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Dynamic Subgoal based Path Formation and Task Allocation: A NeuroFleets Approach to Scalable Swarm robotics

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arXiv 2024年

作者： Peter, Robinroy Ratnabala, Lavanya Andrew Charles, Eugene Yugarajah Tsetserukou, Dzmitry LTD Jaffna Sri Lanka Intelligent Space Robotics Laboratory Skolkovo Institute of Science and Technology Bolshoy Boulevard 30 bld. 1 Moscow121205 Russia Department of Computer Science University of Jaffna Jaffna Sri Lanka

This paper addresses the challenges of exploration and navigation in unknown environments from the perspective of evolutionary swarm robotics. A key focus is on path formation, which is essential for enabling cooperative swarm robots to navigate effectively. We designed the task allocation and path formation process based on a finite state machine, ensuring systematic decision-making and efficient state transitions. The approach is decentralized, allowing each robot to make decisions independently based on local information, which enhances scalability and robustness. We present a novel subgoal-based path formation method that establishes paths between locations by leveraging visually connected subgoals. Simulation experiments conducted in the Argos simulator show that this method successfully forms paths in the majority of trials. However, inter-collision (traffic) among numerous robots during path formation can negatively impact performance. To address this issue, we propose a task allocation strategy that uses local communication protocols and light signal-based communication to manage robot deployment. This strategy assesses the distance between points and determines the optimal number of robots needed for the path formation task, thereby reducing unnecessary exploration and traffic congestion. The performance of both the subgoal-based path formation method and the task allocation strategy is evaluated by comparing the path length, time, and resource usage against the A* algorithm. Simulation results demonstrate the effectiveness of our approach, highlighting its scalability, robustness, and fault tolerance. Copyright © 2024, The Authors. All rights reserved.

关键词： Traffic congestion

Generative De-Quantization for Neural Speech Codec Via Latent Diffusion

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Generative De-Quantization for Neural Speech Codec Via Laten...

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Haici Yang Inseon Jang Minje Kim Department of Intelligent Systems Engineering Indiana University Bloomington IN USA Electronics and Telecommunications Research Institute Daejeon South Korea Department of Computer Science University of Illinois at Urbana-Champaign IL USA

End-to-end speech coding models achieve high coding gains by learning compact yet expressive features and a powerful decoder in a single network. A challenging problem as such results in unwelcome complexity increase and inferior speech quality. In this paper, we propose to separate the representation learning and information reconstruction tasks. We leverage an end-to-end codec for learning low-dimensional discrete tokens. Instead of using its decoder, we employ a latent diffusion model to de-quantize coded features into a high-dimensional continuous space, relieving the decoder’s burden of de-quantizing and upsampling. To mitigate the issue of over-smooth generation, we introduce midway-infilling with less noise reduction and stronger conditioning. We investigate the hyperparameters for midway-infilling and latent diffusion space with different dimensions in ablation studies. Subjective listening tests show that our model outperforms the state-of-the-art at two low bitrates, 1.5 and 3 kbps. We open-source the project for reproducibility 1 .

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Allocation and Migration of Virtual Machines Using Machine Learning

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computers, Materials & Continua 2022年第2期70卷 3349-3364页

作者： Suruchi Talwani Khaled Alhazmi Jimmy Singla Hasan JAlyamani Ali Kashif Bashir School of Computer Science&Engg. Lovely Professional UniversityPunjabIndia National Center for Robotics and Internet of Things Technology Communication and Information Technology Research InstituteKing Abdulaziz City for Science and Technology(KACST)Riyadh11442Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology in RabighKing Abdulaziz UniversityRabigh21911Saudi Arabia Department of Computing and Mathematics ManchesterMetropolitan UniversityManchesterM156BHUK

Cloud computing promises the advent of a new era of service boosted by means of virtualization *** process of virtualization means creation of virtual infrastructure,devices,servers and computing resources needed to deploy an application *** extensively practiced technology involves selecting an efficient Virtual Machine(VM)to complete the task by transferring applications from Physical Machines(PM)to VM or from VM to *** whole process is very challenging not only in terms of computation but also in terms of energy and *** research paper presents an energy aware VM allocation and migration approach to meet the challenges faced by the growing number of cloud data *** Learning(ML)based Artificial Bee Colony(ABC)is used to rank the VM with respect to the load while considering the energy efficiency as a crucial *** most efficient virtual machines are further selected and thus depending on the dynamics of the load and energy,applications are migrated fromoneVMto *** simulation analysis is performed inMatlab and it shows that this research work results in more reduction in energy consumption as compared to existing studies.

