Online material 1 for the article: Towards a Clinical Implementation of Measuring the Elastic Modulus of the Aorta from Cardiac Computed Tomography Images

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Even though ,my work is under construction,  I have uploaded some recordings with 5g network proof.

This dataset is in support of my 2 Research Papers.  This dataset is in support of my research paper     " initially submitted to IEEE 

Paper Abstract

Instructions: 

Read Me

  1. This is an open access ,so everything  can be downloaded after login (free signup). You have to click on 'Title'.
  2. I am just doing this work as I have noticed many things have been shown as false and some people falsely accused as spreading dis-information (by some people or media etc.).
  3.  Those who wish can use this as legitimate scientific proof,  in court. Any doctor or physics professor or school student (depending on hypocrisy ,as elementary or this is taught in PG/phd) may tell what this reading is? Even Published by WHO, as guidelines.
  4.  Others can use this for research or making policies .
  5.  I wont be able to get live data from hospitals or  other places, due to restrictions, (I m not govt agency). Moreover, they try to confiscate IDs (happened with me).  So Rest all will be simulation and results, which are also considered as scientific evidence.
  6.  As I am still in this field (only because of pending research papers but looking non r&d job), so will make model & submit but models made by me, wont be uploaded except results obtained from them.
  7.  Details of model block diagram and parameters  will be in the Research Paper.
  8.   5gproof.zip has screenshots from wifi detection

(1) The folder 'Experimental' has recordings of Magnetic fields measured using  Magnetic Sensor,  mobile app(software) and mobile phone

  • Physical Magnetic Sensor(hardware)

                  Resolution of the sensor is 0.0976 uT

                  Maximum Range of the sensor : 3000.0044 uT

  • Physical orientation and angular velocity  Sensor  (hardware)

                        Resolution of the sensor is 0.0012216975 rad/s

                        Maximum Range of the sensor : 34.90549 rad/s

  •  Physical Proximity Sensor (hardware)

                          Resolution of the sensor : 1.0 cm 

                          Maximum Range of the sensor : 5.0 cm

  •  Physical Gravity Sensor (hardware)

                        Resolution of the sensor :  0.01 m/s2

                       Maximum Range of the sensor :156.98999 m/s2

 

Experimental Result

Lowest Recorded Reading : 11 uT

Highest Recorded Reading : 479 uT

 

Around 300 uT can be measured anywhere, if nearby has 5G equipment ( fluctuates to 50 uT then 111, then 200 , 286,  ...)    .   

Reading of 479 was measured, as few people were feeling unwell and when I checked, it was 420 uT, stationary and fluctuating to it around but that is not recorded.   So after some time, this was recorded.

 

Scripts

Script is uploaded on only IEEE Codeocean, pls  follow instructions in the capsule given in ReadMe.txt.  Capsule of IEEE Codeocean (Matlab) is:

  • Code:

Funding: There are no funders for this submission. The  author has himself fully self-financed.

Acknowlegement :  The author as such thankful to none.

 

The model used is given in  https://dx.doi.org/10.21227/9ab4-tv57

But for this study, extra circuits has been added, details are given in the Paper

 (2)  There are  main  folders

  • Simulation_   Hz
  • Simulation_   Hz
  • Simulation_ 
  • Simulation_  
  • Simulation_ 
  • Simulation_4G
  • Simulation_5G
  • Experimental

(3)

 The folder 'Simulation_4G' has folder

  • Heart
  • Ear
  • Tissue
  • Lungs

(4) The folder 'Simulation_5G' has 3  sub-folders

  • Low
  • Mid
  • High

(5) Each of the subfolder of 5G has folders

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The world faces difficulties in terms of eye care, including treatment, quality of prevention, vision rehabilitation services, and scarcity of trained eye care experts. Early detection and diagnosis of ocular pathologies would enable forestall of visual impairment. One challenge that limits the adoption of computer-aided diagnosis tool by ophthalmologists is the number of sight-threatening rare pathologies, such as central retinal artery occlusion or anterior ischemic optic neuropathy, and others are usually ignored.

