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Добірка наукової літератури з теми "Pointer Analysi"

Автор: Grafiati

Опубліковано: 11 березня 2023

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Статті в журналах з теми "Pointer Analysi"

1

Shields, Denis C., Andrew Collins, and Angela Marlow. "Coding of pointers in the segregation analysis program POINTER." Genetic Epidemiology 11, no. 4 (1994): 385–87. http://dx.doi.org/10.1002/gepi.1370110408.

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2

Matsumoto, Aki, D. S. Han, and Takao Tsuda. "Alias analysis of pointers in Pascal and Fortran 90: dependence analysis between pointer references." Acta Informatica 33, no. 2 (March 1996): 99–130. http://dx.doi.org/10.1007/s002360050037.

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3

Smaragdakis, Yannis, and George Balatsouras. "Pointer Analysis." Foundations and Trends® in Programming Languages 2, no. 1 (2015): 1–69. http://dx.doi.org/10.1561/2500000014.

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4

Hirzel, Martin, Daniel Von Dincklage, Amer Diwan, and Michael Hind. "Fast online pointer analysis." ACM Transactions on Programming Languages and Systems 29, no. 2 (April 2007): 11. http://dx.doi.org/10.1145/1216374.1216379.

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5

Zhu, Jianwen, and Silvian Calman. "Symbolic pointer analysis revisited." ACM SIGPLAN Notices 39, no. 6 (June 9, 2004): 145–57. http://dx.doi.org/10.1145/996893.996860.

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6

Peng-Sheng Chen, Yuan-Shin Hwang, R. D. C. Ju, and J. K. Lee. "Interprocedural probabilistic pointer analysis." IEEE Transactions on Parallel and Distributed Systems 15, no. 10 (October 2004): 893–907. http://dx.doi.org/10.1109/tpds.2004.56.

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7

Spoto, Fausto. "Precise null-pointer analysis." Software & Systems Modeling 10, no. 2 (October 2, 2009): 219–52. http://dx.doi.org/10.1007/s10270-009-0132-5.

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8

Heintze, Nevin, and Olivier Tardieu. "Demand-driven pointer analysis." ACM SIGPLAN Notices 36, no. 5 (May 2001): 24–34. http://dx.doi.org/10.1145/381694.378802.

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9

Hind, Michael, Michael Burke, Paul Carini, and Jong-Deok Choi. "Interprocedural pointer alias analysis." ACM Transactions on Programming Languages and Systems 21, no. 4 (July 1999): 848–94. http://dx.doi.org/10.1145/325478.325519.

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10

Wang, Shao-Chung, Lin-Ya Yu, Li-An Her, Yuan-Shin Hwang, and Jenq-Kuen Lee. "Pointer-Based Divergence Analysis for OpenCL 2.0 Programs." ACM Transactions on Parallel Computing 8, no. 4 (December 31, 2021): 1–23. http://dx.doi.org/10.1145/3470644.

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Анотація:

A modern GPU is designed with many large thread groups to achieve a high throughput and performance. Within these groups, the threads are grouped into fixed-size SIMD batches in which the same instruction is applied to vectors of data in a lockstep. This GPU architecture is suitable for applications with a high degree of data parallelism, but its performance degrades seriously when divergence occurs. Many optimizations for divergence have been proposed, and they vary with the divergence information about variables and branches. A previous analysis scheme viewed pointers and return values from functions as divergence directly, and only focused on OpenCL 1.x. In this article, we present a novel scheme that reports the divergence information for pointer-intensive OpenCL programs. The approach is based on extended static single assignment (SSA) and adds some special functions and annotations from memory SSA and gated SSA. The proposed scheme first constructs extended SSA, which is then used to build a divergence relation graph that includes all of the possible points-to relationships of the pointers and initialized divergence states. The divergence state of the pointers can be determined by propagating the divergence state of the divergence relation graph. The scheme is further extended for interprocedural cases by considering function-related statements. The proposed scheme was implemented in an LLVM compiler and can be applied to OpenCL programs. We analyzed 10 programs with 24 kernels, with a total analyzed program size of 1,306 instructions in an LLVM intermediate representation, with 885 variables, 108 branches, and 313 pointer-related statements. The total number of divergent pointers detected was 146 for the proposed scheme, 200 for the scheme in which the pointer was always divergent, and 155 for the current LLVM default scheme; the total numbers of divergent variables detected were 458, 519, and 482, respectively, with 31, 34, and 32 divergent branches. These experimental results indicate that the proposed scheme is more precise than both a scheme in which a pointer is always divergent and the current LLVM default scheme.

