Research

1358 KiB

Open AccessArticle

Novel Approach for the Recognition and Prediction of Multi-Function Radar Behaviours Based on Predictive State Representations

by Jian Ou, Yongguang Chen, Feng Zhao, Jin Liu and Shunping Xiao

Sensors 2017, 17(3), 632; https://doi.org/10.3390/s17030632 - 19 Mar 2017

Cited by 22 | Viewed by 4822

Abstract

The extensive applications of multi-function radars (MFRs) have presented a great challenge to the technologies of radar countermeasures (RCMs) and electronic intelligence (ELINT). The recently proposed cognitive electronic warfare (CEW) provides a good solution, whose crux is to perceive present and future MFR [...] Read more.

The extensive applications of multi-function radars (MFRs) have presented a great challenge to the technologies of radar countermeasures (RCMs) and electronic intelligence (ELINT). The recently proposed cognitive electronic warfare (CEW) provides a good solution, whose crux is to perceive present and future MFR behaviours, including the operating modes, waveform parameters, scheduling schemes, etc. Due to the variety and complexity of MFR waveforms, the existing approaches have the drawbacks of inefficiency and weak practicability in prediction. A novel method for MFR behaviour recognition and prediction is proposed based on predictive state representation (PSR). With the proposed approach, operating modes of MFR are recognized by accumulating the predictive states, instead of using fixed transition probabilities that are unavailable in the battlefield. It helps to reduce the dependence of MFR on prior information. And MFR signals can be quickly predicted by iteratively using the predicted observation, avoiding the very large computation brought by the uncertainty of future observations. Simulations with a hypothetical MFR signal sequence in a typical scenario are presented, showing that the proposed methods perform well and efficiently, which attests to their validity. Full article

(This article belongs to the Special Issue Non-Contact Sensing)

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7879 KiB

Open AccessArticle

Detection of the Vibration Signal from Human Vocal Folds Using a 94-GHz Millimeter-Wave Radar

by Fuming Chen, Sheng Li, Yang Zhang and Jianqi Wang

Sensors 2017, 17(3), 543; https://doi.org/10.3390/s17030543 - 08 Mar 2017

Cited by 16 | Viewed by 7915

Abstract

The detection of the vibration signal from human vocal folds provides essential information for studying human phonation and diagnosing voice disorders. Doppler radar technology has enabled the noncontact measurement of the human-vocal-fold vibration. However, existing systems must be placed in close proximity to [...] Read more.

The detection of the vibration signal from human vocal folds provides essential information for studying human phonation and diagnosing voice disorders. Doppler radar technology has enabled the noncontact measurement of the human-vocal-fold vibration. However, existing systems must be placed in close proximity to the human throat and detailed information may be lost because of the low operating frequency. In this paper, a long-distance detection method, involving the use of a 94-GHz millimeter-wave radar sensor, is proposed for detecting the vibration signals from human vocal folds. An algorithm that combines empirical mode decomposition (EMD) and the auto-correlation function (ACF) method is proposed for detecting the signal. First, the EMD method is employed to suppress the noise of the radar-detected signal. Further, the ratio of the energy and entropy is used to detect voice activity in the radar-detected signal, following which, a short-time ACF is employed to extract the vibration signal of the human vocal folds from the processed signal. For validating the method and assessing the performance of the radar system, a vibration measurement sensor and microphone system are additionally employed for comparison. The experimental results obtained from the spectrograms, the vibration frequency of the vocal folds, and coherence analysis demonstrate that the proposed method can effectively detect the vibration of human vocal folds from a long detection distance. Full article

(This article belongs to the Special Issue Non-Contact Sensing)

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6335 KiB

Open AccessArticle

Real Time Apnoea Monitoring of Children Using the Microsoft Kinect Sensor: A Pilot Study

by Ali Al-Naji, Kim Gibson, Sang-Heon Lee and Javaan Chahl

Sensors 2017, 17(2), 286; https://doi.org/10.3390/s17020286 - 03 Feb 2017

Cited by 64 | Viewed by 11732

Abstract

The objective of this study was to design a non-invasive system for the observation of respiratory rates and detection of apnoea using analysis of real time image sequences captured in any given sleep position and under any light conditions (even in dark environments). [...] Read more.

The objective of this study was to design a non-invasive system for the observation of respiratory rates and detection of apnoea using analysis of real time image sequences captured in any given sleep position and under any light conditions (even in dark environments). A Microsoft Kinect sensor was used to visualize the variations in the thorax and abdomen from the respiratory rhythm. These variations were magnified, analyzed and detected at a distance of 2.5 m from the subject. A modified motion magnification system and frame subtraction technique were used to identify breathing movements by detecting rapid motion areas in the magnified frame sequences. The experimental results on a set of video data from five subjects (3 h for each subject) showed that our monitoring system can accurately measure respiratory rate and therefore detect apnoea in infants and young children. The proposed system is feasible, accurate, safe and low computational complexity, making it an efficient alternative for non-contact home sleep monitoring systems and advancing health care applications. Full article

(This article belongs to the Special Issue Non-Contact Sensing)

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12940 KiB

Open AccessArticle

Software Defined Doppler Radar as a Contactless Multipurpose Microwave Sensor for Vibrations Monitoring

by Antonio Raffo, Sandra Costanzo and Giuseppe Di Massa

Sensors 2017, 17(1), 115; https://doi.org/10.3390/s17010115 - 08 Jan 2017

Cited by 14 | Viewed by 9449

Abstract

A vibration sensor based on the use of a Software-Defined Radio (SDR) platform is adopted in this work to provide a contactless and multipurpose solution for low-cost real-time vibrations monitoring. In order to test the vibration detection ability of the proposed non-contact method, [...] Read more.

A vibration sensor based on the use of a Software-Defined Radio (SDR) platform is adopted in this work to provide a contactless and multipurpose solution for low-cost real-time vibrations monitoring. In order to test the vibration detection ability of the proposed non-contact method, a 1 GHz Doppler radar sensor is simulated and successfully assessed on targets at various distances, with various oscillation frequencies and amplitudes. Furthermore, an SDR Doppler platform is practically realized, and preliminary experimental validations on a device able to produce a harmonic motion are illustrated to prove the effectiveness of the proposed approach. Full article

(This article belongs to the Special Issue Non-Contact Sensing)

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7261 KiB

Open AccessArticle

Visualization of Venous Compliance of Superficial Veins Using Non-Contact Plethysmography Based on Digital Red-Green-Blue Images

by Kazuya Nakano, Yuta Aoki, Ryota Satoh, Hiroyuki Suzuki and Izumi Nishidate

Sensors 2016, 16(12), 1996; https://doi.org/10.3390/s16121996 - 25 Nov 2016

Cited by 3 | Viewed by 7972

Abstract

We propose the visualization of venous compliance (VC) using a digital red-green-blue (RGB) camera. The new imaging method, which transforms RGB values into VC, combines VC evaluation with blood concentration estimation from the RGB values of each pixel. We evaluate a non-contact plethysmography [...] Read more.

