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Sensor Signal and Information Processing III
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Sensor Signal and Information Processing III

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

Deadline for manuscript submissions: closed (31 July 2020) | Viewed by 87017

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

Special Issue Editors

Prof. Dr. Wai Lok Woo

E-Mail Website
Guest Editor
Department of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne, UK
Interests: machine learning; artificial intelligence; computational intelligence; data analytics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Bin Gao

E-Mail Website
Guest Editor
School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 610056, China
Interests: audio and image processing; social signal processing; multi-physics mathematical modeling; non-destructive evaluation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Sensor Signal and Information Processing (SSIP) is an overarching field of research focusing on the mathematical foundations and practical applications of signal processing algorithms that learn, reason and act. It bridges the boundary between theory and application, developing novel theoretically-inspired methodologies targeting both longstanding and emergent signal processing applications. The core of SSIP lies in its use of nonlinear and non-Gaussian signal processing methodologies combined with convex and non-convex optimization. SSIP encompasses new theoretical frameworks for statistical signal processing (e.g., Hidden Markov Model, latent component analysis, tensor factorization, Bayesian methods) coupled with information theoretic learning, and novel developments in these areas specialized to the processing of a variety of signal modalities including audio, bio-signals, multi-physics signals, images, multispectral, and video among others. In recent years, many signal processing algorithms have incorporated some forms of computational intelligence as part of its core framework in problem solving. These algorithms have the capacity to generalize and discover knowledge for themselves and learning to learn new information whenever unseen data is captured.

The focus of the Special Issue will be on a broad range of sensors, signal and information processing involving the introduction and development of new advanced theoretical and practical algorithms. Potential topics include, but are not limited to:

  • Biomedical signal processing and instrumentation
  • Pattern recognition and analysis
  • Machine learning for signal and image processing
  • Multimodality sensor fusion techniques
  • Compressed sensing and sparsity aware processing
  • Data science and analytics for big data
  • Deep learning: Theory, algorithms and applications
  • Multi-objective signal processing optimization
  • Multimodal information processing for healthcare, monitoring and surveillance
  • Computer vision and 3D reconstruction with multimodal data fusion
  • Wearable sensors and IoT for personalized health monitoring and social computing
  • Non-destructive testing and evaluation for material characterization, structural integrity, defect detection and identification, stress and lifecycle assessment
  • Signal processing for smart grid, load forecasting and energy management
  • Precision farming combining sensors and imaging with real-time data analytics
  • Other emerging applications of signal and information processing

Prof. Dr. Wai Lok Woo
Prof. Dr. Bin Gao
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 submissions that pass pre-check are 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 2600 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

  • Sensors
  • Signal processing
  • Image processing
  • Video processing
  • Information fusion
  • Machine learning
  • Compressive sensing
  • Latent component analysis
  • Low-rank sparse decomposition
  • Deep learning neural network
  • Computational intelligence
  • Social signal processing
  • Non-destructive testing and evaluation

Published Papers (21 papers)

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Editorial

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6 pages, 175 KiB  
Editorial
Sensor Signal and Information Processing III
by Wai Lok Woo and Bin Gao
Sensors 2020, 20(23), 6749; https://0-doi-org.brum.beds.ac.uk/10.3390/s20236749 - 26 Nov 2020
Viewed by 1476
Abstract
n/a Full article
(This article belongs to the Special Issue Sensor Signal and Information Processing III)

