Physico-Mechanical Properties of Nano Silica-Filled Epoxy-Based Mono and Hybrid Composites for Structural Applications

Silicon - This research article describes the results of nano-silica (nSiO2) filled epoxy (Ep) mono composites with different contents (0.5, 1.0, 1.5, 3, and 5 wt.%) and carbon...     

Performance of orthogonal frequency division multiplexing based M-ary quadrature amplitude modulation in asymmetrical dual-hop mixed RF-FSO transmission system

This paper investigates the performance of a dual-hop mixed relay system with radio frequency (RF) and free-space optics (FSO) communication under the effect of pointing error (PE) and atmospheric turbulence (AT). This paper considers a system where RF and FSO links are cascaded. The RF link is modeled by Nakagami-m fading, and the FSO link is modeled as gamma–gamma (G-G) fading channel. Both the channel models use orthogonal frequency division multiplexing (OFDM) with M-ary quadrature amplitude modulation (QAM). The expressions for probability density function, cumulative distribution function, signal-to-noise ratio, and ergodic capacity are derived. The moment generating function (MGF) of fading and the bit error rate (BER) of the OFDM-based M-ary QAM scheme is derived in terms of Meijer's G-function. It has been observed that, in fixed gain relay systems, the modulation scheme's BER is dominated by the SNR of the RF link. While in a variable gain relay system, the turbulence conditions of the FSO system affect the SNR and the BER of the modulation method. The feasibility of heterodyne detection and intensity modulation direct detection (IM/DD) is analyzed in terms of outage probability and ergodic capacity. The results can be used to choose the optimal modulation order and relay system for QAM-OFDM-based optical wireless systems.     

Fast and accurate fuzzy C‐means algorithm for MR brain image segmentation

Fuzzy theory based intelligent techniques are widely preferred for medical applications because of high accuracy. Among the fuzzy based techniques, Fuzzy C‐Means (FCM) algorithm is popular than the other approaches due to the availability of expert knowledge. But, one of the hidden facts is that the computational complexity of the FCM algorithm is significantly high. Since medical applications need to be time effective, suitable modifications must be made in this algorithm for practical feasibility. In this study, necessary changes are included in the FCM approach to make the approach time effective without compromising the segmentation efficiency. An additional data reduction approach is performed in the conventional FCM to minimize the computational complexity and the convergence rate. A comparative analysis with the conventional FCM algorithm and the proposed Fast and Accurate FCM (FAFCM) is also given to show the superior nature of the proposed approach. These techniques are analyzed in terms of segmentation efficiency and convergence rate. Experimental results show promising results for the proposed approach. © 2016 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 26, 188–195, 2016     

Hand posture and gesture recognition techniques for virtual reality applications: a survey

Motion recognition is a topic in software engineering and dialect innovation with a goal of interpreting human signals through mathematical algorithm. Hand gesture is a strategy for nonverbal communication for individuals as it expresses more liberally than body parts. Hand gesture acknowledgment has more prominent significance in planning a proficient human computer interaction framework, utilizing signals as a characteristic interface favorable to circumstance of movements. Regardless, the distinguishing proof and acknowledgment of posture, gait, proxemics and human behaviors is furthermore the subject of motion to appreciate human nonverbal communication, thus building a richer bridge between machines and humans than primitive text user interfaces or even graphical user interfaces, which still limits the majority of input to electronics gadget. In this paper, a study on various motion recognition methodologies is given specific accentuation on available motions. A survey on hand posture and gesture is clarified with a detailed comparative analysis of hidden Markov model approach with other classifier techniques. Difficulties and future investigation bearing are also examined.     

On the fractal characteristics of low Damköhler number flames

Knowledge of the fractal properties of premixed flame surfaces can potentially be used to help develop turbulent combustion models. Here, direct numerical simulations of low Damköhler number flames are used to analyse the fractal nature of the flames. Two sets of data are considered: (i) thermochemical hydrogen–air turbulent premixed plane-jet flames with detailed chemistry and (ii) thermonuclear flames in type Ia supernovae. A three-dimensional box counting method is used to investigate fractal dimension of the flame surface, characterising the self similarity of flame fronts. In the premixed flames, the fractal dimension is found to vary in time between 2.1 and 2.7. The supernovae flames in distributed combustion regimes yield fractal dimension about 2.7. The results for the maximum fractal dimensions are higher than previously reported. They are explained theoretically by a Reynolds number similarity argument which posits that the high Reynolds number, low Damköhler number limiting value of the fractal dimension is 8/3. Also tested is Mandelbrot’s fractal additive law which relates the fractal dimension determined in two dimensions, which is typical of experimental measurements, to that in three dimensions. The comparison of the fractal dimension in both two-dimensional and three-dimensional spaces supports the additive law, even though the flames considered do not formally satisfy isotropy. Finally, the inner-cut off is extracted from the hydrogen flames and found to be consistent in order of magnitude with Kolmogorov scaling.     