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Дисертації з теми "Pointer Analysi"

1

Ghiya, Rakesh. "Putting pointer analysis to work." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape10/PQDD_0002/NQ44439.pdf.

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2

Sims, Elodie-Jane. "Pointer analysis and separation logic." Diss., Manhattan, Kan. : Kansas State University, 2007. http://hdl.handle.net/2097/506.

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3

Fu, Zhoulai. "Static analysis of numerical properties in the presence of pointers." Phd thesis, Université Rennes 1, 2013. http://tel.archives-ouvertes.fr/tel-00918593.

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The fast and furious pace of change in computing technology has become an article of faith for many. The reliability of computer-based systems cru- cially depends on the correctness of its computing. Can man, who created the computer, be capable of preventing machine-made misfortune? The theory of static analysis strives to achieve this ambition. The analysis of numerical properties of programs has been an essential research topic for static analysis. These kinds of properties are commonly modeled and handled by the concept of numerical abstract domains. Unfor- tunately, lifting these domains to heap-manipulating programs is not obvious. On the other hand, points-to analyses have been intensively studied to an- alyze pointer behaviors and some scale to very large programs but without inferring any numerical properties. We propose a framework based on the theory of abstract interpretation that is able to combine existing numerical domains and points-to analyses in a modular way. The static numerical anal- ysis is prototyped using the SOOT framework for pointer analyses and the PPL library for numerical domains. The implementation is able to analyze large Java program within several minutes. The second part of this thesis consists of a theoretical study of the com- bination of the points-to analysis with another pointer analysis providing information called must-alias. Two pointer variables must alias at some pro- gram control point if they hold equal reference whenever the control point is reached. We have developed an algorithm of quadruple complexity that sharpens points-to analysis using must-alias information. The algorithm is proved correct following a semantics-based formalization and the concept of bisimulation borrowed from the game theory, model checking etc.

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4

Sims, Elodie-Jane. "Analyses de Pointeurs et Logique de Séparation." Phd thesis, Ecole Polytechnique X, 2007. http://pastel.archives-ouvertes.fr/pastel-00003506.

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Анотація:

Le cadre de cette thèse est l'analyse statique modulaire par interprétation abstraite de logiciels en vue de leur vérification automatique. Nous nous intéressons en particulier aux programmes comportant des objets alloués dynamiquement sur un tas et repérés par des pointeurs. Le but final étant de trouver des erreurs dans un programme (problèmes de déréférencements et d'alias) ou de prouver qu'un programme est correct (relativement à ces problèmes) de façon automatique. Isthiaq, Pym, O'Hearn et Reynolds ont développé récemment des logiques de fragmentation (separation logics) qui sont des logiques de Hoare avec un langage d'assertions/de prédicats permettant de démontrer qu'un programme manipulant des pointeurs sur un tas est correct. La sémantique des triplets de la logique ({P}C{P

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5

Raiskup, Pavel. "Vylepšení analýzy živých proměnných pomocí points-to analýzy." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2012. http://www.nusl.cz/ntk/nusl-236511.

