Switchable filtering in vivaldi antenna

Presented is a new frequency switchable Vivaldi antenna that has a capability to operate in a wideband mode (1-3 GHz) and reconfigure to six different subbands of operations. The reconfiguration is realised by coupling and changing the effective electrical length of ring slots inserted in the structure by means of pin diode switches. To examine antenna performances, simulated and measured results are presented. Good impedance matches and radiation patterns have been achieved. The proposed antenna is suitable for wideband and multimode radio applications

Integration of Absolute Orientation Measurements in the KinectFusion Reconstruction pipeline

In this paper, we show how absolute orientation measurements provided by low-cost but high-fidelity IMU sensors can be integrated into the KinectFusion pipeline. We show that integration improves both runtime, robustness and quality of the 3D reconstruction. In particular, we use this orientation data to seed and regularize the ICP registration technique. We also present a technique to filter the pairs of 3D matched points based on the distribution of their distances. This filter is implemented efficiently on the GPU. Estimating the distribution of the distances helps control the number of iterations necessary for the convergence of the ICP algorithm. Finally, we show experimental results that highlight improvements in robustness, a speed-up of almost 12%, and a gain in tracking quality of 53% for the ATE metric on the Freiburg benchmark.Comment: CVPR Workshop on Visual Odometry and Computer Vision Applications Based on Location Clues 201

Coarse-graining the dynamics of coupled oscillators

We present an equation-free computational approach to the study of the coarse-grained dynamics of {\it finite} assemblies of {\it non-identical} coupled oscillators at and near full synchronization. We use coarse-grained observables which account for the (rapidly developing) correlations between phase angles and oscillator natural frequencies. Exploiting short bursts of appropriately initialized detailed simulations, we circumvent the derivation of closures for the long-term dynamics of the assembly statistics.Comment: accepted for publication in Phys. Rev. Let

Multi-class pattern classification in imbalanced data

The majority of multi-class pattern classification techniques are proposed for learning from balanced datasets. However, in several real-world domains, the datasets have imbalanced data distribution, where some classes of data may have few training examples compared for other classes. In this paper we present our research in learning from imbalanced multi-class data and propose a new approach, named Multi-IM, to deal with this problem. Multi-IM derives its fundamentals from the probabilistic relational technique (PRMs-IM), designed for learning from imbalanced relational data for the two-class problem. Multi-IM extends PRMs-IM to a generalized framework for multi-class imbalanced learning for both relational and non-relational domains.<br /

Rethinking the Pipeline of Demosaicing, Denoising and Super-Resolution

Incomplete color sampling, noise degradation, and limited resolution are the three key problems that are unavoidable in modern camera systems. Demosaicing (DM), denoising (DN), and super-resolution (SR) are core components in a digital image processing pipeline to overcome the three problems above, respectively. Although each of these problems has been studied actively, the mixture problem of DM, DN, and SR, which is a higher practical value, lacks enough attention. Such a mixture problem is usually solved by a sequential solution (applying each method independently in a fixed order: DM $\to$ DN $\to$ SR), or is simply tackled by an end-to-end network without enough analysis into interactions among tasks, resulting in an undesired performance drop in the final image quality. In this paper, we rethink the mixture problem from a holistic perspective and propose a new image processing pipeline: DN $\to$ SR $\to$ DM. Extensive experiments show that simply modifying the usual sequential solution by leveraging our proposed pipeline could enhance the image quality by a large margin. We further adopt the proposed pipeline into an end-to-end network, and present Trinity Enhancement Network (TENet). Quantitative and qualitative experiments demonstrate the superiority of our TENet to the state-of-the-art. Besides, we notice the literature lacks a full color sampled dataset. To this end, we contribute a new high-quality full color sampled real-world dataset, namely PixelShift200. Our experiments show the benefit of the proposed PixelShift200 dataset for raw image processing.Comment: Code is available at: https://github.com/guochengqian/TENe

Learning in imbalanced relational data

Traditional learning techniques learn from flat data files with the assumption that each class has a similar number of examples. However, the majority of real-world data are stored as relational systems with imbalanced data distribution, where one class of data is over-represented as compared with other classes. We propose to extend a relational learning technique called Probabilistic Relational Models (PRMs) to deal with the imbalanced class problem. We address learning from imbalanced relational data using an ensemble of PRMs and propose a new model: the PRMs-IM. We show the performance of PRMs-IM on a real university relational database to identify students at risk

Locating NAPLs in Ground Water Using Partitioning Fluorescent Dyes

A major challenge in ground water remediation is locating nonaqueous phase liquids (NAPLs). Partitioning tracers can be used to identify NAPL sources between injection and extraction wells. NAPLs are only slightly soluble in water, pose a long-term source of groundwater contamination, and can be difficult to remove. The complexity of recovery processes requires the development of new technologies that guarantee cost effective methods for locating and quantifying NAPLs. Traditional methods like soil coring have been inefficient since they underestimate the quantity of NAPLs and are expensive. Partitioning tracer tests are some of the most recent methods developed for locating these contaminants and determining the volume of the NAPL present in the inter-well zone. The results of the tests can be used to develop remediation techniques to recover NAPLs entrapped in the contaminated zone. Fluorescent dyes may be useful as partitioning tracers. They can be analyzed quickly at the field site, resulting in a shorter analysis time and lower costs than other partitioning tracers. This project pursued the selection of suitable tracers and the development of partitioning tracer techniques to locate and quantify NAPLs in the subsurface

Enantioselective Gas Chromatographic Analysis of Cyclopropane Derivatives

Chirasil-β-Dex was used as chiral stationary phase for the enantioselective gas chromatographic analysis of several new chiral cyclopropane derivatives. The GC method provides information about the chemical yields of the cyclopropane products, enantioselectivity, substrate specifity, and catalytic activity of the chiral catalysts used in the inter- and intra-molecular cyclopropanation reactions and avoids time-consuming work-up procedure

Иерархия факторов, мотивирующих деятельность человека в социально-экономической системе

Приведены результаты исследований потребностей как мотивирующих факторов деятельности человека. Многообразие мотивирующих факторов человеческой деятельности представлено в виде структуры витально-социальных (Пирамида Маслоу), социально-экономических и производственных потребностей, применимой к изучению сложных социально-экономических процессов и явлений

Exhaustive and Efficient Constraint Propagation: A Semi-Supervised Learning Perspective and Its Applications

This paper presents a novel pairwise constraint propagation approach by decomposing the challenging constraint propagation problem into a set of independent semi-supervised learning subproblems which can be solved in quadratic time using label propagation based on k-nearest neighbor graphs. Considering that this time cost is proportional to the number of all possible pairwise constraints, our approach actually provides an efficient solution for exhaustively propagating pairwise constraints throughout the entire dataset. The resulting exhaustive set of propagated pairwise constraints are further used to adjust the similarity matrix for constrained spectral clustering. Other than the traditional constraint propagation on single-source data, our approach is also extended to more challenging constraint propagation on multi-source data where each pairwise constraint is defined over a pair of data points from different sources. This multi-source constraint propagation has an important application to cross-modal multimedia retrieval. Extensive results have shown the superior performance of our approach.Comment: The short version of this paper appears as oral paper in ECCV 201