Relevant bibliographies by topics / Mapping Problem

Academic literature on the topic 'Mapping Problem'

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Published: 4 June 2021
Last updated: 7 February 2022

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Journal articles on the topic "Mapping Problem"

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Thien, Nguyen Duc, Le Hoang Son, Pier Luca Lanzi, and Pham Huy Thong. "Heuristic Optimization Algorithms for Terrain Splitting and Mapping Problem." International Journal of Engineering and Technology 3, no. 4 (2011): 376–83. http://dx.doi.org/10.7763/ijet.2011.v3.255.

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Sergeyeva, I. S. "MAPPING GLOBAL PROBLEMS OF TODAY: THE FOOD PROBLEM." Mapping Sciences and Remote Sensing 27, no. 1 (January 1990): 38–50. http://dx.doi.org/10.1080/07493878.1990.10641788.

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Fu, Xiao-Ming, Jian-Ping Su, Zheng-Yu Zhao, Qing Fang, Chunyang Ye, and Ligang Liu. "Inversion-free geometric mapping construction: A survey." Computational Visual Media 7, no. 3 (July 27, 2021): 289–318. http://dx.doi.org/10.1007/s41095-021-0233-9.

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AbstractA geometric mapping establishes a correspondence between two domains. Since no real object has zero or negative volume, such a mapping is required to be inversion-free. Computing inversion-free mappings is a fundamental task in numerous computer graphics and geometric processing applications, such as deformation, texture mapping, mesh generation, and others. This task is usually formulated as a non-convex, nonlinear, constrained optimization problem. Various methods have been developed to solve this optimization problem. As well as being inversion-free, different applications have various further requirements. We expand the discussion in two directions to (i) problems imposing specific constraints and (ii) combinatorial problems. This report provides a systematic overview of inversion-free mapping construction, a detailed discussion of the construction methods, including their strengths and weaknesses, and a description of open problems in this research field.
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Ali, Bashir, and M. H. Harbau. "Convergence Theorems for BregmanK-Mappings and Mixed Equilibrium Problems in Reflexive Banach Spaces." Journal of Function Spaces 2016 (2016): 1–18. http://dx.doi.org/10.1155/2016/5161682.

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We introduce a new mixed equilibrium problem with a relaxed monotone mapping in a reflexive Banach space and prove the existence of solution of the equilibrium problem. Using Bregman distance, we introduce the concept of BregmanK-mapping for a finite family of Bregman quasiasymptotically nonexpansive mappings and show the fixed point set of the BregmanK-mapping is the set of common fixed points of{Ti}i=1N. Using the BregmanK-mapping, we introduce an iterative sequence for finding a common point in the set of a common fixed points of the finite family of Bregman quasiasymptotically nonexpansive mappings and the set of solutions of some mixed equilibrium problems. Strong convergence of the iterative sequence is proved. Our results generalise and improve many recent results in the literature.
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Kent, Stuart, and Robert Smith. "The Bidirectional Mapping Problem." Electronic Notes in Theoretical Computer Science 82, no. 7 (June 2003): 151–65. http://dx.doi.org/10.1016/s1571-0661(04)80753-9.

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Harrison, Jenny. "The loxodromic mapping problem." Journal of Differential Equations 90, no. 1 (March 1991): 136–42. http://dx.doi.org/10.1016/0022-0396(91)90164-5.

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Kantere, Verena, Dimos Bousounis, and Timos Sellis. "Mapping Discovery Over Revealing Schemas." International Journal of Cooperative Information Systems 24, no. 04 (December 2015): 1550006. http://dx.doi.org/10.1142/s0218843015500069.

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In a world of wide-scale information sharing, data are described in different formats, i.e. data structures, values and schemas. Querying such sources entails techniques that can bridge the data formats. Some existing techniques deal with schema mapping and view complementary aspects of the problem. Important ones, consider producing all the possible mappings for a pair of schemas, insinuating accompanying semantics in the mappings and adapting correct mappings as schemas evolve. In this work, we consider the problem of discovering mappings as schemas of autonomous sources are gradually revealed. Using as an example setting an overlay of peer databases, we present a schema mapping solution that discovers correct mappings as peer schemas are gradually revealed to remote peers. Mapping discovery is schema-centric and incorporates new semantics as they are unveiled. Mapping experience is reused and possible mappings are ranked so that the best choice is presented to the user. The experimental study confirms the suitability of the proposed solution to dynamic settings of heterogeneous sources.
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Kangtunyakarn, Atid. "A New Iterative Algorithm for the Set of Fixed-Point Problems of Nonexpansive Mappings and the Set of Equilibrium Problem and Variational Inequality Problem." Abstract and Applied Analysis 2011 (2011): 1–24. http://dx.doi.org/10.1155/2011/562689.

