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Classification of skin lesions using morpho-granulometry features derived from color clusters

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Classification of skin lesions using morpho-granulometry fea...

International Conference on System Theory, control, and Computing (ICSTCC)

作者： Gigi Tăbăcaru Simona Moldovanu Marian Barbu Department of Automatic Control and Electrical Engineering Faculty of Automation Computers Electrical Engineering and Electronics "Dunarea de Jos" University of Galati Galati Romania Computer Science and Information Technology Faculty of Automation Computers Electrical Engineering and Electronics The Modelling & Simulation Laboratory "Dunarea de Jos" University of Galati Galati Romania

ISBN: (数字)9798350364293

ISBN: (纸本)9798350364309

The skin lesion can be thought of as a biological system, so the morpho-granulometry of significant color clusters found in skin lesions is one of the elements that reproduce in a natural way the structure of the lesion, this novelty is highlighted in this study. Important features of skin lesions can be modulated by fusing neural networks (NN) and machine learning (ML). By choosing the nevus and melanoma classes, the primary goal was accomplished, and three databases were used to test the methodology. The characteristics based on morpho-granulometry allowed for the identification of microstructure within the images, which can be very helpful in characterizing the biological system. Based on random forest (RF) and extreme gradient boosting (XGboost) classifiers, this work aimed to improve the classification performance of important feature selection. The selected features from three free image databases with three NNs were classified. In a binary classification of nevus vs. melanoma, the results showed that the pattern recognition neural network (PRNN), according to the PH2 database, provided an accuracy of 0.923 and an F1-score of 0.876. The classification is interpretable if it is not validated. In our study, the best results were verified with a logistic regression (LR) classifier.

关键词： Accuracy Recurrent neural networks Image color analysis Melanoma Biological systems Artificial neural networks Feature extraction Skin Lesions Classification tree analysis

New On-line Algorithms for Modelling, Identification and Simulation of Dynamic Systems Using Modulating Functions and Non-asymptotic State Estimators: Case Study for a Chosen Physical Process 21st

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New On-line Algorithms for Modelling, Identification and Sim...

21st International Conference on Computational Science, ICCS 2021

作者： Byrski, Witold Drapala, Michal Byrski, Jędrzej Department of Automatic Control and Robotics AGH University of Science and Technology Al. Mickiewicza 30 Kraków30-059 Poland Department of Applied Computer Science AGH University of Science and Technology Al. Mickiewicza 30 Kraków30-059 Poland

ISBN: (纸本)9783030779696

The paper presents an advanced application of computation methodology with complicated algorithms and calculation methods dedicated to optimal identification and simulation of dynamic processes. These models may have an unknown structure (the order of a differential equation) and unknown parameters. The presented methodology uses non-standard algorithms for identification of such continuous-time models that can represent linear and non-linear physical processes. Typical approaches, presented in the literature, most often utilize discrete-time models. However, for the case of continuous-time differential equation models, in which both, the parameters and the derivatives of the output variable are unknown, the solution is not easy. In the paper, for the solution of the identification task, the convolution transformation of the differential equation with a special Modulating Function will be used. Also, to be able to properly simulate the behaviour of the process based on the obtained model, the exact state integral observers with minimal norm will be used for the reconstruction of the exact value of the initial conditions (not their estimate). For multidimensional process case, with multiple control signals (many inputs), additional problems arise that make continuous identification and observation of the vector state (and hence simulation) impossible by the use of the standard methods. Application of the above-mentioned methods for solving this problem will be also presented. Both algorithms, for the parameter identification and the state observation, will be implemented on-line in two independent but cooperating windows that will simultaneously move along the time axis. The presented algorithms will be tested using data collected during the heat exchange process in an industrial glass melting installation. © 2021, Springer Nature Switzerland AG.

