Open AccessReview
Fiber Bragg Grating Sensors for the Oil Industry
Sensors 2017, 17(3), 429; doi:10.3390/s17030429 (registering DOI) -
Abstract
With the oil and gas industry growing rapidly, increasing the yield and profit require advances in technology for cost-effective production in key areas of reservoir exploration and in oil-well production-management. In this paper we review our group’s research into fiber Bragg gratings (FBGs)
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With the oil and gas industry growing rapidly, increasing the yield and profit require advances in technology for cost-effective production in key areas of reservoir exploration and in oil-well production-management. In this paper we review our group’s research into fiber Bragg gratings (FBGs) and their applications in the oil industry, especially in the well-logging field. FBG sensors used for seismic exploration in the oil and gas industry need to be capable of measuring multiple physical parameters such as temperature, pressure, and acoustic waves in a hostile environment. This application requires that the FBG sensors display high sensitivity over the broad vibration frequency range of 5 Hz to 2.5 kHz, which contains the important geological information. We report the incorporation of mechanical transducers in the FBG sensors to enable enhance the sensors’ amplitude and frequency response. Whenever the FBG sensors are working within a well, they must withstand high temperatures and high pressures, up to 175 °C and 40 Mpa or more. We use femtosecond laser side-illumination to ensure that the FBGs themselves have the high temperature resistance up to 1100 °C. Using FBG sensors combined with suitable metal transducers, we have experimentally realized high- temperature and pressure measurements up to 400 °C and 100 Mpa. We introduce a novel technology of ultrasonic imaging of seismic physical models using FBG sensors, which is superior to conventional seismic exploration methods. Compared with piezoelectric transducers, FBG ultrasonic sensors demonstrate superior sensitivity, more compact structure, improved spatial resolution, high stability and immunity to electromagnetic interference (EMI). In the last section, we present a case study of a well-logging field to demonstrate the utility of FBG sensors in the oil and gas industry. Full article
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Open AccessArticle
PTZ Camera-Based Displacement Sensor System with Perspective Distortion Correction Unit for Early Detection of Building Destruction
Sensors 2017, 17(3), 430; doi:10.3390/s17030430 (registering DOI) -
Abstract
This paper presents a pan-tilt-zoom (PTZ) camera-based displacement measurement system, specially based on the perspective distortion correction technique for the early detection of building destruction. The proposed PTZ-based vision system rotates the camera to monitor the specific targets from various distances and controls
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This paper presents a pan-tilt-zoom (PTZ) camera-based displacement measurement system, specially based on the perspective distortion correction technique for the early detection of building destruction. The proposed PTZ-based vision system rotates the camera to monitor the specific targets from various distances and controls the zoom level of the lens for a constant field of view (FOV). The proposed approach adopts perspective distortion correction to expand the measurable range in monitoring the displacement of the target structure. The implemented system successfully obtains the displacement information in structures, which is not easily accessible on the remote site. We manually measured the displacement acquired from markers which is attached on a sample of structures covering a wide geographic region. Our approach using a PTZ-based camera reduces the perspective distortion, so that the improved system could overcome limitations of previous works related to displacement measurement. Evaluation results show that a PTZ-based displacement sensor system with the proposed distortion correction unit is possibly a cost effective and easy-to-install solution for commercialization. Full article
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Open AccessArticle
An Improved Metal-Packaged Strain Sensor Based on A Regenerated Fiber Bragg Grating in Hydrogen-Loaded Boron–Germanium Co-Doped Photosensitive Fiber for High-Temperature Applications
Sensors 2017, 17(3), 431; doi:10.3390/s17030431 (registering DOI) -
Abstract
Local strain measurements are considered as an effective method for structural health monitoring of high-temperature components, which require accurate, reliable and durable sensors. To develop strain sensors that can be used in higher temperature environments, an improved metal-packaged strain sensor based on a
