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Journal articles on the topic 'Design pattern detection'

Author: Grafiati

Published: 9 March 2023

Last updated: 10 March 2023

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1

HAYASHI, S., J. KATADA, R. SAKAMOTO, T. KOBAYASHI, and M. SAEKI. "Design Pattern Detection by Using Meta Patterns." IEICE Transactions on Information and Systems E91-D, no. 4 (April 1, 2008): 933–44. http://dx.doi.org/10.1093/ietisy/e91-d.4.933.

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2

Wang, Lei, Tian Song, Hui-Na Song, and Shuai Zhang. "Research on Design Pattern Detection Method Based on UML Model with Extended Image Information and Deep Learning." Applied Sciences 12, no. 17 (August 30, 2022): 8718. http://dx.doi.org/10.3390/app12178718.

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Detecting relevant design patterns from system design or source code helps software developers and maintainers understand the ideas behind the design of large-scale, highly complicated software systems, thereby improving the quality of software systems. Currently, design pattern detection based on machine learning has become a hot research direction. Scholars have proposed many design pattern detection methods based on machine learning. However, most of the existing literature only reports the utilization of traditional machine learning algorithms such as KNN, decision trees, ANN, SVM, etc., which require manual feature extraction and feature selection. It is very difficult to find suitable and effective features for the detection of design patterns. In the previous research, we have initially explored a design pattern detection method based on graph theory and ANN. Based on the research work done, we speculate that if we can realize the end-to-end design pattern detection from system design or source code to design pattern with the help of the powerful automatic feature extraction and other advantages of deep learning, the detection effect can be further improved. This paper intends to first explore a UML model that extends image information, called colored UML, so as to transform the design pattern detection problem into an image classification problem; on this basis, the positive and negative sample sets and the system to be recognized are all expressed in the form of colored UML models, the convolutional neural network VGGNet is used to train the data set to extract features, and the extracted features are trained by the SVM for binary classification to judge the pattern instances. Experiments were carried out on three open-source projects. We used three non-machine learning design pattern detection methods and five design pattern detection methods based on traditional machine learning algorithms, as well as the method used in this paper. In general, the method proposed in this paper achieved higher precision and recall, and for different programs and their patterns, the precision and recall were stable at more than 85% in most cases. The experimental results demonstrate that this paper can achieve a better effect in recognizing design patterns. The research is, therefore, of both theoretical significance and application value.

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Nazar, Najam, Aldeida Aleti, and Yaokun Zheng. "Feature-based software design pattern detection." Journal of Systems and Software 185 (March 2022): 111179. http://dx.doi.org/10.1016/j.jss.2021.111179.

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4

Tsantalis, Nikolaos, Alexander Chatzigeorgiou, George Stephanides, and Spyros Halkidis. "Design Pattern Detection Using Similarity Scoring." IEEE Transactions on Software Engineering 32, no. 11 (November 2006): 896–909. http://dx.doi.org/10.1109/tse.2006.112.

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5

Liu, Cong. "A General Framework to Detect Design Patterns by Combining Static and Dynamic Analysis Techniques." International Journal of Software Engineering and Knowledge Engineering 31, no. 01 (January 2021): 21–54. http://dx.doi.org/10.1142/s0218194021400027.

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Design pattern detection can provide useful insights to support software comprehension. Accurate and complete detection of pattern instances are extremely important to enable software usability improvements. However, existing design pattern detection approaches and tools suffer from the following problems: incomplete description of design pattern instances, inaccurate behavioral constraint checking, and inability to support novel design patterns. This paper presents a general framework to detect design patterns while solving these issues by combining static and dynamic analysis techniques. The framework has been instantiated for typical behavioral and creational patterns, such as the observer pattern, state pattern, strategy pattern, and singleton pattern to demonstrate the applicability. Based on the open-source process mining toolkit ProM, we have developed an integrated tool that supports the whole detection process for these patterns. We applied and evaluated the framework using software execution data containing around 1,000,000 method calls generated from eight synthetic software systems and three open-source software systems. The evaluation results show that our approach can guarantee a higher precision and recall than existing approaches and can distinguish state and strategy patterns that are indistinguishable by the state-of-the-art.

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Arcelli Fontana, Francesca, Marco Zanoni, and Stefano Maggioni. "Using Design Pattern Clues to Improve the Precision of Design Pattern Detection Tools." Journal of Object Technology 10 (2011): 4:1. http://dx.doi.org/10.5381/jot.2011.10.1.a4.

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Kouli, Mariam, and Abbas Rasoolzadegan. "A Feature-Based Method for Detecting Design Patterns in Source Code." Symmetry 14, no. 7 (July 21, 2022): 1491. http://dx.doi.org/10.3390/sym14071491.

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Design patterns are common solutions to existing issues in software engineering. In recent decades, design patterns have been researched intensively because they increase the quality factors of software systems such as flexibility, maintainability, and reusability. Design pattern detection refers to the determination of the symmetry between a code fragment and the definition of a design pattern. One of the major challenges in design pattern detection is how to obtain accurate information about the design patterns used in the software system due to the existence of different design pattern variants. Increasing the number of design pattern variants covered by a detection method is one of the main factors that increase its accuracy. In this paper, a step toward solving this challenge was taken by proposing a new feature-based method that builds on concrete definitions of existing design pattern variants and supports the definition and detection of new variants. In this proposed method, the needed features are extracted from the signatures of the design patterns. This method was applied to the 23 Gang of Four (GoF) design patterns and evaluated using four open-source Java projects. Afterward, it was compared with some previous methods using automatically generated testbeds. The experimental results demonstrated that the proposed method has better performance in terms of precision and recall compared to the other methods.

