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IEEE SENSORS

作者： Mohamed Benomar Mao-Hsiang Huang Sung Sik Chu Xing Xia Hung Cao Department of Electrical Engineering and Computer Science University of California Irvine Department of Biomedical Engineering University of California Irvine Department of Computer Science University of California Irvine Irvine CA USA

ISBN: (数字)9798350363517

ISBN: (纸本)9798350363524

Long-term monitoring of neurotransmitter levels in vivo is crucial for understanding the mechanisms behind neurological disorders. Electrochemical sensors offer advantages such as miniaturization and fast response times, but their biocompatibility is often overlooked. The widely used Ag/ AgCl wire pseudo-reference electrode, despite its stability, has poor biocompatibility and necessitates additional brain insertion, causing more damage. This work presents a polarized IrOx pseudo-reference integrated L-glutamate sensor for long-term neurotransmitter measurements. The on-probe IrOx thin film, electrochemically deposited, has been tested for stability and biocompatibility. Additionally, strategic polarization of IrOx is introduced to enhance stability. The polarized IrOx on-probe L-glutamate sensor significantly improves the biocompatibility of the electrochemical sensing platform, facilitating the investigation of neurological disorders through long-term neurotransmitter monitoring.

关键词： Neurological diseases Electrodes In vivo Neurotransmitters Sensors Biosensors Wire Time factors Monitoring

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A new control-oriented METANET model to encompass service stations on highways

arXiv

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arXiv 2024年

作者： Kamalifar, Ayda Cenedese, Carlo Cucuzzella, Michele Ferrara, Antonella Department of Electrical Computer Science and Biomedical Engineering Univeristy of Pavia Pavia27100 Italy Department of Information Technology and Electrical Engineering ETH Zürich Zürich Switzerland

In this paper, we propose the METANET with service station (METANET-s) model, a second-order macroscopic traffic model that, compared to the classical METANET, incorporates the dynamics of service stations on highways. Specifically, we employ the (so-called) store-and-forward links to model the stop of vehicles and the possible queue forming in the process of merging back into the highway mainstream. We explore the capability of the METANET-s to capture well both traffic back propagation and capacity drops, which are typically caused by the presence of vehicles joining again the mainstream traffic from the service station. Therefore, capturing these effects is crucial to improving the model's predictive capabilities. Finally, we perform a comparative analysis with the Cell Transmission Model with service station (CTM-s), showcasing that the METANET-s describes the traffic evolution much better than its first-order counterpart. © 2024, CC BY.

关键词： Backpropagation

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Finite-volume method and observability analysis for core-shell enhanced single particle model for lithium iron phosphate batteries

Finite-volume method and observability analysis for core-she...

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IEEE Conference on Decision and Control

作者： Le Xu Simone Fasolato Simona Onori Energy Science and Engineering Stanford University Palo Alto USA Department of Electrical Computer and Biomedical Engineering University of Pavia Pavia Italy

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

The increasing adoption of Lithium Iron Phosphate (LFP) batteries in Electric Vehicles is driven by their affordability, abundant material supply, and safety advantages. However, challenges arise in controlling/estimating unmeasurable LFP states such as state of charge (SOC), due to its flat open circuit voltage, hysteresis, and path dependence dynamics during intercalation and de-intercalation processes. The Core-Shell Average Enhanced Single Particle Model (CSa-ESPM) effectively captures the electrochemical dynamics and phase transition behavior of LFP batteries by means of Partial Differential-Algebraic Equations (PDAEs). These governing PDAEs, including a moving boundary Ordinary Differential Equation (ODE), require a fine-grained spatial grid for accurate and stable solutions when employing the Finite Difference Method (FDM). This, in turn, leads to a computationally expensive system intractable for the design of real-time battery management system algorithms. In this study, we demonstrate that the Finite Volume Method (FVM) effectively discretizes the CSa-ESPM and provides accurate solutions with fewer than 4 control volumes while ensuring mass conservation across multiple operational cycles. The resulting control-oriented reduced-order FVM-based CSa-ESPM is experimentally validated using various C-rate load profiles and its observability is assessed through nonlinear observability analysis. Our results reveal that different current inputs and discrete equation numbers influence model observability, with non-observable regions identified where solid-phase concentration gradients are negligible.

关键词： Analytical models Lithium Mathematical models Iron Batteries State of charge Observability Vehicle dynamics Finite volume methods Load modeling

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Density Based Spatial Clustering of Applications with Noise and Sentence Bert Embedding for Indonesian Utterance Clustering

Density Based Spatial Clustering of Applications with Noise ...

