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Sensors, Volume 20, Issue 5 (March-1 2020) – 303 articles

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Cover Story (view full-size image) Raman spectroscopy is a powerful optical detection technique widely used in biological research, [...] Read more.
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Open AccessArticle
Analysis of the Functionality of the Feed Chain in Olive Pitting, Slicing and Stuffing Machines by IoT, Computer Vision and Neural Network Diagnosis
Sensors 2020, 20(5), 1541; https://doi.org/10.3390/s20051541 - 10 Mar 2020
Viewed by 487
Abstract
Olive pitting, slicing and stuffing machines (DRR in Spanish) are characterized by the fact that their optimal functioning is based on appropriate adjustments. Traditional systems are not completely reliable because their minimum error rate is 1–2%, which can result in fruit loss, since [...] Read more.
Olive pitting, slicing and stuffing machines (DRR in Spanish) are characterized by the fact that their optimal functioning is based on appropriate adjustments. Traditional systems are not completely reliable because their minimum error rate is 1–2%, which can result in fruit loss, since the pitting process is not infallible, and food safety issues can arise. Such minimum errors are impossible to remove through mechanical adjustments. In order to achieve this objective, an innovative solution must be provided in order to remove errors at operating speed rates over 2500 olives/min. This work analyzes the appropriate placement of olives in the pockets of the feed chain by using the following items: (1) An IoT System to control the DRR machine and the data analysis. (2) A computer vision system with an external shot camera and a LED lighting system, which takes a picture of every pocket passing in front of the camera. (3) A chip with a neural network for classification that, once trained, classifies between four possible pocket cases: empty, normal, incorrectly de-stoned olives at any angles (also known as a “boat”), and an anomalous case (foreign elements such as leafs, small branches or stones, two olives or small parts of olives in the same pocket). The main objective of this paper is to illustrate how with the use of a system based on IoT and a physical chip (NeuroMem CM1K, General Vision Inc.) with neural networks for sorting purposes, it is possible to optimize the functionality of this type of machine by remotely analyzing the data obtained. The use of classifying hardware allows it to work at the nominal operating speed for these machines. This would be limited if other classifying techniques based on software were used. Full article
(This article belongs to the Special Issue IoT Technologies and the Agricultural Value Chain)
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Open AccessArticle
Refining Network Lifetime of Wireless Sensor Network Using Energy-Efficient Clustering and DRL-Based Sleep Scheduling
Sensors 2020, 20(5), 1540; https://doi.org/10.3390/s20051540 - 10 Mar 2020
Viewed by 373
Abstract
Over the recent era, Wireless Sensor Network (WSN) has attracted much attention among industrialists and researchers owing to its contribution to numerous applications including military, environmental monitoring and so on. However, reducing the network delay and improving the network lifetime are always big [...] Read more.
Over the recent era, Wireless Sensor Network (WSN) has attracted much attention among industrialists and researchers owing to its contribution to numerous applications including military, environmental monitoring and so on. However, reducing the network delay and improving the network lifetime are always big issues in the domain of WSN. To resolve these downsides, we propose an Energy-Efficient Scheduling using the Deep Reinforcement Learning (DRL) (E2S-DRL) algorithm in WSN. E2S-DRL contributes three phases to prolong network lifetime and to reduce network delay that is: the clustering phase, duty-cycling phase and routing phase. E2S-DRL starts with the clustering phase where we reduce the energy consumption incurred during data aggregation. It is achieved through the Zone-based Clustering (ZbC) scheme. In the ZbC scheme, hybrid Particle Swarm Optimization (PSO) and Affinity Propagation (AP) algorithms are utilized. Duty cycling is adopted in the second phase by executing the DRL algorithm, from which, E2S-DRL reduces the energy consumption of individual sensor nodes effectually. The transmission delay is mitigated in the third (routing) phase using Ant Colony Optimization (ACO) and the Firefly Algorithm (FFA). Our work is modeled in Network Simulator 3.26 (NS3). The results are valuable in provisions of upcoming metrics including network lifetime, energy consumption, throughput and delay. From this evaluation, it is proved that our E2S-DRL reduces energy consumption, reduces delays by up to 40% and enhances throughput and network lifetime up to 35% compared to the existing cTDMA, DRA, LDC and iABC methods. Full article
(This article belongs to the Special Issue Energy-Efficient Sensing in Wireless Sensor Networks)
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Open AccessArticle
Radiomics Driven Diffusion Weighted Imaging Sensing Strategies for Zone-Level Prostate Cancer Sensing
Sensors 2020, 20(5), 1539; https://doi.org/10.3390/s20051539 - 10 Mar 2020
Viewed by 417
Abstract
Prostate cancer is the most commonly diagnosed cancer in North American men; however, prognosis is relatively good given early diagnosis. This motivates the need for fast and reliable prostate cancer sensing. Diffusion weighted imaging (DWI) has gained traction in recent years as a [...] Read more.