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8

BOUASSIDA, NADIA, HANENE BEN-ABDALLAH, and IMENE ISSAOUI. "EVALUATION OF AN AUTOMATED MULTI-PHASE APPROACH FOR PATTERNS DISCOVERY." International Journal of Software Engineering and Knowledge Engineering 23, no. 10 (December 2013): 1367–98. http://dx.doi.org/10.1142/s0218194013500435.

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Design patterns capitalize the knowledge of expert designers and offer reuse that provides for higher design quality and overall faster development. To attain these advantages, a designer must, however, overcome the difficulties in understanding design patterns and determining those appropriate for his/her particular application. On the other hand, one way to benefit from design patterns is to assist inexperienced designers in pattern detection during the design elaboration. Such detection should tolerate variations between the design and the pattern since the exact instantiation of a pattern is infrequent in a design. However, not all variations of a pattern are tolerated. In particular, some structural variations may result in non-optimal instantiations where the requirements are respected but the structure is different; such variations are called spoiled patterns and should also be detected and transformed into acceptable pattern instantiations. This paper first presents an improvement of our design/spoiled pattern detection approach, named MAPeD (Multi-phase Approach for Pattern Discovery). The latter uses an XML information retrieval technique to identify design/spoiled pattern occurrences in a design using, first, static and semantic information and, secondly, dynamic information. This multi-phase detection approach tolerates structural differences between the examined design and the identified design pattern. Furthermore, thanks to the matching information it collects, our identification technique can offer assistance for the improvement of a design. In its second contribution, this paper evaluates MAPeD by comparing its recall and precision rates for five open source systems: JHotDraw, JUnit, JRefactory, MapperXML, QuickUML. The latter were used by other approaches in experimental evaluations. Our evaluation shows that our design pattern identification approach has an average improvement of 9.98% in terms of precision over the best known approach.

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Lee, Se-Yul, and Yong-Soo Kim. "Design and Analysis of Probe Detection Systems for TCP Networks." Journal of Advanced Computational Intelligence and Intelligent Informatics 8, no. 4 (July 20, 2004): 369–72. http://dx.doi.org/10.20965/jaciii.2004.p0369.

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Advanced computer network technology enables the connectivity of computers in an open network environment. Despite the growing numbers of security threats to networks, most intrusion detection identifies security attacks mainly by detecting misuse using a set of rules based on past hacking patterns. This pattern matching has a high rate of false positives and cannot detect new hacking patterns, making it vulnerable to previously unidentified attack patterns and variations in attack and increasing false negatives. Intrusion detection and prevention technologies are thus required. We propose a network-based intrusion detection model using fuzzy cognitive maps (FCM) that detects intrusion by Denial of Service (DoS) attack detection using packet analysis. A DoS attack typically appears as a Probe and Syn Flooding attack. Syn Flooding Preventer using Fuzzy cognitive maps (SPuF) model captures and analyzes packet information to detect Syn flooding attacks. Using the result of decision module analysis, which used FCM, the decision module measures the degree of danger of the DoS and trains the response module to deal with attacks. The result of simulation using the "KDD’99 Competition Data Set" for the SPuF model shows that Probe detection exceeded 97%.

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Pettersson, Niklas, Welf Löwe, and Joakim Nivre. "Evaluation of Accuracy in Design Pattern Occurrence Detection." IEEE Transactions on Software Engineering 36, no. 4 (July 2010): 575–90. http://dx.doi.org/10.1109/tse.2009.92.

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11

Bafandeh Mayvan, Bahareh, and Abbas Rasoolzadegan. "Design pattern detection based on the graph theory." Knowledge-Based Systems 120 (March 2017): 211–25. http://dx.doi.org/10.1016/j.knosys.2017.01.007.

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12

Sidhi, Thomas Adi Purnomo, B. Yudi Dwiandiyanta, and Findra Kartika Sari Dewi. "Batik Motifs Detection Using Pattern Recognition Method." Jurnal Buana Informatika 11, no. 1 (May 1, 2020): 55. http://dx.doi.org/10.24002/jbi.v11i1.3234.

