Silicon-based carbonaceous electrocatalysts for oxygen reduction and evolution properties in alkaline conditions

In this contribution, new electrocatalyst materials, namely silicon-multiwalled carbon nanotubes (Si/MWCNTs), nitrogen-doped multiwalled carbon nano-tubes (Si/NCNTs), and silicon–carbon black (Si/CB), were developed and characterized in an effort to investigate less expensive and more efficient alternatives to Pt-based catalysis for energy storage cells applications. The role of structural behavior of obtained specimens and corresponding electrochemical performances were characterized through X-ray diffraction and scanning electron microscopy, while cyclic voltammetry and electrochemical impedance spectroscopy were analyzed for electrochemical measurements and evaluation of oxygen evolution reaction (OER) along with oxygen reduction reaction (ORR). The electrochemical studies have shown that these materials exhibit reasonable performance for both the ORR and the OER. The findings concluded that the Si/CB base catalyst has shown both OER and ORR activities in comparison to Si/MWCNTs and Si/NCNTs where only ORR performance was monitored. However, Si/NCNTs have shown much higher ORR activity compared to the others. This work highlights the comparison of three possible alternative materials as a potential catalyst to develop optimum alternatives of Pt-free catalysts for fuel cell and lithium-based battery systems

Sorption of As(V) from aqueous solution using in situ growth MgAl–NO3 layered double hydroxide thin film developed on AA6082

In this work, the MgAl–NO3 layered double hydroxide (LDH) developed by the single-step in situ growth method is used as a robust sorbent to remove arsenic from aqueous solution. The MgAl-LDH exhibiting two different distinct morphologies (platelet structure and cauliflower-shaped structure) was developed on the AA6082 substrate with the variation in synthesis parameters, where AA6082 specimen acts as both the reactant and support. The structural characterizations were investigated through scanning electron microscopy, X-ray diffraction analysis, and energy dispersion spectroscopy, while the adsorption of arsenic on MgAl-LDH was studied through Langmuir and Freundlich models. The Langmuir isotherms have shown a maximum adsorption capacity of around 213 and 239 mg/g for platelet and cauliflower-like MgAl-LDH, respectively. The pseudo-first-order and pseudo-second-order Lagergren kinetic models were studied for the understanding of the adsorption kinetics. The results depicted that anion exchange and the electrostatic interaction are the possible reasons of arsenic sorption on MgAl-LDH, but the ion exchange mechanism is found to be the dominant mechanism. The maximum adsorption capacity of cauliflower-shaped MgAl-LDH was found to be slightly higher than platelet structure, but overall maximum arsenic adsorption uptake values of both in situ growth structures have found to be exceeded the mostly reported MgAl-LDH maximum adsorption capacities

Image Local Features Description through Polynomial Approximation

This work introduces a novel local patch descriptor that remains invariant under varying conditions of orientation, viewpoint, scale, and illumination. The proposed descriptor incorporate polynomials of various degrees to approximate the local patch within the image. Before feature detection and approximation, the image micro-texture is eliminated through a guided image filter with the potential to preserve the edges of the objects. The rotation invariance is achieved by aligning the local patch around the Harris corner through the dominant orientation shift algorithm. Weighted threshold histogram equalization (WTHE) is employed to make the descriptor in-sensitive to illumination changes. The correlation coefficient is used instead of Euclidean distance to improve the matching accuracy. The proposed descriptor has been extensively evaluated on the Oxford's affine covariant regions dataset, and absolute and transition tilt dataset. The experimental results show that our proposed descriptor can categorize the feature with more distinctiveness in comparison to state-of-the-art descriptors. - 2013 IEEE.This work was supported by the Qatar National Library.Scopu

EEG-Based Multi-Modal Emotion Recognition using Bag of Deep Features: An Optimal Feature Selection Approach

Much attention has been paid to the recognition of human emotions with the help of electroencephalogram (EEG) signals based on machine learning technology. Recognizing emotions is a challenging task due to the non-linear property of the EEG signal. This paper presents an advanced signal processing method using the deep neural network (DNN) for emotion recognition based on EEG signals. The spectral and temporal components of the raw EEG signal are first retained in the 2D Spectrogram before the extraction of features. The pre-trained AlexNet model is used to extract the raw features from the 2D Spectrogram for each channel. To reduce the feature dimensionality, spatial, and temporal based, bag of deep features (BoDF) model is proposed. A series of vocabularies consisting of 10 cluster centers of each class is calculated using the k-means cluster algorithm. Lastly, the emotion of each subject is represented using the histogram of the vocabulary set collected from the raw-feature of a single channel. Features extracted from the proposed BoDF model have considerably smaller dimensions. The proposed model achieves better classification accuracy compared to the recently reported work when validated on SJTU SEED and DEAP data sets. For optimal classification performance, we use a support vector machine (SVM) and k-nearest neighbor (k-NN) to classify the extracted features for the different emotional states of the two data sets. The BoDF model achieves 93.8% accuracy in the SEED data set and 77.4% accuracy in the DEAP data set, which is more accurate compared to other state-of-the-art methods of human emotion recognition