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Local strain measurements are considered as an effective method for structural health monitoring of high-temperature components, which require accurate, reliable and durable sensors. To develop strain sensors that can be used in higher temperature environments, an improved metal-packaged strain sensor based on a regenerated fiber Bragg grating (RFBG) fabricated in hydrogen (H2)-loaded boron–germanium (B–Ge) co-doped photosensitive fiber is developed using the process of combining magnetron sputtering and electroplating, addressing the limitation of mechanical strength degradation of silica optical fibers after annealing at a high temperature for regeneration. The regeneration characteristics of the RFBGs and the strain characteristics of the sensor are evaluated. Numerical simulation of the sensor is conducted using a three-dimensional finite element model. Anomalous decay behavior of two regeneration regimes is observed for the FBGs written in H2-loaded B–Ge co-doped fiber. The strain sensor exhibits good linearity, stability and repeatability when exposed to constant high temperatures of up to 540 °C. A satisfactory agreement is obtained between the experimental and numerical results in strain sensitivity. The results demonstrate that the improved metal-packaged strain sensors based on RFBGs in H2-loaded B–Ge co-doped fiber provide great potential for high-temperature applications by addressing the issues of mechanical integrity and packaging. Full article
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Open AccessArticle
Lightdrum—Portable Light Stage for Accurate BTF Measurement on Site
Sensors 2017, 17(3), 423; doi:10.3390/s17030423 (registering DOI) -
Abstract
We propose a miniaturised light stage for measuring the bidirectional reflectance distribution function (BRDF) and the bidirectional texture function (BTF) of surfaces on site in real world application scenarios. The main principle of our lightweight BTF acquisition gantry is a compact hemispherical skeleton
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We propose a miniaturised light stage for measuring the bidirectional reflectance distribution function (BRDF) and the bidirectional texture function (BTF) of surfaces on site in real world application scenarios. The main principle of our lightweight BTF acquisition gantry is a compact hemispherical skeleton with cameras along the meridian and with light emitting diode (LED) modules shining light onto a sample surface. The proposed device is portable and achieves a high speed of measurement while maintaining high degree of accuracy. While the positions of the LEDs are fixed on the hemisphere, the cameras allow us to cover the range of the zenith angle from 0 to 75 and by rotating the cameras along the axis of the hemisphere we can cover all possible camera directions. This allows us to take measurements with almost the same quality as existing stationary BTF gantries. Two degrees of freedom can be set arbitrarily for measurements and the other two degrees of freedom are fixed, which provides a tradeoff between accuracy of measurements and practical applicability. Assuming that a measured sample is locally flat and spatially accessible, we can set the correct perpendicular direction against the measured sample by means of an auto-collimator prior to measuring. Further, we have designed and used a marker sticker method to allow for the easy rectification and alignment of acquired images during data processing. We show the results of our approach by images rendered for 36 measured material samples. Full article
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Open AccessArticle
A Topology Control Strategy with Reliability Assurance for Satellite Cluster Networks in Earth Observation
Sensors 2017, 17(3), 445; doi:10.3390/s17030445 (registering DOI) -
Abstract
This article investigates the dynamic topology control problemof satellite cluster networks (SCNs) in Earth observation (EO) missions by applying a novel metric of stability for inter-satellite links (ISLs). The properties of the periodicity and predictability of satellites’ relative position are involved in the
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This article investigates the dynamic topology control problemof satellite cluster networks (SCNs) in Earth observation (EO) missions by applying a novel metric of stability for inter-satellite links (ISLs). The properties of the periodicity and predictability of satellites’ relative position are involved in the link cost metric which is to give a selection criterion for choosing the most reliable data routing paths. Also, a cooperative work model with reliability is proposed for the situation of emergency EO missions. Based on the link cost metric and the proposed reliability model, a reliability assurance topology control algorithm and its corresponding dynamic topology control (RAT) strategy are established to maximize the stability of data transmission in the SCNs. The SCNs scenario is tested through some numeric simulations of the topology stability of average topology lifetime and average packet loss rate. Simulation results show that the proposed reliable strategy applied in SCNs significantly improves the data transmission performance and prolongs the average topology lifetime. Full article
