The set of sleep measures computed by ActiLife, including definitions for each measure and the aggregation methods utilized when multiple sleep periods were detected within the sam... by Louis Faust (6807728)

The randomness detection value of LCS and SCS. by Ye Tao (326899)

CBAM module architecture. by Buyu Wang (17889178)

Table 1_A machine learning approach to predict postoperative sleep disturbance after total knee arthroplasty: a comparative study of multiple algorithms.docx by Yi-xiang Zhang (22563236)

Workflow to extract and define myocardial motion and deformation patterns during early heart morphogenesis. by Morena Raiola (22439390)

“…By interpolating this mesh with the set of T-transformation, we derived ‘Live-Shape’, a continuous description of heart tissue motion. 2) Integrating Multiple Live Images into the Atlas (red shapes): Individual live image motions were integrated into a high-resolution Atlas. …”

Table 4_The potential of short-wave infrared hyperspectral imaging and deep learning for dietary assessment: a prototype on predicting closed sandwiches fillings.docx by Esther Kok (716077)

“…</p>Methods<p>Individual spectra were selected from 24 hyperspectral images of assembled closed sandwiches, measured in a spectral range of 1116.14 nm to 1670.62 nm over 108 bands, pre-processed with Standard Normal Variate filtering, derivatives, and subsampling, and fed into multiple algorithms, among which PLS-DA, multiple classifiers, and a simple neural network.…”

Table 3_The potential of short-wave infrared hyperspectral imaging and deep learning for dietary assessment: a prototype on predicting closed sandwiches fillings.docx by Esther Kok (716077)

“…</p>Methods<p>Individual spectra were selected from 24 hyperspectral images of assembled closed sandwiches, measured in a spectral range of 1116.14 nm to 1670.62 nm over 108 bands, pre-processed with Standard Normal Variate filtering, derivatives, and subsampling, and fed into multiple algorithms, among which PLS-DA, multiple classifiers, and a simple neural network.…”

Table 2_The potential of short-wave infrared hyperspectral imaging and deep learning for dietary assessment: a prototype on predicting closed sandwiches fillings.docx by Esther Kok (716077)

“…</p>Methods<p>Individual spectra were selected from 24 hyperspectral images of assembled closed sandwiches, measured in a spectral range of 1116.14 nm to 1670.62 nm over 108 bands, pre-processed with Standard Normal Variate filtering, derivatives, and subsampling, and fed into multiple algorithms, among which PLS-DA, multiple classifiers, and a simple neural network.…”

Table 1_The potential of short-wave infrared hyperspectral imaging and deep learning for dietary assessment: a prototype on predicting closed sandwiches fillings.docx by Esther Kok (716077)

“…</p>Methods<p>Individual spectra were selected from 24 hyperspectral images of assembled closed sandwiches, measured in a spectral range of 1116.14 nm to 1670.62 nm over 108 bands, pre-processed with Standard Normal Variate filtering, derivatives, and subsampling, and fed into multiple algorithms, among which PLS-DA, multiple classifiers, and a simple neural network.…”