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The rational polynomial function is widely accepted as the preferred sensor model for high-resolution satellite imagery (HRSI). However, satellite images and their associated rational polynomial coefficients (RPCs) often suffer from nonlinear systematic errors, which were caused by attitude oscillation, sensor deformation, and many other imperfect calibration errors, thus affecting the geo-referencing accuracy. Instead of modeling the biases by polynomials in the image space or refining RPCs directly, this study proposes an approach of going back to the physical model and correcting the local distortions in a self-calibration block adjustment. The algorithm of an equivalent geometric sensor model (EGSM) recovery from RPCs is described in detail. As an equivalent form of the physical sensor models, EGSM reflects the complete viewing geometry of push-broom HRSI. The interior and exterior orientation parameters of EGSM can be stably recovered from RPCs without using any metadata. An approach of RPCs refinement by self-calibration block adjustment based on EGSM is introduced. This approach can effectively compensate for the nonlinear systematic errors caused by platforms and sensors similar to the approach of a rigorous sensor model. The performance of EGSM-based block adjustment is compared with the RFM-based bias compensation method. Experiments using ZY-3 images show the EGSM-based approach can effectively eliminate the nonlinear distortions in satellite images caused by sensor deformation and attitude vibrations. Furthermore, experiments using images from various satellites show that the original RFM can be well fitted with the EGSM and the residuals are smaller than 0.1 pixels for all test images.

High-resolution satellite images such as KOMPSAT-3 data provide detailed geospatial information over interest areas that are evenly located in an inaccessible area. The high-resolution satellite cameras are designed with a long focal length and a narrow field of view to increase spatial resolution. Thus, images show relatively narrow swath widths (10–15 km) compared to dozens or hundreds of kilometers in mid-/low-resolution satellite data. Therefore, users often face obstacles to orthorectify and mosaic a bundle of delivered images to create a complete image map. With a single mosaicked image at the sensor level delivered only with radiometric correction, users can process and manage simplified data more efficiently. Thus, we propose sensor-level mosaicking to generate a seamless image product with geometric accuracy to meet mapping requirements. Among adjacent image data with some overlaps, one image is the reference, whereas the others are projected using the sensor model information with shuttle radar topography mission. In the overlapped area, the geometric discrepancy between the data is modeled in spline along the image line based on image matching with outlier removals. The new sensor model information for the mosaicked image is generated by extending that of the reference image. Three strips of KOMPSAT-3 data were tested for the experiment. The data showed that irregular image discrepancies between the adjacent data were observed along the image line. This indicated that the proposed method successfully identified and removed these discrepancies. Additionally, sensor modeling information of the resulted mosaic could be improved by using the averaging effects of input data.

In this paper the authors propose the design of a complete measurement system of clearance based on an ‘on-component’ triaxial capacitive probe for static and dynamic measurement of distance. The capacitive probe is designed to be deposited onto machinery components or complex structures and is aimed at reducing the impact of probe mounting on the monitored component. The problem is discussed from a theoretical point of view and then supported by the realization of a sensor prototype in screen printed technology and of two possible and alternative conditioning systems. The results prove the feasibility of this innovative measurement system.

In this paper, a system for the measurement of the instantaneous speed of rotating parts in turbines is proposed and modeled. The system is based on traditional and widely used variable reluctance sensors often mounted on-board for other purposes. By coupling this sensors with ad hoc rotating target (novel design) and front-end circuit, it is possible to extend the application of this sensor to accurate instantaneous angular speed monitoring, and to exploit it for torsional vibration monitoring or, in general, for the measurement of any other meaningful quantity related to the variation of the angular speed, useful for machine health monitoring and incipient fault diagnosis. The proposed rotating target produces a signal close to the pure sine wave, with the specific purpose of improving accuracy in torsional vibration monitoring. The complete system was modeled, also taking into account the effect of nonidealities in order to evaluate its performance. Experimental results confirm the satisfactory results obtained from simulations, and show the reduction of the magnitude of the higher order harmonics in the output signal spectrum ensuring improved performance for any torsional vibration monitoring assembly employing the proposed system.

