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Advanced Intelligent Control through Versatile Intelligent Portable Platforms
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Advanced Intelligent Control through Versatile Intelligent Portable Platforms

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

Deadline for manuscript submissions: closed (31 March 2020) | Viewed by 101072

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Editor

Prof. Dr. Luige Vladareanu

E-Mail Website1 Website2
Guest Editor
Institute of Solid Mechanics of the Romanian Academy, 010141 Bucharest, Romania
Interests: robot control; intelligent control; artificial intelligence; intelligent agents; intelligent sensor systems; advanced intelligent control methods and techniques; intelligent decision support systems; versatile intelligent portable platforms; human–robot (H2R) interaction systems; machine-to-machine (M2M) interfaces; prediction; machine learning; IoT technologies; cyberphysical systems; IT Industry 4.0 concept; industrial systems in the digital age; intelligent sensors applied to rescue robots; firefighting robots; rehabilitation robots; robot-assisted surgery; domestic robots
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Advanced intelligent control is a rapidly developing, complex, challenging field with great practical importance and potential. It is an inter-disciplinary field, which combines and extends theories and methods from control theory, computer science, and operations research areas with the aim of developing controllers that are highly adaptable to significant unanticipated changes.

Intelligent control imitates human intelligence for learning, decision-making, and problem solving. These human characteristics encompass experience, learning, adapting, and changing methods of approach to solve problems. Intelligent control techniques allow the development of an environment to recreate the advantages of natural intelligence with artificial intelligence.

Advances in sensors, actuators, computation technology, and communication networks provide the necessary tools for the implementation of intelligent control hardware. Practical applications using intelligent sensors for this control method have emerged from artificial intelligence and computer-controlled systems as an interdisciplinary field. These are aimed at a variety of relevant scientific research fields involving machine learning, including deep learning, bio-inspired algorithms, petri nets, recurrent neural networks, neuro-fuzzy control, Bayesian control, genetic control, and intelligent agents (cognitive/conscious control) as well as extensions to traditional techniques such as neutrosophic logic, extenics control, and artificial intelligence in general.

This Special Issue aims to present and communicate new trends in the design, control, and applications of real-time intelligent sensor system control using advanced intelligent control methods and techniques. Thus, we welcome the submission of original research papers and review articles that report recent advancements in intelligent control using intelligent sensors. In particular, we encourage submissions related to the use of innovative multi-sensor fusion techniques integrated through Versatile Intelligent Portable (VIP) Platforms that combine computer vision, virtual and augmented reality (VR&AR), intelligent communication (e.g., remote control), adaptive sensor networks, and intelligent decision support systems (IDSS, e.g., remote sensing) and their integration with DSS, such as GA-based DSS, fuzzy sets DSS, rough sets-based DSS, intelligent agent-assisted DSS, process mining integration to decision support, adaptive DSS; computer vision based DSS, sensory and robotic DSS, human–robot (H2R) interaction systems, and machine-to-machine (M2M) interfaces.

We also invite authors to submit articles related to the utilization of new technologies with advanced intelligent control through Versatile Intelligent Portable Platforms, such as enhanced IoT technologies and applications in the 5G densification era, bio-inspired techniques for future manufacturing enterprise control, a cyber-physical systems approach to cognitive enterprise, development of the IT Industry 4.0 concept , industrial systems in the digital age, cloud computing, robotics and automation with applications such as human aid mechatronics, movement in unstructured and uneven environments for military applications, rescue robots, firefighting robots, rehabilitation robots, robot-assisted surgery, and domestic robots.

