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Special Issue "Data Analysis for Smart Sensor Systems"

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

Deadline for manuscript submissions: 30 June 2020.

Special Issue Editor

Dr. Jan Steinbrener
Website
Guest Editor
Control of Networked Systems Group Institute of Intelligent System Technologies Universität Klagenfurt 9020 Klagenfurt Austria
Interests: autonomous systems; data analysis; machine learning; imaging systems

Special Issue Information

Dear Colleagues,

Smart sensor systems combine advanced data analysis methods and wireless connectivity with cutting-edge sensor technology in a compact package. Driven by recent advances in miniaturization of sensor hardware as well as in machine learning algorithms, these systems are able to interactively and autonomously extract and communicate high-level information about their environment and are thus fueling the current wave of digitalization across a large range of applications in various sectors, such as industry, healthcare, and consumer electronics. Despite recent successful examples, much research is needed on all integration levels to achieve truly robust, autonomous operation and to fully harness the available sensor data in an efficient and secure way, covering the whole range from a single sensor system up to large wireless networks of smart sensor systems. The Special Issue “Data Analysis for Smart Sensor Systems” focuses on recent advances in data analysis methods for smart sensor systems and their applications and is calling for high-impact submissions in the following areas:

  • Data analysis methods for sensor systems;
  • Cloud/fog/edge computing for sensor data analysis;
  • Data analysis methods for wireless sensor networks;
  • Machine learning for sensor systems;
  • Embedded machine learning methods and applications;
  • Modelling of wireless sensor networks;
  • Novel applications of smart sensor systems from all areas;
  • Sensor system enabled autonomous vehicles and applications;
  • Sensor system enabled digital twins;
  • Virtual sensing;
  • As well as all other related areas.

Dr. Jan Steinbrener
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 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

  • Smart sensor systems
  • Sensor networks
  • Machine learning
  • Embedded computing
  • Edge computing
  • Virtual sensing

Published Papers (6 papers)

