Swedish Defence Research Agency

About: Swedish Defence Research Agency is a government organization based out in Stockholm, Sweden. It is known for research contribution in the topics: Radar & Synthetic aperture radar. The organization has 1413 authors who have published 2731 publications receiving 56083 citations. The organization is also known as: Totalförsvarets forskningsinstitut.

Evaluation of a micro-Doppler classification method on mm-wave data

Abstract: Radar micro-Doppler signatures (MDS), which show how different parts of the target move, can be utilized for security and safety applications like detection and assessment of human activity at airports, nuclear power plants etc. We have evaluated a MDS classification method on measured data at 77 GHz. The important part of the method is the feature extraction, which is based on selecting the strongest parts of a Cadence-Velocity Diagram (CVD), which expresses how the curves in the MDS repeat. By our classification of MDSs of human gaits we study also how MDSs of more general target types and activities can be distinguished. We have analyzed and improved the method. The method is sound with good classification results but needs further evaluations and improvements.

Hyperspectral image analysis using deep learning — A review

Abstract: Deep learning is a rather new approach to machine learning that has achieved remarkable results in a large number of different image processing applications. Lately, application of deep learning to detect and classify spectral and spatio-spectral signatures in hyperspectral images has emerged. The high dimensionality of hyperspectral images and the limited amount of labelled training data makes deep learning an appealing approach for analysing hyperspectral data. Auto-Encoder can be used to learn a hierarchical feature representation using solely unlabelled data, the learnt representation can be combined with a logistic regression classifier to achieve results in-line with existing state-of-the-art methods. In this paper, we compare results between a set of available publications and find that deep learning perform in line with state-of-the-art on many data sets but little evidence exists that deep learning outperform the reference methods.

Separation of plutonium from soil and sediment prior to determination by inductively coupled plasma mass spectrometry

Abstract: The purpose of this work was to develop an optimised sample-preparation procedure for the determination of Pu in soil/sediment with ICP-MS. To start with, several different procedures were screened for their ability to separate plutonium and remove uranium. After the screening, two methods were applied on one soil (IAEA Soil6) and two sediment reference materials (IAEA300 and IAEA135). These methods were based on separation of Pu using TEVA and a combination of UTEVA and TRU resins, followed by elution of Pu with 0.1% hydroxylethylidene diphosphonic acid (HEDPA). A comparison was also made between sample preparation based on acid-leaching and complete digestion using lithium borate fusion. The highest yield of Pu (80%) was found with the procedure consisting of fusion followed by TEVA, while the decontamination from U showed large variations (RSD varying from 16–52%) with all procedures. No difference in the recovery of Pu was found between the two sample preparation techniques. The results of the quantitative determination in low resolution of 239Pu and 240Pu from the UTEVA + TRU-separation were significantly higher than those obtained by the TEVA procedure. An analysis in higher mass resolution displayed interfering peaks in the mass-region of Pu, and lanthanide-containing polyatomic ions were found to be a likely cause for these interferences. The procedure based on lithium borate fusion and separation using the TEVA-resin avoided such interferences and was therefore tested for repeatability over time on IAEA300. The stability of the method was good (RSD = 2.49% (n = 8)), with the exception of one value being significantly higher than the others. This result was confirmed by analysis in higher mass-resolution, which indicates an inhomogeneous distribution of Pu in the reference material, despite a sample intake of about 1 g.

Soman-Induced Interleukin-1β mRNA and Protein in Rat Brain

Abstract: Exposure to high doses of the toxic organophosphate compound soman, also known as a chemical warfare agent, causes a progression of toxic symptoms including hyper-secretions, convulsions, respiratory depression, and finally death. In previous studies, we have demonstrated pronounced effects following soman intoxication in dopaminergic, GABAergic, and cholinergic systems in rat brain. The aim of this study was to investigate the effects on the pro-inflammatory cytokine interleukin-1beta (IL-1beta), indicated as mRNA and protein production, at different time intervals following soman intoxication. The peak levels of mRNA was observed 30 min following soman exposure, while a significant increase in the protein was observed at 6 h. Immunohistochemistry analysis revealed the presence of IL-1beta protein in astrocytes and endothelial cells. In addition to the previously observed effects of soman, there is an induction of IL-1beta in the brain. This effect, which is highly correlated to convulsions, implicates IL-1beta as a possible mediator for long-term brain damage observed after soman intoxication.