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Iranian Conference of Biomedical engineering (ICBME)

作者： Neda Abdollahpour Mohammadreza Yazdchi Zahra Baharlouei Department of Biomedical Engineering Ragheb Isfahani Institute of Higher Education Isfahan Iran Medical Image and Signal Processing Research Center School of Advanced Technologies in Medicine Isfahan University of Medical Sciences Isfahan Iran

In this article, a new framework is proposed to address multi-class Motor imagery Brain-Computer Interface (MIBCI) problems containing a small portion of labeled datasets. In this framework, the combination of Independent Component Analysis (ICA), multi-class Common Spatial Pattern (CSP), and a functional Application Programming Interface (API) model assumes a pivotal role. In the feature extraction stage of the work, a concatenated altered signal affected by spatial weights is proposed for each trial in three frequency ranges. This distribution of features can both provide suitable feature maps for augmentation, preparing data for the deep learning analysis, and underscore distinguishable features of MI classes. In the classification stage, spatial and temporal features are dominated by using the effective combination of a one-dimensional Convolutional Neural Network (CNN) and a two-staged Bidirectional Long Short-Term Memory (BLSTM) in three branches containing different distributions of frequency. Given that, the model simultaneously learns past-to-future and future-to-past patterns in two stages. The experimental result on datasets 2a BCI-Competition IV illustrates that the proposed method can be liable, practical and more competitive than the other popular methods pointed out in this paper. All in all, the proposed framework can alleviate the issue of small portions of labeled datasets in MI problems.

关键词： Deep learning Biological system modeling Pipelines Independent component analysis Feature extraction Brain modeling Brain-computer interfaces

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An Improved Parameter Estimation of Hfm signals Based on Irls Linear Fitting of Extracted Group Delay

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SSRN 2023年

作者： Yao, Shuai Zhang, Yixuan Xu, Yanan Gu, Yian Cao, Hongli Key Laboratory of Underwater Acoustic Signal Processing of Ministry of Education Southeast University Nanjing210096 China Hangzhou Applied Acoustic Research Institute Hangzhou310012 China Department of Electronic and Electrical Engineering University College London London United Kingdom

To address the issue of robust parameter estimation of hyperbolic frequency modulated (HFM) signals under low signal-to-noise ratios (SNRs), an improved algorithm based on iteratively reweighted least squares (IRLS) linear fitting of extracted group delay is proposed. The algorithm includes three main steps. First, a novel method for estimating the frequency distribution range (FDR) of an HFM signal is presented, which involves analyzing the transient features of the amplitude spectrum (AS). Second, the spectrum phase (SP) is unwrapped in the estimated FDR to extract the group delay (GD) of the HFM signal. Last, parameter estimation of the HFM signal is achieved by fitting the GD, which is a linear function of the reciprocal of the frequency, using the IRLS linear fitting method. The simulation results have shown that for SNR ≥ -14 dB, the normalized root mean square errors (NRMSEs) estimated by the proposed estimator are 1.5 dB higher than those estimated by the spectrum phase unwrapping (SPU) estimator, including the starting frequency and period slope. Furthermore, experimental results based on sea trial data have also verified the validity and feasibility of the proposed estimator. © 2023, The Authors. All rights reserved.

关键词： Group delay

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AI-enabled CT-guided end-to-end quantification of total cardiac activity in 18FDG cardiac PET/CT for detection of cardiac sarcoidosis

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Journal of Nuclear Cardiology 2025年 48卷 102195页

作者： Miller, Robert JH. Shanbhag, Aakash Marcinkiewicz, Anna M. Struble, Helen Gransar, Heidi Hijazi, Waseem Fujito, Hidesato Kransdorf, Evan Kavanagh, Paul Liang, Joanna X. Builoff, Valerie Dey, Damini Berman, Daniel S. Slomka, Piotr J. Departments of Medicine (Division of Artificial Intelligence in Medicine) Imaging and Biomedical Sciences Cedars-Sinai Medical Center Los Angeles CA United States Department of Cardiac Sciences University of Calgary Calgary AB Canada Signal and Image Processing Institute Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los Angeles CA United States Center of Radiological Diagnostics National Medical Institute of the Ministry of the Interior and Administration Warsaw Poland

