Efficient 3D point clouds classification for face detection using linear programming and data mining

Most of the applications related to security and biometric rely on skin region detection such as face detection, adult 3D objects filtering, and gesture recognition. In this paper, we propose a robust method for skin detection on 3D coloured point clouds. Then, we extend this method to solve the problem of 3D face detection. To do so, we construct a weighted graph from initial coloured 3D point clouds. Then, we present a linear programming algorithm using a predictive model based on a data mining approach to classify and label graph vertices as skin and non-skin regions. Moreover, we apply some refinement rules on skin regions to confirm the presence of a face. Furthermore, we demonstrate the robustness of our method by showing and analysing some experimental results. Finally, we show that our method deals with many data that can be represented by a weighted graph such as 2D images and 3D models.     

An efficient numerical algorithm for multi-dimensional time dependent partial differential equations

An efficient and robust numerical scheme based on Haar wavelets and finite differences is suggested for the solution of two-dimensional time dependent linear and nonlinear partial differential equations (PDEs). Excellent feature of the scheme is the conversion of linear and non-linear PDEs to algebraic equations which are comparatively easy to handle. Convergence of the scheme, which guarantees small error norm as the resolution level increases, is also an important part of this work. Different error norms are computed to check efficiency of the technique. Computations verify accuracy, flexibility and low computational cost of the method.     

Exercise classification and event segmentation in Hammersmith Infant Neurological Examination videos

Image and video processing techniques are being frequently used in medical science applications. Computer vision-based systems have successfully replaced various manual medical processes such as analyzing physical and biomechanical parameters, physical examination of patients. These systems are gaining popularity because of their robustness and the objectivity they bring to various medical procedures. Hammersmith Infant Neurological Examinations (HINE) is a set of physical tests that are carried out on infants in the age group of 3–24 months with neurological disorders. However, these tests are graded through visual observations, which can be highly subjective. Therefore, computer vision-aided approach can be used to assist the experts in the grading process. In this paper, we present a method of automatic exercise classification through visual analysis of the HINE videos recorded at hospitals. We have used scale-invariant-feature-transform features to generate a bag-of-words from the image frames of the video sequences. Frequency of these visual words is then used to classify the video sequences using HMM. We also present a method of event segmentation in long videos containing more than two exercises. Event segmentation coupled with a classifier can help in automatic indexing of long and continuous video sequences of the HINE set. Our proposed framework is a step forward in the process of automation of HINE tests through computer vision-based methods. We conducted tests on a dataset comprising of 70 HINE video sequences. It has been found that the proposed method can successfully classify exercises with accuracy as high as 84%. The proposed work has direct applications in automatic or semiautomatic analysis of “vertical suspension” and “ventral suspension” tests of HINE. Though some of the critical tests such as “pulled-to-sit,” “lateral tilting,” or “adductor’s angle measurement” have already been addressed using image- and video-guided techniques, scopes are there for further improvement.     

Neutron radiation effects on metal oxide semiconductor (MOS) devices

The main purpose of this study is to provide the knowledge and data on Deuterium-Tritium (D-T) fusion neutron induced damage in MOS devices. Silicon metal oxide semiconductor (MOS) devices are currently the cornerstone of the modern microelectronics industry. However, when a MOS device is exposed to a flux of energetic radiation or particles, the resulting effects from this radiation can cause several degradation of the device performance and of its operating life. The part of MOS structure (metal oxide semiconductor) most sensitive to neutron radiation is the oxide insulating layer (SiO₂). When ionizing radiation passes through the oxide, the energy deposited creates electron-hole pairs. These electron-hole pairs have been seriously hazardous to the performance of these electronic components. The degradation of the current gain of the dual n-channel depletion mode MOS caused by neutron displacement defects, was measured using in situ method during neutron irradiation. The average degradation of the gain of the current is about 35 mA, and the change in channel current gain increased proportionally with neutron fluence. The total fusion neutron displacement damage was found to be 4.8 × 10⁻²¹ dpa per n/cm², while the average fraction of damage in the crystal of silicon was found to be 1.24 × 10⁻¹². All the MOS devices tested were found to be controllable after neutron irradiation and no permanent damage was caused by neutron fluence irradiation below 10¹⁰n/cm². The calculation results shows that (n,α) reaction induced soft-error cross-section about 8.7 × 10⁻¹⁴ cm², and for recoil atoms about 2.9 × 10⁻¹⁵ cm², respectively.     

Composite mixed matrix membranes incorporating microporous carbon molecular sieve as filler in polyethersulfone for CO2/CH4 separation

Membrane technology has been considered a key factor for sustainable growth in high-efficiency gas separation. Current mixed matrix membranes (MMMs) technology is rising, but these membranes in the dense structure are having difficulties in operating at high pressures and scale up for commercialization. The purpose of this research is to synthesize composite MMMs (CMMMs) consisting of polyethersulfone (PES), carbon molecular sieve (CMS 1–5 wt %), and Novatex 2471 nonwoven fabric (support layer). The membranes' physical, chemical, and thermal properties were evaluated by different analytical equipment. The morphology of both PES and PES-CMS composite membranes had a porous and asymmetric structure, in which CMS was uniformly distributed in the polymer matrix. The thermal properties showed that the membranes were stable up to 350 °C with a single glass transition temperature. The functional groups in the membrane were confirmed by spectral analysis. The gas performance results showed that carbon dioxide permeance increased with increased CMS concentration and methane permeance decreased due to the hindering effect of CMS under similar operating conditions. The highest selectivity achieved was 12.774 using CMMM of 5 wt % of CMS at 10 bar, which on average was 137.80%, improved selectivity compared to pure PES membrane. The support layer was able to withstand high operating pressures and showed the ability to scale up. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2020 , 137, 48476.     

