Special Issue "10th Anniversary of Electronics: Recent Advances in Computer Science & Engineering"

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: 31 December 2021.

Special Issue Editors

Prof. Dr. Juan M. Corchado
E-Mail Website
Guest Editor
BISITE Research Group, Edificio Multiusos I+D+i, University of Salamanca, 37007 Salamanca, Spain
Interests: artificial Intelligence; machine learning; edge computing; distributed computing; Blockchain; consensus model; smart cities; smart grid
Special Issues and Collections in MDPI journals
Prof. Dr. Stefanos Kollias
E-Mail Website1 Website2
Guest Editor
School of Computer Science, University of Lincoln, Lincoln LN6 7TS, UK
Interests: image and video processing, analysis, coding, storage, retrieval; multimedia systems; computer graphics and virtual reality; artificial intelligence; neural networks; human–computer interaction; medical imaging
Special Issues and Collections in MDPI journals
Prof. Dr. Javid Taheri
E-Mail Website
Guest Editor
Department of Computer Science, Karlstad University, 651 88 Karlstad, Sweden
Interests: cloud computing; distributed computing; parallel computing; virtualization techniques; virtualized Networking; resource allocation and scheduling algorithms; optimization techniques; artificial intelligence (neural networks, fuzzy, etc)

Special Issue Information

Dear Colleagues,

It has now been ten years since the first paper was published in Electronics back in 2011. It has been a rocky road with many highs and many lows, but we are extremely proud to have reached this very important milestone of the 10th anniversary of the journal. To celebrate this momentous occasion, a Special Issue is being prepared which invites both members of the Editorial Board and outstanding renowned authors, including past editors and authors, to submit their high-quality works on the topic of “Computer Science & Engineering”.

Prof. Dr. Juan M. Corchado
Prof. Dr. Stefanos Kollias
Prof. Dr. Javid Taheri
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • AI
  • IoT
  • AIoT
  • Machine Learning
  • Industry 4.0
  • Smart Cities
  • Networks
  • Software Engineering
  • Intelligent Interaction
  • Edge Computing and Fog Computing

Published Papers (19 papers)

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Research

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Article
Communication Cost Reduction with Partial Structure in Federated Learning
Electronics 2021, 10(17), 2081; https://doi.org/10.3390/electronics10172081 - 27 Aug 2021
Viewed by 132
Abstract
Federated learning is a distributed learning algorithm designed to train a single server model on a server using different clients and their local data. To improve the performance of the server model, continuous communication with clients is required, and since the number of [...] Read more.
Federated learning is a distributed learning algorithm designed to train a single server model on a server using different clients and their local data. To improve the performance of the server model, continuous communication with clients is required, and since the number of clients is very large, the algorithm must be designed in consideration of the cost required for communication. In this paper, we propose a method for distributing a model with a structure different from that of the server model, distributing a model suitable for clients with different data sizes, and training a server model using the reconstructed model trained by the client. In this way, the server model deploys only a subset of the sequential model, collects gradient updates, and selectively applies updates to the server model. This method of delivering the server model at a lower cost to clients who only need smaller models can reduce the communication cost of training server models compared to standard methods. An image classification model was designed to verify the effectiveness of the proposed method via three data distribution situations and two datasets, and it was confirmed that training was accomplished only with a cost 0.229 times smaller than the standard method. Full article
Article
Motor Unit Discharges from Multi-Kernel Deconvolution of Single Channel Surface Electromyogram
Electronics 2021, 10(16), 2022; https://doi.org/10.3390/electronics10162022 - 21 Aug 2021
Viewed by 224
Abstract
Surface electromyogram (EMG) finds many applications in the non-invasive characterization of muscles. Extracting information on the control of motor units (MU) is difficult when using single channels, e.g., due to the low selectivity and large phase cancellations of MU action potentials (MUAPs). In [...] Read more.
