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11th International Japan-Africa Conference on Electronics, Communications, and Computations, JAC-ECC 2023

作者： Bakr, Hend A. Salama, Ahmed M. Fares, Ahmed Zaky, Ahmed B. Cairo University Biomedical Engineering Program Faculty of Engineering Giza Egypt Benha University Computer Systems Engineering Program Faculty of Engineering at Shoubra Banha Egypt Computer science and information technology Programs Alexandria Egypt Benha University On leave from Computer Systems Engineering Program Faculty of Engineering at Shoubra Egypt

ISBN: (纸本)9798350369328

Physician scheduling is a critical task that impacts the quality of patient care, staff satisfaction, and operational efficiency in healthcare institutions. The traditional approach to physician scheduling is manual and time-consuming, which can result in errors, staff burnout, and suboptimal schedules. To address these challenges, researchers have turned to optimization techniques like CSP, which has shown promise in solving physician scheduling problems. This paper reviews the existing literature on CSP for physician scheduling and highlights the benefits and limitations of this approach. CSP's benefits include generating schedules quickly and efficiently, incorporating complex constraints and preferences, and handling changes and disruptions in real time. However, CSP also has some limitations, such as the need for a formalized model and the fact that it may not always generate the most intuitive schedules. Overall, the findings suggest that CSP is a promising approach to physician scheduling that can produce high-quality schedules while minimizing staff burnout and improving operational efficiency. © 2023 IEEE.

关键词： Constraint satisfaction problems

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High-Throughput Independent Component Analysis Processor for Full Duplex Systems

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IEEE Journal on Selected Areas in Communications 2023年第9期41卷 2821-2832页

作者： Cheng, Jen-Hao Chang, Tien-Min Shen, Chung-An Fouda, Mohammed E. Eltawil, Ahmed M. National Taiwan University of Science and Technology Department of Electronic and Computer Engineering Taipei10607 Taiwan University of California at Irvine Electrical Engineering and Computer Science Department IrvineCA92697 United States King Abdullah University of Science and Technology Electrical and Computer Engineering Program Thuwal23955 Saudi Arabia

This paper presents the algorithm and very-large-scale integration (VLSI) architecture of a high-throughput and highly efficient independent component analysis (ICA) processor for self-interference cancellation (SIC) in in-band full-duplex (IBFD) systems. This is the first VLSI architecture reported in the literature based on the state-of-the-art entropy bound minimization (EBM) approach. A novel ICA algorithm is presented in this paper with momentum gradient descent optimization. Simulation results show that the number of iterations for the proposed algorithm is significantly reduced compared to the conventional ICA algorithms. Furthermore, a novel early-distribution estimation scheme is proposed in the designed ICA processor to compute multiple distribution functions with low latency and low complexity. The processing flow and the efficiency for the hardware utilization are specifically designed so that the processing speed is maximized with minimum employment of hardware components. The proposed ICA processor is designed and implemented based on the application-specific-integrated circuit (ASIC) flow. The post-layout estimations show that compared with the conventional EBM-based scheme, the proposed design improves the throughput and efficiency by 30x. In addition, compared to prior designs shown in the literature, the proposed ICA processor also demonstrates a significant enhancement in terms of throughput and efficiency. © 1983-2012 IEEE.

关键词： VLSI circuits

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On the particle approximation of lagged Feynman–Kac formulae

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Stochastic Processes and their Applications 2025年 188卷

作者： Awadelkarim, Elsiddig Caffarel, Michel Del Moral, Pierre Jasra, Ajay Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia University of Toulouse CNRS-Lab. de Chimie et Physique Quantiques Toulouse31062 France Centre de Recherche Inria Bordeaux Sud-Ouest Talence33405 France School of Data Science The Chinese University of Hong Kong Shenzhen Shenzhen China

