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In this study, we explore a hybrid non-orthogonal multiple access and time division multiple access (NOMA-TDMA) approach designed to maximize sum throughput in a wireless powered Internet of Things (IoT) network (WPIN). Hybrid access points send energy signals to users on downlink, and users in various groups utilize that harvested energy to transmit information on uplink. This process is facilitated by the NOMA-TDMA scheme wherein users of the same group use NOMA for simultaneous transmissions, and separate time slots are assigned to each group through TDMA. Under this hybrid NOMA-TDMA scheme, the main objective is to enhance the network’s sum throughput by jointly optimizing both the allocation of time for downlink and uplink and the downlink beamforming vectors. Given the complex interdependence of variables, the problem is inherently non-convex, making it difficult to solve numerically. Therefore, we reformulate the problem as a bi-level programming problem—the outer-level sub-problem addresses beamforming vectors using a genetic algorithm while the inner-level sub-problem deals with the allocation of downlink and uplink time through the Lagrange multiplier method. Numerical results show that the proposed hybrid NOMA-TDMA scheme outperforms baseline schemes like orthogonal multiple access and equal time allocation, in terms of the sum throughput of the WPIN.

Curves are the leading cause of road departure crashes that turn into deaths and severe injuries. Most crashes occur on curved roads because they have different geometrical circumstances from straight roads. A sharp turn on the road creates more difficulty for the driver than a normal curve road. To Avoid crashes on the curved road, there is a prerequisite to identify the curved road for drivers that this turn is problematic. Responding intelligently to save precious human life and the country’s gross domestic product is necessary. Our paper proposed a Curve Crash Avoidance Protocol (2CAP) in Vehicular Ad-hoc Network (VANET). The Intelligent Curve Crash Avoidance Algorithm is proposed to avoid disastrous vehicle crashes. The sensing Node Operation algorithm works for multiple sensors embedded in the Onboard Unit to gather information on vehicles and road environments. On the other side, road-side unit requests for this collected data via a secure communication channel for processing. A Linear Regression machine learning technique implements the intelligent Unit Operations algorithm. The Intelligent Unit decides to notify the onboard Unit based on gathered data and trained dataset. We implement the scheme using the Linear Regression Machine Learning Model. Multiple sensors, Global Positioning Systems, and Global Information systems use the dataset methodology to classify and predict results. The proposed model is expected to be effective for proper coordination with wireless sensor network equipment.

Ever-increasing dynamic surges in renewable-based electric power systems, notably wind and photovoltaic farms bring adverse impacts and challenges in terms of reliability and stability. The intermittency of renewable sources imposes significant deviations in frequency due to variations in demand. Wind power induces instability in the grid due to its vulnerable nature, and reduction in system inertia. To mitigate these dynamics issues, an optimal control technique based on flatness-based Active disturbance rejection control (FADRC) and utilizing an enhanced Beluga Whale optimization algorithm (EBWO) for a multi-area interconnected power system with photovoltaic generation. The proposed LFC model addresses the load perturbation and the deviation of tie-line power, with system uncertainties considered as lumped disturbances that are approximated by extended state observers. To achieve optimal performance, the Enhanced Beluga Whale optimization algorithm is adopted and integrated with the suggested controller to fine-tune the controller. To validate the formidable performance of the suggested scheme, different cases have been studied with the existing approaches. The simulation results reveal the supremacy and robustness of the dynamic response of the Flatness-based active disturbance rejection control as compared to other approaches under load variations and parameter uncertainty.

Vehicular ad-hoc network (VANET) is an advanced mobile wireless network. The escalation of equipped vehicles on the road grabs the attention of researchers and is constantly striving to take it further. This type of wireless network infrastructure helps to establish communication between vehicles. Roadside units, sensors, and vehicular nodes are the critical components of our work to protect the vehicular network. This research is focused on detecting and preventing fake vehicular nodes and their messages by applying fake node detection and prevention algorithms and counterfeit message detection and prevention algorithms. In our proposed approach, the fake node detection and prevention algorithms check the node profile after establishing the mesh structure. If the profile attribute named “Pen/Rew” satisfies the condition that should be less than or equal to a threshold value (zero), the fake message detection and prevention process starts. The message will be accepted once the situation is satisfied; otherwise, it is declared fake. We utilized ONE (opportunistic network environment) simulator to generate node movement models, route messages between nodes, and visualize mobility and messaging passing in real time. The results indicated that our proposed work perfectly detects and prevents fake nodes and messages.

Symmetry is everywhere, found in objects around us, whether artificial or natural, and acts as a mid-level cue for both human and machine perception of the chaotic real world. From real-world symmetries, humans take advantage of various tasks, but their computational treatment remains elusive. This study proposes a novel approach for detecting rotational symmetry and the order of rotation within a single object digital image. The proposed method relies on the extraction of Scale Invariant Features Transform (SIFT) features and the robust centroid point. The centroid is computed on the basis of extracted features, to be drawn in xy-plan so that the centroid is on the origin. Later on, converted to the polar domain to facilitate the extraction of rotationally symmetric pairs and the order of rotation. The symmetry exhibited by each pair in the transform domain is the function of the features’ location, orientation, magnitude, and descriptor vector. Experimental results show that the approach correctly identifies the rotational symmetry if enough features are detected and the centroid is robust one.

