Pattern Matching Algorithm Research Articles

Point Pattern Matching Algorithm for Planar Point Sets under Euclidean Transform

Point pattern matching is an important topic of computer vision and pattern recognition. In this paper, we propose a point pattern matching algorithm for two planar point sets under Euclidean transform. We view a point set as a complete graph, establish the relation between the point set and the complete graph, and solve the point pattern matching problem by finding congruent complete graphs. Experiments are conducted to show the effectiveness and robustness of the proposed algorithm.

Short-term sales forecasting with change-point evaluation and pattern matching algorithms

The hotel and car manufacturing industries share many common points in their sales forecasting. For example, both are greatly affected by the fluctuation of economy, and closely related to the inertia. According to the principle characters of forecasting problem concerning these two kinds of industries, a short-term quantitative sales forecasting model is proposed based on the economic fluctuation analysis and the naı¨ve forecasting technology. The sales time series and its curve are used to construct this model. The relative concepts of the model are presented and corresponding algorithms are brought forward. Firstly, economic fluctuation of products sales is analyzed and the historical patterns of economic fluctuation change are divided. According to the geometric characteristics of a sales curve, the best historical matching for the current status is then found out, which corresponds to the process of activating the historical experiences of a manager. Finally the changing trend of the sales curve in the next period is determined, from which the short-term sales forecasting results can be obtained. The number of scattered guests of a hotel and the short-term sales for cars manufactured by a factory are forecasted by means of the model, which shows satisfactory forecasting accuracy. In fact, the forecasting approach proposed herein is the mathematical representation of the naïve forecasting method that is a kind of regular deduction based on the similarity between historical pattern and current status. Thus, this approach is good at forecasting the time series with the similarity between historical pattern and current status no matter whether the time series is seasonal or not, and gives better forecasting accuracy than ARMA and ANN models.

An Algorithm to Compute the Character Access Count Distribution for Pattern Matching Algorithms

We propose a framework for the exact probabilistic analysis of window-based pattern matching algorithms, such as Boyer–Moore, Horspool, Backward DAWG Matching, Backward Oracle Matching, and more. In particular, we develop an algorithm that efficiently computes the distribution of a pattern matching algorithm’s running time cost (such as the number of text character accesses) for any given pattern in a random text model. Text models range from simple uniform models to higher-order Markov models or hidden Markov models (HMMs). Furthermore, we provide an algorithm to compute the exact distribution of differences in running time cost of two pattern matching algorithms. Methodologically, we use extensions of finite automata which we call deterministic arithmetic automata (DAAs) and probabilistic arithmetic automata (PAAs) [1]. Given an algorithm, a pattern, and a text model, a PAA is constructed from which the sought distributions can be derived using dynamic programming. To our knowledge, this is the first time that substring- or suffix-based pattern matching algorithms are analyzed exactly by computing the whole distribution of running time cost. Experimentally, we compare Horspool’s algorithm, Backward DAWG Matching, and Backward Oracle Matching on prototypical patterns of short length and provide statistics on the size of minimal DAAs for these computations.

Research on TCAM Match Algorithm in High-Speed DPI System

The backbone network throughput has climbed up to 40Gbps, it demands a high-speed pattern matching algorithm. In this paper, we apply Half byte matching and SRAM-based Expansion into the traditional TCAM lookup algorithm, to meet accuracy requirements. Theoretical analysis and experimental results show that, the HBS-TCAM algorithms can significantly improve the service identification accuracy, thus enhancing the performance of TCAM lookup system.

Commercialization of Prognostics Systems Leveraging Commercial Off- The-Shelf Instrumentation, Analysis, and Data Base Technologies

There are many facets of a prognostics and health management system. Facets include data collection systems that monitor machine parameters; signal processing facilities that sort, analyze and extract features from collected data; pattern matching algorithms that work to identify machine degradation modes; database systems that organize, trend, compare and report information; communications that synchronize prognostic system information with business functions including plant operations; and finally visualization features that allow interested personnel the ability to view data, reports, and information from within the intranet or across the internet.A prognostic system includes all of these facets, with details of each varying to match specific needs of specific machinery. To profitably commercialize a prognostic system, a generic yet flexible framework is adopted which allows customization of individual facets. Customization of one facet does not materially impact another. This paper describes the framework, and provides case studies of successful implementation.

