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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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Calibrated Peer Reviews in Software Engineering Instruction: Experiences and Application 35

Calibrated Peer Reviews in Software Engineering Instruction:...

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35th IEEE International Conference on Software Engineering Education and Training, CSEE and T 2023

作者： Tenbergen, Bastian Daun, Marian State University of New York at Oswego Department of Computer Science OswegoNY United States Technical University of Applied Sciences Würzburg-Schweinfurt Center for Robotics Schweinfurt Germany

ISBN: (纸本)9798350322026

One of the key aspects of software engineering is to ensure the developed software meets stakeholder expectations in terms of functional requirements as well as the apparent quality. But achieving high software quality is not just a question of conducting thorough requirements engineering and quality assurance, it is also a question of doing so meticulously and with the necessary level of attention to detail. Yet, software engineering education is mostly concerned with instructing technical knowledge (e.g., unit testing) and organizational soft skills (e.g., working in teams). Teaching the learner proper appreciation of quality, not just in terms of executing techniques, but also in terms of alternative solutions, design trade-offs, advantages, and limitations of engineering decisions is rarely an explicit learning outcome in specialized software engineering courses. We therefore propose the systematic application of calibrated peer reviews as a mechanism to expose the learner to as many different solutions as possible during skill-based knowledge acquisition. In this paper, we discuss in detail the application of calibrated peer reviews in a requirements engineering course and in a quality assurance course. Application results show that using calibrated peer reviews during skill acquisition (i.e., homework assignments) positively affects students' ability to apply theory and skills in both specification and quality assurance tasks. Our results show further that while calibrated peer reviews have a positive significant effect to apply and retain course theory, the effect on their ability to produce high quality engineering documents is marginally positive and limited to specification tasks. © 2023 IEEE.

关键词： Requirements engineering

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APriCoT: Action Primitives based on Contact-state Transition for In-Hand Tool Manipulation

APriCoT: Action Primitives based on Contact-state Transition...

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IEEE-RAS International Conference on Humanoid Robots

作者： Daichi Saito Atsushi Kanehira Kazuhiro Sasabuchi Naoki Wake Jun Takamatsu Hideki Koike Katsushi Ikeuchi Department of Computer Science Institute of Science Tokyo Tokyo Japan Applied Robotics Research Microsoft Redmond WA USA

ISBN: (数字)9798350373578

ISBN: (纸本)9798350373585

In-hand tool manipulation is an operation that not only manipulates a tool within the hand (i.e., in-hand manipulation) but also achieves a grasp suitable for a task after the manipulation. This study aims to achieve an in-hand tool manipulation skill through deep reinforcement learning. The difficulty of learning the skill arises because this manipulation requires (A) exploring long-term contact-state changes to achieve the desired grasp and (B) highly-varied motions depending on the contact-state transition. (A) leads to a sparsity of a reward on a successful grasp, and (B) requires an RL agent to explore widely within the state-action space to learn highly-varied actions, leading to sample inefficiency. To address these issues, this study proposes Action Primitives based on Contact-state Transition (APriCoT). APriCoT decomposes the manipulation into short-term action primitives by describing the operation as a contact-state transition based on three action representations (detach, crossover, attach). In each action primitive, fingers are required to perform short-term and similar actions. By training a policy for each primitive, we can mitigate the issues from (A) and (B). This study focuses on a fundamental operation as an example of in-hand tool manipulation: rotating an elongated object grasped with a precision grasp by half a turn to achieve the initial grasp. Experimental results demonstrated that ours succeeded in both the rotation and the achievement of the desired grasp, unlike existing studies. Additionally, it was found that the policy was robust to changes in object shape.

关键词： Training Shape Humanoid robots Deep reinforcement learning

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Degradable Tracking System based on Hardware Multi-Model Estimators

Degradable Tracking System based on Hardware Multi-Model Est...

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2022 Resilience Week, RWS 2022

作者： Kiriakidis, Kiriakos Croteau, Brien Severson, Tracie Rodriguez-Seda, Erick Robucci, Ryan Islam, Riadul Rahman, Saad United States Naval Academy Department of Weapons Robotics and Control Engineering Department of Cyber Science United States University of Maryland Department of Computer Science and Electrical Engineering United States

ISBN: (数字)9781665488198

ISBN: (纸本)9781665488198

Sensing systems onboard unmanned vehicles operate in an environment of constrained computational resources. A cyber-Attack may primarily aim to degrade these computing devices and, ultimately, incapacitate the sensing system itself. To prepare a prototype tracking system for degradation, this paper proposes distributed hardware implementation of a Multiple Model estimator on two FPGA units and, after an attack, adaptation of the estimator by leveraging Dynamic Partial Reconfiguration of the single surviving FPGA. The method ensures that the most likely models of the estimator are loaded on to the fabric of the surviving FPGA with minimal interruption. © 2022 IEEE.

