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12th European Conference on Artificial Intelligence, ECAI 1996, 2nd International Conference on Multiagent systems, ICMAS 1996, Workshop on Learning in Distributed Artificial Intelligence systems, LDAIS 1996 and Workshop on Learning, Interaction, and Organization in Multiagent Environments, LIOME 1996

作者： Friedrich, Holger Kaiser, Michael Rogalla, Oliver Dillmann, Rüdigex University of Karlsruhe Institute for Real-Time Computer Systems and Robotics KarlsruheD-76128 Germany

ISBN: (纸本)3540629343

This paper discusses the significance of communication between individual agents that are embedded into learning Multi-Agent systems. For several learning tasks occurring within a Multi-Agent System, communication activities are investigated and the need for a mutual understanding of agents participating in the learning process is made explicit. Thus, the need for a common ontology to exchange learning-related information is shown. Building this ontogy is an additional learning task that is not only extremely important, but also extremely difficult. We propose a solution that is motiwtted by the human ability to understand each other even in the absence of a common language by using alternative commuification channels, such as gestures. We show some results for the task of cooperative material handling by several manipulators. © 1997, Springer Verlag.

关键词： Multi agent systems

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Theory of Mind Abilities Predict Robot's Gaze Effects on Object Preference

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IEEE Transactions on Affective Computing 2025年

作者： Manzi, Federico Ishikawa, Mitsuhiko Dio, Cinzia Di Itakura, Shoji Kanda, Takayuki Ishiguro, Hiroshi Massaro, Davide Marchetti, Antonella Università Cattolica del Sacro Cuore Research Unit on Theory of Mind Department of Psychology Milan Italy Università Cattolica del Sacro Cuore Research Unit in Psychology and Robotics in the Lifespan Department of Psychology Milan Italy IRCCS Fondazione Don Carlo Gnocchi Milan Italy Hitotsubashi University Hitotsubashi Institute for Advanced Study Japan Ritsumeikan University Kyoto Japan Kyoto University Department of Computer Science Human-Robot Interaction Laboratory Kyoto Japan IRC'HIL Keihana Science City Advanced Telecommunications Research Institute International Kyoto Japan Osaka University Department of Intelligent Systems Toyonaka Japan

This study investigated the differences between human and robot gaze in influencing preference formation, and examined the role of Theory of Mind (ToM) abilities in this process. Human eye gaze is one of the most important sources of information for social interaction and research has demonstrated its effectiveness in influencing people's preference. With increasing technological development, we will interact with robots that can exhibit gaze behavior and influence people's preference. It is unclear whether there are any differences between humans and robots in this process. The present study aimed to analyze the role of the gaze of a robot and a human in influencing the ascription of a preference to the gazer and the participants' preference. Furthermore, we examined the role of ToM abilities in preference formation. The results showed that the gaze has a greater effect on the gazer preference compared to participants' preference regardless of the agent (human or robot). In addition, ToM abilities predict both gazer and individual preferences in the robot's condition only even though different socio-cognitive mechanisms are involved. The study suggests that adults are cognitively able to process the gaze of a robot similar to a human, recognizing the underlying mental state. However, only for the robot, different cognitive mechanisms are involved in the gazer (i.e., perspective taking) and participants' preference formation (i.e., advanced ToM). © 2010-2012 IEEE.

关键词： Social robots

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Fast model-based test case classification for performance analysis of multimedia MPSoC platforms 09

Fast model-based test case classification for performance an...

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Embedded systems Week 2009, ESWEEK 2009 - 7th IEEE/ACM International Conference on Hardware/Software-Co-Design and System Synthesis, CODES+ISSS 2009

作者： Gangadharan, Deepak Chakraborty, Samarjit Zimmermann, Roger Department of Computer Science National University of Singapore Singapore Institute for Real-Time Computer Systems TU Munich Germany

