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7th International Conference on Intelligent Computing and Control Systems, ICICCS 2023

作者： Pratheeba, C. Muthuvinayagam, M. Siva Ramkumar, M. Rohith Bhat, C. Maniraj, P. Kumar, Neelam Sanjeev Nandha Engineering College Department of Electrical and Electronics Engineering Erode India Mahendra Engineering College Department of Electrical and Electronics Engineering Namakkal Mallasamudram India Karpagam Academy of Higher Education Faculty of Engineering Department of Electrical and Electronics Engineering Coimbatore India SIMATS Saveetha School of Engineering Department of Computer Science and Engineering Chennai India M.Kumarasamy College of Engineering Thalavapalayam Department of Electrical and Electronics Engineering Tamilnadu Karur639113 India SIMATS Saveetha School of Engineering Department of Biomedical Engineering Chennai India

ISBN: (纸本)9798350397253

Despite its importance. more than 800 million people still lack access to electricity. Individuals in many rural areas lack access to a consistent supply of energy due to the high cost of grid extension. Off-grid solar systems enable rural residents to connect to the energy 2rid and power their homes' lights. appliances. and other electronic equipment. This document provides a brief overview of a solar power system that can be used to Dower remote homes. Solar panels. a charge controller. batteries. and an inverter make up the system. Sunlight is converted into direct current by solar panels. which is then stored in batteries. The charge controller regulates the charging and discharging cycles of the battery pack in order to keep the cells healthy and long-lasting. The inverter converts the stored DC electricity into the alternating current (AC) power that appliances and other AC loads require. The proposed method is intended to be simple and low-cost. making it an appropriate option for regions with limited resources and low growth. The system can be customized to meet the exact energy needs of the house by adding additional solar panels or batteries. Furthermore. the structure is made up of discrete components. making it easier to assemble and maintain. Off-grid solar power systems are a cost-effective and efficient wav of bringing electricity to areas that are not connected to the power grid. The proposed system describes the design and installation of a solar-powered. direct current (DC) power source for isolated dwellings. The strategy promotes long-term economic growth and raises the living standards of rural families. © 2023 IEEE.

关键词： Solar panels

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KNX-ZigBee Gateway

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IFAC-PapersOnLine 2024年第9期58卷 85-90页

作者： Dominik Grzegorz David Vala VSB - Technical University of Ostrava Faculty of electrical engineering and computer science Department of cybernetics and biomedical engineering CZ

This work is about building automation. It deals with the implementation of the connection between KNX TP bus and ZigBee wireless technology. The connection focuses on the control of lighting end devices supporting th... 详细信息

关键词： ZigBee ZigBee Lighting IEEE 802.15.4 ISO/OSI KNX TP CC2652RB

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Multi-Class Skin Cancer Detection Using Fusion of Textural Features Based CAD Tool

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computers, Materials & Continua 2024年第12期81卷 4217-4263页

作者： Khushmeen Kaur Brar Bhawna Goyal Ayush Dogra Sampangi Rama Reddy Ahmed Alkhayyat Rajesh Singh Manob Jyoti Saikia Department of Electronics and Communication Engineering Chandigarh UniversityMohali140413India Chitkara University Institute of Engineering and Technology Chitkara UniversityChandigarh140401India Department of Physics&Electronics School of SciencesJAIN(Deemed to be University)Bangalore560001India College of Technical Engineering The Islamic UniversityNajaf54001Iraq Department of Electronics and Communication Engineering Uttaranchal Institute of TechnologyUttaranchal UniversityDehradun248007India Electrical and Computer Engineering Department University of MemphisMemphisTN 38152USA Biomedical Sensors&Systems Lab University of MemphisMemphisTN 38152USA