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We introduce a new iterative scheme and a new mapping generated by infinite family of nonexpansive mappings and infinite real number. By using both of these ideas, we obtain strong convergence theorem for finding a common element of the set of solution of equilibrium problem and the set of variational inequality and the set of fixed-point problems of infinite family of nonexpansive mappings. Moreover, we apply our main result to obtain strong convergence theorems for finding a common element of the set of solution of equilibrium problem and the set of variational inequality and the set of common fixed point of pseudocontractive mappings.
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O’Gorman, Bryan, Eleanor Gilbert Rieffel, Minh Do, Davide Venturelli, and Jeremy Frank. "Comparing planning problem compilation approaches for quantum annealing." Knowledge Engineering Review 31, no. 5 (November 2016): 465–74. http://dx.doi.org/10.1017/s0269888916000278.

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AbstractOne approach to solving planning problems is to compile them to other problems for which powerful off-the-shelf solvers are available; common targets include SAT, CSP, and MILP. Recently, a novel optimization technique has become available: quantum annealing (QA). QA takes as input problem instances of quadratic unconstrained binary optimization (QUBO) problem. Early quantum annealers are now available, though their constraints restrict the types of QUBOs they can take as input. Here, we introduce the planning community to the key steps in compiling planning problems to QA hardware: a hardware-independent step, mapping, and a hardware-dependent step, embedding. After describing two approaches to mapping general planning problems to QUBO, we describe preliminary results from running an early quantum annealer on a parametrized family of hard planning problems. The results show that different mappings can substantially affect performance, even when many features of the resulting instances are similar. We conclude with insights gained from this early study that suggest directions for future work.
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Dranishnikov, Alexander. "On the mapping intersection problem." Pacific Journal of Mathematics 173, no. 2 (April 1, 1996): 403–12. http://dx.doi.org/10.2140/pjm.1996.173.403.

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Dissertations / Theses on the topic "Mapping Problem"

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Fletcher, George H. L. "On the data mapping problem." [Bloomington, Ind.] : Indiana University, 2007. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3276692.

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Balavendran, Joseph Rani Deepika. "Gamification to Solve a Mapping Problem in Electrical Engineering." Thesis, University of North Texas, 2020. https://digital.library.unt.edu/ark:/67531/metadc1703330/.

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Coarse-Grained Reconfigurable Architectures (CGRAs) are promising in developing high performance low-power portable applications. In this research, we crowdsource a mapping problem using gamification to harnass human intelligence. A scientific puzzle game, Untangled, was developed to solve a mapping problem by encapsulating architectural characteristics. The primary motive of this research is to draw insights from the mapping solutions of players who possess innate abilities like decision-making, creative problem-solving, recognizing patterns, and learning from experience. In this dissertation, an extensive analysis was conducted to investigate how players' computational skills help to solve an open-ended problem with different constraints. From this analysis, we discovered a few common strategies among players, and subsequently, a library of dictionaries containing identified patterns from players' solutions was developed. The findings help to propose a better version of the game that incorporates these techniques recognized from the experience of players. In the future, an updated version of the game that can be developed may help low-performance players to provide better solutions for a mapping problem. Eventually, these solutions may help to develop efficient mapping algorithms, In addition, this research can be an exemplar for future researchers who want to crowdsource such electrical engineering problems and this approach can also be applied to other domains.
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Phinjaroenphan, Panu, and s2118294@student rmit edu au. "An Efficient, Practical, Portable Mapping Technique on Computational Grids." RMIT University. Computer Science and Information Technology, 2007. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20080516.145808.