Surface electromyogram (EMG) finds many applications in the non-invasive characterization of muscles. Extracting information on the control of motor units (MU) is difficult when using single channels, e.g., due to the low selectivity and large phase cancellations of MU action potentials (MUAPs). In this paper, we propose a new method to face this problem in the case of a single differential channel. The signal is approximated as a sum of convolutions of different kernels (adapted to the signal) and firing patterns, whose sum is the estimation of the cumulative MU firings. Three simulators were used for testing: muscles of parallel fibres with either two innervation zones (IZs, thus, with MUAPs of different phases) or one IZ and a model with fibres inclined with respect to the skin. Simulations were prepared for different fat thicknesses, distributions of conduction velocity, maximal firing rates, synchronizations of MU discharges, and variability of the inter-spike interval. The performances were measured in terms of cross-correlations of the estimated and simulated cumulative MU firings in the range of 0–50 Hz and compared with those of a state-of-the-art single-kernel algorithm. The median cross-correlations for multi-kernel/single-kernel approaches were 92.2%/82.4%, 98.1%/97.6%, and 95.0%/91.0% for the models with two IZs, one IZ (parallel fibres), and inclined fibres, respectively (all statistically significant differences, which were larger when the MUAP shapes were of greater difference). Full article
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Article
A Metaheuristic Based Approach for the Customer-Centric Perishable Food Distribution Problem
Electronics 2021, 10(16), 2018; https://doi.org/10.3390/electronics10162018 - 20 Aug 2021
Viewed by 231
Abstract
High transportation costs and poor quality of service are common vulnerabilities in various logistics networks, especially in food distribution. Here we propose a many-objective Customer-centric Perishable Food Distribution Problem that focuses on the cost, the quality of the product, and the service level [...] Read more.
High transportation costs and poor quality of service are common vulnerabilities in various logistics networks, especially in food distribution. Here we propose a many-objective Customer-centric Perishable Food Distribution Problem that focuses on the cost, the quality of the product, and the service level improvement by considering not only time windows but also the customers’ target time and their priority. Recognizing the difficulty of solving such model, we propose a General Variable Neighborhood Search (GVNS) metaheuristic based approach that allows to efficiently solve a subproblem while allowing us to obtain a set of solutions. These solutions are evaluated over some non-optimized criteria and then ranked using an a posteriori approach that requires minimal information about decision maker preferences. The computational results show (a) GVNS achieved same quality solutions as an exact solver (CPLEX) in the subproblem; (b) GVNS can generate a wide number of candidate solutions, and (c) the use of the a posteriori approach makes easy to generate different decision maker profiles which in turn allows to obtain different rankings of the solutions. Full article
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Article
Multiple-Searching Genetic Algorithm for Whole Test Suites
Electronics 2021, 10(16), 2011; https://doi.org/10.3390/electronics10162011 - 19 Aug 2021
Viewed by 200
Abstract
A test suite is a set of test cases that evaluate the quality of software. The aim of whole test suite generation is to create test cases with the highest coverage scores possible. This study investigated the efficiency of a multiple-searching genetic algorithm [...] Read more.
A test suite is a set of test cases that evaluate the quality of software. The aim of whole test suite generation is to create test cases with the highest coverage scores possible. This study investigated the efficiency of a multiple-searching genetic algorithm (MSGA) for whole test suite generation. In previous works, the MSGA has been effectively used in multicast routing of a network system and in the generation of test cases on individual coverage criteria for small- to medium-sized programs. The performance of the algorithms varies depending on the problem instances. In this experiment were generated whole test suites for complex programs. The MSGA was expanded in the EvoSuite test generation tool and compared with the available algorithms on EvoSuite in terms of the number of test cases, the number of statements, mutation score, and coverage score. All algorithms were evaluated on 14 problem instances with different corpus to satisfy multiple coverage criteria. The problem instances were Java open-source projects. Findings demonstrate that the MSGA generated test cases reached greater coverage scores and detected a larger number of faults in the test class when compared with the others. Full article
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Article
Algorithms for Finding Vulnerabilities and Deploying Additional Sensors in a Region with Obstacles
Electronics 2021, 10(12), 1504; https://doi.org/10.3390/electronics10121504 - 21 Jun 2021
Viewed by 383
Abstract
Consider a two-dimensional rectangular region guarded by a set of sensors, which may be smart networked surveillance cameras or simpler sensor devices. In order to evaluate the level of security provided by these sensors, it is useful to find and evaluate the path [...] Read more.
