Search

Hera mission is the European Space Agency's contribution to the international collaboration with NASA for the planetary defence, i.e. Asteroid Impact Deflection Assessment aiming to deflect the trajectory of its target binary asteroid system (65803) Didymos. The Early Characterization Phase and the Detailed Characterization Phase of Hera mission are two phases of the proximity operations with the objective to physically and dynamically characterize Didymos. During these phases, an Image Processing algorithm is required to estimate the position of the centroid of the primary to enable Line of Sight navigation. However, the performance of standard Image Processing algorithms is affected by the disturbances of the image, such as poor illumination conditions, the presence of external bodies and the irregular shape of the target. This research addresses this challenge by developing a robust Convolutional Neural Networks-based Image Processing algorithm to estimate the position of the centroids of Didymos and its moon Dimorphos, the pseudorange from the primary and the Sun phase angle. The training, validation and testing datasets are generated with the software Planet and Asteroid Natural scene Generation Utility using the Early Characterization Phase and the Detailed Characterization Phase trajectories as case scenario. The position in the image of the centroids of Didymos and Dimorphos is estimated using their respective position vectors. To estimate the pseudorange, the developed algorithm regresses a set of keypoints on the visible border of Didymos and evaluates its apparent radius. For the Sun phase angle, the pixel position of the subsolar point of the primary is leveraged. The High-Resolution Network is the Convolutional Neural Network architecture applied to detect keypoints with superior spatial precision. Even with the considered disturbances, the analysis shows that the proposed algorithm is able to provide an accurate estimation of the mentioned outputs for all the Early Characterization Phase trajectory and for 77.33% of the Detailed Characterization Phase trajectory, improving the robustness and autonomy of the mission navigation. • The Image Processing of Hera is investigated using Convolutional Neural Networks. • The centroid regression of both Didymos and Dimorphos is solved. • The range and the Sun phase angle are estimated using the pinhole camera model. • The subsolar point position on the surface of Didymos is estimated. [ABSTRACT FROM AUTHOR]

Consider the recent upswing in interest around multi-view clustering procedures. Such methods aim to boost clustering efficiency by leveraging information from numerous perspectives. Much research has been devoted to tensorial representation to exploit high-order correlations underlying disparate views while preserving the local geometric structure inside each view. Our research introduces a novel multi-view clustering approach. This approach creates a 3rd-order tensor, assimilating features from all perspectives. We use the t-product in the tensor space to generate the self-representation tensor from the tensorial data. We incorporate the ℓ 1 -based sliced-Laplacian regularization to increase our model's resilience and introduce a fresh column-wise sparse norm: the ℓ 2 , p -norm with 0 < p < 1. This norm displays attributes of invariance, continuity, and differentiability. We present a closed-form answer to the ℓ 2 , p -regularized shrinkage problem, broadening its relevance to other generalized problems. Simultaneously, we propose a tensorial arctan -function as an improved surrogate for the tensor rank. This function has proven more proficient at assessing consistency across multiple viewpoints. By integrating these two components, we formulate an effective algorithm that refines our suggested model, ensuring that the constructed sequence gravitates toward the stationary KKT point. Our team conducts extensive experiments on various datasets to evaluate our model's effectiveness, spanning diverse situations and scales. Results from these experiments emphasize that our approach establishes a novel performance standard. • We integrate ℓ 1 -sliced Laplacian with ℓ 2 , p regularization for multi-view clustering. It reduces redundancy and enhances outlier resilience, achieving an efficient, innovative solution. • An algorithm with augmented Lagrange multipliers tackles multi-view clustering, providing solvable sub-problems with closed-form solutions. • Within the tensor framework adept at leveraging intrinsic cluster structures, our algorithm approaches an optimal Karush–Kuhn–Tucker (KKT) point. • Extensive experiments on four datasets demonstrate our technique's superiority over contemporary methods through thorough comparisons. [ABSTRACT FROM AUTHOR]

In this work, the application of a new strategy called NWFE ensemble (nonparametric weighted feature extraction ensemble) method is proposed. Subspace-supervised projections based on NWFE are incorporated into the construction of ensembles of classifiers to facilitate the correct classification of wrongly classified instances without being detrimental to the overall performance of the ensemble. The performance of NWFE is investigated with a c-Jun N-terminal kinase-3 inhibitor benchmark dataset using different chemical compound representation models. Compared with the standard method, the results obtained show that the applied method improves the prediction performance using two classifiers based on decision trees and support vector machines. Image 1 • The new nonparametric weighted feature extraction (NWFE) method is proposed. • It improves classifier performance compared to standard ensemble method. • The fast clustering-based feature selection algorithm showed be appropriated to find an optimal subset of descriptors. • The results were validated using different molecular representation, with JNK3 inhibition models as an example. [ABSTRACT FROM AUTHOR]