We propose the visualization of venous compliance (VC) using a digital red-green-blue (RGB) camera. The new imaging method, which transforms RGB values into VC, combines VC evaluation with blood concentration estimation from the RGB values of each pixel. We evaluate a non-contact plethysmography (NCPG) system for VC based on comparisons with conventional strain gauge plethysmography (SPG). We conduct in vivo measurements using both systems and investigate their differences by evaluating the VC. The results show that the two methods measure different blood vessels and that errors caused by interstitial fluid accumulation are negligible for the NCPG system, whereas SPG is influenced by such errors. Additionally, we investigate the relationship between VC and physical activity using NCPG. Full article

(This article belongs to the Special Issue Non-Contact Sensing)

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2193 KiB

Open AccessArticle

Micro-Doppler Based Classification of Human Aquatic Activities via Transfer Learning of Convolutional Neural Networks

by Jinhee Park, Rios Jesus Javier, Taesup Moon and Youngwook Kim

Sensors 2016, 16(12), 1990; https://doi.org/10.3390/s16121990 - 24 Nov 2016

Cited by 108 | Viewed by 7886

Abstract

Accurate classification of human aquatic activities using radar has a variety of potential applications such as rescue operations and border patrols. Nevertheless, the classification of activities on water using radar has not been extensively studied, unlike the case on dry ground, due to [...] Read more.

Accurate classification of human aquatic activities using radar has a variety of potential applications such as rescue operations and border patrols. Nevertheless, the classification of activities on water using radar has not been extensively studied, unlike the case on dry ground, due to its unique challenge. Namely, not only is the radar cross section of a human on water small, but the micro-Doppler signatures are much noisier due to water drops and waves. In this paper, we first investigate whether discriminative signatures could be obtained for activities on water through a simulation study. Then, we show how we can effectively achieve high classification accuracy by applying deep convolutional neural networks (DCNN) directly to the spectrogram of real measurement data. From the five-fold cross-validation on our dataset, which consists of five aquatic activities, we report that the conventional feature-based scheme only achieves an accuracy of 45.1%. In contrast, the DCNN trained using only the collected data attains 66.7%, and the transfer learned DCNN, which takes a DCNN pre-trained on a RGB image dataset and fine-tunes the parameters using the collected data, achieves a much higher 80.3%, which is a significant performance boost. Full article

(This article belongs to the Special Issue Non-Contact Sensing)

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2065 KiB

Open AccessArticle

Identification-While-Scanning of a Multi-Aircraft Formation Based on Sparse Recovery for Narrowband Radar

by Yuan Jiang, Jia Xu, Shi-Bao Peng, Er-Ke Mao, Teng Long and Ying-Ning Peng

Sensors 2016, 16(11), 1972; https://doi.org/10.3390/s16111972 - 23 Nov 2016

Cited by 3 | Viewed by 4648

Abstract

It is known that the identification performance of a multi-aircraft formation (MAF) of narrowband radar mainly depends on the time on target (TOT). To realize the identification task in one rotated scan with limited TOT, the paper proposes a novel identification-while-scanning (IWS) method [...] Read more.

It is known that the identification performance of a multi-aircraft formation (MAF) of narrowband radar mainly depends on the time on target (TOT). To realize the identification task in one rotated scan with limited TOT, the paper proposes a novel identification-while-scanning (IWS) method based on sparse recovery to maintain high rotating speed and super-resolution for MAF identification, simultaneously. First, a multiple chirp signal model is established for MAF in a single scan, where different aircraft may have different Doppler centers and Doppler rates. Second, based on the sparsity of MAF in the Doppler parameter space, a novel hierarchical basis pursuit (HBP) method is proposed to obtain satisfactory sparse recovery performance as well as high computational efficiency. Furthermore, the parameter estimation performance of the proposed IWS identification method is analyzed with respect to recovery condition, signal-to-noise ratio and TOT. It is shown that an MAF can be effectively identified via HBP with a TOT of only about one hundred microseconds for IWS applications. Finally, some numerical experiment results are provided to demonstrate the effectiveness of the proposed method based on both simulated and real measured data. Full article

(This article belongs to the Special Issue Non-Contact Sensing)

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3352 KiB

Open AccessArticle

Microwave Chemical Sensor Using Substrate-Integrated-Waveguide Cavity

by Muhammad Usman Memon and Sungjoon Lim

Sensors 2016, 16(11), 1829; https://doi.org/10.3390/s16111829 - 31 Oct 2016

Cited by 33 | Viewed by 7467 | Correction

Abstract

This research proposes a substrate-integrated waveguide (SIW) cavity sensor to detect several chemicals using the microwave frequency range. The frequency response of the presented SIW sensor is switched by filling a very small quantity of chemical inside of the fluidic channel, which also [...] Read more.

This research proposes a substrate-integrated waveguide (SIW) cavity sensor to detect several chemicals using the microwave frequency range. The frequency response of the presented SIW sensor is switched by filling a very small quantity of chemical inside of the fluidic channel, which also causes a difference in the effective permittivity. The fluidic channel on this structure is either empty or filled with a chemical; when it is empty the structure resonates at 17.08 GHz. There is always a different resonant frequency when any chemical is injected into the fluidic channel. The maximum amount of chemical after injection is held in the center of the SIW structure, which has the maximum magnitude of the electric field distribution. Thus, the objective of sensing chemicals in this research is achieved by perturbing the electric fields of the SIW structure. Full article

(This article belongs to the Special Issue Non-Contact Sensing)

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4289 KiB

Open AccessArticle

Detection and Identification of Multiple Stationary Human Targets Via Bio-Radar Based on the Cross-Correlation Method

by Yang Zhang, Fuming Chen, Huijun Xue, Zhao Li, Qiang An, Jianqi Wang and Yang Zhang

Sensors 2016, 16(11), 1793; https://doi.org/10.3390/s16111793 - 27 Oct 2016

Cited by 14 | Viewed by 5243

Abstract

Ultra-wideband (UWB) radar has been widely used for detecting human physiological signals (respiration, movement, etc.) in the fields of rescue, security, and medicine owing to its high penetrability and range resolution. In these applications, especially in rescue after disaster (earthquake, collapse, mine accident, [...] Read more.

Ultra-wideband (UWB) radar has been widely used for detecting human physiological signals (respiration, movement, etc.) in the fields of rescue, security, and medicine owing to its high penetrability and range resolution. In these applications, especially in rescue after disaster (earthquake, collapse, mine accident, etc.), the presence, number, and location of the trapped victims to be detected and rescued are the key issues of concern. Ample research has been done on the first issue, whereas the identification and localization of multi-targets remains a challenge. False positive and negative identification results are two common problems associated with the detection of multiple stationary human targets. This is mainly because the energy of the signal reflected from the target close to the receiving antenna is considerably stronger than those of the targets at further range, often leading to missing or false recognition if the identification method is based on the energy of the respiratory signal. Therefore, a novel method based on cross-correlation is proposed in this paper that is based on the relativity and periodicity of the signals, rather than on the energy. The validity of this method is confirmed through experiments using different scenarios; the results indicate a discernible improvement in the detection precision and identification of the multiple stationary targets. Full article

(This article belongs to the Special Issue Non-Contact Sensing)

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5100 KiB

Open AccessArticle

Development and Application of a Wireless Sensor for Space Charge Density Measurement in an Ultra-High-Voltage, Direct-Current Environment

by Encheng Xin, Yong Ju and Haiwen Yuan

Sensors 2016, 16(10), 1743; https://doi.org/10.3390/s16101743 - 20 Oct 2016

Cited by 3 | Viewed by 4724

Abstract

A space charge density wireless measurement system based on the idea of distributed measurement is proposed for collecting and monitoring the space charge density in an ultra-high-voltage direct-current (UHVDC) environment. The proposed system architecture is composed of a number of wireless nodes connected [...] Read more.

