Example of the fully-compensated phase reconstruction package for off-axis DHM holograms recorded in telecentric configuration. by Raul Castañeda (13932971)

Comparison of edge detector and CHT in detecting droplets. by Manibarathi Vaithiyanathan (6655274)

Glioblastoma multiforme (GBM) pathway modules identified by the netboxr package from cancer genomics alteration data without the use of pre-defined gene sets. by Eric Minwei Liu (9592176)

Supporting data for "Personalized Electromyography Based Control Algorithm Development and Training System for Hand Prosthesis" for HKU Libraries by Xiaojun Wang (9067289)

<b>Algorithm Pseudocode</b> by Yibin Zhao (22425801)

DataSheet2_Benefits and Challenges of Pre-clustered Network-Based Pathway Analysis.PDF by Miguel Castresana-Aguirre (12521683)

“…<p>Functional analysis of gene sets derived from experiments is typically done by pathway annotation. …”

DataSheet1_Benefits and Challenges of Pre-clustered Network-Based Pathway Analysis.CSV by Miguel Castresana-Aguirre (12521683)

“…<p>Functional analysis of gene sets derived from experiments is typically done by pathway annotation. …”

Modeling Molecularly Imprinted Nanoparticles with LNKD: A Resource Efficient Algorithm for Polymer Cross-Linking by Emma Stevens (22257358)

“…Here, we propose LNKD (Linking Nodes in KD-trees), a resource-efficient algorithm for predicting pairs of reactive atoms in pre-cross-linked 3D structures of monomers that applies not only to the modeling of MIPs, but also chemical cross-linking in other materials. …”

Python code, input data, and outputs of Fourier analysis and Dynamic Time Warping from McGlasson et al. by Riley McGlasson (13876874)

“…</p> <h4><strong>Python Function Files:</strong></h4> <p>radarfuncs.py: Holds three functions used for FFT and DTW analysis.…”

ClaritySpectra: Raman spectra analysis tool by Aaron Celestian (9395696)

“…</li></ul><h3>PEAK FITTING </h3><ul><li>Automated background subtraction using asymmetric least squares fitting</li><li>A new suggested background feature that lets you preview the background that you like best</li><li>Interactive background fitting lets you further tune the background to perfection</li><li>Four choice of peaks: Gaussian, Lorentzian, Pseudo-Voigt, and the new Asymmetric Voigt functions</li><li>Overlapping view of how well the peaks fit with quality metrics</li><li>No need to define regions, the algorithm is smart enough to what a peak looks like.…”

ROC curves of our out-of-class algorithm (blue) and a threshold decision boundary (red) representing the true positive rates (TPR) compared to the false positive rates (FPR) for te... by G. Christopher Marais (21492084)

Membership function of output for tank 1. by Tamiru Demelash Kassie (20678515)

Membership function of output for tank 2. by Tamiru Demelash Kassie (20678515)

Table_1_NeuroRA: A Python Toolbox of Representational Analysis From Multi-Modal Neural Data.pdf by Zitong Lu (9913770)

Presentation_2_NeuroRA: A Python Toolbox of Representational Analysis From Multi-Modal Neural Data.pdf by Zitong Lu (9913770)

Table_2_NeuroRA: A Python Toolbox of Representational Analysis From Multi-Modal Neural Data.pdf by Zitong Lu (9913770)

Image_1_NeuroRA: A Python Toolbox of Representational Analysis From Multi-Modal Neural Data.pdf by Zitong Lu (9913770)

Presentation_3_NeuroRA: A Python Toolbox of Representational Analysis From Multi-Modal Neural Data.pdf by Zitong Lu (9913770)

Presentation_1_NeuroRA: A Python Toolbox of Representational Analysis From Multi-Modal Neural Data.pdf by Zitong Lu (9913770)

Table_3_NeuroRA: A Python Toolbox of Representational Analysis From Multi-Modal Neural Data.pdf by Zitong Lu (9913770)