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检索条件"机构=Dept. of Electrical and Computer Engineering and Dept. of Biomedical Informatics"

共 2527 条记录，以下是151-160 订阅

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Tillage Monitoring: Determining the Optimal Number of Features in Multi-Spectral Images: a Case Study in the Indo-Gangetic Plains

Tillage Monitoring: Determining the Optimal Number of Featur...

引用

Remote Sensing Symposium (InGARSS), 2020 IEEE India Geoscience and

作者： Sara Rajabzadeh Jit Mukherjee Fabio Dell’Acqua Dept. of Electrical Computer & Biomedical Engineering University of Pavia Italy

ISBN: (数字)9798350390346

ISBN: (纸本)9798350390353

Accurate monitoring and classification of tillage practices, such as Zero Tillage (ZT) and Conventional Tillage ($C T$), are essential for sustainable agricultural management due to their significant environmental, economic, and agricultural impacts. Traditional methods for monitoring tillage practices are often costly and limited in scope, whereas remote sensing provides a more efficient, and scalable solution. Building upon a previous work that achieved a 75% accuracy using random forest, this study on the Indo-Gangetic Plains (IGP) uses Sentinel-2 data and advanced machine learning ($M L$) techniques such as XGBoost, Logistic Regression ($L R$), and LightGBM, to improve the classification accuracy while optimizing the number of features. Different feature engineering and selection techniques are employed here to reduce the number of features from 171 to an optimal 35, while maintaining the classification accuracy, or even slightly improving it like for the XGBoost and LightGBM models (76%), demonstrating their potential for effective tillage monitoring.

关键词： Training Accuracy Biological system modeling Computational modeling Soft sensors Feature extraction Satellite images Monitoring Remote sensing Random forests

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Compact D-Band Passive Phase Shifters with Fine and Coarse Control Steps in BiCMOS-55nm

Compact D-Band Passive Phase Shifters with Fine and Coarse C...

引用

Topical Meeting on Silicon Monolithic Integrated Circuits in RF Systems

作者： Lorenzo Piotto Guglielmo De Filippi Mahmoud M. Pirbazari Andrea Mazzanti Dept. of Electrical Computer and Biomedical Engineering University of Pavia Italy

Programmable phase shifters are key building blocks in emerging Sub-THz phased array transceivers. This work proposes different structures to implement compact passive phase shifters in D-Band (110-170GHz). Tunable bandpass filters and variable-delay transmission lines are evaluated as phase shifters with fine control resolution over a limited range. To extend the range, 0°/180° and 0°/90° phase shifters are also investigated and implemented with a hybrid coupler and a switched $\lambda/4$ transmission line. Experimental results are presented on a test chip in a 9 metal BiCMOS technology with 55 nm MOS transistors used as switches. By combining the different structures, the full °/360° programmable range is covered from 120 GHz to 170GHz with a size of only $0.13\mathrm{m}\mathrm{m}^{2}$. The estimated insertion loss is aligned with previous works, but the remarkable footprint reduction facilitates the adoption in dense sub-THz phase array systems.

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Auto-Adaptive Stator Ground Fault Protection for Synchronous Generators in Diesel-Electric Locomotives 14

Auto-Adaptive Stator Ground Fault Protection for Synchronous...

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14th IEEE International Symposium on Diagnostics for electrical Machines, Power Electronics and Drives, SDEMPED 2023

作者： Mahtani, Kumar Guerrero, Jose M. Beites, Luis F. Platero, Carlos A. Universidad Politécnica de Madrid Automation Electrical and Electronic Engineering and Industrial Informatics Dept. Madrid Spain Mondragon Unibertsitatea Electronics and Computer Science Dept. Arrasate-Mondragon Spain Universidad Politécnica de Madrid Automation Electrical and Electronic Engineering and Industrial Informatics Department Madrid Spain

ISBN: (纸本)9798350320770

Diesel-electric locomotives are equipped with one or more synchronous generators driven by diesel engines, that provide the necessary electrical energy for their operation. These generators are usually provided with some protection relays, depending on their rated power and their type of service. Standard stator-ground fault protection has a setting for rated conditions. In case of variable speed and voltage operation, the protection does not cover all the operation range of the machine. In addition, unwanted tripping may occur due to incorrect filtering of the third harmonic. In this paper, an auto-adaptative stator ground protection function is presented, for the operation of synchronous machines at variable voltage and frequency. A specific case study is tackled, focused on the main generators of a diesel-electric locomotive. Numerous simulations are carried out to verify the proposed protection method. The obtained results are promising, and they can be used in other synchronous machines operating at variable speed. © 2023 IEEE.

关键词： Stators

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A Simple Defected Ground Structure to Reduce Backside Radiation for biomedical Applications

A Simple Defected Ground Structure to Reduce Backside Radiat...

