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[AFNI Academy] SUMA introduction: Background and start GUI - YouTube
[AFNI Academy] SUMA introduction: Background and start GUI - YouTube
An overview of the SUMA (SUrface Mapping) functionality within AFNI. + We discuss standard SUMA concepts, inputs and inputs. + We also go through basic "suma" GUI functionality (including its interaction with the "afni" GUI). SUMA is useful for analyzing and visualizing data on surfaces + It complements our volumetric way of analyzing data - instead of slicewise views, we can have data points on surfaces or other geometries like tracts embedded within three dimensions + We generally use detailed surfaces created by other software, e.g., FreeSurfer (FS), and then translate their results to NIFTI/GIFTI and we make *standardized meshes* - note that AFNI *can* generate surfaces of volumetric info, but we generally use this more for visualization of things like ROIs and typically don't estimate surfaces for quantitative purposes. - note that we also provide volumetric versions of FS parcellations that are renumbered (for AFNI colorbar viewing) and also conveniently grouped by tissue type (WM, GM, ROI-like GM, ventricles, CSF, other); these are the *_REN_* files. + We also visualize more than surfaces, for example tracts and edges between graph nodes, slices and more. What do you need to use SUMA? Pretty standard things: + For most default functionality, a standard (1 mm isotropic) T1w anatomical volume can be used to generate one or more surface meshes. + One can "unifize" the brightness of the T1w volume (e.g., 3dUnifize) but many mesh-generating softwares do that themselves (e.g., FS's recon-all does) Many AFNI programs work on surface datasets directly, too! + Any AFNI program that just treats spatial elements individual (i.e., "voxelwise" or "nodewise" calculations) can be run on surface dsets + Programs that need spatial information (blurring/smoothing, clustering, averaging, calculating area, ...) are different. If you just want to display volumetric-processed results on surfaces, you can do so. + we have already-created FS output for MNI and TT_N27 dsets, e.g., - https://afni.nimh.nih.gov/pub/dist/tgz/suma_MNI152_2009.tgz - https://afni.nimh.nih.gov/pub/dist/tgz/suma_MNI_N27.tgz - https://afni.nimh.nih.gov/pub/dist/tgz/suma_TT_N27.tgz There is an @SUMA_Make_Spec_* program for each of the major surface-generating softwares + translates their outputs into NIFTI/GIFTI files + makes a *standardized mesh* of each (see Argall, Saad & Beauchamp, 2006) - other projects have started making standard meshes now, too, but seem to have different standardization... Run example case of running suma+afni GUIs + there are lots of keypresses, buttons and options for viewing data in SUMA + when we say a lot, we mean a *lot* + many basic functionalities are demonstrated here: - the "suma_keystrokes.txt" file in the Bootcamp handouts (also linked below) lists many of the most fundamental keypresses - translate, rotate, zoom, turn surfaces on/off, "open" brain as walnut - can toggle through list of surface "states": WM boundary, pial, inflated, sphere, etc. (same topology, different geometry) - find help (button help, online-connected help), see mesh nodes/edges - motion brain, save images, save movies, see RGB crosshair grids - change colormap (Cmp), rightclick on menu buttons for scrollable list - open multiple SUMA views, lockable - have AFNI and SUMA GUIs "talk": send info back and forth, continue to interact (fuuuun! and useful), can check quality of surfaces well PRESENTATIONS: https://afni.nimh.nih.gov/pub/dist/edu/class_lectures/2020-03-NIH/suma/suma.pdf https://afni.nimh.nih.gov/pub/dist/edu/class_lectures/2020-03-NIH/suma/suma_keystrokes.txt -
·youtube.com·
[AFNI Academy] SUMA introduction: Background and start GUI - YouTube
[PDF] Correlation based Dynamic Time Warping | Semantic Scholar
[PDF] Correlation based Dynamic Time Warping | Semantic Scholar
Dynamic Time Warping (DTW) is a widely used technique for univariate time series comparison. This paper proposes a new algorithm for the comparison of multivariate time series which generalize DTW for the needs of correlated multivariate time series.
·semanticscholar.org·
[PDF] Correlation based Dynamic Time Warping | Semantic Scholar
최신 자료 4195
최신 자료 4195
레포트, 리포트, 기말레포트, 기말리포트, 논문, 학술논문, 졸업논문, 레포트표지, 리포트표지, 이력서, 자기소개서, 감상문, 독후감, 방통대자료, 사업계획서 등의 지식 자료를 회원 간에 자유롭게 거래할 수 있습니다.
·happycampus.com·
최신 자료 4195
Two-dimensional correlation optimized warping algorithm for aligning GC x GC-MS data - PubMed
Two-dimensional correlation optimized warping algorithm for aligning GC x GC-MS data - PubMed
A two-dimensional (2-D) correlation optimized warping (COW) algorithm has been developed to align 2-D gas chromatography coupled with time-of-flight mass spectrometry (GC x GC/TOF-MS) data. By partitioning raw chromatographic profiles and warping the grid points simultaneously along the first and se …
·pubmed.ncbi.nlm.nih.gov·
Two-dimensional correlation optimized warping algorithm for aligning GC x GC-MS data - PubMed
US Patent for Method, apparatus, server and system for vital sign detection and monitoring Patent (Patent # 10,735,298 issued August 4, 2020) - Justia Patents Search
US Patent for Method, apparatus, server and system for vital sign detection and monitoring Patent (Patent # 10,735,298 issued August 4, 2020) - Justia Patents Search
Methods, apparatus and systems for detecting and monitoring vital signs and other periodic motions of an object are disclosed. In one example, a system for monitoring object motion in a venue is disclosed. The system comprises a transmitter, a receiver, and a vital sign estimator. The transmitter is located at a first position in the venue and configured for transmitting a wireless signal through a wireless multipath channel impacted by a pseudo-periodic motion of an object in the venue. The receiver is located at a second position in the venue and configured for: receiving the wireless signal through the wireless multipath channel impacted by the pseudo-periodic motion of the object in the venue, and obtaining at least one time series of channel information (TSCI) of the wireless multipath channel based on the wireless signal. The vital sign estimator is configured for: determining that at least one portion of the at least one TSCI in a current sliding time window is associated with the pseudo-periodic motion of the object in the venue, and computing a current characteristics related to the pseudo-periodic motion of the object in the current sliding time window based on at least one of: the at least one portion of the at least one TSCI in the current sliding time window, at least one portion of the at least one TSCI in a past sliding time window, and a past characteristics related to the pseudo-periodic motion of the object in the past sliding time window.
·patents.justia.com·
US Patent for Method, apparatus, server and system for vital sign detection and monitoring Patent (Patent # 10,735,298 issued August 4, 2020) - Justia Patents Search
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Two level parallelism and I/O reduction in genome comparisons | SpringerLink
Two level parallelism and I/O reduction in genome comparisons | SpringerLink
Cluster Computing - Genome comparison poses important computational challenges, especially in CPU-time, memory allocation and I/O operations. Although there already exist parallel approaches of...
·link.springer.com·
Two level parallelism and I/O reduction in genome comparisons | SpringerLink
Volume 51 Issue 2 | Optical Engineering
Volume 51 Issue 2 | Optical Engineering
Optical Engineering (OE) publishes peer-reviewed papers reporting on research, development, and applications of optics, photonics, and imaging science and engineering.
·spiedigitallibrary.org·
Volume 51 Issue 2 | Optical Engineering