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arXiv 2020年

作者： Przewlocka, Dominika Wasala, Mateusz Szolc, Hubert Blachut, Krzysztof Kryjak, Tomasz Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics AGH University of Science and Technology Krakow Poland

In this paper the research on optimisation of visual object tracking using a Siamese neural network for embedded vision systems is presented. It was assumed that the solution shall operate in real-time, preferably for a high resolution video stream, with the lowest possible energy consumption. To meet these requirements, techniques such as the reduction of computational precision and pruning were considered. Brevitas, a tool dedicated for optimisation and quantisation of neural networks for FPGA implementation, was used. A number of training scenarios were tested with varying levels of optimisations - from integer uniform quantisation with 16 bits to ternary and binary networks. Next, the influence of these optimisations on the tracking performance was evaluated. It was possible to reduce the size of the convolutional filters up to 10 times in relation to the original network. The obtained results indicate that using quantisation can significantly reduce the memory and computational complexity of the proposed network while still enabling precise tracking, thus allow to use it in embedded vision systems. Moreover, quantisation of weights positively affects the network training by decreasing overfitting. Copyright © 2020, The Authors. All rights reserved.

关键词： Field programmable gate arrays (FPGA)

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Landscape Change Monitoring using Satellite Data and Open Data Cube Platform

Landscape Change Monitoring using Satellite Data and Open Da...

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IEEE International Conference on Intelligent computer Communication and Processing (ICCP)

作者： Cătălin-Mihai Vâjâială-Tomici Ion-Dorinel Filip Florin Pop Faculty of Automatic Control and Computer Science University POLITEHNICA of Bucharest Romania National Institute for Research and Development in Informatics – ICI Bucharest Romania

ISBN: (数字)9781728190808

ISBN: (纸本)9781728190815

The project presented in this paper aims to bring to the Romanian stage a system capable of providing information on various land changes that occur in Romania. These changes can have multiple causes, either natural or caused by human activities. Local authorities, business people, and researchers can use this information to make informed decisions that can influence Romanian citizens' well-being. This paper presents a method to build a complex system at the national level, aiming to highlight the usefulness of using geospatial data. In order to interact in a pleasant way with geospatial data providers, this work is based on an open-source solution called Open Data Cube, which incorporates in a library different services, acting as an interface between the system itself and the data providers, but also as a standard for data storage. This paper explores three different use cases, namely the detection of deforestation, monitoring the evolution of urbanization, and the evolution of the Black Sea coast, especially in the area of the mouth of the Danube river. Although different, with totally different causes, these three examples highlight the versatility of such a system.

关键词： Indexes Satellites Monitoring Vegetation mapping Urban areas Open source software Rivers

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Optimisation of a siamese neural network for real-time energy efficient object tracking

TechRxiv

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TechRxiv 2020年

In this paper the research on optimisation of visual object tracking using a Siamese neural network for embedded vision systems is presented. It was assumed that the solution shall operate in real-time, preferably for a high resolution video stream, with the lowest possible energy consumption. To meet these requirements, techniques such as the reduction of computational precision and pruning were considered. Brevitas, a tool dedicated for optimisation and quantisation of neural networks for FPGA implementation, was used. A number of training scenarios were tested with varying levels of optimisations – from integer uniform quantisation with 16 bits to ternary and binary networks. Next, the influence of these optimisations on the tracking performance was evaluated. It was possible to reduce the size of the convolutional filters up to 10 times in relation to the original network. The obtained results indicate that using quantisation can significantly reduce the memory and computational complexity of the proposed network while still enabling precise tracking, thus allow to use it in embedded vision systems. Moreover, quantisation of weights positively affects the network training by decreasing overfitting. © 2020, CC BY-NC-SA.

关键词： Field programmable gate arrays (FPGA)

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A novel framework employing deep multi-attention channels network for the autonomous detection of metastasizing cells through fluorescence microscopy

arXiv

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arXiv 2023年

作者： Mamalakis, Michail Macfarlane, Sarah C. Notley, Scott V. Gad, Annica K.B. Panoutsos, George Insigneo Institute for in-silico Medicine University of Sheffield Sheffield United Kingdom Department of Infection Immunity and Cardiovascular Disease Department of Computer science Sheffield United Kingdom Department of Psychiatry Cambridge University Cambridge United Kingdom Department of Oncology and Metabolism The Medical School University of Sheffield Sheffield United Kingdom Department of Automatic Control and Systems Engineering University of Sheffield Sheffield United Kingdom Madeira Chemistry Research Centre University of Madeira Funchal Portugal Department of Oncology-Pathology Karolinska Institutet Stockholm Sweden

