Longest Common Subsequence Algorithm Research Articles

Image Stitching Method of Bone Stick Fragment Based on Similarity Freeman Code Matching

A large number of bone stick fragments were unearthed at the Weiyang Palace, and the manual stitching method is inefficient and time-consuming, therefore Image Stitching Method of Bone Stick Fragment Based on Similarity Freeman code Matching is proposed by this paper to improve bone stick stitching efficiency. The Freeman code is used to represent the contour of the true color RGB image of bone stick fragments, and the direction change dense feature of the bone stick fragment fracture is fused with the Freeman code difference to represent the change direction feature to locate the fracture boundary. At the same time, the integer basic operation is used to replace the complex calculus calculation to improve the efficiency of the algorithm; The longest common subsequence algorithm is used to perform local similarity rough matching on Freeman code of the contour at the fragment fracture, and then the angle and distance error threshold is set according to the prior knowledge of manual stitching to reduce the matching error; Finally, according to the matching results, the complete bone stick contour binary image is drawn. Compared with other algorithms, the contour Cosine similarity accuracy of the bone stick image stitched by this algorithm is improved by 21.68%, which can be accurate to 97.80%; the Editing distance similarity accuracy is improved by 14.29%, which can be accurate to 91.87%;the Peak signal-to-noise ratio is improved by 83%, which can be accurate to 31.13%;the Structural Similarity is improved by 29%, which can be accurate to 0.87; the Figure of merit is improved by 39%, which can be accurate to 0.85. The experiment results show that the algorithm proposed in this paper can effectively assist the stitching work of bone stick cultural relics.

Dangerous Driving Behavior Recognition Based on Hand Trajectory

Dangerous driving behaviors in the process of driving will produce road traffic safety hazards, and even cause traffic accidents. Common dangerous driving behavior includes: eating, smoking, fetching items, using a handheld phone, and touching a control monitor. In order to accurately identify the dangerous driving behaviors, this study first uses the hand trajectory data to construct the dangerous driving behavior recognition model based on the dynamic time warping algorithm (DTW) and the longest common sub-sequence algorithm (LCS). Secondly, 45 subjects’ hand trajectory data were obtained by driving simulation test, and 30 subjects’ hand trajectory data were used to determine the dangerous driving behavior label. The matching degree of hand trajectory data of 15 subjects was calculated based on the dangerous driving behavior recognition model, and the threshold of dangerous driving behavior recognition was determined according to the calculation results. Finally, the dangerous driving behavior recognition algorithm and neural network algorithm are compared and analyzed. The dangerous driving behavior recognition algorithm has a fast calculation speed, small memory consumption, and simple program structure. The research results can be applied to dangerous driving behavior recognition and driving distraction warning based on wrist wearable devices.

Time-series anomaly detection using dynamic programming based longest common subsequence on sensor data

This study proposes a novel approach to time-series anomaly detection by solving the longest common subsequence (LCS) problem of two time-series data. The conventional LCS problem is used to measure the similarity of two input strings. However, it cannot be applied to real-time data collected from sensors in smart manufacturing, which are mostly real numbers instead of strings. Thus, two algorithms—an extension of fixed gap longest common subsequence (extent FGLCS) and the modified dynamic programming-based LCS (MDP-LCS) algorithm—are proposed to deal with real-time series data. Further, a gap search constraint is investigated to limit the search range in finding the LCS. This study also provides a threshold to define the matching of a pair that contains real numbers rather than a string in the conventional LCS or FGLCS problems. In addition, the proposed methods employ the gap search constraint and the threshold to reduce computational time. Furthermore, the proposed algorithms can be implemented on both univariate and multivariate time-series data. The LCS length, known as the similarity of the two time-series data, is used to detect the anomalies based on the user’s expectation. The accuracy and computational time of the proposed algorithm show a significant positive result compared with the exact solution provided by the dynamic programming-based LCS algorithm. Moreover, this study also uses the proposed methods for anomaly detection in a case study of big multisensory data in smart manufacturing. The result shows that the MDP-LCS can detect approximately 98% of normal data and more than 85% of anomalies with a shorter computational time.

A Review Study on DNA Sequence Similarity Using Longest Common Subsequence Algorithm

A Review Study on DNA Sequence Similarity Using Longest Common Subsequence Algorithm

High-performance computing for SARS-CoV-2 RNAs clustering: a data science‒based genomics approach.