In this paper we examine the numerical approximation of the limiting invariant measure associated with Feynman–Kac formulae. These are expressed in a discrete time formulation and are associated with a Markov chain and a potential function. The typical application considered here is the computation of eigenvalues associated with non-negative operators as found, for example, in physics or particle simulation of rare-events. We focus on a novel lagged approximation of this invariant measure, based upon the introduction of a ratio of time-averaged Feynman–Kac marginals associated with a positive operator iterated l∈N times;a lagged Feynman–Kac formula. This estimator and its approximation using Diffusion Monte Carlo (DMC) are commonly used in the physics literature. In short, DMC is an iterative algorithm involving N∈N particles or walkers simulated in parallel, that undergo sampling and resampling operations. In this work, it is shown that for the DMC approximation of the lagged Feynman–Kac formula, one has an almost sure characterization of the L1-error as the time parameter (iteration) goes to infinity and this is at most of O(exp{−κl}/N), for κ>0. In addition a non-asymptotic in time, and time uniform L1−bound is proved which is O(l/N). We also prove a novel central limit theorem to give a characterization of the exact asymptotic in time variance. This analysis demonstrates that the strategy used in physics, namely, to run DMC with N and l small and, for long time enough, is mathematically justified. Our results also suggest how one should choose N and l in practice. We emphasize that these results are not restricted to physical applications;they have broad relevance to the general problem of particle simulation of the Feynman–Kac formula, which is utilized in a great variety of scientific and engineering fields. © 2025 Elsevier B.V.

关键词： Iterative methods

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Deep Semantic and Attentive Network for Unsupervised Video Summarization

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ACM Transactions on Multimedia Computing, Communications and Applications 2022年第2期18卷 1-21页

作者： Zhong, Sheng-Hua Lin, Jingxu Lu, Jianglin Fares, Ahmed Ren, Tongwei College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Electrical Engineering Computer Systems Engineering Program Faculty of Engineering at Shoubra Benha University Cairo Egypt State Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China

With the rapid growth of video data, video summarization is a promising approach to shorten a lengthy video into a compact version. Although supervised summarization approaches have achieved state-of-the-art performance, they require frame-level annotated labels. Such an annotation process is time-consuming and tedious. In this article, we propose a novel deep summarization framework named Deep Semantic and Attentive Network for Video Summarization (DSAVS) that can select the most semantically representative summary by minimizing the distance between video representation and text representation without any frame-level labels. Another challenge associated with video summarization tasks mainly originates from the difficulty of considering temporal information over a long time. Long Short-Term Memory (LSTM) performs well for temporal dependencies modeling but does not work well with long video clips. Therefore, we introduce a self-attention mechanism into our summarization framework to capture the long-range temporal dependencies among the frames. Extensive experiments on two popular benchmark datasets, i.e., SumMe and TVSum, show that our proposed framework outperforms other state-of-the-art unsupervised approaches and even most supervised methods. © 2022 Association for Computing Machinery.

关键词： Semantics

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Fast Smith-Waterman hardware implementation

Fast Smith-Waterman hardware implementation

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2010 IEEE International Symposium on Parallel and Distributed Processing, Workshops and Phd Forum, IPDPSW 2010

作者： Nawaz, Zubair Bertels, Koen Sümbül, H. Ekin Computer Engineering Lab. Delft University of Technology Netherlands Electronic Engineering Program SabancI University Turkey

ISBN: (纸本)9781424465347

The Smith-Waterman (SW) algorithm is one of the widely used algorithms for sequence alignment in computational biology. With the growing size of the sequence database, there is always a need for even faster implementation of SW. In this paper, we have implemented two Recursive Variable Expansion (RVE) based techniques, which are proved to give better speedup than any best dataflow approach at the cost of extra area. Compared to dataflow approach, our HW implementation is 2.29 times faster at the expense of 2.82 times more area. © 2010 IEEE.

关键词： Computational biology Hardware acceleration RVE Sequence alignment

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LSTM with Adam Optimization-Powered High Accuracy Preeclampsia Classification 21

LSTM with Adam Optimization-Powered High Accuracy Preeclamps...

