Relevant bibliographies by topics / Selection Combining

Contents

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

Academic literature on the topic 'Selection Combining'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Selection Combining"

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Zaremba, Adam. "Combining Equity Country Selection Strategies." Contemporary Economics 11, no. 1 (March 31, 2017): 107–26. http://dx.doi.org/10.5709/ce.1897-9254.231.

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Alouini, Mohamed-slim, and Hong-chuan Yang. "Minimum estimation and combining generalized selection combining (MEC-GSC)." IEEE Transactions on Wireless Communications 6, no. 2 (February 2007): 526–32. http://dx.doi.org/10.1109/twc.2007.05225.

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Ali, Shaik Mohammed, and Hemanta Kumar Sahu. "SSK modulation performance with relay selection and selection combining." Internet Technology Letters 2, no. 6 (February 20, 2019): e92. http://dx.doi.org/10.1002/itl2.92.

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Dobbie, Lorraine, John C. Maule, and Anthony J. Brookes. "Combining methods for direct cDNA selection." Technical Tips Online 3, no. 1 (January 1998): 66–68. http://dx.doi.org/10.1016/s1366-2120(08)70102-8.

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Adachi, F., and J. D. Parsons. "Random FM noise with selection combining." IEEE Transactions on Communications 36, no. 6 (June 1988): 752–50. http://dx.doi.org/10.1109/26.2796.

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Albanis, George, and Roy Batchelor. "Combining heterogeneous classifiers for stock selection." Intelligent Systems in Accounting, Finance and Management 15, no. 1-2 (January 2007): 1–21. http://dx.doi.org/10.1002/isaf.282.

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Proschan, Michael A., and Sally A. Hunsberger. "Combining Treatment Selection and Definitive Testing." Biometrical Journal 48, no. 4 (August 2006): 690–92. http://dx.doi.org/10.1002/bimj.200610247.

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Chrysostomou, Kyriacos, Sherry Y. Chen, and Xiaohui Liu. "Combining multiple classifiers for wrapper feature selection." International Journal of Data Mining, Modelling and Management 1, no. 1 (2008): 91. http://dx.doi.org/10.1504/ijdmmm.2008.022539.

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Lioumpas, Athanasios, George Karagiannidis, and Theodoros Tsiftsis. "Adaptive generalized selection combining (A-GSC) receivers." IEEE Transactions on Wireless Communications 7, no. 12 (December 2008): 5214–19. http://dx.doi.org/10.1109/t-wc.2008.071087.

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Verheijen, J. H. M., F. P. A. Coolent, and P. van der Laan. "Combining two classical approaches for statistical selection." Communications in Statistics - Theory and Methods 26, no. 6 (January 1997): 1291–312. http://dx.doi.org/10.1080/03610929708831983.

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Dissertations / Theses on the topic "Selection Combining"

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Formeister, Richard. "DIVERSITY BRANCH SELECTION IN REAL WORLD APPLICATION." International Foundation for Telemetering, 2007. http://hdl.handle.net/10150/604498.

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ITC/USA 2007 Conference Proceedings / The Forty-Third Annual International Telemetering Conference and Technical Exhibition / October 22-25, 2007 / Riviera Hotel & Convention Center, Las Vegas, Nevada
Multipath propagation continues to be the dominant channel impairment in many aeronautical mobile telemetry (AMT) applications. Avoidance and diversity techniques continue to be the only practical means to combat this problem. In 2004 limited results from the development of a new post-detection, no-hit diversity selector were reported. Late breaking results from flight test were reported orally. A review of the methodology, flight test results and conclusions are presented in this paper. Also presented is an update describing enhancements of the implementation which emphasize operational flexibility as well as support for alternate demodulator products.
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Jefferis, Robert P. "TELEMETRY LINK RELIABILITY IMPROVEMENT VIA “NO-HIT” DIVERSITY BRANCH SELECTION." International Foundation for Telemetering, 2004. http://hdl.handle.net/10150/605302.

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International Telemetering Conference Proceedings / October 18-21, 2004 / Town & Country Resort, San Diego, California
Multipath propagation consisting largely of specular reflection components is known to be the major channel impairment in many aeronautical mobile telemetry (AMT) applications. Adaptive equalizers are not effective against flat fading commonly created by strong power delay profile components representing small fractions of the transmitted symbol period. Avoidance and diversity techniques are the only practical means of combating this problem. A new post-detection, no-hit diversity branch selector is described in this paper. Laboratory and limited flight test data comparing non-diversity, selection diversity and intermediate frequency (IF) combining techniques are presented.
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Smith, Shaun. "Combining Markowitz's selection model with different investment styles." Diss., University of Pretoria, 2017. http://hdl.handle.net/2263/64816.

