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arXiv 2024年

作者： Engelaar, Maico H.W. Zhang, Zengjie Vlahakis, Eleftherios E. Dimarogonas, Dimos V. Lazar, Mircea Haesaert, Sofie Department of Electrical Engineering Control Systems Group Eindhoven University of Technology Netherlands Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden

This paper addresses the control synthesis of heterogeneous stochastic linear multi-agent systems with real-time allocation of signal temporal logic (STL) specifications. Based on previous work, we decompose specifications into sub-specifications on the individual agent level. To leverage the efficiency of task allocation, a heuristic filter evaluates potential task allocation based on STL robustness, and subsequently, an auctioning algorithm determines the definitive allocation of specifications. Finally, a control strategy is synthesized for each agent-specification pair using tube-based model predictive control (MPC), ensuring provable probabilistic satisfaction. We demonstrate the efficacy of the proposed methods using a multi-shuttle scenario that highlights a promising extension to automated driving applications like vehicle routing. © 2024, CC BY-NC-ND.

关键词： Stochastic systems

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Slow Convergence of Interacting Kalman Filters in Word-of-Mouth Social Learning

arXiv

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arXiv 2024年

作者： Krishnamurthy, Vikram Rojas, Cristian R. School of Electrical and Computer Engineering Cornell University United States Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden

We consider word-of-mouth social learning involving m Kalman filter agents that operate sequentially. The first Kalman filter receives the raw observations, while each subsequent Kalman filter receives a noisy measurement of the conditional mean of the previous Kalman filter. The prior is updated by the m-th Kalman filter. When m = 2, and the observations are noisy measurements of a Gaussian random variable, the covariance goes to zero as k−1/3 for k observations, instead of O(k−1) in the standard Kalman filter. In this paper we prove that for m agents, the covariance decreases to zero as k−(2m−1), i.e, the learning slows down exponentially with the number of agents. We also show that by artificially weighing the prior at each time, the learning rate can be made optimal as k−1. The implication is that in word-of-mouth social learning, artificially re-weighing the prior can yield the optimal learning rate. Copyright © 2024, The Authors. All rights reserved.

关键词： Kalman filters

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RLVNA: a Platform for Experimenting with Virtual Networks Adaptations over Public Testbeds

RLVNA: a Platform for Experimenting with Virtual Networks Ad...

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Communications and Networking (MeditCom), IEEE International Mediterranean Conference on

作者： Antonino Angi Alessio Sacco Enrico Alberti Guido Marchetto Flavio Esposito Department of Control and Computer Engineering Politecnico di Torino Italy Computer Science Department Saint Louis University USA

Network emulators and simulation environments traditionally support computer networking and distributed system research. The continued use of multiple approaches highlights both the value and inadequacy of each approach. To this end, several large-scale virtual networks testbeds, such as GENI and CloudLab, have emerged, allowing testing of a networked system in controlled yet realistic environments, focusing in particular on facilitating the test of network management schema in Software-Defined Network (SDN) scenarios. Nevertheless, setting up those experiments first and integrating machine learning models later in these deployments is challenging. In this paper, we propose designing and implementing a web-based platform that integrates Reinforcement Learning (RL)-based models with a virtual network experiment using resources acquired within a real-world testbed, e.g., GENI. Users are able to reserve the network resources (links, switches, and hosts) and configure them through our intuitive interface with little effort. The RL algorithm is then launched to learn how to steer traffic dynamically and according to diverse traffic network conditions. Such a model can be easily customized by the user, while our architecture enables fast reprogramming of the Open Virtual Switches via the SDN controller instantiated. We experimented with trace-based traffic to validate this user-friendly platform and evaluated how centralized and decentralized RL algorithms can effectively lead to self-driving networks. While in this paper, the system focuses on the deployment of experiments for virtual network adaptation, the platform can be easily extended to other network management mechanisms and machine learning algorithms.

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Numerical approach to design of low frequency magnetic field irradiation system for lab on chip experiments 14

Numerical approach to design of low frequency magnetic field...

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14th International Conference ELEKTRO, ELEKTRO 2022

作者： Radil, Roman Bajtos, Marek Carnecka, Lucia Hargasova, Klaudia Kamencay, Patrik University of Zilina Department of Electromagnetic and Biomedical Engineering Zilina Slovakia Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Department of Systems Biology and Engineering Gliwice Poland

ISBN: (纸本)9781665467261

The article is focused on design of specific electromagnetic coil system using numerical modelling and simulation methods. The proposed solution would be capable of delivering magnetic field of desired strength/ flux density and homogeneity within the specific area to affect the cultivation process of single cells in lab on chip experiments. © 2022 IEEE.

