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Multiple air-data sensors placed on different positions on an aircraft are usually expected to provide identical measurements. This is because aircraft are assumed to fly in no-wind or uniform wind conditions. There are cases, however, where an aircraft flies in nonuniform wind vector field with significant variations in magnitude and direction over the dimensions of the aircraft. For example, in an aerial refueling operation, the receiver aircraft needs to fly within the nonuniform wind field induced by the wake of the tanker aircraft. This paper shows that an air-data sensor fault detection and isolation algorithm, if developed with an underlying assumption of identical sensor measurements, yields very high false detection rate when the aircraft enters the wake of another aircraft. This paper also presents a novel modification to the redundant-sensor-based fault detection and isolation algorithm that eliminates this problem. The new method is based on a model of the wind field as a function of relative position with respect to the tanker aircraft. This model is used to compute the expected values of air-data sensor measurements given the position of the receiver aircraft relative to the tanker. [ABSTRACT FROM AUTHOR]

A enhanced fusion algorithm for generating a super resolution image from a sequence of low-resolution images captured from identical scene apparently a video, based on framelet have been designed and analyzed. In this paper an improved analytical method of image registration is used which integrates nearest neighbor method and gradient method. Comparing to Discrete Wavelet Transform (DWT) the Framelet Transform (FrT) have tight frame ilter bank that offers symmetry and permits shift in invariance. Therefore using framelet this paper also present a framelet based enhanced fusion for choosing the fused framelet co-eficient that provides detailed edges and good spatial information with adequate de-noising. The proposed algorithm also has high advantage and computationally fast which are most needed for satellite imaging, medical imaging diagnosis, military surveillance, remote sensing etc. [ABSTRACT FROM AUTHOR]

In Scalable Video Coding (SVC), motion estimation and inter-layer prediction play an important role in elimination of temporal and spatial redundancies between consecutive layers. This paper evaluates the performance of widely accepted block matching algorithms used in various video compression standards, with emphasis on the performance of the algorithms for a didactic scalable video codec. Many different implementations of Fast Motion Estimation Algorithms have been proposed to reduce motion estimation complexity. The block matching algorithms have been analyzed with emphasis on Peak Signal to Noise Ratio (PSNR) and computations using MATLAB. In addition to the above comparisons, a survey has been done on Spiral Search Motion Estimation Algorithms for Video Coding. A New Modified Spiral Search (NMSS) motion estimation algorithm has been proposed with lower computational complexity. The proposed algorithm achieves 72% reduction in computation with a minimal (<1dB) reduction in PSNR. A brief introduction to the entire flow of video compression H.264/SVC is also presented in this paper. [ABSTRACT FROM AUTHOR]

This paper presents a decentralized, model predictive control algorithm for the optimal guidance and reconfiguration of swarms of spacecraft composed of hundreds to thousands of agents with limited capabilities. In previous work, J2-invariant orbits have been found to provide collision-free motion for hundreds of orbits in a low Earth orbit. This paper develops real-time optimal control algorithms for the swarm reconfiguration that involve transferring from one J2-invariant orbit to another while avoiding collisions and minimizing fuel. The proposed model predictive control-sequential convex programming algorithm uses sequential convex programming to solve a series of approximate path planning problems until the solution converges. By updating the optimal trajectories during the reconfiguration, the model predictive control algorithm results in decentralized computations and communication between neighboring spacecraft only. Additionally, model predictive control reduces the horizon of the convex optimizations, which reduces the run time of the algorithm. Multiple time steps, time-varying collision constraints, and communication requirements are developed to guarantee stability, feasibility, and robustness of the model predictive control-sequential convex programming algorithm. [ABSTRACT FROM AUTHOR]

This paper analyzes the results of a diagnostic study carried out with second year students of the computational sciences majors at University of Oriente, Cuba, to determine the limitations that they present in computational algorithmization. An exploratory research was developed using quantitative and qualitative methods. The results allowed verifying insufficiencies that have their base in the processes of interpretation and understanding of the problematic situations and in the insufficient design of algorithms, using pseudocodes, before implementing the solutions in a programming language. [ABSTRACT FROM AUTHOR]