Skin cancer has been recognized as one of the most lethal and complex types of cancer for over a *** diagnosis of skin cancer is of paramount importance,yet the process is intricate and *** analysis and modeling of human skin pose significant difficulties due to its asymmetrical nature,the visibility of dense hair,and the presence of various substitute *** texture of the epidermis is notably different from that of normal skin,and these differences are often evident in cases of unhealthy *** a consequence,the development of an effective method for monitoring skin cancer has seen little ***,the task of diagnosing skin cancer from dermoscopic images is particularly *** is crucial to diagnose skin cancer at an early stage,despite the high cost associated with the procedure,as it is an expensive ***,the advancement of diagnostic techniques for skin cancer has been *** address this issue,there is a need for a more accurate and efficient method for identifying and categorizing skin cancer *** involves the evaluation of specific characteristics to distinguish between benign and malignant skin cancer *** present and evaluate several techniques for segmentation,categorized into three main types:thresholding,edge-based,and *** techniques are applied to a dataset of 200 benign and melanoma lesions from the Hospital Pedro Hispano(PH2)*** evaluation is based on twelve distinct metrics,which are designed to measure various types of errors with particular clinical ***,we assess the effectiveness of these techniques independently for three different types of lesions:melanocytic nevi,atypical nevi,and *** first technique is capable of classifying lesions into two categories:atypical nevi and melanoma,achieving the highest accuracy score of 90.00%with the Otsu(3-level)*** second technique also classifies lesions int

关键词： Melanoma computer-aided diagnosis segmentation PH2 ISIC(International Skin Imaging Collaboration) dermoscopy non-melanoma

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Automatic detection of autism spectrum disorder based on fMRI images using a novel convolutional neural network

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Research on biomedical engineering 2023年第2期39卷 407-413页

作者： Sabegh, Amin Majidzadeh Samadzadehaghdam, Nasser Seyedarabi, Hadi Ghadiri, Tahereh Department of Biomedical Engineering Faculty of Advanced Medical Sciences Tabriz University of Medical Sciences Tabriz Iran Faculty of Electrical and Computer Engineering University of Tabriz Tabriz Iran Department of Neuroscience and Cognition Faculty of Advanced Medical Sciences Tabriz University of Medical Sciences Tabriz Iran

Purpose: Heterogeneous mental disorders such as autism spectrum disorder (ASD) are difficult to diagnose, especially in children. The current psychiatric diagnosis process is based solely on the observation of behavioral symptoms. To make a supplementary decision system for a more accurate diagnosis, we can rely on advanced and scalable machine learning methods, namely, deep learning networks. Method: We have developed our model based on resting-state fMRI data from the ABIDE1 database, which includes 17 different imaging sites. After performing a preprocessing pipeline and registering the data onto the atlases, the average time series of brain regions are extracted and the correlation matrix is computed. Then, the most important features of this matrix are identified by using the chi-square feature selection method. A novel architecture of convolutional neural network (CNN) with two-dimensional convolutional layers is proposed to analyze and classify the data. Results: To evaluate the model, three sets of experiments were designed based on different data sets and atlases. Using this method, the highest accuracy obtained in the experiments was 73.53%, which was higher than previous works on classifying ASD from typical controls. Conclusion: This work presented a novel CNN-based model that automatically classifies ASD from healthy controls, showing good classification performance. Most of the previous works done on this database have focused on a limited number of sites. But in this research, the data acquired by all the sites have been used to increase the generalizability of the proposed model. © 2023, The Author(s), under exclusive licence to The Brazilian Society of biomedical engineering.

关键词： Magnetic resonance imaging

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E-Unet: A deep learning method for photoacoustic signal enhancement

E-Unet: A deep learning method for photoacoustic signal enha...

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Photons Plus Ultrasound: Imaging and Sensing 2023

作者： Aggrawal, Deepika Zafar, Mohsin Islam, Md Tarikul Manwar, Rayyan Schonfeld, Dan Avanaki, Kamran Department of Electrical and Computer Engineering University of Illinois ChicagoIL United States Richard and Loan Hill Department of Biomedical Engineering University of Illinois ChicagoIL United States

ISBN: (纸本)9781510658639

We developed a deep learning algorithm, called enhancement Unet (E-Unet), to improve the signal-to-noise ratio (SNR) of signals acquired in a photoacoustic computed microscopy (PAM) system. We tried various combination of custom loss functions which included peak-amplitude, peak-position and mean-squared signal value with Adam optimizer for training purposes. For the testing purposes, we acquired PAM data with complicated phantoms in biological tissue. The performance of the improved signals is evaluated in terms of SNR, structural similarity index (SSIM), root mean square error (RMSE) and Pearson correlation. © 2023 SPIE.

关键词： Signal to noise ratio

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Microfabrication of 3D Spheroid Tumor Models and Imaging using Spectral Domain Optical Coherence Tomography 10

Microfabrication of 3D Spheroid Tumor Models and Imaging usi...