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Research

Open AccessArticle
An Intelligent Multi-Sensor Variable Spray System with Chaotic Optimization and Adaptive Fuzzy Control
Sensors 2020, 20(10), 2954; https://doi.org/10.3390/s20102954 - 22 May 2020
Abstract
During the variable spray process, the micro-flow control is often held back by such problems as low initial sensitivity, large inertia, large hysteresis, nonlinearity as well as the inevitable difficulties in controlling the size of the variable spray droplets. In this paper, a [...] Read more.
During the variable spray process, the micro-flow control is often held back by such problems as low initial sensitivity, large inertia, large hysteresis, nonlinearity as well as the inevitable difficulties in controlling the size of the variable spray droplets. In this paper, a novel intelligent double closed-loop control with chaotic optimization and adaptive fuzzy logic is developed for a multi-sensor based variable spray system, where a Bang-Bang relay controller is used to speed up the system operation, and adaptive fuzzy nonlinear PID is employed to improve the accuracy and stability of the system. With the chaotic optimization of controller parameters, the system is globally optimized in the whole solution space. By applying the proposed double closed-loop control, the variable pressure control system includes the pressure system as the inner closed-loop and the spray volume system as the outer closed-loop. Thus, the maximum amount of spray droplets deposited on the plant surface may be achieved with the minimum medicine usage for plants. Multiple sensors (for example: three pressure sensors and two flow rate sensors) are employed to measure the system states. Simulation results show that the chaotic optimized controller has a rise time of 0.9 s, along with an adjustment time of 1.5 s and a maximum overshoot of 2.67% (in comparison using PID, the rise time is 2.2 s, the adjustment time is 5 s, and the maximum overshoot is 6.0%). The optimized controller parameters are programmed into the hardware to control the established variable spray system. The experimental results show that the optimal spray pressure of the spray system is approximately 0.3 MPa, and the flow rate is approximately 0.08 m3/h. The effective droplet rate is 89.4%, in comparison to 81.3% using the conventional PID control. The proposed chaotically optimized composite controller significantly improved the dynamic performance of the control system, and satisfactory control results are achieved. Full article
(This article belongs to the Special Issue Data Analysis for Smart Sensor Systems)
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Open AccessArticle
Dynamically Reconfigurable Data Readout of Pixel Detectors for Automatic Synchronization with Data Acquisition Systems
Sensors 2020, 20(9), 2560; https://doi.org/10.3390/s20092560 - 30 Apr 2020
Abstract
Reconfigurable detectors with dynamically selectable sensing and readout modes are highly desirable for implementing edge computing as well as enabling advanced imaging techniques such as foveation. The concept of a camera system capable of simultaneous passive imaging and dynamic ranging in different regions [...] Read more.
Reconfigurable detectors with dynamically selectable sensing and readout modes are highly desirable for implementing edge computing as well as enabling advanced imaging techniques such as foveation. The concept of a camera system capable of simultaneous passive imaging and dynamic ranging in different regions of the detector is presented. Such an adaptive-autonomous detector with both spatial and temporal control requires programmable window of exposure (time frames), ability to switch between readout modes such as full-frame imaging and zero-suppressed data, modification of the number of pixel data bits and independent programmability for distinct detector regions. In this work, a method is presented for seamlessly changing time frames and readout modes without data corruption while still ensuring that the data acquisition system (DAQ) does not need to stop and resynchronize at each change of setting, thus avoiding significant dead time. Data throughput is maximized by using a minimum unique data format, rather than lengthy frame headers, to differentiate between consecutive frames. A data control and transmitter (DCT) synchronizes data transfer from the pixel to the periphery, reconfigures the data to transmit it serially off-chip, while providing optimized decision support based on a DAQ definable mode. Measurements on a test structure demonstrate that the DCT can operate at 1 GHz in a 65 nm LP CMOS process. Full article
(This article belongs to the Special Issue Data Analysis for Smart Sensor Systems)
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Open AccessArticle
Automatic Anomaly Detection on In-Production Manufacturing Machines Using Statistical Learning Methods
Sensors 2020, 20(8), 2344; https://doi.org/10.3390/s20082344 - 20 Apr 2020
Abstract
Anomaly detection is becoming increasingly important to enhance reliability and resiliency in the Industry 4.0 framework. In this work, we investigate different methods for anomaly detection on in-production manufacturing machines taking into account their variability, both in operation and in wear conditions. We [...] Read more.
Anomaly detection is becoming increasingly important to enhance reliability and resiliency in the Industry 4.0 framework. In this work, we investigate different methods for anomaly detection on in-production manufacturing machines taking into account their variability, both in operation and in wear conditions. We demonstrate how the nature of the available data, featuring any anomaly or not, is of importance for the algorithmic choice, discussing both statistical machine learning methods and control charts. We finally develop methods for automatic anomaly detection, which obtain a recall close to one on our data. Our developed methods are designed not to rely on a continuous recalibration and hand-tuning by the machine user, thereby allowing their deployment in an in-production environment robustly and efficiently. Full article
(This article belongs to the Special Issue Data Analysis for Smart Sensor Systems)
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Open AccessArticle
Data Augmentation with Suboptimal Warping for Time-Series Classification
Sensors 2020, 20(1), 98; https://doi.org/10.3390/s20010098 - 23 Dec 2019
Abstract
In this paper, a novel data augmentation method for time-series classification is proposed. In the introduced method, a new time-series is obtained in warped space between suboptimally aligned input examples of different lengths. Specifically, the alignment is carried out constraining the warping path [...] Read more.
