We consider the problem of biosonar landmark classification as an example of random and non-stationary signal classification in which finding robust and structure independent features for classification is not trivial. Timefrequency domain studies show that despite the seemingly randomness of those signals, there are local temporal similarities, independent of the position of occurrence in echo...

Target based sentiment classification is able to provide more fine grained sentiment analysis. In this paper, we propose a similarity based approach for this problem. Firstly, a new measure of PMI-TFIDF by combining PMI (Pointwise mutual information) and TF-IDF (term frequency-inverse document frequency) is proposed to measure the association of words for extending related features for a given ...

A recent neuro-spiking coding scheme for feature extraction from biosonar echoes of various plants is examined with a variety of stochastic classifiers. Feature vectors derived are employed in well-known stochastic classifiers, including nearest-neighborhood, single Gaussian and a Gaussian mixture with EM optimization. Classifiers’ performances are evaluated by using cross-validation and bootst...

Speech production variations due to perceptually induced stress contribute significantly to reduced speech processing performance. One approach for assessment of production variations due to stress is to formulate an objective classification of speaker stress based upon the acoustic speech signal. This study proposes an algorithm for estimation of the probability of perceptually induced stress....

In this paper, a new subband-based classification scheme is developed for classifying underwater mines and mine-like targets from the acoustic backscattered signals. The system consists of a feature extractor using wavelet packets in conjunction with linear predictive coding (LPC), a feature selection scheme, and a backpropagation neural-network classifier. The data set used for this study cons...

This paper describes an end-to-end method for extracting moving targets from a real-time video stream, classifying them into predefined categories according to imagebased properties, and then robustly tracking them. Moving targets are detected using the pixel wise difference between consecutive image frames. A classification metric is applied these targets with a temporal consistency constraint...

This paper proposes the use of new target vectors for MLP learning in EEG signal classification. A large Euclidean distance provided by orthogonal bipolar vectors as new target ones is explored to improve the learning and generalization abilities of MLPs. The data set consisted of EEG signals captured from normal individuals and individuals under brain-death protocol. Experimental results are r...

We proposed a spiking neural network (SNN) to detect moving target in video streams and classify them into real categorization in this paper. The proposed SNN uses spike trains to encoding information such as the gray value of pixels or feature parameters of the target, detects moving target by simulating the visual cortex for motion detection in biological system with axonal delays and classif...

Wireless sensor networks (WSNs) are autonomous networks that have been frequently deployed to collaboratively perform target localization and classification tasks. Their autonomous and collaborative features resemble the characteristics of agents. Such similarities inspire the development of heterogeneous agent architecture for WSN in this paper. The proposed agent architecture views WSN as mul...

The theory of compressive sensing (CS) enables the reconstruction of a sparse or compressible image or signal from a small set of linear, non-adaptive (even random) projections. However, in many applications, including object and target recognition, we are ultimately interested in making a decision about an image rather than computing a reconstruction. We propose here a framework for compressiv...