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The accuracy of classifying motor imagery (MI) activities is a significant challenge when using brain–computer interfaces (BCIs). BCIs allow people with motor impairments to control external devices directly with their brains using electroencephalogram (EEG) patterns that translate brain activity into control signals. Many researchers have been working to develop MI-based BCI recognition systems using various time-frequency feature extraction and classification approaches. However, the existing systems still face challenges in achieving satisfactory performance due to large amount of non-discriminative and ineffective features. To get around these problems, we suggested a multiband decomposition-based feature extraction and classification method that works well, along with a strong feature selection method for MI tasks. Our method starts by splitting the preprocessed EEG signal into four sub-bands. In each sub-band, we then used a common spatial pattern (CSP) technique to pull out narrowband-oriented useful features, which gives us a high-dimensional feature vector. Subsequently, we utilized an effective feature selection method, Relief-F, which reduces the dimensionality of the final features. Finally, incorporating advanced classification techniques, we classified the final reduced feature vector. To evaluate the proposed model, we used the three different EEG-based MI benchmark datasets, and our proposed model achieved better performance accuracy than existing systems. Our model's strong points include its ability to effectively reduce feature dimensionality and improve classification accuracy through advanced feature extraction and selection methods. [ABSTRACT FROM AUTHOR]

Analyzing electroencephalography (EEG) signals with machine learning approaches has become an attractive research domain for linking the brain to the outside world to establish communication in the name of the Brain-Computer Interface (BCI). Many researchers have been working on developing successful motor imagery (MI)-based BCI systems. However, they still face challenges in producing better performance with them because of the irrelevant features and high computational complexity. Selecting discriminative and relevant features to overcome the existing issues is crucial. In our proposed work, different feature selection algorithms have been studied to reduce the dimension of multiband feature space to improve MI task classification performance. In the procedure, we first decomposed the MI-based EEG signal into four sets of the narrowband signal. Then a common spatial pattern (CSP) approach was employed for each narrowband to extract and combine effective features, producing a high-dimensional feature vector. Three feature selection approaches, named correlation-based feature selection (CFS), minimum redundancy and maximum relevance (mRMR), and multi-subspace randomization and collaboration-based unsupervised feature selection (SRCFS), were used in this study to select the relevant and effective features for improving classification accuracy. Among them, the SRCFS feature selection approach demonstrated outstanding performance for MI classification compared to other schemes. The SRCFS is based on the multiple k-nearest neighbour graphs method for learning feature weight based on the Laplacian score and then discarding the irrelevant features based on the weight value, reducing the feature dimension. Finally, the selected features are fed into the support vector machines (SVM), linear discriminative analysis (LDA), and multi-layer perceptron (MLP) for classification. The proposed model is evaluated with two benchmark datasets, namely BCI Competition III dataset IVA and dataset IIIB, which are publicly available and mainly used to recognize the MI tasks. The LDA classifier with the SRCFS feature selection algorithm exhibits better performance. It proves the superiority of our proposed study compared to the other state-of-the-art BCI-based MI task classification systems. [ABSTRACT FROM AUTHOR]

The current research was undertaken to compare the proximate composition and antioxidant activity of different parts of green and ripe passion fruit. The proximate composition, bioactive compounds and antioxidant activity of different parts of passion fruit were analyzed by standard methods. According to the results, the ash content of the peel and seed of ripe passion fruit was higher than that of the green one, but only significant (P<0.05) for the seed. Besides, the crude fiber, carbohydrates, and protein content of the peel, pulp, and seed of ripe passion fruit were relatively higher (P<0.05) than those of the green one. At P<0.05, the fat content of green peel and pulp was higher than that of ripe, but the seed content of ripe was higher. The peel and pulp of green passion fruit contain the highest amounts of total polyphenol content (TPC) and total flavonoid content (TFC) than ripe passion fruit (P<0.05). The highest amount of TPC is contained in the seed of green passion fruit than in ripe fruit (P>0.05), but the seed of ripe fruit contains TFC almost twice as much as green fruit (P<0.05). It was also found that the peel and pulp of ripe passion fruit contain a higher percentage of total antioxidant activity (TAA) and vitamin C (P<0.05) than the pulp of a green one. The seed of the green one contains a slightly higher percentage of TAA than the seed of the ripe fruit (P<0.05), but vitamin C was opposite (P<0.05). It is concluded that the pulp, peel, and seed of green and ripe passion fruit have great potential as a health-promoting source that is normally underutilized. [ABSTRACT FROM AUTHOR]

Using first-principles within density functional (DFT) theory, adsorption of CO, CO2, and O3 gas molecules on pristine B24N24 (BN) and Silicon-decorated B24N24 (SiBN) nanosheets has been investigated. Adsorption energies, Mulliken charge, dipole moments, and global indicies are estimated to predict the adsorption behavior of CO, CO2, and O3 by using DFT method at 6-31 G (d, p)/B3LYP level. After silicon doping, unlike CO and CO2, sensing performance improves for the case of O3. The findings reveal that the interaction between O3 and SiBN is stronger than that between O3 and bare BN, suggesting that silicon helps to strengthen the interaction of O3 molecule with BN nanosheet. DFT calculations clearly indicate that the Si-doped BN nanosheet would be a promising O3 gas sensor. To prove the successful interactions among the adsorbents (BN and SiBN) and adsorbates (CO, CO2, and O3), the UV-vis spectra have been analyzed for every system where both the blue shift and red shift have been confirmed. Finally, it can be said that pristine BN nanosheet is a better sensor than SiBN for CO, CO2 gas molecules while SiBN is better for O3 molecules. [ABSTRACT FROM AUTHOR]

