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Special Issue "Selected Papers from MFI 2017 - International Conference on Multisensor Fusion and Integration for Intelligent Systems"

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

Deadline for manuscript submissions: closed (20 February 2018).

Special Issue Editors

Prof. Sukhan Lee
E-Mail Website
Guest Editor
Intelligent System Research Institute (ISRI), Room no.836978, 6th floor, Building C, Research Complex 2, Natural Sciences Campus, Sungkyunkwan University, 2066, Seobu-ro, Jangan-gu, Suwon-si, Gyeonggi-do 440-746, South Korea
Interests: robotics; computer vision; artificial intelligence; MEMS/NEMS
Special Issues and Collections in MDPI journals
Prof. Hanseok Ko
E-Mail Website
Guest Editor
School of Electrical Engineering, Korea University, Intelligent Signal Processing Center, Korea University, Anam-dong, Seongbuk-gu, Seoul 02841, South Korea
Interests: computer vision; acoustic signal processing; multi-sensor fusion; deep learning; big data analytics
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue will include selected papers from the 2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2017), to be held in Daegu, Korea, 16–18 November, 2017. MFI 2017 theme will be “Multisensor Fusion and Integration in the wake of Big Data, Deep Learning and Cyber Physical System”. Practically, autonomous vehicles, machine learning and intelligent systems require the integrations based on the heterogeneous networks of sensors and actuators. MFI has provided fundamental theories and tools for implementing intelligent devices. MFI 2017 will address with how the multisensor fusion and integration technologies are applied to smart machines, which mark the beginning of things-to-things integration and human-to-machines integration. The authors of selected papers from MFI 2017 within the scope of this journal will be invited to submit extended and enhanced versions of their papers to this Special Issue. These extended papers must contain considerable amounts of new material, and will be subject to a new round of reviews before being published in the Special Issue.

Prof. Dr. Sukhan Lee
Prof. Dr. Hanseok Ko
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

  • Autonomous system
  • multisensor
  • sensor fusion
  • integration
  • human-robot-interaction
  • human-computer-integration
  • big data
  • deep learning
  • cyber physical systems

Published Papers (7 papers)

