Relevant bibliographies by topics / Frequency-clustering

Contents

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

Academic literature on the topic 'Frequency-clustering'

Author: Grafiati
Published: 2 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Frequency-clustering"

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Banerjee, Arko, Suvendu Chandan Nayak, and Chhabi Rani Panigrahi. "Weighted Clustering Ensemble with Base Clustering Frequency and Diversity." International Journal of Electronics Engineering and Applications 10, no. 2 (2021): 41–50. http://dx.doi.org/10.30696/ijeea.x.ii.2022.41-50.

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Xue, Yi, and Ramazan Gençay. "Trading frequency and volatility clustering." Journal of Banking & Finance 36, no. 3 (2012): 760–73. http://dx.doi.org/10.1016/j.jbankfin.2011.09.008.

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SUN, Shuping, Zhongwei JIANG, and Haibin WANG. "1204 Heart Sound Clustering Method Using Time-Frequency Distribution Energy." Proceedings of Conference of Chugoku-Shikoku Branch 2010.48 (2010): 365–66. http://dx.doi.org/10.1299/jsmecs.2010.48.365.

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Shumway, Robert H. "Time-frequency clustering and discriminant analysis." Statistics & Probability Letters 63, no. 3 (2003): 307–14. http://dx.doi.org/10.1016/s0167-7152(03)00095-6.

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Rahozin, D. V., and A. Yu Doroshenko. "Low frequency signal classification using clustering methods." PROBLEMS IN PROGRAMMING, no. 1 (January 2024): 48–56. http://dx.doi.org/10.15407/pp2024.01.048.

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The article considers the problem of low frequency signal classification, as sound or vibration pattern footprints may describe types of objects very well. In cases of a priori absence of object signal pattern information, the unsupervised learning methods based on clustering looks good enough for classification, and outperform neural net-based methods in case of limited power envelope. We have used big real-world sound and vibration data set to check several clustering methods (K-Means, OPTICS) for classification without any a priori data and have got good enough results. The article consider
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Adhikari, Animesh. "Clustering local frequency items in multiple databases." Information Sciences 237 (July 2013): 221–41. http://dx.doi.org/10.1016/j.ins.2013.02.043.

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Bao, Jun Peng, Jun Yi Shen, Xiao Dong Liu, and Hai Yan Liu. "The heavy frequency vector-based text clustering." International Journal of Business Intelligence and Data Mining 1, no. 1 (2005): 42. http://dx.doi.org/10.1504/ijbidm.2005.007317.

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Aki, Hazar Cenk, M. Erturk, and Huseyin Arslan. "Fractional Reuse Partitioning Schemes for Overlay Cellular Architectures." International Journal of Interdisciplinary Telecommunications and Networking 2, no. 4 (2010): 15–29. http://dx.doi.org/10.4018/jitn.2010100102.

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In this paper, the authors propose three partitioning schemes for adaptive clustering with fractional frequency reuse namely maximal fractional frequency reuse partitioning (MFRP), optimal fractional reuse partitioning (OFRP), and GoS-oriented frequency reuse partitioning. The authors propose that an overlaid cellular clustering scheme, which uses adaptive fractional frequency reuse factors, would provide a better capacity by exploiting the high level of signal to interference ratio (SIR). The proposed methods are studied via simulations and the results show that the adaptive clustering with d
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Al-Obaydy, Wasseem N. Ibrahem, Hala A. Hashim, Yassen AbdelKhaleq Najm, and Ahmed Adeeb Jalal. "Document classification using term frequency-inverse document frequency and K-means clustering." Indonesian Journal of Electrical Engineering and Computer Science 27, no. 3 (2022): 1517. http://dx.doi.org/10.11591/ijeecs.v27.i3.pp1517-1524.

