Relevant bibliographies by topics / Design pattern detection

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Design pattern detection'

Author: Grafiati
Published: 9 March 2023
Last updated: 10 March 2023

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Journal articles on the topic "Design pattern detection"

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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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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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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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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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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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Dissertations / Theses on the topic "Design pattern detection"

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SAEKI, Motoshi, Takashi KOBAYASHI, Ryota SAKAMOTO, Junya KATADA, and Shinpei HAYASHI. "Design Pattern Detection by Using Meta Patterns." Institute of Electronics, Information and Communication Engineers, 2008. http://hdl.handle.net/2237/14977.

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Binun, Alexander [Verfasser]. "High Accuracy Design Pattern Detection / Alexander Binun." Bonn : Universitäts- und Landesbibliothek Bonn, 2012. http://d-nb.info/1043911294/34.

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Alshira'H, Mohammad H. "Integrating user knowledge into design pattern detection." Thesis, University of Leicester, 2015. http://hdl.handle.net/2381/36232.

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Design pattern detection is useful for a range of software comprehension and maintenance tasks. Tools that rely on static or dynamic analysis alone can produce inaccurate results, especially for patterns that rely on the run-time information. Some tools provide facilities for the developer to refine the results by adding their own knowledge. Currently, however, the ability of tools to accommodate this knowledge is very limited; it can only pertain to the detected patterns and cannot provide additional knowledge about the source code, or about its behaviour. In this thesis, we propose an approach to combine existing pattern detection techniques with a structured feedback mechanism. This enables the developer to refine the detection results by feeding-in additional knowledge about pattern implementations and software behaviour. The motivation is that a limited amount of user input can complement the automated detection process, to produce results that are more accurate. To evaluate the approach we applied it to a selection of openly available software systems. The evaluation was carried in two parts. First, an evaluation case study was carried out to detect pattern instances in the selected systems with the help of the user knowledge. Second, a user study of a broader range of expert users of design patterns was conducted in order to investigate the impact of their knowledge on the detection process, and to see whether it is realistic that the user can identify useful knowledge for the detection process. The evaluation results indicate that the proposed approach can yield a significant improvement in the accuracy whilst requiring a relatively small degree of user input from the developer. Moreover, the results show that expert users can supplement the design pattern detection process with a useful feedback that can enhance the detection of design pattern instances in the source code.
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ZANONI, MARCO. "Data mining techniques for design pattern detection." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2012. http://hdl.handle.net/10281/31515.

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The main objective of design pattern detection is to gain better comprehension of a software system, and of the kind of problems addressed during the development of the system itself. Design patterns have informal specifications, leading to many implementation variants caused by the subjective interpretation of the pattern by developers. This thesis applies a supervised classification approach to make the detection more subjective, bringing to developers the patterns they want to find, ranked by a confidence value.
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Hatzipantelis, Eleftherios. "The design and implementation of a statistical pattern recognition system for induction machine condition monitoring." Thesis, University of Aberdeen, 1995. http://digitool.abdn.ac.uk/R?func=search-advanced-go&find_code1=WSN&request1=AAIU086061.

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Automated fault diagnosis in induction machines is a difficult task and normally requires background information of electrical machines. Here a different methodology to the condition monitoring problem is devised. The approach is based entirely on Digital Signal Processing (DSP) and Statistical Pattern Recognition (PR). Description of machine conditions is extracted from empirical data. The main tasks that must be carried out by a PR-based condition monitoring system are: condition identification, knowledge reinforcement and knowledge creation for previously unseen conditions. The DSP operations are employed to quickly isolate sensor faults and to remove noise using data acquired from a single channel. DSP transformations may seem promising in making the monitoring system portable. Most importantly, they can compensate for operational changes in the machine. These changes affect the supply line currents and the primary signal quantities to be measured, i.e. the current and the axial leakage flux. The data which is input to the statistical monitoring system may be transformed, in the form of features, or remain unaltered. The system exploits the statistical properties of the feature vectors. The particular features, namely the LAR coefficients, convey short-term, high-resolution spectral information. For a long record, the feature vector sequence may provide information about changes in the record spectral characteristics, with time. Many induction machine processes are stationary and they can be properly be dealt with by a simple statistical classifier, e.g. a Gaussian model. For nonstationary processes, the system may employ a more comprehensive tool, namely the Hidden Markov Model. which may track the changing behaviour of the process in question. Initially a limited number of machine conditions are available to the process engineer. By identifying their boundaries, new faulty conditions could be signalled for and adopted into the database.
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MAGGIONI, STEFANO. "Design pattern detection and software architecture reconstruction: an integrated approach based on software micro-structures." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2010. http://hdl.handle.net/10281/7817.