Prof. Dr. Luige Vladareanu
Guest Editor

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Keywords

  • intelligent control
  • robot control
  • intelligent sensor systems
  • intelligent decision support systems
  • Versatile Intelligent Portable Platforms
  • new technologies
  • adaptive sensor networks
  • virtual and augmented reality
  • intelligent remote control and communication

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Published Papers (16 papers)

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Editorial

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6 pages, 171 KiB  
Editorial
Advanced Intelligent Control through Versatile Intelligent Portable Platforms
by Luige Vladareanu
Sensors 2020, 20(13), 3644; https://doi.org/10.3390/s20133644 - 29 Jun 2020
Cited by 5 | Viewed by 2723
Abstract
Deep research and communicating new trends in the design, control and applications of the real time control of intelligent sensors systems using advanced intelligent control methods and techniques is the main purpose of this research. The innovative multi-sensor fusion techniques, integrated through the [...] Read more.
Deep research and communicating new trends in the design, control and applications of the real time control of intelligent sensors systems using advanced intelligent control methods and techniques is the main purpose of this research. The innovative multi-sensor fusion techniques, integrated through the Versatile Intelligent Portable (VIP) platforms are developed, combined with computer vision, virtual and augmented reality (VR&AR) and intelligent communication, including remote control, adaptive sensor networks, human-robot (H2R) interaction systems and machine-to-machine (M2M) interfaces. Intelligent decision support systems (IDSS), including remote sensing, and their integration with DSS, GA-based DSS, fuzzy sets DSS, rough sets-based DSS, intelligent agent-assisted DSS, process mining integration into decision support, adaptive DSS, computer vision based DSS, sensory and robotic DSS, are highlighted in the field of advanced intelligent control. Full article
(This article belongs to the Special Issue Advanced Intelligent Control through Versatile Intelligent Portable Platforms)