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Portfolio selection has been a well-researched topic since the mid 1950Õs. Researchers such as Harry Markowitz obtained the Noble Prize for his work on portfolio selection. His model, which is underpinned by the concept that the market is efficient, has been the cornerstone of many investment strategies over the years. Recently, however, many authors have claimed that the markets are inefficient, and that one cannot rely on a model that assumes a linear and static relationship between risk and reward, making the Markowitz Portfolio Selection Model (MPSM) obsolete. Literature suggests that much of this inefficiency is created through the use of different styles; that is, styles in which shares are grouped together based on certain fundamental characteristics, to inform the investment strategies of investors. Therefore, this study endeavours to supplement the MPSM with different investment styles. Firstly, testing whether the risk adjustment afforded by the MPSM is positively influenced by the different investment styles. Secondly, to determine which style achieves the highest returns over the selected period. Monthly total return data from the JSE was used and portfolio rebalancing took place every six months for a period of 10 years. The share weightings of the portfolios were informed by risk adjusted style based predicted returns. The performance of these portfolios was subsequently compared. Results indicated that style influenced portfolios outperform the non-style influenced MPSM, with some styles providing greater returns than others over the period selected.
Mini Dissertation (MBA)--University of Pretoria, 2017.
lt2018
Gordon Institute of Business Science (GIBS)
MBA
Unrestricted
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Jensen, Richard. "Combining rough and fuzzy sets for feature selection." Thesis, University of Edinburgh, 2004. http://hdl.handle.net/1842/24740.

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Feature selection (FS) refers to the problem of selecting those input attributes that are most predictive of a given outcome; a problem encountered in many areas such as machine learning, pattern recognition and signal processing. Unlike other dimensionality reduction methods, feature selectors preserve the original meaning of the features after reduction. This has found application in tasks that involve datasets containing huge numbers of features (in the order of tens of thousands), which would be impossible to process further. Recent examples include text processing and web content classification. FS techniques have also been applied to small and medium-sized datasets in order to locate the most informative features for later use. Many feature selection methods have been developed and are reviewed critically in this thesis, with particular emphasis on their current limitations. The leading methods in this field are presented in a consistent algorithmic framework. One of the many successful applications of rough set theory has been to this area. The rough set ideology of using only the supplied data and no other information has many benefits in FS, where most other methods require supplementary knowledge. However, the main limitation of rough set-based feature selection in the literature is the restrictive requirement that all data is discrete. In classical rough set theory, it is not possible to consider real-valued or noisy data. This thesis proposes and develops an approach based on fuzzy-rough sets, fuzzy rough feature selection (FRFS), that addresses these problems and retains dataset semantics. Complexity analysis of the underlying algorithms is included. FRFS is applied to two domains where a feature reducing step is important; namely, web content classification and complex systems monitoring. The utility of this approach is demonstrated and is compared empirically with several dimensionality reducers. In the experimental studies, FRFS is shown to equal or improve classification accuracy when compared to the results from unreduced data. Classifiers that use a lower dimensional set of attributes which are retained by fuzzy-rough reduction outperform those that employ more attributes returned by the existing crisp rough reduction method. In addition, it is shown that FRFS is more powerful than the other FS techniques in the comparative study. Based on the new fuzzy-rough measure of feature significance^ further develop­ment of the FRFS technique is presented in this thesis. This is developed from the new area of feature grouping that considers the selection of groups of attributes in the search for the best subset. A novel framework is also given for the application of ant-based search mechanisms within feature selection in general, with particular emphasis on its employment in FRFS. Both of these developments are employed and evaluated within the complex systems monitoring application.
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JAIN, VISHESH. "PERFORMANCE ANALYSIS OF GENERALIZED SELECTION COMBINING IN ARBITRARILY CORRELATED NAKAGAMI FADING CHANNELS." University of Cincinnati / OhioLINK, 2005. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1131175492.

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Zhu, Chao. "Performance Analysis of Minimum Selection GSC with Channel Estimation Errors in Rayleigh Fading Channels." University of Cincinnati / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1250184723.

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Suen, Martin L. "Syndrome selection diversity combining in an indoor mobile radio environment." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk2/tape17/PQDD_0026/MQ31645.pdf.

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Romano, Eduardo O. "Selection indices for combining marker genetic data and animal model information /." This resource online, 1993. http://scholar.lib.vt.edu/theses/available/etd-09192009-040546/.

