Signal and related image processing methods offer a rich source of alternative descriptors as they are designed to work in the presence of noisy data without the need for exact matching. Here we introduce a method, multi-resolution local binary patterns (MLBP) adapted from image processing to ...
The first step is to choose the scale at which to observe the process, the most informative scale being the one that includes the important features while disregarding noisy details in the data. In the investigation of spatial patterns, the optimal scale defines the optimal bin size of the ...
We use the traditional method to do the clustering. The processes are described as below: 1. Compute the l-mers feature vector for each fragment. 2. Randomly select k vectors, each as the center of a group. 3. Cluster all the vectors to the nearest center. 4. For each group, ...
This was able to process 100 data files using 4 Central Processing Units (CPU) workers in only 55 seconds with a maximum memory usage of 1.36 GB. Conclusion Spectral binning is a fast and robust method for the post-acquisition processing of FIE-HRMS data. The open- source R package ...
Moreover, their performance limits are well characterized, at least in the asymptotic limit of large block lengths, via the density evolution method.doi:10.1109/msp.2007.904816Wainwright, M. J.IEEE SIGNAL PROCESSING MAGAZINEM. J. Wainwright, “Sparse graph codes for side information and binning,”...
By accurately characterizing the sensor data that has been binned, we propose a post-capture binning data processing solution that succeeds in suppressing noise and preserving image details. We verify experimentally that the proposed method outperforms the existing alternatives by a substantial margin....