Research

Jump to: Editorial, Review

22 pages, 3158 KiB  
Article
Detection of Anomalous Behavior in Modern Smartphones Using Software Sensor-Based Data
by Victor Vlădăreanu, Valentin-Gabriel Voiculescu, Vlad-Alexandru Grosu, Luige Vlădăreanu, Ana-Maria Travediu, Hao Yan, Hongbo Wang and Laura Ruse
Sensors 2020, 20(10), 2768; https://doi.org/10.3390/s20102768 - 13 May 2020
Cited by 3 | Viewed by 3028
Abstract
This paper describes the steps involved in obtaining a set of relevant data sources and the accompanying method using software-based sensors to detect anomalous behavior in modern smartphones based on machine-learning classifiers. Three classes of models are investigated for classification: logistic regressions, shallow [...] Read more.
This paper describes the steps involved in obtaining a set of relevant data sources and the accompanying method using software-based sensors to detect anomalous behavior in modern smartphones based on machine-learning classifiers. Three classes of models are investigated for classification: logistic regressions, shallow neural nets, and support vector machines. The paper details the design, implementation, and comparative evaluation of all three classes. If necessary, the approach could be extended to other computing devices, if appropriate changes were made to the software infrastructure, based upon mandatory capabilities of the underlying hardware. Full article
(This article belongs to the Special Issue Advanced Intelligent Control through Versatile Intelligent Portable Platforms)
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17 pages, 2006 KiB  
Article
High Precision Positioning with Multi-Camera Setups: Adaptive Kalman Fusion Algorithm for Fiducial Markers
by Dragos Constantin Popescu, Ioan Dumitrache, Simona Iuliana Caramihai and Mihail Octavian Cernaianu
Sensors 2020, 20(9), 2746; https://doi.org/10.3390/s20092746 - 11 May 2020
Cited by 3 | Viewed by 3569
Abstract
The paper addresses the problem of fusing the measurements from multiple cameras in order to estimate the position of fiducial markers. The objectives are to increase the precision and to extend the working area of the system. The proposed fusion method employs an [...] Read more.
The paper addresses the problem of fusing the measurements from multiple cameras in order to estimate the position of fiducial markers. The objectives are to increase the precision and to extend the working area of the system. The proposed fusion method employs an adaptive Kalman algorithm which is used for calibrating the setup of cameras as well as for estimating the pose of the marker. Special measures are taken in order to mitigate the effect of the measurement noise. The proposed method is further tested in different scenarios using a Monte Carlo simulation, whose qualitative precision results are determined and compared. The solution is designed for specific positioning and alignment tasks in physics experiments, but also, has a degree of generality that makes it suitable for a wider range of applications. Full article
(This article belongs to the Special Issue Advanced Intelligent Control through Versatile Intelligent Portable Platforms)
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21 pages, 8088 KiB  
Article
Facial Expressions Recognition for Human–Robot Interaction Using Deep Convolutional Neural Networks with Rectified Adam Optimizer
by Daniel Octavian Melinte and Luige Vladareanu
Sensors 2020, 20(8), 2393; https://doi.org/10.3390/s20082393 - 23 Apr 2020
Cited by 73 | Viewed by 10806
Abstract
The interaction between humans and an NAO robot using deep convolutional neural networks (CNN) is presented in this paper based on an innovative end-to-end pipeline method that applies two optimized CNNs, one for face recognition (FR) and another one for the facial expression [...] Read more.
The interaction between humans and an NAO robot using deep convolutional neural networks (CNN) is presented in this paper based on an innovative end-to-end pipeline method that applies two optimized CNNs, one for face recognition (FR) and another one for the facial expression recognition (FER) in order to obtain real-time inference speed for the entire process. Two different models for FR are considered, one known to be very accurate, but has low inference speed (faster region-based convolutional neural network), and one that is not as accurate but has high inference speed (single shot detector convolutional neural network). For emotion recognition transfer learning and fine-tuning of three CNN models (VGG, Inception V3 and ResNet) has been used. The overall results show that single shot detector convolutional neural network (SSD CNN) and faster region-based convolutional neural network (Faster R-CNN) models for face detection share almost the same accuracy: 97.8% for Faster R-CNN on PASCAL visual object classes (PASCAL VOCs) evaluation metrics and 97.42% for SSD Inception. In terms of FER, ResNet obtained the highest training accuracy (90.14%), while the visual geometry group (VGG) network had 87% accuracy and Inception V3 reached 81%. The results show improvements over 10% when using two serialized CNN, instead of using only the FER CNN, while the recent optimization model, called rectified adaptive moment optimization (RAdam), lead to a better generalization and accuracy improvement of 3%-4% on each emotion recognition CNN. Full article
(This article belongs to the Special Issue Advanced Intelligent Control through Versatile Intelligent Portable Platforms)
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27 pages, 5237 KiB  
Article
3D Object Reconstruction from Imperfect Depth Data Using Extended YOLOv3 Network
by Audrius Kulikajevas, Rytis Maskeliūnas, Robertas Damaševičius and Edmond S. L. Ho