Consider a two-dimensional rectangular region guarded by a set of sensors, which may be smart networked surveillance cameras or simpler sensor devices. In order to evaluate the level of security provided by these sensors, it is useful to find and evaluate the path with the lowest level of exposure to the sensors. Then, if desired, additional sensors can be placed at strategic locations to increase the level of security provided. General forms of these two problems are presented in this paper. Next, the minimum exposure path is found by first using the sensing limits of the sensors to compute an approximate “feasible area” of interest, and then using a grid within this feasible area to search for the minimum exposure path in a systematic manner. Two algorithms are presented for the minimum exposure path problem, and an additional subsequently executed algorithm is proposed for sensor deployment. The proposed algorithms are shown to require significantly lower computational complexity than previous methods, with the fastest proposed algorithm requiring O(n2.5) time, as compared to O(mn3) for a traditional grid-based search method, where n is the number of sensors, m is the number of obstacles, and certain assumptions are made on the parameter values. Full article
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Article
RYEL: An Experimental Study in the Behavioral Response of Judges Using a Novel Technique for Acquiring Higher-Order Thinking Based on Explainable Artificial Intelligence and Case-Based Reasoning
Electronics 2021, 10(12), 1500; https://doi.org/10.3390/electronics10121500 - 21 Jun 2021
Viewed by 374
Abstract
The need for studies connecting machine explainability with human behavior is essential, especially for a detailed understanding of a human’s perspective, thoughts, and sensations according to a context. A novel system called RYEL was developed based on Subject-Matter Experts (SME) to investigate new [...] Read more.
The need for studies connecting machine explainability with human behavior is essential, especially for a detailed understanding of a human’s perspective, thoughts, and sensations according to a context. A novel system called RYEL was developed based on Subject-Matter Experts (SME) to investigate new techniques for acquiring higher-order thinking, the perception, the use of new computational explanatory techniques, support decision-making, and the judge’s cognition and behavior. Thus, a new spectrum is covered and promises to be a new area of study called Interpretation-Assessment/Assessment-Interpretation (IA-AI), consisting of explaining machine inferences and the interpretation and assessment from a human. It allows expressing a semantic, ontological, and hermeneutical meaning related to the psyche of a human (judge). The system has an interpretative and explanatory nature, and in the future, could be used in other domains of discourse. More than 33 experts in Law and Artificial Intelligence validated the functional design. More than 26 judges, most of them specializing in psychology and criminology from Colombia, Ecuador, Panama, Spain, Argentina, and Costa Rica, participated in the experiments. The results of the experimentation have been very positive. As a challenge, this research represents a paradigm shift in legal data processing. Full article
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Article
Remote Laboratory for Online Engineering Education: The RLAB-UOC-FPGA Case Study
Electronics 2021, 10(9), 1072; https://doi.org/10.3390/electronics10091072 - 01 May 2021
Viewed by 948
Abstract
Practical experiments are essential for engineering studies. Regarding the acquisition of practical and professional competences in a completely online scenario, the use of technology that allows students to carry out practical experiments is important. This paper presents a remote laboratory designed and developed [...] Read more.
