matlab polyarea function Search Results


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MathWorks Inc matlab polyarea function
Matlab Polyarea Function, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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MathWorks Inc polyarea function
Computation of a and A along a contour line w (see figure 10a for the location of w). (a) and (b) show the contour curvature kc (equation (2.9)) and the specific drainage area a (obtained by integration of equation (2.7)) along the slope lines going from discrete points of w to the local maximum (red triangle). Black solid lines in (c) and (d) show the values of a and A along the contour line w (analogously to the conceptual representation of figure 3d). A is computed by integration of a along the contour line w and is compared with the total area of the polygon enclosed by w and the two external projected slope lines, i.e. the area inside the green lines in figure 10 (grey dashed line, computed using the Matlab <t>polyarea</t> function). (Online version in colour.)
Polyarea Function, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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MathWorks Inc in-built matlab function polyarea(x,y)
Computation of a and A along a contour line w (see figure 10a for the location of w). (a) and (b) show the contour curvature kc (equation (2.9)) and the specific drainage area a (obtained by integration of equation (2.7)) along the slope lines going from discrete points of w to the local maximum (red triangle). Black solid lines in (c) and (d) show the values of a and A along the contour line w (analogously to the conceptual representation of figure 3d). A is computed by integration of a along the contour line w and is compared with the total area of the polygon enclosed by w and the two external projected slope lines, i.e. the area inside the green lines in figure 10 (grey dashed line, computed using the Matlab <t>polyarea</t> function). (Online version in colour.)
In Built Matlab Function Polyarea(X,Y), supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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MathWorks Inc polyarea
Computation of a and A along a contour line w (see figure 10a for the location of w). (a) and (b) show the contour curvature kc (equation (2.9)) and the specific drainage area a (obtained by integration of equation (2.7)) along the slope lines going from discrete points of w to the local maximum (red triangle). Black solid lines in (c) and (d) show the values of a and A along the contour line w (analogously to the conceptual representation of figure 3d). A is computed by integration of a along the contour line w and is compared with the total area of the polygon enclosed by w and the two external projected slope lines, i.e. the area inside the green lines in figure 10 (grey dashed line, computed using the Matlab <t>polyarea</t> function). (Online version in colour.)
Polyarea, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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Average 90 stars, based on 1 article reviews
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MathWorks Inc polyarea function in matlab 2014a
Computation of a and A along a contour line w (see figure 10a for the location of w). (a) and (b) show the contour curvature kc (equation (2.9)) and the specific drainage area a (obtained by integration of equation (2.7)) along the slope lines going from discrete points of w to the local maximum (red triangle). Black solid lines in (c) and (d) show the values of a and A along the contour line w (analogously to the conceptual representation of figure 3d). A is computed by integration of a along the contour line w and is compared with the total area of the polygon enclosed by w and the two external projected slope lines, i.e. the area inside the green lines in figure 10 (grey dashed line, computed using the Matlab <t>polyarea</t> function). (Online version in colour.)
Polyarea Function In Matlab 2014a, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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MathWorks Inc polyarea.m function
Selection transforms task-relevant information into a common subspace. (A) Population response for selected colors (binned into 4 color bins, indicated by marker color) at different locations (upper vs. lower, indicated by marker shape). Population response is taken as the vector of mean firing rate of all recorded neurons before the cue (pre-cue, left; taken at 400 ms) and after the cue (post-cue, right; taken just prior to target onset, see methods for details). Responses are projected into a reduced dimensionality subspace defined by the first three principle components (PCs) of all 8 color/location pairs. Grey lines connect adjacent colors along the color wheel. Gray shaded region reflects the best fitting planes to each location (see methods for details). (B) Color representations for upper and lower items become correlated after selection. Line shows the mean correlation between the population representation for each color when it was presented/remembered in the ‘upper’ or ‘lower’ position, over time. Correlation was measured after subtracting the mean response at each location (see methods for details). Error bars reflect standard error of the mean. (C) Color planes (seen in A) become aligned after selection, reflected in an increase in the cosine of the angle between the two color planes around the time of cue onset. Black line shows the best-fitting logistic function. (D) Alignment of color representations before (left) and after (right) selection. Colored markers indicate vector of population firing rate for both upper and lower items (markers as in A). Here, all vectors are projected into the ‘lower’ subspace, defined by the first two PCs that maximally explain