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Sample 1_Augmented Reality Reference in APA format Penza, V., Ortiz, J., Mattos, L. S., Forgione, A., & Momi, E. D XXXXXXXXXXDense soft tissue 3D reconstruction refined with super-pixel segmentation...

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Sample 1_Augmented Reality
    Reference in APA format
    Penza, V., Ortiz, J., Mattos, L. S., Forgione, A., & Momi, E. D XXXXXXXXXXDense soft tissue 3D reconstruction refined with super-pixel segmentation for robotic abdominal surgery. International Journal of Computer Assisted Radiology and Surgery, Volum 3, Issue 2, XXXXXXXXXX.
    URL of the Reference
    Level of Journal (Q1, Q2, …Qn)
    Keywords in this Reference
    DOI XXXXXXXXXX/s XXXXXXXXXX
    Journal Level: Q2
    Augmented reality, 3D surface reconstruction, Visualization, Depth estimation, Super pixel Segmentation
    The Name of the Cu
ent Solution (Technique/ Method/ Scheme/ Algorithm/ Model/ Tool/ Framework/ ... etc )
    The Goal (Purpose) of this Solution & What is the Problem that need to be solved
    What are the components of it?
     Techniques:
· Stereoscopic imaging
· 3D reconstruction
· Non-parametric modified census transformation
· Simple Linear Iterative Clustering (SLIC)
· (super pixel segmentation)
Tools:
CT Scan
OpenCV
Stereoscopic Cameras (288x360)
Applied Area:
Liver Surgery
    Problem: the author identified the problem in the complexity of minimally invasive surgery where surgeons are limited to movements and visualization of dense soft tissues accurately.
Purpose (Goal): to propose a system that will enhance intra-operative visualization by exploiting 3D information about the surgical site using SLIC refinement.
    Pre-Operation:
· CT Scan
· Extraction of organ models
· Creation of virtual environment
· Real-virtual camera cali
ation
During Surgery:
· Real-virtual patient registration
· Dense 3D reconstruction
    The Process (Mechanism) of this Work; Means How the Problem has Solved & Advantage & Disadvantage of Each Step in This Process
        
    Process Steps - Pre Operation
    Advantage
    Disadvantage (Limitation)
    1
    CT Scan
    
    
    2
    Extraction of 3D surface models
    
    
    3
    Real-virtual camera cali
ation
    Cali
ated using OpenCV Li
ary
It is able to remove artifacts from the images; thus enhancing quality.
    
    
    Process Steps - During Surgery
    Advantage
    Disadvantage (Limitation)
    1
    Semi-automatic registration of virtual environment on the real patient (not stated if uses machine learning). They use “virtual assistance system”
This semi-automatic registration is done using real-virtual camera plane and the view of the abdomen where it is possible to run the visualization.
    Produces the view of the model for visualization
    
    2
    Soft tissue 3D surface reconstruction
Composed of 2 different approaches in which the author called it “Method 1” and “Method 2”
    
    
    3
    Equalization
Histogram Equalization
Used as (equalizedHist) in OpenCV Li
ary.
Reconstruction Method 1 (Consists of 3 steps)
1. Matching Computation Cost
Absolute intensity Difference (AD)
Matching is done to determine the similarity of the intensity of the pixels of the left and right images to detect and eliminate e
ors.
2. Aggregation Cost Computation
Sum of Absolute Difference (SAD)
A function to combine these pixels into a more meaningful data set using sum of absolute difference.
3. Disparity Computation
Winner takes all strategy computation (WTA)
The disparity computation is responsible for validating the matched pixels on the surface by using winner takes all from the values.
Method 1 is a surface reconstruction technique which uses equalizations to improve the details within the surface area.
    Histogram Equalization – improves the contrast and enhances the details that are over or under exposed.
AD – To calculate the similarity between two pixels.
SAD – the sum of Absolute difference from the computed value in matching cost
WTA – used to find the minimum aggregation cost value at each pixel. Helps validate the co
ect pixels.
    Problems in disparity refinement can arise if the motion and tissue deformation is not identified.
    4
    Reconstruction Method 2 (3 main steps without equalization)
1. Matching cost computation
Non parametric census transform
Matching is done to determine the similarity of pixels of the images in a moving window for comparison.
2. Aggregation cost computation
Sum of Hamming Distance (SHD)
A function to combine these pixels into a more meaningful data set using sum of Hamming Distance.
3. Disparity Computation
The disparity computation is responsible for validating the matched pixels on the surface by using winner takes all from the values.
Method 2 is also a surface reconstruction which does not use equalization like method 1, but it uses NCT to track the moving pixels within the surface area.
    NCT – converts each pixel inside a moving window into a string of bits that represents neighbour pixels for comparison with central pixel and mean pixels within.
SHD – compares the bit of strings that represents the pixels and identifies the position to decrease the computational time of the algorithm.
    Problems in disparity refinement can arise if the motion and tissue deformation is not identified.
    5
    
