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15 pages, 11247 KiB

Open AccessArticle

Adaptive Method for Quantitative Estimation of Glucose and Fructose Concentrations in Aqueous Solutions Based on Infrared Nanoantenna Optics

by Benjamin Schuler, Lucca Kühner, Mario Hentschel, Harald Giessen and Cristina Tarín

Sensors 2019, 19(14), 3053; https://doi.org/10.3390/s19143053 - 11 Jul 2019

Cited by 8 | Viewed by 3766

Abstract

In life science and health research one observes a continuous need for new concepts and methods to detect and quantify the presence and concentration of certain biomolecules—preferably even in vivo or aqueous solutions. One prominent example, among many others, is the blood glucose [...] Read more.

In life science and health research one observes a continuous need for new concepts and methods to detect and quantify the presence and concentration of certain biomolecules—preferably even in vivo or aqueous solutions. One prominent example, among many others, is the blood glucose level, which is highly important in the treatment of, e.g., diabetes mellitus. Detecting and, in particular, quantifying the amount of such molecular species in a complex sensing environment, such as human body fluids, constitutes a significant challenge. Surface-enhanced infrared absorption (SEIRA) spectroscopy has proven to be uniquely able to differentiate even very similar molecular species in very small concentrations. We are thus employing SEIRA to gather the vibrational response of aqueous glucose and fructose solutions in the mid-infrared spectral range with varying concentration levels down to 10 g/l. In contrast to previous work, we further demonstrate that it is possible to not only extract the presence of the analyte molecules but to determine the quantitative concentrations in a reliable and automated way. For this, a baseline correction method is applied to pre-process the measurement data in order to extract the characteristic vibrational information. Afterwards, a set of basis functions is fitted to capture the characteristic features of the two examined monosaccharides and a potential contribution of the solvent itself. The reconstruction of the actual concentration levels is then performed by superposition of the different basis functions to approximate the measured data. This software-based enhancement of the employed optical sensors leads to an accurate quantitative estimate of glucose and fructose concentrations in aqueous solutions. Full article

(This article belongs to the Special Issue Novel Modeling, Signal Processing and Machine Learning Techniques for Sensor Data)

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14 pages, 4401 KiB

Open AccessArticle

An Adaptive Bluetooth/Wi-Fi Fingerprint Positioning Method based on Gaussian Process Regression and Relative Distance

by Hongji Cao, Yunjia Wang, Jingxue Bi and Hongxia Qi

Sensors 2019, 19(12), 2784; https://doi.org/10.3390/s19122784 - 21 Jun 2019

Cited by 11 | Viewed by 2981

Abstract

Trusted positioning data are very important for the fusion of Bluetooth fingerprint positioning (BFP) and Wi-Fi fingerprint positioning (WFP). This paper proposes an adaptive Bluetooth/Wi-Fi fingerprint positioning method based on Gaussian process regression (GPR) and relative distance (RD), which can choose trusted positioning [...] Read more.

Trusted positioning data are very important for the fusion of Bluetooth fingerprint positioning (BFP) and Wi-Fi fingerprint positioning (WFP). This paper proposes an adaptive Bluetooth/Wi-Fi fingerprint positioning method based on Gaussian process regression (GPR) and relative distance (RD), which can choose trusted positioning results for fusion. In the offline stage, measurements of the Bluetooth and Wi-Fi received signal strength (RSS) were collected to construct Bluetooth and Wi-Fi fingerprint databases, respectively. Then, fingerprint positioning error prediction models were built with GPR and data from the fingerprint databases. In the online stage, online Bluetooth and Wi-Fi RSS readings were matched with the fingerprint databases to get a Bluetooth fingerprint positioning result (BFPR) and a Wi-Fi fingerprint positioning result (WFPR). Then, with the help of RD and fingerprint positioning error prediction models, whether the positioning results are trusted was determined. The trusted result is selected as the position estimation result when there is only one trusted positioning result among the BFPR and WFPR. The mean is chosen as the position estimation result when both the BFPR and WFPR results are trusted or untrusted. Experimental results showed that the proposed method was better than BFP and WFP, with a mean positioning error of 2.06 m and a root-mean-square error of 1.449 m. Full article

