Pattern Matching

This paper sets out some findings of a research project carried out in private unaided schools in low-income areas of Hyderabad, India. The part of the research project documented here was designed to examine the question: ‘Is the regulatory regime conducive to entrepreneurial action and market discovery’ with particular reference to the low-income schools in Hyderabad. This paper is narrowly focused, setting out the results of pattern matching empirical data with the Austrian economic concepts of entrepreneurship, rivalry, and market discovery. The research discovered that two regulatory regimes exist, one that is set out ‘on paper’ in the Education Acts and associated rules, and another that operates ‘in practice’. That is, there is a combination of regulations ‘on paper’ and regulations existing in an ‘extra-legal’ sector. Generally it was found that the regulations ‘in practice’ are consistent with market principles. Conversely the regulations set out ‘on paper’ are not conducive to entrepreneurial innovation and market discovery. Recommendations for potential policy initiatives include the possibility of legitimising the ‘extra-legal’ sector by introducing self-regulation possibly via self-evaluation systems for the private unaided schools.

Graphs are widely used to model complicated structures and link them with each other. Some of such structures are XML documents, social networks, and computer networks. Information and model extraction from graph databases is a graph mining process. Efficient query search in graph databases, known as query processing, is one of the heated debates in the field of graph mining. One of the query processing techniques is sequential search over the whole dataset and isomorphism test on all sub-graphs in the database, which is not an optimal technique as to response time and storage. This problem brought in the issues of indexing graph databases to improve query processing performance. As the method implies, part of the database where the answer is expected to be found there is pruned and the number of needed isomorphism tests decreases. It might not be easy to compare the methods and techniques of graph query techniques as different techniques have different objectives. For instance, similarity search techniques reduce query time, while they cannot compete with exact matching techniques as to accuracy and vice versa. Input data volume might be also effective on query time as with immense datasets, similarity search techniques are more preferred than exact matching techniques. The present study is a survey of graph query processing techniques with emphasis on similarity search and exact matching.

Present paper describes the details of the study of the work that has been done in the field of text searching, a sub-division of Natural Language Processing (NLP) till date. The work in this project includes the study and analysis of some of the algorithms devised under this topic, finding the faults or loopholes and trying to increase the efficiency of these algorithms devised, taking forward the range of work done on it. Experiment is done on the various text search algorithms that have been devised namely Knuth-Morris Pratt Algorithm, Naïve Search Algorithm and Boyer-Moore Algorithm by providing text input of various sizes and analyzing their behavior on these variable inputs. After analyzing and doing the study on these algorithms the results states that BoyerMoore‟s Algorithm worked quite well and efficiently than the rest of them when dealing with larger data sets. When working on larger alphabets the Knuth-Morris Pratt Algorithm works quite well. These algorithms do have dra...

We have developed new algorithms for efficient and flexible pattern-matching and pattern-discovery in multidimensional datasets. (A multidimensional dataset is simply any set of points in an N-dimensional space.) These algorithms could be used as the basis of new commercially exploitable applications for data compression, information retrieval and data mining or structural analysis of data. The new algorithms are particularly appropriate for use with databases in which each item in the database is represented as a multidimensional dataset, as is the case, for example, in computer-based music libraries and databases of audio data, databases of 2- and 3-dimensional molecular structures, computer-based image and video libraries and collections of graphs representing scientific results or financial data.

Geometric hashing is a model-based recognition technique based on matching of transformation-invariant object representations stored in a hash table. In the last decade, a number of enhancements have been suggested to the basic method improving its performance and reliability. One of the important enhancements is rehashing, improving the computational performance by dealing with the problem of non-uniform occupancy of hash bins. However, the proposed rehashing schemes aim to redistribute the hash entries uniformly, which is not appropriate for Bayesian approach, another enhancement optimizing the recognition rate in presence of noise. In this paper, we derive the rehashing for Bayesian voting scheme, thus improving the computational performance by minimizing the hash table size and the number of bins accessed, while maintaining optimal recognition rate.

The basic theory of Markov chains has been known to mathematicians and engineers for close to 80 years, but it is only in the past decade that it has been applied explicitly to problems in speech processing. One of the major reasons why speech models, based on Markov chains, have not been developed until recently was the lack of a method for optimizing the parameters of the Markov model to match observed signal patterns. Such a method was proposed in the late 1960's and was immediately applied to speech processing in several research institutions. Continued refinements in the theory and implementation of Markov modelling techniques have greatly enhanced the method, leading to a wide range of applications of these models. It is the purpose of this tutorial paper to give an introduction to the theory of Markov models, and to illustrate how they have been applied to problems in speech recognition.

Phoneme recognition can mean two things, conscious phoneme-naming and p reconscious phone-categorization. Phoneme naming is based on a (learned) label i n the mental lexicon. Tasks requiring phoneme awareness will therefore exhibit all the features of r etrieving lexical it ems. Phone ca tegorization is a hypothetical pre-lexical and p reconscious process that forms the basis of both word recognition and phon eme naming. Evidence from the literature indicates that phone-categorization can be described within a pattern-matching framework with weak links between acoustic c ues and phon eme categories. This is illustrated with two experiments. The c urrent evidence favors a laxphoneme theory, in which all phonemic ca tegories s upported by the ac oustic e vidence and phonemic context are available to access the lexicon. However, the current evidence only supports segment-sized categories. It is inconclusive as to whether these categories are the same in number and content as the phonemes.