Prostate cancer is the most commonly diagnosed cancer in North American men; however, prognosis is relatively good given early diagnosis. This motivates the need for fast and reliable prostate cancer sensing. Diffusion weighted imaging (DWI) has gained traction in recent years as a fast non-invasive approach to cancer sensing. The most commonly used DWI sensing modality currently is apparent diffusion coefficient (ADC) imaging, with the recently introduced computed high-b value diffusion weighted imaging (CHB-DWI) showing considerable promise for cancer sensing. In this study, we investigate the efficacy of ADC and CHB-DWI sensing modalities when applied to zone-level prostate cancer sensing by introducing several radiomics driven zone-level prostate cancer sensing strategies geared around hand-engineered radiomic sequences from DWI sensing (which we term as Zone-X sensing strategies). Furthermore, we also propose Zone-DR, a discovery radiomics approach based on zone-level deep radiomic sequencer discovery that discover radiomic sequences directly for radiomics driven sensing. Experimental results using 12,466 pathology-verified zones obtained through the different DWI sensing modalities of 101 patients showed that: (i) the introduced Zone-X and Zone-DR radiomics driven sensing strategies significantly outperformed the traditional clinical heuristics driven strategy in terms of AUC, (ii) the introduced Zone-DR and Zone-SVM strategies achieved the highest sensitivity and specificity, respectively for ADC amongst the tested radiomics driven strategies, (iii) the introduced Zone-DR and Zone-LR strategies achieved the highest sensitivities for CHB-DWI amongst the tested radiomics driven strategies, and (iv) the introduced Zone-DR, Zone-LR, and Zone-SVM strategies achieved the highest specificities for CHB-DWI amongst the tested radiomics driven strategies. Furthermore, the results showed that the trade-off between sensitivity and specificity can be optimized based on the particular clinical scenario we wish to employ radiomic driven DWI prostate cancer sensing strategies for, such as clinical screening versus surgical planning. Finally, we investigate the critical regions within sensing data that led to a given radiomic sequence generated by a Zone-DR sequencer using an explainability method to get a deeper understanding on the biomarkers important for zone-level cancer sensing. Full article
(This article belongs to the Special Issue Biomedical Imaging and Sensing)
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Open AccessArticle
Performance Analysis of Geiger–Müller and Cadmium Zinc Telluride Sensors Envisaging Airborne Radiological Monitoring in NORM Sites
Sensors 2020, 20(5), 1538; https://doi.org/10.3390/s20051538 - 10 Mar 2020
Viewed by 477
Abstract
Radiological monitoring is fundamental for compliance with radiological protection policies in the aftermath of radiological events, such as nuclear accidents, terrorism, and out-of-commission uranium mines. An effective strategy for radiation monitoring is to use radiation detectors coupled with Unmanned Aerial Vehicles (UAVs), enabling [...] Read more.
Radiological monitoring is fundamental for compliance with radiological protection policies in the aftermath of radiological events, such as nuclear accidents, terrorism, and out-of-commission uranium mines. An effective strategy for radiation monitoring is to use radiation detectors coupled with Unmanned Aerial Vehicles (UAVs), enabling for quicker surveillance of large areas without involving the need of human presence in the target area. The main aim of this study was to formulate the parameters for a UAV flight strategy in preparation for future field measurements using Geiger–Muller Counters (GMC) and Cadmium Zinc Telluride (CZT) spectrometers. As a proof of concept, the prepared flight strategy will be used to survey out-of-commission uranium mines in northern Portugal. Procedures to assure the calibration of the CZT and verification of the GMCs were conducted, as well as a sensitivity analysis of the sensors considering different acquisition times, distance to source, and detector response time. This article reports specific parameters, such as UAV distance to ground, time of exposition, speed, and the methodology to perform the identification and calculate the activity of possible radioactive sources. An effective flight strategy is also presented, aiming to use radiation detectors coupled with UAVs to undertake extensive monitoring of areas with enhanced levels of environmental radiation, which is of prime importance due to the lasting hazardous effects of enhanced environmental radiation in the nearby ecosystem and population. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle
Precision and Reliability of Tightly Coupled PPP GNSS and Landmark Monocular Vision Positioning
Sensors 2020, 20(5), 1537; https://doi.org/10.3390/s20051537 - 10 Mar 2020
Viewed by 345
Abstract
This paper presents an approach to analyse the quality, in terms of precision and reliability, of a system which integrates—at the observation-level—landmark positions and GNSS measurements, obtained with a single camera and a digital map, and a single frequency GNSS receiver respectively. We [...] Read more.
This paper presents an approach to analyse the quality, in terms of precision and reliability, of a system which integrates—at the observation-level—landmark positions and GNSS measurements, obtained with a single camera and a digital map, and a single frequency GNSS receiver respectively. We illustrate the analysis by means of design computations, and we present the actual performance by means of a small experiment in practice. It is shown that the integration model is able to produce a position solution even when both sensors individually fail to do so. With realistic assumptions on measurement noise, the proposed integrated, low-cost system can deliver a horizontal position with a precision of better than half a meter. The external reliability of the integrated system is at the few decimetre-level, showing that the impact of undetected faults in the measurements, for instance incorrectly identified landmarks in the image, on the horizontal position is limited and acceptable, thereby confirming the fault-robustness of the system. Full article
(This article belongs to the collection Positioning and Navigation)
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Open AccessArticle
Non-Local SVD Denoising of MRI Based on Sparse Representations
Sensors 2020, 20(5), 1536; https://doi.org/10.3390/s20051536 - 10 Mar 2020
Viewed by 349
Abstract
Magnetic Resonance (MR) Imaging is a diagnostic technique that produces noisy images, which must be filtered before processing to prevent diagnostic errors. However, filtering the noise while keeping fine details is a difficult task. This paper presents a method, based on sparse representations [...] Read more.
Magnetic Resonance (MR) Imaging is a diagnostic technique that produces noisy images, which must be filtered before processing to prevent diagnostic errors. However, filtering the noise while keeping fine details is a difficult task. This paper presents a method, based on sparse representations and singular value decomposition (SVD), for non-locally denoising MR images. The proposed method prevents blurring, artifacts, and residual noise. Our method is composed of three stages. The first stage divides the image into sub-volumes, to obtain its sparse representation, by using the KSVD algorithm. Then, the global influence of the dictionary atoms is computed to upgrade the dictionary and obtain a better reconstruction of the sub-volumes. In the second stage, based on the sparse representation, the noise-free sub-volume is estimated using a non-local approach and SVD. The noise-free voxel is reconstructed by aggregating the overlapped voxels according to the rarity of the sub-volumes it belongs, which is computed from the global influence of the atoms. The third stage repeats the process using a different sub-volume size for producing a new filtered image, which is averaged with the previously filtered images. The results provided show that our method outperforms several state-of-the-art methods in both simulated and real data. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle
Embedded Bio-Mimetic System for Functional Electrical Stimulation Controlled by Event-Driven sEMG
Sensors 2020, 20(5), 1535; https://doi.org/10.3390/s20051535 - 10 Mar 2020
Viewed by 347
Abstract
The analysis of the surface ElectroMyoGraphic (sEMG) signal for controlling the Functional Electrical Stimulation (FES) therapy is being widely accepted as an active rehabilitation technique for the restoration of neuro-muscular disorders. Portability and real-time functionalities are major concerns, and, among others, two correlated [...] Read more.
