Single Sensor System Research Articles

Detecting rigid links between sensors for automatic sensor space alignment in virtual environments

Simultaneous use of multiple sensor systems provides improved accuracy and tracking range compared to use of a single sensor system for virtual reality applications. However, calibration of multiple sensor technologies is non-trivial and at a minimum will require significant, and likely regular, user actioned calibration procedures. To enable ambient sensor calibration, we present techniques for automatically identifying relations between rigidly linked 6DoF and 3DoF sensors belonging to different sensor systems for body tracking. The techniques allow for subsequent automatic alignment of the sensor systems. Two techniques are presented, analysed in simulation for performance under varying noise and latency conditions, and are applied to two case studies. The first study identified sensors tracked by a gold standard rigid body tracker with one of six rigid bodies tracked by the first generation Kinect sensor with each sensor identified correctly in at least 76% of estimates. The second case study was an interactive version of the system that can detect a change in sensor configuration in 1–2 s and only requires movements of less than 15 cm or $$90^\circ$$ . Our methods represent a key step in creating highly accessible multi-device 3D virtual environments.

Development of A Textile Capacitive Proximity Sensor and Gait Monitoring System for Smart Healthcare.

Gait is not only one of the most important functions and activities in daily life but is also a parameter to monitor one's health status. We propose a single channel capacitive proximity pressure sensor (TCPS) and gait monitoring system for smart healthcare. Insole-type TCPS (270mm in length) was designed consisting of three layers including two shield layers and a sensor layer. Analyzing the step count and stride time are the basic indicators in gait analysis, thus they were selected as evaluation indicators. A total of 12 subjects participated in the experiment to evaluate the resolution of our TCPS. To evaluate the accuracy of TCPS, step count and its error rates were simultaneously detected by naked eye, ZIKTO Walk (ZIKTO Co., Korea), and HJ-203-K pedometer (Omron Co., Japan) as reference. Results showed that the error rate of 1.77% in TCPS was lower than those of other devices and correlation coefficient was 0.958 (p-value = 0.000). ZIKTO Walk and pedometer do not provide information on stride time, therefore it was detected by F-scan (Tekscan, USA) to evaluate the performance of TCPS. As a result, error rate of stride time measured by TCPS was found to be 1% and the correlation coefficient was 0.685 (p-value=0.000). According to these results, our proposed system may be helpful in development of gait monitoring and measurement system as smart healthcare.

Angular-Dependent Radius Measurements at Rotating Objects Using Underdetermined Sensor Systems

Precise and contactless shape measurements of rotating objects is important, e.g., for monitoring and controlling the manufacturing quality in lathes. For this purpose, multisensor and single-sensor approaches based on optical distance and surface velocity measurements are state-of-the-art techniques. Two- and single-sensor systems are particularly promising to measure the angular-dependent radius of the cross section of the rotating object in a scanning regime with minimal optical access. Since a comparison between the different sensor systems is missing, the potential of these underdetermined sensor systems is unclear. In addition, displacements of the rotational axis and sensor misalignments are suspected to be crucial error sources, but the error is unknown. For this reason, an error analysis is performed regarding the resulting systematic error and the random error for the two- and single-sensor systems. As a result, the different sensor systems have an equal cross-sensitivity with respect to lateral displacements of the rotational axis from the sensor axes, but the two-sensor approach has the lowest sensitivity regarding sensor misalignments. For the studied measurement conditions, the systematic error dominates the sensor noise for the two-sensor system and the single-sensor system with combined distance and velocity measurement at an object mean radius >6 mm. The smallest total measurement uncertainty is obtained with the two-sensor system. Finally, the relevance of systematic error depends on the utilization, i.e., for instance on the absolute rotor radius, the stability of the rotor axis, the sensor position, the accuracy of the sensor alignment, and the uncertainty of the distance and/or velocity measurements.

