The new High‐Efficiency Video Coding (HEVC) standard doubles the video compression ratio compared to the previous H.264/AVC at the same video quality and without any degradation. However, this important performance is achieved by increasing the encoder computational complexity. That's why HEVC complexity is a crucial subject. The most time consuming and the most intensive computing part of HEVC is the motion estimation based principally on the sum of absolute differences (SAD) or the sum of square differences (SSD) algorithms. For these reasons, the authors proposed an implementation of these algorithms on a low cost NVIDIA GPU (graphics processing unit) using the Fermi architecture developed with Compute Unified Device Architecture language. The proposed algorithm is based on the parallel‐difference and the parallel‐reduction process. The investigational results show a significant speed‐up in terms of execution time for most 64 × 64 pixel blocks. In fact, the proposed parallel algorithm permits a significant reduction in the execution time that reaches up to 56.17 and 30.4%, compared to the CPU, for SAD and SSD algorithms, respectively. This improvement proves that parallelising the algorithm with the new proposed reduction process for the Fermi‐GPU generation leads to better results. These findings are based on a static study that determines the PU percentage utilisation for each dimension in the HEVC. This study shows that the larger PUs are the most utilised in temporal levels 3 and 4, which attain 84.56% for class E. This improvement is accompanied by an average peak signal‐to‐noise ratio loss of 0.095 dB and a decrease of 0.64% in terms of BitRate.

Fluid deformations around a cylinder combined with an applied electric field are used to enhance the kinetics rate and the response time of heterogeneous immunosensors in microfluidic systems. The insertion of an obstacle in the microchannel as well as the application an applied electric field are used to change the fluid motion topology that improves the transport of diffusion-limited proteins. The response time is affected by various parameters such as the inlet flow velocity, the initial analyte concentration and the obstacle position. The effects of the parameters related to the kinetics reaction on the sensitivity and the performance of the biosensor have been studied numerically. Numerical results reveal that an appropriate choice of the inlet analyte and inlet flow velocity with applied electric field may reduce considerably the response time and enhance the microfluidic sensor performance.