Multistatic human micro-Doppler classification of armed/unarmed personnel

Classification of different human activities using multistatic micro-Doppler data and features is considered in this paper, focusing on the distinction between unarmed and potentially armed personnel. A database of real radar data with more than 550 recordings from 7 different human subjects has been collected in a series of experiments in the field with a multistatic radar system. Four key features were extracted from the micro-Doppler signature after Short Time Fourier Transform analysis. The resulting feature vectors were then used as individual, pairs, triplets, and all together before inputting to different types of classifiers based on the discriminant analysis method. The performance of different classifiers and different feature combinations is discussed aiming at identifying the most appropriate features for the unarmed vs armed personnel classification, as well as the benefit of combining multistatic data rather than using monostatic data only

Personnel recognition based on multistatic micro-Doppler and singular value decomposition features

The use of micro-Doppler signatures experimentally collected by a multistatic radar system to recognise and classify different people walking is discussed. A suitable feature based on singular value decomposition of the spectrograms is proposed and tested with different types of classifiers. It is shown that high accuracy of between 97 and 99% can be achieved when multistatic data are used to perform the classification

Genome-wide tests for introgression between cactophilic Drosophila implicate a role of inversions during speciation

K.L. was funded by a junior research fellowship from the National Environmental Research Council, UK (NE/I020288/1, NBAF659).Models of speciation-with-gene-flow have shown that the reduction in recombination between alternative chromosome arrangements can facilitate the fixation of locally adaptive genes in the face of gene flow and contribute to speciation. However, it has proven frustratingly difficult to show empirically that inversions have reduced gene flow and arose during or shortly after the onset of species divergence rather than represent ancestral polymorphisms. Here, we present an analysis of whole genome data from a pair of cactophilic fruit flies, Drosophila mojavensis and D. arizonae, which are reproductively isolated in the wild and differ by several large inversions on three chromosomes. We found an increase in divergence at rearranged compared to colinear chromosomes. Using the density of divergent sites in short sequence blocks we fit a series of explicit models of species divergence in which gene flow is restricted to an initial period after divergence and may differ between colinear and rearranged parts of the genome. These analyses show that D. mojavensis and D. arizonae have experienced postdivergence gene flow that ceased around 270 KY ago and was significantly reduced in chromosomes with fixed inversions. Moreover, we show that these inversions most likely originated around the time of species divergence which is compatible with theoretical models that posit a role of inversions in speciation with gene flow.Publisher PDFPeer reviewe

Experimental analysis of multistatic multiband radar signatures of wind turbines

This study presents the analysis of recent experimental data acquired using two radar systems at S-band and X-band to measure simultaneous monostatic and bistatic signatures of operational wind turbines near Shrivenham, UK. Bistatic and multistatic radars are a potential approach to mitigate the adverse effects of wind farm clutter on the performance of radar systems, which is a well-known problem for air traffic control and air defence radar. This analysis compares the simultaneous monostatic and bistatic micro-Doppler signatures of two operational turbines and investigates the key differences at bistatic angles up to 23°. The variations of the signature with different polarisations, namely vertical transmitted and vertical received and horizontal transmitted and horizontal received, are also discussed

FreezeOut: Accelerate Training by Progressively Freezing Layers

The early layers of a deep neural net have the fewest parameters, but take up the most computation. In this extended abstract, we propose to only train the hidden layers for a set portion of the training run, freezing them out one-by-one and excluding them from the backward pass. Through experiments on CIFAR, we empirically demonstrate that FreezeOut yields savings of up to 20% wall-clock time during training with 3% loss in accuracy for DenseNets, a 20% speedup without loss of accuracy for ResNets, and no improvement for VGG networks. Our code is publicly available at https://github.com/ajbrock/FreezeOutComment: Extended Abstrac

Precursor ion scanning for detection and structural characterization of heterogeneous glycopeptide mixtures