A space charge density wireless measurement system based on the idea of distributed measurement is proposed for collecting and monitoring the space charge density in an ultra-high-voltage direct-current (UHVDC) environment. The proposed system architecture is composed of a number of wireless nodes connected with space charge density sensors and a base station. The space charge density sensor based on atmospheric ion counter method is elaborated and developed, and the ARM microprocessor and Zigbee radio frequency module are applied. The wireless network communication quality and the relationship between energy consumption and transmission distance in the complicated electromagnetic environment is tested. Based on the experimental results, the proposed measurement system demonstrates that it can adapt to the complex electromagnetic environment under the UHVDC transmission lines and can accurately measure the space charge density. Full article

(This article belongs to the Special Issue Non-Contact Sensing)

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15491 KiB

Open AccessArticle

A Fast Superpixel Segmentation Algorithm for PolSAR Images Based on Edge Refinement and Revised Wishart Distance

by Yue Zhang, Huanxin Zou, Tiancheng Luo, Xianxiang Qin, Shilin Zhou and Kefeng Ji

Sensors 2016, 16(10), 1687; https://doi.org/10.3390/s16101687 - 13 Oct 2016

Cited by 29 | Viewed by 5549

Abstract

The superpixel segmentation algorithm, as a preprocessing technique, should show good performance in fast segmentation speed, accurate boundary adherence and homogeneous regularity. A fast superpixel segmentation algorithm by iterative edge refinement (IER) works well on optical images. However, it may generate poor superpixels [...] Read more.

The superpixel segmentation algorithm, as a preprocessing technique, should show good performance in fast segmentation speed, accurate boundary adherence and homogeneous regularity. A fast superpixel segmentation algorithm by iterative edge refinement (IER) works well on optical images. However, it may generate poor superpixels for Polarimetric synthetic aperture radar (PolSAR) images due to the influence of strong speckle noise and many small-sized or slim regions. To solve these problems, we utilized a fast revised Wishart distance instead of Euclidean distance in the local relabeling of unstable pixels, and initialized unstable pixels as all the pixels substituted for the initial grid edge pixels in the initialization step. Then, postprocessing with the dissimilarity measure is employed to remove the generated small isolated regions as well as to preserve strong point targets. Finally, the superiority of the proposed algorithm is validated with extensive experiments on four simulated and two real-world PolSAR images from Experimental Synthetic Aperture Radar (ESAR) and Airborne Synthetic Aperture Radar (AirSAR) data sets, which demonstrate that the proposed method shows better performance with respect to several commonly used evaluation measures, even with about nine times higher computational efficiency, as well as fine boundary adherence and strong point targets preservation, compared with three state-of-the-art methods. Full article

(This article belongs to the Special Issue Non-Contact Sensing)

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1180 KiB

Open AccessArticle

LPI Radar Waveform Recognition Based on Time-Frequency Distribution

by Ming Zhang, Lutao Liu and Ming Diao

Sensors 2016, 16(10), 1682; https://doi.org/10.3390/s16101682 - 12 Oct 2016

Cited by 120 | Viewed by 9417

Abstract

In this paper, an automatic radar waveform recognition system in a high noise environment is proposed. Signal waveform recognition techniques are widely applied in the field of cognitive radio, spectrum management and radar applications, etc. We devise a system to classify the modulating [...] Read more.

In this paper, an automatic radar waveform recognition system in a high noise environment is proposed. Signal waveform recognition techniques are widely applied in the field of cognitive radio, spectrum management and radar applications, etc. We devise a system to classify the modulating signals widely used in low probability of intercept (LPI) radar detection systems. The radar signals are divided into eight types of classifications, including linear frequency modulation (LFM), BPSK (Barker code modulation), Costas codes and polyphase codes (comprising Frank, P1, P2, P3 and P4). The classifier is Elman neural network (ENN), and it is a supervised classification based on features extracted from the system. Through the techniques of image filtering, image opening operation, skeleton extraction, principal component analysis (PCA), image binarization algorithm and Pseudo–Zernike moments, etc., the features are extracted from the Choi–Williams time-frequency distribution (CWD) image of the received data. In order to reduce the redundant features and simplify calculation, the features selection algorithm based on mutual information between classes and features vectors are applied. The superiority of the proposed classification system is demonstrated by the simulations and analysis. Simulation results show that the overall ratio of successful recognition (RSR) is 94.7% at signal-to-noise ratio (SNR) of −2 dB. Full article

(This article belongs to the Special Issue Non-Contact Sensing)

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6659 KiB

Open AccessArticle

Eddy-Current Sensors with Asymmetrical Point Spread Function

by Janusz Gajda and Marek Stencel

Sensors 2016, 16(10), 1642; https://doi.org/10.3390/s16101642 - 04 Oct 2016

Cited by 2 | Viewed by 5349

Abstract

This paper concerns a special type of eddy-current sensor in the form of inductive loops. Such sensors are applied in the measuring systems classifying road vehicles. They usually have a rectangular shape with dimensions of 1 × 2 m, and are installed under [...] Read more.

This paper concerns a special type of eddy-current sensor in the form of inductive loops. Such sensors are applied in the measuring systems classifying road vehicles. They usually have a rectangular shape with dimensions of 1 × 2 m, and are installed under the surface of the traffic lane. The wide Point Spread Function (PSF) of such sensors causes the information on chassis geometry, contained in the measurement signal, to be strongly averaged. This significantly limits the effectiveness of the vehicle classification. Restoration of the chassis shape, by solving the inverse problem (deconvolution), is also difficult due to the fact that it is ill-conditioned. An original approach to solving this problem is presented in this paper. It is a hardware-based solution and involves the use of inductive loops with an asymmetrical PSF. Laboratory experiments and simulation tests, conducted with models of an inductive loop, confirmed the effectiveness of the proposed solution. In this case, the principle applies that the higher the level of sensor spatial asymmetry, the greater the effectiveness of the deconvolution algorithm. Full article

(This article belongs to the Special Issue Non-Contact Sensing)

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7934 KiB

Open AccessArticle

Fast Industrial Inspection of Optical Thin Film Using Optical Coherence Tomography

by Muhammad Faizan Shirazi, Kibeom Park, Ruchire Eranga Wijesinghe, Hyosang Jeong, Sangyeob Han, Pilun Kim, Mansik Jeon and Jeehyun Kim

Sensors 2016, 16(10), 1598; https://doi.org/10.3390/s16101598 - 28 Sep 2016

Cited by 44 | Viewed by 7388

Abstract

An application of spectral domain optical coherence tomography (SD-OCT) was demonstrated for a fast industrial inspection of an optical thin film panel. An optical thin film sample similar to a liquid crystal display (LCD) panel was examined. Two identical SD-OCT systems were utilized [...] Read more.

An application of spectral domain optical coherence tomography (SD-OCT) was demonstrated for a fast industrial inspection of an optical thin film panel. An optical thin film sample similar to a liquid crystal display (LCD) panel was examined. Two identical SD-OCT systems were utilized for parallel scanning of a complete sample in half time. Dual OCT inspection heads were utilized for transverse (fast) scanning, while a stable linear motorized translational stage was used for lateral (slow) scanning. The cross-sectional and volumetric images of an optical thin film sample were acquired to detect the defects in glass and other layers that are difficult to observe using visual inspection methods. The rapid inspection enabled by this setup led to the early detection of product defects on the manufacturing line, resulting in a significant improvement in the quality assurance of industrial products. Full article

(This article belongs to the Special Issue Non-Contact Sensing)

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4288 KiB

Open AccessArticle

Multi-Frequency Target Detection Techniques for DVB-T Based Passive Radar Sensors

by Tatiana Martelli, Fabiola Colone, Enrico Tilli and Annarita Di Lallo

Sensors 2016, 16(10), 1594; https://doi.org/10.3390/s16101594 - 28 Sep 2016

Cited by 30 | Viewed by 6095

Abstract

This paper investigates the possibility to improve target detection capability in a DVB-T- based passive radar sensor by jointly exploiting multiple digital television channels broadcast by the same transmitter of opportunity. Based on the remarkable results obtained by such a multi-frequency approach using [...] Read more.

