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18th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2025

作者： Han, Shuning Duan, Feng Vilaseca, Gemma Vilaró, Núria Caiafa, Cesar F. Sun, Zhe Solé-Casals, Jordi Data and Signal Processing Research Group University of Vic-Central University of Catalonia Vic Catalonia Spain Image Processing Research Group RIKEN Center for Advanced Photonics RIKEN Saitama Wako-Shi Japan Tianjin Key Laboratory of Brain Science and Intelligent Rehabilitation Nankai University Tianjin China Psychological Department Oms and Prat school Fundació Catalunya- La Pedrera Catalonia Manresa Spain Oms Foundation Catalonia Manresa Spain Instituto Argentino de Radioastronomıa-CCT La Plata CONICET / CIC-PBA / UNLP Argentina Faculty of Health Data Science Juntendo University Chiba Urayasu Japan Department of Psychiatry University of Cambridge Cambridge United Kingdom

Advances in non-invasive neuroimaging, such as structural magnetic resonance imaging (sMRI), have en abled the construction of structural brain networks (SBNs), allowing in vivo mapping of anatomical connec tions. This study investigates brain network structural differences linked to different intelligence levels in children by individual morphometric similarity networks (MSNs) derived from sMRI data. Through group and individual-level analyses, we aim to uncover key topological features associated with cognitive perfor mance and to identify a suitable connection density for SBN analysis. Connection density strongly affects global and nodal topological features, with a range of p = 0.05 to 0.15 recommended for stable and optimal results. Gifted individuals exhibit stronger intra-hemispheric and intra-modular connectivity, a more balanced distribution of left-to-right intra-hemispheric connections, and lower mean versatility, supporting efficient and stable cognitive processing. Moreover, anatomical modularity analyses based on von Economo indicate that higher cognitive performance is linked to enhanced connectivity in specific areas (such as secondary sensory area, motor to association area and secondary sensory to limbic area), alongside selective reduction in cer tain modular connections (such as motor to insular area, association to secondary sensory area and motor to secondary sensory area). Furthermore, topological features, including participation coefficient and local effi ciency, are linked to cognitive performance. These findings provide valuable insights into the SBNs underlying cognitive levels in children. © 2025 by SCITEPRESS– Science and Technology Publications, Lda.

关键词： Brain mapping

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Efficient Fault Detection in Nonlinear Industrial Process: A Reduced Kernel PCA-Based Spectral Clustering Approach

Efficient Fault Detection in Nonlinear Industrial Process: A...

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Artificial Intelligence, Applications, Innovation and Ethics (AI2E), International Conference for

作者： Khadija Attouri Majdi Mansouri Mansour Hajji Kais Bouzrara Laboratory of Advanced Materials and Nanotechnologies Higher Institute of Applied Sciences and Technology of Kasserine Kairouan University Tunisia Department of Electrical and Computer Engineering College of Engineering Sultan Qaboos University Muscat Oman Research Laboratory of Automation Signal Processing and Image National Engineering School of Monastir Tunisia

ISBN: (数字)9798331513733

ISBN: (纸本)9798331513740

The challenges posed by nonlinearities in industrial systems necessitate innovative techniques that outperform the limitations of traditional methods such as principal component analysis (PCA). While Kernel Principal Component Analysis (KPCA) offers a robust solution to handle nonlinear data, its computational requirements are a significant issue, especially for large-sized datasets. In this work, we propose a novel technique, namely, reduced kernel principal component analysis-based spectral clustering (RKPCA SpC ), to monitor and detect faults in the benchmark Tennessee Eastman process. The suggested approach addresses the complexity associated with KPCA by reducing data size during the model training phase. This reduction involves retaining only the principal components, preserving informative features, and selecting pertinent samples without compromising the original data's content. The efficacy of the proposed method is evaluated through key performance metrics, including false alarm rate (FAR), missed detection rate (MDR), detection time delay (DTD), and computation time (CT). Additionally, gained execution time (GET), gained storage space (GSP), and loss function (LF) are considered, providing a comprehensive assessment of the developed paradigms' effectiveness. The results demonstrate the promising capabilities of our proposed scheme.

关键词： Training Measurement Technological innovation Fault detection Delay effects Computational modeling Complexity theory Kernel Monitoring Principal component analysis

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Short-Term Photovoltaic Power Forecasting-Based Kernel Ridge Regression

Short-Term Photovoltaic Power Forecasting-Based Kernel Ridge...