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Abstract. Batik motif is one of the factors that makes batik unique and attractive. There are various kinds of batik motif designs in various areas. Each of these design motifs implies symbols/illustrations that contain certain meanings.The design of the batik motif is used in different events according to the occasions. But unfortunately, not many people understand this, even though local wisdom on the design of batik motifs is one form of cultural heritage of the archipelago that must be preserved. Related to this, development of information technology and multimedia should be used as a solution. However, until now, there is no accurate and fast information system in detecting batik motifs. This study applies pattern recognition methods to find the most appropriate and accurate method for detecting and interpreting batik motifs. The method will be used to build a batik motif detection information system to help users get information quick and accurately.Keywords: pattern recognition, batik motifs, analysis and design of information systems.Abstrak. Motif batik merupakan salah satu faktor yang menjadikan batik unik dan menarik. Terdapat berbagai macam desain motif batik di berbagai area. Setiap desain motif tersebut mengisyaratkan simbol-simbol/ilustrasi yang mengandung makna tertentu. Tentu saja desain motif batik tersebut digunakan dalam acara yang berbeda-beda sesuai dengan keperluanya. Namun sayang, tidak banyak orang yang mengerti hal ini, padahal kearifan lokal pada desain motif batik tersebut merupakan salah satu bentuk warisan budaya nusantara yang wajib dilestarikan. Terkait hal tersebut, seharusnya perkembangan teknologi informatika dan multimedia dapat digunakan sebagai solusi. Namun demikian, sampai saat ini, belum ada system informasi yang akurat dan cepat dalam mendeteksi dan menginterpretasi motif batik. Penelitian ini menerapkan metode-metode pengenalan pola guna menemukan metode yang paling tepat dan akurat untuk mendeteksi dan menginterpretasi motif batik. Metode tersebut akan digunakan untuk membangun system informasi deteksi motif batik untuk membantu pengguna yang tidak mengenal motif batik mendapatkan informasi secara lebih cepat dan akurat.Kata Kunci: pattern recognition, batik motifs, analysis and design of information systems.

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13

Al-Obeidallah, Mohammed Ghazi, Miltos Petridis, and Stelios Kapetanakis. "A Multiple Phases Approach for Design Patterns Recovery Based on Structural and Method Signature Features." International Journal of Software Innovation 6, no. 3 (July 2018): 36–52. http://dx.doi.org/10.4018/ijsi.2018070103.

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Design patterns describe both structure, behavior of classes and their relationships. They can improve software documentation, speed up the development process and enable large-scale reuse of software architectures. This article presents a multiple levels detection approach (MLDA) to recover design pattern instances from Java source code. MLDA is able to recover design pattern instances based on a generated class level representation of a subject system. Specifically, MLDA presents what is the so-called Structural Search Model (SSM) which incrementally builds the structure of each design pattern based on the generated source code model. Moreover, MLDA uses a rule-based approach to match the method signatures of the candidate design instances to that of the subject system. As the experiment results illustrate, MLDA is able to recover 23 design patterns with reasonable detection accuracy.

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14

Chaturvedi, A. "Present Approaches for Detection of Design Pattern A Survey." International Journal of Computer Sciences and Engineering 6, no. 8 (August 31, 2018): 948–58. http://dx.doi.org/10.26438/ijcse/v6i8.948958.

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15

Zanoni, Marco, Francesca Arcelli Fontana, and Fabio Stella. "On applying machine learning techniques for design pattern detection." Journal of Systems and Software 103 (May 2015): 102–17. http://dx.doi.org/10.1016/j.jss.2015.01.037.

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16

Sun, Ying, Zishu He, and Jun Li. "Cognitive Space-Time Transmit Pattern Design." International Journal of Antennas and Propagation 2014 (2014): 1–6. http://dx.doi.org/10.1155/2014/141568.

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This paper provides a cognitive space-time (angle-Doppler) transmit pattern design method for mitigating clutter effect. This pattern takes full advantage of degrees-of-freedom (DOF) on transmit, which can preserve the maximum response for the target of interest while prenulling the mainlobe clutter on transmit, potentially simplifying processing on receiver and reducing the requirement for receiver dynamic range. The output signal-to-clutter-and-noise ratio (SCNR) can be improved significantly after applying the cognitive space-time transmit pattern to airborne-phased array radar. In addition, the traditional transmit antenna pattern and the proposed cognitive scheme are compared in terms of the probability of target detection. Simulations are conducted to demonstrate proof-of-concept.

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17

YIN, HONGHUAN, HONGBIN YU, JUNQIANG PENG, and HONGYU SHAO. "Design of the detecting platform for the electronic control rotary dobby." Industria Textila 72, no. 03 (June 30, 2021): 261–67. http://dx.doi.org/10.35530/it.072.03.1751.

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In order to meet the factory detection requirements of the rotary electronic dobby, a new design of the detecting platformfor the rotary electronic dobby was proposed in the present study. It was composed with three main modules to meetthe different levels of detecting process all over the factory, including the embedded control system, human machineinterface system and the information management system. For the information management system of the dobbyworkshop, the proposed platform performed the remote management, results monitor, and data analysis to the dobbymanufactured at the factory. For the operators of the dobby detecting process, the human machine interface systemrealized the edit of pattern, the upload and download of the pattern data based through Wi-Fi, the control of motor speed,the test process record and the upload of the test results. Moreover, the embedded control system was developed andbuilt to realize the real-time control to electromagnet and swing arms of dobby according to the pattern stored in flashchip and status display of the detecting results on the platform. The designed detecting platform in this article can bewidely applied in the factory for detection of the rotary electronic dobby. The reliability and practicability of the proposedplatform have been confirmed by the practical application of the dobby manufacturing enterprise.

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18

Aladib, Loay, and Sai Peck Lee. "Pattern detection and design rationale traceability: an integrated approach to software design quality." IET Software 13, no. 4 (August 2019): 249–59. http://dx.doi.org/10.1049/iet-sen.2017.5017.

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19

Tokunaga, Yusuke, Toshihide Hakukawa, and Takahiro Inoue. "Algorithm and Design of an Intelligent Digital Integrated Circuit for a Watermelon Harvesting Robot." Journal of Robotics and Mechatronics 11, no. 3 (June 20, 1999): 173–82. http://dx.doi.org/10.20965/jrm.1999.p0173.