This paper presents a method for permanent parameter tuning of optimization algorithms. We describe mathematical formalization of parameter tuning problem and meta-model used for it. There are also the scheme of the proposed method, the description of search stages and meta-algorithms used. A way to classify optimization problems is also introduced. It defines how the tuning process will be divided for different types of problems in order to reuse tuning results. Besides, several ways to organize tuning process are suggested. The proposed method is implemented in the program system for automated parameter tuning. The results of experiments indicate the effectiveness of developed system. [ABSTRACT FROM AUTHOR]

Nowadays, navigation and guidance is widely applied in many different fields. The high accuracy is very important and necessary in most of applications, but it sometimes needs to have a balance between cost and performance of navigation system. Hence, there are many new algorithms, new integrated methods are proposed to integrate or embed into INS/GPS integration systems to enhance accuracy, to reduce size and have an acceptable cost. In the recent study of our group, we have succeeded in finding out a new algorithm named Street Return Algorithm and embedding into a INS/GPS integration system. However, that research only obtains high accuracy when errors determined by INS are traverse of roads but in remanent cases the accuracy could not be determined. In this paper, we have theoretically proposed to use a compass sensor and a corresponding algorithm with this kind of sensor in order to overcome that limitation. [ABSTRACT FROM AUTHOR]

Since the introduction of hexapod-type motion systems for flight simulation in the 1970s, Motion Drive Algorithm tuning has been primarily based on the subjective judgement of experienced pilots. This subjective method is often not transparent and often leads to ambiguous process of adjustment of the tuning parameters. Consequently, there are large variations in the motion cueing characteristics of flight training devices, a variability that subsequently raises questions regarding the value of motion cueing for pilot training itself. The third revision of ICAO 9625 Manual of Criteria for the Qualification of Flight Simulation Training Devices offered the opportunity to take a closer look at simulator motion cueing requirements in general. This led to the concept of the objective motion cueing test (OMCT), which was reported in 2006. After the method was evaluated on three research flight simulators, the results were published in 2007, demonstrating a larger spread in dynamic behaviour of cueing algorithms than expected. After discussions with the simulator industry regarding the form and methodology of the OMCT, an evaluation of the test in cooperation with the industry started in 2011. This led to the final form of the OMCT and cueing parameter criterion for the in-flight mode of transport aircraft. This paper describes the OMCT, the evaluation results and the criterion. [ABSTRACT FROM PUBLISHER]

In this paper is proposed a control algorithm based on the first order sliding mode technique. The control design adds an exponential reaching law and a disturbance estimator to improve performance, achieving a reduction of the convergence time to the reference, as well as a reduction of the reaching time towards the sliding surface. Also, by compensating the estimated disturbance, it is possible to reduce the amplitude of the chattering in the control signal. As the control design is intended to be applied in mechanical systems, a velocity observer design is also proposed. Bringing together the above aspects, the proposed controller renders an improved performance over the classical first order sliding mode controller. The stability of the closed-loop system is proved using quadratic functions. The performance of the proposed control structure is illustrated and compared with other controllers via numerical simulations and real-time experiments in a mechanical system. [ABSTRACT FROM AUTHOR]

The aim of this paper is to examine the earnings dynamic in Italy, in order to explain earnings differences between southern Italy and centralnorthern Italy. In our analysis we use different techniques: ordinary least squares (OLS), quantile regression models and the algorithm developed by Machado and Mata (2005). In particular, the Machado and Mata (2005) algorithm allows us to examine the relative importance of both differences in workers' characteristics and in their returns in explaining southern, central and northern Italy earnings differences at a point in time, as well as across time within each macro-area. We focus on the role of differences in educational endowment and returns to education, one of the most important components of human capital in the stylised literature. The level of education determines the substantial disparities in terms of wage returns. However, this holds only for levels of education related to compulsory education. [ABSTRACT FROM AUTHOR]

