Probability, Statistics, and Stochastic Processes

Olofsson, Peter; Andersson, Mikael

doi:10.1002/9780471743064

Cited by 45 publications

(31 citation statements)

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“…As specific examples, the expected value of the convolution of two Gaussian distributions with means µ 1 and µ 2 , is exactly µ 12 = µ 1 + µ 2 , and we note that the pdf of the convolution is also Gaussian [20]. Similarly for the expected value of two uniform distributions, however, as we saw in Example 3, the resulting pdf is no longer uniform.…”

Section: For Each Pointmentioning

confidence: 83%

“…Another interpretation is that V iq can be viewed as a sum V i + (−V q ). Since V i and V q (consequently, −V q ) are independent variables [5,35], it is a well-known fact from the probability theory that the random variable V iq has a pdfiq that is a convolution of the corresponding pdf s of V i and −V q ) [20]. In other words, we have [20]).…”

Section: For Each Pointmentioning

confidence: 99%

“…Now, the key observation is that V i − V q is another random variable, which we denote with V iq . In probability and signal/image processing, V iq is known as the cross-correlation of V i and V q [17,20]. Another interpretation is that V iq can be viewed as a sum V i + (−V q ).…”

Section: For Each Pointmentioning

confidence: 99%

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Continuous probabilistic nearest-neighbor queries for uncertain trajectories

Trajcevski¹,

Tamassia

Scheuermann³

et al. 2009

Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology

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This work addresses the problem of processing continuous nearest neighbor (NN ) queries for moving objects trajectories when the exact position of a given object at a particular time instant is not known, but is bounded by an uncertainty region. As has already been observed in the literature, the answers to continuous NN-queries in spatio-temporal settings are time parameterized in the sense that the objects in the answer vary over time. Incorporating uncertainty in the model yields additional attributes that affect the semantics of the answer to this type of queries. In this work, we formalize the impact of uncertainty on the answers to the continuous probabilistic NN-queries, provide a compact structure for their representation and efficient algorithms for constructing that structure. We also identify syntactic constructs for several qualitative variants of continuous probabilistic NN-queries for uncertain trajectories and present efficient algorithms for their processing.

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Section: For Each Pointmentioning

confidence: 83%

Section: For Each Pointmentioning

confidence: 99%

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Continuous probabilistic nearest-neighbor queries for uncertain trajectories

Trajcevski¹,

Tamassia

Scheuermann³

et al. 2009

Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology

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“…Selection of the subset of options can be performed using either of two main statistical sampling principles: random or targeted [31]. A form of targeted search includes selection of options based on their quality with the solution yielding a Pareto line [6] of job execution options.…”

Section: The Controllermentioning

confidence: 99%

Scheduling and planning job execution of loosely coupled applications

2011

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Growth in availability of data collection devices has allowed individual researchers to gain access to large quantities of data that needs to be analyzed. As a result, many labs and departments have acquired considerable compute resources. However, effective and efficient utilization of those resources remains a barrier for the individual researchers because the distributed computing environments are difficult to understand and control. We introduce a methodology and a tool that automatically manipulates and understands job submission parameters to realize a range of job execution alternatives across a distributed compute infrastructure. Generated alternatives are presented to a user at the time of job submission in the form of tradeoffs mapped onto two conflicting objectives, namely job cost and runtime. Such presentation of job execution alternatives allows a user to immediately and quantitatively observe viable options regarding their job execution, and thus allows the user to interact with the environment at a true service level. Generated job execution alternatives have been tested through simulation and on real-world resources and, in both cases, the average accuracy of the runtime of the generated and perceived job alternatives is within 5%.

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“…That means the estimator aims at the true value or is correct average [24]. In (5) Monte Carlo simulation.…”

Section: --Minimize Operationamentioning

confidence: 99%

Studies on Radar and Non-radar Sensor Networks

Liang¹

2006

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During the period of 12/1/2005 -5/30/2006, we expanded our research from generic wireless sensor networks to radar sensor networks. For radar sensor networks, we performed the following preliminary studies:1. Waveform design and diversity in radar sensor networks with applications to automatic target recognition without or with delay-doppler uncertainty. We used constant frequency (CF) pulse waveform and linear frequency modulation (LFM) waveform in this study. 2. We proposed a Knowledge-based Ubiquitous and Persistent Sensor networks (KUPS) for threat assessment, of which "sensor" is a broad characterization concept. It means diverse data or information from ubiquitous and persistent sensor sources such as organic sensors (e.g., radar) and human intelligence sensors.3. Spatial-temporal-frequency diversity to improve the detection performance of Radar Sensor Networks in the presence of certain types of interference (clutter, jamming, noise and interference between radar sensors) was studied.For non-radar sensor networks, we continuously conducted the following research tasks:1. 20060620043RSN and propose maximum-likehood (ML)-ATR algorithms for nonfluctuating target as well as fluctuating target. Simulation results show that our waveform diversity-based ML-ATI{ algorithm performs much better than single-waveform ML-ATR algorithm for nonfluctuating targets or fluctuating targets. Conclusions are drawn based on our analysis and simulations and future research works on this research topic are discussed. In the above ATR using CF pulse waveform design and diversity, we assumed no delay-doppler uncertainty. It is not true for ATR in target search phase because the target range and mobility are not yet perfectly known, which results in delay-doppler uncertainty. In [3] [4], we studied linear frequency modulation (LFM) waveform design and diversity, and applied it to ATR with delaydoppler uncertainty using ML-ATR algorithm. Simulation results show that our RSN vastly reduces the ATR error comparing to a single radar system in ATR with delay-doppler uncertainty.In [6], we studied waveform design and diversity using some concepts from physical layer communications such as orthogonal, non-coherent detection, and coherent detection. We also used signal representation from communication domain, i.e., taking its real part when carriers are considered. We proposed orthogonal waveforms for RSN, which eliminates interference when no doppler shift is introduced. Additionally, this approach applies the advantage of spacial diversity through equal gain combination performed by clusterhead. When doppler shift is considered and interference is unavoidable, we analyzed the performance of this design not only in coherent RSN, but in noncoherent systems as well. The latter scenario is more challenging as doppler-shift uncertainty results in more complicated implementation. Monte Carlo simulation shows that our technique provides much better detection performance than single radar for fluctuating targets, in terms of probabili...

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Probability, Statistics, and Stochastic Processes

Cited by 45 publications

References 0 publications

Continuous probabilistic nearest-neighbor queries for uncertain trajectories

Continuous probabilistic nearest-neighbor queries for uncertain trajectories

Scheduling and planning job execution of loosely coupled applications

Studies on Radar and Non-radar Sensor Networks

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