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2024 IEEE International Conference on robotics and Biomimetics, ROBIO 2024

作者： Kaya, Mertcan Kuhnlenz, Kolja Coburg University of Applied Sciences and Arts Robotics Research Lab Department of Electrical Engineering and Computer Science Coburg Germany

ISBN: (纸本)9781665481090

This paper investigates the influence of a static robot head on deviations of human hand movements from task direction (motor interference) during simultaneous human and robot arm movements using a collaborative robot arm. Synchronous vertical and horizontal human and robot arm movements are conducted in all combinations (within-subjects factors direction and congruency). The presence of the head (head/ no head) is varied as a between-subjects factor. In this first step, only its motionless presence is deliberately focused at, in order to exclude contributions by eye- or head-movement behavior or facial expressions being difficult variables to generalize from. Participants' hands are tracked optically marker-based and deviations from task-related vertical, respectively, horizontal movements are evaluated. In contrast to expectations, results do not show a significant impact of the robot head on motor interference. Thus, there is evidence, that appearance might not be an activating factor for motor interference in HRI. © 2024 IEEE.

关键词： Robotic arms

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Continuous Query-based Data Trading

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Performance Evaluation Review 2024年第1期52卷 73-74页

作者： Cheng, Jin Ding, Ningning Lui, John C.S. Huang, Jianwei School of Science and Engineering Shenzhen Institute of Artificial Intelligence and Robotics for Society The Chinese University of Hong Kong Shenzhen Shenzhen China Department of Electrical and Computer Engineering Northwestern University Evanston United States Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Hong Kong

In the era of big data, traditional data trading methods designed for one-time queries on static databases fail to meet the demands of continuous query-based trading on streaming data, often resulting in repeated and inaccurate charges due to neglecting computation sharing in continuous query execution. To address this, we introduce CQTrade, the first trading framework tailored for continuous queries, which incorporates computation sharing across different time windows and queries, enhancing integration with existing trading systems. Our contributions include a theoretical analysis of computation-sharing techniques, the development of a general optimization problem to maximize seller revenue adaptable to various techniques, and the proposal of a branch-and-price algorithm to handle the problem's complexity. Our evaluation shows that the proposed framework improves the success rate of data trading by 12.8% and boosts the seller's revenue by an average of 28.7%, compared to the one-time query-based data trading methods used in the current data market. © 2024 Owner/Author.

关键词： Integer programming

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A Novel Approach to Continual Knowledge Transfer in Multilingual Neural Machine Translation Using Autoregressive and Non-Autoregressive Models for Indic Languages

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IEEE Access 2025年 13卷 89775-89810页

作者： Sheshadri, Shailashree K. Gupta, Deepa Paul, Biswajit Bhavani, J. Siva Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Bengaluru560035 India DRDO CV Raman Nagar Centre for Artificial Intelligence and Robotics Bangalore India

Recent progress in multilingual pre-trained models has significantly improved translation quality for Indic languages. However, extending these models to new languages via fine-tuning or retraining remains computationally costly and often leads to parameter interference, degrading performance on previously learned or typologically distant languages. While continual learning offers a promising alternative for incremental language addition, its application in Indic contexts is still limited and faces challenges in generalization across diverse linguistic settings. To overcome these issues, we propose a Continual Knowledge Transfer (CKT) framework for efficient and scalable multilingual adaptation. CKT is realized in both autoregressive (MNMT) and non-autoregressive (Switch-GLAT) architectures, yielding two variants: MNMT+CKT and Switch-GLAT+CKT. Rather than retraining the entire model, CKT freezes the multilingual base and updates only parameters relevant to the newly added language. Key innovations include gradient-based knowledge pruning, sequential teacher integration, and dynamic vocabulary expansion for minimizing interference and maximizing cross-lingual retention. Comprehensive evaluations on the IN22-Conv and IN22-Gen benchmark datasets demonstrate that both MNMT+CKT and Switch-GLAT+CKT consistently outperform established baselines, such as IndicTrans2, Google Translate, GPT-4-32K, LLaMA-2-17B, and NLIP-LAB-IITH. The proposed multi-step distillation approach, MNMT+CKT, consistently outperforms conventional fine-tuning and Knowledge Transfer (MNMT+KT) strategies for incremental adaptation of linguistically diverse Indic languages. On IN22-Conv, BLEU improvements range from +4.93 (Kashmiri → English) to +11.48 (Assamese → English) and similar improvements are seen for IN22-Gen. The method also achieves substantial reductions in trainable parameters - 19.80% (Nepali) to 66.87% (Kannada) - while enabling up to 4x faster inference when integrated with the Switch-GL