Geometric correction is fundamental in producing high quality satellite data products. However, the geometric correction for ocean color sensors, e.g., Geostationary Ocean Color Imager (GOCI), is challenging because the traditional method based on ground control points (GCPs) cannot be applied when the shoreline is absent. In this study, we develop a hybrid geometric correction method, which applies shoreline matching and frequency matching on slots with shorelines and without shorelines, respectively. Frequency matching has been proposed to estimate the relative orientation between GOCI slots without a shoreline. In this paper, we extend our earlier research for absolute orientation and geometric correction by combining frequency matching results with shoreline matching ones. The proposed method consists of four parts: Initial sensor modeling of slots without shorelines, precise sensor modeling through shoreline matching, relative orientation modeling by frequency matching, and generation of geometric correction results using a combination of the two matching procedures. Initial sensor modeling uses the sensor model equation for GOCI and metadata in order to remove geometric distortion due to the Earth&rsquo, s rotation and curvature in the slots without shorelines. Precise sensor modeling is performed with shoreline matching and random sample consensus (RANSAC) in the slots with shorelines. Frequency matching computes position shifts for slots without shorelines with respect to the precisely corrected slots with shorelines. GOCI Level 1B scenes are generated by combining the results from shoreline matching and frequency matching. We analyzed the accuracy of shoreline matching alone against that of the combination of shoreline matching and frequency matching. Both methods yielded a similar accuracy of 1.2 km, which supports the idea that frequency matching can replace traditional shoreline matching for slots without visible shorelines.

In this paper a system for the measurement of the instantaneous speed of rotating parts in turbine is proposed and modeled. The system is based on traditional and widely used variable reluctance sensors (VRSs) and on a simple, robust and accurate front-end electronics, which allows for improving the overall performance. The complete system was modeled, also taking into account the effect of non-idealities, and characterized by means of both simulations and experimental tests.

Signal strength-based positioning in wireless sensor networks is a key technology for seamless, ubiquitous localization, especially in areas where Global Navigation Satellite System (GNSS) signals propagate poorly. To enable wireless local area network (WLAN) location fingerprinting in larger areas while maintaining accuracy, methods to reduce the effort of radio map creation must be consolidated and automatized. Gaussian process regression has been applied to overcome this issue, also with auspicious results, but the fit of the model was never thoroughly assessed. Instead, most studies trained a readily available model, relying on the zero mean and squared exponential covariance function, without further scrutinization. This paper studies the Gaussian process regression model selection for WLAN fingerprinting in indoor and outdoor environments. We train several models for indoor/outdoor- and combined areas, we evaluate them quantitatively and compare them by means of adequate model measures, hence assessing the fit of these models directly. To illuminate the quality of the model fit, the residuals of the proposed model are investigated, as well. Comparative experiments on the positioning performance verify and conclude the model selection. In this way, we show that the standard model is not the most appropriate, discuss alternatives and present our best candidate.

Terrestrial laser scanning typically requires the use of artificial targets for registration and georeferencing the data. This equipment can be burdensome to transport and set up, representing expense in both time and labor. Environmental factors such as terrain can sometimes make target placement dangerous or impossible, or lead to weak network geometry and therefore degraded product accuracy. The use of additional sensors can help reduce the required number of artificial targets and, in some cases, eliminate the need for them altogether. The research presented here extends methods for direct georeferencing of terrestrial laser scanner data using a dual GNSS antenna apparatus with additional photogrammetric observations from a scanner-mounted camera. Novel combinations of observations and processing methods were tested on data collected at two disparate sites in order to find the best method in terms of processing efficiency and product quality. In addition, a general model for the scanner and auxiliary data is given which can be used for least-squares adjustment and uncertainty estimation in similar systems with varied and diverse configurations. We found that the dual-antenna system resulted in cm-level accuracy practical for many applications and superior to conventional one-antenna systems, and that auxiliary photogrammetric observation significantly increased accuracy of the dual-antenna solution.