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Grid computing provides a powerful, virtual parallel system known as a computational Grid on which users can run parallel applications to solve problems quickly. However, users must be careful to allocate tasks to nodes properly because improper allocation of only one task could result in lengthy executions of applications, or even worse, applications could crash. This allocation problem is called the mapping problem, and an entity that tackles this problem is called a mapper. In this thesis, we aim to develop an efficient, practical, portable mapper. To study the mapping problem, researchers often make unrealistic assumptions such as that nodes of Grids are always reliable, that execution times of tasks assigned to nodes are known a priori, or that detailed information of parallel applications is always known. As a result, the practicality and portability of mappers developed in such conditions are uncertain. Our review of related work suggested that a more efficient tool is required to study this problem; therefore, we developed GMap, a simulator researchers/developers can use to develop practical, portable mappers. The fact that nodes are not always reliable leads to the development of an algorithm for predicting the reliability of nodes and a predictor for identifying reliable nodes of Grids. Experimental results showed that the predictor reduced the chance of failures in executions of applications by half. The facts that execution times of tasks assigned to nodes are not known a priori and that detailed information of parallel applications is not alw ays known, lead to the evaluation of five nearest-neighbour (nn) execution time estimators: k-nn smoothing, k-nn, adaptive k-nn, one-nn, and adaptive one-nn. Experimental results showed that adaptive k-nn was the most efficient one. We also implemented the predictor and the estimator in GMap. Using GMap, we could reliably compare the efficiency of six mapping algorithms: Min-min, Max-min, Genetic Algorithms, Simulated Annealing, Tabu Search, and Quick-quality Map, with none of the preceding unrealistic assumptions. Experimental results showed that Quick-quality Map was the most efficient one. As a result of these findings, we achieved our goal in developing an efficient, practical, portable mapper.
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Ramineni, Narahari. "Tree Restructuring Approach to Mapping Problem in Cellular Architecture FPGAS." PDXScholar, 1995. https://pdxscholar.library.pdx.edu/open_access_etds/4914.

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This thesis presents a new technique for mapping combinational circuits to Fine-Grain Cellular-Architecture FPGAs. We represent the netlist as the binary tree with decision variables associated with each node of the tree. The functionality of the tree nodes is chosen based on the target FPGA architecture. The proposed tree restructuring algorithms preserve local connectivity and allow direct mapping of the trees to the cellular array, thus eliminating the traditional routing phase. Also, predictability of the signal delays is a very important advantage of the developed approach. The developed bus-assignment algorithm efficiently utilizes the medium distance routing resources (buses). The method is general and can be used for any Fine Grain CA-type FPGA. To demonstrate our techniques, ATMEL 6000 series FPGA was used as a target architecture. The area and delay comparison between our methods and commercial tools is presented using a set of MCNC benchmarks. Final layouts of the implemented designs are included. Results show that the proposed techniques outperform the available commercial tools for ATMEL 6000 FPGAs, both in area and delay optimization.
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Mu, Wangshu, and Wangshu Mu. "Geocomputational Approaches to Improve Problem Solution in Spatial Optimization: A Case Study of the p-Median Problem." Diss., The University of Arizona, 2018. http://hdl.handle.net/10150/626752.

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The p-Median problem (PMP) is one of the most widely applied location problems in urban and regional planning to support spatial decision-making. As an NP-hard problem, the PMP remains challenging to solve optimally, especially for large-sized problems. This research focuses on developing geocomputational approaches to improve the effectiveness and efficiency of solving the PMP. This research also examines existing PMP methods applied to choropleth mapping and proposes a new approach to address issues associated with uncertainty. Chapter 2 introduces a new algorithm that solves the PMP more effectively. In this chapter, a method called the spatial-knowledge enhanced Teitz and Bart heuristic (STB) is proposed to improve the classic Teitz and Bart (TB) heuristic.. The STB heuristic prioritizes candidate facility sites to be examined in the solution set based on the spatial distribution of demand and candidate facility sites. Tests based on a range of PMPs demonstrate the effectiveness of the STB heuristic. Chapter 3 provides a high performance computing (HPC) based heuristic, Random Sampling and Spatial Voting (RSSV), to solve large PMPs. Instead of solving a large-sized PMP directly, RSSV solves multiple sub-PMPs with each sub-PMP containing a subset of facility and demand sites. Combining all the sub-PMP solutions, a spatial voting strategy is introduced to select candidate facility sites to construct a PMP for obtaining the final problem solution. The RSSV algorithm is well-suited to the parallel structure of the HPC platform. Tests with the BIRCH dataset show that RSSV provides high-quality solutions and reduces computing time significantly. Tests also demonstrate the dynamic scalability of the algorithm; it can start with a small amount of computing resources and scale up or down when the availability of computing resources changes. Chapter 4 provides a new classification scheme to draw choropleth maps when data contain uncertainty. Considering that units in the same class on a choropleth map are assigned the same color or pattern, the new approach assumes the existence of a representative value for each class. A maximum likelihood estimation (MLE) based approach is developed to determine class breaks so that the overall within-class deviation is minimized while considering uncertainty. Different methods, including mixed integer programming, dynamic programming, and an interchange heuristic, are developed to solve the new classification problem. The proposed mapping approach is then applied to map two American Community Survey datasets. The effectiveness of the new approach is demonstrated, and the linkage of the approach with the PMP method and the Jenks Natural Breaks is discussed.
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Leung, Wing-sum. "Applying computer-mediated concept mapping on problem-based learning in biology." Click to view the E-thesis via HKUTO, 2003. http://sunzi.lib.hku.hk/hkuto/record/B40040318.