Background: [18F]-fluorodeoxyglucose ([18F]FDG) positron emission tomography (PET) plays a central role in diagnosing and managing cardiac sarcoidosis. We propose a fully automated pipeline for quantification of [18F]FDG PET activity using deep learning (DL) segmentation of cardiac chambers on computed tomography (CT) attenuation maps and evaluate quantitative approaches based on this framework. Methods: We included consecutive patients undergoing [18F]FDG PET/CT for suspected cardiac sarcoidosis. DL segmented left atrium, left ventricle (LV), right atrium, right ventricle, aorta, LV myocardium, and lungs from CT attenuation scans. CT-defined anatomical regions were applied to [18F]FDG PET images automatically to quantify target to background ratio (TBR), volume of inflammation (VOI) and cardiometabolic activity (CMA) using full sized and shrunk segmentations. Results: A total of 69 patients were included, with mean age of 56.1 ± 13.4 and cardiac sarcoidosis present in 29 (42 %). CMA had highest prediction performance (area under the receiver operating characteristic curve [AUC] .919, 95 % confidence interval [CI] .858 – .980) followed by VOI (AUC .903, 95 % CI .834 – .971), TBR (AUC .891, 95 % CI .819 – .964), and maximum standardized uptake value (AUC .812, 95 % CI .701 – .923). Abnormal CMA (≥1) had a sensitivity of 100 % and specificity 65 % for cardiac sarcoidosis. Lung quantification was able to identify patients with pulmonary abnormalities. Conclusion: We demonstrate that fully automated volumetric quantification of [18F]FDG PET for cardiac sarcoidosis based on CT attenuation map-derived volumetry is feasible, rapid, and has high prediction performance. This approach provides objective measurements of cardiac inflammation with consistent definition of myocardium and background region. © 2025 American Society of Nuclear Cardiology

关键词： Cardiac sarcoidosis Deep learning Hypermetabolism Positron emission tomography Quantification

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40 Gb/s High-speed mode-division multiplexing transmission employing NRZ modulation format

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Journal of Optical Communications 2023年第S1期44卷 S1157-S1165页

作者： Sabitu, Rabiu Imam Khan, Nafizah Goriman Malekmohammadi, Amin Affiliated to the George Green Institute for Electromagnetics Research Nottingham United Kingdom Photonics Communication Systems and Signal Processing Research Group Kano University of Science and Technology Wudil KanoPMB 3244 Nigeria Semenyih Malaysia Department of Electrical and Computer Engineering California State University BakersfieldCA93311 United States Photonics Communication Systems and Signal Processing Research Group Kano University of Science and Technology Wudil KanoPMB 3244 Nigeria

This report examines the performance of a high-speed MDM transmission system supporting four nondegenerate spatial modes at 10 Gb/s. The analysis adopts the NRZ modulation format to evaluate the system performance in terms of a minimum power required (PN) and the nonlinear threshold power (PTH) at a BER of 10−9. The receiver sensitivity, optical signal-to-noise ratio, and the maximum transmission distance were investigated using the direct detection by employing a multimode erbium-doped amplifier (MM-EDFA). It was found that by properly optimizing the MM-EDFA, the system performance can significantly be improved. © 2023 Walter de Gruyter GmbH. All rights reserved.

关键词： Erbium doped fiber amplifiers

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EEG Artifact Removal Based on Brain Dipoles' Regions Using ICA and Dipfit in Motor imagery Tasks

EEG Artifact Removal Based on Brain Dipoles' Regions Using I...

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Iranian Conference of Biomedical engineering (ICBME)

In this article, a new semi-automatic Electroencephalogram (EEG) artifact removal has been proposed for Motor imagery (MI) tasks to improve the system's performance. There are eight reference clusters whose locations have been calculated based on the precise coordinates of a copious amount of brain dipoles acquired from a large number of users performing MI tasks. In this method, called 8 Ref-Clusters, a kind of Blind Source Separation (BSS) algorithm along with the DIPFIT plugin of the EEGLAB platform take on a decisive role. These eight clusters demonstrate which dipoles are brain sources and which ones are artifacts to eliminate. In the case of improving the performance of a system for a particular subject, we defined a specific threshold that could alter the size of clusters in three dimensions. The elaborate threshold pointed out above is unquestionably user-dependent. Having made a comparison between results before and after applying the 8-Ref-Clusters on the BCI-Competition IV 2a datasets, the average performance increased by roughly 4% which is promising when the datasets used in these evaluations had been filtered, between 8 and 30 Hz, only before applying Independent Component Analysis (ICA). Making a comparison between the results of the proposed method and those of other CSP-based methods shows that applying the proposed artifact removal only before CSP can significantly enhance the performance of the system at about 15.6%, in the case of the mCSP method. All in all, the proposed artifact removal method is a semi-automatic method that is computationally fast and able to detect the various types of artifacts like a heartbeat, muscle and head movements, eye blink, line noise, and so on.

