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2023 Italia Intelligenza Artificiale - Thematic Workshops, Ital-IA 2023

作者： Ledda, Emanuele Putzu, Lorenzo Delussu, Rita Fumera, Giorgio Roli, Fabio Department of Computer Control and Management Engineering Sapienza University of Rome Via Ariosto 25 Rome00185 Italy Department of Informatics Bioengineering Robotics and Systems Engineering University of Genova Via Dodecaneso 35 Genova16146 Italy Department of Electric and Electronic Engineering University of Cagliari Via Marengo 3 Cagliari09100 Italy

The increasing adoption of machine learning and deep learning models in critical applications raises the issue of ensuring their trustworthiness, which can be addressed by quantifying the uncertainty of their predictions. However, the black-box nature of many such models allows only to quantify uncertainty through ad hoc superstructures, which require to develop and train a model in an uncertainty-aware fashion. However, for applications where previously trained models are already in operation, it would be interesting to develop uncertainty quantification approaches acting as lightweight "plug-ins" that can be applied on top of such models without modifying and re-training them. In this contribution we present a research activity of the Pattern Recognition and Applications Lab of the University of Cagliari related to a recently proposed post hoc uncertainty quantification method, we named dropout injection, which is a variant of the well-known Monte Carlo dropout, and does not require any re-training nor any further gradient descent-based optimization;this makes it a promising, lightweight solution for integrating uncertainty quantification on any already-trained neural network. We are investigating a theoretically grounded solution to make dropout injection as effective as Monte Carlo dropout through a suitable rescaling of its uncertainty measure;we are also evaluating its effectiveness in the computer vision tasks of crowd counting and density estimation for intelligent video surveillance, thanks to our participation in a project funded by the European Space Agency. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Pattern recognition

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Parallel Byzantine fault tolerance method for blockchain

Parallel Byzantine fault tolerance method for blockchain

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International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Dubey, Kumar Pradyot Gnanaprakasam, C.N. Khan, Ihtiram Raza Sadik, Md Shibli Al-Farhani, Liwa H. Ray, Samrat Department of Computer and Information Science Jagadguru Ram Bhadracharya Divyanga University UP Chitrakoot India Department of Electronics and Instrumentation Engineering St. Joseph’s College of Engineering Tamilnadu Chennai India Computer Science Department Jamia Hamdard Delhi India University of Portsmouth Portsmouth United Kingdom System Analysis Control and Information Processing Dep Academy of Engineering RUDN University Moscow Russia The Institute of Industrial Management Economics and Trade Peter The Great Saint Petersburg Polytechnic University Russia

ISBN: (纸本)9781032669663

Enterprise-focused blockchain applications that offer safe, anonymous, and immutable transactions are growing as blockchain technology matures. Traditional blockchain architecture has low performance and scalability, making it unsuitable for enterpriselevel applications with high concurrency and massive data. This paper studies and proposes a simplified Byzantine fault-tolerant (SBFT) consensus algorithm to improve consensus stage efficiency, a Task parallel smart contract model to maximise multi-core system parallel efficiency, and an improved blockchain system architecture to reflect the lightweight, low coupling nature of blockchain technology. It facilitates corporate application secondary development and the ParaChain blockchain and smart contract technology. Experimental verification compares the parallelization - based ParaChain blockchain’s TPS and scalability. PBFT-based blockchain systems have been greatly improved. © 2024 The Author(s).

关键词： Smart contract

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A Novel Conv-Mamba-Hybrid Network for Medical Image Segmentation

A Novel Conv-Mamba-Hybrid Network for Medical Image Segmenta...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Qiao, Sibo Zhao, Zhiyuan Xie, Pengfei Yin, Wenjing Wang, Min Zhang, Yuanyuan Pang, Shanchen Tiangong University The College of Software Tianjin China China University of Petroleum The College of Computer Science and Technology Qingdao China Khalifa University of Science and Technology The Department of Electrical Engineering and Computer Science Abu Dhabi United Arab Emirates Tiangong University The College of Life Sciences Tianjin China Qingdao University of Technology The College of Information and Control Engineering Qingdao China

ISBN: (纸本)9798350386226

In the rapidly advancing landscape of intelligent medical technology, high-precision and widely applicable medical image segmentation techniques are pivotal in advancing personalized treatment plans and enhancing patient experiences. To address inherent obstacles, such as complex structures and features that are difficult to capture in segmentation tasks, we first propose a CMH module that merges convolutional neural networks (CNNs) with state space models (e.g., Mamba). This module hierarchically integrates CNN's local priors into the Mamba layer, adeptly capturing specific details of target objects and broader contextual information from different perspectives. Subsequently, we introduce an information fusion module based on Mamba (MIH), aimed at dynamically fusing feature information from different paths and levels to enhance feature expression and discrimination. Building upon the abovementioned modules, we develop an unexplored segmentation model, CMHNet, capable of flexibly capturing feature maps of varying scales and angles from medical images. In the experiments, we present CMHNet's distinguished performance in segmentation tasks through massive experiments on four medical datasets. © 2024 IEEE.