In this paper, a star tracker attitude estimation procedure with increased robustness and efficiency, using the AIM algorithm, is presented and validated. The unique approach of the AIM algorithm allows one to introduce a reliable quality check that can be efficiently calculated. Unlike existing validation methods, this quality check not only detects that some of the data are unreliable but it also determines which star measurements are unreliable. These unreliable measurements can be removed from the data set, and a new attitude quaternion can be calculated without having to repeat the entire AIM algorithm. This greatly improves the robustness of the attitude estimation, while limiting the computational expense. Furthermore, the structure of AIM allows one to reuse previously calculated data when the change in attitude between subsequent measurements is small. This way, the efficiency of the entire attitude estimation cycle can be increased significantly. These enhancements are validated with simulated star tracker data. The results show that the improvements significantly improve the robustness and lower the computational cost of the star tracker attitude estimation. As a consequence, the overall performance of the attitude determination and control system greatly increases. The increased efficiency of the attitude estimation could also allow the use of star trackers in smaller satellite projects with smaller budgets. [ABSTRACT FROM AUTHOR]

A combination of multiple gravity-assist and impulsive Delta-V maneuvers is often required for interplanetary and interstellar space missions, such as NASA's Voyager 1 and 2, Galileo, and Cassini missions. The design of such complex interplanetary missions is difficult with traditional mission analysis techniques because the mission must be prepruned to determine potential trajectories. Prepruning has been necessary because these mission often require the optimization of dozens of variables in a highly nonlinear and discontinuous design space. This process risks pruning nonintuitive solutions, which may potentially contain the optimal trajectory. In this paper, a hybrid optimization algorithm that is capable of determining optimal interplanetary trajectories, including the number of gravity assists and the planetary flyby order, is developed. The hybrid optimization algorithm uses a stochastic genetic algorithm to globally search the design space, as well as traditional nonlinear programming gradient-based optimization tools to determine locally optimal trajectories. By combining the global convergence properties of genetic algorithms with the accuracy of gradient-based solvers, a robust optimization algorithm capable of determining near-optimal solutions for a complex interplanetary space mission is developed. [ABSTRACT FROM AUTHOR]

Data clustering plays an important role in partitioning the large set of data objects into known/unknown number of groups or clusters so that the objects in each cluster are having high degree of similarity while objects in different clusters are dissimilar to each other. Recently a number of data clustering methods are explored by using traditional methods as well as nature inspired swarm intelligence algorithms. In this paper, a new data clustering method using modified bat algorithm is presented. The experimental results show that the proposed algorithm is suitable for data clustering in an efficient and robust way. [ABSTRACT FROM AUTHOR]

In wavelength division multiplexed all optical networks; lightpath establishes a connection between sending and receiving nodes bypassing the electronic processing at intermediate nodes. One of the prime objectives of Routing and Wavelength Assignment (RWA) problem is to maximize the number of connections efficiently by choosing the best routes. Although there are several algorithms available, improving the blocking performance in optical networks and finding optimal solutions for RWA problem has still remained a challenging issue. Wavelength conversion can be helpful in restricting the problem of wavelength continuity constraint but it increases complexity in the network. In this paper, we propose new weight dependent routing and wavelength assignment strategy for all optical networks without use of wavelength converters. Proposed weight function reduces blocking probability significantly, improving the network performance at various load conditions. Further, due to absence of wavelength converters, the cost and complexity of network reduces. Results show that the proposed strategy performs better than earlier reported methods. [ABSTRACT FROM AUTHOR]

Heat transfer and fluid flow correlation are generally derived from experimental data, which are affected by various kinds of errors. Transition from laminar to turbulent regime is a sensitive issue that depends on many factors. In complex geometry it is not possible to predict a transition Reynolds number value which is coincident to the real thing. The paper proposes a highly efficient and fast algorithm for identification of heat transfer and flow friction correlations for compact heat surfaces. Simultaneously, the algorithm provides the Reynolds number for which transition from laminar to turbulent flow occurs. The algorithm is based on damped least-squares (DLS) algorithm and divides successively the Re axis attempting to minimize the least squares sums of deviations. The algorithm was implemented in a program to automate it and some specific examples are presented [ABSTRACT FROM AUTHOR]