关键词： Numerical models

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HINT: Supporting Congestion control Decisions with P4-driven In-Band Network Telemetry

HINT: Supporting Congestion Control Decisions with P4-driven...

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IEEE Workshop on High Performance Switching and Routing

作者： Alessio Sacco Antonino Angi Flavio Esposito Guido Marchetto Department of Control and Computer Engineering Politecnico di Torino Italy Department of Computer Science Saint Louis University USA

Years of research on congestion controls have highlighted how end-to-end and in-network protocols might perform poorly in some contexts. Recent advances in data plane network programmability could also bring advantages in transport protocols, enabling mining and processing in-network congestion signals. However, the new machine learning-based congestion control class has only partially used data from the network, favoring a more sophisticated model design but neglecting possibly precious pieces of data. In this paper, we present HINT, an in-band network telemetry architecture designed to provide insights into network congestion to the end-host TCP algorithm during the learning process. In particular, the key idea is to adapt switches’ behavior via P4 and instruct them to insert simple device information, such as processing delay and queue occupancy, directly into transferred packets. Initial experimental results show that this approach comes with a little network overhead but can improve the visibility and, consequently, the accuracy of TCP decisions of the end-host. At the same time, the programmability of both switches and hosts also enables customization of the default behavior as the user’s needs change.

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Hide & Seek: Traffic Matrix Completion and Inference Using Hidden Information

Hide & Seek: Traffic Matrix Completion and Inference Using H...

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Consumer Communications and Networking Conference, CCNC IEEE

作者： Alessio Sacco Flavio Esposito Guido Marchetto Department of Control and Computer Engineering Politecnico di Torino Italy Department of Computer Science Saint Louis University USA

Traffic matrices are used for many network management operations, from planning to repairing. Despite years of research on the topic, their estimation and inference on the Internet are still challenging and error-prone. For example, missing values are unavoidable due to flaws in the measurement systems and possible failure in data collection systems. It is thus helpful for many network operators to recover the missing data from the partial direct measurements. Some existing matrix completion methods do not fully consider network traffic behavior and hidden traffic characteristics, showing the inability to adapt to multiple scenarios. Others instead make assumptions on the matrix structure that may be invalid or impractical, curtailing the applicability. In this paper, we propose Hide & Seek, a novel matrix completion and prediction algorithm based on a combination of generative autoencoders and Hidden Markov Models. We demonstrate with an extensive experimental evaluation on real-world datasets how our algorithm can accurately reconstruct missing values while predicting their short-term evolution.

关键词： Hidden Markov models Estimation Telecommunication traffic Data collection Prediction algorithms Inference algorithms Encoding

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Risk-Aware Real-Time Task Allocation for Stochastic Multi-Agent Systems under STL Specifications

Risk-Aware Real-Time Task Allocation for Stochastic Multi-Ag...

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IEEE Conference on Decision and control

作者： M. H. W. Engelaar Z. Zhang E. E. Vlahakis D.V. Dimarogonas M. Lazar S. Haesaert Department of Electrical Engineering (Control Systems Group) Eindhoven University of Technology The Netherlands Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

This paper addresses the control synthesis of heterogeneous stochastic linear multi-agent systems with realtime allocation of signal temporal logic (STL) specifications. Based on previous work, we decompose specifications into subspecifications on the individual agent level. To leverage the efficiency of task allocation, a heuristic filter evaluates potential task allocation based on STL robustness, and subsequently, an auctioning algorithm determines the definitive allocation of specifications. Finally, a control strategy is synthesized for each agent-specification pair using tube-based model predictive control (MPC), ensuring provable probabilistic satisfaction. We demonstrate the efficacy of the proposed methods using a multi-shuttle scenario that highlights a promising extension to automated driving applications like vehicle routing.

关键词： Vehicle routing Stochastic processes Probabilistic logic Prediction algorithms Real-time systems Robustness Resource management Vehicle dynamics Multi-agent systems Predictive control

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Hierarchical Retrieval of High-Resolution Fingerprints Based on Pore Feature

Hierarchical Retrieval of High-Resolution Fingerprints Based...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Jing Ma Yuanrong Xu Suyu Dong Wei Wang School of Computer Science and Technology Harbin Institute of Technology Shenzhen China School of Computer Science and Technology Harbin Institute of Technology Weihai China College of Computer and Control Engineering Northeast Forestry University Harbin China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Faced with an escalating number of fingerprint images, most existing retrieval approachs suffer from a common problem: diminishing computational efficiency. This paper presents a hierarchical retrieval system tailored for high-resolution fingerprint images that utilizes abundant pore features and robust recognizability to improve retrieval performance. The framework comprises two core components. Firstly, a CNN-based feature extraction network is established, incorporating an attention mechanism to capture pore features in fingerprint images comprehensively. Subsequently, a hierarchical fingerprint retrieval approach is introduced, involving connection graph construction and a hierarchy of jump table structures for efficient retrieval of query pores. Empirical experiments conducted on high-resolution fingerprint image datasets underscore the system’s effectiveness. Compared with other advanced pore-based fingerprint retrieval methods, the proposed method exhibits a notable rise in the hit rate with reduced penetration rates, significantly reducing the retrieval time.