Perturbation-Based Eigenvector Updates for On-Line Principal Components Analysis and Canonical Correlation Analysis

Principal components analysis is an important and well-studied subject in statistics and signal processing. Several algorithms for solving this problem exist, and could be mostly grouped into one of the following three approaches: adaptation based on Hebbian updates and deflation, optimization of a second order statistical criterion (like reconstruction error or output variance), and fixed point update rules with deflation. In this study, we propose an alternate approach that avoids deflation and gradient-search techniques. The proposed method is an on-line procedure based on recursively updating the eigenvector and eigenvalue matrices with every new sample such that the estimates approximately track their true values as would be calculated analytically from the current sample estimate of the data covariance matrix. The perturbation technique is theoretically shown to be applicable for recursive canonical correlation analysis, as well. The performance of this algorithm is compared with that of a structurally similar matrix perturbation-based method and also with a few other traditional methods like Sanger’s rule and APEX.

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Distance metric-based time-efficient fuzzy algorithm for abnormal magnetic resonance brain image segmentation

Image segmentation is one of the significant computational applications of the biomedical field. Automated computational methodologies are highly preferred for medical image segmentation since these techniques are immune to human perception error. Artificial intelligence (AI)-based techniques are often used for this process since they are superior to other automated techniques in terms of accuracy and convergence time period. Fuzzy systems hold a significant position among the AI techniques because of their high accuracy. Even though these systems are exceptionally accurate, the time period required for convergence is exceedingly high. In this work, a novel distance metric-based fuzzy C-means (FCM) algorithm is proposed to tackle the low-convergence-rate problem of the conventional fuzzy systems. This modified approach involves the concept of distance-based dimensionality reduction of the input vector space that substantially reduces the iterative time period of the conventional FCM algorithm. The effectiveness of the modified FCM algorithm is explored in the context of magnetic resonance brain tumor image segmentation. Experimental results show promising results for the proposed approach in terms of convergence time period and segmentation efficiency. Thus, this algorithm proves to be highly feasible for time-oriented real-time applications.     

Inkjet Printing of Self‐Assembled 2D Titanium Carbide and Protein Electrodes for Stimuli‐Responsive Electromagnetic Shielding

2D titanium carbides (MXene) possess significant characteristics including high conductivity and electromagnetic interference shielding efficiency (EMI SE) that are important for applications in printed and flexible electronics. However, MXene‐based ink formulations are yet to be demonstrated for proper inkjet printing of MXene patterns. Here, tandem repeat synthetic proteins based on squid ring teeth (SRT) are employed as templates of molecular self‐assembly to engineer MXene inks that can be printed as stimuli‐responsive electrodes on various substrates including cellulose paper, glass, and flexible polyethylene terephthalate (PET). MXene electrodes printed on PET substrates are able to display electrical conductivity values as high as 1080 ± 175 S cm^?1, which significantly exceeds electrical conductivity values of state‐of‐the‐art inkjet‐printed electrodes composed of other 2D materials including graphene (250 S cm^?1) and reduced graphene oxide (340 S cm^?1). Furthermore, this high electrical conductivity is sustained under excessive bending deformation. These flexible electrodes also exhibit effective EMI SE values reaching 50 dB at films with thicknesses of 1.35 µm, which mainly originate from their high electrical conductivity and layered structure.     

LES of a premixed jet flame DNS using a strained flamelet model

Turbulent premixed flames in the thin and broken reaction zones regimes are difficult to model with Large Eddy Simulation (LES) because turbulence strongly perturbs subfilter scale flame structures. This study addresses the difficulty by proposing a strained flamelet model for LES of high Karlovitz number flames. The proposed model extends a previously developed premixed flamelet approach to account for turbulence’s perturbation of subfilter premixed flame structures. The model describes combustion processes by solving strained premixed flamelets, tabulating the results in terms of a progress variable and a hydrogen radical, and invoking a presumed PDF framework to account for subfilter physics. The model is validated using two dimensional laminar flame studies, and is then tested by performing an LES of a premixed slot-jet direct numerical simulation (DNS). In the premixed regime diagram this slot-jet is found at the edge of the broken reaction zones regime. Comparisons of the DNS, the strained flamelet model LES, and an unstrained flamelet model LES confirm that turbulence perturbs flame structure to leading order effect, and that the use of an unstrained flamelet LES model under-predicts flame height. It is shown that the strained flamelet model captures the physics characterizing interactions of mixing and chemistry in highly turbulent regimes.     

Solidification behavior of water-cooled and subzero-chilled cast iron on mechanical properties

Hypoeutectic cast iron specimens cast using chills that are water-cooled and liquid-nitrogen-cooled (subzero chilling) were compared with specimens of the same chemical composition, which were sand-cast without any chill. The solidification behavior, number of eutectic cells, grain size, and effects of these on the mechanical properties such as strength and fracture toughness were recorded and analyzed in this paper. It is revealed from the above investigation that subzero and water-cooled chilled cast irons exhibit severe under cooling as compared to that of normal sand-cast iron. Thus, it is concluded from the investigation that nucleation conditions are completely altered during solidification, which is considered to be responsible for the variation in eutectic cell size, grain size, microstructure, and hence mechanical properties of the cast iron.