关键词： Convolutional neural networks

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FMCW Radar-Based Human Activity Recognition: A Machine Learning Approach for Elderly Care

FMCW Radar-Based Human Activity Recognition: A Machine Learn...

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2025 IEEE Wireless Communications and Networking Conference, WCNC 2025

作者： Mashhadigholamali, Mohammadreza Fard, Ali Samimi Zolfaghari, Samaneh Abedi, Hajar Chakraborty, Mainak Borzì, Luigi Daneshtalab, Masoud Shaker, George Politecnico di Torino Department of Control and Computer Engineering Turin Italy School of Innovation Design and Engineering Mädlardalen University Västerås Sweden University of Waterloo Department of Electrical and Computer Engineering WaterlooON Canada

ISBN: (纸本)9798350368369

In this paper, we propose a novel system prototype for human activity recognition using a low-cost, low-power millimeter-wave (mmWave) frequency-modulated continuous wave (FMCW) radar. Our approach applies the Fast Fourier Transform on the slow time axis and employs a Capon filter to generate range-Doppler, range-azimuth, and range-elevation maps, respectively. It can also effectively mitigate noise and multipath effects. We then use principal component analysis for feature reduction, reducing the dimensionality of the feature vectors extracted from these maps, which can be used to train conventional machine learning classifiers. This approach aims to achieve a balance between computational complexity, accuracy, and overall system performance. Our proposed system demon-strates promising recognition rates and robustness across varying levels of activity granularity, achieving recognition rates from 90.28% for four activities up to 70.97% for seven fine-grained ac-tivities. These findings highlight the potential of millimeter wave radar and suggested range maps combined with conventional machine learning classifiers for noninvasive, privacy-preserving activity recognition, with significant implications for healthcare, elderly care, and ambient assisted living. © 2025 IEEE.

关键词： Deep learning

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Fuzzy controller of Evaluation System Access Distribution 13

Fuzzy Controller of Evaluation System Access Distribution

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13th International Conference on Advanced computer Information Technologies, ACIT 2023

作者： Dubchak, Lesia Vasylkiv, Nadiia Turchenko, Iryna Nadvynychna, Tetiana Savka, Nadiia Akimjak, Amantius West Ukrainian National University Department of Computer Engineering Ternopil Ukraine West Ukrainian National University Department of Information Computer Systems and Control Ternopil Ukraine West Ukrainian National University Subdepartment of Psyhology and Social Work Ternopil Ukraine Catholic University in Ruzomberok Department of Catholic Theology Ruzomberok Slovakia

control of users' access to the information system is the main part of the security system. Moreover, it must work in real time and be easily reconfigured. Such controller can be built by using fuzzy logic. In this paper, the authors propose a fuzzy controller for access distribution to the student assessment system. This approach allows applying the proposed fuzzy controller to any evaluation system, as it can be easily reconfigured. Hardware implementation of this controller allows to increase the stability of the information security system because of subjectivity exclusion. © 2023 IEEE.

关键词： controllers

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Model predictive control for tracking using artificial references: Fundamentals, recent results and practical implementation 63

Model predictive control for tracking using artificial refer...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Krupa, P. Kohler, J. Ferramosca, A. Alvarado, I. Zeilinger, M.N. Alamo, T. Limon, D. Computer Science Group L'Aquila Italy Institute for Dynamic Systems and Control Eth Zürich ZürichCH-8092 Switzerland University of Bergamo Information and Production Engineering Department of Management Via Marconi 5 BG Dalmine24044 Italy Universidad de Sevilla Department of Systems Engineering and Automation Seville Spain

ISBN: (纸本)9798350316339

This paper provides a comprehensive tutorial on a family of Model Predictive control (MPC) formulations, known as MPC for tracking, which are characterized by including an artificial reference as part of the decision variables in the optimization problem. These formulations have several benefits with respect to the classical MPC formulations, including guaranteed recursive feasibility under online reference changes, as well as asymptotic stability and an increased domain of attraction. This tutorial paper introduces the concept of using an artificial reference in MPC, presenting the benefits and theoretical guarantees obtained by its use. We then provide a survey of the main advances and extensions of the original linear MPC for tracking, including its non-linear extension. Additionally, we discuss its application to learning-based MPC, and discuss optimization aspects related to its implementation. © 2024 IEEE.

关键词： Predictive control systems

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Modeling and Simulation of Two Axes Gimbal Using Fuzzy control

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computers, Materials & Continua 2022年第7期72卷 93-107页

作者： Ayman A.Aly Mohamed O.Elhabib Bassem F.Felemban B.Saleh Dac-Nhuong Le Department of Mechanical Engineering College of EngineeringTaif UniversityTaif21944Saudi Arabia Control and Mechatronics Division School of Electrical EngineeringUTMJohor Bahru81300Malaysia Institute of Research and Development Duy Tan UniversityDanang550000Vietnam School of Computer Science Duy Tan UniversityDanang550000Vietnam

The application of the guided missile seeker is to provide stability to the sensor’s line of sight toward a target by isolating it from the missile motion and *** main objective of this paper is not only to present the physical modeling of two axes gimbal system but also to improve its performance through using fuzzy logic controlling *** paper is started by deriving the mathematical model for gimbals motion using Newton’s second law,followed by designing the mechanical parts of model using SOLIDWORKS and converted to xml file to connect dc motors and sensors using MATLAB/***,a Mamdani-type fuzzy and a Proportional-Integral-Derivative(PID)controllers were designed using MATLAB *** performance of both controllers was evaluated and tested for different types of input *** simulation results showed that self-tuning fuzzy controller provides better performance,since no overshoot,small steady-state error and small settling time compared to PID controller.