Flanking sequence effects on oligonucleotide hybridization

Microarray platforms measure both sequence content and prevalence of sequence by leveraging oligonucleotide hybridization. Computational probe design centers on short regions of high sequence similarity, most often between 25 and 50 base pairs in length. A variety of pattern-matching algorithms specific to the probe length are employed to determine the uniqueness of a target against its background, and a subset of these consider internal structure of the probe as a contra-indicator of successful hybridization. Since it is assumed that the target structure will mirror that of the probe, no consideration is given to the flanking sequences. In this study, we start to examine on the effects of total target sequence length in the context of common hybridization assay conditions. There are two considerations: due to the flexible nature of the single-stranded backbone, loops and bubbles can still yield stable hybridization between species with less than perfect sequence homology; the flanking sequences may loop back and occupy or obstruct the intended duplex region. Selecting a small subset of 33mer probes from the Affymetrix SNP6.0 Array we generated a pool of potentially cross-hybridizing sequence regions found by using the SeqNFindTM platform. Pools of length variant targets centered around the potentially cross hybridizing sequence were generated from human reference genome 36.3 and computationally examined using OMPTM to calculate the affinity constants. To demonstrate that the modeled properties affect measurements a small subset of these targets and probes were tested on microarrays.

High-Resolution Tactile Imaging Sensor Using Total Internal Reflection and Nonrigid Pattern Matching Algorithm

A novel tactile imaging sensor, that is capable of measuring the elasticity of the touched object, is designed, implemented, and tested. In the proposed sensor, a multilayer Polydimethylsiloxane optical waveguide has been fabricated as the sensing probe. The light is illuminated at the critical angle to totally reflect within the flexible and transparent waveguide. When a waveguide is compressed by an object, the contact area of the waveguide deforms and causes the light to scatter. The scattered light is captured by a high-resolution camera. To find the elastic modulus of a touched object, multiple tactile images are taken from slightly different loading force values. The applied force has been estimated using the integrated pixel values of the tactile image. The strain has been estimated by matching the series of tactile images using the proposed nonrigid pattern matching algorithm. The measurement method was validated by the commercial soft polymer samples with the known elastic modulus. The experimental results showed that the tactile imaging sensor can measure the elastic modulus with the error less than 5.38%.

Temperature Measurements of the Air Plasma Flow Using Optical Emission Spectroscopy

The rotational temperature of the freestream flow generated by the Scirocco arcjet plasma wind-tunnel facility of the Italian Aerospace Research Center (CIRA) is measured by means of optical emission spectroscopy. The gas mixture used to perform the atmospheric reentry simulation tests is air, and the optical spectrum emitted 17 cmaway from the exit section of the nozzle has been investigated over the 220–310 nm wavelength range. The optical setup is based on a 1200-grooves=mmmonochromator and a 1024 256 charge-coupled device camera, and the spectrum has been calibrated using a combined deuterium–halogen radiation source. The rotational temperature is determined by comparing the experimental spectra with those obtained by the spectral simulation software LIFBASE 2.0. A pattern-matching algorithm is used. Attention is mainly focused on the NO emission bands, and partial agreement between experimental results and numerical predictions is achieved for six test campaigns conducted in the arcjet facility. Finally, an error analysis has been performed to assess the error bands of the measures.

SU-E-I-194: Motion Detection Based Adaptive Temporal Filtering for Image Guided Procedures Using the High Resolution Microangiographic Fluoroscope (MAF)

Purpose: To implement real-time adaptive temporal filtering based on amount of motion detected for an object of interest during fluoroscopy. For a stationary object, higher-weight temporal filtering is used and during movement the weight is reduced in real-time. Methods: A phantom consisting of two stents (stainless steel and nitinol) mounted on a linear stage, and a stationary third stent (nitinol stent with platinum markers) was assembled. The stent strut sizes ranged between 80 to 100 micron. Fluoroscopy was done using the custom high resolution Micro Angiographic Fluoroscope (MAF) with an x-ray spectrum hardened by a head equivalent phantom. The stainless steel stent was selected as the object of interest and was localized using a pattern matching algorithm available as a part of LabVIEW IMAQ Vision software. A real time adaptive temporal filter was developed. When the stent was found to be stationary, the temporal filtering weight was increased. Depending on the amount of motion the weight was reduced; more the motion, lesser the weight. Results: Without temporal filtering, the nitinol stent could not be seen. When the stents were stationary, and the temporal filter weight was increased, the nitinol stent could be clearly visualized due to the quantum noise reduction. When the same temporal filter was used while the stents were in motion, then motion blur reduced the visibility of the nitinol stent and the struts of stainless steel stent also could not be distinguished from each other. In this case when the temporal filtering weight was reduced the stainless steel stent struts were visualized better. Conclusions: Motion based adaptive temporal filtering can be implemented to aid during interventional procedures by tracking the motion of the high contrast interventional device to improve the visualization of lower contrast neurovascular objects. Support: NIH Grants R01-EB008425, R01-EB002873