Open AccessReview
Emerging Cytokine Biosensors with Optical Detection Modalities and Nanomaterial-Enabled Signal Enhancement
Sensors 2017, 17(2), 428; doi:10.3390/s17020428 -
Abstract
Protein biomarkers, especially cytokines, play a pivotal role in the diagnosis and treatment of a wide spectrum of diseases. Therefore, a critical need for advanced cytokine sensors has been rapidly growing and will continue to expand to promote clinical testing, new biomarker development,
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Protein biomarkers, especially cytokines, play a pivotal role in the diagnosis and treatment of a wide spectrum of diseases. Therefore, a critical need for advanced cytokine sensors has been rapidly growing and will continue to expand to promote clinical testing, new biomarker development, and disease studies. In particular, sensors employing transduction principles of various optical modalities have emerged as the most common means of detection. In typical cytokine assays which are based on the binding affinities between the analytes of cytokines and their specific antibodies, optical schemes represent the most widely used mechanisms, with some serving as the gold standard against which all existing and new sensors are benchmarked. With recent advancements in nanoscience and nanotechnology, many of the recently emerging technologies for cytokine detection exploit various forms of nanomaterials for improved sensing capabilities. Nanomaterials have been demonstrated to exhibit exceptional optical properties unique to their reduced dimensionality. Novel sensing approaches based on the newly identified properties of nanomaterials have shown drastically improved performances in both the qualitative and quantitative analyses of cytokines. This article brings together the fundamentals in the literature that are central to different optical modalities developed for cytokine detection. Recent advancements in the applications of novel technologies are also discussed in terms of those that enable highly sensitive and multiplexed cytokine quantification spanning a wide dynamic range. For each highlighted optical technique, its current detection capabilities as well as associated challenges are discussed. Lastly, an outlook for nanomaterial-based cytokine sensors is provided from the perspective of optimizing the technologies for sensitivity and multiplexity as well as promoting widespread adaptations of the emerging optical techniques by lowering high thresholds currently present in the new approaches. Full article
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Open AccessArticle
A Temperature-Dependent Battery Model for Wireless Sensor Networks
Sensors 2017, 17(2), 422; doi:10.3390/s17020422 -
Abstract
Energy consumption is a major issue in in Wireless Sensor Networks (WSNs), as nodes are powered by chemical batteries with an upper bounded lifetime. Estimating the lifetime of batteries is a difficult task, as it depends on several factors, such as operating temperatures
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Energy consumption is a major issue in in Wireless Sensor Networks (WSNs), as nodes are powered by chemical batteries with an upper bounded lifetime. Estimating the lifetime of batteries is a difficult task, as it depends on several factors, such as operating temperatures and discharge rates. Analytical battery models can be used for estimating both the battery lifetime and the voltage behavior over time. Still, available models usually do not consider the impact of operating temperatures on the battery behavior. The target of this work is to extend the widely-used Kinetic Battery Model (KiBaM) to include the effect of temperature on the battery behavior. The proposed Temperature-Dependent KiBaM (T-KiBaM) is able to handle operating temperatures, providing better estimates for the battery lifetime and voltage behavior. The performed experimental validation shows that T-KiBaM achieves an average accuracy error smaller than 0.33%, when estimating the lifetime of Ni-MH batteries for different temperature conditions. In addition, T-KiBaM significantly improves the original KiBaM voltage model. The proposed model can be easily adapted to handle other battery technologies, enabling the consideration of different WSN deployments. Full article
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Open AccessArticle
Dual MIMU Pedestrian Navigation by Inequality Constraint Kalman Filtering
Sensors 2017, 17(2), 427; doi:10.3390/s17020427 -
Abstract
The foot-mounted inertial navigation system is an important method of pedestrian navigation as it, in principle, does not rely any external assistance. A real-time range decomposition constraint method is proposed in this paper to combine the information of dual foot-mounted inertial navigation systems.
