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Smart World Congress (SWC), IEEE

作者： Michele Guagnano Sara Groppo Luigi Pugliese Massimo Violante Dept. of Control and Computer Engineering Politecnico di Torino Turin Italy

Sleep covers approximately one-third of life, providing the necessary recovery to fundamental body functions. Sleep deprivation and poor sleep quality cause several side effects in everyday life; among others, the efficiency of the immune system progressively decreases, enhancing the arise of pathologies. Therefore, the evaluation of sleep quality is crucial for providing information about personal health status. However, a uniform and robust method for the assessment of sleep quality is still missing. In this preliminary study, Obstructive Sleep Apneas (OSAs), sleep macro pattern and sleep micro pattern are identified and then combined to provide a comprehensive sleep evaluation. For this purpose, a subset of physiological variables (HR, HRV, SpO 2 , body movements) is derived from Polysomnography (PSG) and exploited for the development of rule-based algorithms. This reduced parameter set is selected considering further implementations on wearable off-the-shelf commercial devices.

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Enhancing Graph Representation of the Environment through Local and Cloud Computation

arXiv

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arXiv 2023年

作者： Argenziano, Francesco Suriani, Vincenzo Nardi, Daniele Dept. of Computer Control and Management Engineering Sapienza University of Rome Italy

Enriching the robot representation of the operational environment is a challenging task that aims at bridging the gap between low-level sensor readings and high-level semantic understanding. Having a rich representation often requires computationally demanding architectures and pure point cloud based detection systems that struggle when dealing with everyday objects that have to be handled by the robot. To overcome these issues, we propose a graph-based representation that addresses this gap by providing a semantic representation of robot environments from multiple sources. In fact, to acquire information from the environment, the framework combines classical computer vision tools with modern computer vision cloud services, ensuring computational feasibility on onboard hardware. By incorporating an ontology hierarchy with over 800 object classes, the framework achieves cross-domain adaptability, eliminating the need for environment-specific tools. The proposed approach allows us to handle also small objects and integrate them into the semantic representation of the environment. The approach is implemented in the Robot Operating System (ROS) using the RViz visualizer for environment representation. This work is a first step towards the development of a general-purpose framework, to facilitate intuitive interaction and navigation across different domains. © 2023, CC BY.

关键词： Robots

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Further Results on the Structure of Normal Forms of Input-Affine Nonlinear MIMO Systems

Further Results on the Structure of Normal Forms of Input-Af...

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IEEE Conference on Decision and control

作者： Alberto Isidori Dept. of Computer Control and Management Engineering University “Sapienza” Rome Italy

In a recent paper it has been shown that the existence, for a MIMO nonlinear system, of normal forms with a special structure that proves to be useful in the design of feedback laws is implied by an assumption introduced a long time ago by Hirschorn in his work on systems invertibility. In this paper, we provide an alternative viewpoint and prove that a necessary and sufficient condition for the existence of such kind of normal forms can be identified in a special feature of the so-called maximal controlled invariant distribution algorithm.

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Closed-Loop System Identification of an Unstable Nonlinear System Using Koopman Operator

Closed-Loop System Identification of an Unstable Nonlinear S...

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First International Symposium on Instrumentation, control, Artificial Intelligence, and Robotics (ICA-SYMP)

作者： Benjaset Maneeloke Sudchai Boonto Peerayot Sanposh Yodyium Tipsuwan Natthawut Chinthaned Warit Tanaprasitpattana Dept. of Electrical Engineering Kasetsart University Bangkok Thailand Dept. of Control Systems and Instrumentation Engineering King Mongkut's University of Technology Thonburi Bangkok Thailand Dept. of Computer Engineering Kasetsart University Bangkok Thailand

ISBN: (数字)9798331505400

ISBN: (纸本)9798331505417

This paper presents a novel methodology for closed-loop system identification of unstable nonlinear systems using the Koopman operator with Extended Dynamic Mode Decomposition with control (EDMDc). The study highlights the critical role of selecting appropriate observable functions to develop accurate and efficient Koopman models. We demonstrate that the resulting Koopman models exhibit excellent fitting and validation properties and retain the stabilizability of the original nonlinear systems. These models are verified through time and frequency responses under closed-loop control using a Linear Quadratic Regulator (LQR), confirming their effectiveness. Future work will extend this framework to more complex systems and incorporate machine learning techniques to refine the selection of observable functions. This approach aims further to enhance the adaptability and robustness of Koopman-based control strategies.