In this paper, a novel data augmentation method for time-series classification is proposed. In the introduced method, a new time-series is obtained in warped space between suboptimally aligned input examples of different lengths. Specifically, the alignment is carried out constraining the warping path and reducing its flexibility. It is shown that the resultant synthetic time-series can form new class boundaries and enrich the training dataset. In this work, the comparative evaluation of the proposed augmentation method against related techniques on representative multivariate time-series datasets is presented. The performance of methods is examined using the nearest neighbor classifier with the dynamic time warping (NN-DTW), LogDet divergence-based metric learning with triplet constraints (LDMLT), and the recently introduced time-series cluster kernel (NN-TCK). The impact of the augmentation on the classification performance is investigated, taking into account entire datasets and cases with a small number of training examples. The extensive evaluation reveals that the introduced method outperforms related augmentation algorithms in terms of the obtained classification accuracy. Full article
(This article belongs to the Special Issue Data Analysis for Smart Sensor Systems)
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Open AccessArticle
AI Radar Sensor: Creating Radar Depth Sounder Images Based on Generative Adversarial Network
Sensors 2019, 19(24), 5479; https://doi.org/10.3390/s19245479 - 12 Dec 2019
Abstract
Significant resources have been spent in collecting and storing large and heterogeneous radar datasets during expensive Arctic and Antarctic fieldwork. The vast majority of data available is unlabeled, and the labeling process is both time-consuming and expensive. One possible alternative to the labeling [...] Read more.
Significant resources have been spent in collecting and storing large and heterogeneous radar datasets during expensive Arctic and Antarctic fieldwork. The vast majority of data available is unlabeled, and the labeling process is both time-consuming and expensive. One possible alternative to the labeling process is the use of synthetically generated data with artificial intelligence. Instead of labeling real images, we can generate synthetic data based on arbitrary labels. In this way, training data can be quickly augmented with additional images. In this research, we evaluated the performance of synthetically generated radar images based on modified cycle-consistent adversarial networks. We conducted several experiments to test the quality of the generated radar imagery. We also tested the quality of a state-of-the-art contour detection algorithm on synthetic data and different combinations of real and synthetic data. Our experiments show that synthetic radar images generated by generative adversarial network (GAN) can be used in combination with real images for data augmentation and training of deep neural networks. However, the synthetic images generated by GANs cannot be used solely for training a neural network (training on synthetic and testing on real) as they cannot simulate all of the radar characteristics such as noise or Doppler effects. To the best of our knowledge, this is the first work in creating radar sounder imagery based on generative adversarial network. Full article
(This article belongs to the Special Issue Data Analysis for Smart Sensor Systems)
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Open AccessArticle
Data-Analytics Modeling of Electrical Impedance Measurements for Cell Culture Monitoring
Sensors 2019, 19(21), 4639; https://doi.org/10.3390/s19214639 - 25 Oct 2019
Abstract
High-throughput data analysis challenges in laboratory automation and lab-on-a-chip devices’ applications are continuously increasing. In cell culture monitoring, specifically, the electrical cell-substrate impedance sensing technique (ECIS), has been extensively used for a wide variety of applications. One of the main drawbacks of ECIS [...] Read more.
High-throughput data analysis challenges in laboratory automation and lab-on-a-chip devices’ applications are continuously increasing. In cell culture monitoring, specifically, the electrical cell-substrate impedance sensing technique (ECIS), has been extensively used for a wide variety of applications. One of the main drawbacks of ECIS is the need for implementing complex electrical models to decode the electrical performance of the full system composed by the electrodes, medium, and cells. In this work we present a new approach for the analysis of data and the prediction of a specific biological parameter, the fill-factor of a cell culture, based on a polynomial regression, data-analytic model. The method was successfully applied to a specific ECIS circuit and two different cell cultures, N2A (a mouse neuroblastoma cell line) and myoblasts. The data-analytic modeling approach can be used in the decoding of electrical impedance measurements of different cell lines, provided a representative volume of data from the cell culture growth is available, sorting out the difficulties traditionally found in the implementation of electrical models. This can be of particular importance for the design of control algorithms for cell cultures in tissue engineering protocols, and labs-on-a-chip and wearable devices applications. Full article
(This article belongs to the Special Issue Data Analysis for Smart Sensor Systems)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Smart sensor systems combine advanced data analysis methods and wireless connectivity with cutting-edge sensor technology in a compact package. Driven by recent advances in miniaturization of sensor hardware as well as in machine learning algorithms, these systems are able to interactively and autonomously extract and communicate high-level information about their environment and are thus fueling the current wave of digitalization across a large range of applications in various sectors, such as industry, healthcare, and consumer electronics. Despite recent successful examples, much research is needed on all integration levels to achieve truly robust, autonomous operation and to fully harness the available sensor data in an efficient and secure way, covering the whole range from a single sensor system up to large wireless networks of smart sensor systems. The Special Issue “Data Analysis for Smart Sensor Systems” focuses on recent advances in data analysis methods for smart sensor systems and their applications and is calling for high-impact submissions in the following areas:

  • Data analysis methods for sensor systems;
  • Cloud/fog/edge computing for sensor data analysis;
  • Data analysis methods for wireless sensor networks;
  • Machine learning for sensor systems;
  • Embedded machine learning methods and applications;
  • Modelling of wireless sensor networks;
  • Novel applications of smart sensor systems from all areas;
  • Sensor system enabled autonomous vehicles and applications;
  • Sensor system enabled digital twins;
  • Virtual sensing;
  • As well as all other related areas.
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