Pattern Matching Algorithm Research Articles

Evaluation of Dry Electrodes in Canine Heart Rate Monitoring.

The functionality of three dry electrocardiogram electrode constructions was evaluated by measuring canine heart rate during four different behaviors: Standing, sitting, lying and walking. The testing was repeated (n = 9) in each of the 36 scenarios with three dogs. Two of the electrodes were constructed with spring-loaded test pins while the third electrode was a molded polymer electrode with Ag/AgCl coating. During the measurement, a specifically designed harness was used to attach the electrodes to the dogs. The performance of the electrodes was evaluated and compared in terms of heartbeat detection coverage. The effect on the respective heart rate coverage was studied by computing the heart rate coverage from the measured electrocardiogram signal using a pattern-matching algorithm to extract the R-peaks and further the beat-to-beat heart rate. The results show that the overall coverage ratios regarding the electrodes varied between 45–95% in four different activity modes. The lowest coverage was for lying and walking and the highest was for standing and sitting.

Local HMM for indoor positioning based on fingerprinting and displacement ranging

Received signal strength (RSS) in wireless networks is widely adopted for indoor positioning purpose because of its low cost and open access properties. However due to the sophisticated propagation of radio signals, the RSS shows a significant variation during pedestrian walking, which introduces critical errors in deterministic indoor positioning. To solve this problem, the authors present a novel method to improve the indoor pedestrian positioning accuracy by modelling fingerprinting and information on the movement into a hidden Markov models (HMMs). They divide the whole continuous positioning process into specified-size sub-processes, which could efficiently reduce the accumulative and resonance error caused by iterative estimation. They use an accelerometer sensor to provide the information on the movement distance to calculate the transition probability of the HMMs. In their experiments, they demonstrate that, compared with the deterministic pattern matching algorithm, the proposed method greatly improves the positioning accuracy and shows robust environmental adaptability.

A Brain-Computer Interface Based on Miniature-Event-Related Potentials Induced by Very Small Lateral Visual Stimuli.

Traditional visual brain-computer interfaces (BCIs) preferred to use large-size stimuli to attract the user's attention and elicit distinct electroencephalography (EEG) features. However, the visual stimuli are of no interest to the users as they just serve as the hidden codes behind the characters. Furthermore, using stronger visual stimuli could cause visual fatigue and other adverse symptoms to users. Therefore, it's imperative for visual BCIs to use small and inconspicuous visual stimuli to code characters. This study developed a new BCI speller based on miniature asymmetric visual evoked potentials (aVEPs), which encodes 32 characters with a space-code division multiple access scheme and decodes EEG features with a discriminative canonical pattern matching algorithm. Notably, the visual stimulus used in this study only subtended 0.5° of visual angle and was placed outside the fovea vision on the lateral side, which could only induce a miniature potential about 0.5 μV in amplitude and about 16.5dB in signal-to-noise rate. A total of 12 subjects were recruited to use the miniature aVEP speller in both offline and online tests. Information transfer rates up to 63.33b/min could be achieved from online tests (online demo URL: https://www.youtube.com/edit?o=U&video_id=kC7btB3mvGY ). Experimental results demonstrate the feasibility of using very small and inconspicuous visual stimuli to implement an efficient BCI system, even though the elicited EEG features are very weak. The proposed innovative technique can broaden the category of BCIs and strengthen the brain-computer communication.

Tree pattern matching from regular tree expressions

In this work we deal with tree pattern matching over ranked trees, where the pattern set to be matched against is defined by a regular tree expression. We present a new method that uses a tree automaton constructed inductively from a regular tree expression. First we construct a special tree automaton for the regular tree expression of the pattern E, which is somehow a generalization of Thompson automaton for strings. Then we run the constructed automaton on the subject tree t. The pattern matching algorithm requires an O(|t||E|) time complexity, where |t| is the number of nodes of t and |E| is the size of the regular tree expression E. The novelty of this contribution besides the low time complexity is that the set of patterns can be infinite, since we use regular tree expressions to represent patterns.

Individual Biometric Identification Using Multi-Cycle Electrocardiographic Waveform Patterns.