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Leung, Wing-sum, and 梁穎琛. "Applying computer-mediated concept mapping on problem-based learning in biology." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2003. http://hub.hku.hk/bib/B40040318.

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Wu, Bian, and 吴忭. "Supporting problem solving and knowledge construction using a dual-mapping learning environment." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2013. http://hdl.handle.net/10722/197136.

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Problem-Based Learning (PBL) has been widely adopted as an important approach to medical education in order to help students master knowledge by solving authentic problems. However, the effect of PBL on students’ construction of a well-organized knowledge base is found not satisfactory. It is assumed that learning a concept found in a problem will automatically result in that concept being used to solve other problems, which is not always the case. Knowledge gained from practice is found difficult to retain and reuse as a result of contextualization and dynamic aspects of actual problem-solving practice. Reciprocity between practice and knowledge has been insufficiently investigated in existing studies. The purpose of this study was to address the challenge by aligning knowledge construction with problem solving through the design of a computer-based cognitive tool and implementation of the tool into an online learning environment. The tool consists of the argument mapping technique to represent the problem-solving processes and the concept mapping technique to represent the knowledge constructed from the problem-solving experience, so called dual-mapping tool. This study adopted a design-based research paradigm with two rounds of design and evaluation to explore how the proposed dual-mapping learning (DML) environment could be designed to externalize and connect the problem-solving and knowledge-construction processes and to evaluate how effectively the DML environment could support PBL. The design of the DML environment was underpinned by the cognitive apprenticeship model, which is widely used in situated learning contexts such as PBL. The model highlights the importance of offering an authentic learning context, externalizing complex cognitive processes, and providing expert guidance on learning processes. Accordingly, the proposed DML environment consists of an authentic problem context for exploration, a dual-mapping tool for articulation and reflection of problem-solving and knowledge-construction processes, and expert support for modeling, coaching, and scaffolding these complex processes. The evaluation study aimed to investigate the effectiveness of the DML environment in terms of its acceptance by students, students’ problem-solving and knowledge-construction performances, and its impact on learning emotions and motivation to learn. Medical students from two medical schools in Mainland China participated in the study to use the DML environment. Multiple-source data was collected from questionnaire surveys, pre-and post-competency tests, semi-structured interviews, and log file data of online learning records, and was analyzed through descriptive statistical analysis, analysis of means, correlation analysis, analysis of variance, and content analysis. The evaluation results suggested that the students found the DML environment useful, and that the DML environment was effective in improving clinical problem-solving and medical-knowledge construction performance, as well as activating positive emotions and motivation in PBL. The findings of the study have practical implications for educators and learning technology designers as well as theoretical implications for educational researchers.
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Rosato, Matthew J. "Applying conformal mapping to the vertex correspondence problem for 3D face models." Diss., Online access via UMI:, 2007.

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Lin, Xinyi. "A machine learning based approach for the link-to-system mapping problem." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-214852.

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The quality of mobile communication is related to signal transmissions. Early detection of the errors in transmissions may reduce the time delay of communications. The traditional error detection methods are not accurate enough. Therefore, in this report, a machine learning based approach is proposed for the link-to-system mapping problem, which can predict the outcomes (received correctly or not) of the link-level simulations without knowing the exact signals that are being transmitted. In this method, the transmission state is assumed to be a function of the features of a channel environment like the interference and the noise, the relative motion between the transmitter and the receiver and this function is obtained using a machine learning method. The training dataset is generated by simulations of the channel environment. Logistic regression, support vector machine and neural networks are the three algorithms implemented in this thesis. Experimental results show that all three algorithms work well compared to traditional methods. Neural networks provide the best results for this problem. Furthermore, the neural network model is tested with a dataset consisting of features of ten different channel environments, which verified the generalization ability of the model.
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Books on the topic "Mapping Problem"

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Eberlein, Burkard. Regulating public utilities in Europe: Mapping the problem. San Domenico: European University Institute, 1998.