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The foot-mounted inertial navigation system is an important method of pedestrian navigation as it, in principle, does not rely any external assistance. A real-time range decomposition constraint method is proposed in this paper to combine the information of dual foot-mounted inertial navigation systems. It is well known that low-cost inertial pedestrian navigation aided with both ZUPT (zero velocity update) and the range decomposition constraint performs better than those in their own respective methods. This paper recommends that the separation distance between the position estimates of the two foot-mounted inertial navigation systems be restricted by an ellipsoidal constraint that relates to the maximum step length and the leg height. The performance of the proposed method is studied by utilizing experimental data, and the results indicate that the method can effectively correct the dual navigation systems’ position over the traditional spherical constraint. Full article
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Open AccessArticle
A New Deep Learning Model for Fault Diagnosis with Good Anti-Noise and Domain Adaptation Ability on Raw Vibration Signals
Sensors 2017, 17(2), 425; doi:10.3390/s17020425 -
Abstract
Intelligent fault diagnosis techniques have replaced time-consuming and unreliable human analysis, increasing the efficiency of fault diagnosis. Deep learning models can improve the accuracy of intelligent fault diagnosis with the help of their multilayer nonlinear mapping ability. This paper proposes a novel method
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Intelligent fault diagnosis techniques have replaced time-consuming and unreliable human analysis, increasing the efficiency of fault diagnosis. Deep learning models can improve the accuracy of intelligent fault diagnosis with the help of their multilayer nonlinear mapping ability. This paper proposes a novel method named Deep Convolutional Neural Networks with Wide First-layer Kernels (WDCNN). The proposed method uses raw vibration signals as input (data augmentation is used to generate more inputs), and uses the wide kernels in the first convolutional layer for extracting features and suppressing high frequency noise. Small convolutional kernels in the preceding layers are used for multilayer nonlinear mapping. AdaBN is implemented to improve the domain adaptation ability of the model. The proposed model addresses the problem that currently, the accuracy of CNN applied to fault diagnosis is not very high. WDCNN can not only achieve 100% classification accuracy on normal signals, but also outperform the state-of-the-art DNN model which is based on frequency features under different working load and noisy environment conditions. Full article
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Open AccessArticle
Experimental Validation of Depth Cameras for the Parameterization of Functional Balance of Patients in Clinical Tests
Sensors 2017, 17(2), 424; doi:10.3390/s17020424 -
Abstract
In clinical practice, patients’ balance can be assessed using standard scales. Two of the most validated clinical tests for measuring balance are the Timed Up and Go (TUG) test and the MultiDirectional Reach Test (MDRT). Nowadays, inertial sensors (IS) are employed for kinematic
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In clinical practice, patients’ balance can be assessed using standard scales. Two of the most validated clinical tests for measuring balance are the Timed Up and Go (TUG) test and the MultiDirectional Reach Test (MDRT). Nowadays, inertial sensors (IS) are employed for kinematic analysis of functional tests in the clinical setting, and have become an alternative to expensive, 3D optical motion capture systems. In daily clinical practice, however, IS-based setups are yet cumbersome and inconvenient to apply. Current depth cameras have the potential for such application, presenting many advantages as, for instance, being portable, low-cost and minimally-invasive. This paper aims at experimentally validating to what extent this technology can substitute IS for the parameterization and kinematic analysis of the TUG and the MDRT tests. Twenty healthy young adults were recruited as participants to perform five different balance tests while kinematic data from their movements were measured by both a depth camera and an inertial sensor placed on their trunk. The reliability of the camera’s measurements is examined through the Interclass Correlation Coefficient (ICC), whilst the Pearson Correlation Coefficient (r) is computed to evaluate the correlation between both sensor’s measurements, revealing excellent reliability and strong correlations in most cases. Full article
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Open AccessTechnical Note
Inertial Navigation System/Doppler Velocity Log (INS/DVL) Fusion with Partial DVL Measurements
Sensors 2017, 17(2), 415; doi:10.3390/s17020415 -
Abstract
The Technion autonomous underwater vehicle (TAUV) is an ongoing project aiming to develop and produce a small AUV to carry on research missions, including payload dropping, and to demonstrate acoustic communication. Its navigation system is based on an inertial navigation system (INS) aided
