Effects of multiple laser shock processing (LSP) impacts on mechanical properties and wear behaviors of AISI 8620 steel

The aim of this paper was to address the effects of multiple laser shock processing (LSP) impacts with different pulse energy on mechanical properties and wear behaviors of AISI 8620 steel. Wear analyses were conducted by means of calculation of volume loss and scanning electron microscope (SEM) of the wear surface. Surface profiles, roughness and micro-hardness were measured. The micro-structures in the surface layer of the untreated and LSPed samples (treated by multiple LSP impacts) were investigated by using transmission electron microscopy (TEM) observations. Experimental results and analyses indicated that multiple LSP impacts can remarkably improve the wear resistance of AISI 8620 steel, and the wear mechanism of multiple LSP impacts on AISI 8620 steel was also entirely revealed. The wear process of the unpolished sample subjected to multiple LSP impacts can be described as follows: the wear rate was big at the beginning of sliding dry wear, but then decreased after the micro-indention in the sample surface was polished to the disappear. This phenomenon can be attributed to the fact that multiple LSP impacts generate many micro-indents in the sample surface.

Joint learning and dictionary construction for pattern recognition

We propose a joint representation and classification framework that achieves the dual goal of finding the most discriminative sparse overcomplete encoding and optimal classifier parameters. Formulating an optimization problem that combines the objective function of the classification with the representation error of both labeled and unlabeled data, constrained by sparsity, we propose an algorithm that alternates between solving for subsets of parameters, whilst preserving the sparsity. The method is then evaluated over two important classification problems in computer vision: object categorization of natural images using the Caltech 101 database and face recognition using the Extended Yale B face database. The results show that the proposed method is competitive against other recently proposed sparse overcomplete counterparts and considerably outperforms many recently proposed face recognition techniques when the number training samples is small.

Human identification from ECG signals via sparse representation of local segments

This work proposes a novel framework to extract compact and discriminative features from Electrocardiogram (ECG) signals for human identification based on sparse representation of local segments. Specifically, local segments extracted from an ECG signal are projected to a small number of basic elements in a dictionary, which is learned from training data. A final representation is extracted by performing a max pooling procedure over all the sparse coefficient vectors in the ECG signal. Unlike most of existing methods for human identification from ECG signals which require segmentation of individual heartbeats or extraction of fiducial points, the proposed method does not need to segment individual heartbeats or detect any fiducial points. The method achieves an 99.48% accuracy on a 100 subjects dataset constructed from a publicly available database, which demonstrates that both local and global structural information are well captured to characterize the ECG signals.

The dictionary

The Portia Geach Memorial Award is a celebration of female Australian artists. It’s Australia’s most prestigious portrait prize for female artists and has greatly contributed to the development of female artists in this country. It was established by Florence Kate Geach in 1961 in memory of her sister, Portia Geach and is awarded each year to the best portrait painted from life of some man or woman distinguished in Art, Letters or the Sciences. Our judges this year are Jane Watters, Director S.H. Ervin Gallery, Dr Lindy Lee, Senior Lecturer, Sydney College of the Arts and Ben Quilty, Artist. A media release with the full list of finalists will be distributed tomorrow morning with further details available at www.thetrustcompany.com.au/portiageach. The artwork The Dictionary will be hung in the S.H. Ervin Gallery from 3 October until 16 November, with the exhibition open from 4 October, 1st Prize money $30,000

Sparse Representations of Image via Overcomplete Dictionary Learned by Adaptive Non-orthogonal Sparsifying Transform

How to learn an over complete dictionary for sparse representations of image is an important topic in machine learning, sparse coding, blind source separation, etc. The so-called K-singular value decomposition (K-SVD) method [3] is powerful for this purpose, however, it is too time-consuming to apply. Recently, an adaptive orthogonal sparsifying transform (AOST) method has been developed to learn the dictionary that is faster. However, the corresponding coefficient matrix may not be as sparse as that of K-SVD. For solving this problem, in this paper, a non-orthogonal iterative match method is proposed to learn the dictionary. By using the approach of sequentially extracting columns of the stacked image blocks, the non-orthogonal atoms of the dictionary are learned adaptively, and the resultant coefficient matrix is sparser. Experiment results show that the proposed method can yield effective dictionaries and the resulting image representation is sparser than AOST.

Junior English - Arabic Maths Dictionary

Junior English - Arabic Maths Dictionary

When compressive sensing meets data hiding

Personal use is permitted.We present a novel framework of performing multimedia data hiding using an over-complete dictionary, which brings compressive sensing to the application of data hiding. Unlike the conventional orthonormal full-space dictionary, the over-complete dictionary produces an underdetermined system with infinite transform results. We first discuss the minimum norm formulation (ℓ2-norm) which yields a closed-form solution and the concept of watermark projection, so that higher embedding capacity and an additional privacy preserving feature can be obtained. Furthermore, we study the sparse formulation (ℓ0-norm) and illustrate that as long as the ℓ0-norm of the sparse representation of the host signal is less than the signal's dimension in the original domain, an informed sparse domain data hiding system can be established by modifying the coefficients of the atoms that have not participated in representing the host signal. A single support modification-based data hiding system is then proposed and analyzed as an example. Several potential research directions are discussed for further studies. More generally, apart from the ℓ2 and ℓ0-norm constraints, other conditions for reliable detection performance are worth of future investigation.