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Convergence of Intelligent Data Acquisition and Advanced Computing Systems

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (30 September 2020) | Viewed by 42625

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Dr. Grigore Stamatescu

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Department of Automation and Industrial Informatics, Faculty of Automatic Control and Computers, University “Politehnica” of Bucharest, 313 Splaiul Independentei, 060042 Bucharest, Romania
Interests: networked-embedded sensing; information processing; control engineering; building automation; smart city; data analytics; computational intelligence; industry and energy applications
Special Issues, Collections and Topics in MDPI journals

Prof. Anatoly Sachenko

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Guest Editor

Department for Information Computer Systems and Control, Ternopil National Economic University, Ternopil, Ukraine
Interests: precision sensor measuring systems; artificial neural network applications; wireless sensor networks; intelligent cybersecurity systems; image processing and pattern recognition

Prof. Dr. Dan Popescu

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Guest Editor

Department of Automation and Industrial Informatics, Faculty of Automatic Control and Computer Science, University POLITEHNICA of Bucharest, 060042 Bucharest, Romania
Interests: image acquisition; image processing; feature extraction; image classification; image segmentation; artificial neural networks; deep learning; wireless sensor networks; unmanned aerial vehicles; data fusion; data processing in medicine; data processing in agriculture
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems (IEEE IDAACS 2019) will be held on September 18 to 21, 2019 at University of Lorraine, Metz, France. The main goal of the IDAACS is to provide a forum for high-quality reports on the state-of-the-art theory, technology, and applications of intelligent data acquisition and advanced computer systems as used in measurement, automation, and scientific research, in industry and in business. This Special Issue will contain a selection of papers submitted and accepted at IDAACS 2019. We warmly invite researchers to submit their contributions to this Special Issue. Potential topics include but are not limited to:

Advanced instrumentation and data acquisition systems;
Advanced mathematical methods for data acquisition and computing systems;
Big Data;
Computational intelligence for instrumentation and data acquisition systems;
Data analysis and modeling;
Embedded systems
Intelligent distributed systems and remote control;
Intelligent information systems, data mining, and ontology;
Internet of Things;
Pattern recognition, digital image, and signal processing;
Virtual instrumentation systems;
Special stream in intelligent instrumentation and data acquisition systems in advanced manufacturing for Industry 4;
Special stream in intelligent robotics and sensors;
Special stream in machine learning;
Special stream in smart buildings and smart cities;
Special stream in smart meters;
Special stream in wireless systems;
Workshop cyber physical systems and Internet of Things dependability.

The Special Issue also welcomes external contributions from interested researchers on the above mentioned topics.

Dr. Grigore Stamatescu
Prof. Anatoly Sachenko
Prof. Dan Popescu
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 submissions that pass pre-check are 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. Sensors 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 2600 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

intelligent sensors
data acquisition
advanced computing systems
information processing
wireless sensor networks
internet of things

Published Papers (10 papers)

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Editorial

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4 pages, 159 KiB

Open AccessEditorial

Convergence of Intelligent Data Acquisition and Advanced Computing Systems

by Grigore Stamatescu, Anatoliy Sachenko and Dan Popescu

Sensors 2021, 21(7), 2262; https://doi.org/10.3390/s21072262 - 24 Mar 2021

Cited by 1 | Viewed by 2132

Abstract

This editorial article briefly outlines the objectives and achieved goals of the Special Issue on “Convergence of Intelligent Data Acquisition and Advanced Computing Systems” running between September 2019 and September 2020 in the Sensors journal [...] Full article

(This article belongs to the Special Issue Convergence of Intelligent Data Acquisition and Advanced Computing Systems)

Research

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16 pages, 15628 KiB

Open AccessArticle

Power-Oriented Monitoring of Clock Signals in FPGA Systems for Critical Application

by Oleksandr Drozd, Grzegorz Nowakowski, Anatoliy Sachenko, Viktor Antoniuk, Volodymyr Kochan and Myroslav Drozd

