Embedded SQL inserts SQL statements into the host programming language and executes them at program run time. SQL injection is a known attack technique; however, detection techniques are not introduced in embedded SQL. This paper introduces a technique based on candidate code generation that can detect SQL injection vulnerability in the C/C++ host programming language.

The Long Term Evolution (LTE) of mobile communication standard was designed by the 3rd generation partnership project (3GPP) to serve the requirements. Nowadays, the combining of the orthogonal frequency division multiplexing (OFDM) and the multiple input multiple output (MIMO) is supported in LTE system. The MIMO-OFDM is considered to improve data rate and channel capacity without additional bandwidth. Because the receivers get all transmission signals from all transmitters at the same time, many detection schemes have been developed for accurate estimation and low complexity. Among the detection schemes, the QR decomposition with M algorithm (QRD-M) achieves optimal error performance with low complexity. Nevertheless, the conventional QRD-M has high complexity for implementation. To overcome the problem, this letter proposes the low complexity QRD-M detection scheme in MIMO-OFDM systems. The proposed scheme has two elements which decide layer value and the limited candidates. The two elements are defined by the number of transmit antennas and the cardinality of modulation set respectively. From simulation results, the proposed scheme has the same error performance with the conventional QRD-M and very lower complexity than the conventional QRD-M.

The paper presents a recognition method of character-position-free on-line handwritten Japanese text patterns to allow a user to overlay characters freely without confirming previously written characters. To develop this method, we first collected text patterns written without wrist or elbow support and without visual feedback and then prepared large sets of character-position-free handwritten Japanese text patterns artificially from normally handwritten text patterns. The proposed method sets each off-stroke between real strokes as undecided and evaluates the segmentation probability by SVM model. Then, the optimal segmentation-recognition path can be effectively found by Viterbi search in the candidate lattice, combining the scores of character recognition, geometric features, linguistic context, as well as the segmentation scores by SVM classification. We test this method on variously overlaid sample patterns, as well as on the above-mentioned collected handwritten patterns, and verify that its recognition rates match those of the latest recognizer for normally handwritten horizontal Japanese text with no serious speed restriction in practical applications.

Even with the recent development of new types of social networking services such as microblogs, Bulletin Board Systems (BBS) remains popular for local communities and vertical discussions. These BBS sites have high volume of traffic everyday with user discussions on a variety of topics. Therefore it is difficult for BBS visitors to find the posts that they are interested in from the large amount of discussion threads. We attempt to explore several main characteristics of BBS, including organizational flexibility of BBS texts, high data volume and aging characteristic of BBS topics. Based on these characteristics, we propose a novel method of Online Topic Detection (OTD) on BBS, which mainly includes a representative post selection procedure based on Markov chain model and an efficient topic clustering algorithm with candidate topic set generation based on Aging Theory. Experimental results show that our method improves the performance of OTD in BBS environment in both detection accuracy and time efficiency. In addition, analysis on the aging characteristic of discussion topics shows that the generation and aging of topics on BBS is very fast, so it is wise to introduce candidate topic set generation strategy based on Aging Theory into the topic clustering algorithm.

In this letter, parallel orthogonal matching pursuit (POMP) is proposed to supplement orthogonal matching pursuit (OMP) which has been widely used as a greedy algorithm for sparse signal recovery. Empirical simulations show that POMP outperforms the existing sparse signal recovery algorithms including OMP, compressive sampling matching pursuit (CoSaMP), and linear programming (LP) in terms of the exact recovery ratio (ERR) for the sparse pattern and the mean-squared error (MSE) between the estimated signal and the original signal.

Network topology significantly affects network cost, path length, link load distribution, and reliability, so we need to consider multiple criteria with different units simultaneously when designing a network's topology. The analytic hierarchy process (AHP) is a technique of balancing multiple criteria in order to reach a rational decision. Using AHP, we can reflect the relative importance of each criterion on the evaluation result; therefore, we have applied it to network topology evaluation in past research. When evaluating network topologies using AHP, we need to construct the set of topology candidates prior to the evaluation. However, the time required to construct this set greatly increases as the network size grows. In this paper, we propose applying a binary partition approach for constructing a topology candidate set with dramatically reduced calculation time. To reduce the calculation time, we introduce an upper limit for the total link length. Although the results of AHP are affected by introducing the upper limit of the total link length, we show that desirable topologies are still selected in AHP.