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Increased advancement in a variety of study subjects and information technologies, has increased the number of published research articles. However, researchers are facing difficulties and devote a significant time amount in locating scientific research publications relevant to their domain of expertise. In this article, an approach of document classification is presented to cluster the text documents of research articles into expressive groups that encompass a similar scientific field. The main focus and scopes of target groups were adopted in designing the proposed method, each group include
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Al-Obaydy, Wasseem N. Ibrahem, Hala A. Hashim, Yassen AbdulKhaleq Najm, and Ahmed Adeeb Jalal. "Document classification using term frequency-inverse document frequency and K-means clustering." Indonesian Journal of Electrical Engineering and Computer Science 27, no. 3 (2022): 1517–24. https://doi.org/10.11591/ijeecs.v27.i3.pp1517-1524.

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Increased advancement in a variety of study subjects and information technologies, has increased the number of published research articles. However, researchers are facing difficulties and devote a significant time amount in locating scientific research publications relevant to their domain of expertise. In this article, an approach of document classification is presented to cluster the text documents of research articles into expressive groups that encompass a similar scientific field. The main focus and scopes of target groups were adopted in designing the proposed method, each group include
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Dissertations / Theses on the topic "Frequency-clustering"

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White, Parker Douglas. "Constrained Clustering for Frequency Hopping Spread Spectrum Signal Separation." Thesis, Virginia Tech, 2019. http://hdl.handle.net/10919/93726.

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Frequency Hopping Spread Spectrum (FHSS) signaling is used across many devices operating in both regulated and unregulated bands. In either situation, if there is a malicious device operating within these bands, or more simply a user operating out of the required specifications, the identification this user important to insure communication link integrity and interference mitigation. The identification of a user involves the grouping of that users signal transmissions, and the separation of those transmission from transmissions of other users in a shared frequency band. Traditional signal se
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Verousis, Thanos. "Price discreteness and clustering in ultra high frequency equity and options data." Thesis, Aberystwyth University, 2009. http://hdl.handle.net/2160/63fefd3e-0d27-4f50-b1cb-490d1d063dd2.

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There is only sparse evidence on the implications of price discreteness in empirical studies using high frequency data. This thesis deals with the issue of discreteness in irregularly spaced data, and its theme is the investigation of two important aspects of discreteness. In particular, the compass rose pattern and price clustering in the individual equity options market are primarily studied. Also, price clustering is covered in the Initial Public Offerings (IPOs) primary market and its evolution in the secondary market is documented. The first contribution of the thesis is the design of a n
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Telha, Cornejo Claudio (Claudio A. ). "Algorithms and hardness results for the jump number problem, the joint replenishment problem, and the optimal clustering of frequency-constrained maintenance jobs." Thesis, Massachusetts Institute of Technology, 2012. http://hdl.handle.net/1721.1/70446.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2012.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (p. 107-110).<br>In the first part of this thesis we present a new, geometric interpretation of the jump number problem on 2-dimensional 2-colorable (2D2C) partial order. We show that the jump number of a 2D2C poset is equivalent to the maximum cardinality of an independent set in a properly defined collection of rectangles in the plane. We then model the geometric problem as a linear program. Even
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Hanna, Peter, and Erik Swartling. "Anomaly Detection in Time Series Data using Unsupervised Machine Learning Methods: A Clustering-Based Approach." Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-273630.

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For many companies in the manufacturing industry, attempts to find damages in their products is a vital process, especially during the production phase. Since applying different machine learning techniques can further aid the process of damage identification, it becomes a popular choice among companies to make use of these methods to enhance the production process even further. For some industries, damage identification can be heavily linked with anomaly detection of different measurements. In this thesis, the aim is to construct unsupervised machine learning models to identify anomalies on un
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Abraham, Etimbuk. "Adaptive supervisory control scheme for voltage controlled demand response in power systems." Thesis, University of Manchester, 2018. https://www.research.manchester.ac.uk/portal/en/theses/adaptive-supervisory-control-scheme-for-voltage-controlled-demand-response-in-power-systems(3e64537d-52c7-4eb5-87f2-b73fe920b9cb).html.

