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3rd URSI Atlantic and Asia Pacific Radio Science Meeting, AT-AP-RASC 2022

作者： Di Meo, Simona Cannata, Alessia MacChello, Chiara Morganti, Simone Pasian, Marco Matrone, Giulia University of Pavia Department of Electrical Computer and Biomedical Engineering Pavia Italy

ISBN: (纸本)9789463968058

Tissue-mimicking phantoms represent a key point for the development of biomedical systems for diagnostic imaging. In this paper, new recipes for tissue-mimicking breast phantoms are proposed and tested, both dielectrically and mechanically. Phantoms mimicking human breast neoplastic tissues are considered, as they are anatomically stiffer than the surrounding healthy tissues. In our recipes, only cheap, easy-to-manage and safe components are involved, and the performance of two solidifying agents (i.e., gelatin and agar) are evaluated both from a dielectric and a mechanical point of view. Dielectric measurements are performed from 500 MHz to 40 GHz, and mechanical tests are performed with the unconfined compression approach, using a preload of 0.2 N and a test speed of 0.5 mm/min. This analysis shows that agar is more suitable for the fabrication of stiffer phantoms as compared to gelatin. © 2022 URSI.

关键词： Phantoms

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Evaluating Non-Expert Stakeholder Interaction with Artificial Intelligence on Energy Urban Domain Using VIRTSI: The Case of ChatGPT 15

Evaluating Non-Expert Stakeholder Interaction with Artificia...

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15th International Conference on Information, Intelligence, Systems and Applications, IISA 2024

作者： Tsihrintzis, George A. Sarmas, Elissaios Marinakis, Vangelis Panagoulias, Dimitrios Tsihrintzi, Evangelia-Aikaterini Virvou, Maria University of Piraeus Department of Informatics Piraeus Greece School of Electrical & Computer Engineering National Technical University of Athens Decision Support Systems Laboratory Greece National and Kapodistrian University of Athens Department of Informatics and Telecommunicatios Athens Greece

ISBN: (纸本)9798350368833

This paper investigates the interaction of non-expert stakeholders with Artificial Intelligence (AI) in the energy urban domain, using the VIRTSI model and focusing on the capabilities of ChatGPT. VIRTSI (Variability and Impact of Reciprocal Trust States towards Intelligent systems), is a rigorous computational model for human-AI Interaction that simulates human trust states, spanning from overtrust to distrust, through user modelling and quantifies the efficiency of the interaction in VIRTSI-adapted confusion matrices. The research employed an 16-question survey, evaluating the accuracy and usefulness of ChatGPT's responses regarding energy consumption, cost-effective solutions, and renewable energy production for residential buildings in Greece. Each answer was assessed by human stakeholders who were non-expert in the energy Urban Domain (e.g house owners, building managers, etc.), who either accepted or rejected the responses based on validation processes. The analysis highlighted key aspects such as repetition, specification, and objections in the interaction with ChatGPT, offering insights into the effectiveness of AI in supporting energy-related decisions. The findings reveal that while AI can provide valuable information, user validation and expert consultation are critical for practical implementation. This highlights both the potential and limitations of integrating AI tools like ChatGPT in enhancing non-expert stakeholder engagement and decision-making in urban energy management, emphasizing the ongoing need for human expert involvement. © 2024 IEEE.

关键词： Decision making

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Evaluation of Different Magnet Materials and Skewed Geometries for IPMSM at High Speed

Evaluation of Different Magnet Materials and Skewed Geometri...

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2022 International Conference on electrical Machines, ICEM 2022

作者： Mantione, Lorenzo Frosini, Lucia Minervini, Marcello University of Pavia Computer and Biomedical Engineering Department of Electrical Pavia Italy

ISBN: (数字)9781665414326

ISBN: (纸本)9781665414326

In this paper, an investigation on various permanent magnet materials and rotor geometries for Interior Permanent Magnet Synchronous Motors is presented, to achieve optimal performance both at base speed and in a wide flux-weakening region. The research starts from the updated state of the art, which suggests single V-shape magnets, to obtain a good compromise between performance and industrial interest, and rotor step-skewing. To design, analyze and compare several rotor topologies, two software (FluxMotor and Flux2D) have been used. The results highlight that less performing magnet materials are not convenient, due to the need of greater mass of material to reach the same performance, while a simple three steps skewing, combined with the best choice of rotor geometry, provides a strong reduction of torque ripple, which remains low up to 8000 rpm. © 2022 IEEE.