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The Technion autonomous underwater vehicle (TAUV) is an ongoing project aiming to develop and produce a small AUV to carry on research missions, including payload dropping, and to demonstrate acoustic communication. Its navigation system is based on an inertial navigation system (INS) aided by a Doppler velocity log (DVL), magnetometer, and pressure sensor (PS). In many INSs, such as the one used in TAUV, only the velocity vector (provided by the DVL) can be used for aiding the INS, i.e., enabling only a loosely coupled integration approach. In cases of partial DVL measurements, such as failure to maintain bottom lock, the DVL cannot estimate the vehicle velocity. Thus, in partial DVL situations no velocity data can be integrated into the TAUV INS, and as a result its navigation solution will drift in time. To circumvent that problem, we propose a DVL-based vehicle velocity solution using the measured partial raw data of the DVL and additional information, thereby deriving an extended loosely coupled (ELC) approach. The implementation of the ELC approach requires only software modification. In addition, we present the TAUV six degrees of freedom (6DOF) simulation that includes all functional subsystems. Using this simulation, the proposed approach is evaluated and the benefit of using it is shown. Full article
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Open AccessArticle
A Soft Sensor-Based Three-Dimensional (3-D) Finger Motion Measurement System
Sensors 2017, 17(2), 420; doi:10.3390/s17020420 -
Abstract
In this study, a soft sensor-based three-dimensional (3-D) finger motion measurement system is proposed. The sensors, made of the soft material Ecoflex, comprise embedded microchannels filled with a conductive liquid metal (EGaln). The superior elasticity, light weight, and sensitivity of soft sensors allows
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In this study, a soft sensor-based three-dimensional (3-D) finger motion measurement system is proposed. The sensors, made of the soft material Ecoflex, comprise embedded microchannels filled with a conductive liquid metal (EGaln). The superior elasticity, light weight, and sensitivity of soft sensors allows them to be embedded in environments in which conventional sensors cannot. Complicated finger joints, such as the carpometacarpal (CMC) joint of the thumb are modeled to specify the location of the sensors. Algorithms to decouple the signals from soft sensors are proposed to extract the pure flexion, extension, abduction, and adduction joint angles. The performance of the proposed system and algorithms are verified by comparison with a camera-based motion capture system. Full article
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Open AccessArticle
Efficient Data Collection in Widely Distributed Wireless Sensor Networks with Time Window and Precedence Constraints
Sensors 2017, 17(2), 421; doi:10.3390/s17020421 -
Abstract
In addition to the traditional densely deployed cases, widely distributed wireless sensor networks (WDWSNs) have begun to emerge. In these networks, sensors are far away from each other and have no network connections. In this paper, a special application of data collection for
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In addition to the traditional densely deployed cases, widely distributed wireless sensor networks (WDWSNs) have begun to emerge. In these networks, sensors are far away from each other and have no network connections. In this paper, a special application of data collection for WDWSNs is considered where each sensor (Unmanned Ground Vehicle, UGV) moves in a hazardous and complex terrain with many obstacles. They have their own work cycles and can be accessed only at a few locations. A mobile sink cruises on the ground to collect data gathered from these UGVs. Considerable delay is inevitable if the UGV and the mobile sink miss the meeting window or wait idly at the meeting spot. The unique challenge here is that, for each cycle of an UGV, there is only a limited time window for it to appear in front of the mobile sink. Therefore, we propose scheduling the path of a single mobile sink, targeted at visiting a maximum number of UGVs in a timely manner with the shortest path, according to the timing constraints bound by the cycles of UGVs. We then propose a bipartite matching based algorithm to reduce the number of mobile sinks. Simulation results show that the proposed algorithm can achieve performance close to the theoretical maximum determined by the duty cycle instance. Full article
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Open AccessArticle
Long Term Amperometric Recordings in the Brain Extracellular Fluid of Freely Moving Immunocompromised NOD SCID Mice
Sensors 2017, 17(2), 419; doi:10.3390/s17020419 -
Abstract
We describe the in vivo characterization of microamperometric sensors for the real-time monitoring of nitric oxide (NO) and oxygen (O2) in the striatum of immunocompromised NOD SCID mice. The latter strain has been utilized routinely in the establishment of humanized models
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We describe the in vivo characterization of microamperometric sensors for the real-time monitoring of nitric oxide (NO) and oxygen (O2) in the striatum of immunocompromised NOD SCID mice. The latter strain has been utilized routinely in the establishment of humanized models of disease e.g., Parkinson’s disease. NOD SCID mice were implanted with highly sensitive and selective NO and O2 sensors that have been previously characterized both in vitro and in freely moving rats. Animals were systemically administered compounds that perturbed the amperometric current and confirmed sensor performance. Furthermore, the stability of the amperometric current was investigated and 24 h recordings examined. Saline injections caused transient changes in both currents that were not significant from baseline. l-NAME caused significant decreases in NO (p < 0.05) and O2 (p < 0.001) currents compared to saline. l-Arginine produced a significant increase (p < 0.001) in NO current, and chloral hydrate and Diamox (acetazolamide) caused significant increases in O2 signal (p < 0.01) compared against saline. The stability of both currents were confirmed over an eight-day period and analysis of 24-h recordings identified diurnal variations in both signals. These findings confirm the efficacy of the amperometric sensors to perform continuous and reliable recordings in immunocompromised mice. Full article
