scholarly journals Visual sensor with resolution enhancement by mechanical vibrations

2002 ◽  
Author(s):  
O. Landolt ◽  
A. Mitros ◽  
C. Koch
Keyword(s):  
Resolution Enhancement ◽  
Visual Sensor ◽  
Mechanical Vibrations

Off-axis electron holography applied to the study of interfaces

1992 ◽  
Vol 50 (1) ◽  
pp. 244-245
Author(s):  
J.K. Weiss ◽  
M. Gajdardziska-Josifovska ◽  
M. R. McCartney ◽  
David J. Smith
Keyword(s):  
Electric Fields ◽  
Resolution Enhancement ◽  
Interfacial Structure ◽  
Atomic Scale ◽  
Bulk Property ◽  
Electron Holography ◽  
Specimen Thickness ◽  
Composition And Structure ◽  
Chemical Segregation ◽  
Diffraction Effects

Interfacial structure is a controlling parameter in the behavior of many materials. Electron microscopy methods are widely used for characterizing such features as interface abruptness and chemical segregation at interfaces. The problem for high resolution microscopy is to establish optimum imaging conditions for extracting this information. We have found that off-axis electron holography can provide useful information for the study of interfaces that is not easily obtained by other techniques.Electron holography permits the recovery of both the amplitude and the phase of the image wave. Recent studies have applied the information obtained from electron holograms to characterizing magnetic and electric fields in materials and also to atomic-scale resolution enhancement. The phase of an electron wave passing through a specimen is shifted by an amount which is proportional to the product of the specimen thickness and the projected electrostatic potential (ignoring magnetic fields and diffraction effects). If atomic-scale variations are ignored, the potential in the specimen is described by the mean inner potential, a bulk property sensitive to both composition and structure. For the study of interfaces, the specimen thickness is assumed to be approximately constant across the interface, so that the phase of the image wave will give a picture of mean inner potential across the interface.

RESEARCH RESPONSE QUENCHER MECHANICAL VIBRATIONS BY THE FORMULA Of CARDANO

2013 ◽  
Vol 33 (0) ◽  
pp. 181-185
Author(s):  
М. І. Ігнатишин ◽  
І. Д. Рубіш
Keyword(s):  
Mechanical Vibrations

A prototype 3D target tracking system based on wireless visual sensor network

2014 ◽  
Author(s):  
Kaishun XiaHou ◽  
Shanxing Chen ◽  
Shunjian Liang ◽  
Yilin Wu
Keyword(s):  
Target Tracking ◽  
Sensor Network ◽  
Tracking System ◽  
Visual Sensor ◽  
Visual Sensor Network

scholarly journals Seeing under the cover with a 3D U-Net: point cloud-based weight estimation of covered patients

2021 ◽  
Author(s):  
Alexander Bigalke ◽  
Lasse Hansen ◽  
Jasper Diesel ◽  
Mattias P. Heinrich
Keyword(s):  
Point Cloud ◽  
Drug Dosage ◽  
Sensor Data ◽  
Patient Specific ◽  
Point Cloud Data ◽  
Weight Estimation ◽  
Visual Sensor ◽  
Cloud Data ◽  
Emergency Situations ◽  
3D Cnn

Abstract Purpose Body weight is a crucial parameter for patient-specific treatments, particularly in the context of proper drug dosage. Contactless weight estimation from visual sensor data constitutes a promising approach to overcome challenges arising in emergency situations. Machine learning-based methods have recently been shown to perform accurate weight estimation from point cloud data. The proposed methods, however, are designed for controlled conditions in terms of visibility and position of the patient, which limits their practical applicability. In this work, we aim to decouple accurate weight estimation from such specific conditions by predicting the weight of covered patients from voxelized point cloud data. Methods We propose a novel deep learning framework, which comprises two 3D CNN modules solving the given task in two separate steps. First, we train a 3D U-Net to virtually uncover the patient, i.e. to predict the patient’s volumetric surface without a cover. Second, the patient’s weight is predicted from this 3D volume by means of a 3D CNN architecture, which we optimized for weight regression. Results We evaluate our approach on a lying pose dataset (SLP) under two different cover conditions. The proposed framework considerably improves on the baseline model by up to $${16}{\%}$$ 16 % and reduces the gap between the accuracy of weight estimates for covered and uncovered patients by up to $${52}{\%}$$ 52 % . Conclusion We present a novel pipeline to estimate the weight of patients, which are covered by a blanket. Our approach relaxes the specific conditions that were required for accurate weight estimates by previous contactless methods and thus constitutes an important step towards fully automatic weight estimation in clinical practice.

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