关键词： Neural machine translation

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Digital Twins and Cyber-Physical Systems:A New Frontier in computer Modeling

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computer Modeling in Engineering & sciences 2025年第4期143卷 51-113页

作者： Vidyalakshmi G S Gopikrishnan Wadii Boulila Anis Koubaa Gautam Srivastava Department of Computer Science and Engineering SRM University APAmaravati522240India School of Computer Science and Engineering VIT-AP UniversityAmaravathi522240India Robotics and Internet-of-Things Laboratory Prince Sultan UniversityRiyadh12435Saudi Arabia Department of Math and Computer Science Brandon UniversityBrandonMB R7A6A9Canada Research Centre for Interneural Computing China Medical UniversityTaichung40402Taiwan

Cyber-Physical Systems(CPS)represent an integration of computational and physical elements,revolutionizing industries by enabling real-time monitoring,control,and optimization.A complementary technology,Digital Twin(DT),acts as a virtual replica of physical assets or processes,facilitating better decision making through simulations and predictive *** and DT underpin the evolution of Industry 4.0 by bridging the physical and digital *** survey explores their synergy,highlighting how DT enriches CPS with dynamic modeling,realtime data integration,and advanced simulation *** layered architecture of DTs within CPS is examined,showcasing the enabling technologies and tools vital for seamless *** study addresses key challenges in CPS modeling,such as concurrency and communication,and underscores the importance of DT in overcoming these *** in various sectors are analyzed,including smart manufacturing,healthcare,and urban planning,emphasizing the transformative potential of CPS-DT *** addition,the review identifies gaps in existing methodologies and proposes future research directions to develop comprehensive,scalable,and secure CPSDT *** synthesizing insights fromthe current literature and presenting a taxonomy of CPS and DT,this survey serves as a foundational reference for academics and *** findings stress the need for unified frameworks that align CPS and DT with emerging technologies,fostering innovation and efficiency in the digital transformation era.

关键词： Cyber physical systems digital twin efficiency Industry 4.0 robustness and intelligence

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Audio-Visual Navigation with Anti-Backtracking 27th

Audio-Visual Navigation with Anti-Backtracking

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Zhao, Zhenghao Tang, Hao Yan, Yan Department of Computer Science University of Illinois Chicago Chicago United States Robotics Institute Carnegie Mellon University Pittsburgh United States

ISBN: (纸本)9783031784552

Embodied navigation, which involves robotic agents exploring an unknown environment to reach target locations with egocentric observation, is a complex problem in the field of embodied AI. Audio-visual navigation extends this concept by equipping agents with both visual and auditory sensors. Recent studies have explored the audio-visual navigation task, exploring its potential and intricacies. Current methodologies, despite their achievements, often fail to fully utilize the capabilities of these sensory modalities, leading to sub-optimal designs and inefficiencies. In addition, the prevalent backtracking problem in navigation tasks often leads to redundant movements by robots. To address these challenges, we introduce the Audio-Visual Guided Navigation (AVGN) model. At its core, AVGN champions the fusion of visual and auditory data through sophisticated modality fusion layers. Our model represents a significant advancement by employing transformers for visual encoding and deploying a novel Dual Stage Feature Integration (DSFI) to decode latent interrelationships between the visual and audio realms. With AVGN, the backtracking issue is mitigated, as the acoustic map synergizes more effectively with visual features for informed action decisions. Furthermore, targeting the backtracking problem, we propose a unique set of reward structures to guide and refine the actions of the agent. Benchmark evaluations on Replica and Matterport3D datasets validate our claims, and AVGN notably surpasses existing methodologies in audio-visual navigation tasks. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Reinforcement learning

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Real-Time Multi-object Tracking Using YOLOv8 and SORT on a SoC FPGA 21st

Real-Time Multi-object Tracking Using YOLOv8 and SORT on a...