SU-E-T-504: Development of An Offline Based Internal Organ Motion Verification System during Treatment Using Sequential Cine EPID Images

Purpose: We developed an offline based organ motion verification system using cine EPID images and evaluated its accuracy and availability through phantom study. Methods: The system was designed for import of cine EPID images, which are obtained sequentially during treatment through a DICOM format and reconstructed into a live image. For verification of organ motion, a pattern matching algorithm using an internal surrogate was employed in the self-developed analysis software. For the system performance test, we developed a linear motion phantom, which consists of a human body shaped phantom with a fake tumor in the lung and linear motion cart with control software. The phantom was operated with a motion of 2 cm at 4 sec per cycle and cine EPID images were obtained at a rate of 3.3 frames/sec with 1024 × 768 pixel counts in a linear accelerator (10 MVX). Organ motion of the target was tracked using self-developed analysis software and compared with data from the RPM system (Varian, USA). For quantitative analysis, we analyzed correlation between two data sets in terms of average cycle, amplitude, and pattern (root mean square, RSM) of motion. Results: Averages for the cycle of motion from cine EPID and RPM system were 3.95±0.02 and 3.98±0.11 sec, respectively, and showed good agreement on real value (4 sec). Average of the amplitude of motion tracked by our system (1.71 ±0.02 cm) showed a slightly different value, compared with the actual value (2 cm), due to time resolution for image acquisition. The value of the RMS from the cine EPID image (0.379) grew slightly, by 3.8%, compared with data from the RPM (0.365). Conclusions: Our system showed good representation of its motion in a preliminary phantom study. The system can be implemented for clinical purposes, which include organ motion verification and its feedback for accurate dose delivery to the moving target.

Perception of sinewave vowels.

There is a significant body of research examining the intelligibility of sinusoidal replicas of natural speech. Discussion has followed about what the sinewave speech phenomenon might imply about the mechanisms underlying phonetic recognition. However, most of this work has been conducted using sentence material, making it unclear what the contributions are of listeners' use of linguistic constraints versus lower level phonetic mechanisms. This study was designed to measure vowel intelligibility using sinusoidal replicas of naturally spoken vowels. The sinusoidal signals were modeled after 300 /hVd/ syllables spoken by men, women, and children. Students enrolled in an introductory phonetics course served as listeners. Recognition rates for the sinusoidal vowels averaged 55%, which is much lower than the ∼95% intelligibility of the original signals. Attempts to improve performance using three different training methods met with modest success, with post-training recognition rates rising by ∼5-11 percentage points. Follow-up work showed that more extensive training produced further improvements, with performance leveling off at ∼73%-74%. Finally, modeling work showed that a fairly simple pattern-matching algorithm trained on naturally spoken vowels classified sinewave vowels with 78.3% accuracy, showing that the sinewave speech phenomenon does not necessarily rule out template matching as a mechanism underlying phonetic recognition.

Performance Evaluation of Full Search Equivalent Pattern Matching Algorithms

Pattern matching is widely used in signal processing, computer vision, and image and video processing. Full search equivalent algorithms accelerate the pattern matching process and, in the meantime, yield exactly the same result as the full search. This paper proposes an analysis and comparison of state-of-the-art algorithms for full search equivalent pattern matching. Our intention is that the data sets and tests used in our evaluation will be a benchmark for testing future pattern matching algorithms, and that the analysis concerning state-of-the-art algorithms could inspire new fast algorithms. We also propose extensions of the evaluated algorithms and show that they outperform the original formulations.

Machine vision system for automated spectroscopy

This paper describes a novel system based on the machine vision and machine learning techniques for fully automated, real-time identification of constituent elements in a sample specimen using laser-induced breakdown spectroscopy (LIBS) images. The proposed system is developed as a compact spectrum analyzer for rapid element detection using a commercially available video camera. We proposed a correlation-based pattern matching algorithm for analyzing single element spectra. However, the use of a high-speed laser and presence of numerous imperfections in the experimental setup require advanced techniques for analyzing multi-element spectra. We cast the element detection problem as a multi-label classification problem that uses support vector machines and artificial neural networks for multi-element classification. The proposed algorithms were evaluated using actual LIBS images. The machine learning approaches yielded correct identification of elements to an accuracy of 99%. Our system is useful in instances where a qualitative analysis is sufficient over a quantitative element analysis.