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Artificial Intelligence, Applications, Innovation and Ethics (AI2E), International Conference for

作者： Manel Marweni Majdi Mansouri Mansour Hajji Mohamed Faouzi Mimouni Research Laboratory of Automation Signal Processing and Image National Engineering School of Monastir Tunisia Department of Electrical and Computer Engineering College of Engineering Sultan Qaboos University Muscat Oman Laboratory of Advanced Materials and Nanotechnologies Higher Institute of Applied Sciences and Technology of Kasserine Kairouan University Kasserine Tunisia

ISBN: (数字)9798331513733

ISBN: (纸本)9798331513740

The modern energy landscape is undergoing a seismic change from traditional, finite energy sources and toward cleaner, renewable alternatives. The restrictions faced by traditional sources, which are not only finite but increasingly shrink in the face of burgeoning global energy demands driven by population increase and industrial expansion, are driving this change. Although promising, renewable energy poses complications, particularly the reliance on climatic conditions. An important aspect of addressing these difficulties is effective energy management within distribution systems, which includes forecasting and optimization phases. This research focuses on forecasting using an advanced machine learning (ML) approach. Accurately forecasting renewable energy generation over time is critical for improving energy management. This technique is evaluated using a variety of performance indicators, including Mean Error (ME), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the Coefficient of Determination (R 2 ). Empirical studies support the method's usefulness, demonstrating noteworthy performance with low error rates.

关键词： Photovoltaic systems Renewable energy sources Technological innovation Accuracy Machine learning Predictive models Forecasting Kernel Energy management Optimization

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A two-stage approach for managing actuators redundancy and its application to fault tolerant flight control

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Chinese Journal of Aeronautics 2015年第2期28卷 469-477页

作者： Zhong Lunlong Félix Mora-Camino Tianjin Key Laboratory for Advanced Signal and Image Processing Civil Aviation University of China Automation Research Group of MAIAA ENAC Toulouse University

In safety-critical systems such as transportation aircraft, redundancy of actuators is introduced to improve fault tolerance. How to make the best use of remaining actuators to allow the system to continue achieving a desired operation in the presence of some actuators failures is the main subject of this paper. Considering that many dynamical systems, including flight dynamics of a transportation aircraft, can be expressed as an input affine nonlinear system, a new state repre- sentation is adopted here where the output dynamics are related with virtual inputs associated with the intended operation. This representation, as well as the distribution matrix associated with the effectiveness of the remaining operational actuators, allows us to define different levels of fault tol- erant governability with respect to actuators＇ failures. Then, a two-stage control approach is devel- oped, leading frst to the inversion of the output dynamics to get nominal values for the virtual inputs and then to the solution of a linear quadratic （LQ） problem to compute the solicitation of each operational actuator. The proposed approach is applied to the control of a transportation air- craft which performs a stabilized roll maneuver while a partial failure appears. Two fault scenarios are considered and the resulting performance of the proposed approach is displayed and discussed.

关键词： Actuator allocation Fault tolerant control Flight control Linear quadratic （LQ）problem Nonlinear control

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Simulation-Framework for purely digital CNN/MRF-architectures

Simulation-Framework for purely digital CNN/MRF-architecture...

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9th IEEE International Workshop on Cellular Neural Networks and their Applications, CNNA

作者： Stilkerich, Stephan C. EADS Corporate Research Center Image and Signal Processing Group D-81663 Munich Germany

Any kind of hardware-relevant modeling, simulation and analysis of purely digital and massively parallel architectures, which are based on CNN/MRF 1 processing principles is a time consuming, computing resources intensive, fault-prone and complex task. Until now there is no industrially qualified toolkit available to systematically support these tasks in a single closed environment In this contribution we present a novel Simulation-Framework for purely digital CNN/MRF processing systems. The unique modeling, hardware-relevant simulation and analysis capabilities unified in this Simulation-Framework allows it to systematically investigate (1) the massively parallel processing dynamic of digital CNN/MRF devices, (2) the model's convergence behavior and (3) complete CNN/MRF systems with an application-specific size. The paper is finalized by simulation results demonstrating the ability of the framework to handle CNN/MRF processing systems of realistic size and complexity. This manifests the industrial relevance of the proposed CNN/MRF Simulation-Framework.

关键词： Cellular neural networks

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Implementation and Performance Analysis of FFT and Inverse FFT Using Openmp, MPI and Hybrid Programming on Virtual SMP/HPC Architecture 1

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53rd Annual Convention of the Computer Society of India, CSI 2020

作者： Singla, Jai G. Padia, Kirti Signal and Image Processing Group Space Applications Centre Indian Space Research Organization Ahmedabad India

ISBN: (数字)9789811627231

ISBN: (纸本)9789811627224

A Fast Fourier transform (FFT) algorithm computes the discrete Fourier transform (DFT) of a sequence, or it’s inverse. Fourier analysis converts a signal from its original domain (often time or space) to a representation in the frequency domain and vice versa. An FFT rapidly computes such transformations by factorizing the DFT matrix into a product of sparse (mostly zero) factors. Fast Fourier transforms are widely used for many applications in engineering, science, and mathematics & signal and image processing. As, FFT remains a time consuming task on limited core machine and with a larger data set, we intended to implement this Fourier transformation technique on virtual SMP (HPC) with 144 CPU cores setup to improve the timings or to simultaneously handle multiple data sets. This paper consists of details about vSMP architecture designed by Scalemp [1], programming paradigms used, implementation details, performance and scalability analysis of Fast Fourier Transformation (FFT) on virtual Symmetric multiprocessor system (vSMP) using programming paradigms such as openMP, MPI and Hybrid programming, results and observations. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Discrete Fourier transforms

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Automatic Cloud Detection in Resourcesat LISS-3 Data using Spectral Angle Similarity and Mode Seeking Reference Spectra 15

Automatic Cloud Detection in Resourcesat LISS-3 Data using S...