Research

Jump to: Editorial

18 pages, 5968 KiB  
Article
Laplacian Scores-Based Feature Reduction in IoT Systems for Agricultural Monitoring and Decision-Making Support
by Giorgos Tsapparellas, Nanlin Jin, Xuewu Dai and Gerhard Fehringer
Sensors 2020, 20(18), 5107; https://0-doi-org.brum.beds.ac.uk/10.3390/s20185107 - 08 Sep 2020
Cited by 3 | Viewed by 3736
Abstract
Internet of things (IoT) systems generate a large volume of data all the time. How to choose and transfer which data are essential for decision-making is a challenge. This is especially important for low-cost and low-power designs, for example Long-Range Wide-Area Network (LoRaWan)-based [...] Read more.
Internet of things (IoT) systems generate a large volume of data all the time. How to choose and transfer which data are essential for decision-making is a challenge. This is especially important for low-cost and low-power designs, for example Long-Range Wide-Area Network (LoRaWan)-based IoT systems, where data volume and frequency are constrained by the protocols. This paper presents an unsupervised learning approach using Laplacian scores to discover which types of sensors can be reduced, without compromising the decision-making. Here, a type of sensor is a feature. An IoT system is designed and implemented for a plant-monitoring scenario. We have collected data and carried out the Laplacian scores. The analytical results help choose the most important feature. A comparative study has shown that using fewer types of sensors, the accuracy of decision-making remains at a satisfactory level. Full article
(This article belongs to the Special Issue Sensor Signal and Information Processing III)
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18 pages, 14251 KiB  
Article
Indoor Scene Change Captioning Based on Multimodality Data
by Yue Qiu, Yutaka Satoh, Ryota Suzuki, Kenji Iwata and Hirokatsu Kataoka
Sensors 2020, 20(17), 4761; https://0-doi-org.brum.beds.ac.uk/10.3390/s20174761 - 23 Aug 2020
Cited by 14 | Viewed by 4071
Abstract
This study proposes a framework for describing a scene change using natural language text based on indoor scene observations conducted before and after a scene change. The recognition of scene changes plays an essential role in a variety of real-world applications, such as [...] Read more.
This study proposes a framework for describing a scene change using natural language text based on indoor scene observations conducted before and after a scene change. The recognition of scene changes plays an essential role in a variety of real-world applications, such as scene anomaly detection. Most scene understanding research has focused on static scenes. Most existing scene change captioning methods detect scene changes from single-view RGB images, neglecting the underlying three-dimensional structures. Previous three-dimensional scene change captioning methods use simulated scenes consisting of geometry primitives, making it unsuitable for real-world applications. To solve these problems, we automatically generated large-scale indoor scene change caption datasets. We propose an end-to-end framework for describing scene changes from various input modalities, namely, RGB images, depth images, and point cloud data, which are available in most robot applications. We conducted experiments with various input modalities and models and evaluated model performance using datasets with various levels of complexity. Experimental results show that the models that combine RGB images and point cloud data as input achieve high performance in sentence generation and caption correctness and are robust for change type understanding for datasets with high complexity. The developed datasets and models contribute to the study of indoor scene change understanding. Full article
(This article belongs to the Special Issue Sensor Signal and Information Processing III)
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21 pages, 638 KiB  
Article
An Automatic Sleep Stage Classification Algorithm Using Improved Model Based Essence Features
by Huaming Shen, Feng Ran, Meihua Xu, Allon Guez, Ang Li and Aiying Guo
Sensors 2020, 20(17), 4677; https://0-doi-org.brum.beds.ac.uk/10.3390/s20174677 - 19 Aug 2020
Cited by 28 | Viewed by 3022
Abstract
The automatic sleep stage classification technique can facilitate the diagnosis of sleep disorders and release the medical expert from labor-consumption work. In this paper, novel improved model based essence features (IMBEFs) were proposed combining locality energy (LE) and dual state space models (DSSMs) [...] Read more.
The automatic sleep stage classification technique can facilitate the diagnosis of sleep disorders and release the medical expert from labor-consumption work. In this paper, novel improved model based essence features (IMBEFs) were proposed combining locality energy (LE) and dual state space models (DSSMs) for automatic sleep stage detection on single-channel electroencephalograph (EEG) signals. Firstly, each EEG epoch is decomposed into low-level sub-bands (LSBs) and high-level sub-bands (HSBs) by wavelet packet decomposition (WPD), separately. Then, the DSSMs are estimated by the LSBs and the LE calculation is carried out on HSBs. Thirdly, the IMBEFs extracted from the DSSM and LE are fed into the appropriate classifier for sleep stage classification. The performance of the proposed method was evaluated on three public sleep databases. The experimental results show that under the Rechtschaffen’s and Kale’s (R&K) standard, the sleep stage classification accuracies of six classes on the Sleep EDF database and the Dreams Subjects database are 92.04% and 78.92%, respectively. Under the American Academy of Sleep Medicine (AASM) standard, the classification accuracies of five classes in the Dreams Subjects database and the ISRUC database reached 79.90% and 81.65%. The proposed method can be used for reliable sleep stage classification with high accuracy compared with state-of-the-art methods. Full article