关键词： Training Head Heart beat System performance Independent component analysis Muscles Lead

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Deep Neural Network Dataset Collection for Optimal Positioning of a Capacitive Compensated Schiffman Phase Shifter

Deep Neural Network Dataset Collection for Optimal Positioni...

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International Technical Conference o Circuits/Systems, Computers and Communications (ITC-CSCC)

作者： Raymond Gyaang Ahmed Abdul-Rahman Dennis Agyemanh Nana Gookyi Sung-Joon Jang Sang-Seol Lee Department of Electrical and Electronics Engineering Bolgatanga Technical University Bolgatanga Ghana Department of Telecommunication Engineering Kwame Nkrumah University of Science and Technology Kumasi Ghana Institute for Scientific and Technological Information Council for Scientific and Industrial Research Accra Ghana Intelligent Image Processing Research Center Korea Electronics Technology Institute Seongnam-si Republic of Korea

This paper presents an accurate analysis and design of a Schiffman phase shifter with optimally positioned compensation capacitances for improved performances. The approach synthesized the microstrip coupler's even and odd mode characteristic impedance and its coupling effect to enhance the phase shift with minimal phase deviation. This was achieved by positioning artificial capacitances along the microstrip directional coupler. The new design method is valid for all substrate thicknesses and permittivity thereby overcoming the bandwidth limitation presented by the minimum spacing between the coupled lines in a conventional microstrip directional coupler. The simulation result demonstrated a maximum phase deviation of ± 3° and a maximum return loss of -0.613 dB. The source and load reflections were well below - 10 dB. The obtained dataset is highly accurate and can be used to train a deep neural network for improved phase error and return loss of a Schiffman phase shifter.

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Automated Colorectal Polyps Detection from Endoscopic images using MultiResUNet Framework with Attention Guided Segmentation

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Human-Centric Intelligent Systems 2024年第2期4卷 299-315页

作者： Ahamed, Md. Faysal Islam, Md. Rabiul Nahiduzzaman, Md. Chowdhury, Muhammad E. H. Alqahtani, Abdulrahman Murugappan, M. Department of Computer Science & Engineering Rajshahi University of Engineering & Technology Rajshahi Bangladesh Department of Electrical & Computer Engineering Rajshahi University of Engineering & Technology Rajshahi Bangladesh Department of Electrical Engineering Qatar University Doha Qatar Department of Biomedical Technology College of Applied Medical Sciences Al-Kharj Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia Department of Medical Equipment Technology College of Applied Medical Science Majmaah University Majmaah City Saudi Arabia Intelligent Signal Processing (ISP) Research Lab Department of Electronics and Communication Engineering Kuwait College of Science and Technology Doha Kuwait Department of Electronics and Communication Engineering Vels Institute of Sciences Technology and Advanced Studies Chennai India DCenter for Unmanned Autonomous Systems (CoEUAS) Universiti Malaysia Perlis Perlis Malaysia

The early detection of colorectal polyps is crucial for the reduction of mortality rates. However, manually identifying polyps is time-consuming and expensive, increasing the risk of missing them. Our paper aims to address this issue by presenting an automated segmentation approach for colorectal polyps. This paper proposes a method that combines a skip connection with hybrid attention guidance (AG) using attention guidance (AG) and residual path frameworks to identify salient features. Furthermore, we augment test samples using original, horizontal flip, and vertical flip transformations to enhance model robustness through Test Time Augmentation (TTA). The model was trained with Kvasir-seg samples and evaluated on Kvasir-seg and CVC-ClinicDB datasets to gauge generalizability. A significant accuracy (0.9546), a Dice Similarity Coefficient (DSC) of 0.8557, a Cross-section over Union (IoU) of 0.8824, a Recall (0.8221), a Precision (0.8922), an area under Receiver Operating Characteristics (ROC-AUC) of 0.9454, and an area under Precision-Recall (AUC-PR) of 0.8717 were achieved without TTA. Through TTA integration, accuracy (0.9993), DSC (0.8663), IoU (0.8277), Recall (0.8060), Precision (0.9364), and ROC-AUC (0.9587) have been improved. A comparison of our framework with state-of-the-art models demonstrated its effectiveness and segmentation capabilities. Additionally, the proposed model contains only 0.47 million parameters and a weight size of 6.71 MB, illustrating its potential for clinical diagnostics. A computer-aided diagnosis (CAD) system improves patient outcomes by detecting colorectal polyps early and improving segmentation accuracy.