关键词： Finite element method

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Gamified Rehabilitation and Physiotherapy Using MediaPipe for Posture and Hand Gesture Recognition 2

Gamified Rehabilitation and Physiotherapy Using MediaPipe fo...

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2nd International Conference on Recent Trends in Microelectronics, Automation, Computing, and Communications Systems, ICMACC 2024

作者： Vidyasagar, K.E. Ch Sai, G. N. K. Anantha Ruchita, Munagala Saikia, Manob Jyoti University of Memphis Biomedical Sensors and Systems Lab MemphisTN38152 United States Osmania University Department of Biomedical Engineering Hyderabad500007 India University of Memphis Electrical and Computer Engineering Department MemphisTN38152 United States

ISBN: (纸本)9798350366570

Introduction: Rehabilitation for conditions affecting mobility, such as stroke, muscle atrophy, and others, poses significant challenges in traditional physiotherapy practices. Engaging patients in enjoyable exercises is crucial for maintaining compliance with rehabilitation protocols. In response, this paper introduces a pioneering game-based approach to rehabilitation, leveraging posture detection technology. Materials and Methods: A game-based rehabilitation system was developed utilizing deep learning techniques through the MediaPipe framework for posture and hand gesture recognition. The system analyzes user posture in real time via a webcam and translates these movements into game controls, allowing patients to interact with popular games like Subway Surfers to simulate physical activities such as running and jumping. Latency between patient movements and in-game responses was measured across three hardware configurations (8GB, 12GB, and 16GB RAM) to evaluate system performance and responsiveness. Results and Discussion: Latency decreased with higher RAM, with the lowest measured at 430 ms on a 16GB configuration. This real-time responsiveness enhances patient engagement, making the rehabilitation process more immersive and effective. By leveraging posture detection technology, this innovative system bridges the gap between the necessity of rehabilitation exercises and the appeal of gaming, offering a promising solution for improving treatment outcomes across diverse age groups and conditions. © 2024 IEEE.

关键词： Gesture recognition

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Cognitive Analysis based on Brain Connectivity for Learning Task fMRI Data using CONN Software Package 1

Cognitive Analysis based on Brain Connectivity for Learning ...

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1st IEEE Karachi Section Humanitarian Technology Conference, Khi-HTC 2024

作者： Jatoi, Munsif Ali Teevino, Sadam Hussain Dharejo, Fayaz Ali Salim Habib University Department of Biomedical Engineering Sindh Karachi Pakistan Tenologico de Monterrey School of Engineering and Sciences Mexico Khalifa University Department of Electrical Engineering and Computer Science United Arab Emirates

ISBN: (纸本)9798350373042

The educational psychology is dependent upon the cognition and learning. The productive research in this domain yields the results for improvements in novel pedagogical methods at various educational level. The learning and memory are closely affiliated with brain structure and functioning i.e., neurobiology which has been the topic of research in philosophy, psychology and social sciences. Hence, to study the learning mechanism for improvements in educational methods, its essential to understand theoretical background of cognitive analysis through brain sciences. The human brain consists of billions of neurons with trillions of synaptic connections structurally and functionally organized in multiple scales of space and time, respectively. This complex structure is responsible for various complex and simple mental/physical tasks such as learning, memory, listening, sequencing, reasoning, motion, thinking, controlling, etc. The brain activity is analyzed through either source analysis or connectivity analysis. The connectivity analysis is carried out by either anatomical way, which is termed as anatomical connectivity, or through the functional way, which is termed as functional connectivity. However, the third explores the cause and effect relationship for a set of neurons and is termed as effective connectivity. This research work produces a generalized review for educational learning and then its relationship with connectivity analysis, including its introduction, graph theory implication, discussion for its types, and parameter discussion. Finally, functional connectivity was implemented on brain data using the CONN software package to compare various degrees, Eigenvalue, and betweenness centrality using fMRI data. The results show that the Eigenvalue centrality measure performs the best result for functional connectivity compared with other centrality measures. © 2024 IEEE.

