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Linear Discriminant Analysis (LDA) is widely-used for supervised dimension reduction and linear classification. Classical LDA, however, suffers from the ill-posed estimation problem on data with high dimension and low sample size (HDLSS). To cope with this problem, in this paper, we propose an Adaptive Wishart Discriminant Analysis (AWDA) for classification, that makes predictions in an ensemble way...
In given paper offered methods and algorithms of determination of complexity of test questions for formation a database system of the adaptive test control for objective estimation of knowledge of students (pupils) in the course of training learning systems.
Background: Software effort estimates are necessary and critical at an early phase for decision makers to establish initial budgets, and in a government context to select the most competitive bidder for a contract. The challenge is that estimated software requirements is the only size information available at this stage, compounded with the newly increasing adoption of agile processes in the US DoD...
Starting from a hypothetical open shop production quantity estimation problem with weakly formulated objective function, this paper comparatively discusses a manual heuristic solution and an IBM-ILOG based numerical solution. Problem complexity is managed by specific partitioning strategies in the construction of heuristic solution instances. The study may serve as starting point in the systematic...
In the set disjointess problem, we have k players, each with a private input X^i ⊆ [n], and the goal is for the players to determine whether or not their sets have a global intersection. The players communicate over a shared blackboard, and we charge them for each bit that they write on the board.We study the trade-off between the number of interaction rounds we allow the players, and the...
The paper addresses a fast implementation algorithm about distributed fusion with CPHD filter. An implementation method of distributed fusion based on maximum probability association (MPA), called MPA-DF, is presented. Though the performance of MPA-DF is a little worse than traditional Generalization Covariance Intersection (GCI) distributed fusion method, called GCI-DF, the former needs lower computational...
Given a set of points P⊄ R^d and a kernel k, the Kernel Density Estimate at a point x∊R^d is defined as \mathrm{KDE}_{P}(x)=\frac{1}{|P|}\sum_{y\in P} k(x,y). We study the problem of designing a data structure that given a data set P and a kernel function, returns approximations to the kernel density} of a query point in sublinear time}. We introduce a class of unbiased estimators...
In this paper, the problem of echo cancellation in long acoustic impulse responses (AIRs) is highlighted. Three of the mostly-used recent NLMS-based sparse adaptive filtering algorithms are presented; and their performances in the context of acoustic echo cancellation (AEC) are studied and compared. The algorithms of interest include the improved proportionate normalized least mean square (IPNLMS),...
The problem of computing the Fourier Transform of a signal whose spectrum is dominated by a small number k of frequencies quickly and using a small number of samples of the signal in time domain (the Sparse FFT problem) has received significant attention recently. It is known how to approximately compute the k-sparse Fourier transform in \approx k\log^2 n time [Hassanieh et alSTOC12], or using the...
We propose an efficient meta-algorithm for Bayesian inference problems based on low-degree polynomials, semidefinite programming, and tensor decomposition. The algorithm is inspired by recent lower bound constructions for sum-of-squares and related to the method of moments. Our focus is on sample complexity bounds that are as tight as possible (up to additive lower-order terms) and often achieve statistical...
Generalized frequency division multiplexing (GFDM) with the flexible structure is one of the promising candidates for the fifth generation wireless communication system. This paper focuses on training sequence design that is used in the estimation of in-phase(I) and quadrature(Q) imbalance parameters on GFDM receivers as well as frequency selective channel. Combining with the structure of low complexity...
Most of real-life signals are nonGaussian, therefore the linear parametrization/modeling methods, suitable for Gaussian signals are neither adequate nor optimal. Using a nonlinear approach, we can significantly improve filtering results. However, the price that has to be paid is the complexity of the nonlinear treatment. In this paper we consider the nonlinear Schurtype orthogonal transformations...
We present nonlinear Schur-type orthogonal representations of nonlinear filters of the Volterra-Wiener class for higher-order and non-Gaussian stochastic processes, and propose efficient and numerically attractive solutions of the orthogonal transformations (innovations, stochastic modeling) for this class of processes.
The Frisch-Waugh-Lovell (FWL) Recursive Least Squares (RLS) algorithm has been recently proposed as an RLS algorithm with lower computational cost and better numerical properties. We propose a VHDL implementation that has been successfully implemented on a Xilinx Virtex-7 FPGA. The FWL RLS algorithm has a complexity of L2 + O(L) products, instead of 1.5L2 O(L) as in conventional RLS algorithms. Because...
Use case analysis has been widely adopted in modern software engineering due to its strength in capturing the functional requirements of a system. It is often done with a UML use case model that formalizes the interactions between actors and a system in the requirements elicitation iteration, and with architectural alternatives explored and user interface details specified in the following analysis...
Clock frequency estimation is a key issue in many signal processing applications, e.g. network clock estimation in wireless sensor networks. In wireless systems or harsh environments, it is likely that clock events can be missed and, therefore, the observed process has to be treated as a sparse periodic process. To parameterize the clock, current research is applying periodogram estimators at a complexity...
Good planning and managing software test process require accurate estimation of software test effort. This becomes particularly significant when validation and verification activities are to be performed by an independent organization. This study presents a systematic literature review and a follow up industrial survey, which was performed to investigate the state of the art on software test effort...
Power management becomes an integral part of hardware-systems design. In modern complex systems, the powermanagement design is not a simple task and it is quite difficult to evaluate whether the designed strategy is the best. In this paper, we propose a new method for overhead estimation of the required power-management unit, based on system-level abstract specification. It enables a designer to explore...
In this paper, a joint-domain dictionary learning-based error concealment approach is proposed. We extend the existing joint-domain dictionary learning methods to make more suitable scheme for error concealment. The main idea is to train an offline over-complete dictionary pair and learn two mapping matrices using two sets from the original and corrupted patches in a coupled manner, such that the...
High Efficiency Video Coding (HEVC) employs the complex rate-distortion optimization (RDO) to choose the optimal mode decison which leads to the drastical increase of computation complexity. In order to solve the problem, this paper proposed a new RD estimation method for HEVC intra mode decision that uses transformed coefficients to do the rate-distortion optimization. For the rate part, the method...
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