The high power DC-DC converters are widely used in industrial and renewable application due to variable dc source. For this purpose, the interleaved dual cascade DCDC converter (IDCC) is the most advantageous due to its and low ripple with high boosting factor and high potential efficiency. To optimize IDCC performance, an integrated regulator ensures equal current sharing between levels, with voltage cascading to boost the output voltage. The integrated regulator technique (IRT) is designed to control all regulators at each level to maintain an optimal switching period, thereby reducing switching dissipation and decreasing ripple. Its ability to handle high voltages conversion with a consistent low ripple output and modular structure enhance performance. It has demonstrated a 1.5% current ripple and up to 94% efficiency in high-power density applications. Simulation and experimental results of a 2 kW prototype validate the IRT's performance.

A flux-switching permanent-magnet synchronous machine (FSPMSM) has shown advantages, including strong mechanical robustness, high torque density, and acceptable fault redundancy potential, and started to find a market in various fields in electric vehicle, ship, airplane, and wind generation. However, a double salient structure and a high number of pole pairs cause the FSPMSM to experience great torque ripple and converter switching reduction, compromising its performance. Optimizing the machine design can significantly decrease the speed ripple and torque, often resulting in increased manufacturing costs, lower efficiency, and lower power density. Alternatively, several control-based solutions have been explored. One of the existing methods to minimize the torque control ripple is model predictive control (MPC); the most attractive method among them is model predictive current control (MPCC). In the speed outer loop design of MPC, the traditional PI control approach is often employed in FSPMSM controller design due to its ease of use and stability. However, it is hard to obtain suitable results due to its low control accuracy. In order to address this issue, this paper suggests MPCC combined sliding mode control (SMC) for three phases of flux-switching permanent magnet motor to improve the dynamic response of the MPCC. The simulated results imply that the suggested SMC combined MPCC scheme presents acceptable dynamic performances compared to the conventional MPCC strategy.

To overcome the adverse effects of external disturbances on grid current quality, this article presents a novel integral terminal sliding mode control (IT-SMC) for a grid-connected three-phase inverter. The integral terminal SMC guarantees finite-time convergence, minimal overshoot, and chattering-free operation. In the mathematical design of the controller, a derivative term is introduced in the capacitor voltages to achieve a better damping effect and prevent overshoot. Additionally, the integral of the grid and inverter current error is incorporated into the sliding surface to achieve precise tracking of the current reference. A 3-kW system is designed using the MATLAB/Simulink tool to analyze the controller’s performance. The grid-current quality and the stability of the controller are analyzed under conditions of grid voltage distortions, impedance variations, and filter resonance frequency. Experimental results confirm that the designed controller ensures nearly zero steady-state error, low grid current harmonics, rapid dynamic response, and system stability under weak grid conditions.

Video memorability prediction has become an important research topic in computer vision in recent years. The movie's input is highly remembered that gains much attention with unbounded time constraints. Episodic memory is a fascinating research area that needs much attention using video processing tools and techniques. Episodic memories are long-lasting with complete detail. Movies are one of the best instances of episodic memory. This paper proposes a novel framework to fuse deep features to predict the probability of recalling episodic events. Memories are reproducible and sensitive to sophisticated set of properties rather than low-level propertiesthe proposed framework pin up the fusion of text, visual and motion features. A fuzzy-based FastText model, a supervised text extraction module, is designed to extract the annotations with their relevant classes. The colour histogram analysis is done to determine the dominant colour region that performs as a connected fragment to form episodic video sequences. A novel Faster R-CNN is designed to discover the scene objects using an informative regional proposal network formation. Here, the modified loss function sorts out the lowest overlapping regions yielding the best proposals. The ‘high-level’ properties are collected using Principal Component Analysis (PCA) to form episodic shots. These are fused to estimate the memorability score. The proposed framework is implemented in Mediaeval 2018 datasets. A superior spearman's rank correlation result is achieved as 0.6428 short-term and 0.4285 long-term memorability than the latest comparable methods.

Currently, most of the adopted myoelectric schemes for upper limb prostheses do not provide users with intuitive control. Higher accuracies have been reported using different classification algorithms but investigation on the reliability over time for these methods is very limited. In this study, we compared for the first time the longitudinal performance of selected state-of-the-art techniques for electromyography (EMG) based classification of hand motions. Experiments were conducted on ten able-bodied and six transradial amputees for seven continuous days. Linear discriminant analysis (LDA), artificial neural network (ANN), support vector machine (SVM), K-nearest neighbor (KNN), and decision trees (TREE) were compared. Comparative analysis showed that the ANN attained highest classification accuracy followed by LDA. Three-way repeated ANOVA test showed a significant difference (P < 0.001) between EMG types (surface, intramuscular, and combined), days (1-7), classifiers, and their interactions. Performance on the last day was significantly better (P < 0.05) than the first day for all classifiers and EMG types. Within-day, classification error (WCE) across all subject and days in ANN was: surface (9.12 ± 7.38%), intramuscular (11.86 ± 7.84%), and combined (6.11 ± 7.46%). The between-day analysis in a leave-one-day-out fashion showed that the ANN was the optimal classifier (surface (21.88 ± 4.14%), intramuscular (29.33 ± 2.58%), and combined (14.37 ± 3.10%). Results indicate that within day performances of classifiers may be similar but over time, it may lead to a substantially different outcome. Furthermore, training ANN on multiple days might allow capturing time-dependent variability in the EMG signals and thus minimizing the necessity for daily system recalibration.

This paper presents a dual‐band coplanar waveguide (CPW) fed printed antenna with rectangular shape design blocks having ultrawideband characteristics, proposed and implemented on an FR4 substrate. The size of the proposed antenna is just 25 mm × 35 mm. A novel rounded corners technique is used to enhance not only the impedance bandwidth but also the gain of the antenna. The proposed antenna design covers two ultrawide bands which include 1.1–2.7 GHz and 3.15–3.65 GHz, thus covering 2.4 GHz Bluetooth/Wi‐Fi band and most of the bands of 3G, 4G, and a future expected 5G band, that is, 3.4–3.6 GHz. Being a very low‐profile antenna makes it very suitable for the future 5G Internet of Things (IoT) portable applications. A step‐by‐step design process is carried out to obtain an optimized design for good impedance matching in the two bands. The current densities and the reflection coefficients at different stages of the design process are plotted and discussed to get a good insight into the final proposed antenna design. This antenna exhibits stable radiation patterns on both planes, having low cross polarization and low back lobes with a maximum gain of 8.9 dB. The measurements are found to be in good accordance with the simulated results.