A Bayesian Data Fusion Approach to Spatio-Temporal Fusion of Remotely Sensed Images

Remote sensing provides rich sources of data for the monitoring of land surface dynamics. However, single-sensor systems are constrained from providing spatially high-resolution images with high revisit frequency due to the inherent sensor design limitation. To obtain images high in both spatial and temporal resolutions, a number of image fusion algorithms, such as spatial and temporal adaptive reflectance fusion model (STARFM) and enhanced STARFM (ESTARFM), have been recently developed. To capitalize on information available in a fusion process, we propose a Bayesian data fusion approach that incorporates the temporal correlation information in the image time series and casts the fusion problem as an estimation problem in which the fused image is obtained by the Maximum A Posterior (MAP) estimator. The proposed approach provides a formal framework for the fusion of remotely sensed images with a rigorous statistical basis; it imposes no requirements on the number of input image pairs; and it is suitable for heterogeneous landscapes. The approach is empirically tested with both simulated and real-life acquired Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) images. Experimental results demonstrate that the proposed method outperforms STARFM and ESTARFM, especially for heterogeneous landscapes. It produces surface reflectances highly correlated with those of the reference Landsat images. It gives spatio-temporal fusion of remotely sensed images a solid theoretical and empirical foundation that may be extended to solve more complicated image fusion problems.

Conflicting Information Fusion Based on an Improved DS Combination Method

An effective and reliable fusion method for conflicting information is proposed in this paper. Compared with a single-sensor system, a multi-sensor fusion system can comprehensively combine the redundancy and complementarity of multi-sensor information to obtain better system performance. Hence, the multi-sensor fusion system has become one of the research hotspots. However, due to lack knowledge about the measurement environment and limited sensor accuracy, the multi-sensor system inevitably appears to have imperfect, uncertain and inconsistent information. To solve the problem, we introduce one powerful uncertainty reasoning method: Dempster–Shafer theory (DS theory). With convincing measurement and a forceful combination of uncertain information, DS theory is widely applied in various fields, like decision-making, expert systems, target tracking, monitoring systems, etc. Nevertheless, DS theory will produce counter-intuitive fusion results when the pieces of evidence are highly conflicting. To address this issue, we raise an improved DS combination method for conflicting information fusion in this paper. First of all, the modified Minkowski distance function and the betting-commitment distance function are separately employed to revise potentially conflicting pieces of evidence. The procedure availably solves the conflicting situations caused by unreliable and imprecise evidence sources, which enhances the consistency among pieces of evidence. Then, based on two revised pieces of evidence, a conflicting redistribution strategy based on locally conflicting analyses is put forward. The approach dexterously combines two revised pieces of evidence to avoid conflicting situations caused by compulsive normalization, which further improves the accuracy and convergence speed of the multi-sensor fusion system. Finally, two experimental analyses with consistent information and conflicting information reveal the remarkable effectiveness and priority of the proposed algorithm for the multi-sensor fusion system. Consequently, this paper has certain value for the multi-sensor fusion system.

Comparison of single and double electrostatic sensors for rotational speed measurement

Accurate and reliable measurement of rotational speed is crucial in many industrial processes. Recent research provides an alternative approach to rotational speed measurement of dielectric rotors through electrostatic sensing and signal processing. This paper aims to explore the electrostatic phenomenon of rotational machineries, design considerations of the spacing between double electrostatic sensors and effect of dielectric rotors on the performance of the rotational speed measurement systems based on single and double electrostatic sensors. Through a series of experimental tests with rotors of different material types, including polytetrafluoroethylene (PTFE), polyvinyl chloride (PVC) and Nylon, different surface roughness (Ra 3.2 and Ra 6.3) and difference diameters (60mm and 120mm), the accuracy and reliability of the two measurement systems are assessed and compared. Experimental results suggest that more electrostatic charge is generated on the PTFE rotors with a larger diameter and coarser surface and hence better performance of the measurement systems. The single-sensor system yields a relative error of within ±1% while the double-sensor system produces an error within ±1.5% over the speed range of 500–3000rpm for all tested rotors. However, the single-sensor system outperforms the double-sensor system at high rotational speeds (>2000rpm) with a relative error less than ±0.05%.

Compressed Sensing and Parallel Acquisition

Parallel acquisition systems arise in various applications in order to moderate problems caused by insufficient measurements in single-sensor systems. These systems allow simultaneous data acquisition in multiple sensors, thus alleviating such problems by providing more overall measurements. In this work we consider the combination of compressed sensing with parallel acquisition. We establish the theoretical improvements of such systems by providing recovery guarantees for which, subject to appropriate conditions, the number of measurements required per sensor decreases linearly with the total number of sensors. Throughout, we consider two different sampling scenarios -- distinct (corresponding to independent sampling in each sensor) and identical (corresponding to dependent sampling between sensors) -- and a general mathematical framework that allows for a wide range of sensing matrices (e.g., subgaussian random matrices, subsampled isometries, random convolutions and random Toeplitz matrices). We also consider not just the standard sparse signal model, but also the so-called sparse in levels signal model. This model includes both sparse and distributed signals and clustered sparse signals. As our results show, optimal recovery guarantees for both distinct and identical sampling are possible under much broader conditions on the so-called sensor profile matrices (which characterize environmental conditions between a source and the sensors) for the sparse in levels model than for the sparse model. To verify our recovery guarantees we provide numerical results showing phase transitions for a number of different multi-sensor environments.

