Mathematical Analysis and Performance Evaluation of the Cell-Free mMIMO Networks Based on Cognitive Relays

Wireless Personal Communications - The cell-Free massive multiple input multiple output “mMIMO” networks can provide a satisfied performance for the fifth generation “5G”...     

2D and 3D image localization,compression and reconstruction using new hybrid moments

In this article, we will present a new set of hybrid polynomials and their corresponding moments, with a view to using them for the localization, compression and reconstruction of 2D and 3D images. These polynomials are formed from the Hahn and Krawtchouk polynomials. The process of calculating these is successfully stabilized using the modified recurrence relations with respect to the n order, the variable x and the symmetry property. The hybrid polynomial generation process is carried out in two forms: the first form contains the separable discrete orthogonal polynomials of Krawtchouk–Hahn (DKHP) and Hahn–Krawtchouk (DHKP). The latter are generated as the product of the discrete orthogonal Hahn and Krawtchouk polynomials, while the second form is the square equivalent of the first form, it consists of discrete squared Krawtchouk–Hahn polynomials (SKHP) and discrete polynomials of Hahn–Krawtchouk squared (SHKP). The experimental results clearly show the efficiency of hybrid moments based on hybrid polynomials in terms of localization property and computation time of 2D and 3D images compared to other types of moments; on the other hand, encouraging results have also been shown in terms of reconstruction quality and compression despite the superiority of classical polynomials.

     

Detection of Abnormal Activities from Various Signals Based on Statistical Analysis

We perceive big data with massive datasets of complex and variegated structures in the modern era. Such attributes formulate hindrances while analyzing and storing the data to generate apt aftermaths. Privacy and security are the colossal perturb in the domain space of extensive data analysis. In this paper, our foremost priority is the computing technologies that focus on big data, IoT (Internet of Things), Cloud Computing, Blockchain, and fog computing. Among these, Cloud Computing follows the role of providing on-demand services to their customers by optimizing the cost factor. AWS, Azure, Google Cloud are the major cloud providers today. Fog computing offers new insights into the extension of cloud computing systems by procuring services to the edges of the network. In collaboration with multiple technologies, the Internet of Things takes this into effect, which solves the labyrinth of dealing with advanced services considering its significance in varied application domains. The Blockchain is a dataset that entertains many applications ranging from the fields of crypto-currency to smart contracts. The prospect of this research paper is to present the critical analysis and review it under the umbrella of existing extensive data systems. In this paper, we attend to critics' reviews and address the existing threats to the security of extensive data systems. Moreover, we scrutinize the security attacks on computing systems based upon Cloud, Blockchain, IoT, and fog. This paper lucidly illustrates the different threat behaviour and their impacts on complementary computational technologies. The authors have mooted a precise analysis of cloud-based technologies and discussed their defense mechanism and the security issues of mobile healthcare.

     

Schedule-Based Cooperative Multi-agent Reinforcement Learning for Multi-channel Communication in Wireless Sensor Networks

Wireless sensor networks (WSNs) have become an important component in the Internet of things (IoT) field. In WSNs, multi-channel protocols have been developed to overcome some limitations related to the throughput and delivery rate which have become necessary for many IoT applications that require sufficient bandwidth to transmit a large amount of data. However, the requirement of frequent negotiation for channel assignment in distributed multi-channel protocols incurs an extra-large communication overhead which results in a reduction of the network lifetime. To deal with this requirement in an energy-efficient way is a challenging task. Hence, the Reinforcement Learning (RL) approach for channel assignment is used to overcome this problem. Nevertheless, the use of the RL approach requires a number of iterations to obtain the best solution which in turn creates a communication overhead and time-wasting. In this paper, a Self-schedule based Cooperative multi-agent Reinforcement Learning for Channel Assignment (SCRL CA) approach is proposed to improve the network lifetime and performance. The proposal addresses both regular traffic scheduling and assignment of the available orthogonal channels in an energy-efficient way. We solve the cooperation between the RL agents problem by using the self-schedule method to accelerate the RL iterations, reduce the communication overhead and balance the energy consumption in the route selection process. Therefore, two algorithms are proposed, the first one is for the Static channel assignment (SSCRL CA) while the second one is for the Dynamic channel assignment (DSCRL CA). The results of extensive simulation experiments show the effectiveness of our approach in improving the network lifetime and performance through the two algorithms.

     

Architectural Design,Improvement, and Challenges of Distributed Software-Defined Wireless Sensor Networks

Wireless Personal Communications - Software-defined networking (SDN) is widely perceived to simplify network management and monitoring. The introduction of the SDN model into wireless sensor...     