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Research

Open AccessFeature PaperArticle
Coalition Formation Based Compressive Sensing in Wireless Sensor Networks
Sensors 2018, 18(7), 2331; https://doi.org/10.3390/s18072331 - 18 Jul 2018
Cited by 2
Abstract
Compressive sensing originates in the field of signal processing and has recently become a topic of energy-efficient data gathering in wireless sensor networks. In this paper, we propose an energy efficient distributed compressive sensing solution for sensor networks. The proposed solution utilizes sparsity [...] Read more.
Compressive sensing originates in the field of signal processing and has recently become a topic of energy-efficient data gathering in wireless sensor networks. In this paper, we propose an energy efficient distributed compressive sensing solution for sensor networks. The proposed solution utilizes sparsity distribution of signals to group sensor nodes into several coalitions and then implements localized compressive sensing inside coalitions. This solution improves data-gathering performance in terms of both data accuracy and energy consumption. The approach curbs both data-transmission costs and number of measurements. Coalition-based data gathering cuts transmission costs, and the number of measurements is reduced by scheduling sensor nodes and adjusting their sampling frequency. Our simulation showed that our approach enhances network performance by minimizing energy cost and improving data accuracy. Full article
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Open AccessArticle
Robust Target Tracking with Multi-Static Sensors under Insufficient TDOA Information
Sensors 2018, 18(5), 1481; https://doi.org/10.3390/s18051481 - 08 May 2018
Cited by 2
Abstract
This paper focuses on underwater target tracking based on a multi-static sonar network composed of passive sonobuoys and an active ping. In the multi-static sonar network, the location of the target can be estimated using TDOA (Time Difference of Arrival) measurements. However, since [...] Read more.
This paper focuses on underwater target tracking based on a multi-static sonar network composed of passive sonobuoys and an active ping. In the multi-static sonar network, the location of the target can be estimated using TDOA (Time Difference of Arrival) measurements. However, since the sensor network may obtain insufficient and inaccurate TDOA measurements due to ambient noise and other harsh underwater conditions, target tracking performance can be significantly degraded. We propose a robust target tracking algorithm designed to operate in such a scenario. First, track management with track splitting is applied to reduce performance degradation caused by insufficient measurements. Second, a target location is estimated by a fusion of multiple TDOA measurements using a Gaussian Mixture Model (GMM). In addition, the target trajectory is refined by conducting a stack-based data association method based on multiple-frames measurements in order to more accurately estimate target trajectory. The effectiveness of the proposed method is verified through simulations. Full article
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Open AccessArticle
An Embodied Multi-Sensor Fusion Approach to Visual Motion Estimation Using Unsupervised Deep Networks
Sensors 2018, 18(5), 1427; https://doi.org/10.3390/s18051427 - 04 May 2018
Cited by 1
Abstract
Aimed at improving size, weight, and power (SWaP)-constrained robotic vision-aided state estimation, we describe our unsupervised, deep convolutional-deconvolutional sensor fusion network, Multi-Hypothesis DeepEfference (MHDE). MHDE learns to intelligently combine noisy heterogeneous sensor data to predict several probable hypotheses for the dense, pixel-level correspondence [...] Read more.
Aimed at improving size, weight, and power (SWaP)-constrained robotic vision-aided state estimation, we describe our unsupervised, deep convolutional-deconvolutional sensor fusion network, Multi-Hypothesis DeepEfference (MHDE). MHDE learns to intelligently combine noisy heterogeneous sensor data to predict several probable hypotheses for the dense, pixel-level correspondence between a source image and an unseen target image. We show how our multi-hypothesis formulation provides increased robustness against dynamic, heteroscedastic sensor and motion noise by computing hypothesis image mappings and predictions at 76–357 Hz depending on the number of hypotheses being generated. MHDE fuses noisy, heterogeneous sensory inputs using two parallel, inter-connected architectural pathways and n (1–20 in this work) multi-hypothesis generating sub-pathways to produce n global correspondence estimates between a source and a target image. We evaluated MHDE on the KITTI Odometry dataset and benchmarked it against the vision-only DeepMatching and Deformable Spatial Pyramids algorithms and were able to demonstrate a significant runtime decrease and a performance increase compared to the next-best performing method. Full article
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Open AccessArticle
Defense Strategies for Asymmetric Networked Systems with Discrete Components
Sensors 2018, 18(5), 1421; https://doi.org/10.3390/s18051421 - 03 May 2018
Cited by 7
Abstract
We consider infrastructures consisting of a network of systems, each composed of discrete components. The network provides the vital connectivity between the systems and hence plays a critical, asymmetric role in the infrastructure operations. The individual components of the systems can be attacked [...] Read more.
We consider infrastructures consisting of a network of systems, each composed of discrete components. The network provides the vital connectivity between the systems and hence plays a critical, asymmetric role in the infrastructure operations. The individual components of the systems can be attacked by cyber and physical means and can be appropriately reinforced to withstand these attacks. We formulate the problem of ensuring the infrastructure performance as a game between an attacker and a provider, who choose the numbers of the components of the systems and network to attack and reinforce, respectively. The costs and benefits of attacks and reinforcements are characterized using the sum-form, product-form and composite utility functions, each composed of a survival probability term and a component cost term. We present a two-level characterization of the correlations within the infrastructure: (i) the aggregate failure correlation function specifies the infrastructure failure probability given the failure of an individual system or network, and (ii) the survival probabilities of the systems and network satisfy first-order differential conditions that capture the component-level correlations using multiplier functions. We derive Nash equilibrium conditions that provide expressions for individual system survival probabilities and also the expected infrastructure capacity specified by the total number of operational components. We apply these results to derive and analyze defense strategies for distributed cloud computing infrastructures using cyber-physical models. Full article