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Radical changes to present day power systems will lead to power systems with a significant penetration of renewable energy sources and smartness, expressed in an extensive utilization of novel sensors and cyber secure Information and Communication Technology. Although these renewable energy sources prove to contribute to the reduction of CO2 emissions into the environment, its high penetration affects power system dynamic performance as a result of reduced power system inertia as well as less flexibility with regards to dispatching generation to balance future demand. These pose a threat both
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Banerjee, Debashis. "Intelligent real-time environment and process adaptive radio frequency front-ends for ultra low power applications." Diss., Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/53882.

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In the thesis the design of process tolerant, use-aware radio-frequency front-ends were explored. First, the design of fuzzy logic and equation based controllers, which can adapt to multi-dimensional channel conditions, are proposed. Secondly, the thesis proves that adaptive systems can have multiple modes of operation depending upon the throughput requirements of the system. Two such modes were demonstrated: one optimizing the energy-per-bit (energy priority mode) and another achieving the lowest power consumption at the highest throughput (data priority mode). Finally, to achieve process tol
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Nguyen, Linh Trung. "Estimation and separation of linear frequency- modulated signals in wireless communications using time - frequency signal processing." Queensland University of Technology, 2004. http://eprints.qut.edu.au/15984/.

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Signal processing has been playing a key role in providing solutions to key problems encountered in communications, in general, and in wireless communications, in particular. Time-Frequency Signal Processing (TFSP) provides eective tools for analyzing nonstationary signals where the frequency content of signals varies in time as well as for analyzing linear time-varying systems. This research aimed at exploiting the advantages of TFSP, in dealing with nonstationary signals, into the fundamental issues of signal processing, namely the signal estimation and signal separation. In particular, i
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Nguyen, Linh-Trung. "Estimation and separation of linear frequency- modulated signals in wireless communications using time - frequency signal processing." Thesis, Queensland University of Technology, 2004. https://eprints.qut.edu.au/15984/1/Nguyen_Linh-Trung_Thesis.pdf.

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Signal processing has been playing a key role in providing solutions to key problems encountered in communications, in general, and in wireless communications, in particular. Time-Frequency Signal Processing (TFSP) provides eective tools for analyzing nonstationary signals where the frequency content of signals varies in time as well as for analyzing linear time-varying systems. This research aimed at exploiting the advantages of TFSP, in dealing with nonstationary signals, into the fundamental issues of signal processing, namely the signal estimation and signal separation. In particular,
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Moreira, Rodrigo Bossini Tavares. "Construção do livro de ofertas a partir de dados de alta frequência e um algoritmo de predição de valores baseado em técnicas de agrupamento e regressão linear." Universidade de São Paulo, 2013. http://www.teses.usp.br/teses/disponiveis/45/45134/tde-24072013-231212/.

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A negociação algorítmica oferece algoritmos que tomam decisões de compra e/ou venda com base em parâmetros pré-determinados, oscilações de preços no mercado, dados históricos etc. Uma vantagem oferecida por ela é a possibilidade de atuação rápida no mercado, possivelmente aproveitando as melhores ofertas disponíveis. A Bovespa disponibiliza dados referentes à troca de mensagens entre as partes que constituem o mercado nanceiro. A partir dessas mensagens, geralmente é possível fazer a construção do livro de ofertas, que contém informações referentes às ofertas de compra e venda disponíveis em d
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Rulewski, Stenberg Louis. "High frequency rainfall data disaggregation with a random cascade model : Identifying regional differences in hyetographs in Sweden." Thesis, Uppsala universitet, Institutionen för geovetenskaper, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-434661.

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The field of urban hydrology is in need of high temporal resolution data series in order to effectively model and analyse existing and future trends in extreme precipitation. When high resolution data sets are, for any number of reasons, not available for a given location, the technique of disaggregation using a random cascade model can be applied. Previous studies have demonstrated the relevance of random cascades in the context of rainfall data disaggregation with temporal resolutions usually down to 1 hour. In this study, an attempt at disaggregation to a resolution of 1 minute was made. Us
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Books on the topic "Frequency-clustering"

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Rajakumar, P. S., S. Geetha, and T. V. Ananthan. Fundamentals of Image Processing. Jupiter Publications Consortium, 2023. http://dx.doi.org/10.47715/jpc.b.978-93-91303-80-8.

