Mitigation of Through-Wall Distortions of Frontal Radar Images using Denoising Autoencoders

Radar images of humans and other concealed objects are considerably distorted by attenuation, refraction and multipath clutter in indoor through-wall environments. While several methods have been proposed for removing target independent static and dynamic clutter, there still remain considerable challenges in mitigating target dependent clutter especially when the knowledge of the exact propagation characteristics or analytical framework is unavailable. In this work we focus on mitigating wall effects using a machine learning based solution -- denoising autoencoders -- that does not require prior information of the wall parameters or room geometry. Instead, the method relies on the availability of a large volume of training radar images gathered in through-wall conditions and the corresponding clean images captured in line-of-sight conditions. During the training phase, the autoencoder learns how to denoise the corrupted through-wall images in order to resemble the free space images. We have validated the performance of the proposed solution for both static and dynamic human subjects. The frontal radar images of static targets are obtained by processing wideband planar array measurement data with two-dimensional array and range processing. The frontal radar images of dynamic targets are simulated using narrowband planar array data processed with two-dimensional array and Doppler processing. In both simulation and measurement processes, we incorporate considerable diversity in the target and propagation conditions. Our experimental results, from both simulation and measurement data, show that the denoised images are considerably more similar to the free-space images when compared to the original through-wall images

Representation of Radar Micro-Dopplers Using Customized Dictionaries

Human motions give rise to frequency modulations, known as micro-Dopplers, to continuous wave radar signals. Micro-Doppler signals have been extensively researched for the classification of different types of human motions as well as to distinguish humans from other moving targets. However, there are two main scenarios where the performance of existing algorithms deteriorates significantly—one, when the channel consists of multiple moving targets resulting in distorted signatures, and two, when the systems conditions during the training stage deviate significantly from the conditions during the test stage. In this chapter, it is demonstrated that both of these limitations can be overcome by representing the radar data through customized dictionaries, fine-tuned to provide sparser representations of the data, than traditional data-independent dictionaries such as Fourier or wavelets. The performances of the algorithms are evaluated with both simulated and measured radar data gathered from moving humans in indoor line-of-sight conditions

Radar simulation of human activities in non line-of-sight environments

textThe capability to detect, track and monitor human activities behind building walls and other non-line-of-sight environments is an important component of security and surveillance operations. Over the years, both ultrawideband and Doppler based radar techniques have been researched and developed for tracking humans behind walls. In particular, Doppler radars capture some interesting features of the human radar returns called microDopplers that arise from the dynamic movements of the different body parts. All the current research efforts have focused on building hardware sensors with very specific capabilities. This dissertation focuses on developing a physics based Doppler radar simulator to generate the dynamic signatures of complex human motions in nonline-of-sight environments. The simulation model incorporates dynamic human motion, electromagnetic scattering mechanisms, channel propagation effects and radar sensor parameters. Detailed, feature-by-feature analyses of the resulting radar signatures are carried out to enhance our fundamental understanding of human sensing using radar. First, a methodology for simulating the radar returns from complex human motions in free space is presented. For this purpose, computer animation data from motion capture technologies are exploited to describe the human movements. Next, a fast, simple, primitive-based electromagnetic model is used to simulate the human body. The microDopplers of several human motions such as walking, running, crawling and jumping are generated by integrating the animation models of humans with the electromagnetic model of the human body. Next, a methodology for generating the microDoppler radar signatures of humans moving behind walls is presented. This involves combining wall propagation functions derived from the finite-difference time-domain (FDTD) simulation with the free space radar simulations of humans. The resulting hybrid simulator of the human and wall is used to investigate the effects of both homogeneous and inhomogeneous walls on human microDopplers. The results are further corroborated by basic point-scatterer analysis of different wall effects. The wall studies are followed by an analysis of the effects of flat grounds on human radar signatures. The ground effect is modeled using the method of images and a ground reflection coefficient. A suitable Doppler radar testbed is developed in the laboratory for simulation validation. Measured data of different human activities are collected in both line-of-sight and through-wall environments and the resulting microDoppler signatures are compared with the simulation results. The human microDopplers are best observed in the joint timefrequency space. Hence, suitable joint time-frequency transforms are investigated for improving the display and the readability of both simulated and measured spectrograms. Finally, two new Doppler radar paradigms are considered. First, a scenario is considered where multiple, spatially distributed Doppler radars are used to measure the microDopplers of a moving human from different viewing angles. The possibility of using these microDoppler data for estimating the positions of different point scatterers on the human body is investigated. Second, a scenario is considered where multiple Doppler radars are collocated in a two-dimensional (2-D) array configuration. The possibility of generating frontal images of human movements using joint Doppler and 2-D spatial beamforming is considered. The performance of this concept is compared with that of conventional 2-D array processing without Doppler processing.Electrical and Computer Engineerin