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Hilbers, Peter A. J. Processor networks and aspects of the mapping problem. Cambridge: Cambridge University Press, 1991.

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Chrisochoides, Nikos. A comparison of optimization heruistics for the data mapping problem. Ithaca, N.Y: Cornell Theory Center, Cornell University, 1995.

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Bryan, Kurt. An inverse problem in thermal imaging. Hampton, VA: Institute for Computer Applications in Science and Engineering, NASA Langley Research Center, 1994.

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Bryan, Kurt. An inverse problem in thermal imaging. Hampton, VA: Institute for Computer Applications in Science and Engineering, NASA Langley Research Center, 1994.

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Hu, Thomas S. Z. Torsion problem of elastostatics: An application of boundary element method and relation with conformal mapping. Dartmouth, N.S: Defence Research Establishment Atlantic, 1992.

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Strategic lean mapping: Blending improvement processes for the perfect solution. New York: McGraw-Hill Professional, 2012.

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Mistaken identity: The mind-brain problem reconsidered. Albany: State University of New York Press, 2001.

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M, Crinella Francis, and Yu Jen, eds. Brain mechanisms in problem solving and intelligence: A lesion survey of the rat brain. New York: Plenum Press, 1990.

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Apanasov, B. N. Discrete groups in space and uniformization problems. Dordrecht: Boston, 1991.

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Book chapters on the topic "Mapping Problem"

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Saltz, Joel. "Methods for Automated Problem Mapping." In The IMA Volumes in Mathematics and Its Applications, 173–95. New York, NY: Springer US, 1988. http://dx.doi.org/10.1007/978-1-4684-6357-6_11.

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McGeer, Rick, David G. Andersen, and Stephen Schwab. "The Network Testbed Mapping Problem." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 383–98. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-17851-1_30.

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Maffli, E. "Problem drinking and relatives." In Mapping the Social Consequences of Alcohol Consumption, 79–91. Dordrecht: Springer Netherlands, 2001. http://dx.doi.org/10.1007/978-94-015-9725-8_7.

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Berman, Francine. "The Mapping Problem in Parallel Computation." In Mathematical Aspects of Scientific Software, 41–57. New York, NY: Springer New York, 1988. http://dx.doi.org/10.1007/978-1-4684-7074-1_2.

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Melo, Márcio, Jorge Carapinha, Susana Sargento, Luis Torres, Phuong Nga Tran, Ulrich Killat, and Andreas Timm-Giel. "Virtual Network Mapping – An Optimization Problem." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 187–200. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-30422-4_14.

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Harff, Jan, Ricardo A. Olea, John C. Davis, and Geoffrey C. Bohling. "Geostatistical Solution for the Classification Problem with an Application to Oil Prospecting." In Geologic Modeling and Mapping, 263–79. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4613-0363-3_13.

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Rinderknecht, Jakob Karl. "The Problem of Differentiated Consensus." In Mapping the Differentiated Consensus of the Joint Declaration, 39–52. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-40099-0_3.

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Binh, Huynh Thi Thanh, Bach Hoang Vinh, Nguyen Hong Nhat, and Le Hoang Linh. "Heuristic Algorithm for Virtual Network Mapping Problem." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 43–53. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-13326-3_5.

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Glasberg, Elena. "Antarctic Convergence: The Problem of Antarctic Mapping." In Antarctica as Cultural Critique, 1–17. New York: Palgrave Macmillan US, 2012. http://dx.doi.org/10.1057/9781137014436_1.

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Sándor, Zsolt, and Maria Helena Morais. "A Mapping Model for the Coorbital Problem." In Dynamics of Natural and Artificial Celestial Bodies, 263–64. Dordrecht: Springer Netherlands, 2001. http://dx.doi.org/10.1007/978-94-017-1327-6_36.

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Conference papers on the topic "Mapping Problem"

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Clerckx, Tim, Kris Luyten, and Karin Coninx. "The mapping problem back and forth." In the 3rd annual conference. New York, New York, USA: ACM Press, 2004. http://dx.doi.org/10.1145/1045446.1045455.

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Rosson, M. B., and E. Gold. "Problem-solution mapping in object-oriented design." In Conference proceedings. New York, New York, USA: ACM Press, 1989. http://dx.doi.org/10.1145/74877.74880.

