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Researchers have been working to magnify mental workload (MWL) modeling for a long time. An important aspect of its modeling is feature selection as it interprets bulky and high-dimensional EEG data and enhances the accuracy of the classification model. In this study, a feature selection technique is proposed to obtain an optimized feature set with multiple domain features that can contribute to classifying the MWL at three distinct levels. The brain signals from thirteen healthy subjects were examined while they attended an intrinsic MWL of spotting differences in a set of similar pictures. The Recursive Feature Elimination (RFE) technique selects the robust features from the feature matrix by eliminating all the least contributing features. Along with the Support Vector Machine (SVM), the overall classification accuracy with the proposed RFE reached 0.913 from 0.791 surpassing the other techniques mentioned. The results of the study also significantly display the variation in the mean values of the selected features at the three workload levels (p

Billion-dollar investment has been made globally in different industries, and hence, protection of organisational assets against theft or leakage of confidential information becomes a prime objecti...

This study presents online-tuning approach using the moth flame optimization (MFO) algorithm to optimize the parameters of PID and modified PID (I-PD) controllers used in the split-range scheme to control the temperature of the mixing process. The performance of these controllers is investigated for the individual temperature setpoints in terms of settling time and compared with performances obtained using offline-tuning approach with the MFO algorithm. The simulation results show a significant improvement with online-tuning approach as compared to offline approach. To further improve the performance, this study proposes modifications in the original MFO algorithm in three phases: by changing the spiral path, by changing the initial population based on the opposition theory, and by a change in the selection of the flames for the updating mechanism. A new version of MFO algorithm is obtained by combining the above-mentioned modifications and used to tune the PID and I-PD controllers in both offline and online modes. Further, the new algorithm is tested for both the controllers with respect to the effect of system dynamics and the effect of process disturbance. The results obtained after validation show that the use of the new version of the MFO algorithm further improves the online tuning of both the controllers. The simulation results also clearly establish the superior performance of the modified PID (I-PD) controller over the PID controller under all conditions.

With the recent advancement in the field of information technology, online frauds have also been increased and hence password-based systems are no more secure. Therefore need for a more reliable au...

This article presents a nature-inspired optimization technique, namely the moth flame optimization (MFO) algorithm, for tuning the PID controller parameters in the variable range of split-range control scheme to regulate the temperature of the mixing process. Further, the performance of the controller using Ziegler–Nichols (Z–N) tuning method and MFO algorithm is compared for the same. In this study, the controller parameters are tuned using both the methods for various temperature setpoints, and the performance of the controller for the individual temperature setpoints is analyzed in terms of settling time and demand of utilities. Investigations are conducted on the basis of the effect of dead time in the valve and the effect of process disturbance. The performance of the controller is also investigated on the basis of utility consumption using a valve with no dead time. The results show that MFO-based tuning approach provides a significant improvement as compared to Z–N tuning method.

Background The brain performs a very significant job in our body by processing the information associated with human critical inclinations, intentions, sensory attention and awareness, execution, and mental state sustenance during a specific task. The attention of every human being gets altered while undergoing two or more cognitive tasks collaterally. Methods In this research, subjects were asked to perform two tasks collaterally in which one task was considered as primary task whereas the other task as secondary. The EEG (Electroencephalography) signals of the subjects undergoing those collateral cognitive tasks were recorded using RMS EEG-32 Super Spec machine. Result The relative band powers ratio (Theta to Beta band power ratio) helped in tracing the point of time when the attention devoted to the primary task got deviated by the secondary task. An auditory P300 peak generation validated the deviation in this research. Conclusion This research track could pave the way for designing a battery that can analyze the subject’s performance during multi-tasking. Other than this, such an investigation will help in avoiding the disasters caused by the attention deviation of the operator.

Due to great advances in the field of information technology, the need for a more reliable authentication system has been growing rapidly for protecting the individual or organizational assets from online frauds. In the past, many authentication techniques have been proposed like password and tokens but these techniques suffer from many shortcomings such as offline attacks (guessing) and theft. To overcome these shortcomings, in this paper brain signal based authentication system is proposed. A Brain–Computer Interface (BCI) is a tool that provides direct human–computer interaction by analyzing brain signals. In this study, a person authentication approach that can effectively recognize users by generating unique brain signal features in response to pictures of different objects is presented. This study focuses on a P300 BCI for authentication system design. Also, three classifiers were tested: Quadratic Discriminant Analysis (QDA), K-Nearest Neighbor, and Quadratic Support Vector Machine. The results showed that the proposed visual stimuli with pictures as selection attributes obtained significantly higher classification accuracies (97%) and information transfer rates (37.14 bits/min) as compared to the conventional paradigm. The best performance was observed with the QDA as compare to other classifiers. This method is advantageous for developing brain signal based authentication application as it cannot be forged (like Shoulder surfing) and can still be used for disabled users with a brain in good running condition. The results show that reduced matrix size and modified visual stimulus typically affects the accuracy and communication speed of P300 BCI performance.

A novel improved meta heuristic method, Hybrid salp swarm using sine cosine algorithm (Hybrid SS-SC) for maximum power point tracking (MPPT) under partial shading condition is proposed in this paper. The proposed algorithm envisages finding the best GMPP. The simulation studies clearly demonstrate that the method compares favourably with the conventional P&O method as well as with some other nature inspired algorithms in tracking GMPP in less time.