variance in the color of the lower item (defined in the full N-dimensional neural space on held-out data; see methods). Timepoints and markers are as in (A). (E) Timecourse of population responses to the color of the upper item, projected into the upper subspace defined before selection (left) and after selection (right). Upper subspaces were defined as in D, but for the upper item. (F) Before selection, color representations are better separated using the pre-cue subspace. After selection, colors are better separated in the post-cue subspace. Separability was measured as the area of the <t>quadrilateral</t> defined by the population vectors for each color, projected into either the pre-cue or post-cue subspaces (left and right columns in each plot; area averaged across upper and lower items). Subspaces are defined as in D and E. Violin plots show bootstrapped distributions. (G) Schematic of how selection transforms color representations. Initially, the colors of the upper and lower item are encoded in orthogonal subspaces specific to each item’s location. The selected item is then transformed into a common subspace, regardless of its initial location. (H) Upper and lower representations become aligned after selection (left column) but immediately after stimulus presentation during attention (right column). Histograms show bootstrapped distribution of the cosine of the angle between the best-fitting planes for the upper and lower stimuli in either an ‘early’ (150-350 ms post-stimulus offset) or ‘late’ (200-0 ms before color wheel onset) time period during the delay. Green lines indicate median values. · p < 0.10, * p < 0.05, ** p < 0.01, *** p < 0.001.
Polyarea.M Function, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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MathWorks Inc contourf
Selection transforms task-relevant information into a common subspace. (A) Population response for selected colors (binned into 4 color bins, indicated by marker color) at different locations (upper vs. lower, indicated by marker shape). Population response is taken as the vector of mean firing rate of all recorded neurons before the cue (pre-cue, left; taken at 400 ms) and after the cue (post-cue, right; taken just prior to target onset, see methods for details). Responses are projected into a reduced dimensionality subspace defined by the first three principle components (PCs) of all 8 color/location pairs. Grey lines connect adjacent colors along the color wheel. Gray shaded region reflects the best fitting planes to each location (see methods for details). (B) Color representations for upper and lower items become correlated after selection. Line shows the mean correlation between the population representation for each color when it was presented/remembered in the ‘upper’ or ‘lower’ position, over time. Correlation was measured after subtracting the mean response at each location (see methods for details). Error bars reflect standard error of the mean. (C) Color planes (seen in A) become aligned after selection, reflected in an increase in the cosine of the angle between the two color planes around the time of cue onset. Black line shows the best-fitting logistic function. (D) Alignment of color representations before (left) and after (right) selection. Colored markers indicate vector of population firing rate for both upper and lower items (markers as in A). Here, all vectors are projected into the ‘lower’ subspace, defined by the first two PCs that maximally explain variance in the color of the lower item (defined in the full N-dimensional neural space on held-out data; see methods). Timepoints and markers are as in (A). (E) Timecourse of population responses to the color of the upper item, projected into the upper subspace defined before selection (left) and after selection (right). Upper subspaces were defined as in D, but for the upper item. (F) Before selection, color representations are better separated using the pre-cue subspace. After selection, colors are better separated in the post-cue subspace. Separability was measured as the area of the <t>quadrilateral</t> defined by the population vectors for each color, projected into either the pre-cue or post-cue subspaces (left and right columns in each plot; area averaged across upper and lower items). Subspaces are defined as in D and E. Violin plots show bootstrapped distributions. (G) Schematic of how selection transforms color representations. Initially, the colors of the upper and lower item are encoded in orthogonal subspaces specific to each item’s location. The selected item is then transformed into a common subspace, regardless of its initial location. (H) Upper and lower representations become aligned after selection (left column) but immediately after stimulus presentation during attention (right column). Histograms show bootstrapped distribution of the cosine of the angle between the best-fitting planes for the upper and lower stimuli in either an ‘early’ (150-350 ms post-stimulus offset) or ‘late’ (200-0 ms before color wheel onset) time period during the delay. Green lines indicate median values. · p < 0.10, * p < 0.05, ** p < 0.01, *** p < 0.001.
Contourf, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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MathWorks Inc convhull function
Parameters measured to evaluate the deficit in coordination, perception, and grasping in a reaching and grab experience.
Convhull Function, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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MathWorks Inc matlab r2012a
Parameters measured to evaluate the deficit in coordination, perception, and grasping in a reaching and grab experience.
Matlab R2012a, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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Image Search Results