The analysis of the surface ElectroMyoGraphic (sEMG) signal for controlling the Functional Electrical Stimulation (FES) therapy is being widely accepted as an active rehabilitation technique for the restoration of neuro-muscular disorders. Portability and real-time functionalities are major concerns, and, among others, two correlated challenges are the development of an embedded system and the implementation of lightweight signal processing approaches. In this respect, the event-driven nature of the Average Threshold Crossing (ATC) technique, considering its high correlation with the muscle force and the sparsity of its representation, could be an optimal solution. In this paper we present an embedded ATC-FES control system equipped with a multi-platform software featuring an easy-to-use Graphical User Interface (GUI). The system has been first characterized and validated by analyzing CPU and memory usage in different operating conditions, as well as measuring the system latency (fulfilling the real-time requirements with a 140 ms FES definition process). We also confirmed system effectiveness, testing it on 11 healthy subjects: The similarity between the voluntary movement and the stimulate one has been evaluated, computing the cross-correlation coefficient between the angular signals acquired during the limbs motion. We obtained high correlation values of 0.87 ± 0.07 and 0.93 ± 0.02 for the elbow flexion and knee extension exercises, respectively, proving good stimulation application in real therapy-scenarios. Full article
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Open AccessArticle
Extra-Wide Lane Ambiguity Resolution and Validation for a Single Epoch Based on the Triple-Frequency BeiDou Navigation Satellite System
Sensors 2020, 20(5), 1534; https://doi.org/10.3390/s20051534 - 10 Mar 2020
Viewed by 288
Abstract
The ambiguity resolution (AR) and validation of the global navigation satellite system (GNSS) have been challenging tasks for some decades. Considering the reliability problem of extra-wide-lane (EWL) ambiguity in the triple-carrier ambiguity resolution (TCAR), a method for validating the reliability of the EWL [...] Read more.
The ambiguity resolution (AR) and validation of the global navigation satellite system (GNSS) have been challenging tasks for some decades. Considering the reliability problem of extra-wide-lane (EWL) ambiguity in the triple-carrier ambiguity resolution (TCAR), a method for validating the reliability of the EWL ambiguity using a single epoch was proposed for the BeiDou Navigation Satellite System (BDS). For the initial EWL ambiguity, obtained using a rounding estimator with a geometry-free (GF) model, the double-difference ionospheric delay was first estimated to construct a relative positioning model with an initial fixed ambiguity. Second, based on the theory of gross error detection and the AR characteristics of EWL, the second-best ambiguity candidate was constructed. Finally, among the two sets of ambiguities, the one with the smaller posterior variance was taken as the reliable ambiguity. The study showed that, for a single epoch, when only one or two satellites had incorrect ambiguities, the AR success rate after ambiguity validation and correction could reach 100% for medium baselines. For long baselines, due to the increase of atmospheric error, the validation was affected to some extent. However, the AR success rates for two long baselines increased from 96.82% and 98.44% to 98.80% and 99.67%, respectively. Full article
(This article belongs to the Section Remote Sensors, Control, and Telemetry)
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Open AccessArticle
A Multi-Scale U-Shaped Convolution Auto-Encoder Based on Pyramid Pooling Module for Object Recognition in Synthetic Aperture Radar Images
Sensors 2020, 20(5), 1533; https://doi.org/10.3390/s20051533 - 10 Mar 2020
Viewed by 281
Abstract
Although unsupervised representation learning (RL) can tackle the performance deterioration caused by limited labeled data in synthetic aperture radar (SAR) object classification, the neglected discriminative detailed information and the ignored distinctive characteristics of SAR images can lead to performance degradation. In this paper, [...] Read more.
Although unsupervised representation learning (RL) can tackle the performance deterioration caused by limited labeled data in synthetic aperture radar (SAR) object classification, the neglected discriminative detailed information and the ignored distinctive characteristics of SAR images can lead to performance degradation. In this paper, an unsupervised multi-scale convolution auto-encoder (MSCAE) was proposed which can simultaneously obtain the global features and local characteristics of targets with its U-shaped architecture and pyramid pooling modules (PPMs). The compact depth-wise separable convolution and the deconvolution counterpart were devised to decrease the trainable parameters. The PPM and the multi-scale feature learning scheme were designed to learn multi-scale features. Prior knowledge of SAR speckle was also embedded in the model. The reconstruction loss of the MSCAE was measured by the structural similarity index metric (SSIM) of the reconstructed data and the images filtered by the improved Lee sigma filter. A speckle suppression restriction was also added in the objective function to guarantee that the speckle suppression procedure would take place in the feature learning stage. Experimental results with the MSTAR dataset under the standard operating condition and several extended operating conditions demonstrated the effectiveness of the proposed model in SAR object classification tasks. Full article
(This article belongs to the Special Issue Recent Advancements in Radar Imaging and Sensing Technology)
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Open AccessReview
A Review of Thin-Film Magnetoelastic Materials for Magnetoelectric Applications
Sensors 2020, 20(5), 1532; https://doi.org/10.3390/s20051532 - 10 Mar 2020
Viewed by 356
Abstract
Since the revival of multiferroic laminates with giant magnetoelectric (ME) coefficients, a variety of multifunctional ME devices, such as sensor, inductor, filter, antenna etc. have been developed. Magnetoelastic materials, which couple the magnetization and strain together, have recently attracted ever-increasing attention due to [...] Read more.
Since the revival of multiferroic laminates with giant magnetoelectric (ME) coefficients, a variety of multifunctional ME devices, such as sensor, inductor, filter, antenna etc. have been developed. Magnetoelastic materials, which couple the magnetization and strain together, have recently attracted ever-increasing attention due to their key roles in ME applications. This review starts with a brief introduction to the early research efforts in the field of multiferroic materials and moves to the recent work on magnetoelectric coupling and their applications based on both bulk and thin-film materials. This is followed by sections summarizing historical works and solving the challenges specific to the fabrication and characterization of magnetoelastic materials with large magnetostriction constants. After presenting the magnetostrictive thin films and their static and dynamic properties, we review micro-electromechanical systems (MEMS) and bulk devices utilizing ME effect. Finally, some open questions and future application directions where the community could head for magnetoelastic materials will be discussed. Full article
(This article belongs to the Special Issue Magnetoelastic Sensors)
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Open AccessArticle
A Camera Sensors-Based System to Study Drug Effects on In Vitro Motility: The Case of PC-3 Prostate Cancer Cells
Sensors 2020, 20(5), 1531; https://doi.org/10.3390/s20051531 - 10 Mar 2020
Viewed by 345
Abstract
Cell motility is the brilliant result of cell status and its interaction with close environments. Its detection is now possible, thanks to the synergy of high-resolution camera sensors, time-lapse microscopy devices, and dedicated software tools for video and data analysis. In this scenario, [...] Read more.
