Aberration Corrected Emittance Exchange

Full exploitation of emittance exchange (EEX) requires aberration-free performance of a complex imaging system including active radio-frequency (RF) elements which can add temporal distortions. We investigate the performance of an EEX line where the exchange occurs between two dimensions with normalized emittances which differ by multiple orders of magnitude. The transverse emittance is exchanged into the longitudinal dimension using a double dog-leg emittance exchange setup with a five cell RF deflector cavity. Aberration correction is performed on the four most dominant aberrations. These include temporal aberrations that are corrected with higher order magnetic optical elements located where longitudinal and transverse emittance are coupled. We demonstrate aberration-free performance of an EEX line with emittances differing by four orders of magnitude, \textit{i.e.} an initial transverse emittance of 1~pm-rad is exchanged with a longitudinal emittance of 10~nm-rad

Nano-modulated electron beams via electron diffraction and emittance exchange for coherent x-ray generation

We present a new method for generation of relativistic electron beams with current modulation on the nanometer scale and below. The current modulation is produced by diffracting relativistic electrons in single crystal Si, accelerating the diffracted beam and imaging the crystal structure, then transferring the image into the temporal dimension via emittance exchange. The modulation period can be tuned by adjusting electron optics after diffraction. This tunable longitudinal modulation can have a period as short as a few angstroms, enabling production of coherent hard x-rays from a source based on inverse Compton scattering with total accelerator length of approximately ten meters. Electron beam simulations from cathode emission through diffraction, acceleration and image formation with variable magnification are presented along with estimates of the coherent x-ray output properties

Spectrogram classification using dissimilarity space

In this work, we combine a Siamese neural network and different clustering techniques to generate a dissimilarity space that is then used to train an SVM for automated animal audio classification. The animal audio datasets used are (i) birds and (ii) cat sounds, which are freely available. We exploit different clustering methods to reduce the spectrograms in the dataset to a number of centroids that are used to generate the dissimilarity space through the Siamese network. Once computed, we use the dissimilarity space to generate a vector space representation of each pattern, which is then fed into an support vector machine (SVM) to classify a spectrogram by its dissimilarity vector. Our study shows that the proposed approach based on dissimilarity space performs well on both classification problems without ad-hoc optimization of the clustering methods. Moreover, results show that the fusion of CNN-based approaches applied to the animal audio classification problem works better than the stand-alone CNNs

Development of new FRP reinforcement for optimized concrete structures

With the goal of achieving sustainable design, being able to combine optimized geometries with durable construction materials is a major challenge for Civil Engineering. Recent research at the University of Bath has demonstrated that fibre-reinforced polymers (FRP) can be woven into geometrically appropriate cages for the reinforcement of optimised concrete beams. This innovative construction method enables the replacement of conventional steel with non-corrosive reinforcement that can provide the required strength exactly where needed. The manufacturing of the reinforcement is achieved by means of an automated process based on a filament winding technique. Being extremely lightweight, the wound-FRP (WFRP) cages are well suited to speeding up construction processes, as they can be delivered on site ready to be cast. In this paper, the results of flexural tests on optimised full-scale flexibly formed concrete elements are reported and discussed. Two different case studies are taken in consideration: A structurally optimized joist supporting a lightweight floor;A structurally optimized beam with an in-situ casting of a concrete floor. The optimization objective is to obtain the minimal mass of concrete required to achieve the structural capacity design requirements from widely recognized design codes. The experimental results demonstrate the reliability of the technical solution proposed and provide the basis of a new concept for sustainable and durable reinforced concrete structures

Filament winding fabrication of FRP reinforcement cages

This project has made progress towards the development of a novel alternative reinforcement technique for concrete structures with complex geometries, which are difficult to reinforce with conventional steel. Fibre-reinforced polymers (FRP) are woven into geometrically appropriate reinforcement cages to provide the required strength exactly where it is needed. Automated fabrication of the reinforcement utilises a modification of the filament winding technique. Being extremely lightweight, the resulting wound- FRP (W-FRP) cages are well suited to automation of the construction process, as they can be delivered ready for casting in optimized concrete elements. This is a key advance in research progress towards achieving minimum embodied energy, optimised, concrete structures. Experimental tests conducted on full-size W-FRP reinforced concrete beams demonstrate the reliability of the solution proposed, showing a new frontier for sustainable and durable reinforced concrete structures

Animal sound classification using dissimilarity spaces

The classifier system proposed in this work combines the dissimilarity spaces produced by a set of Siamese neural networks (SNNs) designed using four different backbones with different clustering techniques for training SVMs for automated animal audio classification. The system is evaluated on two animal audio datasets: one for cat and another for bird vocalizations. The proposed approach uses clustering methods to determine a set of centroids (in both a supervised and unsupervised fashion) from the spectrograms in the dataset. Such centroids are exploited to generate the dissimilarity space through the Siamese networks. In addition to feeding the SNNs with spectrograms, experiments process the spectrograms using the heterogeneous auto-similarities of characteristics. Once the similarity spaces are computed, each pattern is \u201cprojected\u201d into the space to obtain a vector space representation; this descriptor is then coupled to a support vector machine (SVM) to classify a spectrogram by its dissimilarity vector. Results demonstrate that the proposed approach performs competitively (without ad-hoc optimization of the clustering methods) on both animal vocalization datasets. To further demonstrate the power of the proposed system, the best standalone approach is also evaluated on the challenging Dataset for Environmental Sound Classification (ESC50) dataset

Postprocessing for skin detection

Skin detectors play a crucial role in many applications: face localization, person tracking, objectionable content screening, etc. Skin detection is a complicated process that involves not only the development of apposite classifiers but also many ancillary methods, including techniques for data preprocessing and postprocessing. In this paper, a new postprocessing method is described that learns to select whether an image needs the application of various morphological sequences or a homogeneity function. The type of postprocessing method selected is learned based on categorizing the image into one of eleven predetermined classes. The novel postprocessing method presented here is evaluated on ten datasets recommended for fair comparisons that represent many skin detection applications. The results show that the new approach enhances the performance of the base classifiers and previous works based only on learning the most appropriate morphological sequences

Feature transforms for image data augmentation

A problem with convolutional neural networks (CNNs) is that they require large datasets to obtain adequate robustness; on small datasets, they are prone to overfitting. Many methods have been proposed to overcome this shortcoming with CNNs. In cases where additional samples cannot easily be collected, a common approach is to generate more data points from existing data using an augmentation technique. In image classification, many augmentation approaches utilize simple image manipulation algorithms. In this work, we propose some new methods for data augmentation based on several image transformations: the Fourier transform (FT), the Radon transform (RT), and the discrete cosine transform (DCT). These and other data augmentation methods are considered in order to quantify their effectiveness in creating ensembles of neural networks. The novelty of this research is to consider different strategies for data augmentation to generate training sets from which to train several classifiers which are combined into an ensemble. Specifically, the idea is to create an ensemble based on a kind of bagging of the training set, where each model is trained on a different training set obtained by augmenting the original training set with different approaches. We build ensembles on the data level by adding images generated by combining fourteen augmentation approaches, with three based on FT, RT, and DCT, proposed here for the first time. Pretrained ResNet50 networks are finetuned on training sets that include images derived from each augmentation method. These networks and several fusions are evaluated and compared across eleven benchmarks. Results show that building ensembles on the data level by combining different data augmentation methods produce classifiers that not only compete competitively against the state-of-the-art but often surpass the best approaches reported in the literature