To optimize the exploitation of oil and gas reservoirs both on- and offshore, Biodentfiy has developed a method to predict prospectivity of hydrocarbons before drilling. This method uses microbiological DNA analysis of shallow soil or seabed samples to detect vertical upward microseepage from hydrocarbon accumulations, which change the composition of microbes at the surface.
Microbiological DNA analysis of shallow soil or seabed samples results in a high-dimensional dataset, which is interpreted using machine learning. Using the machine learning method Elastic Net, features (microbes) are selected from an existing DNA database to classify new shallow soil or seabed samples. Multiple models, each with a different combination of externally set parameters (called hyperparameters), are trained to improve accuracy, essentially creating a grid of models. The aim of this thesis is to investigate if it is possible to accelerate feature selection on high-dimensional datasets by implementing a parallel design on a GPU to train this grid of models, and to investigate the performance of this GPU implementation. Inspired by an implementation called Shotgun, which is able to improve performance by exploiting parallelism across features when training a single model on a CPU, an implementation, named GPU Shotgun (GPU-SG) was devised, which could exploit parallelism across samples, features, and multiple models in the grid (of combinations of hyperparameters). Depending on the size of the grid and the hardware, using GPU-SG, a speedup of between 0.2 and 5.26 can be reached for sparse datasets (a datasets with lots of 0 values) when compared to standard CPU implementations. When considering dense datasets (a dataset with few 0 values), using GPU-SG, a speedup of between 0.5 and 10 can be achieved. The amount of memory available to store a dataset is lower for GPU's than for a CPU, and currently the design is limited by this, because the design does not allow a dataset that is larger than the memory available. GPU-SG can be used to design improved implementations, which reduce the time when the GPU or CPU is idle to improve performance. ... Read more

Inaccurate and slow positional tracking are fundamental problems of low-cost VR solutions. These factors can induce motion sickness and therefore significantly deteriorate the user experience. There are already a variety of solutions available to track the position of a VR HMD, some of which can be made robust and
low-latency by inexpensive means.

This thesis proposes a low-cost, outside-in, optical tracking solution. As optical sensor a stationary camera with an IR pass filter is used to capture an array of IR LEDs, which are integrated at strategic locations within the frame of the HMD. By using the OpenCV library the 3D pose of the LEDs can be estimated, and used to change the perspective of the user within the virtual world.

In particular, this tracking solution incorporates a fast, cost-effective, modified camera, capable of capturing over 180 frames per second. With this camera and a custom designed polyhedral structure to incorporate the LEDs, the LED array can be calibrated quickly and tracked accurately even when oriented at an obtuse
angle with the camera. The high frame rate also allows for a redundancy in pose estimations, obtained by a mix of algebraic and iterative PnP solvers to further improve numerical stability and accuracy.

The tracking algorithm accurately tracks the developed prototype at short distances, even when placed in a noisy environment. The calibration procedure can also be performed fast, so that tracking can be resumed within a second when tracking has been lost due to occlusion. Recommendations for further improvements would be to integrate an IMU to provide tracking information in case that the HMD becomes occluded, and to overall improve tracking of rotational movement. Also, a higher resolution optical sensor should be
used to improve the range of this tracking solution. ... Read more