关键词： Time-frequency analysis Adaptation models Regulators Accuracy Fitting Machine learning Robustness Closed loop systems Complex systems Frequency control

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Kernel-based multi-step predictors for data-driven analysis and control of nonlinear systems through the velocity form

arXiv

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arXiv 2024年

作者： Verhoek, Chris Tóth, Roland The Control Systems group The Dept. of Electrical Engineering The Eindhoven University of Technology Netherlands The Institute for Computer Science and Control Budapest Hungary

We propose kernel-based approaches for the construction of a single-step and multi-step predictor of the velocity form of nonlinear (NL) systems, which describes the time-difference dynamics of the corresponding NL system and admits a highly structured representation. The predictors in turn allow to formulate completely data-driven representations of the velocity form. The kernel-based formulation that we derive, inherently respects the structured quasi-linear and specific time-dependent relationship of the velocity form. This results in an efficient multi-step predictor for the velocity form and hence for nonlinear systems. Moreover, by using the velocity form, our methods open the door for data-driven behavioral analysis and control of nonlinear systems with global stability and performance guarantees. © 2024, CC BY-NC-SA.

关键词： Nonlinear simulations

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Differential Co-expression Network Analysis to Investigate Sexual Dimorphism in Colon Cancer

Differential Co-expression Network Analysis to Investigate S...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Alfano, Caterina Farina, Lorenzo Petti, Manuela Sapienza University of Rome PhD Program in Network Oncology and Precision Medicine Rome Italy Sapienza University of Rome PhD Program in Network Oncology and Precision Medicine Dept. of Computer Control and Management Engineering Rome Italy Sapienza University of Rome Dept. of Computer Control and Management Engineering Rome Italy

ISBN: (纸本)9781665468190

Colorectal cancer is the third most diagnosed cancer in the world, but it has a higher mortality rate in men compared to women. However, we are not close to understanding how and why sex influences the outcome of the disease. This study focuses on mRNA expression profiles of colon cancer patients to look for molecular differences in the development of colon cancer between men and women. We used paired expression data (i.e., data collected in pairs of normal and cancer cells, by taking samples from the same individual), we identified differentially expressed genes (cancer vs normal) and computed co-expression and differential co-expression gene networks (men vs women). Doing so, we inferred the main changes and alterations happening in cancer tissues, and specifically how these changes were different among men and women. We found that the co-expression networks of women and men affected by colon cancer are quite different and we reported the genes that show the most differences in this comparison, checking if they could also be associated to sexual dimorphism or sexual hormones. Among these genes we found a interesting presence of genes associated to the Wnt signaling pathway which has been found to be regulated by estrogen and whose activation is strongly linked with colon cancer. © 2022 IEEE.

关键词： Diseases

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Real-Time and Low-Overhead Graph Task Scheduling over Vehicular Computing-Assisted Edge Networks

Real-Time and Low-Overhead Graph Task Scheduling over Vehicu...

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IEEE International Conference on Communications (ICC)

作者： Bingshuo Guo Minghui Liwang Fan Yang Seyyedali Hosseinalipour Xianbin Wang Huaiyu Dai Dept. of Information and Communication Engineering Xiamen University Xiamen China Dept. of Control Science and Engineering Tongji University Shanghai China Dept. of Electrical Engineering University at Buffalo-SUNY NY USA Dept. of Electrical and Computer Engineering Western University Ontario Canada Dept. of Electrical and Computer Engineering North Carolina State University Raleigh USA

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Modern vehicular networks encounter a multitude of computation-intensive tasks that have unique processing topologies represented by graph structures. The integration of edge computing and vehicular networks has provided a unique platform for handling these tasks at the network edge. However, the complex structure of these tasks makes their scheduling and execution challenging. This paper proposes a Vehicular Computing-assisted Edge Network (VCEN) architecture, where graph tasks are scheduled over a Vehicle-Edge Collaborative Cloud (VECC) for parallel execution. Our goal is to obtain feasible mappings between task components and computing nodes in the VECC while minimizing task execution latency and energy consumption. We show that achieving this goal requires solving an NP-hard optimization problem with complex constraints related to task structure and VECC topology. We then propose a fast and lightweight approach for graph task scheduling over VECC that comprises two key phases. In the former phase, we introduce a preprocessing algorithm that reduces the graph task's dimensionality by merging important components and cutting redundant edges. In the latter phase, we deploy a cost-reduction-preferred mapping algorithm to obtain feasible mappings between task components and VECC. Through simulations, we demonstrate our superior performance in different network settings.