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Анотація:

Languages such as C use pointers very heavily. Implementation of operations on dynamically linked structures is, however, quite difficult. This can cause the programmer to make more mistakes than usual. One method for dealing with this situation is to use the static analysis tools. This thesis elaborates on the extension to the Code Listener architecture which is an interface for building static analysis tools. Code Listener is able to construct a call-graph or a control flow graph for a given source code and send it to the analyzing tool. One ability of the architecture is that it can conduct the live variable analysis internally. It detects places in the control flow graph where some subset of variables may be killed. The problem was that every variable for which a pointer address was assigned could not been killed, before. This decision had been made because there was no assurance that the variable could never been used through the pointer. So the goal of this work was to design and incorporate a points-to analysis which is able to exclude some references from the set of considered pointers to improve the live variable analysis.

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6

GARIBOLDI, BIANCA MARIA. "Norms of the lattice point discrepancy." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2017. http://hdl.handle.net/10281/151708.

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Анотація:

Main topic is the discrepancy, studied for different kind of sets under different hypothesis (curvature, dimensions,..). In particular we try to estimate the norms of the discrepancy respect to translations, rotations and dilations.
Main topic is the discrepancy, studied for different kind of sets under different hypothesis (curvature, dimensions,..). In particular we try to estimate the norms of the discrepancy respect to translations, rotations and dilations.

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7

Rombourg, Romain. "Analyse, modélisation et détection de bruits pour scanners laser terrestres." Thesis, Université Grenoble Alpes (ComUE), 2019. http://www.theses.fr/2019GREAM064.

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Анотація:

Dans cette thèse, nous nous sommes concentrés sur plusieurs sujets liés à la détection du bruit dans les nuages de points générés par les scanners laser terrestres (TLS). Premièrement, les méthodes de projection pour calculer une image à partir d'un balayage TLS. Deuxièmement, la détection du bruit du ciel, c'est-à-dire le bruit produit lorsqu'une mesure d'un TLS à onde continue modulée en amplitude n'est effectuée que sur du rayonnement ambiant. Et enfin, la détection du bruit de points mixtes, c'est-à-dire les points acquis lorsque le TLS reçoit des signaux de retour de plusieurs surfaces différentes. Pour relever ces défis, nous avons d'abord analysé l'échantillonnage de l'espace du TLS et déduit des propriétés sur la densité locale de points en fonction de l'altitude, ce qui nous a permis de montrer les limites des techniques classiques de détection. Nous avons ensuite défini un cadre théorique pour analyser les méthodes de projection, fondements des méthodes de détection 2D. Ce cadre nous a permis de mettre en évidence deux propriétés fondamentales devant être satisfaites par une projection. En se basant sur ces propriétés, nous avons conçu un algorithme de projection les satisfaisant au mieux. Nous avons ensuite défini une quantification de la qualité d'une projection et comparé notre algorithme avec l'algorithme classique et montré que la méthode classique n'est pas adaptée. La projection proposée a quant à elle donné de très bons résultats. Comme le bruit du ciel n'a jamais été étudié dans de précédents travaux, nous l'avons formellement analysé pour construire des bases théoriques pour la détection du ciel. L'analyse nous a permis de montrer théoriquement et expérimentalement que la distribution de distance du bruit de ciel est indépendante des propriétés sous-jacentes du rayonnement ambiant. à partir de notre projection et des propriétés découvertes, nous avons conçu un détecteur de ciel et un détecteur de points mixtes. Les détecteurs ont été testés via une validation approfondie en conditions contrôlées. Les résultats ont montré que nos détecteurs combinés à notre projection sont capables de détecter correctement presque tous le bruit présenté avec peu de mauvaises détections pour le détecteur de ciel et une quantité raisonnable pour le détecteur de point mixte
In this thesis, we focused on several topics related to noise detection in point cloud generated by Terrestrial Laser Scanners (TLS). First, the projection methods to compute an image from a TLS scan. Second, the detection of sky noise, i.e. noise produced when a Amplitude Modulated Continuous Wave TLS measures range only from background radiation. And finally, the detection of mixed point noise, i.e. points acquired when the TLS was receiving return signals from several different surfaces. To tackle these challenges, we first analysed how the TLS samples space and deduced properties on how the local point cloud density evolves with respect to the elevation, this allowed us to show the limits of usual noise detection techniques and oriented our focus on 2D non density based detection techniques. We then defined a theoretical framework to analyse projection methods, unavoidable foundations for 2D detection methods. This framework allowed us to bring to light two fundamental properties that should be satisfied by a projection. Following these properties, we designed a projection algorithm that satisfied them as much as possible. We then defined a way to quantify projection quality and compared our proposed algorithm with the widely used classic algorithm and showed that the classic projection method is not adapted. Our proposed projection however showed very good results. Since the sky noise was never studied in previous works, we formally analysed it to build some theoretical foundations for sky detection. The analysis allowed us to show theoretically and experimentally that the range distribution of sky noise is independent of the underlying properties of the background radiation signal. From our projection and the discovered properties, we designed a sky detector and a mixed point detector. The detectors were tested via an extensive validation in controlled conditions. The results showed that our proposed detectors combined with the proposed projection are able to correctly detect almost all presented noise with few bad detection for the sky detectors and reasonable amount for the mixed point detector