The article extends the cycle of papers dedicated to programming and verificatoin of PLC-programs by LTL-specification. This approach provides the availability of correctness analysis of PLC-programs by the model checking method. The model checking method application presupposes constructing a finite model of a PLC-program. For successful verification of the required properties it is important to take into consideration that not all combinations of input signals from the sensors can occur while PLC is working with a control object. This fact requires more advertence to the construction of the PLC-program model. In this paper we propose to describe a consistent behavior of sensors by three groups of LTL-formulas. They will affect the program model, approximating it to the actual behavior of the PLC-program. The idea of LTL-requirements is shown by an example. A PLC-program is a description of reactions on input signals from sensors, switches and buttons. In constructing a PLC-program model, the approach to modeling a consistent behavior of PLC sensors allows to focus on modeling precisely these reactions without an extension of the program model by additional structures for design of the realistic behavior of sensors. The consistent behavior of sensors is taken into account only at the stage of checking the conformity of the programming model to required properties, i.e. a property satisfaction proof for the constructed model occurs with the condition that the model contains only such executions of the program that comply with the consistent behavior of sensors.

Flexibility, lightness, wearability and cheapness are the most important features for a successful adoption of bend sensors, being they fundamental elements to realize systems able to convert static positions and movements into electrical signals. Even though these sensors have been employed for many different applications, the focal aspect of their mechanical modeling is still not adequately considered. For such a reason the aim of this paper is to fill a lack concerning the method of bend sensors mechanical characterization and modeling. The results have been exploited to realize an instrumented glove able to measure finger joints movements.

The device consists of a camera which gives the HO an indirect view of a scene (real world); proprioceptive and exteroceptive sensors allowing the recreating of the 3D geometric database of an environment (virtual world). The virtual world is projected onto a video display terminal (VDT). Computer-generated and video images are superimposed. The man-machine interface functions deal mainly with on line building of graphic aids to improve perception, updating the geometric database of the robotic site, and video control of the robot. The superimposition of the real and virtual worlds is carried out through a calibration of the multisensor system. First, the sensing system and several methods used for modeling an environment\'s geometric database in telerobotics have been presented. The operator models unknown objects (of cylindrical or polyhedral pattern) using a video camera(VC) image and a range finder(RF). When the quality of images is poor, the range finder, which is mounted on a site and azimuth rotation turret, brings an indispensable complement by measures of depth. The improvement offered by the cooperation VC/RF has been shown for small depths (below 1.6m). In some particular cases we propose to achieve the fusion of multisensor redundant data, in order to reduce the uncertainty. Then the updating of environment's 3D geometric database in telerobotics by single camera view is presented. It concerns the pose determination of known objects using 2D clues obtained through video images. A two step algorithm is implemented and assessed. The first step is available in case of object large motion, it is a geometric algorithm, which doesn\'t use redundant data; .it provides a first estimate of the pose. The second step is available in case of object small motion. A linear approach is carried out to determine the pose. It allows the use of redundant data. so it improves the accuracy achieved after the first step. KEY WORDS: Teleoperation, Telerobotics, Range finder; Video camera; Sensor modeling; 3D database updating, Image matching. Global Journal of Pure and Applied Sciences Vol.11(1) 2005:153-163

Recent robots target safety, reconfigurability and interactivity by addressing the “softness” of the hardware either by endowing additional degrees-of-freedom or through inherent compliancy. These robots require distributed sensing with flexibility and softness that would not interfere with the robot's agility. There have been various sensing solutions using soft conductive materials including conductive silicone, liquid metal-filled micro-channels, and conductive-ink based sensors. However, we still lack a comprehensive study on their potentials, drawbacks, and the different parameters that affect their response. We present our design, fabrication process and characterization results for conductive silicone polymer and carbon ink-based curvature sensors. These sensors are flexible, mechanically robust under large strains, scalable, and easy to fabricate in large numbers. We propose an equivalent mechanical system to model sensors’ response. This model is unique for its extensive characterization of these polymer based sensors. Based on the characterization results, we systematically categorize and compare the performance of conductive silicone and carbon ink-based sensors with different design parameters.