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Romano, Eduardo. "Selection indices for combining marker genetic data and animal model information." Thesis, Virginia Tech, 1993. http://hdl.handle.net/10919/44879.

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It was suggested that marker and phenotypic information be combined in order to obtain more accurate or earlier genetic evaluations. An improvement in accuracy or time of evaluation due to utilization of marker assisted selection (MAS) increases genetic progress. Fernando and Grossman (1989) suggested including marker information directly into the Animal Model, Best Linear Unbiased Prediction system, but several problems need to be solved before their approach becomes feasible. Other selection indices were suggested but either do not use all the available information or are suitable only for evaluation of the offspring of the sire from which the marker information was established. A selection index combining marker and Animal Model information was developed to allow comparisons involving offspring, grandoffspring and great-grandoffspring of a sire. Marker information was assumed to be a least square estimate of the difference between the average effects of the two quantitative trait loci (QTL) alleles present in a sire (Dp) and the standard error of this estimate (SE(Dp)). Estimates may have been obtained from a daughter or granddaughter design. Comparisons among grandoffspring and great-grandoffspring also require an estimate of the recombination rate (r) between the marker and the QTL. The Animal Model information consists of predicted transmitting ability (PTA) and reliability of PTA. PTA was assumed not to include any marker information. The expected percentage of the gain in accuracy (PGA) due to the inclusion of marker information in the selection indices is affected by the degree of polymorphism at the marker locus. The polymorphism information content (PIC) of a marker locus was computed for the second and third generations and for mates genotyped or not. PGA increased with larger Dos lower SE(Dp), lower r, a smaller number of own and progeny records, and larger PIC. PGA and PIC reduce over generations. Marker information in dairy cattle is likely to be used in generations beyond offspring. Then, only the use of highly polymorphic markers with a large and accurately estimated effect may be economically justified.
Master of Science
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Reid, Eric. "ENCRYPTED CORRELATING SOURCE SELECTOR." International Foundation for Telemetering, 2006. http://hdl.handle.net/10150/604265.

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ITC/USA 2006 Conference Proceedings / The Forty-Second Annual International Telemetering Conference and Technical Exhibition / October 23-26, 2006 / Town and Country Resort & Convention Center, San Diego, California
Modern telemetry and data streams are often encrypted. The majority of range testing activities require multiple ground stations to collect these streams and send them to a central processing location. Each of these streams currently needs to be individually decrypted before best source selection, processing and analysis. Using innovative techniques, it is possible to time correlate these encrypted streams, compare them with each other and create an output stream of better quality than any of the individual streams. This stream can then be decrypted by a single decryption device, greatly reducing cost and complexity.
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Books on the topic "Selection Combining"

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F, Drach Robert, ed. High-return, low-risk investment: Combining market timing, stock selection, and closed-end funds. New York u.a: Mentor u.a., 1985.

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Conka, Tahir. Performance analysis of noncoherent differential phase shift keying using Post-Detection Selection Combining over a Rayleigh fading channel. Monterey, Calif: Naval Postgraduate School, 1998.

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Ong, Choon Kwee. Performance analysis of DPSK signals with selection combing and convolutional coding in fading channel. Monterey, Calif: Naval Postgraduate School, 1998.

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Fernández-González, Eduardo. Public Portfolio Selection Combining Genetic Algorithms and Mathematical Decision Analysis. INTECH Open Access Publisher, 2012.

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Schmitt, Neal. Combining Cognitive and Noncognitive Measures. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780199373222.003.0012.

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The use of noncognitive measures enhances the prediction of various individual outcomes. Although structured measures of noncognitive constructs are routinely used in employee selection, they are rarely used to predict college student success. Situational judgment, biodata measures, and other methods of measurement address constructs that add to the prediction of grade point average and are major correlates of other student outcomes. Employers and college administrators indicate that outcomes other than task performance and grades are important. Because noncognitive attributes are the best predictors of these alternative outcomes, it seems they should be included in the set of criteria used by college admissions personnel. Several issues should be addressed if noncognitive measures are used more frequently, including the possibility of their use in other ways than for selection only, the minimizing of the influence of faking, and the reaction of various constituencies to their use in college admissions decisions.
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Performance Analysis of Noncoherent Binary Frequency Shift Keying Using Equal Gain Combining and Post Detection Selection Combining over a Nakagami Fading Channel. Storming Media, 1998.

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Performance Analysis of DPSK Signals with Selection Combining and Convolutional Coding in Fading Channel. Storming Media, 1998.

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Performance Analysis of Noncoherent Differential Phase Shift Keying Using Post-Detection Selection Combining Over a Rayleigh Fading Channel. Storming Media, 1998.