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Nikitina, Asya F. "Design and implementation of pattern recognition algorithms for the detection of chemicals with a microcantilever sensor array." abstract and full text PDF (free order & download UNR users only), 2007. http://0-gateway.proquest.com.innopac.library.unr.edu/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:1447605.

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Manhaeve, Hans A. R. "Single pattern detection and identification of of CMOS transistor faults, requirements and methods : design and realisation of the OCIMU I←D←D←Q monitor; single pattern CMOS transistor fault testing." Thesis, University of Hull, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.361497.

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Karvir, Hrishikesh. "Design and Validation of a Sensor Integration and Feature Fusion Test-Bed for Image-Based Pattern Recognition Applications." Wright State University / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=wright1291753291.

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Atojoko, Achimugu A. "Design and Modelling of Passive UHF RFID Tags for Energy Efficient Liquid Level Detection Applications. A study of various techniques in the design, modelling, optimisation and deployment of RFID reader and passive UHF RFID tags to achieve effective performance for liquid sensing applications." Thesis, University of Bradford, 2016. http://hdl.handle.net/10454/15906.

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Sewer and oil pipeline spillage issues have become major causes of pollution in urban and rural areas usually caused by blockages in the water storage and drainage system, and oil spillage of underground oil pipelines. An effective way of avoiding this problem will be by deploying some mechanism to monitor these installations at each point in time and reporting unusual liquid activity to the relevant authorities for prompt action to avoid a flooding or spillage occurrence. This research work presents a low cost energy efficient liquid level monitoring technique using Radio Frequency Identification Technology. Passive UHF RFID tags have been designed, modelled and optimized. A simple rectangular tag, the P-shaped tag and S-shaped tag with UHF band frequency of operation (850-950 MHz) has been designed and modelled. Detailed parametric analysis of the rectangular tag is made and the optimised design results analysed and presented in HFSS and Matlab. The optimised rectangular tag designs are then deployed as level sensors in a gully pot. Identical tags were deployed to detect 4 distinct levels in alternate positions and a few inches in seperation distance within the gully pot height (Low, Mid, High and Ultra high). The radiation characteristic of tag sensors in deployment as modelled on HFSS is observed to show consistent performance with application requirements. An in-manhole chamber antenna for an underground communication system is analysed, designed, deployed and measured. The antenna covers dual-band impedance bandwidths (i.e. 824 to 960 MHz, and 1710 to 2170 MHz). The results show that the antenna prototype exhibits sufficient impedance bandwidth, suitable radiation characteristics, and adequate gains for the required underground wireless sensor applications. Finally, a Linearly Shifted Quadrifilar Helical Antenna (LSQHA) designed using Genetic Algorithm optimisation technique for adoption as an RFID reader antenna is proposed and investigated. The new antenna confirms coverage of the RFID bandwidth 860-960 MHz with acceptable power gain of 13.1 dBi.
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Books on the topic "Design pattern detection"

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Jean, Jared St. Kinect Hacks: Tips and Tools for Motion and Pattern Detection. O'Reilly Media, Incorporated, 2012.

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Jean, Jared St. Kinect Hacks: Tips and Tools for Motion and Pattern Detection. O'Reilly Media, Incorporated, 2012.

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Favre, Diane. Coloring Book for Detective: Beautiful Adults Relaxation Funny Appreciation Gift Idea Coloring Book with Stress Relieving Patterns, Animals Designs, and More. Independently Published, 2020.

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Darcy, Alison, and Shiri Sadeh-Sharvit. Mobile Device Applications for the Assessment and Treatment of Eating Disorders. Edited by W. Stewart Agras and Athena Robinson. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780190620998.013.27.

