Three-dimensional VLC indoor positioning system using smart device camera receiver with image processing technique

2020 ◽  
Author(s):  
Punramon Rattakorn ◽  
UKRIT MANKONG ◽  
SANGDUAN POTHA
Keyword(s):  
Image Processing ◽  
Three Dimensional ◽  
Indoor Positioning ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Smart Device ◽  
Positioning System ◽  
Indoor Positioning System

Analysis of Insole Geometry and Deformity by Using a Three-Dimensional Image Processing Technique: A Preliminary Study

2019 ◽  
Vol 109 (2) ◽  
pp. 98-107
Author(s):  
Kit-lun Yick ◽  
Wai-ting Lo ◽  
Sun-pui Ng ◽  
Joanne Yip ◽  
Hung-hei Kwan ◽  
...  
Keyword(s):  
Image Processing ◽  
Cross Sections ◽  
Plantar Pressure ◽  
Three Dimensional ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Diabetic Patients ◽  
Clinical Practices ◽  
Cross Sectional ◽  
Orthotic Treatment

Background: Accurate representation of the insole geometry is crucial for the development and performance evaluation of foot orthoses designed to redistribute plantar pressure, especially for diabetic patients. Methods: Considering the limitations in the type of equipment and space available in clinical practices, this study adopted a simple portable three-dimensional (3-D) desktop scanner to evaluate the 3-D geometry of an orthotic insole and the corresponding deformities after the insole has been worn. The shape of the insole structure along horizontal cross sections is defined with 3-D scanning and image processing. Accompanied by an in-shoe pressure measurement system, plantar pressure distribution in four foot regions (hallux, metatarsal heads, midfoot, and heel) is analyzed and evaluated for insole deformity. Results: Insole deformities are quantified across the four foot regions. The hallux region tends to show the greatest changes in shape geometry (17%–50%) compared with the other foot regions after 2 months of insole wear. As a result of insole deformities, plantar peak pressures change considerably (–4.3% to +69.5%) during the course of treatment. Conclusions: Changes in shape geometry of the insoles could be objectively quantified with 3-D scanning techniques and image processing. This investigation finds that, in general, the design of orthotic insoles may not be adequate for diabetic individuals with similar foot problems. The drastic changes in the insole shape geometry and cross-sectional areas during orthotic treatment may reduce insole fit and conformity. An inadequate insole design may also affect plantar pressure reduction. The approach proposed herein, therefore, allows for objective quantification of insole shape geometry, which results in effective and optimal orthotic treatment.

A New Indoor Positioning System Using Artificial Encoded Magnetic Fields

2017 ◽  
Vol 71 (2) ◽  
pp. 299-316 ◽  
Author(s):  
Falin Wu ◽  
Yuan Liang ◽  
Yong Fu ◽  
Chenghao Geng
Keyword(s):  
Magnetic Fields ◽  
Three Dimensional ◽  
Indoor Positioning ◽  
Global Navigation Satellite Systems ◽  
Positioning System ◽  
Indoor Environments ◽  
Step Method ◽  
Satellite Systems ◽  
Multipath Effects ◽  
Indoor Positioning System

The demand for accurate indoor positioning continues to grow but the predominant positioning technologies such as Global Navigation Satellite Systems (GNSS) are not suitable for indoor environments due to multipath effects and Non-Line-Of-Sight (NLOS) conditions. This paper presents a new indoor positioning system using artificial encoded magnetic fields, which has good properties for NLOS conditions and fewer multipath effects. The encoded magnetic fields are generated by multiple beacons; each beacon periodically generates unique magnetic field sequences, which consist of a gold code sequence and a beacon location sequence. The position of an object can be determined with measurements from a tri-axial magnetometer using a three-step method: performing time synchronisation between sensor and beacons, identifying the beacon field and the beacon location, and estimating the position of the object. The results of the simulation and experiment show that the proposed system is capable of achieving Two-Dimensional (2D) and Three-Dimensional (3D) accuracy at sub-decimetre and decimetre levels, respectively.

scholarly journals Diagnosing Chronic Glaucoma Using Watershed and Convolutional Neural Network

2021 ◽  
Vol 9 (9) ◽  
pp. 272-283
Author(s):  
Naureen Fathima
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Convolutional Neural Network ◽  
Language Processing ◽  
Three Dimensional ◽  
Image Plane ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Computational Technique ◽  
Fundus Imaging

Abstract: Glaucoma is a disease that relates to the vision of human eye,Glaucoma is a disease that affects the human eye's vision. This sickness is regarded as an irreversible condition that causes eyesight degeneration. One of the most common causes of lifelong blindness is glaucoma in persons over the age of 40. Because of its trade-off between portability, size, and cost, fundus imaging is the most often utilised screening tool for glaucoma detection. Fundus imaging is a two-dimensional (2D) depiction of the three-dimensional (3D), semitransparent retinal tissues projected on to the imaging plane using reflected light. The idea plane that depicts the physical display screen through which a user perceives a virtual 3D scene is referred to as the "image plane”. The bulk of current algorithms for autonomous glaucoma assessment using fundus images rely on handcrafted segmentation-based features, which are influenced by the segmentation method used and the retrieved features. Convolutional neural networks (CNNs) are known for, among other things, their ability to learn highly discriminative features from raw pixel intensities. This work describes a computational technique for detecting glaucoma automatically. The major goal is to use a "image processing technique" to diagnose glaucoma using a fundus image as input. It trains datasets using a convolutional neural network (CNN). The Watershed algorithm is used for segmentation and is the most widely used technique in image processing. The following image processing processes are performed: region of interest, morphological procedures, and segmentation. This technique can be used to determine whether or not a person has Glaucoma. Keywords: Recommender system, item-based collaborative filtering, Natural Language Processing, Deep learning.

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