An Introduction to Statistical Signal Processing

Gray, Robert M.; Davisson, L.

doi:10.1017/cbo9780511801372

Cited by 162 publications

(115 citation statements)

References 33 publications

Supporting

Mentioning

115

Contrasting

Order By: Relevance

“…The details can be found in the Appendix. To calculate the conditional expectation of the X(t + 7) given the observation set B(t), we express X(t + 7) as where yt = Sw(t)+l -t is the time until the node hits the next waypoint, and 6P(7-yt) is the displacement of the node after reaching the next waypoint until time t + 7. Figure 1 …”

Section: Theorem 2 the Optimal Mobility Predictor Of X(t + T) Givenmentioning

confidence: 99%

Last Encounter Routing under Random Waypoint Mobility

Sarafijanovic-Djukic

Grossglauser

2004

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. Last Encounter Routing (LER) algorithms for mobile ad hoc networks rely only on encounter histories at every node to raute packets, and therefore do not need control traffic to track topology changes due to node mobility. LER exploits the fact that past information about a node's mobility helps to locate that node in the future. As we have pointed out in earlier work [1], the performance of LER algorithms depends on the mobility processes of nodes. In this paper, we ask whether LER can work under the random waypoint (RWP) mobility model. This question is important for several reasons. First, as shown in [1], a good performance for the RWP model is harder to achieve than for another prominent mobility model, the random walk. This is because the RWP model has a much shorter relaxation time, i.e. , a time-horizon over which past information is still useful. Also, the RWP model has a much less favorable ratio of number of encounters between nodes and the traveled distance. Second, in centrast to the random walk, the RWP model is predictable. This provides us with an opportunity to exploit additional information collected in an encounter (such as speed, direction, etc.) to improve routing. We formally define the RWP model, and compute the optimal predictors for several observation sets, i.e., observed parameters of node mobility. We develop a new LER algorithm tuned to the RWP model called GREASE-RWP, and present simulation results that dernarrstrate that an efficient and scalable LER for the RWP model is possible.

show abstract

Section: Theorem 2 the Optimal Mobility Predictor Of X(t + T) Givenmentioning

confidence: 99%

Last Encounter Routing under Random Waypoint Mobility

Sarafijanovic-Djukic

Grossglauser

2004

Lecture Notes in Computer Science

View full text Add to dashboard Cite

show abstract

“…denotes the pmf of the Poisson law (Gray & Davisson 2004) 2 . The adoption of this probabilistic model is common in contemporary astrometry (e.g., in Gaia, see Lindegren 2008).…”

Section: Astrometrymentioning

confidence: 99%

Analysis of the Bayesian Cramér-Rao lower bound in astrometry

Echeverria

Silva

Mendez

et al. 2016

A&A

View full text Add to dashboard Cite

Context. The best precision that can be achieved to estimate the location of a stellar-like object is a topic of permanent interest in the astrometric community. Aims. We analyze bounds for the best position estimation of a stellar-like object on a CCD detector array in a Bayesian setting where the position is unknown, but where we have access to a prior distribution. In contrast to a parametric setting where we estimate a parameter from observations, the Bayesian approach estimates a random object (i.e., the position is a random variable) from observations that are statistically dependent on the position. Methods. We characterize the Bayesian Cramér-Rao (CR) that bounds the minimum mean square error (MMSE) of the best estimator of the position of a point source on a linear CCD-like detector, as a function of the properties of detector, the source, and the background.Results. We quantify and analyze the increase in astrometric performance from the use of a prior distribution of the object position, which is not available in the classical parametric setting. This gain is shown to be significant for various observational regimes, in particular in the case of faint objects or when the observations are taken under poor conditions. Furthermore, we present numerical evidence that the MMSE estimator of this problem tightly achieves the Bayesian CR bound. This is a remarkable result, demonstrating that all the performance gains presented in our analysis can be achieved with the MMSE estimator. Conclusions. The Bayesian CR bound can be used as a benchmark indicator of the expected maximum positional precision of a set of astrometric measurements in which prior information can be incorporated. This bound can be achieved through the conditional mean estimator, in contrast to the parametric case where no unbiased estimator precisely reaches the CR bound.

show abstract

“…Assuming that X is a Gaussian random vector with mean vector M X and covariance matrix Σ X and using the properties of a characteristic function of random variable, we can compute the mean M Y and covariance matrix Σ Y of Y as [9]:…”

Section: Transient Fault Propagation Modelmentioning

confidence: 99%

“…When propagating transient fault, we use the coefficients of d and a from the transient fault description in (1) and (2), and the gate delay coefficients (6) and apply equation (9) to find the output glitch duration and amplitude in terms of process parameter variations. In case of reconvergent glitches, as shown in the pseudocode in Figure 3, we first apply glitch merging, if necessary, as described in Section 4.3.…”

Section: Transient Fault Modelingmentioning

confidence: 99%

Process variability-aware transient fault modeling and analysis

Chopra

Zhuo

Blaauw

et al. 2008

2008 IEEE/ACM International Conference on Computer-Aided Design

View full text Add to dashboard Cite

-Due to reduction in device feature size and supply voltage, the sensitivity of digital systems to transient faults is increasing dramatically. As technology scales further, the increase in transistor integration capacity also leads to the increase in process and environmental variations. Despite these difficulties, it is expected that systems remain reliable while delivering the required performance. Reliability and variability are emerging as new design challenges, thus pointing to the importance of modeling and analysis of transient faults and variation sources for the purpose of guiding the design process. This work presents a symbolic approach to modeling the effect of transient faults in digital circuits in the presence of variability due to process manufacturing. The results show that using a nominal case and not including variability effects, can underestimate the SER by 5% for the 50% yield point and by 10% for the 90% yield point.

show abstract

An Introduction to Statistical Signal Processing

Cited by 162 publications

References 33 publications

Last Encounter Routing under Random Waypoint Mobility

Last Encounter Routing under Random Waypoint Mobility

Analysis of the Bayesian Cramér-Rao lower bound in astrometry

Process variability-aware transient fault modeling and analysis

Contact Info

Product

Resources

About