A fast pattern matching algorithm with multi-byte search unit for high-speed network security

A signature-based intrusion detection system identifies intrusions by comparing the data traffic with known signature patterns. In this process, matching of packet strings against signature patterns is the most time-consuming step and dominates the overall system performance. Many signature-based network intrusion detection systems (NIDS), e.g., the Snort, employ one or multiple pattern matching algorithms to detect multiple attack types. So far, many pattern matching algorithms have been proposed. Most of them use single-byte standard unit for search, while a few algorithms such as the Modified Wu–Manber (MWM) algorithm use typically two-byte unit, which guarantees better performance than others even as the number of different signatures increases. Among those algorithms, the MWM algorithm has been known as the fastest pattern matching algorithm when the patterns in a rule set rarely appear in packets. However, the matching time of the MWM algorithm increases as the length of the shortest pattern in a signature group decreases. In this paper, by extending the length of the shortest pattern, we minimize the pattern matching time of the algorithm which uses multi-byte unit. We propose a new pattern matching algorithm called the L +1-MWM algorithm for multi-pattern matching. The proposed algorithm minimizes the performance degradation that is originated from the dependency on the length of the shortest pattern. We show that the L +1-MWM algorithm improves the performance of the MWM algorithm by as much as 20% in average under various lengths of shortest patterns and normal traffic conditions. Moreover, when the length of the shortest pattern in a rule set is less than 5, the L +1-MWM algorithm shows 38.87% enhancement in average. We also conduct experiments on a real campus network and show that 12.48% enhancement is obtained in average. In addition, it is shown that the L +1-MWM algorithm provides a better performance than the MWM algorithm by as much as 25% in average under various numbers of signatures and normal traffic conditions, and 20.12% enhancement in average with real on-line traffic.

Exact Multiple Pattern Matching Algorithm using DNA Sequence and Pattern Pair

string matching algorithms are essential components in DNA applications of the computational biology. Pattern matching is an important task of the pattern discovery process in today's world for finding the structural and functional behavior in proteins and genes. Although pattern matching is commonly used in computer science and information processing, it can be found in everyday tasks. Molecular biologists often search for the important information from the databases in different directions of different uses. With the increasing need for instant information, pattern matching will continue to grow and change as needed from time to time. In this research we propose a new pattern matching technique called an Exact multiple pattern matching algorithms using DNA sequence and pattern pair. The current approach is used to avoid unnecessary comparisons in the DNA sequence. Due to this, the number of comparisons gradually decreases and comparison per character ratio of the proposed algorithm reduces accordingly when compared to the some of the existing popular methods. Proposed algorithm is implemented and compared with existing algorithms. Comparison results demonstrate that index based algorithm is efficient than the number of the existing techniques.

Scalable Pattern Matching on Multicore Platform via Dynamic Differentiated Distributed Detection (D⁴)

Pattern Matching (PM) is a key building block for many emerging network applications. Modern multicore platforms are becoming performance competitive with traditional hardware solutions, which are expensive and hard to adapt to the rapid diversification of Internet applications. However, due to uneven network flow sizes and the need to retain packet order within each flow, traditional parallel processing models using packet flows as the basic unit to partition the workload cannot fully take advantage of multicore platforms' power, exhibiting low CPU utilization and poor scalability with increasing numbers of CPUs or cores. In this paper, we propose a novel parallel inspection model called Dynamic Differentiated Distributed Detection (D4). D4 deploys balanced parallel detection by adding one more dimension on PM workload partition. The pattern set is prepartitioned into several subsets so as to distribute the workload of the hot flows across multiple cores while still maintaining packet order within each flow. We also show theoretically that higher number of subsets leads to higher algorithmic overhead. To achieve optimal throughput for all flow size distributions, D4 prepartitions the pattern set in several ways for use in different detection modes beforehand, and then, dynamically switches among these modes on-the-fly according to the flow and runtime information it senses. D4 also allows multiple PM algorithms to work simultaneously on different pattern subsets. According to several heuristics and the algorithms' characteristics, the detection mode selection and subset partitioning algorithms are designed to maximize the CPU/core utilization while avoiding unnecessary overheads. Experiments show that D4 features high core utilization and low overhead, thus achieving distinct performance gains against traditional load balancing schemes, as shown by experimental results using real-world pattern sets and traffic traces.

