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We present a solution to a specific version of one of the most fundamental computer science problem - the nearest neighbour problem (NN). The new, proposed variant of the NN problem is the multispace, dynamic, fixed-radius, all nearest neighbours problem, where the NN data structure handles queries that concern different subsets of input dimensions. In other words, solutions to this problem allow...
Adapting a model to changes in the data distribution is a relevant problem in machine learning and pattern recognition since such changes degrade the performances of classifiers trained on undistorted samples. This paper tackles the problem of domain adaptation in the context of hyper spectral satellite image analysis. We propose a new correlated correspondence algorithm based on network analysis...
We extend the standard choice model of multinomial logit model (MLM) into a hierarchical Bayesian model to simultaneously estimate the preferences of customers and the visibility of items from purchasing history. We say that an item has high visibility when customers well consider that item as a candidate before making a choice. We design two algorithms for estimating the parameters of the proposed...
With this paper, we present an algorithm for the anti-aliased Euclidean distance transform, based on wave front propagation, that can easily be extended to images of arbitrary dimensionality and sampling lattices. We investigate the behavior and weaknesses of the algorithm, applied to synthetic two-dimensional area-sampled images, and suggest an enhancement to the original method, with complexity...
A new algorithm evaluation method is presented for coning and large angular rate rotation coexisting environments. Normalized quaternion and its high order items expressed with the rotation vector are used for theoretical error analysis. The performances of two types of different coning algorithms are discussed, one based on conventional frequency-series and the other based on Savage's explicit frequency...
An objective of blind source separation (BSS) is to recover potential source signals from their mixtures without a prior knowledge of the mixing process. In this paper, a new underdetermined blind source separation (UDBSS) approach, based on the local mean decomposition (LMD) method and the AMUSE algorithm, is proposed. To make the UDBSS problem simpler, some extra observation signals are first constructed...
Three-dimensional virtual surgery has received a significant amount of attention in recent years. It is necessary to mark organ lesions and add ablation path on virtual organ model. The contribution of this paper is that we propose and implement a new shortest path algorithm based on PLY triangular mesh model. The algorithm follows the topology structure of model and is simple to implement. We add...
An algorithm of the recursive parametric identification is analyzed, oriented to applying within noise-free systems. Its analytical form, conditions of the convergence and applications are specified. Theoretical inferences are confirmed by simulation results.
Minimum mean square error (MMSE) algorithm is near-optimal for uplink large-scale MIMO systems, but involves high-complexity matrix inversion. In this paper, based on a special property of uplink large-scale MIMO systems that the filtering matrix of the MMSE algorithm is symmetric positive definite as we will prove, we propose to exploit the Gauss-Seidel method to iteratively realize the MMSE algorithm...
Direct single shooting for optimal control by means of gradient-based numerical optimizers requires sensitivity information. We provide this sensitivity information by means of the full algorithmic differentiation (AD), also referred to as first-discretize-then-differentiate, of a stiffly accurate Rosenbrock-type method with variable step size. In this context, full algorithmic differentiation means...
Full search (FS) motion estimation algorithm compares the current block with every possible block of the reference block to find the most accurate motion vector (MV) with the least matching error by the mean square error (MSE). Using FS motion estimation algorithm in real time applications is impractical because of the cumbersome computations. In order to decrease the amount of significant computation...
Standard interpolatory subspaces for model reduction of linear descriptor systems may produce unbounded ℋ2 or ℋ∞ error. In this paper we investigate this issue and discuss modified interpolatory subspaces based on spectral projectors that ensure bounded errors. In the special case of index-3 descriptor systems, we show how to transform the system to an equivalent system that enables the use of standard...
The use of machine-learning in neuroimaging offers new perspectives in early diagnosis and prognosis of brain diseases. Although such multivariate methods can capture complex relationships in the data, traditional approaches provide irregular (ℓ2 penalty) or scattered (£1 penalty) predictive pattern with a very limited relevance. A penalty like Total Variation (TV) that exploits the natural 3D structure...
We present a new efficient algorithm for the search version of the approximate Closest Vector Problem with Preprocessing (CVPP). Our algorithm achieves an approximation factor of O(n/sqrt{log n}), improving on the previous best of O(n^{1.5}) due to Lag arias, Lenstra, and Schnorr {hkzbabai}. We also show, somewhat surprisingly, that only O(n) vectors of preprocessing advice are sufficient to solve...
Robustness of nonlinear systems can be analyzed by computing robust forward invariant sets (RFIS). The smallest RFIS provides the least conservative estimate of system performance under perturbations. However, computation of the smallest RFIS through brute force search can be a difficult task. We develop a novel algorithm to find the smallest RFIS for two-dimensional systems subjected to bounded additive...
We consider physical-layer security in a novel MISO cooperative overlay cognitive radio network (CRN) with a single eavesdropper. We aim to design an artificial noise (AN) aided secondary transmit strategy to maximize the joint achievable secrecy rate of both primary and secondary links, subject to a global secondary transmit power constraint and guaranteeing any transmission of secondary should at...
In the cooperative data exchange problem, a group of wireless clients use a shared broadcast channel to exchange packets from a given set X. Each of the clients has a subset of packets in X available to it as a side information. The clients transmit linear combinations of the packets one after the other, until all clients are able to recover all packets in X. This problem arises naturally in many...
Multidimensional stochastic optimization plays an important role in analysis and control of many technical systems. To solve the challenging problems of multidimensional optimization, it was suggested to use the randomized algorithms of stochastic approximation with perturbed input which have simple forms and provide consistent estimates of the unknown parameters for observations under “almost arbitrary”...
In the past decade, adaptive dynamic programming (ADP) has been widely used to realize online learning tracking control of dynamical systems, where neural networks with manually designed features are commonly used. In order to improve the generalization capability and learning efficiency of ADP, this paper presents a novel framework of ADP with sparse kernel machines by integrating kernel methods...
The exact value iteration for POMDP planning is so complex that we use approximation to solve the problems in practice. In recent years, point-based algorithm has become a research hotspot. PBVI algorithm selects successors that improve the worst case density as rapidly as possible. The smaller the gaps between all belief points, the faster the value function converges to the optimal solutions. PBVI...
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