The singular points of fingerprints, viz. core and delta, are important referential points for the classification of fingerprints. Several conventional approaches such as the Poincare index method have been proposed; however, these approaches are not reliable with poor-quality fingerprints. This paper proposes a new core and delta detection employing singular candidate analysis and an extended relational graph. Singular candidate analysis allows the use both the local and global features of ridge direction patterns and realizes high tolerance to local image noise; this involves the extraction of locations where there is high probability of the existence of a singular point. Experimental results using the fingerprint image databases FVC2000 and FVC2002, which include several poor-quality images, show that the success rate of the proposed approach is 10% higher than that of the Poincare index method for singularity detection, although the average computation time is 15%-30% greater.

In this paper, a new local matching algorithm, to estimate dense disparity map in stereo vision, consisting of two stages is presented. At the first stage, the reduction of search space is carried out with a high efficiency, i.e. remarkable decrease in the average number of candidates per pixel, with low computational cost and high assurance of retaining the correct answer. This outcome being due to the effective use of multiple radial windows, intensity information, and some usual and new constraints, in a reasonable manner, retains those candidates which satisfy more constraints and especially being more promising to satisfy the implied assumption in using support windows; i.e., the disparity consistency of the window pixels. Such an output from the first stage, while speeding up the final selection of disparity in the second stage due to search space reduction, is also promising a more accurate result due to having more reliable candidates. In the second stage, the weighted window, although not necessarily being the exclusive choice, is employed and examined. The experimental results on the standard stereo benchmarks for the developed algorithm are presented, confirming that the massive computations to obtain more precise matching costs in weighted window is reduced to about 1/11 and the final disparity map is also improved.

This paper describes a vision-based and real-time system for detecting road signs from within a moving vehicle. The system architecture which is proposed in this paper consists of two parts, the learning and the detection part of road sign images. The proposed system has the standard architecture with adaboost algorithm. Adaboost is a popular algorithm which used to detect an object in real time. To improve the detection rate of adaboost algorithm, this paper proposes a new combination method of classifiers in every stage. In the case of detecting road signs in real environment, it can be ambiguous to decide to which class input images belong. To overcome this problem, we propose a method that applies fuzzy measure and fuzzy integral which use the importance and the evaluated values of classifiers within one stage. It is called fuzzy-boosting in this paper. Also, to improve the speed of a road sign detection algorithm using adaboost at the detection step, we propose a method which chooses several candidates by using MC generator. In this paper, as the sub-windows of chosen candidates pass classifiers which are made from fuzzy-boosting, we decide whether a road sign is detected or not. Using experiment result, we analyze and compare the detection speed and the classification error rate of the proposed algorithm applied to various environment and condition.

This paper proposes a "structuring search space" (SSS) method aimed to accelerate recognition of large character sets. We divide the feature space of character categories into smaller clusters and derive the centroid of each cluster as a pivot. Given an input pattern, it is compared with all the pivots and only a limited number of clusters whose pivots have higher similarity (or smaller distance) to the input pattern are searched in, thus accelerating the recognition speed. This is based on the assumption that the search space is a distance space. We also consider two ways of candidate selection and finally combine them the method has been applied to a practical off-line Japanese character recognizer with the result that the coarse classification time is reduced to 56% and the whole recognition time is reduced to 52% while keeping its recognition rate as the original.

We propose a modified Hidden Markov Model (HMM) with a view to improve gesture recognition using a moving camera. The conventional HMM is formulated so as to deal with only one feature candidate per frame. However, for a mobile robot, the background and the lighting conditions are always changing, and the feature extraction problem becomes difficult. It is almost impossible to extract a reliable feature vector under such conditions. In this paper, we define a new gesture recognition framework in which multiple candidates of feature vectors are generated with confidence measures and the HMM is extended to deal with these multiple feature vectors. Experimental results comparing the proposed system with feature vectors based on DCT and the method of selecting only one candidate feature point verifies the effectiveness of the proposed technique.

