Journal: Scientific Reports

Article Title: Super Sub-Nyquist Single-Pixel Imaging by Total Variation Ascending Ordering of the Hadamard Basis

doi: 10.1038/s41598-020-66371-5

Figure Lengend Snippet: Image reconstructions with different subsets of normal Hadamard masks. ( a ) The measured signal intensity distribution of the image using normal order Hadamard. ( b,c ) are the measured signal intensity distribution in real value ( y ) descending order and absolute value (| y |) descending order. ( d,i ) are the ground truth of the phantom image and natural image. ( e,f,j,k ) give the reconstructed images at sampling ratio of 15% and 40% based on normal order Hadamard. ( g,h,l–m ) give the reconstructed images of the sparse image and natural image at sampling ratios of 15% and 40% based on absolute value descending order Hadamard.

Article Snippet: In this work, we make use of the Hadamard matrix generated using the built-in function in MATLAB 2018b (otherwise known as Sylvester’s construction), which we call the ‘normal order’ to form other reordered Hadamard matrices.

Techniques: Sampling

Journal: Scientific Reports

Article Title: Super Sub-Nyquist Single-Pixel Imaging by Total Variation Ascending Ordering of the Hadamard Basis

doi: 10.1038/s41598-020-66371-5

Figure Lengend Snippet: Different Hadamard ordering example. ( a–e ) are the normal order, TV order, TG order, CC order and Paley order of 16 × 16 Hadamard matrices and their mask sequence orders respectively, each mask is one row of Hadamard matrix which are reshaped into a 4 × 4 2D matrix.

Article Snippet: In this work, we make use of the Hadamard matrix generated using the built-in function in MATLAB 2018b (otherwise known as Sylvester’s construction), which we call the ‘normal order’ to form other reordered Hadamard matrices.

Techniques: Sequencing

Journal: Scientific Reports

Article Title: Super Sub-Nyquist Single-Pixel Imaging by Total Variation Ascending Ordering of the Hadamard Basis

doi: 10.1038/s41598-020-66371-5

Figure Lengend Snippet: Total variation of the proposed four reordered Hadamard matrices comparison. The blue, orange, red and black dots are the total variation value distribution of CC order, TG order, TV order and Paley Hadamard matrices.

Article Snippet: In this work, we make use of the Hadamard matrix generated using the built-in function in MATLAB 2018b (otherwise known as Sylvester’s construction), which we call the ‘normal order’ to form other reordered Hadamard matrices.

Techniques: Comparison

Journal: Scientific Reports

Article Title: Super Sub-Nyquist Single-Pixel Imaging by Total Variation Ascending Ordering of the Hadamard Basis

doi: 10.1038/s41598-020-66371-5

Figure Lengend Snippet: Image reconstruction with different fractions of four Hadamard basis. ( a ) is the 12.5% of full measured signal intensity sorted in descending order according to its absolute value. ( b–e ) are the measured signals intensity using subset of natural order, Paley order, CC order, TG order and TV order Hadamard basis respectively. ( f ) is the ground truth image. ( g–j ) are the reconstructed images using subset of Paley order, CC order, TG order and TV order Hadamard basis respectively.

Article Snippet: In this work, we make use of the Hadamard matrix generated using the built-in function in MATLAB 2018b (otherwise known as Sylvester’s construction), which we call the ‘normal order’ to form other reordered Hadamard matrices.

Techniques:

Journal: eNeuro

Article Title: Background EEG Connectivity Captures the Time-Course of Epileptogenesis in a Mouse Model of Epilepsy

doi: 10.1523/ENEURO.0059-19.2019

Figure Lengend Snippet: Analysis of background functional connectivity reveals changes over the time course of epileptogenesis. A , E , I , Individual connectivity matrices represented as dots in the first two principal dimensions of the multidimensional scaling of Frobenius distances between the individual connectivity matrices. Each dot represents a single matrix (green, Day 0; yellow, Day 7; red, Day 28; gray, Sham control; empty symbols: circle, diamond, and square represent the median of the connectivity matrices). The first three principal multidimensional scaling dimensions represent ∼70% of the relations encoded in the raw Frobenius distances ( R 2 ABS =0.66, R 2 MAX =0.72, R 2 MIN =0.7; R is Pearson’s correlation coefficient between the Frobenius distances in the matrix space and the Euclidian distances in the reconstructed space); for clarity only the first two coordinates are plotted. B – D , F – H , J – L , Median functional connectivity matrices (indicated with empty symbols in A , E , I ) resulting from the three different measures at different days with color-coded connection weights (Day 0 over 11 matrices, Day 7 over 6 matrices, Day 28 over 8 matrices; different numbers of matrices for individual days because of quality of recordings; see Materials and Methods).

Article Snippet: Next, we used classical multidimensional scaling (MDS) to visualize relations captured by the similarity matrix , using MATLAB (Release 2018b, MathWorks) function cmdscale.

Techniques: Functional Assay, Control