We consider the problem of weak signal detection in strong disturbance with a subspace structure. Unlike conventional subspace detection techniques relying on the availability of a large amount of training data, we consider a knowledge-aided (KA) subspace detection approach for training limited scenarios by incorporating partial prior knowledge of the subspace. A unique feature of the proposed ...

Artificial Immune Systems (AIS) have long been used in the field of computer security and especially in Intrusion Detection systems. Intrusion detection based on AISs falls into two main categories. The first generation of AIS is inspired from adaptive immune reactions but, the second one which is called danger theory focuses on both adaptive and innate reactions to build a more biologically-re...

In this paper, the application of the emerging compressed sensing (CS) theory and the geometric characteristics of the targets in radar images are investigated. Currently, the signal detection algorithms based on the CS theory require knowing the prior knowledge of the sparsity of target signals. However, in practice, it is often impossible to know the sparsity in advance. To solve this problem...

Contrast resolution and detail resolution are two important parameters in ultrasound imaging. This paper presents a new method to enhance these parameters, simultaneously. A parallel auxiliary beamformer has been employed whose weightings are such that an estimation of the leaked signal through the main beamformer is obtained. Then the output of main beamformer is modified according to the ...

The combination of multiple-input multiple-output (MIMO) signal processing with orthogonal frequency division multiplexing (OFDM) using Cyclic Prefix (CP) is regarded as a highly promising solution to achieving the data rates of next-generation wireless communication systems operating in frequency selective fading environments. In this paper, we propose a novel detection scheme for MIMO based t...

The generalized likelihood ratio test (GLRT) for a stochastic partially homogeneous model is proposed for adaptive signal detection. The stochastic partially homogeneous model generalizes the standard partially homogeneous model by treating the disturbance covariance matrix as a random matrix. Specifically, it assumes that, R, the disturbance covariance matrix of training signals, is a random m...

A detection method based on sensitization of a squared double differentiated signal is developed which discriminates the turbulent zones from laminar zones quite accurately. The procedure adopts a variable threshold and a variable hold time of the order of the Kolmogorov time scale. The output file so generated, includes all the information for further analysis of the turbulent signal.

Detection, estimation and filtering of desired signal in the presence of noise are some of the most common and practical problems in the analysis of time series. The analysis in the time domain often involves the comparison of two different signals and stochastic signals are usually more profitably analyzed in the time domain. Extraction of signal are considerably improves quality of signal. In...

We introduce a new model of adaptive criterion setting within a signal detection framework, and show how this provides psychological insights that allow us to segregate causes of suboptimality in perceptual learning. We apply this to a perceptual learning task for both neurotypical and autistic participants. The model parameters provide a bridge between the mechanisms of an aberrant precision a...

Thresholding plays a vital role in image segmentation. A wrong selection of threshold may lead to improper segmentation. Adaptive approach will be good enough for the selection of threshold in many occasions. This paper proposes an iterative approach for segmentation of traffic signal based on the prior knowledge about the size of the traffic signals. Region of Interest is found based on a tent...