Cell motility is the brilliant result of cell status and its interaction with close environments. Its detection is now possible, thanks to the synergy of high-resolution camera sensors, time-lapse microscopy devices, and dedicated software tools for video and data analysis. In this scenario, we formulated a novel paradigm in which we considered the individual cells as a sort of sensitive element of a sensor, which exploits the camera as a transducer returning the movement of the cell as an output signal. In this way, cell movement allows us to retrieve information about the chemical composition of the close environment. To optimally exploit this information, in this work, we introduce a new setting, in which a cell trajectory is divided into sub-tracks, each one characterized by a specific motion kind. Hence, we considered all the sub-tracks of the single-cell trajectory as the signals of a virtual array of cell motility-based sensors. The kinematics of each sub-track is quantified and used for a classification task. To investigate the potential of the proposed approach, we have compared the achieved performances with those obtained by using a single-trajectory paradigm with the scope to evaluate the chemotherapy treatment effects on prostate cancer cells. Novel pattern recognition algorithms have been applied to the descriptors extracted at a sub-track level by implementing features, as well as samples selection (a good teacher learning approach) for model construction. The experimental results have put in evidence that the performances are higher when a further cluster majority role has been considered, by emulating a sort of sensor fusion procedure. All of these results highlighted the high strength of the proposed approach, and straightforwardly prefigure its use in lab-on-chip or organ-on-chip applications, where the cell motility analysis can be massively applied using time-lapse microscopy images. Full article
(This article belongs to the Special Issue Machine Learning for Biomedical Imaging and Sensing)
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Open AccessArticle
Pedestrian Navigation Method Based on Machine Learning and Gait Feature Assistance
Sensors 2020, 20(5), 1530; https://doi.org/10.3390/s20051530 - 10 Mar 2020
Viewed by 293
Abstract
In recent years, as the mechanical structure of humanoid robots increasingly resembles the human form, research on pedestrian navigation technology has become of great significance for the development of humanoid robot navigation systems. To solve the problem that the wearable inertial navigation system [...] Read more.
In recent years, as the mechanical structure of humanoid robots increasingly resembles the human form, research on pedestrian navigation technology has become of great significance for the development of humanoid robot navigation systems. To solve the problem that the wearable inertial navigation system based on micro-inertial measurement units (MIMUs) installed on feet cannot effectively realize its positioning function when the body movement is too drastic to be measured correctly by commercial grade inertial sensors, a pedestrian navigation method based on construction of a virtual inertial measurement unit (VIMU) and gait feature assistance is proposed. The inertial data from different positions of pedestrians’ lower limbs are collected synchronously via actual IMUs as training samples. The nonlinear mapping relationship between inertial information from the human foot and leg is established by a visual geometry group-long short term memory (VGG-LSTM) neural network model, based on which the foot VIMU and virtual inertial navigation system (VINS) are constructed. The VINS experimental results show that, combined with zero-velocity update (ZUPT), the integrated method of error modification proposed in this paper can effectively reduce the accumulation of positioning errors in situations where the gait type exceeds the measurement range of the inertial sensors. The positioning performance of the proposed method is more accurate and stable in complex gait types than that merely using ZUPT. Full article
(This article belongs to the Special Issue Wearable Electronic Sensors)
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Open AccessArticle
Optimization of a Single Tube Practical Acoustic Thermometer
Sensors 2020, 20(5), 1529; https://doi.org/10.3390/s20051529 - 10 Mar 2020
Viewed by 295
Abstract
When designing a single tube practical acoustic thermometer (PAT), certain considerations should be addressed for optimal performance. This paper is concerned with the main issues involved in building a reliable PAT. It has to be emphasised that a PAT measures the ratio of [...] Read more.
When designing a single tube practical acoustic thermometer (PAT), certain considerations should be addressed for optimal performance. This paper is concerned with the main issues involved in building a reliable PAT. It has to be emphasised that a PAT measures the ratio of the time delay between the single temperature calibration point (ice point) and any other temperature. Here, we present different models of the speed of sound in tubes, including the effects of real gases and an error analysis of the most accurate model with a Monte Carlo simulation. Additionally, we introduce the problem of acoustic signal overlap and some possible solutions, one of which is acoustic signal cancellation, which aims to eliminate the unwanted parts of an acoustic signal, and another is to optimize the tube length for the parameters of the gas used and specific temperature range. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle
Using Class-Specific Feature Selection for Cancer Detection with Gene Expression Profile Data of Platelets
Sensors 2020, 20(5), 1528; https://doi.org/10.3390/s20051528 - 10 Mar 2020
Viewed by 272
Abstract
A novel multi-classification method, which integrates the elastic net and probabilistic support vector machine, was proposed to solve this problem in cancer detection with gene expression profile data of platelets, whose problems mainly are a kind of multi-class classification problem with high dimension, [...] Read more.
A novel multi-classification method, which integrates the elastic net and probabilistic support vector machine, was proposed to solve this problem in cancer detection with gene expression profile data of platelets, whose problems mainly are a kind of multi-class classification problem with high dimension, small samples, and collinear data. The strategy of one-against-all (OVA) was employed to decompose the multi-classification problem into a series of binary classification problems. The elastic net was used to select class-specific features for the binary classification problems, and the probabilistic support vector machine was used to make the outputs of the binary classifiers with class-specific features comparable. Simulation data and gene expression profile data were intended to verify the effectiveness of the proposed method. Results indicate that the proposed method can automatically select class-specific features and obtain better performance of classification than that of the conventional multi-class classification methods, which are mainly based on global feature selection methods. This study indicates the proposed method is suitable for general multi-classification problems featured with high-dimension, small samples, and collinear data. Full article
(This article belongs to the Special Issue Multimodal Data Fusion and Machine-Learning for Healthcare)
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Open AccessArticle
Multiuser Chirp Spread Spectrum Transmission in an Underwater Acoustic Channel Applied to an AUV Fleet
Sensors 2020, 20(5), 1527; https://doi.org/10.3390/s20051527 - 10 Mar 2020
Viewed by 320
Abstract
The objective of this paper is to provide a multiuser transmission technique for underwater acoustic communication in the framework of an Autonomous Underwater Vehicle (AUV) fleet. By using a variant of a Hyperbolically Frequency-Modulated (HFM) signal, we describe a new family of transmission [...] Read more.
