to incorporate the computational principles found in animal brains, into modern technological systems. Following this approach, in this work we propose a closed-loop neuromorphic control system for an event-based robotic arm. The proposed system consists of a shifted Winner-Take-All spiking network for interpolating a reference trajectory and a spiking comparator network responsible for controlling the flow continuity of the trajectory, which is fed back to the actual position of the robot. The comparator model is based on a differential position comparison neural network, which governs the execution of the next trajectory points to close the control loop between both components of the system. To evaluate the system, we implemented and deployed the model on a mixed-signal analog-digital neuromorphic platform, the DYNAP-SE2, to facilitate integration and communication with the ED-Scorbot robotic arm platform. Experimental results on one joint of the robot validate the use of this architecture and pave the way for future neuro-inspired control of the entire robot.

关键词： Interpolation Neuromorphics Event detection Spiking neural networks Arms Manipulators Control systems Hardware Trajectory Analog-digital conversion

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EVEN SMALL CORRELATION AND DIVERSITY SHIFTS POSE DATASET-BIAS ISSUES

arXiv

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arXiv 2023年

作者： Bissoto, Alceu Barata, Catarina Valle, Eduardo Avila, Sandra Institute of Computing University of Campinas Brazil Institute for Systems and Robotics Instituto Superior Técnico Portugal School of Electrical and Computing Engineering University of Campinas Brazil Recod.ai Lab. University of Campinas Brazil

Distribution shifts are common in real-world datasets and can affect the performance and reliability of deep learning models. In this paper, we study two types of distribution shifts: diversity shifts, which occur when test samples exhibit patterns unseen during training, and correlation shifts, which occur when test data present a different correlation between seen invariant and spurious features. We propose an integrated protocol to analyze both types of shifts using datasets where they co-exist in a controllable manner. Finally, we apply our approach to a real-world classification problem of skin cancer analysis, using out-of-distribution datasets and specialized bias annotations. Our protocol reveals three findings: 1) Models learn and propagate correlation shifts even with low-bias training;this poses a risk of accumulating and combining unaccountable weak biases;2) Models learn robust features in high- and low-bias scenarios but use spurious ones if test samples have them;this suggests that spurious correlations do not impair the learning of robust features;3) Diversity shift can reduce the reliance on spurious correlations;this is counter-intuitive since we expect biased models to depend more on biases when invariant features are missing. Our work has implications for distribution shift research and practice, providing new insights into how models learn and rely on spurious correlations under different types of shifts. Copyright © 2023, The Authors. All rights reserved.

关键词： Classification (of information)

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