Quantitative results on DRPD dataset. by Dunlu Lu (19964225)

“…In comparative experiments on four plant datasets, MAR-YOLOv9 improved the mAP@0.5 accuracy by 39.18% compared to seven mainstream object detection algorithms, and by 1.28% compared to the YOLOv9 model. …”

Architecture of MAR-YOLOv9. by Dunlu Lu (19964225)

“…In comparative experiments on four plant datasets, MAR-YOLOv9 improved the mAP@0.5 accuracy by 39.18% compared to seven mainstream object detection algorithms, and by 1.28% compared to the YOLOv9 model. …”

Quantitative results on MTDC-UAV dataset. by Dunlu Lu (19964225)

“…In comparative experiments on four plant datasets, MAR-YOLOv9 improved the mAP@0.5 accuracy by 39.18% compared to seven mainstream object detection algorithms, and by 1.28% compared to the YOLOv9 model. …”

Counting results on WEDU dataset. by Dunlu Lu (19964225)

“…In comparative experiments on four plant datasets, MAR-YOLOv9 improved the mAP@0.5 accuracy by 39.18% compared to seven mainstream object detection algorithms, and by 1.28% compared to the YOLOv9 model. …”

Example images from four plant datasets. by Dunlu Lu (19964225)

“…In comparative experiments on four plant datasets, MAR-YOLOv9 improved the mAP@0.5 accuracy by 39.18% compared to seven mainstream object detection algorithms, and by 1.28% compared to the YOLOv9 model. …”

Counting results on RFRB dataset. by Dunlu Lu (19964225)

“…In comparative experiments on four plant datasets, MAR-YOLOv9 improved the mAP@0.5 accuracy by 39.18% compared to seven mainstream object detection algorithms, and by 1.28% compared to the YOLOv9 model. …”

Detection visualization results on WEDU dataset. by Dunlu Lu (19964225)

“…In comparative experiments on four plant datasets, MAR-YOLOv9 improved the mAP@0.5 accuracy by 39.18% compared to seven mainstream object detection algorithms, and by 1.28% compared to the YOLOv9 model. …”

Effective connectivity analysis. by Akke Mats Houben (21572855)

Structure of optimized model parameters in the high-dimensional cases. by Kevin J. Wischnewski (21354521)

“…The number and size of the clusters were determined with help of the -means clustering method. Both were set to zero if the absolute mean value of the off-diagonal elements in the correlation matrix (cf. …”

Design of stiffened panels for stress and buckling via topology optimization: data by Sheng Chu (19655605)

“…To solve the optimization problem, a semi-analytical sensitivity analysis is performed, and the optimization algorithm is outlined. Numerical investigations demonstrate and validate the proposed method.…”

Data Sheet 1_Integrated diagnostics and time series sensitivity assessment for growth monitoring of a medicinal plant (Glycyrrhiza uralensis Fisch.) based on unmanned aerial vehicl... by Ao Zhang (372387)

“…PIs collectively achieved high-precision predictions (mean 0.42 ≤ R² ≤ 0.94), with the prediction of PH using green leaf index (GLI) in BP algorithm attaining peak accuracy (R² = 0.94). VIs and PIs exhibited comparable predictive capacity for yield, with multi-indicators integrated modeling significantly enhancing performance: VIs achieved R² = 0.87 under RF algorithms, whereas PIs reached R² = 0.81 using BP algorithms. …”

Data Sheet 2_Integrated diagnostics and time series sensitivity assessment for growth monitoring of a medicinal plant (Glycyrrhiza uralensis Fisch.) based on unmanned aerial vehicl... by Ao Zhang (372387)

“…PIs collectively achieved high-precision predictions (mean 0.42 ≤ R² ≤ 0.94), with the prediction of PH using green leaf index (GLI) in BP algorithm attaining peak accuracy (R² = 0.94). VIs and PIs exhibited comparable predictive capacity for yield, with multi-indicators integrated modeling significantly enhancing performance: VIs achieved R² = 0.87 under RF algorithms, whereas PIs reached R² = 0.81 using BP algorithms. …”

Methodological overview. by Jinming Xiao (12517096)

“…<p>(A) The source reconstruction of TMS-evoked potential of each subject was performed using dSPM method based on MNE software library. The time series of cortical activity were extracted through Schaefer 200 parcellation atlas. …”

Mean squared Error on all unseen data. by Edward Antonian (21453161)

“…The first extension we consider is the case of graph signals that have only been partially recorded, meaning a subset of their elements is missing at observation time. Next, we examine the statistical effect of correlated prediction error and propose a method for Generalized Least Squares (GLS) on graphs. …”

Possible graph filter functions. by Edward Antonian (21453161)

“…The first extension we consider is the case of graph signals that have only been partially recorded, meaning a subset of their elements is missing at observation time. Next, we examine the statistical effect of correlated prediction error and propose a method for Generalized Least Squares (GLS) on graphs. …”

The notational conventions used in this paper. by Edward Antonian (21453161)

“…The first extension we consider is the case of graph signals that have only been partially recorded, meaning a subset of their elements is missing at observation time. Next, we examine the statistical effect of correlated prediction error and propose a method for Generalized Least Squares (GLS) on graphs. …”

Video 1_TDE-3: an improved prior for optical flow computation in spiking neural networks.mp4 by Matthew Yedutenko (5142461)

“…Proposed in the literature bioinspired neuromorphic Time-Difference Encoder (TDE-2) combines event-based sensors and processors with spiking neural networks to provide real-time and energy-efficient motion detection through extracting temporal correlations between two points in space. However, on the algorithmic level, this design leads to a loss of direction-selectivity of individual TDEs in textured environments. …”

Data Sheet 1_TDE-3: an improved prior for optical flow computation in spiking neural networks.pdf by Matthew Yedutenko (5142461)

“…Proposed in the literature bioinspired neuromorphic Time-Difference Encoder (TDE-2) combines event-based sensors and processors with spiking neural networks to provide real-time and energy-efficient motion detection through extracting temporal correlations between two points in space. However, on the algorithmic level, this design leads to a loss of direction-selectivity of individual TDEs in textured environments. …”

Confusion_Matrix_Data.zip by Mohammad Farhad Bulbul (21003494)

“…<p dir="ltr">This research paper proposes a novel approach for human activity recognition using depth video data, focusing on improving accuracy by effectively capturing motion information and utilizing a robust classification method. Here's a breakdown of the key elements:</p><p dir="ltr"><b>. …”

supporting data for PHD thesis entitled " Arousal Regulation and Neurofeedback Treatment for ADHD Children" by Yuliang Wang (9151616)

“…Analyses use standardized mean differences (Hedges g) under random-effects models, stratified by comparator type (medicine, active, sham, passive) and, where applicable, contrasted across protocol families (customised algorithm, SCP, SMR, TBR).</p><p dir="ltr">The supporting dataset contains the <b>raw arm-level descriptive statistics</b> required to compute effect sizes: per study, outcome, and timepoint it lists group means, standard deviations, and sample sizes for neurofeedback and control arms, along with rater, comparator category, protocol type, and outcome direction coding (so higher values consistently reflect the intended construct). …”