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Performance Analysis of Binary FSK Signals with L-Fold Diversity Selection Combining Techniques in a Nakagami-M Fading Channel. Storming Media, 1998.

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Jackson & Perkins Selecting, Growing, and Combining Outstanding Perennials: Northwestern Edition (Jackson & Perkins Selecting, Growing and Combining Outstanding Perinnials). Cool Springs Press, 2004.

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Book chapters on the topic "Selection Combining"

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Färber, Michael, and Cezary Kaliszyk. "Random Forests for Premise Selection." In Frontiers of Combining Systems, 325–40. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-24246-0_20.

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Yee, Kenton K. "Combining Fundamental Measures for Stock Selection." In Handbook of Quantitative Finance and Risk Management, 185–202. Boston, MA: Springer US, 2010. http://dx.doi.org/10.1007/978-0-387-77117-5_11.

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Skurichina, Marina, and Robert P. W. Duin. "Combining Feature Subsets in Feature Selection." In Multiple Classifier Systems, 165–75. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11494683_17.

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Aridas, Christos K., Sotiris B. Kotsiantis, and Michael N. Vrahatis. "Combining Prototype Selection with Local Boosting." In IFIP Advances in Information and Communication Technology, 94–105. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-44944-9_9.

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Teng, Choh Man. "Combining Noise Correction with Feature Selection." In Data Warehousing and Knowledge Discovery, 340–49. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-45228-7_34.

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Jones, Chris L. D., and K. Suzanne Barber. "Combining Job and Team Selection Heuristics." In Coordination, Organizations, Institutions and Norms in Agent Systems IV, 33–47. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-00443-8_3.

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Prudêncio, Ricardo B. C., Carlos Soares, and Teresa B. Ludermir. "Combining Meta-learning and Active Selection of Datasetoids for Algorithm Selection." In Lecture Notes in Computer Science, 164–71. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21219-2_22.

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Zaremba, Adam, and Jacob Shemer. "What Next? Combining and Improving Country Selection Strategies." In Country Asset Allocation, 223–40. New York: Palgrave Macmillan US, 2016. http://dx.doi.org/10.1057/978-1-137-59191-3_14.

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Petelin, Dejan, and Juš Kocijan. "Streaming-Data Selection for Gaussian-Process Modelling." In Towards Advanced Data Analysis by Combining Soft Computing and Statistics, 177–90. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-30278-7_15.

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Dhir, Chandra Shekhar, and Soo Young Lee. "Hybrid Feature Selection: Combining Fisher Criterion and Mutual Information for Efficient Feature Selection." In Advances in Neuro-Information Processing, 613–20. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02490-0_75.

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Conference papers on the topic "Selection Combining"

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Kong, N. "Performance Comparison Among Conventional Selection Combining, Optimum Selection Combining and Maximal Ratio Combining." In ICC 2009 - 2009 IEEE International Conference on Communications. IEEE, 2009. http://dx.doi.org/10.1109/icc.2009.5199319.

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Roy, Sebastien. "Hierarchical selection diversity combining architecture." In ICC 2012 - 2012 IEEE International Conference on Communications. IEEE, 2012. http://dx.doi.org/10.1109/icc.2012.6363900.

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Jorgensen, Stefan. "Intercluster beamforming using selection combining." In GLOBECOM 2014 - 2014 IEEE Global Communications Conference. IEEE, 2014. http://dx.doi.org/10.1109/glocom.2014.7036823.

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Alouini, M. S., and Hong-Chuan Yang. "Minimum estimation and combining generalized selection combining (MEC-GSC)." In Proceedings. International Symposium on Information Theory, 2005. ISIT 2005. IEEE, 2005. http://dx.doi.org/10.1109/isit.2005.1523401.

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Yao Ma, Z. Wang, and S. Pasupathy. "Asymptotic gains of generalized selection combining." In 2003 IEEE 58th Vehicular Technology Conference. VTC 2003-Fall (IEEE Cat. No.03CH37484). IEEE, 2003. http://dx.doi.org/10.1109/vetecf.2003.1286210.

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Stroud, Michael, and M. Michele Moss. "Tele-Transport: Combining Mobile Patient Assessment with Critical Care Outside Tertiary Care Centers." In Selection of Abstracts From NCE 2015. American Academy of Pediatrics, 2017. http://dx.doi.org/10.1542/peds.140.1_meetingabstract.136.