Extended XML Tree Pattern Matching: Theories and Algorithms

As business and enterprises generate and exchange XML data more often, there is an increasing need for efficient processing of queries on XML data. Searching for the occurrences of a tree pattern query in an XML database is a core operation in XML query processing. Prior works demonstrate that holistic twig pattern matching algorithm is an efficient technique to answer an XML tree pattern with parent-child (P-C) and ancestor-descendant (A-D) relationships, as it can effectively control the size of intermediate results during query processing. However, XML query languages (e.g., XPath and XQuery) define more axes and functions such as negation function, order-based axis, and wildcards. In this paper, we research a large set of XML tree pattern, called extended XML tree pattern, which may include P-C, A-D relationships, negation functions, wildcards, and order restriction. We establish a theoretical framework about “matching cross” which demonstrates the intrinsic reason in the proof of optimality on holistic algorithms. Based on our theorems, we propose a set of novel algorithms to efficiently process three categories of extended XML tree patterns. A set of experimental results on both real-life and synthetic data sets demonstrate the effectiveness and efficiency of our proposed theories and algorithms.

Enhancing particle image tracking performance with a sequential Monte Carlo method: The bootstrap filter

One of the common problems of current pattern match and particle image tracking algorithms is the deployment of constant velocity assumption for particle motion between two frames, which would result in serious errors when high velocity gradient flows are measured. To address this issue, a new particle image tracking method—bootstrap filter tracking is proposed. In this new method, a simple nonlinear dynamic model which takes particle acceleration into account is employed and a sequential Monte Carlo method—bootstrap filter is used in conjunction with pattern match algorithm to strengthen the particle image tracking performance. By using the nonlinear system model and bootstrap filter, particle location at next time step can be predicted accurately and the new method is able to measure high velocity gradient flows with better performance than the traditional particle image tracking algorithms. This new method is validated by using numerically generated particle images. Its accuracy in terms of particle image density, out-of-plane displacement and displacement gradient is compared with the Kalman filter tracking (Takehara et al., 2000 [34]) and the Super-PIV (Keane et al., 1995 [30]) methods. The three algorithms are also compared by using a set of real turbulent jet images. The test results demonstrate that the bootstrap filter tracking method is superior than the Kalman filter tracking and the Super-PIV methods for measuring low density, high velocity gradient flows.

The development of an automated PIV image processing software—SmartPIV

Since the popularity of digital particle image velocimetry technique (DPIV), many PIV image processing algorithms have been proposed. Amongst them, fast Fourier transform (FFT) Cross Correlation, Discrete Window Offset Cross Correlation, Iterative Multigrid Cross Correlation, Iterative Image Deformation Cross Correlation and cross correlation based particle tracking methods are widely used algorithms and have been extensively studied by researchers. All of these algorithms have their advantages and disadvantages in terms of computational load and measurement accuracy. To choose a suitable algorithm, researchers not only need to understand the complex principles of these algorithms, but also need to find out their applicable flow conditions. This could greatly increase work load for PIV users who focus more on flow structure itself instead of PIV algorithms. It is therefore necessary to develop a method which can choose PIV algorithms wisely according to the input PIV images. This paper firstly reviews the development of PIV algorithm with mainly focus on analysing advantages and disadvantages of six widely used algorithms. By using both synthetic and real PIV images, comparative studies are then carried out among these algorithms. The tests give a rate for the performance of the algorithms and provide a parameter to automatically separate pattern match and particle tracking algorithms. Based on qualitative and quantitative analysis, an automated PIV image processing method—SmartPIV is proposed and tested by both synthetic and real PIV images. For all the three test cases, the SmartPIV successfully picks the most suitable algorithm and gives very promising results.

An Improved Pattern Matching Algorithm in the Intrusion Detection System

This paper proposed a matching algorithm FBMH(Fast Boyer Moor Horspool),which made an improvement on the BMH(Boyer Moor Horspool) and BMHS(Boyer Moor Horspool Sundy) matching algorithm based on the study of several typical pattern matching algorithms used in intrusion detection. The result shows that, the FBMH algorithm has less intrusion detection matching time than BMH and BMHS algorithm. The FBMH algorithm accelerated the speed of pattern matching effectively, therefore enhanced the efficiency of the intrusion detection system.