>Magister Scientiae - MSc / The SASL project is in the process of developing a machine translation system that can translate fully-fledged phrases between SASL and English in real-time. To-date, several systems have been developed by the project focusing on facial expression, hand shape, hand motion, hand orientation and hand location recognition and estimation. Achmed developed a highly accurate upper body pose recognition and estimation system. The system is capable of recognizing and estimating the location of the arms from a twodimensional video captured from a monocular view at an accuracy of 88%. The system operates at well below real-time speeds. This research aims to investigate the use of optimizations and parallel processing techniques using the CUDA framework on Achmed’s algorithm to achieve real-time upper body pose recognition and estimation. A detailed analysis of Achmed’s algorithm identified potential improvements to the algorithm. Are- implementation of Achmed’s algorithm on the CUDA framework, coupled with these improvements culminated in an enhanced upper body pose recognition and estimation system that operates in real-time with an increased accuracy.

In telecommunication, messages sent through a wireless medium often experience noise interfering with the signal in a way that corrupts the messages. As the demand for high throughput in the mobile network is increasing, algorithms that can detectand correct these corrupted messages quickly and accurately are of interest to the industry. Polar codes have been chosen by the Third Generation Partnership Project as the error correction code for 5G New Radio control channels. This thesis work aimed to investigate whether the polar code Successive Cancellation (SC) could be parallelized and if a graphics processing unit (GPU) can be utilized to optimize the execution time of the algorithm. The polar code Successive Cancellation was enhanced by implementing tree pruning and support for GPUs to leverage their parallelization. The difference in execution time between the concurrent and sequential versions of the SC algorithm with and without tree pruning was evaluated. The tree pruning SC algorithm almost always offered shorter execution times than the SC algorithm that did not employ treepruning. However, the support for GPUs did not reduce the execution time in these tests. Thus, the GPU is not certain to be able to improve this type of enhanced SC algorithm based on these results. / Meddelanden som överförs över ett mobilt nät utsätts ofta för brus som distorterar dem. I takt med att intresset ökat för hög genomströmning i mobilnätet har också intresset för algoritmer som snabbt och tillförlitligt kan upptäcka och korrigera distorderade meddelanden ökat. Polarkoder har valts av "Third Generation Partnership Project" som den klass av felkorrigeringskoder som ska användas för 5G:s radiokontrollkanaler. Detta examensarbete hade som syfte att undersöka om polarkoden "Successive Cancellation" (SC) skulle kunna parallelliseras och om en grafisk bearbetningsenhet (GPU) kan användas för att optimera exekveringstiden för algoritmen. SC utökades med stöd för trädbeskärning och parallellisering med hjälp av GPU:er. Skillnaden i exekveringstid mellan de parallella och sekventiella versionerna av SC-algoritmen med och utan trädbeskärning utvärderades. SC-algoritmen för trädbeskärning erbjöd nästan alltid kortare exekveringstider än SC-algoritmen som inte använde trädbeskärning. Stödet för GPU:er minskade dock inte exekveringstiden. Således kan man med dessa resultat inte med säkerhet säga att GPU-stöd skulle gynna SC-algoritmen.

Low-Density Parity-Check (LDPC) codes are linear error-correcting codes used to establish reliable communication between units on a noisy transmission channel in mobile telecommunications. LDPC algorithms detect and recover altered or corrupted message bits using sparse parity-check matrices in order to decipher messages correctly. LDPC codes have been shown to be fitting coding schemes for the fifth generation (5G) New Radio (NR), according to the third generation partnership project (3GPP).  TietoEvry, a consultant in telecom, has discovered that optimizations of LDPC decoding algorithms can be achieved/obtained with the use of a parallel computing platform called Compute Unified Device Architecture (CUDA), developed by NVIDIA. This platform utilizes the capabilities of a graphics processing unit (GPU) rather than a central processing unit (CPU), which in turn provides parallel computing. An optimized version of an LDPC decoding algorithm, the Min-Sum Algorithm (MSA), is implemented in CUDA and in C++ for comparison in terms of CPU execution time, to explore the capabilities that CUDA offers. The testing is done with a set of 12 sparse parity-check matrices and input-channel messages with different sizes. As a result, the CUDA implementation executes approximately 55% faster than a standard, unoptimized C++ implementation.