We derive an approximate maximum a posteriori (MAP) method for detecting NDE defect signals using hidden Markov random fields (HMRFs). In the proposed HMRF framework, a set of spatially distributed NDE measurements is assumed to form a noisy realization of an underlying random field that has a simple structure with Markovian dependence. Here, the random field describes the defect signals to be estimated or detected. The HMRF models incorporate measurement locations into the statistical analysis, which is important in scenarios where the same defect affects measurements at multiple locations. We also discuss initialization of the proposed HMRF detector and apply to simulated eddy-current data and experimental ultrasonic C-scan data from an inspection of a cylindrical Ti 6-4 billet.

We develop a nonparametric method for estimating the probability distribution function (pdf) describing the physical phenomenon measured by a sensor network. The measurements are collected by sensor-processor elements (nodes) deployed in the region of interest; the nodes quantize these measurements and transmit only one bit per observation to a fusion center. We model the measurement pdf as a Gaussian mixture and develop a Fisher scoring algorithm for computing the maximum likelihood (ML) estimates of the unknown mixture probabilities. We also estimate the number of mixture components as well as their means and standard deviation. Numerical simulations demonstrate the performance of the proposed method.

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Artificial intelligence-empowered path selection plays an important role in wireless sensor networks (WSNs), because it can exceed the cognitive performance of humans and determine multiple aspects of the network performance. Ant colony optimization (ACO) is an effective intelligence algorithm which succeeds in addressing several issues of WSNs, including data transmission, node deployment, etc. There exist several ACO-based transmission strategies for WSNs, but the summary and comparison of such works are very limited. This paper provides a comprehensive overview of ACO-based transmission strategies for static and mobile WSNs. First, we provide a classification of existing ACO-based transmission methods, which distinguishes itself from other works in network types. Second, the highly typical ACObased transmission strategies for WSNs are illustrated and discussed. Finally, we summarize the paper and present several open issues concerning the design of such networks. This survey contributes to system design guidance and network performance improvement. INDEX TERMS Wireless sensor networks, transmission protocol, ant colony optimization.

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