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A hyperjerk system pertains to a dynamical system regulated by an ordinary differential equation of $n$ th order, where $n \geq 4$ . The main contribution of this work is the finding of a new autonomous hyperjerk system with a half line equilibrium. The mathematical framework of the proposed hyperjerk system contains eight terms with an absolute function nonlinearity. The essential dynamic characteristics of the model are explored, encompassing analysis of equilibrium points and their stability, depiction of the phase trajectories, illustration of bifurcation patterns, and visualization of Lyapunov exponent graphs. Our finding shows that the new 4D hyperjerk system exhibits special behavior like multistability, period doubling reversals and antimonotonocity. The proposed hyperjerk system has been implemented with an electronic circuit using MultiSim 14.0. Moreover, the FPGA implementation of the proposed hyperjerk system is performed by applying two numerical methods: Forward Euler and Trapezoidal. Experimental attractors are given from an oscilloscope by using the Zybo Z7-20 FPGA development board, which are in good agreement with the MATLAB and MultiSim 14.0 simulations. Finally, based on the chaotic dynamical behavior of the proposed chaotic hyperjerk system, a new image encryption approach is proposed. The experimental outcomes of the presented encryption algorithm prove its efficiency and security.

Computer vision (CV) was developed for computers and other systems to act or make recommendations based on visual inputs, such as digital photos, movies, and other media. Deep learning (DL) methods are more successful than other traditional machine learning (ML)methods inCV.DL techniques can produce state-of-the-art results for difficult CV problems like picture categorization, object detection, and face recognition. In this review, a structured discussion on the history, methods, and applications of DL methods to CV problems is presented. The sector-wise presentation of applications in this papermay be particularly useful for researchers in niche fields who have limited or introductory knowledge of DL methods and CV. This review will provide readers with context and examples of how these techniques can be applied to specific areas. A curated list of popular datasets and a brief description of them are also included for the benefit of readers. [ABSTRACT FROM AUTHOR]

In this paper, a bulk arrival and two-phase bulk service with active Bernoulli feedback, vacation, and breakdown is considered. The server provides service in two phases as mandatory according to the general bulk service rule, with minimum bulk size ′ a ′ and maximum bulk size ′ b ′ . In the first essential service (FES) completion epoch, if the server fails, with probability ′ δ ′ , then the renewal of the service station is considered. On the other hand, if there is no server failure, with a probability ′ 1 − δ ′ , then the server switches to a second essential service (SES) in succession. A customer who requires further service as feedback is given priority, and they join the head of the queue with probability β . On the contrary, a customer who does not require feedback leaves the system with a probability ′ 1 − β ′ . If the queue length is less than ′ a ′ after SES, the server may leave for a single vacation with probability ′ 1 − β ′ . When the server finds an inadequate number of customers in the queue after vacation completion, the server becomes dormant. After vacation completion, the server requires some time to start service, which is attained by including setup time. The setup time is initiated only when the queue length is at least ′ a ′ . Even after setup time completion, the service process begins only with a queue length 'N' (N > b). The novelty of this paper is that it introduces an essential two-phase bulk service, immediate Bernoulli feedback for customers, and renewal service time of the first essential service for the bulk arrival and bulk service queueing model. We aim to develop a model that investigates the probability-generating function of the queue size at any time. Additionally, we analyzed various performance characteristics using numerical examples to demonstrate the model's effectiveness. An optimum cost analysis was also carried out to minimize the total average cost with appropriate practical applications in existing data transmission and data processing in LTE-A networks using the DRX mechanism. [ABSTRACT FROM AUTHOR]

Aerodynamically generated noise affects passenger comfort in cars, high-speed trains, and airplanes, and thus, automobile manufacturers aim for its reduction. Investigation methods of noise and vibration sources can be divided into two groups, i.e. experimental research and mathematical research. Recently, owing to the increase in computing power, research in aerodynamically generated noise (aero-acoustics) is beginning to use modern methods such as computational fluid dynamics or fluid-structure interaction. The mathematical model of turbulent flow is given by the system of partial differential equations, its solution is ambiguous and thus requires verification by physical experiment. The results of numerical methods are affected by the boundary conditions of high quality gained from the actual experiment. This article describes an application of complex measurement methodology in the aerodynamic and acoustic (vibro-acoustic) fields. The first part of the paper is focused on the specification of the experimental equipment, i.e. the wind tunnel, which was significantly upgraded in order to obtain the relevant aerodynamics and vibro-acoustics data. The paper presents specific results from the measurement of the aerodynamic and vibro-acoustic fields.

