No. 1 (2023)
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Published:
2023-03-30
ARTICLES FROM THIS ISSUE
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Beam Pattern Optimization Via Unequal Ascending Clusters
Abstract
In this paper, two different architectures based on completely and sectionally clustered arrays are proposed to improve the array patterns. In the wholly clustered arrays, all elements of the ordinary array are divided into multiple unequal ascending clusters. In the sectionally clustered arrays, two types of architectures are proposed by dividing a part of the array into clusters based on the position of specific elements. In the first architecture of sectionally clustered arrays, only those elements that are located on the sides of the array are grouped into unequal ascending clusters, and other elements located in the center are left as individual and unoptimized items (i.e. uniform excitation). In the second architecture, only some of the elements close the center are grouped into unequal ascending clusters, and the side elements were left individually and without optimization. The research proves that the sectionally clustered architecture has many advantages compared to the completely clustered structure, in terms of the complexity of the solution. Simulation results show that PSLL in the side clustered array can be reduced to more than −28 dB for an array of 40 elements. The PSLL was −17 dB in the case of a centrally clustered array, whereas the complexity percentage in the wholly clustered array method was 12 .5 %, while the same parameter for the partially clustered array method equaled 10%.
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Synthesis of Reconfigurable Multiple Shaped Beams of a Concentric Circular Ring Array Antenna Using Evolutionary Algorithms
Abstract
The approach described in this paper uses evolutionary algorithms to create multiple-beam patterns for a concentric circular ring array (CCRA) of isotropic antennas using a common set of array excitation amplitudes. The flat top, cosec2, and pencil beam patterns are examples of multiple-beam patterns. All of these designs have an upward angle of θ = 0◦. All the patterns are further created in three azimuth planes (φ = 0◦, 5◦, and 10◦). To create the necessary patterns, non-uniform excitations are used in combination with evenly spaced isotropic components. For the flat top and cosecant-squared patterns, the best combination of common components, amplitude and various phases is applied, whereas the pencil beam pattern is produced using the common amplitude only. Differential evolutionary algorithm (DE), genetic algorithm (GA), and firefly algorithm (FA) are used to generate the best 4-bit discrete magnitudes and 5-bit discrete phases. These discrete excitations aid in lowering the feed network design complexity and the dynamic range ratio (DRR). A variety of randomly selected azimuth planes are used to verify the excitations as well. With small modifications in the desired parameters, the patterns are formed using the same excitation. The results proved both the efficacy of the suggested strategy and the dominance of DE over GA as well as FA.
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CG-SCMA Codebook Design Based on Maximized Euclidian Distance
Abstract
Sparse code multiple access (SCMA) is a multi-dimensional codebook based on a class of non-orthogonal multiple access (NOMA) technologies enabling the delivery of non-orthogonal resource elements to numerous users in 5G wireless communications without increasing complexity. This paper proposes a computer-generated sparse code multiple access (CG-SCMA) technique, where the minimum Euclidian distance (MED) of a star 16-point quadrature amplitude modulation is maximized by CG-SCMA, thus creating a complex SCMA codebook based on optimizing the difference between the first and other radiuses over rotated constellations. To specify the most suitable values for this constellation, it is divided into four sub-constellations using trellis coded modulation (TCM) in an effort to optimize MED. The new codebook has four sub-constellations with MED values of 3.85, 2.26, 2.26, and 3.85, respectively. Application of the message passing algorithm (MPA) ensures low complexity of the decoding process
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A New Class of Fractional Cumulative Residual Entropy - Some Theoretical Results
Abstract
In this paper, by differentiating the entropy’s generating function (i.e., h(t) = R SX̄F tX (x)dx) using a Caputo fractional-order derivative, we derive a generalized non-logarithmic fractional cumulative residual entropy (FCRE). When the order of differentiation α → 1, the ordinary Rao CRE is recovered, which corresponds to the results from first-order ordinary differentiation. Some properties and examples of the proposed FCRE are also presented.
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Concept of Joint Functioning of Access Control Systems
Abstract
Modern integrated information and telecommunication systems are upgraded on a continuous basis. Such systems contain both new and old components. The approaches to developing individual components of access control systems are different in the majority of cases. As a rule, modernization of outdated but efficient systems that have been operating without any failures for long periods of time is economically unfeasible. Such an approach requires that different subsystems function based on shared data. This necessitates the coordination of various access control systems in order to ensure proper information security levels. This article examines how joint functioning of various versions of access control systems deployed in IT and telecommunication spheres may be achieved at the stage of their modernization. Potential ways in which information flows may bypass the security policies of one of the access control systems concerned are determined. The authors discuss traditional access control models. For role-based and thematic access control models, specific hypotheses are formulated to comply with security policies when different versions of access control systems work together. The structure of the model assuming that different versions of access control systems operate jointly has been developed. Based on the model, the necessary and sufficient conditions are determined under which unauthorized information flows are prevented. The security theorem for the joint functioning of different versions of access control systems is presented and proved. The results of the study showed that the methodological basis for coordinating access control models applicable to information and telecommunication systems undergoing modernization consists in observing, separately, the equality of information flows between shared objects in each of the versions of the access control systems. The approaches developed in this article can be extended to combined access control systems.