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21st International Electronics Symposium, IES 2019

作者： Sakinah, Nur Tahir, Muhlis Badriyah, Tessy Syarif, Iwan Politeknik Elektronika Negeri Surabaya Graduate Program of Engineering Technology Surabaya Indonesia Computer Graphic Engineering Technology Study Program Politeknik Negeri Sambas Kalimantan Barat Indonesia

ISBN: (纸本)9781728144498

Preeclampsia is a pregnancy complication characterized by high blood pressure and signs of damage to another organ system, most often the liver and kidneys. Preeclampsia usually begins after 20 weeks of pregnancy in women whose blood pressure had been normal. To anticipate the risk of preeclampsia and prevent the death of pregnant women, a predictive system is needed for preeclampsia. to predict this, the data obtained will be studied to recognize patterns for predicting preeclampsia. Artificial neural network is one method that has the ability to learn patterns from a data. The system built in this study is to use the artificial neural network method by using the architecture of Long Short-Term Memory (LSTM) Neural Networks. But this technique requires the right parameters to get accurate predictions. In this paper analyze several parameters such as number of time series patterns, number of hidden neurons, max epoch, and composition of training and test data on the accuracy of the predictions obtained. The results of the analysis showed that the system built was able to predict Preeclampsia well, with an average accuracy rate of 90.22% for data testing. © 2019 IEEE.

关键词： Long short-term memory

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2D and 3D photogrammetric models for respiratory analysis in adolescents

2D and 3D photogrammetric models for respiratory analysis in...

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13th Mediterranean Conference on Medical and Biological engineering and Computing 2013, MEDICON 2013

作者： Ripka, W.L. Ulbricht, Leandra Neves, E.B. Gewehr, P.M. Federal University of Technology-Paraná Graduate Program in Electrical Engineering and Computer Science Curitiba Brazil Federal University of Technology-Paraná Graduate Program in Biomedical Engineering Curitiba Brazil

ISBN: (纸本)9783319008455

The aim of this study was to test the correlation between a two dimensional (2D) and three dimensional (3D) biomechanical photogrammetric model, with inspiratory and expiratory lung capacities in adolescents. This was a cross sectional study, in which 40 adolescents of both genders were assessed, aged between 14 and 17 years old. Anthropometric data were collected (body mass, height and thorax length). Then, three tests were performed for forced vital capacity (FVC) in the supine position, using a spirometer. For FVC proof assessments, the volunteers were subjected to a mapping of body structures with adhesive markers and, after the test, one was selected with better respiratory performance for computational analysis of photogrammetric thoracoabdominal mobility. The results showed average values of 15.40, 61.29, 1.66 and 25.54, for age (years), body mass (kg), height (m) and thorax length (cm), respectively. The analysis found respiratory values of: 3:22 l for forced expiratory volume in one second, 3.87 l for forced vital capacity, 6.50 l/s for peak expiratory flow and 3.59 l for deep inspiration. The correlation showed better values for 3D photogrammetry as compared to 2D. The inclusion of the third dimension has been applied in order to adequate photogrammetric methodology to existing kinematics analysis tools as videogrammetry computed and plethysmography. © Springer International Publishing Switzerland 2014.

关键词： Photogrammetry

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Absolute gradient algorithm for adaptive IIR notch filter

Absolute gradient algorithm for adaptive IIR notch filter

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2012 9th International Conference on Electrical engineering/Electronics, computer, Telecommunications and Information technology, ECTI-CON 2012

作者： Nosan, A. Punchalard, R. Computer Engineering Graduate Program Faculty of Engineering Mahanakorn University of Technology Bangkok Thailand Department of Telecommunication Engineering Mahanakorn University of Technology Bangkok Thailand

ISBN: (纸本)9781467320245

The gradient algorithm used absolute value of error signal as a criterion to adjust the filter coefficient of adaptive IIR notch filter is presented in this paper. The objective is to estimate or track the frequency of a single sinusoidal signal corrupted by additive white Gaussian noise. The proposed algorithm can solve the problem of selecting an initial condition as met in the previous method. Moreover, it also has a high convergence speed. © 2012 IEEE.