Computation of a and A along a contour line w (see figure 10a for the location of w). (a) and (b) show the contour curvature kc (equation (2.9)) and the specific drainage area a (obtained by integration of equation (2.7)) along the slope lines going from discrete points of w to the local maximum (red triangle). Black solid lines in (c) and (d) show the values of a and A along the contour line w (analogously to the conceptual representation of figure 3d). A is computed by integration of a along the contour line w and is compared with the total area of the polygon enclosed by w and the two external projected slope lines, i.e. the area inside the green lines in figure 10 (grey dashed line, computed using the Matlab polyarea function). (Online version in colour.)

Journal: Proceedings. Mathematical, Physical, and Engineering Sciences

Article Title: On the theory of drainage area for regular and non-regular points

doi: 10.1098/rspa.2017.0693

Figure Lengend Snippet: Computation of a and A along a contour line w (see figure 10a for the location of w). (a) and (b) show the contour curvature kc (equation (2.9)) and the specific drainage area a (obtained by integration of equation (2.7)) along the slope lines going from discrete points of w to the local maximum (red triangle). Black solid lines in (c) and (d) show the values of a and A along the contour line w (analogously to the conceptual representation of figure 3d). A is computed by integration of a along the contour line w and is compared with the total area of the polygon enclosed by w and the two external projected slope lines, i.e. the area inside the green lines in figure 10 (grey dashed line, computed using the Matlab polyarea function). (Online version in colour.)

Article Snippet: A is computed by integration of a along the contour line w and is compared with the total area of the polygon enclosed by w and the two external projected slope lines, i.e. the area inside the green lines in (grey dashed line, computed using the Matlab polyarea function). (Online version in colour.)

Techniques:

Selection transforms task-relevant information into a common subspace. (A) Population response for selected colors (binned into 4 color bins, indicated by marker color) at different locations (upper vs. lower, indicated by marker shape). Population response is taken as the vector of mean firing rate of all recorded neurons before the cue (pre-cue, left; taken at 400 ms) and after the cue (post-cue, right; taken just prior to target onset, see methods for details). Responses are projected into a reduced dimensionality subspace defined by the first three principle components (PCs) of all 8 color/location pairs. Grey lines connect adjacent colors along the color wheel. Gray shaded region reflects the best fitting planes to each location (see methods for details). (B) Color representations for upper and lower items become correlated after selection. Line shows the mean correlation between the population representation for each color when it was presented/remembered in the ‘upper’ or ‘lower’ position, over time. Correlation was measured after subtracting the mean response at each location (see methods for details). Error bars reflect standard error of the mean. (C) Color planes (seen in A) become aligned after selection, reflected in an increase in the cosine of the angle between the two color planes around the time of cue onset. Black line shows the best-fitting logistic function. (D) Alignment of color representations before (left) and after (right) selection. Colored markers indicate vector of population firing rate for both upper and lower items (markers as in A). Here, all vectors are projected into the ‘lower’ subspace, defined by the first two PCs that maximally explain variance in the color of the lower item (defined in the full N-dimensional neural space on held-out data; see methods). Timepoints and markers are as in (A). (E) Timecourse of population responses to the color of the upper item, projected into the upper subspace defined before selection (left) and after selection (right). Upper subspaces were defined as in D, but for the upper item. (F) Before selection, color representations are better separated using the pre-cue subspace. After selection, colors are better separated in the post-cue subspace. Separability was measured as the area of the quadrilateral defined by the population vectors for each color, projected into either the pre-cue or post-cue subspaces (left and right columns in each plot; area averaged across upper and lower items). Subspaces are defined as in D and E. Violin plots show bootstrapped distributions. (G) Schematic of how selection transforms color representations. Initially, the colors of the upper and lower item are encoded in orthogonal subspaces specific to each item’s location. The selected item is then transformed into a common subspace, regardless of its initial location. (H) Upper and lower representations become aligned after selection (left column) but immediately after stimulus presentation during attention (right column). Histograms show bootstrapped distribution of the cosine of the angle between the best-fitting planes for the upper and lower stimuli in either an ‘early’ (150-350 ms post-stimulus offset) or ‘late’ (200-0 ms before color wheel onset) time period during the delay. Green lines indicate median values. · p < 0.10, * p < 0.05, ** p < 0.01, *** p < 0.001.