Instructions: 

The dataset is divided into two parts:

A. RFMiD_All_Classes_Dataset: It consists of

1. Original color fundus images (3200 images divided into a training set (1920 images), validation (640 images), and testing set (640 images) - PNG Files)

2.  Groundtruth Labels for normal and abnormal (comprising of 45 different types of diseases/pathologies) categories (Divided into training, validation, and testing set - CSV Files)

 

B. RFMiD_Challenge_Dataset: It consists of

1. Original color fundus images (3200 images divided into a training set (1920 images), validation (640 images), and testing set (640 images) - PNG Files)

2. Groundtruth Labels for 28* different categories (Divided into training, validation, and testing set - CSV Files)

 

* The diseases having more than 10 images belong to an independent class and all other disease categories are merged and labeled as “OTHER”. This finally constitutes 28 classes for disease classification.

 

Detailed instructions about this dataset are available on the challenge website: https://riadd.grand-challenge.org/.

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2144 Views

This dataset contains 1944 data, which are scanned by the HIS-RING PACT system.

the data sampling rate of our system is 40 MSa/s, a 128-elements 2.5MHz full-view ring-shaped transducer with 30mm radius. 

 continuous updating.....

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62 Views

This is our generated 3890 3D skin vessel tissue models which could be used for medical image analysis such as classification, segmentation, reconstruction and quantitative medical image analysis.

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124 Views

This dataset contains 1216 data, which are scanned by HIS-RING PACT system.

the data sampling rate of our system is 40 MSa/s, a 128-elements 2.5MHz full-view ring-shaped transducer with 30mm radius. 

 continuous updating.....

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230 Views

Diabetic Retinopathy is the second largest cause of blindness in diabetic patients. Early diagnosis or screening can prevent the visual loss. Nowadays , several computer aided algorithms have been developed to detect the early signs of Diabetic Retinopathy ie., Microaneurysms. The AGAR300 dataset presented here facilitate the researchers for benchmarking MA detection algorithms using digital fundus images. Currently, we have released the first set of database which consists of 28 color fundus images, shows the signs of Microaneurysm.

Instructions: 

The files corresponding to the work reported in paper titled " A novel automated system of discriminating Microaneurysms in fundus images”. The images  are taken from Fundus photography machine with the resolution of 2448x3264. This dataset contains Diabetic Retinopathy images and users of this dataset should cite the following article.

 

D. Jeba Derwin, S. Tamil Selvi, O. Jeba Singh, B. Priestly Shan,”A novel automated system of discriminating Microaneurysms in fundus images”, Biomedical Signal Processing and Control,Vol.58, 2020, pages: 101839,ISSN 1746-8094, https://doi.org/10.1016/j.bspc.2019.101839.

(http://www.sciencedirect.com/science/article/pii/S1746809419304203)

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1070 Views

The dataset is genrated by the fusion of three publicly available datasets: COVID-19 cxr image (https://github.com/ieee8023/covid-chestxray-dataset), Radiological Society of North America (RSNA) (https://www.kaggle.com/c/rsna-pneumonia-detection-challenge), and U.S.  national  library  of  medicine  (USNLM) collected  Montgomery  country - NLM(MC) (http

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929 Views

We chose 8 publicly available CT volumes of COVID-19 positive patients which were available from https://doi.org/10.5281/zenodo.3757476 and used 3D slicer to generate volumetric annotations of 512*512 dimension for 5 lung lobes namely right upper lobe, right middle lobe, right lower lobe, left upper lobe and left lower lobe. These annotations are validated by a radiologist with over 15 years of experience. 

Instructions: 

CT volumes can be downloaded from https://doi.org/10.5281/zenodo.3757476

Volumetric annotations for 5 lobe segments namely right upper lobe, right middle lobe, right lower lobe, left upper lobe and left lower lobe are saved as segments 1 to 5 respectively. 

For scans with prefix coronacases_00x their corresponding annotations are uploaded with suffix lobes

The scans and annotations measure 512*512 and are in .nii format

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