关键词： Cloud computing VM allocation VM migration machine learning

DBF-Net: A Dual-Branch Network with Feature Fusion for Ultrasound Image Segmentation

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arXiv 2024年

作者： Xu, Guoping Wu, Xiaming Liao, Wentao Wu, Xinglong Huang, Qing Li, Chang Hubei Key Laboratory of Intelligent Robotics School of Computer Science and Engineering Wuhan Institute of Technology Hubei Wuhan430205 China Department of Biomedical Engineering Hefei University of Technology Anhui Hefei230009 China

Objective: Accurately segmenting lesions in ultrasound images remains a challenging task due to the inherent ambiguity in distinguishing boundaries between lesions and adjacent tissues. Although deep learning has made significant progress in accurate ultrasound image segmentation, there is still a lack of attention to the quality of boundary segmentation and its relationship with the body. As a result, the current accuracy of ultrasound image segmentation still has room for improvement in enhancing the precision of lesion delineation. We aim to improve the overall segmentation accuracy and the quality of boundary segmentation by exploring the relationship between the body and the boundary in ultrasound images. Methods: We introduce a dual-branch structure based on a deep neural network that allows the model to learn the relationship between the body and boundary parts under supervision, leading to an improvement in overall segmentation accuracy and boundary segmentation quality. Additionally, we propose a novel feature fusion module aimed at facilitating the integration and interaction of body and boundary information. Based on the dual-branch structure and feature fusion strategy, we have designed UBBS-Net for ultrasound image segmentation. Results: The proposed approach is evaluated on three challenging public ultrasound image datasets. Our experimental results demonstrate the superiority of the proposed method compared to existing state-of-the-art methods. Specifically, we achieve a Dice Similarity Coefficient of 81.05±10.44% for breast cancer segmentation, 76.41±5.52% for brachial plexus nerves segmentation, and 87.75±4.18% for infantile hemangioma segmentation on BUSI, UNS and UHES datasets, respectively. Conclusions: We propose UBBS-Net, a novel network for ultrasound image segmentation that combines body and boundary information with a proposed feature fusion module. Our method outperforms existing approaches on three challenging public datasets, demonstratin

关键词： Ultrasonic imaging

TiCoSS: Tightening the Coupling between Semantic Segmentation and Stereo Matching within A Joint Learning Framework

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arXiv 2024年

作者： Tang, Guanfeng Wu, Zhiyuan Li, Jiahang Zhong, Ping Chen, Xieyuanli Lu, Huimin Fan, Rui The College of Electronics & Information Engineering Shanghai Research Institute for Intelligent Autonomous Systems Shanghai Institute of Intelligent Science and Technology Tongji University The State Key Laboratory of Intelligent Autonomous Systems Frontiers Science Center for Intelligent Autonomous Systems Tongji University Shanghai201804 China The Department of Engineering King's College London LondonWC2R 2LS United Kingdom The Department of Computer Science and Technology Central South University China The College of Intelligence Science and Technology National University of Defense Technology China

Semantic segmentation and stereo matching, respectively analogous to the ventral and dorsal streams in our human brain, are two key components of autonomous driving perception systems. Addressing these two tasks with separate networks is no longer the mainstream direction in developing computer vision algorithms, particularly with the recent advances in large vision models and embodied artificial intelligence. The trend is shifting towards combining them within a joint learning framework, especially emphasizing feature sharing between the two tasks. The major contributions of this study lie in comprehensively tightening the coupling between semantic segmentation and stereo matching. Specifically, this study introduces three key novelties: (1) a tightly coupled, gated feature fusion strategy, (2) a hierarchical deep supervision strategy, and (3) a coupling tightening loss function. All these technical contributions help tightly couple semantic segmentation and stereo matching tasks, collectively resulting in TiCoSS, a state-of-the-art joint learning framework. Through extensive experiments on the KITTI and vKITTI2 datasets, along with both qualitative and quantitative analyses, we validate the effectiveness of our developed strategies and loss function. Our approach demonstrates superior performance compared to prior arts, with a notable increse in mIoU by over 9%. Our source code will be publicly available at ***/TiCoSS upon publication. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantic Segmentation

Mesh-based Object Tracking for Dynamic Semantic 3D Scene Graphs via Ray Tracing

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arXiv 2024年

作者： Niecksch, Lennart Mock, Alexander Igelbrink, Felix Wiemann, Thomas Hertzberg, Joachim German Research Centre for Artificial Intelligence Plan-Based Robot Control Osnabrück Germany Osnabrück University Institute of Computer Science Knowledge-Based Systems Osnabrück Germany Fulda University of Applied Sciences Department of Applied Computer Science Robotics in Computer Science Fulda Germany

In this paper, we present a novel method for 3D geometric scene graph generation using range sensors and RGB cameras. We initially detect instance-wise keypoints with a YOLOv8s model to compute 6D pose estimates of known objects by solving PnP. We use a ray tracing approach to track a geometric scene graph consisting of mesh models of object instances. In contrast to classical point-to-point matching, this leads to more robust results, especially under occlusions between objects instances. We show that using this hybrid strategy leads to robust self-localization, pre-segmentation of the range sensor data and accurate pose tracking of objects using the same environmental representation. All detected objects are integrated into a semantic scene graph. This scene graph then serves as a front end to a semantic mapping framework to allow spatial reasoning. Copyright © 2024, The Authors. All rights reserved.