The electrocardiogram (ECG) waveform conveys information regarding the electrical property of the heart. The patterns vary depending on the individual heart characteristics. ECG features can be potentially used for biometric recognition. This study presents a new method using the entire ECG waveform pattern for matching and demonstrates that the approach can potentially be employed for individual biometric identification. Multi-cycle ECG signals were assessed using an ECG measuring circuit, and three electrodes can be patched on the wrists or fingers for considering various measurements. For biometric identification, our-fold cross validation was used in the experiments for assessing how the results of a statistical analysis will generalize to an independent data set. Four different pattern matching algorithms, i.e., cosine similarity, cross correlation, city block distance, and Euclidean distances, were tested to compare the individual identification performances with a single channel of ECG signal (3-wire ECG). To evaluate the pattern matching for biometric identification, the ECG recordings for each subject were partitioned into training and test set. The suggested method obtained a maximum performance of 89.9% accuracy with two heartbeats of ECG signals measured on the wrist and 93.3% accuracy with three heartbeats for 55 subjects. The performance rate with ECG signals measured on the fingers improved up to 99.3% with two heartbeats and 100% with three heartbeats of signals for 20 subjects.

String Matching Algorithms

To analyze the content of the documents, the various pattern matching algorithms are used to find all the occurrences of a limited set of patterns within an input text or input document. In order to perform this task, this research work used four existing string matching algorithms; they are Brute Force algorithm, Knuth-Morris-Pratt algorithm (KMP), Boyer Moore algorithm and Rabin Karp algorithm. This work also proposes three new string matching algorithms. They are Enhanced Boyer Moore algorithm, Enhanced Rabin Karp algorithm and Enhanced Knuth-Morris-Pratt algorithm.
 Findings: For experimentation, this work has used two types of documents, i.e. .txt and .docx. Performance measures used are search time, number of iterations and accuracy. From the experimental results, it is realized that the enhanced KMP algorithm gives better accuracy compared to other string matching algorithms. Application/Improvements: Normally, these algorithms are used in the field of text mining, document classification, content analysis and plagiarism detection. In future, these algorithms have to be enhanced to improve their performance and the various types of documents will be used for experimentation.

Adaptive on-line calibration for around-view monitoring system using between-camera homography estimation

The around-view monitoring (AVM) system is one of the major applications of advanced driver assistance systems and intelligent transportation systems. We propose an on-line calibration method, which can compensate misalignments for AVM systems. Most AVM systems use fisheye undistortion, inverse perspective transformation, and geometrical registration methods. To perform these procedures, the parameters for each process must be known; the procedure by which the parameters are estimated is referred to as the initial calibration. However, when only using the initial calibration data, we cannot compensate misalignments, caused by changing equilibria of cars. Moreover, even small changes such as tire pressure levels, passenger weight, or road conditions can affect a car’s equilibrium. Therefore, to compensate for this misalignment, additional techniques are necessary, specifically an on-line calibration method. On-line calibration can recalculate homographies, which can correct any degree of misalignment using the unique features of ordinary parking lanes. To extract features from the parking lanes, this method uses corner detection and a pattern matching algorithm. From the extracted features, homographies are estimated using random sample consensus and parameter estimation. Finally, the misaligned epipolar geographies are compensated via the estimated homographies. Thus, the proposed method can render image planes parallel to the ground. This method does not require any designated patterns and can be used whenever cars are placed in a parking lot. The experimental results show the robustness and efficiency of the method.

Mining location information from life- and earth-sciences studies to facilitate knowledge discovery

Location information in published studies represents an untapped resource for literature discovery, applicable to a range of domains. The ability to easily discover scientific articles from specific places, nearby locales, or similar (but geographically separate) areas worldwide is important for advancing science and addressing global sustainability challenges. However, the thematic and not geographic nature of current search tools makes location-based searches challenging and inefficient. Manually geolocating studies is labor intensive, and place-name recognition algorithms have performed poorly due to prevalence of irrelevant place names in scientific articles. These challenges have hindered past efforts to create map-based literature search tools. Thus, automated approaches are needed to sustain article georeferencing efforts. Common pattern-matching algorithms (parsers) can be used to identify and extract geographic coordinates from the text of published articles. Pattern-matching algorithms (geoparsers) were developed using regular expressions and lexical parsing and tested their performance against sets of full-text articles from multiple journals that were manually scanned for coordinates. Both geoparsers performed well at recognizing and extracting coordinates from articles with accuracy ranging from 85.1% to 100%, and the lexical geoparser performing marginally better. Omission errors (i.e. missed coordinates) were 0% to 14.9% for the regular expression geoparser and 0% to 10.3% for the lexical geoparser. Only a single commission error (i.e. erroneous coordinate) was encountered with the lexical geoparser. The ability to automatically identify and extract location information from published studies opens new possibilities for transforming scientific literature discovery and supporting novel research.