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21st International Symposium on Applied Reconfigurable Computing, ARC 2025

作者： Danilowicz, Michal Kryjak, Tomasz Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics AGH University of Science and Technology Krakow Poland

ISBN: (纸本)9783031879944

Multi-object tracking (MOT) is one of the most important problems in computer vision and a key component of any vision-based perception system used in advanced autonomous mobile robotics. Therefore, its implementation on low-power and real-time embedded platforms is highly desirable. Modern MOT algorithms should be able to track objects of a given class (e.g. people or vehicles). In addition, the number of objects to be tracked is not known in advance, and they may appear and disappear at any time, as well as be obscured. For these reasons, the most popular and successful approaches have recently been based on the tracking paradigm. Therefore, the presence of a high quality object detector is essential, which in practice accounts for the vast majority of the computational and memory complexity of the whole MOT system. In this paper, we propose an FPGA (Field-Programmable Gate Array) implementation of an embedded MOT system based on a quantized YOLOv8 detector and the SORT (Simple Online Realtime Tracker) tracker. We use a modified version of the FINN framework to utilize external memory for model parameters and to support operations necessary required by YOLOv8. We discuss the evaluation of detection and tracking performance using the COCO and MOT15 datasets, where we achieve 0.21 mAP and 38.9 MOTA respectively. As the computational platform, we use an MPSoC system (Zynq UltraScale+ device from AMD/Xilinx) where the detector is deployed in reprogrammable logic and the tracking algorithm is implemented in the processor system. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： System-on-chip

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Secrecy Performance Review for IoT-based Cognitive Farming System

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Recent Patents on Engineering 2024年第7期18卷 66-75页

作者： Chopra, Khyati Alam, M. Afshar Department of CSE Jamia Hamdard University New Dehli India University School of Automation and Robotics GGSIPU New Delhi110032 India Department of Computer Science and Engineering Jamia Hamdard University New Delhi110062 India

Smart farm management has complex challenges;therefore, there is a need to improve the overall adaptability, profitability, and environmental soundness of the farming systems. Agricultural production can be enhanced with the help of IoT-based cognitive tools. The rapid increase in IoT-based smart systems has raised critical concerns about efficiently utilizing the wireless spectrum. To drastically bring the improvement in the utilization of the wireless spectrum, cognitive radio (CR) has emerged as an effective solution. The inherent openness and broadcast nature of the wireless medium have given rise to many security issues in cooperative networks. Information-theoretic security or physical layer security (PLS) is evolved as an efficacious wireless prototype for reliable transmissions to avert the eavesdropper from expropriating transmittal data on the wireless connections. Security is the key factor in these networks as they are exposed and active in be-havior, thus, are susceptible to fraudulent activities. In this patent study, we investigated the performance of the CR system in terms of secrecy and also examined the system in terms of diversity receipt and selection of relay. The secrecy outcome of channel state information at the sender was also studied. The detrimental effects of fading in cooperative networks were also extensively discussed. Cooperative diversity was examined by deploying MRC and SC strategies at receptors. It has been deduced that CSI knowledge at the sender also has an eloquent concussion on the secrecy of the cognitive system. © 2024 Bentham science Publishers.

关键词： Cognitive radio

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Hand Gestures and Machine Learning: A Novel Path to Number Prediction

Hand Gestures and Machine Learning: A Novel Path to Number P...

