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16th International Conference on Developments in eSystems engineering, DeSE 2023

作者： Gad, Abdalla Fteiha, Bara' Alatawi, Jawaher Mohammed, Mahra Almansoori, Mahra Ghazal, Mohammed Yousaf, Jawad Almajali, Eqab Hussain, Abir Jafar Abu Dhabi University Electrical Computer and Biomedical Engineering Department Abu Dhabi United Arab Emirates University of Sharjah Department of Electrical Engineering Sharjah United Arab Emirates

ISBN: (纸本)9798350381344

This work proposes a flexible, foldable, lightweight, robust, and smart cane to aid visually impaired individuals and patients in navigating unfamiliar environments safely. The developed smart cane is equipped with an ultrasonic sensor, RFID reader, and an AI machine vision sensor for object detection and identification in the range of 30 cm to 9 m. A mobile application with a text-to-voice feature is developed that connects with the cane using Bluetooth to provide voice warnings to the user based on processed sensor output. The developed cane is also equipped with a vibration sensor activated when an obstacle is closer than 30 cm, serving as a backup of voice warnings. Object detection and classification using multiple sources (AI vision sensor, ultrasonic sensor, and RFID readers) ensures robust and uninterrupted operation in case of failure of one sensor. In emergencies, users can send text messages with their location to a pre-selected caretaker's phone number using the installed SOS button. The rechargeable battery offers more than 24 hours of operation and warns users when the battery is low. The prototype has been successfully executed and tested in various environments and aims to improve the safety and quality of life for visually impaired individuals and hospital patients. © 2023 IEEE.

关键词： Object detection

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Assessment of a mini stereotactic guidance system for percutaneous focal liver tumour ablation

Assessment of a mini stereotactic guidance system for percut...

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Medical Imaging 2025: Image-Guided Procedures, Robotic Interventions, and Modeling

作者： Romero, Joeana Cambranis Peters, Terry M. Chen, Elvis C.S. School of Biomedical Engineering Western University ON Canada Robarts Research Institute Western University ON Canada Department of Medical Biophysics Western University ON Canada Department of Electrical and Computer Engineering Western University ON Canada

ISBN: (纸本)9781510685949

Percutaneous liver tumour ablation is becoming the preferred treatment option for patients ineligible for surgery. Percutaneous insertion of ablation applicator is often assisted by ultrasound (US) as it provides real-time visualization of subcutaneous anatomy and needle advancement. However, US-guided approach often requires multiple needle repositioning, leading to increased risk of tumour seeding and patient harm. Surgical Navigation Systems (SNS) have the potential to mitigate these limitations of the US-guided approach by providing additional visual and mechanical support. In this paper, we present a SNS comprising a mini stereotactic, patient-attached, mechanical needle guider with virtual reality (VR) visualization to facilitate needle positioning. We present a preliminary user trial comparing traditional US needle guidance puncture against our Mini-SNS. Nineteen (19) non-experts performed two randomized needle insertions into a designated tumour in a custom liver phantom. Preparation time, insertion time, total procedural time, needle repositioning, and final needle tip (tracked and virtual) position were recorded and analyzed. The Mini-SNS results show a reduction in insertion time compared to the traditional approach, without significant difference in the total procedure time. Compared to the traditional manual US-guided method, our SNS improves needle tip placement accuracy from 17 mm to less than 10 mm. The number of repositioning was significantly reduced using the Mini-SNS, being just 3 in times total compared to an average of 8 times per user using the traditional approach. These initial findings suggest that the Mini-SNS enhances efficiency in needle punctures, with reduced insertion time and repositioning, supporting its potential to minimize patient harm. © 2025 SPIE

关键词： Transplants

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Infrastructure-dependent ramp-metering control for METANET-s 63

Infrastructure-dependent ramp-metering control for METANET-s

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Kamalifar, Ayda Cenedese, Carlo Cucuzzella, Michele Ferrara, Antonella University of Pavia Department of Electrical Computer Science and Biomedical Engineering Pavia27100 Italy Eth Zürich Department of Information Technology and Electrical Engineering Zürich Switzerland Nijenborgh 4 Groningen9747 AG Netherlands