The projection of a 3D object point onto a 2D image plane (i.e., the object-to-image projection) is one of the fundamental operations in photogrammetric data processing. Unlike that for a frame image, the object-to-image projection for a pushbroom image is not a straightforward process because each scan line is exposed at a distinct instant of time, and a preliminary step is therefore required to determine the corresponding scan-line coordinate of the ground point to be projected. This paper presents a new scan-line search algorithm for the object-to-image projection of airborne pushbroom images, which employs a coarse-to-fine strategy following four consecutive steps: affine transformation, iterative search, sequential search, and sub-pixel interpolation. The experimental results with 255 pushbroom images captured in various flight conditions show that the proposed algorithm is robust and can save at least 15 percent of the computational time when compared to the previous methods, while the latter often cannot yield correct results in strong turbulence scenarios. [ABSTRACT FROM AUTHOR]

Typical algorithms in remote sensing-based mapping, such as geometric correction, image fusion, image mosaic, and automatic DEM extractions, are data- and computation-intensive; processing on multi-core computers can improve their performance. Therefore, parallel computing methods that can fully leverage state-of-the-art hardware platforms and that can be easily adapted to these algorithms are required. In this paper, a method with high parallelism is adopted. The method integrates a recursive procedure with a parallel mechanism that is capable of concurrently processing multiple blocks on multiple cores. The parallel experiments of five categories of typical algorithms on two multi-core computers with Windows and Linux operating systems, respectively, were fulfilled. The experimental results show that although the gains of parallel performance vary for different algorithms, the processing performance achieved on multi-core computers is significantly improved. The best case on a computer with two CPUs is able to perform the DEM extractions up to 13.6 times faster than serial execution. According to these experiments, the factors influencing parallel performance on a multi-core computer are discussed. [ABSTRACT FROM AUTHOR]

Copyright of Automatika: Journal for Control, Measurement, Electronics, Computing & Communications is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Aerocapture is the maneuver by an interplanetary spacecraft to fly through the atmosphere of a planet with the aim of attaining a specified orbit around the planet. By appropriately controlling the aerodynamic lift and/or drag force vectors, the spacecraft can exit the atmosphere and enter the target orbit without the need for large propellant consumption in post-atmospheric orbital correction burns. The focus of this paper is to develop an algorithm to guide the spacecraft accurately and reliably during the aerocapture maneuver with lift vector control while ensuring the least possible post-atmospheric propellant expenditure for inserting into the target orbit. The analysis of optimal aerocapture flight in this work shows that the optimal aerocapture trajectory in general has a bang-bang control structure in which the spacecraft first flies with the largest possible vertical lift up, then the largest possible vertical lift down. Based on this understanding, a two-phase numerical predictor-corrector guidance algorithm is developed. It is demonstrated that this algorithm not only exhibits the strengths of adaptivity and high accuracy of predictor-corrector guidance algorithms, but also produces an optimal performance in terms of propellant consumption that is significantly better than existing aerocapture numerical predictor-corrector guidance algorithms. [ABSTRACT FROM AUTHOR]

Conventional distances used in clustering basically grow with the square of the distance in means, and are therefore insensitive when the clusters are tightly separated. This paper proposes a new separation measure between two Gaussians by blending the concept of non-Gaussianity and information theoretic distances with the goal of improving the separation of tightly coupled clusters. The measure called Quadratic Renyi's Negentropy Separation (QRNS) relies on the estimation of negentropy between the Gaussian fitted clusters and the Gaussian mixture constructed by individual clusters. An analytical form to estimate separation is derived using the quadratic Rényi entropy instead of the common Shannon entropy. QRNS is not a distance, and its properties are analyzed and discussed. Finally, QRNS is used to develop a simple clustering algorithm, which is preliminary validated in several classical problems with separable clusters. Although the proposed clustering algorithm is not among the state of the art in clustering, it shows very good results when using the new measure. [ABSTRACT FROM AUTHOR]

