Pattern Matching Algorithm Research Articles

Research on Information Technology with Voiceprint Authentication Algorithm and its Implementation on Mobile Terminal

Mobile internet and its application is hot information technology. The study integrates and improves some speech processing algorithms to make them suitable for the mobile terminal environment and to realize user identity authentication. It uses one body's unique voiceprint information which has the characteristics of convenient, concise, efficient and safe. By studying pre-emphasis, hamming window and other speech signal pretreatment, the voiceprint feature extraction technique of Mel cepstrum parameters, pattern matching algorithm DTW, a algorithm scheme is established. The scheme has been implemented and verified. It is suitable for treating small amount of words voice authentication.

Spaced Seed Design Using Perfect Rulers

A widely used class of approximate pattern matching algorithms work in two stages, the first being a filtering stage that uses spaced seeds to quickly discards regions where a match is not likely to occur. The design of effective spaced seeds is known to be a hard problem. In this setting, we propose a family of lossless spaced seeds for matching with up to two errors based on mathematical objects known as perfect rulers. We analyze these seeds with respect to the tradeoff they offer between seed weight and the minimum length of the pattern to be matched. We identify a specific property of rulers, namely their skewness, which is closely related to the minimum pattern length of the derived seeds. In this context, we study in depth the specific case of Wichmann rulers and investigate the generalization of our approach to the larger class of unrestricted rulers. Although our analysis is mainly of theoretical interest, we show that for pattern lengths of practical relevance our seeds have a larger weight, hence a better filtration efficiency, than the ones known in the literature.

Medical Image Registration Using the Fourier Transform

A Fourier Transform (FT) based pattern-matching algorithm was adapted for use in medical image registration. This algorithm obtained the FT of two images, determined the normalized cross-power spectrum of the transformed images, and then applied an inverse FT. The result was a delta function with a maximum value at the location corresponding to the distance between the two images; a similar method was used to recover rotations. This algorithm was first tested using a simple two-dimensional image, with induced shifts of ±20 pixels and ±10 degrees. All translations were recovered with no error and all rotations were recovered within 0.18 degrees. Subsequently, this algorithm was tested on eight clinical kV images drawn from four different body sites. Twenty-five random shifts and rotations were applied to each image. The average mean error of the registration solution was -0.002 ± 0.077 mm in the x direction, 0.002 ± 0.075 mm in the y direction, and -0.012 ± 0.099 degrees. These initial results suggest that a FT algorithm has a high degree of accuracy when registering clinical kV images.

Design and analysis of periodic multiple seeds

A wide class of approximate pattern matching algorithms are based on a filtration phase in which spaced seeds are used to discard regions where a match is not likely to occur. The problem of determining the “optimal” shape of a spaced seed in a particular setting is known to be a hard one: in practice spaced seeds are chosen using heuristics or considering a restricted family of seeds with “reasonably good” performances.In this paper we consider the family of spaced seeds with a periodic structure. Such seeds have been already proven valuable both as a theoretical tool and in bioinformatics applications. We show that known combinatorial objects, namely Difference Sets and Families and Steiner Systems, naturally lead to the design of lossless periodic seeds for approximate pattern matching with k=2 and k=3 mismatches. We analyze in depth the properties of the resulting seeds obtaining insights also on seeds without a periodic structure. The results of the analysis are then used to guide an experimental evaluation of the effectiveness of periodic seeds for pattern lengths of practical interest. Our results give a complete picture of strengths and limitations of periodic seeds, and can be used by practitioners for the design of effective approximate pattern matching algorithms.

Perbandingan Bubble Sort dengan Insertion Sort pada Bahasa Pemrograman C dan Fortran

Sorting is a basic algorithm studied by students of computer science major. Sorting algorithm is the basis of other algorithms such as searching algorithm, pattern matching algorithm. Bubble sort is a popular basic sorting algorithm due to its easiness to be implemented. Besides bubble sort, there is insertion sort. It is lesspopular than bubble sort because it has more difficult algorithm. This paper discusses about process time between insertion sort and bubble sort with two kinds of data. First is randomized data, and the second is data of descending list. Comparison of process time has been done in two kinds of programming language that is C programming language and FORTRAN programming language. The result shows that bubble sort needs more time than insertion sort does.

