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We offer an algorithm that can identify aircraft categories from Inverse Synthetic Aperture Radar (ISAR) images that use both the radar reflection pulse shape, which includes the duration or size of the radar pulse that is reflected, and the Doppler shifts of different parts of the aircraft caused by rotational motions of the aircraft as it maneuvers. We investigated the practicality of determining which of seven different aircraft categories a radar return indicates. The object of this research is to very quickly tell from an ISAR return how an aircraft compares to the seven different categories where the aircraft is in any position of a prescribed holding pattern. We propose a new method in which we compare each ISAR image to unions of images of the different aircraft categories. This method gave us results that are superior to the results we obtained in [8].

SUMMARYDiabetic retinopathy is the progressive pathological alterations in the retinal microvasculature that very often causes blindness. Because of its clinical significance, it will be helpful to have regular cost-effective eye screening for diabetic patients by developing algorithms to perform retinal image analysis, fundus image enhancement, and monitoring. The two cost-effective algorithms are proposed for exudates detection and optic disk extraction aimed for retinal images classification and diagnosis assistance. They represent the effort made to offer a cost-effective algorithm for optic disk identification, which will enable easier exudates extraction, exudates detection and retinal images classification aimed to assist ophthalmologists while making diagnoses. The proposed algorithms apply mathematical modeling, which enables light intensity levels emphasis, easier optic disk and exudates detection, efficient and correct classification of retinal images. The algorithm is robust to various appearance changes of retinal fundus images and shows very promising results. Fundus images are classified into those that are healthy and those affected by diabetes, based on the detected optic disk and exudates. The obtained results indicate that the proposed algorithm successfully and correctly classifies more than 98% of the observed retinal images because of the changes in the appearance of retinal fundus images typically encountered in clinical environments.

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