Journal: bioRxiv

Article Title: Selective control of working memory in prefrontal, parietal, and visual cortex

doi: 10.1101/2020.04.07.030718

Figure Lengend Snippet: Selection transforms task-relevant information into a common subspace. (A) Population response for selected colors (binned into 4 color bins, indicated by marker color) at different locations (upper vs. lower, indicated by marker shape). Population response is taken as the vector of mean firing rate of all recorded neurons before the cue (pre-cue, left; taken at 400 ms) and after the cue (post-cue, right; taken just prior to target onset, see methods for details). Responses are projected into a reduced dimensionality subspace defined by the first three principle components (PCs) of all 8 color/location pairs. Grey lines connect adjacent colors along the color wheel. Gray shaded region reflects the best fitting planes to each location (see methods for details). (B) Color representations for upper and lower items become correlated after selection. Line shows the mean correlation between the population representation for each color when it was presented/remembered in the ‘upper’ or ‘lower’ position, over time. Correlation was measured after subtracting the mean response at each location (see methods for details). Error bars reflect standard error of the mean. (C) Color planes (seen in A) become aligned after selection, reflected in an increase in the cosine of the angle between the two color planes around the time of cue onset. Black line shows the best-fitting logistic function. (D) Alignment of color representations before (left) and after (right) selection. Colored markers indicate vector of population firing rate for both upper and lower items (markers as in A). Here, all vectors are projected into the ‘lower’ subspace, defined by the first two PCs that maximally explain variance in the color of the lower item (defined in the full N-dimensional neural space on held-out data; see methods). Timepoints and markers are as in (A). (E) Timecourse of population responses to the color of the upper item, projected into the upper subspace defined before selection (left) and after selection (right). Upper subspaces were defined as in D, but for the upper item. (F) Before selection, color representations are better separated using the pre-cue subspace. After selection, colors are better separated in the post-cue subspace. Separability was measured as the area of the quadrilateral defined by the population vectors for each color, projected into either the pre-cue or post-cue subspaces (left and right columns in each plot; area averaged across upper and lower items). Subspaces are defined as in D and E. Violin plots show bootstrapped distributions. (G) Schematic of how selection transforms color representations. Initially, the colors of the upper and lower item are encoded in orthogonal subspaces specific to each item’s location. The selected item is then transformed into a common subspace, regardless of its initial location. (H) Upper and lower representations become aligned after selection (left column) but immediately after stimulus presentation during attention (right column). Histograms show bootstrapped distribution of the cosine of the angle between the best-fitting planes for the upper and lower stimuli in either an ‘early’ (150-350 ms post-stimulus offset) or ‘late’ (200-0 ms before color wheel onset) time period during the delay. Green lines indicate median values. · p < 0.10, * p < 0.05, ** p < 0.01, *** p < 0.001.

Article Snippet: To measure separability of the colors, we computed the area of this quadrilateral (polyarea.m function in MATLAB).

Techniques: Selection, Marker, Plasmid Preparation, Transformation Assay

Parameters measured to evaluate the deficit in coordination, perception, and grasping in a reaching and grab experience.

Journal: Brain Sciences

Article Title: Mixed Reality-Based Smart Occupational Therapy Personalized Protocol for Cerebellar Ataxic Patients

doi: 10.3390/brainsci14101023

Figure Lengend Snippet: Parameters measured to evaluate the deficit in coordination, perception, and grasping in a reaching and grab experience.

Article Snippet: Sway area , Total area occupied by the trajectory calculated building a polygon figure with the most external samples as the vertices (combination of “convhull” and “polyarea” MATLAB (version R2024a) functions) , Accuracy of movement (hand near the target).

Techniques: Activity Assay, Comparison