Fig 12 - by Nicholas H. Vitale (20469289)

Predicted SNR normalized to the acquisition time of 1s versus input excitation power for CW and TG systems. by Nicholas H. Vitale (20469289)

The impact of extrinsic noise sources on the measured SNR is plotted for both CW and TG against varying optical filter magnitudes. by Nicholas H. Vitale (20469289)

Functional Projection <i>K</i>-means by Roberto Rocci (16022201)

“…<p>A new technique for simultaneous clustering and dimensionality reduction of functional data is proposed. The observations are projected into a low-dimensional subspace and clustered by means of a functional <i>K</i>-means. …”

Path planning chart for Environment 3 (15 × 15, U-shaped obstacles). by Tengfei Ma (597633)

Path planning chart for Environment 4 (15 × 15, U-shaped obstacles). by Tengfei Ma (597633)

Schematic representation of the placenta and feto-placental vascular structures. by Diana C. de Oliveira (9220729)

Associative memory paradigm used to train the feature decoders. by Sam Hall-McMaster (10343795)

Choice behavior on individual test tasks. by Sam Hall-McMaster (10343795)

Data Sheet 1_CU Cilia – an application for image analysis by machine learning – reveals significance of cysteine cathepsin K activity for primary cilia of human thyroid epithelial... by Maren Rehders (2879318)

Fig 1 - by Michiel Stock (1627249)

G4SNVHunter workflow for identifying variants that affect G4 formation. by Rongxin Zhang (1618159)

“…Subsequently, the impact of the variants on the formation potential of the identified G4s will be assessed based on the G4Hunter algorithm (Middle panel). Finally, candidate variants can be filtered and visualized using functions provided by G4SNVHunter to screen out those that can potentially disrupt the formation of G4 structures (Right panel). …”

Overview of archaic SNPs (aSNPs) potentially affecting G4 formation, identified by G4SNVHunter. by Rongxin Zhang (1618159)

An miRNA panel to predict RRiF. by Nabarun Chakraborty (535772)

AE positioning projection. by Li Jiaqing (21212614)

“…The K-SVD algorithm functions as an adaptive filter, learning from AE signals in various damage states to remove background noise and retain critical structural characteristics. …”

K-SVD dictionary learning process. by Li Jiaqing (21212614)

“…The K-SVD algorithm functions as an adaptive filter, learning from AE signals in various damage states to remove background noise and retain critical structural characteristics. …”

Evaluation of denoising effect. by Li Jiaqing (21212614)

“…The K-SVD algorithm functions as an adaptive filter, learning from AE signals in various damage states to remove background noise and retain critical structural characteristics. …”

Coati optimization algorithm for brain tumor identification based on MRI with utilizing phase-aware composite deep neural network by Rajesh Kumar Thangavel (20591164)

“…<p>Brain tumors can cause difficulties in normal brain function and are capable of developing in various regions of the brain. …”

Ablation study visualization results. by Xiaozhou Feng (2918222)

“…Finally, the MPDIoU loss function is adopted for bounding box regression, enhancing the precision of localization. …”

Experimental parameter configuration. by Xiaozhou Feng (2918222)

“…Finally, the MPDIoU loss function is adopted for bounding box regression, enhancing the precision of localization. …”