Wavelet based multicarrier CDMA system

Emerging demands for high data rate services, high user capacity and low power consumption systems are the key driving forces behind the continued technology evolution in wireless communications. Multicarrier Modulation techniques support variety of services requiring different data rates and different QoS (quality of service) levels. Multicarrier CDMA is a wireless communication system that can be seen as a combination of direct sequence CDMA and Orthogonal Frequency Division Multiplexing techniques. The main benefits of this system are its robustness to inter symbol interference and multipath propagation in fading channels. This paper studies and simulates the Discrete Wavelet Transform based Multicarrier CDMA and compares it with the  Discrete Fourier Transform based one using different number of sub carriers, and different modulation techniques. The results shows that the Wavelet based system outperforms the Fourier based one since it has lower bit error rate BER performance, lower peak to verage power ratio PAPR and higher user capacity

Security vulnerabilities related to web-based data

In this digital era, organizations and industries are moving towards replacing websites with web applications for many obvious reasons. With this transition towards web-based applications, organizations and industries find themselves surrounded by several threats and vulnerabilities. One of the largest concerns is keeping their infrastructure safe from attacks and misuse. Web security entails applying a set of procedures and practices, by applying several security principles at various layers to protect web servers, web users, and their surrounding environment. In this paper, we will discuss several attacks that may affect web-based applications namely: SQL injection attacks, cookie poisoning, cross-site scripting, and buffer overflow. Additionally, we will discuss detection and prevention methods from such attacks

PSO-SVM hybrid system for melanoma detection from histo-pathological images

This paper introduces an automated system for skin cancer (melanoma) detection from Histo-pathological images sampled from microscopic slides of skin biopsy. The proposed system is a hybrid system based on Particle Swarm Optimization and Support Vector Machine (PSO-SVM). The features used are extracted from the grayscale image histogram, the co-occurrence matrix and the energy of the wavelet coefficients resulting from the wavelet packet decomposition. The PSO-SVM system selects the best feature set and the best values for the SVM parameters (C and γ) that optimize the performance of the SVM classifier.   The system performance is tested on a real dataset obtained from the Southern Pathology Laboratory in Wollongong NSW, Australia. Evaluation results show a classification accuracy of 87.13%, a sensitivity of 94.1% and a specificity of 80.22%.The sensitivity and specificity results are comparable to those obtained by dermatologists

Support Vector Machine for Photovoltaic System Efficiency Improvement

Photovoltaic panels are promising source for renewable energy. They serve as a clean source of electricity by converting the radiation coming from the sun to electric energy. However, the amount of energy produced by the photovoltaic panels is dependent on many variables including the irradiation and the ambient temperature, leading to nonlinear characteristics. Finding the optimal operating point in the photovoltaic characteristic curve and operating the photovoltaic panels at that point ensures improved system efficiency. This paper introduces a unique method to improve the efficiency of the photovoltaic panel using Support Vector Machines. The dataset, which is obtained from a real photovoltaic setup in Spain, include temperature, radiation, output current, voltage and power for a period of one year. The results obtained show that the system is capable of accurately driving the photovoltaic panel to produce optimal output power for a given temperature and irradiation levels

A Review of Recent Developments in Driver Drowsiness Detection Systems

Continuous advancements in computing technology and artificial intelligence in the past decade have led to improvements in driver monitoring systems. Numerous experimental studies have collected real driver drowsiness data and applied various artificial intelligence algorithms and feature combinations with the goal of significantly enhancing the performance of these systems in real-time. This paper presents an up-to-date review of the driver drowsiness detection systems implemented over the last decade. The paper illustrates and reviews recent systems using different measures to track and detect drowsiness. Each system falls under one of four possible categories, based on the information used. Each system presented in this paper is associated with a detailed description of the features, classification algorithms, and used datasets. In addition, an evaluation of these systems is presented, in terms of the final classification accuracy, sensitivity, and precision. Furthermore, the paper highlights the recent challenges in the area of driver drowsiness detection, discusses the practicality and reliability of each of the four system types, and presents some of the future trends in the field