An Efficient Algorithm to Identify DNA Motifs

We consider the problem of identifying motifs that abstracts the task of finding short conserved sites in genomic DNA. The planted (l, d)-motif problem, PMP, is the mathematical abstraction of this problem, which consists of finding a substring of length l that occurs in each si in a set of input sequences S = {s1, s2, . . . ,st} with at most d substitutions. Our propose algorithm combines the voting algorithm and pattern matching algorithm to find exact motifs. The combined algorithm is achieved by running the voting algorithm on t′ sequences, t′ < t. After that we use the pattern matching on the output of the voting algorithm and the reminder sequences, t − t′. Two values of t′ are calculated. The first value of t′ makes the running time of our proposed algorithm less than the running time of voting algorithm. The second value of t′ makes the running time of our proposed algorithm is minimal. We show that our proposed algorithm is faster than the voting algorithm by testing both algorithms on simulated data from (9, d ≤ 2) to (19, d ≤ 7). Finally, we test the performance of the combined algorithm on realistic biological data.

Auto-Pattern Programmable Kernel Filter (Auto-PPKF) for Suppression of Bot Generated Traffic

Bots usually vary from their other malicious counter parts by periodically reporting to the botmaster through regular exchange of messages. Our experiments on bot attack generation showed a continuous exchange of packets with similar content between the botmaster and the zombie machine at various time intervals. Though there were also genuine packets with similar content being sent out of the victim machine challenge was to differentiate between the two and pass only the genuine ones. In this paper, an algorithm namely Auto-Pattern Programmable Kernel Filter (Auto-PPKF), for automatic detection of patterns from packet payload for filtering out malicious packets generated by bots is proposed. The significant feature of our proposed Auto-PPKF algorithm is that, the malicious pattern is deduced at kernel level on the fly from packet payload. Traditional algorithms such as Boyer Moore, Knuth Morris Patt, and Naive Pattern search algorithms require the pattern to be identified available a priori. Currently, Longest Common Subsequence (LCS) algorithm stands as the most preferred algorithm for pattern matching. But the disadvantage is that common sequences can also exist in many genuine packets. Hence, the challenge lies in automatic detection of malicious patterns and filtering of the packets having such malicious patterns. This would not only put off the communication between the Botmaster and Zombie machine, but will also thus prevent user information from being sent to the botmaster.

Recognition of Grasping Motion Based on Modal Space Haptic Information Using DP Pattern-Matching Algorithm

Human motion recognition systems play an important role in improving the ability of robots to support human life. This paper proposes a novel motion recognition method based on haptic data (i.e., position and force information). First, haptic data are obtained using a bilateral control system. Second, the haptic data are divided into action components, using modal transformation. Finally, both the position and the force information in each action component are compared with recorded data. A dynamic programming pattern-matching algorithm is used to recognize the desired motion, and the validity of the proposed method is verified experimentally. In the experiments, the proposed method is applied to a grasping motion. The proposed method can trigger scaled bilateral control and assist the operator in real time.

Exact online two-dimensional pattern matching using multiple pattern matching algorithms

Baker and Bird and Baeza-Yates and Regnier are two of the most efficient and widely used algorithms for exact online two-dimensional pattern matching. Both use the automaton of the Aho-Corasick multiple pattern matching algorithm to locate all the occurrences of a two-dimensional pattern in a two-dimensional input string, a data structure that is considered by many as inefficient, especially when used to process long patterns or data using large alphabet sizes. This article presents variants of the Baker and Bird and the Baeza-Yates and Regnier algorithms that use the data structures of the Set Horspool, Wu-Manber, Set Backward Oracle Matching, and SOG multiple pattern matching algorithms in place of the automaton of Aho-Corasick and evaluates their performance experimentally in terms of preprocessing and searching time.

