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BIOPHYSICAL SIGNAL PROCESSING FOR AUTOMATIC ANXIETY CLASSIFICATION IN A VIRTUAL REALITY EXPOSURE THERAPY SYSTEM

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UPB Scientific Bulletin, Series C: Electrical Engineering an...

UPB Scientific Bulletin, Series C: Electrical engineering and computer Science 2024年第2期86卷 73-84页

作者： Jinga, Nicolae Petrescu, Cătălin-Dumitru Mitruț, Oana Moldoveanu, Alin Moldoveanu, Florica Petrescu, Livia Dept. of Computer Science National University of Science and Technology POLITEHNICA Bucharest Romania Dept. of Automatic Control and Systems Engineering National University of Science and Technology POLITEHNICA Bucharest Romania Faculty of Biology University of Bucharest Romania

This paper presents a signal processing framework for automatic anxiety level classification in a virtual reality exposure therapy system. Two types of biophysical data (heart rate and electrodermal activity) were recorded, pre-processed and passed through a feature extraction procedure that provided input for the real-time anxiety level classification algorithm. We showcase the mathematical and engineering techniques behind the procedures and conclude with the challenges encountered in our research and future development ideas. The proposed method provides a good estimate of the level of anxiety while having at the same time a reduced level of complexity, allowing implementation on equipment with limited computing resources. © 2024, Politechnica University of Bucharest. All rights reserved.

关键词： Classification (of information)

Low power implementation of Geometric High-order Decorrelation-based Source Separation on an FPGA board

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Low power implementation of Geometric High-order Decorrelati...

IEEE Cool Chips

作者： Ziquan Qin Kaijie Wei Hideharu Amano Kazuhiro Nakadai Dept. of Information and Computer Science Keio University Yokohama Japan Dept. of Systems and Control Engineering Tokyo Institute of Technology Tokyo Japan

Open source software for robot audition called HARK aims to make “OpenCV” in audio signal processing, providing comprehensive functions from multichannel audio input to sound localization, sound source separation, and au-tomatic speech recognition. Since each of these HARK modules takes considerable energy when executed on PC, we propose to implement each module on an FPGA board called M-KUBOS connected. Here, we focus on the most computationally expensive function of HARK; the sound source separation, and implement it on a Zynq Ultrascale+ board. More than twice a performance improvement was achieved by using the sound frequency level parallelization in the HLS description compared to the software execution on the Ryzen 3990X64-core server. Power evaluation of the real board showed that the energy consumption is only 1/23.4 of the server.

关键词： Location awareness Energy consumption Source separation Power demand Speech recognition Parallel processing Servers

LLCoach: Generating Robot Soccer Plans using Multi-Role Large Language Models

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arXiv 2024年

作者： Brienza, Michele Musumeci, Emanuele Suriani, Vincenzo Affinita, Daniele Pennisi, Andrea Nardi, Daniele Bloisi, Domenico Daniele Dept. of Computer Control and Management Engineering Sapienza University of Rome Rome Italy School of Engineering University of Basilicata Potenza Italy Dept. of International Humanities and Social Sciences International University of Rome Rome Italy

The deployment of robots into human scenarios necessitates advanced planning strategies, particularly when we ask robots to operate in dynamic, unstructured environments. RoboCup offers the chance to deploy robots in one of those scenarios, a human-shaped game represented by a soccer match. In such scenarios, robots must operate using predefined behaviors that can fail in unpredictable conditions. This paper introduces a novel application of Large Language Models (LLMs) to address the challenge of generating actionable plans in such settings, specifically within the context of the RoboCup Standard Platform League (SPL) competitions where robots are required to autonomously execute soccer strategies that emerge from the interactions of individual agents. In particular, we propose a multi-role approach leveraging the capabilities of LLMs to generate and refine plans for a robotic soccer team. The potential of the proposed method is demonstrated through an experimental evaluation, carried out simulating multiple matches where robots with AI-generated plans play against robots running human-built code. © 2024, CC BY.

关键词： Robot programming

Direct data-driven state-feedback control of general nonlinear systems

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arXiv 2023年

作者： Verhoek, Chris Koelewijn, Patrick J.W. Haesaert, Sofie Tóth, Roland The Dept. of Electrical Engineering The Eindhoven University of Technology Netherlands The Institute for Computer Science and Control Budapest Hungary

Through the use of the Fundamental Lemma for linear systems, a direct data-driven state-feedback control synthesis method is presented for a rather general class of nonlinear (NL) systems. The core idea is to develop a data-driven representation of the so-called velocity-form, i.e., the time-difference dynamics, of the NL system, which is shown to admit a direct linear parameter-varying (LPV) representation. By applying the LPV extension of the Fundamental Lemma in this velocity domain, a state-feedback controller is directly synthesized to provide asymptotic stability and dissipativity of the velocity-form. By using realization theory, the synthesized controller is realized as a NL state-feedback law for the original unknown NL system with guarantees of universal shifted stability and dissipativity, i.e., stability and dissipativity w.r.t. any (forced) equilibrium point, of the closed-loop behavior. This is achieved by the use of a single sequence of data from the system and a predefined basis function set to span the scheduling map. The applicability of the results is demonstrated on a simulation example of an unbalanced disc. © 2023, CC BY-NC-SA.

关键词： Linear systems

Tailored Output Layers of Neural Networks for Satisfaction of State Constraints in Nonlinear control Systems

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Tailored Output Layers of Neural Networks for Satisfaction o...