(This article belongs to the Special Issue Novel Modeling, Signal Processing and Machine Learning Techniques for Sensor Data)

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13 pages, 5976 KiB

Open AccessArticle

Spherical Reverse Beamforming for Sound Source Localization Based on the Inverse Method

by Chao Sun and Yuechan Liu

Sensors 2019, 19(11), 2618; https://doi.org/10.3390/s19112618 - 9 Jun 2019

Cited by 6 | Viewed by 3756

Abstract

A spherical array is not limited to providing an acoustic map in all directions by the azimuth of the array. In this paper, spherical reverse beamforming for sound source localization based on spherical harmonic beamforming and the principle of sound field reconstruction is [...] Read more.

A spherical array is not limited to providing an acoustic map in all directions by the azimuth of the array. In this paper, spherical reverse beamforming for sound source localization based on spherical harmonic beamforming and the principle of sound field reconstruction is proposed in order to output a sharper scanning beam. It is assumed that there is an imaginary sound source at each scan point, and the acoustic map of a spherical array to the actual sound source is regarded as the combination of all of the imaginary sound sources. Sound source localization can be realized by calculating the contribution of each imaginary sound source to the sound field. Also in this work, the non-convex constrained optimization problem is established using p-norm. Combined with the norm method, the sparse solution of the imaginary sources is obtained through iterative weighted techniques, and the resolution of sound source localization is improved significantly. The performance of this method is investigated in comparison to conventional spherical beamforming. The numerical results show that the proposed method can achieve higher resolution for the localization of sound sources without being limited by the frequency and array aperture, and has a stronger ability to suppress fluctuations in background noise. Full article

(This article belongs to the Special Issue Novel Modeling, Signal Processing and Machine Learning Techniques for Sensor Data)

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15 pages, 4959 KiB

Open AccessArticle

ECNet: Efficient Convolutional Networks for Side Scan Sonar Image Segmentation

by Meihan Wu, Qi Wang, Eric Rigall, Kaige Li, Wenbo Zhu, Bo He and Tianhong Yan

Sensors 2019, 19(9), 2009; https://doi.org/10.3390/s19092009 - 29 Apr 2019

Cited by 49 | Viewed by 6012

Abstract

This paper presents a novel and practical convolutional neural network architecture to implement semantic segmentation for side scan sonar (SSS) image. As a widely used sensor for marine survey, SSS provides higher-resolution images of the seafloor and underwater target. However, for a large [...] Read more.

This paper presents a novel and practical convolutional neural network architecture to implement semantic segmentation for side scan sonar (SSS) image. As a widely used sensor for marine survey, SSS provides higher-resolution images of the seafloor and underwater target. However, for a large number of background pixels in SSS image, the imbalance classification remains an issue. What is more, the SSS images contain undesirable speckle noise and intensity inhomogeneity. We define and detail a network and training strategy that tackle these three important issues for SSS images segmentation. Our proposed method performs image-to-image prediction by leveraging fully convolutional neural networks and deeply-supervised nets. The architecture consists of an encoder network to capture context, a corresponding decoder network to restore full input-size resolution feature maps from low-resolution ones for pixel-wise classification and a single stream deep neural network with multiple side-outputs to optimize edge segmentation. We performed prediction time of our network on our dataset, implemented on a NVIDIA Jetson AGX Xavier, and compared it to other similar semantic segmentation networks. The experimental results show that the presented method for SSS image segmentation brings obvious advantages, and is applicable for real-time processing tasks. Full article

(This article belongs to the Special Issue Novel Modeling, Signal Processing and Machine Learning Techniques for Sensor Data)

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15 pages, 2597 KiB

Open AccessArticle

Environment Sound Classification Using a Two-Stream CNN Based on Decision-Level Fusion

by Yu Su, Ke Zhang, Jingyu Wang and Kurosh Madani

Sensors 2019, 19(7), 1733; https://doi.org/10.3390/s19071733 - 11 Apr 2019

Cited by 153 | Viewed by 12557

Abstract

With the popularity of using deep learning-based models in various categorization problems and their proven robustness compared to conventional methods, a growing number of researchers have exploited such methods in environment sound classification tasks in recent years. However, the performances of existing models [...] Read more.