We developed a transparent computational large-scale imaging-based framework that can distinguish between normal and metastasizing human cells. The method relies on fluorescence microscopy images showing the spatial organization of actin and vimentin filaments in normal and metastasizing single cells, using a combination of multiattention channels network and global explainable techniques. We test a classification between normal cells (Bj primary fibroblast), and their isogenically matched, transformed and invasive counterpart (BjTertSV40TRasV12). Manual annotation is not trivial to automate due to the intricacy of the biologically relevant features. In this research, we utilized established deep learning networks and our new multi-attention channel architecture. To increase the interpretability of the network - crucial for this application area - we developed an interpretable global explainable approach correlating the weighted geometric mean of the total cell images and their local GradCam scores. The significant results from our analysis unprecedently allowed a more detailed, and biologically relevant understanding of the cytoskeletal changes that accompany oncogenic transformation of normal to invasive and metastasizing cells. We also paved the way for a possible spatial micrometre-level biomarker for future development of diagnostic tools against metastasis (spatial distribution of vimentin). © 2023, CC BY-SA.

关键词： Cells

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Artificial Intelligence Based Segmentation for Skin-Layer Visualization from Optical Coherence Tomography Images

Artificial Intelligence Based Segmentation for Skin-Layer Vi...

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Gilang Titah Ramadhan Yori Pusparani Ardha Ardea Prisilla Wei-Cheng Shen Hsu-Tang Cheng Ben-Yi Liau Yih-Kuen Jan Wen-Thong Chang Chih-Yang Lin Chi-Wen Lung Department of Computer Science and Information Engineering Asia University Taichung Taiwan Department of Visual Communication Design Budi Luhur University Jakarta Indonesia Department of Digital Media Design Asia University Taichung Taiwan Department of Fashion Design LaSalle College Jakarta Jakarta Indonesia Department of Creative Product Design Asia University Taichung Taiwan Division of Plastic Surgery Asia University Hospital Taichung Taiwan Department of Automatic Control Engineering Feng Chia University Taichung Taiwan Department of Kinesiology and Community Health University of Illinois Urbana USA Department of Mechanical Engineering National Central University Taoyuan Taiwan

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

Changes in epidermal thickness are linked to various skin diseases, such as diabetic foot. Optical Coherence Tomography (OCT), a noninvasive imaging technology, enables detailed visualization of skin layers. This study employed a deep learning approach using the U-Net architecture to analyze OCT images, specifically focusing on the stratum corneum (SC) and epidermis (ED) layers. Utilizing a dataset of 10,000 images, the U-Net model achieved 92 % accuracy in predicting SC and 95% accuracy for ED. These results highlight the potential of this method for accurate 3D visualization of SC and ED layers, paving the way for precise assessment of skin layer thickness and its changes in medical applications.

关键词： Deep learning Visualization Solid modeling Image segmentation Accuracy Three-dimensional displays Optical coherence tomography Medical services Predictive models Epidermis

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Lockdown interventions in SIR models: Is the reproduction number the right control variable?

arXiv

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arXiv 2021年

作者： Cianfanelli, Leonardo Parise, Francesca Acemoglu, Daron Como, Giacomo Ozdaglar, Asuman Dipartimento di Scienze Matematiche Politecnico di Torino Italy Department of Electrical and Computer Engineering Cornell University United States Department of Economics Massachusetts Institute of Technology MIT United States Department of Automatic Control Lund University Sweden Department of Electrical Engineering and Computer Science MIT United States

The recent COVID-19 pandemic highlighted the need of non-pharmaceutical interventions in the first stages of a pandemic. Among these, lockdown policies proved unavoidable yet extremely costly from an economic perspective. To better understand the tradeoffs between economic and epidemic costs of lockdown interventions, we here focus on a simple SIR epidemic model and study lockdowns as solutions to an optimal control problem. We first show numerically that the optimal lockdown policy exhibits a phase transition from suppression to mitigation as the time horizon grows, i.e., if the horizon is short the optimal strategy is to impose severe lockdown to avoid diffusion of the infection, whereas if the horizon is long the optimal control steers the system to herd immunity to reduce economic loss. We then consider two alternative policies, motivated by government responses to the COVID-19 pandemic, where lockdown levels are selected to either stabilize the reproduction number (i.e., "flatten the curve") or the fraction of infected (i.e., containing the number of hospitalizations). We compute analytically the performance of these two feedback policies and compare them to the optimal control. Interestingly, we show that in the limit of infinite horizon stabilizing the number of infected is preferable to controlling the reproduction number, and in fact yields close to optimal performance. Copyright © 2021, The Authors. All rights reserved.

