Experimental Results of High-Resolution ISAR Imaging of Ground-Moving Vehicles with a Stationary FMCW Rada

In the paper experimental results of ISAR (Inverse Synthetic Aperture Radar) processing obtained with highresolution radar are presented. Targets under observation were ground moving vehicles, such as cars, trucks and tractors. The experiments were performed with a FMCW (Frequency- Modulated Continuous-Wave) radar operating at 94 GHz with almost 1 GHz of bandwidth. Due to the measurement scenario more typical for SAR (Synthetic Aperture Radar), than ISAR, i.e. targets moving along straight line crossing the antenna beam, algorithms usually applied for SAR processing have been used

Feasibility study of the space synthetic aperture radar for the SSETI-ESMO project, Journal of Telecommunications and Information Technology, 2007, nr 1

The following paper presents the analysis of the feasibility study of the SAR radar for lunar space missions. The European StudentsMoon Orbiter (ESMO) project is con- ducted by the Students Space Exploration and Technology Initiative (SSETI) association. The phase A of this project is supported by the European Space Agency (ESA)

Short-Range C-Band Noise Radar for Meteorological Application

his paper concerns a ground-based continuous- wave noise radar system that was built from Commercial-Off-The-Shelf measurement equipment (Vector Signal Analyser, Ar- bitrary Waveform Generator, amplifiers, antennas) and operated at 5.6 GHz in the C-band. This electronic system was tested as a meteorological radar in the experiment devoted to atmospheric precipitation sensing. The result of the processing is a set of Range-Doppler plots containing visible echo of rain, based on which its intensity can be calculated. There are the system setup, signal processing and results described in the paper

Implementation and Results of New High Resolution SAR Modes for an Airborne Maritime Patrol Radar

The paper presents new high resolution SAR results of real-life measurements using an updated ARS-400/ARS-800 SAR sensor installed on the maritime patrol aircraft M-28. The main role for such radars is surveying the sea surface, and the imaging of selected targets (e.g. ships, roads, vehicles, buildings, etc.) to help the operator in classifying them. In the present day increasing computing power, improved algorithms and general technological progress has allowed the obtaining of better results in SAR imagery

The simple analysis method of nonlinear frequency distortions in FMCW radar, Journal of Telecommunications and Information Technology, 2001, nr 4

The paper presents a simple method for estimating nonlinear frequency distortions of linear frequency modulated (LFM) signals used in FMCW radars. This method, derived from the polynomial model of the nonlinear FM signal phase, is based on finding the maximum of two-dimensional chirp-like transform of the IF video signal. The IF signal is obtained by mixing transmitted FM signal with its delayed copy. Using suggested transform we show that the presented method is able to detect and classify signal distortions

Distributed physical sensors network for the protection of critical infrastractures against physical attacks

The SCOUT project is based on the use of multiple innovative and low impact technologies for the protection of space control ground stations and the satellite links against physical and cyber-attacks, and for intelligent reconfiguration of the ground station network (including the ground node of the satellite link) in the case that one or more nodes fail. The SCOUT sub-system devoted to physical attacks protection, SENSNET, is presented. It is designed as a network of sensor networks that combines DAB and DVB-T based passive radar, noise radar, Ku-band radar, infrared cameras, and RFID technologies. The problem of data link architecture is addressed and the proposed solution described

Machine learning profiles of cardiovascular risk in patients with diabetes mellitus: the Silesia Diabetes-Heart Project

AimsAs cardiovascular disease (CVD) is a leading cause of death for patients with diabetes mellitus (DM), we aimed to find important factors that predict cardiovascular (CV) risk using a machine learning (ML) approach.Methods and resultsWe performed a single center, observational study in a cohort of 238 DM patients (mean age ± SD 52.15 ± 17.27 years, 54% female) as a part of the Silesia Diabetes-Heart Project. Having gathered patients' medical history, demographic data, laboratory test results, results from the Michigan Neuropathy Screening Instrument (assessing diabetic peripheral neuropathy) and Ewing's battery examination (determining the presence of cardiovascular autonomic neuropathy), we managed use a ML approach to predict the occurrence of overt CVD on the basis of five most discriminative predictors with the area under the receiver operating characteristic curve of 0.86 (95% CI 0.80-0.91). Those features included the presence of past or current foot ulceration, age, the treatment with beta-blocker (BB) and angiotensin converting enzyme inhibitor (ACEi). On the basis of the aforementioned parameters, unsupervised clustering identified different CV risk groups. The highest CV risk was determined for the eldest patients treated in large extent with ACEi but not BB and having current foot ulceration, and for slightly younger individuals treated extensively with both above-mentioned drugs, with relatively small percentage of diabetic ulceration.ConclusionsUsing a ML approach in a prospective cohort of patients with DM, we identified important factors that predicted CV risk. If a patient was treated with ACEi or BB, is older and has/had a foot ulcer, this strongly predicts that he/she is at high risk of having overt CVD