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Open AccessArticle
FPGA Based Adaptive Rate and Manifold Pattern Projection for Structured Light 3D Camera System
Sensors 2018, 18(4), 1139; https://doi.org/10.3390/s18041139 - 08 Apr 2018
Cited by 1
Abstract
The quality of the captured point cloud and the scanning speed of a structured light 3D camera system depend upon their capability of handling the object surface of a large reflectance variation in the trade-off of the required number of patterns to be [...] Read more.
The quality of the captured point cloud and the scanning speed of a structured light 3D camera system depend upon their capability of handling the object surface of a large reflectance variation in the trade-off of the required number of patterns to be projected. In this paper, we propose and implement a flexible embedded framework that is capable of triggering the camera single or multiple times for capturing single or multiple projections within a single camera exposure setting. This allows the 3D camera system to synchronize the camera and projector even for miss-matched frame rates such that the system is capable of projecting different types of patterns for different scan speed applications. This makes the system capturing a high quality of 3D point cloud even for the surface of a large reflectance variation while achieving a high scan speed. The proposed framework is implemented on the Field Programmable Gate Array (FPGA), where the camera trigger is adaptively generated in such a way that the position and the number of triggers are automatically determined according to camera exposure settings. In other words, the projection frequency is adaptive to different scanning applications without altering the architecture. In addition, the proposed framework is unique as it does not require any external memory for storage because pattern pixels are generated in real-time, which minimizes the complexity and size of the application-specific integrated circuit (ASIC) design and implementation. Full article
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Open AccessArticle
Divide and Conquer-Based 1D CNN Human Activity Recognition Using Test Data Sharpening
Sensors 2018, 18(4), 1055; https://doi.org/10.3390/s18041055 - 01 Apr 2018
Cited by 12
Abstract
Human Activity Recognition (HAR) aims to identify the actions performed by humans using signals collected from various sensors embedded in mobile devices. In recent years, deep learning techniques have further improved HAR performance on several benchmark datasets. In this paper, we propose one-dimensional [...] Read more.
Human Activity Recognition (HAR) aims to identify the actions performed by humans using signals collected from various sensors embedded in mobile devices. In recent years, deep learning techniques have further improved HAR performance on several benchmark datasets. In this paper, we propose one-dimensional Convolutional Neural Network (1D CNN) for HAR that employs a divide and conquer-based classifier learning coupled with test data sharpening. Our approach leverages a two-stage learning of multiple 1D CNN models; we first build a binary classifier for recognizing abstract activities, and then build two multi-class 1D CNN models for recognizing individual activities. We then introduce test data sharpening during prediction phase to further improve the activity recognition accuracy. While there have been numerous researches exploring the benefits of activity signal denoising for HAR, few researches have examined the effect of test data sharpening for HAR. We evaluate the effectiveness of our approach on two popular HAR benchmark datasets, and show that our approach outperforms both the two-stage 1D CNN-only method and other state of the art approaches. Full article
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Open AccessArticle
Optimally Distributed Kalman Filtering with Data-Driven Communication
Sensors 2018, 18(4), 1034; https://doi.org/10.3390/s18041034 - 29 Mar 2018
Cited by 7
Abstract
For multisensor data fusion, distributed state estimation techniques that enable a local processing of sensor data are the means of choice in order to minimize storage and communication costs. In particular, a distributed implementation of the optimal Kalman filter has recently been developed. [...] Read more.
For multisensor data fusion, distributed state estimation techniques that enable a local processing of sensor data are the means of choice in order to minimize storage and communication costs. In particular, a distributed implementation of the optimal Kalman filter has recently been developed. A significant disadvantage of this algorithm is that the fusion center needs access to each node so as to compute a consistent state estimate, which requires full communication each time an estimate is requested. In this article, different extensions of the optimally distributed Kalman filter are proposed that employ data-driven transmission schemes in order to reduce communication expenses. As a first relaxation of the full-rate communication scheme, it can be shown that each node only has to transmit every second time step without endangering consistency of the fusion result. Also, two data-driven algorithms are introduced that even allow for lower transmission rates, and bounds are derived to guarantee consistent fusion results. Simulations demonstrate that the data-driven distributed filtering schemes can outperform a centralized Kalman filter that requires each measurement to be sent to the center node. Full article
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