Probability of Detection

Cognitive Radios have been advanced as a technology for the opportunistic use of under-utilized spectrum since they are able to sense the spectrum and use frequency bands if no Primary user is detected. However, the required sensitivity is very demanding since any individual radio might face a deep fade. We propose light-weight cooperation in sensing based on hard decisions to mitigate the sensitivity requirements on individual radios. We show that the "link budget" that system designers have to reserve for fading is a significant function of the required probability of detection. Even a few cooperating users (~10-20) facing independent fades are enough to achieve practical threshold levels by drastically reducing individual detection requirements. Hard decisions perform almost as well as soft decisions in achieving these gains. Cooperative gains in a environment where shadowing is correlated, is limited by the cooperation footprint (area in which users cooperate). In essence, a few independent users are more robust than many correlated users. Unfortunately, cooperative gain is very sensitive to adversarial/failing Cognitive Radios. Radios that fail in a known way (always report the presence/absence of a Primary user) can be compensated for by censoring them. On the other hand, radios that fail in unmodeled ways or may be malicious, introduce a bound on achievable sensitivity reductions. As a rule of thumb, if we believe that 1/N users can fail in an unknown way, then the cooperation gains are limited to what is possible with N trusted users.

Predicting the occurrence of hailstorms is one of the most complicated tasks in weather forecasting because of the small area of land that is usually affected, and because of the short time hail events last. Today there are a number of parameters available that may be used to characterize preconvective conditions and predict the beginning of convection. However, forecast models based on stability indices should be developed separately for each geographic area.The databases available were used in this study with two aims in mind: determining which meteorological variables or indices obtained from a radiosonde in the study zone are more relevant in characterizing preconvective conditions; and, secondly, setting up an objective short-term forecast model for storms on the basis of one or more meteorological parameters.The logistic regression was used to establish the dichotomy risk/no risk of storms. A function was developed combining seven meteorological variables. The results show that the forecast model has a Probability of Detection of 0.87 and a False Alarm Ratio of 0.18.

It is a common practice in the oil and gas industry to employ various Non-Destructive Evaluation (NDE)
methods during in-service inspections to detect the cracks in the pipeline. The capability of NDE method
is expressed by the metric called as the Probability of Detection (POD), which is stated as a function of crack size through the POD curve. This manuscript thus deals with various aspects of POD. Firstly, a brief discussion about NDE reliability and POD is presented. Thereafter, based on the literature review the history and development of POD curves is provided. Finally, a brief discussion about the future of POD i.e. model assisted POD is presented.

One of the commonly used tools for detecting changes in climatic and hydrologic time series is trend analysis. A number of statistical tests exist to assess the significance of trends in time series. One of the commonly used non-parametric trend tests is the Mann-Kendall trend test. The null hypothesis in the Mann-Kendall test is that the data are independent and randomly ordered. However, the existence of positive autocorrelation in the data increases the probability of detecting trends when actually none exist, and vice versa. Although this is a well-known fact, few studies have addressed this issue, and autocorrelation in the data is often ignored. In this study, the effect of autocorrelation on the variance of the Mann-Kendall trend test statistic is discussed. A theoretical relationship is derived to calculate the variance of the Mann-Kendall test statistic for autocorrelated data. The special cases of AR(1) and MA(1) dependence are discussed as examples. An approximation to the theoretical relationship is also presented in order to reduce computation time for long time series. Based on the modified value of the variance of the Mann-Kendall trend test statistic, a modified non-parametric trend test which is suitable for autocorrelated data is proposed. The accuracy of the modified test in terms of its empirical significance level was found to be superior to that of the original Mann-Kendall trend test without any loss of power. The modified test is applied to rainfall as well as streamflow data to demonstrate its performance as compared to the original Mann-Kendall trend test.

Various  Non-Destructive Evaluation (NDE) methods are used in offshore industry during in-service inspections to detect the cracks in the structures and mechanical items. The metric generally used to quantify the capability of NDE method is Probability of Detection (POD), which is expressed as a function of crack size through POD curve. Human skills play a major role in determining overall reliability of NDE method used for crack detection. Thus, a brief discussion related to human factors in NDE reliability is presented. Thereafter, based on literature review the history and development of POD curves is provided. Likewise, the statistics of the POD curve, along with the statistical models of POD curve for different NDE methods is also presented. Furthermore, the limitations of POD curves are discussed in the paper. Finally, a brief discussion about model assisted POD is presented.

Reliability quantification is a critical and necessary process for the evaluation and assessment of any inspection technology that may be classified either as a Nondestructive Evaluation (NDE) or Structural Health Monitoring (SHM) technique. Based on the sensitivity characterization of NDE techniques, appropriate processes have been developed and established for the reliability quantification of their performance with respect to damage/flaw detection in materials or structures. However, in the case of SHM-based methods, no such well-defined and general applicable approaches have been established for neither active nor passive sensing techniques that allow for their accurate reliability quantification.

The objective of this study is to characterize the sensitivity of active sensing acousto- ultrasound-based SHM techniques with respect to damage detection, as well as to identify the parameters that influence their sensitivity. With such an understanding, it is believed that adequate quantitative methods could then be established to enable the practical use of acousto- ultrasound SHM methods in the aerospace and mechanical engineering communities.

In order to evaluate the sensitivity of a pre-selected active sensing acousto-ultrasound SHM system, both numerical simulations and experiments were performed on thirty aluminum coupons each outfitted with a pair of Lead-Zirconate-Titanate (PZT) based piezoelectric sensors/actuators. A damage index versus damage size relationship was investigated numerically and experimentally to assess the applicability of the traditional NDE linear regression framework for Probability of Detection (POD) for an active sensing SHM system. The results of the study show that the position of each sensor-actuator pair with respect to a known damage location and the damage growth pattern are the two most critical parameters influencing the reliability of the same SHM system applied to identical structural components under the same environmental conditions.

Typical location determination systems require the presence of a physical device that is attached to the person that is being tracked. In addition, they usually require the tracked device to participate actively in the localization process. In this paper, we introduce the concept of Device-free Passive (DfP) localization. A DfP system is envisioned to be able to detect, track, and identify entities that do not carry any device, nor participate actively in the localization process. The system works by monitoring and processing changes in the ...