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Mobile devices and applications (apps) are increasingly used in clinical practice, offering reconceptualization of and novel avenues to tracking symptoms and delivery of more personalized interventions. This chapter reviews the burgeoning approaches to the integration of mobile in screening and treating individuals with eating disorders. Promising methods of data collection such as ecological momentary assessments enhance the capabilities of detecting symptoms and recognizing patterns—both are fundamental to the screening, evaluation, and monitoring of eating disorders and lay the foundations for better treatment design. More recent advances in machine learning allow ecological momentary interventions to be delivered and continuously optimized at the individual level in real time. This chapter explores what this means for the future of personalized treatment for eating disorders, referring to apps that integrate these mechanisms. Finally, the chapter provides a framework for evaluating mobile device mental health apps in clinical care.
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Book chapters on the topic "Design pattern detection"

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Bouassida, Nadia, and Hanêne Ben-Abdallah. "A New Approach for Pattern Problem Detection." In Notes on Numerical Fluid Mechanics and Multidisciplinary Design, 150–64. Cham: Springer International Publishing, 2010. http://dx.doi.org/10.1007/978-3-642-13094-6_13.

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Issaoui, Imène, Nadia Bouassida, and Hanêne Ben-Abdallah. "A Design Pattern Detection Approach Based on Semantics." In Software Engineering Research, Management and Applications 2012, 49–63. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-30460-6_4.

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Lee, Byung-kwan, Seung-hae Yang, Dong-Hyuck Kwon, and Dai-Youn Kim. "PGNIDS(Pattern-Graph Based Network Intrusion Detection System) Design." In Computational Science and Its Applications - ICCSA 2006, 38–47. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11751595_5.

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Arcelli, Francesca, Fabrizio Perin, Claudia Raibulet, and Stefano Ravani. "Design Pattern Detection in Java Systems: A Dynamic Analysis Based Approach." In Communications in Computer and Information Science, 163–79. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14819-4_12.

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Nikita, P., and Satyadhyan Chickerur. "Design and Implementation of Edge Detection Algorithms Using FPGA." In Proceedings of the 13th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2021), 563–72. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-96302-6_53.

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Neha, N., M. R. Gauthama Raman, Nivethitha Somu, R. Senthilnathan, and V. Shankar Sriram. "An Improved Feedforward Neural Network Using Salp Swarm Optimization Technique for the Design of Intrusion Detection System for Computer Network." In Computational Intelligence in Pattern Recognition, 867–75. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9042-5_74.

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Li, Runyu, Zhizhong Zhao, Zhiqiang Wang, Haifeng Wang, Wensheng Zhang, and Bowen Wang. "Design and Output Characteristics of Magnetostrictive Sensor Array for Tire Pattern Detection." In Proceedings of 2021 Chinese Intelligent Automation Conference, 90–97. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-6372-7_11.

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Srivastava, Romesh, Aparna Parameswaran, and Hrishikesh S. Sonalikar. "On Design of Airborne Radomes with Brent’s Method for Radiation Pattern Peak Detection." In Lecture Notes in Electrical Engineering, 163–72. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2818-4_17.

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Chaturvedi, Arti, Manjari Gupta, and Sanjay Kumar Gupta. "DPVO: Design Pattern Detection Using Vertex Ordering a Case Study in JHotDraw with Documentation to Improve Reusability." In Communications in Computer and Information Science, 452–65. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-2372-0_40.

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Zhang, Zhixiang, and Qinghua Li. "Automated Detection of Design Patterns." In Grid and Cooperative Computing, 694–97. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24680-0_110.

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Conference papers on the topic "Design pattern detection"

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Lebon, Maurice, and Vassilios Tzerpos. "Fine-Grained Design Pattern Detection." In 2012 IEEE 36th Annual Computer Software and Applications Conference - COMPSAC 2012. IEEE, 2012. http://dx.doi.org/10.1109/compsac.2012.37.

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Dabain, Haneen, Ayesha Manzer, and Vassilios Tzerpos. "Design pattern detection using FINDER." In SAC 2015: Symposium on Applied Computing. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2695664.2695900.

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Nagy, Akos, and Bence Kovari. "Programming language neutral design pattern detection." In 2015 16th IEEE International Symposium on Computational Intelligence and Informatics (CINTI). IEEE, 2015. http://dx.doi.org/10.1109/cinti.2015.7382925.