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10th International Conference on Biosignals, Images and Instrumentation, ICBSII 2024

作者： John, Pauline Sukumar, Pavithra Sami, Christopher Qasaimeh, Mohammad A. Zam, Azhar New York University Abu Dhabi Division of Engineering Abu Dhabi United Arab Emirates New York University Department of Mechanical and Aerospace Engineering New York United States New York University Department of Biomedical Engineering Department of Electrical and Computer Engineering New York United States

ISBN: (纸本)9798350350951

A low-cost, robust, and flexible device for generating an array of miniaturized 3D spheroid tumor models was designed and developed. The device consisting of two layers: a layer with through-hole array and a plain lamination sheet, in which both are bonded so the through-holes acting as confining barriers. The hydrophobic bottom lamination sheet led to drop formation in each through-hole, confirmed by experimental characterization using food color dye of different volumes. Further, 3D tumor spheroid models were grown within through-holes on the lamination sheet by the hanging drop approach. With preliminary experiments, spheroids were gown in a flat substrate using simple micro-pipetting and the classical hanging drop method. The formed 3D spheroid tumor models were consequently characterized by live/dead assay. To verify formation of the 3D tumor spheroid models on days 1, 4, 7, 14, and 21, label-free spectral domain based optical coherence tomography (SD-OCT) technique was employed. Results showed an increase in the height of the spheroids up to 600 μm. By utilizing SD-OCT, we were able to achieve enhanced penetration within the 3D spheroids, allowing us to visualize the structural information at various depths. With spheroids growth, the SD-OCT was performed to quantitatively analyze their sizes and necrotic cores. The length, width and diameter of the spheroids were measured for volumetric analysis, based on the cross-sectional and en face SD-OCT images. Next experiments will consider using the developed device with the SD-OCT technique for personalized medication in clinical field and drug-screening/drug-discovery in pharmaceutical industry. © 2024 IEEE.

关键词： Optical tomography

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TEFISTA-NET: GTD Parameter Estimation of Low-Frequency Ultra- Wideband Radar via Model-Based Deep Learning 48

TEFISTA-NET: GTD Parameter Estimation of Low-Frequency Ultra...

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

作者： Li, Rui Wang, Xueqian Li, Gang Zhang, Xiao-Ping Tsinghua University Department of Electronic Engineering China Tsinghua University Tsinghua-Berkeley Shenzhen Institute China Toronto Metropolitan University Department of Electrical Computer & Biomedical Engineering Japan

ISBN: (纸本)9781728163277

The geometrical theory of diffraction (GTD) has been widely investigated to describe the target scattering behaviors with the low-frequency ultra-wideband (LFW) radar. In this paper, we propose a new model-based deep learning method for GTD parameter estimation. The proposed method is designed by unfolding the fast iterative shrinkage thresholding algorithm (FISTA) into a deep neural network. Unlike existing methods based on compressed sensing (CS), the key parameters in our algorithm are fully learnable, avoiding nontrivial parameter tuning procedures. Our network with simple convolution operations is more computationally efficient than existing methods, which require matrix inversions or quadratic programming and have low convergence speed. A novel loss function is designed for the new network to improve the capacity of target enhancement. Experiments on simulation data show that the new method achieves higher computational efficiency while maintaining or improving the precision of GTD parameter estimation compared with existing methods. © 2023 IEEE.

关键词： compressive sensing deep learning geometrical theory of diffraction Low-frequency ultra-wideband radar

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A Reinforcement Learning-Based Computational Model of Human Elbow Joint Operation for Effective Human-Machine Interface

A Reinforcement Learning-Based Computational Model of Human ...

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2024 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2024

作者： Yoo, Eunsoo Choi, Kyungrak Park, Hangue Sungkyunkwan University Department of MetaBioHealth Suwon Korea Republic of Texas A & M University Department of Electrical and Computer Engineering United States Sungkyunkwan University Department of Biomedical Engineering Department of Intelligent Precision Healthcare Convergence Department of MetaBioHealth Suwon Korea Republic of

ISBN: (纸本)9798331530839

Designing effective human-machine interfaces requires understanding complex interactions between humans and machines. However, even bi-directional communication that relies on sensor data is often insufficient due to the unpredictability of human behaviors. To better anticipate these behaviors, a computational model that reflects the operating principles of the human nervous system is essential. This study presents a computational neuromechanical model that focuses on human sensorimotor operations, particularly in the context of elbow joint rotation. The model leverages reinforcement learning (RL) to simulate the brain's reward mechanism in motor adjustment, with the reward function adjusted by a multiplication constant (M) that reflects individual variability in sensorimotor processing. Measurement and simulation data were evaluated based on their overlap ratios. The test results demonstrate that the RL-based model, when calibrated with an optimal M value, closely matches with measured motor outputs, indicating its potential for improving the effectiveness of the human-machine interface. © 2024 IEEE.