Nowadays, Genomic data constitutes one of the fastest growing datasets in the world. As of 2025, it is supposed to become the fourth largest source of Big Data, and thus mandating adequate high-performance computing (HPC) platform for processing. With the latest unprecedented and unpredictable mutations in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the research community is in crucial need for ICT tools to process SARS-CoV-2 RNA data, e.g., by classifying it (i.e., clustering) and thus assisting in tracking virus mutations and predict future ones. In this paper, we are presenting an HPC-based SARS-CoV-2 RNAs clustering tool. We are adopting a data science approach, from data collection, through analysis, to visualization. In the analysis step, we present how our clustering approach leverages on HPC and the longest common subsequence (LCS) algorithm. The approach uses the Hadoop MapReduce programming paradigm and adapts the LCS algorithm in order to efficiently compute the length of the LCS for each pair of SARS-CoV-2 RNA sequences. The latter are extracted from the U.S. National Center for Biotechnology Information (NCBI) Virus repository. The computed LCS lengths are used to measure the dissimilarities between RNA sequences in order to work out existing clusters. In addition to that, we present a comparative study of the LCS algorithm performance based on variable workloads and different numbers of Hadoop worker nodes.

A Many-Objective Simultaneous Feature Selection and Discretization for LCS-Based Gesture Recognition

Discretization and feature selection are two relevant techniques for dimensionality reduction. The first one aims to transform a set of continuous attributes into discrete ones, and the second removes the irrelevant and redundant features; these two methods often lead to be more specific and concise data. In this paper, we propose to simultaneously deal with optimal feature subset selection, discretization, and classifier parameter tuning. As an illustration, the proposed problem formulation has been addressed using a constrained many-objective optimization algorithm based on dominance and decomposition (C-MOEA/DD) and a limited-memory implementation of the warping longest common subsequence algorithm (WarpingLCSS). In addition, the discretization sub-problem has been addressed using a variable-length representation, along with a variable-length crossover, to overcome the need of specifying the number of elements defining the discretization scheme in advance. We conduct experiments on a real-world benchmark dataset; compare two discretization criteria as discretization objective, namely Ameva and ur-CAIM; and analyze recognition performance and reduction capabilities. Our results show that our approach outperforms previous reported results by up to 11% and achieves an average feature reduction rate of 80%.

OpenMP Implementation of Parallel Longest Common Subsequence Algorithm for Mathematical Expression Retrieval

Given a mathematical expression in LaTeX or MathML format, retrieval algorithm extracts similar expressions from a database. In our previous work, we have used Longest Common Subsequence (LCS) algorithm to match two expressions of lengths, [Formula: see text] and [Formula: see text], which takes [Formula: see text] time complexity. If there are [Formula: see text] database expressions, total complexity is [Formula: see text], and an increase in [Formula: see text] also increases this complexity. In the present work, we propose to use parallel LCS algorithm in our retrieval process. Parallel LCS has [Formula: see text] time complexity with [Formula: see text] processors and total complexity can be reduced to [Formula: see text]. For our experimentation, OpenMP based implementation has been used on Intel [Formula: see text] processor with 4 cores. However, for smaller expressions, parallel version takes more time as the implementation overhead dominates the algorithmic improvement. As such, we have proposed to use parallel version, selectively, only on larger expressions, in our retrieval algorithm to achieve better performance. We have compared the sequential and parallel versions of our ME retrieval algorithm, and the performance results have been reported on a database of 829 mathematical expressions.

New Construction of Family of MLCS Algorithms.

The multiple longest common subsequence (MLCS) problem involves finding all the longest common subsequences of multiple character sequences. This problem is encountered in a variety of areas, including data mining, text processing, and bioinformatics, and is particularly important for biological sequence analysis. By taking the MLCS problem and algorithms for its solution as research domain, this study analyzes the domain of multiple longest common subsequence algorithms, extracts features that algorithms in the domain do and do not have in common, and creates a domain feature model for the MLCS problem by using generic programming, domain engineering, abstraction, and related technologies. A component library for the domain is designed based on the feature model for the MLCS problem, and the partition and recur (PAR) platform is used to ensure that highly reliable MLCS algorithms can be quickly assembled through component assembly. This study provides a valuable reference for obtaining rapid solutions to problems of biological sequence analysis and improves the reliability and assembly flexibility of assembling algorithms.