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"Fundamentals of Image Processing" offers a comprehensive exploration of image processing's pivotal techniques, tools, and applications. Beginning with an overview, the book systematically categorizes and explains the multifaceted steps and methodologies inherent to the digital processing of images. The text progresses from basic concepts like sampling and quantization to advanced techniques such as image restoration and feature extraction. Special emphasis is given to algorithms and models crucial to image enhancement, restoration, segmentation, and application. In the initial segments, the i
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Book chapters on the topic "Frequency-clustering"

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Rahman, Md Abdur, Madhu Chetty, Dieter Bulach, and Pramod P. Wangikar. "Frequency Decomposition Based Gene Clustering." In Neural Information Processing. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-26535-3_20.

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Zare-Mirakabad, Mohammad-Reza, Aman Jantan, and Stéphane Bressan. "Clustering-Based Frequency l-Diversity Anonymization." In Advances in Information Security and Assurance. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02617-1_17.

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Balzanella, Antonio, Giada Adelfio, Marcello Chiodi, Antonino D’Alessandro, and Dario Luzio. "Time-Frequency Filtering for Seismic Waves Clustering." In Studies in Classification, Data Analysis, and Knowledge Organization. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-06692-9_1.

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Adhikari, Animesh, Jhimli Adhikari, and Witold Pedrycz. "Clustering Local Frequency Items in Multiple Data Sources." In Data Analysis and Pattern Recognition in Multiple Databases. Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-03410-2_5.

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Yang, Shen, Qifeng Zhou, and Qing Wang. "Clustering of Bandit with Frequency-Dependent Information Sharing." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-28238-6_18.

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Adhikari, Animesh, and Jhimli Adhikari. "Clustering Local Frequency Items in Multiple Data Sources." In Advances in Knowledge Discovery in Databases. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-13212-9_11.

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Gu, Xiaowei, Plamen P. Angelov, German Gutierrez, Jose Antonio Iglesias, and Araceli Sanchis. "Parallel Computing TEDA for High Frequency Streaming Data Clustering." In Advances in Big Data. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47898-2_25.

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Hajjar, Ali, and Joe Tekli. "Unsupervised Extractive Text Summarization Using Frequency-Based Sentence Clustering." In New Trends in Database and Information Systems. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-15743-1_23.

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Swinney, Carolyn J., and John C. Woods. "GNSS Jamming Clustering Using Unsupervised Learning and Radio Frequency Signals." In Proceedings of the International Conference on Cybersecurity, Situational Awareness and Social Media. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-6974-6_2.

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Kapoor, Shreya, Manu S. Pillai, and Ashish Nagpal. "Effective Background and Foreground Segmentation Using Unsupervised Frequency Domain Clustering." In Lecture Notes in Networks and Systems. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-9712-1_8.

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Conference papers on the topic "Frequency-clustering"

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Li, Xiang, Yu Tang, Erkang Li, and Di Lin. "Unsupervised Identification Method of Radio-Frequency Fingerprint Based on Deep Clustering." In 2024 4th International Conference on Intelligent Technology and Embedded Systems (ICITES). IEEE, 2024. https://doi.org/10.1109/icites62688.2024.10777458.

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Gong, Weizheng, Shaoqi Yu, Yin Yao, Zhenfei Yao, and Bo Xu. "System Partitioning Primary Frequency Regulation Capability Evaluation Based on DBSCAN Clustering." In 2024 IEEE 4th International Conference on Digital Twins and Parallel Intelligence (DTPI). IEEE, 2024. https://doi.org/10.1109/dtpi61353.2024.10778703.

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Paramo, Gian, Arturo Bretas, Sean Meyn, and Newton Bretas. "A Frequency Stability Scheme Based on Dynamic Estimation and Spectral Clustering." In 2024 IEEE International Conference on Environment and Electrical Engineering and 2024 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe). IEEE, 2024. http://dx.doi.org/10.1109/eeeic/icpseurope61470.2024.10751571.

