Advanced Aviation Weather Radar Data Processing and Real-Time Implementations

The objectives of this dissertation work are developing an enhanced intelligent radar signal and data processing framework for aviation hazard detection, classification and monitoring, and real-time implementation on massive parallel platforms. Variety of radar sensor platforms are used to prove the concept including airborne precipitation radar and different ground weather radars. As a focused example of the proposed approach, this research applies evolutionary machine learning technology to turbulence level classification for civil aviation. An artificial neural network (ANN) machine learning approach based on radar observation is developed for classifying the cubed root of the Eddy Dissipation Rate (EDR), a widely-accepted measure of turbulence intensity. The approach is validated using typhoon weather data collected by Hong Kong Observatory’s (HKO) Terminal Doppler Weather Radar (TDWR) located near Hong Kong International Airport (HKIA) and comparing HKO-TDWR EDR$^{1/3}$ detections and predictions with in situ EDR$^{1/3}$ measured by commercial aircrafts. The testing results verified that machine learning approach performs reasonably well for both detecting and predicting tasks. As the preliminary step to explore the possibility of acceleration by integrating General Purpose Graphic Processing Unit (GPGPU), this research introduces a practical approach to implement real-time processing algorithms for general surveillance radar based on NVIDIA graphical processing units (GPUs). The pulse compression algorithms are implemented using compute unified device architecture (CUDA) libraries such as CUDA basic linear algebra subroutines and CUDA fast Fourier transform library, which are adopted from open source libraries and optimized for the NVIDIA GPUs. For more advanced, adaptive processing algorithms such as adaptive pulse compression, customized kernel optimization is investigated. A statistical optimization approach is developed for this purpose without needing much knowledge of the physical configurations of the kernels. It was found that the kernel optimization approach can significantly improve the performance. Benchmark performance is compared with the CPU performance in terms of processing accelerations. The proposed implementation framework can be used in various radar systems including ground-based phased array radar, airborne sense and avoid radar, and aerospace surveillance radar. After the investigation of the GPGPU on radar signal processing chain, the benchmark of applying machine learning approach on embedded GPU platform was performed. According to the performance, real-time requirement of the machine learning method of turbulence detection developed in this research could be met as well as Size, Weight and Power (SWaP) restrictions on embedded GPGPU platforms

Mobile graphics: SIGGRAPH Asia 2017 course

Peer ReviewedPostprint (published version

Smoothed particle hydrodynamics method for free surface flow based on MPI parallel computing

In the field of computational fluid dynamics (CFD), smoothed particle hydrodynamics (SPH) is very suitable for simulating problems with large deformation, free surface flow and other types of flow scenarios. However, traditional smoothed particle hydrodynamics methods suffer from the problem of high computation complexity, which constrains their application in scenarios with accuracy requirements. DualSPHysics is an excellent smoothed particle hydrodynamics software proposed in academia. Based on this tool, this paper presents a largescale parallel smoothed particle hydrodynamics framework: parallelDualSPHysics, which can solve the simulation of large-scale free surface flow. First, an efficient domain decomposition algorithm is proposed. And the data structure of DualSPHysics in a parallel framework is reshaped. Secondly, we proposed a strategy of overlapping computation and communication to the parallel particle interaction and particle update module, which greatly improves the parallel efficiency of the smoothed particle hydrodynamics method. Finally, we also added the pre-processing and post-processing modules to enable parallelDualSPHysics to run in modern high performance computers. In addition, a thorough evaluation shows that the 3 to 120 million particles tested can still maintain more than 90% computing efficiency, which demonstrates that the parallel strategy can achieve superior parallel efficiency

Crystallographic fragment screening - improvement of workflow, tools and procedures, and application for the development of enzyme and protein-protein interaction modulators

One of the great societal challenges of today is the fight against diseases which reduce life expectancy and lead to high economic losses. Both the understanding and the addressing of these diseases need research activities at all levels. One aspect of this is the discovery and development of tool compounds and drugs. Tool compounds support disease research and the development of drugs. For about 20 years, the discovery of new compounds has been attempted by screening small organic molecules by high-throughput methods. More recently, X-ray crystallography has emerged as the most promising method to conduct such screening. Crystallographic fragment-screening (CFS) generates binding information as well as 3D-structural information of the target protein in complex with the bound fragment. This doctoral research project is focused primarily on the optimization of the crystallographic fragment screening workflow. Investigated were the requirements for more successful screening campaigns with respect to the crystal system studied, the fragment libraries, the handling of the crystalline samples, as well as the handling of the data associated with a screening campaign. The improved CFS workflow was presented as a detailed protocol and as an accompanying video to train future CFS users in a streamlined and accessible way. Together, these improvements make CFS campaigns a more high-throughput method, offering the ability to screen larger fragment libraries and allowing higher numbers of campaigns performed per year. The protein targets throughout the project were two enzymes and a spliceosomal protein-protein complex. The enzymes comprised the aspartic protease Endothiapepsin and the SARS-Cov-2 main protease. The protein-protein complex was the RNaseH-like domain of Prp8, a vital structural protein in the spliceosome, together with its nuclear shuttling factor Aar2. By performing the CFS campaigns against disease-relevant targets, the resulting fragment hits could be used directly to develop tool compounds or drugs. The first steps of optimization of fragment hits into higher affinity binders were also investigated for improvements. In summary, a plethora of novel starting points for tool compound and drug development was identified