This paper investigates the possibility to improve target detection capability in a DVB-T- based passive radar sensor by jointly exploiting multiple digital television channels broadcast by the same transmitter of opportunity. Based on the remarkable results obtained by such a multi-frequency approach using other signals of opportunity (i.e., FM radio broadcast transmissions), we propose appropriate modifications to the previously devised signal processing techniques for them to be effective in the newly considered scenarios. The resulting processing schemes are extensively applied against experimental DVB-T-based passive radar data pertaining to different surveillance applications. The obtained results clearly show the effectiveness of the proposed multi-frequency approaches and demonstrate their suitability for application in the considered scenarios. Full article

(This article belongs to the Special Issue Non-Contact Sensing)

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2290 KiB

Open AccessArticle

Research on the Lift-off Effect of Receiving Longitudinal Mode Guided Waves in Pipes Based on the Villari Effect

by Jiang Xu, Yong Sun and Jinhai Zhou

Sensors 2016, 16(9), 1529; https://doi.org/10.3390/s16091529 - 20 Sep 2016

Cited by 11 | Viewed by 6323

Abstract

The magnetostrictive guided wave technology as a non-contact measurement can generate and receive guided waves with a large lift-off distance up to tens of millimeters. However, the lift-off distance of the receiving coil would affect the coupling efficiency from the elastic energy to [...] Read more.

The magnetostrictive guided wave technology as a non-contact measurement can generate and receive guided waves with a large lift-off distance up to tens of millimeters. However, the lift-off distance of the receiving coil would affect the coupling efficiency from the elastic energy to the electromagnetic energy. In the existing magnetomechanical models, the change of the magnetic field in the air gap was ignored since the permeability of the rod is much greater than that of air. The lift-off distance of the receiving coil will not affect the receiving signals based on these models. However, the experimental phenomenon is in contradiction with these models. To solve the contradiction, the lift-off effect of receiving the longitudinal mode guided waves in pipes is investigated based on the Villari effect. A finite element model of receiving longitudinal guided waves in pipes is obtained based on the Villari effect, which takes into account the magnetic field in the pipe wall and the air zone at the same time. The relation between the amplitude of the induced signals and the radius (lift-off distance) of the receiving coil is obtained, which is verified by experiment. The coupling efficiency of the receiver is a monotonic decline with the lift-off distance increasing. The decay rate of the low frequency wave is slower than the high frequency wave. Additionally, the results show that the rate of change of the magnetic flux in the air zone and in the pipe wall is the same order of magnitude, but opposite. However, the experimental results show that the error of the model in the large lift-off distance is obvious due to the diffusion of the magnetic field in the air, especially for the high frequency guided waves. Full article

(This article belongs to the Special Issue Non-Contact Sensing)

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2059 KiB

Open AccessArticle

Target Tracking Using SePDAF under Ambiguous Angles for Distributed Array Radar

by Teng Long, Honggang Zhang, Tao Zeng, Xinliang Chen, Quanhua Liu and Le Zheng

Sensors 2016, 16(9), 1456; https://doi.org/10.3390/s16091456 - 09 Sep 2016

Cited by 5 | Viewed by 4615

Abstract

Distributed array radar can improve radar detection capability and measurement accuracy. However, it will suffer cyclic ambiguity in its angle estimates according to the spatial Nyquist sampling theorem since the large sparse array is undersampling. Consequently, the state estimation accuracy and track validity [...] Read more.

Distributed array radar can improve radar detection capability and measurement accuracy. However, it will suffer cyclic ambiguity in its angle estimates according to the spatial Nyquist sampling theorem since the large sparse array is undersampling. Consequently, the state estimation accuracy and track validity probability degrades when the ambiguous angles are directly used for target tracking. This paper proposes a second probability data association filter (SePDAF)-based tracking method for distributed array radar. Firstly, the target motion model and radar measurement model is built. Secondly, the fusion result of each radar’s estimation is employed to the extended Kalman filter (EKF) to finish the first filtering. Thirdly, taking this result as prior knowledge, and associating with the array-processed ambiguous angles, the SePDAF is applied to accomplish the second filtering, and then achieving a high accuracy and stable trajectory with relatively low computational complexity. Moreover, the azimuth filtering accuracy will be promoted dramatically and the position filtering accuracy will also improve. Finally, simulations illustrate the effectiveness of the proposed method. Full article

(This article belongs to the Special Issue Non-Contact Sensing)

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2399 KiB

Open AccessArticle

Low-Rank Matrix Recovery Approach for Clutter Rejection in Real-Time IR-UWB Radar-Based Moving Target Detection

by Donatien Sabushimike, Seung You Na, Jin Young Kim, Ngoc Nam Bui, Kyung Sik Seo and Gil Gyeom Kim

Sensors 2016, 16(9), 1409; https://doi.org/10.3390/s16091409 - 01 Sep 2016

Cited by 13 | Viewed by 6247

Abstract

The detection of a moving target using an IR-UWB Radar involves the core task of separating the waves reflected by the static background and by the moving target. This paper investigates the capacity of the low-rank and sparse matrix decomposition approach to separate [...] Read more.

The detection of a moving target using an IR-UWB Radar involves the core task of separating the waves reflected by the static background and by the moving target. This paper investigates the capacity of the low-rank and sparse matrix decomposition approach to separate the background and the foreground in the trend of UWB Radar-based moving target detection. Robust PCA models are criticized for being batched-data-oriented, which makes them inconvenient in realistic environments where frames need to be processed as they are recorded in real time. In this paper, a novel method based on overlapping-windows processing is proposed to cope with online processing. The method consists of processing a small batch of frames which will be continually updated without changing its size as new frames are captured. We prove that RPCA (via its Inexact Augmented Lagrange Multiplier (IALM) model) can successfully separate the two subspaces, which enhances the accuracy of target detection. The overlapping-windows processing method converges on the optimal solution with its batch counterpart (i.e., processing batched data with RPCA), and both methods prove the robustness and efficiency of the RPCA over the classic PCA and the commonly used exponential averaging method. Full article

(This article belongs to the Special Issue Non-Contact Sensing)

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Open AccessArticle

Through Wall Radar Classification of Human Micro-Doppler Using Singular Value Decomposition Analysis

by Matthew Ritchie, Matthew Ash, Qingchao Chen and Kevin Chetty

Sensors 2016, 16(9), 1401; https://doi.org/10.3390/s16091401 - 31 Aug 2016

Cited by 31 | Viewed by 9679

Abstract

The ability to detect the presence as well as classify the activities of individuals behind visually obscuring structures is of significant benefit to police, security and emergency services in many situations. This paper presents the analysis from a series of experimental results generated [...] Read more.