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8

Sălcianu, Alexandru D. (Alexandru Doru) 1975. "Pointer analysis and its applications for Java programs." Thesis, Massachusetts Institute of Technology, 2001. http://hdl.handle.net/1721.1/86781.

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Анотація:

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2001.
Includes bibliographical references (p. 135-137).
by Alexandru D. Sălcianu.
S.M.

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9

Whaley, John. "Context-sensitive pointer analysis using binary decision diagrams /." May be available electronically:, 2007. http://proquest.umi.com/login?COPT=REJTPTU1MTUmSU5UPTAmVkVSPTI=&clientId=12498.

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10

Mensi, Amira. "Analyse des pointeurs pour le langage C." Phd thesis, Ecole Nationale Supérieure des Mines de Paris, 2013. http://pastel.archives-ouvertes.fr/pastel-00944703.

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Анотація:

Les analyses statiques ont pour but de déterminer les propriétés des programmes au moment de la compilation. Contrairement aux analyses dynamiques, le comportement exact du programme ne peut être connu. Par conséquent, on a recours à des approximations pour remédier à ce manque d'information. Malgré ces approximations, les analyses statiques permettent des optimisations et des transformations efficaces pour améliorer les performances des programmes. Parmi les premières analyses du processus d'optimisation figure l'analyse des pointeurs. Son but est d'analyser statiquement un programme en entrée et de fournir en résultat une approximation des emplacements mémoire vers lesquels pointent ses variables pointeurs. Cette analyse est considérée comme l'une des analyses de programmes les plus délicates et l'information qu'elle apporte est très précieuse pour un grand nombre d'autres analyses clientes. En effet, son résultat est nécessaire à d'autres optimisations, comme la propagation de constante, l'élimination du code inutile, le renommage des scalaires ainsi que la parallélisation automatique des programmes. L'analyse des pointeurs est très nécessaire pour l'exploitation du parallélisme présent dans les applications scientifiques écrites en C. Ceci est dû au fait que les tableaux, très présents dans ce type d'applications, sont accédés via les pointeurs. Il devient nécessaire d'analyser les dépendances entre les éléments de tableau dans le but de paralléliser les boucles. Le langage C présente beaucoup de difficultés lors de son analyse par la liberté qu'il offre aux utilisateurs pour gérer et manipuler la mémoire par le biais des pointeurs. Ces difficultés apparaissent par exemple lors de l'accès aux tableaux par pointeurs, l'allocation dynamique (via "malloc") ainsi que les structures de données récursives. L'un des objectifs principaux de cette thèse est de déterminer les emplacements mémoire vers lesquels les pointeurs pointent. Ceci se fait en assurant plusieurs dimensions comme : - la sensibilité au flot de contrôle, c'est-à-dire la mise à jour des informations d'un point programme à un autre ; - la non-sensibilité au contexte, c'est-à-dire l'utilisation de résumés au lieu de l'analyse du corps de la fonction à chaque appel ; - la modélisation des champs pointeurs des structures de données agrégées, dans laquelle chaque champ représente un emplacement mémoire distinct. D'autres aspects sont pris en compte lors de l'analyse des programmes écrits en C comme la précision des emplacements mémoire alloués au niveau du tas, l'arithmétique sur pointeurs ou encore les pointeurs vers tableaux. Notre travail permet l'amélioration des résultats des analyses clientes et en particulier il permet la parallélisation des boucles lorsqu'on accède aux éléments de tableaux via les pointeurs, la détection de code inutile ou le calcul du graphe de dépendances. Il est implémenté dans le compilateur parallélliseur PIPS (Parallélisation Interprocédurale de Programmes Scientifiques) et permet d'analyser, en particulier, les applications scientifiques de traitement du signal tout en assurant une analyse intraprocédurale précise et une analyse interprocédurale efficace via les résumés.