关键词： Software packages

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High-fidelity super-resolution imaging with deconvolution by pixel reassignment (DPR)

High-fidelity super-resolution imaging with deconvolution by...

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2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Zhao, Bingying Mertz, Jerome Department of Electrical and Computer Engineering Boston University MA02215 United States Department of Biomedical Engineering Boston University MA02215 United States

We demonstrated a novel super-resolution analytical approach, deconvolution by pixel reassignment (DPR), which can enhance the spatial resolution in fluorescence microscopy beyond the diffraction limit with high fidel... 详细信息

ISBN: (纸本)9781957171258

关键词： Pixels

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Efficiency and fairness improvement for elastic optical networks using reinforcement learning-based traffic prediction

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Journal of Optical Communications and Networking 2022年第3期14卷 25-42页

作者： Valkanis, Anastasios Papadimitriou, Georgios Beletsioti, Georgia Varvarigos, Emmanouel Nicopolitidis, Petros Department of Informatics Aristotle University of Thessaloniki Thessaloniki Greece School of Electrical and Computer Engineering National Technical University of Athens Athens Greece

The characteristics of traffic in core networks have changed in recent years, with the data flows among network nodes now showing large volumes but also large fluctuations during the day. In such a dynamic environment, the static allocation of network resources by making reservations at the peak rates is wasteful, both in terms of energy consumption and resource efficiency. Periodic reconfiguration of network resources is necessary for the full exploitation of an elastic optical network's potential. In this paper, we present a resource reconfiguration scheme for an IP over elastic optical core network. The cornerstone of the proposed scheme is a novel traffic prediction mechanism based on reinforcement learning. Using the forecasting mechanism, a heuristic routing and spectrum assignment algorithm was designed to efficiently and fairly allocate network resources periodically. Simulation results show both a reduction of the bandwidth blocking probability and an increase of the fairness index regarding the network resources allocation over corresponding algorithms in the literature. © 2009-2012 Optica Publishing Group.

关键词： Reinforcement learning

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14-Bit SAR ADC with on-Chip Digital Bubble Sorting Calibration Technology

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Chinese Journal of Electronics 2025年第1期34卷 125-136页

作者： Hua Fan Zhuorui Chen Tongrui Xu Franco Maloberti Qi Wei Quanyuan Feng School of Integrated Circuit Science and Engineering(Exemplary School of Microelectronics) University of Electronic Science and Technology of China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Chongqing Institute of Microelectronics Industry Technology University of Electronic Science and Technology of China Department of Electrical Computer and Biomedical Engineering University of Pavia Department of Precision Instrument Tsinghua University Institute of Microelectronics Southwest Jiaotong University

This article designs a 14-bit successive approximation register analog-to-digital converter（SAR ADC）.A novel digital bubble sorting calibration method is proposed and applied to eliminate the effect of capacitor mismatch on the linearity of the SAR ADC. To reduce the number of capacitors, a hybrid architecture of a high 8-bit binary-weighted capacitor array and a low 6-bit resistor array is adopted by the digital-to-analog（DAC）. The common-mode voltage VCM-based switching scheme is chosen to reduce the switching energy and area of the DAC. The time-domain comparator is employed to obtain lower power consumption. Sampling is performed through a gate voltage bootstrapped switch to reduce the nonlinear errors introduced when sampling the input signal. Moreover, the SAR logic and the whole calibration is totally implemented on-chip through digital integrated circuit（IC） tools such as design compiler, IC compiler, etc. Finally, a prototype is designed and implemented using 0.18 μm bipolar-complementary metal oxide semiconductor（CMOS）-double-diffused MOS 1.8 V CMOS technology. The measurement results show that the SAR ADC with on-chip bubble sorting calibration method achieves the signal-to-noise-and-distortion ratio of 69.75 dB and the spurious-free dynamic range of 83.77 dB.