关键词： Mesh generation

A Bi-population Cooperative Optimization Algorithm Assisted by an Autoencoder for Medium-scale Expensive Problems

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IEEE/CAA Journal of Automatica Sinica 2022年第11期9卷 1952-1966页

作者： Meiji Cui Li Li MengChu Zhou Jiankai Li Abdullah Abusorrah Khaled Sedraoui IEEE the School of Intelligent Manufacturing Nanjing University of Science and TechnologyNanjing 210094China the Department of Electronics and Information Engineering Tongji UniversityShanghai 201804China the Helen and John C.Hartmann Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer EngineeringFaculty of Engineeringand K.A.CARE Energy Research and Innovation CenterKing Abdulaziz UniversityJeddah 21589Saudi Arabia

This study presents an autoencoder-embedded optimization(AEO)algorithm which involves a bi-population cooperative strategy for medium-scale expensive problems(MEPs).A huge search space can be compressed to an informative lowdimensional space by using an autoencoder as a dimension reduction *** search operation conducted in this low space facilitates the population with fast convergence towards the *** strike the balance between exploration and exploitation during optimization,two phases of a tailored teaching-learning-based optimization(TTLBO)are adopted to coevolve solutions in a distributed fashion,wherein one is assisted by an autoencoder and the other undergoes a regular evolutionary ***,a dynamic size adjustment scheme according to problem dimension and evolutionary progress is proposed to promote information exchange between these two phases and accelerate evolutionary convergence *** proposed algorithm is validated by testing benchmark functions with dimensions varying from 50 to *** indicated in our experiments,TTLBO is suitable for dealing with medium-scale problems and thus incorporated into the AEO framework as a base *** with the state-of-the-art algorithms for MEPs,AEO shows extraordinarily high efficiency for these challenging problems,t hus opening new directions for various evolutionary algorithms under AEO to tackle MEPs and greatly advancing the field of medium-scale computationally expensive optimization.

关键词： Autoencoder dimension reduction evolutionary algorithm medium-scale expensive problems teaching-learning-based optimization

Personalized Neural Speech Codec

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Personalized Neural Speech Codec

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Inseon Jang Haici Yang Wootaek Lim Seungkwon Beack Minje Kim Electronics and Telecommunications Research Institute Daejeon South Korea Department of Intelligent Systems Engineering Indiana University Bloomington IN USA Department of Computer Science University of Illinois at Urbana-Champaign IL USA

In this paper, we propose a personalized neural speech codec, envisioning that personalization can reduce the model complexity or improve perceptual speech quality. Despite the common usage of speech codecs where only a single talker is involved on each side of the communication, personalizing a codec for the specific user has rarely been explored in the literature. First, we assume speakers can be grouped into smaller subsets based on their perceptual similarity. Then, we also postulate that a group-specific codec can focus on the group’s speech characteristics to improve its perceptual quality and computational efficiency. To this end, we first develop a Siamese network that learns the speaker embeddings from the LibriSpeech dataset, which are then grouped into underlying speaker clusters. Finally, we retrain the LPCNet-based speech codec baselines on each of the speaker clusters. Subjective listening tests show that the proposed personalization scheme introduces model compression while maintaining speech quality. In other words, with the same model complexity, personalized codecs produce better speech quality.

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Exploring the Impact of Random Distribution Choices on Particle Swarm Optimization: An Initial Analysis 27

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Exploring the Impact of Random Distribution Choices on Parti...

27th International Conference on Knowledge Based and intelligent Information and Engineering Sytems, KES 2023

作者： Kolodziejczyk, Joanna Tarasenko, Yuliia Sulikowski, Piotr Research Team on Intelligent Decision Support Systems Department of Artificial Intelligence Methods and Applied Mathematics Faculty of Computer Science and Information Technology West Pomeranian University of Technology in Szczecin ul. Zolnierska 49 Szczecin71-210 Poland National Institute of Telecommunications ul. Szachowa 1 Warsaw04-894 Poland Department of Information Systems Engineering Faculty of Computer Science and Information Technology West Pomeranian University of Technology in Szczecin ul. Zolnierska 49 Szczecin71-210 Poland

Particle Swarm Optimization (PSO) is a robust stochastic optimization algorithm for solving complex and constrained optimization problems. This paper aims to systematically investigate the influence of diverse random number distributions on the learning parameters of PSO and evaluate the algorithm's sensitivity based on these parameters. To this end, several exhaustive numerical experiments were conducted on several test functions, including asymmetric Sphere, Easom, and Csendes. Initially, appropriate random distributions were identified for the learning parameters and proceeded to compare these distributions using all potential combinations. The sensitivity of PSO to these parameters was evaluated using the mean best score, the relative proximity of the best particle to the theoretical optimum, and the average objective function evaluations. The most significant contribution of this research lies in the insights derived from the experimental data. The discovery was that highly skewed and long-tailed distributions such as Chi, Chi-square, Lomax, and Exponential often underperform. On the other hand, distributions that are bounded proportionally to the problem domain consistently surpass the performance of the standard uniform distribution. These findings significantly enhance the understanding of PSO algorithms and provide valuable guidelines for their practical deployment. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： Particle Swarm Optimization random number distribution stochastic optimization