The SLIC refinement is a Superpixels segmentation process where it fills the holes disparity map and produces better reconstructed images.
This method is applied after the initial disparity map is constructed then will be refined through this algorithm to fill the holes and create a more dense disparity map.
    SLIC provides better matched pixels because the disparity map will have less holes; thus, will provide better output for visualization in the AR.
    There is a trade-off between accuracy and computation time. SLIC adds about 0.1s in computation time but will increase the accuracy of matched pixels providing a dense disparity map.
    Major Impact Factors in this Work
    
    Dependent Variable
    Independent Variable
    Accuracy of e
or mapping
    Using 2 heart phantoms(Method 2 with SLIC refinement which is most accurate), it was able to achieve an accuracy in disparity e
or mapping of :
Heart 1 – 1.75mm
Heart 2 – 1.79mm
    Processing time (computational time)
    1.29s / frame and 1.30s / frame (using image resolution of 288x360)
SLIC refinement adds about 0.1s for both heart phantoms.
    Depth Perception (pixels matching)
    Using 2 heart phantoms(Method 2 with SLIC refinement), it was able to match pixels with the percentage of :
Heart 1 – 72.6% matched pixels
Heart 2 – 66.5% matched pixels
    
    Input and Output
    Feature of This Solution
    Contribution & The Value of This Work
    
    Input - Pre Operation
    Output - Pre Operation
    CT Scan Data
    Preoperative 3D model

    Input - During Surgery
    Output - During Surgery
    3D model
    AR view
    
This proposed enhanced system was able to provide accurate results which was able to address, instrument interaction with tissue,
eathing, heartbeat using depth estimation methods (real-time 3D reconstruction with Superpixels refinements).
The most notable feature in this system is the ability to create a 3D reconstruction of the preoperative model while in the intraoperative considering other factors such movements, and deformations. This enables better visualizations for the surgeon as they are aware of real-time happenings within the surgery as there are always uncertainty that can happen which are not planned.
    
The main contribution of this method is to be able to provide a “virtual assistive system” in which it is able to do 3D reconstructions which overcomes the limitations of the state of the arts limitation. Reconstructing the soft tissues in real-time will provide better visualization for the surgeons as they will see deformations happening and will update based from the previous frame and not the original CT data. Though the main contribution of the paper is within the segmentations refinement method which is SLIC refinements where it fills the holes of the disparity map by matching the pixels to form a better disparity map that will be inserted in the virtual environment.
    1. what in the method could have been better?
    2. what in the author analyses were missed?
    3. was there a technique that could have been used, or a question that could have been asked, that the researchers did not use or ask?
    
Based on the proposed method, the segmentation phase that does the disparity map refinement added on the computational time. There have been modifications within the SLIC algorithm that increases computational speed and accuracy with pixel matching; thus would improve the overall refinement and the accuracy of the system.
    Surgical tools may be a hindrance to this proposed solution since large occlusions may cause the 3D reconstruction not to identify the soft tissue or deformation accurately since it relies on the previous data and not the preoperative data.
     Occlusion handling could be addressed in a form of additional technique or integrate a system that would address the tools such as geometric aware systems where in would register the tools in the preoperative phase.
    4. were the conclusion justified and How?
    Analyse This Work By Critical Thinking
    The Tools That Assessed this Work
    The conclusions were justified since the main goal of the proposed system is to enhance visualization of the state of the art by proposing 3D reconstruction methods to defeat the static nature of preoperative CT scans. The used a novel SLIC refinement algorithm to enhance the accuracy of the images in terms of disparity maps that will be used in the virtual environment. They were also able to provide suggestions to improve their system, such stating that they would use a GPU based workstation.
    The proposed system was able to overcome common problems of AR systems in which it relies heavily on pre-operative data. This solution uses dense 3D reconstruction so that it is able to handle soft tissue deformations happening in real-time. They also use SLIC refinement to improve the accuracy which increased the matched pixels of the disparity map outputs by up to 70% compared to the non-refined disparity maps(images).
However, there are still limitations to this system. It focuses mainly on reconstructing the soft tissue deformations but given if there are large occlusions(tools) within the area of the reconstructions, it will not be able to accurately reconstruct the tissue which is changing. This can be solved by using geometrically aware systems which seen in other proposed methods within AR.
     The experiment was ca
ied out using phantoms hearts where there are 2 methods involved that uses 3D reconstructions. Method 1 with SLIC refinement was able to provide 3.11mm and 72.6% matched pixels within 1.21s.
Method 2 with SLIC refinement was able to achieve even better results with 1.75mm and 72.6% matched pixels within 1.29s.
Sample 2_Networking
    Reference in APA format
    Wu, J., Cheng, B., Wang, M., & Chen, J XXXXXXXXXXPriority-Aware FEC Coding for High-Definition Mobile Video Delivery Using TCP. IEEE Transactions On Mobile Computing, 16(4), XXXXXXXXXX.
    URL of the Reference
    Level of Journal (Q1, Q2, …Qn)
    Keywords in this Reference
    http:
ieeexplore.ieee.org.ezproxy.csu.edu.a u/stamp/stamp.jsp?amumber=7498667 
    Q2
    · High definition mobile video 
· TCP
· Stringent delay constraint
· Forward e
or co
ection
· Priority-awareness
· Wireless networks
    The Name of the Cu
ent Solution (Technique/ Method/ Scheme/ Algorithm/ Model/ Tool/ Framework/ ... etc )
    The Goal (Purpose) of this Solution & What is the Problem that need to be solved
    What are the components of it?
    Techniques:
Priority Aware and TCP Oriented coding (PATON)
    Problem: delivering HD video using TCP over wireless network seems challenging because of high transmission rate and stringent delay constraint of HD video, bandwidth limited and e
or prone wireless network, and the retransmission scheme of TCP that causes delay.
Goal: to
Answered Same Day Aug 11, 2020 Swinburne University of Technology

