Weak and strong stabilization of linear systems

We study the problem of stabilizing a distributed linear system on a subregion of its geometrical domain. We are concerned with two methods： the first approach enables us to characterize a stabilizing control via the steady state Riccati equation, and the second one is based on decomposing the state space into two suitable subspaces and studying the projections of the initial system onto such subspaces. The obtained results are performed through various examples.     

DeepClassRooms: a deep learning based digital twin framework for on-campus class rooms

Neural Computing and Applications - A lot of different methods are being opted for improving the educational standards through monitoring of the classrooms. The developed world uses Smart...     

Fuzzy-based hunger games search algorithm for global optimization and feature selection using medical data

Neural Computing and Applications - Feature selection (FS) is one of the basic data preprocessing steps in data mining and machine learning. It is used to reduce feature size and increase model...     

Discrete GWO Optimized Data Aggregation for Reducing Transmission Rate in IoT

The conventional hospital environment is transformed into digital transformation that focuses on patient centric remote approach through advanced technologies. Early diagnosis of many diseases will improve the patient life. The cost of health care systems is reduced due to the use of advanced technologies such as Internet of Things (IoT), Wireless Sensor Networks (WSN), Embedded systems, Deep learning approaches and Optimization and aggregation methods. The data generated through these technologies will demand the bandwidth, data rate, latency of the network. In this proposed work, efficient discrete grey wolf optimization (DGWO) based data aggregation scheme using Elliptic curve Elgamal with Message Authentication code (ECEMAC) has been used to aggregate the parameters generated from the wearable sensor devices of the patient. The nodes that are far away from edge node will forward the data to its neighbor cluster head using DGWO. Aggregation scheme will reduce the number of transmissions over the network. The aggregated data are preprocessed at edge node to remove the noise for better diagnosis. Edge node will reduce the overhead of cloud server. The aggregated data are forward to cloud server for central storage and diagnosis. This proposed smart diagnosis will reduce the transmission cost through aggregation scheme which will reduce the energy of the system. Energy cost for proposed system for 300 nodes is 0.34μJ. Various energy cost of existing approaches such as secure privacy preserving data aggregation scheme (SPPDA), concealed data aggregation scheme for multiple application (CDAMA) and secure aggregation scheme (ASAS) are 1.3 μJ, 0.81 μJ and 0.51 μJ respectively. The optimization approaches and encryption method will ensure the data privacy.     

Self-generation of reward by logarithmic transformation of multiple sensor evaluations

Artificial Life and Robotics - Although the design of the reward function in reinforcement learning is important, it is difficult to design a system that can adapt to a variety of environments and...     

Design and simulation of autonomous military vehicle control system based on machine vision and ensemble movement approach

The Journal of Supercomputing - On the battlefield, early detection of armored vehicles can have a positive effect. Because according to this issue, timely and appropriate reactions can be done....     

Investigating the effect of varying block size on power and energy consumption of GPU kernels

The Journal of Supercomputing - Power consumption is likely to remain a significant concern for exascale performance in the foreseeable future. In addition, graphics processing units (GPUs) have...     

Design of backpropagated neurocomputing paradigm for Stuxnet virus dynamics in control infrastructure

Neural Computing and Applications - In the present study, a novel application of backpropagated neurocomputing heuristics (BNCH) is presented for epidemic virus model that portrays the Stuxnet...     

Advanced Authentication Mechanisms for Identity and Access Management in Cloud Computing

Identity management is based on the creation and management of user identities for granting access to the cloud resources based on the user attributes. The cloud identity and access management (IAM) grants the authorization to the end-users to perform different actions on the specified cloud resources. The authorizations in the IAM are grouped into roles instead of granting them directly to the end-users. Due to the multiplicity of cloud locations where data resides and due to the lack of a centralized user authority for granting or denying cloud user requests, there must be several security strategies and models to overcome these issues. Another major concern in IAM services is the excessive or the lack of access level to different users with previously granted authorizations. This paper proposes a comprehensive review of security services and threats. Based on the presented services and threats, advanced frameworks for IAM that provide authentication mechanisms in public and private cloud platforms. A threat model has been applied to validate the proposed authentication frameworks with different security threats. The proposed models proved high efficiency in protecting cloud platforms from insider attacks, single sign-on failure, brute force attacks, denial of service, user privacy threats, and data privacy threats.     

The Double Edge Sword Based Distributed Executor Service

Scalability is one of the most important quality attribute of software-intensive systems, because it maintains an effective performance parallel to the large fluctuating and sometimes unpredictable workload. In order to achieve scalability, thread pool system (TPS) (which is also known as executor service) has been used extensively as a middleware service in software-intensive systems. TPS optimization is a challenging problem that determines the optimal size of thread pool dynamically on runtime. In case of distributed-TPS (DTPS), another issue is the load balancing b/w available set of TPSs running at backend servers. Existing DTPSs are overloaded either due to an inappropriate TPS optimization strategy at backend servers or improper load balancing scheme that cannot quickly recover an overload. Consequently, the performance of software-intensive system is suffered. Thus, in this paper, we propose a new DTPS that follows the collaborative round robin load balancing that has the effect of a double-edge sword. On the one hand, it effectively performs the load balancing (in case of overload situation) among available TPSs by a fast overload recovery procedure that decelerates the load on the overloaded TPSs up to their capacities and shifts the remaining load towards other gracefully running TPSs. And on the other hand, its robust load deceleration technique which is applied to an overloaded TPS sets an appropriate upper bound of thread pool size, because the pool size in each TPS is kept equal to the request rate on it, hence dynamically optimizes TPS. We evaluated the results of the proposed system against state of the art DTPSs by a client-server based simulator and found that our system outperformed by sustaining smaller response times.