Motion estimation is a very important but computationally complex task in video coding. Process of determining motion vectors based on the temporal correlation of consecutive frame is used for video compression. In order to reduce the computational complexity of motion estimation and maintain the quality of encoding during motion compensation, different fast search techniques are available. These block based motion estimation algorithms use the sum of absolute difference (SAD) between corresponding macroblock in current frame and all the candidate macroblocks in the reference frame to identify best match. Existing implementations can perform SAD between two blocks using sequential or pipeline approach but performing multi operand SAD in single clock cycle with optimized recourses is state of art. In this paper various parallel architectures for computation of the fixed block size SAD is evaluated and fast parallel SAD architecture is proposed with optimized resources. Further SAD processor is described with 9 processing elements which can be configured for any existing fast search block matching algorithm. Proposed SAD processor consumes 7% fewer adders compared to existing implementation for one processing elements. Using nine PE it can process 84 HD frames per second in worse case which is good outcome for real time implementation. In average case architecture process 325 HD frames per second. [ABSTRACT FROM AUTHOR]

The chief aim of this paper is to develop an effective approach to the issue of load profile clustering by applying Improved Replacement In Bee Optimization algorithm (IRIBO). While, intelligent metering solutions like Automated Meter Reading (AMR), Automated Meter Infrastructure (AMI) are in place to address the current issues prevailing in the domain of electricity markets, algorithm using Improved Replacement In Bee Optimization has been proved beneficial and uncomplicated to apply within a selective database. In this study Load Profile (LP) clustering distribution networks based on the shape of the load profile was studied for fitness function in the selected LP clustering. The results clearly indicate that LP clustering has advantages in providing metering solutions to consumers who do not possess digital metering which can be easily operated with trivial changes in the calibrations. [ABSTRACT FROM AUTHOR]

Registration is a process of spatially aligning the data in two or more coordinate systems. It is required when maps, images or vector data are in different coordinate systems and need to be brought into a common reference frame. Registration is an important step in data fusion and change detection problems. It is usually achieved by using common points or lines in different coordinate systems. However, in most cases the datasets do not have common points or lines, especially when the data are multimodal or multitemporal. In this paper the authors demonstrate a feature-based registration method that registers 2D datasets without explicitly using point correspondences. This is achieved by iteratively minimizing the area formed between multiple pairs of corresponding free-form lines. The algorithm was successfully tested for 2D similarity transformation with synthetic and real data. The recovered parameters were approximately 99 percent accurate, though no point correspondences exist between datasets. [ABSTRACT FROM AUTHOR]

Research on vision-based navigation of unmanned air vehicles (UAVs) has been in focus in the recent years, both in military and civilian application domains. Vision-based navigation uses features as cues to match successive images and compute the location of the UAV. The method proposed in this paper is an extension to the existing work to automatically compute scale weights of dual-tree complex wavelet transform (DTCWT) coefficients to improve the performance in terms of detection of features as against discrete wavelets or its Fourier counterparts. Existing DTCWT coefficients give improved time-shifted sensitivity, better directional selectivity with local phase information and limited redundancy. However, the drawback of this method is that the results depend on appropriate selection of scale weights α and β, which are different for different scenes. The existing technique incorporates a rule of thumb-based recommended values of α and β, irrespective of the scene which generates too many key-points, making the registration process computationally intensive. As a real-time application, the need of the problem is to extract less number of strong features. Thus, an algorithmic approach to compute optimal values of scale weights considering precision and accuracy is proposed. The method is tested on various synthetic, simulated, and aerial images having different transformations and in the presence of noise between successive images. It is observed that DTCWT descriptor performs the best and gives better results than scale invariant feature transform and Speeded up robust features. [ABSTRACT FROM AUTHOR]

