A Radar Sensing Algorithm by Gabor Theory

In this paper, an alternative Target Density Function( TDF) is proposed for narrowband radar model. This is achieved by estimating a new target density function by Gabor theory. It is shown how Gabor transform can be used to obtaining wideband target density function by transmitting a waveform which is a kernel for this transform. The windowing characteristics of this theory is plausible to reaching an accurate result. The presented wideband target density function is developed in a various manner different from the conventional methods

An Approach to Active Sensor Imaging

In this paper, an alternative Target Density Function (TDF) is proposed to image the radar targets in a dense target environment. It is obtained by considering a novel range and scanning angle plane different from the conventional methods. An alternative method is briefly proposed for smoothing the target density function by taking advantage of Walsh functions. Although the imaging is obtained via the phased array radars, the problem associated with beamforming in linear phased array radar system is bypassed in this new algorithm

An Alternative Target Density Function for Radar Imaging

In this paper, an alternative Target Density Function (TDF) is proposed to image the radar targets in a dense target environment. It is produced by wavelet theory considering a new range and angle plane different from the conventional methods. It is shown that Wavelet theory can be used as approach to imaging by active sensors by transmitting a waveform which is a kernel for this transform such as a window function. Although the imaging is obtained via the phased array radars, the problem associated with beamforming in linear phased array radar system is bypassed in this new algorithm

Input variable selection in time-critical knowledge integration applications: A review, analysis, and recommendation paper

This is the post-print version of the final paper published in Advanced Engineering Informatics. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2013 Elsevier B.V.The purpose of this research is twofold: first, to undertake a thorough appraisal of existing Input Variable Selection (IVS) methods within the context of time-critical and computation resource-limited dimensionality reduction problems; second, to demonstrate improvements to, and the application of, a recently proposed time-critical sensitivity analysis method called EventTracker to an environment science industrial use-case, i.e., sub-surface drilling. Producing time-critical accurate knowledge about the state of a system (effect) under computational and data acquisition (cause) constraints is a major challenge, especially if the knowledge required is critical to the system operation where the safety of operators or integrity of costly equipment is at stake. Understanding and interpreting, a chain of interrelated events, predicted or unpredicted, that may or may not result in a specific state of the system, is the core challenge of this research. The main objective is then to identify which set of input data signals has a significant impact on the set of system state information (i.e. output). Through a cause-effect analysis technique, the proposed technique supports the filtering of unsolicited data that can otherwise clog up the communication and computational capabilities of a standard supervisory control and data acquisition system. The paper analyzes the performance of input variable selection techniques from a series of perspectives. It then expands the categorization and assessment of sensitivity analysis methods in a structured framework that takes into account the relationship between inputs and outputs, the nature of their time series, and the computational effort required. The outcome of this analysis is that established methods have a limited suitability for use by time-critical variable selection applications. By way of a geological drilling monitoring scenario, the suitability of the proposed EventTracker Sensitivity Analysis method for use in high volume and time critical input variable selection problems is demonstrated.E

Machine-Part cell formation through visual decipherable clustering of Self Organizing Map

Machine-part cell formation is used in cellular manufacturing in order to process a large variety, quality, lower work in process levels, reducing manufacturing lead-time and customer response time while retaining flexibility for new products. This paper presents a new and novel approach for obtaining machine cells and part families. In the cellular manufacturing the fundamental problem is the formation of part families and machine cells. The present paper deals with the Self Organising Map (SOM) method an unsupervised learning algorithm in Artificial Intelligence, and has been used as a visually decipherable clustering tool of machine-part cell formation. The objective of the paper is to cluster the binary machine-part matrix through visually decipherable cluster of SOM color-coding and labelling via the SOM map nodes in such a way that the part families are processed in that machine cells. The Umatrix, component plane, principal component projection, scatter plot and histogram of SOM have been reported in the present work for the successful visualization of the machine-part cell formation. Computational result with the proposed algorithm on a set of group technology problems available in the literature is also presented. The proposed SOM approach produced solutions with a grouping efficacy that is at least as good as any results earlier reported in the literature and improved the grouping efficacy for 70% of the problems and found immensely useful to both industry practitioners and researchers.Comment: 18 pages,3 table, 4 figure

Mycobacterial catalase–peroxidase is a tissue antigen and target of the adaptive immune response in systemic sarcoidosis

Sarcoidosis is a disease of unknown etiology characterized by noncaseating epithelioid granulomas, oligoclonal CD4+ T cell infiltrates, and immune complex formation. To identify pathogenic antigens relevant to immune-mediated granulomatous inflammation in sarcoidosis, we used a limited proteomics approach to detect tissue antigens that were poorly soluble in neutral detergent and resistant to protease digestion, consistent with the known biochemical properties of granuloma-inducing sarcoidosis tissue extracts. Tissue antigens with these characteristics were detected with immunoglobulin (Ig)G or F(ab′)2 fragments from the sera of sarcoidosis patients in 9 of 12 (75%) sarcoidosis tissues (150–160, 80, or 60–64 kD) but only 3 of 22 (14%) control tissues (all 62–64 kD; P = 0.0006). Matrix-assisted laser desorption/ionization time of flight mass spectrometry identified Mycobacterium tuberculosis catalase–peroxidase (mKatG) as one of these tissue antigens. Protein immunoblotting using anti-mKatG monoclonal antibodies independently confirmed the presence of mKatG in 5 of 9 (55%) sarcoidosis tissues but in none of 14 control tissues (P = 0.0037). IgG antibodies to recombinant mKatG were detected in the sera of 12 of 25 (48%) sarcoidosis patients compared with 0 of 11 (0%) purified protein derivative (PPD)− (P = 0.0059) and 4 of 10 (40%) PPD+ (P = 0.7233) control subjects, suggesting that remnant mycobacterial catalase–peroxidase is one target of the adaptive immune response driving granulomatous inflammation in sarcoidosis

First Dark Matter Results from the XENON100 Experiment

The XENON100 experiment, in operation at the Laboratori Nazionali del Gran Sasso in Italy, is designed to search for dark matter WIMPs scattering off 62 kg of liquid xenon in an ultra-low background dual-phase time projection chamber. In this letter, we present first dark matter results from the analysis of 11.17 live days of non-blind data, acquired in October and November 2009. In the selected fiducial target of 40 kg, and within the pre-defined signal region, we observe no events and hence exclude spin-independent WIMP-nucleon elastic scattering cross-sections above 3.4 x 10^-44 cm^2 for 55 GeV/c^2 WIMPs at 90% confidence level. Below 20 GeV/c^2, this result constrains the interpretation of the CoGeNT and DAMA signals as being due to spin-independent, elastic, light mass WIMP interactions.Comment: 5 pages, 5 figures. Matches published versio