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2023 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2023

作者： Nusrat, Tasin Roy, Sayan Dept. of Electrical Engineering and Computer Science Rapid CitySD57701 United States

ISBN: (纸本)9781665442282

This paper presents a defected ground structure (DGS)-based microstrip patch antenna for applications in the Industrial, Scientific, and Medical (ISM) band. The resonant frequency of the antenna is 2.33 GHz. The -10 dB impedance bandwidth of the antenna is 20 MHz (2.32-2.34 GHz), which satisfies the bandwidth requirement for most applications in the ISM band. The front-to-back ratio improves from 12 dB to 18 dB after incorporating the proposed DGS design without varying the overall gain. In addition, the proposed design has a reduced specific absorption rate from 1.54 W /kg to 0.53 W /kg, as averaged over 1 gm of tissue. This simple and low-profile antenna with the DGS is therefore suitable for biomedical, wearable devices, on-body sensors, and microwave imaging applications. © 2023 IEEE.

关键词： Defected ground structures

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The Metaverse for Stroke Motor Rehabilitation∗

The Metaverse for Stroke Motor Rehabilitation∗

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2023 IEEE Conference on engineering informatics, EI 2023

作者： Choy, Chi Sang Fang, Qiang Neville, Katrina Jelfs, Beth School of Engineering Rmit University Melbourne Australia Shantou University Dept. Biomedical Engineering Shantou China University of Birmingham Dept. Electronic Electrical & Systems Engineering Birmingham United Kingdom

ISBN: (纸本)9798350338522

Stroke is a critical illness that causes disabilities and fatalities. The current standard motor rehabilitation usually requires some residual movement of stroke patients, limiting participation. Movement imagination, known as motor imagery (MI), potentially expedites motor rehabilitation by activating the motor brain areas, which may facilitate neuroplasticity. MI is, however, a difficult ability that may deteriorate over time. The Metaverse, a virtual reality (VR) social media platform, provides an immersive and interactive environment that allows stroke patients to experience the embodiment of their MI performance via their avatars. This paper provides a framework to design a Metaverse stroke motor rehabilitation program, outlining the essential elements required from the Metaverse and the challenges in implementing the Metaverse in stroke motor rehabilitation. Future research directions are identified that provide solutions and facilitate stroke recovery in motor functions through more advanced and innovative digital technologies suitable for vulnerable stroke patients. © 2023 IEEE.

关键词： Patient rehabilitation

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ML-Based Interpretation of Cardiotocography Data: Current State and Future Research 7

ML-Based Interpretation of Cardiotocography Data: Current St...

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7th IEEE International Conference of computer Science and Information Technology, ICOSNIKOM 2023

作者： Kadarina, Trie Maya Basari Gunawan, Dadang Universitas Indonesia Dept. of Electrical Engineering Indonesia Research Center for Biomedical Engineering Universitas Indonesia Faculty of Engineering Indonesia

ISBN: (纸本)9798350360752

To evaluate the health and well-being of an unborn child throughout pregnancy, fetal risk prediction is a crucial component of prenatal care. The evaluation of potential risks and issues related to fetal development may now be done by healthcare professionals using a variety of instruments and methodologies due to developments in medical technology and research. One way to predict fetal risk is with a machine learning algorithm using Cardiotocographic (CTG) data. Massive amounts of medical data can be analyzed using machine learning algorithms to aid clinicians in making more precise diagnoses. The objective of this study was to provide the current state and future research in the field of ML-based interpretation of CTG data by mapping current knowledge in the field. The search was performed on Science Direct, SCOPUS, IEEE, and ProQuest databases. RepOrting standards for Systematic Evidence Syntheses (ROSES) in environmental research guidelines were followed. After screening process, we obtained 42 recent studies that examined the application of machine learning algorithms for interpreting CTG tracings. Future research should focus on a larger and more diverse dataset, comparative evaluations, real-world clinical testing, and developing models that combine accuracy with interpretability. © 2023 IEEE.

关键词： Diagnosis

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BOD and COD Estimation of Wastewater Based on Low Cost Sensors Using Random Forest Regression Technique 9

BOD and COD Estimation of Wastewater Based on Low Cost Senso...

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9th International Conference on Information Technology, computer and electrical engineering, ICITACEE 2022

作者： Soetedjo, Aryuanto Hendriarianti, Evy Prasetya, Renaldi Primaswara Marhananda, Achmad Akbar Widyatamara, Amandarika Prastyo, Muhammad Edo Igam, Rachmad Albi Pradana, Andika Yoga Sindy, Suhaena Wisma Ernia Malang Dept. of Electrical Engineering Malang Indonesia Malang Dept. of Environmental Engineering Malang Indonesia Malang Dept. of Informatics Engineering Malang Indonesia

ISBN: (数字)9781665471503

ISBN: (纸本)9781665471480

The Biological Oxygen Demand (BOD) and Chemical Oxygen Demand (COD) of the wastewater should be monitored regularly to maintain the quality standard. This paper presents a method to estimate the BOD and COD of the wastewater using the machine learning technique, namely the Random Forest Regression technique. The multi-sensor system consists of the pH, Turbidity, Dissolved Oxygen (DO), and Total Dissolved Solids (TDS) sensors employed to provide the dataset to the machine learning implemented on the Raspberry Pi module. The proposed multi-sensor system uses IoT technology for storing the data in the cloud during the data collection. The experimental results showed that the proposed method achieves a high accuracy of 94.7 % and R-Squared (R2) of 0.97 for the BOD estimation and high accuracy of 94.14 % and R-Squared (R2) of 0.97 for the COD estimation. Furthermore, the proposed system provides a fast computation time suitable for the wastewater's real-time BOD and COD monitoring. © 2022 IEEE.

