Classification of compound images based on transform coefficient likelihood
Applications like distance learning and teleconferencing often require compression of images that contain both text and graphics. Because text and graphics have different properties, a compression scheme can benefit by treating the textual and graphical portions of such compound images separately. In this paper, we propose new methods, called transform coefficient likelihood (TCL) schemes, for separating the textual and graphical portions of a compound image. TCL schemes examine the DCT coefficient values of an 8/spl times/8 block. For each coefficient, they refer to stored histograms that give the likelihood that a certain value occurs in a text block, or in a graphics block. They then examine the differences in these two likelihoods over all the coefficients in the block to decide whether it contains text or graphics. Experimental results show that the best TCL methods significantly outperform previously proposed techniques.

title: On the Verge of One Petabyte - the Story Behind the BaBar Database System
abstract: The BaBar database has pioneered the use of a commercial ODBMS within the HEP community. The unique object-oriented architecture of Objectivity/DB has made it possible to manage over 700 terabytes of production data generated since May'99, making the BaBar database the world's largest known database. The ongoing development includes new features, addressing the ever-increasing luminosity of the detector as well as other changing physics requirements. Significant efforts are focused on reducing space requirements and operational costs. The paper discusses our experience with developing a large scale database system, emphasizing universal aspects which may be applied to any large scale system, independently of underlying technology used.

title: IMDS: intelligent malware detection system
abstract: The proliferation of malware has presented a serious threat to the security of computer systems. Traditional signature-based anti-virus systems fail to detect polymorphic and new, previously unseen malicious executables. In this paper, resting on the analysis of Windows API execution sequences called by PE files, we develop the Intelligent Malware Detection System (IMDS) using Objective-Oriented Association (OOA) mining based classification. IMDS is an integrated system consisting of three major modules: PE parser, OOA rule generator, and rule based classifier. An OOA_Fast_FP-Growth algorithm is adapted to efficiently generate OOA rules for classification. A comprehensive experimental study on a large collection of PE files obtained from the anti-virus laboratory of King-Soft Corporation is performed to compare various malware detection approaches. Promising experimental results demonstrate that the accuracy and efficiency of our IMDS system out perform popular anti-virus software such as Norton AntiVirus and McAfee VirusScan, as well as previous data mining based detection systems which employed Naive Bayes, Support Vector Machine (SVM) and Decision Tree techniques.

title: Monaural Speech Separation
abstract: Monaural speech separation has been studied in previous systems that incorporate auditory scene analysis principles. A major problem for these systems is their inability to deal with speech in the high-frequency range. Psychoacoustic evidence suggests that different perceptual mechanisms are involved in handling resolved and unresolved harmonics. Motivated by this, we propose a model for monaural separation that deals with low-frequency and high-frequency signals differently. For resolved harmonics, our model generates segments based on temporal continuity and cross-channel correlation, and groups them according to periodicity. For unresolved harmonics, the model generates segments based on amplitude modulation (AM) in addition to temporal continuity and groups them according to AM repetition rates derived from sinusoidal modeling. Underlying the separation process is a pitch contour obtained according to psychoacoustic constraints. Our model is systematically evaluated, and it yields substantially better performance than previous systems, especially in the high-frequency range.

title: Hardware-software partitioning of real-time operating systems using Hopfield neural networks
abstract: The hardware–software automated partitioning of a real-time operating system in the system-on-a-chip (SoC-RTOS partitioning) is a NP-complete problem, and a crucial step in the hardware–software co-design of SoC. In this paper, a new model for SoC-RTOS partitioning is introduced, which can help in understanding the essence of the SoC-RTOS partitioning. A discrete Hopfield neural network approach for implementing the SoC-RTOS partitioning is proposed, where a novel energy function, operating equation and coefficients of the neural network are redefined. Simulations are carried out with comparison to other optimization techniques. Experimental results demonstrate the feasibility and effectiveness of the proposed method.

title: Segmentation of medical images using LEGION
abstract: Advances in visualization technology and specialized graphic workstations allow clinicians to virtually interact with anatomical structures contained within sampled medical-image datasets. A hindrance to the effective use of this technology is the difficult problem of image segmentation. In this paper, the authors utilize a recently proposed oscillator network called the locally excitatory globally inhibitory oscillator network (LEGION) whose ability to achieve fast synchrony with local excitation and desynchrony with global inhibition makes it an effective computational framework for grouping similar features and segregating dissimilar ones in an image. The authors extract an algorithm from LEGION dynamics and propose an adaptive scheme for grouping. They show results of the algorithm to two-dimensional (2-D) and three-dimensional (3-D) (volume) computerized topography (CT) and magnetic resonance imaging (MRI) medical-image datasets. In addition, the authors compare their algorithm with other algorithms for medical-image segmentation, as well as with manual segmentation. 's computational and architectural properties make it a promising approach for real-time medical-image segmentation.

title: Wavelet transform-based frequency tuning ILC
abstract: A discrete wavelet transform-based cutoff frequency tuning method is proposed and experimental investigation is reported. In the method, discrete wavelet packet algorithm, as a time-frequency analysis tool, is employed to decompose the tracking error into different frequency regions so that the maximal error component can be identified at any time step. At each time step, the passband of the filter is from zero to the upper limit of frequency region where the maximal error component resides. Hence, the filter is a function of time as well as index of cycle. The experimental results show that this method can suppress higher frequency error components at proper time steps. While at the time steps where the major tracking error falls into lower frequency range, the cutoff frequency of the filter is set lower to reduce the influence of noises and uncertainties. This way, learning transient and long-term stability can be improved.