Practical experiments are essential for engineering studies. Regarding the acquisition of practical and professional competences in a completely online scenario, the use of technology that allows students to carry out practical experiments is important. This paper presents a remote laboratory designed and developed by the Open University of Catalonia (RLAB-UOC), which allows engineering students studying online to carry out practical experiments anywhere and anytime with real electronic and communications equipment. The features of the remote laboratory and students’ satisfaction with its use are analyzed in real subjects across six semesters using a self-administered questionnaire in an FPGA-based case study. The results for the FPGA-based case study present the perception and satisfaction of students using the proposed remote laboratory in the acquisition of subject competences and content. Full article
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Article
Machine Learning Methods for Preterm Birth Prediction: A Review
Electronics 2021, 10(5), 586; https://doi.org/10.3390/electronics10050586 - 03 Mar 2021
Viewed by 849
Abstract
Preterm births affect around 15 million children a year worldwide. Current medical efforts focus on mitigating the effects of prematurity, not on preventing it. Diagnostic methods are based on parent traits and transvaginal ultrasound, during which the length of the cervix is examined. [...] Read more.
Preterm births affect around 15 million children a year worldwide. Current medical efforts focus on mitigating the effects of prematurity, not on preventing it. Diagnostic methods are based on parent traits and transvaginal ultrasound, during which the length of the cervix is examined. Approximately 30% of preterm births are not correctly predicted due to the complexity of this process and its subjective assessment. Based on recent research, there is hope that machine learning can be a helpful tool to support the diagnosis of preterm births. The objective of this study is to present various machine learning algorithms applied to preterm birth prediction. The wide spectrum of analysed data sets is the advantage of this survey. They range from electrohysterogram signals through electronic health records to transvaginal ultrasounds. Reviews of works on preterm birth already exist; however, this is the first review that includes works that are based on a transvaginal ultrasound examination. In this work, we present a critical appraisal of popular methods that have employed machine learning methods for preterm birth prediction. Moreover, we summarise the most common challenges incurred and discuss their possible application in the future. Full article
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Article
Machine Learning for Predictive Modelling of Ambulance Calls
Electronics 2021, 10(4), 482; https://doi.org/10.3390/electronics10040482 - 18 Feb 2021
Viewed by 722
Abstract
A novel machine learning approach is presented in this paper, based on extracting latent information and using it to assist decision making on ambulance attendance and conveyance to a hospital. The approach includes two steps: in the first, a forward model analyzes the [...] Read more.
A novel machine learning approach is presented in this paper, based on extracting latent information and using it to assist decision making on ambulance attendance and conveyance to a hospital. The approach includes two steps: in the first, a forward model analyzes the clinical and, possibly, non-clinical factors (explanatory variables), predicting whether positive decisions (response variables) should be given to the ambulance call, or not; in the second, a backward model analyzes the latent variables extracted from the forward model to infer the decision making procedure. The forward model is implemented through a machine, or deep learning technique, whilst the backward model is implemented through unsupervised learning. An experimental study is presented, which illustrates the obtained results, by investigating emergency ambulance calls to people in nursing and residential care homes, over a one-year period, using an anonymized data set provided by East Midlands Ambulance Service in United Kingdom. Full article
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Article
On the Selection of Process Mining Tools
Electronics 2021, 10(4), 451; https://doi.org/10.3390/electronics10040451 - 11 Feb 2021
Viewed by 584
Abstract
Process mining is a research discipline that applies data analysis and computational intelligence techniques to extract knowledge from event logs of information systems. It aims to provide new means to discover, monitor, and improve processes. Process mining has gained particular attention over recent [...] Read more.
Process mining is a research discipline that applies data analysis and computational intelligence techniques to extract knowledge from event logs of information systems. It aims to provide new means to discover, monitor, and improve processes. Process mining has gained particular attention over recent years and new process mining software tools, both academic and commercial, have been developed. This paper provides a survey of process mining software tools. It identifies and describes criteria that can be useful for comparing the tools. Furthermore, it introduces a multi-criteria methodology that can be used for the comparative analysis of process mining software tools. The methodology is based on three methods, namely ontology, decision tree, and Analytic Hierarchy Process (AHP), that can be used to help users decide which software tool best suits their needs. Full article
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Article
Creating Customized CGRAs for Scientific Applications
Electronics 2021, 10(4), 445; https://doi.org/10.3390/electronics10040445 - 11 Feb 2021
Viewed by 496
Abstract
Executing complex scientific applications on Coarse Grain Reconfigurable Arrays (CGRAs) offers improvements in the execution time and/or energy consumption when compared to optimized software implementations or even fully customized hardware solutions. In this work, we explore the potential of application analysis methods in [...] Read more.