(This article belongs to the Special Issue Sensor Signal and Information Processing III)
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22 pages, 10004 KiB  
Article
Mask Gradient Response-Based Threshold Segmentation for Surface Defect Detection of Milled Aluminum Ingot
by Ying Liang, Ke Xu and Peng Zhou
Sensors 2020, 20(16), 4519; https://0-doi-org.brum.beds.ac.uk/10.3390/s20164519 - 12 Aug 2020
Cited by 17 | Viewed by 3258
Abstract
The surface quality of aluminum ingot is crucial for subsequent products, so it is necessary to adaptively detect different types of defects in milled aluminum ingots surfaces. In order to quickly apply the calculations to a real production line, a novel two-stage detection [...] Read more.
The surface quality of aluminum ingot is crucial for subsequent products, so it is necessary to adaptively detect different types of defects in milled aluminum ingots surfaces. In order to quickly apply the calculations to a real production line, a novel two-stage detection approach is proposed. Firstly, we proposed a novel mask gradient response-based threshold segmentation (MGRTS) in which the mask gradient response is the gradient map after the strong gradient has been eliminated by the binary mask, so that the various defects can be effectively extracted from the mask gradient response map by iterative threshold segmentation. In the region of interest (ROI) extraction, we combine the MGRTS and the Difference of Gaussian (DoG) to effectively improve the detection rate. In the aspect of the defect classification, we train the inception-v3 network with a data augmentation technology and the focal loss in order to overcome the class imbalance problem and improve the classification accuracy. The comparative study shows that the proposed method is efficient and robust for detecting various defects on an aluminum ingot surface with complex milling grain. In addition, it has been applied to the actual production line of an aluminum ingot milling machine, which satisfies the requirement of accuracy and real time very well. Full article
(This article belongs to the Special Issue Sensor Signal and Information Processing III)
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17 pages, 2914 KiB  
Article
On the Better Performance of Pianists with Motor Imagery-Based Brain-Computer Interface Systems
by José-Vicente Riquelme-Ros, Germán Rodríguez-Bermúdez, Ignacio Rodríguez-Rodríguez, José-Víctor Rodríguez and José-María Molina-García-Pardo
Sensors 2020, 20(16), 4452; https://0-doi-org.brum.beds.ac.uk/10.3390/s20164452 - 10 Aug 2020
Cited by 16 | Viewed by 3763
Abstract
Motor imagery (MI)-based brain-computer interface (BCI) systems detect electrical brain activity patterns through electroencephalogram (EEG) signals to forecast user intention while performing movement imagination tasks. As the microscopic details of individuals’ brains are directly shaped by their rich experiences, musicians can develop certain [...] Read more.
Motor imagery (MI)-based brain-computer interface (BCI) systems detect electrical brain activity patterns through electroencephalogram (EEG) signals to forecast user intention while performing movement imagination tasks. As the microscopic details of individuals’ brains are directly shaped by their rich experiences, musicians can develop certain neurological characteristics, such as improved brain plasticity, following extensive musical training. Specifically, the advanced bimanual motor coordination that pianists exhibit means that they may interact more effectively with BCI systems than their non-musically trained counterparts; this could lead to personalized BCI strategies according to the users’ previously detected skills. This work assessed the performance of pianists as they interacted with an MI-based BCI system and compared it with that of a control group. The Common Spatial Patterns (CSP) and Linear Discriminant Analysis (LDA) machine learning algorithms were applied to the EEG signals for feature extraction and classification, respectively. The results revealed that the pianists achieved a higher level of BCI control by means of MI during the final trial (74.69%) compared to the control group (63.13%). The outcome indicates that musical training could enhance the performance of individuals using BCI systems. Full article
(This article belongs to the Special Issue Sensor Signal and Information Processing III)
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23 pages, 4278 KiB  
Article
Efficient Noisy Sound-Event Mixture Classification Using Adaptive-Sparse Complex-Valued Matrix Factorization and OvsO SVM
by Phetcharat Parathai, Naruephorn Tengtrairat, Wai Lok Woo, Mohammed A. M. Abdullah, Gholamreza Rafiee and Ossama Alshabrawy
Sensors 2020, 20(16), 4368; https://0-doi-org.brum.beds.ac.uk/10.3390/s20164368 - 05 Aug 2020
Cited by 9 | Viewed by 2383
Abstract
This paper proposes a solution for events classification from a sole noisy mixture that consist of two major steps: a sound-event separation and a sound-event classification. The traditional complex nonnegative matrix factorization (CMF) is extended by cooperation with the optimal adaptive L1 [...] Read more.
