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Polyphase induction motors are the most commonly available industrial machines utilized in a wide range of real-world applications. Any impending fault within these motors is generally very difficult to isolate by conventional fault sensors or experts. The fact is generally attributed to the nonlinear behavior of the motor's terminal characteristics. Extracting anomalous behavior from such data is a challenging task and predominantly relies on the historical machine data pattern. Based on the abovementioned context, this paper presents a novel time-series condition monitoring data assessment methodology to identify developing faults in induction motors. The technique employs Hidden Markov Models to identify anomalous machine behavior. The identification thus obtained is further improved via a Naïve Bayes classifier to further eliminate false positives from healthy and faultcontaining data. The overall Bayes classification outcome showed a marked increase in detection accuracy at 84.55% with a substantial reduction in false positives.

Background subtraction is a well-known technique in computer vision to extract foreground objects from background reference frames. In real-time video processing applications such as surveillance, behavioral profiling and intelligent transport systems, the domain presents a number of challenges. Video frames used to train such models contain a range of dynamic background activities such as waving trees, moving cloud cover or abrupt intensity variations that make the foreground detection a challenging task.Dynamic neural networks are known for their capability to predict time-series-based nonlinear models via previous feature data. The proposed scenario models each pixel's intensity/color-alternating behavior based on its previous activity patterns. Any significant or unusual variation in the underlying intensity or color value therefore is modeled as a foreground activity. Based on this concept, this paper presents a non-linear autoregressive neural (BG-NARX) classifier with the pixels' chromatic values as the exogenous vectors to improve background detection accuracy.The proposed model was evaluated against three benchmarking video datasets and reported promising detection accuracies ranging from 67-94% for pedestrians and vehicles against highly variable backgrounds with low false positives and negatives.

Audio event detection (AED) and recognition is a signal processing and analysis domain used in a wide range of applications including surveillance, home automation and behavioral assessment. The field presents numerous challenges to the current state-of-the-art due to its highly nonlinear nature. High false alarm rates (FARs) in such applications particularly limit the capabilities of vision-based perimeter monitoring systems by inducing high operator dependence. On the other hand, conventional fence-based vibration detectors and pressure-driven "taut wires" offer high sensitivity at the cost of a high FAR due to debris, animals and weather. This work reports an audio event identification methodology implemented as a test-bed system for a surveillance application to reduce FAR, maximize blind-spot coverage and improve audio event classification accuracy. The first phase utilizes a nonlinear autoregressive classifier to locate and classify discrete audio events via an exogenous sound direction variable to improve classifier confidence. The second phase implements a time-series-based system to recognize various audio activity groups from nominal everyday sound events such as traffic and muffled speech. The discretely labeled data is thus trained with HMM and Conditional Random Field classifiers and reports a substantial improvement in classification accuracies of indoor human activities.

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