关键词： Accuracy Image recognition Attention mechanisms Image matching Fingerprint recognition Feature extraction Robustness Computational efficiency Bioinformatics

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Automatic Liver Segmentation from Multiphase CT Using Modified SegNet and ASPP Module

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SN computer science 2024年第4期5卷 377页

作者： Nayantara, P. Vaidehi Kamath, Surekha Kadavigere, Rajagopal Manjunath, Kanabagatte Nanjundappa Department of Instrumentation and Control Engineering Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal 576104 India Department of Radiodiagnosis and Imaging Kasturba Medical College Manipal Academy of Higher Education Karnataka Manipal 576104 India Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal 576104 India

Liver cancer is one of the dominant causes of cancer death worldwide. Computed Tomography (CT) is the commonly used imaging modality for diagnosing it. computer-based liver cancer diagnosis systems can assist radiologists in image interpretation and improve diagnosis speed and accuracy. Since liver segmentation is crucial to such systems, researchers are relentlessly pursuing various segmentation approaches. A clinically viable computer-aided system requires examining multiphase CT images. However, most of the research focuses only on the portal venous phase. In this work, we developed an automatic and efficient Deep Learning (DL) method using SegNet, atrous spatial pyramid pooling module and leaky ReLU layers for liver segmentation from quadriphasic abdominal CT volumes. The proposed method was validated on two datasets, an internal institutional dataset consisting of multiphase CT and a public dataset of portal venous phase CT volumes. The Dice Coefficients (DC) obtained were greater than 96% for the latter dataset and the portal venous phase of the former. For arterial, delayed and plain CT phases of the former dataset, the DC achieved were 94.61%, 95.01% and 93.23%, respectively. Experiments showed that our model performed better than the other state-of-the-art DL models. Ablation studies have revealed that the proposed model leverages the strengths of all the three components that make it up. The promising performance of the proposed method suggests that it is appropriate for incorporation in hepatic cancer diagnosis systems. © The Author(s) 2024.

关键词： Atrous spatial pyramid pooling Computed tomography computer-aided diagnosis Liver cancer Liver segmentation SegNet

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RISense: 6G-Enhanced Human Activity Recognition System with RIS and Deep LDA 25

RISense: 6G-Enhanced Human Activity Recognition System with ...

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25th IEEE International Conference on Mobile Data Management, MDM 2024

作者： Rizk, Hamada Hashima, Sherief Osaka University Graduate School of Information Science and Technology Suita565-0871 Japan Tanta University Computer and Control Engineering Department Tanta31733 Egypt RIKEN-AIP Computational Learning Theory Team Fukuoka819-0395 Japan Egyptian Atomic Energy Authority Engineering Dept Cairo13759 Egypt

ISBN: (纸本)9798350374551

Human Activity Recognition (HAR) systems hold great potential in aiding disabled and elderly individuals to live independently. Various approaches have been suggested for identifying human activities, including sensors, cameras, wearables, and contactless microwave sensing. This latter method is gaining significant attention due to its ability to address privacy concerns arising from cameras and alleviate discomfort caused by wearables. However, current microwave sensing techniques have a key limitation, requiring controlled and ideal conditions to achieve accurate activity detection. In this paper, we propose RISense, a deep learning-aided system for HAR using Re-configurable Intelligent Surface (RIS). RISense introduces novel modules designed to generate a human activity representational space that ensures separability between activity classes, even in the presence of noisy and distorted Channel State Information (CSI) measurements. These representations are then fed into a Recurrent Neural Network (RNN), which learns the sequential changes in features to accurately estimate the user activity. The evaluation of RISense across two realistic settings, encompassing both non-line-of-sight and multi-floor scenarios, showcases its efficacy. Specifically, RISense achieves an activity recognition accuracy of 98.7%. This performance overcomes the accuracy of state-of-the-art systems by over 15%. These findings highlight the superior efficacy of the proposed methodologies, including integrating RIS and advanced learning techniques. © 2024 IEEE.

关键词： Channel state information

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