Temperature sensing performance of Er3 +/Yb3 + doped CaMoO4 phosphor prepared via polyol method is reported herein. The X-ray diffraction, Fourier transform infrared spectroscopy and field emission scanning electron microscopy are done to confirm the phase, structure and purity of the synthesized phosphor. The infrared to green upconversion emission is investigated using 980 nm diode laser excitation along with its dependence on input pump power and external temperature. The temperature dependent fluorescence intensity ratio of two upconversion emission bands assigned to 2H11/2 → 4I15/2 (530 nm) and 4S3/2 → 4I15/2 (552 nm) transitions has shown two distinct slopes in the studied temperature range - 300 to 760 K and therefore, dual nature of temperature sensitivity is observed in this phosphor. This phenomenon in rare earth doped materials is either scarcely reported or overlooked. The material has shown higher sensitivity in the high temperature region (535 K

The brain plays an important role in our body as it manages majority human judgmental tendencies, intentions, resolution towards particular situation, performance and mental state sustenance during any particular task. The psychologists have been trying to create a link between the observed pattern with respect to these factors and the person’s brain. In this study, participants were supposed to pinpoint ten spots between sought of analogous pictures that make them different from each other within a given time. The neural response to the particular cognitive task was acquired by RMS EEG-32 Super Spec machine. Further, frequency bands were extracted from the signals obtained employing EEG (Electroencephalography). They were analysed to know the point in time when the brain settles at a steady state after the completion of the task. The beginning of the steady state was validated by the manual signaling. This study can be used for tracking the recovery of many neurologically impaired patients.

In the recent past, the web (internet) has emerged as the most interactive authentication system for all of us (i.e. Internet banking passwords, system or building access, and e-payment platforms, etc.) and as a result, traditional authentication systems (like passwords or token-based) are never again more secure i.e. they are vulnerable to attacks. As a result, the security of individual information and safe access to a system winds up prime necessities. Therefore, the EEG-based authentication system has recently become a reasonable key for high-level security. This study centers upon P300 evoked potential-based authentication system designing. In this paper, a new visual stimulus paradigm (i.e. [Formula: see text] P300 speller) using pictures of different objects as stimuli for a person authentication system is designed instead of the conventional character-based paradigm (i.e. [Formula: see text] speller) for increasing the classification accuracy and Information Transfer Rate (ITR). The trial begins by exhibiting a collection of pictures of various objects on four corners of the PC screen comprising of random object pictures (non-target) alongside password pictures (target) that trigger P300 reactions. The P300 reaction’s rightness then checks the identity of the subject concerning the focused pictures (Target). The proposed investigation model achieves higher classification accuracy of 96.78%, along with 0.03075 False Rejection Rate (FRR), 0.03297 False Acceptation Rate (FAR), and ITR of [Formula: see text]. This study has shown that P300-based authentication system has an advantage over conventional methods (Password, Token, etc.) as EEG-based systems cannot be mimicked or forged (like Shoulder surfing in case of password) and can still be used for disabled users with a brain in good running condition. The classification results revealed that the performance of the QDA classifier outperformed other classifiers based on accuracy and ITR.

The conventional perturb and observe algorithm used for maximum power point tracking of PV system suffers from major drawbacks such as steady state oscillation around final maximum power point and loss of tracking direction i.e. divergence from actual maximum power point for rapid change in insolation. Both these problems result in significant power loss as the PV system operating point is getting diverted from actual maximum power point. As Buck-Boost converter plays an important role while changing the operating point of PV panel, it contributes to significant amount of power loss in the form of switching loss, conduction loss etc. This paper proposes a method to avoid the above given shortcomings of perturb & observe algorithm. Major focus is given on the designing of Buck-Boost converter as to date nobody has taken into consideration the losses occurring in the converter even though converter forms an integral part of the MPPT set up. The losses occurring in the conventional buck boost converter have been computed and its effect on the efficiency of MPPT system is investigated. The soft-switched buck boost converter for PV applications is designed so as to minimize the losses taking place in converter.

The synthesis and spectroscopy of the upconverting nanoparticles, cubic NaYF4:Er3+/Yb3+ phosphor is developed for latent fingermark detection. The cubic phase of NaYF4: Er3+/Yb3+ phosphor is synthesized by thermal decomposition method using trifluoroacetate precursor with coordinating ligand octadecene and oleic acid in a mixture of technical grade. The synthesized samples showed intense green emission using 976 nm diode laser as an excitation source. Because of excellent property of luminescence in green regime the sample is used to detect the latent fingermark on a porous glass surface.

Upconversion emission and temperature sensing of the Er 3+ /Yb 3+ doped ZnO–CaTiO 3 nano-composite phosphor is studied by varying the ZnO concentration. The XRD and EDX studies reveal the formation of composite phase when ZnO doping exceeds above 10 mw%. Five prominent upconversion emission bands at 410, 492, 524, 545 and 662 nm corresponding to 2 H 9/2 → 4 I 15/2 , 4 F 3/2 → 4 I 15/2 , 2 H 11/2 → 4 I 15/2 , 4 S 3/2 → 4 I 15/2 and 4 F 9/2 → 4 I 15/2 transitions, respectively are found under 980 nm excitation from a diode laser. On the basis of rise time analysis it was found that energy transfer process is responsible for the intense upconversion emission. Large reduction in decay time of 4 S 3/2 level is observed on the ZnO incorporation in host matrix. Moreover, the absolute sensor sensitivity, relative sensor sensitivity and calculated color coordinates of the samples are also determined. These results indicate the potentiality of this composite phosphor for various applications.