Perform Pattern Matching Research Articles

Split-based elevational localization of photoacoustic guidewire tip by 1D array probe using spatial impulse response.

Objective. Photoacoustic emitters on the tip of a therapeutic device have been intensively studied for echo-guided intervention purposes. In this study, a novel method for localizing the guidewire tip emitter in the elevation direction using a 1D array probe is proposed to resolve the issue of the tip potentially deviating from the ultrasound-imaged plane.Approach. Our method uses the 'interference split' that appears when the emitter is off-plane. Here, a point source from the emitter splits into two points in images. Based on the split, 'split-based elevation localization (SEL)' is introduced to estimate the absolute elevation position of the emitter. Additionally, 'Signed SEL' incorporates an asymmetric feature into the 1D probe to obtain the sign of the elevation localization. An attenuative coupler is attached to the half side of the probe to control the interference split. In SEL and Signed SEL, we propose a modeled split matching (MSM) algorithm to localize the tip position. MSM performs pattern matching of a measured split waveform with modeled split waveforms corresponding to all emitter positions in a region of interest. The modeled waveforms are precalculated using the spatial impulse response. The proposed method is numerically and experimentally validated.Main results. Numerical simulations for time-domain wave propagation clearly demonstrated the interference split phenomena. In the experimental validation with a vessel-mimicking phantom, the proposed methods successfully estimated the elevation positions,yb.SEL exhibited a root-mean-squared error (RMSE) of 2.0 mm for the range of 0 mm ≤yb≤ 30 mm, while Signed SEL estimated the absolute position with an RMSE of 2.4 mm and the sign with an accuracy of 80.8% for the range of -30 mm ≤yb≤ 30 mm.Significance.These results suggest that the proposed method could provide approximate tip positions and help sonographers track it by fanning the probe.

High-Throughput Bit-Pattern Matching under Heavy Interference on FPGA

Bit-pattern matching is an important technological capability, used in many fields such as network intrusion detection (NID) and packet classification systems. Essentially, it involves the matching of an input bit pattern to a bit-pattern entry of a memory structure inside the system. Contemporary methods focus on the decomposition of the input bit pattern into smaller and more manageable parts, with the subsequent parallel processing of these elements. This fragmentation promotes the use of advanced pipeline techniques and hardware optimizations, enabling these methods to achieve very high throughputs and reasonable efficiency. However, the functionality of their respective circuits is limited to only performing pattern matching when there is no interference. In this article, we intend to present a circuit that performs pattern matching under heavy interference; instead of fragmentation, a more holistic approach will be adopted. To improve the throughput of the circuit, long bit sequences will be directly compared to many memory entries simultaneously. The minimization of hardware consumption and maximization of efficiency in these comparisons will be achieved with the use of novel hardware architecture that is based on pipelined adder trees and comparators. The platform of implementation is an FPGA (Field-Programmable Gate Array).

Permuted Pattern Matching Algorithms on Multi-Track Strings

A multi-track string is a tuple of strings of the same length. Given the pattern and text of two multi-track strings, the permuted pattern matching problem is to find the occurrence positions of all permutations of the pattern in the text. In this paper, we propose several algorithms for permuted pattern matching. Our first algorithm, which is based on the Knuth–Morris–Pratt (KMP) algorithm, has a fast theoretical computing time with O ( m k ) as the preprocessing time and O ( n k log σ ) as the matching time, where n, m, k, σ , and occ denote the length of the text, the length of the pattern, the number of strings in the multi-track, the alphabet size, and the number of occurrences of the pattern, respectively. We then improve the KMP-based algorithm by using an automaton, which has a better experimental running time. The next proposed algorithms are based on the Boyer–Moore algorithm and the Horspool algorithm that try to perform pattern matching. These algorithms are the fastest experimental algorithms. Furthermore, we propose an extension of the AC-automaton algorithm that can solve dictionary matching on multi-tracks, which is a task to find multiple multi-track patterns in a multi-track text. Finally, we propose filtering algorithms that can perform permuted pattern matching quickly in practice.

The Associative Memory System Infrastructures for the ATLAS Fast Tracker

The associative memory (AM) system of fast tracker (FTK) processor has been designed for the tracking trigger upgrade to the ATLAS detector at the Conseil Europeen Pour La Recherche Nucleaire large hadron collider. The system performs pattern matching (PM) using the detector hits of particles in the ATLAS silicon tracker. The AM system is the main processing element of FTK and is mainly based on the use of application-specified integrated circuits (ASICs) (AM chips) designed to execute PM with a high degree of parallelism. It finds track candidates at low resolution which become seeds for a full resolution track fitting. The AM system implementation is based on a collection of large 9U Versa Module Europa (VME) boards, named “serial link processors” (AMBSLPs). On these boards, a huge traffic of data is implemented on a network of 900 2-Gb/s serial links. The complete AM-based processor consumes much less power (~50 kW) than its CPU equivalent and its size is much smaller. The AMBSLP has a power consumption of ~250 W and there will be 16 of them in a crate. This results in unusually large power consumption for a VME crate and the need for complex custom infrastructure in order to have sufficient cooling. This paper reports on the design and testing of the infrastructures needed to run and cool the system which will include 16 AMBSLPs in the same crate, the integration of the AMBSLP inside a first FTK slice, the performance of the produced prototypes (both hardware and firmware), as well as their tests in the global FTK integration. This is an important milestone to be satisfied before the FTK production.