The ability to detect the presence as well as classify the activities of individuals behind visually obscuring structures is of significant benefit to police, security and emergency services in many situations. This paper presents the analysis from a series of experimental results generated using a through-the-wall (TTW) Frequency Modulated Continuous Wave (FMCW) C-Band radar system named Soprano. The objective of this analysis was to classify whether an individual was carrying an item in both hands or not using micro-Doppler information from a FMCW sensor. The radar was deployed at a standoff distance, of approximately 0.5 m, outside a residential building and used to detect multiple people walking within a room. Through the application of digital filtering, it was shown that significant suppression of the primary wall reflection is possible, significantly enhancing the target signal to clutter ratio. Singular Value Decomposition (SVD) signal processing techniques were then applied to the micro-Doppler signatures from different individuals. Features from the SVD information have been used to classify whether the person was carrying an item or walking free handed. Excellent performance of the classifier was achieved in this challenging scenario with accuracies up to 94%, suggesting that future through wall radar sensors may have the ability to reliably recognize many different types of activities in TTW scenarios using these techniques. Full article

(This article belongs to the Special Issue Non-Contact Sensing)

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2382 KiB

Open AccessArticle

Through-Wall Multiple Targets Vital Signs Tracking Based on VMD Algorithm

by Jiaming Yan, Hong Hong, Heng Zhao, Yusheng Li, Chen Gu and Xiaohua Zhu

Sensors 2016, 16(8), 1293; https://doi.org/10.3390/s16081293 - 15 Aug 2016

Cited by 72 | Viewed by 7177

Abstract

Targets located at the same distance are easily neglected in most through-wall multiple targets detecting applications which use the single-input single-output (SISO) ultra-wideband (UWB) radar system. In this paper, a novel multiple targets vital signs tracking algorithm for through-wall detection using SISO UWB [...] Read more.

Targets located at the same distance are easily neglected in most through-wall multiple targets detecting applications which use the single-input single-output (SISO) ultra-wideband (UWB) radar system. In this paper, a novel multiple targets vital signs tracking algorithm for through-wall detection using SISO UWB radar has been proposed. Taking advantage of the high-resolution decomposition of the Variational Mode Decomposition (VMD) based algorithm, the respiration signals of different targets can be decomposed into different sub-signals, and then, we can track the time-varying respiration signals accurately when human targets located in the same distance. Intensive evaluation has been conducted to show the effectiveness of our scheme with a 0.15 m thick concrete brick wall. Constant, piecewise-constant and time-varying vital signs could be separated and tracked successfully with the proposed VMD based algorithm for two targets, even up to three targets. For the multiple targets’ vital signs tracking issues like urban search and rescue missions, our algorithm has superior capability in most detection applications. Full article

(This article belongs to the Special Issue Non-Contact Sensing)

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Open AccessArticle

A Study of a Handrim-Activated Power-Assist Wheelchair Based on a Non-Contact Torque Sensor

by Ki-Tae Nam, Dae-Jin Jang, Yong Chol Kim, Yoon Heo and Eung-Pyo Hong

Sensors 2016, 16(8), 1251; https://doi.org/10.3390/s16081251 - 08 Aug 2016

Cited by 5 | Viewed by 7991

Abstract

Demand for wheelchairs is increasing with growing numbers of aged and disabled persons. Manual wheelchairs are the most commonly used assistive device for mobility because they are convenient to transport. Manual wheelchairs have several advantages but are not easy to use for the [...] Read more.

Demand for wheelchairs is increasing with growing numbers of aged and disabled persons. Manual wheelchairs are the most commonly used assistive device for mobility because they are convenient to transport. Manual wheelchairs have several advantages but are not easy to use for the elderly or those who lack muscular strength. Therefore, handrim-activated power-assist wheelchairs (HAPAW) that can aid driving power with a motor by detecting user driving intentions through the handrim are being researched. This research will be on HAPAW that judge user driving intentions by using non-contact torque sensors. To deliver the desired motion, which is sensed from handrim rotation relative to a fixed controller, a new driving wheel mechanism is designed by applying a non-contact torque sensor, and corresponding torques are simulated. Torques are measured by a driving wheel prototype and compared with simulation results. The HAPAW prototype was developed using the wheels and a driving control algorithm that uses left and right input torques and time differences are used to check if the non-contact torque sensor can distinguish users’ driving intentions. Through this procedure, it was confirmed that the proposed sensor can be used effectively in HAPAW. Full article

(This article belongs to the Special Issue Non-Contact Sensing)

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Open AccessArticle

Time-Varying Vocal Folds Vibration Detection Using a 24 GHz Portable Auditory Radar

by Hong Hong, Heng Zhao, Zhengyu Peng, Hui Li, Chen Gu, Changzhi Li and Xiaohua Zhu

Sensors 2016, 16(8), 1181; https://doi.org/10.3390/s16081181 - 28 Jul 2016

Cited by 20 | Viewed by 5993

Abstract

Time-varying vocal folds vibration information is of crucial importance in speech processing, and the traditional devices to acquire speech signals are easily smeared by the high background noise and voice interference. In this paper, we present a non-acoustic way to capture the human [...] Read more.

Time-varying vocal folds vibration information is of crucial importance in speech processing, and the traditional devices to acquire speech signals are easily smeared by the high background noise and voice interference. In this paper, we present a non-acoustic way to capture the human vocal folds vibration using a 24-GHz portable auditory radar. Since the vocal folds vibration only reaches several millimeters, the high operating frequency and the 4 × 4 array antennas are applied to achieve the high sensitivity. The Variational Mode Decomposition (VMD) based algorithm is proposed to decompose the radar-detected auditory signal into a sequence of intrinsic modes firstly, and then, extract the time-varying vocal folds vibration frequency from the corresponding mode. Feasibility demonstration, evaluation, and comparison are conducted with tonal and non-tonal languages, and the low relative errors show a high consistency between the radar-detected auditory time-varying vocal folds vibration and acoustic fundamental frequency, except that the auditory radar significantly improves the frequency-resolving power. Full article

(This article belongs to the Special Issue Non-Contact Sensing)

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Open AccessArticle

Robust Ground Target Detection by SAR and IR Sensor Fusion Using Adaboost-Based Feature Selection

by Sungho Kim, Woo-Jin Song and So-Hyun Kim

Sensors 2016, 16(7), 1117; https://doi.org/10.3390/s16071117 - 19 Jul 2016

Cited by 27 | Viewed by 12255

Abstract

Long-range ground targets are difficult to detect in a noisy cluttered environment using either synthetic aperture radar (SAR) images or infrared (IR) images. SAR-based detectors can provide a high detection rate with a high false alarm rate to background scatter noise. IR-based approaches [...] Read more.

Long-range ground targets are difficult to detect in a noisy cluttered environment using either synthetic aperture radar (SAR) images or infrared (IR) images. SAR-based detectors can provide a high detection rate with a high false alarm rate to background scatter noise. IR-based approaches can detect hot targets but are affected strongly by the weather conditions. This paper proposes a novel target detection method by decision-level SAR and IR fusion using an Adaboost-based machine learning scheme to achieve a high detection rate and low false alarm rate. The proposed method consists of individual detection, registration, and fusion architecture. This paper presents a single framework of a SAR and IR target detection method using modified Boolean map visual theory (modBMVT) and feature-selection based fusion. Previous methods applied different algorithms to detect SAR and IR targets because of the different physical image characteristics. One method that is optimized for IR target detection produces unsuccessful results in SAR target detection. This study examined the image characteristics and proposed a unified SAR and IR target detection method by inserting a median local average filter (MLAF, pre-filter) and an asymmetric morphological closing filter (AMCF, post-filter) into the BMVT. The original BMVT was optimized to detect small infrared targets. The proposed modBMVT can remove the thermal and scatter noise by the MLAF and detect extended targets by attaching the AMCF after the BMVT. Heterogeneous SAR and IR images were registered automatically using the proposed RANdom SAmple Region Consensus (RANSARC)-based homography optimization after a brute-force correspondence search using the detected target centers and regions. The final targets were detected by feature-selection based sensor fusion using Adaboost. The proposed method showed good SAR and IR target detection performance through feature selection-based decision fusion on a synthetic database generated by OKTAL-SE. Full article

(This article belongs to the Special Issue Non-Contact Sensing)

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Open AccessArticle

Real-Time Detection and Measurement of Eye Features from Color Images

by Diana Borza, Adrian Sergiu Darabant and Radu Danescu

Sensors 2016, 16(7), 1105; https://doi.org/10.3390/s16071105 - 16 Jul 2016

Cited by 18 | Viewed by 9569

Abstract

The accurate extraction and measurement of eye features is crucial to a variety of domains, including human-computer interaction, biometry, and medical research. This paper presents a fast and accurate method for extracting multiple features around the eyes: the center of the pupil, the [...] Read more.