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We propose an algorithm and design of an intelligent digital integrated circuit for recognition of circular patterns in a binary image based on template matching using a modified matching degree. The proposed system consists of a preprocessor #1 (noise reduction, coarsening, and edge detection), a preprocessor #2 (noise filtering and location parameter detection), and a circular pattern recognition block. The proposed system is implementable onto field programmable gate arrays (FPGAs) and forming part of the vision system for a watermelon harvesting robot. Functional verification, logic synthesis, and implementation are detailed for the FPGA circular pattern recognition block.

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20

Arcelli Fontana, Francesca, and Marco Zanoni. "A tool for design pattern detection and software architecture reconstruction." Information Sciences 181, no. 7 (April 1, 2011): 1306–24. http://dx.doi.org/10.1016/j.ins.2010.12.002.

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Bernardi, Mario Luca, Marta Cimitile, and Giuseppe Di Lucca. "Design pattern detection using a DSL-driven graph matching approach." Journal of Software: Evolution and Process 26, no. 12 (September 29, 2014): 1233–66. http://dx.doi.org/10.1002/smr.1674.

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Yarahmadi, Hadis, and Seyed Mohammad Hossein Hasheminejad. "Design pattern detection approaches: a systematic review of the literature." Artificial Intelligence Review 53, no. 8 (April 20, 2020): 5789–846. http://dx.doi.org/10.1007/s10462-020-09834-5.

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23

Issaoui, Imène, Nadia Bouassida, and Hanêne Ben-Abdallah. "Using metric-based filtering to improve design pattern detection approaches." Innovations in Systems and Software Engineering 11, no. 1 (December 9, 2014): 39–53. http://dx.doi.org/10.1007/s11334-014-0241-3.

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24

Guo, Junbo, Jintao Li, Yongdong Zhang, Dongming Zhang, and Xiao Wu. "Video Copy Detection Based on Trajectory Behavior Pattern." Journal of Computer-Aided Design & Computer Graphics 22, no. 6 (July 1, 2010): 943–48. http://dx.doi.org/10.3724/sp.j.1089.2010.10842.

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25

Almadi, Sara H. S., Danial Hooshyar, and Rodina Binti Ahmad. "Bad Smells of Gang of Four Design Patterns: A Decade Systematic Literature Review." Sustainability 13, no. 18 (September 14, 2021): 10256. http://dx.doi.org/10.3390/su131810256.

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Gang of Four (GoF) design patterns are widely approved solutions for recurring software design problems, and their benefits to software quality are extensively studied. However, the occurrence of bad smells in design patterns increases the crisis of degenerating design patterns’ structure and behavior. Their occurrences are detrimental to the benefits of design patterns and they influence software sustainability by increasing maintenance costs and energy consumption. Despite the destructive roles of bad smells in such designs, there are an absence of studies systematically reviewing bad smells of GoF design patterns. This study systematically reviews a 10-year state of the art sample, identifying 16 studies investigating this phenomenon. Following a thorough evaluation of the full contents, we observed that the occurrence of bad smells have been investigated in proportion to four granularity levels of analysis: Design level, category level, pattern level, and role level. We identified 28 bad smells, categorized under code smells and grime symptoms, and emphasized their relationship with GoF pattern types and categories. The utilization of design pattern bad smell detection approaches and datasets were also discussed. Consequently, we observed that the research phenomenon is growing intensively, with a prominent focus of studies analyzing code smell occurrences rather than grime occurrences, at various granularity levels. Finally, we uncovered research gaps and areas with significant potentials for future research.

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WANG, LUSHENG, and LIANG DONG. "RANDOMIZED ALGORITHMS FOR MOTIF DETECTION." Journal of Bioinformatics and Computational Biology 03, no. 05 (October 2005): 1039–52. http://dx.doi.org/10.1142/s0219720005001508.

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Motivation: Motif detection for DNA sequences has many important applications in biological studies, e.g. locating binding sites regulatory signals, designing genetic probes etc. In this paper, we propose a randomized algorithm, design an improved EM algorithm and combine them to form a software tool. Results: (1) We design a randomized algorithm for consensus pattern problem. We can show that with high probability, our randomized algorithm finds a pattern in polynomial time with cost error at most ∊ × l for each string, where l is the length of the motif and ∊ can be any positive number given by the user. (2) We design an improved EM algorithm that outperforms the original EM algorithm. (3) We develop a software tool, MotifDetector, that uses our randomized algorithm to find good seeds and uses the improved EM algorithm to do local search. We compare MotifDetector with Buhler and Tompa's PROJECTION which is considered to be the best known software for motif detection. Simulations show that MotifDetector is slower than PROJECTION when the pattern length is relatively small, and outperforms PROJECTION when the pattern length becomes large. Availability: It is available for free at , subject to copyright restrictions.

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Chihada, Abdullah, Saeed Jalili, Seyed Mohammad Hossein Hasheminejad, and Mohammad Hossein Zangooei. "Source code and design conformance, design pattern detection from source code by classification approach." Applied Soft Computing 26 (January 2015): 357–67. http://dx.doi.org/10.1016/j.asoc.2014.10.027.