A Coprocessor for the Fast Tracker Simulation

The Fast Tracker (FTK) executes real-time tracking for online event selection in the ATLAS experiment. Data processing speed is achieved by exploiting pipelining and parallel processing. Track reconstruction is executed in two stages. The first stage, implemented on custom application-specific integrated circuit (ASICs) called associative memory (AM) chips, performs pattern matching (PM) to identify track candidates in low resolution. The second stage, implemented on field programmable gate arrays (FPGAs), builds on the PM results, performing track fitting in full resolution. The use of such a parallelized architecture for real-time event selection opens up a new, huge computing problem related to the analysis of the acquired samples. Millions of events have to be simulated to determine the efficiency and the properties of the reconstructed tracks with a small statistical error. The AM chip emulation is a computationally intensive task when implemented in software running on commercial resources. This paper proposes the use of a hardware coprocessor to solve this problem efficiently. We report on the implementation and performance of all the functions requiring massive computing power in a modern, compact embedded system for track reconstruction. That system is the miniaturization of the complex FTK processing unit, which is also well suited for powering applications outside the realm of high energy physics.

Non-Boolean Associative Processing: Circuits, System Architecture, and Algorithms

We present the design and the performance of a hierarchical associative memory (AM) based on phase locking of coupled oscillators used for pattern recognition. The use of coupled oscillators, rather than Boolean logic, provides for implementations using emerging nanotechnology, such as magnetic spin-torque oscillators and resonant body transistor oscillators, which have the potential of lower energy and higher density than CMOS solutions. We develop a model for the general behavior of weakly coupled nonlinear oscillators that perform pattern matching using a simulation of coupled CMOS ring oscillators. We derive a simple analytic model for their phase locking behavior and use this reduced model in a hierarchical AM for image recognition tasks, such as identifying handwritten numbers.

A Clinical Heart Disease Decision Supportive Optimized Mining Method for Effective Disease Diagnosis

Healthcare industry collects enormous amounts of healthcare data and required to mine and ascertain hidden information for constructive decision making. In recent years, the computer technology and machine learning methods with data mining approach increase techniques in assisting the doctors for productive decision making related to heart disease and stroke identification at an early stage. The needs to reduce the pattern matching loss by performing pattern matching while effectively improving the heart disease identification at much early stage poses severe challenges to the database community. In this paper a method called Clinical Heart Disease-Decision Supportive Optimized Mining (CHDDSOM) is presented to overcome the pattern matching loss, and perform perfect pattern matching. The method CHDDSOM categories to enable decision support with multidimensional analysis using Mahalanobis distance measure for obtaining dynamic data table information and typically built to support early stage of stroke identification and levels of heart disease. This helps in identifying the solution for different patterns and therefore reducing the pattern matching loss. With the objective of identifying the level of heart disease in a very accurate manner, the CHDDSOM method uses the Iterative Dichotomiser 3 based decision tree model.With Iterative Dichotomiser 3 based decision tree model, the stroke is also identified very easily by starting with the original set S as the root node. The entropy value of every attribute is calculated and is placed in the decision tree. Decision tree are constructed from the higher to lower values to perform easy pattern matching, aiming at reducing the processing time for pattern matching. Experiment is conducted with the Cleveland Clinic Foundation Heart disease data set available from UCI repository using the factors such as pattern matching loss rate, accuracy, processing time for pattern matching, computational cost. Experimental analysis shows that the CHD-DSOM method is able to reduce the processing time for pattern matching by 33.23% and reduce the pattern matching loss rate by 29.67% compared to the state-of-the-art works.

EFFICIENTLY MATCHING FREQUENT PATTERNS BASED ON BITMAP INVERTED FILES BUILT FROM CLOSED ITEMSETS

Online pattern matching has become an increasingly important method for utilizing the discovered frequent patterns to build various intelligent systems, and obviously it demands high pattern matching efficiency. Memory-based online pattern matching is a solution, but it requires a frequent pattern base with small storage size. The closed itemsets are a lossless and condensed representation of all frequent itemsets, however, their itemset format does not facilitate implementing efficient pattern matching. This paper proposes a novel method that builds the bitmap inverted file from the closed itemsets and uses it to substitute for the closed itemsets to perform pattern matching. The bitmap inverted files use bitmaps instead of the referential lists to keep the maps of frequent items in the closed itemsets, so as to reduce the storage size and promote the intersecting operation efficiency. In order to completely substitute for the closed itemsets in pattern matching, the bitmap inverted files also keep other information relevant to the closed itemsets, such as their supports and lengths. Experiments show that on dense datasets, the storage size of the bitmap inverted files is much smaller than that of the set of closed itemsets, and the pattern matching based on the bitmap inverted files resident in memory is orders of magnitude more efficient than that based on the closed itemsets.