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Open AccessArticle
Data Collection and Analysis Using Wearable Sensors for Monitoring Knee Range of Motion after Total Knee Arthroplasty
Sensors 2017, 17(2), 418; doi:10.3390/s17020418 -
Abstract
Total knee arthroplasty (TKA) is the most common treatment for degenerative osteoarthritis of that articulation. However, either in rehabilitation clinics or in hospital wards, the knee range of motion (ROM) can currently only be assessed using a goniometer. In order to provide continuous
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Total knee arthroplasty (TKA) is the most common treatment for degenerative osteoarthritis of that articulation. However, either in rehabilitation clinics or in hospital wards, the knee range of motion (ROM) can currently only be assessed using a goniometer. In order to provide continuous and objective measurements of knee ROM, we propose the use of wearable inertial sensors to record the knee ROM during the recovery progress. Digitalized and objective data can assist the surgeons to control the recovery status and flexibly adjust rehabilitation programs during the early acute inpatient stage. The more knee flexion ROM regained during the early inpatient period, the better the long-term knee recovery will be and the sooner early discharge can be achieved. The results of this work show that the proposed wearable sensor approach can provide an alternative for continuous monitoring and objective assessment of knee ROM recovery progress for TKA patients compared to the traditional goniometer measurements. Full article
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Open AccessArticle
The Impact of 3D Stacking and Technology Scaling on the Power and Area of Stereo Matching Processors
Sensors 2017, 17(2), 426; doi:10.3390/s17020426 -
Abstract
Recently, stereo matching processors have been adopted in real-time embedded systems such as intelligent robots and autonomous vehicles, which require minimal hardware resources and low power consumption. Meanwhile, thanks to the through-silicon via (TSV), three-dimensional (3D) stacking technology has emerged as a practical
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Recently, stereo matching processors have been adopted in real-time embedded systems such as intelligent robots and autonomous vehicles, which require minimal hardware resources and low power consumption. Meanwhile, thanks to the through-silicon via (TSV), three-dimensional (3D) stacking technology has emerged as a practical solution to achieving the desired requirements of a high-performance circuit. In this paper, we present the benefits of 3D stacking and process technology scaling on stereo matching processors. We implemented 2-tier 3D-stacked stereo matching processors with GlobalFoundries 130-nm and Nangate 45-nm process design kits and compare them with their two-dimensional (2D) counterparts to identify comprehensive design benefits. In addition, we examine the findings from various analyses to identify the power benefits of 3D-stacked integrated circuit (IC) and device technology advancements. From experiments, we observe that the proposed 3D-stacked ICs, compared to their 2D IC counterparts, obtain 43% area, 13% power, and 14% wire length reductions. In addition, we present a logic partitioning method suitable for a pipeline-based hardware architecture that minimizes the use of TSVs. Full article
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Open AccessArticle
An Adaptive Multi-Sensor Data Fusion Method Based on Deep Convolutional Neural Networks for Fault Diagnosis of Planetary Gearbox
Sensors 2017, 17(2), 414; doi:10.3390/s17020414 -
Abstract
A fault diagnosis approach based on multi-sensor data fusion is a promising tool to deal with complicated damage detection problems of mechanical systems. Nevertheless, this approach suffers from two challenges, which are (1) the feature extraction from various types of sensory data and
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A fault diagnosis approach based on multi-sensor data fusion is a promising tool to deal with complicated damage detection problems of mechanical systems. Nevertheless, this approach suffers from two challenges, which are (1) the feature extraction from various types of sensory data and (2) the selection of a suitable fusion level. It is usually difficult to choose an optimal feature or fusion level for a specific fault diagnosis task, and extensive domain expertise and human labor are also highly required during these selections. To address these two challenges, we propose an adaptive multi-sensor data fusion method based on deep convolutional neural networks (DCNN) for fault diagnosis. The proposed method can learn features from raw data and optimize a combination of different fusion levels adaptively to satisfy the requirements of any fault diagnosis task. The proposed method is tested through a planetary gearbox test rig. Handcraft features, manual-selected fusion levels, single sensory data, and two traditional intelligent models, back-propagation neural networks (BPNN) and a support vector machine (SVM), are used as comparisons in the experiment. The results demonstrate that the proposed method is able to detect the conditions of the planetary gearbox effectively with the best diagnosis accuracy among all comparative methods in the experiment. Full article