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Bonello, D. K., Y. Iano, and U. B. Neto. "An Algorithm for the Detection of Circular Elements in Engineering Design." Journal of Engineering Sciences 7, no. 1 (2020): E6—E9. http://dx.doi.org/10.21272/jes.2020.7(1).e2.

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Various concentrated works have been done in the area of computational vision regarding the circle and texture detections. Detection of circles in images can be beneficial for PCB components industries for the detection of capacitors in printed circuit boards, also for medicine in the detection of red cells, white blood cells, and leukocytes, and for applications which requires precision and assignments regarding the detection of circles in a digital image. In this work is utilized a benchmarking of images to detection circle boards of different radio values for the comparison with the work [1] of this article. The benchmarking of images is composed of five main images that are tested in the algorithm of detection of circles in MATLAB with different values of radio for each image. The results appoint an enhancement of 300 % concerning the algorithm proposed in work [1] showed in this article. In this work also would be plotted graphs concerning the accuracy of the new proposed algorithm with relation to the algorithm proposed in work [1], indicating better results concerning the GUI interfaces and capacity of detection circles. Keywords: computer vision, pattern recognition, an algorithm of detection, circle detection, parameter identification.

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Gan, Yuanying, Chuntong Liu, Zhenxin He, Hongcai Li, and Zhongye Liu. "Digital Camouflage Pattern Design Based on the Biased Random Walk." Modelling and Simulation in Engineering 2022 (June 16, 2022): 1–12. http://dx.doi.org/10.1155/2022/2986346.

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Digital camouflage is a common countermeasure against military reconnaissance. In the face of high-tech imaging reconnaissance, battlefield detection means tend to be automated and refined. In order to adapt to the concealment requirements under various environmental backgrounds, combined with the camouflage performance of digital camouflage and its feedback mechanism in camouflage pattern design, this paper proposed a digital camouflage pattern design method based on biased random walk. Firstly, the original background is preprocessed, and the background texture’s direction, corner, step length, and pixel intensity difference are statistically analyzed, and the boundary probability between pixel nodes is estimated. Then, a biased random walk is used to outline the camouflage patches. The edge scatter is enriched according to the density of the patches, and the camouflage patches are filled according to the proportion of the main color of the background. Finally, a digital camouflage pattern is obtained. The quantitative analysis results show that the mean heart rate of the digital camouflage pattern based on multiscene design is at least 31.0% higher than that of the original background segmentation texture, and the standard deviation index of equivalent diameter is increased by 14.9% on average. In addition, the results of simulation camouflage image detection in multiple scenes show that the proposed method can effectively deal with camouflage target detection on the basis of fully retaining the original background texture information and has strong camouflage concealment effect in the scene.

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Xiang, Zhong, Yujia Shen, Zhitao Cheng, Miao Ma, and Feng Lin. "Periodic Pattern Detection of Printed Fabric Based on Deep Learning Algorithm." Journal of Physics: Conference Series 2148, no. 1 (January 1, 2022): 012013. http://dx.doi.org/10.1088/1742-6596/2148/1/012013.

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Abstract Printed fabric patterns contain multiple repeat pattern primitives, which have a significant impact on fabric pattern design in the textile industry. The pattern primitive is often composed of multiple elements, such as color, form, and texture structure. Therefore, the more pattern elements it contains, the more complex the primitive is. In order to segment fabric primitives, this paper proposes a novel convolutional neural network (CNN) method with spatial pyramid pooling module as a feature extractor, which enables to learn the pattern feature information and determine whether the printed fabric has periodic pattern primitives. Furthermore, by choosing pair of activation peaks in a filter, a set of displacement vectors can be calculated. The activation peaks that are most accordant with the optimum displacement vector contribute to pick out the final size of primitives. The results show that the method with the powerful feature extraction capabilities of the CNN can segment the periodic pattern primitives of complex printed fabrics. Compared with the traditional algorithm, the proposed method has higher segmentation accuracy and adaptability.

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Omprakash Hajari, Kamal, Ujwalla Haridas Gawande, and Yogesh Golhar. "Motion Pattern Based Anomalous Pedestrian Activity Detection." International Journal of Image, Graphics and Signal Processing 14, no. 6 (December 8, 2022): 15–25. http://dx.doi.org/10.5815/ijigsp.2022.06.02.

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LIU, JINZHU, and LEQUAN MIN. "ROBUST DESIGNS FOR GRAY-SCALE GLOBAL CONNECTIVITY DETECTION CNN TEMPLATES." International Journal of Bifurcation and Chaos 17, no. 08 (August 2007): 2827–38. http://dx.doi.org/10.1142/s0218127407018816.

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The cellular neural/nonlinear network (CNN) is a powerful tool for image and video signal processing, as well as robotic and biological visions. Practically, an engineer always hopes to design a CNN that has both universality and robustness. Based on research on the designs for the global connectivity detection (GCD) CNN [Chua, 1997] used in binary pattern, this paper establishes a theorem on robust designs for gray-scale global connectivity detection (GGCD) CNN templates. The theorem provides template parameter inequalities for determining parameter intervals for implementing the GCD functions. As a first example, two gray-scale labyrinth patterns with Gaussian noise are constructed. Using the GGCD, CNN designed by the theorem detects the connectivity of the two labyrinth patterns with gray-scales. In the other three examples, using GGCD CNNs simulate the spreads of an infectious diseases at nonuniform speeds.