This paper proposes a solution for events classification from a sole noisy mixture that consist of two major steps: a sound-event separation and a sound-event classification. The traditional complex nonnegative matrix factorization (CMF) is extended by cooperation with the optimal adaptive L1 sparsity to decompose a noisy single-channel mixture. The proposed adaptive L1 sparsity CMF algorithm encodes the spectra pattern and estimates the phase of the original signals in time-frequency representation. Their features enhance the temporal decomposition process efficiently. The support vector machine (SVM) based one versus one (OvsO) strategy was applied with a mean supervector to categorize the demixed sound into the matching sound-event class. The first step of the multi-class MSVM method is to segment the separated signal into blocks by sliding demixed signals, then encoding the three features of each block. Mel frequency cepstral coefficients, short-time energy, and short-time zero-crossing rate are learned with multi sound-event classes by the SVM based OvsO method. The mean supervector is encoded from the obtained features. The proposed method has been evaluated with both separation and classification scenarios using real-world single recorded signals and compared with the state-of-the-art separation method. Experimental results confirmed that the proposed method outperformed the state-of-the-art methods. Full article
(This article belongs to the Special Issue Sensor Signal and Information Processing III)
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17 pages, 12193 KiB  
Article
Nonlocal Total Variation Using the First and Second Order Derivatives and Its Application to CT image Reconstruction
by Yongchae Kim and Hiroyuki Kudo
Sensors 2020, 20(12), 3494; https://0-doi-org.brum.beds.ac.uk/10.3390/s20123494 - 20 Jun 2020
Cited by 10 | Viewed by 4816
Abstract
We propose a new class of nonlocal Total Variation (TV), in which the first derivative and the second derivative are mixed. Since most existing TV considers only the first-order derivative, it suffers from problems such as staircase artifacts and loss in smooth intensity [...] Read more.
We propose a new class of nonlocal Total Variation (TV), in which the first derivative and the second derivative are mixed. Since most existing TV considers only the first-order derivative, it suffers from problems such as staircase artifacts and loss in smooth intensity changes for textures and low-contrast objects, which is a major limitation in improving image quality. The proposed nonlocal TV combines the first and second order derivatives to preserve smooth intensity changes well. Furthermore, to accelerate the iterative algorithm to minimize the cost function using the proposed nonlocal TV, we propose a proximal splitting based on Passty’s framework. We demonstrate that the proposed nonlocal TV method achieves adequate image quality both in sparse-view CT and low-dose CT, through simulation studies using a brain CT image with a very narrow contrast range for which it is rather difficult to preserve smooth intensity changes. Full article
(This article belongs to the Special Issue Sensor Signal and Information Processing III)
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15 pages, 627 KiB  
Article
Fingerprinting-Based Indoor Localization Using Interpolated Preprocessed CSI Phases and Bayesian Tracking
by Wenxu Wang, Damián Marelli and Minyue Fu
Sensors 2020, 20(10), 2854; https://0-doi-org.brum.beds.ac.uk/10.3390/s20102854 - 18 May 2020
Cited by 24 | Viewed by 4209
Abstract
Indoor positioning using Wi-Fi signals is an economic technique. Its drawback is that multipath propagation distorts these signals, leading to an inaccurate localization. An approach to improve the positioning accuracy consists of using fingerprints based on channel state information (CSI). Following this line, [...] Read more.
Indoor positioning using Wi-Fi signals is an economic technique. Its drawback is that multipath propagation distorts these signals, leading to an inaccurate localization. An approach to improve the positioning accuracy consists of using fingerprints based on channel state information (CSI). Following this line, we propose a new positioning method which consists of three stages. In the first stage, which is run during initialization, we build a model for the fingerprints of the environment in which we do localization. This model permits obtaining a precise interpolation of fingerprints at positions where a fingerprint measurement is not available. In the second stage, we use this model to obtain a preliminary position estimate based only on the fingerprint measured at the receiver’s location. Finally, in the third stage, we combine this preliminary estimation with the dynamical model of the receiver’s motion to obtain the final estimation. We compare the localization accuracy of the proposed method with other rival methods in two scenarios, namely, when fingerprints used for localization are similar to those used for initialization, and when they differ due to alterations in the environment. Our experiments show that the proposed method outperforms its rivals in both scenarios. Full article
(This article belongs to the Special Issue Sensor Signal and Information Processing III)
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13 pages, 8402 KiB  
Article
Fault Diagnosis of Wind Turbine Gearbox Based on the Optimized LSTM Neural Network with Cosine Loss
by Aijun Yin, Yinghua Yan, Zhiyu Zhang, Chuan Li and René-Vinicio Sánchez
Sensors 2020, 20(8), 2339; https://0-doi-org.brum.beds.ac.uk/10.3390/s20082339 - 20 Apr 2020
Cited by 61 | Viewed by 4818
Abstract
The gearbox is one of the most fragile parts of a wind turbine (WT). Fault diagnosis of the WT gearbox is of great importance to reduce operation and maintenance (O&M) costs and improve cost-effectiveness. At present, intelligent fault diagnosis methods based on long [...] Read more.