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15th IEEE International Conference on Industrial and Information Systems, ICIIS 2020

作者： Misra, Indranil Manthira Moorthi, S. Dhar, Debajyoti Signal and Image Processing Group Space Applications Centre Indian Space Research Organisation Ahmedabad India

ISBN: (纸本)9781728185248

Optical remote sensing images contain cloud pixels, which need to be detected and masked for different earth observation studies and applications. In literature, various algorithms are reported which can detect cloud in multi-spectral remote sensing images. In this paper, we have developed an approach based on spectral angle computation that measures the radiometric gap between a reference pixel and input image pixels. Reference spectra is determined using mode seeking technique by taking multiple cloud pixels from multi-temporal images covering different land terrain. The processing chain is developed and tested in Resourcesat LISS-3 surface reflectance data. Result section shows that technique developed can determine thick cloud pixels with high confidence interval and compared with other novel cloud detection methods. © 2020 IEEE.

关键词： Pixels

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A fast algorithm for automated independent process separation from single channel biomedical signal recordings: FastIPA

A fast algorithm for automated independent process separatio...

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4th IET International Conference on Advances in Medical, signal and Information processing, MEDSIP 2008

作者： James, C.J. Davies, M.E. Signal Processing and Control Group ISVR University of Southampton United Kingdom IDCOM Joint Research Institute for Signal and Image Processing University of Edinburgh United Kingdom

ISBN: (纸本)9780863419348

Independent component analysis (ICA) has found many uses in source separation in biomedical signals. We highlight a methodology and put forward an algorithm which allows single channel ICA to be performed on single channel biomedical signal recordings. The algorithm uses a fast, deflationary approach to efficiently extract independent processes underlying the single channel recordings. We show that for processes which are reasonably spectrally disjoint the algorithm can separate out individual sources. We show examples of this using brain signal recordings and abdominal foetal recordings.

关键词： Independent component analysis

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Computer vision-based apple grading for golden delicious apples based on surface features

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Information processing in Agriculture 2017年第1期4卷 33-40页

作者： Payman Moallem Alireza Serajoddin Hossein Pourghassem Department of Electrical Engineering Faculty of EngineeringUniversity of IsfahanHezarjerib StreetIsfahanIran Applied Image and Signal Processing Research Group Research Center for Signal Processing and Intelligent SystemsUniversity of IsfahanHezarjerib StreetIsfahanIran Department of Electrical Engineering Najafabad BranchIslamic Azad UniversityNajafabadIsfahanIrand Digital Processing and Machine Vision Research Center Najafabad BranchIslamic Azad UniversityNajafabadIran

In this paper,a computer vision-based algorithm for golden delicious apple grading is proposed which works in six ***-apple pixels as background are firstly removed from input ***,stem end is detected by combination of morphological methods and Mahalanobis distant *** region is also detected by applying K-means clustering on the Cb component in YCbCr color *** that,defects segmentation is achieved using Multi-Layer Perceptron(MLP)neural *** the next step,stem end and calyx regions are removed from defected regions to refine and improve apple grading ***,statistical,textural and geometric features from refined defected regions are ***,for apple grading,a comparison between performance of Support Vector Machine(SVM),MLP and K-Nearest Neighbor(KNN)classifiers is *** is done in two manners which in the first one,an input apple is classified into two categories of healthy and *** the second manner,the input apple is classified into three categories of first rank,second rank and rejected *** both grading steps,SVM classifier works as the best one with recognition rate of 92.5%and 89.2%for two categories(healthy and defected)and three quality categories(first rank,second rank and rejected ones),among 120 different golden delicious apple images,respectively,considering K-folding with K=***,the accuracy of the proposed segmentation algorithms including stem end detection and calyx detection are evaluated for two different apple image databases.

关键词： Golden delicious apple Grading Computer vision Segmentation Classification

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Role of microfluctuations in accommodation: a novel approach to reduce non-accommodative noise

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International Journal of Ophthalmology(English edition) 2019年第4期12卷 681-684页

作者： Núria Lupón Joan Gispets Genis Cardona Alba Tàpia Hector Abril Department of Optics and Optometry Universitat Politècnica de Catalunya Applied Optics and Image Processing Research Group Universitat Politècnica de Catalunya

Accommodative response and its possible role in myopia development has been explored through the study of the microfluctuations(MFs) of accommodation, which are commonly divided in high(1.0 to 2.3 Hz) and low(0.1 to 0.6 Hz) frequency components. Previous research efforts have evidenced that a certain percentage of the amplitude of MFs seems not to originate in the accommodative response. We aimed to develop and test a new approach to reduce this non-accommodative noise. For this purpose, ten healthy participants were enrolled to determine the difference between the amplitude of MFs at near and distance for each range of frequencies, which was defined as the relative amplitude of MFs. The findings support the exploration of the relative rather than absolute values of the amplitude of MFs to better understand the contribution of both accommodative and non-accommodative factors to MFs.

关键词： accommodation cycloplegia microfluctuations myopia photorefraction

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