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Barbudo, Rafael, Aurora Ramírez, Francisco Servant, and José Raúl Romero. "GEML: A grammar-based evolutionary machine learning approach for design-pattern detection." Journal of Systems and Software 175 (May 2021): 110919. http://dx.doi.org/10.1016/j.jss.2021.110919.

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Liu, Lilong, Xuelin Yang, and Weisheng Hu. "Experimental evaluation of pilot pattern design in direct-detection optical OFDM transmission." Optics Communications 294 (May 2013): 83–87. http://dx.doi.org/10.1016/j.optcom.2012.12.014.

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Ordin, Mikhail, Leona Polyanskaya, David Maximiliano Gómez, and Arthur G. Samuel. "The Role of Native Language and the Fundamental Design of the Auditory System in Detecting Rhythm Changes." Journal of Speech, Language, and Hearing Research 62, no. 4 (April 15, 2019): 835–52. http://dx.doi.org/10.1044/2018_jslhr-s-18-0299.

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Purpose We investigated whether rhythm discrimination is mainly driven by the native language of the listener or by the fundamental design of the human auditory system and universal cognitive mechanisms shared by all people irrespective of rhythmic patterns in their native language. Method In multiple experiments, we asked participants to listen to 2 continuous acoustic sequences and to determine whether their rhythms were the same or different (AX discrimination). Participants were native speakers of 4 languages with different rhythmic properties (Spanish, French, English, and German) to understand whether the predominant rhythmic patterns of a native language affect sensitivity, bias, and reaction time in detecting rhythmic changes in linguistic (Experiment 2) and in nonlinguistic (Experiments 1 and 2) acoustic sequences. We examined sensitivity and bias measures, as well as reaction times. We also computed Bayes factors in order to assess the effect of native language. Results All listeners performed better (i.e., responded faster and manifested higher sensitivity and accuracy) when detecting the presence or absence of a rhythm change when the 1st stimulus in an AX test pair exhibited regular rhythm (i.e., a syllable-timed rhythmic pattern) than when the 1st stimulus exhibited irregular rhythm (i.e., stress-timed rhythmic pattern). This result pattern was observed both on linguistic and nonlinguistic stimuli and was not modulated by the native language of the participant. Conclusion We conclude that rhythm change detection is a fundamental function of a processing system that relies on general auditory mechanisms and is not modulated by linguistic experience.

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Rousseau, Sylvain, and David Helbert. "Compressive Color Pattern Detection Using Partial Orthogonal Circulant Sensing Matrix." IEEE Transactions on Image Processing 29 (2020): 670–78. http://dx.doi.org/10.1109/tip.2019.2927334.

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Li, Xiaotong, Anthony Gachagan, and Paul Murray. "Design of 2D Sparse Array Transducers for Anomaly Detection in Medical Phantoms." Sensors 20, no. 18 (September 19, 2020): 5370. http://dx.doi.org/10.3390/s20185370.

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Aperiodic sparse 2D ultrasonic array configurations, including random array, log spiral array, and sunflower array, have been considered for their potential as conformable transducers able to image within a focal range of 30–80 mm, at an operating frequency of 2 MHz. Optimisation of the imaging performance of potential array patterns has been undertaken based on their simulated far field directivity functions. Two evaluation criteria, peak sidelobe level (PSL) and integrated sidelobe ratio (ISLR), are used to access the performance of each array configuration. Subsequently, a log spiral array pattern with −19.33 dB PSL and 2.71 dB ISLR has been selected as the overall optimal design. Two prototype transducers with the selected log spiral array pattern have been fabricated and characterised, one using a fibre composite element composite array transducer (CECAT) structure, the other using a conventional 1–3 composite (C1–3) structure. The CECAT device demonstrates improved coupling coefficient (0.64 to 0.59), reduced mechanical cross-talk between neighbouring array elements (by 10 dB) and improved operational bandwidth (by 16.5%), while the C1–3 device performs better in terms of sensitivity (~50%). Image processing algorithms, such as Hough transform and morphological opening, have been implemented to automatically detect and dimension particles located within a fluid-filled tube structure, in a variety of experimental scenarios, including bespoke phantoms using tissue mimicking material. Experiments using the fabricated CECAT log spiral 2D array transducer demonstrated that this algorithmic approach was able to detect the walls of the tube structure and stationary anomalies within the tube with a precision of ~0.1 mm.

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Sizov, F. F. "Detection of IR and sub/THz radiation using MCT thin layer structures: design of the chip, optical elements and antenna pattern." Semiconductor Physics Quantum Electronics and Optoelectronics 19, no. 2 (July 6, 2016): 149–55. http://dx.doi.org/10.15407/spqeo19.02.149.

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Wang, Jianming, and Jing Chen. "Design and Research of Dynamic Evolution System in Football Tactics Under Computational Intelligence." Mathematical Problems in Engineering 2022 (May 11, 2022): 1–12. http://dx.doi.org/10.1155/2022/3772236.