Synergetic of PALSAR-2 and Sentinel-1A SAR Polarimetry for Retrieving Aboveground Biomass in Dipterocarp Forest of Malaysia

Space borne synthetic aperture radar (SAR) data have become one of the primary sources for aboveground biomass (AGB) estimation of forests. However, studies have indicated that limitations occur when a single sensor system is employed, especially in tropical forests. Hence, there is potential for improving estimates if two or more different sensor systems are used. Studies on integrating multiple sensor systems for estimation of AGB over Malaysia’s tropical forests are scarce. This study investigated the use of PALSAR-2 L-band and Sentinel-1A C-band SAR polarizations to estimates the AGB over 5.25 million ha of the lowland, hill, and upper hill forests in Peninsular Malaysia. Polarized images, i.e., HH–HV from PALSAR-2 and VV–VH from Sentinel-1A have been utilized to produce several variables for predictions of the AGB. Simple linear and multiple linear regression analysis was performed to identify the best predictor. The study concluded that although limitations exist in the estimates, the combination of all polarizations from both PALSAR-2 and Sentiel-1A SAR data able to increase the accuracy and reduced the root means square error (RMSE) up to 14 Mg ha−1 compared to the estimation resulted from single polarization. A spatially distributed map of AGB reported the total AGB within the study area was about 1.82 trillion Mg of the year 2016.

Automatic detection of avalanches: evaluation of three different approaches

Automated detection of snow avalanches is an important tool for avalanche forecasting and for assessing the effectiveness of avalanche control measures at bad visibility. Avalanche detection systems are usually based on infrasound, seismic, or radar signals. Within this study, we compared three different types of avalanche detection systems: one avalanche radar, one infrasound array system consisting of four infrasound sensors, and a newly developed single sensor infrasound system. A special focus is given to the new single sensor system, which is a low cost, easy to install system, originally designed for the detection of debris flows and debris floods. Within this work, we analysed how this single sensor system could be adapted to detect also snow avalanches. All three systems were installed close to a road near Ischgl (Tyrol, Austria) at the avalanche-exposed Paznaun Valley. The valley is endangered by two avalanche paths which are controlled by several avalanche towers. The radar system detected avalanches accurately and reliably but was limited to the particular avalanche path towards which the radar beam was directed. The infrasound array could detect avalanches from all surrounding avalanche paths, however, with a higher effort for installation. The newly tested single infrasound sensor system was significantly cheaper and easier to install than the other two systems. It could also detect avalanches form all directions, although without information about the direction. In summary, each of the three different systems was able to successfully detect avalanches and had its particular strengths and weaknesses, which should be considered according to the specific requirements of a particular practical application.

Multi-scale referencing and coordinate unification of optical sensors in multi-axis machines

Abstract Multi-scale optical sensor systems help to overcome the area of conflict between resolution, field size, and inspection time if it comes to the frequent problem of detecting small defects on large areas. The sensors of such systems are chosen according to two main properties: On the one hand, they should measure in opposed scales; on the other hand, their measurement principles should vary as well in order to be suitable for different material and surface properties. However, these systems can only operate at full capacity if it is possible to unify the acquired data from each sensor into one common coordinate system such that an overall analysis is possible, or subsequent sub-measurements can be triggered. In this paper, a general approach for a common sensor referencing is proposed, whose focus lies on microscopic optical sensors for both scattering and reflecting surfaces. The method is able to handle resolutions from the nanometer to millimeter scale in one single system, but is also feasible for a coordinate unification across several single sensor systems.