关键词： Energy consumption Processor scheduling Network topology Merging computer architecture Real-time systems Topology

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Game-Theoretic-Based Resource Allocation Algorithm for SCMA Max-Min Problem to Maximize Fairness

Game-Theoretic-Based Resource Allocation Algorithm for SCMA ...

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IEEE International Conference on Electronics and Communication engineering (ICECE)

作者： Maryam Cheraghy Meysam Soltanpour Belal Abuhaija Hemn Barzan Abdalla Kennedy Ehimwenma dept. Computer Science and Technology Wenzhou-Kean University Wenzhou China dept. Control Science and Engineering Shandong University Jinan China

ISBN: (数字)9798331517755

ISBN: (纸本)9798331517762

This paper tackles a joint optimization problem in uplink sparse code multiple access (SCMA) networks to enhance network fairness, i.e., Jain's fairness index (JFI). We propose a novel game-theoretic approach, the worst-case player first-strategy selection (WCPFSS) algorithm, to maximize the minimum achievable rate for all subcarriers while ensuring fair power allocation and optimized subcarrier assignment. This approach effectively manages user interference because each subcarrier acts as a player to exchange their strategies, which are clusters of users associated with each subcarrier. The simulation results confirm the superiority of the proposed WCPFSS method w.r.t. JFI for differential setup parameters.

关键词： Codes Simulation Clustering algorithms Interference Minimax techniques Resource management Indexes Uplink Game theory Optimization

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Deep Reinforcement Learning Platooning control of Non-Cooperative Autonomous Vehicles in a Mixed Traffic Environment

Deep Reinforcement Learning Platooning Control of Non-Cooper...

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IEEE International Conference on Automation Science and engineering (CASE)

作者： Danilo Menegatti Andrea Wrona Antonio Di Paola Simone Gentile Alessandro Giuseppi Dept. of Computer Control and Management Engineering "Antonio Ruberti" Sapienza University of Rome Rome Italy Dept. of Electrical and Information Engineering Polytechnic of Bari Bari Italy

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

Ensuring secure spacing between vehicles is vital for road safety, efficient traffic flow, and system stability in autonomous driving. While traditional cooperative platooning approach, relying on centralized coordination exploiting wireless network, faces practical implementation challenges due to communication constraints and diverse driving behaviors, this work introduces a scalable non-cooperative multi-agent platooning strategy based on Deep Reinforcement Learning, leveraging on decentralized decision-making principles. The agents’ aim is to adjust their velocities dynamically to ensure safe following distances and adapt to surrounding vehicle behavior, without the possibility of exchanging information over a wireless network. Extensive simulations validate the effectiveness and robustness of the proposed approach, making it suitable for real-world autonomous driving scenarios.

关键词： Wireless networks Deep reinforcement learning control systems Aerodynamics Robustness Stability analysis Sensors Vehicle dynamics Autonomous vehicles Optimization

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Multi-Point Positional Insertion Tuning for Small Object Detection

arXiv

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arXiv 2024年

作者： Goto, Kanoko Karasawa, Takumi Hirose, Takumi Kawakami, Rei Inoue, Nakamasa Dept. of Computer Science Institute of Science Tokyo Tokyo Japan Dept. of Systems and Control Engineering Institute of Science Tokyo Tokyo Japan

Small object detection aims to localize and classify small objects within images. With recent advances in large-scale vision-language pretraining, finetuning pretrained object detection models has emerged as a promising approach. However, finetuning large models is computationally and memory expensive. To address this issue, this paper introduces multi-point positional insertion (MPI) tuning, a parameter-efficient finetuning (PEFT) method for small object detection. Specifically, MPI incorporates multiple positional embeddings into a frozen pretrained model, enabling the efficient detection of small objects by providing precise positional information to latent features. Through experiments, we demonstrated the effectiveness of the proposed method on the SODA-D dataset. MPI performed comparably to conventional PEFT methods, including CoOp and VPT, while significantly reducing the number of parameters that need to be tuned. Copyright © 2024, The Authors. All rights reserved.

关键词： Visual languages

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