Optimal Resource Allocation and Task Segmentation in IoT Enabled Mobile Edge Cloud

<div>This work examines the convex optimization problem. The objective is to minimize the task duration by optimal allocation of the resources like local and edge computational capabilities, transmission power, and optimal task segmentation. For optimal allocation of resources, an algorithm name Estimation of Optimal Resource Allocator (EORA) is designed to optimize the function by keeping track of statistics of each candidate of the population. </div>

Optimal Resource Allocation and Task Segmentation in IoT Enabled Mobile Edge Cloud

<div>This work examines the convex optimization problem. The objective is to minimize the task duration by optimal allocation of the resources like local and edge computational capabilities, transmission power, and optimal task segmentation. For optimal allocation of resources, an algorithm name Estimation of Optimal Resource Allocator (EORA) is designed to optimize the function by keeping track of statistics of each candidate of the population. </div>

The methodology of synthesis of dynamically reconfigurable computing systems with temporal partitioning of homogeneous resources

The main motivation factors for the proposed research were the increase of cost-efficiency of FPGA based systems and the simplification of the design process. The first factor is optimization of design in multi-parametric constraint space. The second factor is the design of reconfigurable systems based on higher level of abstraction in a form of macro-functions rather than conventional HDL primitives. Main goal of this work was to create a methodology for automated cost-effective design synthesis of FPGA systems by utilizing temporal partitioning concept. Temporal partitioning provides powerful mechanism that allows to design cost-effective multi-parametrically optimized architectures. Another feature of these architectures is the ability for run-time self-restoration from hardware faults. As the result of the proposed research this methodology was created and successfully verified on the first prototype of Multi-mode Adaptive Reconfigurable System (MARS) with embedded Temporal Partitioning Mechanism (TPM). A special CAD software system was developed for automated application programming, automated task segmentation, and further high-level synthesis of segment specific processors (SSPs). Several novel methodologies were proposed, developed, and verified including: a methodology for creation of macro-operators (MOs) and associated set of optimized virtual hardware components (VHCs); an automated task segmentation methodology and synthesis of segment specific processors from the VHCs; methodology for integration of fault tolerance mechanisms with the self-restoration capability. The latter mechanism made possible the mitigation of transient and permanent hardware faults in run-time. The proof-of-concept component of this research consists of implementation of the above methodologies and mechanisms in the special software CAD system and verification on the experimental setup based on the prototype of system with TPM (MARS platform). As the result, all the developed methodologies and architectural solutions were tested and their effectiveness was demonstrated.

The methodology of synthesis of dynamically reconfigurable computing systems with temporal partitioning of homogeneous resources

The main motivation factors for the proposed research were the increase of cost-efficiency of FPGA based systems and the simplification of the design process. The first factor is optimization of design in multi-parametric constraint space. The second factor is the design of reconfigurable systems based on higher level of abstraction in a form of macro-functions rather than conventional HDL primitives. Main goal of this work was to create a methodology for automated cost-effective design synthesis of FPGA systems by utilizing temporal partitioning concept. Temporal partitioning provides powerful mechanism that allows to design cost-effective multi-parametrically optimized architectures. Another feature of these architectures is the ability for run-time self-restoration from hardware faults. As the result of the proposed research this methodology was created and successfully verified on the first prototype of Multi-mode Adaptive Reconfigurable System (MARS) with embedded Temporal Partitioning Mechanism (TPM). A special CAD software system was developed for automated application programming, automated task segmentation, and further high-level synthesis of segment specific processors (SSPs). Several novel methodologies were proposed, developed, and verified including: a methodology for creation of macro-operators (MOs) and associated set of optimized virtual hardware components (VHCs); an automated task segmentation methodology and synthesis of segment specific processors from the VHCs; methodology for integration of fault tolerance mechanisms with the self-restoration capability. The latter mechanism made possible the mitigation of transient and permanent hardware faults in run-time. The proof-of-concept component of this research consists of implementation of the above methodologies and mechanisms in the special software CAD system and verification on the experimental setup based on the prototype of system with TPM (MARS platform). As the result, all the developed methodologies and architectural solutions were tested and their effectiveness was demonstrated.

Automatic Functional Shoulder Task Identification and Sub-Task Segmentation Using Wearable Inertial Measurement Units for Frozen Shoulder Assessment

Advanced sensor technologies have been applied to support frozen shoulder assessment. Sensor-based assessment tools provide objective, continuous and quantitative information for evaluation and diagnosis. However, the current tools for assessment of functional shoulder tasks mainly rely on manual operation. It may cause several technical issues to the reliability and usability of the assessment tool, including manual bias during the recording and additional efforts for data labeling. To tackle these issues, this pilot study aims to propose an automatic functional shoulder task identification and sub-task segmentation system using inertial measurement units to provide reliable shoulder task labeling and sub-task information for clinical professionals. The proposed method combines machine learning models and rule-based modification to identify shoulder tasks and segment sub-tasks accurately. A hierarchical design is applied to enhance the efficiency and performance of the proposed approach. Nine healthy subjects and nine frozen shoulder patients are invited to perform five common shoulder tasks in the lab-based and clinical environments, respectively. The experimental results show that the proposed method can achieve 87.11% F-score for shoulder task identification, and 83.23% F-score and 427 mean absolute time errors (milliseconds) for sub-task segmentation. The proposed approach demonstrates the feasibility of the proposed method to support reliable evaluation for clinical assessment.

Improved Version of Graph-Cut Algorithm for CT Images of Lung Cancer With Clinical Property Condition

In a clinical evaluation, the detection of lung cancer is a challenging task. Segmentation methods are used to detect the extra growing nodule. Early diagnosis of lung cancer is significant in clinical research. The early stage of lung nodules is very soft tissues and tough to segment accurately. Generally, conservative graph cut methods are very weak to detect those soft edges in medical images. In this article, the proposed algorithm is improved to obtain the accuracy of the process to segment the edges than the conventional graph cut methods. This investigation is executed to shows the accuracy of lung segmentation.