关键词： Gimbal system self-tuning fuzzy proportional-integral-derivative(PID)control cross coupling

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On a novel tracking differentiator design based on iterative learning in a moving window

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control Theory and Technology 2023年第1期21卷 46-55页

作者： Xiangyang Li Rafal Madonski Zhiqiang Gao Senping Tian Key Laboratory of Autonomous Systems and Network Control Ministry of EducationSchool of Automation Science and EngineeringSouth China University of TechnologyGuangzhou 510641China Energy and Electricity Research Center Jinan UniversityZhuhai 519070China Department of Electrical and Computer Science Cleveland State UniversityClevelandOH 44115USA

Differential signals are key in control engineering as they anticipate future behavior of process variables and therefore are critical in formulating control laws such as proportional-integral-derivative(PID).The practical challenge,however,is to extract such signals from noisy measurements and this difficulty is addressed first by *** in the form of linear and nonlinear tracking differentiator(TD).While improvements were made,TD did not completely resolve the conflict between the noise sensitivity and the accuracy and timeliness of the *** two approaches proposed in this paper start with the basic linear TD,but apply iterative learning mechanism to the historical data in a moving window(MW),to form two new iterative learning tracking differentiators(IL-TD):one is a parallel IL-TD using an iterative ladder network structure which is implementable in analog circuits;the other a serial IL-TD which is implementable digitally on any computer *** algorithms are validated in simulations which show that the proposed two IL-TDs have better tracking differentiation and de-noise performance compared to the existing linear TD.

关键词： Tracking differentiator(TD) Iterative learning Iterative learning tracking differentiator(IL-TD) Active disturbance rejection control(ADRC)-Two-dimensional system(2-D system)

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Robust Parameter Estimation with Orthogonal Regression Using Interval Constraint Satisfaction Technique

Robust Parameter Estimation with Orthogonal Regression Using...

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Intelligent and Fuzzy Systems - Intelligence and Sustainable Future Proceedings of the INFUS 2023 Conference

作者： Raval, Sejal K. Raval, Kamlesh N. Makwana, Dipesh P. Kotak, Paresh Instrument and Control Department Government Polytechnic Ahmedabad India Computer Engineering Department R.C.Technical Institute Ahmedabad India I.C. Department L.D. College of Engineering Ahmedabad India S. and S. Gandhi College Surat India

ISBN: (纸本)9783031397769

Parameter estimation is a first and foremost task to design a proper mathematical model. Outliers in a data can often lead to improper parameter estimation. In present work a novel technique using interval constraint satisfaction technique is suggested for getting robust parameter estimation of a system using linear orthogonal regression. In this proposed method we used M estimator with tukey’s biweight function and modified it for orthogonal regression. Tukey’s biweight is generally computed using IRLS which has computational issues of convergence and local minima. Here, benefits of interval analysis based method is utilized to develop the proposed method. Two well-known examples of datasets are used to validate the proposed method. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Parameter estimation

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Exploring the Diagnostic Potential of LLMs in Schizophrenia Detection through EEG Analysis

Exploring the Diagnostic Potential of LLMs in Schizophrenia ...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Guerra, Michele Milanese, Roberto Deodato, Michele Ciobanu, Madalina G. Fasano, Fausto University of Molise Mosaic Research Center DiBT Pesche Italy Politecnico di Torino Department of Control and Computer Engineering Torino Italy New York University Division of Science Abu Dhabi United Arab Emirates Fisciano Italy

ISBN: (纸本)9798350386226

Schizophrenia is a psychiatric disorder that presents significant diagnostic challenges due to its complex neurophysiological characteristics. This paper investigates the potential of Large Language Models (LLMs), such as OpenAI's GPT-4 and GPT-o1, in detecting schizophrenia through electroencephalography (EEG) analysis. Using the LMSU public ScZ EEG dataset, we conducted a series of experiments involving different types of input data, including raw EEG signals, frequency band summaries, and graphical representations of brain activity. Our findings demonstrate that LLMs can accurately classify schizophrenic and healthy individuals while offering interpretable, clinically relevant insights aligned with established EEG markers. By integrating these models into the diagnostic workflow, we explore the concept of Symbiotic AI, where LLMs act as cognitive collaborators, enhancing clinicians' ability to analyze complex data efficiently and transparently. This approach not only improves diagnostic accuracy but also facilitates real-time decision-making, paving the way for earlier and more precise detection of schizophrenia in clinical settings. © 2024 IEEE.

关键词： Electroencephalography

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