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Книги з теми "Pointer Analysi"

1

Costello, Brian J. Creole Pointe Coupee: A sociological analysis. New Roads, La: John and Noelie Laurent Ewing, 2002.

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2

Linking methods in critical point theory. Boston: Birkhauser, 1999.

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3

Michel, Willem, ed. Critical point theory and Hamiltonian systems. New York: Springer-Verlag, 1989.

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4

Dreger, J. Brian. Function point analysis. Englewood Cliffs, N.J: Prentice Hall, 1989.

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5

McFetridge, D. G. Les technologies de pointe au Canada: Analyse des données récentes sur leur utilisation. Ottawa, Ont: Conseil économique du Canada, 1992.

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6

Pawłucki, Wiesław. Points de Nash des ensembles sous-analytiques. Providence, R.I., USA: American Mathematical Society, 1990.

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7

Kavian, Otared. Introduction à la théorie des points critiques et applications aux problèmes elliptiques. Paris: Springer-Verlag, 1993.

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8

Plenge, Waagepetersen Rasmus, ed. Statistical inference and simulation for spatial point processes. Boca Raton, Fla: Chapman & Hall/CRC, 2004.

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9

Applications of point set theory in real analysis. Dordrecht: Kluwer Academic, 1998.

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10

Masiello, Antonio. Variational methods in Lorentzian geometry. Harlow: Longman Scientific & Technical, 1994.

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Частини книг з теми "Pointer Analysi"

1

Yong, Suan Hsi, and Susan Horwitz. "Pointer-Range Analysis." In Static Analysis, 133–48. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-27864-1_12.

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2

Khedker, Uday P., Alan Mycroft, and Prashant Singh Rawat. "Liveness-Based Pointer Analysis." In Static Analysis, 265–82. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33125-1_19.

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3

Guyer, Samuel Z., and Calvin Lin. "Client-Driven Pointer Analysis." In Static Analysis, 214–36. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-44898-5_12.

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4

Simon, Luke. "Optimizing Pointer Analysis Using Bisimilarity." In Static Analysis, 222–37. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03237-0_16.

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5

Petruşel, Adrian, and Gabriela Petruşel. "Coupled Fixed Points and Coupled Coincidence Points via Fixed Point Theory." In Mathematical Analysis and Applications, 661–707. Hoboken, NJ, USA: John Wiley & Sons, Inc, 2018. http://dx.doi.org/10.1002/9781119414421.ch19.

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6

Zhang, Wei, and Yu Zhang. "Lightweight Function Pointer Analysis." In Information Security Practice and Experience, 439–53. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-17533-1_30.

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7

Chen, Chaomei. "Knowledge Domain Analysis." In Turning Points, 139–75. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19160-2_6.