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Zhang, Jie, Hongji Shi, Yongji Cao, et al. "Feature Extraction for Post-Disturbance Frequency Prediction Based on Hierarchical Agglomerative Clustering." In 2024 IEEE 7th International Conference on Automation, Electronics and Electrical Engineering (AUTEEE). IEEE, 2024. https://doi.org/10.1109/auteee62881.2024.10869776.

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Zheng, Zhichao, Liangzhong Yao, Gang Zhang, Xianbo Ke, Yifei Wang, and Lin Cheng. "Operation Mode Extraction for Frequency Stability Control of Power Systems Based on Improved Clustering Algorithm." In 2024 6th International Conference on Power and Energy Technology (ICPET). IEEE, 2024. https://doi.org/10.1109/icpet62369.2024.10941480.

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Burkhardt, Daniel B., Jay S. Stanley III, Guy Wolf, and Smita Krishnaswamy. "Vertex-Frequency Clustering." In 2019 IEEE Data Science Workshop (DSW). IEEE, 2019. http://dx.doi.org/10.1109/dsw.2019.8755591.

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Baniasadi, Amirali, and Andreas Moshovos. "Asymmetric-frequency clustering." In the 2002 international symposium. ACM Press, 2002. http://dx.doi.org/10.1145/566408.566474.

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Aziz, Ravsan, Gokhan Kilic, Tolga Girici, Tolga Numanoglu, Guven Yenihayat, and Halime Koca. "Clustering and frequency allocation in frequency hopping tactical networks." In MILCOM 2015 - 2015 IEEE Military Communications Conference. IEEE, 2015. http://dx.doi.org/10.1109/milcom.2015.7357573.

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Singh, Nirmal, and Sawtantar Singh Khurmi. "ByteFreq: Malware clustering using byte frequency." In 2016 5th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO). IEEE, 2016. http://dx.doi.org/10.1109/icrito.2016.7784976.

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Long, Fuhui, Hanchuan Peng, and David D. Feng. "Image categorization based on clustering spatial frequency maps." In Electronic Imaging 2004, edited by Minerva M. Yeung, Rainer W. Lienhart, and Chung-Sheng Li. SPIE, 2003. http://dx.doi.org/10.1117/12.527284.

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Reports on the topic "Frequency-clustering"

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Phalen, William J., Robert M. Yadrick, and Walter G. Albert. Validation of a Procedure for Clustering Expressions of Frequency and Amount. Defense Technical Information Center, 1999. http://dx.doi.org/10.21236/ada364121.

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Engel, Bernard, Yael Edan, James Simon, Hanoch Pasternak, and Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, 1996. http://dx.doi.org/10.32747/1996.7613033.bard.

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The objectives of this project were to develop procedures and models, based on neural networks, for quality sorting of agricultural produce. Two research teams, one in Purdue University and the other in Israel, coordinated their research efforts on different aspects of each objective utilizing both melons and tomatoes as case studies. At Purdue: An expert system was developed to measure variances in human grading. Data were acquired from eight sensors: vision, two firmness sensors (destructive and nondestructive), chlorophyll from fluorescence, color sensor, electronic sniffer for odor detecti
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Paynter, Robin A., Celia Fiordalisi, Elizabeth Stoeger, et al. A Prospective Comparison of Evidence Synthesis Search Strategies Developed With and Without Text-Mining Tools. Agency for Healthcare Research and Quality (AHRQ), 2021. http://dx.doi.org/10.23970/ahrqepcmethodsprospectivecomparison.

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Background: In an era of explosive growth in biomedical evidence, improving systematic review (SR) search processes is increasingly critical. Text-mining tools (TMTs) are a potentially powerful resource to improve and streamline search strategy development. Two types of TMTs are especially of interest to searchers: word frequency (useful for identifying most used keyword terms, e.g., PubReminer) and clustering (visualizing common themes, e.g., Carrot2). Objectives: The objectives of this study were to compare the benefits and trade-offs of searches with and without the use of TMTs for evidence
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