Prediction of Polyphenol Fraction in Virgin Olive Oil Using Mid-Infrared Attenuated Total Reflectance Attenuated Total Reflectance Accessory–Mid-Infrared Coupled With Partial Least Squares Regression

This study focuses on the quantification of virgin olive oil phenolic compound while using mid-infrared spectroscopy with chemometrics. For that, 100 samples of virgin olive oils in phenolic compounds which varied between 1.04 and 10.33 g/L were picked up in Morocco’s regions and subjected to infrared analysis. The aim was to develop a calibration model for the prediction of phenolic compounds in olive oil by using Fourier transform-infrared spectroscopy. The spectral transmissions of all samples were obtained in the spectral range of 600–4000 cm^–1. The values obtained for correlation coefficient and root mean square errors of prediction were 0.99 and 0.11, respectively. These results showed the capability of the Fourier transform-infrared spectroscopy and the chemometric in developing accurate models to predict the phenolic compounds in virgin olive oil.     

Joint channel tracking and intercarrier-interference equalisation for vertical Bell Labs layered space time-orthogonal frequency division multiplexing in vehicle ad hoc network

Vertical Bell Labs layered space time-orthogonal frequency division multiplexing (VBLAST-OFDM) systems can achieve high spectral efficiency in quasi-stationary links and with channel state information (CSI) matrix knowledge. Owing to the high speeds of nodes in vehicle ad hoc network (VANET), the channel is fast fading thus raising the need for channel tracking. Furthermore, inter-carrier interference (ICI) causes an error floor at high signal-to-noise ratio (SNR) even with perfect CSI knowledge. In this paper we investigate channel tracking and ICI mitigation for VBLAST-OFDM. The analysis of ICI shows that it increases with speed, number of subcarriers and/or number of transmit antennas. The authors then introduce a simple channel tracking algorithm for VBLAST-OFDM. Simulation results show that our algorithm reduces the bit error rate (BER) of a 2 x 4 VBLAST system by 102² at 40 dB SNR and 100 km/h speed compared to obtaining a channel estimate from a training sequence only. The change in the channel response is estimated using the channel tracking algorithm and then passed to an ICI equaliser to enhance performance and reduce the error floor caused by ICI at high SNR. Equalising five pairs of subcarriers gives 4 dB improvement for 2 x 4 VBLAST at 180 km/h relative speed. The performance is enhanced as more subcarriers are included in the ICI equaliser at the expense of increased receiver complexity.     

Vertical Handover Decision Algorithm for Multimedia Streaming in VANET

Wireless Personal Communications - Vehicular ad hoc networks represent one of the most important applications of wireless ad hoc networks. In highways, where different networks exist, frequent and...     

Belief functions in telecommunications and network technologies: an overview

In the last few years, evidence theory, also known as Dempster-Shafer theory or belief functions theory, have received growing attention in many fields such as artificial intelligence, computer vision, telecommunications and networks, robotics, and finance. This is due to the fact that imperfect information permeates the real-world applications, and as a result, it must be incorporated into any information system that aims to provide a complete and accurate model of the real world. Although, it is in an early stage of development relative to classical probability theory, evidence theory has proved to be particularly useful to represent and reason with imperfect information in a wide range of real-world applications. In such cases, evidence theory provides a flexible framework for handling and mining uncertainty and imprecision as well as combining evidence obtained from multiple sources and modeling the conflict between them. The purpose of this paper is threefold. First, it introduces the basics of the belief functions theory with emphasis on the transferable belief model. Second, it provides a practical case study to show how the belief functions theory was used in a real network application, thereby providing guidelines for how the evidence theory may be used in telecommunications and networks. Lastly, it surveys and discusses a number of examples of applications of the evidence theory in telecommunications and network technologies.     

