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2024 IEEE BiCMOS and Compound Semiconductor Integrated Circuits and Technology Symposium, BCICTS 2024

作者： Serra, Lorenzo De Filippi, Guglielmo Piotto, Lorenzo Mazzanti, Andrea University of Pavia Department of Electrical Computer and Biomedical Engineering Italy

ISBN: (纸本)9798331541248

Cascode amplifiers with a matching network between the common-emitter (CE) and the common-base (CB) HBTs are popular in the sub-THz band for their enhanced gain. The stage is analyzed in this paper with focus on the large-signal behavior. It is first observed that the CE-CB interstage network can push the CB out of the linear class-A regime, limiting the input power at 1 dB gain compression. It is then shown that the issue can be avoided with a suitable biasing scheme, leading to enhanced dynamic range with superior power efficiency. The concept is applied to the design of a D-band LNA in BiCMOS technology. With a quiescent power consumption of 20 mW from 2 V supply, the LNA shows 17.5 dB gain and 5.5 dB NF at 170 GHz. The output-referred 1dB compression point is 4.2dBm with 9% power efficiency at 175 GHz. Compared to previously reported LNAs in the same band, measured results demonstrate a remarkable dynamic range improvement with the the highest power efficiency. © 2024 IEEE.

关键词： BiCMOS technology

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Early Diagnosis of Osteoporosis: An Artificial Intelligence-Based Framework 7

Early Diagnosis of Osteoporosis: An Artificial Intelligence-...

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7th International Conference on Advances in biomedical engineering, ICABME 2023

作者： Zaylaa, Amira J. Daher, Alaa Ayache, Mohammad Omeirat, Niveen Mahmoud, Razane Hawili, Bader Abou Beirut Arab University Biomedical Engineering Laboratory Biomedical Engineering Program Electrical and Computer Engineering Faculty of Engineering Lebanon

ISBN: (纸本)9798350325850

Osteoporosis is a common disease characterized by low bone density and structural deterioration of bone tissue. For a successful course of treatment and fracture avoidance, early diagnosis of this disease is essential. The aim was to provide a novel method for osteoporosis prediction using Artificial Intelligence (AI)-based framework. The purpose was to predict the likelihood of osteoporosis based on the Bone Mineral Density (BMD), along with other characteristics such as age, weight, height, gender, and Body Mass Index (BMI) extracted from medical reports and images collected from a comprehensive medical center in Lebanon. Three machine-learning algorithms were implemented and tested, Logistic Regression (LR), Support Vector Machines (SVM), and Decision Trees (DT). Variety of quantitative statistical metrics were used to evaluate the performance of our framework, upon training and testing our algorithms. The metrics that were employed to evaluate our results included accuracy, precision, sensitivity, and F-score, in addition to the Receiver Operating Characteristic (ROC) Curve and the Area Under the Curve (AUC). Experimental Results demonstrated that both the SVM and LR algorithms achieved the highest accuracy of detection of osteoporosis as compared to existing algorithms applied in this field, with an accuracy of 89%. The sensitivity of diagnosis obtained was 98% by LR and 97% by SVM and surpassed the sensitivity obtained by DT. As such LR showed the best performance. The output of the algorithms could help medical doctors assess patients automatically. These findings demonstrated the potential of AI in osteoporosis prediction and thus prevention, highlighting the significance of early diagnosis. Thereby as a future prospect, choosing carefully the framework is crucial and additional algorithms have to be considered and tested. © 2023 IEEE.

关键词： Decision trees

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Compact Model Build Upon Dynamic Weighting Artificial Neural Network Approach for Complementary Field Effect Transistors

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IEEE Transactions on Electron Devices 2024年第1期71卷 246-253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 Institute of Pioneer Semiconductor Innovation The Institute of Artificial Intelligence Innovation National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program The Institute of Communications Engineering the Institute of Biomedical Engineering Department of Electronics and Electrical Engineering Hsinchu300093 Taiwan

In this work, a dynamic weighting-artificial neural network (DW-ANN) methodology is presented for quick and automated compact model (CM) generation. It takes advantage of both TCAD simulations for high accuracy and SPICE simulations for cost-effectiveness. This methodology is developed for gate-all-around (GAA) silicon (Si) nanosheet (NS) complementary field effect transistor (CFET), a potential candidate for future CMOS technology due to its innate properties. The critical process variation (PV) sources that severely degrade the CFET performance are estimated using DW-ANN. It reduces the computation cost and predicts the effects of PV sources with less than 1% error. Furthermore, a compact DW-ANN-based Verilog-A model has been developed that captures the dc as well as transient behavior accurately for circuit-level analysis. CFET-based circuits such as inverter, 6T-static random access memory (SRAM), and ring oscillator (RO) are characterized and implemented using DW-ANN-based CM. The overall average error of the model is reported as less than 2%. Therefore, the proposed device and circuit modeling approach provides a feasible solution for the rapid compact modeling of new emerging devices with good convergence and high accuracy. © 1963-2012 IEEE.