Evaluation of initial setting time due to superplasticizers

This paper shows how polycarboxylate based superplasticizer affects the initial setting time of cement paste. Three superplasticizers are used in this study with different properties and aiming to determine the delay in initial setting time due to superplasticizer. Initial setting time is calculated as per IS: 4031-PART 5-1988 with different SP dosages (0.5%, 0.75%, 1.0% and 1.5% of weight of cement). Superplasticizer is an admixture which reduces the water-cement ratio or increase the workability at the same water content. This paper deals with the evaluation of initial setting time due to superplasticizers

Acute disseminated encephalomyelitis mimicking late CNS relapse of acute lymphoblastic leukaemia: case report

BACKGROUND: Acute encephalomyelopathy occurring after an allogeneic bone marrow transplant for leukaemia is a diagnostic emergency. The diagnosis can be challenging since there is a wide set of alternative diagnoses, including opportunistic infections and relapse of the leukaemia. CASE PRESENTATION: A 13-year old girl presented with a severe acute myelopathy and encephalopathy. She was in prolonged remission from a central nervous system and bone marrow relapse of an acute lymphoblastic leukaemia, treated with allogeneic bone marrow transplantation. Neuroimaging showed multifocal grey and white matter lesions of demyelinating appearance in the brain and entire spine. Immunophenotyping and cytogenetic investigations of the girl's cerebrospinal fluid lymphocytosis excluded a late central nervous system relapse of her leukaemia. The diagnosis was acute disseminated encephalomyelitis. With standard immunosuppressive therapy, the girl had early cerebral recovery but a prolonged period of recovery from her myelopathy. CONCLUSION: Acute disseminated encephalomyelitis should be considered in the differential diagnosis of acute encephalomyelopathy after bone marrow transplantation for leukaemia. Demyelinating syndromes such as acute disseminated encephalomyelitis may be late sequelae of bone marrow transplantation

Radar Enhanced Multi-Armed Bandit for Rapid Beam Selection in Millimeter Wave Communications

Multi-arm bandit (MAB) algorithms have been used to learn optimal beams for millimeter wave communication systems. Here, the complexity of learning the optimal beam linearly scales with the number of beams, leading to high latency when there are a large number of beams. In this work, we propose to integrate radar with communication to enhance the MAB learning performance by searching only those beams where the radar detects a scatterer. Further, we use radar to distinguish the beams that show mobile targets from those which indicate the presence of static clutter, thereby reducing the number of beams to scan. Simulations show that our proposed radar-enhanced MAB reduces the exploration time by searching only the beams with distinct radar mobile targets resulting in improved throughput.Comment: 5 pages, 6 figure

Near-Real-Time Global Biomass Burning Emissions Product from Geostationary Satellite Constellation

