Automatic Recognition of Sound Direction in the Median Plane of Human Ears

2019 ◽  
Author(s):  
Siqing Han ◽  
Delong Xu
Keyword(s):  
Median Plane ◽  
Automatic Recognition ◽  
Sound Direction

A High Performance Energy Analyzer for Use in Electron Scanning Microscopy

1969 ◽  
Vol 27 ◽  
pp. 14-15
Author(s):  
A. V. Crewe ◽  
M. Isaacson ◽  
D. Johnson
Keyword(s):  
High Performance ◽  
Vertical Plane ◽  
Median Plane ◽  
Electron Gun ◽  
Uniform Field ◽  
Loss Mechanism ◽  
Electron Scanning Microscopy ◽  
Sector Type ◽  
Double Focusing ◽  
Electron Energy Loss

A double focusing magnetic spectrometer has been constructed for use with a field emission electron gun scanning microscope in order to study the electron energy loss mechanism in thin specimens. It is of the uniform field sector type with curved pole pieces. The shape of the pole pieces is determined by requiring that all particles be focused to a point at the image slit (point 1). The resultant shape gives perfect focusing in the median plane (Fig. 1) and first order focusing in the vertical plane (Fig. 2).

Doll Reflex as a Function of Body Tilt in the Median Plane

1973 ◽  
Author(s):  
Wayne Shebilske ◽  
Sheldon M. Ebenholtz
Keyword(s):  
Median Plane ◽  
Body Tilt

scholarly journals Automatic Recognition of Fetal Facial Standard Plane in Ultrasound Image via Fisher Vector

PLoS ONE ◽  
2015 ◽  
Vol 10 (5) ◽  
pp. e0121838 ◽  
Author(s):  
Baiying Lei ◽  
Ee-Leng Tan ◽  
Siping Chen ◽  
Liu Zhuo ◽  
Shengli Li ◽  
...  
Keyword(s):  
Ultrasound Image ◽  
Automatic Recognition ◽  
Fisher Vector

scholarly journals Smart Care Using a DNN-Based Approach for Activities of Daily Living (ADL) Recognition

2020 ◽  
Vol 11 (1) ◽  
pp. 10
Author(s):  
Muchun Su ◽  
Diana Wahyu Hayati ◽  
Shaowu Tseng ◽  
Jiehhaur Chen ◽  
Hsihsien Wei
Keyword(s):  
Neural Network ◽  
Health Care ◽  
Activities Of Daily Living ◽  
Video Processing ◽  
Daily Living ◽  
Deep Neural Network ◽  
Recognition System ◽  
Automatic Recognition ◽  
Data Filtering ◽  
Accuracy Rate

Health care for independently living elders is more important than ever. Automatic recognition of their Activities of Daily Living (ADL) is the first step to solving the health care issues faced by seniors in an efficient way. The paper describes a Deep Neural Network (DNN)-based recognition system aimed at facilitating smart care, which combines ADL recognition, image/video processing, movement calculation, and DNN. An algorithm is developed for processing skeletal data, filtering noise, and pattern recognition for identification of the 10 most common ADL including standing, bending, squatting, sitting, eating, hand holding, hand raising, sitting plus drinking, standing plus drinking, and falling. The evaluation results show that this DNN-based system is suitable method for dealing with ADL recognition with an accuracy rate of over 95%. The findings support the feasibility of this system that is efficient enough for both practical and academic applications.

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