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Open AccessArticle
Dynamic Fluid in a Porous Transducer-Based Angular Accelerometer
Sensors 2017, 17(2), 416; doi:10.3390/s17020416 -
Abstract
This paper presents a theoretical model of the dynamics of liquid flow in an angular accelerometer comprising a porous transducer in a circular tube of liquid. Wave speed and dynamic permeability of the transducer are considered to describe the relation between angular acceleration
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This paper presents a theoretical model of the dynamics of liquid flow in an angular accelerometer comprising a porous transducer in a circular tube of liquid. Wave speed and dynamic permeability of the transducer are considered to describe the relation between angular acceleration and the differential pressure on the transducer. The permeability and streaming potential coupling coefficient of the transducer are determined in the experiments, and special prototypes are utilized to validate the theoretical model in both the frequency and time domains. The model is applied to analyze the influence of structural parameters on the frequency response and the transient response of the fluidic system. It is shown that the radius of the circular tube and the wave speed affect the low frequency gain, as well as the bandwidth of the sensor. The hydrodynamic resistance of the transducer and the cross-section radius of the circular tube can be used to control the transient performance. The proposed model provides the basic techniques to achieve the optimization of the angular accelerometer together with the methodology to control the wave speed and the hydrodynamic resistance of the transducer. Full article
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Open AccessArticle
A Sensor Data Fusion System Based on k-Nearest Neighbor Pattern Classification for Structural Health Monitoring Applications
Sensors 2017, 17(2), 417; doi:10.3390/s17020417 -
Abstract
Civil and military structures are susceptible and vulnerable to damage due to the environmental and operational conditions. Therefore, the implementation of technology to provide robust solutions in damage identification (by using signals acquired directly from the structure) is a requirement to reduce operational
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Civil and military structures are susceptible and vulnerable to damage due to the environmental and operational conditions. Therefore, the implementation of technology to provide robust solutions in damage identification (by using signals acquired directly from the structure) is a requirement to reduce operational and maintenance costs. In this sense, the use of sensors permanently attached to the structures has demonstrated a great versatility and benefit since the inspection system can be automated. This automation is carried out with signal processing tasks with the aim of a pattern recognition analysis. This work presents the detailed description of a structural health monitoring (SHM) system based on the use of a piezoelectric (PZT) active system. The SHM system includes: (i) the use of a piezoelectric sensor network to excite the structure and collect the measured dynamic response, in several actuation phases; (ii) data organization; (iii) advanced signal processing techniques to define the feature vectors; and finally; (iv) the nearest neighbor algorithm as a machine learning approach to classify different kinds of damage. A description of the experimental setup, the experimental validation and a discussion of the results from two different structures are included and analyzed. Full article
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Open AccessArticle
Dynamic Method of Neutral Axis Position Determination and Damage Identification with Distributed Long-Gauge FBG Sensors
Sensors 2017, 17(2), 411; doi:10.3390/s17020411 -
Abstract
The neutral axis position (NAP) is a key parameter of a flexural member for structure design and safety evaluation. The accuracy of NAP measurement based on traditional methods does not satisfy the demands of structural performance assessment especially under live traffic loads. In
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The neutral axis position (NAP) is a key parameter of a flexural member for structure design and safety evaluation. The accuracy of NAP measurement based on traditional methods does not satisfy the demands of structural performance assessment especially under live traffic loads. In this paper, a new method to determine NAP is developed by using modal macro-strain (MMS). In the proposed method, macro-strain is first measured with long-gauge Fiber Bragg Grating (FBG) sensors; then the MMS is generated from the measured macro-strain with Fourier transform; and finally the neutral axis position coefficient (NAPC) is determined from the MMS and the neutral axis depth is calculated with NAPC. To verify the effectiveness of the proposed method, some experiments on FE models, steel beam and reinforced concrete (RC) beam were conducted. From the results, the plane section was first verified with MMS of the first bending mode. Then the results confirmed the high accuracy and stability for assessing NAP. The results also proved that the NAPC was a good indicator of local damage. In summary, with the proposed method, accurate assessment of flexural structures can be facilitated. Full article
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