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Wei-Jung Shiang, Hsin-Chih Chen, and Hsin Rau. "An intelligent matcher for schema mapping problem." In 2008 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2008. http://dx.doi.org/10.1109/icmlc.2008.4620953.

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Hayes, M. J. D., and P. J. Zsombor-Murray. "Solving the Burmester Problem Using Kinematic Mapping." In ASME 2002 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASME, 2002. http://dx.doi.org/10.1115/detc2002/mech-34378.

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Mao, Ming, Yefei Peng, and Michael Spring. "Ontology Mapping: As a Binary Classification Problem." In 2008 Fourth International Conference on Semantics, Knowledge and Grid (SKG). IEEE, 2008. http://dx.doi.org/10.1109/skg.2008.101.

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Sanen, Frans, Eddy Truyen, and Wouter Joosen. "Mapping problem-space to solution-space features." In the eighth international conference. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1621607.1621633.

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Oldridge, Steve, Gregor Miller, and Sidney Fels. "Mapping the Problem Space of Image Registration." In 2011 Canadian Conference on Computer and Robot Vision (CRV). IEEE, 2011. http://dx.doi.org/10.1109/crv.2011.48.

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Korff, Joshua Von, Dehui Hu, N. Sanjay Rebello, N. Sanjay Rebello, Paula V. Engelhardt, and Chandralekha Singh. "Assessment of vertical transfer in problem solving: Mapping the problem design space." In 2011 PHYSICS EDUCATION RESEARCH CONFERENCE. AIP, 2012. http://dx.doi.org/10.1063/1.3680074.

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Zhou, Xiang, Xi Zhou, and Yanfei Liu. "Gaussian Mixture Model Mapping in face recognition." In 2013 International Conference on Computational Problem-solving (ICCP). IEEE, 2013. http://dx.doi.org/10.1109/iccps.2013.6893566.

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Ziavras, S. G. "On the mapping problem for multi-level systems." In the 1989 ACM/IEEE conference. New York, New York, USA: ACM Press, 1989. http://dx.doi.org/10.1145/76263.76307.

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Reports on the topic "Mapping Problem"

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Humke, C. J. Bias Removal Techniques for the Target-Object Mapping Problem. Fort Belvoir, VA: Defense Technical Information Center, July 2002. http://dx.doi.org/10.21236/ada403913.

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Ramineni, Narahari. Tree Restructuring Approach to Mapping Problem in Cellular Architecture FPGAS. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.6790.

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Marrone, B. L., L. L. Deaven, D. J. Chen, Min S. Park, M. A. MacInnes, G. C. Salzman, and T. M. Yoshida. Directly labeled fluorescent DNA probes for chromosome mapping. Office of Scientific and Technical Information (OSTI), December 1995. http://dx.doi.org/10.2172/205135.

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Memoli, Facundo, Guillermo Sapiro, and Stanley Osher. Solving Variational Problems and Partial Differential Equations Mapping into General Target Manifolds. Fort Belvoir, VA: Defense Technical Information Center, January 2002. http://dx.doi.org/10.21236/ada526351.

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Yu, Bin. Statistical Problems in Remote Sensing, Image Compression, and Mapping of Human Chromosomes. Fort Belvoir, VA: Defense Technical Information Center, May 2002. http://dx.doi.org/10.21236/ada413806.

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Kelley, Troy D. Using a Cognitive Architecture to Solve Simultaneous Localization and Mapping (SLAM) Problems. Fort Belvoir, VA: Defense Technical Information Center, April 2006. http://dx.doi.org/10.21236/ad1016045.

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Kelley, Troy D. Using a Cognitive Architecture to Solve Simultaneous Localization and Mapping (SLAM) Problems. Fort Belvoir, VA: Defense Technical Information Center, April 2006. http://dx.doi.org/10.21236/ada636872.

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Berezovsky, Jesse. Final report: Mapping Interactions in Hybrid Systems with Active Scanning Probes. Office of Scientific and Technical Information (OSTI), September 2017. http://dx.doi.org/10.2172/1395561.

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Oh, Hae-Soo, and Ivo M. Babuska. The Method of Auxiliary Mapping for the Finite Element Solutions of Elasticity Problems Containing Singularities. Fort Belvoir, VA: Defense Technical Information Center, September 1994. http://dx.doi.org/10.21236/ada285689.

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Strausbaugh, L. D., and C. M. Berg. A probe-based mapping strategy for DNA sequencing with mobile primers. Office of Scientific and Technical Information (OSTI), January 1991. http://dx.doi.org/10.2172/5827345.

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