Mobility and network connectivity to the rest of the fleet are critical capabilities of battlefield assets. Battlefield Mobile Ad-Hoc Networks (MANETs) face a dynamic and unpredictable operating environment. Bottlenecks and fluctuating connectivity can stem from a number of factors: such as clusters of vehicles separated by buildings or terrain obstacles; intentional interference, such as jamming; and by failure or damage. Exploiting node mobility is a common solution to reintegrate disjointed clusters of mobile nodes in a network, however given that each mobile node in the battlefield has a task of given criticality to perform as part of an overall mission plan, one must recognise that in a realistic battlefield scenario, a Topology Management Algorithm (TMA) cannot assume that all mobile nodes are available to be relocated to recover a partitioned network and that further nodes deployed in the area not equally be lost due to a persistent localised danger. In this paper, a mission aware network topology healing algorithm is presented which harvests mission information from a fleet wide shared data model and relocates mobile nodes within their mission parameters taking into account mission goals, criticality of goals and waypoints, mission critical neighbours and terrain danger zones. This novel algorithm is verified by modelling and simulation by demonstrating a significant performance gain when comparing it to a traditional TMA without mission awareness. [ABSTRACT FROM AUTHOR]

This paper proposes a multifocus image fusion algorithm based on cloud model. First, each source images are divided into overlapping image blocks of size (2N+1) x (2N+1) and then the mean and entropy of every image pixels over this neighborhood window was calculated and compared in Cloud domain. The pixel with higher magnitude of the calculated image features was selected to form the fused image. The results of multifocus image fusion using this algorithm hold favorable consistency in terms of root mean square error, peak signa to noise ratio and quality index for three pairs of test images and confirm the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Copyright of Automatika: Journal for Control, Measurement, Electronics, Computing & Communications is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper we propose extensions to the existing utility-based reputation model for virtual organizations (VOs) in grids, and present a novel approach for integrating reputation into grid resource management system. The proposed extensions include: incorporation of statistical model of user behaviour (SMUB) to assess user reputation; a new approach for assigning initial reputation to a new entity in a VO; capturing alliance between consumer and resource; time decay and score functions. The addition of the SMUB model provides robustness and dynamics to the user reputation model comparing to the policy-based user reputation model in terms of adapting to user actions. We consider a problem of integrating reputation into grid scheduler as a multi-criteria optimization problem. A non-linear trade-off scheme is applied to form a composition of partial criteria to provide a single objective function. The advantage of using such a scheme is that it provides a Pareto-optimal solution partially satisfying criteria with corresponding weights. Experiments were run to evaluate performance of the model in terms of resource management using data collected within the EGEE Grid-Observatory project. Results of simulations showed that on average a 45% gain in performance can be achieved when using a reputation-based resource scheduling algorithm. [ABSTRACT FROM AUTHOR]

This paper proposes a novel hyperspectral matching technique by integrating the Jeffries-Matusita measure (JM) and the Spectral Angle Mapper (SAM) algorithm. The deterministic Spectral Angle Mapper and stochastic Jeffries-Matusita measure are orthogonally projected using the sine and tangent functions to increase their spectral ability. The developed JM-SAM algorithm is implemented in effectively discriminating the landcover classes and cover types in the hyperspectral images acquired by PROBA/CHRIS and EO-1 Hyperion sensors. The reference spectra for different land-cover classes were derived from each of these images. The performance of the proposed measure is compared with the performance of the individual SAM and JM approaches. From the values of the relative spectral discriminatory probability (RSDPB) and relative discriminatory entropy value (RSDE), it is inferred that the hybrid JM-SAM approach results in a high spectral discriminability than the SAM and JM measures. Besides, the use of the improved JM-SAM algorithm for supervised classification of the images results in 92.9% and 91.47% accuracy compared to 73.13%, 79.41%, and 85.69% of minimum-distance, SAM and JM measures. It is also inferred that the increased spectral discriminability of JM-SAM measure is contributed by the JM distance. Further, it is seen that the proposed JM-SAM measure is compatible with varying spectral resolutions of PROBA/CHRIS (62 bands) and Hyperion (242 bands). [ABSTRACT FROM AUTHOR]