The gearbox is one of the most fragile parts of a wind turbine (WT). Fault diagnosis of the WT gearbox is of great importance to reduce operation and maintenance (O&M) costs and improve cost-effectiveness. At present, intelligent fault diagnosis methods based on long short-term memory (LSTM) networks have been widely adopted. As the traditional softmax loss of an LSTM network usually lacks the power of discrimination, this paper proposes a fault diagnosis method for wind turbine gearboxes based on optimized LSTM neural networks with cosine loss (Cos-LSTM). The loss can be converted from Euclid space to angular space by cosine loss, thus eliminating the effect of signal strength and improve the diagnosis accuracy. The energy sequence features and the wavelet energy entropy of the vibration signals are used to evaluate the Cos-LSTM networks. The effectiveness of the proposed method is verified with the fault vibration data collected on a gearbox fault diagnosis experimental platform. In addition, the Cos-LSTM method is also compared with other classic fault diagnosis techniques. The results demonstrate that the Cos-LSTM has better performance for gearbox fault diagnosis. Full article
(This article belongs to the Special Issue Sensor Signal and Information Processing III)
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16 pages, 1651 KiB  
Article
Modulation Classification Using Compressed Sensing and Decision Tree–Support Vector Machine in Cognitive Radio System
by Xiaoyong Sun, Shaojing Su, Zhen Zuo, Xiaojun Guo and Xiaopeng Tan
Sensors 2020, 20(5), 1438; https://0-doi-org.brum.beds.ac.uk/10.3390/s20051438 - 06 Mar 2020
Cited by 20 | Viewed by 3006
Abstract
In this paper, a blind modulation classification method based on compressed sensing using a high-order cumulant and cyclic spectrum combined with the decision tree–support vector machine classifier is proposed to solve the problem of low identification accuracy under single-feature parameters and reduce the [...] Read more.
In this paper, a blind modulation classification method based on compressed sensing using a high-order cumulant and cyclic spectrum combined with the decision tree–support vector machine classifier is proposed to solve the problem of low identification accuracy under single-feature parameters and reduce the performance requirements of the sampling system. Through calculating the fourth-order, eighth-order cumulant and cyclic spectrum feature parameters by breaking through the traditional Nyquist sampling law in the compressed sensing framework, six different cognitive radio signals are effectively classified. Moreover, the influences of symbol length and compression ratio on the classification accuracy are simulated and the classification performance is improved, which achieves the purpose of identifying more signals when fewer feature parameters are used. The results indicate that accurate and effective modulation classification can be achieved, which provides the theoretical basis and technical accumulation for the field of optical-fiber signal detection. Full article
(This article belongs to the Special Issue Sensor Signal and Information Processing III)
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13 pages, 1668 KiB  
Article
Semantically Guided Large Deformation Estimation with Deep Networks
by In Young Ha, Matthias Wilms and Mattias Heinrich
Sensors 2020, 20(5), 1392; https://0-doi-org.brum.beds.ac.uk/10.3390/s20051392 - 04 Mar 2020
Cited by 9 | Viewed by 2690
Abstract
Deformable image registration is still a challenge when the considered images have strong variations in appearance and large initial misalignment. A huge performance gap currently remains for fast-moving regions in videos or strong deformations of natural objects. We present a new semantically guided [...] Read more.
Deformable image registration is still a challenge when the considered images have strong variations in appearance and large initial misalignment. A huge performance gap currently remains for fast-moving regions in videos or strong deformations of natural objects. We present a new semantically guided and two-step deep deformation network that is particularly well suited for the estimation of large deformations. We combine a U-Net architecture that is weakly supervised with segmentation information to extract semantically meaningful features with multiple stages of nonrigid spatial transformer networks parameterized with low-dimensional B-spline deformations. Combining alignment loss and semantic loss functions together with a regularization penalty to obtain smooth and plausible deformations, we achieve superior results in terms of alignment quality compared to previous approaches that have only considered a label-driven alignment loss. Our network model advances the state of the art for inter-subject face part alignment and motion tracking in medical cardiac magnetic resonance imaging (MRI) sequences in comparison to the FlowNet and Label-Reg, two recent deep-learning registration frameworks. The models are compact, very fast in inference, and demonstrate clear potential for a variety of challenging tracking and/or alignment tasks in computer vision and medical image analysis. Full article
(This article belongs to the Special Issue Sensor Signal and Information Processing III)
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16 pages, 2665 KiB  
Article
Compressive Sensing Spectroscopy Using a Residual Convolutional Neural Network
by Cheolsun Kim, Dongju Park and Heung-No Lee
Sensors 2020, 20(3), 594; https://0-doi-org.brum.beds.ac.uk/10.3390/s20030594 - 21 Jan 2020
Cited by 22 | Viewed by 5032
Abstract
Compressive sensing (CS) spectroscopy is well known for developing a compact spectrometer which consists of two parts: compressively measuring an input spectrum and recovering the spectrum using reconstruction techniques. Our goal here is to propose a novel residual convolutional neural network (ResCNN) for [...] Read more.
