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

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

Fabronia Raddi (Musci) in Libya

Fabronia pusilla Raddi var. ciliata Lesq. & James is recorded for the first time from Libya. This record adds family Fabroniaceae to the moss flora of Libya and increases the number of taxa known from there to 107

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

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 /

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

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

A novel scheme for control by active and reactive power utilized in gearless variable speed wind turbine system with PMSG connected to the grid

Introduction. As a result of increasing fossil fuel price and state-of-the-art technology, more and more residential and commercial consumers of electricity have been installing wind turbines. The motivation being to cut energy bills and carbon dioxide emissions. Purpose. The main goal of this work is developing a control scheme for a variable speed wind turbine generator in order to produce utmost power from varying wind types, and variable wind speed. Novelty. This research paper presents an IGBT power converter control scheme for active power in relation to wind speed and reactive power by adjusting Q-reference (Qref) value in a gearless variable speed wind turbine with permanent magnet synchronous generator. Methods. An effective modelling and control of the wind turbine with the suggested power converter is executed by utilizing MATLAB/Simulink software. The control scheme consists of both the wind turbine control and the power converter control. Simulation results are utilized in the analysis and deliberation of the ability of the control scheme, which reveals that the wind turbine generator has the capability to actively sustain an electric power grid network, owing to its ability to independently control active and reactive power according to applied reference values at variable wind speed. Practical value. This research can be utilized for assessing the control methodology, the dynamic capabilities and influence of a gearless variable-speed wind energy conversion system on electric power grids. A case study has been presented with a (3×10 MW = 30 MW) wind farm scheme.Вступ. Внаслідок зростання цін на викопне паливо та використання найсучасніших технологій, дедалі більше побутових та комерційних споживачів електроенергії встановлюють вітряні турбіни. Мотивація полягає в тому, щоб скоротити рахунки за електроенергію та викиди вуглекислого газу. Мета. Основною метою цієї роботи є розробка схеми управління вітряним генератором зі змінною швидкістю для отримання максимальної потужності від різних типів вітру та змінної швидкості вітру. Новизна. У даній дослідницькій роботі представлена схема управління силовим IGBT перетворювачем для активної потужності в залежності від швидкості вітру та реактивної потужності шляхом регулювання значенняQ-еталона (Qref) у безредукторній вітровій турбіні з регульованою швидкістю та синхронним генератором із постійними магнітами. Методи. Ефективне моделювання та керування вітровою турбіною з запропонованим перетворювачем потужності здійснюється з використанням програмного забезпечення MATLAB/Simulink. Схема управління складається з управління вітряною турбіною і з управління силовим перетворювачем. Результати моделювання використовуються для аналізу та обговорення можливостей схеми управління, що показує, що генератор вітрової турбіни здатний активно підтримувати електроенергетичну мережу завдяки своїй здатності незалежно контролювати активну та реактивну потужність відповідно до застосовуваних еталонних значень при змінній швидкості вітру. Практична цінність. Це дослідження може бути використане для оцінки методології управління, динамічних можливостей та впливу безредукторної системи перетворення енергії вітру зі змінною швидкістю на електричні мережі. Наведено тематичне дослідження зі схемою вітряної електростанції (3×10 МВт = 30 МВт)

Data Dependent Randomized Smoothing

Randomized smoothing is a recent technique that achieves state-of-art performance in training certifiably robust deep neural networks. While the smoothing family of distributions is often connected to the choice of the norm used for certification, the parameters of these distributions are always set as global hyper parameters independent of the input data on which a network is certified. In this work, we revisit Gaussian randomized smoothing and show that the variance of the Gaussian distribution can be optimized at each input so as to maximize the certification radius for the construction of the smoothed classifier. This new approach is generic, parameter-free, and easy to implement. In fact, we show that our data dependent framework can be seamlessly incorporated into 3 randomized smoothing approaches, leading to consistent improved certified accuracy. When this framework is used in the training routine of these approaches followed by a data dependent certification, we achieve 9\% and 6\% improvement over the certified accuracy of the strongest baseline for a radius of 0.5 on CIFAR10 and ImageNet.Comment: First two authors contributed equally to this wor

Synthesis and Characterizations of Titanium Tungstosilicate and Tungstophosphate Mesoporous Materials

The work reports a development approach for the synthesis of novel multi-components mesoporous materials of titanium tungstate (meso-TiW) titanium tungstosilicate (meso-TiWSi) and tungstophosphate (meso-TiWP) mixed oxides that have high surface area and ordered mesoporous structures at nanometer length scale. Using the solvent evaporation-induced self-assembly (EISA) new oxides of bi- and tri-component of meso-TiW, meso-TiWSi and meso-TiWP oxides with different compositions and porosity were achieved. The physicochemical properties of the mesoporous oxides were characterized by X-ray diffraction, BET surface area analyzer, scanning, and transmission electron microscopes. Subject to the oxide composition, the obtained meso-TiW, meso-TiWSi and meso-TiWP exhibits high surface area, ordered 2D hexagonal mesostructured with order channels extended over a large area. The produced meso-TiW, meso-TiWSi, and meso-TiWP adsorbents exhibit good adsorption efficiency for the removal of Pb(II), Cd(II) and Hg(II) ions from water solution due to the presence of high surface area and accessibility of surface active sites. The adsorption efficiency of these mesoporous oxide reaches up to 95% and is found to be dependent contact time and adsorbents dose. The synthesis strategy is particularly advantageous for the production of new complex (multi-component) inorganic mesoporous materials that might have an application in the field of environmental, catalysis or energy storage and production