Sensors 2021, 21(3), 792; https://doi.org/10.3390/s21030792 - 25 Jan 2021

Cited by 5 | Viewed by 2029

Abstract

This paper presents a power-oriented monitoring of clock signals that is designed to avoid synchronization failure in computer systems such as FPGAs. The proposed design reduces power consumption and increases the power-oriented checkability in FPGA systems. These advantages are due to improvements in the evaluation and measurement of corresponding energy parameters. Energy parameter orientation has proved to be a good solution for detecting a synchronization failure that blocks logic monitoring circuits. Key advantages lay in the possibility to detect a synchronization failure hidden in safety-related systems by using traditional online testing that is based on logical checkability. Two main types of power-oriented monitoring are considered: detecting a synchronization failure based on the consumption and the dissipation of power, which uses temperature and current consumption sensors, respectively. The experiments are performed on real FPGA systems with the controlled synchronization disconnection and the use of the computer-aided design (CAD) utility to estimate the decreasing values of the energy parameters. The results demonstrate the limited checkability of FPGA systems when using the thermal monitoring of clock signals and success in monitoring by the consumption current. Full article

(This article belongs to the Special Issue Convergence of Intelligent Data Acquisition and Advanced Computing Systems)

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21 pages, 3409 KiB

Open AccessArticle

A Novel Framework Using Deep Auto-Encoders Based Linear Model for Data Classification

by Ahmad M. Karim, Hilal Kaya, Mehmet Serdar Güzel, Mehmet R. Tolun, Fatih V. Çelebi and Alok Mishra

Sensors 2020, 20(21), 6378; https://doi.org/10.3390/s20216378 - 09 Nov 2020

Cited by 15 | Viewed by 2859

Abstract

This paper proposes a novel data classification framework, combining sparse auto-encoders (SAEs) and a post-processing system consisting of a linear system model relying on Particle Swarm Optimization (PSO) algorithm. All the sensitive and high-level features are extracted by using the first auto-encoder which is wired to the second auto-encoder, followed by a Softmax function layer to classify the extracted features obtained from the second layer. The two auto-encoders and the Softmax classifier are stacked in order to be trained in a supervised approach using the well-known backpropagation algorithm to enhance the performance of the neural network. Afterwards, the linear model transforms the calculated output of the deep stacked sparse auto-encoder to a value close to the anticipated output. This simple transformation increases the overall data classification performance of the stacked sparse auto-encoder architecture. The PSO algorithm allows the estimation of the parameters of the linear model in a metaheuristic policy. The proposed framework is validated by using three public datasets, which present promising results when compared with the current literature. Furthermore, the framework can be applied to any data classification problem by considering minor updates such as altering some parameters including input features, hidden neurons and output classes. Full article

(This article belongs to the Special Issue Convergence of Intelligent Data Acquisition and Advanced Computing Systems)

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17 pages, 2814 KiB

Open AccessArticle

Contribution to Speeding-Up the Solving of Nonlinear Ordinary Differential Equations on Parallel/Multi-Core Platforms for Sensing Systems

by Vahid Tavakkoli, Kabeh Mohsenzadegan, Jean Chamberlain Chedjou and Kyandoghere Kyamakya

