Study of critical ricochet angle for conical nose shape projectiles

2012 ◽  
Author(s):  
Vijayalakshmi Murali ◽  
Manish G. Law ◽  
Smita D. Naik
Keyword(s):  
Nose Shape ◽  
Critical Ricochet Angle

scholarly journals Nasal Morphology and Its Correlation to Craniofacial Morphology in Lateral Cephalometric Analysis

2021 ◽  
Vol 18 (6) ◽  
pp. 3064
Author(s):  
Agnieszka Jankowska ◽  
Joanna Janiszewska-Olszowska ◽  
Katarzyna Grocholewicz
Keyword(s):  
Soft Tissue ◽  
Nasal Bone ◽  
Correlation Coefficients ◽  
Craniofacial Morphology ◽  
Base Angle ◽  
Nasolabial Angle ◽  
Orthodontic Patients ◽  
Nose Shape ◽  
The Relationship ◽  
Spearman’S Correlation

Nose shape, size, and inclination influence facial appearance, but few studies concern the relationship between the nasal profile and craniofacial structures. The objective of this study was to analyze association of nasal cephalometric variables with skeletal structures, age, and sex. Cephalometric and nasal analysis was performed in 386 Polish orthodontic patients (aged 9–25 years). Student t-test and Mann–Whitney test were used to compare quantitative variables and Pearson’s or Spearman’s correlation coefficients—to find correlations. Soft tissue facial convexity angle correlates to Holdaway ratio, ANB (A-Nasion-B), and Wits appraisal. Nasal dorsum axis, nose length, nose depth (1) and nose depth (2), nose hump, lower dorsum convexity, and columella convexity increase with age. Nasal base angle, nasolabial angle, nasomental angle, soft tissue facial convexity and nasal bone angle decrease with age. Nasal base angle and nasomental angle are smaller in females. Thus, a relationship exists between nasal morphology and sagittal jaw configuration. Nasal parameters significantly change with age. Sexual dimorphism characterizes nasal bone angle and nasomental angle.

scholarly journals Analysis of Scour Depth Around Bridge Piers With Round Nose Shape by HEC-RAS 5.0.7 Software

2021 ◽  
Vol 1764 (1) ◽  
pp. 012151
Author(s):  
C S Silvia ◽  
M Ikhsan ◽  
A Wirayuda ◽  
Mastiar
Keyword(s):  
Bridge Piers ◽  
Scour Depth ◽  
Nose Shape

Tolerance of Composite Pipes to Local Impact Damage

1996 ◽  
Vol 210 (3) ◽  
pp. 181-192 ◽  
Author(s):  
S R Reid ◽  
C Peng ◽  
J N Ashton
Keyword(s):  
Damage Tolerance ◽  
Impact Damage ◽  
Local Loading ◽  
Experimental Programme ◽  
Filament Wound ◽  
Nose Shape ◽  
Local Impact ◽  
Chopped Strand Mat ◽  
Composite Pipes

The results of an extensive experimental programme on local loading of composite pipes are presented. Particular attention is directed to the influence of projectile nose shape on the damage tolerance of the pipes. Both filament wound pipes and lined chopped-strand mat pipes are considered.

The influence of initial nose shape in eroding penetration

1994 ◽  
Vol 15 (2) ◽  
pp. 139-148 ◽  
Author(s):  
James D. Walker ◽  
Charles E. Anderson
Keyword(s):  
Nose Shape

scholarly journals Content-Aware Eye Tracking for Autostereoscopic 3D Display

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4787
Author(s):  
Dongwoo Kang ◽  
Jingu Heo
Keyword(s):  
Eye Tracking ◽  
Near Infrared ◽  
Descent Method ◽  
Light Conditions ◽  
3D Display ◽  
Scale Invariant ◽  
Detection And Tracking ◽  
Position Detection ◽  
Nose Shape ◽  
Content Aware

This study develops an eye tracking method for autostereoscopic three-dimensional (3D) display systems for use in various environments. The eye tracking-based autostereoscopic 3D display provides low crosstalk and high-resolution 3D image experience seamlessly without 3D eyeglasses by overcoming the viewing position restriction. However, accurate and fast eye position detection and tracking are still challenging, owing to the various light conditions, camera control, thick eyeglasses, eyeglass sunlight reflection, and limited system resources. This study presents a robust, automated algorithm and relevant systems for accurate and fast detection and tracking of eye pupil centers in 3D with a single visual camera and near-infrared (NIR) light emitting diodes (LEDs). Our proposed eye tracker consists of eye–nose detection, eye–nose shape keypoint alignment, a tracker checker, and tracking with NIR LED on/off control. Eye–nose detection generates facial subregion boxes, including the eyes and nose, which utilize an Error-Based Learning (EBL) method for the selection of the best learnt database (DB). After detection, the eye–nose shape alignment is processed by the Supervised Descent Method (SDM) with Scale-invariant Feature Transform (SIFT). The aligner is content-aware in the sense that corresponding designated aligners are applied based on image content classification, such as the various light conditions and wearing eyeglasses. The conducted experiments on real image DBs yield promising eye detection and tracking outcomes, even in the presence of challenging conditions.

3D Nose shape net for human gender and ethnicity classification

2019 ◽  
Vol 126 ◽  
pp. 51-57
Author(s):  
Chenlei Lv ◽  
Zhongke Wu ◽  
Dan Zhang ◽  
Xingce Wang ◽  
Mingquan Zhou
Keyword(s):  
Nose Shape ◽  
Ethnicity Classification
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