The Padding of Vein Image Features and Hardware Designs in M-Health Environments

2021 ◽  
pp. 125-158
Keyword(s):  
Cloud Computing ◽  
Median Filter ◽  
Threshold Value ◽  
Image Features ◽  
Moving Window ◽  
Gaussian Filter ◽  
Timing Diagrams ◽  
Segmentation Task ◽  
Hardware Designs ◽  
Distributed Cloud

This chapter describes the timing diagrams of padding features and hardware designs of segmentation, controllers, and filters. Further, the authors have described that the hardware design concept of segmentation task can be performed online in a distributed cloud computing m-health environment. The segmentation phase uses two Gaussian filter functions with different sizes of filter masks and standard deviation with a threshold value to make a distinction between veins image patterns and the corresponding backgrounds in the cloud IoT-based m-health environment. In order to design the hardware architecture of the median filter, the superior moving window architecture is used by researchers to accommodate a larger size median filter in the cloud IoT-based m-health environment.

Secure and Effective Key Management Using Secret Sharing Schemes in Cloud Computing

2020 ◽  
Vol 16 (1) ◽  
pp. 1-15
Author(s):  
Shahin Fatima ◽  
Shish Ahmad
Keyword(s):  
Cloud Computing ◽  
Secret Sharing ◽  
Key Management ◽  
Divided Difference ◽  
Threshold Value ◽  
Secret Sharing Schemes ◽  
Sharing Scheme ◽  
Random Threshold ◽  
Crucial Problem ◽  
Distributed Cloud

Security is a crucial problem in Cloud computing. Storing and accessing the data in the Cloud is very popular nowadays but the security of data is still lagging behind. Secret sharing schemes are widely used to improve the security of data. In this article, a threshold secret sharing scheme using Newton divided difference interpolating polynomial (TSSNIP) is proposed in a distributed Cloud environment to enhance security of keys used for encryption. The proposed method uses a Newton divided difference interpolating polynomial for key splitting and key reconstruction. A threshold value is used to reconstruct the shares in secret sharing schemes. The proposed work made use of dynamic and random threshold generation method to ensure security of key. The experimental output shows reduced execution time, better security, efficiency, and robustness in the proposed scheme. Furthermore, the proposed scheme also outperformed other secret sharing schemes.

scholarly journals Ultrasound Image Classification of Thyroid Nodules Using Machine Learning Techniques

Medicina ◽  
2021 ◽  
Vol 57 (6) ◽  
pp. 527
Author(s):  
Vijay Vyas Vadhiraj ◽  
Andrew Simpkin ◽  
James O’Connell ◽  
Naykky Singh Singh Ospina ◽  
Spyridoula Maraka ◽  
...  
Keyword(s):  
Decision Making ◽  
Sensitivity And Specificity ◽  
Thyroid Nodules ◽  
Median Filter ◽  
Ultrasound Image ◽  
Multiple Instance Learning ◽  
Image Features ◽  
Classification Model ◽  
Support Vector ◽  
Accuracy Score

Background and Objectives: Thyroid nodules are lumps of solid or liquid-filled tumors that form inside the thyroid gland, which can be malignant or benign. Our aim was to test whether the described features of the Thyroid Imaging Reporting and Data System (TI-RADS) could improve radiologists’ decision making when integrated into a computer system. In this study, we developed a computer-aided diagnosis system integrated into multiple-instance learning (MIL) that would focus on benign–malignant classification. Data were available from the Universidad Nacional de Colombia. Materials and Methods: There were 99 cases (33 Benign and 66 malignant). In this study, the median filter and image binarization were used for image pre-processing and segmentation. The grey level co-occurrence matrix (GLCM) was used to extract seven ultrasound image features. These data were divided into 87% training and 13% validation sets. We compared the support vector machine (SVM) and artificial neural network (ANN) classification algorithms based on their accuracy score, sensitivity, and specificity. The outcome measure was whether the thyroid nodule was benign or malignant. We also developed a graphic user interface (GUI) to display the image features that would help radiologists with decision making. Results: ANN and SVM achieved an accuracy of 75% and 96% respectively. SVM outperformed all the other models on all performance metrics, achieving higher accuracy, sensitivity, and specificity score. Conclusions: Our study suggests promising results from MIL in thyroid cancer detection. Further testing with external data is required before our classification model can be employed in practice.

scholarly journals Dense cellular segmentation for EM using 2D–3D neural network ensembles

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Matthew D. Guay ◽  
Zeyad A. S. Emam ◽  
Adam B. Anderson ◽  
Maria A. Aronova ◽  
Irina D. Pokrovskaya ◽  
...  
Keyword(s):  
Blood Platelets ◽  
Semantic Segmentation ◽  
3D Models ◽  
Image Features ◽  
Cell Models ◽  
Whole Cells ◽  
Large Numbers ◽  
The Creation ◽  
Segmentation Task ◽  
Neural Network Ensembles

