Ömer Nezih Gerek

A 2-D orientation-adaptive prediction filter in lifting structures for image coding

Lifting-style implementations of wavelets are widely used in image coders. A two-dimensional (2-D) edge adaptive lifting structure, which is similar to Daubechies 5/3 wavelet, is presented. The 2-D prediction filter predicts the value of the next polyphase component according to an edge orientation estimator of the image. Consequently, the prediction domain is allowed to rotate /spl plusmn/45/spl deg/ in regions with diagonal gradient. The gradient estimator is computationally inexpensive with additional costs of only six subtractions per lifting instruction, and no multiplications are required.

Power-quality event analysis using higher order cumulants and quadratic classifiers

In this paper, we present a novel power-quality (PQ) event detection and classification method using higher order cumulants as the feature parameter, and quadratic classifiers as the classification method. We have observed that local higher order statistical parameters that are estimated from short segments of 50-Hz notch-filtered voltage waveform data carry discriminative features for PQ events analyzed herein. A vector with six parameters consisting of local minimas and maximas of higher order central cumulants starting from the second (variance) up to the fourth cumulant is used as the feature vector. Local vector magnitudes and simple thresholding provide an immediate event detection criterion. After the detection of a PQ event, local maxima and minima of the cumulants around the event instant are used for the event-type classification. We have observed that the minima and maxima for each statistical order produces clusters in the feature space. These clusters were observed to exhibit noncircular topology; hence, quadratic-type classifiers that require the Mahalanobis distance metric are proposed. The events investigated and presented are line-to-ground arcing faults and voltage sags due to the induction motor starting. Detection and classification results obtained from an experimentally staged PQ event data set are presented.

Power quality event detection using joint 2-D-wavelet subspaces

In this work, we present a novel two-dimensional (2-D) representation of power system waveforms for the automatic analysis and detection of transient events. The representation is composed of a matrix whose rows are formed by time segments of digital waveforms. By the appropriate selection of the time segment length, the 2-D data exhibits wave-like image shapes. The general shape is immediately disturbed whenever a power quality transient event occurs. We propose the use of two dimensional discrete wavelet transforms (2-D-DWT) to detect these disturbances. It has been observed that, after omitting the approximation space signals of the wavelet transform and denoising the detail space signals, the inverse 2-D-DWT provides good detection and localization results, even for cases where conventional methods fail. Examples are presented.

2-D analysis and compression of power-quality event data

This paper introduces a novel two-dimensional (2-D) representation of the power quality event data. 2-D discrete-time wavelet transform is applied to the 2-D representation of real-life event data. The proposed representation and the transform is tested in terms of both event analysis and data compression. The experimental results indicate that the 2-D transform of the event data outperforms the results obtained by conventional one-dimensional (1-D) wavelet transform-based methods.

Covariance analysis of voltage waveform signature for power-quality event classification

In this paper, covariance behavior of several features (signature identifiers) that are determined from the voltage waveform within a time window for power-quality (PQ) event detection and classification is analyzed. A feature vector using selected signature identifiers such as local wavelet transform extrema at various decomposition levels, spectral harmonic ratios, and local extrema of higher order statistical parameters, is constructed. It is observed that the feature vectors corresponding to power quality event instances can be efficiently classified according to the event type using a covariance based classifier known as the common vector classifier. Arcing fault (high impedance fault) type events are successfully classified and distinguished from motor startup events under various load conditions. It is also observed that the proposed approach is even able to discriminate the loading conditions within the same class of events at a success rate of 70%. In addition, the common vector approach provides a redundancy and usefulness information about the feature vector elements. Implication of this information is experimentally justified with the fact that some of the signature identifiers are more important than others for the discrimination of PQ event types

An image-processing based automated bacteria colony counter

This paper presents an image processing based automated counting system to detect the number of bacteria colonies that develop in Petri dishes of microbiology laboratories. The visible colonies represent the initial number of bacteria present in the aqueous environment. The counting system contains shape based segmentation and classification algorithms. Colonies are considered as (possibly overlapping with some amount of amorphous deviations from) discs and classified as a cluster of bacteria with respect to their compactness ratio. The system is implemented using Matlab, and tested using ground truth data provided from Anadolu University, Dept. of Environmental Engineering microbiology laboratory. Results are presented.

A wearable head-mounted sensor-based apparatus for eye tracking applications

This work presents a novel approach to eye-tracking systems using eye-glass like apparatus equipped with relatively cheap IrDA sensors and IrDA LEDs connected to a computer. The proposed system produces very low dimensional feature vectors for processing as compared to its competitors that process video data acquired from a digital camera. Consequently, the computational requirements of the proposed system are low. Furthermore, the apparatus is lightweight and can be directly worn. A prototype system is developed in our laboratories and tested to observe its capabilities. Preliminary results show that the approach provides a promising human-computer interface system with plausible accuracy.

A head-mounted sensor-based eye tracking device: eye touch system

In this study, a new eye tracking system, namely Eye Touch, is introduced. Eye Touch is based on an eyeglasses-like apparatus on which IrDA sensitive sensors and IrDA light sources are mounted. Using inexpensive sensors and light sources instead of a camera leads to lower system cost and need for the computation power. A prototype of the proposed system is developed and tested to show its capabilities. Based on the test results obtained, Eye Touch is proved to be a promising human-computer interface system.

Self organizing map (SOM) approach for classification of power quality events

In this work, Self Organizing Map (SOM) is used in order to classify the types of defections in electrical systems, known as Power Quality (PQ) events. The features for classifications are extracted from real time voltage waveform within a sliding time window and a signature vector is formed. The signature vector consists of different types of features such as local wavelet transform extrema at various decomposition levels, spectral harmonic ratios and local extrema of higher order statistical parameters. Before the classification, the clustering has been achieved using SOM in order to define codebook vectors, then LVQ3 (Learning Vector Quantizer) algorithm is applied to find exact classification borders. The k-means algorithm with Davies-Boulding clustering index method is applied to figure out the classification regions. Here it has been observed that, successful classification of two major PQ event types corresponding to arcing faults and motor start-up events for different load conditions has been achieved.

A Low-Computational Approach on Gaze Estimation With Eye Touch System

Among various approaches to eye tracking systems, light-reflection based systems with non-imaging sensors, e.g., photodiodes or phototransistors, are known to have relatively low complexity; yet, they provide moderately accurate estimation of the point of gaze. In this paper, a low-computational approach on gaze estimation is proposed using the Eye Touch system, which is a light-reflection based eye tracking system, previously introduced by the authors. Based on the physical implementation of Eye Touch, the sensor measurements are now utilized in low-computational least-squares algorithms to estimate arbitrary gaze directions, unlike the existing light reflection-based systems, including the initial Eye Touch implementation, where only limited predefined regions were distinguished. The system also utilizes an effective pattern classification algorithm to be able to perform left, right, and double clicks based on respective eye winks with significantly high accuracy. In order to avoid accuracy problems for sensitive sensor biasing hardware, a robust custom microcontroller-based data acquisition system is developed. Consequently, the physical size and cost of the overall Eye Touch system are considerably reduced while the power efficiency is improved. The results of the experimental analysis over numerous subjects clearly indicate that the proposed eye tracking system can classify eye winks with 98% accuracy, and attain an accurate gaze direction with an average angular error of about 0.93 °. Due to its lightweight structure, competitive accuracy and low-computational requirements relative to video-based eye tracking systems, the proposed system is a promising human-computer interface for both stationary and mobile eye tracking applications.