In this paper, a physically based two-dimensional analytical model of potential profile is proposed for both symmetric and asymmetric p-type-doped Double Gate Junctionless Field Effect Transistors (DG JLFETs). The proposed model keeps its validity in the subthreshold region. In order to establish the model, 2-D Poisson's equation is solved in the channel region while assuming cubic potential distribution along the channel region. We also propose a 2-D analytical model of symmetric Double Gate Stack-Oxide Junctionless Field Effect Transistors (DGS JLFETs). In order to check out the validity of our proposed model, we have used SILVACO ATLAS simulator. Comparison of potential distribution is made between different structures. Between symmetric and asymmetric structures, symmetric structure shows better potential profile as they have shown higher potential distribution in channel region. But when DG JLFET is compared with DGS JLFET, we find better potential profile distribution in DGS JLFET. Important performance parameters like Ion/Ioff ratio, SS, DIBL, Threshold Voltage, I–V characteristics for different structures are found out and compared among them. Asymmetric structure with same electrode materials in both gates but having lower bottom gate oxide thickness showed better performances like higher Ion/Ioff ratio, lower SS, less DIBL compared to other DG JLFETs with and without stack oxide. [ABSTRACT FROM AUTHOR]

Perfect absorption is one of the important application of epsilon-near-zero (ENZ) material. We analyze here the coherent perfect absorption (CPA) in single and bilayer of ENZ thin film of ITO by admittance matching method. To enhance the absorption, a coupling layer of dielectric is to deposit on the substrate with an ENZ ITO layer. CPA conditions are presented here in simulation results which are obtained for p-polarized light. This mode is comparable with the transverse resonance effect. In order to obtain the symmetric results, the optimum polarization direction of illumination is also extracted here. Employing the ENZ CPA property of ITO, this structure is applicable in optical switching, modulation and sensor at the telecommunication regime. [ABSTRACT FROM AUTHOR]

Molecular communication in nanonetworks is an emerging communication paradigm that uses molecules as information carriers. In molecule shift keying (MoSK), where different types of molecules are used for encoding, transmitter and receiver complexities increase as the modulation order increases. We propose a modulation technique called depleted MoSK (D-MoSK) in which, molecules are released if the information bit is 1 and no molecule is released for 0. The proposed scheme enjoys reduced number of the types of molecules for encoding. Numerical results show that the achievable rate is considerably higher and symbol error rate (SER) performance is better in the proposed technique. [ABSTRACT FROM PUBLISHER]

Machine translation (MT) is the process of translating text from one language to another using bilingual data sets and grammatical rules. Recent works in the field of MT have popularized sequence-to-sequence models leveraging neural attention and deep learning. The success of neural attention models is yet to be construed into a robust framework for automated English-to-Bangla translation due to a lack of a comprehensive dataset that encompasses the diverse vocabulary of the Bangla language. In this study, we have proposed an English-to-Bangla MT system using an encoder-decoder attention model using the CCMatrix corpus. Our method shows that this model can outperform traditional SMT and RBMT models with a Bilingual Evaluation Understudy (BLEU) score of 15.68 despite being constrained by the limited vocabulary of the corpus. We hypothesize that this model can be used successfully for state-of-the-art machine translation with a more diverse and accurate dataset. This work can be extended further to incorporate several newer datasets using transfer learning techniques. [ABSTRACT FROM AUTHOR]

The Wireless Personal Area Network (WPAN) is one of the fledging paradigms that the next generation of wireless systems is sprouting towards. Among them, a more specific category is the Wireless Body Area Network (WBAN) used for health monitoring. On the other hand, Ultra-Wideband (UWB) comes with a number of desirable features at the physical layer for wireless communications. One big challenge in adoption of UWB in WBAN is the fact that signals get attenuated exponentially. Due to the intrinsic structural complexity in human body, electromagnetic waves show a profound variation during propagation through it. The reflection and transmission coefficients of human body are highly dependent upon the dielectric constants as well as upon the frequency. The difference in structural materials such as fat, muscles and blood essentially makes electromagnetic wave attenuation to be different along the way. Thus, a complete characterization of body channel is a challenging task. The connection between attenuation and frequency of the signal makes the investigation of UWB in WBAN an interesting proposition. In this paper, we study analytically the impact of body channels on electromagnetic signal propagation with reference to UWB. In the process, scattering, reflectivity and transmitivity have been addressed with analysis of approximate layer-wise modeling, and with numerical depictions. Pulses with Gaussian profile have been employed in our analysis. It shows that, under reasonable practical approximations, the human body channel can be modeled in layers so as to have the effects of total reflections or total transmissions in certain frequency bands. This could help decide such design issues as antenna characteristics of implant devices for WBAN employing UWB. [ABSTRACT FROM AUTHOR]