Automatic target detection and localization using ultra-wideband radar

<span>The pulse ultra-wide band (UWB) radar consists of switching of energy of very short duration in an ultra-broadband emission chain, and the UWB signal emitted is an ultrashort pulse, of the order of nanoseconds, without a carrier. These systems can indicate the presence and distances of a distant object, call a target, and determine its size, shape, speed, and trajectory. In this paper, we present a UWB radar system allowing the detection of the presence of a target and its localization in a road environment based on the principle of correlation of the reflected signal with the reference and the determination of its correlation peak.</span>

Determination of chemical shifts in 6-condensed syringylic lignin model compounds

A small library of 6-substituted syringyl model compounds with aliphatic, carboxylic, phenylic, benzylic alcohols and brominated substituents were prepared. The influence of the substituents on the chemical shifts of the compounds was analyzed. All of model compounds showed a characteristic increase in the 13C NMR chemical shift of the methoxy group vicinal to the substitution. This 13C NMR peak and its corresponding correlation peak in HSQC could potentially be used to identify 6-condensation in syringylic lignin samples.

Correlation dispersion as a measure to better estimate uncertainty of remotely sensed glacier displacements

In recent years a vast amount of glacier surface velocity data from satellite imagery has emerged based on correlation between repeat images. Thereby, much emphasis has been put on fast processing of large data volumes. The metadata of such measurements are often highly simplified when the measurement precision is lumped into a single number for the whole dataset, although the error budget of image matching is in reality not isotropic and constant over the whole velocity field. The spread of the correlation peak of individual image offset measurements is dependent on the image content and the non-uniform flow of the ice. Precise dispersion estimates for each individual velocity measurement can be important for inversion of, for instance, rheology, ice thickness and/or bedrock friction. Errors in the velocity data can propagate into derived results in a complex and exaggerating way, making the outcomes very sensitive to velocity noise and errors. Here, we present a computationally fast method to estimate the matching precision of individual displacement measurements from repeat imaging data, focussing on satellite data. The approach is based upon Gaussian fitting directly on the correlation peak and is formulated as a linear least squares estimation, making its implementation into current pipelines straightforward. The methodology is demonstrated for Sermeq Kujalleq, Greenland, a glacier with regions of strong shear flow and with clearly oriented crevasses, and Malaspina Glacier, Alaska. Directionality within an image seems to be dominant factor influencing the correlation dispersion. In our cases these are crevasses and moraine bands, while a relation to differential flow, such as shear, is less pronounced.

Optical detection of values of separate aberrations using a multi-channel filter matched with phase Zernike functions

The use of a multichannel wavefront sensor matched with phase Zernike functions to determine the type and magnitude of aberration in the analyzed wavefront is investigated. The approach is based on stepwise compensation of wavefront aberrations based on a dynamically tunable spatial light modulator. As criteria for successful detection, not only the magnitude of the correlation peak, but also the maximum intensity, compactness, and orientation of the distribution in each diffraction order are considered. On the basis of numerical simulation, the efficiency of the proposed approach is shown for detecting both weak and strong (up to a wavelength) wavefront aberrations.

Optical detection of values of separate aberrations using a multi-channel filter matched with phase Zernike functions

The use of a multichannel wavefront sensor matched with phase Zernike functions to determine the type and magnitude of aberration in the analyzed wavefront is investigated. The approach is based on stepwise compensation of wavefront aberrations based on a dynamically tunable spatial light modulator. As criteria for successful detection, not only the magnitude of the correlation peak, but also the maximum intensity, compactness, and orientation of the distribution in each diffraction order are considered. On the basis of numerical simulation, the efficiency of the proposed approach is shown for detecting both weak and strong (up to a wavelength) wavefront aberrations.

Particle Retracking Algorithm Capable of Quantifying Large, Local Matrix Deformation for Traction Force Microscopy

Deformation measurement is a key process in traction force microscopy (TFM). Conventionally, particle image velocimetry (PIV) or correlation-based particle tracking velocimetry (cPTV) have been used for such a purpose. Using simulated bead images, we show that those methods fail to capture large displacement vectors and that it is due to a poor cross-correlation. Here, to redeem the potential large vectors, we propose a two-step deformation tracking algorithm that combines cPTV, which performs better for small displacements than PIV methods and a newly-designed retracking algorithm that exploits statistically confident vectors from the initial cPTV to guide the selection of correlation peak which are not necessarily the global maximum. As a result, the new method, named ‘cPTV-Retracking’, or cPTVR, was able to track more than 92% of large vectors whereas conventional methods could track 43-77% of those. Correspondingly, traction force reconstructed from cPTVR showed better recovery of large traction than the old methods. cPTVR applied on the experimental bead images has shown a better resolving power of the traction with different-sized cell-matrix adhesions than conventional methods. Altogether, cPTVR method enhances the accuracy of TFM in the case of large deformations present in soft substrates. We share this advance via our TFMPackage software.

Bond Structures between Wood Components and Citric Acid in Wood-Based Molding

Citric acid-based wood adhesive is considered a chemical-bonding wood adhesive. However, the detailed structures of the bonds between wood components and citric acid remain unknown. Here, we examine the chemical bonding structures between citric acid and wood by heteronuclear single quantum coherence-nuclear magnetic resonance (HSQC-NMR) analysis of wood-based molding using Japanese cedar (Cryptomeria japonica) and citric acid. In the HSQC-NMR spectrum of the wood molding, some esterified C/H correlation peaks appeared. The primary hydroxyl groups of polysaccharides, such as cellulose and galactoglucomannan, and the primary hydroxyl groups of the β-O-4 and β-5 substructures in lignin were found to be esterified with citric acid. In contrast, the secondary hydroxyl groups, except for xylan, barely reacted because of the steric hindrance. Additionally, the C/H correlation peak volumes of the reducing ends of mannan and xylan in the anomeric region increased after molding. It was clarified that the glycosidic bonds in the hemicelluloses were cleaved under the acidic molding condition with citric acid. The HSQC-NMR analysis revealed that the esterification of hemicellulose and lignin, and degradation of hemicellulose, proceeded under the molding condition. These results will promote understanding of the adhesive mechanism of citric acid-based wood adhesive and of the properties of the molding.

Influence of Neighborhood Size and Cross-Correlation Peak-Fitting Method on Location Accuracy

A known technique to obtain subpixel resolution by using object tracking through cross-correlation consists of interpolating the obtained correlation function and then refining peak location. Although the technique provides accurate results, peak location is usually biased toward the closest integer coordinate. This effect is known as the peak-locking error and it strongly limits this calculation technique’s experimental accuracy. This error may differ depending on the scene and algorithm used to fit and interpolate the correlation peak, but in general, it may be attributed to a sampling problem and the presence of aliasing. Many studies in the literature analyze this effect in the Fourier domain. Here, we propose an alternative analysis on the spatial domain. According to our interpretation, the peak-locking error may be produced by a non-symmetrical sample distribution, thus provoking a bias in the result. According to this, the peak interpolant function, the size of the local domain and low-pass filters play a relevant role in diminishing the error. Our study explores these effects on different samples taken from the DIC Challenge database, and the results show that, in general, peak fitting with a Gaussian function on a relatively large domain provides the most accurate results.