ISBN: (纸本)9798350316339

In this paper, we propose a novel infrastructure-dependent ramp-metering control for the recently proposed METANET with service station (METANET-s) model, i.e., a second-order macroscopic traffic model that, compared to the classical METANET, incorporates the dynamics of service stations on highways. We study the effect of a ramp-metering control scheme on a highway stretch with a service station and show that it is capable of actively regulate internal traffic demand attempting to exit the service station via its on-ramp, on top of contributing to decrease the traffic congestion on the mainstream. In fact, the proposed control scheme effectively prevents the backlog of vehicles attempting to merge back onto the mainstream. This dynamic control mechanism is further endowed by a route guidance control strategy increasing the share of vehicles stopping at the service station during main-stream congestion periods, e.g. via incentives. The combined effect of our control schemes allows to take full advantage of the presence of service stations, reducing the overall traffic congestion. Simulation results demonstrate the effectiveness of the proposed control strategies. © 2024 IEEE.

关键词： Traffic congestion

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Quantifying Deep Learning Model Uncertainty in Conformal Prediction

Quantifying Deep Learning Model Uncertainty in Conformal Pre...

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2023 AAAI Summer Symposium Series, SuSS 2023

作者： Karimi, Hamed Samavi, Reza Department of Electrical Computer and Biomedical Engineering Toronto Metropolitan University TorontoON Canada Vector Institute TorontoON Canada

Precise estimation of predictive uncertainty in deep neural networks is a critical requirement for reliable decisionmaking in machine learning and statistical modeling, particularly in the context of medical AI. Conformal Prediction (CP) has emerged as a promising framework for representing the model uncertainty by providing well-calibrated confidence levels for individual predictions. However, the quantification of model uncertainty in conformal prediction remains an active research area, yet to be fully addressed. In this paper, we explore state-of-the-art CP methodologies and their theoretical foundations. We propose a probabilistic approach in quantifying the model uncertainty derived from the produced prediction sets in conformal prediction and provide certified boundaries for the computed uncertainty. By doing so, we allow model uncertainty measured by CP to be compared by other uncertainty quantification methods such as Bayesian (e.g., MC-Dropout and DeepEnsemble) and Evidential approaches. © 2023, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Deep neural networks

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A Comprehensive Technique Based on Machine Learning for Device and Circuit Modeling of Gate-All-Around Nanosheet Transistors

IEEE Open Journal of Nanotechnology

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IEEE Open Journal of Nanotechnology 2023年 4卷 181-194页

作者： Butola, Rajat Li, Yiming Kola, Sekhar Reddy National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Institute of Communications Engineering the Institute of Biomedical Engineering the Department of Electronics and Electrical Engineering the Institute of Pioneer Semiconductor Innovation and the Institute of Artificial Intelligence Innovation Hsinchu300093 Taiwan

Machine learning (ML) is poised to play an important part in advancing the predicting capability in semiconductor device compact modeling domain. One major advantage of ML-based compact modeling is its ability to capture complex relationships and patterns in large datasets. Therefore, in this paper a novel design scheme based on dynamically adaptive neural network (DANN) is proposed to develop fast and accurate compact model (CM). This framework constitutes a powerful yet computationally efficient methodology and exhibits emergent dynamic behaviors. This paper demonstrates that the compact model based on ML can be designed to replicate the performance of conventional compact model for nanodevices. For this work, gate-all-around (GAA) nanosheet (NS) device characteristics are comprehensively analyzed for process variability sources using the proposed model. The device geometry parameters such as channel length, nanosheet width and nanosheet thickness are fed as input features to the DANN model. The adaptive neural network learns dynamically by updating weights of the model in accordance with the input features and achieves accurate neural weight convergence. The proposed model predicted the electrical characteristics of NS devices with less than 1% error rate. The model is also implemented and validated for the simulations of digital circuit designs such as inverter, and logic gates. ©;2023 The Authors.

