Frontiers in Signal Processing

7

Authors: Nematollah Zarmehi, Mohammad Javad Barikbin

This paper proposes a robust watermarking method for uncompressed video data against H.264/AVC and H.265/HEVC compression standards. We embed the watermark data in the midrange transform coefficients of a block that is less similar to its corresponding block in the previous and next frames. This idea makes the watermark robust against the compression standards that use the inter prediction technique. The last two video compression standards also use inter prediction for motion compensation like previous video compression standards. Therefore, the proposed method is also well suited with these standards. Simulation results show the adequate robustness and transparency of our watermarking scheme.

8

Authors: Mantosh Biswas, Hari Om

This paper proposes a hybrid image denoising technique using neighbouring wavelet coefficients. The NeighShrink method groups the wavelet coefficients in non overlapping blocks and then thresholds empirically them blockwise. This method does not give good quality of image since it removes too many small wavelet coefficients. Our proposed scheme retains the modified coefficients and also gives good performance in terms of peak signal-to-noise ratio.

17

Authors: Xiaolong Cui, Taieb Znati, Rami Melhem

With the concerted efforts from researchers in hardware, software, algorithm, and data management, HPC is moving towards extreme-scale, featuring a computing capability of quintillion (1018) FLOPS. As we approach the new era of computing, however, several daunting scalability challenges remain to be conquered. Delivering extreme-scale performance will require a computing platform that supports billion-way parallelism, necessitating a dramatic increase in the number of computing, storage, and networking components. At such a large scale, failure would become a norm rather than an exception, driving the system to significantly lower efficiency with unprecedented amount of power consumption. To tackle this challenge, we propose an adaptive and power-aware algorithm, referred to as Lazy Shadowing, as an efficient and scalable approach to achieve high-levels of resilience, through forward progress, in extreme-scale, failure-prone computing environments. Lazy Shadowing associates with each process a “shadow” (process) that executes at a reduced rate, and opportunistically rolls forward each shadow to catch up with the its leading process during failure recovery. Compared to existing fault tolerance methods, our approach can achieve 20% energy saving with potential reduction in solution time at scale.