The accurate extraction and measurement of eye features is crucial to a variety of domains, including human-computer interaction, biometry, and medical research. This paper presents a fast and accurate method for extracting multiple features around the eyes: the center of the pupil, the iris radius, and the external shape of the eye. These features are extracted using a multistage algorithm. On the first stage the pupil center is localized using a fast circular symmetry detector and the iris radius is computed using radial gradient projections, and on the second stage the external shape of the eye (of the eyelids) is determined through a Monte Carlo sampling framework based on both color and shape information. Extensive experiments performed on a different dataset demonstrate the effectiveness of our approach. In addition, this work provides eye annotation data for a publicly-available database. Full article

(This article belongs to the Special Issue Non-Contact Sensing)

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Open AccessArticle

Passive UHF RFID Tag for Multispectral Assessment

by Pablo Escobedo, Miguel A. Carvajal, Luis F. Capitán-Vallvey, José Fernández-Salmerón, Antonio Martínez-Olmos and Alberto J. Palma

Sensors 2016, 16(7), 1085; https://doi.org/10.3390/s16071085 - 14 Jul 2016

Cited by 29 | Viewed by 11750

Abstract

This work presents the design, fabrication, and characterization of a passive printed radiofrequency identification tag in the ultra-high-frequency band with multiple optical sensing capabilities. This tag includes five photodiodes to cover a wide spectral range from near-infrared to visible and ultraviolet spectral regions. [...] Read more.

This work presents the design, fabrication, and characterization of a passive printed radiofrequency identification tag in the ultra-high-frequency band with multiple optical sensing capabilities. This tag includes five photodiodes to cover a wide spectral range from near-infrared to visible and ultraviolet spectral regions. The tag antenna and circuit connections have been screen-printed on a flexible polymeric substrate. An ultra-low-power microcontroller-based switch has been included to measure the five magnitudes issuing from the optical sensors, providing a spectral fingerprint of the incident electromagnetic radiation from ultraviolet to infrared, without requiring energy from a battery. The normalization procedure has been designed applying illuminants, and the entire system was tested by measuring cards from a colour chart and sensing fruit ripening. Full article

(This article belongs to the Special Issue Non-Contact Sensing)

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Open AccessArticle

An Optical Sensor for Measuring the Position and Slanting Direction of Flat Surfaces

by Yu-Ta Chen, Yen-Sheng Huang and Chien-Sheng Liu

Sensors 2016, 16(7), 1061; https://doi.org/10.3390/s16071061 - 09 Jul 2016

Cited by 11 | Viewed by 6309

Abstract

Automated optical inspection is a very important technique. For this reason, this study proposes an optical non-contact slanting surface measuring system. The essential features of the measurement system are obtained through simulations using the optical design software Zemax. The actual propagation of laser [...] Read more.

Automated optical inspection is a very important technique. For this reason, this study proposes an optical non-contact slanting surface measuring system. The essential features of the measurement system are obtained through simulations using the optical design software Zemax. The actual propagation of laser beams within the measurement system is traced by using a homogeneous transformation matrix (HTM), the skew-ray tracing method, and a first-order Taylor series expansion. Additionally, a complete mathematical model that describes the variations in light spots on photoelectric sensors and the corresponding changes in the sample orientation and distance was established. Finally, a laboratory prototype system was constructed on an optical bench to verify experimentally the proposed system. This measurement system can simultaneously detect the slanting angles (x, z) in the x and z directions of the sample and the distance (y) between the biconvex lens and the flat sample surface. Full article

(This article belongs to the Special Issue Non-Contact Sensing)

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Open AccessArticle

Hyperspectral Image-Based Night-Time Vehicle Light Detection Using Spectral Normalization and Distance Mapper for Intelligent Headlight Control

by Heekang Kim, Soon Kwon and Sungho Kim

Sensors 2016, 16(7), 1058; https://doi.org/10.3390/s16071058 - 08 Jul 2016

Cited by 7 | Viewed by 8529

Abstract

This paper proposes a vehicle light detection method using a hyperspectral camera instead of a Charge-Coupled Device (CCD) or Complementary metal-Oxide-Semiconductor (CMOS) camera for adaptive car headlamp control. To apply Intelligent Headlight Control (IHC), the vehicle headlights need to be detected. Headlights are [...] Read more.

This paper proposes a vehicle light detection method using a hyperspectral camera instead of a Charge-Coupled Device (CCD) or Complementary metal-Oxide-Semiconductor (CMOS) camera for adaptive car headlamp control. To apply Intelligent Headlight Control (IHC), the vehicle headlights need to be detected. Headlights are comprised from a variety of lighting sources, such as Light Emitting Diodes (LEDs), High-intensity discharge (HID), and halogen lamps. In addition, rear lamps are made of LED and halogen lamp. This paper refers to the recent research in IHC. Some problems exist in the detection of headlights, such as erroneous detection of street lights or sign lights and the reflection plate of ego-car from CCD or CMOS images. To solve these problems, this study uses hyperspectral images because they have hundreds of bands and provide more information than a CCD or CMOS camera. Recent methods to detect headlights used the Spectral Angle Mapper (SAM), Spectral Correlation Mapper (SCM), and Euclidean Distance Mapper (EDM). The experimental results highlight the feasibility of the proposed method in three types of lights (LED, HID, and halogen). Full article

(This article belongs to the Special Issue Non-Contact Sensing)

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Open AccessArticle

Microwave Imaging under Oblique Illumination

by Qingyang Meng, Kuiwen Xu, Fazhong Shen, Bin Zhang, Dexin Ye, Jiangtao Huangfu, Changzhi Li and Lixin Ran

Sensors 2016, 16(7), 1046; https://doi.org/10.3390/s16071046 - 06 Jul 2016

Cited by 6 | Viewed by 5986

Abstract

Microwave imaging based on inverse scattering problem has been attracting many interests in the microwave society. Among some major technical challenges, the ill-posed, multi-dimensional inversion algorithm and the complicated measurement setup are critical ones that prevent it from practical applications. In this paper, [...] Read more.

Microwave imaging based on inverse scattering problem has been attracting many interests in the microwave society. Among some major technical challenges, the ill-posed, multi-dimensional inversion algorithm and the complicated measurement setup are critical ones that prevent it from practical applications. In this paper, we experimentally investigate the performance of the subspace-based optimization method (SOM) for two-dimensional objects when it was applied to a setup designed for oblique incidence. Analytical, simulation, and experimental results show that, for 2D objects, neglecting the cross-polarization scattering will not cause a notable loss of information. Our method can be potentially used in practical imaging applications for 2D-like objects, such as human limbs. Full article

(This article belongs to the Special Issue Non-Contact Sensing)

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Open AccessArticle

Microsoft Kinect Visual and Depth Sensors for Breathing and Heart Rate Analysis

by Aleš Procházka, Martin Schätz, Oldřich Vyšata and Martin Vališ

Sensors 2016, 16(7), 996; https://doi.org/10.3390/s16070996 - 28 Jun 2016

Cited by 67 | Viewed by 10507

Abstract

This paper is devoted to a new method of using Microsoft (MS) Kinect sensors for non-contact monitoring of breathing and heart rate estimation to detect possible medical and neurological disorders. Video sequences of facial features and thorax movements are recorded by MS Kinect [...] Read more.

