benchtop and phantom testing raw data Search Results


machine learning algorithm for benchtop and phantom expriments  (Mendeley Ltd)
86
Mendeley Ltd machine learning algorithm for benchtop and phantom expriments
<t>Benchtop</t> recordings of the sensor (A) Illustration of the scanning device positioned over modular cube structure, where red cubes (high conductivity) represent hemorrhage and transparent cubes represent healthy brain tissue. The scanning area is arranged in a 4 × 4 grid with an additional row and column along the edges to reduce edge effects. The device records the signal over each cube for 10 s; the reported value at each coordinate is the average of that 10-s measurement. Scanning all cubes yields a total of 16 data points in a 4 × 4 matrix. (B) Each setup is scanned at distances of 0, 1, 2, and 3 cm (from the bottom of the device to the top of the cube structure) to simulate varying lesion depths in a real-world scenario. (C and D) The distinction between control setups (all transparent cubes) and setups containing at least one lesion (red cube) is shown for different device distances and for two coil sizes (6 and 9 cm). (C) Plots the data range (max – min) per setup, while (D) shows the average signal value across all 16 cubes.
Machine Learning Algorithm For Benchtop And Phantom Expriments, supplied by Mendeley Ltd, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/benchtop+and+phantom+testing+raw+data/learning+machine/pmc12719177-45-0-12
Average 86 stars, based on 1 article reviews
machine learning algorithm for benchtop and phantom expriments - by Bioz Stars, 2026-11
86/100 stars
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benchtop and phantom testing raw data  (Mendeley Ltd)
86
Mendeley Ltd benchtop and phantom testing raw data
<t>Benchtop</t> recordings of the sensor (A) Illustration of the scanning device positioned over modular cube structure, where red cubes (high conductivity) represent hemorrhage and transparent cubes represent healthy brain tissue. The scanning area is arranged in a 4 × 4 grid with an additional row and column along the edges to reduce edge effects. The device records the signal over each cube for 10 s; the reported value at each coordinate is the average of that 10-s measurement. Scanning all cubes yields a total of 16 data points in a 4 × 4 matrix. (B) Each setup is scanned at distances of 0, 1, 2, and 3 cm (from the bottom of the device to the top of the cube structure) to simulate varying lesion depths in a real-world scenario. (C and D) The distinction between control setups (all transparent cubes) and setups containing at least one lesion (red cube) is shown for different device distances and for two coil sizes (6 and 9 cm). (C) Plots the data range (max – min) per setup, while (D) shows the average signal value across all 16 cubes.
Benchtop And Phantom Testing Raw Data, supplied by Mendeley Ltd, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/benchtop+and+phantom+testing+raw+data/associated+cardiac+data+dataset+diseases+fqrs+in+of+outcomes+pediatric+prognostic+structured+with/pmc12719177-37-0-10
Average 86 stars, based on 1 article reviews
benchtop and phantom testing raw data - by Bioz Stars, 2026-11
86/100 stars
  Buy from Supplier

Image Search Results


Benchtop recordings of the sensor (A) Illustration of the scanning device positioned over modular cube structure, where red cubes (high conductivity) represent hemorrhage and transparent cubes represent healthy brain tissue. The scanning area is arranged in a 4 × 4 grid with an additional row and column along the edges to reduce edge effects. The device records the signal over each cube for 10 s; the reported value at each coordinate is the average of that 10-s measurement. Scanning all cubes yields a total of 16 data points in a 4 × 4 matrix. (B) Each setup is scanned at distances of 0, 1, 2, and 3 cm (from the bottom of the device to the top of the cube structure) to simulate varying lesion depths in a real-world scenario. (C and D) The distinction between control setups (all transparent cubes) and setups containing at least one lesion (red cube) is shown for different device distances and for two coil sizes (6 and 9 cm). (C) Plots the data range (max – min) per setup, while (D) shows the average signal value across all 16 cubes.

Journal: iScience

Article Title: Machine learning enhanced noninvasive transcranial stroke detection using a portable eddy current damping sensor

doi: 10.1016/j.isci.2025.114130

Figure Lengend Snippet: Benchtop recordings of the sensor (A) Illustration of the scanning device positioned over modular cube structure, where red cubes (high conductivity) represent hemorrhage and transparent cubes represent healthy brain tissue. The scanning area is arranged in a 4 × 4 grid with an additional row and column along the edges to reduce edge effects. The device records the signal over each cube for 10 s; the reported value at each coordinate is the average of that 10-s measurement. Scanning all cubes yields a total of 16 data points in a 4 × 4 matrix. (B) Each setup is scanned at distances of 0, 1, 2, and 3 cm (from the bottom of the device to the top of the cube structure) to simulate varying lesion depths in a real-world scenario. (C and D) The distinction between control setups (all transparent cubes) and setups containing at least one lesion (red cube) is shown for different device distances and for two coil sizes (6 and 9 cm). (C) Plots the data range (max – min) per setup, while (D) shows the average signal value across all 16 cubes.

Article Snippet: Machine Learning Algorithm for Benchtop and Phantom Expriments , This paper , Mendeley Data: https://doi.org/10.17632/bd5gvn6hvg.1.

Techniques: Control

K-nearest neighbor model training and nested cross-validation (A) Schematic overview of K-nearest neighbors model. (B) Schematic overview of nested cross-validation resampling strategy. (C) Nested cross-validation ROC (receiver operating characteristic) curves showing AUC (area under curve) and classification error for the 9 cm benchtop device (BT-9) at each lesion depth (0, 1, 2, and 3 cm). (D) Nested cross-validation ROC curve showing AUC and classification error for the 9 cm device on Phantom across all lesion depths. (E) Nested cross-validation ROC curves showing AUC and classification error for the 6 cm device on benchtop (BT-6) at each lesion depth (0, 1, 2, and 3 cm). (F) Nested cross-validation ROC curve showing AUC and classification error for the 6 cm device on Phantom across all lesion depths.

Journal: iScience

Article Title: Machine learning enhanced noninvasive transcranial stroke detection using a portable eddy current damping sensor

doi: 10.1016/j.isci.2025.114130

Figure Lengend Snippet: K-nearest neighbor model training and nested cross-validation (A) Schematic overview of K-nearest neighbors model. (B) Schematic overview of nested cross-validation resampling strategy. (C) Nested cross-validation ROC (receiver operating characteristic) curves showing AUC (area under curve) and classification error for the 9 cm benchtop device (BT-9) at each lesion depth (0, 1, 2, and 3 cm). (D) Nested cross-validation ROC curve showing AUC and classification error for the 9 cm device on Phantom across all lesion depths. (E) Nested cross-validation ROC curves showing AUC and classification error for the 6 cm device on benchtop (BT-6) at each lesion depth (0, 1, 2, and 3 cm). (F) Nested cross-validation ROC curve showing AUC and classification error for the 6 cm device on Phantom across all lesion depths.

Article Snippet: Machine Learning Algorithm for Benchtop and Phantom Expriments , This paper , Mendeley Data: https://doi.org/10.17632/bd5gvn6hvg.1.

Techniques: Biomarker Discovery