With the popularity of using deep learning-based models in various categorization problems and their proven robustness compared to conventional methods, a growing number of researchers have exploited such methods in environment sound classification tasks in recent years. However, the performances of existing models use auditory features like log-mel spectrogram (LM) and mel frequency cepstral coefficient (MFCC), or raw waveform to train deep neural networks for environment sound classification (ESC) are unsatisfactory. In this paper, we first propose two combined features to give a more comprehensive representation of environment sounds Then, a fourfour-layer convolutional neural network (CNN) is presented to improve the performance of ESC with the proposed aggregated features. Finally, the CNN trained with different features are fused using the Dempster–Shafer evidence theory to compose TSCNN-DS model. The experiment results indicate that our combined features with the four-layer CNN are appropriate for environment sound taxonomic problems and dramatically outperform other conventional methods. The proposed TSCNN-DS model achieves a classification accuracy of 97.2%, which is the highest taxonomic accuracy on UrbanSound8K datasets compared to existing models. Full article

(This article belongs to the Special Issue Novel Modeling, Signal Processing and Machine Learning Techniques for Sensor Data)

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23 pages, 3990 KiB

Open AccessArticle

Reducing False Arrhythmia Alarms Using Different Methods of Probability and Class Assignment in Random Forest Learning Methods

by Krzysztof Gajowniczek, Iga Grzegorczyk and Tomasz Ząbkowski

Sensors 2019, 19(7), 1588; https://doi.org/10.3390/s19071588 - 2 Apr 2019

Cited by 9 | Viewed by 3680

Abstract

The literature indicates that 90% of clinical alarms in intensive care units might be false. This high percentage negatively impacts both patients and clinical staff. In patients, false alarms significantly increase stress levels, which is especially dangerous for cardiac patients. In clinical staff, [...] Read more.

The literature indicates that 90% of clinical alarms in intensive care units might be false. This high percentage negatively impacts both patients and clinical staff. In patients, false alarms significantly increase stress levels, which is especially dangerous for cardiac patients. In clinical staff, alarm overload might lead to desensitization and could result in true alarms being ignored. In this work, we applied the random forest method to reduce false arrhythmia alarms and specifically explored different methods of probability and class assignment, as these affect the classification accuracy of the ensemble classifiers. Due to the complex nature of the problem, i.e., five types of arrhythmia and several methods to determine probability and the alarm class, a synthetic measure based on the ranks was proposed. The novelty of this contribution is the design of a synthetic measure that helps to leverage classification results in an ensemble model that indicates a decision path leading to the best result in terms of the area under the curve (AUC) measure or the global accuracy (score). The results of the research are promising. The best performance in terms of the AUC was 100% accuracy for extreme tachycardia, whereas the poorest results were for ventricular tachycardia at 87%. Similarly, in terms of the accuracy, the best results were observed for extreme tachycardia (91%), whereas ventricular tachycardia alarms were the most difficult to detect, with an accuracy of only 51%. Full article

(This article belongs to the Special Issue Novel Modeling, Signal Processing and Machine Learning Techniques for Sensor Data)

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20 pages, 8943 KiB

Open AccessArticle

A Novel Underdetermined Blind Source Separation Method and Its Application to Source Contribution Quantitative Estimation

by Jiantao Lu, Wei Cheng and Yanyang Zi

Sensors 2019, 19(6), 1413; https://doi.org/10.3390/s19061413 - 22 Mar 2019

Cited by 12 | Viewed by 3103

Abstract

To identify the major vibration and radiation noise, a source contribution quantitative estimation method is proposed based on underdetermined blind source separation. First, the single source points (SSPs) are identified by directly searching the identical normalized time-frequency vectors of mixed signals, which can [...] Read more.