Sensors 2020, 20(7), 2025; https://doi.org/10.3390/s20072025 - 3 Apr 2020
Cited by 19 | Viewed by 5111
Abstract
State-of-the-art intelligent versatile applications provoke the usage of full 3D, depth-based streams, especially in the scenarios of intelligent remote control and communications, where virtual and augmented reality will soon become outdated and are forecasted to be replaced by point cloud streams providing explorable [...] Read more.
State-of-the-art intelligent versatile applications provoke the usage of full 3D, depth-based streams, especially in the scenarios of intelligent remote control and communications, where virtual and augmented reality will soon become outdated and are forecasted to be replaced by point cloud streams providing explorable 3D environments of communication and industrial data. One of the most novel approaches employed in modern object reconstruction methods is to use a priori knowledge of the objects that are being reconstructed. Our approach is different as we strive to reconstruct a 3D object within much more difficult scenarios of limited data availability. Data stream is often limited by insufficient depth camera coverage and, as a result, the objects are occluded and data is lost. Our proposed hybrid artificial neural network modifications have improved the reconstruction results by 8.53% which allows us for much more precise filling of occluded object sides and reduction of noise during the process. Furthermore, the addition of object segmentation masks and the individual object instance classification is a leap forward towards a general-purpose scene reconstruction as opposed to a single object reconstruction task due to the ability to mask out overlapping object instances and using only masked object area in the reconstruction process. Full article
(This article belongs to the Special Issue Advanced Intelligent Control through Versatile Intelligent Portable Platforms)
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27 pages, 7312 KiB  
Article
Connected Bike-smart IoT-based Cycling Training Solution
by George Catargiu, Eva-H. Dulf and Liviu C. Miclea
Sensors 2020, 20(5), 1473; https://doi.org/10.3390/s20051473 - 7 Mar 2020
Cited by 7 | Viewed by 13385
Abstract
The Connected Bike project combines several technologies, both hardware and software, to provide cycling enthusiasts with a modern alternative solution for training. Therefore, a trainer can monitor online through a Web Application some of the important parameters for training, more specifically the speed, [...] Read more.
The Connected Bike project combines several technologies, both hardware and software, to provide cycling enthusiasts with a modern alternative solution for training. Therefore, a trainer can monitor online through a Web Application some of the important parameters for training, more specifically the speed, cadence and power generated by the cyclist. Also, the trainer can see at every moment where the rider is with the aid of a GPS module. The system is built out of both hardware and software components. The hardware is in charge of collecting, scaling, converting and sending data from sensors. On the software side, there is the server, which consists of the Back-End and the MQTT (Message Queues Telemetry Transport) Broker, as well as the Front-End of the Web Application that displays and manages data as well as collaboration between cyclists and trainers. Finally, there is the Android Application that acts like a remote command for the hardware module on the bike, giving the rider control over how and when the ride is monitored. Full article
(This article belongs to the Special Issue Advanced Intelligent Control through Versatile Intelligent Portable Platforms)
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23 pages, 17204 KiB  
Article
Automatic Distortion Rectification of Wide-Angle Images Using Outlier Refinement for Streamlining Vision Tasks
by Vijay Kakani, Hakil Kim, Jongseo Lee, Choonwoo Ryu and Mahendar Kumbham
Sensors 2020, 20(3), 894; https://doi.org/10.3390/s20030894 - 7 Feb 2020
Cited by 15 | Viewed by 7310
Abstract
The study proposes an outlier refinement methodology for automatic distortion rectification of wide-angle and fish-eye lens camera models in the context of streamlining vision-based tasks. The line-members sets are estimated in a scene through accumulation of line candidates emerging from the same edge [...] Read more.
The study proposes an outlier refinement methodology for automatic distortion rectification of wide-angle and fish-eye lens camera models in the context of streamlining vision-based tasks. The line-members sets are estimated in a scene through accumulation of line candidates emerging from the same edge source. An iterative optimization with an outlier refinement scheme was applied to the loss value, to simultaneously remove the extremely curved outliers from the line-members set and update the robust line members as well as estimating the best-fit distortion parameters with lowest possible loss. The proposed algorithm was able to rectify the distortions of wide-angle and fish-eye cameras even in extreme conditions such as heavy illumination changes and severe lens distortions. Experiments were conducted using various evaluation metrics both at the pixel-level (image quality, edge stretching effects, pixel-point error) as well as higher-level use-cases (object detection, height estimation) with respect to real and synthetic data from publicly available, privately acquired sources. The performance evaluations of the proposed algorithm have been investigated using an ablation study on various datasets in correspondence to the significance analysis of the refinement scheme and loss function. Several quantitative and qualitative comparisons were carried out on the proposed approach against various self-calibration approaches. Full article
