Emma E. Regentova

Diagnosability of star graphs under the comparison diagnosis model

In this paper, the diagnosability of n-dimensional star graph Sn under the comparison diagnosis model has been studied. It is proved that Sn is (n - 1)-diagnosable under the comparison diagnosis model when n ≥ 4.

Architectures and routing schemes for optical network-on-chips

As indicated in the latest version of ITRS roadmap, optical wiring is a viable interconnect technology for future SoC/SiC/SiP designs that can provide broad band data transfer rates unmatchable by the existing metal/low-k dielectric interconnects. In this paper, we present an interconnection architecture, referred as the wavelength routed optical network (WRON), suitable to build on-chip optical micro-networks. The routing scheme for WRON, using any two of the three routing parameters (the source node address, the destination node address, and the routing wavelength), is generalized in this paper. With WRON as the primitive platform, we further propose a new recursive architecture, the recursive wavelength routed optical network (RCWRON), and it serves as the basis of a redundant architecture, the redundant wavelength routed optical network (RDWRON). The routing schemes for RCWRON and RDWRON are also detailed in this paper.

An algorithm with reduced operations for connected components detection in ITU-T group 3/4 coded images

An algorithm, which performs connected components detection in the course of decoding ITU-T (former CCITT) facsimile Group 3/4, i.e., MH/MR/MMR compressed images is presented. New definitions of mode color and a new transition element are introduced that allow MR/MMR codes to analyze and derive information about connection of black runs in two adjacent scan lines in the course of decoding. The experiments on the standard set of eight CCITT documents have shown that, on the average, the complexity of direct processing of MR/MMR codes is lower by a factor of 20 and 2.5 than that for raster images and MH codes processing respectively. Data structures for image vector description are discussed.

Microcalcification detection based on wavelet domain hidden markov tree model: study for inclusion to computer aided diagnostic prompting system.

In this paper we investigate the performance of statistical modeling of digital mammograms by means of wavelet domain hidden Markov trees for its inclusion to a computer-aided diagnostic prompting system. The system is designed for detecting clusters of microcalcifications. Their further discrimination as benign or malignant is to be done by radiologists. The model is used for segmenting images based on the maximum likelihood classifier enhanced by the weighting technique. Further classification incorporates spatial filtering for a single microcalcification (MC) and microcalcification cluster (MCC) detection. Contrast filtering applied for the digital database for screening mammography (DDSM) dataset prior to spatial filtering greatly improves the classification accuracy. For all MC clusters of 40 mammograms from the mini-MIAS database of Mammographic Image Analysis Society, 92.5%-100% of true positive cases can be detected under 2-3 false positives per image. For 150 cases of DDSM cases, the designed system is capable to detect up to 98% of true positives under 3.3% of false positive cases.

Packet switching optical network-on-chip architectures

In this paper we propose three packet switching optical network-on-chip architectures, i.e., TON-I, TON-II and TON-III. Micro-ring resonator (MRR)-based optical switches are adopted for wavelength-based routing in TONs. Direct optical channels (DOCs) are introduced as the direct optical paths between nodes. For each node in TON-I, II, and III, the number of DOCs is 4, 8, and 10 respectively. We present the implementations of a packet switching optical NoC with TONs with a limited number of wavelengths. The design of routers and schema for wavelength assignment are presented for each TON. The number of different wavelengths needed for in TON-I, II, and III is 2, 4, and 6. The proposed architectures yield highly scalabilities, high bandwidth, low latency and low power consumption. TON network performances are evaluated by simulation as presented. The transmission power loss analysis is provided as well. Simulation and analysis results show that the proposed architectures can be considered as a viable solution for future NoCs.