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Ren, Wu, and Wenyun Zhao. "An observer design-pattern detection technique." In 2012 IEEE International Conference on Computer Science and Automation Engineering (CSAE). IEEE, 2012. http://dx.doi.org/10.1109/csae.2012.6273011.

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Arcelli, Francesca, Claudia Raibulet, Yann-gael Gueheneuc, Giuliano Antoniol, and Jason Smith. "Design Pattern Detection for Reverse Engineering." In 2006 13th Working Conference on Reverse Engineering. IEEE, 2006. http://dx.doi.org/10.1109/wcre.2006.23.

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Stencel, Krzysztof, and Patrycja Wegrzynowicz. "Detection of Diverse Design Pattern Variants." In 2008 15th Asia-Pacific Software Engineering Conference. IEEE, 2008. http://dx.doi.org/10.1109/apsec.2008.67.

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Dong, Jing, Yongtao Sun, and Yajing Zhao. "Design pattern detection by template matching." In the 2008 ACM symposium. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1363686.1363864.

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Shiozawa, Kazufumi, Taiki Kimura, Tetsuaki Matsunawa, Shigeki Nojima, and Toshiya Kotani. "Multi-criteria hotspot detection using pattern classification." In Design-Process-Technology Co-optimization for Manufacturability XIII, edited by Jason P. Cain and Chi-Min Yuan. SPIE, 2019. http://dx.doi.org/10.1117/12.2515665.

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Chen, Jingsong, James Shiely, and Evangeline F. Y. Young. "Fast detection of largest repeating layout pattern." In Design-Process-Technology Co-optimization for Manufacturability XIII, edited by Jason P. Cain and Chi-Min Yuan. SPIE, 2019. http://dx.doi.org/10.1117/12.2517194.

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Chaturvedi, Shivam, Amrita Chaturvedi, Anurag Tiwari, and Shalini Agarwal. "Design Pattern Detection using Machine Learning Techniques." In 2018 7th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO). IEEE, 2018. http://dx.doi.org/10.1109/icrito.2018.8748282.

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Reports on the topic "Design pattern detection"

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Tao, Yang, Amos Mizrach, Victor Alchanatis, Nachshon Shamir, and Tom Porter. Automated imaging broiler chicksexing for gender-specific and efficient production. United States Department of Agriculture, December 2014. http://dx.doi.org/10.32747/2014.7594391.bard.

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Extending the previous two years of research results (Mizarch, et al, 2012, Tao, 2011, 2012), the third year’s efforts in both Maryland and Israel were directed towards the engineering of the system. The activities included the robust chick handling and its conveyor system development, optical system improvement, online dynamic motion imaging of chicks, multi-image sequence optimal feather extraction and detection, and pattern recognition. Mechanical System Engineering The third model of the mechanical chick handling system with high-speed imaging system was built as shown in Fig. 1. This system has the improved chick holding cups and motion mechanisms that enable chicks to open wings through the view section. The mechanical system has achieved the speed of 4 chicks per second which exceeds the design specs of 3 chicks per second. In the center of the conveyor, a high-speed camera with UV sensitive optical system, shown in Fig.2, was installed that captures chick images at multiple frames (45 images and system selectable) when the chick passing through the view area. Through intensive discussions and efforts, the PIs of Maryland and ARO have created the protocol of joint hardware and software that uses sequential images of chick in its fall motion to capture opening wings and extract the optimal opening positions. This approached enables the reliable feather feature extraction in dynamic motion and pattern recognition. Improving of Chick Wing Deployment The mechanical system for chick conveying and especially the section that cause chicks to deploy their wings wide open under the fast video camera and the UV light was investigated along the third study year. As a natural behavior, chicks tend to deploy their wings as a mean of balancing their body when a sudden change in the vertical movement was applied. In the latest two years, this was achieved by causing the chicks to move in a free fall, in the earth gravity (g) along short vertical distance. The chicks have always tended to deploy their wing but not always in wide horizontal open situation. Such position is requested in order to get successful image under the video camera. Besides, the cells with checks bumped suddenly at the end of the free falling path. That caused the chicks legs to collapse inside the cells and the image of wing become bluer. For improving the movement and preventing the chick legs from collapsing, a slowing down mechanism was design and tested. This was done by installing of plastic block, that was printed in a predesign variable slope (Fig. 3) at the end of the path of falling cells (Fig.4). The cells are moving down in variable velocity according the block slope and achieve zero velocity at the end of the path. The slop was design in a way that the deacceleration become 0.8g instead the free fall gravity (g) without presence of the block. The tests showed better deployment and wider chick's wing opening as well as better balance along the movement. Design of additional sizes of block slops is under investigation. Slops that create accelerations of 0.7g, 0.9g, and variable accelerations are designed for improving movement path and images.
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2