Executing complex scientific applications on Coarse Grain Reconfigurable Arrays (CGRAs) offers improvements in the execution time and/or energy consumption when compared to optimized software implementations or even fully customized hardware solutions. In this work, we explore the potential of application analysis methods in such customized hardware solutions. We offer analysis metrics from various scientific applications and tailor the results that are to be used by MC-Def, a novel Mixed-CGRA Definition Framework targeting a Mixed-CGRA architecture that leverages the advantages of CGRAs and those of FPGAs by utilizing a customized cell-array along, with a separate LUT array being used for adaptability. Additionally, we present the implementation results regarding the VHDL-created hardware implementations of our CGRA cell concerning various scientific applications. Full article
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Article
Construction and Evaluation of QOL Specialized Dictionary SqolDic Using Vocabulary Meaning and QOL Scale
Electronics 2021, 10(4), 417; https://doi.org/10.3390/electronics10040417 - 08 Feb 2021
Viewed by 537
Abstract
Agents that build interactive relationships with people can provide appropriate support and generate behaviors by accurately grasping the state of the person. This study focuses on the quality of life (QOL), which can be assessed multidimensionally, and aims to estimate QOL scores in [...] Read more.
Agents that build interactive relationships with people can provide appropriate support and generate behaviors by accurately grasping the state of the person. This study focuses on the quality of life (QOL), which can be assessed multidimensionally, and aims to estimate QOL scores in the process of human interaction. Although vision-based estimation has been the main method for QOL estimation, we proposed a new text-based estimation method. We created a QOL-specific dictionary called SqolDic, which is based on large-scale Japanese textual data. To evaluate the effectiveness of SqolDic, we implemented a system that outputs the time-series variation of a user’s conversation content and the QOL scores based on it. In an experiment for estimating the content of user conversations based on a QOL scale by inputting data from actual human conversations, we achieved a maximum estimation accuracy of 91.2%. Additionally, in an experiment to estimate QOL score variability, we successfully estimated the mental health state and one of the QOL scales with a smaller distribution of error than that in previous studies. The experimental results demonstrated the effectiveness of our system in estimating conversation content and QOL scores as well as the effectiveness of our newly proposed QOL dictionary. Full article
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Article
WRGAN: Improvement of RelGAN with Wasserstein Loss for Text Generation
Electronics 2021, 10(3), 275; https://doi.org/10.3390/electronics10030275 - 25 Jan 2021
Viewed by 499
Abstract
Generative adversarial networks (GANs) were first proposed in 2014, and have been widely used in computer vision, such as for image generation and other tasks. However, the GANs used for text generation have made slow progress. One of the reasons is that the [...] Read more.
Generative adversarial networks (GANs) were first proposed in 2014, and have been widely used in computer vision, such as for image generation and other tasks. However, the GANs used for text generation have made slow progress. One of the reasons is that the discriminator’s guidance for the generator is too weak, which means that the generator can only get a “true or false” probability in return. Compared with the current loss function, the Wasserstein distance can provide more information to the generator, but RelGAN does not work well with Wasserstein distance in experiments. In this paper, we propose an improved neural network based on RelGAN and Wasserstein loss named WRGAN. Differently from RelGAN, we modified the discriminator network structure with 1D convolution of multiple different kernel sizes. Correspondingly, we also changed the loss function of the network with a gradient penalty Wasserstein loss. Our experiments on multiple public datasets show that WRGAN outperforms most of the existing state-of-the-art methods, and the Bilingual Evaluation Understudy(BLEU) scores are improved with our novel method. Full article
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Article
Privacy-Preserving Surveillance as an Edge Service Based on Lightweight Video Protection Schemes Using Face De-Identification and Window Masking
Electronics 2021, 10(3), 236; https://doi.org/10.3390/electronics10030236 - 21 Jan 2021
Cited by 1 | Viewed by 613
Abstract
With a myriad of edge cameras deployed in urban and suburban areas, many people are seriously concerned about the constant invasion of their privacy. There is a mounting pressure from the public to make the cameras privacy-conscious. This paper proposes a Privacy-preserving Surveillance [...] Read more.