This paper discusses the effects of an incorrectly measured DC link voltage in a synchronous drive system. As the investigations show, the control algorithm can become unstable when the measured value for the DC link voltage is much smaller than the actual value. Therefore, this paper also presents two approaches for estimating the DC link voltage based on the measured currents, the motor speed and the duty cycle. If measured and estimated values of the DC link voltage deviate too much from each other, a fault diagnosis system detects this fault and enables different flags based on the fault scenario. The flags are then used to reconfigure the control algorithm, so that instead of the measured voltage its estimated value is used. The analysis shows the need for the reconfiguration of a faulty DC link voltage measurement and the experimental results demonstrate the effectiveness of the approach.

The number of smartphone users and mobile devices has increased significantly. The Mobile Cloud Applications based on cloud computing have also been increased. The mobile apps can be used in Augmented Reality, E-Transportation, 2D/3-D Games, E-Healthcare, and Education. The modern cloud-based frameworks provide such services on Virtual Machines. The existing frameworks worked well, but these suffered the problems such as overhead, resource utilization, lengthy boot-time, and cost of running Mobile Applications. This study addresses these problems by proposing a Dynamic Decision-Based Task Scheduling Technique for Microservice-based Mobile Cloud Computing Applications (MSCMCC). The MSCMCC runs delay-sensitive applications and mobility with less cost than existing approaches. The study focused on Task Scheduling problems on heterogeneous Mobile Cloud servers. We further propose Task Scheduling and Microservices based Computational Offloading (TSMCO) framework to solve the Task Scheduling in steps, such as Resource Matching, Task Sequencing, and Task Scheduling. Furthermore, the experimental results elaborate that the proposed MSCMCC and TSMCO enhance the Mobile Server Utilization. The proposed system effectively minimizes the cost of healthcare applications by 25%, augmented reality by 23%, E-Transport tasks by 21%, and 3-D games tasks by 19%, the average boot-time of microservices applications by 17%, resource utilization by 36%, and tasks arrival time by 16%.

The current article is an understanding of heat transfer and non-Newtonian fluid flow with implications of the power-law fluid on a facing surface of the circular cylinder embedded at the end of the channel containing the screen. The cylinder is fixed with an aspect ratio of 4:1 from height to the radius of the cylinder. The simulation for the fluid flow and heat transfer was obtained with variation of the angle of screen $\frac {\pi }{6}\le \theta \le \frac {\pi }{3}$ , Reynolds number 1000 ≤ Re ≤ 10, 000 and the power-law index $0.7\le n\le 1.3$ by solving two-dimensional incompressible Navier-Stokes equations and the energy equation with screen boundary condition and slip walls. The results will be in a good match with asymptotic solution given in the literature. The results are presented through graph plots for non-dimensional velocity, temperature, mean effective thermal conductivity, heat transfer coefficient, and the local Nusselt number on the front surface of the circular cylinder. It was found that the ratio between the input velocity to the present velocity on the surface of the circular cylinder remains consistent and reaches up to a maximum of 2.2% and the process of heat transfer does not affect by the moving of the screen and clearly with the raise of power-law indexes the distribution of the heat transfer upsurges. On validation with two experimentally derived correlations, it was also found that the results obtained for the shear-thinning fluid are more precise than the numerically calculated results for Newtonian as well as shear-thickening cases. Finally, we suggest necessary measures to enrich the development of convection when observing with strong effects influenced by the screens or screen boundary conditions.

Over the last few decades, eye gaze estimation techniques have been thoroughly investigated by many researchers. However, predicting a 3D gaze from a 2D natural image remains challenging because it has to deal with several issues such as diverse head positions, face shape transformation, illumination variations, and subject individuality. Many previous studies employ convolutional neural networks (CNNs) for this task, and yet the accuracy needs improvement for its practical use. In this paper, we propose a 3D gaze estimation framework based on the data science perspective: First, a novel neural network architecture is designed to exploit every possible visual attribute such as the states of both eyes and the head position, including several augmentations; secondly, the data fusion method is utilized by incorporating multiple gaze datasets. Extensive experiments were carried out using two standard eye gaze datasets, including comparative analysis. The experimental results suggest that our method outperforms state-of-the-art with 2.8 degrees for MPIIGaze and 3.05 degrees for EYEDIAP dataset, respectively, indicating that it has a potential for real applications.

Batteries are essential for efficiently utilizing the energy from the photovoltaic (PV) modules. However, integration of batteries with PV plants at large scale needs more attention in terms of size, location, and times for the charging and discharging of the batteries. This paper addresses these aspects. It presents a mixed integer optimization using genetic algorithm for determining the optimum size and placement of battery-sourced distributed PV generation (B-SDPVG) in distribution networks. The total energy loss index is formulated as the main objective function and simultaneously, the bus voltage deviations and penetrations of the B-SDPVG are calculated. The yields from the PV plants are estimated using 15 years of weather data modeled with the aid of beta probability density function. Furthermore, a novel charge-discharge control model is developed for determining the choice of the charging and discharging of batteries at each hour. By considering different time varying voltage-dependent load models, the proposed algorithm is applied on the IEEE 33 bus and the IEEE 69 bus test distribution networks. The numerical results of two distribution networks with time-varying loads show the advantages of the proposed methodology. It was revealed that integration of battery storage and intelligent scheduling for charging and discharging of batteries produced much better results and improved the quality of distribution networks. The supply of power from the B-SDPVG during the peak load hours and at night was made possible for each load model. The proposed charge-discharge control model for scheduling the charging and discharging of the batteries was found dynamic. Results of this paper can be of potential importance in planning for the integration of battery storage in distribution networks.