Meaningful Learning for Deep Facial Emotional Features

Neural Processing Letters - Facial expression is an important aspect to recognize emotions between humans. However, this task remains difficult for machines. Several approaches have been developed...     

An Efficient Digital Confidentiality Scheme Based on Commutative Chaotic Polynomial

Multimedia Tools and Applications - One of the most critical aspects of this technologically progressive era is the propagation of information through an unsecured communication channel. The...     

Learning a Deep Predictive Coding Network for a Semi-Supervised 3D-Hand Pose Estimation

In this paper we present a CNN based approach for a real time 3D-hand pose estimation from the depth sequence. Prior discriminative approaches have achieved remarkable success but are facing two main challenges: Firstly, the methods are fully supervised hence require large numbers of annotated training data to extract the dynamic information from a hand representation. Secondly, unreliable hand detectors based on strong assumptions or a weak detector which often fail in several situations like complex environment and multiple hands. In contrast to these methods, this paper presents an approach that can be considered as semi-supervised by performing predictive coding of image sequences of hand poses in order to capture latent features underlying a given image without supervision. The hand is modelled using a novel latent tree dependency model (LDTM) which transforms internal joint location to an explicit representation. Then the modeled hand topology is integrated with the pose estimator using data dependent method to jointly learn latent variables of the posterior pose appearance and the pose configuration respectively. Finally, an unsupervised error term which is a part of the recurrent architecture ensures smooth estimations of the final pose. Experiments on three challenging public datasets, ICVL, MSRA, and NYU demonstrate the significant performance of the proposed method which is comparable or better than state-of-the-art approaches.      

Halloysite Nanotubes Modified by Fe3O4 Nanoparticles and Applied as a Natural and Efficient Nanocatalyst for the SymmetricalHantzsch Reaction

Halloysite as an impressive natural eco-friendly nanotube with aluminosilicate structure has been investigated recently due to its unique features such as specific morphology and excellent bio-adaptability. In this research, Fe₃O₄ nanoparticles have been loaded on the tubular halloysite by co-precipitation method in order to synthesis magnetic halloysite (Hal-Fe₃O₄₎. To characterize this recoverable nanocatalyst, applicable analyses such as Fourier-transform infrared (FT-IR) spectroscopy, energy-dispersive X-ray (EDX) analysis, field-emission scanning electron microscopy (FE-SEM) images, X-ray diffraction (XRD) pattern, Thermogravimetric analysis (TGA) and vibrating sample magnetometer (VSM) curves have been carried out. The results confirmed that Fe₃O₄ nanoparticles with cubic structure, and uniform distribution, were located at halloysite nanotubes (HNTs). This aluminosilicate nanocomposite with high thermal stability, crystalline structure, and stable morphology was evaluated as a heterogeneous catalyst in the symmetrical Hantzsch reaction for the first time. Easy synthesis process, green media, high performance, recoverable catalyst and reusing of the Hal-Fe₃O₄ as a nanocatalyst for 8 times are the main features of this protocol.

     

Dielectric,ferroelectric, and energy storage properties in dysprosium doped sodium bismuth titanate ceramics

In this work, Na_0.5(Bi_1-xDy_x)_0.5TiO₃ (0?≤?x?≤?15%) ceramics were prepared via solid state reaction method and were characterized. A stable and pure perovskite phase was revealed by X-ray diffraction analysis for all compositions and a symmetry change from rhombohedral to orthorhombic phase was detected beyond 10% of Dy substitution. The incorporation of Dy³⁺ into Sodium Bismuth Titanate (Na_0.5Bi_0.5TiO₃) matrix allows a substantial decrease of the coercive field, an increase in the resistivity, and leads to a high stability of the dielectric permittivity (??/?_(150?°C) ≤?±?15%) over a wide temperature range. Furthermore, this system was found to exhibit improved energy storage properties at high temperatures with a maximum energy density of 1.2?J/cm³ obtained for 2%Dy composition at 200?°C. The obtained results are very promising for energy storage capacitors operating at high temperatures.     

Electrophoretic deposition of graphene oxide on carbon brush as bioanode for microbial fuel cell operated with real wastewater

Microbial fuel cell (MFC) is a promising technology for simultaneous wastewater treatment and energy harvesting. The properties of the anode material play a critical role in the performance of the MFC. In this study, graphene oxide was prepared by a modified hummer's method. A thin layer of graphene oxide was incorporated on the carbon brush using an electrophoretic technique. The deoxygenated graphene oxide formed on the surface of the carbon brush (RGO-CB) was investigated as a bio-anode in MFC operated with real wastewater. The performance of the MFC using the RGO-CB was compared with that using plain carbon brush anode (PCB). Results showed that electrophoretic deposition of graphene oxide on the surface of carbon brush significantly enhanced the performance of the MFC, where the power density increased more than 10 times (from 33 mWm^?2 to 381 mWm^?2). Although the COD removal was nearly similar for the two MFCs, i.e., with PCB and RGO-CB; the columbic efficiency significantly increased in the case of RGO-CB anode. The improved performance in the case of the modified electrode was related to the role of the graphene in improving the electron transfer from the microorganism to the anode surface, as confirmed from the electrochemical impedance spectroscopy measurements.     

Evaluation of TBM performance prediction models and sensitivity analysis of input parameters

Bulletin of Engineering Geology and the Environment - In recent years, anumber of models and formulas have been proposed to estimate the penetration rate of hard rock tunnel boring machines (TBMs)....