Automatic recommendation to omitted steps in use case specification

Completeness is one of the key attributes for a high-quality software requirements specification. Although incomplete requirements frequently occur in the requirements specification, it is rarely discovered. This turns out to be one of the major causes of software project failure. In order to handle this issue, this paper proposes an automatic approach to recommending omitted steps in a use case-based requirements specification. First, we automatically extract diverse scenario patterns by using the verb clustering algorithm and scenario flow graphs. Based on the scenario patterns, our approach detects omitted steps of user’s scenarios by the pattern matching algorithm and automatically recommends appropriate steps for the omitted parts. For validation of our approach, we have developed tool support, named ScenarioAmigo, and collected 231 use case specifications composing of 1874 scenario steps from 12 academic or proprietary projects. We first carried out the preliminary study to decide appropriate thresholds and weights. Then, we conducted three experiments as a quantitative performance evaluation. First, the cross-validation for the collected scenarios shows the 76% precision and 80% recall. Second, the comparison of recall of ScenarioAmigo to that of human experts obtained the 20% higher score. As the last experiment, we compared the result of ScenarioAmigo and human experts in terms of severity of each scenario and found that our approach could recommend normal as well as important scenarios, compared to the human experts.

Practical privacy‐preserving deep packet inspection outsourcing

SummaryHardware‐based middleboxes are ubiquitous in computer networks, which usually incur high deployment and management expenses. A recently arising trend aims to address those problems by outsourcing the functions of traditional hardware‐based middleboxes to high volume servers in a cloud. This technology is promising but still faces a few challenges from different aspects, including privacy concerns, middlebox functionality, and performance. In this paper, we propose two practical approaches to implementing a cloud‐based DPI middlebox. The outsourced DPI middlebox performs payload inspection over encrypted traffic while preserving the privacy of both communication data and inspection rules. Our first approach employs a modified reversible sketch structure, which is used for efficient error‐free membership testing, and our second approach extends the famous AC pattern matching algorithm to the cipher text domain. We utilize unkeyed one‐way hash functions instead of complex cryptographic protocols to achieve the privacy preservation requirements. Our system supports a wide range of real‐world inspection rules. We conduct evaluations on the ClamAV rule set, and the experiment results demonstrate the effectiveness of our proposals.

On-line Maneuver Identification in Highway Traffic Using Elastic Template Matching

Early identification and interpretation of traffic maneuvers will become key elements of modern driver assistance systems. Currently, only simple traffic situations can be detected by using a decision tree (DT) procedure in most cases. Even though this approach is straightforward, it still requires an expert to build a different DT for every maneuver. Moreover, for complex driving behaviours such simple logic identification does not work. In the present work we develop a template (or pattern) matching (TM) algorithm for maneuver recognition in highway traffic with a particular focus on a cut-in maneuver. By using maneuver patterns we are able to consider scenarios of arbitrary complexity and make the identification procedure more systematic and with minimal man expert involvement. The key element of the proposed TM algorithm is the settlement of a distance measure. We use dynamic time warping (DTW) method for an optimal non-linear alignment between two sequences under certain restrictions. It enables a correct assignment of the same maneuver but having different dynamics (speed, aggressiveness, etc.), and is robust to data with missing values. We illustrate the proposed method by cut-in detection from real traffic data and compare it to the standard DT identification.