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With the rapid development of computer vision, it has been widely used in football. Aiming at the problems of incomplete collection of information, poor operability of system, and bad practical application in the field of football match, an analysis system of football tactics is designed in this paper. Combining the requirements of functions and performance, the analysis system is divided into management of basic information, collection of tactical information, detection of passing pattern, inquiry of tactical information, and visual display, which puts forward the method of the detection of passing pattern based on model. Through the intelligent design, the visual display of the information of athlete, team, competition, tactics, and passing pattern is realized under computational intelligence, which has certain practical value in application.

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Wu, Qinghong, and Minghui Zhu. "Application of Image Processing Sensor and Pattern Recognition in Detection of Bearing Surface Defects." Journal of Sensors 2022 (July 1, 2022): 1–7. http://dx.doi.org/10.1155/2022/7924982.

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In order to solve the problem that the traditional detection technology can not meet the requirements of online detection, a visual detection device for bearing inner ring defects based on image processing and pattern recognition is proposed in this paper. The device systematically designs an image acquisition device of bearing inner ring based on CCD. In the hardware scheme, the appropriate lens, camera, light source, and other related hardware are selected according to the actual needs, a complete image acquisition platform of bearing inner ring is built, and the software function design is completed. The simulation results show that the qualified rate of machine detection is 98.6%, the missed detection rate is 0, and the false detection rate is 1.4%, which are better than manual detection. Conclusion. The test results show that the bearing inner ring defect detection system can detect the surface defects of bearing inner ring quickly, stably, and reliably, and the detection efficiency and accuracy are higher than the original manual detection method, so it has a good application prospect.

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Chavat, Juan, Sergio Nesmachnow, Andrei Tchernykh, and Vladimir Shepelev. "Active Safety System for Urban Environments with Detecting Harmful Pedestrian Movement Patterns Using Computational Intelligence." Applied Sciences 10, no. 24 (December 17, 2020): 9021. http://dx.doi.org/10.3390/app10249021.

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This article presents a system for detecting pedestrian movement patterns in urban environments, by applying computational intelligence methods for image processing and pattern detection. The proposed system is capable of processing multiple images and video sources in real-time. Furthermore, it has a flexible design, as it is based on a pipes and filters architecture that makes it easy to evaluate different computational intelligence techniques to address the subproblems involved in each stage of the process. Two main stages are implemented in the proposed system: the first stage is in charge of extracting relevant features of the processed images, by applying image processing and object tracking, and the second stage is responsible for the patterns detection. The experimental analysis of the proposed system was performed over more than 1450 problem instances, using PETS09-S2L1 videos, and the results were compared with part of the Multiple Object Tracking Challenge benchmark results. Experiments covered the two main stages of the system. Results indicate that the proposed system is competitive yet simpler than other similar software methods. Overall, this article provides the theoretical frame and a proof of concept needed for the implementation of a real-time system that takes as input a group of image sequences, extracts relevant features, and detects a set of predefined patterns. The proposed implementation is a reliable proof of the viability of building pedestrian movement pattern detection systems.

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Lu, Zhe-Ming, and Yong Shi. "Fast Video Shot Boundary Detection Based on SVD and Pattern Matching." IEEE Transactions on Image Processing 22, no. 12 (December 2013): 5136–45. http://dx.doi.org/10.1109/tip.2013.2282081.

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Miranda, Michael Gabriel, Renato Alberto Salinas, Ulrich Raff, and Oscar Magna. "Wavelet Design for Automatic Real-Time Eye Blink Detection and Recognition in EEG Signals." International Journal of Computers Communications & Control 14, no. 3 (May 31, 2019): 375–87. http://dx.doi.org/10.15837/ijccc.2019.3.3516.

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The blinking of an eye can be detected in electroencephalographic (EEG) recordings and can be understood as a useful control signal in some information processing tasks. The detection of a specific pattern associated with the blinking of an eye in real time using EEG signals of a single channel has been analyzed. This study considers both theoretical and practical principles enabling the design and implementation of a system capable of precise real-time detection of eye blinks within the EEG signal. This signal or pattern is subject to considerable scale changes and multiple incidences. In our proposed approach, a new wavelet was designed to improve the detection and localization of the eye blinking signal. The detection of multiple occurrences of the blinking perturbation in the recordings performed in real-time operation is achieved with a window giving a time-limited projection of an ongoing analysis of the sampled EEG signal.

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Lee, Sang-Hoon, and Jin Soh. "Design and Implementation of the Intrusion Detection Pattern Algorithm Based on Data Mining." KIPS Transactions:PartC 10C, no. 6 (October 1, 2003): 717–26. http://dx.doi.org/10.3745/kipstc.2003.10c.6.717.

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Park, Chan-Jun, Sun-Hwan Kim, Sung-Kwun Oh, and Jin-Yul Kim. "Design of RBFNNs Pattern Classifier Realized with the Aid of Face Features Detection." Journal of Korean Institute of Intelligent Systems 26, no. 2 (April 25, 2016): 120–26. http://dx.doi.org/10.5391/jkiis.2016.26.2.120.

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Vazquez-Lopez, J. A., I. Lopez-Juarez, and M. Peña-Cabrera. "On the Use of the FuzzyARTMAP Neural Network for Pattern Recognition in Statistical Process Control using a Factorial Design." International Journal of Computers Communications & Control 5, no. 2 (June 1, 2010): 205. http://dx.doi.org/10.15837/ijccc.2010.2.2475.