This paper is devoted to a new method of using Microsoft (MS) Kinect sensors for non-contact monitoring of breathing and heart rate estimation to detect possible medical and neurological disorders. Video sequences of facial features and thorax movements are recorded by MS Kinect image, depth and infrared sensors to enable their time analysis in selected regions of interest. The proposed methodology includes the use of computational methods and functional transforms for data selection, as well as their denoising, spectral analysis and visualization, in order to determine specific biomedical features. The results that were obtained verify the correspondence between the evaluation of the breathing frequency that was obtained from the image and infrared data of the mouth area and from the thorax movement that was recorded by the depth sensor. Spectral analysis of the time evolution of the mouth area video frames was also used for heart rate estimation. Results estimated from the image and infrared data of the mouth area were compared with those obtained by contact measurements by Garmin sensors (www.garmin.com). The study proves that simple image and depth sensors can be used to efficiently record biomedical multidimensional data with sufficient accuracy to detect selected biomedical features using specific methods of computational intelligence. The achieved accuracy for non-contact detection of breathing rate was 0.26% and the accuracy of heart rate estimation was 1.47% for the infrared sensor. The following results show how video frames with depth data can be used to differentiate different kinds of breathing. The proposed method enables us to obtain and analyse data for diagnostic purposes in the home environment or during physical activities, enabling efficient human–machine interaction. Full article

(This article belongs to the Special Issue Non-Contact Sensing)

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Open AccessArticle

An Ultrahigh Frequency Partial Discharge Signal De-Noising Method Based on a Generalized S-Transform and Module Time-Frequency Matrix

by Yushun Liu, Wenjun Zhou, Pengfei Li, Shuai Yang and Yan Tian

Sensors 2016, 16(6), 941; https://doi.org/10.3390/s16060941 - 22 Jun 2016

Cited by 33 | Viewed by 6576

Abstract

Due to electromagnetic interference in power substations, the partial discharge (PD) signals detected by ultrahigh frequency (UHF) antenna sensors often contain various background noises, which may hamper high voltage apparatus fault diagnosis and localization. This paper proposes a novel de-noising method based on [...] Read more.

Due to electromagnetic interference in power substations, the partial discharge (PD) signals detected by ultrahigh frequency (UHF) antenna sensors often contain various background noises, which may hamper high voltage apparatus fault diagnosis and localization. This paper proposes a novel de-noising method based on the generalized S-transform and module time-frequency matrix to suppress noise in UHF PD signals. The sub-matrix maximum module value method is employed to calculate the frequencies and amplitudes of periodic narrowband noise, and suppress noise through the reverse phase cancellation technique. In addition, a singular value decomposition de-noising method is employed to suppress Gaussian white noise in UHF PD signals. Effective singular values are selected by employing the fuzzy c-means clustering method to recover the PD signals. De-noising results of simulated and field detected UHF PD signals prove the feasibility of the proposed method. Compared with four conventional de-noising methods, the results show that the proposed method can suppress background noise in the UHF PD signal effectively, with higher signal-to-noise ratio and less waveform distortion. Full article

(This article belongs to the Special Issue Non-Contact Sensing)

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Open AccessArticle

Estimating 3D Leaf and Stem Shape of Nursery Paprika Plants by a Novel Multi-Camera Photography System

by Yu Zhang, Poching Teng, Yo Shimizu, Fumiki Hosoi and Kenji Omasa

Sensors 2016, 16(6), 874; https://doi.org/10.3390/s16060874 - 14 Jun 2016

Cited by 47 | Viewed by 8885

Abstract

For plant breeding and growth monitoring, accurate measurements of plant structure parameters are very crucial. We have, therefore, developed a high efficiency Multi-Camera Photography (MCP) system combining Multi-View Stereovision (MVS) with the Structure from Motion (SfM) algorithm. In this paper, we measured six [...] Read more.

For plant breeding and growth monitoring, accurate measurements of plant structure parameters are very crucial. We have, therefore, developed a high efficiency Multi-Camera Photography (MCP) system combining Multi-View Stereovision (MVS) with the Structure from Motion (SfM) algorithm. In this paper, we measured six variables of nursery paprika plants and investigated the accuracy of 3D models reconstructed from photos taken by four lens types at four different positions. The results demonstrated that error between the estimated and measured values was small, and the root-mean-square errors (RMSE) for leaf width/length and stem height/diameter were 1.65 mm (R² = 0.98) and 0.57 mm (R² = 0.99), respectively. The accuracies of the 3D model reconstruction of leaf and stem by a 28-mm lens at the first and third camera positions were the highest, and the number of reconstructed fine-scale 3D model shape surfaces of leaf and stem is the most. The results confirmed the practicability of our new method for the reconstruction of fine-scale plant model and accurate estimation of the plant parameters. They also displayed that our system is a good system for capturing high-resolution 3D images of nursery plants with high efficiency. Full article

(This article belongs to the Special Issue Non-Contact Sensing)

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Open AccessArticle

A Multidisciplinary Approach to High Throughput Nuclear Magnetic Resonance Spectroscopy

by Hossein Pourmodheji, Ebrahim Ghafar-Zadeh and Sebastian Magierowski

Sensors 2016, 16(6), 850; https://doi.org/10.3390/s16060850 - 09 Jun 2016

Cited by 12 | Viewed by 11074

Abstract

Nuclear Magnetic Resonance (NMR) is a non-contact, powerful structure-elucidation technique for biochemical analysis. NMR spectroscopy is used extensively in a variety of life science applications including drug discovery. However, existing NMR technology is limited in that it cannot run a large number of [...] Read more.

Nuclear Magnetic Resonance (NMR) is a non-contact, powerful structure-elucidation technique for biochemical analysis. NMR spectroscopy is used extensively in a variety of life science applications including drug discovery. However, existing NMR technology is limited in that it cannot run a large number of experiments simultaneously in one unit. Recent advances in micro-fabrication technologies have attracted the attention of researchers to overcome these limitations and significantly accelerate the drug discovery process by developing the next generation of high-throughput NMR spectrometers using Complementary Metal Oxide Semiconductor (CMOS). In this paper, we examine this paradigm shift and explore new design strategies for the development of the next generation of high-throughput NMR spectrometers using CMOS technology. A CMOS NMR system consists of an array of high sensitivity micro-coils integrated with interfacing radio-frequency circuits on the same chip. Herein, we first discuss the key challenges and recent advances in the field of CMOS NMR technology, and then a new design strategy is put forward for the design and implementation of highly sensitive and high-throughput CMOS NMR spectrometers. We thereafter discuss the functionality and applicability of the proposed techniques by demonstrating the results. For microelectronic researchers starting to work in the field of CMOS NMR technology, this paper serves as a tutorial with comprehensive review of state-of-the-art technologies and their performance levels. Based on these levels, the CMOS NMR approach offers unique advantages for high resolution, time-sensitive and high-throughput bimolecular analysis required in a variety of life science applications including drug discovery. Full article

(This article belongs to the Special Issue Non-Contact Sensing)

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Open AccessArticle

A Novel Method for Proximity Detection of Moving Targets Using a Large-Scale Planar Capacitive Sensor System

by Yong Ye, Jiahao Deng, Sanmin Shen, Zhuo Hou and Yuting Liu

Sensors 2016, 16(5), 699; https://doi.org/10.3390/s16050699 - 16 May 2016

Cited by 25 | Viewed by 7963

Abstract

A novel method for proximity detection of moving targets (with high dielectric constants) using a large-scale (the size of each sensor is 31 cm × 19 cm) planar capacitive sensor system (PCSS) is proposed. The capacitive variation with distance is derived, and a [...] Read more.