The objective of this paper is to provide a multiuser transmission technique for underwater acoustic communication in the framework of an Autonomous Underwater Vehicle (AUV) fleet. By using a variant of a Hyperbolically Frequency-Modulated (HFM) signal, we describe a new family of transmission techniques called MultiUser Chirp Spread Spectrum (MU-CSS), which allows a very simple matched-filter-based decoding. These techniques are expected to provide good resilience against multiuser interference while keeping good robustness to Underwater Acoustic (UWA) channel impairments like Doppler shift. Their implementation for the UWA scenario is described, and the performance results over a simulated shallow-water UWA channel are analyzed and compared against conventional Code-Division Multiple Access (CDMA) and Time-Division Multiple Access (TDMA) transmission. Finally, the feasibility and robustness of the proposed methods are verified over the underWater AcousTic channEl Replay benchMARK (Watermark), fed by several channel responses from sounding experiments performed in a lake. Full article
(This article belongs to the Special Issue Underwater Sensor Networks)
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Open AccessArticle
High Precision Low-Speed Control for Permanent Magnet Synchronous Motor
Sensors 2020, 20(5), 1526; https://doi.org/10.3390/s20051526 - 10 Mar 2020
Viewed by 279
Abstract
Due to the process defects and imperfection of drivers, permanent magnet synchronous motors (PMSM) are problematic to control. There is still a lack of effective high-performance control methods for inertial stabilized platforms based on PMSM currently. At present, the most frequently used method [...] Read more.
Due to the process defects and imperfection of drivers, permanent magnet synchronous motors (PMSM) are problematic to control. There is still a lack of effective high-performance control methods for inertial stabilized platforms based on PMSM currently. At present, the most frequently used method is sliding mode control (SMC), but traditional sliding mode control cannot overcome the contradiction between high performance and system chattering. In order to solve this problem and improve the system reliability and pointing accuracy, a new approach law for the sliding mode controller is proposed in this paper. In view of the large periodic torque ripple in PMSM, an iterative learning controller (ILC) is introduced to compensate for the disturbance. Based on these, aimed at suppressing all kinds of real-time disturbances in the working environment of the system, the extended state observer (ESO) is brought into the servo system to observe the lumped disturbance of the system, and the total disturbance observed is compensated into the sliding mode controller, so as to better suppress the system chattering and enhance the system’s ability of resisting external disturbance. Experiments are carried out on an inertial stabilization platform based on DSP + CPLD. The final experiments verify that the SMC with the new approach, combined with ILC and ESO, is of outstanding performance when compared with the traditional proportional integral (PI) + disturbance observer (DOB) control scheme. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle
Design of a Fully Integrated Inductive Coupling System: A Discrete Approach Towards Sensing Ventricular Pressure
Sensors 2020, 20(5), 1525; https://doi.org/10.3390/s20051525 - 10 Mar 2020
Viewed by 368
Abstract
In this paper, an alternative strategy for the design of a bidirectional inductive power transfer (IPT) module, intended for the continuous monitoring of cardiac pressure, is presented. This new integrated implantable medical device (IMD) was designed including a precise ventricular pressure sensor, where [...] Read more.
In this paper, an alternative strategy for the design of a bidirectional inductive power transfer (IPT) module, intended for the continuous monitoring of cardiac pressure, is presented. This new integrated implantable medical device (IMD) was designed including a precise ventricular pressure sensor, where the available implanting room is restricted to a 1.8 × 1.8 cm2 area. This work considers a robust magnetic coupling between an external reading coil and the implantable module: a three-dimensional inductor and a touch mode capacitive pressure sensor (TMCPS) set. In this approach, the coupling modules were modelled as RCL circuits tuned at a 13.56 MHz frequency. The analytical design was validated by means of Comsol Multiphysics, CoventorWare, and ANSYS HFSS software tools. A power transmission efficiency (PTE) of 94% was achieved through a 3.5 cm-thick biological tissue, based on high magnitudes for the inductance (L) and quality factor (Q) components. A specific absorption rate (SAR) of less than 1.6 W/Kg was attained, which suggests that this IPT system can be implemented in a safe way, according to IEEE C95.1 safety guidelines. The set of inductor and capacitor integrated arrays were designed over a very thin polyimide film, where the 3D coil was 18 mm in diameter and approximately 50% reduced in size, considering any conventional counterpart. Finally, this new approach for the IMD was under development using low-cost thin film manufacturing technologies for flexible electronics. Meanwhile, as an alternative test, this novel system was fabricated using a discrete printed circuit board (PCB) approach, where preliminary electromagnetic characterization demonstrates the viability of this bidirectional IPT design. Full article
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Open AccessArticle
Ratiometric Strategy for Electrochemical Sensing of Carbaryl Residue in Water and Vegetable Samples
Sensors 2020, 20(5), 1524; https://doi.org/10.3390/s20051524 - 10 Mar 2020
Viewed by 279
Abstract
Accurate analysis of pesticide residue in real samples is essential for food safety and environmental protection. However, a traditional electrochemical sensor based on single-signal output is easily affected by background noise, environmental conditions, electrode diversity, and a complex matrix of samples, leading to [...] Read more.