(This article belongs to the Special Issue Advanced Intelligent Control through Versatile Intelligent Portable Platforms)
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12 pages, 2847 KiB  
Article
The Growth of Ga2O3 Nanowires on Silicon for Ultraviolet Photodetector
by Badriyah Alhalaili, Ruxandra Vidu and M. Saif Islam
Sensors 2019, 19(23), 5301; https://doi.org/10.3390/s19235301 - 2 Dec 2019
Cited by 26 | Viewed by 5231
Abstract
We investigated the effect of silver catalysts to enhance the growth of Ga2O3 nanowires. The growth of Ga2O3 nanowires on a P+-Si (100) substrate was demonstrated by using a thermal oxidation technique at high temperatures [...] Read more.
We investigated the effect of silver catalysts to enhance the growth of Ga2O3 nanowires. The growth of Ga2O3 nanowires on a P+-Si (100) substrate was demonstrated by using a thermal oxidation technique at high temperatures (~1000 °C) in the presence of a thin silver film that serves as a catalyst layer. We present the results of morphological, compositional, and electrical characterization of the Ga2O3 nanowires, including the measurements on photoconductance and transient time. Our results show that highly oriented, dense and long Ga2O3 nanowires can be grown directly on the surface of silicon. The Ga2O3 nanowires, with their inherent n-type characteristics formed a pn heterojunction when grown on silicon. The heterojunction showed rectifying characteristics and excellent UV photoresponse. Full article
(This article belongs to the Special Issue Advanced Intelligent Control through Versatile Intelligent Portable Platforms)
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21 pages, 5449 KiB  
Article
Novel PDMS-Based Sensor System for MPWM Measurements of Picoliter Volumes in Microfluidic Devices
by Mihăiţă Nicolae Ardeleanu, Ileana Nicoleta Popescu, Iulian Nicolae Udroiu, Emil Mihai Diaconu, Simona Mihai, Emil Lungu, Badriyah Alhalaili and Ruxandra Vidu
Sensors 2019, 19(22), 4886; https://doi.org/10.3390/s19224886 - 8 Nov 2019
Cited by 8 | Viewed by 3814
Abstract
In order for automatic microinjection to serve biomedical and genetic research, we have designed and manufactured a PDMS-based sensor with a circular section channel using the microwire molding technique. For the very precise control of microfluidic transport, we developed a microfluidic pulse width [...] Read more.
In order for automatic microinjection to serve biomedical and genetic research, we have designed and manufactured a PDMS-based sensor with a circular section channel using the microwire molding technique. For the very precise control of microfluidic transport, we developed a microfluidic pulse width modulation system (MPWM) for automatic microinjections at a picoliter level. By adding a computer-aided detection and tracking of fluid-specific elements in the microfluidic circuit, the PDMS microchannel sensor became the basic element in the automatic control of the microinjection sensor. With the PDMS microinjection sensor, we precise measured microfluidic volumes under visual detection, assisted by very precise computer equipment (with precision below 1 μm) based on image processing. The calibration of the MPWM system was performed to increase the reproducibility of the results and to detect and measure microfluidic volumes. The novel PDMS-based sensor system for MPWM measurements of microfluidic volumes contributes to the advancement of intelligent control methods and techniques, which could lead to new developments in the design, control, and in applications of real-time intelligent sensor system control. Full article
(This article belongs to the Special Issue Advanced Intelligent Control through Versatile Intelligent Portable Platforms)
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20 pages, 9472 KiB  
Article
Detection of Participation and Training Task Difficulty Applied to the Multi-Sensor Systems of Rehabilitation Robots
by Hao Yan, Hongbo Wang, Luige Vladareanu, Musong Lin, Victor Vladareanu and Yungui Li
Sensors 2019, 19(21), 4681; https://doi.org/10.3390/s19214681 - 28 Oct 2019
Cited by 15 | Viewed by 3535
Abstract
In the process of rehabilitation training for stroke patients, the rehabilitation effect is positively affected by how much physical activity the patients take part in. Most of the signals used to measure the patients’ participation are EMG signals or oxygen consumption, which increase [...] Read more.
In the process of rehabilitation training for stroke patients, the rehabilitation effect is positively affected by how much physical activity the patients take part in. Most of the signals used to measure the patients’ participation are EMG signals or oxygen consumption, which increase the cost and the complexity of the robotic device. In this work, we design a multi-sensor system robot with torque and six-dimensional force sensors to gauge the patients’ participation in training. By establishing the static equation of the mechanical leg, the man–machine interaction force of the patient can be accurately extracted. Using the impedance model, the auxiliary force training mode is established, and the difficulty of the target task is changed by adjusting the K value of auxiliary force. Participation models with three intensities were developed offline using support vector machines, for which the C and σ parameters are optimized by the hybrid quantum particle swarm optimization and support vector machines (Hybrid QPSO-SVM) algorithm. An experimental statistical analysis was conducted on ten volunteers’ motion representation in different training tasks, which are divided into three stages: over-challenge, challenge, less challenge, by choosing characteristic quantities with significant differences among the various difficulty task stages, as a training set for the support vector machines (SVM). Experimental results from 12 volunteers, with tasks conducted on the lower limb rehabilitation robot LLR-II show that the rehabilitation robot can accurately predict patient participation and training task difficulty. The prediction accuracy reflects the superiority of the Hybrid QPSO-SVM algorithm. Full article