关键词： Field programmable gate arrays (FPGA)

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SelfMedHPM: Self Pre-Training with Hard Patches Mining Masked Autoencoders for Medical Image Segmentation 22

SelfMedHPM: Self Pre-Training with Hard Patches Mining Maske...

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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Lv, Yunhao Chen, Lingyu Wang, Jian Li, Yangxi Chen, Fang Nanjing University of Aeronautics and Astronautics Department of Computer Science and Engineering Nanjing China School of Biomedical Engineering Institute of Medical Robotics Shanghai JiaoTong University Shanghai200240 China

ISBN: (纸本)9798331520526

In recent years, deep learning methods such as convolutional neural network (CNN) and transformers have made significant progress in CT multi-organ segmentation. However, CT multi-organ segmentation methods based on masked image modeling (MIM) are very limited. There are already methods using MAE for CT multi-organ segmentation task, we believe that the existing methods do not identify the most difficult areas to reconstruct. To this end, we propose a MIM self-training framework with hard patches mining masked autoencoders for CT multi-organ segmentation tasks (self-MedHPM). The method performs ViT self-pretraining on the training set of the target data and introduces an auxiliary loss predictor, which first predicts the patch loss and determines the location of the next mask. SelfMedHPM implementation is better than various competitive methods in abdominal CT multi-organ segmentation and body CT multi-organ segmentation. We have validated the performance of our method on the Multi Atlas Labeling Beyond The Cranial Vault (BTCV) dataset for abdomen mult-organ segmentation and the SinoMed Whole Body (SMWB) dataset for body multi-organ segmentation tasks. © 2025 IEEE.

关键词： Convolutional neural networks

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Efficient-Net ASPP Deep Network for Malignant Ultrasound Breast Cancer Segmentation

Efficient-Net ASPP Deep Network for Malignant Ultrasound Bre...

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2023 IEEE International Conference on Emerging Trends in Engineering, sciences and Technology, ICES and T 2023

作者： Azam, Komal M.A., Azam M.A., Qureshi K.B., Khan M.A., Azam College Women University The Government Sadiq Department of Computer Science Bahawalpur Pakistan The Islamia University of Bahawalpur The Faculty of Engineering Bahawalpur Pakistan Istituto Italiano di Tecnologia Department of Advanced Robotics Genoa Italy

ISBN: (纸本)9781665455602

Worldwide, breast cancer is one of the main causes of mortality among women. Only through early recognition of symptoms is it possible to limit the incidence of premature deaths. Utilizing standard deep-learning semantic segmentation, tumors are automatically segmented. It requires minimal human contact. The standard methods are less efficient and precise in accuracy. In this paper, we propose an Efficient-Net based Atrous Spatial Pyramid Pooling (Efficient-Net ASPP) model for efficient and improved segmentation. The proposed model is comprised of an EfficientNetV2B3 backbone with dilated convolutional, skip connection, max pooling, and an ASSP block. A publicly available Malignant Breast Cancer Ultrasound (MBCU) subset image dataset was investigated in our study. Several baseline segmentation methods were compared with our proposed method. The quantitative benchmark analysis shows the proposed model for segmenting MBCU images achieved 70% intersection over union (IOU) and a 62% score on the Dice Similarity Coefficient (DSC) metrics. The proposed network improves MBCU image segmentation by 14% on DSC and 18% on IOU when compared to baseline models. © 2023 IEEE.

关键词： Ultrasonics

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Preprocessing-based Kinodynamic Motion Planning Framework for Intercepting Projectiles using a Robot Manipulator

Preprocessing-based Kinodynamic Motion Planning Framework fo...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Ramkumar Natarajan Hanlan Yang Qintong Xie Yash Oza Manash Pratim Das Fahad Islam Muhammad Suhail Saleem Howie Choset Maxim Likhachev The Robotics Institute School of Computer Science Carnegie Mellon University USA Department of Engineering Science University of Oxford UK

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

We are interested in studying sports with robots and starting with the problem of intercepting a projectile moving toward a robot manipulator equipped with a shield. To successfully perform this task, the robot needs to (i) detect the incoming projectile, (ii) predict the projectile’s future motion, (iii) plan a minimum-time rapid trajectory that can evade obstacles and intercept the projectile, and (iv) execute the planned trajectory. These four steps must be performed under the manipulator’s dynamic limits and extreme time constraints (≤ 350ms in our setting) to successfully intercept the projectile. In addition, we want these trajectories to be smooth to reduce the robot’s joint torques and the impulse on the platform on which it is mounted. To this end, we propose a kinodynamic motion planning framework that preprocesses smooth trajectories offline to allow real-time collision-free executions online. We present an end-to-end pipeline along with our planning framework, including perception, prediction, and execution modules. We evaluate our framework experimentally in simulation and show that it has a higher blocking success rate than the baselines. Further, we deploy our pipeline on a robotic system comprising an industrial arm (ABB IRB-1600) and an onboard stereo camera (ZED 2i), which achieves a 78% success rate in projectile interceptions.

