Nonlinear Stabilization of High-Energy and Ultrashort Pulses in Passively Modelocked Lasers with Fast Saturable Absorption

The two most commonly used models for passively modelocked lasers with fast saturable absorbers are the Haus modelocking equation (HME) and the cubic-quintic modelocking equation (CQME). The HME corresponds to a special limit of the CQME in which only a cubic nonlinearity in the fast saturable absorber is kept in the model. Here, we use singular perturbation theory to demonstrate that the CQME has a stable high-energy solution for an arbitrarily small but non-zero quintic contribution to the fast saturable absorber. As a consequence, we find that the CQME predicts the existence of stable modelocked pulses when the cubic nonlinearity is orders of magnitude larger than the value at which the HME predicts that modelocked pulses become unstable. This intrinsically larger stability range is consistent with experiments. Our results suggest a possible path to obtain high-energy and ultrashort pulses by fine tuning the higher-order nonlinear terms in the fast saturable absorber.Comment: 8 pages, 6 figures, submitted to PR

Matching Image Sets via Adaptive Multi Convex Hull

Traditional nearest points methods use all the samples in an image set to construct a single convex or affine hull model for classification. However, strong artificial features and noisy data may be generated from combinations of training samples when significant intra-class variations and/or noise occur in the image set. Existing multi-model approaches extract local models by clustering each image set individually only once, with fixed clusters used for matching with various image sets. This may not be optimal for discrimination, as undesirable environmental conditions (eg. illumination and pose variations) may result in the two closest clusters representing different characteristics of an object (eg. frontal face being compared to non-frontal face). To address the above problem, we propose a novel approach to enhance nearest points based methods by integrating affine/convex hull classification with an adapted multi-model approach. We first extract multiple local convex hulls from a query image set via maximum margin clustering to diminish the artificial variations and constrain the noise in local convex hulls. We then propose adaptive reference clustering (ARC) to constrain the clustering of each gallery image set by forcing the clusters to have resemblance to the clusters in the query image set. By applying ARC, noisy clusters in the query set can be discarded. Experiments on Honda, MoBo and ETH-80 datasets show that the proposed method outperforms single model approaches and other recent techniques, such as Sparse Approximated Nearest Points, Mutual Subspace Method and Manifold Discriminant Analysis.Comment: IEEE Winter Conference on Applications of Computer Vision (WACV), 201

The research on explosion suppression effect of aluminum alloy explosion-proof materials cleaned by ultrasonic

Premixed gas explosion pipe system was established to study the change rule of explosion pressure and pressure rise rate of 10% methane/ air premixed gas under four condition that no material was filled, used material was filled, new materials was filled and cleaned materials was filled in explosive pipe. The results show that compared with the used material and cleaned material, the average maximum explosion pressure was reduced by 21.62% and the average pressure rise rate decreased by 84.80%. The results show that the suppression performance of used aluminum alloy explosion-proof materials improved greatly after the used materials is cleaned

Robust Face Recognition for Data Mining

While the technology for mining text documents in large databases could be said to be relatively mature, the same cannot be said for mining other important data types such as speech, music, images and video. Yet these forms of multimedia data are becoming increasingly prevalent on the internet and intranets as bandwidth rapidly increases due to continuing advances in computing hardware and consumer demand. An emerging major problem is the lack of accurate and efficient tools to query these multimedia data directly, so we are usually forced to rely on available metadata such as manual labeling. Currently the most effective way to label data to allow for searching of multimedia archives is for humans to physically review the material. This is already uneconomic or, in an increasing number of application areas, quite impossible because these data are being collected much faster than any group of humans could meaningfully label them - and the pace is accelerating, forming a veritable explosion of non-text data. Some driver applications are emerging from heightened security demands in the 21st century, postproduction of digital interactive television, and the recent deployment of a planetary sensor network overlaid on the internet backbone

Person Location Service on the Planetary Sensor Network

This paper gives a prototype application which can provide a person location service on the IrisNet. Two crucial technologies face detection and face recognition underpinning such image and video data mining service are explained. For the face detection, authors use 4 types of simple rectangles as features, Adaboost as the learning algorithm to select the important features for classification, and finally generate a cascade of classifiers which is extremely fast on the face detection task. As for the face recognition, the authors develop Adaptive Principle Components Analysis (APCA) to improve the robustness of principal Components Analysis (PCA) to nuisance factors such as lighting and expression. APCA also can recognize faces from single face which is suitable in a data mining situatio

Illumination and Expression Invariant Face Recognition With One Sample Image

Most face recognition approaches either assume constant lighting condition or standard facial expressions, thus cannot deal with both kinds of variations simultaneously. This problem becomes more serious in applications when only one sample image per class is available. In this paper, we present a linear pattern classification algorithm, Adaptive Principal Component Analysis (APCA), which first applies PCA to construct a subspace for image representation; then warps the subspace according to the within-class covariance and between-class covariance of samples to improve class separability. This technique performed well under variations in lighting conditions. To produce insensitivity to expressions, we rotate the subspace before warping in order to enhance the representativeness of features. This method is evaluated on the Asian Face Image Database. Experiments show that APCA outperforms PCA and other methods in terms of accuracy, robustness and generalization ability