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Seismic traces recorded in a single sensor from multiple shots demonstrate significant correlation. We propose a memory-assisted seismic signal compression method based on dictionary learning and sparse coding that would explore this correlation. Different from traditional methods, the dictionary used for compression is learned and updated by the information extracted from the common memory between the sender (sensor) node and the receiver node, over a fixed window of the most recent traces. The common memory is formed by the previously transmitted traces.

It is of particular interest to reconstruct or estimate bandlimited graph signals, which are smoothly varying signals defined over graphs, from partial noisy measurements. However, choosing an optimal subset of nodes to sample is NP-hard. We formularize the problem as the experimental design of a linear regression model if we allow multiple measurements on a single node. By relaxing it to a convex optimization problem, we get the proportion of sample for each node given the budget of total sample size. Then, we use a probabilistic quantization to get the number of each node to be sampled.

We propose a super-resolution method to convert spatial resolution from 2K to 8K to utilize existing 2K HDTV video for 8K UHDTV broadcasting. The proposed method uses image registration for wavelet multi-scale bands of 2K video frames considering future hardware implementation to real-time processing. This image registration consists of alignment and assignment procedures. The wavelet multi-scale bands are extracted using wavelet decomposition of 2K video frames. The alignment is processed from 2K video frames to its wavelet low-frequency bands.

Gaining information about heart and coronary arteries such as estimating the flow velocity and coronary flow reserve (CFR) by only using 2D + time X-ray angiography sequence is of great interest due to its availability. We propose to segment the coronary arteries from 2D+time X-ray angiography sequences during contrast fluid propagation, by using a multi-step method based on unsharp masking followed by an iterative process of segmenting and non-rigid registration, until the alignment from the registration process is satisfactory.

Afternoon sleepiness in daily life reduces arousal level, performance, and so on. It has been cleared that short naps are effective to cancel the sleepiness. Sleep stage 2 is one of important factors about sleeping especially in short time nap. Sleep spindles are especially important hallmarks of sleep stage 2. Therefore, it is necessary to find a spindle for analysis in sleep stage 2.