The identification of cavitation is very important in technical practice for operational and especially economic reasons. The article deals with the use of another way to measure noise during cavitation. The current approach of measuring noise with an intensity probe is used in practice for identification, but it does not immediately address the position of the cavitation source for a given frequency range. Measurement by an acoustic camera is not entirely common in practice, but it allows to determine the location of the noise source for a given frequency range. To test the acoustic camera, the authors focused on the cavitating flow in a hydraulic circuit with three previously tested nozzles. Noise was measured for these nozzles using an acoustic intensity probe with two microphones. The results were evaluated by statistical methods and compared with measurements using an acoustic camera. The aim of the article is to point out the advantages of using this approach for accurate area identification of the problem. Research background: The work is focused on the issue of cavitation and its identification in the hydraulic circuit. For cavitation research, a variant of cavitation identification by noise was chosen. However, this measurement brings problems that are only revealed through more sophisticated and accurate measurements. Purpose of the article: The purpose of the article is to point out other possibilities of measuring cavitation noise using modern technologies and subsequently verify the results. Methods: Metody: A common way of measuring noise is to measure it with a suitably located acoustic intensity probe. A more modern approach is area noise measurement. Measurement methodology and benefits are described. Findings & Value added: The commonly used way of measuring noise using an acoustic intensity probe has proved to be insufficient, as it is not possible to distinguish the location of sources in the case of complex measurements. When using an acoustic camera, there are more sources of noise in a given circuit and they are detected according to the required frequencies in different places than expected. The article points out the specific identification of noise sources using the frequency spectrum of noise for selected elements.

The article deals with experimental investigation of water cavitation in the convergent-divergent nozzle of rectangular cross-section. In practice, a quick and simple determination of cavitation is essential, especially if it is basic cavitation or cavitation generated additionally by the air being sucked. Air influences the formation, development and size of the cavity area in hydraulic elements. Removal or reduction of the cavity area is possible by structural changes of the element. In case of the cavitation with the suction air, it is necessary to find the source of the air and seal it. The pressure gradient, the flow, the oxygen content in the tank, and hence the air dissolved in the water, the air flow rate, the noise intensity and the vibration velocity on the nozzle wall were measured on laboratory equipment. From the selected measurements the frequency spectrum of the variation of the water flow of the cavity with cavitation without air saturation and with air saturation was compared and evaluated.

An optimal network refers to a computer or communication network designed, configured, and managed to maximize efficiency, performance, and effectiveness while minimizing cost and resource utilization. In a network design and management context, optimal typically implies achieving the best possible outcomes between various factors. This research investigated the use of fuzzy graph edge coloring for various fuzzy graph operations, and it focused on the efficacy and efficiency of the fuzzy network product using the minimal spanning tree and the chromatic index of the fuzzy network product. As a network made of nodes and vertices, measurement with vertices is a parameter for domination, and edge measurement is a parameter for edge coloring, so we used these two parameters in the algorithm. This paper aims to identify an optimal network that can be established using product outcomes. This study shows a way to find an optimal fuzzy network based on comparative optimal parameter domination and edge coloring, which can be elaborated with applications. An algorithm was generated using an optimal approach, which was subsequently implemented in the form of applications. [ABSTRACT FROM AUTHOR]