Near-real-time estimates of biomass burning emissions are crucial for air quality monitoring and forecasting. We present here the first near-real-time global biomass burning emission product from geostationary satellites (GBBEP-Geo) produced from satellite-derived fire radiative power (FRP) for individual fire pixels. Specifically, the FRP is retrieved using WF_ABBA V65 (wildfire automated biomass burning algorithm) from a network of multiple geostationary satellites. The network consists of two Geostationary Operational Environmental Satellites (GOES) which are operated by the National Oceanic and Atmospheric Administration, the Meteosat second-generation satellites (Meteosat-09) operated by the European Organisation for the Exploitation of Meteorological Satellites, and the Multifunctional Transport Satellite (MTSAT) operated by the Japan Meteorological Agency. These satellites observe wildfires at an interval of 15–30 min. Because of the impacts from sensor saturation, cloud cover, and background surface, the FRP values are generally not continuously observed. The missing observations are simulated by combining the available instantaneous FRP observations within a day and a set of representative climatological diurnal patterns of FRP for various ecosystems. Finally, the simulated diurnal variation in FRP is applied to quantify biomass combustion and emissions in individual fire pixels with a latency of 1 day. By analyzing global patterns in hourly biomass burning emissions in 2010, we find that peak fire season varied greatly and that annual wildfires burned 1.33 × 1012 kg dry mass, released 1.27 × 1010 kg of PM2.5 (particulate mass for particles with diameter \u3c2.5 μm) and 1.18 × 1011kg of CO globally (excluding most parts of boreal Asia, the Middle East, and India because of no coverage from geostationary satellites). The biomass burning emissions were mostly released from forest and savanna fires in Africa, South America, and North America. Evaluation of emission result reveals that the GBBEP-Geo estimates are comparable with other FRP-derived estimates in Africa, while the results are generally smaller than most of the other global products that were derived from burned area and fuel loading. However, the daily emissions estimated from GOES FRP over the United States are generally consistent with those modeled from GOES burned area and MODIS (Moderate Resolution Imaging Spectroradiometer) fuel loading, which produces an overall bias of 5.7% and a correlation slope of 0.97 ± 0.2. It is expected that near-real-time hourly emissions from GBBEP-Geo could provide a crucial component for atmospheric and chemical transport modelers to forecast air quality and weather conditions

Changing trends in circulating rotavirus strains in Pune, western India in 2009–2012: Emergence of a rare G9P[4] rotavirus strain

AbstractBackgroundA vast diversity in rotaviruses at inter- and intra-genotypic level underscores the need for monitoring of circulating rotavirus strains. The aim of this study was to update the data on rotavirus disease and strains for the period from January 2009 to December 2012 in Pune, western India which has been one of the sites of the Indian Rotavirus Strain Surveillance Network since November 2005.MethodsChildren aged <5 years admitted for acute gastroenteritis in three different hospitals from Pune city were included in the study. The stool specimens were collected and tested for rotavirus antigen by a commercial enzyme immunoassay. The rotavirus strains were genotyped by multiplex reverse transcription polymerase chain reaction.ResultsDuring the study period, we found 35.1% of 685 stool specimens contained rotavirus antigen. Frequency of rotavirus detection was greatest (58.5%) among children aged 7–12 months. The G1P[8] (31.4%), G2P[4] (20.2%) and G9P[8] (11.8%) strains were the most common types. We noted predominance of G1P[8] strains (39.6%-46.1%) in all the years of study except 2009 wherein G9P[8] strains scored highest level (15.3%). Subsequent to this, we identified G9P[8] strains at the second highest position in 2010, their sudden decline and rise in G9P[4] strains in 2011–2012. We detected G12 strains in combination with P[6] and P[8] at variable rates (0–10.2%) and highest level (27.1%) of mixed rotavirus infections in 2009 as compared to 2010–2012 (0–3.8%).ConclusionThe study highlights the huge burden of rotavirus disease and changing profile of circulating rotavirus strains displaying emergence of G9P[4] reassortant strains in Pune, western India and emphasizes the need to analyze the entire genomic constellation of rotavirus strains for better evaluation of the impact of rotavirus