Accelerating Pattern Matching Using a Novel Parallel Algorithm on GPUs

Graphics processing units (GPUs) have attracted a lot of attention due to their cost-effective and enormous power for massive data parallel computing. In this paper, we propose a novel parallel algorithm for exact pattern matching on GPUs. A traditional exact pattern matching algorithm matches multiple patterns simultaneously by traversing a special state machine called an Aho-Corasick machine. Considering the particular parallel architecture of GPUs, in this paper, we first propose an efficient state machine on which we perform very efficient parallel algorithms. Also, several techniques are introduced to do optimization on GPUs, including reducing global memory transactions of input buffer, reducing latency of transition table lookup, eliminating output table accesses, avoiding bank-conflict of shared memory, coalescing writes to global memory, and enhancing data transmission via peripheral component interconnect express. We evaluate the performance of the proposed algorithm using attack patterns from Snort V2.8 and input streams from DEFCON. The experimental results show that the proposed algorithm performed on NVIDIA GPUs achieves up to 143.16-Gbps throughput, 14.74 times faster than the Aho-Corasick algorithm implemented on a 3.06-GHz quad-core CPU with the OpenMP. The library of the proposed algorithm is publically accessible through Google Code.

A vision-based system for dynamic displacement measurement of long-span bridges: algorithm and verification

Dynamic displacement of structures is an important index for in-service structural condition and behavior assessment, but accurate measurement of structural displacement for large-scale civil structures such as long-span bridges still remains as a challenging task. In this paper, a vision-based dynamic displacement measurement system with the use of digital image processing technology is developed, which is featured by its distinctive characteristics in non-contact, long-distance, and high-precision structural displacement measurement. The hardware of this system is mainly composed of a high-resolution industrial CCD (charge-coupled-device) digital camera and an extended-range zoom lens. Through continuously tracing and identifying a target on the structure, the structural displacement is derived through cross-correlation analysis between the predefined pattern and the captured digital images with the aid of a pattern matching algorithm. To validate the developed system, MTS tests of sinusoidal motions under different vibration frequencies and amplitudes and shaking table tests with different excitations (the El-Centro earthquake wave and a sinusoidal motion) are carried out. Additionally, in-situ verification experiments are performed to measure the mid-span vertical displacement of the suspension Tsing Ma Bridge in the operational condition and the cable-stayed Stonecutters Bridge during loading tests. The obtained results show that the developed system exhibits an excellent capability in real-time measurement of structural displacement and can serve as a good complement to the traditional sensors.

Acquisition of the tide-induced Lagrangian residual current field by the PIV technique in the laboratory

In this paper, a new particle image velocimetry (PIV)-based measurement method is proposed to obtain the high-resolution tide-induced Lagrangian residual current field in the laboratory. A long gravity wave was generated to simulate the tide in a narrow tank full of water laden with PIV particles. Consecutive charge-coupled device (CCD) images were recorded with the studied layer illuminated with a laser beam. Two images separated by one tidal period were processed by applying the pattern-matching algorithm to get the horizontal tide-induced Lagrangian residual current field. The results coincide with sporadic results from the traditional surface-float tracing method, but with much higher spatial resolution and accuracy. Furthermore, it is found that the direct acquisition of the Lagrangian residual current may reduce the error at least by one order compared with those acquisition methods that require the detailed information of the tidal cycle.

LED Chips Locating Algorithm Based on Wavelet Transformation

LED chips position is of crucial significance in chip testing, scribing, die spreading, and die bonding. It’s a great solution to indicate electrical characteristics of chips, examine whether the chip pins are up to the standard, and distinguish LED chip quality. Concerning this, an LED chip positioning method based on wavelet transform is proposed in this paper. Firstly, CCD, light and motion control module are adopted to construct and acquire the visual system of LED chip images. Then the images are processed with lowpass filtering and normalization to obtain Hi-Q chips image, and image features are extracted by further using multi-scale wavelet transform. Lastly, high accuracy positioning of LED chips is achieved by employing high-accuracy point pattern matching algorithm. Experimental results show that the LED chip image positioning error is less than 1μm and the position speed is faster than 5 particles per second, which offer new approaches for high-accuracy chip positioning system of detection machines, sorting machines, die bonders, etc.

A Case Study of Distributed Network Fault Detection Technology in Distance Education

The intrusion detection under the environment of IPv6 is an important security technology along with firewall in system security defense system, which can be used for real-time detection and monitoring of the system in the whole process of system invasion. This paper puts forward an intrusion detection system under IPv6 platform based on intrusion detection feature attribute reduction by using pattern matching, so as to expand the range of application and user group of the security products. By the analysis and comparison of various pattern matching algorithms, the new algorithm realizes the intrusion feature module matching under IPv6, and make detection system be of high efficiency. Later experiments have proved this view.