To identify the major vibration and radiation noise, a source contribution quantitative estimation method is proposed based on underdetermined blind source separation. First, the single source points (SSPs) are identified by directly searching the identical normalized time-frequency vectors of mixed signals, which can improve the efficiency and accuracy in identifying SSPs. Then, the mixing matrix is obtained by hierarchical clustering, and source signals can also be recovered by the least square method. Second, the optimal combination coefficients between source signals and mixed signals can be calculated based on minimum redundant error energy. Therefore, mixed signals can be optimally linearly combined by source signals via the coefficients. Third, the energy elimination method is used to quantitatively estimate source contributions. Finally, the effectiveness of the proposed method is verified via numerical case studies and experiments with a cylindrical structure, and the results show that source signals can be effectively recovered, and source contributions can be quantitatively estimated by the proposed method. Full article

(This article belongs to the Special Issue Novel Modeling, Signal Processing and Machine Learning Techniques for Sensor Data)

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19 pages, 8803 KiB

Open AccessArticle

Double Q-Learning for Radiation Source Detection

by Zheng Liu and Shiva Abbaszadeh

Sensors 2019, 19(4), 960; https://doi.org/10.3390/s19040960 - 24 Feb 2019

Cited by 38 | Viewed by 6732

Abstract

Anomalous radiation source detection in urban environments is challenging due to the complex nature of background radiation. When a suspicious area is determined, a radiation survey is usually carried out to search for anomalous radiation sources. To locate the source with high accuracy [...] Read more.

Anomalous radiation source detection in urban environments is challenging due to the complex nature of background radiation. When a suspicious area is determined, a radiation survey is usually carried out to search for anomalous radiation sources. To locate the source with high accuracy and in a short time, different survey approaches have been studied such as scanning the area with fixed survey paths and data-driven approaches that update the survey path on the fly with newly acquired measurements. In this work, we propose reinforcement learning as a data-driven approach to conduct radiation detection tasks with no human intervention. A simulated radiation environment is constructed, and a convolutional neural network-based double Q-learning algorithm is built and tested for radiation source detection tasks. Simulation results show that the double Q-learning algorithm can reliably navigate the detector and reduce the searching time by at least 44% compared with traditional uniform search methods and gradient search methods. Full article

(This article belongs to the Special Issue Novel Modeling, Signal Processing and Machine Learning Techniques for Sensor Data)

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11 pages, 4876 KiB

Open AccessArticle

A New Model and Its Application for the Dynamic Response of RGO Resistive Gas Sensor

by Hongfei Du, Guangzhong Xie, Yuanjie Su, Huiling Tai, Xiaosong Du, He Yu and Qiuping Zhang

Sensors 2019, 19(4), 889; https://doi.org/10.3390/s19040889 - 20 Feb 2019

Cited by 24 | Viewed by 4979

Abstract

An reduced graphene oxide (RGO) resistive gas sensor was prepared to detect ammonia at room temperature, the result indicated that the desorption of gas (NH

_{3}

) molecules from a graphene-based sensor was difficult, which lead to a baseline drift. The responses of [...] Read more.

An reduced graphene oxide (RGO) resistive gas sensor was prepared to detect ammonia at room temperature, the result indicated that the desorption of gas (NH

_{3}

) molecules from a graphene-based sensor was difficult, which lead to a baseline drift. The responses of different concentrations were compared and studied. It was found that both the response rate and its acceleration were affected by the gas concentration. An Intermolecular Forces Based Model was established to explain the adsorption and desorption dynamic response curves. A new method was proposed based on this model. The first and second derivative extrema (FSDE) of the response curve can be attained quickly to calibrate the gas concentrations. The experiment results demonstrated that this new method could eliminate the baseline drift and was capable of increasing the efficiency of gas calibration significantly. Full article

(This article belongs to the Special Issue Novel Modeling, Signal Processing and Machine Learning Techniques for Sensor Data)

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17 pages, 6855 KiB

Open AccessArticle

Weak Degradation Characteristics Analysis of UAV Motors Based on Laplacian Eigenmaps and Variational Mode Decomposition

by Xiaohong Wang, Wenhui Fan, Xinjun Li and Lizhi Wang

Sensors 2019, 19(3), 524; https://doi.org/10.3390/s19030524 - 27 Jan 2019

Cited by 14 | Viewed by 3759

Abstract

Brushless direct current (BLDC) motors are the source of flight power during the operation of rotary-wing unmanned aerial vehicles (UAVs), and their working state directly affects the safety of the whole system. To predict and avoid motor faults, it is necessary to accurately [...] Read more.