Compressive sensing (CS) spectroscopy is well known for developing a compact spectrometer which consists of two parts: compressively measuring an input spectrum and recovering the spectrum using reconstruction techniques. Our goal here is to propose a novel residual convolutional neural network (ResCNN) for reconstructing the spectrum from the compressed measurements. The proposed ResCNN comprises learnable layers and a residual connection between the input and the output of these learnable layers. The ResCNN is trained using both synthetic and measured spectral datasets. The results demonstrate that ResCNN shows better spectral recovery performance in terms of average root mean squared errors (RMSEs) and peak signal to noise ratios (PSNRs) than existing approaches such as the sparse recovery methods and the spectral recovery using CNN. Unlike sparse recovery methods, ResCNN does not require a priori knowledge of a sparsifying basis nor prior information on the spectral features of the dataset. Moreover, ResCNN produces stable reconstructions under noisy conditions. Finally, ResCNN is converged faster than CNN. Full article
(This article belongs to the Special Issue Sensor Signal and Information Processing III)
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21 pages, 3315 KiB  
Article
Cognitive Frequency-Hopping Waveform Design for Dual-Function MIMO Radar-Communications System
by Yu Yao, Xuan Li and Lenan Wu
Sensors 2020, 20(2), 415; https://0-doi-org.brum.beds.ac.uk/10.3390/s20020415 - 11 Jan 2020
Cited by 8 | Viewed by 2978
Abstract
A frequency-hopping (FH)-based dual-function multiple-input multiple-output (MIMO) radar communications system enables implementation of a primary radar operation and a secondary communication function simultaneously. The set of transmit waveforms employed to perform the MIMO radar task is generated using FH codes. For each transmit [...] Read more.
A frequency-hopping (FH)-based dual-function multiple-input multiple-output (MIMO) radar communications system enables implementation of a primary radar operation and a secondary communication function simultaneously. The set of transmit waveforms employed to perform the MIMO radar task is generated using FH codes. For each transmit antenna, the communication operation can be realized by embedding one phase symbol during each FH interval. However, as the radar channel is time-variant, it is necessary for a successive waveform optimization scheme to continually obtain target feature information. This research work aims at enhancing the target detection and feature estimation performance by maximizing the mutual information (MI) between the target response and the target returns, and then minimizing the MI between successive target-scattering signals. The two-step cognitive waveform design strategy is based upon continuous learning from the radar scene. The dynamic information about the target feature is utilized to design FH codes. Simulation results show an improvement in target response extraction, target detection probability and delay-Doppler resolution as the number of iterations increases, while still maintaining high data rate with low bit error rates between the proposed system nodes. Full article
(This article belongs to the Special Issue Sensor Signal and Information Processing III)
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21 pages, 4058 KiB  
Article
A Neuron-Based Kalman Filter with Nonlinear Autoregressive Model
by Yu-ting Bai, Xiao-yi Wang, Xue-bo Jin, Zhi-yao Zhao and Bai-hai Zhang
Sensors 2020, 20(1), 299; https://0-doi-org.brum.beds.ac.uk/10.3390/s20010299 - 05 Jan 2020
Cited by 59 | Viewed by 5960
Abstract
The control effect of various intelligent terminals is affected by the data sensing precision. The filtering method has been the typical soft computing method used to promote the sensing level. Due to the difficult recognition of the practical system and the empirical parameter [...] Read more.
The control effect of various intelligent terminals is affected by the data sensing precision. The filtering method has been the typical soft computing method used to promote the sensing level. Due to the difficult recognition of the practical system and the empirical parameter estimation in the traditional Kalman filter, a neuron-based Kalman filter was proposed in the paper. Firstly, the framework of the improved Kalman filter was designed, in which the neuro units were introduced. Secondly, the functions of the neuro units were excavated with the nonlinear autoregressive model. The neuro units optimized the filtering process to reduce the effect of the unpractical system model and hypothetical parameters. Thirdly, the adaptive filtering algorithm was proposed based on the new Kalman filter. Finally, the filter was verified with the simulation signals and practical measurements. The results proved that the filter was effective in noise elimination within the soft computing solution. Full article
(This article belongs to the Special Issue Sensor Signal and Information Processing III)
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33 pages, 6880 KiB  
Article
Stable Tensor Principal Component Pursuit: Error Bounds and Efficient Algorithms
by Wei Fang, Dongxu Wei and Ran Zhang
Sensors 2019, 19(23), 5335; https://0-doi-org.brum.beds.ac.uk/10.3390/s19235335 - 03 Dec 2019
Cited by 3 | Viewed by 3144
Abstract
The rapid development of sensor technology gives rise to the emergence of huge amounts of tensor (i.e., multi-dimensional array) data. For various reasons such as sensor failures and communication loss, the tensor data may be corrupted by not only small noises but also [...] Read more.
The rapid development of sensor technology gives rise to the emergence of huge amounts of tensor (i.e., multi-dimensional array) data. For various reasons such as sensor failures and communication loss, the tensor data may be corrupted by not only small noises but also gross corruptions. This paper studies the Stable Tensor Principal Component Pursuit (STPCP) which aims to recover a tensor from its corrupted observations. Specifically, we propose a STPCP model based on the recently proposed tubal nuclear norm (TNN) which has shown superior performance in comparison with other tensor nuclear norms. Theoretically, we rigorously prove that under tensor incoherence conditions, the underlying tensor and the sparse corruption tensor can be stably recovered. Algorithmically, we first develop an ADMM algorithm and then accelerate it by designing a new algorithm based on orthogonal tensor factorization. The superiority and efficiency of the proposed algorithms is demonstrated through experiments on both synthetic and real data sets. Full article