Accurate analysis of pesticide residue in real samples is essential for food safety and environmental protection. However, a traditional electrochemical sensor based on single-signal output is easily affected by background noise, environmental conditions, electrode diversity, and a complex matrix of samples, leading to extremely low accuracy. Hence, in this paper, a ratiometric strategy based on dual-signal output was adopted to build inner correction for sensing of widely-used carbaryl (CBL) for the first time. By comparison, Nile blue A (NB) was selected as reference probe, due to its well-defined peak, few effects on the target peak of CBL, and excellent stability. The effects of a derivatization method, technique mode, and pH were also investigated. Then the performance of the proposed ratiometric sensor was assessed in terms of three aspects including the elimination of system noise, electrode deviation and matrix effect. Compared with traditional single-signal sensor, the ratiometric sensor showed a much better linear correlation coefficient (r > 0.99), reproducibility (RSD < 10%), and limit of detection (LOD = 1.0 μM). The results indicated the introduction of proper reference probe could ensure the interdependence of target and reference signal on the same sensing environment, thus inner correction was fulfilled, which provided a promising tool for accurate analysis. Full article
(This article belongs to the Special Issue Sensors for Environmental and Life Science Applications)
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Open AccessArticle
Motion Assessment for Accelerometric and Heart Rate Cycling Data Analysis
Sensors 2020, 20(5), 1523; https://doi.org/10.3390/s20051523 - 10 Mar 2020
Viewed by 312
Abstract
Motion analysis is an important topic in the monitoring of physical activities and recognition of neurological disorders. The present paper is devoted to motion assessment using accelerometers inside mobile phones located at selected body positions and the records of changes in the heart [...] Read more.
Motion analysis is an important topic in the monitoring of physical activities and recognition of neurological disorders. The present paper is devoted to motion assessment using accelerometers inside mobile phones located at selected body positions and the records of changes in the heart rate during cycling, under different body loads. Acquired data include 1293 signal segments recorded by the mobile phone and the Garmin device for uphill and downhill cycling. The proposed method is based upon digital processing of the heart rate and the mean power in different frequency bands of accelerometric data. The classification of the resulting features was performed by the support vector machine, Bayesian methods, k-nearest neighbor method, and neural networks. The proposed criterion is then used to find the best positions for the sensors with the highest discrimination abilities. The results suggest the sensors be positioned on the spine for the classification of uphill and downhill cycling, yielding an accuracy of 96.5% and a cross-validation error of 0.04 evaluated by a two-layer neural network system for features based on the mean power in the frequency bands 3 , 8 and 8 , 15 Hz. This paper shows the possibility of increasing this accuracy to 98.3% by the use of more features and the influence of appropriate sensor positioning for motion monitoring and classification. Full article
(This article belongs to the Special Issue Intelligent Sensors for Monitoring Physical Activities)
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Open AccessCorrection
Correction: Gulimire Tuerdi, Nuerguli Kari, Yin Yan, Patima Nizamidin and Abliz Yimit *. A Functionalized Tetrakis(4-Nitrophenyl)Porphyrin Film Optical Waveguide Sensor for Detection of H2S and Ethanediamine Gases. Sensors 2017, 17, 2717
Sensors 2020, 20(5), 1522; https://doi.org/10.3390/s20051522 - 10 Mar 2020
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Abstract
The authors wish to make the following corrections to this paper [...] Full article
Open AccessArticle
An Efficient Certificateless Aggregate Signature Scheme for Blockchain-Based Medical Cyber Physical Systems
Sensors 2020, 20(5), 1521; https://doi.org/10.3390/s20051521 - 10 Mar 2020
Viewed by 274
Abstract
Different from the traditional healthcare field, Medical Cyber Physical Systems (MCPS) rely more on wireless wearable devices and medical applications to provide better medical services. The secure storage and sharing of medical data are facing great challenges. Blockchain technology with decentralization, security, credibility [...] Read more.
Different from the traditional healthcare field, Medical Cyber Physical Systems (MCPS) rely more on wireless wearable devices and medical applications to provide better medical services. The secure storage and sharing of medical data are facing great challenges. Blockchain technology with decentralization, security, credibility and tamper-proof is an effective way to solve this problem. However, capacity limitation is one of the main reasons affecting the improvement of blockchain performance. Certificateless aggregation signature schemes can greatly tackle the difficulty of blockchain expansion. In this paper, we describe a two-layer system model in which medical records are stored off-blockchain and shared on-blockchain. Furthermore, a multi-trapdoor hash function is proposed. Based on the proposed multi-trapdoor hash function, we present a certificateless aggregate signature scheme for blockchain-based MCPS. The purpose is to realize the authentication of related medical staffs, medical equipment, and medical apps, ensure the integrity of medical records, and support the secure storage and sharing of medical information. The proposed scheme is highly computationally efficient because it does not use bilinear maps and exponential operations. Many certificateless aggregate signature schemes without bilinear maps in Internet of things (IoT) have been proposed in recent years, but they are not applied to the medical field, and they do not consider the security requirements of medical data. The proposed scheme in this paper has high computing and storage efficiency, while meeting the security requirements in MCPS. Full article
(This article belongs to the Special Issue Security and Privacy in Wireless Sensor Network)
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Open AccessReview
Applications of Deep Learning for Dense Scenes Analysis in Agriculture: A Review
Sensors 2020, 20(5), 1520; https://doi.org/10.3390/s20051520 - 10 Mar 2020
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Abstract
Deep Learning (DL) is the state-of-the-art machine learning technology, which shows superior performance in computer vision, bioinformatics, natural language processing, and other areas. Especially as a modern image processing technology, DL has been successfully applied in various tasks, such as object detection, semantic [...] Read more.
Deep Learning (DL) is the state-of-the-art machine learning technology, which shows superior performance in computer vision, bioinformatics, natural language processing, and other areas. Especially as a modern image processing technology, DL has been successfully applied in various tasks, such as object detection, semantic segmentation, and scene analysis. However, with the increase of dense scenes in reality, due to severe occlusions, and small size of objects, the analysis of dense scenes becomes particularly challenging. To overcome these problems, DL recently has been increasingly applied to dense scenes and has begun to be used in dense agricultural scenes. The purpose of this review is to explore the applications of DL for dense scenes analysis in agriculture. In order to better elaborate the topic, we first describe the types of dense scenes in agriculture, as well as the challenges. Next, we introduce various popular deep neural networks used in these dense scenes. Then, the applications of these structures in various agricultural tasks are comprehensively introduced in this review, including recognition and classification, detection, counting and yield estimation. Finally, the surveyed DL applications, limitations and the future work for analysis of dense images in agriculture are summarized. Full article
(This article belongs to the Special Issue Computer Vision for Remote Sensing)
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Open AccessArticle
An Automatic Bearing Fault Diagnosis Method Based on Characteristics Frequency Ratio
Sensors 2020, 20(5), 1519; https://doi.org/10.3390/s20051519 - 10 Mar 2020
Viewed by 262
Abstract
Bearing is a key component of satellite inertia actuators such as moment wheel assemblies (MWAs) and control moment gyros (CMGs), and its operating state is directly related to the performance and service life of satellites. However, because of the complexity of the vibration [...] Read more.