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2023 International Conference on computer Science, Information Technology and engineering, ICCoSITE 2023

作者： Hasani, Muhammad Fikri Heryadi, Yaya Arifin, Yulyani Lukas Suparta, Wayan Bina Nusantara University BINUS Graduate Program Computer Science School of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University BINUS Graduate Program Computer Science Computer Science Department Jakarta11480 Indonesia Jakarta12930 Indonesia Institut Teknologi Nasional Yogyakarta Faculty of Industrial Technology Department of Electrical Engineering Yogyakarta55281 Indonesia

ISBN: (纸本)9798350320954

Task oriented chatbots are a sub-topic related to chatbots, where chatbots will perform certain tasks with specific goals. One part of creating a task-oriented chatbot is doing intent classification. Intent classification is a task of text classification. As in general text classification, the required dataset requires a label to carry out the classification process. To speed up and help the utterance analysis process, there is already a method, namely clustering, and Density-based clustering is a part of clustering that can determine cluster patterns based on arbitrary data, with DBScan as one of its algorithms. This research used 10000 client utterance data of awhatsapp based e-commerce conversation. SentenceBert also used as a state of art sentence embedding. This research yield silhouette score of 0.327 as the best result from eps of 0.1 and MinPts of 95. However, based on the cluster result, sentences labelled as noise can be further clustered. Text Preprocessing, text augmentation and sentence embedding techniques can be explored to increase the cluster performance. © 2023 IEEE.

关键词： Embeddings

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Deep Learning Model for Classifying Public Opinions on Energy Sector IHSG Stocks on the Twitter Social Media Platform

Deep Learning Model for Classifying Public Opinions on Energ...

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2023 International Conference on Artificial Intelligence Robotics, Signal and Image Processing, AIRoSIP 2023

作者： Sulistio, Bambang Heryadi, Yaya Lukas Suparta, Wayan Sonata, Ilvico Binus Graduate Program - Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia Universitas Katolik Indonesia Atma Jaya Cognitive Engineering Research Group Jakarta Indonesia Institut Teknologi Nasional Yogyakarta Faculty of Industrial Technology Department of Electrical Engineering Yogyakarta Indonesia

ISBN: (纸本)9798350327472

Capital market transactions provide an opportunity for investors to acquire ownership of company shares and capital gains, as well as dividends. However, alongside the benefits, there are risks of capital loss and liquidation, leading to stress and depression due to profit targets and decision-making errors. To mitigate the risk of decision-making errors in investment, data analysis is needed, including sentiment analysis, which influences stock prices. This study aims to develop a new deep learning model to classify Indonesian public opinion on JCI stocks, especially the Energy sector, obtained from the Twitter social media platform. The model will perform sentiment analysis and categorize opinions as negative, neutral, or positive. We created a dataset that was trained using Bidirectional Encoder Representations from Transformers (BERT) to summarize the analysis of public sentiment above so that it can assist investors in studying public sentiment as a reference for investing with a yield precision of 76%, Recall of 77%, and F1-score on 76%. © 2023 IEEE.

关键词： Tweets

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Role of Deep Learning Models in Intelligent Classification of Various Date Fruit Bunches

Role of Deep Learning Models in Intelligent Classification o...

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International Conference on Developments in eSystems engineering (DESE)

作者： Zainab Abuowda Nour Salam Shorouk Ramadan Taimur Hassan Mohammed Ghazal Eqab Almajali Abir Jafar Hussain Jawad Yousaf Electrical Computer and Biomedical Engineering Department Abu Dhabi University Abu Dhabi United Arab Emirates Department of Electrical Engineering University of Sharjah Sharjah United Arab Emirates

ISBN: (数字)9798350368697

ISBN: (纸本)9798350368703

This study presents a performance comparison of different deep machine learning models for the intelligent segregation of date fruit bunches of numerous varieties. The shape, size, and color of the various types of dates with different maturity conditions require experienced farmers to estimate the accrual class. This study has used a transfer learning approach on various machine learning models (VGG-19, MobileNetV2, DenseNet, and NASNet) to classify five different kinds of date bunches (Barhi, Khalas, Menifi, Nabout saif, and Sullaj). Each model is trained on a dataset of 11,944 images of five different date classes. The comparison deduces that DenseNet produces the best accuracy, precision, and recall when compared with the performance of other models. The maximum achieved accuracy of the trained DenseNet model is $100 \%$ for the validation dataset and 98.4% for the test dataset.

关键词： Deep learning Analytical models Visualization Accuracy Shape Image color analysis Biological system modeling Computational modeling Transfer learning Testing

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Analyzing Ablation Zones of Curved Microwave Ablation Antennas

Analyzing Ablation Zones of Curved Microwave Ablation Antenn...

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IEEE MTT-S International Microwave Bio Conference ((IMBIOC)

作者： Andrew Fry Emily Porter Chandra Deparment of Electrical and Computer Engineering University of Texas at Austin Austin Texas Department of Biomedical Engineering McGill University Montreal Canada

ISBN: (数字)9798350351057

ISBN: (纸本)9798350351064

This work proposes 4 different methods for visualizing and quantifying ablation zones produced by flexible microwave ablation antennas. Importantly, three of these methods enable quantification of the ablation zone in different ways and could readily be used as features in a database of ablation zones produced by flexible microwave ablation antennas. These methods provide a framework for flexible microwave ablation researchers to compare and optimize their designs.

