Natasa Reljin

Feature extraction and classification of EEG sleep recordings in newborns

Visual evaluation of long-term EEG recordings is very difficult, time consuming and subjective process. This paper aims to present the research and development of a comprehensive scheme for computer-assisted recognition of behavioral states of sleep in newborns. In clinical practice, the ratio of behavioral states (wakefulness, quiet and active sleep) is used as an important indicator of the brain maturation. Analysis was performed offline, on real clinical data, with the assumption that each EEG channel in recording was independent from others and equally important for analysis and classification. The proposed solution comprises several consecutive steps of signal preprocessing and processing, with focus on segmentation, feature extraction and selection, and classification. Performed classification was based on linear support vector machines and performance was evaluated through cross validation. Obtained results can be used as a reference for developing or enhancing neonatal sleep EEG/PSG classification algorithms.

Small Moving Targets Detection Using Outlier Detection Algorithms

Recent research in motion detection has shown that various outlier detection methods could be used for efficient detection of small moving targets. These algorithms detect moving objects as outliers in a properly defined attribute space, where outlier is defined as an object distinct from the objects in its neighborhood. In this paper, we compare the performance of two incremental outlier detection algorithms, namely the incremental connectivity-based outlier factor and the incremental local outlier factor to modified Stauffer-Grimson algorithm. Each video sequence is represented with spatial-temporal blocks extracted from the raw video. Principal component analysis (PCA) is applied on these blocks in order to reduce the dimensionality of extracted data. Extensive experiments performed on several data sets, including infrared sequences from OSU Thermal Pedestrian Database repository, and data collected at Delaware State University from FLIR Systems PTZ cameras have shown promising results in using outlier detection for detection of small moving targets.

Detection of moving objects using incremental connectivity outlier factor algorithm

In this paper, we describe a technique for detection of moving objects in RGB and infra-red (IR) videos. The technique is based on novel incremental connectivity-based outlier factor (IncCOF). The main idea of the proposed approach is to detect moving blocks as outliers---objects dissimilar to objects in their vicinity--within a properly defined feature space. As the feature space, we use representation of videos by spatial-temporal blocks combined with principal component analysis for dimensionality reduction. Experimental evaluation of the proposed approach on a variety of test videos, including PETS repository, demonstrates its applicability and robustness on the choice of parameters.

Performance of multilayer perceptrons for classification of LIBS protein spectra

We investigate performance of neural networks for classification of laser-induced breakdown spectroscopic data of four proteins: Bovine Serum Albumin, Osteopontin, Leptin and Insulin-like Growth Factor II. We utilize principal component analysis algorithm for feature extraction and multilayer perceptrons algorithms with one and two hidden layers. We employ leave-one-out procedure for classifier evaluation. Our experimental results indicate that methods with linear convergence can provide classification accuracy superior to methods with quadratic convergence.

Detection of suspicious activity using incremental outlier detection algorithms

Detection of unusual trajectories of moving objects can help in identifying suspicious activity on convoy routes and thus reduce casualties caused by improvised explosive devices. In this paper, using video imagery we compare efficiency of various techniques for incremental outlier detection on detecting unusual trajectories on simulated and real-life data obtained from SENSIAC database. Incremental outlier detection algorithms that we consider in this paper include incremental Support Vector Classifier (incSVC), incremental Local Outlier Factor (incLOF) algorithm and incremental Connectivity Outlier Factor (incCOF) algorithm. Our experiments performed on ground truth trajectory data indicate that incremental LOF algorithm can provide better detection of unusual trajectories in comparison to other examined techniques.

Classification of performers using support vector machines

Huge amount of different music material in digital form, that can be found on the Internet, represents a big problem for a user who wants to find some particular music piece. Indexing, retrieving and classification are some of the techniques that can be used to provide faster search. In this paper, one of the methods for classification of the performers is described. We created audio databases, which consist of short audio sequences from several (nine) songs, sang by three distinct performers. Wavelet coefficients were used as feature descriptors for these music sequences. Classification was performed based on linear support vector machines. Our system exhibits very good results in the experiment with two performers (two classes): partial accuracies for classes were 100% and 98%, respectively. In the experiment with three classes (three performers), we used one-versus-one approach, and obtained partial accuracies of 50%, 82% and 35%, respectively.

Adaptive Filters for Processing Water Level Data

Salt marshes are composed of various habitats contributing to high levels of habitat diversity and increased productivity (Kennish, 2002; Zharikov et al., 2005), making them among the most productive ecosystems on the Earth. The salt marsh consists of a halophytic vegetation community growing near saline waters (Mitsch & Gosselink, 2000) characterized by grasses, herbs, and low shrubs (Adam, 2002). Salt marshes exist between the upper limit of the high tide and the lower limit of the mean high water tide (Adam, 2002). They represent an important factor in the support of surrounding food chains, and due to the high level of productivity their economic and aesthetic value is increasing (Delaware Department of Natural Resources and Environmental Control, 2002; Zharikov et al. 2005). The survival and reproduction of many species of commercial fish and shellfish is dependent upon salt marshes (Zharikov & Skilleter, 2004). In addition, salt marshes provide critical habitat and food supply to crustaceans (Zharikov et al., 2005) and shorebirds (Potter et al., 1991). They are often considered as a primary indicator of the ecosystem health (Zhang et al., 1997). Because of their ability to transfer and store nutrients, salt marshes are an important factor in the maintenance and improvement of water quality (Delaware Department of Natural Resources and Environmental Control, 2002; Zhang et al., 1997). In addition, they provide significant economic value as a cost-effective means of flood and erosion control (Delaware Department of Natural Resources and Environmental Control, 2002; Morris et al., 2004). This economic value makes coastal systems the site of elevated human activity (Kennish, 2002). Determining the effects of sea level rise on tidal marsh systems is currently a very popular research area (Temmerman et al., 2004). While average sea level has increased 10-25 cm in the past century (Kennish, 2002), the Atlantic coast has experienced a sea level rise of 30 cm (Hull & Titus, 1986). Local relative sea level has risen an average rate of 0.12 cm yr-1 in the past 2000 years, but at Breakwater Harbor in Lewes, DE sea level is rising at the average rate of 0.33 cm yr-1, nearly three times that rate (Kraft et al., 1992). According to the National Academy of Sciences and the Environmental Protection Agency, sea level rise within the next century could increase 60 cm to 150 cm (Hull & Titus, 1986). The changes in sea level rise are particularly affecting tidal marshes, since they are located between the sea and the terrestrial edge (Adam, 2002; Temmerman et al., 2004). The prediction is that sea level rise will have the most negative effect on marshes in the areas where the landward migration of the marsh is restricted by dams and levees (Rooth & Stevenson, 2000).