Journal: Journal of visualized experiments : JoVE

Article Title: Semi-Automatic Graphical Tool for Measuring Coronary Artery Spatially Weighted Calcium Score from Gated Cardiac Computed Tomography Images

doi: 10.3791/65458

Figure Lengend Snippet:

Article Snippet: ​ Name of Material/Equipment Company Catalog Number Comments/Description Standard Computer N/A N/A macOS or Windows operating system MATLAB Mathworks R2019a Requires Image Processing Toolbox and Statistics and Machine Learning Toolbox; Any version compatible with and able to run version R2019a scripts is sufficient Clinical Cardiac CT Scanner Siemens SOMATOM Force Dual Source CT Used for the source images; Any cardiac CT will be sufficient Calcium Hydroxyapatite Sigma-Aldrich 289396–100G Suspended in EpoxAcast 690 resin for phantom creation EpoxAcast 690 Smooth-On 03641 Used for phantom creation syngo.via Siemens VB60A_HF04 Commercial software used for computing Agatston score for validation study Open in a separate window

Techniques: Software

Journal: Data Analytics and Artificial Intelligence

Article Title: Enhancing Attendance Operations with MATLAB Image Processing

doi: 10.46632/daai/4/2/6

Figure Lengend Snippet: FIGURE 1. Face Acquisition 1. Image Capture: The high-resolution camera which is used for capturing video is used to take frontal images of the students. 2. 2. Pre-processing: The images are converted from RGB to Grayscale and are scaled down by a factor of 1.2. Real-time Face Detection and Recognition:

Article Snippet: After pre-processing, the system employs facial recognition algorithms from MATLAB's Computer Vision Toolbox.

Techniques:

Journal: Data Analytics and Artificial Intelligence

Article Title: Enhancing Attendance Operations with MATLAB Image Processing

doi: 10.46632/daai/4/2/6

Figure Lengend Snippet: FIGURE 2. Face Recognition

Article Snippet: After pre-processing, the system employs facial recognition algorithms from MATLAB's Computer Vision Toolbox.

Techniques:

Journal: Agronomy

Article Title: Self-Organizing Map-Based Assessment of Compost Maturity and Cu/Zn Passivation in Biochar-Amended Pig Manure

doi: 10.3390/agronomy15040778

Figure Lengend Snippet: Figure 2. PARAFAC-SOM analysis of four organic components: (a) U-matrix; (b) sample distribution; (c) multiple-hit histogram; (d–g) fluorescence intensity maps of Components 1–4. The U-matrix visualizes sample similarity, where identical neurons indicate similar fluorescence properties. Color shifts from blue to red denote increasing fluorescence signal variation. In intensity maps, redder hues indicate higher fluorescence levels. CK-1 represents day 1 of the control group, with other labels following the same format.

Article Snippet: In this study, fluorescence data were processed using the SOM toolbox (version 2.0) in MATLAB R2021b.

Techniques: Fluorescence, Control

Journal: Agronomy

Article Title: Self-Organizing Map-Based Assessment of Compost Maturity and Cu/Zn Passivation in Biochar-Amended Pig Manure

doi: 10.3390/agronomy15040778

Figure Lengend Snippet: Figure 4. Fmax%-SOM analysis of Cu: (a) U-matrix; (b) best matching unit (BMU); (c) Exc-Cu; (d) Red-Cu; (e) Oxi-Cu; (f) Res-Cu fluorescence intensity mapping. The U-matrix represents sample similarity, where samples in the same neuron share fluorescence characteristics. Color transitions from blue to red indicate increasing fluorescence signal differences, with redder hues signifying lower similarity. In fluorescence maps, blue-to-red shifts denote rising fluorescence intensity, reflecting higher Cu concentrations. CK-1 represents the day 1 sample from the control without BC; all other labels follow this format.

Article Snippet: In this study, fluorescence data were processed using the SOM toolbox (version 2.0) in MATLAB R2021b.

Techniques: Fluorescence, Control

Journal: Agronomy

Article Title: Self-Organizing Map-Based Assessment of Compost Maturity and Cu/Zn Passivation in Biochar-Amended Pig Manure

doi: 10.3390/agronomy15040778

Figure Lengend Snippet: Figure 6. Fmax%-SOM analysis of Zn: (a) U-matrix; (b) best matching unit (BMU); and fluorescence intensity maps for (c) Exc-Zn; (d) Red-Zn; (e) Oxi-Zn; and (f) Res-Zn. The U-matrix illustrates sample similarity, where samples in the same neuron exhibit comparable fluorescence characteristics. Color shifts from blue to red indicate increasing fluorescence differences, with redder hues denoting lower similarity. In intensity maps, a blue-to-red transition signifies enhanced fluorescence, reflecting higher Zn content. CK-1 represents the day 1 sample from the control without BC, with all other labels following this format.

Article Snippet: In this study, fluorescence data were processed using the SOM toolbox (version 2.0) in MATLAB R2021b.

Techniques: Fluorescence, Control