Fast and Accurate ISAR Focusing Based on a Doppler Parameter Estimation Algorithm

This letter deals with inverse synthetic aperture radar (ISAR) autofocusing of noncooperative moving targets. The relative motion between the target and the sensor, which provides the angular diversity necessary for ISAR imagery, is also responsible for unwanted range migration and phase changes generating defocusing. In the case of noncooperative targets, the relative motion is unknown: the ISAR needs, hence, to implement an autofocus step [motion compensation (MoCo)] to achieve high resolution imaging. This task is typically carried out via the optimization of functionals based on general image quality parameters. In this letter, we propose the use of a fast and accurate MoCo algorithm based on the estimation of the Doppler parameters, thus fully coping with the nature of the imaging system. The effectiveness of the proposed method is proven on both simulated data and data acquired by operational systems

SECCA procedure for anal incontinence and antibiotic treatment: a case report of anal abscess

Background: Fecal Incontinence (FI) can seriously affect quality of life. The treatment of fecal incontinence starts conservatively but in case of failure, different surgical approaches may be proposed to the patient. Recently several not invasive approaches have been developed. One of these is the radiofrequency (RF) energy application to the internal anal sphincter. Case presentation: We report a rare case of an anal abscess related to a SECCA procedure in a 66-year-old woman affected by gas and FI for twenty years. Conclusions: The complications post-SECCA procedure reported in literature are generally not serious and often self-limited, such as bleeding or anal pain. This is a case of an anal abscess. We suggest that this finding could consolidate the importance of administering antibiotic therapy to patients and to run a full course of at least 6 days rather than a short-term (24 h) therapy, with the aim to minimize the incidence of this complication

Cognitive analytics management of the customer lifetime value: an artificial neural network approach

Purpose: The purpose of this study is to show that the use of CAM (cognitive analytics management) methodology is a valid tool to describe new technology implementations for businesses. Design/methodology/approach: Starting from a dataset of recipes, we were able to describe consumers through a variant of the RFM (recency, frequency and monetary value) model. It has been possible to categorize the customers into clusters and to measure their profitability thanks to the customer lifetime value (CLV). Findings: After comparing two machine learning algorithms, we found out that self-organizing map better classifies the customer base of the retailer. The algorithm was able to extract three clusters that were described as personas using the values of the customer lifetime value and the scores of the variant of the RFM model. Research limitations/implications: The results of this methodology are strictly applicable to the retailer which provided the data. Practical implications: Even though, this methodology can produce useful information for designing promotional strategies and improving the relationship between company and customers. Social implications: Customer segmentation is an essential part of the marketing process. Improving further segmentation methods allow even small and medium companies to effectively target customers to better deliver to society the value they offer. Originality/value: This paper shows the application of CAM methodology to guide the implementation and the adoption of a new customer segmentation algorithm based on the CLV