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8

Ye, Sen, Yulei Sui, and Jingling Xue. "Region-Based Selective Flow-Sensitive Pointer Analysis." In Static Analysis, 319–36. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10936-7_20.

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9

Shapiro, Marc, and Susan Horwitz. "The effects of the precision of pointer analysis." In Static Analysis, 16–34. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/bfb0032731.

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10

Venet, Arnaud. "A Scalable Nonuniform Pointer Analysis for Embedded Programs." In Static Analysis, 149–64. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-27864-1_13.

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Тези доповідей конференцій з теми "Pointer Analysi"

1

Hind, Michael. "Pointer analysis." In the 2001 ACM SIGPLAN-SIGSOFT workshop. New York, New York, USA: ACM Press, 2001. http://dx.doi.org/10.1145/379605.379665.

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2

Zhu, Jianwen. "Symbolic pointer analysis." In the 2002 IEEE/ACM international conference. New York, New York, USA: ACM Press, 2002. http://dx.doi.org/10.1145/774572.774594.

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3

Biallas, Sebastian, Mads Chr Olesen, Franck Cassez, and Ralf Huuck. "PtrTracker: Pragmatic pointer analysis." In 2013 IEEE 13th International Working Conference on Source Code Analysis and Manipulation (SCAM). IEEE, 2013. http://dx.doi.org/10.1109/scam.2013.6648186.

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4

Zhu, Jianwen, and Silvian Calman. "Symbolic pointer analysis revisited." In the ACM SIGPLAN 2004 conference. New York, New York, USA: ACM Press, 2004. http://dx.doi.org/10.1145/996841.996860.

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5

Heintze, Nevin, and Olivier Tardieu. "Demand-driven pointer analysis." In the ACM SIGPLAN 2001 conference. New York, New York, USA: ACM Press, 2001. http://dx.doi.org/10.1145/378795.378802.

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6

Liu, Peng, Omer Tripp, and Xiangyu Zhang. "IPA: improving predictive analysis with pointer analysis." In ISSTA '16: International Symposium on Software Testing and Analysis. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2931037.2931046.

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7

Narinyan, Nataliya. "Golden points and bifurcation points as red light." In Systems Analysis in Economics - 2020. Moscow, "Science" Publishing House, 2021. http://dx.doi.org/10.33278/sae-2020.book1.246-249.

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8

Emami, Maryam, Rakesh Ghiya, and Laurie J. Hendren. "Context-sensitive interprocedural points-to analysis in the presence of function pointers." In the ACM SIGPLAN 1994 conference. New York, New York, USA: ACM Press, 1994. http://dx.doi.org/10.1145/178243.178264.

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9

Horwitz, S., P. Pfeiffer, and T. Reps. "Dependence analysis for pointer variables." In the ACM SIGPLAN 1989 Conference. New York, New York, USA: ACM Press, 1989. http://dx.doi.org/10.1145/73141.74821.

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10

Rugina, Radu, and Martin Rinard. "Pointer analysis for multithreaded programs." In the ACM SIGPLAN 1999 conference. New York, New York, USA: ACM Press, 1999. http://dx.doi.org/10.1145/301618.301645.

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Звіти організацій з теми "Pointer Analysi"

1

Berney, Ernest, Naveen Ganesh, Andrew Ward, J. Newman, and John Rushing. Methodology for remote assessment of pavement distresses from point cloud analysis. Engineer Research and Development Center (U.S.), April 2021. http://dx.doi.org/10.21079/11681/40401.