Numerical modeling of ZnSnO/CZTS based solar cells

Numerical simulation has been performed to improve the performance of Cu2ZnSnS4 (CZTS) solar cells by replacing CdS with Zn1–xSnxO buffer layer. The influences of thickness, donor concentration and defect density of buffer layers on the performance of CZTS solar cells were investigated. It has been found that Zn1–xSnxO buffer layer for Sn content of 0.20 is better for CZTS solar cell. A higher efficiency can be achieved with thinner buffer layer. The optimized solar cell demonstrated a maximum power conversion efficiency of 13%.     

Novel chaos game optimization tuned-fractional-order PID fractional-order PI controller for load–frequency control of interconnected power systems

In this work, chaos game optimization (CGO), a robust optimization approach, is employed for efficient design of a novel cascade controller for four test systems with interconnected power systems (IPSs) to tackle load–frequency control (LFC) difficulties. The CGO method is based on chaos theory principles, in which the structure of fractals is seen via the chaotic game principle and the fractals’ self-similarity characteristics are considered. CGO is applied in LFC studies as a novel application, which reveals further research gaps to be filled. For practical implementation, it is also highly desirable to keep the controller structure simple. Accordingly, in this paper, a CGO-based controller of fractional-order (FO) proportional–integral–derivative–FO proportional–integral (FOPID–FOPI) controller is proposed, and the integral time multiplied absolute error performance function is used. Initially, the proposed CGO-based FOPID–FOPI controller is tested with and without the nonlinearity of the governor dead band for a two-area two-source model of a non-reheat unit. This is a common test system in the literature. A two-area multi-unit system with reheater–hydro–gas in both areas is implemented. To further generalize the advantages of the proposed scheme, a model of a three-area hydrothermal IPS including generation rate constraint nonlinearity is employed. For each test system, comparisons with relevant existing studies are performed. These demonstrate the superiority of the proposed scheme in reducing settling time, and frequency and tie-line power deviations.     

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.     

An industrial portrait background removal solution based on knowledge infusion

Background removal of an identity (ID) picture consists in separating the foreground (face, body, hair and clothes) from the background of the image. It is a necessary groundwork for all modern identity documents that also has many benefits for improving ID security. State of the art image processing techniques encountered several segmentation issues and offer only partial solutions. It is due to the presence of erratic components like hairs, poor contrast, luminosity variation, shadow, color overlap between clothes and background. In this paper, a knowledge infused approach is proposed that hybridizes smart image processing tasks and prior knowledge. The research is based on a divide and conquer strategy aiming at simulating the sequential attention of human when performing a manual segmentation. Knowledge is infused by considering the spatial relation between anatomic elements of the ID image (face feature, forehead, body and hair) as well as their “signal properties”. The process consists in first determining a convex hull around the person’s body including all the foreground while keeping very close to the contour between the background and the foreground. Then, a body map generated from biometric analysis associated to an automatic grab cut process is applied to reach a finer segmentation. Finally, a heuristic-based post-processing step consisting in correcting potential hair and fine boundary issues leads to the final segmentation. Experimental results show that the newly proposed architecture achieves better performances than tested current state-of-the-art methodologies including active contours, generalist popular deep learning techniques, and also two other ones considered as the smartest for portrait segmentation. This new technology has been adopted by an international company as its industrial ID foreground solution.

     

Radiation Synthesis and Magnetic Property Investigations of the Graft Copolymer Poly(Ethylene-g-Acrylic Acid)/Fe<Subscript>3</Subscript>O<Subscript>4</Subscript> Film

In this work, acrylic acid (AAc) monomer was grafted onto low-density polyethylene (LDPE) films by the direct method to obtain acid (LDPE-grafted poly(acrylic acid) (LDPE-g-PAAc)) graft copolymers. The presence of the grafted PAAc with COOH groups allows coupling with Fe^2+/3+ ions. The stabilization of Fe₃O₄ particles onto the graft copolymers was done by in situ reduction of LDPE-g-PAAc/Fe^2+/3+ with sodium borohydride (NaBH₄) in aqueous solution. The LDPE-g-PAAc graft copolymer and LDPE-g-PAAc/Fe₃O₄ composite films were characterized by scanning electron microscopy (SEM), X-ray diffraction (XRD), thermogravimetric analysis (TGA), Fourier transform infrared spectroscopy (FT-IR), differential scanning calorimetry (DSC), and electron spin resonance (ESR). The synthesized composites exhibit excellent magnetic properties. The results indicated that the magnetic oxide (Fe₃O₄) was embedded and homogenously dispersed into the surfaces of the graft copolymer films as indicated by SEM. The FT-IR analysis clearly suggests that an AAc monomer was effectively grafted onto LDPE. The XRD studies elucidate the change in the crystallinity of the graft copolymers.

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Graphical Abstract The procedures for magnetic film manufacture LDPE-g-PAAc/Fe₃O₄ using gamma irradiation technique