A Narrow-Band Indoor Positioning System by Fusing Time and Received Signal Strength via Ensemble Learning

The Internet of Things (IoT) is an emerging paradigm to integrate the physical world into cyber systems by connecting various devices, which sense and control the surrounding objects. For the upcoming IoT era, narrow-band signals will play an important role in the exchange of sensor data and control signals in IoT networks. Indoor positioning is critical for many IoT applications, such as asset tracking, smart buildings, smart e-health, and smart cities, but it is very challenging to achieve accurate indoor positioning with narrow-band signals because of multipath propagation. In this paper, we design a positioning system using narrow-band signals, particularly ZigBee signals, based on an enhanced fingerprinting algorithm by fusing received signal strength (RSS) and time information. We first investigate the feasibility of time-based fingerprinting by handling the challenges as synchronization compensation among anchor nodes and design of sub-sample timestamps with a resolution of nanoseconds. Second, we design a feature-based fusion approach to fuse and standardize time and RSS fingerprints. Third, we adopt a random forest regression model to design an enhanced pattern matching algorithm for fingerprinting. We implement the proposed algorithms with software-defined radio techniques. In the given experiment results, we find that the positioning system achieves a mean positioning accuracy of 1.61 m, which represents a 36.1% improvement over traditional RSS-based fingerprinting.

Performance Evaluation of Different Pattern Matching Algorithms of Snort

Snort is the most widely deployed Network Intrusion Detection System (NIDS) whose performance is dominated by the pattern matching of packets in the network. In this paper, we present an experimental evaluation and comparison of the performance of different pattern matching algorithms of Snort NIDS namely ac-q, ac-bnfa, acsplit, ac-banded and ac-sparsebands on Linux Operating System (Ubuntu Server 16.04). Snort's performance is measured by subjecting the server running Snort v2.9.9.1 to live malicious traffic and a standard dataset. The performance is calculated and compared in terms of throughput, memory utilization and CPU utilization.

Micro Sequence Identification of DNA Data Using Pattern Mining Techniques

A rapid development of NGS(Next generation Sequencing) technologies can be able to produce large amounts of sequence data,which is leading in to a wide range of new applications. Sequence matches between a translated nucleotide sequence and a well known biological protein can able to provide a strong evidence for the presence of homologous coding region, and such similarities often be identified even between a distantly related genes. Common techniques often restrict indels in the alignment to improve time,speed, whereas more Flexible aligners are too slow for large-scale applications. Moreover, many current aligners are becoming inefficient as generated reads grow ever larger. To perform such high dimensional process, it requires a special hardware implementation & designing, such implementation can also increases a complexity of efficiency and hardware. The Field Programmable Gate Array is the well-known to design and we propose a high efficient algorithm for sequence detection in any of bioinformatics data. Unlike previous methods, the proposed pattern matching algorithm can identifies the sequence of each factor on the basis of their occurrences. This method can computes the multi-level similarity measure with an available sequences. Based on the multi-level sequence similarity measure computed a single sequence of bioinformatics data can be identified. The proposed method produces efficient result in sequence searching and detection and improves the hardware utilization which in terms reduces the time complexity as well.

An Optimal Image Processing Method for Simultaneous Detection of Weld Seam Position and Weld Gap in Robotic Arc Welding

For robot path planning the weld seam positions need to be known in advance as the industrial robot generally work in teach and playback mode. In this paper, a vision sensor has been utilized for automation of robotic welding path planning. A seam tracking algorithm has been proposed for a butt type of weld joint with varying weld gap for effective measurement of weld path positions and weld gap simultaneously. For this first an image acquisition algorithm technique has been proposed for capturing of image of weld seam in gray scale mode. Then in image processing at first one pattern matching algorithm for tracking of weld seam path is performed. Then different edge detection techniques have been applied to find the most efficient edge detection method for obtaining the characteristics of weld seam edge. Then best edge fitting method has been applied to fit the edges along the weld seam edge and the pixel values on the edges were measured. The weld gap and the midpoint between edges points are measured simultaneously by vision assistant toolbox in LabVIEW software background.

A review on parameterized string matching algorithms

Identification of candidate genes and nucleotides are basic uses of the Bioinformatics research. Biology that deals with molecule has functional as well as structural behavior imparting need of well-furnished techniques to distinguish the molecules. Parameterized matching is one of those techniques which deal in identifying molecules having different functional and structural behavior. The word “Parameterized” represent the characters or symbols in the form of parameters. It implies that the sequence of characters forming text and pattern are matched if and only if the bijective mapping is formed between them. Exponential increase in the size of data sets leads the requirement to solve this matching problem in sub-linear time. This paper displays the review of single pattern parameterized pattern matching algorithms using q-grams having linear time complexities. It also discusses about the best algorithm that has less number of false matches. The comparisons done are based on certain criteria's presenting pros and cons of the algorithms.