(This article belongs to the Special Issue Sensor Signal and Information Processing III)
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14 pages, 2768 KiB  
Article
Reverse Dispersion Entropy: A New Complexity Measure for Sensor Signal
by Yuxing Li, Xiang Gao and Long Wang
Sensors 2019, 19(23), 5203; https://0-doi-org.brum.beds.ac.uk/10.3390/s19235203 - 27 Nov 2019
Cited by 55 | Viewed by 3699
Abstract
Permutation entropy (PE), as one of the powerful complexity measures for analyzing time series, has advantages of easy implementation and high efficiency. In order to improve the performance of PE, some improved PE methods have been proposed through introducing amplitude information and distance [...] Read more.
Permutation entropy (PE), as one of the powerful complexity measures for analyzing time series, has advantages of easy implementation and high efficiency. In order to improve the performance of PE, some improved PE methods have been proposed through introducing amplitude information and distance information in recent years. Weighted-permutation entropy (W-PE) weight each arrangement pattern by using variance information, which has good robustness and stability in the case of high noise level and can extract complexity information from data with spike feature or abrupt amplitude change. Dispersion entropy (DE) introduces amplitude information by using the normal cumulative distribution function (NCDF); it not only can detect the change of simultaneous frequency and amplitude, but also is superior to the PE method in distinguishing different data sets. Reverse permutation entropy (RPE) is defined as the distance to white noise in the opposite trend with PE and W-PE, which has high stability for time series with varying lengths. To further improve the performance of PE, we propose a new complexity measure for analyzing time series, and term it as reverse dispersion entropy (RDE). RDE takes PE as its theoretical basis and combines the advantages of DE and RPE by introducing amplitude information and distance information. Simulation experiments were carried out on simulated and sensor signals, including mutation signal detection under different parameters, noise robustness testing, stability testing under different signal-to-noise ratios (SNRs), and distinguishing real data for different kinds of ships and faults. The experimental results show, compared with PE, W-PE, RPE, and DE, that RDE has better performance in detecting abrupt signal and noise robustness testing, and has better stability for simulated and sensor signal. Moreover, it also shows higher distinguishing ability than the other four kinds of PE for sensor signals. Full article
(This article belongs to the Special Issue Sensor Signal and Information Processing III)
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18 pages, 20555 KiB  
Article
Signal Denoising Method Using AIC–SVD and Its Application to Micro-Vibration in Reaction Wheels
by Xianbo Yin, Yang Xu, Xiaowei Sheng and Yan Shen
Sensors 2019, 19(22), 5032; https://0-doi-org.brum.beds.ac.uk/10.3390/s19225032 - 18 Nov 2019
Cited by 12 | Viewed by 4919
Abstract
To suppress noise in signals, a denoising method called AIC–SVD is proposed on the basis of the singular value decomposition (SVD) and the Akaike information criterion (AIC). First, the Hankel matrix is chosen as the trajectory matrix of the signals, and its optimal [...] Read more.
To suppress noise in signals, a denoising method called AIC–SVD is proposed on the basis of the singular value decomposition (SVD) and the Akaike information criterion (AIC). First, the Hankel matrix is chosen as the trajectory matrix of the signals, and its optimal number of rows and columns is selected according to the maximum energy of the singular values. On the basis of the improved AIC, the valid order of the optimal matrix is determined for the vibration signals mixed with Gaussian white noise and colored noise. Subsequently, the denoised signals are reconstructed by inverse operation of SVD and the averaging method. To verify the effectiveness of AIC–SVD, it is compared with wavelet threshold denoising (WTD) and empirical mode decomposition with Savitzky–Golay filter (EMD–SG). Furthermore, a comprehensive indicator of denoising (CID) is introduced to describe the denoising performance. The results show that the denoising effect of AIC–SVD is significantly better than those of WTD and EMD–SG. On applying AIC–SVD to the micro-vibration signals of reaction wheels, the weak harmonic parameters can be successfully extracted during pre-processing. The proposed method is self-adaptable and robust while avoiding the occurrence of over-denoising. Full article
(This article belongs to the Special Issue Sensor Signal and Information Processing III)
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18 pages, 4216 KiB  
Article
Non-Rigid Multi-Modal 3D Medical Image Registration Based on Foveated Modality Independent Neighborhood Descriptor
by Feng Yang, Mingyue Ding and Xuming Zhang
Sensors 2019, 19(21), 4675; https://0-doi-org.brum.beds.ac.uk/10.3390/s19214675 - 28 Oct 2019
Cited by 15 | Viewed by 3667
Abstract
The non-rigid multi-modal three-dimensional (3D) medical image registration is highly challenging due to the difficulty in the construction of similarity measure and the solution of non-rigid transformation parameters. A novel structural representation based registration method is proposed to address these problems. Firstly, an [...] Read more.