关键词： Decision trees

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Predicting electrically evoked action potentials in image-based cochlear implants models using ResNet

Predicting electrically evoked action potentials in image-ba...

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

作者： Vu, Minh Bratu, Erin L. Noble, Jack H. Dept. of Electrical Engineering and Computer Engineering Vanderbilt University Nashville 37235 TN United States

ISBN: (纸本)9781510685949

Cochlear implants (CI) are highly successful neural-prosthetic devices, recreating the sensation of hearing for individuals experiencing severe to profound hearing loss. CI user experiences vary greatly due to several reasons, with changes in neural health within the cochlea being a major contributing factor. Estimating a patient's auditory neural health is thus very important for patient-specific programming. One common method used to derive the number of healthy auditory nerve fibers (ANF) is through electrically evoked compound action potential (ECAP). Comparison between clinically measured ECAP responses and simulation can provide insights into a patient's neural health condition. This simulation is currently done through in-house software that calculates simulated nerve fibers' responses to stimulation but is very time-consuming. In this work we attempt to generate ECAP response using ResNet18 trained on the in-house ECAP simulations of 6 patients, augmented with varying resistivity parameters and CI locations. Our goal is to reduce computation time, allowing for model creation and patient-specific programming recommendations during a single patient visit, while also eliminating common problems with outliers and artifacts during ECAP simulation. Our method generated ECAP responses with peaks that were within 10% of the traditional model output on average, unaffected by stimulus artifacts, and with an average inference time of 1.12 ±0.08 s. This represents a large decrease in computational overhead for our procedure and could facilitate same-day patient-specific programming. © 2025 SPIE

关键词： auditorial nerve cochlear implant deep learning electrical model inner ear multilayer perceptron nerve fiber Neural modelling

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Look-Up Table Based Reduced Order Model of Synchronous Motors for Digital Twin Applications

Look-Up Table Based Reduced Order Model of Synchronous Motor...

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IEEE Workshop on electrical Machines Design, Control and Diagnosis (WEMDCD)

作者： Gabriele De Boni Lorenzo Mantione Marcello Minervini Lucia Frosini Dept. of Electrical Computer and Biomedical Engineering University of Pavia SPIN Applicazioni Magnetiche Pavia Italy Dept. of Electrical Computer and Biomedical Engineering University of Pavia Pavia Italy Dept. of Engineering and Applied Sciences University of Bergamo Dalmine Italy

ISBN: (数字)9798331520748

ISBN: (纸本)9798331520755

Electric motors can be represented as non-linear (NL), multiple input multiple output (MIMO), highly interconnected systems. Finite Element (FE) simulations are powerful tools to represent electric motors. However, the process can be time consuming. A Reduced Order Model (ROM) approach emerged as a possible alternative to describe the physical behavior of an electric motor. This paper presents a reduced order model for a Synchronous Reluctance motor, built from a general method for synchronous machines. Currents from simulations and measurement have been compared in time and frequency domains, to explore the possible application of such generated model in Digital Twin applications.

关键词： Heuristic algorithms Frequency-domain analysis Current measurement Predictive models Harmonic analysis Reduced order systems Digital twins Read only memory Reluctance motors Electric motors

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SARdBScene: Dataset and Resnet Baseline for Audio Scene Source Counting and Analysis 48

SARdBScene: Dataset and Resnet Baseline for Audio Scene Sour...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Nigro, Michael Krishnan, Sridhar Toronto Metropolitan University Dept. of Electrical Computer Biomedical Engineering TorontoONM5B 2K3 Canada

ISBN: (纸本)9781728163277

This paper introduces a first of its kind dataset for audio scene analysis (ASA) and presents a baseline approach for audio source counting. SARdBScene is developed to promote research for audio source counting, as a relatively new ASA task, and present a comprehensive dataset that covers a variety of scenarios and audio-based tasks. It contains 80 hours of audio scene mixtures depicting four distinct environments with detailed annotations that make it a unique collection of curated data in the audio analysis landscape. Our baseline approach using ResNet establishes state-of-the-art results of 77.3% and 85.7% accuracy for audio source counting up to 12 sources and speaker counting up to 4 speakers, respectively. © 2023 IEEE.

关键词： Classification (of information)

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