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We examine the problem of classifying biological sequences, and in particular the challenge of generalizing to novel input data. The high dimensionality of sequence results in an extremely sparsely populated input space. This motivates a need for regularization (a form of inductive bias), in order to achieve generalization. We discuss regularization in the context of regular Neural Networks and Deep...
Self-labeled training data in semi-supervised learning may contain much noise due to the initial insufficient training data, which may hurt the generalization ability of the final hypothesis. In this paper, we propose an Active Semi-Supervised framework with Data Editing(ASSDE) to improve sparsely labeled text classification. A data editing technique is used to identify and remove noise introduced...
Pilot power management is an important issue for coverage planning in UMTS systems. We consider the problem of minimizing the pilot power subject to the constraint of full service coverage. For this planning problem, which is NP-hard in complexity, effective methods being able to deal with large-scale networks of heterogeneous cell coverage patterns are highly desirable. We propose an integer linear...
This paper presents a new soft detection scheme for multi-input multi-output (MIMO) system with turbo codes, which inherently requires soft input information from the MIMO detection part. The MIMO system operates for spatial multiplexing, in which an exhaustive search is the only option for a maximum likelihood (ML) soft detection. Although the maximum performance can be achieved using the ML soft...
This paper proposes a generic criterion that defines the optimum number of basis functions for radial basis function (RBF) neural networks. The generalization performance of an RBF network relates to its prediction capability on independent test data. This performance gives a measure of the quality of the chosen model. An RBF network with an overly restricted basis gives poor predictions on new data,...
When comparing clustering results, any evaluation metric breaks down the available information to a single number. However, a lot of evaluation metrics are around, that are not always concordant nor easily interpretable in judging the agreement of a pair of clusterings. Here, we provide a tool to visually support the assessment of clustering results in comparing multiple clusterings. Along the way,...
Recently, Delay-Tolerant Space-Time Codes (DT-STCs) have been designed for cooperative communications. They are optimal in synchronous transmission and preserve their full-diversity order for arbitrary delays in asynchronous case. In this paper, we investigate these DT-STCs in femtocell networks where Femtocell Access Points can assist Macrocell Base Stations to transmit data information to users...
To date there is no practical means to evaluate the true word error probability (WEP) of a given turbo or LDPC code because typical decoders cannot achieve the performance of ML decoding. In this paper, we propose a viable methodology to establish tight bounds on the ML-decoding WEP for these codes through empirical simulation. Our framework centers on the efficient use of multiple-output decoding...
The design of the MAP decoder for signals in impulsive noise modeled using the symmetric α-stable (SαS) distribution is considered. The conventional MAP decoder, which optimizes the a posteriori probability for Gaussian noise, performs poorly in SαS noise. On the other hand, the optimal MAP decoder possesses impractical complexity due to the lack of a closed form expression of the probability density...
Knowledge of a network's topology and internal characteristics such as delay times or losses is crucial to maintain seamless operation of network services. Network tomography is a useful approach to infer such knowledge from end-to-end measurements between nodes at the periphery of the network, as it does not require cooperation of routers and other internal nodes. Most current tomography algorithms...
This paper introduces a new optimization technique termed as Recursive Ant Colony Optimization (RACO), a modified form of ant colony method (ACO), for finding best probable solution to a combinatorial problem. ACO simulates the social behavior of ants, optimizing their path from the nest to food source. The movement of an ant is random and the shortest path is found on the basis of the pheromone laid...
In this work, we study the localization of mobile signal emitters using time of arrival (TOA) measurement and additional urban street information. Two algorithms are proposed to improve the localization performance by integrating street information with the TOA measurement. The first algorithm exhaustively searches of all possible road paths. For each possible path, the source location is estimated...
The problem of sum-rate maximization in two-way amplify-and-forward (AF) multiple-input multiple-output (MIMO) relaying is considered. Mathematically, this problem is equivalent to the constrained maximization of the product of quadratic ratios that is a non-convex problem. Such problems appear also in many other applications. This problem can be further relaxed into a difference-of-convex functions...
This paper deals with different techniques for linear equalization of multipath channels with imperfect channel estimation (CE). We develop a unified framework based on Krylov subspace expansion, which allows us to compare the performance of the conjugate gradient (CG) method, diagonal loading (DL), and a hybrid scheme. Our analysis shows that the DL method generally outperforms its alternatives,...
We study the problem of learning ridge functions of the form f(x) = g(aT x), x ∈ ℝd, from random samples. Assuming g to be a twice continuously differentiable function, we leverage techniques from low rank matrix recovery literature to derive a uniform approximation guarantee for estimation of the ridge function f. Our new analysis removes the de facto compressibility assumption on the parameter a...
This paper introduces new minor (noise) subspace tracking (MST) algorithms based on the minimum noise subspace (MNS) technique. The latter has been introduced as a computationally efficient subspace method for blind system identification. We exploit here the principle of the MNS, to derive the most efficient algorithms for MST. The proposed method joins the advantages of low complexity and fast convergence...
A robust and reduced-complexity H-infinity (H-inf) channel estimator for multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems is addressed. The proposed estimator is realized by taking the following procedures: first, a simplified objective function is considered to guarantee the design simplicity of the H-inf channel estimator. Second, an expectation maximization...
Vector quantization schemes are proposed to extract secret keys from correlated wireless fading channels. By assuming that the channel between two terminals are reciprocal, its estimates can be used as the common randomness for generating secret keys at the two terminals. Most schemes in the literature assume that channels are independent over time and utilize scalar quantization on each element of...
In this paper, a novel plane fitting algorithm with low complexity and high accuracy is proposed to refine the depth maps generated by stereo matching. We first compute the confidence coefficient for each pixel in the depth map by cross checking and stable pixel calculation. According to the outlier pixel percentage for each segment, we choose one method, either proposed weighted least square error...
A depth-map is used to synthesize virtual texture views in the multi-view plus depth (MVD) format. In conventional video coding, a coded depth-map often suffers from compression artifacts along object boundaries, which have a negative effect on the quality of rendered images in the view synthesis process. To address this problem, we propose a depth-map boundary filtering technique to eliminate coding...
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