The non-rigid multi-modal three-dimensional (3D) medical image registration is highly challenging due to the difficulty in the construction of similarity measure and the solution of non-rigid transformation parameters. A novel structural representation based registration method is proposed to address these problems. Firstly, an improved modality independent neighborhood descriptor (MIND) that is based on the foveated nonlocal self-similarity is designed for the effective structural representations of 3D medical images to transform multi-modal image registration into mono-modal one. The sum of absolute differences between structural representations is computed as the similarity measure. Subsequently, the foveated MIND based spatial constraint is introduced into the Markov random field (MRF) optimization to reduce the number of transformation parameters and restrict the calculation of the energy function in the image region involving non-rigid deformation. Finally, the accurate and efficient 3D medical image registration is realized by minimizing the similarity measure based MRF energy function. Extensive experiments on 3D positron emission tomography (PET), computed tomography (CT), T1, T2, and PD weighted magnetic resonance (MR) images with synthetic deformation demonstrate that the proposed method has higher computational efficiency and registration accuracy in terms of target registration error (TRE) than the registration methods that are based on the hybrid L-BFGS-B and cat swarm optimization (HLCSO), the sum of squared differences on entropy images, the MIND, and the self-similarity context (SSC) descriptor, except that it provides slightly bigger TRE than the HLCSO for CT-PET image registration. Experiments on real MR and ultrasound images with unknown deformation have also be done to demonstrate the practicality and superiority of the proposed method. Full article
(This article belongs to the Special Issue Sensor Signal and Information Processing III)
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21 pages, 18218 KiB  
Article
A Deep Learning Framework for Signal Detection and Modulation Classification
by Xiong Zha, Hua Peng, Xin Qin, Guang Li and Sihan Yang
Sensors 2019, 19(18), 4042; https://0-doi-org.brum.beds.ac.uk/10.3390/s19184042 - 19 Sep 2019
Cited by 51 | Viewed by 13134
Abstract
Deep learning (DL) is a powerful technique which has achieved great success in many applications. However, its usage in communication systems has not been well explored. This paper investigates algorithms for multi-signals detection and modulation classification, which are significant in many communication systems. [...] Read more.
Deep learning (DL) is a powerful technique which has achieved great success in many applications. However, its usage in communication systems has not been well explored. This paper investigates algorithms for multi-signals detection and modulation classification, which are significant in many communication systems. In this work, a DL framework for multi-signals detection and modulation recognition is proposed. Compared to some existing methods, the signal modulation format, center frequency, and start-stop time can be obtained from the proposed scheme. Furthermore, two types of networks are built: (1) Single shot multibox detector (SSD) networks for signal detection and (2) multi-inputs convolutional neural networks (CNNs) for modulation recognition. Additionally, the importance of signal representation to different tasks is investigated. Experimental results demonstrate that the DL framework is capable of detecting and recognizing signals. And compared to the traditional methods and other deep network techniques, the current built DL framework can achieve better performance. Full article
(This article belongs to the Special Issue Sensor Signal and Information Processing III)
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14 pages, 3926 KiB  
Article
Proposal of a Geometric Calibration Method Using Sparse Recovery to Remove Linear Array Push-Broom Sensor Bias
by Jun Chen, Zhichao Sha, Jungang Yang and Wei An
Sensors 2019, 19(18), 4003; https://0-doi-org.brum.beds.ac.uk/10.3390/s19184003 - 16 Sep 2019
Cited by 2 | Viewed by 2155
Abstract
The rational function model (RFM) is widely used in the most advanced Earth observation satellites, replacing the rigorous imaging model. The RFM method achieves the desired calibration performance when image distortion is caused by long-period errors. However, the calibration performance of the RFM [...] Read more.
The rational function model (RFM) is widely used in the most advanced Earth observation satellites, replacing the rigorous imaging model. The RFM method achieves the desired calibration performance when image distortion is caused by long-period errors. However, the calibration performance of the RFM method deteriorates when short-period errors—such as attitude jitter error—are present, and the insufficient and uneven ground control points (GCPs) can also lower the calibration precision of the RFM method. Hence, this paper proposes a geometric calibration method using sparse recovery to remove the linear array push-broom sensor bias. The most important issue regarding this method is that the errors related to the imaging process are approximated to the equivalent bias angles. By using the sparse recovery method, the number and distribution of GCPs needed are greatly reduced. Meanwhile, the proposed method effectively removes short-period errors by recognizing periodic wavy patterns in the first step of the process. The image data from Earth Observing 1 (EO-1) and the Advanced Land Observing Satellite (ALOS) are used as experimental data for the verification of the calibration performance of the proposed method. The experimental results indicate that the proposed method is effective for the sensor calibration of both satellites. Full article
(This article belongs to the Special Issue Sensor Signal and Information Processing III)
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