On the Performance of FFT/DWT/DCT-based OFDM Systems with Chaotic Interleaving and Channel Estimation Algorithms

The efficiency of data transmission over fading channels in orthogonal frequency division multiplexing (OFDM) systems depends on the employed interleaving method. In this study, we propose an improved chaotic interleaving scheme which aims to improve the performance of OFDM system under fading channel. In the proposed scheme, the binary data is interleaved with chaotic Baker map prior to the modulation process. In the sequel, significant degree of encryption is being added during data transmission. The performance of the proposed approach is tested on the conventional fast Fourier transform OFDM, discrete wavelet transform OFDM, and discrete cosine transform OFDM with and without chaotic interleaving. Furthermore, an expectation–maximization (EM) algorithm is proposed for improving channel impulse response (CIR) estimation based on a maximum likelihood principle. The proposed scheme makes use of EM algorithm to update the channel estimates until convergence is reached. The simulation results show the efficiency of the proposed algorithms under Rayleigh fading environments where the symbol error rate essentially coincides with that of the perfect channel case after the fifth EM iteration.     

Impact of interconnection photovoltaic/wind system with utility on their reliability using a fuzzy scheme

Reliability analysis has been considered as an important step in any system design process. A reliable electrical power system means a system which has sufficient power to feed the load demand during a certain period or, in other words, has small Loss of Load Probability (LOLP). LOLP is defined as an expected fraction of load not met by its power needs from electrical power system during its lifetime. Photovoltaic (PV)/Wind Energy System (WES) Hybrid Electric Power System (PV/WES HEPS) differs considerably from the Utility Grid (UG) in its performance and operating characteristics. With the interconnection of PV/WES as a HEPS into the UG, the fluctuating nature of the energy produced by these systems has a different effect on the overall system reliability than that of the fluctuating nature of energy produced by UG. Therefore, this paper presents a complete study, from reliability point of view, to determine the impact of interconnecting PV/WES HEPS into UG. Four different configurations of PV/WES/UG have been investigated and a comparative study between these four different configurations has been carried out. The overall system is divided into three subsystems, containing the UG, PV and WES. The generation capacity outage table has been built for each configuration of these subsystems. These capacity outage tables of UG, PV/UG, WES/UG and PV/WES/UG are calculated and updated to incorporate their fluctuating energy production. This paper also presents a fuzzy logic technique to calculate and assess the reliability index for each HEPS configuration under study.     

Spectroscopic ellipsometry studies on hydrogenated amorphous silicon thin films deposited using DC saddle field plasma enhanced chemical vapor deposition system

Hydrogenated amorphous silicon (a-Si H) films deposited on crystalline silicon substrates using the DC saddle field (DCSF) plasma enhanced chemical vapor deposition (PECVD) system have been investigated. We have determined the complex dielectric function, ε(E) = ε₁(E) + iε₂(E) for hydrogenated amorphous silicon (a-Si:H) thin films by spectroscopic ellipsometry (SE) in the 1.5-4.5 eV energy range at room temperature. The results indicate that there is a change in the structure of the a-Si:H films as the thickness is increased above 4 nm. This is attributed to either an increase in the bonded hydrogen content and, or a decrease of voids during the growth of a-Si:H films. The film thickness and deposition temperature are two important parameters that lead to both hydrogen content variation and silicon bonding change as well as significant variations in the optical band gap. The influence of substrate temperature during deposition on film and interface properties is also included.     

Face Recognition Based on Gabor Feature Extraction Followed by FastICA and LDA

Over the past few decades, face recognition has become the most effective biometric technique in recognizing people’s identity, as it is widely used in many areas of our daily lives. However, it is a challenging technique since facial images vary in rotations, expressions, and illuminations. To minimize the impact of these challenges, exploiting information from various feature extraction methods is recommended since one of the most critical tasks in face recognition system is the extraction of facial features. Therefore, this paper presents a new approach to face recognition based on the fusion of Gabor-based feature extraction, Fast Independent Component Analysis (FastICA), and Linear Discriminant Analysis (LDA). In the presented method, first, face images are transformed to grayscale and resized to have a uniform size. After that, facial features are extracted from the aligned face image using Gabor, FastICA, and LDA methods. Finally, the nearest distance classifier is utilized to recognize the identity of the individuals. Here, the performance of six distance classifiers, namely Euclidean, Cosine, Bray-Curtis, Mahalanobis, Correlation, and Manhattan, are investigated. Experimental results revealed that the presented method attains a higher rank-one recognition rate compared to the recent approaches in the literature on four benchmarked face datasets: ORL, GT, FEI, and Yale. Moreover, it showed that the proposed method not only helps in better extracting the features but also in improving the overall efficiency of the facial recognition system.     

