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).     

Perfect tracking of ZMP trajectory for humanoid locomotion using repetitive control

Journal of Mechanical Science and Technology - In this paper, the tracking of the zero moment point (ZMP) trajectory for humanoid locomotion is addressed. For that, a combination of a repetitive...     

Mesoscopic approach for steady‐state free convection in a diamond array

The purpose of the present paper is testing an in‐house efficiency algorithm based on lattice Boltzmann method (LBM) and using it to resolve the obtained coupled nondimensional governing equations to analyze two‐dimensional free convection inside a cold outer cavity subjected to a heated cylindrical diamond array. Steady state or oscillatory results are obtained using the Bhatnagar‐Gross‐Krook collision model associated to the thermal LBM. Both the velocity and temperature fields are solved using the D2Q9 models. With different Rayleigh numbers (Ra), the tested free convection can either achieve to steady state or oscillatory. We extended our in house Fortran 90 code using curved boundary conditions and implemented them into a cavity with a diamond array. The numerical simulations were done using distinct Ra (10⁶ and 10 ⁷) and distances between the four neighboring circular cylinders aligned in a diamond array. The effects of several physical parameters, including Ra and position of the hot body array on flow and heat transfer characteristics are investigated. The obtained results are highlighted in the form of streamlines, isotherms, and velocities plots. We show in this paper the stability and the efficiency of the LBM to deal with a complex geometry and its ability to reach suitable convergence criteria for high Ra (10 ⁶ and 10 ⁷). The numerical results indicate that LBM can simulate numerical problems with a high Ra reaching a steady state where we can depict the change of the flow pattern and enhancement of the heat transfer in the presence of heated diamond array.     

Decision Bayes Criteria for Optimal Classifier Based on Probabilistic Measures

This paper addresses the high dimension sample problem in discriminate analysis under nonparametric and supervised assumptions.Since there is a kind of equivalence between the probabilistic dependence measure and the Bayes classification error probability,we propose to use an iterative algorithm to optimize the dimension reduction for classification with a probabilistic approach to achieve the Bayes classifier.The estimated probabilities of different errors encountered along the different phases of the system are realized by the Kernel estimate which is adjusted in a means of the smoothing parameter.Experiment results suggest that the proposed approach performs well.     

Automatic speech recognition system for Tunisian dialect

Although Modern Standard Arabic is taught in schools and used in written communication and TV/radio broadcasts, all informal communication is typically carried out in dialectal Arabic. In this work, we focus on the design of speech tools and resources required for the development of an Automatic Speech Recognition system for the Tunisian dialect. The development of such a system faces the challenges of the lack of annotated resources and tools, apart from the lack of standardization at all linguistic levels (phonological, morphological, syntactic and lexical) together with the mispronunciation dictionary needed for ASR development. In this paper, we present a historical overview of the Tunisian dialect and its linguistic characteristics. We also describe and evaluate our rule-based phonetic tool. Next, we go deeper into the details of Tunisian dialect corpus creation. This corpus is finally approved and used to build the first ASR system for Tunisian dialect with a Word Error Rate of 22.6%.     

Extracting TSK-type Neuro-Fuzzy model using the Hunting search algorithm

This paper proposes a Takagi-Sugeno-Kang (TSK) type Neuro-Fuzzy model tuned by a novel metaheuristic optimization algorithm called Hunting Search (HuS). The HuS algorithm is derived based on a model of group hunting of animals such as lions, wolves, and dolphins when looking for a prey. In this study, the structure and parameters of the fuzzy model are encoded into a particle. Thus, the optimal structure and parameters are achieved simultaneously. The proposed method was demonstrated through modeling and control problems, and the results have been compared with other optimization techniques. The comparisons indicate that the proposed method represents a powerful search approach and an effective optimization technique as it can extract the accurate TSK fuzzy model with an appropriate number of rules.     

New pulse mode neuro-fuzzy hardware architecture and application to image denoising

This paper focuses on a new digital architecture of pulse mode neuro-fuzzy system (PMNFS) with on-chip learning ability. The main purpose goal is to make use of the outstanding features of neuro-fuzzy in function approximation, and implement a reconfigurable architecture with on-chip learning on a field-programmable gate array (FPGA) platform. Details of the whole design with on-chip learning solutions are given. As an application illustrating the efficiency and scalability of the proposed PMNFS, we have considered the approximation of image denoising, which is a very important step in image processing. Experimental results show great efficiency of the proposed method, outperforming other denoising techniques. It was also demonstrated that such a system is strongly adaptive and gives good restored images independently of the kind of noises. Owing to learning, such feature cannot be met with conventional denoising techniques. Design synthesis results on a virtex II PRO FPGA platform are presented. Comparisons with conventional techniques as well as neural ones show higher performances of the designed PMNFS.     

Deflection Prediction of Steel and FRP-Reinforced Concrete-Filled FRP Tube Beams

This paper presents the results of experimental and theoretical investigations that study the flexural behavior of reinforced concrete-filled fiber-reinforced polymer (FRP) tubes (RCFFTs) beams. The experimental program consists of 10 circular beams [6 RCFFT and 4 control reinforced concrete (RC) beams] with a total length of 2,000?mm, tested under four-point bending load. The experimental results were used to review and verify the applicability of various North American code provisions and some available equations in the literature to predict deflection of RCFFT beams. The measured deflections and the experimental values of the effective moment of inertia were analyzed and compared with those predicted using available models. The results of the analysis indicated that the behavior of steel and FRP-RCFFT beams under the flexural load was significantly different than that of steel and FRP-RC members. This is attributed to the confining effect of the FRP tubes and their axial contribution. This confining behavior in turn enhanced the overall flexural behavior and improved the tension stiffening of RCFFT beams. For that, the predicted tension stiffening of steel and FRP-RCFFT beams using the conventional equations (steel or FRP-RC member) underestimates the flexural response; therefore, the predicted deflections are overestimated. Based on the analysis of the test results, the Branson’s equation for the effective moment of inertia of RC structures is modified, and new equations are developed to accurately predict the deflection of concrete-filled FRP tube (CFFT) beams reinforced with steel or FRP bars.