ISBN: (纸本)9781605586281

Currently, performance analysis of multimedia-MPSoC platforms largely rely on simulation. The execution of one or more applications on such a platform is simulated for a library of test video clips. If all specified performance constraints are satisfied for this library, then the architecture is assumed to be well-designed. This is similar to testing software for functional correctness. However, in contrast to functional testing, simulating a set of video clips for a complex application/architecture is extremely time consuming. In this paper we propose a technique for clustering a library of video clips, such that it is sufficient to simulate only one clip from each cluster rather than the entire library. Our clustering is scalable, i.e., the number of clusters may be determined based on the number of clips that the system designer wishes to simulate (which is independent of the input library size). For each video clip in the library, we perform a fast bitstream analysis from which the workload generated while processing this clip on the given architecture may be estimated. This workload information, in conjunction with a workload model and a performance model of the architecture, is used for the clustering. This entire process does not involve any simulation and is hence extremely fast. We illustrate its utility through a detailed case study using an MPEG-2 decoder application running on an MPSoC platform. As part of validation of our methodology, it was observed that video clips falling into the same cluster exhibit similar worst case buffer backlogs and worst case delays for one macroblock. Overall the results demonstrate that the proposed method provides a very fast and accurate analysis and hence can be of significant benefit to the system designer. Copyright 2009 ACM.

关键词： Software testing

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PLACING REGISTRATION MARKS

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 1994年第1期41卷 51-59页

作者： RAO, AS GOLDBERG, KY Department of Computer Science University of Utrecht Utrecht Netherlands Departments of Computer Science and Electrical Engineering-Systems Institute of Robotics and Intelligent Systems University of Southern California Los Angeles CA USA

In industrial assembly, a registration mark can be placed on parts to aid a computer vision system in determining the position and orientation (pose) of parts. However, when sensor noise and limits on resolution introduce errors in the measured location of the registration mark, these errors can propagate into the measurement of part pose. In this paper, we define the registration mark problem: given an n-sided rigid planar polygonal part-and a set of k poses for the part, locate a point on the surface of the part that maximizes the minimum distance between transformed points. A registration mark at this point will be maximally robust to sensor imperfections. We give an 0(n log n + k4 log k log* k) time algorithm to solve this planar problem using a result from Schwartz and Sharir [21], and demonstrate the algorithm using a commercial vision system. Our results extend to classes of curved planar parts and polyhedral parts.

关键词： computer vision computer errors Noise measurement Manufacturing automation robotics and automation computer science Intelligent robots computer industry Assembly systems Robustness

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Environment-Driven Online LiDAR-Camera Extrinsic Calibration

arXiv

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arXiv 2025年

作者： Huang, Zhiwei Li, Jiaqi Zhong, Ping Fan, Rui The Department of Control Science & Engineering The College of Electronics & Information Engineering Tongji University Shanghai201804 China The School of Computer Science and Engineering Central South University Hunan Changsha410083 China The National Key Laboratory of Science and Technology on Automatic Target Recognition National University of Defense Technology Hunan Changsha410073 China The Department of Control Science & Engineering The College of Electronics & Information Engineering Shanghai Research Institute for Intelligent Autonomous Systems The State Key Laboratory of Intelligent Autonomous Systems Frontiers Science Center for Intelligent Autonomous Systems Tongji University Shanghai201804 China The National Key Laboratory of Human-Machine Hybrid Augmented Intelligence Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Shaanxi Xi’an710049 China

LiDAR-camera extrinsic calibration (LCEC) is the core for data fusion in computer vision. Existing methods typically rely on customized calibration targets or fixed scene types, lacking the flexibility to handle variations in sensor data and environmental contexts. This paper introduces EdO-LCEC, the first environment-driven, online calibration approach that achieves human-like adaptability. Inspired by the human perceptual system, EdO-LCEC incorporates a generalizable scene discriminator to actively interpret environmental conditions, creating multiple virtual cameras that capture detailed spatial and textural information. To overcome cross-modal feature matching challenges between LiDAR and camera, we propose dual-path correspondence matching (DPCM), which leverages both structural and textural consistency to achieve reliable 3D-2D correspondences. Our approach formulates the calibration process as a spatial-temporal joint optimization problem, utilizing global constraints from multiple views and scenes to improve accuracy, particularly in sparse or partially overlapping sensor views. Extensive experiments on real-world datasets demonstrate that EdO-LCEC achieves state-of-the-art performance, providing reliable and precise calibration across diverse, challenging environments. Copyright © 2025, The Authors. All rights reserved.