The aim of the study was to define a simple system for the identification of unknown bodies by using CT images of frontal sinus and to discuss whether it was worth to add measurements to the system or not. The system was including simple features as F (presence or absence of frontal sinus), S (intersinus and intrasinus septum) and S (scalloping), and named as FSS system. Measurements selected for the study were width, height, anteroposterior length, total width of two sinuses, the distance between the highest points of the two sinuses and the distance of each sinus to its maximum lateral limit.
The study was conducted retrospectively on the paranasal CT scans of 100 cases (38 male and 62 female) who had no apparent sinonasal pathology. All the features and measurements were coded according to the system defined by the authors for each case and coded formulas were compared. At least 93 % of the formulas could be eliminated for a case by using FSS system. The rate of success was increased to 98 % by adding measurements. Contrary to objective criteria of FSS system, measurements were prone to bias. Therefore, in practice success rate would be expected to be lower than calculated.
In the study population, instead of making 100 measurements, eliminating the most of the cases with FSS system and later discriminating the rest by pattern matching was seen logical.

In recent years, the increase in cyber threats has raised many concerns about security and privacy in the digital world. However, new attack methods are often limited to a few core techniques. Here, in order to detect new threat patterns, the authors use an attack graph structure to model unprecedented network traffic. This graph for the unknown attack is matched to a pre-known threat database, which contains attack graphs related to each known threat. The main challenge is to associate unknown traffics to a family of known threats. For this, the authors utilise random walks and pattern theorem. The authors utilise the pattern theorem and apply it to a set of proposed algorithms for detecting new generations of malicious traffics. Under the assumption of having a proper threat database, the authors argue that for each unknown threat, which belongs to a family of threats, it is possible to find at least one matching pattern with high matching rate and sensitivity.

This paper investigates the use of facial gestures for identity recogni- tion. This is the first time that such a quantitative evaluation is conducted, comparing the analyses of 2D versus 3D dynamic data of verbal and non- verbal facial actions. Suitable data processing and feature extraction meth- ods are examined, then a number of pattern matching techniques including the Fr´

Abstract: Palm vein authentication is one of the most reliable authentication. Which contain physiological body characteristics that can be used to distinguish between users. In this paper we represent in new approach for the personal identification in the palm vein image.This paper represent the contactless palm vein authentication device that takes blood vessel pattern as a personal identification. It is one of the most critical and challenging task to meet upcoming demand for stringent security. Vein are internal in the body and have wealth of differentiating feature assuming false identity through forgery is externally difficult, thereby enabling and externally high level of security. The palm secure works by capturing a person’s vein pattern image while radiating it with near infrared rays. The palm secure detects the structure of the pattern of veins on palm of the human hand with the at most precision .The sensor emits a near infrared beam towards the palm of the hand and the blood flowing through these back to the heart with reduced oxygen absorb as black pattern. This pattern is a recorded by the sensor and is stored in encrypted from in a database, on a token or an a smart card. It is consist of small palm vein scanner. That is easy and nature to use, fast and highly accurate.
Keywords: Biometric authentication, Palm-Vein Recognition, Vascular Patterns, ROI extraction, feature extraction, Pattern matching.
Title: Palm Vein Authentication
Author:  Ukirde Archana , Momale Shraddha, Waghmare Shital, Mandlik Swati
International Journal of Computer Science and Information Technology Research
ISSN 2348-1196 (print), ISSN 2348-120X (online)
Research Publish Journals

Herein, it is shown that by exploiting integral definitions of well known special functions, through generalizations and differentiations, broad classes of definite integrals can be solved in closed form or in terms of special functions. This is especially useful when there is no closed form solution to the indefinite form of the integral. In this paper, three such classes of definite integrals are presented. Two of these classes incorporate and supercede all of Kölbig's integration formulae [11], including his formulation for the computation of Cauchy principal values. Also presented are the mathematical derivations that support the implementation of a third class which exploits the incomplete Gamma function. The resulting programs, based on pattern matching, differentiation, and occasionally limits, are very efficient.

Content-based image retrieval consists in retrieving from an image database the most similar image with respect to a query, according to some similarity measure. This scenario has numerous specic applications that include bioinformatics and medical imaging, among others. However, because the size of image repositories grows very fast, nding patterns in images requires an index to avoid a sequential scan,

Template Matching is one of the most basic techniques in computer vision, where the algorithm should search for a template image T in an image to analyze I. It is heavily used in signal, image and video processing. A lot of applications are based on template matching in object detection, superresolution, image denoising and image compression. In this thesis, the rotation, scale, brightness and contrast invariant grayscale template matching problem is considered. An algorithm is proposed that uses a sufficient condition for distinguishing between candidate matching positions and other positions that cannot provide a better degree of match with respect to the current best candidate. The proposed algorithm is compared with eight existing state-of-the-art techniques. Theoretical analysis and experiments on eight image datasets show that the proposed algorithm, under some conditions, can maintain exhaustive accuracy while providing a significant speedup over state-of-the-art techniques with over than one order of magnitude in some cases.