Sky Segmentation by Fusing Clustering with Neural Networks

Sky segmentation is an important task for many applications related to obstacle detection and path planning for autonomous air and ground vehicles. In this paper, we present a method for the automated sky segmentation by fusing K-means clustering and Neural Network (NN) classifications. The performance of the method has been tested on images taken by two Hazcams (ie., Hazard Avoidance Cameras) on NASA’s Mars rover. Our experimental results show high accuracy in determining the sky area. The effect of various parameters is demonstrated using Receiver Operating Characteristic (ROC) curves.

Document analysis by processing JBIG-encoded images

Abstract.Techniques are presented to directly process JBIG-encoded document images. Two experimental processing pipelines are designed to evaluate the performance of the methods from the application perspective. They are document segmentation for obtaining the global layout and the form processing system for form type identification and the form dropout. The JBIG coding context is employed to perform horizontal smearing and connected-component detection concurrently in the course of decoding the base layer of the JBIG images. It is shown that, using a simple segmentation algorithm, the global layout is identified 50 times faster compared to the case of processing the full resolution images. In addition, an original solution is presented for form type identification by use of the Hough transform of the JBIG base layer images, thus expediting it by a factor of 16 in the designed form dropout system. Advantages of the compressed domain processing include fast procedures, reduced memory requirements, and the possibility of hardware implementation.

A Machine Learning Approach to Horizon Line Detection Using Local Features

Planetary rover localization is a challenging problem since no conventional methods such as GPS, structural landmarks etc. are available. Horizon line is a promising visual cue which can be exploited for estimating the rovers position and orientation during planetary missions. By matching the horizon line detected in 2D images captured by the rover with virtually generated horizon lines from 3D terrain models e.g., Digital Elevation MapsDEMs, the localization problem can be solved in principle. In this paper, we propose a machine learning approach for horizon line detection using edge images and local features i.e., SIFT. Given an image, first we apply Canny edge detection using various parameters and keep only those edges which survive over a wide range of thresholds. We refer to these edges as Maximally Stable Extremal Edges MSEEs. Using ground truth information, we then train an Support Vector Machine SVM classifier to classify MSEE pixels into two categories: horizon and non-horizon. Each MSSE pixel is described using SIFT features which becomes input to the SVM classifier. Given a novel image, we use the same procedure to extract MSSEs; then, we classify each MSEE pixel as horizon or non-horizon using the SVM classifier. MSEE pixels classified as horizon are then provided to a Dynamic Programming shortest path finding algorithm which returns a consistent horizon line. In general, Dynamic Programming returns different solutions i.e., due to gaps when searching for the optimum horizon line in a left-to-right or right-to-left fashion. We use the actual output of the SVM classifier to resolve ambiguities in places where the left-to-right and right-to-left solutions are different. The final solution, is typically a combination of edge segments from the left-to-right or right-to-left solutions. Moreover, we use the SVM classifier to fill in small gaps in the horizon line; this is in contrast to the traditional dynamic programming approach which relies on mere interpolation. We report promising experimental results using a set of real images.

Detecting microcalcifications in digital mammograms using wavelet domain hidden Markov tree model.

In this paper we investigate the performance of statistical modeling of digital mammograms by means of wavelet domain hidden Markov tree model (WHMT) for its inclusion to a computer-aided diagnostic prompting system for detecting microcalcification (MC) clusters. The system incorporates: (1) gross-segmentation of mammograms for obtaining the breast region; (2) eliminating the pepper-type noise, (3) block-wise wavelet transform of the breast signal and likelihood calculation; (4) image segmentation; (5) postprocessing for retaining MC clusters. FROC curves are obtained for all MC clusters containing mammograms of mini-MIAS database. 100% of true positive cases are detected by the system at 2.9 false positives per case

Virtual guard channel for handoff calls in integrated voice/data wireless networks

In this paper, we propose a virtual guard channel (VGC) scheme for handoff calls in integrated voice/data wireless networks. By utilizing the multi-channel capability of data service, the proposed scheme can provide better performance in quality of service (QoS) provisioning and utilize the limited channel resources more efficiently compared with the conventional guard channel (GC) scheme.