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LTCC patch antenna array for 5G mobile applications featuring embedded air cavities

LTCC patch antenna array for 5G mobile applications featurin...

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International Conference on Electronics Packaging (ICEP)

作者： A. Schulz K. Blau J. Mü ller Department of Electrical Engineering and Information Technology Technische Universit&#x00E4 t Ilmenau Electronics Technology Group Electronic Measurements and Signal Processing Group Institute of Micro- and Nanotechnologies MacroNano&#x00AE Ilmenau Germany

A LTCC patch antenna array was designed and fabricated in Low Temperature Co-fired Ceramic (LTCC) multilayer technology using picosecond laser structuring for precise manufacturing of embedded air cavities and feeding structures, which are beneficial for the antenna RF performance. Moreover, free-standing feeding vias in the embedded air cavities were successfully implemented in LTCC technology that enable a very simple and low-loss feed at the antenna ports. The 2x2 dual-polarized patch antenna array with embedded air cavities and transitions was first characterized by reflection measurements. The measured center frequency of the antenna is about 29.5 GHz and a bandwidth of nearly 1 GHz was achieved.

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Assisting Visually Impaired People using Autonomous Navigation System and Computer Vision for Grocery Shopping

Assisting Visually Impaired People using Autonomous Navigati...

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2022 IEEE International Conference on Electro Information Technology, eIT 2022

作者： Miralles, Patricia Sanchez Gonzalez, Laura Fernandez Yu, Xinrui Saniie, Jafar Illinois Institute of Technology Embedded Computing and Signal Processing Department of Electrical and Computer Engineering Research Laboratory ChicagoIL60616 United States Universidad Politécnica de Madrid E.T.S.I. Industriales Madrid Spain Universidad Politécnica de Madrid E.T.S.I. Telecommunications Madrid Spain

ISBN: (纸本)9781665480093

Visually impaired people face lots of challenges when doing daily tasks like grocery shopping. Hence, a grocery shopping navigation assistant that covers the shopping preparation, the route creation, the navigation through the store and the product identification has been designed. In this paper we present the design flow of a novel system using the Internet of Things (IoT) and Computer Vision (CV) to assist people with visual impairment (PVI) for grocery shopping. The system starts with assisting the user creating his shopping list using the personal smart phone and Speech-to-Text (STT). Then, the system accesses the database of grocery store to optimizes the route for locating the items in the shopping list with minimal time and effort. Once the route is created and the user is in the store, the smart phone physical navigation system starts using landmarks-QR codes and using Text-to-Speech (TTS) for guidance. When the user is correctly placed, the product recognition starts and TTS is used to fetch the product. © 2022 IEEE.

关键词： Computer vision

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Improving Automatic Sleep Staging Via Temporal Smoothness Regularization

Improving Automatic Sleep Staging Via Temporal Smoothness Re...

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International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Huy Phan Elisabeth Heremans Oliver Y. Chén Philipp Koch Alfred Mertins Maarten De Vos Amazon Alexa Cambridge MA USA Department of Electrical Engineering KU Leuven Belgium School of Economics Finance and Management University of Bristol UK Institute for Signal Processing University of Lübeck Germany German Research Center for Artificial Intelligence (DFKI) Germany

We propose a regularization method, so-called temporal smoothness regularization, for training deep neural networks for automatic sleep staging in small data settings. In intuition, we constrain the cross-entropy losses of any two adjacent epochs in the sequential input to be as close to each other as possible. The regularization closely reflects the slow transition nature of sleep process which implies small information changes between two consecutive sleep epochs. Via the regularization, we essentially discourage the network from overfitting to these small changes. Our experiments show that training the SeqSleepNet base network with the proposed regularization leads to performance improvement over the baseline without the regularization applied. Furthermore, our developed method achieves the performance on par with the state-of-the-art performance while outperforming other existing methods.

关键词： Training Deep learning Neural networks signal processing Acoustics Speech processing

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