关键词： Optimal control systems

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Gradient Boosting Application in Forecasting of Performance Indicators Values for Measuring the Efficiency of Promotions in FMCG Retail

arXiv

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arXiv 2020年

作者： Henzel, Joanna Sikora, Marek Department of Computer Networks and System Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology ul. Akademicka 16 Gliwice44-100 Poland

—In the paper, a problem of forecasting promotion efficiency is raised. The authors propose a new approach, using the gradient boosting method for this task. Six performance indicators are introduced to capture the promotion effect. For each of them, within predefined groups of products, a model was trained. A description of using these models for forecasting and optimising promotion efficiency is provided. Data preparation and hyperparameters tuning processes are also described. The experiments were performed for three groups of products from a large grocery company. Copyright © 2020, The Authors. All rights reserved.

关键词： Benchmarking

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Activation maps of convolutional neural networks as a tool for brain degeneration tracking in early diagnosis of dementia in Parkinson's disease based on magnetic resonance imaging

Research Square

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Research Square 2021年

作者： Suwalska, Aleksandra Siuda, Joanna Kocot, Szymon Zmuda, Weronika Rudzinska-Bar, Monika Polanska, Joanna Department of Data Science and Engineering Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice Poland Department of Neurology School of Medicine Medical University of Silesia Katowice Poland Department of Neurology Faculty of Medicine and Health Sciences Andrzej Frycz Modrzewski Krakow University Krakow Poland

Identification of Parkinson’s Disease (PD) patients at risk for development of dementia is crucial for early intervention. However, diagnosing dementia in PD patients requires advanced, time-consuming and expensive imaging techniques (such as DaT-SCAN-SPECT). The study aims to prove the usefulness of convolutional neural networks (CNN) for the identification of brain areas related to the progress of cognitive impairment by using standard Magnetic Resonance Imaging (MRI) sequences. T1&T2 sequences of 18 patients were used in the pilot study. Activation maps were generated, and the brain regions most involved in the classification process were identified, showing areas potentially significant in the diagnosis of cognitive impairment severity. The cerebellum was proven significant for distinguishing the above-mentioned classes in relative cerebellum volume (ANOVA p-value=0.0038 with large effect size η2=0.5254) and folding (p-value=0.0031, η2=0.5357), which is consistent with reports by other authors. Our analysis demonstrates that convolutional neural networks combined with a proper image preprocessing pipeline could be used for feature extraction in MRI sequences and can successfully support the identification of disease-specific abnormalities of the brain image. © 2021, CC BY.

关键词： Magnetic resonance imaging

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Reduct-based ranking of attributes

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Procedia computer science 2020年 176卷 2576-2585页

作者： Beata Zielosko Urszula Stańczyk Institute of Computer Science University of Silesia in Katowice Bȩdzińska 39 41-200 Sosnowiec Poland Department of Graphics Computer Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 2A 44-100 Gliwice Poland

The paper is dedicated to the area of feature selection, in particular a notion of attribute rankings that allow to estimate importance of variables. In the research presented for ranking construction a new weighting factor was defined, based on relative reducts. A reduct constitutes an embedded mechanism of feature selection, specific to rough set theory. The proposed factor takes into account the number of reducts in which a given attribute exists, as well as lengths of reducts. Two approaches for reduct generation were employed and compared, with search executed by a genetic algorithm. To validate the usefulness of the reduct-based rankings in the process of feature reduction, for gradually decreasing subsets of attributes, selected through rankings, sets of decision rules were induced in classical rough set approach. The performance of all rule classifiers was evaluated, and experimental results showed that the proposed rankings led to at least the same, or even increased classification accuracy for reduced sets of features than in the case of operating on the entire set of condition attributes. The experiments were performed on datasets from stylometry domain, with treating authorship attribution as a classification task, and stylometric descriptors as characteristic features defining writing styles.

关键词： feature reduction reduct ranking of attributes rough sets decision rules classification stylometry

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Assessing quality of decision reducts

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Procedia computer science 2020年 176卷 3273-3282页

作者： Urszula Stańczyk Beata Zielosko Department of Graphics Computer Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 2A 44-100 Gliwice Poland Institute of Computer Science University of Silesia in Katowice Bȩdzińska 39 41-200 Sosnowiec Poland

The paper presents research focused on decision reducts, a feature reduction mechanism inherent to rough sets theory. As a reduct enables to protect the discriminative properties of attributes with respect to described concepts, from the point of data representation, a reduct length is considered to be the most important measure of its quality. However, such approach is insufficient while taking into account the performance of a reduct-based rule classifier applied to test samples. When many reducts of the same length are available, they can lead to vastly different predictions. The paper provides a description for the proposed procedure for iterative reduct generation, which results in decrease of diversity in the observed levels of accuracy, supporting reduct selection. The procedure was applied for binary classification with balanced classes, for the stylometric task of authorship attribution.

关键词： reduct rough sets decision rules classification stylometry feature reduction

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