UAS See-and-Avoid Strategy using a Fuzzy Logic Controller Optimized by Cross-Entropy in Scaling Factors and Membership Functions

This work aims to develop a novel Cross-Entropy (CE) optimization-based fuzzy controller for Unmanned Aerial Monocular Vision-IMU System (UAMVIS) to solve the seeand- avoid problem using its accurate autonomous localization information. The function of this fuzzy controller is regulating the heading of this system to avoid the obstacle, e.g. wall. In the Matlab Simulink-based training stages, the Scaling Factor (SF) is adjusted according to the specified task firstly, and then the Membership Function (MF) is tuned based on the optimized Scaling Factor to further improve the collison avoidance performance. After obtained the optimal SF and MF, 64% of rules has been reduced (from 125 rules to 45 rules), and a large number of real flight tests with a quadcopter have been done. The experimental results show that this approach precisely navigates the system to avoid the obstacle. To our best knowledge, this is the first work to present the optimized fuzzy controller for UAMVIS using Cross-Entropy method in Scaling Factors and Membership Functions optimization.

Simplified small-signal stability analysis for optimized power system architecture

The optimization of power architectures is a complex problem due to the plethora of different ways to connect various system components. This issue has been addressed by developing a methodology to design and optimize power architectures in terms of the most fundamental system features: size, cost and efficiency. The process assumes various simplifications regarding the utilized DC/DC converter models in order to prevent the simulation time to become excessive and, therefore, stability is not considered. The objective of this paper is to present a simplified method to analyze small-signal stability of a system in order to integrate it into the optimization methodology. A black-box modeling approach, applicable to commercial converters with unknown topology and components, is based on frequency response measurements enabling the system small-signal stability assessment. The applicability of passivity-based stability criterion is assessed. The stability margins are stated utilizing a concept of maximum peak criteria derived from the behavior of the impedance-based sensitivity function that provides a single number to state the robustness of the stability of a well-defined minor-loop gain.

High-Power High-Efficiency Laser Power Transmission at 100m Using Optimized Multi-Cell GaAs Converter

A high-power high-efficiency laser power transmission system at 100m based on an optimized multi-cell GaAs converter capable of supplying 9.7W of electricity is demonstrated. An I-V testing system integrated with a data acquisition circuit and an analysis software is designed to measure the efficiency and the I-V characteristics of the laser power converter (LPC). The dependencies of the converter’s efficiency with respect to wavelength, laser intensity and temperature are analyzed. A diode laser with 793nm of wavelength and 24W of power is used to test the LPC and the software. The maximum efficiency of the LPC is 48.4% at an input laser power of 8W at room temperature. When the input laser power is 24W (laser intensity of 60000W/m2), the efficiency is 40.4% and the output voltage is 4 V. The overall efficiency from electricity to electricity is 11.6%.

Optimized design of the prestress in continuous bridge decks

In this paper a method for automatic design of the prestress in continuous bridge decks is presented. In a first step of the procedure the optimal prestressed force for a completely geometrically defined and feasible prestress layout is obtained by means of linear programming techniques. Further on, in a second step the prestress geometry and minimum force are automatically found by steepest descent optimization techniques. Finally this methodology is applied to two-span continuous bridge decks and from the obtained results some preliminary design rules can be drawn.

Land Cover Mapping by an Optimized Object-Oriented Approach. Case of Study Mediterranean Landscapes

Remote sensing information from spaceborne and airborne platforms continues to provide valuable data for different environmental monitoring applications. In this sense, high spatial resolution im-agery is an important source of information for land cover mapping. For the processing of high spa-tial resolution images, the object-based methodology is one of the most commonly used strategies. However, conventional pixel-based methods, which only use spectral information for land cover classification, are inadequate for classifying this type of images. This research presents a method-ology to characterise Mediterranean land covers in high resolution aerial images by means of an object-oriented approach. It uses a self-calibrating multi-band region growing approach optimised by pre-processing the image with a bilateral filtering. The obtained results show promise in terms of both segmentation quality and computational efficiency.

Optimized HOG for on-road video based vehicle verification

Vision-based object detection from a moving platform becomes particularly challenging in the field of advanced driver assistance systems (ADAS). In this context, onboard vision-based vehicle verification strategies become critical, facing challenges derived from the variability of vehicles appearance, illumination, and vehicle speed. In this paper, an optimized HOG configuration for onboard vehicle verification is proposed which not only considers its spatial and orientation resolution, but descriptor processing strategies and classification. An in-depth analysis of the optimal settings for HOG for onboard vehicle verification is presented, in the context of SVM classification with different kernels. In contrast to many existing approaches, the evaluation is realized in a public and heterogeneous database of vehicle and non-vehicle images in different areas of the road, rendering excellent verification rates that outperform other similar approaches in the literature.

UAS See-and-Avoid Strategy using a Fuzzy Logic Controller Optimized by Cross-Entropy in Scaling Factors and Membership Functions.

This work aims to develop a novel Cross-Entropy (CE) optimization-based fuzzy controller for Unmanned Aerial Monocular Vision-IMU System (UAMVIS) to solve the seeand-avoid problem using its accurate autonomous localization information. The function of this fuzzy controller is regulating the heading of this system to avoid the obstacle, e.g. wall. In the Matlab Simulink-based training stages, the Scaling Factor (SF) is adjusted according to the specified task firstly, and then the Membership Function (MF) is tuned based on the optimized Scaling Factor to further improve the collison avoidance performance. After obtained the optimal SF and MF, 64% of rules has been reduced (from 125 rules to 45 rules), and a large number of real flight tests with a quadcopter have been done. The experimental results show that this approach precisely navigates the system to avoid the obstacle. To our best knowledge, this is the first work to present the optimized fuzzy controller for UAMVIS using Cross-Entropy method in Scaling Factors and Membership Functions optimization.