A CPU‐FPGA heterogeneous approach for biological sequence comparison using high‐level synthesis

SummaryThis article presents a high‐level synthesis implementation of the longest common subsequence (LCS) algorithm combined with a weighted‐based scheduler for comparing biological sequences prioritizing energy consumption or execution time. The LCS algorithm has been thoroughly tailored using Vivado High‐Level Synthesis tool, which is able to synthesize register transfer level (RTL) from high‐level language descriptions, such as C/C++. Performance and energy consumption results were obtained with a CPU Intel Core i7‐3770 CPU and an Alpha‐Data ADM‐PCIE‐KU3 board that has a Xilinx Kintex UltraScale XCKU060 FPGA chip. We executed a batch of 20 comparisons of sequences on 10k, 20k, and 50k sizes. Our experiments showed that the energy consumption on the combined approach was significantly lower when compared to the CPU, achieving 75% energy reduction on 50k comparisons. We also used the tool proposed in this article to do a case study on Covid‐19, with real SARS‐CoV‐2 sequences, comparing their LCS scores.

Predictability measurement for the sequence of travel time by combining the Lempel-Ziv compression and the Longest Common Subsequence algorithm

It is commonly recognized that reliability in travel time is a significant factor to consider, as it not only wields a significant impact on traveller behaviour, but also substantially impacts the general efficiency of transport systems. Predictability measure for Travel time Sequence is probabilistic approach that depends on historical data. To measure the reliability of travel time sequence from the perspective of predictability, entropy plays an important role. To estimate the entropy of such a travel time sequence Lempel-Ziv data compression algorithm can be used but Lempel-Ziv scanned the whole historical string for the exactly identical substring. So, in entropy estimation process some partially similar substring or subsequence gets overlooked. For deep analysis, longest common subsequence (LCS) algorithm is employed to calculate exactly identical substring and most similar subsequence. LCS algorithm discarded redundant subsequence's which further reduce entropy. This lead to increase in predictability of travel time sequence. Thus, the combination of LZ and LCS give more accurate predictability and easily can be implemented for travel time forecasting in ITS to calculate prediction rate for next travel time based on given travel time sequence.

Efficient algorithms for Longest Common Subsequence of two bucket orders to speed up pairwise genetic map comparison.

Genetic maps order genetic markers along chromosomes. They are, for instance, extensively used in marker-assisted selection to accelerate breeding programs. Even for the same species, people often have to deal with several alternative maps obtained using different ordering methods or different datasets, e.g. resulting from different segregating populations. Having efficient tools to identify the consistency and discrepancy of alternative maps is thus essential to facilitate genetic map comparisons. We propose to encode genetic maps by bucket order, a kind of order, which takes into account the blurred parts of the marker order while being an efficient data structure to achieve low complexity algorithms. The main result of this paper is an O(n log(n)) procedure to identify the largest agreements between two bucket orders of n elements, their Longest Common Subsequence (LCS), providing an efficient solution to highlight discrepancies between two genetic maps. The LCS of two maps, being the largest set of their collinear markers, is used as a building block to compute pairwise map congruence, to visually emphasize maker collinearity and in some scaffolding methods relying on genetic maps to improve genome assembly. As the LCS computation is a key subroutine of all these genetic map related tools, replacing the current LCS subroutine of those methods by ours –to do the exact same work but faster– could significantly speed up those methods without changing their accuracy. To ease such transition we provide all required algorithmic details in this self contained paper as well as an R package implementing them, named LCSLCIS, which is freely available at: https://github.com/holtzy/LCSLCIS.

A Dynamic Hand Gesture Recognition System Incorporating Orientation-based Linear Extrapolation Predictor and Velocity-assisted Longest Common Subsequence Algorithm

A Dynamic Hand Gesture Recognition System Incorporating Orientation-based Linear Extrapolation Predictor and Velocity-assisted Longest Common Subsequence Algorithm

A Segment-Based Trajectory Similarity Measure in the Urban Transportation Systems.

