Simulation data of complex obstacle environment. حسب Yu Gu (162342)

Simulation data of simple obstacle environment. حسب Yu Gu (162342)

Simulation data of 3D environment. حسب Yu Gu (162342)

Distribution of (A) temporal difference set (DistT) and (B) spatial distance set (DistS) values by sample number, for the lightning data subset (Fig 3(B)) identified by the CC3D al... حسب Manxing Shi (22367181)

DataSheet1_Extreme Point Sort Transformation Combined With a Long Short-Term Memory Network Algorithm for the Raman-Based Identification of Therapeutic Monoclonal Antibodies.DOCX حسب Jin Ling (7474442)

Study design flow chart. حسب Müge Yalçin (16486563)

7-segmentation of 200 scenes from BDD100k حسب Sabarish Vadarevu (9272924)

Fitness score statistics for BADASS optimization of alpha-amylase (AMY_BACSU). حسب Carlos A. Gomez-Uribe (21492788)

<b>Geographic-dependent Parameter Optimization based on A-4DEnVar: Simulation with an </b><b>Idealized 2-D </b><b>Coupled Model</b> حسب Jianxin He (20589917)

"…In this study, a novel dynamic independent point (DIP) scheme combined with a sample-space variable replacement algorithm, which enhances the convexity of the cost function, reduces computational dimensionality, and further expands the parameter subspace, is introduced to A-4DEnVar. …"

Robust Multi-Model Subset Selection حسب Anthony-Alexander Christidis (6949055)

"…We establish the finite-sample breakdown point of the models generated by RMSS, including that of the Robust Best Subset Selection (RBSS) estimator as a special case. …"

REDUCTION OF SAMPLE SIZE IN THE ANALYSIS OF SPATIAL VARIABILITY OF NONSTATIONARY SOIL CHEMICAL ATTRIBUTES حسب Tamara C. Maltauro (7366898)

"…<div><p>ABSTRACT In the study of spatial variability of soil attributes, it is essential to define a sampling plan with adequate sample size. This study aimed to evaluate, through simulated data, the influence of parameters of the geostatistical model and sampling configuration on the optimization process, and resize and reduce the sample size of a sampling configuration of a commercial area composed of 102 points. …"

The repair algorithm successfully recovered artificially removed dendrites from mouse CA1 pyramidal cells and restored their Sholl profiles. حسب Moritz Groden (18018276)

A method to concatenate multiple short time series for evaluating dynamic behaviour during walking حسب Stefan Orter (6786098)

"…The collected time series were cut into multiple shorter time series of varying lengths and subsequently concatenated using a novel algorithm that identifies similar poses in successive time series in order to determine an optimal concatenation time point. …"

SimSPPF module structure. حسب Mengqi Yuan (4852555)

"…A publicly available 5-class object mmWave radar heatmap dataset, including 2,500 annotated images, were selected for data modeling and method evaluation. The results show that the mean average precision (mAP), precision and recall of the S2DB-mmWave YOLOv8n model were 93.1% mAP@0.5, 55.8% mAP@0.5:0.95, 89.4% and 90.6%, respectively, which is 3.3, 1.6, 4.5 and 7.7 percentage points higher than the baseline YOLOv8n network without increasing the parameter count.…"

mAP@0.5 results. حسب Mengqi Yuan (4852555)

"…A publicly available 5-class object mmWave radar heatmap dataset, including 2,500 annotated images, were selected for data modeling and method evaluation. The results show that the mean average precision (mAP), precision and recall of the S2DB-mmWave YOLOv8n model were 93.1% mAP@0.5, 55.8% mAP@0.5:0.95, 89.4% and 90.6%, respectively, which is 3.3, 1.6, 4.5 and 7.7 percentage points higher than the baseline YOLOv8n network without increasing the parameter count.…"

YOLOv8n network architecture diagram. حسب Mengqi Yuan (4852555)

"…A publicly available 5-class object mmWave radar heatmap dataset, including 2,500 annotated images, were selected for data modeling and method evaluation. The results show that the mean average precision (mAP), precision and recall of the S2DB-mmWave YOLOv8n model were 93.1% mAP@0.5, 55.8% mAP@0.5:0.95, 89.4% and 90.6%, respectively, which is 3.3, 1.6, 4.5 and 7.7 percentage points higher than the baseline YOLOv8n network without increasing the parameter count.…"

Radar parameters. حسب Mengqi Yuan (4852555)

"…A publicly available 5-class object mmWave radar heatmap dataset, including 2,500 annotated images, were selected for data modeling and method evaluation. The results show that the mean average precision (mAP), precision and recall of the S2DB-mmWave YOLOv8n model were 93.1% mAP@0.5, 55.8% mAP@0.5:0.95, 89.4% and 90.6%, respectively, which is 3.3, 1.6, 4.5 and 7.7 percentage points higher than the baseline YOLOv8n network without increasing the parameter count.…"

Confusion matrix of the improved model. حسب Mengqi Yuan (4852555)

"…A publicly available 5-class object mmWave radar heatmap dataset, including 2,500 annotated images, were selected for data modeling and method evaluation. The results show that the mean average precision (mAP), precision and recall of the S2DB-mmWave YOLOv8n model were 93.1% mAP@0.5, 55.8% mAP@0.5:0.95, 89.4% and 90.6%, respectively, which is 3.3, 1.6, 4.5 and 7.7 percentage points higher than the baseline YOLOv8n network without increasing the parameter count.…"

Improved network structure of the YOLOv8n model. حسب Mengqi Yuan (4852555)

"…A publicly available 5-class object mmWave radar heatmap dataset, including 2,500 annotated images, were selected for data modeling and method evaluation. The results show that the mean average precision (mAP), precision and recall of the S2DB-mmWave YOLOv8n model were 93.1% mAP@0.5, 55.8% mAP@0.5:0.95, 89.4% and 90.6%, respectively, which is 3.3, 1.6, 4.5 and 7.7 percentage points higher than the baseline YOLOv8n network without increasing the parameter count.…"

PANet network design. حسب Mengqi Yuan (4852555)

"…A publicly available 5-class object mmWave radar heatmap dataset, including 2,500 annotated images, were selected for data modeling and method evaluation. The results show that the mean average precision (mAP), precision and recall of the S2DB-mmWave YOLOv8n model were 93.1% mAP@0.5, 55.8% mAP@0.5:0.95, 89.4% and 90.6%, respectively, which is 3.3, 1.6, 4.5 and 7.7 percentage points higher than the baseline YOLOv8n network without increasing the parameter count.…"