AbstractThe structure of N-linked glycans is determined by a complex, anabolic, intracellular pathway but the exact role of individual glycans is not always clear. Characterization of carbohydrates attached to glycoproteins is essential to aid understanding of this complex area of biology. Specific mass spectral detection of glycopeptides from protein digests may be achieved by on-line HPLC-MS, with selected ion monitoring (SIM) for diagnostic product ions generated by cone voltage fragmentation, or by precursor ion scanning for terminal saccharide product ions, which can yield the same information more rapidly. When glycosylation is heterogeneous, however, these approaches can result in spectra that are complex and poorly resolved. We have developed methodology, based around precursor ion scanning for ions of high m/z, that allows site specific detection and structural characterization of glycans at high sensitivity and resolution. These methods have been developed using the standard glycoprotein, fetuin, and subsequently applied to the analysis of the N-linked glycans attached to the scrapie-associated prion protein, PrPSc. These glycans are highly heterogeneous and over 30 structures have been identified and characterized site specifically. Product ion spectra have been obtained on many glycopeptides confirming structure assignments. The glycans are highly fucosylated and carry Lewis X or sialyl Lewis X epitopes and the structures are in-line with previous results. [Abbreviations: Hex–Hexose, C6H12O6 carbohydrates, including mannnose and galactose; HexNAc—N-acetylhexosamine, C8H15NO6 carbohydrates, including N-acetylglucosamine and N-acetylgalactosamine; GlcNAc—N-acetylglucosamine; GalNAc—N-acetylgalactosamine; Fuc–Fucose; NeuAC—N-acetylneuraminic acid or sialic acid; TSE—Transmissible Spongiform Encephalopathy.

SMASH: One-Shot Model Architecture Search through HyperNetworks

Designing architectures for deep neural networks requires expert knowledge and substantial computation time. We propose a technique to accelerate architecture selection by learning an auxiliary HyperNet that generates the weights of a main model conditioned on that model's architecture. By comparing the relative validation performance of networks with HyperNet-generated weights, we can effectively search over a wide range of architectures at the cost of a single training run. To facilitate this search, we develop a flexible mechanism based on memory read-writes that allows us to define a wide range of network connectivity patterns, with ResNet, DenseNet, and FractalNet blocks as special cases. We validate our method (SMASH) on CIFAR-10 and CIFAR-100, STL-10, ModelNet10, and Imagenet32x32, achieving competitive performance with similarly-sized hand-designed networks. Our code is available at https://github.com/ajbrock/SMAS

Generative and Discriminative Voxel Modeling with Convolutional Neural Networks

When working with three-dimensional data, choice of representation is key. We explore voxel-based models, and present evidence for the viability of voxellated representations in applications including shape modeling and object classification. Our key contributions are methods for training voxel-based variational autoencoders, a user interface for exploring the latent space learned by the autoencoder, and a deep convolutional neural network architecture for object classification. We address challenges unique to voxel-based representations, and empirically evaluate our models on the ModelNet benchmark, where we demonstrate a 51.5% relative improvement in the state of the art for object classification.Comment: 9 pages, 5 figures, 2 table

Some interactions among driver, vehicle, and roadway variables in normal driving

Effects of road and vehicle conditions, visual warning signs, direction of turns, night time, and skill on automobile driver performance are studied in several experiments. Considered criteria are variability in speed and acceleration

Multi-Frequency Micro-Doppler Based Classification Of Micro-Drone Payload Weight

The use of drones for recreational, commercial and military purposes has seen a rapid increase in recent years. The ability of counter-drone detection systems to sense whether a drone is carrying a payload is of strategic importance as this can help determine the potential threat level posed by a detected drone. This paper presents the use of micro-Doppler signatures collected using radar systems operating at three different frequency bands for the classification of carried payload of two different micro-drones performing two different motions. Use of a KNN classifier with six features extracted from micro-Doppler signatures enabled mean payload classification accuracies of 80.95, 72.50 and 86.05%, for data collected at S-band, C-band and W-band, respectively, when the drone type and motion type are unknown. The impact on classification performance of different amounts of situational information is also evaluated in this paper