Solution

Monika answered on Aug 11 2020
138 Votes
Cloud computing with AES encryption for Health secto
(
18
Cloud
computing with AES encryption for Health secto
)
Table of contents
1) Journal 1
2) Journal 2
3) Journal3
    Version 1.0 _ Week 1 (5 Journal Papers from CSU Li
ary)
    1
    Reference in APA format that will be in 'Reference List'
    Babitha,M.P. & Babu Ramesh, K.R.(2016). Secure cloud storage using AES encryption
    Citation that will be in the content
    
    URL of the Reference
    Level of Journal (Q1, Q2, …Qn)
    Keywords in this Reference
    
https:
ieeexplore.ieee.org/document/7877709/keywords
     Q1
    Cloud Computing
AES Encryption
Data security
    The Name of the Cu
ent Solution (Technique/ Method/ Scheme/ Algorithm/ Model/ Tool/ Framework/ ... etc )
    The Goal (Objective) of this Solution & What is the Problem that need to be solved
    What are the components of it?
    Technique/Algorithm name:
Secure cloud storage using AES encryption
Tools:
Advanced Encryption standard
Applied Area:
La
Medical
Hospital
    Problem: due to the multiple users and multiple users, data accessibility and privacy is very crucial. So there is a need to cloud data before encrypted.
Goal: data is secured by encryption. The advanced encryption standard performed both encryption and decryption process. The main goal is to make data secure and scalable while patient centric.
    
Cloud serve
Personal Health Records
Personal Doctor, Family, Friends
Emergency department
    The Process (Mechanism) of this Work; The process steps of the Technique/system
    
    
    Process Steps
    Advantage (Purpose of this step)
    Disadvantage (Limitation/Challenge)
    1
    Initially attribute is selected by owner and then provide access right in public domain. File encrypted with AES and ABS and data stored on cloud server. User request data from cloud server. After that access key is provide to emergency department by owner.
    Data encrypted: specific data of a patient share with doctor but others are not able to see information
    Security issue: the uploaded data in the cloud can be modified and tempered.
    2
    AES is more suitable in encryption. ABS is more expensive. So data encrypted with AES 128 key and again data decrypted with AES 128.
    Improved Processing time.
    
    3
    The encrypted data and encrypted data key both are stored on cloud server.
    Improved performance.
    
    4
    Data of patient records stored on cloud server by using AES technique. Data is more secure and private.
    Secured privacy
    
    5
    
    
    
    Validation Criteria (Measurement Criteria)
    
    Dependent Variable
    Independent Variable
    Attributes
    Access keys
    
    Delegates Keys
    
     Encrypted data using AES
    
    Processing time
    
    
    
    
    
    Input and Output
    Critical Thinking: Feature of this work, and Why (Justify)
    Critical Thinking: Limitations of the research cu
ent solution, and Why (Justify)
        Input (Data)
    Output (View)
    AES encrypted data
    X-Ray
     It has Data Privacy and security of Electronic health records (EHR) on cloud server.
    Data hacking is the main limitation of this research. To secure unauthorized access of sensitive data, encrypted standard are used. Sometime co
ectness of end results such as X ray is affected by the laziness of cloud server.
    (Describe the research/cu
ent solution)
    Evaluation Criteria
    How this research/cu
ent solution is valuable for your project
    There are several cryptographic algorithm are used for encryption of data....
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