Crystallization of P Type Amorphous Silicon (a-Si: H) by AIC Method: Effect of Aluminum Thickness

In this work, we will study the crystallization of P type hydrogenated amorphous silicon (a-Si:H) by Aluminum Induced Crystallization technique (CIA) by varying the thickness of the aluminum films. We have deposited a 100 nm thickness of p-type a-Si:H layer on Corning glass substrates using PECVD technique. An aluminum layer with thickness ranging from 10 to 400 nm was thermally evaporated on the a-Si:H surface. The thermal annealing was performed in a conventional furnace at temperature of 550 °C for 4 h in flowing N2 ambient. The study of the crystallization of the Al/a-Si:H/Glass structure according the aluminum thickness was carried out by using Raman spectroscopy, X-rays diffraction and Hall Effect measurements. Raman results reveal the presence of the peaks between 510 and 520 cm−1, which are close to the peak of crystallized Si (about 521 cm−1) proving the crystallization of all samples. The XRD measurements show the presence of the characteristic peaks of the crystalline silicon, thus the a-Si: H (p) layer was effectively crystallized by the AIC method in a short time. Through Hall measurements we found an improvement in electrical properties and an increase in dopant concentration (+ 5.3 1014 to + 2.9 1017 cm2).     

The Effect of Ti Addition on Microstructure and Magnetic Properties of Nanocrystalline FeAl40 Alloy Powders Prepared by Mechanical Alloying

Powder Metallurgy and Metal Ceramics - Recent research on nanocrystalline FeAl alloys has shown that these alloys are of high importance due to their promising structural and mechanical properties,...     

Support vector machine based decision for mechanical fault condition monitoring in induction motor using an advanced Hilbert-Park transform

In this work we suggest an original fault signature based on an improved combination of Hilbert and Park transforms. Starting from this combination we can create two fault signatures: Hilbert modulus current space vector (HMCSV) and Hilbert phase current space vector (HPCSV). These two fault signatures are subsequently analysed using the classical fast Fourier transform (FFT). The effects of mechanical faults on the HMCSV and HPCSV spectrums are described, and the related frequencies are determined. The magnitudes of spectral components, relative to the studied faults (air-gap eccentricity and outer raceway ball bearing defect), are extracted in order to develop the input vector necessary for learning and testing the support vector machine with an aim of classifying automatically the various states of the induction motor.     

An Innovative Genetic Algorithm for a Multi-Objective Optimization of Two-Dimensional Cutting-Stock Problem

This paper addressed an important variant of two-dimensional cutting stock problem. The objective was not only to minimize trim loss, as in traditional cutting stock problems, but rather to minimize the number of machine setups. This additional objective is crucial for the life of the machines and affects both the time and the cost of cutting operations. Since cutting stock problems are well known to be NP-hard, we proposed an approximate method to solve this problem in a reasonable time. This approach differs from the previous works by generating a front with many interesting solutions. By this way, the decision maker or production manager can choose the best one from the set based on other additional constraints. This approach combined a genetic algorithm with a linear programming model to estimate the optimal Pareto front of these two objectives. The effectiveness of this approach was evaluated through a set of instances collected from the literature. The experimental results for different-size problems show that this algorithm provides Pareto fronts very near to the optimal ones.     

Dual-Color Antenna-Coupled LEKID for Next-Generation Multi-chroic CMB Focal Planes

We present an antenna-coupled Kinetic Inductance Detector for millimeter wave astronomy. Next-generation telescopes for observing the cosmic microwave background are demanding in terms of number of detectors and focal plane area filling efficiency. Moreover, foreground reduction in B-Mode polarimetry requires sky observation with multiple frequency bands. In this context, KIDs are a promising technology because of their large multiplexing rate, while antenna coupling can provide multi-band and dual-polarization solutions in compact design. We have developed polarization-sensitive dual-band pixel at 140 and 160 GHz with a bandwidth of almost 8% for each sub-band. The design involves a microstrip-excited slot antenna and two open-stub band-pass filters to direct the signal toward two resonators. These are lumped elements capacitively coupled to the antenna and include an aluminum strip as absorber. The architecture proposed is particularly simple to fabricate, via-less and only involves two metallization levels. The transition does not require any dielectric deposition above the resonator, thus preventing limitations from any source of noise due to a non-monocrystalline substrate. Furthermore, the same coupling technique can be applied to many types of microstrip-excited antennas, which allow to accommodate band-pass filters.     

Segmentation of brain MRI using active contour model

Alzheimer disease is a neurodegenerative disorder that impairs memory, cognitive function, and gradually leads to dementia, physical deterioration, loss of independence, and death of the affected individual. In this context, segmentation of medical images is a very important technique in the field of image analysis and Computer‐Assisted Diagnosis. In this article, we introduce a new automatic method of brain images’ segmentation based on the Active Contour (AC) model to extract the Hippocampus and the Corpus Callosum (CC). Our contribution is to combine the geometric method with the statistical method of the AC. We used the Caselle Level Set and added a learning phase to build an average shape and to make the initialization task automatic. For the step of contour evolution, we used the principle of Level set and we added to it the a priori knowledge. Experimental results are very promising. © 2017 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 27, 3–11, 2017     

Elderly fall detection based on multi-stream deep convolutional networks

Multimedia Tools and Applications - Fall is the biggest threat to seniors, with significant emotional, physical and financial implications. It is the major cause of serious injuries, disabilities,...     

Energy modelling of fabrics wrinkle recovery behaviour

Wrinkle recovery behaviour is an important property of fabrics for apparel applications. Fifty fabrics of a variety of constructions have been objectively evaluated in order to study the wrinkle recovery after a deformation. The wrinkle recovery behaviour was studied based on energy modelling. To study their wrinkle recovery properties, fabrics are modelled with the energy method. It is shown that the more the energy dissipation is low, the more fabric is able to recover to its initial state and then the more residual deformation is low. Thus, the energy dissipation can be used to estimate the ability of the fabric to recover to its initial state. In this paper, we also studied the effect of the fatigue test after the repeated wrinkling. Further, the increase of the fatigue cycle number after repeated wrinkling resulted in an increase of the residual deformation and permanent deformation. This method allows us for better approximation of the real behaviour of multidirectional wrinkling of clothing during wearing.