关键词： Neural networks

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Enhancing waste sorting and recycling efficiency: robust deep learning-based approach for classification and detection

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Neural Computing and Applications 2025年第6期37卷 4567-4583页

作者： Sayem, Faizul Rakib Islam, Md. Sakib Bin Naznine, Mansura Nashbat, Mohammad Hasan-Zia, Mazhar Kunju, Ali K Ansaruddin Khandakar, Amith Ashraf, Azad Majid, Molla Ehsanul Kashem, Saad Bin Abul Chowdhury, Muhammad E. H. Department of Electrical and Electronic Engineering University of Dhaka Dhaka Bangladesh Department of Biomedical Engineering Military Institute of Science and Technology Dhaka Bangladesh Department of Computer Science & Engineering Rajshahi University of Engineering & Technology Rajshahi6204 Bangladesh Chemical Engineering Department University of Doha for Science and Technology Doha Qatar Department of Electrical Engineering Qatar University Doha2713 Qatar Computer Applications Department Academic Bridge Program Qatar Foundation Doha Qatar Department of Computing Science AFG College With the University of Aberdeen Doha Qatar

Given the severity of waste pollution as a major environmental concern, intelligent and sustainable waste management is becoming increasingly crucial in both developed and developing countries. The material composition and volume of urban solid waste are key considerations in processing, managing, and utilizing city waste. Deep learning technologies have emerged as viable solutions to address waste management issues by reducing labor costs and automating complex tasks. However, the limited number of trash image categories and the inadequacy of existing datasets have constrained the proper evaluation of machine learning model performance across a large number of waste classes. In this paper, we present robust waste image classification and object detection studies using deep learning models, utilizing 28 distinct recyclable categories of waste images comprising a total of 10,406 images. For the waste classification task, we proposed a novel dual-stream network that outperformed several state-of-the-art models, achieving an overall classification accuracy of 83.11%. Additionally, we introduced the GELAN-E (generalized efficient layer aggregation network) model for waste object detection tasks, obtaining a mean average precision (mAP50) of 63%, surpassing other state-of-the-art detection models. These advancements demonstrate significant progress in the field of intelligent waste management, paving the way for more efficient and effective solutions. © The Author(s) 2024.

关键词： Deep learning for waste management Intelligent waste management Object detection in waste management Sustainable waste processing Urban solid waste Waste image classification

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Fabrication and characterization of magnesium-based nanocomposites reinforced with Baghdadite and carbon nanotubes for orthopaedical applications

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Journal of Magnesium and Alloys 2024年第12期12卷 5144-5163页

作者： Mojtaba Ansari Shiva Mahdavikia Hossein Eslami Mozhdeh Saghalaini Hamid Taghipour Fatemeh Zare Shahin Shirani Mohammad Hossein Alizadeh Roknabadi Department of Biomedical Engineering Meybod UniversityMeybodIran Department of Biomedical Engineering Amirkabir University of TechnologyTehranIran Center for Education and Research on Macromolecules(CERM) CESAM Research UnitDepartment of ChemistryUniversity of LiègeLiègeBelgium Department of Electrical and Computer Engineering Texas A&M UniversityCollege StationTXUSA Department of Aerospace Engineering Amirkabir University of TechnologyTehranIran

This study explores the potential of Mg/Carbon Nanotubes/Baghdadite composites as biomaterials for bone regeneration and repair while addressing the obstacles to their clinical *** powder was synthesized using the sol-gel method to ensure a fine distribution within the Mg/CNTs ***/1.5 wt.%CNT composites were reinforced with BAG at weight fractions of 0.5,1.0,and 1.5 wt.%using spark plasma sintering at 450℃and 50 MPa after homogenization via ball *** cellular bioactivity of these nanocomposites was evaluated using human osteoblast-like cells and adipose-derived mesenchymal stromal *** proliferation and attachment of MG-63cells were assessed and visualized using the methylthiazol tetrazolium(MTT)assay and SEM,while AD-MSC differentiation was measured using alkaline phosphatase activity *** were also generated to visualize the diameter distributions of particles in SEM images using image processing *** Mg/CNTs/0.5 wt.%BAG composite demonstrated optimal mechanical properties,with compressive strength,yield strength,and fracture strain of 259.75 MPa,180.25 MPa,and 31.65%,*** learning models,including CNN,LSTM,and GRU,were employed to predict stress-strain relationships across varying BAG amounts,aiming to accurately model these curves without requiring extensive physical *** shown by contact angle measurements,enhanced hydrophilicity promoted better cell adhesion and ***,corrosion resistance improved with a higher BAG *** study concludes that Mg/CNTs composites reinforced with BAG concentrations below 1.0 wt.%offer promising biodegradable implant materials for orthopedic applications,featuring adequate load-bearing capacity and improved corrosion resistance.