A novel method for proximity detection of moving targets (with high dielectric constants) using a large-scale (the size of each sensor is 31 cm × 19 cm) planar capacitive sensor system (PCSS) is proposed. The capacitive variation with distance is derived, and a pair of electrodes in a planar capacitive sensor unit (PCSU) with a spiral shape is found to have better performance on sensitivity distribution homogeneity and dynamic range than three other shapes (comb shape, rectangular shape, and circular shape). A driving excitation circuit with a Clapp oscillator is proposed, and a capacitance measuring circuit with sensitivity of 0.21

V_{p - p} / pF

is designed. The results of static experiments and dynamic experiments demonstrate that the voltage curves of static experiments are similar to those of dynamic experiments; therefore, the static data can be used to simulate the dynamic curves. The dynamic range of proximity detection for three projectiles is up to 60 cm, and the results of the following static experiments show that the PCSU with four neighboring units has the highest sensitivity (the sensitivities of other units are at least 4% lower); when the attack angle decreases, the intensity of sensor signal increases. This proposed method leads to the design of a feasible moving target detector with simple structure and low cost, which can be applied in the interception system. Full article

(This article belongs to the Special Issue Non-Contact Sensing)

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Open AccessArticle

A Simulation Study of a Radiofrequency Localization System for Tracking Patient Motion in Radiotherapy

by Mark Ostyn, Siyong Kim and Woon-Hong Yeo

Sensors 2016, 16(4), 534; https://doi.org/10.3390/s16040534 - 13 Apr 2016

Cited by 3 | Viewed by 5878

Abstract

One of the most widely used tools in cancer treatment is external beam radiotherapy. However, the major risk involved in radiotherapy is excess radiation dose to healthy tissue, exacerbated by patient motion. Here, we present a simulation study of a potential radiofrequency (RF) [...] Read more.

One of the most widely used tools in cancer treatment is external beam radiotherapy. However, the major risk involved in radiotherapy is excess radiation dose to healthy tissue, exacerbated by patient motion. Here, we present a simulation study of a potential radiofrequency (RF) localization system designed to track intrafraction motion (target motion during the radiation treatment). This system includes skin-wearable RF beacons and an external tracking system. We develop an analytical model for direction of arrival measurement with radio frequencies (GHz range) for use in a localization estimate. We use a Monte Carlo simulation to investigate the relationship between a localization estimate and angular resolution of sensors (signal receivers) in a simulated room. The results indicate that the external sensor needs an angular resolution of about 0.03 degrees to achieve millimeter-level localization accuracy in a treatment room. This fundamental study of a novel RF localization system offers the groundwork to design a radiotherapy-compatible patient positioning system for active motion compensation. Full article

(This article belongs to the Special Issue Non-Contact Sensing)

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Open AccessArticle

Exploitation of Ubiquitous Wi-Fi Devices as Building Blocks for Improvised Motion Detection Systems

by Francesco Soldovieri and Gianluca Gennarelli

Sensors 2016, 16(3), 307; https://doi.org/10.3390/s16030307 - 27 Feb 2016

Cited by 13 | Viewed by 5499

Abstract

This article deals with a feasibility study on the detection of human movements in indoor scenarios based on radio signal strength variations. The sensing principle exploits the fact that the human body interacts with wireless signals, introducing variations of the radiowave fields due [...] Read more.

This article deals with a feasibility study on the detection of human movements in indoor scenarios based on radio signal strength variations. The sensing principle exploits the fact that the human body interacts with wireless signals, introducing variations of the radiowave fields due to shadowing and multipath phenomena. As a result, human motion can be inferred from fluctuations of radiowave power collected by a receiving terminal. In this paper, we investigate the potentialities of widely available wireless communication devices in order to develop an improvised motion detection system (IMDS). Experimental tests are performed in an indoor environment by using a smartphone as a Wi-Fi access point and a laptop with dedicated software as a receiver. Simple detection strategies tailored for real-time operation are implemented to process the received signal strength measurements. The achieved results confirm the potentialities of the simple system here proposed to reliably detect human motion in operational conditions. Full article

(This article belongs to the Special Issue Non-Contact Sensing)

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Review

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2636 KiB

Open AccessReview

Six-Port Based Interferometry for Precise Radar and Sensing Applications

by Alexander Koelpin, Fabian Lurz, Sarah Linz, Sebastian Mann, Christoph Will and Stefan Lindner

Sensors 2016, 16(10), 1556; https://doi.org/10.3390/s16101556 - 22 Sep 2016

Cited by 67 | Viewed by 13189

Abstract

Microwave technology plays a more important role in modern industrial sensing applications. Pushed by the significant progress in monolithic microwave integrated circuit technology over the past decades, complex sensing systems operating in the microwave and even millimeter-wave range are available for reasonable costs [...] Read more.

Microwave technology plays a more important role in modern industrial sensing applications. Pushed by the significant progress in monolithic microwave integrated circuit technology over the past decades, complex sensing systems operating in the microwave and even millimeter-wave range are available for reasonable costs combined with exquisite performance. In the context of industrial sensing, this stimulates new approaches for metrology based on microwave technology. An old measurement principle nearly forgotten over the years has recently gained more and more attention in both academia and industry: the six-port interferometer. This paper reviews the basic concept, investigates promising applications in remote, as well as contact-based sensing and compares the system with state-of-the-art metrology. The significant advantages will be discussed just as the limitations of the six-port architecture. Particular attention will be paid to impairment effects and non-ideal behavior, as well as compensation and linearization concepts. It will be shown that in application fields, like remote distance sensing, precise alignment measurements, as well as interferometrically-evaluated mechanical strain analysis, the six-port architecture delivers extraordinary measurement results combined with high measurement data update rates for reasonable system costs. This makes the six-port architecture a promising candidate for industrial metrology. Full article

(This article belongs to the Special Issue Non-Contact Sensing)

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Open AccessReview

Short-Range Noncontact Sensors for Healthcare and Other Emerging Applications: A Review

by Changzhan Gu

Sensors 2016, 16(8), 1169; https://doi.org/10.3390/s16081169 - 26 Jul 2016

Cited by 87 | Viewed by 12619

Abstract

Short-range noncontact sensors are capable of remotely detecting the precise movements of the subjects or wirelessly estimating the distance from the sensor to the subject. They find wide applications in our day lives such as noncontact vital sign detection of heart beat and [...] Read more.

Short-range noncontact sensors are capable of remotely detecting the precise movements of the subjects or wirelessly estimating the distance from the sensor to the subject. They find wide applications in our day lives such as noncontact vital sign detection of heart beat and respiration, sleep monitoring, occupancy sensing, and gesture sensing. In recent years, short-range noncontact sensors are attracting more and more efforts from both academia and industry due to their vast applications. Compared to other radar architectures such as pulse radar and frequency-modulated continuous-wave (FMCW) radar, Doppler radar is gaining more popularity in terms of system integration and low-power operation. This paper reviews the recent technical advances in Doppler radars for healthcare applications, including system hardware improvement, digital signal processing, and chip integration. This paper also discusses the hybrid FMCW-interferometry radars and the emerging applications and the future trends. Full article

(This article belongs to the Special Issue Non-Contact Sensing)

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