With a myriad of edge cameras deployed in urban and suburban areas, many people are seriously concerned about the constant invasion of their privacy. There is a mounting pressure from the public to make the cameras privacy-conscious. This paper proposes a Privacy-preserving Surveillance as an Edge service (PriSE) method with a hybrid architecture comprising a lightweight foreground object scanner and a video protection scheme that operates on edge cameras and fog/cloud-based models to detect privacy attributes like windows, faces, and perpetrators. The Reversible Chaotic Masking (ReCAM) scheme is designed to ensure an end-to-end privacy while the simplified foreground-object detector helps reduce resource consumption by discarding frames containing only background-objects. A robust window-object detector was developed to prevent peeping via windows; whereas human faces are detected by using a multi-tasked cascaded convolutional neural network (MTCNN) to ensure de-identification. The extensive experimental studies and comparative analysis show that the PriSE scheme (i) can efficiently detect foreground objects, and scramble those frames that contain foreground objects at the edge cameras, and (ii) detect and denature window and face objects, and identify perpetrators at a fog/cloud server to prevent unauthorized viewing via windows, to ensure anonymity of individuals, and to deter criminal activities, respectively. Full article
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Article
EdgeAvatar: An Edge Computing System for Building Virtual Beings
Electronics 2021, 10(3), 229; https://doi.org/10.3390/electronics10030229 - 20 Jan 2021
Viewed by 685
Abstract
Dialogue systems, also known as conversational agents, are computing systems that use algorithms for speech and language processing to engage in conversation with humans or other conversation-capable systems. A chatbot is a conversational agent that has, as its primary goal, to maximize the [...] Read more.
Dialogue systems, also known as conversational agents, are computing systems that use algorithms for speech and language processing to engage in conversation with humans or other conversation-capable systems. A chatbot is a conversational agent that has, as its primary goal, to maximize the length of the conversation without any specific targeted task. When a chatbot is embellished with an artistic approach that is meant to evoke an emotional response, then it is called a virtual being. On the other hand, conversational agents that interact with the physical world require the use of specialized hardware to sense and process captured information. In this article we describe EdgeAvatar, a system based on Edge Computing principles for the creation of virtual beings. The objective of the EdgeAvatar system is to provide a streamlined and modular framework for virtual being applications that are to be deployed in public settings. We also present two implementations that use EdgeAvatar and are inspired by historical figures to interact with visitors of the Venice Biennale 2019. EdgeAvatar can adapt to fit different approaches for AI powered conversations. Full article
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Article
Muon–Electron Pulse Shape Discrimination for Water Cherenkov Detectors Based on FPGA/SoC
Electronics 2021, 10(3), 224; https://doi.org/10.3390/electronics10030224 - 20 Jan 2021
Cited by 1 | Viewed by 989
Abstract
The distinction of secondary particles in extensive air showers, specifically muons and electrons, is one of the requirements to perform a good measurement of the composition of primary cosmic rays. We describe two methods for pulse shape detection and discrimination of muons and [...] Read more.