Sensors 2020, 20(21), 6130; https://doi.org/10.3390/s20216130 - 28 Oct 2020

Cited by 2 | Viewed by 1834

Abstract

Solving ordinary differential equations (ODE) on heterogenous or multi-core/parallel embedded systems does significantly increase the operational capacity of many sensing systems in view of processing tasks such as self-calibration, model-based measurement and self-diagnostics. The main challenge is usually related to the complexity of the processing task at hand which costs/requires too much processing power, which may not be available, to ensure a real-time processing. Therefore, a distributed solving involving multiple cores or nodes is a good/precious option. Also, speeding-up the processing does also result in significant energy consumption or sensor nodes involved. There exist several methods for solving differential equations on single processors. But most of them are not suitable for an implementation on parallel (i.e., multi-core) systems due to the increasing communication related network delays between computing nodes, which become a main and serious bottleneck to solve such problems in a parallel computing context. Most of the problems faced relate to the very nature of differential equations. Normally, one should first complete calculations of a previous step in order to use it in the next/following step. Hereby, it appears also that increasing performance (e.g., through increasing step sizes) may possibly result in decreasing the accuracy of calculations on parallel/multi-core systems like GPUs. In this paper, we do create a new adaptive algorithm based on the Adams–Moulton and Parareal method (we call it PAMCL) and we do compare this novel method with other most relevant implementations/schemes such as the so-called DOPRI5, PAM, etc. Our algorithm (PAMCL) is showing very good performance (i.e., speed-up) while compared to related competing algorithms, while thereby ensuring a reasonable accuracy. For a better usage of computing units/resources, the OpenCL platform is selected and ODE solver algorithms are optimized to work on both GPUs and CPUs. This platform does ensure/enable a high flexibility in the use of heterogeneous computing resources and does result in a very efficient utilization of available resources when compared to other comparable/competing algorithm/schemes implementations. Full article

(This article belongs to the Special Issue Convergence of Intelligent Data Acquisition and Advanced Computing Systems)

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19 pages, 1991 KiB

Open AccessArticle

A Cost-Effective Electric Vehicle Intelligent Charge Scheduling Method for Commercial Smart Parking Lots Using a Simplified Convex Relaxation Technique

by Muhammad Jawad, Muhammad Bilal Qureshi, Sahibzada Muhammad Ali, Noman Shabbir, Muhammad Usman Shahid Khan, Afnan Aloraini and Raheel Nawaz

Sensors 2020, 20(17), 4842; https://doi.org/10.3390/s20174842 - 27 Aug 2020

Cited by 14 | Viewed by 3374

Abstract

Deployment of efficient and cost-effective parking lots is a known bottleneck for the electric vehicles (EVs) sector. A comprehensive solution incorporating the requirements of all key stakeholders is required. Taking up the challenge, we propose a real-time EV smart parking lot model to attain the following objectives: (a) maximize the smart parking lot revenue by accommodating maximum number of EVs and (b) minimize the cost of power consumption by participating in a demand response (DR) program offered by the utility since it is a tool to answer and handle the electric power usage requirements for charging the EV in the smart parking lot. With a view to achieving these objectives, a linear programming-based binary/cyclic (0/1) optimization technique is developed for the EV charge scheduling process. It is difficult to solve the problems of binary optimization in real-time given that the complexity of the problem increases with the increase in number of EV. We deploy a simplified convex relaxation technique integrated with the linear programming solution to overcome this problem. The algorithm achieves: minimum power consumption cost of the EV smart parking lot; efficient utilization of available power; maximization of the number of the EV to be charged; and minimum impact on the EV battery lifecycle. DR participation provide benefits by offering time-based and incentive-based hourly intelligent charging schedules for the EV. A thorough comparison is drawn with existing variable charging rate-based techniques in order to demonstrate the comparative validity of our proposed technique. The simulation results show that even under no DR event, the proposed scheme results in 2.9% decrease in overall power consumption cost for a 500 EV scenario when compared to variable charging rate method. Moreover, in similar conditions, such as no DR event and for 500 EV arrived per day, there is a 2.8% increase in number of EV charged per day, 3.2% improvement in the average state-of-charge (SoC) of the EV, 12.47% reduction in the average time intervals required to achieve final SoC. Full article

(This article belongs to the Special Issue Convergence of Intelligent Data Acquisition and Advanced Computing Systems)