A simple fluorophore/surfactant ensemble as single discriminative sensor platform: Identifying multiple metal ions in aqueous solution

Abstract A simple imidazolium-modified mono-pyrene derivative (Py-IM) is synthesized. SDS assemblies modulate its fluorescence emission from monomer emission in neat aqueous solution to monomer-excimer co-emission in pre-micelles and to monomer emission again in micellar solutions. The ensemble based on Py-IM and SDS assemblies exhibiting both monomer and excimer emission displays multiple-wavelength cross-reactive responses to cationic metal ions. This single sensor system could well discriminate 6 quenching metal ions (Fe3+, Cu2+, Pb2+, Ni2+, Co2+, and Mn2+) through principal component analysis method. Control experiments with Py-IM in the absence of SDS assemblies and in SDS micellar solutions reveal that the modulation effect of surfactant assemblies plays an important role in the cross-reactive responses of the cationic probe, which provides a general way of fabricating discriminative single sensor systems.

Flexible 3D reconstruction method based on phase-matching in multi-sensor system.

Considering the measuring range limitation of a single sensor system, multi-sensor system has become essential in obtaining complete image information of the object in the field of 3D image reconstruction. However, for the traditional multi-sensors worked independently in its system, there was some point in calibrating each sensor system separately. And the calibration between all single sensor systems was complicated and required a long time. In this paper, we present a flexible 3D reconstruction method based on phase-matching in multi-sensor system. While calibrating each sensor, it realizes the data registration of multi-sensor system in a unified coordinate system simultaneously. After all sensors are calibrated, the whole 3D image data directly exist in the unified coordinate system, and there is no need to calibrate the positions between sensors any more. Experimental results prove that the method is simple in operation, accurate in measurement, and fast in 3D image reconstruction.

Simple and accurate empirical absolute volume calibration of a multi-sensor fringe projection system

This paper suggests a novel absolute empirical calibration method for a multi-sensor fringe projection system. The optical setup of the projector-camera sensor can be arbitrary. The term absolute calibration here means that the centre of the three dimensional coordinates in the resultant calibrated volume coincides with a preset centre to the three-dimensional real-world coordinate system. The use of a zero-phase fringe marking spot is proposed to increase depth calibration accuracy, where the spot centre is determined with sub-pixel accuracy. Also, a new method is proposed for transversal calibration. Depth and transversal calibration methods have been tested using both single sensor and three-sensor fringe projection systems. The standard deviation of the error produced by this system is 0.25mm. The calibrated volume produced by this method is 400mm×400mm×140mm.

A Skill Acquisition Based Framework for Aiding Lower Limb Injury Rehabilitation using a Single Inertial Sensor with Concurrent Visual Feedback

Technology to aid the acquisition and performance of motor skills is becoming increasingly commonplace however there is distinct disconnect between these technological interventions and a detailed understanding of how to design technology to best instruct a learner. Using a single inertial sensor with bespoke concurrent visual feedback, based in a MATLAB data visualisation environment, this paper presents a skill acquisition framework to facilitate home based physiotherapy interventions. When athletes and patients are prescribed at home based physiotherapy interventions the current literature reports low rates of adherence. In addition the lack of monitoring and exercise classification raises concerns towards the quality of rehabilitation program outcomes. A trial was conducted randomly assigning twenty two uninjured participants to two categories, one with the aid of the rehabilitation software and the other a control group with no feedback. Both groups received the same visual instructions on the three simple leg static stretching tasks that are indicative of lower limb injury physiotherapy interventions. The results showed statically significant improvements in both the program adherence as well as the error mitigation of the feedback group in comparison to the control. Substantiating the skill acquisition framework errors for the feedback group seemed to lessen over time synonymous with an immediate learning effect as a result of the concurrent feedback. The findings suggest that at home physiotherapy interventions could be enhanced by using a concurrent biofeedback skill acquisition based single inertial sensor system. Evidently improving adherence, technique and allowing for the data to be accrued over time and relayed to practitioners and coaching staff ultimately giving them heightened confidence in monitoring physiotherapy progress. The wider implications means this research could be useful in tracking and providing feedback for a range of sports injury circumstances to ultimately improve the outcomes of the physiotherapy interventions.

Self-compensating displacement sensor based on hydramatic structured transducer and fiber Bragg grating

An optical fiber displacement sensor with a large measuring range for simultaneous displacement and temperature measurement is presented in this paper. We developed a specific transducer based on the piston and hydraumatic structure to realize a large displacement measurement, which combined the large measuring range and high precision into a single sensor system. The spectrum showed two reflection peaks used to compensate for cross-sensitivity in the displacement detection. This displacement sensor can linearly work in a large measuring displacement range greater than 45 mm with a high sensitivity of 0.036 nm/mm. The sensor we reported can be developed for real-time displacement monitoring in many industrial environments such as the mechanical shape or liquid level monitoring.