关键词： Notch filters

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AI-driven 3D Spatial Transcriptomics

arXiv

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arXiv 2025年

作者： Almagro-Pérez, Cristina Song, Andrew H. Weishaupt, Luca Kim, Ahrong Jaume, Guillaume Williamson, Drew F.K. Hemker, Konstantin Lu, Ming Y. Singh, Kritika Chen, Bowen Le, Long Phi Baras, Alexander S. Jiang, Sizun Bashashati, Ali Liu, Jonathan T.C. Mahmood, Faisal Department of Pathology Mass General Brigham Harvard Medical School BostonMA United States Cancer Program Broad Institute of Harvard and MIT CambridgeMA United States Data Science Program Dana-Farber Cancer Institute BostonMA United States Harvard-MIT Division of Health Sciences and Technology Massachusetts Institute of Technology CambridgeMA United States Department of Pathology Pusan National University Busan Korea Republic of Department of Pathology and Laboratory Medicine Emory University School of Medicine AtlantaGA United States Department of Computer Science & Technology University of Cambridge Cambridge United Kingdom Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA United States Rutgers Robert Wood Johnson Medical School New BrunswickNJ United States Department of Pathology Johns Hopkins Hospital BaltimoreMD United States Department of Biomedical Engineering Johns Hopkins Hospital BaltimoreMD United States Broad Institute of Harvard and MIT CambridgeMA United States Center for Virology and Vaccine Research Beth Israel Deaconess Medical Center Harvard Medical School BostonMA United States Department of Pathology Dana Farber Cancer Institute BostonMA United States Department of Pathology and Laboratory Medicine University of British Columbia VancouverBC Canada School of Biomedical Engineering University of British Columbia VancouverBC Canada Department of Mechanical Engineering Bioengineering Laboratory Medicine & Pathology University of Washington SeattleWA United States Harvard Data Science Initiative Harvard University CambridgeMA United States

A comprehensive three-dimensional (3D) map of tissue architecture and gene expression is crucial for illuminating the complexity and heterogeneity of tissues across diverse biomedical applications1. However, most spatial transcriptomics (ST) approaches remain limited to two-dimensional (2D) sections of tissue2–4. Although current 3D ST methods hold promise, they typically require extensive tissue sectioning, are complex, are not compatible with non-destructive 3D tissue imaging technologies, and often lack scalability5–8. Here, we present VOlumetrically Resolved Transcriptomics EXpression (VORTEX), an AI framework that leverages 3D tissue morphology and minimal 2D ST to predict volumetric 3D ST. By pretraining on diverse 3D morphology–transcriptomic pairs from heterogeneous tissue samples and then fine-tuning on minimal 2D ST data from a specific volume of interest, VORTEX learns both generic tissue-related and sample-specific morphological correlates of gene expression. This approach enables dense, high-throughput, and fast 3D ST, scaling seamlessly to large tissue volumes far beyond the reach of existing 3D ST techniques. By offering a cost-effective and minimally destructive route to obtaining volumetric molecular insights, we anticipate that VORTEX will accelerate biomarker discovery and our understanding of morphomolecular associations and cell states in complex tissues. Interactive 3D ST volumes can be viewed at https://***/. © 2025, CC BY-NC-ND.

关键词： Vortex flow

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Cancer risk assessment system by android application in smartphone 17

Cancer risk assessment system by android application in smar...

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6th International Conference on Bioinformatics and Biomedical Science, ICBBS 2017

作者： Chunngam, Bunthida Computer Engineering Program Faculty of Industrial Education Rajamangala University of Technology Suvarnabhumi Suphanburi Thailand

ISBN: (纸本)9781450352222

This research was cancer risk analysis system development on Android operating system. This research had objective to developed cancer risk analysis system development on Android operating system and studied satisfaction of system user. There were 5 experts who were 3 information technology experts, 2 professional level registered nurses. There were 100 sample users to test this system which collected data by questionnaire about system efficient evaluation and satisfaction about this risk assessment system. This system had 3 functions which were system using, system feather evaluation and system accuracy evaluation which analyzed by frequency of rating scales, mean and standard deviation. This data analysis result from these 5 experts found that the system using performance quality had good level satisfaction (3.95 mean), system feather function analysis had good level satisfaction (4.12 mean) and system accuracy had good level satisfaction (4.13 mean). © 2017 Association for Computing Machinery.

关键词： Risk analysis

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