(This article belongs to the Special Issue Advanced Intelligent Control through Versatile Intelligent Portable Platforms)
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18 pages, 4302 KiB  
Article
Exoskeleton Hand Control by Fractional Order Models
by Mircea Ivanescu, Nirvana Popescu, Decebal Popescu, Asma Channa and Marian Poboroniuc
Sensors 2019, 19(21), 4608; https://doi.org/10.3390/s19214608 - 23 Oct 2019
Cited by 12 | Viewed by 3275
Abstract
This paper deals with the fractional order control for the complex systems, hand exoskeleton and sensors, that monitor and control the human behavior. The control laws based on physical significance variables, for fractional order models, with delays or without delays, are proposed and [...] Read more.
This paper deals with the fractional order control for the complex systems, hand exoskeleton and sensors, that monitor and control the human behavior. The control laws based on physical significance variables, for fractional order models, with delays or without delays, are proposed and discussed. Lyapunov techniques and the methods that derive from Yakubovici-Kalman-Popov lemma are used and the frequency criterions that ensure asymptotic stability of the closed loop system are inferred. An observer control is proposed for the complex models, exoskeleton and sensors. The asymptotic stability of the system, exoskeleton hand-observer, is studied for sector control laws. Numerical simulations for an intelligent haptic robot-glove are presented. Several examples regarding these models, with delays or without delays, by using sector control laws or an observer control, are analyzed. The experimental platform is presented. Full article
(This article belongs to the Special Issue Advanced Intelligent Control through Versatile Intelligent Portable Platforms)
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20 pages, 2078 KiB  
Article
A Perceptive Interface for Intelligent Cyber Enterprises
by Ioan Dumitrache, Simona Iuliana Caramihai, Mihnea Alexandru Moisescu, Ioan Stefan Sacala, Luige Vladareanu and Dragos Repta
Sensors 2019, 19(20), 4422; https://doi.org/10.3390/s19204422 - 12 Oct 2019
Cited by 11 | Viewed by 2952
Abstract
Large scale, complex, networked enterprises, as may be considered (trans)national energy systems, multi-national manufacturing enterprises, smart cities a.s.o. are structures that can be characterized as systems of systems (SoS) and, as such, require specific modelling paradigms and control architectures to ensure their successful [...] Read more.
Large scale, complex, networked enterprises, as may be considered (trans)national energy systems, multi-national manufacturing enterprises, smart cities a.s.o. are structures that can be characterized as systems of systems (SoS) and, as such, require specific modelling paradigms and control architectures to ensure their successful running. Their main characteristic is the necessity of solving practically one-of-a-kind problems with respect to the external context and internal configuration, thus dealing with dynamically evolving flows of data and information. The paper introduces the concept of intelligent cyber-enterprise, as an integrating paradigm that uses information and knowledge dynamics, in order to model and control SoS, especially focusing on the importance of appropriately adapt external and internal perception of an enterprise through a new generation of sensorial systems—the perceptive interfaces. The authors analyze sensing and perception in relation to intelligent cyber enterprise model and propose an implementation for a perceptive system interface. Full article
(This article belongs to the Special Issue Advanced Intelligent Control through Versatile Intelligent Portable Platforms)
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18 pages, 4712 KiB  
Article
The Design and Experimental Development of Air Scanning Using a Sniffer Quadcopter
by Endrowednes Kuantama, Radu Tarca, Simona Dzitac, Ioan Dzitac, Tiberiu Vesselenyi and Ioan Tarca
Sensors 2019, 19(18), 3849; https://doi.org/10.3390/s19183849 - 6 Sep 2019
Cited by 27 | Viewed by 4245
Abstract
This study presents a detailed analysis of an air monitoring development system using quadcopters. The data collecting method is based on gas dispersion investigation to pinpoint the gas source location and determine the gas concentration level. Due to its flexibility and low cost, [...] Read more.