Kulhandjian, Hovannes. Detecting Driver Drowsiness with Multi-Sensor Data Fusion Combined with Machine Learning. Mineta Transportation Institute, September 2021. http://dx.doi.org/10.31979/mti.2021.2015.

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In this research work, we develop a drowsy driver detection system through the application of visual and radar sensors combined with machine learning. The system concept was derived from the desire to achieve a high level of driver safety through the prevention of potentially fatal accidents involving drowsy drivers. According to the National Highway Traffic Safety Administration, drowsy driving resulted in 50,000 injuries across 91,000 police-reported accidents, and a death toll of nearly 800 in 2017. The objective of this research work is to provide a working prototype of Advanced Driver Assistance Systems that can be installed in present-day vehicles. By integrating two modes of visual surveillance to examine a biometric expression of drowsiness, a camera and a micro-Doppler radar sensor, our system offers high reliability over 95% in the accuracy of its drowsy driver detection capabilities. The camera is used to monitor the driver’s eyes, mouth and head movement and recognize when a discrepancy occurs in the driver's blinking pattern, yawning incidence, and/or head drop, thereby signaling that the driver may be experiencing fatigue or drowsiness. The micro-Doppler sensor allows the driver's head movement to be captured both during the day and at night. Through data fusion and deep learning, the ability to quickly analyze and classify a driver's behavior under various conditions such as lighting, pose-variation, and facial expression in a real-time monitoring system is achieved.
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3

Mizrach, Amos, Michal Mazor, Amots Hetzroni, Joseph Grinshpun, Richard Mankin, Dennis Shuman, Nancy Epsky, and Robert Heath. Male Song as a Tool for Trapping Female Medflies. United States Department of Agriculture, December 2002. http://dx.doi.org/10.32747/2002.7586535.bard.

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This interdisciplinaray work combines expertise in engineering and entomology in Israel and the US, to develop an acoustic trap for mate-seeking female medflies. Medflies are among the world's most economically harmful pests, and monitoring and control efforts cost about $800 million each year in Israel and the US. Efficient traps are vitally important tools for medfly quarantine and pest management activities; they are needed for early detection, for predicting dispersal patterns and for estimating medfly abundance within infested regions. Early detection facilitates rapid response to invasions, in order to contain them. Prediction of dispersal patterns facilitates preemptive action, and estimates of the pests' abundance lead to quantification of medfly infestations and control efforts. Although olfactory attractants and traps exist for capturing male and mated female medflies, there are still no satisfactorily efficient means to attract and trap virgin and remating females (a significant and dangerous segment of the population). We proposed to explore the largely ignored mechanism of female attraction to male song that the flies use in courtship. The potential of such an approach is indicated by studies under this project. Our research involved the identification, isolation, and augmentation of the most attractive components of male medfly songs and the use of these components in the design and testing of traps incorporating acoustic lures. The project combined expertise in acoustic engineering and instrumentation, fruit fly behavior, and integrated pest management. The BARD support was provided for 1 year to enable proof-of-concept studies, aimed to determine: 1) whether mate-seeking female medflies are attracted to male songs; and 2) over what distance such attraction works. Male medfly calling song was recorded during courtship. Multiple acoustic components of male song were examined and tested for synergism with substrate vibrations produced by various surfaces, plates and loudspeakers, with natural and artificial sound playbacks. A speaker-funnel system was developed that focused the playback signal to reproduce as closely as possible the near-field spatial characteristics of the sounds produced by individual males. In initial studies, the system was tasted by observing the behavior of females while the speaker system played songs at various intensities. Through morning and early afternoon periods of peak sexual activity, virgin female medflies landed on a sheet of filter paper at the funnel outlet and stayed longer during broadcasting than during the silent part of the cycle. In later studies, females were captured on sticky paper at the funnel outlet. The mean capture rates were 67 and 44%, respectively, during sound emission and silent control periods. The findings confirmed that female trapping was improved if a male calling song was played. The second stage of the research focused on estimating the trapping range. Initial results indicated that the range possibly extended to 70 cm, but additional, verification tests remain to be conducted. Further studies are planned also to consider effects of combining acoustic and pheromonal cues.
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4