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Muthukumar, S., K. Balachandar, V. Nagarajan, and Kirubakaran S. "Performance analysis of threshold based selective relaying for Switched Selection Combining." In 2014 International Conference on Communications and Signal Processing (ICCSP). IEEE, 2014. http://dx.doi.org/10.1109/iccsp.2014.6950145.

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Gutierrez-Madronal, Lorena, Antonio Garcia-Dominguez, and Inmaculada Medina-Bulo. "Combining Evolutionary Mutation Testing with Random Selection." In 2020 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2020. http://dx.doi.org/10.1109/cec48606.2020.9185618.

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Kim, Y. G., and N. C. Beaulieu. "Lossless Selection Combining without Channel State Information." In 2008 IEEE International Conference on Communications. IEEE, 2008. http://dx.doi.org/10.1109/icc.2008.258.

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González, Diana, Daniel Costa, and José Filho. "Distributed Transmit-Antenna Selection Scheme for Relaying Systems with Selection Combining." In XXXI Simpósio Brasileiro de Telecomunicações. Sociedade Brasileira de Telecomunicações, 2013. http://dx.doi.org/10.14209/sbrt.2013.36.

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Reports on the topic "Selection Combining"

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Wolf, Lior, and Stanley Bileschi. Combining Variable Selection with Dimensionality Reduction. Fort Belvoir, VA: Defense Technical Information Center, March 2005. http://dx.doi.org/10.21236/ada454990.

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Dorr, Bonnie J., and Maria Katsova. Lexical Selection for Cross-Language Applications: Combining LCS with WordNet. Fort Belvoir, VA: Defense Technical Information Center, October 1998. http://dx.doi.org/10.21236/ada458651.

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Hill, Jonathan C., and John C. Knight. Selective Notification: Combining Forms of Decoupled Addressing for Internet-Scale Command and Alert Dissemination. Fort Belvoir, VA: Defense Technical Information Center, January 2003. http://dx.doi.org/10.21236/ada436806.

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de Caritat, Patrice, Brent McInnes, and Stephen Rowins. Towards a heavy mineral map of the Australian continent: a feasibility study. Geoscience Australia, 2020. http://dx.doi.org/10.11636/record.2020.031.

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Heavy minerals (HMs) are minerals with a specific gravity greater than 2.9 g/cm3. They are commonly highly resistant to physical and chemical weathering, and therefore persist in sediments as lasting indicators of the (former) presence of the rocks they formed in. The presence/absence of certain HMs, their associations with other HMs, their concentration levels, and the geochemical patterns they form in maps or 3D models can be indicative of geological processes that contributed to their formation. Furthermore trace element and isotopic analyses of HMs have been used to vector to mineralisation or constrain timing of geological processes. The positive role of HMs in mineral exploration is well established in other countries, but comparatively little understood in Australia. Here we present the results of a pilot project that was designed to establish, test and assess a workflow to produce a HM map (or atlas of maps) and dataset for Australia. This would represent a critical step in the ability to detect anomalous HM patterns as it would establish the background HM characteristics (i.e., unrelated to mineralisation). Further the extremely rich dataset produced would be a valuable input into any future machine learning/big data-based prospectivity analysis. The pilot project consisted in selecting ten sites from the National Geochemical Survey of Australia (NGSA) and separating and analysing the HM contents from the 75-430 µm grain-size fraction of the top (0-10 cm depth) sediment samples. A workflow was established and tested based on the density separation of the HM-rich phase by combining a shake table and the use of dense liquids. The automated mineralogy quantification was performed on a TESCAN® Integrated Mineral Analyser (TIMA) that identified and mapped thousands of grains in a matter of minutes for each sample. The results indicated that: (1) the NGSA samples are appropriate for HM analysis; (2) over 40 HMs were effectively identified and quantified using TIMA automated quantitative mineralogy; (3) the resultant HMs’ mineralogy is consistent with the samples’ bulk geochemistry and regional geological setting; and (4) the HM makeup of the NGSA samples varied across the country, as shown by the mineral mounts and preliminary maps. Based on these observations, HM mapping of the continent using NGSA samples will likely result in coherent and interpretable geological patterns relating to bedrock lithology, metamorphic grade, degree of alteration and mineralisation. It could assist in geological investigations especially where outcrop is minimal, challenging to correctly attribute due to extensive weathering, or simply difficult to access. It is believed that a continental-scale HM atlas for Australia could assist in derisking mineral exploration and lead to investment, e.g., via tenement uptake, exploration, discovery and ultimately exploitation. As some HMs are hosts for technology critical elements such as rare earth elements, their systematic and internally consistent quantification and mapping could lead to resource discovery essential for a more sustainable, lower-carbon economy.
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