Brushless direct current (BLDC) motors are the source of flight power during the operation of rotary-wing unmanned aerial vehicles (UAVs), and their working state directly affects the safety of the whole system. To predict and avoid motor faults, it is necessary to accurately understand the health degradation process of the motor before any fault occurs. However, in actual working conditions, due to the aerodynamic environmental conditions of the aircraft flight, the background noise components of the vibration signals characterizing the running state of the motor are complex and severely coupled, making it difficult for the weak degradation characteristics to be clearly reflected. To address these problems, a weak degradation characteristic extraction method based on variational mode decomposition (VMD) and Laplacian Eigenmaps (LE) was proposed in this study to precisely identify the degradation information in system health data, avoid the loss of critical information and the interference of redundant information, and to optimize the description of a motor’s degradation process despite the presence of complex background noise. A validation experiment was conducted on a specific type of motor under operation with load, to obtain the degradation characteristics of multiple types of vibration signals, and to test the proposed method. The results proved that this method can improve the stability and accuracy of predicting motor health, thereby helping to predict the degradation state and to optimize the maintenance strategies. Full article

(This article belongs to the Special Issue Novel Modeling, Signal Processing and Machine Learning Techniques for Sensor Data)

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27 pages, 20070 KiB

Open AccessArticle

Algorithm Design for Edge Detection of High-Speed Moving Target Image under Noisy Environment

by Fangfang Han, Bin Liu, Junchao Zhu and Baofeng Zhang

Sensors 2019, 19(2), 343; https://doi.org/10.3390/s19020343 - 16 Jan 2019

Cited by 11 | Viewed by 4750

Abstract

For some measurement and detection applications based on video (sequence images), if the exposure time of camera is not suitable with the motion speed of the photographed target, fuzzy edges will be produced in the image, and some poor lighting condition will aggravate [...] Read more.

For some measurement and detection applications based on video (sequence images), if the exposure time of camera is not suitable with the motion speed of the photographed target, fuzzy edges will be produced in the image, and some poor lighting condition will aggravate this edge blur phenomena. Especially, the existence of noise in industrial field environment makes the extraction of fuzzy edges become a more difficult problem when analyzing the posture of a high-speed moving target. Because noise and edge are always both the kind of high-frequency information, it is difficult to make trade-offs only by frequency bands. In this paper, a noise-tolerant edge detection method based on the correlation relationship between layers of wavelet transform coefficients is proposed. The goal of the paper is not to recover a clean image from a noisy observation, but to make a trade-off judgment for noise and edge signal directly according to the characteristics of wavelet transform coefficients, to realize the extraction of edge information from a noisy image directly. According to the wavelet coefficients tree and the Lipschitz exponent property of noise, the idea of neural network activation function is adopted to design the activation judgment method of wavelet coefficients. Then the significant wavelet coefficients can be retained. At the same time, the non-significant coefficients were removed according to the method of judgment of isolated coefficients. On the other hand, based on the design of Daubechies orthogonal compactly-supported wavelet filter, rational coefficients wavelet filters can be designed by increasing free variables. By reducing the vanishing moments of wavelet filters, more high-frequency information can be retained in the wavelet transform fields, which is benefit to the application of edge detection. For a noisy image of high-speed moving targets with fuzzy edges, by using the length 8-4 rational coefficients biorthogonal wavelet filters and the algorithm proposed in this paper, edge information could be detected clearly. Results of multiple groups of comparative experiments have shown that the edge detection effect of the proposed algorithm in this paper has the obvious superiority. Full article

(This article belongs to the Special Issue Novel Modeling, Signal Processing and Machine Learning Techniques for Sensor Data)

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18 pages, 8957 KiB

Open AccessArticle

SHNN-CAD⁺: An Improvement on SHNN-CAD for Adaptive Online Trajectory Anomaly Detection

by Yuejun Guo and Anton Bardera

Sensors 2019, 19(1), 84; https://doi.org/10.3390/s19010084 - 27 Dec 2018

Cited by 8 | Viewed by 3908

Abstract

To perform anomaly detection for trajectory data, we study the Sequential Hausdorff Nearest-Neighbor Conformal Anomaly Detector (SHNN-CAD) approach, and propose an enhanced version called SHNN-CAD

^{+}

. SHNN-CAD was introduced based on the theory of conformal prediction dealing with the problem of online [...] Read more.

