The carrier phase tracking loop is the primary focus of the current work. In particular, two carrier phase tracking techniques are compared, the standard phase tracking loop, i.e., the phase lock loop (PLL), and the extended Kalman filter (EKF) tracking loop. In order to compare these two different techniques and taking into consideration the different models adopted in each, it is important to bring them to one common ground. In order to accomplish this, the equivalent PLL for a given EKF has to be determined in terms of steady-state response to both thermal noise and signal dynamics. A novel method for experimentally calculating the equivalent bandwidth of the EKF is presented and used to evaluate the performance of the equivalent PLL. Results are shown for both the L1 and L5 signals. Even though the two loops are designed to track equivalent dynamics and to have equivalent carrier phase standard deviations, the EKF outperforms the equivalent PLL in terms of both the transient response and sensitivity.
Noise can affect images while acquired, transmitted, stored or compressed. One of the best methods for noise removal is the sparse representation algorithm (SR). The Quantum Particle Swarm Optimization (QPSO) is one of the meta-heuristic algorithms. This paper shows excellent results in noise reduction in the quick version of QPSO, which uses benefit of the SRs and meta-heuristic algorithms. This approach is known as FQPSO-MP, depending on the matching pursuit algorithm (MP). A proposed Dynamic-Multi-Swarm (DMS) and a pre-learned dictionary (FQPSO-MP) method saves the time of calculating the learning dictionary. These modifications contribute to important benefits of computing efficiency (productivity improvements of approximately 90% are achieved) without sacred image quality in comparison with the initial QPSO-MP technique (the bigger reduction relative to the PSNR indexes is lower than 0.58 dB and 0.019). The proposed FQPSO-MP method compared to the original QPSO-MP method after modification. The scientific results show that the FQPSO-MP algorithm is more effective and quicker without sacrificing image quality than the FQPSO-MP algorithm. The experimental results show, in comparison to state-of-the-art denoising algorithms, that both quantitative and image quality results are achieved with the suggested FQPSO-MP method.
Multi-core processor is viewed as the future production of microprocessor design. It is not only a solution for CPU speeds but also it decreases the power consumption, because many cores in a lower frequency collectively produce less heat dissipation than one core with their total frequency. From this point of view, Cloud computing will be mostly built on top of multi-core technologies. However, to fully take advantage of the computational capability and the other advantages of multi-cores, a lot of new techniques must be proposed and considered. In this research paper, a resourcescheduling algorithm along with a server consolidation algorithm is applied to multi-core processors. It is be shown by experimental results that adding cores to the processors in data centers increases the system performance, decreases the power consumption, along with other benefits.
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