Limitations,challenges, and solution approaches in grid-connected renewable energy systems

In the modern world, only conventional energy resources cannot fulfil the growing energy demand. Electricity is a fundamental building block of a technological revolution. Today, most of the electricity demand is met by the burning of fossil fuels but at the cost of adverse environmental impact. In order to bridge the gap between electricity demand and supply, nonconventional and eco-friendly means of energy generation are considered. Renewable energy systems (RESs) offer an adequate solution to mitigate the challenges originated due to greenhouse gasses (GHG). However, they have an unpredictable power generation with specific site requirements. Grid integration of RESs may lead to new challenges related to power quality, reliability, power system stability, harmonics, subsynchronous oscillations (SSOs), power quality, and reactive power compensation. The integration with energy storage systems (ESSs) can reduce these complexities that arise due to the intermittent nature of RESs. In this paper, a comprehensive review of renewable energy sources has been presented. Application of ESSs in RESs and their development phase has been discussed. Role of ESSs in increasing lifetime, efficiency, and energy density of power system having RESs has been reviewed. Moreover, different techniques to solve the critical issues like low efficiency, harmonics, and inertia reduction in photovoltaic (PV) systems have been presented. Unlike most of the available review papers, this article also investigates the impact of FACTS technology in RESs-based power system using multitype flexible AC transmission system (FACTS) controllers. Three simulation models have been developed in MATLAB/Simulink. The results show that FACTS devices help to maintain the stability of RESs integrated power system. This review paper is believed to be of potential benefit for researchers from both the industry and academia to develop better understanding of challenges and solution techniques for REs-based power systems and future research dimensions in this area.     

Transesterification of Palm-based Methyl Palmitate into Esteramine Catalyzed by Calcium Oxide Catalyst

The technology for transesterification reactions between methyl esters and alcohols is well established by using classical homogeneous alkaline catalysts, which provide high conversion of methyl esters to specialty or nonindigenous esters. However, in certain products where the purity of the esters is of concern, the removal of homogeneous catalysts after the completion of the reaction is a challenge in terms of production cost and water footprint. Therefore, a study to investigate the potential of heterogeneous catalysts was conducted on reactions between methyl palmitate and triethanolamine. The degree of basicity and active surface area of calcium oxide (CaO), zinc oxide (ZnO), and magnesium oxide (MgO) were first characterized by using temperature-programmed desorption (TPD-CO₂) and Brunauere–Emmett–Teller (BET), respectively. Among the metal oxides investigated, the CaO catalyst showed the best catalytic activity toward the transesterification process as it gave the highest conversion of methyl palmitate and yielded fatty esteramine compositions similar to the conventional homogeneous catalyst. The optimum transesterification condition by using the CaO catalyst utilized a lower vacuum system of approximately 200 mbar, which could minimize a considerable amount of energy consumption. Furthermore, low CaO dosage of 0.1% was able to give a conversion of 94.5% methyl ester and formed esteramine at 170 °C for 2 h. Therefore, the production of esterquats from esteramine may become more economically feasible through the methyl ester route by using the CaO catalyst, which can be recycled three times.     

Fuzzy modeling and particle swarm optimization for determining the optimal operating parameters to enhance the bio-methanol production from sugar cane bagasse

This work aims to maximize the production of bio-methanol from sugar cane bagasse through pyrolysis. The maximum value of the bio-methanol yield can be obtained as soon as the optimal operating parameters in a pyrolysis batch reactor are well defined. Using the experimental data, the fuzzy logic technique is used to build a robust model that describes the yield of bio-methanol production. Then, Particle Swarm Optimization (PSO) algorithm is utilized to estimate the optimal values of the operating parameters that maximize the bio-methanol yield. Three different operating parameters influence the yield of bio-methanol from sugar cane bagasse through pyrolysis. The controlling parameters are considered as the reaction temperature (°C), reaction time (min), and nitrogen flow (L/min). Accordingly, during the optimization process, these parameters are used as the decision variables set for the PSO optimizer in order to maximize the yield of bio-methanol, which is considered as a cost function. The results demonstrated a well-fitting between the fuzzy model and the experimental data compared with previous predictions obtained by an artificial neural network (ANN) model. The mean square errors of the model predictions are 0.11858 and 0.0259, respectively, for the ANN and fuzzy-based models, indicating that fuzzy modeling increased the prediction accuracy to 78.16% compared with ANN. Based on the built model, the PSO optimizer accomplished a substantial improvement in the yield of bio-methanol by 20% compared to that obtained experimentally, without changing system design or the materials used.     

Highly porous and thermally stable tribopositive hybrid bimetallic cryogel to boost up the performance of triboelectric nanogenerators

There have been tremendous efforts made to investigate various materials to enhance the electrical performance of triboelectric nanogenerators (TENGs) but there is still demand for some techniques to further enhance the performance of tribomaterials. Therefore, we fabricated a bimetallic hybrid cryogel via cheap and facile UV-radiation as well as in situ reduction method. Fabricated TENG device made up of porous hybrid bimetallic cryogel film containing silver and gold nanoparticles as tribopositive material and poly dimethyl siloxane (PDMS) as a tribonegative layer with dimension of 1 × 2 cm² has the ability to produced output voltage of 262.14 V with current density of 27.52 mA/m² and 7.44 W/m² peak power density, which was sufficient to light up more than 120 white light emitting-diodes (LEDs). Porous and rough structure, interaction of nanoparticles was the reason behind the performance enhancement of tribopositive material. Thus, this study introduces a very stable and easily synthesized bimetallic hybrid cryogel as a tribopositive material to enhance the performance of tribomaterials to design high performance TENG devices.