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Sarscape Envi 5.0 Crack 16



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sarscape envi 5.0 crack 16



To provide an adequate level of protection for humans from exposure to radio-frequency (RF) electromagnetic fields (EMF) and to assure that any adverse health effects are avoided. The basic restrictions in terms of the specific energy absorption rate (SAR) were prescribed by IEEE and ICNIRP. An example of a therapeutic application of non-ionizing EMF is hyperthermia (HT), in which intense RF energy is focused at a target region. Deep HT in the head and neck (H&N) region involves inducing energy at 434 MHz for 60 min on target. Still, stray exposure of the brain is considerable, but to date only very limited side-effects were observed. The objective of this study is to investigate the stringency of the current basic restrictions by relating the induced EM dose in the brain of patients treated with deep head and neck (H&N) HT to the scored acute health effects. We performed a simulation study to calculate the induced peak 10 g spatial-averaged SAR (psSAR10g) in the brains of 16 selected H&N patients who received the highest SAR exposure in the brain, i.e. who had the minimum brain-target distance and received high forwarded power during treatment. The results show that the maximum induced SAR in the brain of the patients can exceed the current basic restrictions (IEEE and ICNIRP) on psSAR10g for occupational environments by 14 times. Even considering the high local SAR in the brain, evaluation of acute effects by the common toxicity criteria (CTC) scores revealed no indication of a serious acute neurological effect. In addition, this study provides pioneering quantitative human data on the association between maximum brain SAR level and acute adverse effects when brains are exposed to prolonged RF EMF.


A Compact Real-Time Optronic SAR Processor has been successfully developed and tested up to a Technology Readiness Level of 4 (TRL4), the breadboard validation in a laboratory environment. SAR, or Synthetic Aperture Radar, is an active system allowing day and night imaging independent of the cloud coverage of the planet. The SAR raw data is a set of complex data for range and azimuth, which cannot be compressed. Specifically, for planetary missions and unmanned aerial vehicle (UAV) systems with limited communication data rates this is a clear disadvantage. SAR images are typically processed electronically applying dedicated Fourier transformations. This, however, can also be performed optically in real-time. Originally the first SAR images were optically processed. The optical Fourier processor architecture provides inherent parallel computing capabilities allowing real-time SAR data processing and thus the ability for compression and strongly reduced communication bandwidth requirements for the satellite. SAR signal return data are in general complex data. Both amplitude and phase must be combined optically in the SAR processor for each range and azimuth pixel. Amplitude and phase are generated by dedicated spatial light modulators and superimposed by an optical relay set-up. The spatial light modulators display the full complex raw data information over a two-dimensional format, one for the azimuth and one for the range. Since the entire signal history is displayed at once, the processor operates in parallel yielding real-time performances, i.e. without resulting bottleneck. Processing of both azimuth and range information is performed in a single pass. This paper focuses on the onboard capabilities of the compact optical SAR processor prototype that allows in-orbit processing of SAR images. Examples of processed ENVISAT ASAR images are presented. Various SAR processor parameters such as processing capabilities, image quality (point target analysis), weight and


In this paper, personal electromagnetic field exposure of the general public due to 12 different radiofrequency sources is characterized. Twenty-eight different realistic exposure scenarios based upon time, environment, activity, and location have been defined and a relevant number of measurements were performed with a personal exposure meter. Indoor exposure in office environments can be higher than outdoor exposure: 95th percentiles of field values due to WiFi ranged from 0.36 to 0.58 V m(-1), and for DECT values of 0.33 V m(-1) were measured. The downlink signals of GSM and DCS caused the highest outdoor exposures up to 0.52 V m(-1). The highest total field exposure occurred for mobile scenarios (inside a train or bus) from uplink signals of GSM and DCS (e.g., mobile phones) due to changing environmental conditions, handovers, and higher required transmitted signals from mobile phones due to penetration through windows while moving. A method to relate the exposure to the actual whole-body absorption in the human body is proposed. An application is shown where the actual absorption in a human body model due to a GSM downlink signal is determined. Fiftieth, 95th, and 99 th percentiles of the whole-body specific absorption rate (SAR) due to this GSM signal of 0.58 microW kg(-1), 2.08 microW kg(-1), and 5.01 microW kg(-1) are obtained for a 95th percentile of 0.26 V m(-1). A practical usable function is proposed for the relation between the whole-body SAR and the electric fields. The methodology of this paper enables epidemiological studies to make an analysis in combination with both electric field and actual whole-body SAR values and to compare exposure with basic restrictions.


Purpose To evaluate SAR as a dosimetric quantity for EMF bioeffects, and identify ways for increasing the precision in EMF dosimetry and bioactivity assessment. Methods We discuss the interaction of man-made electromagnetic waves with biological matter and calculate the energy transferred to a single free ion within a cell. We analyze the physics and biology of SAR and evaluate the methods of its estimation. We discuss the experimentally observed non-linearity between electromagnetic exposure and biological effect. Results We find that: a) The energy absorbed by living matter during exposure to environmentally accounted EMFs is normally well below the thermal level. b) All existing methods for SAR estimation, especially those based upon tissue conductivity and internal electric field, have serious deficiencies. c) The only method to estimate SAR without large error is by measuring temperature increases within biological tissue, which normally are negligible for environmental EMF intensities, and thus cannot be measured. Conclusions SAR actually refers to thermal effects, while the vast majority of the recorded biological effects from man-made non-ionizing environmental radiation are non-thermal. Even if SAR could be accurately estimated for a whole tissue, organ, or body, the biological/health effect is determined by tiny amounts of energy/power absorbed by specific biomolecules, which cannot be calculated. Moreover, it depends upon field parameters not taken into account in SAR calculation. Thus, SAR should not be used as the primary dosimetric quantity, but used only as a complementary measure, always reporting the estimating method and the corresponding error. Radiation/field intensity along with additional physical parameters (such as frequency, modulation etc) which can be directly and in any case more accurately measured on the surface of biological tissues, should constitute the primary measure for EMF exposures, in spite of similar uncertainty to predict


Long-range ground targets are difficult to detect in a noisy cluttered environment using either synthetic aperture radar (SAR) images or infrared (IR) images. SAR-based detectors can provide a high detection rate with a high false alarm rate to background scatter noise. IR-based approaches can detect hot targets but are affected strongly by the weather conditions. This paper proposes a novel target detection method by decision-level SAR and IR fusion using an Adaboost-based machine learning scheme to achieve a high detection rate and low false alarm rate. The proposed method consists of individual detection, registration, and fusion architecture. This paper presents a single framework of a SAR and IR target detection method using modified Boolean map visual theory (modBMVT) and feature-selection based fusion. Previous methods applied different algorithms to detect SAR and IR targets because of the different physical image characteristics. One method that is optimized for IR target detection produces unsuccessful results in SAR target detection. This study examined the image characteristics and proposed a unified SAR and IR target detection method by inserting a median local average filter (MLAF, pre-filter) and an asymmetric morphological closing filter (AMCF, post-filter) into the BMVT. The original BMVT was optimized to detect small infrared targets. The proposed modBMVT can remove the thermal and scatter noise by the MLAF and detect extended targets by attaching the AMCF after the BMVT. Heterogeneous SAR and IR images were registered automatically using the proposed RANdom SAmple Region Consensus (RANSARC)-based homography optimization after a brute-force correspondence search using the detected target centers and regions. The final targets were detected by feature-selection based sensor fusion using Adaboost. The proposed method showed good SAR and IR target detection performance through feature selection-based decision fusion on a synthetic database generated 350c69d7ab


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