关键词： Differential equations

An iterative coordinate descent algorithm to compute sparse low-rank approximations

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arXiv 2021年

作者： Rusu, Cristian The Faculty of Automatic Control and Computer Science University Politehnica of Bucharest Romania Department of Computer Science Faculty of Mathematics and Computer Science University of Bucharest Romania

In this paper, we describe a new algorithm to build a few sparse principal components from a given data matrix. Our approach does not explicitly create the covariance matrix of the data and can be viewed as an extension of the Kogbetliantz algorithm to build an approximate singular value decomposition for a few principal components. We show the performance of the proposed algorithm to recover sparse principal components on various datasets from the literature and perform dimensionality reduction for classification applications. Copyright © 2021, The Authors. All rights reserved.

关键词： Singular value decomposition

Policy and Value Deep RL for Temporal Language-Agnostic Street Image Captioning 3

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Policy and Value Deep RL for Temporal Language-Agnostic Stre...

3rd IEEE Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability, ECBIOS 2021

作者： Chan, Christopher Chun Ki Chen, Chih-Cheng Delaney, Steven Yang, Cheng-Fu Department of Information Management Chaoyang University of Technology Taichung Taiwan Department of Automatic Control Engineering Feng Chia University Taichung40724 Taiwan Department of Aeronautical Engineering Chaoyang University of Technology Taichung Taiwan Department of Computer Science Ryerson University Canada Department of Chemical and Materials Engineering National University of Kaohsiung Kaohsiung Taiwan

ISBN: (纸本)9781728193045

Dashcams and street cameras capture a huge amount of street and traffic video data. Temporal image captioning for such video data has been approached with an encoder-decoder framework and achieved substantial success in captioning accuracy. Most recently, policy and value deep reinforcement learning (PVRL) emerged as an outperformer over other decision-making frameworks for image captioning. In this paper, we design a framework that utilizes PVRL on an in-house dataset containing temporal images of East Asia streets as a step towards designing a language-agnostic street image captioning framework that is capable of captioning temporal images of any street regardless of location. For language-invariance, the framework includes cross-modal retrieval at the character level so that similar words in different languages but in the same word-embedding space are grouped together. Our results show that PVRL can be applied successfully to temporal video captured the streets and achieve natural semantic captions;preliminary studies on our dataset suggest that the framework is capable of use in multi-language scenarios. © 2021 ECBIOS 2021. All rights reserved.

关键词： Deep learning

Learning to control Linear Systems can be Hard

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arXiv 2022年

作者： Tsiamis, Anastasios Ziemann, Ingvar Morari, Manfred Matni, Nikolai Pappas, George J. Automatic Control Laboratory ETH Zurich Switzerland School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden Department of Electrical and Systems Engineering University of Pennsylvania United States

In this paper, we study the statistical difficulty of learning to control linear systems. We focus on two standard benchmarks, the sample complexity of stabilization, and the regret of the online learning of the Linear Quadratic Regulator (LQR). Prior results state that the statistical difficulty for both benchmarks scales polynomially with the system state dimension up to system-theoretic quantities. However, this does not reveal the whole picture. By utilizing minimax lower bounds for both benchmarks, we prove that there exist non-trivial classes of systems for which learning complexity scales dramatically, i.e. exponentially, with the system dimension. This situation arises in the case of underactuated systems, i.e. systems with fewer inputs than states. Such systems are structurally difficult to control and their system theoretic quantities can scale exponentially with the system dimension dominating learning complexity. Under some additional structural assumptions (bounding systems away from uncontrollability), we provide qualitatively matching upper bounds. We prove that learning complexity can be at most exponential with the controllability index of the system, that is the degree of underactuation. Copyright © 2022, The Authors. All rights reserved.