IDPM: An Improved Degenerate Pattern Matching Algorithm for Biological Sequences

Let [Formula: see text] be a string, with symbols from an alphabet. [Formula: see text] is said to be degenerate if for some positions, say [Formula: see text], [Formula: see text] can contain a subset of symbols from the symbol alphabet, rather than just one symbol. Given a text string [Formula: see text] and a pattern [Formula: see text], both with symbols from an alphabet [Formula: see text], the degenerate string matching problem, is to find positions in [Formula: see text] where [Formula: see text] occured, such that [Formula: see text], [Formula: see text], or both are allowed to be degenerate. Though some algorithms have been proposed, their huge computational cost pose a significant challenge to their practical utilization. In this work, we propose IDPM, an improved degenerate pattern matching algorithm based on an extension of the Boyer–Moore algorithm. At the preprocessing phase, the algorithm defines an alphabet-independent compatibility rule, and computes the shift arrays using respective variants of the bad character and good suffix heuristics. At the search phase, IDPM improves the matching speed by using the compatibility rule. On average, the proposed IDPM algorithm has a linear time complexity with respect to the text size, and to the overall size of the pattern. IDPM demonstrates significance performance improvement over state-of-the-art approaches. It can be used in fast practical degenerate pattern matching with large data sizes, with important applications in flexible and scalable searching of huge biological sequences.

APPLYING A Q-GRAM BASED MULTIPLE STRING MATCHING ALGORITHM FOR APPROXIMATE MATCHING

We consider the application of multiple pattern matching (Multi AOSO on q-Grams) algorithm for approximate pattern matching. We propose the on-line approach which translates the problem from approximate pattern matching into a multiple pattern one (called partitioning into exact search). Presented solution allows relatively fast search multiple patterns in text with given k-differences(or mismatches). This paper presents comparison of solution based on MAG algorithm, and [4]. Experiments on DNA, English, Proteins and XML texts with up to k errors show that the new proposed algorithm achieves relatively good results in practical use.

Pattern Matching Performance Analysis Based on Linear Models with Information Backscattered from RFID Tags

Pattern matching localization algorithms are commonly proposed in complex indoor environments. Received signal strength indicator (RSSI) of radio signals backscattered from passive ultra high frequency radio frequency identification (RFID) tags is commonly used as the pattern in matching process. Considering RSSI is severely affected by multipath propagation and noise, etc., more types of patterns will be a better choice for further study. In recent years, phase information is becoming more and more important for RFID-based systems. As for the period ambiguity problem of wrapped phase information, phase difference of arrival (PDOA) of double-frequency signals can be utilized as a type of pattern instead of phase of arrival corresponding to the propagation distance. In this paper, we combine RSSI and PDOA as a brand new type of pattern named RP. In previous study, only localization accuracy is considered to prove effective. It is hard to show the matching performance of the type of pattern with the following process of localization algorithms. Here we take advantage of the linear relationship between pattern Euclidean distance and geographic locations Euclidean distance of tags fixed in reference points to evaluate the distinctiveness performance of RP in matching. The linear correlation of the model is tested at each reference point with experimental data captured from indoor scenario. By evaluating these models statistically, we think RP is better than RSSI and PDOA as a pattern.

Analysis of SQL Injection Detection and Prevention

Objectives: SQL Injection Attack (SQLIA) is a frequent and a severe security issue in the web applications. In SQLIA, hacker can obtain the benefit of poor input validation and weak coded web application. Due to the successful execution of a SQLIA, integrity and confidentiality of data are lost which results in the degrading organization’s market value. This paper gives a valuable analysis of various types of SQLIAs, methods and mechanisms. It also explores various detection and prevention techniques. Methods/Analysis: A rigorous survey has been conducted and consequently, comparative analysis of various detection and prevention techniques is done with respect to various types of attacks. In current research various pattern matching algorithms for the detection and prevention of SQLIA are analyzed and few are tested. Findings: Comparative analysis of Boyer Moore pattern matching algorithm is done with Naive String pattern matching algorithm. The time and memory consumption taken by both the algorithms has been analyzed. The results show that Boyer Moore is more efficient to detect and prevent the SQLIAs as compared to Naive string. Novelty/Improvement: In future it aims to propose an algorithm which will enhance in terms of efficiency and resource usage. The approach needs to be implemented for every pattern matching algorithm to find the best solution regarding detection and prevention of SQLIA.