Bearing is a key component of satellite inertia actuators such as moment wheel assemblies (MWAs) and control moment gyros (CMGs), and its operating state is directly related to the performance and service life of satellites. However, because of the complexity of the vibration frequency components of satellite bearing assemblies and the small loading, normal running bearings normally present similar fault characteristics in long-term ground life experiments, which makes it difficult to judge the bearing fault status. This paper proposes an automatic fault diagnosis method for bearings based on a presented indicator called the characteristic frequency ratio. First, the vibration signals of various MWAs were picked up by the bearing vibration test. Then, the improved ensemble empirical mode decomposition (EEMD) method was introduced to demodulate the envelope of the bearing signals, and the fault characteristic frequencies of the vibration signals were acquired. Based on this, the characteristic frequency ratio for fault identification was defined, and a method for determining the threshold of fault judgment was further proposed. Finally, an automatic diagnosis process was proposed and verified by using different bearing fault data. The results show that the presented method is feasible and effective for automatic monitoring and diagnosis of bearing faults. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle
Finite Element Analysis of Interface Dependence on Nanomechanical Sensing
Sensors 2020, 20(5), 1518; https://doi.org/10.3390/s20051518 - 10 Mar 2020
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Abstract
Nanomechanical sensors and their arrays have been attracting significant attention for detecting, discriminating and identifying target analytes. The sensing responses can be partially explained by the physical properties of the receptor layers coated on the sensing elements. Analytical solutions of nanomechanical sensing are [...] Read more.
Nanomechanical sensors and their arrays have been attracting significant attention for detecting, discriminating and identifying target analytes. The sensing responses can be partially explained by the physical properties of the receptor layers coated on the sensing elements. Analytical solutions of nanomechanical sensing are available for a simple cantilever model including the physical parameters of both a cantilever and a receptor layer. These analytical solutions generally rely on the simple structures, such that the sensing element and the receptor layer are fully attached at their boundary. However, an actual interface in a real system is not always fully attached because of inhomogeneous coatings with low affinity to the sensor surface or partial detachments caused by the exposure to some analytes, especially with high concentration. Here, we study the effects of such macroscopic interfacial structures, including partial attachments/detachments, for static nanomechanical sensing, focusing on a Membrane-type Surface stress Sensor (MSS), through finite element analysis (FEA). We simulate various macroscopic interfacial structures by changing the sizes, numbers and positions of the attachments as well as the elastic properties of receptor layers (e.g., Young’s modulus and Poisson’s ratio) and evaluate the effects on the sensitivity. It is found that specific interfacial structures lead to efficient sensing responses, providing a guideline for designing the coating films as well as optimizing the interfacial structures for higher sensitivity including surface modification of the substrate. Full article
(This article belongs to the Section Chemical Sensors)
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Open AccessArticle
Smart Privacy Protection for Big Video Data Storage Based on Hierarchical Edge Computing
Sensors 2020, 20(5), 1517; https://doi.org/10.3390/s20051517 - 10 Mar 2020
Viewed by 310
Abstract
Recently, the rapid development of the Internet of Things (IoT) has led to an increasing exponential growth of non-scalar data (e.g., images, videos). Local services are far from satisfying storage requirements, and the cloud computing fails to effectively support heterogeneous distributed IoT environments, [...] Read more.
Recently, the rapid development of the Internet of Things (IoT) has led to an increasing exponential growth of non-scalar data (e.g., images, videos). Local services are far from satisfying storage requirements, and the cloud computing fails to effectively support heterogeneous distributed IoT environments, such as wireless sensor network. To effectively provide smart privacy protection for video data storage, we take full advantage of three patterns (multi-access edge computing, cloudlets and fog computing) of edge computing to design the hierarchical edge computing architecture, and propose a low-complexity and high-secure scheme based on it. The video is divided into three parts and stored in completely different facilities. Specifically, the most significant bits of key frames are directly stored in local sensor devices while the least significant bits of key frames are encrypted and sent to the semi-trusted cloudlets. The non-key frame is compressed with the two-layer parallel compressive sensing and encrypted by the 2D logistic-skew tent map and then transmitted to the cloud. Simulation experiments and theoretical analysis demonstrate that our proposed scheme can not only provide smart privacy protection for big video data storage based on the hierarchical edge computing, but also avoid increasing additional computation burden and storage pressure. Full article
(This article belongs to the Special Issue Fog/Edge Computing based Smart Sensing System)
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Open AccessArticle
Liver Tumor Segmentation in CT Scans Using Modified SegNet
Sensors 2020, 20(5), 1516; https://doi.org/10.3390/s20051516 - 10 Mar 2020
Viewed by 313
Abstract
The main cause of death related to cancer worldwide is from hepatic cancer. Detection of hepatic cancer early using computed tomography (CT) could prevent millions of patients’ death every year. However, reading hundreds or even tens of those CT scans is an enormous [...] Read more.
The main cause of death related to cancer worldwide is from hepatic cancer. Detection of hepatic cancer early using computed tomography (CT) could prevent millions of patients’ death every year. However, reading hundreds or even tens of those CT scans is an enormous burden for radiologists. Therefore, there is an immediate need is to read, detect, and evaluate CT scans automatically, quickly, and accurately. However, liver segmentation and extraction from the CT scans is a bottleneck for any system, and is still a challenging problem. In this work, a deep learning-based technique that was proposed for semantic pixel-wise classification of road scenes is adopted and modified to fit liver CT segmentation and classification. The architecture of the deep convolutional encoder–decoder is named SegNet, and consists of a hierarchical correspondence of encode–decoder layers. The proposed architecture was tested on a standard dataset for liver CT scans and achieved tumor accuracy of up to 99.9% in the training phase. Full article
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Open AccessArticle
Fast Approximations of Activation Functions in Deep Neural Networks when using Posit Arithmetic
Sensors 2020, 20(5), 1515; https://doi.org/10.3390/s20051515 - 10 Mar 2020
Viewed by 315
Abstract
With increasing real-time constraints being put on the use of Deep Neural Networks (DNNs) by real-time scenarios, there is the need to review information representation. A very challenging path is to employ an encoding that allows a fast processing and hardware-friendly representation of [...] Read more.