Gur, Amit, Edward Buckler, Joseph Burger, Yaakov Tadmor, and Iftach Klapp. Characterization of genetic variation and yield heterosis in Cucumis melo. United States Department of Agriculture, January 2016. http://dx.doi.org/10.32747/2016.7600047.bard.

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Project objectives: 1) Characterization of variation for yield heterosis in melon using Half-Diallele (HDA) design. 2) Development and implementation of image-based yield phenotyping in melon. 3) Characterization of genetic, epigenetic and transcriptional variation across 25 founder lines and selected hybrids. The epigentic part of this objective was modified during the course of the project: instead of characterization of chromatin structure in a single melon line through genome-wide mapping of nucleosomes using MNase-seq approach, we took advantage of rapid advancements in single-molecule sequencing and shifted the focus to Nanoporelong-read sequencing of all 25 founder lines. This analysis provides invaluable information on genome-wide structural variation across our diversity 4) Integrated analyses and development of prediction models Agricultural heterosis relates to hybrids that outperform their inbred parents for yield. First generation (F1) hybrids are produced in many crop species and it is estimated that heterosis increases yield by 15-30% globally. Melon (Cucumismelo) is an economically important species of The Cucurbitaceae family and is among the most important fleshy fruits for fresh consumption Worldwide. The major goal of this project was to explore the patterns and magnitude of yield heterosis in melon and link it to whole genome sequence variation. A core subset of 25 diverse lines was selected from the Newe-Yaar melon diversity panel for whole-genome re-sequencing (WGS) and test-crosses, to produce structured half-diallele design of 300 F1 hybrids (MelHDA25). Yield variation was measured in replicated yield trials at the whole-plant and at the rootstock levels (through a common-scion grafted experiments), across the F1s and parental lines. As part of this project we also developed an algorithmic pipeline for detection and yield estimation of melons from aerial-images, towards future implementation of such high throughput, cost-effective method for remote yield evaluation in open-field melons. We found extensive, highly heritable root-derived yield variation across the diallele population that was characterized by prominent best-parent heterosis (BPH), where hybrids rootstocks outperformed their parents by 38% and 56 % under optimal irrigation and drought- stress, respectively. Through integration of the genotypic data (~4,000,000 SNPs) and yield analyses we show that root-derived hybrids yield is independent of parental genetic distance. However, we mapped novel root-derived yield QTLs through genome-wide association (GWA) analysis and a multi-QTLs model explained more than 45% of the hybrids yield variation, providing a potential route for marker-assisted hybrid rootstock breeding. Four selected hybrid rootstocks are further studied under multiple scion varieties and their validated positive effect on yield performance is now leading to ongoing evaluation of their commercial potential. On the genomic level, this project resulted in 3 layers of data: 1) whole-genome short-read Illumina sequencing (30X) of the 25 founder lines provided us with 25 genome alignments and high-density melon HapMap that is already shown to be an effective resource for QTL annotation and candidate gene analysis in melon. 2) fast advancements in long-read single-molecule sequencing allowed us to shift focus towards this technology and generate ~50X Nanoporesequencing of the 25 founders which in combination with the short-read data now enable de novo assembly of the 25 genomes that will soon lead to construction of the first melon pan-genome. 3) Transcriptomic (3' RNA-Seq) analysis of several selected hybrids and their parents provide preliminary information on differentially expressed genes that can be further used to explain the root-derived yield variation. Taken together, this project expanded our view on yield heterosis in melon with novel specific insights on root-derived yield heterosis. To our knowledge, thus far this is the largest systematic genetic analysis of rootstock effects on yield heterosis in cucurbits or any other crop plant, and our results are now translated into potential breeding applications. The genomic resources that were developed as part of this project are putting melon in the forefront of genomic research and will continue to be useful tool for the cucurbits community in years to come.
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