To perform anomaly detection for trajectory data, we study the Sequential Hausdorff Nearest-Neighbor Conformal Anomaly Detector (SHNN-CAD) approach, and propose an enhanced version called SHNN-CAD

^{+}

. SHNN-CAD was introduced based on the theory of conformal prediction dealing with the problem of online detection. Unlike most related approaches requiring several not intuitive parameters, SHNN-CAD has the advantage of being parameter-light which enables the easy reproduction of experiments. We propose to adaptively determine the anomaly threshold during the online detection procedure instead of predefining it without any prior knowledge, which makes the algorithm more usable in practical applications. We present a modified Hausdorff distance measure that takes into account the direction difference and also reduces the computational complexity. In addition, the anomaly detection is more flexible and accurate via a re-do strategy. Extensive experiments on both real-world and synthetic data show that SHNN-CAD

^{+}

outperforms SHNN-CAD with regard to accuracy and running time. Full article

(This article belongs to the Special Issue Novel Modeling, Signal Processing and Machine Learning Techniques for Sensor Data)

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17 pages, 3157 KiB

Open AccessArticle

Computerized Ultrasonic Imaging Inspection: From Shallow to Deep Learning

by Jiaxing Ye, Shunya Ito and Nobuyuki Toyama

Sensors 2018, 18(11), 3820; https://doi.org/10.3390/s18113820 - 7 Nov 2018

Cited by 77 | Viewed by 7235

Abstract

For many decades, ultrasonic imaging inspection has been adopted as a principal method to detect multiple defects, e.g., void and corrosion. However, the data interpretation relies on an inspector’s subjective judgment, thus making the results vulnerable to human error. Nowadays, advanced computer vision [...] Read more.

For many decades, ultrasonic imaging inspection has been adopted as a principal method to detect multiple defects, e.g., void and corrosion. However, the data interpretation relies on an inspector’s subjective judgment, thus making the results vulnerable to human error. Nowadays, advanced computer vision techniques reveal new perspectives on the high-level visual understanding of universal tasks. This research aims to develop an efficient automatic ultrasonic image analysis system for nondestructive testing (NDT) using the latest visual information processing technique. To this end, we first established an ultrasonic inspection image dataset containing 6849 ultrasonic scan images with full defect/no-defect annotations. Using the dataset, we performed a comprehensive experimental comparison of various computer vision techniques, including both conventional methods using hand-crafted visual features and the most recent convolutional neural networks (CNN) which generate multiple-layer stacking for representation learning. In the computer vision community, the two groups are referred to as shallow and deep learning, respectively. Experimental results make it clear that the deep learning-enabled system outperformed conventional (shallow) learning schemes by a large margin. We believe this benchmarking could be used as a reference for similar research dealing with automatic defect detection in ultrasonic imaging inspection. Full article

(This article belongs to the Special Issue Novel Modeling, Signal Processing and Machine Learning Techniques for Sensor Data)

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21 pages, 2600 KiB

Open AccessArticle

Multiple Sound Sources Localization with Frame-by-Frame Component Removal of Statistically Dominant Source

by Maoshen Jia, Yuxuan Wu, Changchun Bao and Jing Wang

Sensors 2018, 18(11), 3613; https://doi.org/10.3390/s18113613 - 24 Oct 2018

Cited by 6 | Viewed by 3166

Abstract

Multiple sound sources localization is a hot topic in audio signal processing and is widely utilized in many application areas. This paper proposed a multiple sound sources localization method based on a statistically dominant source component removal (SDSCR) algorithm by soundfield microphone. The [...] Read more.