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21 pages, 9071 KiB

Open AccessArticle

Improved Pixel-Level Pavement-Defect Segmentation Using a Deep Autoencoder

by Rytis Augustauskas and Arūnas Lipnickas

Sensors 2020, 20(9), 2557; https://doi.org/10.3390/s20092557 - 30 Apr 2020

Cited by 47 | Viewed by 5426

Abstract

Convolutional neural networks perform impressively in complicated computer-vision image-segmentation tasks. Vision-based systems surpass humans in speed and accuracy in quality inspection tasks. Moreover, the maintenance of big infrastructures, such as roads, bridges, or buildings, is tedious and time-demanding work. In this research, we addressed pavement-quality evaluation by pixelwise defect segmentation using a U-Net deep autoencoder. Additionally, to the original neural network architecture, we utilized residual connections, atrous spatial pyramid pooling with parallel and “Waterfall” connections, and attention gates to perform better defect extraction. The proposed neural network configurations showed a segmentation performance improvement over U-Net with no significant computational overhead. Statistical and visual performance evaluation was taken into consideration for the model comparison. Experiments were conducted on CrackForest, Crack500, GAPs384, and mixed datasets. Full article

(This article belongs to the Special Issue Convergence of Intelligent Data Acquisition and Advanced Computing Systems)

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18 pages, 2553 KiB

Open AccessArticle

EUROPA: A Case Study for Teaching Sensors, Data Acquisition and Robotics via a ROS-Based Educational Robot

by Georgios Karalekas, Stavros Vologiannidis and John Kalomiros

Sensors 2020, 20(9), 2469; https://doi.org/10.3390/s20092469 - 27 Apr 2020

Cited by 39 | Viewed by 6853

Abstract

Robots have become a popular educational tool in secondary education, introducing scientific, technological, engineering and mathematical concepts to students all around the globe. In this paper EUROPA, an extensible, open software and open hardware robotic platform is presented focusing on teaching physics, sensors, data acquisition and robotics. EUROPA’s software infrastructure is based οn Robot Operating System (ROS). It includes easy to use interfaces for robot control and interaction with users and thus can easily be incorporated in Science, Technology, Engineering and Mathematics (STEM) and robotics classes. EUROPA was designed taking into account current trends in educational robotics. An overview of widespread robotic platforms is presented, documenting several critical parameters of interest such as their architecture, sensors, actuators and controllers, their approximate cost, etc. Finally, an introductory STEM curriculum developed for EUROPA and applied in a class of high school students is presented. Full article

(This article belongs to the Special Issue Convergence of Intelligent Data Acquisition and Advanced Computing Systems)

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16 pages, 3961 KiB

Open AccessArticle

Data-Driven Living Spaces’ Heating Dynamics Modeling in Smart Buildings using Machine Learning-Based Identification

by Roozbeh Sadeghian Broujeny, Kurosh Madani, Abdennasser Chebira, Veronique Amarger and Laurent Hurtard

Sensors 2020, 20(4), 1071; https://doi.org/10.3390/s20041071 - 16 Feb 2020

Cited by 10 | Viewed by 2683

Abstract

Modeling and control of the heating feature of living spaces remain challenging tasks because of the intrinsic nonlinear nature of the involved processes as well as the strong nonlinearity of the entailed dynamic parameters in those processes. Although nowadays, adaptive heating controllers represent a crucial need for smart building energy management systems (SBEMS) as well as an appealing perspective for their effectiveness in optimizing energy efficiency, unfortunately, the leakage of models competent in handling the complexity of real living spaces’ heating processes means the control strategies implemented in most SBEMSs are still conventional. Within this context and by considering that the living space’s occupation rate (i.e., by users or residents) may affect the model and the issued heating control strategy of the concerned living space, we have investigated the design and implementation of a data-driven machine learning-based identification of the building’s living space dynamic heating conduct, taking into account the occupancy (by the residents) of the heated space. In fact, the proposed modeling strategy takes advantage, on the one hand, of the forecasting capacity of the time-series of the nonlinear autoregressive exogenous (NARX) model, and on the other hand, from the multi-layer perceptron’s (MLP) learning and generalization skills. The proposed approach has been implemented and applied for modeling the dynamic heating conduct of a real five-floor building’s living spaces located at Senart Campus of University Paris-Est Créteil (UPEC), taking into account their occupancy (by users of this public building). The obtained results assessing the accuracy and addictiveness of the investigated hybrid machine learning-based approach are reported and discussed. Full article