AbstractBiologists who use electron microscopy (EM) images to build nanoscale 3D models of whole cells and their organelles have historically been limited to small numbers of cells and cellular features due to constraints in imaging and analysis. This has been a major factor limiting insight into the complex variability of cellular environments. Modern EM can produce gigavoxel image volumes containing large numbers of cells, but accurate manual segmentation of image features is slow and limits the creation of cell models. Segmentation algorithms based on convolutional neural networks can process large volumes quickly, but achieving EM task accuracy goals often challenges current techniques. Here, we define dense cellular segmentation as a multiclass semantic segmentation task for modeling cells and large numbers of their organelles, and give an example in human blood platelets. We present an algorithm using novel hybrid 2D–3D segmentation networks to produce dense cellular segmentations with accuracy levels that outperform baseline methods and approach those of human annotators. To our knowledge, this work represents the first published approach to automating the creation of cell models with this level of structural detail.

Using Distributed Cloud Computing to Solve Resource-Intensive Tasks

2021 ◽  
Vol 12 (5) ◽  
pp. 233-254
Author(s):  
D. Yu. Bulgakov ◽  
Keyword(s):  
Cloud Computing ◽  
Point Of View ◽  
Free Software ◽  
Production Environment ◽  
Learning Models ◽  
Python Programming Language ◽  
Task Processing ◽  
Python Programming ◽  
Distributed Cloud ◽  
Machine Learning Models

A method for solving resource-intensive tasks that actively use the CPU, when the computing resources of one server become insufficient, is proposed. The need to solve this class of problems arises when using various machine learning models in a production environment, as well as in scientific research. Cloud computing allows you to organize distributed task processing on virtual servers that are easy to create, maintain, and replicate. An approach based on the use of free software implemented in the Python programming language is justified and proposed. The resulting solution is considered from the point of view of the theory of queuing. The effect of the proposed approach in solving problems of face recognition and analysis of biomedical signals is described.

scholarly journals Layer contour characterization in additive manufacturing through image binarization

2021 ◽  
Vol 1193 (1) ◽  
pp. 012067
Author(s):  
D Blanco ◽  
A Fernández ◽  
P Fernández ◽  
B J Álvarez ◽  
F Peña
Keyword(s):  
Digital Images ◽  
Median Filter ◽  
Threshold Value ◽  
Image Binarization ◽  
Actual Contour ◽  
Filter Size ◽  
Characterization Procedure ◽  
Flat Bed Scanner ◽  
Selection Of ◽  
Excellent Coverage

Abstract On-Machine Measurement adoption will be key to dimensional and geometrical improvement of additively manufactured parts. One possible approach based on OMM aims at using digital images of manufactured layers to characterize actual contour deviations with respect to their theoretical profile. This strategy would also allow for in-process corrective actions. This work describes a layer-contour characterization procedure based on binarization of digital images acquired with a flat-bed scanner. This procedure has been tested off-line to evaluate the influence of two of the parameters for image treatment, the median filter size (S f ) and the threshold value (T), on the dimensional/geometrical reliability of the contour characterization. Results showed that an appropriate selection of configuration parameters allowed to characterize the proposed test-target with excellent coverage and reasonable accuracy.

scholarly journals Multi-Objective Service Placement Scheme Based on Fuzzy-AHP System for Distributed Cloud Computing

2019 ◽  
Vol 9 (17) ◽  
pp. 3550 ◽  
Author(s):  
A-Young Son ◽  
Eui-Nam Huh
Keyword(s):  
Energy Efficiency ◽  
Cloud Computing ◽  
Response Time ◽  
Data Centers ◽  
Cloud Data ◽  
Multi Objective ◽  
Service Placement ◽  
Service Response Time ◽  
Cloud Data Centers ◽  
Distributed Cloud

With the rapid increase in the development of the cloud data centers, it is expected that massive data will be generated, which will decrease service response time for the cloud data centers. To improve the service response time, distributed cloud computing has been designed and researched for placement and migration from mobile devices close to edge servers that have secure resource computing. However, most of the related studies did not provide sufficient service efficiency for multi-objective factors such as energy efficiency, resource efficiency, and performance improvement. In addition, most of the existing approaches did not consider various metrics. Thus, to maximize energy efficiency, maximize performance, and reduce costs, we consider multi-metric factors by combining decision methods, according to user requirements. In order to satisfy the user’s requirements based on service, we propose an efficient service placement system named fuzzy- analytical hierarchical process and then analyze the metric that enables the decision and selection of a machine in the distributed cloud environment. Lastly, using different placement schemes, we demonstrate the performance of the proposed scheme.

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