An Intrusion Detection Method Study under the Environment of IPv6

The intrusion detection under the environment of IPv6 is an important security technology along with firewall in system security defense system, which can be used for real-time detection and monitoring of the system in the whole process of system invasion. This paper puts forward an intrusion detection system under IPv6 platform based on intrusion detection feature attribute reduction by using pattern matching, so as to expand the range of application and user group of the security products. By the analysis and comparison of various pattern matching algorithms, the new algorithm realizes the intrusion feature module matching under IPv6, and make detection system be of high efficiency. Later experiments have proved this view.

Compressive Sensing Pattern Matching Techniques for Synthesizing Planar Sparse Arrays

In this paper, the design of sparse planar arrays is yielded through a set of innovative and efficient pattern matching algorithms within the Bayesian Compressive Sensing (BCS) framework. Towards this end, the 2D sparse synthesis problem is formulated in a probabilistic fashion and the single-task (ST) and the multi-task (MT) BCS solutions are derived. The results from a numerical validation concerned with different aperture size and target patterns prove that the proposed implementations enable an element saving ranging from 25% up to 87%, while achieving a reliable beam control.

An Improved Algorithm Based on AC-BM Algorithm

The pattern matching algorithm is the mainstream technology in the instruction detection system, and therefore as a pattern-matching methods core string matching algorithm directly affect an intrusion detection system performance and efficiency. So based on the discussions of the most fashionable pattern matching algorithms at present, an improved algorithm of AC-BM is presented. From the experiments in the Snort ,it is concluded that the improved algorithm of the performance and efficiency is higher than AC-BM algorithm.

Improved Fingerprint Recognition Algorithm Application Study on Smart Home

Study on a new type of fingerprint identification algorithm and its application in intelligent home electric control lock problem. The traditional fingerprint recognition algorithms on fingerprint minutiae matching accuracy is low, difficult to accurately extract details, leading to lock malfunction or could not be opened. In order to overcome this problem, improved Point pattern fingerprint recognition matching algorithm, joined the matching weight coefficient on the base of pattern matching algorithm, and gives the hardware structure of fingerprint identification system, the improved algorithm is successfully applied in smart home applications, the example shows that, the improved algorithm can effectively improve the recognition rate , reduce false positives, has a certain practical value.

Localization of RFID-equipped assets during the operation phase of facilities

Indoor localization has gained importance as it has the potential to improve various processes related to the lifecycle management of facilities and to deliver personalized and location-based services (LBSs). Radio Frequency Identification (RFID) based systems, on the other hand, have been widely used in different applications in construction and maintenance. This paper investigates the usage of RFID technology for indoor localization of RFID equipped assets during the operation phase of facilities. The location-related data on RFID tags attached to assets are extracted from a Building Information Model (BIM) and can provide context-aware information inside the building which can improve Facilities Management (FM) processes. First, using the current location of the assets saved on the tags attached to fixed assets, an FM personnel is able to read tags from a distance and locate them on a floor plan. Fixed tags with known positions act as reference tags for RFID reader localization techniques. In this scenario, the user can also estimate his/her location by scanning the surrounding reference tags. Furthermore, the paper investigates an approach to locate moveable assets using received signals from available reference tags in the building based on pattern matching and clustering algorithms. As a result, a user equipped with an RFID reader is able to estimate his/her location, as well as the location of target assets, without having access to any Real-Time Location System (RTLS) infrastructure. Several case studies are used to demonstrate the feasibility of the proposed methods.

Network Threat Behavior Detection and Trend Analysis Based on the TDLC Model

The rapid development of computer networks has accelerated the development of society, but also leads to much more frequent network attacks, and makes the attacks much more complex. Therefore, network intrusion detection becomes a great challenge to security in both industry and academics. In this paper, we present a network detection method based on a collaborative model against network threats and attacks, as well as trend analysis of network structure. First of all, the threat detection level collaboration model is given a specific framework and build process, as well as collaborative mechanisms. We then provide a pattern-matching algorithm and a behavioral sequence template for a simple introduction to this approach, and we explain how to use the collaborative model structure. Finally, the security situation of the entire network is analyzed by a quantitative situation evaluation model, cooperating with the network topology, and the threat type is determined by a D-S evidence theory algorithm. The experiment results show that, while running in an intranet security guard system of a large enterprise, a next-step attack can be predicted by our algorithm, and the security situation of the entire network can be accurately evaluated as well.