关键词： Biodegradable material Magnesium-based composite Carbon nanotube Baghdadite Spark plasma sintering

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Fostering Human-AI interaction: development of a Clinical Decision Support System enhanced by eXplainable AI and Natural Language Processing 2

Fostering Human-AI interaction: development of a Clinical De...

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Joint of the 2nd World Conference on eXplainable Artificial Intelligence Late-Breaking Work, Demos and Doctoral Consortium, xAI-2024:LB/D/DC

作者： Bergomi, Laura Department of Electrical Computer and Biomedical Engineering University of Pavia Pavia Italy

Artificial Intelligence (AI) is increasingly integrated into Decision Support Systems (DSS). The explainability of AI-based systems becomes crucial in sensitive and critical domains, such as healthcare, where ethical considerations and reliability are paramount concerns. In the clinical setting, it is important to evaluate how humans and AI can collaborate on cognitive tasks. Collaboration protocols (HAI-CP) allow for the investigation of the usefulness of AI models and their impact on users (both positive and negative). Although research on the application of these methods is blooming, there is little understanding of the impact on clinical decision-making, especially for eXplainable AI (XAI) systems, due to the lack of user studies. Therefore, the goal of this proposal is to develop a clinical DSS enhanced by XAI and Natural Language Processing (NLP): their synergy can add value to the interaction between users and AI, fostering a more linguistically natural, comprehensible, trustworthy, and supporting interfacing, that blends into the existing workflows. This proposal explores potential solutions to tailor natural language explanations and data visualizations to the end-user, improving the comprehensibility of the reasons behind a decision, and increasing the user’s confidence in the decision;investigates and tests possible strategies to "get the patient-in-the-loop";explores uncertainty quantification and counterfactual approaches, and finally assesses the impact on naturalistic (i.e., real-world) decision-making and long-term effects and biases. © 2024 Copyright for this paper by its authors.

关键词： Decision support systems

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Multi-stage Segmentation of Diabetic Foot Ulcers Using Self-supervised Learning 4th

Multi-stage Segmentation of Diabetic Foot Ulcers Using Self...

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4th Diabetic Foot Ulcers Grand Challenge, DFUC 2024 held in Conjunction 27th International Conference on Medical Image Computing and computer Assisted Intervention, MICCAI 2024

作者： Gupta, Neetika Nangia, Ayush Khan, Naimul Mefraz Department of Electrical Computer and Biomedical Engineering Toronto Metropolitan University TorontoON Canada

ISBN: (纸本)9783031808708

Diabetic Foot Ulcers (DFUs) pose a significant healthcare challenge due to their potential to lead to severe complications, including infections and ischemia, often necessitating limb amputation or causing mortality. Accurate diagnosis of ulcer regions, and prompt response can prevent amputations and fatalities through early and reliable DFU detection. However, manual examination of DFUs is labor-intensive and prone to variability. In response to these challenges, we propose DFUC_CAM, a novel method for instance segmentation of DFUs using self-supervised learning (SSL). The proposed approach leverages a multi-stage methodology. Initially, a convolutional neural network (CNN) trained for classification is utilized to generate Class Activation Maps (CAMs), which localize and highlight discriminative regions within DFU images. These CAMs serve as pseudo-labels to train a segmentation network using a SSL framework. For the MICCAI 2024 Diabetic Foot Ulcer Challenge (DFUC 2024), we trained DFUC_CAM using the DFUC 2024 and FUSeg datasets. The FUSeg dataset primarily serves for the quantitative evaluation of our method. During the validation phase of DFUC 2024, our team, ***, achieved a mean Dice score of 0.342, and in the final testing phase, we improved this to 0.385. These results demonstrate the potential of our approach for enhancing DFU detection and segmentation in clinical settings. The code is available at: https://***/RyersonMultimediaLab/DFUC_CAM. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Self-supervised learning