American control Conference (ACC)

作者： Lukas Markolf Olaf Stursberg Dept. of Electrical Engineering and Computer Science Inst. of Control and System Theory University of Kassel Germany

This work considers the synthesis of state feedback controllers established as deep artificial feed-forward neural networks for the control of discrete-time nonlinear but input-affine systems. The idea is to design output layers of particular structure to guarantee the satisfaction of state constraints in form of control-invariant ellipsoids. Since an analytical expression can be derived for the resulting neural network controller, the latter can be stored and evaluated efficiently. Moreover, the proposed output layer guarantees the satisfaction of the considered state constraints for each specification of the parameter vector. Numerical examples are provided for illustration and evaluation of the approach, in which the approximation of a nonlinear model predictive control law is considered as application.

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Neuro-symbolic Empowered Denoising Diffusion Probabilistic Models for Real-time Anomaly Detection in Industry 4.0

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arXiv 2023年

作者： Capogrosso, Luigi Mascolini, Alessio Girella, Federico Skenderi, Geri Gaiardelli, Sebastiano Dall’Ora, Nicola Ponzio, Francesco Fraccaroli, Enrico Di Cataldo, Santa Vinco, Sara Macii, Enrico Fummi, Franco Cristani, Marco Dept. of Engineering for Innovation Medicine University of Verona Italy Dept. of Control and Computer Engineering Polytechnic of Turin Italy Dept. of Regional and Urban Studies and Planning Polytechnic of Turin Italy Dept. of Computer Science University of North Carolina Chapel Hill United States

关键词： Anomaly detection

Koopman Operators in Robot Learning

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arXiv 2024年

作者： Shi, Lu Haseli, Masih Mamakoukas, Giorgos Bruder, Daniel Abraham, Ian Murphey, Todd Cortes, Jorge Karydis, Konstantinos Dept. of Electrical and Computer Engineering University of California Riverside United States Dept. of Mechanical and Aerospace Engineering University of California San Diego United States Dept. of Planning and Control Zoox Inc. United States Dept. of Mechanical Engineering University of Michigan United States Dept. of Mechanical Engineering Yale University United States Dept. of Mechanical Engineering Northwestern University United States Tsinghua University China

Koopman operator theory offers a rigorous treatment of dynamics and has been emerging as a powerful modeling and learning-based control method enabling significant advancements across various domains of robotics. Due to its ability to represent nonlinear dynamics as a linear operator, Koopman theory offers a fresh lens through which to understand and tackle the modeling and control of complex robotic systems. Moreover, it enables incremental updates and is computationally inexpensive making it particularly appealing for real-time applications and online active learning. This review comprehensively presents recent research results on advancing Koopman operator theory across diverse domains of robotics, encompassing aerial, legged, wheeled, underwater, soft, and manipulator robotics. Furthermore, it offers practical tutorials to help new users get started as well as a treatise of more advanced topics leading to an outlook on future directions and open research questions. Taken together, these provide insights into the potential evolution of Koopman theory as applied to the field of robotics. © 2024, CC BY.

关键词： Adversarial machine learning

MIRROR: miRNA regulation-level differential network to study sex and ethnic disparities in cancer

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MIRROR: miRNA regulation-level differential network to study...

Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Caterina Alfano Marco Filetti Lorenzo Farina Manuela Petti Dept. of Experimental Medicine Sapienza University of Rome Rome Italy Phase 1 Unit Fondazione Policlinico Universitario A. Gemelli IRCCS Rome Italy Dept. of Computer Control and Management Engineering PhD Program in Network Oncology and Precision Medicine Sapienza University of Rome Rome Italy Dept. of Computer Control and Management Engineering Sapienza University of Rome Rome Italy

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Cancer disparities are adverse differences in cancer measures that exist among certain population groups. Given that the role they play not only in the disease prognosis but also in therapy response, there is an urgent need to understand what causes them. Most studies investigate these disparities by analyzing transcriptomic data and in particular miRNAs for their regulatory role, but only focusing on expression levels. To face this challenge we propose MIRROR, a new method which analyzes a differential co-expression network of miRNAs between patients’ cohorts, to study the role they play at the target genes’ level. Doing so, we can study the altered molecular mechanism that are linked to cancer disparities. The application of MIRROR to two different cases of cancer disparities has demonstrated its efficacy in identifying molecular players involved in the considered disparity, presenting itself as a viable option to approach this challenge.

关键词： Proteins Technological innovation Transcriptomics Medical treatment Focusing Mirrors Prostate cancer Prognostics and health management Faces Cancer

Energy Consumption Model for LoRaWAN via Field Experiments for Smart Agriculture

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Energy Consumption Model for LoRaWAN via Field Experiments f...

IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

作者： Satoshi Yamazaki Taiki Ogura Dept. of Control and Computer Engineering National Institute of Technology Numazu College Numazu Japan Dept. of Electrical Electronics and Information Engineering Nagaoka University of Technology Niigata Japan

This paper proposes a scheme to model the energy consumption of LoRaWAN, which is a popular example of low-power wide-area networks (LPWANs), nodes via the results of outdoor field experiments by assuming regional smart agriculture as a use case for internet of things (IoT). Specifically, we derive an experimental approximation formula to estimate the battery lifetime by introducing parameters such as spread factor and payload length. The validity of the proposed scheme is demonstrated by confirming that the results obtained by the approximate formula, the experimental and theoretical results generally agree, regardless of the node state and spread factor. Furthermore, we show that the obtained approximate formula can be used to identify the current consumption value in the sleep state that should be achieved to achieve the desired battery lifetime.

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