The cluster technique involves the creation of clusters and the selection of a cluster head (CH), which connects sensor nodes, known as cluster members (CM), to the CH. The CH receives data from the CM and collects data from sensor nodes, removing unnecessary data to conserve energy. It compresses the data and transmits them to base stations through multi-hop to reduce network load. Since CMs only communicate with their CH and have a limited range, they avoid redundant information. However, the CH's routing, compression, and aggregation functions consume power quickly compared to other protocols, like TPGF, LQEAR, MPRM, and P-LQCLR. To address energy usage in wireless sensor networks (WSNs), heterogeneous high-power nodes (HPN) are used to balance energy consumption. CHs close to the base station require effective algorithms for improvement. The cluster-based glow-worm optimization technique utilizes random clustering, distributed cluster leader selection, and link-based routing. The cluster head routes data to the next group leader, balancing energy utilization in the WSN. This algorithm reduces energy consumption through multi-hop communication, cluster construction, and cluster head election. The glow-worm optimization technique allows for faster convergence and improved multi-parameter selection. By combining these methods, a new routing scheme is proposed to extend the network's lifetime and balance energy in various environments. However, the proposed model consumes more energy than TPGF, and other protocols for packets with 0 or 1 retransmission count in a 260-node network. This is mainly due to the short INFO packets during the neighbor discovery period and the increased hop count of the proposed derived pathways. Herein, simulations are conducted to evaluate the technique's throughput and energy efficiency. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is seen as the most viable solution for real-time monitoring applications. But the faults occurring at the perception layer are prone to misleading the data driven system and consume higher bandwidth and power. Thus, the goal of this effort is to provide an edge deployable sensor-fault detection and identification algorithm to reduce the detection, identification, and repair time, save network bandwidth and decrease the computational stress over the Cloud. Towards this, an integrated algorithm is formulated to detect fault at source and to identify the root cause element(s), based on Random Forest (RF) and Fault Tree Analysis (FTA). The RF classifier is employed to detect the fault, while the FTA is utilized to identify the source. A Methane (CH4) sensing application is used as a case-study to test the proposed system in practice. We used data from a healthy CH4 sensing node, which was injected with different forms of faults, such as sensor module faults, processor module faults and communication module faults, to assess the proposed model's performance. The proposed integrated algorithm provides better algorithm-complexity, execution time and accuracy when compared to FTA or standalone classifiers such as RF, Support Vector Machine (SVM) or K-nearest Neighbor (KNN). Metrics such as Accuracy, True Positive Rate (TPR), Matthews Correlation Coefficient (MCC), False Negative Rate (FNR), Precision and F1-score are used to rank the proposed methodology. From the field experiment, RF produced 97.27% accuracy and outperformed both SVM and KNN. Also, the suggested integrated methodology's experimental findings demonstrated a 27.73% reduced execution time with correct fault-source and less computational resource, compared to traditional FTA-detection methodology. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is rapidly expanding and becoming an integral part of daily life, increasing the potential for security threats such as malware or cyberattacks. Many embedded systems (ESs), responsible for handling sensitive data or facilitating secure online activities, must adhere to stringent security standards. For instance, payment processors employ security-critical components as distinct chips, maintaining physical separation from other network components to prevent the leakage of sensitive information such as cryptographic keys. Establishing a trusted environment in IoT and ESs, where interactions are based on the trust model of communication nodes, is a viable approach to enhance security in IoT and ESs. Although trust management (TM) has been extensively studied in distributed networks, IoT, and ESs, significant challenges remain for real-world implementation. In response, we propose a hybrid fuzzy rule algorithm (FRA) and trust planning mechanism (TPM), denoted FRA + TPM, for effective trust management and to bolster IoT and ESs reliability. The proposed system was evaluated against several conventional methods, yielding promising results: trust prediction accuracy (99%), energy consumption (53%), malicious node detection (98%), computation time (61 s), latency (1.7 ms), and throughput (9 Mbps). [ABSTRACT FROM AUTHOR]

In engineering applications the noisiness of a component or the whole device is a common problem. Currently, a lot of effort is put to eliminate noise of the already produced devices, to prevent generation of acoustic waves during the design of new components, or to specify the operating problems based on noisiness change. The experimental method and the mathematical modelling method belong to these identification methods. With the power of today’s computers the ability to identify the sources of the noise on the mathematical modelling level is a very appreciated tool for engineers. For example, the noise itself may be generated by the vibration of the solid object, combustion, shock, fluid flow around an object or cavitation at the fluid flow in an object. For the given task generating the noise using fluid flow on the selected geometry and propagation of the acoustic waves and their subsequent identification are solved and evaluated. In this paper the principle of measurement of variables describing the fluid flow field and acoustic field are described. For the solution of fluid flow a mathematical model implemented into the CFD code is used. The mathematical modelling evaluation of the flow field is compared to the experimental data.