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Adaptive Rider Feedback Artificial Tree Optimization-Based Deep Neuro-Fuzzy Network for Classification of Sentiment Grade
Abstract
Sentiment analysis is an efficient technique for expressing users’ opinions (neutral, negative or positive) regarding specific services or products. One of the important benefits of analyzing sentiment is in appraising the comments that users provide or service providers or services. In this work, a solution known as adaptive rider feedback artificial tree optimization-based deep neuro-fuzzy network (RFATO-based DNFN) is implemented for efficient sentiment grade classification. Here, the input is pre-processed by employing the process of stemming and stop word removal. Then, important factors, e.g. SentiWordNet-based features, such as the mean value, variance, as well as kurtosis, spam word-based features, term frequency-inverse document frequency (TF-IDF) features and emoticon-based features, are extracted. In addition, angular similarity and the decision tree model are employed for grouping the reviewed data into specific sets. Next, the deep neuro-fuzzy network (DNFN) classifier is used to classify the sentiment grade. The proposed adaptive rider feedback artificial tree optimization (A-RFATO) approach is utilized for the training of DNFN. The A-RFATO technique is a combination of the feedback artificial tree (FAT) approach and the rider optimization algorithm (ROA) with an adaptive concept. The effectiveness of the proposed A-RFATO-based DNFN model is evaluated based on such metrics as sensitivity, accuracy, specificity, and precision. The sentiment grade classification method developed achieves better sensitivity, accuracy, specificity, and precision rates when compared with existing approaches based on Large Movie Review Dataset, Datafiniti Product Database, and Amazon reviews.
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Improved Association Rule Mining-Based Data Sanitization for Privacy Preservation Model in Cloud
Abstract
Data security in cloud services is achieved by imposing a broad range of privacy settings and restrictions. However, the different security techniques used fail to eliminate the hazard of serious data leakage, information loss and other vulnerabilities. Therefore, better security policy requirements are necessary to ensure acceptable data protection levels in the cloud. The two procedures presented in this paper are intended to build a new cloud data security method. Here, sensitive data stored in big datasets is protected from abuse via the data sanitization procedure relying on an improved apriori approach to clean the data. The main objective in this case is to generate a key using an optimization technique known as Corona-integrated Archimedes Optimization with Tent Map Estimation (CIAO-TME). Such a technique deals with both restoration and sanitization of data. The problem of optimizing the data preservation ratio (IPR), the hiding ratio (HR), and the degree of modification (DOM) is formulated and researched as well.
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Single Linkage Weighted Steepest Gradient Adaboost Cluster-Based D2D in 5G Networks
Abstract
Efficiency of data transmissions with minimum latency levels and better resource utilization is a challenging issue in 5 G device-to-device (D 2D) environments. A novel technique referred to as single linkage steepest gradient gentle AdaBoost cluster-based device (SLSGAC) is introduced to improve device-to-device communications with minimum latency. The proposed technique uses the ensemble clustering approach to group mobile devices by constructing a set of weak clusters, based on the Minkowski single linkage clustering technique. In the weak clustering process, residual energy, bandwidth and SINR are estimated, and mobile devices are grouped based on the Minkowski distance measure. Results of the weak clustering process are combined to provide the final ensemble’s clustering output by applying the steepest gradient function to minimize the error rate. For each cluster, a head is selected from among the group members to improve the data transmission rate and minimize latency. Simulations are conducted comparing the proposed technique with the existing methods based on such metrics as energy efficiency, data delivery ratio, packet loss rate, throughput and latency.
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An Adaptive Richardson-Lucy Algorithm for Medical Image Restoration
Abstract
Image restoration is the process of estimating the original image content from a degraded picture. In this paper, the Richardson-Lucy iterative algorithm was developed to improve the quality of degraded medical images. It has been assumed that medical images are exposed to two types of degradation. The first type is the blur function in the Gaussian form with different widths, i.e. σ = 1 , 2, and 3. The second type of degradation was assumed to be of the independent white Gaussian noise type with different signal-to-noise ratio values: SNR = 10, 50 , and 100. The results obtained from the adaptive filter are compared, quantitatively, with different conventional filters: inverse, Wiener, and constraint least square, by applying different measures, such as: power signal to noise ratio (PSNR), structural similarity index (SSID), and root mean square error (RMSE). The comparison showed that the adaptive recovery filter achieves better results.
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Preserving Integrity of Evidence with Blockchain Technology in Cloud Forensics for Immigration Management
Abstract
As the popularity of cloud computing increases, safety concerns are growing as well. Cloud forensics (CF) is a smart adaptation of the digital forensics model that is used for fighting the related offenses. This paper proposes a new forensic method relying on a blockchain network. Here, the log files are accumulated and preserved in the blockchain using different peers. In order to protect the system against illegitimate users, an improved blowfish method is applied. In this particular instance, the system is made up of five distinct components: hypervisor (VMM), IPFS file storage, log ledger, node controller, and smart contract. The suggested approach includes six phases: creation of the log file, key setup and exchange, evidence setup and control, integrity assurance, agreement validation and confidential file release, as well as blockchain-based communication. To ensure efficient exchange of data exchange between the cloud provider and the client, the methodology comprises IPFS. The SSA (FOI-SSA) model, integrated with forensic operations, is used to select the keys in the best possible way. Finally, an analysis is conducted to prove the effectiveness of the proposed FOI-SSA technique
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Remedies to Thermal Radiation in Fused Silica Optical Fibers
Abstract
During fire incidents, optical fibers located with-in a fire-resistant cable are usually exposed to temperatures of 800◦C to 1000◦C. Hot fibers generate narrowband thermal (incandescent) radiation and collect broadband thermal radiation originating from the heated surroundings. The power of the second component, initially negligible, increases with time due to the rising number of fiber cracks and other defects acting as couplers for external radiation. Thermal radiation may interfere with fiber attenuation measurements performed during a fire test, but is rather unlikely to prevent data transmission with typical GbE and 10 GbE transceivers during a fire. This problem may be remedied by combining the following methods: using single mode fibers instead of multimode fibers, using bandpass filters to block thermal radiation, and selecting proper transmitter power, wavelength and photodetector.