关键词： Spatio-temporal data

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Supply Chain Sustainability in Disruption: SC Intelligence, Customer Knowledge Transfer, and the Mediating Role of IS/IT

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IEEE Engineering Management Review 2025年

作者： Ratna, Silvia Saide, Saide Rater, Salya Svyazhanovna, Baizakova Saule Herzavina, Herzavina Faculty of Information Technology Department of Informatics Engineering and Department of Information Systems Banjarmasin Indonesia State Islamic University of Sultan Syarif Kasim Riau Universitas Islam Negeri Sultan Syarif Kasim Riau-UIN SUSKA Riau Department of Information Systems Faculty of Science and Technology Indonesia PRO Knowledge Indonesia Pekanbaru Indonesia Universitas Pahlawan Tuanku Tambusai Indonesia Lembaga Enreach UIN Suska Riau Indonesia Politeknik Aceh Selatan Department of Informatics Engineering Aceh Indonesia Information Communication Technologies Robotics in Arkalyk Pedagogical Institute Master of Natural Sciences Educational Program for the Informatics Computer Science Kazakhstan Universitas Pahlawan Tuanku Tambusai Department of Informatics Engineering Indonesia National Taiwan University of Science and Technology Taiwan

Knowledge resource and information system/technology (IS/IT) capability have been considered to improve firm performance, however there is still a gap regarding the sustainability of supply chain to face and recover from disruption (e.g., COVID-19). Questionnaire-based surveys was adopted and received feedback from sixty (160) Indonesian companies or about 28.07% of response rate. © 1973-2011 IEEE.

关键词： customer knowledge transfer disruption information system/technology (IS/IT) responding knowledge management resource-based view (RBV) Supply chain (SC) sustainability supply chain knowledge transfer

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Modeling buffers with data refresh semantics in automotive architectures

Modeling buffers with data refresh semantics in automotive a...

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6th Embedded systems Week 2010, ESWEEK 2010 - 10th ACM International Conference on Compilers, Architecture and Synthesis for Embedded systems, EMSOFT'10

作者： Phan, Linh T.X. Schneider, Reinhard Chakraborty, Samarjit Lee, Insup Department of Computer and Information Science University of Pennsylvania United States Institute for Real-Time Computer Systems TU Munich Germany

ISBN: (纸本)9781605589046

Automotive architectures consist of multiple electronic control units (ECUs) which run distributed control applications. Such ECUs are connected to sensors and actuators and communicate via shared buses. Resource arbitration at the ECUs and also in the communication medium, coupled with variabilities in execution requirements of tasks results in jitter in the signal/data streams existing in the system. As a result, buffers are required at the ECUs and bus controllers. However, these buffers often implement different semantics - FIFO queuing, which is the most straightforward buffering scheme, and data refreshing, where stale data is overwritten by freshly sampled data. Traditional timing and schedulability analysis that are used to compute, e.g., end-to-end delays, in such automotive architectures can only model FIFO buffering. As a result, they return pessimistic delay and resource estimates because in reality overwritten data items do not get processed by the system. In this paper we propose an analytical framework for accurately modeling such data refresh semantics. Our model exploits a novel feedback control mechanism and is purely functional in nature. As a result, it is scalable and does not involve any explicit state modeling. Using this model we can estimate various timing and performance metrics for automotive ECU networks consisting of buffers implementing different data handling semantics. We illustrate the utility of this model through three case studies from the automotive electronics domain.

关键词： Semantics

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Efficient TD3 based path planning of mobile robot in dynamic environments using prioritized experience replay and LSTM

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Scientific reports 2025年第1期15卷 18331页

作者： Yunhan Lin Zhijie Zhang Yijian Tan Hao Fu Huasong Min School of Computer Science and Technology Wuhan University of Science and Technology Wuhan 430081 China. Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Wuhan 430081 China. Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology Wuhan 430081 China. Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology Wuhan 430081 China. mhuasong@***.

