Stability analysis of a class of digital filters utilizing single saturation nonlinearity

A novel criterion for the global asymptotic stability of a class of digital filters utilizing single saturation nonlinearity is presented. An example showing the effectiveness of the present criterion is given.     

Novel Lyapunov-based rapid and ripple-free MPPT using a robust model reference adaptive controller for solar PV system

The technological, economic, and environmental benefits of photovoltaic (PV) systems have led to their widespread adoption in recent years as a source of electricity generation. However, precisely identifying a PV system''s maximum power point (MPP) under normal and shaded weather conditions is crucial to conserving the maximum generated power. One of the biggest concerns with a PV system is the existence of partial shading, which produces multiple peaks in the P–V characteristic curve. In these circumstances, classical maximum power point tracking (MPPT) approaches are prone to getting stuck on local peaks and failing to follow the global maximum power point (GMPP). To overcome such obstacles, a new Lyapunov-based Robust Model Reference Adaptive Controller (LRMRAC) is designed and implemented to reach GMPP rapidly and ripple-free. The proposed controller also achieves MPP accurately under slow, abrupt and rapid changes in radiation, temperature and load profile. Simulation and OPAL-RT real-time simulators in various scenarios are performed to verify the superiority of the proposed approach over the other state-of-the-art methods, i.e., ANFIS, INC, VSPO, and P&O. MPP and GMPP are accomplished in less than 3.8 ms and 10 ms, respectively. Based on the results presented, the LRMRAC controller appears to be a promising technique for MPPT in a PV system.     

Intrusion Detection System in Wireless Sensor Network Using Conditional Generative Adversarial Network

Wireless communication networks have much data to sense, process, and transmit. It tends to develop a security mechanism to care for these needs for such modern-day systems. An intrusion detection system (IDS) is a solution that has recently gained the researcher’s attention with the application of deep learning techniques in IDS. In this paper, we propose an IDS model that uses a deep learning algorithm, conditional generative adversarial network (CGAN), enabling unsupervised learning in the model and adding an eXtreme gradient boosting (XGBoost) classifier for faster comparison and visualization of results. The proposed method can reduce the need to deploy extra sensors to generate fake data to fool the intruder 1.2–2.6%, as the proposed system generates this fake data. The parameters were selected to give optimal results to our model without significant alterations and complications. The model learns from its dataset samples with the multiple-layer network for a refined training process. We aimed that the proposed model could improve the accuracy and thus, decrease the false detection rate and obtain good precision in the cases of both the datasets, NSL-KDD and the CICIDS2017, which can be used as a detector for cyber intrusions. The false alarm rate of the proposed model decreases by about 1.827%.

     

Performance Analysis of Path Loss Models at 1.8 GHz in an Indoor/Outdoor Environment to Verify Network Capacity and Coverage

Wireless Personal Communications - Cloud is an environment where the resources are outsourced as service to the cloud consumers based on their demand. The cloud providers follows pay as you go...     

Attack Resilient and Efficient Protocol based on Greedy Perimeter Coordinator Routing—Mobility Awareness for Preventing the Attack in the VANET

Wireless Personal Communications - A Greedy Perimeter Coordinator Routing and Mobility Awareness (GPCR-MA) vehicular routing is a widely accepted routing protocol for VANET (Vehicular Ad hoc...     

Investigations on mode-division multiplexed free-space optical transmission for inter-satellite communication link

Wireless Networks - Inter-satellite data transmission links are very crucial for providing global inter-connectivity. We report designing and investigations on high date rate inter-satellite...     

Performance Analysis of Machine Learning Algorithms for Classifying Hand Motion-Based EEG Brain Signals

Brain-computer interfaces (BCIs) records brain activity using electroencephalogram (EEG) headsets in the form of EEG signals; these signals can be recorded, processed and classified into different hand movements, which can be used to control other IoT devices. Classification of hand movements will be one step closer to applying these algorithms in real-life situations using EEG headsets. This paper uses different feature extraction techniques and sophisticated machine learning algorithms to classify hand movements from EEG brain signals to control prosthetic hands for amputated persons. To achieve good classification accuracy, denoising and feature extraction of EEG signals is a significant step. We saw a considerable increase in all the machine learning models when the moving average filter was applied to the raw EEG data. Feature extraction techniques like a fast fourier transform (FFT) and continuous wave transform (CWT) were used in this study; three types of features were extracted, i.e., FFT Features, CWT Coefficients and CWT scalogram images. We trained and compared different machine learning (ML) models like logistic regression, random forest, k-nearest neighbors (KNN), light gradient boosting machine (GBM) and XG boost on FFT and CWT features and deep learning (DL) models like VGG-16, DenseNet201 and ResNet50 trained on CWT scalogram images. XG Boost with FFT features gave the maximum accuracy of 88%.     

Investigation of machining characteristics in rotary ultrasonic machining of alumina ceramic

Alumina ceramic is well documented as a much-demanded advanced ceramic in the present competitive structure of manufacturing and industrial applications owing to its excellent and superior properties. The current article aimed to experimentally investigate the influence of several process variables, namely: spindle speed, feed rate, coolant pressure, and ultrasonic power, on considered machining characteristics of interest, i.e., chipping size and material removal rate in the rotary ultrasonic machining of alumina ceramic. Response surface methodology has been employed in the form of a central composite rotatable design to design the experiments. Variance analysis testing has also been performed with a view to observing the consequence of the considered parameters. The microstructure of machined rod samples was evaluated and analyzed using a scanning electron microscope. This analysis has revealed and confirmed the presence of plastic deformation that caused removal of material along with brittle fractures in rotary ultrasonic machining of alumina ceramic. The validity and competence of the developed mathematical model have been verified with test results. The multi-response optimization of machining responses (material removal rate and chipping size) has also been attempted by employing a desirability approach, and at an optimized parametric setting the obtained experimental values for material removal rate and chipping size were 0.4166?mm³/s and 0.5134?mm, respectively, with a combined desirability index value of 0.849.     

A novel application of micro-EDM process for the generation of nickel nanoparticles with different shapes

This article explains production of nickel nanoparticles through a micro-electrical discharge machining (EDM) process with a combination of different process parameters. The production of nickel nanoparticles was carried out in a dielectric medium (deionized water) with developed micro-EDM while polyvinyl alcohol worked as the stabilizing agent. The characterization of nickel nanoparticle was done by scanning electron microscope (SEM), Energy-dispersive X-ray spectroscopy (EDS), X-ray diffraction (XRD), UV–Vis spectroscopy, and Fourier transform infrared (FTIR) analysis. From this investigation, the mean crystal size of the nickel nanoparticles was found to be in the range of 15–20?mm for a pulse-on time variation of 2–0.3?µs and the crystal size was found to decrease with the decrease of pulse-on time. It was also observed that with this decrease, the shape and size of nickel nanoparticles change from spherical to needle-like. The dispersion stability of nickel nanofluid was determined by viscosity measurements and the dynamic viscosity was noted to decrease by decreasing the pulse duration. From the FTIR spectrum results, it was confirmed that the synthesized nickel nanoparticles in deionized water were pure and monolithic. UV–Vis–NIR spectroscopy depicted that the band gap energy increases with a reduction in the pulse-on time and obtains a higher band gap (5.31?eV) for 0.3?µs pulse-on time.