关键词： Reinforcement learning

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Stability Assessment of Ultramicroelectrode Arrays in Neural Stimulation: An Electrochemical Impedance Spectroscopy Analysis 46

Stability Assessment of Ultramicroelectrode Arrays in Neural...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Dong, Qiwei Ezeh, Cynthia Wu, Yupeng Hetke, Jamille F. Cogan, Stuart Orazem, Mark E. Otto, Kevin J. University of Florida Department of Electrical & Computer Engineering United States University of Florida Department of Chemical Engineering United States The University of Texas Department of Bioengineering Department of Material Sciences & Engineering Dallas United States NeuroNexus United States University of Florida J. Crayton Pruitt Family Department of Biomedical Engineering United States

ISBN: (纸本)9798350371499

The overall focus of this research program is to investigate the viability and safety of microelectrode arrays (MEAs) and ultramicroelectrode arrays (UMEAs) for microstimulation (μStim) in the brain. We aim to assess their potential to deliver adequate electrical stimulation for neural activation without causing electrode or tissue damage. To this end, here we conducted galvanostatic Electrochemical Impedance Spectroscopy (EIS) to assess the behavior of electrodes of various diameters both pre- and post-stimulation in a rat model. Our first-generation electrode arrays (G1) were designed with electrode diameters spanning 3 orders of magnitude and including subcellular dimensions. Using these devices, we delivered 0.4 nC/phase stimulation in vivo. We conducted in vitro EIS measurements before and after the current pulsing. Our findings show that these electrode arrays, including UMEAs, exhibit stable electrochemical behavior following a 0.4 nC/phase current pulse. To validate the EIS frequency range used for the G1 measurements, we designed a second-generation electrode array (G2) that included a terminating lead and a looping lead. This array provided the ability to assess the accuracy contour plot of our EIS system and electrode array platform. This exploratory research contributes to the ongoing knowledge of UMEAs in μStim applications. These developments may result in high-fidelity, non-damaging multichannel μStim, and improved neuromodulation technology for the treatment of neural diseases and injuries. © 2024 IEEE.

关键词： Electrochemical impedance spectroscopy

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Representations of fricatives in subcortical model responses: Comparisons with human consonant perceptiona)

Representations of fricatives in subcortical model responses...

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作者： Hamza, Yasmeen Farhadi, Afagh Schwarz, Douglas M. McDonough, Joyce M. Carney, Laurel H. Department of Biomedical Engineering University of Rochester RochesterNY14627 United States Department of Electrical and Computer Engineering University of Rochester RochesterNY14627 United States Depts. of Neuroscience and Biomedical Engineering University of Rochester RochesterNY14627 United States Department of Linguistics University of Rochester RochesterNY14627 United States Depts. of Biomedical Engineering Neuroscience and Electrical and Computer Engineering University of Rochester RochesterNY14627 United States

Fricatives are obstruent sound contrasts made by airflow constrictions in the vocal tract that produce turbulence across the constriction or at a site downstream from the constriction. Fricatives exhibit significant intra/intersubject and contextual variability. Yet, fricatives are perceived with high accuracy. The current study investigated modeled neural responses to fricatives in the auditory nerve (AN) and inferior colliculus (IC) with the hypothesis that response profiles across populations of neurons provide robust correlates to consonant perception. Stimuli were 270 intervocalic fricatives (10 speakers × 9 fricatives × 3 utterances). Computational model response profiles had characteristic frequencies that were log-spaced from 125 Hz to 8 or 20 kHz to explore the impact of high-frequency responses. Confusion matrices generated by k-nearest-neighbor subspace classifiers were based on the profiles of average rates across characteristic frequencies as feature vectors. Model confusion matrices were compared with published behavioral data. The modeled AN and IC neural responses provided better predictions of behavioral accuracy than the stimulus spectra, and IC showed better accuracy than AN. Behavioral fricative accuracy was explained by modeled neural response profiles, whereas confusions were only partially explained. Extended frequencies improved accuracy based on the model IC, corroborating the importance of extended high frequencies in speech perception. © 2023 Acoustical Society of America.

关键词： Frequency response

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