Parametric Identification and Diagnosis of Integrated Navigation Systems in Bench Test Process

Growth of complexity and functional importance of integrated navigation systems (INS) leads to high losses at the equipment refusals. The paper is devoted to the INS diagnosis system development, allowing identifying the cause of malfunction. The proposed solutions permit taking into account any changes in sensors dynamic and accuracy characteristics by means of the appropriate error models coefficients. Under actual conditions of INS operation, the determination of current values of the sensor models and estimation filter parameters rely on identification procedures. The results of full-scale experiments are given, which corroborate the expediency of INS error models parametric identification in bench test process.

Telehealth Approach for Glaucoma Progression Monitoring

This paper presents a Web-Centric [3] extension to a previously developed glaucoma expert system that will provide access for doctors and patients from any part of the world. Once implemented, this telehealth solution will publish the services of the Glaucoma Expert System on the World Wide Web, allowing patients and doctors to interact with it from their own homes. This web-extension will also allow the expert system itself to be proactive and to send diagnosis alerts to the registered user or doctor and the patient, informing each one of any emergencies, therefore allowing them to take immediate actions. The existing Glaucoma Expert System uses fuzzy logic learning algorithms applied on historical patient data to update and improve its diagnosis rules set. This process, collectively called the learning process, would benefit greatly from a web-based framework that could provide services like patient data transfer and web- based distribution of updated rules [1].

Intelligent Computations for Flood Monitoring

Floods represent the most devastating natural hazards in the world, affecting more people and causing more property damage than any other natural phenomena. One of the important problems associated with flood monitoring is flood extent extraction from satellite imagery, since it is impractical to acquire the flood area through field observations. This paper presents a method to flood extent extraction from synthetic-aperture radar (SAR) images that is based on intelligent computations. In particular, we apply artificial neural networks, self-organizing Kohonen’s maps (SOMs), for SAR image segmentation and classification. We tested our approach to process data from three different satellite sensors: ERS-2/SAR (during flooding on Tisza river, Ukraine and Hungary, 2001), ENVISAT/ASAR WSM (Wide Swath Mode) and RADARSAT-1 (during flooding on Huaihe river, China, 2007). Obtained results showed the efficiency of our approach.