(This article belongs to the Special Issue Convergence of Intelligent Data Acquisition and Advanced Computing Systems)

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25 pages, 8990 KiB

Open AccessArticle

Advanced UAV–WSN System for Intelligent Monitoring in Precision Agriculture

by Dan Popescu, Florin Stoican, Grigore Stamatescu, Loretta Ichim and Cristian Dragana

Sensors 2020, 20(3), 817; https://doi.org/10.3390/s20030817 - 03 Feb 2020

Cited by 150 | Viewed by 11298

Abstract

The growing need for food worldwide requires the development of a high-performance, high-productivity, and sustainable agriculture, which implies the introduction of new technologies into monitoring activities related to control and decision-making. In this regard, this paper presents a hierarchical structure based on the collaboration between unmanned aerial vehicles (UAVs) and federated wireless sensor networks (WSNs) for crop monitoring in precision agriculture. The integration of UAVs with intelligent, ground WSNs, and IoT proved to be a robust and efficient solution for data collection, control, analysis, and decisions in such specialized applications. Key advantages lay in online data collection and relaying to a central monitoring point, while effectively managing network load and latency through optimized UAV trajectories and in situ data processing. Two important aspects of the collaboration were considered: designing the UAV trajectories for efficient data collection and implementing effective data processing algorithms (consensus and symbolic aggregate approximation) at the network level for the transmission of the relevant data. The experiments were carried out at a Romanian research institute where different crops and methods are developed. The results demonstrate that the collaborative UAV–WSN–IoT approach increases the performances in both precision agriculture and ecological agriculture. Full article

(This article belongs to the Special Issue Convergence of Intelligent Data Acquisition and Advanced Computing Systems)

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18 pages, 1880 KiB

Open AccessArticle

Evaluation of Three Different Approaches for Automated Time Delay Estimation for Distributed Sensor Systems of Electric Vehicles

by Jakob Pfeiffer, Xuyi Wu and Ahmed Ayadi

Sensors 2020, 20(2), 351; https://doi.org/10.3390/s20020351 - 08 Jan 2020

Cited by 6 | Viewed by 2789

Abstract

Deviations between High Voltage (HV) current measurements and the corresponding real values provoke serious problems in the power trains of Electric Vehicle (EVs). Examples for these problems have inaccurate performance coordinations and unnecessary power limitations during driving or charging. The main reason for the deviations are time delays. By correcting these delays with accurate Time Delay Estimation (TDE), our data shows that we can reduce the measurement deviations from 25% of the maximum current to below 5%. In this paper, we present three different approaches for TDE. We evaluate all approaches with real data from power trains of EVs. To enable an execution on automotive Electronic Control Unit (ECUs), the focus of our evaluation lies not only on the accuracy of the TDE, but also on the computational efficiency. The proposed Linear Regression (LR) approach suffers even from small noise and offsets in the measurement data and is unsuited for our purpose. A better alternative is the Variance Minimization (VM) approach. It is not only more noise-resistant but also very efficient after the first execution. Another interesting approach are Adaptive Filter (AFs), introduced by Emadzadeh et al. Unfortunately, AFs do not reach the accuracy and efficiency of VM in our experiments. Thus, we recommend VM for TDE of HV current signals in the power train of EVs and present an additional optimization to enable its execution on ECUs. Full article

(This article belongs to the Special Issue Convergence of Intelligent Data Acquisition and Advanced Computing Systems)

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