This study presents a detailed analysis of an air monitoring development system using quadcopters. The data collecting method is based on gas dispersion investigation to pinpoint the gas source location and determine the gas concentration level. Due to its flexibility and low cost, a quadcopter was integrated with air monitoring sensors to collect the required data. The analysis started with the sensor placement on the quadcopter and their correlation with the generated vortex. The reliability and response time of the sensor used determine the duration of the data collection process. The dynamic nature of the environment makes the technique of air monitoring of topmost concern. The pattern method has been adapted to the data collection process in which area scanning was marked using a point of interest or grid point. The experiments were done by manipulating a carbon monoxide (CO) source, with data readings being made in two ways: point source with eight sampling points arranged in a square pattern, and non-point source with 24 sampling points in a grid pattern. The quadcopter collected data while in a hover state with 10 s sampling times at each point. The analysis of variance method (ANOVA) was also used as the statistical algorithm to analyze the vector of gas dispersion. In order to tackle the uncertainty of wind, a bivariate Gaussian kernel analysis was used to get an estimation of the gas source area. The result showed that the grid pattern measurement was useful in obtaining more accurate data of the gas source location and the gas concentration. The vortex field generated by the propeller was used to speed up the accumulation of the gas particles to the sensor. The dynamic nature of the wind caused the gas flow vector to change constantly. Thus, more sampling points were preferred, to improve the accuracy of the gas source location prediction. Full article
(This article belongs to the Special Issue Advanced Intelligent Control through Versatile Intelligent Portable Platforms)
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15 pages, 6410 KiB  
Article
New Motion Intention Acquisition Method of Lower Limb Rehabilitation Robot Based on Static Torque Sensors
by Yongfei Feng, Hongbo Wang, Luige Vladareanu, Zheming Chen and Di Jin
Sensors 2019, 19(15), 3439; https://doi.org/10.3390/s19153439 - 6 Aug 2019
Cited by 15 | Viewed by 5354
Abstract
The rehabilitation robot is an application of robotic technology for people with limb disabilities. This paper investigates a new applicable and effective sitting/lying lower limb rehabilitation robot (the LLR-Ro). In order to improve the patient’s training initiative and accelerate the rehabilitation process, a [...] Read more.
The rehabilitation robot is an application of robotic technology for people with limb disabilities. This paper investigates a new applicable and effective sitting/lying lower limb rehabilitation robot (the LLR-Ro). In order to improve the patient’s training initiative and accelerate the rehabilitation process, a new motion intention acquisition method based on static torque sensors is proposed. This motion intention acquisition method is established through the dynamics modeling of human–machine coordination, which is built on the basis of Lagrangian equations. Combined with the static torque sensors installed on the mechanism leg joint axis, the LLR-Ro can obtain the active force from the patient’s leg. Based on the variation of the patient’s active force and the kinematic functional relationship of the patient’s leg end point, the patient motion intention is obtained and used in the proposed active rehabilitation training method. The simulation experiment demonstrates the correctness of mechanism leg dynamics equations through ADAMS software and MATLAB software. The calibration experiment of the joint torque sensors’ combining limit range filter with an average value filter provides the hardware support for active rehabilitation training. The consecutive variation of the torque sensors from just the mechanism leg weight, as well as both the mechanism leg and the patient leg weights, obtains the feasibility of lower limb motion intention acquisition. Full article
(This article belongs to the Special Issue Advanced Intelligent Control through Versatile Intelligent Portable Platforms)
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14 pages, 12999 KiB  
Article
Analyzing Passive BCI Signals to Control Adaptive Automation Devices
by Ghada Al-Hudhud, Layla Alqahtani, Heyam Albaity, Duaa Alsaeed and Isra Al-Turaiki
Sensors 2019, 19(14), 3042; https://doi.org/10.3390/s19143042 - 10 Jul 2019
Cited by 11 | Viewed by 4245
Abstract
Brain computer interfaces are currently considered to greatly enhance assistive technologies and improve the experiences of people with special needs in the workplace. The proposed adaptive control model for smart offices provides a complete prototype that senses an environment’s temperature and lighting and [...] Read more.
Brain computer interfaces are currently considered to greatly enhance assistive technologies and improve the experiences of people with special needs in the workplace. The proposed adaptive control model for smart offices provides a complete prototype that senses an environment’s temperature and lighting and responds to users’ feelings in terms of their comfort and engagement levels. The model comprises the following components: (a) sensors to sense the environment, including temperature and brightness sensors, and a headset that collects electroencephalogram (EEG) signals, which represent workers’ comfort levels; (b) an application that analyzes workers’ feelings regarding their willingness to adjust to a space based on an analysis of collected data and that determines workers’ attention levels and, thus, engagement; and (c) actuators to adjust the temperature and/or lighting. This research implemented independent component analysis to remove eye movement artifacts from the EEG signals and used an engagement index to calculate engagement levels. This research is expected to add value to research on smart city infrastructures and on assistive technologies to increase productivity in smart offices. Full article
(This article belongs to the Special Issue Advanced Intelligent Control through Versatile Intelligent Portable Platforms)
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Review