Highlights • Although the second-order approximation closely matches the true distribution, it will not lead to a closed-form expression for the likelihood function, while the first-order approximation results in a simple closed-form expression at the expense of some accuracy loss. • Although the likelihood function deviates from its first-order approximation, the position of their maxima are close to each other, so the first-order approximation still results in an appropriate position estimate. • In the presence of orientation uncertainty, the proposed estimator outperforms the state-of-the-art estimators. Abstract As the positioning accuracy of a visible light positioning (VLP) system is highly susceptible to changes in the orientation of the receiver, accurate knowledge of the receiver orientation is required. In practice, the orientation of the receiver is estimated with an external orientation estimation device. However, these devices generally suffer from drift and misalignment, causing an uncertainty in the measured orientation that will degrade the performance of standard positioning algorithms. In this paper, we derive a novel positioning algorithm that takes into account the effect of the orientation uncertainty. To this end, we need to cope with the non-linear relationship between the received signal strength (RSS) and the orientation uncertainty, which makes the likelihood function of the RSS, required to derive the maximum likelihood (ML) estimator, hard to obtain. To solve this issue, we consider the first and second-order Taylor series expansion of the RSS. Although the accuracy of the second-order approximation is better than the first-order approximation, the first-order approximation results in a closed-form expression for the likelihood function, while this is not possible with the second-order approximation. Because of this, we derive the ML estimator using the first-order approximation, and employ the multivariate gradient descent algorithm to obtain the position estimate. Computer simulations show that the proposed algorithm outperforms state-of-the-art VLP algorithms subject to orientation uncertainty. [ABSTRACT FROM AUTHOR]

• Algorithms to calculate center of contrast targets are proprietary. • NIST's new algorithm enables transparency. • NIST's algorithm is robust and precise compared to OEM software. Terrestrial Laser Scanners (TLSs) are used in a variety of large scale scanning applications such as reverse engineering, assembly of aircraft or ships and surveying. Contrast targets are used with such instruments for enabling scene registration or to establish a scale when used on a scale bar. Currently, the algorithms to calculate the centers of contrast targets (CCT) are either proprietary to the original equipment manufacturers (OEMs) or not precise and accurate. Some of these algorithms may also operate only on OEM's proprietary data format. To overcome these limitations, a novel algorithm was developed at the National Institute of Standards and Technology (NIST) to calculate the center of contrast targets. Several targets in various orientations were scanned by one TLS and their centers were calculated by both the NIST algorithm and one OEM software. The results show that the NIST algorithm is robust, addresses many data quality issues and has better precision than the OEM software in most cases. [ABSTRACT FROM AUTHOR]

• A novel modified Jaya algorithm is developed to identify structural damage. • A multi-sample objective function embedding L 0.5 sparsity regularization is presented. • The coupling effects of three types of uncertainties are considered during the calculation. • Satisfactory identifications can be obtained by the proposed method. This study develops an improved Jaya algorithm for structural damage identification considering various uncertainties using vibration data. Most studies consider uncertainties, such as measurement noise, modeling uncertainties, and temperature variations separately but few on their coupling effects. On the other hand, the Jaya algorithm may trap local minimums in optimizing complex objective functions. Therefore, a novel modified Jaya algorithm is developed by integrating the one-step K-means clustering, Hooke–Jeeves pattern search, and linear population reduction strategies. The three modification strategies greatly improve the global optimization performance of the standard Jaya algorithm, which is verified by a series of high-dimension test functions. An L 0.5 regularization is applied to improve the ill-posedness of the damage identification problem and ensure the sparsity of the solution. Numerical and experimental studies on two structures show that the proposed algorithm can identify the structural damage accurately, even considering the coupling effects of various types of uncertainties. [ABSTRACT FROM AUTHOR]