Attacks Analysis of TCP And UDP Of UNCW-NB15 Dataset

TCP (Transmission Control Protocol) and UDP (User Datagram Protocol) are the most important protocols in complete protocol architecture.  There are many types of attacks that can block the communication or reduce the performance of a protocol. This study provides a detail analysis of TCP and UDP attacks and their application layer protocols. The authors will also suggest that where the security administrator should focus for providing best security. The old datasets such as KDD99 and NSLKDD has many limitations. This study uses UNSW-NB15 dataset which has recently been generated

An Innovative Multi-Model Neural Network Approach for Feature Selection in Emotion Recognition Using Deep Feature Clustering

Emotional awareness perception is a largely growing field that allows for more natural interactions between people and machines. Electroencephalography (EEG) has emerged as a convenient way to measure and track a user’s emotional state. The non-linear characteristic of the EEG signal produces a high-dimensional feature vector resulting in high computational cost. In this paper, characteristics of multiple neural networks are combined using Deep Feature Clustering (DFC) to select high-quality attributes as opposed to traditional feature selection methods. The DFC method shortens the training time on the network by omitting unusable attributes. First, Empirical Mode Decomposition (EMD) is applied as a series of frequencies to decompose the raw EEG signal. The spatiotemporal component of the decomposed EEG signal is expressed as a two-dimensional spectrogram before the feature extraction process using Analytic Wavelet Transform (AWT). Four pre-trained Deep Neural Networks (DNN) are used to extract deep features. Dimensional reduction and feature selection are achieved utilising the differential entropy-based EEG channel selection and the DFC technique, which calculates a range of vocabularies using k-means clustering. The histogram characteristic is then determined from a series of visual vocabulary items. The classification performance of the SEED, DEAP and MAHNOB datasets combined with the capabilities of DFC show that the proposed method improves the performance of emotion recognition in short processing time and is more competitive than the latest emotion recognition methods

Unveiling the electrochemical advantages of a scalable and novel aniline-derived polybenzoxazole-reduced graphene oxide composite decorated with manganese oxide nanoparticles for supercapacitor applications

In the quest for better supercapacitor performance, materials with high energy storage, rapid charge transfer and excellent charge-discharge capabilities are crucial. However, improving energy density and optimizing electrode materials for supercapacitors remains a challenge. Within this perspective, we developed a novel aniline-derived polybenzoxazole-reduced graphene oxide composite (Mn3O4-pBOA-rGO) for supercapacitor applications. An electrode for supercapacitor applications was then fabricated using the synthesized composite. Various characterization techniques, such as X-ray diffraction, scanning electron microscopy, Fourier transform infrared spectroscopy and nuclear magnetic resonance spectroscopy, were employed to analyze the structure of the material and cyclic voltammetry, electrochemical impedance spectroscopy and galvanostatic charge-discharge tests were performed to evaluate the supercapacitive performance. Remarkably, the Mn₃O₄-pBOA-rGO ternary nanocomposite-based electrode exhibited enhanced electrochemical performance achieving an energy density of 116 Wh kg−¹ in a 1 M aqueous Na2SO4 electrolyte at a current density of 1 A g−¹. In comparison, the Mn₃O₄-pBOA binary nanocomposite recorded an energy density of 25 Wh kg−¹, while bare Mn₃O₄ yielded 10 Wh kg−¹. The significantly improved electrochemical properties of the Mn₃O₄-pBOA-rGO composite present a promising and viable solution for supercapacitor applications

DFT-Net: Deep Feature Transformation Based Network for Object Categorization and Part Segmentation in 3-Dimensional Point Clouds

Unlike 2-dimensional (2D) images, direct 3-dimensional (3D) point cloud processing using deep neural network architectures is challenging, mainly due to the lack of explicit neighbor relationships. Many researchers attempt to remedy this by performing an additional voxelization preprocessing step. However, this adds additional computational overhead and introduces quantization error issues, limiting an accurate estimate of the underlying structure of objects that appear in the scene. To this end, in this article, we propose a deep network that can directly consume raw unstructured point clouds to perform object classification and part segmentation. In particular, a Deep Feature Transformation Network (DFT-Net) has been proposed, consisting of a cascading combination of edge convolutions and a feature transformation layer that captures the local geometric features by preserving neighborhood relationships among the points. The proposed network builds a graph in which the edges are dynamically and independently calculated on each layer. To achieve object classification and part segmentation, we ensure point order invariance while conducting network training simultaneously—the evaluation of the proposed network has been carried out on two standard benchmark datasets for object classification and part segmentation. The results were comparable to or better than existing state-of-the-art methodologies. The overall score obtained using the proposed DFT-Net is significantly improved compared to the state-of-the-art methods with the ModelNet40 dataset for object categorization