Measurement and prediction of the strength of rubberized concrete

Illegally discarded piles of automobile tires are sources of potential hazards. Current disposal methods are wasteful and costly as they require either consumption of landfill space or continuous costly maintenance. A solution to the problem of scrap-tire disposal is the potential use of tire chips and crumb rubber as mineral aggregate substitutes in Portland cement concrete mixes. In this study, some of the engineering properties of rubberized concrete were examined and a neural network was developed to predict its compressive and tensile strengths. Rubberized concrete was found to possess good esthetics, acceptable workability, and a smaller unit weight than normal concrete. However, rubberized concrete did not perform as well as normal concrete under repeated freeze-thaw cycles. It exhibited lower compressive and tensile strength than that of normal concrete. Unlike normal concrete, rubberized concrete had the ability to absorb a large amount of plastic energy under compressive and tensile loads. It did not demonstrate the typical brittle failure, but rather a ductile, plastic failure mode. Test results were analyzed so that a model can be developed to predict the strength of rubberized concrete. Two neural network models were developed to predict the reduction in the compressive and tensile strength as a result of replacing mineral aggregate with rubber aggregate. A maximum difference of 9·2% between test results and model prediction was detected during the testing of the neural networks.     

Adaptive Fourier Series Neural Network PID Controller

In this work, an Adaptive Neural Networks PID controller structure, called Adaptive Fourier Series Neural Networks PID controller (AFSNNPID), is developed. The main objective is to obtain a simple controller for nonlinear systems that can be tuned online to reject perturbations effect and compensate the system parameters variation. Due to its simple architecture and very attractive proprieties, the Fourier Series Neural Network (FSNN) is used to online adjust the parameters of the PID controller. Furthermore, using the delta-rule algorithm, the adaptation dynamics of the FSNN is globally stable. The design procedure of the proposed controller and the stability analysis of the closed loop system using the small gain theorem are given. To assess the effectiveness of the proposed control scheme, the control of a 3-DOF robot arm manipulator is considered and a comparative study, using the adaptive neural network PID controller and the particle swarm optimization based PID controller, is carried out. The obtained results, through the experimental study, indicate that the AFSNNPID controller presents better control performance than the other controllers.

     

Chaotic watermark for blind forgery detection in images

In this paper, a new chaos based watermarking scheme for effective tamper detection in images is proposed. The proposed scheme is able to detect any possible forgery and spot the areas which have been tampered. To improve the efficiency of the proposed scheme, a binary watermark is constructed from the image itself, which makes the watermark unique for every image and guarantee the blindness in the detection process. To guarantee the security of the proposed scheme, chaotic maps are used to complicate the embedding and the detection process to reduce the vulnerability to different attacks. Experimental results and security analysis show that the proposed scheme achieves superior tamper detection under common attacks.     

Analyzing the ZnO and CH₃NH₃PbI₃ as Emitter Layer for Silicon Based Heterojunction Solar Cells

Today, it has become an important task to modify existing traditional silicon-based solar cell factory to produce high-efficiency silicon-based heterojunction solar cells, at a lower cost. Therefore, the aim of this paper is to analyze CH₃NH₃PbI₃ and ZnO materials as an emitter layer for p-type silicon wafer-based heterojunction solar cells. CH₃NH₃PbI₃ and ZnO can be synthesized using the cheap Sol-Gel method and can form n-type semiconductor. We propose to combine these two materials since CH₃NH₃PbI₃ is a great light absorber and ZnO has an optimal complex refractive index which can be used as antireflection material. The photoelectric parameters of n-CH₃NH₃PbI₃/p-Si, n-ZnO/p-Si, and n-Si/p-Si solar cells have been studied in the range of 20–200 nm of emitter layer thickness. It has been found that the short circuit current for CH₃NH₃PbI₃/p-Si and n-ZnO/p-Si solar cells is almost the same when the emitter layer thickness is in the range of 20–100 nm. Additionally, when the emitter layer thickness is greater than 100 nm, the short circuit current of CH₃NH₃PbI₃/p-Si exceeds that of n-ZnO/p-Si. The optimal emitter layer thickness for n-CH₃NH₃PbI₃/p-Si and n-ZnO/p-Si was found equal to 80 nm. Using this value, the short-circuit current and the fill factor were estimated around 18.27 mA/cm² and 0.77 for n-CH₃NH₃PbI₃/p-Si and 18.06 mA/cm² and 0.73 for n-ZnO/p-Si. Results show that the efficiency of n-CH₃NH₃PbI₃/p-Si and n-ZnO/p-Si solar cells with an emitter layer thickness of 80 nm are 1.314 and 1.298 times greater than efficiency of traditional n-Si/p-Si for the same sizes. These findings will help perovskites materials to be more appealing in the PV industry and accelerate their development to become a viable alternative in the renewable energy sector.