With the rapid spread of built-in GPS handheld smart devices, the trajectory data from GPS sensors has grown explosively. Trajectory data has spatio-temporal characteristics and rich information. Using trajectory data processing techniques can mine the patterns of human activities and the moving patterns of vehicles in the intelligent transportation systems. A trajectory similarity measure is one of the most important issues in trajectory data mining (clustering, classification, frequent pattern mining, etc.). Unfortunately, the main similarity measure algorithms with the trajectory data have been found to be inaccurate, highly sensitive of sampling methods, and have low robustness for the noise data. To solve the above problems, three distances and their corresponding computation methods are proposed in this paper. The point-segment distance can decrease the sensitivity of the point sampling methods. The prediction distance optimizes the temporal distance with the features of trajectory data. The segment-segment distance introduces the trajectory shape factor into the similarity measurement to improve the accuracy. The three kinds of distance are integrated with the traditional dynamic time warping algorithm (DTW) algorithm to propose a new segment–based dynamic time warping algorithm (SDTW). The experimental results show that the SDTW algorithm can exhibit about 57%, 86%, and 31% better accuracy than the longest common subsequence algorithm (LCSS), and edit distance on real sequence algorithm (EDR) , and DTW, respectively, and that the sensitivity to the noise data is lower than that those algorithms.

A case study of TTCN-3 test scripts clone analysis in an industrial telecommunication setting

Context: This paper presents a novel experiment focused on detecting and analyzing clones in test suites written in TTCN-3, a standard telecommunication test script language, for different industrial projects.Objective: This paper investigates frequencies, types, and similarity distributions of TTCN-3 clones in test scripts from three industrial projects in telecommunication. We also compare the distribution of clones in TTCN-3 test scripts with the distribution of clones in C/C++ and Java projects from the telecommunication domain. We then perform a statistical analysis to validate the significance of differences between these distributions.Method: Similarity is computed using CLAN, which compares metrics syntactically derived from script fragments. Metrics are computed from the Abstract Syntax Trees produced by a TTCN-3 parser called Titan developed by Ericsson as an Eclipse plugin. Finally, clone classification of similar script pairs is computed using the Longest Common Subsequence algorithm on token types and token images.Results: This paper presents figures and diagrams reporting TTCN-3 clone frequencies, types, and similarity distributions. We show that the differences between the distribution of clones in test scripts and the distribution of clones in applications are statistically significant. We also present and discuss some lessons that can be learned about the transferability of technology from this study.Conclusion: About 24% of fragments in the test suites are cloned, which is a very high proportion of clones compared to what is generally found in source code. The difference in proportion of Type-1 and Type-2 clones is statistically significant and remarkably higher in TTCN-3 than in source code. Type-1 and Type-2 clones represent 82.9% and 15.3% of clone fragments for a total of 98.2%. Within the projects this study investigated, this represents more and easier potential re-factoring opportunities for test scripts than for code.

Detecting Road Intersections from GPS Traces Using Longest Common Subsequence Algorithm

Intersections are important components of road networks, which are critical to both route planning and path optimization. Most existing methods define the intersections as locations where the road users change their moving directions and identify the intersections from GPS traces through analyzing the road users’ turning behaviors. However, these methods suffer from finding an appropriate threshold for the moving direction change, leading to true intersections being undetected or spurious intersections being falsely detected. In this paper, the intersections are defined as locations that connect three or more road segments in different directions. We propose to detect the intersections under this definition by finding the common sub-tracks of the GPS traces. We first detect the Longest Common Subsequences (LCSS) between each pair of GPS traces using the dynamic programming approach. Second, we partition the longest nonconsecutive subsequences into consecutive sub-tracks. The starting and ending points of the common sub-tracks are collected as connecting points. At last, intersections are detected from the connecting points through Kernel Density Estimation (KDE). Experimental results show that our proposed method outperforms the turning point-based methods in terms of the F-score.

A new algorithm for "the LCS problem" with application in compressing genome resequencing data.