Optimized Monte Carlo method for glasses

A new Monte Carlo algorithm is introduced for the simulation of supercooled liquids and glass formers, and tested in two model glasses. The algorithm thermalizes well below the Mode Coupling temperature and outperforms other optimized Monte Carlo methods.

An optimized pseudorandom generator using packed matrices

Most cryptographic services and information security protocols require a dependable source of random data; pseudorandom generators are convenient and efficient for this application working as one of the basic foundation blocks on which to build the required security infrastructure. We propose a modification of a previously published matricial pseudorandom generator that significantly improves performance and security by using word packed matrices and modifying key scheduling and bit extraction schemes. The resulting generator is then successfully compared to world class standards.

Photocatalytic oxidation of propene in gas phase at low concentration by optimized TiO2 nanoparticles

In the present study, nanocrystalline titanium dioxide (TiO2) was prepared by sol–gel method at low temperature from titanium tetraisopropoxide (TTIP) and characterized by different techniques (gas adsorption, XRD, TEM and FTIR). Variables of the synthesis, such as the hydrolyzing agent (acetic acid or isopropanol) and calcination temperatures (300–800 °C), were analyzed to get uniform size TiO2 nanoparticles. The effect that these two variables have on the structure of the resultant TiO2 nanoparticles and on their photocatalytic activity is investigated. The photocatalytic activities of TiO2 nanoparticles were evaluated for propene oxidation at low concentration (100 ppmv) under two different kinds of UV light (UV-A ∼ 365 nm and UV-C ∼ 257.7 nm) and compared with Degussa TiO2 P-25, used as reference sample. The results show that both hydrolyzing agents allow to prepare TiO2 nanoparticles and that the hydrolyzing agent influences the crystalline structure and its change with the thermal treatments. Interestingly, the prepared TiO2 nanoparticles possess anatase phase with small crystalline size, high surface area and higher photocatalytic activity for propene oxidation than commercial TiO2 (Degussa P-25) under UV-light. Curiously, these prepared TiO2 nanoparticles are more active with the 365 nm source than with the 257.7 nm UV-light, which is a remarkable advantage from an application point of view. Additionally, the obtained results are particularly good when acetic acid is the hydrolyzing agent at both wavelengths used, possibly due to the high crystallinity, low anatase phase size and high surface oxygen groups’ content in the nanoparticles prepared with it, in comparison to those prepared using isopropanol.

Optimized CUDA-Based PDE Solver for Reaction Diffusion Systems on Arbitrary Surfaces

Partial differential equation (PDE) solvers are commonly employed to study and characterize the parameter space for reaction-diffusion (RD) systems while investigating biological pattern formation. Increasingly, biologists wish to perform such studies with arbitrary surfaces representing ‘real’ 3D geometries for better insights. In this paper, we present a highly optimized CUDA-based solver for RD equations on triangulated meshes in 3D. We demonstrate our solver using a chemotactic model that can be used to study snakeskin pigmentation, for example. We employ a finite element based approach to perform explicit Euler time integrations. We compare our approach to a naive GPU implementation and provide an in-depth performance analysis, demonstrating the significant speedup afforded by our optimizations. The optimization strategies that we exploit could be generalized to other mesh based processing applications with PDE simulations.

Optimized methods for extracting circulating small RNAs from long-term stored equine samples.

Circulating miRNAs in body fluids, particularly serum, are promising candidates for future routine biomarker profiling in various pathologic conditions in human and veterinary medicine. However, reliable standardized methods for miRNA extraction from equine serum and fresh or archived whole blood are sorely lacking. We systematically compared various miRNA extraction methods from serum and whole blood after short and long-term storage without addition of RNA stabilizing additives prior to freezing. Time of storage at room temperature prior to freezing did not affect miRNA quality in serum. Furthermore, we showed that miRNA of NGS-sufficient quality can be recovered from blood samples after >10 years of storage at -80 °C. This allows retrospective analyses of miRNAs from archived samples.

Optimized Decoding for Auditory Attention Detection

Thesis (Master's)--University of Washington, 2016-06

Application of nox-restriction fragment length polymorphism for the differentiation of Brachyspira intestinal spirochetes isolated from pigs and poultry in Australia

Sixty-nine intestinal spirochetes isolated from pigs and poultry in eastern Australia were selected to evaluate the effectiveness of a species-specific PCR-based restriction fragment length polymorphism (RFLP) analysis of the Brachyspira nox gene. For comparative purposes, all isolates were subjected to species-specific PCRs for the pathogenic species Brachyspira hyodysenteriae and Brachyspira pilosicoli, and selected isolates were examined further by sequence analysis of the nox and 16S ribosomal RNA genes. Modifications to the original nox-RFLP method included direct inoculation of bacterial cells into the amplification mixture and purification of the PCR product, which further optimized the nox-RFLP for use in a veterinary diagnostic laboratory, producing sufficient product for both species identification and future comparisons. Although some novel profiles that prevented definitive identification were observed, the nox-RFLP method successfully classified 45 of 51 (88%) porcine and 15 of 18 (83%) avian isolates into 5 of the 6 recognized species of Brachyspira. This protocol represents a significant improvement over conventional methods currently used in veterinary diagnostic laboratories for rapid specific identification of Brachyspira spp. isolated from both pigs and poultry.