In this paper, we propose a superpixel based depth map generation scheme for the application to monoscopic to stereoscopic video conversion. The proposed algorithm employs four main processes to generate depth maps for all frames in the video sequences. First, the depth maps of the key frames in the input sequence are generated by superpixel merging and some user interactions. Second, the frames in the input sequences are over-segmented by Simple Linear Iterative Clustering (SLIC) or depth aided SLIC method depending on whether or not they have the depth maps. Third, each superpixel in current frame is used to match the corresponding superpixel in its previous frame. Finally, depth map is propagated with a joint bilateral filter based on the estimated matching vector of each superpixel. We show an improved performance of the proposed algorithm through experimental results.

GPUs have become the dominant computing units to meet the need of high performance in various computational fields. But the long operation latency causes the underutilization of on-chip computing resources, resulting in performance degradation when running parallel tasks on GPUs. A good warp scheduling strategy is an effective solution to hide latency and improve resource utilization. However, most current warp scheduling algorithms on GPUs ignore the ability of long operations to hide latency. In this paper, we propose a long-operation-first warp scheduling algorithm, LFWS, for GPU platforms. The LFWS filters warps in the ready state to a ready queue and updates the queue in time according to changes in the status of the warp. The LFWS divides the warps in the ready queue into long and short operation groups based on the type of operations in their instruction buffers, and it gives higher priority to the long-operating warp in the ready queue. This can effectively use the long operations to hide some of the latency from each other and enhance the system's ability to hide the latency. To verify the effectiveness of the LFWS, we implement the LFWS algorithm on a simulation platform GPGPU-Sim. Experiments are conducted over various CUDA applications to evaluate the performance of LFWS algorithm, compared with other five warp scheduling algorithms. The results show that the LFWS algorithm achieves an average performance improvement of 8.01% and 5.09%, respectively, over three traditional and two novel warp scheduling algorithms, effectively improving computational resource utilization on GPU.

With the advantages of higher blocking voltage, higher operation temperature, fast-switching characteristics, and lower switching losses, the silicon carbide (SiC) MOSFET has attracted more attentions and become an available replacement of traditional silicon (Si) power semiconductor in applications. Despite of all the merits above, electromagnetic interference (EMI) issues will be induced consequently by the ultra-fast switching transitions of the SiC MOSFET. To quickly and precisely assess the switching behaviors of the SiC MOSFET for EMI investigation, an analytical model is proposed. This model has comprehensively considered most of the key factors, including parasitic inductances, non-linearity of the junction capacitors, negative feedback effect of Ls and Cgd shared by the power and the gate stage loops, non-linearity of the trans-conductance, and skin effect during voltage and current ringing stages, which will considerably affect the switching performance of the SiC MOSFET. Additionally, a finite-state machine (FSM) is especially utilized so as to analytically and intuitively describe the switching behaviors of the SiC MOSFET via Stateflow. Based on double pulse test (DPT), the effectiveness and correctness of the proposed model are validated through the comparison between the calculated and the measured waveforms during switching transitions. Besides, the model can appropriately depict the spectrum of the drain-source voltage of the MOSFET and is suitable for EMI investigation in applying of SiC devices.