Traffic reduction in network segments through cache implementations has become an important research topic due to the exponential increase in data requests through the Internet. To support these activities, high-power computing and massive storage must support the creation, retrieval, update, and deletion of large amounts of data. Duplicate requests in the segment from the same clients challenge developing flexible networks. Studies about Information-Centric Networks (ICN) propose to improve the performance of content-based networks because there is a content handling of requests for content, leading to an independent flow from each user. Such as a unicast content delivery ignores identical requests of the other users made to the same service. Therefore, many approaches use Software-Defined Networks (SDN) to provide improved network management to develop a flexible content-based network. This article proposes the PROMID architecture, characterized as an information-centric network through SDN that optimizes the bandwidth consumption in dynamic request-response connections. From the experimental results, we observed that our approach allows us to optimize 84.61% on a total of 32.000 request responses and 34.25% in latency optimization. Moreover, our method measured an increase in the transfer rate from 19.99 Mbps to 164.74 Mbps.

Laminated busbars offer numerous advantages over traditional busbars, cables, and wiring harnesses due to their lower stray inductance, higher capacitance, and compact size. These characteristics make them particularly well-suited for use in electric vehicles, where space is a constraint and reliable power distribution is critical. This study presents the optimization of a laminated busbar for use in an electric car traction inverter. The laminated busbar structure is simulated in the electrostatic, magnetostatic, and thermal domains to optimize its stray parameters and thermal attributes using finite element analysis software. The work has a step-by-step process that includes optimizing insulation material, conductor overlap area, bending of terminals and their combined effect, and reducing the laminated busbar current density and thermal gradient. The results demonstrate that the optimization significantly increases the parasitic capacitance by 14.8%, reduces the stray inductance by 2.73% and thermal gradient by 2.34%, with negligible variation in the stray resistance. This research provides a comprehensive account of the optimization process of laminated busbars for electric vehicle traction inverters.

This paper focuses on developing a non-isolated bidirectional high gain converter suited for EV charging. This converter has two ports with bidirectional features which functions in both buck and boost operation. This converter is shown to attain high voltage gain with high efficiency while using less components. To verify the stability of the bidirectional high gain converter, state space averaging is performed and the stability curves are plotted for both boost and buck operation. The buck and boost characteristics of this converter are observed through simulation using MatLab, for 1.2 kW system and results are presented for the analysis. Based on this analysis the hardware of the bidirectional converter is developed and results are obtained and compared.

Today, there are many patients with Amyotrophic Lateral Sclerosis or paralysis who are unable to perform any body movements other than eye movements. A system has been developed to facilitate communication between patients and their surroundings. This system utilizes binary coded expressions obtained through single and double eye blinks performed by the patients, which are then vocalized. The locations of the eye blinks are determined by analyzing the signals obtained from the patient using a wireless NeuroSky MindWave single-channel EEG device. The windowing process is performed at predetermined intervals to determine whether the blink within these windows was a single or double blink, by using the Cross Correlation method. The system is completed by converting the expressions corresponding to the binary codes to speech and has provided results with a very high degree of accuracy.

The solar water pumping systems (SWPS) is useful in remote or village areas where there is no electricity. In India, SWPS are revolutionizing agriculture by giving farmers access to power for everything, from harvesting crops to pumping water. As the government is working to make solar pumping systems affordable for the agricultural sector, induction motors (IM) are found to be the most appropriate motor type for this application because of its low cost, durability and ruggedness. In this work, the SWPS is powered by an inverter driven three-phase IM with V/f control technique. Various MPPT techniques have been proposed to track maximum power from PV system under partial shading condition. This paper mainly focuses on a hybrid Particle Swarm Optimization-Incremental Conductance (PSO-INC) algorithm to capture the peak power efficiently when IM based SWPS is subjected to partial shading. The results are compared with PSO algorithm, a hybrid PSO-PO method and a modified 0.8 Voc approach. The MATLAB/Simulink environment is used to simulate and test these algorithms on the SWPS system with partial shading. The motor's performance in terms of torque ripple and speed settling time is analyzed.

In the Agricultural Internet of Things (AgIoT), Unmanned Aerial Vehicles (UAVs) can be used to collect sensor data. Thus, UAVs must plan the appropriate data collection paths so that sensors can collect the data under different positions and generate time-constrained data. Therefore, this paper proposes a UAV path planning algorithm based on Deep Reinforcement Learning (DRL), which jointly optimizes location, energy, and time deadline to maximize the data-energy ratio. The path planning process is modeled and decomposed into a Markov Decision Process (MDP), and then a Prioritized Experience Replay Double Deep Q Network (PER-DDQN) model is used to calculate the optimal solution. Furthermore, a time-constrained reward function and an improved adaptive upper confidence bound (UCB) exploration function are proposed to balance exploration and exploitation in the DRL algorithm, affording the developed algorithm to converge quickly and smoothly. The simulations demonstrate that compared with traditional methods, the proposed algorithm presents better path selection during the data collection process, lower execution time, and a higher data-energy ratio. Our algorithm promotes the use of UAV in AgIoT.

Automatic Speech Recognition (ASR) systems are notorious for their poor performance in adverse conditions, leading to high sensitivity and low robustness. Due to the costly and time-consuming nature of creating extensive speech databases, addressing the issue of low robustness has become a prominent area of research, focusing on the synthetic generation of speech data using pre-existing natural speech. This paper examines the impact of standard data augmentation techniques, including pitch shift, time stretch, volume control, and their combination, on the accuracy of isolated-word ASR systems. The performance of three machine learning models, namely Hidden Markov Models (HMM), Support Vector Machines (SVM), and Convolutional Neural Networks (CNN), is analyzed on two Serbian corpora of isolated words. The Whi-Spe speech database in neutral phonation is utilized for augmentation and training, and a specifically developed Python-based software tool is employed for the augmentation process in this research study. The conducted experiments demonstrate a statistically significant reduction in the Word Error Rate (WER) for the CNN-based recognizer on both testing datasets, achieved through a single augmentation technique based on pitch-shifting.

Wind power plants do not provide frequency support subject to disturbances because wind turbine generators (WTGs) are decoupled from system frequency deviations. To provide frequency regulation from a permanent-magnet synchronous generator-based wind energy conversion system following the change in system load demand, this paper presents a basic controller that directly perturbs the rotor-speed reference according to system frequency deviations. In the proposed approach, the kinetic energy (KE) of the rotating parts of the WTGs is utilized for frequency support, thus enriching the power system stability. The prominent aspect of opting for this concept is the immediate KE release or absorption through a step-change in the speed reference. Furthermore, the system proceeds to its normal operation without causing instability issues. To prove this concept, extensive simulations in MATLAB/Simulink have been performed, and the results demonstrate its good capability in providing system frequency regulation, resulting in the enhancement of power system stability. For comparison, simultaneous control of the power-frequency droop control and DC-link inertial support was also prepared.

In the grid-connected systems, LCL filters are widely used for eliminating the generated switching current ripples due to better attenuation in comparison with L or LC filter. However it is known that stability of digital controlled LCL filter can present a challenge due to induced delay. This paper presents conditions of system stability in dependence of filter characteristics such as resonant frequency, inductors and capacitor parameter variations. Stability analysis and system performance are observed and compared. Derived results are tested and confirmed on Hardware-in-the-loop HIL402 device. Obtained results are discussed and suggestions are provided in terms of the practical implementation of the control system.