Particle filtering in the Dempster–Shafer theory

Reineking, Thomas

doi:10.1016/j.ijar.2011.06.003

Cited by 16 publications

(5 citation statements)

References 37 publications

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“…The Data Fusion Engine component was implemented using JDS [229], an API written in java that supports the basic function of DST such as belief function (bel) and plausibility (pl).…”

Section: Data Fusion Enginementioning

confidence: 99%

An orchestration approach for unwanted Internet traffic identification

2012

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The fulfillment of this Thesis would not have been possible without the contribution of a large number of persons. The first persons I am deeply indebted to are my wife Livia Soraya and my children"s Gabriel, Bruna and Luísa. While they did not contribute to this Thesis directly, but I would like to thank them for their support and love. I would also like to thank my parents, Clarice and José Ribamar, for their important support. I owe a big thank-you to my Thesis advisor, Djamel Sadok, who offered the opportunity to join his research team a few years ago. I must say that at that time, I was not really imagining the kind of experience I was about to embark on. I appreciated very much Djamel"s very pragmatic approach to networking. Djamel is also an inexhaustible source of networking references. Through his enthusiasm and unlimited support (time, ideas and experience), he helped me to complete this Thesis. In addition to that, this work contains the fruits of many and lengthy discussions regarding the present contributions with Djamel. My sincere thanks also go to Professor Judith Kelner for giving me as well as to my family a great deal of support when we moved to Recife. She was instrumental in my participation in the GPRT group. I learned a lot from her valuable experience. I am also thankful for the excellent example she has provided as a successful researcher and Professor. The third person I would like to thank is my Thesis co-advisor Eduardo Souto. Souto has been a great friend from Manaus, where we worked together in the University"s Data Processing Center (CPD). He was key to my decision for choosing Recife as the place to do my Doctorate studies. I learned a lot from the references he pointed me to and from the many discussions we had. We collaborated on a lot of problems, especially about the design of the OADS Miner.

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“…The Data Fusion Engine component was implemented using JDS [229], an API written in java that supports the basic function of DST such as belief function (bel) and plausibility (pl).…”

Section: Data Fusion Enginementioning

confidence: 99%

An orchestration approach for unwanted Internet traffic identification

2012

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“…The framework uses the DS library from [9] for combining evidences based on the DS rule of combination and adopts On-Demand Multicast Routing Protocol (ODMRP) [6][13] as the underlying messaging system of the collaborative hosts. In the experimentation, the MANET group was formed by using two laptops which act as contexts providers and two PDAs (HP iPAQ 5500/2200) which acted as the users' devices.…”

Section: Implementation and Experimen-tationmentioning

confidence: 99%

Collaborative context management framework for mobile ad hoc network environments

Wibisono

Ling

Zaslavsky

2010

Proceedings of the 2010 ACM Symposium on Applied Computing

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Managing context in Mobile Ad Hoc Network (MANET) environments is challenging due to environmental characteristics and limitations. Different aspects of context management such as location management, context acquisition, context quality management, context discovery and other related tasks need to be dealt with to provide reliable context management support. In this paper, we propose a new approach to context management in the MANET environments. We model contexts and situations based on Context Spaces (CS) theory and the Dempster-Shafer (DS) theory of evidences. A heuristic approach to computing the relevancy value of context information to the defined situation of context-aware applications is also proposed and developed. The relevancy value is then incorporated in the fusion mechanism based on the discount rule. We have developed a hybrid location model and integrated the approach to the model. The prototype of the framework is built based on the On-Demand Multicast Routing Protocol (ODMRP) as the underlying messaging protocol among the collaborative hosts. Finally, the implementation and experimentation issues of the prototype are discussed.

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“…In [27,20], DS theory is used to improve Kalman/Particle filters. The uncertainty they consider are not on classifications from different algorithms but the positions.…”

Section: Introductionmentioning

confidence: 99%

An evidential fusion approach for gender profiling

Liu

Miller

et al. 2016

Information Sciences

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Abstract. CCTV (Closed-Circuit TeleVision) systems are broadly deployed in the present world. To ensure in-time reaction for intelligent surveillance, it is a fundamental task for real-world applications to determine the gender of people of interest. However, normal video algorithms for gender profiling (usually face profiling) have three drawbacks. First, the profiling result is always uncertain. Second, the profiling result is not stable. The degree of certainty usually varies over time, sometimes even to the extent that a male is classified as a female, and vice versa. Third, for a robust profiling result in cases that a person's face is not visible, other features, such as body shape, are required. These algorithms may provide different recognition results -at the very least, they will provide different degrees of certainties. To overcome these problems, in this paper, we introduce an Dempster-Shafer (DS) evidential approach that makes use of profiling results from multiple algorithms over a period of time, in particular, Denoeux's cautious rule is applied for fusing mass functions through time lines. Experiments show that this approach does provide better results than single profiling results and classic fusion results. Furthermore, it is found that if severe mis-classification has occurred at the beginning of the time line, the combination can yield undesirable results. To remedy this weakness, we further propose three extensions to the evidential approach proposed above incorporating notions of time-window, time-attenuation, and time-discounting, respectively. These extensions also applies Denoeux's rule along with time lines and take the DS approach as a special case. Experiments show that these three extensions do provide better results than their predecessor when mis-classifications occur.

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Particle filtering in the Dempster–Shafer theory

Cited by 16 publications

References 37 publications

An orchestration approach for unwanted Internet traffic identification

An orchestration approach for unwanted Internet traffic identification

Collaborative context management framework for mobile ad hoc network environments

An evidential fusion approach for gender profiling

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