Cyber physical systems: A smart city perspective Khan, Firoz; Kumar, R. Lakshmana; Kadry, Seifedine ...
International journal of electrical and computer engineering (Malacca, Malacca),
08/2021, Letnik:
11, Številka:
4
Journal Article
Cyber-physical system (CPS) is a terminology used to describe multiple systems of existing infrastructure and manufacturing system that combines computing technologies (cyber space) into the physical ...space to integrate human interaction. This paper does a literature review of the work related to CPS in terms of its importance in today’s world. Further, this paper also looks at the importance of CPS and its relationship with internet of things (IoT). CPS is a very broad area and is used in variety of fields and some of these major fields are evaluated. Additionally, the implementation of CPS and IoT is major enabler for smart cities and various examples of such implementation in the context of Dubai and UAE are researched. Finally, security issues related to CPS in general are also reviewed.
Internet of Things (IoT) is transforming the technical setting of conventional systems and finds applicability in smart cities, smart healthcare, smart industry, etc. In addition, the application ...areas relating to the IoT enabled models are resource-limited and necessitate crisp responses, low latencies, and high bandwidth, which are beyond their abilities. Cloud computing (CC) is treated as a resource-rich solution to the above mentioned challenges. But the intrinsic high latency of CC makes it nonviable. The longer latency degrades the outcome of IoT based smart systems. CC is an emergent dispersed, inexpensive computing pattern with massive assembly of heterogeneous autonomous systems. The effective use of task scheduling minimizes the energy utilization of the cloud infrastructure and rises the income of service providers by the minimization of the processing time of the user job. With this motivation, this paper presents an intelligent Chaotic Artificial Immune Optimization Algorithm for Task Scheduling (CAIOA-RS) in IoT enabled cloud environment. The proposed CAIOA-RS algorithm solves the issue of resource allocation in the IoT enabled cloud environment. It also satisfies the makespan by carrying out the optimum task scheduling process with the distinct strategies of incoming tasks. The design of CAIOA-RS technique incorporates the concept of chaotic maps into the conventional AIOA to enhance its performance. A series of experiments were carried out on the CloudSim platform. The simulation results demonstrate that the CAIOA-RS technique indicates that the proposed model outperforms the original version, as well as other heuristics and metaheuristics.
•To construct a 3D model of the kidney blood vessel, an open-source software program is used.•The finite volume approach and SIMPLE scheme are used.•The non-Newtonian blood flow is considered as a ...laminar flow.•The impact of five non-Newtonian viscosity models on blood flow in the cerebral blood vessel represents flow and heat transfer performance in the reported parameters.
Nowadays, cardiovascular illnesses are among the leading causes of death in the world. Thus, many studies have been performed to diagnose and prevention of these diseases. Studies show that the computational hemodynamic method (CHD) is a very effective method to control and prevent the progression of this type of disease. In this computational paper, the impression of five non-Newtonian viscosity models (nNVMs) on cerebral blood vessels (CBV) is investigated by CHD. In this simulation, blood flow is supposed steady, laminar, incompressible, and non-Newtonian. The parameters of Nusselt number (Nu), dimensionless temperature (θ), pressure drop (Δp), and dimensionless average wall shear stress (DAWSS) are also investigated by considering the effects of heat generated by the body. Utilizing the FVM and SIMPLE scheme for pressure-velocity coupling is a good approach to investigating CBVs for five different viscosity models. In the results, it is shown that the θ and Δp+ increase with increasing Reynolds number (Re) in the CBVs. By enhancing the Re from 90 to 120 in the Cross viscosity model, the Δp+ changes about 1.391 times. The DAWSS grows by increasing the Re in all viscosity models. This increase in DAWSS leads to an increasing velocity gradient close to the cerebral vessel wall.
Modern technologies are widely used today to diagnose epilepsy, neurological disorders, and brain tumors. Meanwhile, it is not cost-effective in terms of time and money to use a large amount of ...electroencephalography (EEG) data from different centers and collect them in a central server for processing and analysis. Collecting this data correctly is challenging, and organizations avoid sharing their and client information with others due to data privacy protection. It is difficult to collect these data correctly and it is challenging to transfer them to research centers due to the privacy of the data. In this regard, collaborative learning as an extraordinary approach in this field paves the way for the use of information repositories in research matters without transferring the original data to the centers. This study focuses on the use of a heterogeneous client balancing technique with an interval selection approach and classification of EEG signals with ResNet50 deep architecture. The test results achieved an accuracy of 99.14 compared to similar methods.
Municipal solid waste (MSW)-to-energy systems have gained significant attention in recent years for their potential to produce renewable energy from waste. These systems involve the conversion of MSW ...into electricity, heat or fuel. One of the most promising applications of MSW-to-energy systems is the production of hydrogen, which is considered a clean and sustainable fuel. Machine learning algorithms have the potential to revolutionize the way MSW-to-energy systems are managed. The integration of machine learning into MSW-to-energy systems has the potential to significantly improve the sustainability and profitability of this industry. In this study, a novel integrated MSW-to-energy system is modeled to produce hydrogen, power, and oxygen and with capacities of heating water and air. Hydrogen production, power production, oxygen storage, hot water, hot air, and system emission are predicted using machine learning algorithms based on regression models with high validity and R2 values more than 99.8% having errors smaller than 1%. The reduced regression models are developed by eliminating the insignificant variables from the full algorithms using the analysis of variance. The findings reveal high accuracy for the reduced regression models while their errors slightly decrease to 2%. This suggests that the machine learning algorithms can also be used as an effective tool to further improve MSW-to-energy systems.
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•A novel municipal solid waste-to-energy system is modeled.•Hydrogen and oxygen are produced as well as electrical power, hot water and air.•Machine learning algorithms are developed using regression models.•Reduced regression models are developed using analysis of variance.•Machine learning algorithms show a high accuracy with errors less than 1%.
Categorization of cardiac abnormalities received from several centers is not possible within the quickest time because of privacy and security restrictions. Today, individuals’ security problem is ...considered as one of the most important research fields in most research sciences. This study provides a novel approach for detection of cardiac abnormalities based on federated learning (FL). This approach addresses the challenge of accessing data from remote centers and presents the possibility of learning without the need for transferring data from the main center. We present a novel aggregation approach in the FL for addressing the challenge of imbalanced data using the averaging stochastic weights (SWA) optimizer and a multivariate Gaussian in order to make a better and more accurate detection possible. The advantage of the present proposed approach is robust and secure aggregation for unbalanced electrocardiogram (ECG) data from heterogeneous clients. We were able to achieve 87.98% accuracy in testing with the robust VGG19 architecture.
This paper is concerning with the combination of two enhanced techniques to investigate the system efficiency of non-coherent spectral amplitude coding optical code division multiple access ...(SAC-OCDMA) that based upon zero cross correlation (ZCC) codes. These techniques are: the usage of semiconductor optical amplifier (SOA) method and the utilization of two code keying scheme. The outcomes obtained from OptiSystem simulator prove that the combination of these approaches enables a 5-channel non-coherent SAC-OCDMA system to transmit a data rate of 10 Gbps over 93 km distance at acceptable bit error rate (BER).
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