关键词： Circuit simulation

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Spectral period doubling and encoding of dissipative optical solitons via gain control

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PhotoniX 2024年第1期5卷 290-305页

作者： Kangwen Yang Yi Zhou Yuqing Ling Kevin K.Tsia Heping Zeng Kenneth K.Y.Wong Shanghai Key Laboratory of Modern Optical System and Engineering Research Center of Optical Instrument and SystemMinistry of EducationSchool of Optical Electrical and Computer EngineeringUniversity of Shanghai for Science and TechnologyShanghai 200093China Department of Electrical and Electronic Engineering The University of Hong KongHong KongChina Advanced Biomedical Instrumentation Centre Hong Kong Science ParkHong KongChina State Key Laboratory of Precision Spectroscopy East China Normal UniversityShanghai 200062China

Period-doubling bifurcation,as an intermediate state between order and chaos,is ubiquitous in all disciplines of nonlinear ***,previous experimental observations of period doubling in ultrafast fiber lasers are mainly restricted to self-sustained steady state,controllable manipulation and dynamic switching between period doubling and other intriguing dynamical states are still largely ***,we propose to expand the vision of dissipative soliton periodic doubling,which we illustrate experimentally by reporting original spontaneous,collisional,and controllable spectral period doubling in a polarization-maintaining ultrafast fiber ***,the spontaneous period doubling can be observed in both single-and *** mechanism of the switchable state and periodic doubling was revealed by numerical ***,state transformation of individual solitons can be resolved during the collision of triple solitons involving stationary,oscillating,and period ***,controllable deterministic switching between period doubling and other dynamical states,as well as exemplifying the application of period-doubling-based digital encoding,is achieved under programmable pump *** results open a new window for unveiling complex Hopf bifurcation in dissipative systems and bring useful insights into nonlinear science and applications.

关键词： Period doubling Mode locking Ultrafast fiber lasers

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Machine Learning based Radar Heatmap Generation for Advanced Driver Assistance Systems 5

Machine Learning based Radar Heatmap Generation for Advanced...

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5th International Conference on High Performance Big Data and Intelligent Systems, HDIS 2023

作者： Klassen, Angela Karakuş, Erkan Yang, Qing Dept. of Electrical Computer and Biomedical Engineering University of Rhode Island KingstonRI02881 United States

ISBN: (纸本)9798350394160

Radar sensors are commonly used in Advanced Driver Assistance Systems (ADAS) to detect surrounding objects. Radar signals pass a signal processing chain to compute the range, the velocity and the angular position of potential obstacles and other traffic participants. Major challenges of this procedure are the high complexity and latency of transferring a large amount of raw radar data to subsequent processing units. This paper presents a machine learning model that dramatically reduces the latency of the data transfer by compression of the raw radar data and that generates Range-Angle (RA) Heatmaps directly from the compressed data. RA Heatmaps present the essential visualization technique for the localization of surrounding objects in the radar horizon. In addition, they can be used to detect and identify objects. We use the autoencoder and the U-Net architecture for the compression and the RA Heatmap generation respectively. For the collection of raw radar data, a Texas Instruments (TI) AWR1243 FMCW radar sensor board is utilized. The data to train the U-Net architecture is obtained by applying analytical radar imaging algorithms including a 3-dimensional Fast Fourier Transform (3D FFT) and our proposed framework for Region of Interest (ROI) extraction on the collected data. Experimental results indicate that our final system can achieve a significant reduction of the original raw radar data while still maintaining all relevant features required for object recognition tasks. Excellent accuracy scores exceeding 99 % on input data that has been compressed by a factor of four validate the capabilities of the model and underline its potential for radar data processing in the context of ADAS. ©2023 IEEE.

关键词： Data transfer

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3D-Printable and Robust All-in-One Polymer-Entangled Photocatalytic Microreactors for Visible-Light-Driven Hydrogen Evolution

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ACS Applied Energy Materials 2024年第2期7卷 657-664页

作者： Ting, Li-Yu Li, Bing-Heng Huang, Qian-Ci Chen, An-Rong Sun, Yu-En Chang, Chih-Li Lin, Wei-Cheng Elewa, Ahmed M. Lin, Zong-Hong Lin, Tzu-En Chou, Ho-Hsiu Department of Chemical Engineering National Tsing Hua University Hsinchu300044 Taiwan Institute of Biomedical Engineering Department of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu300044 Taiwan Department of Material Science and Engineering National Tsing Hua University Hsinchu300044 Taiwan Department of Biomedical Engineering National Taiwan University Taiwan106319 Taiwan College of Semiconductor Research National Tsing Hua University Hsinchu300044 Taiwan Photonics Research Center National Tsing Hua University Hsinchu300044 Taiwan