FPGA implementation of biologically-inspired auto-associative memory

The design of an auto-associative memory based on a spiking neural network is described. Delays rather than binary values are used to represent signals and coincidence is used to perform pattern matching. A complete implementation of the memory on a single FPGA is presented.

A cascaded neuro-computational model for spoken word recognition

In human speech recognition, words are analysed at both pre-lexical (i.e., sub-word) and lexical (word) levels. The aim of this paper is to propose a constructive neuro-computational model that incorporates both these levels as cascaded layers of pre-lexical and lexical units. The layered structure enables the system to handle the variability of real speech input. Within the model, receptive fields of the pre-lexical layer consist of radial basis functions; the lexical layer is composed of units that perform pattern matching between their internal template and a series of labels, corresponding to the winning receptive fields in the pre-lexical layer. The model adapts through self-tuning of all units, in combination with the formation of a connectivity structure through unsupervised (first layer) and supervised (higher layers) network growth. Simulation studies show that the model can achieve a level of performance in spoken word recognition similar to that of a benchmark approach using hidden Markov models, while enabling parallel access to word candidates in lexical decision making.

Detection of planar motion objects using Radon transform and one‐dimensional phase‐only matched filtering

AbstractWe describe an algorithm to detect the position and orientation of multiple objects in planar motion using the Radon transform and 1D phase‐only matched filtering (POMF). The proposed vision algorithm performs pattern matching between a template and input image to detect the position and orientation of the objects. First, the Radon transform is applied to the two images to separate the rotation and translation of the objects. Next, 1D POMF along the distance of the Radon transform is applied to the transformed images so as to detect the separated position and orientation. Since it uses 1D POMF to detect both position and orientation, the proposed algorithm can detect the position and orientation of multiple objects. © 2006 Wiley Periodicals, Inc. Syst Comp Jpn, 37(5): 56–66, 2006; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/scj.20398

Speech recognition apparatus and method using two opposite words

A speech recognition apparatus recognizes a speech signal received from a speaker and provides the result of recognition for an external device. In the apparatus, a pattern matching section performs pattern matching between each of reference patterns in a vocabulary and characteristic parameters extracted from the speech signal. The vocabulary includes reference patterns corresponding to words. Further the apparatus has a similar sound group which includes reference patterns corresponding to the sound similar to that of a specific word. The specific word is a word in response to which the external device performs an operation which cannot be easily undone. The speech signal is rerecognized by using the similar sound group. As a result, the pattern matching section outputs a word other than the specific word, if one of the reference patterns in the similar sound group has a high similarity with the characteristic parameters.

An On-Line Correction Technique of Random Loading with a Real-Time Signal Processor for a Laboratory Fatigue Test

Abstract The authors present an application of the Solartron 1200 Real Time Signal Processor to correcting the on-line random loading in a fatigue test system. By inputting a power spectrum described by a data file to a computer, its corresponding random process may be simulated. Since the processor is connected with a computer, results of signal processing done by the processor are sent to the computer periodically during the test. The computer performs pattern matching using the correlation coefficient value (CCV) between a loading spectrum and its predetermined target. If a CCV exceeds its threshold, the old data file will be replaced with a new one and the random loading exerted on a specimen will be updated. When the new data file is obtained based on optimal control strategy, the updated loading spectrum will approach its target. Therefore, an electrohydraulic fatigue test system equipped with a real-time signal processor can realize an on-line correction of a random loading based on the target spectrum desired. Since this updating does not change the original software package and hardware of the system, the performance of the system and testing quality are safely improved.

Successful Pattern Matching with a Large Number of Reference Patterns Using a Joint Fourier-Transform Correlator

Two kinds of modification of the joint transform correlator to perform pattern matching are proposed and the mechanisms are analyzed. Simulation and experimental results show that pattern matching is executed correctly by the modified JTCs; it is shown that one of the two modified joint transform correlators is equivalent to matched filtering and the other corresponds to pure-phase-only filtering.

On String Pattern Matching: A New Model with a Polynomial Time Algorithm

A polynomial time algorithm is presented for string pattern matching. Earley’s parsing algorithm is adapted for context-free patterns and is extended to allow the augmentation of the immediate assignment operation of SNOBOL4 and a powerful descriptive operator not previously implemented, set complement. Canonical pattern definition systems are defined to describe patterns for which our algorithm will perform pattern matching. The languages generated by such systems are called extended context-free languages, and are shown to properly contain the family of context-free languages and all families of k-intersection languages which have been shown to establish an infinite hierarchy between the family of context-free languages and the family of context-sensitive languages. It is also shown that for an alphabet of one character, the immediate assignment operator cannot be expressed in terms of the complement operator. Some results on the closure properties and unsolvable problems for this new family are also sh...