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Correlation filters are attractive for synthetic aperture radar (SAR) automatic target recognition (ATR) because of their shift invariance and potential for distortion-tolerant pattern recognition. In particular, the maximum average correlation height (MACH) filter exhibits better distortion tolerance than other linear correlation filters. Despite its attractive features, it has been shown that the MACH filter relies perhaps too heavily on the average training image leading to poor clutter rejection performance. To improve the clutter rejection performance, we have introduced the extended MACH (EMACH) filter. We have shown that this new filter is better at rejecting clutter images while retaining the distortion tolerance feature of the original MACH filter. In this paper, we introduce a method to decompose the EMACH filter to further improve its performance. The paper describes the theory of this method and shows its potential advantages. Test results of this method using the public domain MSTAR data base are shown.

Smart agriculture is an evolving trend in the agriculture industry, where sensors are embedded into plants to collect vital data and help in decision-making to ensure a higher quality of crops and prevent pests, disease, and other possible threats. One of the most critical pests of palms is the red palm weevil, which is an insect that causes much damage to palm trees and can devastate vast areas of palm trees. The most challenging problem is that the effect of the weevil is not visible by humans until the palm reaches an advanced infestation state. For this reason, there is a pressing need to use advanced technology for early detection and prevention of infestation propagation. In this project, we have developed an IoT-based smart palm monitoring prototype as a proof-of-concept that (1) allows monitoring palms remotely using smart agriculture sensors, (2) contribute to the early detection of red palm weevil infestation. Users can use web/mobile applications to interact with their palm farms and help them in getting early detection of possible infestations. We used an industrial-level IoT platform to interface between the sensor layer and the user layer. Moreover, we have collected data using accelerometer sensors, and we applied signal processing and statistical techniques to analyze collected data and determine a fingerprint of the infestation.

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