Feedforward neural networks have been successfully developed and applied in many areas because of their universal approximation capability. However, there still remains the problem of determining a suitable network structure for the given task. In this paper, we propose a novel self-organizing neural network which automatically adjusts its structure according to the task. Utilizing both the constructive and the pruning procedures, the proposed algorithm finds a near-optimal network which is compact and shows good generalization performance. One of its important features is reliability, which means the randomness of neural networks is effectively reduced. The resultant networks can have suitable numbers of hidden neurons and hidden layers according to the complexity of the given task. The simulation results for the well-known function regression problems show that our method successfully organizes near-optimal networks.

In this letter, base station placement is automatically determined from pre-defined candidate sites using a genetic approach, and the transmit power is obtained taking the interference situation into account in cases of interference-dominant systems. In order to apply a genetic algorithm to the base station placement problem, a real-valued representation scheme is proposed. Corresponding operators such as crossover and mutation are also introduced. The proposed algorithm is applied to an inhomogeneous traffic density environment, where a base station's coverage may be limited by offered traffic loads. An objective function is designed for performing the cell planning in a coverage- and cost-effective manner.

Error concealment is an essential part of reliable video communication systems because transmission errors are inevitable even when the coded bitstream is highly protected. The problem of temporal EC can be factored into two parts regarding candidate motion vectors (MVs) employed and the matching criterion to evaluate the fitness of each candidate MV. In order to obtain more faithful EC results, this paper proposes a novel iterative EC algorithm, in which an efficient way to provide candidate MVs and a new fitness measure are presented. The proposed approach for candidate MVs systematically utilizes all the available neighboring MVs by exploiting a well-known spatiotemporal correlation of block MVs. Also, in order to remove the dependency of a damaged block's quality of concealment on the already concealed adjacent blocks, we develope a new matching criterion. The objective of the proposed fitness measure is to minimize the total boundary matching errors induced by the whole corrupted blocks. Simulations performed using an H.263 codec demonstrate a significant improvement on the subjective and objective concealed video qualities, especially when the corrupted area is wider than a single row of coding blocks.

The performance of conventional error concealment (EC) is significantly affected by the method of selecting candidate motion vectors (MVs). In order to obtain more robust EC results, this letter proposes a new and efficient way to choose candidate MVs. The proposed approach systematically utilizes available neighboring MVs by exploiting a well-known spatiotemporal correlation of block MVs. Through extensive simulations with H.263, this letter demonstrates that the proposed candidate MVs provide superior concealed video quality in comparison to the best results of other existing techniques.

This paper presents a method of multiple fault diagnosis in sequential circuits by input-sequence pairs having sensitizing input pairs. We, first, introduce an input-sequence pair having sensitizing input pairs to diagnose multiple faults in a sequential circuit represented by a combinational array model. We call such input-sequence pair the sensitizing sequence pair in this paper. Next, we describe a diagnostic method for multiple faults in sequential circuits by the sensitizing sequence pair. From a relation between a sensitizing path generated by a sensitizing sequence pair and a subcircuit, the proposed method deduces the suspected faults for the subcircuits, one by one, based on the responses observed at primary outputs without probing any internal line. Experimental results show that our diagnostic method identifies fault locations within small numbers of suspected faults.

Logic synthesizers usually have good area minimization capabilities, producing circuits of minimal area. But good delay minimization techniques are still missing in current logic synthesis technology. In [7], the RENO algorithm (which stands for REsynthesis for Network Optimization) was proposed for minimizing the area of multi-level combinational networks, and its effectiveness in designing minimal-area networks has been demonstrated. In this paper, we present improvements and extensions of the RENO algorithm for network delay minimization by using Boolean resynthesis techniques. We will discuss new algorithms for gate resynthesis which have not only reduced the processing time significantly, but also have improved the quality of minimization. Due to the generality of the gate resynthesis algorithms, we can minimize both delay and area of a network concurrently in a unified way, and network delay is reduced significantly with no or very small area penalty. Extensive experimental results and comparison with the speed_up algorithm in SIS-1.0 are presented.