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Traditional adaptive beamformers have robustness just for specific error condition. They suffer performance degradation in the presence of multiple errors such as sample covariance matrix estimation error and steering vector mismatch. In this article, a new robust adaptive beamforming algorithm based on jointly estimating the covariance matrix and steering vector mismatch is proposed to overcome both...
This paper presents a new target tracking method. The presented method which named marginalized cubature Kalman filter is based on standard cubature Kalman filter and marginalized moment estimator. The marginalized moment estimator uses sigma-points sampling and Guass-Hermite integration to estimate the mean and covariance. The proposed algorithm which is called MCKF in short, uses marginalized moment...
Based on the interpolated all-phase DFT, a new parameters estimation algorithm for the exponential signal is presented. The proposed algorithm utilizes the all-phase preprocessing unit to construct a new signal sequence by continuously cycle shifting signal samples and summing up N buffered exponential signal sample sequences, and estimate the parameters of the exponential signal based on the interpolated...
Detection of moving text of different orientations in video is challenging because of low resolution and complex background of video. In this paper, we propose a method based on motion vectors to identify the moving blocks which have linear and constant velocity. For each block, we compute moments and use k-means clustering algorithm to extract text candidate. We introduce a new criterion based on...
There is the over-sampling method which is one of the methods for realizing the blind identification. In the conventional method, the unknown system is usually assumed by FIR system and the estimation filter is also used by FIR filter. However, if a target system has long impulse length in many real environment, we may have to assume IIR system for the unknown system. If we use FIR filters to estimate...
Anomaly detection starts from a model of normalbehavior and classifies departures from this model as anomalies. This paper introduces a statistical non-parametric approach for anomaly detection that is based on a multivariate extension of the Poisson point process model for univariateextremes. The method is demonstrated on both a synthetic and a real-world data set, the latter being an unbalanced...
This paper studies the problem of distributed parameter estimation in wireless sensor network under energy constraints. Optimization formulas that find the optimal sensors' observations transmission that guarantee the best estimation performance from the available energy are derived. The network consists of sensors that are deployed over an area at random. Sensors' observations are noisy measurements...
In this work, a measure of similarity based on the matching of multivariate probability density functions (PDFs) is proposed. In consonance with the information theoretic learning (ITL) framework, the affinity comparison between the joint PDFs is performed using a quadratic distance, estimated with the aid of the Parzen window method with Gaussian kernels. The motivation underlying this proposal is...
Inter carrier interference and multiple access interference are induced in uplink OFDMA systems due to Carrier Frequency Offset (CFO) which is a mismatch between the transmitter and receiver frequencies that in turn affects the orthogonality among subcarriers. A different CFO value is experienced by each user data and each of these CFO values needs to be estimated and corrected. A novel Extended Viterbi-and-Viterbi...
Direction-of-arrival (DOA) estimation of sound sources by using microphone array is an important technique. Its applications are teleconference system and human-robot communicating system and so on. This paper describes a DOA estimation method using array with arbitrary 3-dimentional configuration. The problem discussed in this study is the case in the presence of spatial aliasing. The proposed method...
In this paper, we analyze joint channel, carrier frequency offset (CFO), and phase noise estimation in orthogonal frequency division multiplexing (OFDM) relaying networks. To achieve this goal, a detailed transmission framework involving both training and data symbols is first presented. Next, a novel algorithm that applies the training symbols to jointly estimate the channel responses, CFO, and phase...
In this paper, we investigate the benefit of inter-node collaboration in multidimensional location estimation. In particular, for networks with reference nodes at known locations and source nodes whose locations are unknown and to be estimated, we establish the value of collaboration for source node position estimation by presenting proof of a decreasing Cramér-Rao lower bound as additional source...
This paper considers the problem of estimating the symmetric and Toeplitz covariance matrix of compressive samples of wide sense stationary random vectors. A new structured deterministic sampling method known as the "Generalized Nested Sampling" is introduced which enables compressive quadratic sampling of symmetric Toeplitz matrices., by fully exploiting the inherent redundancy in the Toeplitz...
This paper proposes a simple sensing and estimation framework, called one-bit sketching, to faithfully recover the principal subspace of a data stream or dataset from a set of one-bit measurements collected at distributed sensors. Each bit indicates the comparison outcome between energy projections of the local sample covariance matrix over a pair of random directions. By leveraging low-dimensional...
In this paper, we propose a new approach to data density estimation based on the total sum of distances from a data point, and the recently introduced Recursive Density Estimation technique. It is suitable for autonomous real-time video analytics problems, and has been specifically designed to be executed very fast; it uses integer-only arithmetic with no divisions and no floating point numbers (no...
We study diffusion based channel estimation in distributed architectures suitable for various communication applications such as cognitive radios. Although the demand for distributed processing is steadily growing, these architectures require a substantial amount of communication among their nodes (or processing elements) causing significant energy consumption and increase in carbon footprint. Due...
Single-input and single-output (SISO) controlled autoregressive moving average system by using a scalar factor input-output data is considered. Through data scaling, a simple identification technique is obtained. Using input-output scaling factors a data Recursive Least Squares (RLS) method for estimating the parameters of a linear model and contemporary sinusoidal disturbance detection is deduced...
This paper deals with an application of stability approach. In such a case, it is very important to be able to estimate the maximum tracking error induced by uncertainties and/or bounded perturbations. In this paper, we present a new approach which enables the estimation by overvaluation of this error between the evolution of Lur'e and Postnikov systems and of its model. It is based on the use of...
Voting Advice Applications (VAAs) are online tools that match the policy preferences of voters' with the policy positions of political parties or candidates. A recent, innovative extension of VAAs has been to draw on the field of computer science to introduce a social vote recommendation borrowing the basic principles of collaborative filtering. The latter takes advantage of the community of VAA users...
An emotion orientated intelligent interface consists of Emotion Generating Calculations (EGC) and Mental State Transition Network (MSTN). We have developed the Android EGC application software which the agent works to evaluate the feelings in the conversation. In this paper, we develop the tourist information system which can estimate the user's feelings at the sightseeing spot. The system can recommend...
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