关键词： Semiconductor device measurement Capacitors Linearity Switches Calibration System-on-chip Frequency measurement Time-domain analysis Sorting Signal to noise ratio

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Machine Learning Applied to Electronic Health Record Data: Opportunities and Challenges

Machine Learning Applied to Electronic Health Record Data: O...

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16th International Joint Conference on biomedical engineering Systems and Technologies, BIOSTEC 2023

作者： Bellazzi, Riccardo Department of Electrical Computer and Biomedical Engineering University of Pavia IRCCS ICS Maugeri Pavia Italy

The increasing success of application of machine and deep learning in many areas of medicine, in particular in imaging diagnostics (Rajpurkar et al., 2020), is pushing towards the implementation of AI-based approaches to extract knowledge from health records data (EHR) (Li et al., 2020). The potential of sophisticated strategies to derive regularities from very large collection of textual data, such as language models, is also generating strong expectations about the capability of extracting information unstructured textual notes as well as in generating biomedical texts (Segura-Bedmar et al., 2022;Luo et al., 2022). The COVID-19 pandemics, being one of the most relevant healthcare challenges synchronously happened worldwide, has represented a strong push towards the timely use of EHR data to characterize the clinical course of the COVID-19 disease. Successful examples are represented by cooperative international efforts, such as the Consortium for Clinical Characterization of COVID-19 by EHR (4CE) initiative (Brat et al., 2020). However, EHR data are particularly complex, due to their multifaceted nature and inherent relationship with the health care organizations generating the data. In a recent paper, Kohane and colleagues summarizing the experience carried on in leading 4CE have identified six main challenges that have proven to be crucial for running EHR-based projects (Kohane et al., 2021): i) data completeness, ii) data collection and handling, iii) data type, iv) robustness of methods against EHR variability (within and across institutions, countries, and time), v) transparency of data and analytic code, and vi) the need of multidisciplinary approach. Those topics, in the context of structured EHR data, have been recently further systematized by a consensus paper by the European Society of Cardiology and the BigData(S?Heart consortium that has defined the CODE-EHR best-practice framework for the use of structured electronic health-care records in clinical re

关键词： Electronic health record

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Numerical Simulations of Static Magnetic Fields with Mu-metal Cage Shielding 24

Numerical Simulations of Static Magnetic Fields with Mu-meta...

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24th International Conference on Computational Problems of electrical engineering, CPEE 2023

作者： Bajtos, Marek Radil, Roman Janousek, Ladislav Bajtos, Marek Dang, Nhat University of Zilina Faculty of Electrical Engineering and Information Technology Department of Electromagnetic and Biomedical Engineering Zilina Slovakia University of Colorado Boulder Department of Electrical Computer and Energy Engineering BoulderCO United States

ISBN: (纸本)9798350330342

This article presents numerical simulations focused on static magnetic fields, employing a Mu-metal cage for shielding against the geomagnetic field. The objective is to verify, how homogeneous and uniform magnetic field within the magnetic field applicator is created. Square Helmholtz coils are employed to ensure this homogeneity. Observations indicate a 5.3% deviation in the B-field when the coils are supplied with a DC current of 110 mA. In the absence of a DC field supply, a 12% deviation is observed. The impact of extremely low-frequency electromagnetic fields and static magnetic fields on living organisms is well-documented, emphasizing the significance of even small changes and deviations in the magnetic field. This underscores the importance of employing a Mu-metal cage when investigating the effects of these fields. Numerical simulations using specialized software demonstrate that the highly permeable Mu-metal box provides approximately 90% shielding efficiency against the geomagnetic field. © 2023 IEEE.

关键词： Magnetic fields

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