Fall detection algorithm in energy efficient multistate sensor system.

Health issues for elderly people may lead to different injuries obtained during simple activities of daily living (ADL). Potentially the most dangerous are unintentional falls that may be critical or even lethal to some patients due to the heavy injury risk. Many fall detection systems are proposed but only recently such health care systems became available. Nevertheless sensor design, accuracy as well as energy consumption efficiency can be improved. In this paper we present a single 3-axial accelerometer energy-efficient sensor system. Power saving is achieved by selective event processing triggered by fall detection procedure. The results in our simulations show 100% accuracy when the threshold parameters are chosen correctly. Estimated energy consumption seems to extend battery life significantly.

Bearings-Only Initial Relative Orbit Determination

A method for performing bearings-only initial relative orbit determination of a nearby space object in the absence of any information regarding the space object’s geometry and relative orbit is presented. To resolve the range ambiguity characteristic of a single optical sensor system, a second optical sensor is included at a known baseline distance on the observing spacecraft. To formulate an initial estimate of the space object’s relative orbit and its associated uncertainty, the angle measurements from both sensors are used to bound a region for all possible relative positions of the space object. A parameterized probability distribution in relative position that reflects uniform relative range uncertainty across the bounded region is constructed at two unique times. Linkage of the positional distributions is performed using a second-order relative Lambert solver to formulate a full-state probability density function in relative position and velocity, which can be further refined through processing subsequent measurement data in a Bayesian framework.

Saliency-Based Position Estimation of Permanent-Magnet Synchronous Machines Using Square-Wave Voltage Injection With a Single Current Sensor

This paper presents a saliency-based position sensorless drive of permanent-magnet (PM) machines using a single current sensor for cost-effective applications. For the single current sensor system, the dc-link current is measured for the reconstruction of three-phase currents. The phase current reconstruction can only be realized when active voltages are applied. This limitation results in six immeasurable areas for the current reconstruction using the pulsewidth modulation. In order to implement the saliency-based sensorless drive, a square-wave voltage is injected to minimize the harmonics of reconstructed saliency current for the rotor position estimation. By injecting a square-wave voltage on the direction of the rotor $d$ -axis, the injection voltage vector is nearly perpendicular to the fundamental voltage vector at low speed. The saliency reflected current or the fundamental current can be separately reconstructed by the dc-link current, leading to the reduced reconstruction errors and improved sensorless performance. By using the proposed signal injection technique on a single sensor system, the saliency-based sensorless drive can work on an interior PM machine with a salient ratio of $L_{q}/L_{d} = 1.35$ at 1% rated speed (60/6000 r/min) under full torque load based on the experimental evaluation.

Passive wireless local area network radar network using compressive sensing technique

Distributed networks constructed from heterogeneous or homogeneous sensors, which are also called multiple-input–multiple-output systems, offer several advantages compared to single sensor systems. In particular for a distributed homogeneous sensor network targets are illuminated from various aspect angles and reflections are received at different locations simultaneously. Hence, these networks outperform single-input–single-output systems easily by several aspects. This spatial diversity improves target detection and parameter estimation dramatically. Therefore these radar networks attract more and more scientists worldwide. This study shows the potential of compressive sensing technique applied to a distributed homogeneous sensor network comprised of several wireless local area network routers as transmitters and dedicated receivers. The CS group sparsity approach is employed to reduce the required data rate, which has to be transferred to a central processing unit for data fusion, without decreasing the performance of the sensor network.

Radio-frequency sensor fusion for relative navigation of formation flying satellites

The increasing need for robustness, reliability and flexibility of relative navigation systems imposed by the current and future autonomous formation flying missions, calls for the implementation of solutions using an alternative approach to single-sensor systems. These schemes possess different levels of availability, mainly driven by sensor failure and mission orbit profiles. The data obtained from the global positioning system (GPS) and formation flying radio-frequency (FFRF) sensors from the nominal phase of the PRISMA mission provide an opportunity for the implementation of an extended approach for the generation of relative navigation solutions. The present study introduces the design of a simple relative navigation filter based on the sensor fusion concept, which makes use of differential GPS and FFRF measurements obtained from the on-board sensors. The filter design uses the Hill-Clohessy-Wiltshire and Yamanaka-Ankersen relative dynamics models as well as numerical integration methods including the J2-perturbation for the state and associated covariance matrix propagation. The design is tested using flight data obtained during representative formation flying operations of the PRISMA mission.