关键词： Microwave antennas Microwave ablation Visualization Databases Microwave theory and techniques Tumors Microwave imaging

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Mammographic Breast Positioning Assessment via Deep Learning 1st

Mammographic Breast Positioning Assessment via Deep Learnin...

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1st Deep Breast Workshop on AI and Imaging for Diagnostic and Treatment Challenges in Breast Care, Deep-Breath 2024

作者： Tanyel, Toygar Denizoglu, Nurper Seker, Mustafa Ege Alis, Deniz Cerekci, Esma Karaarslan, Ercan Aribal, Erkin Oksuz, Ilkay Biomedical Engineering Graduate Program Istanbul Technical University Istanbul Turkey Department of Radiology Acibadem Healthcare Group Istanbul Turkey Department of Radiology University of Wisconsin-Madison MadisonWI United States Department of Radiology School of Medicine Acibadem University Istanbul Turkey Sisli Hamidiye Etfal Training and Research Hospital Istanbul Turkey Department of Computer Engineering Istanbul Technical University Istanbul Turkey

ISBN: (纸本)9783031777882

Breast cancer remains a leading cause of cancer-related deaths among women worldwide, with mammography screening as the most effective method for the early detection. Ensuring proper positioning in mammography is critical, as poor positioning can lead to diagnostic errors, increased patient stress, and higher costs due to recalls. Despite advancements in deep learning (DL) for breast cancer diagnostics, limited focus has been given to evaluating mammography positioning. This paper introduces a novel DL methodology to quantitatively assess mammogram positioning quality, specifically in mediolateral oblique (MLO) views using attention and coordinate convolution modules. Our method identifies key anatomical landmarks, such as the nipple and pectoralis muscle, and automatically draws a posterior nipple line (PNL), offering robust and inherently explainable alternative to well-known classification and regression-based approaches. We compare the performance of proposed methodology with various regression and classification-based models. The CoordAtt UNet model achieved the highest accuracy of 88.63% ± 2.84 and specificity of 90.25% ± 4.04, along with a noteworthy sensitivity of 86.04% ± 3.41. In landmark detection, the same model also recorded the lowest mean errors in key anatomical points and the smallest angular error of 2.42∘. Our results indicate that models incorporating attention mechanisms and CoordConv module increase the accuracy in classifying breast positioning quality and detecting anatomical landmarks. Furthermore, we make the labels and source codes available to the community to initiate an open research area for mammography, accessible at https://***/tanyelai/deep-breast-positioning. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Diseases

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Recent advances in acoustofluidic separation technology in biology

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Microsystems & Nanoengineering 2022年第5期8卷 1-16页

作者： Yanping Fan Xuan Wang Jiaqi Ren Francis Lin Jiandong Wu School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghai 200093China Institute of Biomedical and Health Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China Department of Physics and Astronomy University of ManitobaWinnipegMB R3T 2N2Canada

Acoustofluidic separation of cells and particles is an emerging technology that integrates acoustics and *** the last decade,this technology has attracted significant attention due to its biocompatible,contactless,and labelfree *** has been widely validated in the separation of cells and submicron bioparticles and shows great potential in different biological and biomedical *** review first introduces the theories and mechanisms of acoustofluidic ***,various applications of this technology in the separation of biological particles such as cells,viruses,biomolecules,and exosomes are ***,we discuss the challenges and future prospects of this field.

关键词： fluid separation acoustics

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A ShuffleNet Architecture-Based Multi-Layer Feature Scaling and Fusion Approach for Underwater Net Cage Damage Detection in Aquaculture

A ShuffleNet Architecture-Based Multi-Layer Feature Scaling ...

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IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia)

作者： Xiang-Rui Huang Yi-Cheng Chen Liang-Bi Chen Wu-Chih Hu Department of Electrical Engineering Master Program in Electrical Engineering and Computer Science National Penghu University of Science and Technology Penghu Taiwan Department of Computer Science and Information Engineering National Penghu University of Science and Technology Penghu Taiwan

ISBN: (数字)9798331530839

ISBN: (纸本)9798331530846

In offshore aquaculture operations, personnel equipped with diving gear are often necessary to inspect the underwater net cages for damage, particularly on the sea floor. This manual inspection process is time-consuming and complex. To overcome this problem, this paper proposes a computer vision solution for identifying damage in underwater net cages to address the inefficiencies and challenges of traditional manual inspections. The proposed scheme utilizes a high-performance multi-branch computational architecture designed based on ShuffleNet architecture to detect net cage damage more efficiently. Experimental results demonstrate that this work performs well on the ImageNet ILSVRC-2010 dataset and achieves an accuracy of 88.54% in underwater net damage detection.

关键词： Accuracy Gears Computational modeling Sea floor computer architecture Manuals Inspection Feature extraction Personnel Aquaculture

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