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Artificial visual perception neural system using a solution-processable MoS_(2)-based in-memory light sensor

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Light(Science & Applications) 2023年第8期12卷 1583-1594页

作者： Dayanand Kumar Lana Joharji Hanrui Li Ayman Rezk Ammar Nayfeh Nazek El-Atab Smart Advanced Memory Devices and Applications(SAMA)LaboratoryElectrical and Computer Engineering ProgramComputer Electrical Mathematical Science and Engineering DivisionKing Abdullah University of Science and Technology(KAUST)Thuwal 23955Kingdom of Saudi Arabia Department of Electrical Engineering and Computer Science Khalifa UniversityAbu Dhabi 127788United Arab Emirates

Optoelectronic devices are advantageous in in-memory light sensing for visual information processing,recognition,and storage in an energy-efficient ***,in-memory light sensors have been proposed to improve the energy,area,and time efficiencies of neuromorphic computing *** study is primarily focused on the development of a single sensing-storage-processing node based on a two-terminal solution-processable MoS2 metal-oxide-semiconductor(MOS)charge-trapping memory structure—the basic structure for charge-coupled devices(CCD)—and showing its suitability for in-memory light sensing and artificial visual *** memory window of the device increased from 2.8 V to more than 6V when the device was irradiated with optical lights of different wavelengths during the program ***,the charge retention capability of the device at a high temperature(100 ℃)was enhanced from 36 to 64%when exposed to a light wavelength of 400 *** larger shift in the threshold voltage with an increasing operating voltage confirmed that more charges were trapped at the Al_(2)O_(3)/MoS_(2) interface and in the MoS_(2) layer.A small convolutional neural network was proposed to measure the optical sensing and electrical programming abilities of the *** array simulation received optical images transmitted using a blue light wavelength and performed inference computation to process and recognize the images with 91%*** study is a significant step toward the development of optoelectronic MOS memory devices for neuromorphic visual perception,adaptive parallel processing networks for in-memory light sensing,and smart CCD cameras with artificial visual perception capabilities.

关键词： visual neural process

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Nanofabricated high turn-density spiral coils for on-chip electromagneto-optical conversion

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Microsystems & Nanoengineering 2024年第2期10卷 181-192页

作者： Ilhan Bok Alireza Ashtiani Yash Gokhale Jack Phillips Tianxiang Zhu Aviad Hai Department of Biomedical Engineering University of Wisconsin–MadisonMadisonWIUSA Department of Electrical and Computer Engineering University of Wisconsin–MadisonMadisonWIUSA Wisconsin Institute for Translational Neuroengineering(WITNe) University of Wisconsin–MadisonMadisonWIUSA

Circuit-integrated electromagnets are fundamental building blocks for on-chip signal transduction,modulation,and tunability,with specific applications in environmental and biomedical micromagnetometry.A primary challenge for improving performance is pushing quality limitations while minimizing size and fabrication complexity and retaining spatial *** efforts have exploited highly involved three-dimensional synthesis,advanced insulation,and exotic material ***,we present a rapid nanofabrication process that employs electron beam dose control for high-turn-density diamond-embedded flat spiral coils;these coils achieve efficient on-chip electromagnetic-to-optical signal *** fabrication process relies on fast 12.3 s direct writing on standard poly(methyl methacrylate)as a basis for the metal lift-off *** with 70 micrometer overall diameters and 49–470 nm interturn spacings with corresponding inductances of 12.3–12.8 nH are *** utilize optical micromagnetometry to demonstrate that magnetic field generation at the center of the structure effectively correlates with finite element modeling *** designs based on our process can be integrated with photolithography to broadly enable optical magnetic sensing and spin-based computation.

关键词： utilize retaining high

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Finite-difference time-domain methods

NATURE REVIEWS METHODS PRIMERS

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NATURE REVIEWS METHODS PRIMERS 2023年第1期3卷 1-19页

作者： [Anonymous] Department of Electrical and Computer Engineering The Ohio State University Columbus OH USA Department of Electrical and Computer Engineering University of Toronto Toronto Ontario Canada Department of Electrical and Computer Engineering University of Utah Salt Lake City UT USA Department of Electrical Engineering Pohang University of Science and Technology Pohang Korea Department of Electrical and Electronic Engineering The University of Manchester Manchester UK Biomedical Engineering Department Northwestern University Evanston IL USA Department of Electrical and Software Engineering University of Calgary Calgary Canada Department of Electrical and Computer Engineering Purdue University West Lafayette IN USA

This PrimeView highlights how algorithm and hardware develops could expand the application range of finite-difference time-domain methods.

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