This paper presents a method of extracting car license plates captured from the arbitrary directions by using symmetry features. The generalized symmetry transform (GST) produces continuous features of symmetry between two points by combining locality constraint and reflectional symmetry, but its time complexity of GST is increased by the second power of the radius of a searching window. To achieve considerable reduction of this time complexity, we propose a scan line based GST (SLGST) which calculates the symmetry between a pair of edge pixels along the scan lines. Instead of calculating the global symmetry of a license plate, we assign the symmetry contribution acquired from SLGST to the corner point estimated from two edge points and their gradient orientations. The right angle tuned SLGST (RATSLGST) is newly proposed to detect the right angle corners more effectively. Image normalization by image warping is adopted to make such segmentation of license plate and later identification much easier. We also adopt the verifier that evaluates a candidate license plate to enhance extraction rate. Our experiments show that the proposed method for extracting perspectively distorted license plates is fairly reliable.

We analyze M,MMPP/G/1 finite queues with queue-length-threshold (QLT) scheduling policy and Bernoulli schedule where the arrival of type-1 customers (nonreal-time traffic) is Poisson and the arrival of type-2 customers (real-time traffic) is a Markov-modulated Poisson process (MMPP). The next customer to be served is determined by the queue length in the buffer of type-1 customers. We obtain the joint queue length distribution for customers of both types at departure epochs by using the embedded Markov chain method, and then obtain the queue length distribution at an arbitrary time by using the supplementary variable method. From these results, we obtain the loss probabilities and the mean waiting times for customers of each type. The numerical examples show the effects of the QLT scheduling policy on performance measures of the nonreal-time traffic and the bursty real-time traffic in ATM networks.

A non-deterministic finite automaton (NFA)-based parallel string matching scheme is proposed. To parallelize the operations of NFAs, a graphic processing unit (GPU) is adopted. Considering the resource occupancy of threads and size of the shared memory, the optimized resource allocation is performed in the proposed string matching scheme. Therefore, the performance is enhanced significantly in all evaluations.

An efficient algorithm is proposed for the adaptive sizing of a tracking window in correlation-based video tracking. Since a tracking window specifies a support region when estimating a target displacement, the ability to adapt the window size relative to a moving target significantly influences the performance of video tracking. The basic strategy of the proposed algorithm is to maintain the occupancy rate of the target in the tracking window within a specified range. As such, the proposed algorithm measures the occupancy rate using the ratio of the power of the spatial gradients in the edge subwindows, which edge the tracking window, to that in the tracking window. In addition, the level of any complex background and additive white noise is also evaluated to reduce their effect on the gradients. Experimental results using various artificial and real sequences confirm that the proposed algorithm can effectively adjust a tracking window to a moving target and is robust to a complex background and noise.

This letter proposes a new practical multiuser MIMO (MU-MIMO) scheme, which is an evolution of the well-known Per User Unitary beamforming Rate Control (PU2RC) proposed for 3GPP-LTE and IEEE802.16m standards. The proposed scheme includes an efficient user scheduling algorithm which alleviates the major weakness of the conventional PU2RC. Numerical results show that the proposed scheme provides notable performance improvement especially with small and medium user pool since it effectively exploits the benefit from large codebook size.

A HfO2 as the charge-storage layer with the physical thickness thinner than 4 nm in silicon-oxide-high-k oxide-oxide-silicon (SOHOS) flash memory was investigated. Compared to the conventional silicon-oxide-nitride-oxide-silicon (SONOS) flash memory, the SOHOS shows the slow operational speed and exhibits the poorer retention characteristics. These are attributed to the thin physical thickness below 4 nm and the crystallization of the HfO2 to contribute the lateral migration of the trapped charge in the trapping layer during high temperature annealing process.

Due to the exponentially increasing threat of cyber attacks, many e-commerce organizations around the world have begun to recognize the importance of information security. When considering the importance of security in e-commerce, we need to train e-commerce security experts who can help ensure the reliable deployment of e-commerce. The purpose of this research is to design and evaluate an e-commerce security curriculum useful in training e-commerce security experts. In this paper, we use a phase of the Delphi method and the Analytic Hierarchy Process (AHP) method. To validate our results, we divide the respondents into two groups and compare the survey results.

This paper presents a motion-based boundary tracking method for a moving deformable object in an image sequence using a parametric active contour model. Deciding the local converging directions of the contour points is essential for correctly extracting the boundary of a moving deformable object. Thus, a new energy function for a parametric active contour model is proposed based on the addition of a directional energy term using a frame difference map to the greedy snake. The frame difference map is used to obtain motion information on an object with fast and non-rigid motion. Plus, updating rules for the frame difference map are also developed to encourage the stable convergence of the contour points. Experiments on a set of synthetic and real image sequences show that the proposed method could fully track a speedy deformable object while exactly extracting the boundary of the object in every frame.

The goal of personal-style handwriting synthesis is to produce texts in the same style as an individual writer by analyzing the writer's samples of handwriting. The difficulty of handwriting synthesis is that the output should have the characteristics of the person's handwriting as well as looking natural, based on a limited number of available examples. We develop a synthesis algorithm which produces handwriting that exhibits naturalness based on the probabilistic character model.

In this paper, we propose a method to estimate affine motion parameters from consecutive images with the assumption that the motion in progress can be characterized by an affine model. The motion may be caused either by a moving camera or moving object. The proposed method first extracts motion vectors from a sequence of images and then processes them by adaptive robust estimation to obtain affine parameters. Typically, a robust estimation filters out outliers (velocity vectors that do not fit into the model) by fitting velocity vectors to a predefined model. To filter out potential outliers, our adaptive robust estimation defines a flexible weight function based on a sigmoid function. During the estimation process, we tune the sigmoid function gradually to its hard-limit as the errors between the input data and the estimation model are decreased, so that we can effectively separate non-outliers from outliers with the help of the finally tuned hard-limit form of the weight function. The experimental results show that the suggested approach is very effective in estimating affine parameters.