Detection of Abnormal Activities from Various Signals Based on Statistical Analysis

We perceive big data with massive datasets of complex and variegated structures in the modern era. Such attributes formulate hindrances while analyzing and storing the data to generate apt aftermaths. Privacy and security are the colossal perturb in the domain space of extensive data analysis. In this paper, our foremost priority is the computing technologies that focus on big data, IoT (Internet of Things), Cloud Computing, Blockchain, and fog computing. Among these, Cloud Computing follows the role of providing on-demand services to their customers by optimizing the cost factor. AWS, Azure, Google Cloud are the major cloud providers today. Fog computing offers new insights into the extension of cloud computing systems by procuring services to the edges of the network. In collaboration with multiple technologies, the Internet of Things takes this into effect, which solves the labyrinth of dealing with advanced services considering its significance in varied application domains. The Blockchain is a dataset that entertains many applications ranging from the fields of crypto-currency to smart contracts. The prospect of this research paper is to present the critical analysis and review it under the umbrella of existing extensive data systems. In this paper, we attend to critics' reviews and address the existing threats to the security of extensive data systems. Moreover, we scrutinize the security attacks on computing systems based upon Cloud, Blockchain, IoT, and fog. This paper lucidly illustrates the different threat behaviour and their impacts on complementary computational technologies. The authors have mooted a precise analysis of cloud-based technologies and discussed their defense mechanism and the security issues of mobile healthcare.

     

A novel blind watermarking approach for medical image authentication using MinEigen value features

Developing new watermarking approaches that consider special features of medical images become increasingly necessary. This paper proposes a new watermarking approach to ensure medical images authenticity, using MinEigen value features, chaotic sequence, and Quantization Index Modulation (QIM) in the spatial domain. The idea is to choose the 3?×?3 non overlapping blocks around MinEigen values points, then embed the watermark bits in these blocks using a novel blind way based on chaotic sequence and QIM. The proposed technique is purely blind and fast in terms of execution time. Experimental results demonstrate that the proposed approach is robust against all DICOM JPEG compression attacks while keeping high imperceptibility.

     

Fuzzy Geographic Information Systems for Phytoremediation Plant Selection

Geographic information systems (GIS) technology was combined with fuzzy logic to construct a phytoremediation plant selection tool. Phytoremediation involves the use of vegetation for treatment of contaminated soils, sediments, and water. In this paper, the focus is on one particular phytoremediation mechanism plant-assisted bioremediation of polycyclic aromatic hydrocarbon (PAH) soil contamination. Many plants show potential for PAH remediation, but factors such as growth requirements, climate, and soil conditions need to be considered. Given the spatial nature of the data involved, GIS was chosen as the basis for the plant selection tool. Eight candidate plants were selected because of their PAH phytoremediation potential, and their growth requirements were represented using fuzzy membership functions to account for parameters’ uncertainty. Vermont and South Carolina were selected as case studies, and calculations were performed to determine the suitability ratings for each of the candidate plants in these areas based on the plants’ growth requirements with respect to climate and soil attributes.     

Modelling approaches for the estimation of irreducible water saturation and heterogeneities of the commercial Ashtart reservoir from the Gulf of Gabès, Tunisia

Oil production based on reserves in place in the Ashtart oilfield required the precise knowledge of the main reservoir parameters including porosity, permeability and irreducible water saturation. The reservoir series is comprised of Nummulitid but heterogeneous limestones of the El Garia Formation, the petrofacies texture, geometry and petrophysical parameters of which were apprehended using seismic profiles; gamma-ray and sonic lateral logs, as well as cores and cuttings taken in drillwells. The evaluation of residual oil saturation, multiphase flow and oil production techniques from the Ashtart reservoir also depend on variations and zoning of the irreducible water saturation. Estimation of the initial water saturation and hence variations in the capillary pressure in the reservoir, required compilations of porosity data measured on cores, supplemented by additional but computed porosities based on acoustic log diagrams. Furthermore, Gamma Ray, Sonic log, and well to well correlations tied to core results and well cuttings, help recognize the layered lithologies within the El Garia flat lying but stratified, Ypresian in origin reservoir rocks. Abundant permeability and porosity values compiled in the light of seismic sequence and Gamma Ray and Sonic log details, were integrated in an empirical approach using the Leverett J function, to model the irreducible water saturation depending on the capillary pressure distribution in the whole reservoir. Variations of this principal hydraulic parameter in a wide range (S_wir: 12 to 40%) compared to the preceding lithostratigraphic, petrographical and petrophysical results help recognize four main rock pore types in the commercial Ashtart reservoir. These vary from (1) a zone with a rock pore type showing an irreducible water saturation as low as 12%, and a fairly good reservoir character in the lower third part of the lithologic column which is thought to channelize a multiphase fluid flow in the global oilfield, (2) to those zones built-up of rock pore types with higher initial water saturation amounts which in certain cases tend to indicate zones of degraded reservoir. Our study suggests that diagenesis prevalently controls porosity, due to operative dissolutions of the Nummulitid tests/bioclasts, and cementation; moreover, diagenesis exerts effects on permeability by interconnecting intergranular and intratest pore spaces. In contrast, microfracturing enhances permeability of the reservoir. This is notably the case in the fairly permeable central zone in the Ashtart reservoir with excellent petrophysical parameters, but which were found to degrade gradually towards its peripheries.