The distinction of secondary particles in extensive air showers, specifically muons and electrons, is one of the requirements to perform a good measurement of the composition of primary cosmic rays. We describe two methods for pulse shape detection and discrimination of muons and electrons implemented on FPGA. One uses an artificial neural network (ANN) algorithm; the other exploits a correlation approach based on finite impulse response (FIR) filters. The novel hls4ml package is used to build the ANN inference model. Both methods were implemented and tested on Xilinx FPGA System on Chip (SoC) devices: ZU9EG Zynq UltraScale+ and ZC7Z020 Zynq. The data set used for the analysis was captured with a data acquisition system on an experimental site based on a water Cherenkov detector. A comparison of the accuracy of the detection, resources utilization and power consumption of both methods is presented. The results show an overall accuracy on particle discrimination of 96.62% for the ANN and 92.50% for the FIR-based correlation, with execution times of 848 ns and 752 ns, respectively. Full article
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Article
A Non-Linear Convolution Network for Image Processing
Electronics 2021, 10(2), 201; https://doi.org/10.3390/electronics10020201 - 17 Jan 2021
Cited by 1 | Viewed by 597
Abstract
This paper proposes a new neural network structure for image processing whose convolutional layers, instead of using kernels with fixed coefficients, use space-variant coefficients. The adoption of this strategy allows the system to adapt its behavior according to the spatial characteristics of the [...] Read more.
This paper proposes a new neural network structure for image processing whose convolutional layers, instead of using kernels with fixed coefficients, use space-variant coefficients. The adoption of this strategy allows the system to adapt its behavior according to the spatial characteristics of the input data. This type of layers performs, as we demonstrate, a non-linear transfer function. The features generated by these layers, compared to the ones generated by canonical CNN layers, are more complex and more suitable to fit to the local characteristics of the images. Networks composed by these non-linear layers offer performance comparable with or superior to the ones which use canonical Convolutional Networks, using fewer layers and a significantly lower number of features. Several applications of these newly conceived networks to classical image-processing problems are analyzed. In particular, we consider: Single-Image Super-Resolution (SISR), Edge-Preserving Smoothing (EPS), Noise Removal (NR), and JPEG artifacts removal (JAR). Full article
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Review

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Review
Challenges and Opportunities in Industry 4.0 for Mechatronics, Artificial Intelligence and Cybernetics
Electronics 2021, 10(16), 2001; https://doi.org/10.3390/electronics10162001 - 19 Aug 2021
Viewed by 245
Abstract
Industry 4.0 has risen as an integrated digital manufacturing environment, and it has created a novel research perspective that has thrust research to interdisciplinarity and exploitation of ICT advances. This work presents and discusses the main aspects of Industry 4.0 and how intelligence [...] Read more.
Industry 4.0 has risen as an integrated digital manufacturing environment, and it has created a novel research perspective that has thrust research to interdisciplinarity and exploitation of ICT advances. This work presents and discusses the main aspects of Industry 4.0 and how intelligence can be embedded in manufacturing to create the smart factory. It briefly describes the main components of Industry 4.0, and it focuses on the security challenges that the fully interconnected ecosystem of Industry 4.0 has to meet and the threats for each component. Preserving security has a crucial role in Industry 4.0, and it is vital for its existence, so the main research directions on how to ensure the confidentiality and integrity of the information shared among the Industry 4.0 components are presented. Another view is in light of the security issues that come as a result of enabling new technologies. Full article
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Review
Machine Learning Methods for Histopathological Image Analysis: A Review
Electronics 2021, 10(5), 562; https://doi.org/10.3390/electronics10050562 - 27 Feb 2021
Viewed by 638
Abstract
Histopathological images (HIs) are the gold standard for evaluating some types of tumors for cancer diagnosis. The analysis of such images is time and resource-consuming and very challenging even for experienced pathologists, resulting in inter-observer and intra-observer disagreements. One of the ways of [...] Read more.
Histopathological images (HIs) are the gold standard for evaluating some types of tumors for cancer diagnosis. The analysis of such images is time and resource-consuming and very challenging even for experienced pathologists, resulting in inter-observer and intra-observer disagreements. One of the ways of accelerating such an analysis is to use computer-aided diagnosis (CAD) systems. This paper presents a review on machine learning methods for histopathological image analysis, including shallow and deep learning methods. We also cover the most common tasks in HI analysis, such as segmentation and feature extraction. Besides, we present a list of publicly available and private datasets that have been used in HI research. Full article
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