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Анотація:

The ability to remotely assess road and airfield pavement condition is critical to dynamic basing, contingency deployment, convoy entry and sustainment, and post-attack reconnaissance. Current Army processes to evaluate surface condition are time-consuming and require Soldier presence. Recent developments in the area of photogrammetry and light detection and ranging (LiDAR) enable rapid generation of three-dimensional point cloud models of the pavement surface. Point clouds were generated from data collected on a series of asphalt, concrete, and unsurfaced pavements using ground- and aerial-based sensors. ERDC-developed algorithms automatically discretize the pavement surface into cross- and grid-based sections to identify physical surface distresses such as depressions, ruts, and cracks. Depressions can be sized from the point-to-point distances bounding each depression, and surface roughness is determined based on the point heights along a given cross section. Noted distresses are exported to a distress map file containing only the distress points and their locations for later visualization and quality control along with classification and quantification. Further research and automation into point cloud analysis is ongoing with the goal of enabling Soldiers with limited training the capability to rapidly assess pavement surface condition from a remote platform.

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2

Wiggin, Roger. Functional analysis of Great Basin projectile points. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.2789.

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3

Spears, William M., and Kenneth A. De Jong. An Analysis of Multi-Point Crossover,. Fort Belvoir, VA: Defense Technical Information Center, January 1990. http://dx.doi.org/10.21236/ada294057.

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4

Neher, Daryl E., and II. Research Report Point Reactor Kinetic Analysis. Fort Belvoir, VA: Defense Technical Information Center, December 2002. http://dx.doi.org/10.21236/ada410867.

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5

Meisel, L. V., and M. A. Johnson. Multifractal Analysis of Chaotic Point Sets. Fort Belvoir, VA: Defense Technical Information Center, April 1992. http://dx.doi.org/10.21236/ada250756.

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6

AIR FORCE GLOBAL WEATHER CENTRAL OFFUTTAFB NE. Improved Point Analysis Model (IPAM) (Users Guide). Fort Belvoir, VA: Defense Technical Information Center, February 1991. http://dx.doi.org/10.21236/ada235305.

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7

Carlin, Bradley P., Alan E. Gelfand, and Adrian F. Smith. Hierarchical Bayesian Analysis of Change Point Problems. Fort Belvoir, VA: Defense Technical Information Center, October 1990. http://dx.doi.org/10.21236/ada228179.

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8

Broadhead, B. QADS: A multidimensional point kernel analysis module. Office of Scientific and Technical Information (OSTI), May 1990. http://dx.doi.org/10.2172/6963611.

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9

Egerland, Walter O., and Charles E. Hansen. Fixed Points of Expansive Analytic Maps (II). Fort Belvoir, VA: Defense Technical Information Center, September 1992. http://dx.doi.org/10.21236/ada254737.

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10

Sinclair, Samantha, and Sandra LeGrand. Reproducibility assessment and uncertainty quantification in subjective dust source mapping. Engineer Research and Development Center (U.S.), August 2021. http://dx.doi.org/10.21079/11681/41523.

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Accurate dust-source characterizations are critical for effectively modeling dust storms. A previous study developed an approach to manually map dust plume-head point sources in a geographic information system (GIS) framework using Moderate Resolution Imaging Spectroradiometer (MODIS) imagery processed through dust-enhancement algorithms. With this technique, the location of a dust source is digitized and recorded if an analyst observes an unobscured plume head in the imagery. Because airborne dust must be sufficiently elevated for overland dust-enhancement algorithms to work, this technique may include up to 10 km in digitized dust-source location error due to downwind advection. However, the potential for error in this method due to analyst subjectivity has never been formally quantified. In this study, we evaluate a version of the methodology adapted to better enable reproducibility assessments amongst multiple analysts to determine the role of analyst subjectivity on recorded dust source location error. Four analysts individually mapped dust plumes in Southwest Asia and Northwest Africa using five years of MODIS imagery collected from 15 May to 31 August. A plume-source location is considered reproducible if the maximum distance between the analyst point-source markers for a single plume is ≤10 km. Results suggest analyst marker placement is reproducible; however, additional analyst subjectivity-induced error (7 km determined in this study) should be considered to fully characterize locational uncertainty. Additionally, most of the identified plume heads (> 90%) were not marked by all participating analysts, which indicates dust source maps generated using this technique may differ substantially between users.

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