• A discrete kernel estimator is developed to estimate unknown, time-varying target birth cardinality distribution. • Exponential weighted moving average is employed to address the time-varying characteristics of birth cardinality distribution. • An improved Gaussian Mixture CPHD filter with birth cardinality distribution and birth intensity estimated online is designed for multi-target tracking. Cardinalized probability hypothesis density (CPHD) filter is a promising algorithm for multi-target tracking. However, the performance of standard CPHD is degraded when the target birth process is unknown and dynamically changing. To remove this limitation, a discrete kernel estimator in conjunction with exponential weighted moving average scheme is introduced in this study to estimate the time-varying target birth cardinality distribution (i.e. , probability distribution on number of newborn targets appearing during one sampling time) at each processing step. The target birth intensity is then updated according to the resulting estimated birth cardinality distribution. The estimated birth intensity and cardinality distribution can be employed by a tracker based on Gaussian mixture CPHD (GMCPHD) to modulate its filtering strength for target tracking. The performance of the proposed framework is demonstrated by both numerical simulations and experiment on a video surveillance dataset. Results show that the proposed algorithm can reduce the delay of standard GMCPHD filter's response to the changes in the number of targets, and thus improve the accuracy of cardinality estimates. Satisfactory estimation of cardinality can be obtained even when there is a fast change in the rate of birth. [ABSTRACT FROM AUTHOR]

Abstract: Spike sorting algorithms aim at decomposing complex extracellular signals to independent events from single neurons in the electrode''s vicinity. The decision about the actual number of active neurons is still an open issue, with sparsely firing neurons and background activity the most influencing factors. We introduce a graph-theoretical algorithmic procedure that successfully resolves this issue. Dimensionality reduction coupled with a modern, efficient and progressively executable clustering routine proved to achieve higher performance standards than popular spike sorting methods. Our method is validated extensively using simulated data for different levels of SNR. [Copyright &y& Elsevier]

Abstract: It is well known that neighbor discovery is a critical component of proactive routing protocols in wireless ad hoc networks. However there is no formal study on the performance of proposed neighbor discovery mechanisms. This paper provides a detailed model of key performance metrics of neighbor discovery algorithms, such as node degree and the distribution of the distance to symmetric neighbors. The model accounts for the dynamics of neighbor discovery as well as node density, mobility, radio and interference. The paper demonstrates a method for applying these models to the evaluation of global network metrics. In particular, it describes a model of network connectivity. Validation of the models shows that the degree estimate agrees, within 5% error, with simulations for the considered scenarios. The work presented in this paper serves as a basis for the performance evaluation of remaining performance metrics of routing protocols, vital for large scale deployment of ad hoc networks. [Copyright &y& Elsevier]

Abstract: Automated image quality assessment is highly desirable to evaluate the performance of various image fusion algorithms for night vision applications. In this paper we propose a perceptual quality evaluation method for image fusion which is based on human visual system (HVS) models. Our method assesses the image quality of a fused image using the following steps. First, the source and fused images are filtered by a contrast sensitivity function (CSF) after which a local contrast map is computed for each image. Second, a contrast preservation map is generated to describe the relationship between the fused image and each source image. Finally, the preservation maps are weighted by a saliency map to obtain an overall quality map. The mean of the quality map indicates the quality of the fused image. Experimental results compare the predictions made by our algorithm with human perceptual evaluations for several different parameter settings in our algorithm. The most popular existing algorithms are also evaluated. For some specific parameter settings, we find our algorithm provides better predictions, which are more closely matched to human perceptual evaluations, than the existing algorithms. The evaluations focus on the night vision application, but the algorithm we propose is applicable to other applications also. [Copyright &y& Elsevier]

The objective of this study was to investigate the energy and angle dependence of the RPL dosimetry system in use at the Paul Scherrer Institute (GBFJ-01 dosimeter, FGD 660 reader, and CDEC-Easy algorithm) for the operational dose quantity H *(10) and compare the results with the requirements from the Swiss dosimetry regulation. The energy response was determined for photon energies between 12 keV (N-15) and 1250 keV (60Co) and the angle response was determined for 65 keV (N-80) and 662 keV (137Cs). The data obtained in this study show that the RPL dosimeters satisfy the requirements of the Swiss dosimetry regulation for the energy dependence, but only partially satisfy the requirements for the angle dependence. For directional irradiations around ±90° the dosimeter response can deviate by more than the 20% allowed by the Swiss dosimetry regulation. If a more realistic testing scenario for the 90° irradiation is used, which includes full rotation of the dosimeter around its reference direction, the response is within 10% of the response at 0° irradiation. Full tests according to the IEC 62387 were not performed and, therefore, performance against this standard cannot be fully evaluated from this data. Nevertheless, the data indicate that the asymmetric dosimeter design would have difficulties satisfying the IEC 62387 standard for H *(10) for low energy photons and large angle irradiation, in spite of the allowance of full rotation for the 90° irradiations. • Energy and angle dependence of a radiophotoluminescence dosimetry system was investigated for environmental dosimetry. • The dosimetric requirements of the Swiss radiation protection legislation are fulfilled for the energy dependence. • The RPL relative response deviates by more than 20% for radiation incident with angles around 90°. • Full rotation scenario proposed by the IEC 62387 for 90° irradiation improves the dosimeter response. [ABSTRACT FROM AUTHOR]