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22 pages, 1344 KiB  
Review
The Impact of Technology on People with Autism Spectrum Disorder: A Systematic Literature Review
by Katherine Valencia, Cristian Rusu, Daniela Quiñones and Erick Jamet
Sensors 2019, 19(20), 4485; https://doi.org/10.3390/s19204485 - 16 Oct 2019
Cited by 124 | Viewed by 20647
Abstract
People with autism spectrum disorder (ASD) tend to enjoy themselves and be engaged when interacting with computers, as these interactions occur in a safe and trustworthy environment. In this paper, we present a systematic literature review on the state of the research on [...] Read more.
People with autism spectrum disorder (ASD) tend to enjoy themselves and be engaged when interacting with computers, as these interactions occur in a safe and trustworthy environment. In this paper, we present a systematic literature review on the state of the research on the use of technology to teach people with ASD. We reviewed 94 studies that show how the use of technology in educational contexts helps people with ASD develop several skills, how these approaches consider aspects of user experience, usability and accessibility, and how game elements are used to enrich learning environments. This systematic literature review shows that the development and evaluation of systems and applications for users with ASD is very promising. The use of technological advancements such as virtual agents, artificial intelligence, virtual reality, and augmented reality undoubtedly provides a comfortable environment that promotes constant learning for people with ASD. Full article
(This article belongs to the Special Issue Advanced Intelligent Control through Versatile Intelligent Portable Platforms)
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