sensors-logo

Journal Browser

Journal Browser

Special Issue "Convergence of Intelligent Data Acquisition and Advanced Computing Systems"

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

Deadline for manuscript submissions: 30 September 2020.

Special Issue Editors

Dr. Grigore Stamatescu
Website1 Website2
Guest Editor
Department of Automatic Control and Industrial Informatics, University "Politehnica" of Bucharest, Romania
Interests: networked embedded sensing; information processing; control engineering; building automation; smart city
Special Issues and Collections in MDPI journals
Prof. Anatoly Sachenko
Website
Guest Editor
Department for Information Computer Systems and Control,Ternopil National Economic University, Ukraine
Interests: precision sensor measuring systems; artificial neural network applications; wireless sensor networks; intelligent cybersecurity systems; image processing and pattern recognition
Prof. Dan Popescu
Website
Guest Editor
Department of Automatic Control and Industrial Informatics, University "Politehnica" of Bucharest, Romania
Interests: image processing; pattern recognition; unmanned aerial systems; wireless sensor networks; intelligent data processing

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:

  1. Advanced instrumentation and data acquisition systems;
  2. Advanced mathematical methods for data acquisition and computing systems;
  3. Big Data;
  4. Computational intelligence for instrumentation and data acquisition systems;
  5. Data analysis and modeling;
  6. Embedded systems
  7. Intelligent distributed systems and remote control;
  8. Intelligent information systems, data mining, and ontology;
  9. Internet of Things;
  10. Pattern recognition, digital image, and signal processing;
  11. Virtual instrumentation systems;
  12. Special stream in intelligent instrumentation and data acquisition systems in advanced manufacturing for Industry 4;
  13. Special stream in intelligent robotics and sensors;
  14. Special stream in machine learning;
  15. Special stream in smart buildings and smart cities;
  16. Special stream in smart meters;
  17. Special stream in wireless systems;
  18. 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 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. 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 2000 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 (5 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Open AccessArticle
Improved Pixel-Level Pavement-Defect Segmentation Using a Deep Autoencoder
Sensors 2020, 20(9), 2557; https://doi.org/10.3390/s20092557 - 30 Apr 2020
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 [...] Read more.
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
Show Figures

Figure 1

Open AccessArticle
EUROPA: A Case Study for Teaching Sensors, Data Acquisition and Robotics via a ROS-Based Educational Robot
Sensors 2020, 20(9), 2469; https://doi.org/10.3390/s20092469 - 27 Apr 2020
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, [...] Read more.
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
Show Figures

Figure 1

Open AccessArticle
Data-Driven Living Spaces’ Heating Dynamics Modeling in Smart Buildings using Machine Learning-Based Identification
Sensors 2020, 20(4), 1071; https://doi.org/10.3390/s20041071 - 16 Feb 2020
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 [...] Read more.
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
Show Figures

Figure 1

Open AccessArticle
Advanced UAV–WSN System for Intelligent Monitoring in Precision Agriculture
Sensors 2020, 20(3), 817; https://doi.org/10.3390/s20030817 - 03 Feb 2020
Cited by 1
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 [...] Read more.
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
Show Figures

Figure 1

Open AccessArticle
Evaluation of Three Different Approaches for Automated Time Delay Estimation for Distributed Sensor Systems of Electric Vehicles
Sensors 2020, 20(2), 351; https://doi.org/10.3390/s20020351 - 08 Jan 2020
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 [...] Read more.
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
Show Figures

Figure 1

Back to TopTop