• We propose an improved ℓ 0 -LMS (ℓ 0 -ILMS) algorithm for sparse system identification. • The convergence condition of the proposed ℓ 0 -ILMS algorithm on step size is derived. • The parameter selection criterion for optimal steady-state mean-square deviation (MSD) is discussed. • Compared to ℓ 0 -LMS algorithm, the steady-state MSD of the proposed ℓ 0 -ILMS algorithm is lower and less sensitive to tuning parameters and measurement noise power. The ℓ 0 -norm-constraint LMS (ℓ 0 -LMS) algorithm is one of the widely used sparse LMS algorithms for the identification of sparse system, and its performance is quite attractive compared to other precursors. However, ℓ 0 -LMS is still confronted with some limitations on the optimal parameter selection and the estimated coefficient accuracy of sparse system identification. In this paper, we proposed an improved ℓ 0 -LMS (ℓ 0 -ILMS) algorithm to address these limitations. The convergence condition and the parameter selection rules for optimal steady-state mean-square deviation (MSD) of ℓ 0 -ILMS are discussed. Compared with ℓ 0 -LMS, the steady-state MSD of ℓ 0 -ILMS is lower and less sensitive to the tuning parameters and measurement noise power. Numerical simulations comparing the performance of standard LMS, ℓ 0 -LMS and ℓ 0 -ILMS demonstrate the effectiveness of ℓ 0 -ILMS. [ABSTRACT FROM AUTHOR]

Teach and Repeat (T&R) refers to the technology that allows a robot to autonomously follow a previously traversed route, in a natural scene and using only its onboard sensors. In this paper we present a Visual-Inertial Teach and Repeat (VI-T&R) algorithm that uses stereo and inertial data and targets Unmanned Aerial Vehicles with limited on-board computational resources. We propose a tightly-coupled relative formulation of the visual-inertial constraints that is tailored to the T&R application. In order to achieve real-time operation on limited hardware, we reduce the problem to motion-only visual-inertial Bundle Adjustment. In the repeat stage, we detail how to generate a trajectory and smoothly follow it with a constantly changing relative frame. The proposed method is validated in simulated environments, using real sensor data from the public EuRoC dataset, and using our own robotic setup and closed-loop control. Our experimental results demonstrate high accuracy and real-time performance both on a standard desktop system and on a low-cost Odroid X-U4 embedded computer. • This paper proposes a visual-inertial teach and replay navigation method for unmanned aerial vehicles. • The basis of the method is a tightly-coupled visual-inertial odometry under a relative formulation. • We achieve real-time operation even on a resource-constrained hardware platform such as Odroid X-U4. [ABSTRACT FROM AUTHOR]

• A modified bat algorithm based on new position selection strategies is proposed. • The modified bat algorithm is hybrized with differantial evolution algorithm. • Exploration and exploitation abilities of the bat algorithm are balanced. • The algorithms are tested on classical benchmark functions and CEC test suites. • Hybrid method improved significantly the performance of the bat algorithm. The bat algorithm (BA) is one of the metaheuristic algorithms that are used to solve optimization problems. The differential evolution (DE) algorithm is also applied to optimization problems and has successful exploitation ability. In this study, an advanced modified BA (MBA) algorithm was initially proposed by making some modifications to improve the exploration and exploitation abilities of the BA. A hybrid system (MBADE), involving the use of the MBA in conjunction with the DE, was then suggested in order to further improve the exploitation potential and provide superior performance in various test problem clusters. The proposed hybrid system uses a common population, and the algorithm to be applied to the individual is selected on the basis of a probability value, which is calculated in accordance with the performance of the algorithms; thus, the probability of applying a successful algorithm is increased. The performance of the proposed method was tested on functions that have frequently been studied, such as classical benchmark functions, small-scale CEC 2005 benchmark functions, large-scale CEC 2010 benchmark functions, and CEC 2011 real-world problems. The obtained results were compared with the results obtained from the standard BA and other findings in the literature and interpreted by means of statistical tests. The developed hybrid system showed superior performance to the standard BA in all test problem sets and produced more acceptable results when compared to the published data for the existing algorithms. In addition, the contribution of the MBA and DE algorithms to the hybrid system was examined. [ABSTRACT FROM AUTHOR]