关键词： Linear systems

GETAE: Graph Information Enhanced Deep Neural Network Ensemble ArchitecturE for Fake News Detection

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arXiv 2024年

作者： Truica, Ciprian-Octavian Apostol, Elena-Simona Marogel, Marius Paschke, Adrian Computer Science and Engineering Department Faculty of Automatic Control and Computers National University of Science and Technology Politehnica Bucharest Splaiul Independentei 313 Bucharest060042 Romania Academy of Romanian Scientists Ilfov 3 Bucharest050044 Romania Department of Mathematics and Computer Science Freie Universität Berlin Arnimallee 14 Berlin14195 Germany Fraunhofer Institute for Open Communication Systems Kaiserin-Augusta-Allee 31 Berlin10589 Germany

In today’s digital age, fake news has become a major problem that has serious consequences, ranging from social unrest to political upheaval. To address this issue, new methods for detecting and mitigating fake news are required. In this work, we propose to incorporate contextual and network-aware features into the detection process. This involves analyzing not only the content of a news article but also the context in which it was shared and the network of users who shared it, i.e., the information diffusion. Thus, we propose GETAE, Graph Information Enhanced Deep Neural Network Ensemble ArchitecturE for Fake News Detection, a novel ensemble architecture that uses textual content together with the social interactions to improve fake news detection. GETAE contains two Branches: the Text Branch and the Propagation Branch. The Text Branch uses Word and Transformer Embeddings and a Deep Neural Network based on feed-forward and bidirectional Recurrent Neural Networks ([Bi]RNN) for learning novel contextual features and creating a novel Text Content Embedding. The Propagation Branch considers the information propagation within the graph network and proposes a Deep Learning architecture that employs Node Embeddings to create novel Propagation Embedding. GETAE Ensemble combines the two novel embeddings, i.e., Text Content Embedding and Propagation Embedding, to create a novel Propagation-Enhanced Content Embedding which is afterward used for classification. The experimental results obtained on two real-world publicly available datasets, i.e., Twitter15 and Twitter16, prove that using this approach improves fake news detection and outperforms state-of-the-art models. © 2024, CC BY-NC-ND.

关键词： Graph embeddings

Fixed-time convergent control barrier functions for coupled multi-agent systems under STL tasks

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arXiv 2021年

作者： Sharifi, Maryam Dimarogonas, Dimos V. Department of Automatic Control School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden

This paper presents a control strategy based on a new notion of time-varying fixed-time convergent control barrier functions (TFCBFs) for a class of coupled multi-agent systems under signal temporal logic (STL) tasks. In this framework, each agent is assigned a local STL task regradless of the tasks of other agents. Each task may be dependent on the behavior of other agents which may cause conflicts on the satisfaction of all tasks. Our approach finds a robust solution to guarantee the fixed-time satisfaction of STL tasks in a least violating way and independent of the agents’ initial condition in the presence of undesired violation effects of the neighbor agents. Particularly, the robust performance of the task satisfactions can be adjusted in a user-specified way. Copyright © 2021, The Authors. All rights reserved.

关键词： Multi agent systems

Constrained Visual-Inertial Localization With Application And Benchmark in Laparoscopic Surgery

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Constrained Visual-Inertial Localization With Application An...

IEEE International Conference on Robotics and Automation (ICRA)

作者： Regine Hartwig Daniel Ostler Jean-Claude Rosenthal Hubertus Feuß ner Dirk Wilhelm Dirk Wollherr Surgical Department Research Group MITI Technical University of Munich (TUM) Germany Department of Electrical and Computer Engineering Chair of Automatic Control Engineering Technical University of Munich (TUM) Germany Fraunhofer HHI Berlin Germany

We propose a novel method to tackle the visual-inertial localization problem for constrained camera movements. We use residuals from the different modalities to jointly optimize a global cost function. The residuals emerge from IMU measurements, stereoscopic feature points, and constraints on possible solutions in SE(3). In settings where dynamic disturbances are frequent, the residuals reduce the complexity of the problem and make localization feasible. We verify the advantages of our method in a suitable medical use case and produce a dataset capturing a minimally invasive surgery in the abdomen. Our novel clinical dataset MITI is comparable to state-of-the-art evaluation datasets, contains calibration and synchronization and is available at [1].

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Optimisation of a Siamese Neural Network for Real-Time Energy Efficient Object Tracking 1

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International Conference on computer Vision and Graphics, ICCVG 2020

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

ISBN: (数字)9783030590062

ISBN: (纸本)9783030590055

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, Springer Nature Switzerland AG.

关键词： Field programmable gate arrays (FPGA)