With increasing real-time constraints being put on the use of Deep Neural Networks (DNNs) by real-time scenarios, there is the need to review information representation. A very challenging path is to employ an encoding that allows a fast processing and hardware-friendly representation of information. Among the proposed alternatives to the IEEE 754 standard regarding floating point representation of real numbers, the recently introduced Posit format has been theoretically proven to be really promising in satisfying the mentioned requirements. However, with the absence of proper hardware support for this novel type, this evaluation can be conducted only through a software emulation. While waiting for the widespread availability of the Posit Processing Units (the equivalent of the Floating Point Unit (FPU)), we can already exploit the Posit representation and the currently available Arithmetic-Logic Unit (ALU) to speed up DNNs by manipulating the low-level bit string representations of Posits. As a first step, in this paper, we present new arithmetic properties of the Posit number system with a focus on the configuration with 0 exponent bits. In particular, we propose a new class of Posit operators called L1 operators, which consists of fast and approximated versions of existing arithmetic operations or functions (e.g., hyperbolic tangent (TANH) and extended linear unit (ELU)) only using integer arithmetic. These operators introduce very interesting properties and results: (i) faster evaluation than the exact counterpart with a negligible accuracy degradation; (ii) an efficient ALU emulation of a number of Posits operations; and (iii) the possibility to vectorize operations in Posits, using existing ALU vectorized operations (such as the scalable vector extension of ARM CPUs or advanced vector extensions on Intel CPUs). As a second step, we test the proposed activation function on Posit-based DNNs, showing how 16-bit down to 10-bit Posits represent an exact replacement for 32-bit floats while 8-bit Posits could be an interesting alternative to 32-bit floats since their performances are a bit lower but their high speed and low storage properties are very appealing (leading to a lower bandwidth demand and more cache-friendly code). Finally, we point out how small Posits (i.e., up to 14 bits long) are very interesting while PPUs become widespread, since Posit operations can be tabulated in a very efficient way (see details in the text). Full article
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Open AccessArticle
Prototype of Nitro Compound Vapor and Trace Detector Based on a Capacitive MIS Sensor
Sensors 2020, 20(5), 1514; https://doi.org/10.3390/s20051514 - 10 Mar 2020
Viewed by 310
Abstract
A prototype of a nitro compound vapor and trace detector, which uses the pyrolysis method and a capacitive gas sensor based on the metal–insulator–semiconductor (MIS) structure type Pd–SiO2–Si, was developed and manufactured. It was experimentally established that the detection limit of [...] Read more.
A prototype of a nitro compound vapor and trace detector, which uses the pyrolysis method and a capacitive gas sensor based on the metal–insulator–semiconductor (MIS) structure type Pd–SiO2–Si, was developed and manufactured. It was experimentally established that the detection limit of trinitrotoluene trace for the detector prototype is 1 × 10−9 g, which corresponds to concentration from 10−11 g/cm3 to 10−12 g/cm3. The prototype had a response time of no more than 30 s. The possibility of further improving the characteristics of the prototype detector by reducing the overall dimensions and increasing the sensitivity of the MIS sensors is shown. Full article
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Open AccessArticle
Analysis of the Errors Caused by Disturbed Multimode Fibers in the Interferometer with Fiber-Coupled Delivery
Sensors 2020, 20(5), 1513; https://doi.org/10.3390/s20051513 - 09 Mar 2020
Viewed by 331
Abstract
In this paper, the errors of the displacement measurement interferometer with multi-mode fiber-coupled delivery are analyzed when the fibers are disturbed. Simulation results show that the characteristic frequency of the measurement error is consistent with that of disturbance, and the error has higher [...] Read more.
In this paper, the errors of the displacement measurement interferometer with multi-mode fiber-coupled delivery are analyzed when the fibers are disturbed. Simulation results show that the characteristic frequency of the measurement error is consistent with that of disturbance, and the error has higher order frequency components. The experiments are designed for the effect of fringe contrast on the measurement error. The experimental results show that the measurement error is rather sensitive to the interference angle between the measurement arm and the reference arm in the multi-mode fibers, but not to the irradiance ratio of the measurement arm and the reference arm. In an interferometer with multimode fiber, the interference angle between the measurement arm and the reference arm needs to be restricted. This conclusion provides a theoretical basis for designing an interferometer measurement system with interference angle that is adaptive to wider application. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle
Flow Field Perception of a Moving Carrier Based on an Artificial Lateral Line System
Sensors 2020, 20(5), 1512; https://doi.org/10.3390/s20051512 - 09 Mar 2020
Viewed by 309
Abstract
At present, autonomous underwater vehicles (AUVs) cannot perceive local environments in complex marine environments, where fish can obtain hydrodynamic information about the surrounding environment through a lateral line. Inspired by this biological function, an artificial lateral line system (ALLS) was built on a [...] Read more.
At present, autonomous underwater vehicles (AUVs) cannot perceive local environments in complex marine environments, where fish can obtain hydrodynamic information about the surrounding environment through a lateral line. Inspired by this biological function, an artificial lateral line system (ALLS) was built on a moving bionic carrier using the pressure sensor in this paper. When the carrier operated with different speeds in the flow field, the pressure distribution characteristics surrounding the carrier were analyzed by numerical simulation, where the effect of the flow angle between the fluid velocity direction and the carrier navigation direction was considered. The flume experiment was carried out in accordance with the simulation conditions, and the analysis results of the experiment were consistent with those in the simulation. The relationship between pressure and fluid velocity was established by a fitting method. Subsequently, the pressure difference method was investigated to establish a relationship model between the pressure difference on both sides of the carrier and the flow angle. Finally, a back propagation neural network model was used to predict the fluid velocity, flow angle, and carrier speed successfully in the unknown fluid environment. The local fluid environment perception by moving carrier carrying ALLS was studied which may promote the engineering application of the artificial lateral line in the local perception, positioning, and navigation on AUVs. Full article
(This article belongs to the Special Issue Autonomous Underwater Vehicle Navigation)
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