Road markings are used to provide guidance and instruction to road users for safe and comfortable driving. Enabling rapid, cost-effective and comprehensive approaches to the maintenance of route networks can be greatly improved with detailed information about location, dimension and condition of road markings. Mobile Laser Scanning (MLS) systems provide new opportunities in terms of collecting and processing this information. Laser scanning systems enable multiple attributes of the illuminated target to be recorded including intensity data. The recorded intensity data can be used to distinguish the road markings from other road surface elements due to their higher retro-reflective property. In this paper, we present an automated algorithm for extracting road markings from MLS data. We describe a robust and automated way of applying a range dependent thresholding function to the intensity values to extract road markings. We make novel use of binary morphological operations and generic knowledge of the dimensions of road markings to complete their shapes and remove other road surface elements introduced through the use of thresholding. We present a detailed analysis of the most applicable values required for the input parameters involved in our algorithm. We tested our algorithm on different road sections consisting of multiple distinct types of road markings. The successful extraction of these road markings demonstrates the effectiveness of our algorithm. [ABSTRACT FROM AUTHOR]

Wilson-ϑ method is one of the commonly used numerical algorithms, where the properly set parameters can usually result in absolute convergence of the solution. Based on this characteristic, a retrospective method of load identification can be derived by employing this theory inversely. However, from the calculated results of practical examples, it is shown that the method is not a kind of stable algorithm. The paper indicates the reason why the inverse wilson-ϑ method will diverge and put forward an corrective iterative algorithm using the principle of bisection. The step-by-step corrective method is used to eliminate cumulative errors and improve the stability of the algorithm. Numerical simulation and experimental research have been carried out for several cases, of which the results show good convergence and anti-noise capability of this method. [ABSTRACT FROM AUTHOR]

Identifying submerged objects is critical for several disciplines such as marine archaeology and search and rescue. However, identifying objects in underwater searches presents many challenges, particularly if the only data available to analyze is poor-quality video where the videographer did not plan for photogrammetric techniques to be utilized. In this paper, we discuss the use of adaptive sampling of the underwater video to extract sharp still images for stitching and analysis, and creating mosaicked images by identifying and matching local scale-in-variant feature transform features using computationally efficient algorithms. Computer aided design models of suspected aircraft components were superimposed, and a feature common in multiple mosaicked images was used to identify a common feature between purported objects to assess goodness of it. The super-imposition method was replicated using landing gear from a reference aircraft and a rope of known dimensions, and favorably compared against the remotely operated vehicle (ROV) analysis results. [ABSTRACT FROM AUTHOR]

As an important indicator of cropping intensity, Cropping Index (CI) is defined as the number of peaks in the Vegetation Index (VI) time-series curve in a year. The existing CI mapping algorithms (e.g., the cross-fitting and the second order difference algorithm) are vulnerable to noise contained in the VI time series and need a priori knowledge and some extra constraints which could not be directly derived from the VI time-series data. In this paper, a shape-matching method is developed which can map CI directly from the preprocessed VI time-series data without the de-noising processes. This shape-matching method utilizes a decision-making process to find out the true peaks in the VI time-series curve based on a rank order mathematical morphology algorithm. The processing procedure involves five steps: (a) determination of the temporal moving window size, (b) detection of local maximum/minimum points, (c) exclusion of false maximum/minimum points, (d) determination of the threshold for the minimum growth amplitude, and (e) mapping of CI. This shape-matching method only needs two input parameters, the temporal moving window size and the threshold for the minimum growth amplitude, which can be both directly derived from the VI time-series data with some selected test pixels. Moreover, the response of the shape-matching method is relatively insensitive to the exact values of its design parameters, making it more flexible and effective in adapting to other regions. This new method is applied to map the CIs in Jiangsu Province, China, in 2010, based on the Enhanced Vegetation Index (EVI) time-series data derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) product. The overall CI mapping accuracy for the shape-matching method is 80 percent, which is much higher than the CI mapping accuracy of 60 percent for the second order difference algorithm. This shape-matching method can be further applied to other regions with a grid-search for its optimal parameters using some test pixels. [ABSTRACT FROM AUTHOR]