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Special Issue "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: 31 March 2020.

Special Issue Editor

Prof. Dr. Luige Vladareanu
E-Mail Website
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; cyber-physical 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 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

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 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

  • 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

Published Papers (8 papers)

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Research

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Open AccessArticle
Novel PDMS-Based Sensor System for MPWM Measurements of Picoliter Volumes in Microfluidic Devices
Sensors 2019, 19(22), 4886; https://doi.org/10.3390/s19224886 - 08 Nov 2019
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
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Open AccessArticle
Detection of Participation and Training Task Difficulty Applied to the Multi-Sensor Systems of Rehabilitation Robots
Sensors 2019, 19(21), 4681; https://doi.org/10.3390/s19214681 - 28 Oct 2019
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
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Open AccessArticle
Exoskeleton Hand Control by Fractional Order Models
Sensors 2019, 19(21), 4608; https://doi.org/10.3390/s19214608 - 23 Oct 2019
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
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Open AccessArticle
A Perceptive Interface for Intelligent Cyber Enterprises
Sensors 2019, 19(20), 4422; https://doi.org/10.3390/s19204422 - 12 Oct 2019
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
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Open AccessArticle
The Design and Experimental Development of Air Scanning Using a Sniffer Quadcopter
Sensors 2019, 19(18), 3849; https://doi.org/10.3390/s19183849 - 06 Sep 2019
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
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Open AccessArticle
New Motion Intention Acquisition Method of Lower Limb Rehabilitation Robot Based on Static Torque Sensors
Sensors 2019, 19(15), 3439; https://doi.org/10.3390/s19153439 - 06 Aug 2019
Cited by 1
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
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Open AccessArticle
Analyzing Passive BCI Signals to Control Adaptive Automation Devices
Sensors 2019, 19(14), 3042; https://doi.org/10.3390/s19143042 - 10 Jul 2019
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
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Review

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Open AccessReview
The Impact of Technology on People with Autism Spectrum Disorder: A Systematic Literature Review
Sensors 2019, 19(20), 4485; https://doi.org/10.3390/s19204485 - 16 Oct 2019
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
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