Hydrogels show promise in preventing inorganic catalysis aggregation during hydrogen evolution. However, the incompatible intrinsic properties of inorganics and hydrogels can cause catalyst release during the reaction. One solution is incorporating polymer photocatalysts into hydrogels, but low photocatalytic efficiency due to anti-synergetic effects between polymers and hydrogels remains a challenge. Herein, we developed all-in-one photocatalytic microreactors (PMRs) with excellent stability and efficiency by strongly entangling polymers with hydrogels and designing heterogeneous hydrogels. This approach prevents catalyst loss while achieving a high hydrogen evolution efficiency, self-healability, and stretchability. Moreover, PMRs maintain their efficiency even after they undergo freeze-drying and rehydration. Remarkably, we demonstrate the construction of PMRs into three-dimensional (3D) structures via 3D-printing at room temperature without additional supporting material. In light of these advantages, we have demonstrated a strategy for rapidly manufacturing PMRs with high stability, reactivity, and 3D-printability, which has significant potential for practical applications. © 2024 American Chemical Society

关键词： Hydrogels

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Comparing SAR X and L bands to map the urban extent in a metropolis of South America. The potential of multitemporal data.

Comparing SAR X and L bands to map the urban extent in a met...

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2023 Joint Urban Remote Sensing Event, JURSE 2023

作者： Marzialetti, Flavio Carranza, Maria Laura Sorriso, Antonietta Gamba, Paolo EnvixLab Department of Biosciences and Territory Italy University of Pavia Biomedical and Computer Engineering Department of Electrical Pavia Italy

ISBN: (纸本)9781665493734

Monitoring city boundaries and extents is a mandatory task so as to achieve the 11th Goal of sustainable development agenda 2030. This work aims at exploring the potential of the SAR dataset recorder by the SIASGE constellation for mapping urban areas and their extent. In particular, urban maps have been produced by applying the Urban EXTent algorithm to Very High Resolution (VHR) Cosmo-SkyMed first generation (CSK and CSG, respectively) in X-band and SAOCOM (SAO) data in L-band. Urban extractions based on multi-temporal SAR data and based on a single date have also been compared. SAR-based urban maps have been finally analyzed against existing land cover maps produced by multi-spectral data. All three SAR derived maps presented high accuracy, with CSG being the best one, and CSK and SAO slightly less performant. Urban maps derived from multitemporal SAR information presented higher accuracy than those based on a single date and, at the same time, resulted coherent with land cover maps produced by traditional multispectral data classification. The VHR CSG and CSK, with high computational efforts, well support urban extent mapping at very fine local scale, while SAO data provide a good support for mapping urban areas at a regional or country scale. © 2023 IEEE.

关键词： Mapping

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A novel charging approach to Temperature and State of Charge management in BEV

A novel charging approach to Temperature and State of Charge...

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2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023

作者： Gemma, Filippo Tresca, Giulia Di Salvo, Salvatore Riccardo Formentini, Andrea Zanchetta, Pericle University of Genova Electrical Electronic Telecommunications Engineering and Naval Architecture Dept. Italy University of Pavia Electrical Computer and Biomedical Engineering Dept. Italy University of Nottingham Electrical and Electronic Engineering Dept. United Kingdom

ISBN: (纸本)9798350316445

This paper proposes a novel approach to simultaneously manage the State of Charge and Temperature balancing during the charging process of the battery cells installed within a Reconfigurable Cascaded Multilevel Converter, used in the powertrain of Battery Electric Vehicles. Depending on the charging process scenario, the algorithm prioritizes temperature or SOC balancing, respectively, still ensuring low charging times and adherence to safety temperature limits. The method is here validated through simulations, showing a final balanced dynamic for both temperature and SOC without compromising the overall efficiency of the charging process. © 2023 IEEE.

关键词： Battery Electric Vehicle Battery Model Reconfigurable Cascaded Multilevel Converter SOC balancing Temperature Control

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