BackgroundThe longest common subsequence (LCS) problem is a classical problem in computer science, and forms the basis of the current best-performing reference-based compression schemes for genome resequencing data.MethodsFirst, we present a new algorithm for the LCS problem. Using the generalized suffix tree, we identify the common substrings shared between the two input sequences. Using the maximal common substrings, we construct a directed acyclic graph (DAG), based on which we determine the LCS as the longest path in the DAG. Then, we introduce an LCS-motivated reference-based compression scheme using the components of the LCS, rather than the LCS itself.ResultsOur basic scheme compressed the Homo sapiens genome (with an original size of 3,080,436,051 bytes) to 15,460,478 bytes. An improvement on the basic method further reduced this to 8,556,708 bytes, or an overall compression ratio of 360. This can be compared to the previous state-of-the-art compression ratios of 157 (Wang and Zhang, 2011) and 171 (Pinho, Pratas, and Garcia, 2011).ConclusionWe propose a new algorithm to address the longest common subsequence problem. Motivated by our LCS algorithm, we introduce a new reference-based compression scheme for genome resequencing data. Comparative results against state-of-the-art reference-based compression algorithms demonstrate the performance of the proposed method.

A method for discovering typical process sequence using granular computing and similarity algorithm based on part features

Aiming at the problems of hereditability and reusability for process planning data in the enterprises, a novel method for discovering typical process sequence by applying granular computing and similarity algorithm based on part features is put forward. Firstly, a calculating model of process sequence similarity is built through the analysis and comparison of part features, including machining features and topological relations between features, and a fuzzy similarity matrix of all the process sequences is established. Afterwards, according to the theory of fuzzy quotient space, which is one of the theoretical models of granular computing, a process sequence quotient space family with hierarchical structure is constructed. The granularity of every process sequence quotient space is measured by information entropy, and the information gain, which means the difference of information entropy between two adjacent quotient spaces, is calculated. Finally, a quotient space with the bigger information gain as well as the higher minimal process sequence similarity in process information granules is determined as an optimal process information granular layer, in which some typical process sequences are acquired from process information granules by using longest common subsequence algorithm. An application example verifies the feasibility and validity of the proposed method.

A Method of Recognition of Human Hand and Body Gestures for Task-Level Learning from Demonstration

Different problems of robot learning and planning have received considerable attention, recently. In particular, we can mention robot task learning. Robot learning from demonstration is especially important for robots that operate in unstructured environments. The effectiveness of such learning depends strongly on the quality of vision-based analysis of human hand and body gestures. In this paper, we consider a method of recognition of human hand and body gestures that based on a modified longest common subsequence algorithm with adaptive parameters.

Enhanced Dynamic Algorithm of Genome Sequence Alignments

The merging of biology and computer science has created a new field called computational biology that explore the capacities of computers to gain knowledge from biological data, bioinformatics. Computational biology is rooted in life sciences as well as computers, information sciences, and technologies. The main problem in computational biology is sequence alignment that is a way of arranging the sequences of DNA, RNA or protein to identify the region of similarity and relationship between sequences. This paper introduces an enhancement of dynamic algorithm of genome sequence alignment, which called EDAGSA. It is filling the three main diagonals without filling the entire matrix by the unused data. It gets the optimal solution with decreasing the execution time and therefore the performance is increased. To illustrate the effectiveness of optimizing the performance of the proposed algorithm, it is compared with the traditional methods such as Needleman-Wunsch, Smith-Waterman and longest common subsequence algorithms. Also, database is implemented for using the algorithm in multi-sequence alignments for searching the optimal sequence that matches the given sequence.

Gait Parameter Estimation From a Miniaturized Ear-Worn Sensor Using Singular Spectrum Analysis and Longest Common Subsequence

This paper presents a new approach to gait analysis and parameter estimation from a single miniaturized ear-worn sensor embedded with a triaxial accelerometer. Singular spectrum analysis combined with the longest common subsequence algorithm has been used as a basis for gait parameter estimation. It incorporates information from all axes of the accelerometer to estimate parameters including swing, stance, and stride times. Rather than only using local features of the raw signals, the periodicity of the signals is also taken into account. The hypotheses tested by this study include: 1) how accurate is the ear-worn sensor in terms of gait parameter extraction compared to the use of an instrumented treadmill; 2) does the ear-worn sensor provide a feasible option for assessment and quantification of gait pattern changes. Key gait events for normal subjects such as heel contact and toe off are validated with a high-speed camera, as well as a force-plate instrumented treadmill. Ten healthy adults walked for 20min on a treadmill with an increasing incline of 2% every 2min. The upper and lower limits of the absolute errors using 95% confidence intervals for swing, stance, and stride times were obtained as 35.5 ±3.99 ms, 36.9 ±3.84 ms, and 17.9 ±2.29 ms, respectively.