Multiple sound sources localization is a hot topic in audio signal processing and is widely utilized in many application areas. This paper proposed a multiple sound sources localization method based on a statistically dominant source component removal (SDSCR) algorithm by soundfield microphone. The existence of the statistically weak source (SWS) among soundfield microphone signals is validated by statistical analysis. The SDSCR algorithm with joint an intra-frame and inter-frame statistically dominant source (SDS) discriminations is designed to remove the component of SDS while reserve the SWS component. The degradation of localization accuracy caused by the existence of the SWS is resolved using the SDSCR algorithm. The objective evaluation of the proposed method is conducted in simulated and real environments. The results show that the proposed method achieves a better performance compared with the conventional SSZ-based method both in sources localization and counting. Full article

(This article belongs to the Special Issue Novel Modeling, Signal Processing and Machine Learning Techniques for Sensor Data)

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19 pages, 2889 KiB

Open AccessArticle

Biologically Inspired Hierarchical Contour Detection with Surround Modulation and Neural Connection

by Shuai Li, Yuelei Xu, Wei Cong, Shiping Ma, Mingming Zhu and Min Qi

Sensors 2018, 18(8), 2559; https://doi.org/10.3390/s18082559 - 4 Aug 2018

Cited by 7 | Viewed by 3173

Abstract

Contour is a very important feature in biological visual cognition and has been extensively investigated as a fundamental vision problem. In connection with the limitations of conventional models in detecting image contours in complex scenes, a hierarchical image contour extraction method is proposed [...] Read more.

Contour is a very important feature in biological visual cognition and has been extensively investigated as a fundamental vision problem. In connection with the limitations of conventional models in detecting image contours in complex scenes, a hierarchical image contour extraction method is proposed based on the biological vision mechanism that draws on the perceptual characteristics of the early vision for features such as edges, shapes, and colours. By simulating the information processing mechanisms of the cells’ receptive fields in the early stages of the biological visual system, we put forward a computational model that combines feedforward, lateral, and feedback neural connections to decode and obtain the image contours. Our model simulations and their results show that the established hierarchical contour detection model can adequately fit the characteristics of the biological experiment, quickly and effectively detect the salient contours in complex scenes, and better suppress the unwanted textures. Full article

(This article belongs to the Special Issue Novel Modeling, Signal Processing and Machine Learning Techniques for Sensor Data)

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13 pages, 1041 KiB

Open AccessArticle

Generalized L-Shaped Nested Array Concept Based on the Fourth-Order Difference Co-Array

by Lei Zhang, Shiwei Ren, Xiangnan Li, Guishan Ren and Xiaohua Wang

Sensors 2018, 18(8), 2482; https://doi.org/10.3390/s18082482 - 1 Aug 2018

Cited by 3 | Viewed by 2940

Abstract

In this paper, a generalized L-shaped nested array based on the fourth-order difference co-array is proposed for two-dimensional (2D) directions’ estimation. The new structure framework makes full use of the physical sensor locations to form a virtual uniform rectangular array (URA) as large [...] Read more.

In this paper, a generalized L-shaped nested array based on the fourth-order difference co-array is proposed for two-dimensional (2D) directions’ estimation. The new structure framework makes full use of the physical sensor locations to form a virtual uniform rectangular array (URA) as large as possible. As it utilizes the fourth-order difference instead of the traditional second-order difference result, this structure framework can acquire a much higher degree-of-freedom (DOF) than the existing 2D sparse arrays. The proposed structures have two advantages. One is that the subarrays can be chosen as any nested-class arrays, which makes the sparse array design more flexible. We can choose arbitrary subarray structures for DOF enhancement purposes. Another advantage is that the relative position of two subarrays can be set as any integral multiple of half wavelength. This means that two subarrays can be located as far as possible so that the relative influence between two physical subarrays can be ignored. The DOFs of several typical generalized L-shaped nested arrays (GLNAs) are compared in this paper. By setting the subarrays as different types and the relative position as a special value, a special GLNA is presented. Simulations show that GLNAs have obvious superiority in 2D direction-of-arrival estimation. Full article

(This article belongs to the Special Issue Novel Modeling, Signal Processing and Machine Learning Techniques for Sensor Data)

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