关键词： Service robots Projectiles Pipelines Robot vision systems Trajectory Planning Time factors

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Cleo: Smart Glasses to Monitor Consumption of Alcohol and Cigarettes

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SN computer science 2023年第1期4卷 53页

作者： Sethuraman, Sibi Chakkaravarthy Kompally, Pranav Mohanty, Saraju P. Raju, Bksp Kumar Cruz, Meenalosini Vimal School of Computer Science and Engineering and Center of Excellence in Artificial Intelligence and Robotics (AIR) VIT-AP University Amaravati India Department of Computer Science and Engineering University of North Texas Denton 76207 TX United States Department of Information Technology Allen E. Paulson College of Engineering and Computing Georgia Southern University Statesboro GA United States

It is estimated that over 60% of people around the globe consume alcohol and cigars daily. Many people use them beyond the permitted limit, which causes lung cancer, liver and kidney failure. If there is a system that could monitor their intake level, it will alert them in case of excess consumption, which could help them control the intake. To help the users monitor their consumption, we introduce Cleo Eyeglasses in this paper. Cleo is a wearable spectacle device with a mounted camera and single onboard computer that performs custom trained object recognition to identify alcoholic beverages and cigarettes. Upon recognition, a log is automatically maintained in the corresponding mobile application. If the intake exceeds the user threshold, then our system generates an alert to both the user and concerned medical personnel. Cigarette/alcohol consumption can immediately affect the body without noticeable symptoms. Cleo addresses this with keen monitoring of vital body parameters and generates an alert when abnormalities are detected. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Alcohol monitoring CNN computer vision Deep learning Smart glasses Smoke monitoring

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Constant-time Motion Planning with Anytime Refinement for Manipulation

Constant-time Motion Planning with Anytime Refinement for Ma...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Itamar Mishani Hayden Feddock Maxim Likhachev School of Computer Science Robotics Institute Carnegie Mellon University Pittsburgh PA Electrical and Computer Engineering Department Pittsburgh University

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Robotic manipulators are essential for future autonomous systems, yet limited trust in their autonomy has confined them to rigid, task-specific systems. The intricate configuration space of manipulators, coupled with the challenges of obstacle avoidance and constraint satisfaction, often makes motion planning the bottleneck for achieving reliable and adaptable autonomy. Recently, a class of constant-time motion planners (CTMP) was introduced. These planners employ a preprocessing phase to compute data structures that enable online planning provably guarantee the ability to generate motion plans, potentially sub-optimal, within a user defined time bound. This framework has been demonstrated to be effective in a number of time-critical tasks. However, robotic systems often have more time allotted for planning than the online portion of CTMP requires, time that can be used to improve the solution. To this end, we propose an anytime refinement approach that works in combination with CTMP algorithms. Our proposed framework, as it operates as a constant time algorithm, rapidly generates an initial solution within a user-defined time threshold. Furthermore, functioning as an anytime algorithm, it iteratively refines the solution’s quality within the allocated time budget. This enables our approach to strike a balance between guaranteed fast plan generation and the pursuit of optimization over time. We support our approach by elucidating its analytical properties, showing the convergence of the anytime component towards optimal solutions. Additionally, we provide empirical validation through simulation and real-world demonstrations on a 6 degree-of-freedom robot manipulator, applied to an assembly domain.

关键词： Manipulators Reliability engineering Data structures Real-time systems Planning Time factors Task analysis

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Learning-based Control for PMSM Using Distributed Gaussian Processes with Optimal Aggregation Strategy 49

Learning-based Control for PMSM Using Distributed Gaussian P...

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49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Yin, Zhenxiao Dai, Xiaobing Yang, Zewen Shen, Yang Hattab, Georges Zhao, Hang Robotics and Autonomous Systems Thrust Guangzhou China Technical University of Munich Chair of Information-oriented Control Munich Germany Berlin Germany Freie Universität Berlin Department of Mathematics and Computer Science Berlin Germany The Hong Kong University of Science and Technology Department of Electronic & Computer Engineering Hong Kong

ISBN: (纸本)9798350331820

The growing demand for accurate control in varying and unknown environments has sparked a corresponding increase in the requirements for power supply components, including permanent magnet synchronous motors (PMSMs). To infer the unknown part of the system, machine learning techniques are widely employed, especially Gaussian process regression (GPR) due to its flexibility of continuous system modeling and its guaranteed performance. For practical implementation, distributed GPR is adopted to alleviate the high computational complexity. However, the study of distributed GPR from a control perspective remains an open problem. In this paper, a control-aware optimal aggregation strategy of distributed GPR for PMSMs is proposed based on the Lyapunov stability theory. This strategy exclusively leverages the posterior mean, thereby obviating the need for computationally intensive calculations associated with posterior variance in alternative approaches. Moreover, the straightforward calculation process of our proposed strategy lends itself to seamless implementation in high-frequency PMSM control. The effectiveness of the proposed strategy is demonstrated in the simulations. © 2023 IEEE.

关键词： Synchronous motors

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