To address the challenges of sample utilization efficiency and managing temporal dependencies, this paper proposes an efficient path planning method for mobile robot in dynamic environments based on an improved twin delayed deep deterministic policy gradient (TD3) algorithm. The proposed method, named PL-TD3, integrates prioritized experience replay (PER) and long short-term memory (LSTM) neural networks, which enhance both sample efficiency and the ability to handle time-series data. To verify the effectiveness of the proposed method, simulation and practical experiments were designed and conducted. In the simulation experiments, both static and dynamic obstacles were included in the test environment, along with experiments to assess generalization capabilities. The algorithm demonstrated superior performance in terms of both execution time and path efficiency. The practical experiments, based on the assumptions from the simulation tests, further confirmed that PL-TD3 has improved the effectiveness and robustness of path planning for mobile robot in dynamic environments.

关键词： Long short-term memory (LSTM ) Path planning in dynamic environment Prioritized experience replay (PER) Reinforcement learning Twin delayed deep deterministic policy gradient (TD3) algorithm

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real-time Cell Sorting with Scalable In Situ FPGA-Accelerated Deep Learning

arXiv

引用

arXiv 2025年

作者： Islam, Khayrul Forelli, Ryan F. Han, Jianzhong Bhadane, Deven Huang, Jian Agar, Joshua C. Tran, Nhan Ogrenci, Seda Liu, Yaling Department of Mechanical Engineering Lehigh University BethlehemPA18015 United States Department of Electrical and Computer Engineering Northwestern University EvanstonIL60208 United States Coriell Institute for Medical Research CamdenNJ United States Department of Computer Science Lehigh University BethlehemPA18015 United States Cooper Medical School of Rowan University CamdenNJ08103 United States Center for Metabolic Disease Research Temple University Lewis Katz School of Medicine PhiladelphiaPA19122 United States Department of Mechanical Engineering and Mechanics Drexel University PhiladelphiaPA19104 United States Real-time Processing Systems Division Fermi National Accelerator Laboratory BataviaIL60510 United States Department of Bioengineering Lehigh University BethlehemPA18015 United States

Precise cell classification is essential in biomedical diagnostics and therapeutic monitoring, particularly for identifying diverse cell types involved in various diseases. Traditional cell classification methods such as flow cytometry depend on molecular labeling which is often costly, time-intensive, and can alter cell integrity. To overcome these limitations, we present a label-free machine learning framework for cell classification, designed for real-time sorting applications using bright-field microscopy images. This approach leverages a teacher-student model architecture enhanced by knowledge distillation, achieving high efficiency and scalability across different cell types. Demonstrated through a use case of classifying lymphocyte subsets, our framework accurately classifies T4, T8, and B cell types with a dataset of 80,000 preprocessed images, accessible via an open-source Python package for easy adaptation. Our teacher model attained 98% accuracy in differentiating T4 cells from B cells and 93% accuracy in zero-shot classification between T8 and B cells. Remarkably, our student model operates with only 0.02% of the teacher model’s parameters, enabling field-programmable gate array (FPGA) deployment. Our FPGA-accelerated student model achieves an ultra-low inference latency of just 14.5 µs and a complete cell detection-to-sorting trigger time of 24.7 µs, delivering 12x and 40x improvements over the previous state-of-the-art real-time cell analysis algorithm in inference and total latency, respectively, while preserving accuracy comparable to the teacher model. This framework provides a scalable, cost-effective solution for lymphocyte classification, as well as a new SOTA real-time cell sorting implementation for rapid identification of subsets using in situ deep learning on off-the-shelf computing hardware. © 2025, CC BY.

关键词： Deep learning

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computer vision research at the University of Southern California

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INTERNATIONAL JOURNAL OF computer VISION 1996年第1-2期20卷 5-9页

作者： Nevatia, R Medioni, G Institute of Robotics and Intelligent Systems University of Southern California Computer Science Department 90089 Los Angeles CA USA

A broad computer research program that includes many topics of basic research such as perceptual grouping, stereo and motion analysis, shape description and object recognition as well as many application areas such as aerial image analysis, moving target analysis and vehicle navigation are being conducted at the University of Southern California. The objective of the research is to construct 'generic' vision system. A generic system is one that is capable of working in a variety of domains, or at least is capable of being reconfigured rapidly to do so.

关键词： computer vision

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