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2023 International Conference on Recent Advances in science and Engineering Technology, ICRASET 2023

作者： Karegoudra, Shilpa Veeresha, R.K. Department of Computer Science and Engineering Udupi574110 India Department of Robotics and Artificial Intelligence Udupi574110 India

ISBN: (纸本)9798350306927

The gesture-based communication system proves highly valuable for individuals who are almost deaf and discourse disabled. It allows them to convey messages effectively through gestures. The objective of this application is to provide a platform that accurately interprets these signs, enabling regular individuals to understand the gestures. The system detects and recognizes the gestures and displays the corresponding characters. This technology, which utilizes the PCA method, achieves comparable accuracy to other existing systems and methods. The system we have developed plays a crucial role in enabling individuals to understand and communicate like normal people using static gestures. While various proposals exist for gesture systems with specific languages like ASL, ISL, etc., we introduce a new set of static gestures using MATLAB, building upon existing systems. The system involves capturing input through a camera, after which the system applies preprocessing techniques to enhance the captured image. Next, a set of features is extracted from the preprocessed image using PCA (Principal Component Analysis). To identify the gestures, the system compares these features with those in the training sets, using the Euclidean Distance method. Our hand gesture system has been designed to recognize and interpret numbers from 0 to 5. Once a gesture is identified, the system can produce the output in the form of text ensuring effective communication for the users. © 2023 IEEE.

关键词： Principal component analysis

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Test-Time Adaptation for Nighttime Color-Thermal Semantic Segmentation

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第10期5卷 4893-4904页

作者： Liu, Yexin Zhang, Weiming Zhao, Guoyang Zhu, Jinjing Vasilakos, Athanasios V. Wang, Lin Robotics and Autonomous Systems Thrust Guangzhou511458 China Grimstad4879 Norway Artificial Intelligence Thrust Guangzhou511458 China The Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong999077 Hong Kong

The ability to scene understanding in adverse visual conditions, e.g., nighttime, has sparked active research for color-thermal semantic segmentation. However, it is essentially hampered by two critical problems: 1) the day-night gap of color images is larger than that of thermal images;and 2) the classwise performance of color images at night is not consistently higher or lower than that of thermal images. We propose the first test-time adaptation (TTA) framework, dubbed Night-TTA, to address the problems for nighttime color-thermal semantic segmentation without access to the source (daytime) data during adaptation. Our method enjoys three key technical parts. First, as one modality (e.g., color) suffers from a larger domain gap than that of the other (e.g., thermal), imaging heterogeneity refinement (IHR) employs an interaction branch on the basis of color and thermal branches to prevent cross-modal discrepancy and performance degradation. Then, class aware refinement (CAR) is introduced to obtain reliable ensemble logits based on pixel-level distribution aggregation of the three branches. In addition, we also design a specific learning scheme for our TTA framework, which enables the ensemble logits and three student logits to collaboratively learn to improve the quality of predictions during the testing phase of our Night-TTA. Extensive experiments show that our method achieves state-of-the-art (SoTA) performance with a 13.07% boost in mean intersection over union (mIoU). © 2020 IEEE.

关键词： Color

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India's Stake in Emerging Technology - A Review 18

India's Stake in Emerging Technology - A Review

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Singh, Sugandha China Ramu, S. Lalit, Ruchika Sgt University Department of Computer Science and Engineering Gurugram India Chaitanya Bharathi Institute of Technology Department of Computer Science and Engineering Hyderabad India University School of Automation and Robotics Ggsipu East Campus New Delhi India

ISBN: (纸本)9789380544519

Emerging technologies of today are altering the way we think about the built environment. In the meantime, many technologies have undergone revolutionary changes, and India, an expanding nation, offers opportunities for development in several new areas. The goal of the study is to show how organizations need to adapt their perspective and create new strategies for emerging technologies such as artificial intelligence (AI), machine learning (ML), big data, quantum, and space exploration in light of the swift advancements in technology. This paper presents that although India has come across a lot of advancements in this era of emerging technology, certain challenges have been faced in aspects like adapting, feasibility, and approval which brought many changes across the country, which in turn is impacting the pace to transform the country towards Industry 4.0 and Industry 5.0. Therefore a study is presented to understand the impact of emerging technologies for countries coming up as Industry 4.0 and Industry 5.0. This paper emphasizes on for India's initiatives for emerging technologies to come up as VIKSIT BHARAT @2047. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Artificial Intelligence Big Data Fabrication Hypersonic platforms Industry 4.0 and Industry 5.0 Quantum computing

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