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Time-series statistical pattern recognition is of prime importance in statistics, especially in quality control techniques for manufacturing processes. A frequent problem in this application is the complexity when trying to determine the behaviour (pattern) from sample data. There have been identified standard patterns which are commonly present when using the X chart; its detection depends on human judgement supported by norms and graphical criteria. In the last few years, it has been demonstrated that Artificial Neural Networks (ANN’s) are useful to predict the type of time-series pattern instead of the use of rules. However, the ANN control parameters have to be fixed to values that maximize its performance. This research proposes an experimental design methodology to determine the most appropriate values for the control parameters of the FuzzyARTMAP ANN such as: learning rate (β ) and network vigilance (ρa, ρb, ρab) in order to increment the neural network efficiency during unnatural pattern recognition.

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Yu, Yufeng, Dingsheng Wan, Qun Zhao, and Huan Liu. "Detecting Pattern Anomalies in Hydrological Time Series with Weighted Probabilistic Suffix Trees." Water 12, no. 5 (May 21, 2020): 1464. http://dx.doi.org/10.3390/w12051464.

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Anomalous patterns are common phenomena in time series datasets. The presence of anomalous patterns in hydrological data may represent some anomalous hydrometeorological events that are significantly different from others and induce a bias in the decision-making process related to design, operation and management of water resources. Hence, it is necessary to extract those “anomalous” knowledge that can provide valuable and useful information for future hydrological analysis and forecasting from hydrological data. This paper focuses on the problem of detecting anomalous patterns from hydrological time series data, and proposes an effective and accurate anomalous pattern detection approach, TFSAX_wPST, which combines the advantages of the Trend Feature Symbolic Aggregate approximation (TFSAX) and weighted Probabilistic Suffix Tree (wPST). Experiments with different hydrological real-world time series are reported, and the results indicate that the proposed methods are fast and can correctly detect anomalous patterns for hydrological time series analysis, and thus promote the deep analysis and continuous utilization of hydrological time series data.

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BIGGIO, BATTISTA, GIORGIO FUMERA, and FABIO ROLI. "PATTERN RECOGNITION SYSTEMS UNDER ATTACK: DESIGN ISSUES AND RESEARCH CHALLENGES." International Journal of Pattern Recognition and Artificial Intelligence 28, no. 07 (October 14, 2014): 1460002. http://dx.doi.org/10.1142/s0218001414600027.

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We analyze the problem of designing pattern recognition systems in adversarial settings, under an engineering viewpoint, motivated by their increasing exploitation in security-sensitive applications like spam and malware detection, despite their vulnerability to potential attacks has not yet been deeply understood. We first review previous work and report examples of how a complex system may be evaded either by leveraging on trivial vulnerabilities of its untrained components, e.g. parsing errors in the pre-processing steps, or by exploiting more subtle vulnerabilities of learning algorithms. We then discuss the need of exploiting both reactive and proactive security paradigms complementarily to improve the security by design. Our ultimate goal is to provide some useful guidelines for improving the security of pattern recognition in adversarial settings, and to suggest related open issues to foster research in this area.

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Islamiyati, Dina Suci, and Ahmad Fikri. "Penerapan Algoritma Knuth-Morris-Pratt dalam Mendeteksi Tingkat Kemiripan Judul Skripsi Berbasis Web." Journal of Information System Research (JOSH) 3, no. 2 (January 31, 2022): 58–63. http://dx.doi.org/10.47065/josh.v3i2.1168.

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In terms of finding the problem topic for thesis titles, students often have difficulty in determining thesis titles, in addition, there may be similarities between students. Manually checking the title requires more effort, for it takes a system to detect the level of similarity in the thesis title. To design and build a thesis title-level detection system using the Knuth-Morris-Pratt algorithm. To find out the percentage of similarities in the title, the Knuth-Morris-Pratt algorithm at the time of matching the string is Enter query of the word to be searched, matching the word arrangement pattern used as an example at the beginning of the text. From left to right, this algorithm will match character per character pattern with characters in the corresponding text, the algorithm then shifts the wording pattern until the word arrangement pattern used as an example is at the end of the text. Based on the analysis, design and implementation of the similarity level detection system of two or more thesis titles using the Knuth Morris Pratt algorithm it can be concluded that: The detection system has been successfully created using the Knuth Morris Pratt algorithm (KMP). It has been obtained the results of analysis by the process of string matching between strings with patterns where the system postulates the results of analysis in the form of the number of word similarities and percentages

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Xing, Zhen-hua, Zhen Wang, Hang Xu, Miao Xu, Xiao-lin Mou, Lu-xiao Wu, Kun Yang, and Zhao Liu. "Optimization design and development of UHF omnidirectional wideband antenna for PD detection in substation." E3S Web of Conferences 256 (2021): 01012. http://dx.doi.org/10.1051/e3sconf/202125601012.

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In order to meet the requirements of partial discharge (PD) detection of electrical equipment in substation station area, the ultra-high frequency (UHF) antenna with broadband, high gain and horizontal plane (H-plane) omnidirectional is studied and designed as a PD sensor. By analyzing the characteristic impedance and structural characteristics of the biconic antenna, it is determined to be an UHF omnidirectional wideband antenna for detecting PD. The influence of structural parameters such as half-cone angle and vertebral body height on the antenna performance is simulated. At last, a double cone antenna is made and its bandwidth, directional pattern, gain and equivalent height are tested.

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