Analysis of Spectrum Sensing in Cognitive Radio Network with OFDM

With the development of a new and ever expanding wireless applications and services, spectrum resources are facing in demands. In present scenario, the spectrum allotment has been done by providing each new service with its own fixed frequency Slot. Most of the user’s spectrum is already assigned, so it becomes very difficult to find spectrum for other users or existing users. This leads to the scarcity of available spectrum and inefficient channel utilization. Cognitive radio is a novel technology which improves the spectrum utilization by allowing secondary user to borrow unused radio spectrum from primary licensed users or to share the spectrum with the primary users. Present paper deals with the spectrum sensing in which multiple users detect the spectrum gap through energy detection and investigate the detection performance in an efficient and implementable way. Simulation results showed that the probability of detection is achieved at small value SNR in case of OFDM modulation as compare the other and simple cognitive environment.

Measurement-based Statistical Method for Estimating and Verifying Signal Coverage and Coverage Probability in Urban Microcells

Customer’s complaints and concerns about radio signal coverage at their home are important trigger to performance relevant drive test in the relevant area to observe the coverage quality. In this paper, statistical approach has been employed to assess the quality of the radio coverage and outage probability based on measured radio signals in an established UMTS network, operational in Ikoyi, a typical urban microcell in Nigerian environment. The results shows that the quality of radio signals at the cell edge is very poor in locations 2 and 4, as they recorded poor coverage probability performance of 89.25% and 81.72% and high outage probability performance of 10.74% and 18.28% respectively. It is also observed that the smaller the fade margin, the higher the outage probability and the lower the coverage reliability. This implies that the smaller the fade margin, the smaller the received signal strength at the MS and the more likely outage events. Hence, sufficient signal strength is needed at the mobile terminals at locations 2 and 4 in order to achieve the outage probability and coverage reliability required to effectively operate cellular communication networks.

SOFT COMPUTING: AN INTRODUCTION

Soft computing (SC) is a newly emerging multidisciplinary field. It is a collection of computational techniques, such as expert systems, fuzzy logic, neural networks, and evolutionary algorithms, which provide information processing capabilities to solve complex practical problems. The major benefit of SC lies in its ability to tolerate imprecision, uncertainty, partial truth, and approximation in processing imprecise and inaccurate information and simulating human decision making at low cost. This paper provides a brief introduction on soft computing.

COMPUTATIONAL HUMANITIES

Computational humanities is at the intersection of computing technologies and the disciplines of the humanities. Research in this field has steadily increased over the past years. Computational tools supporting textual search, large database analysis, data mining, network mapping, and natural language processing are employed by the humanities researcher. This opens up new realms for analysis and understanding. This paper provides a brief introduction into computational humanities.

Monitoring and Prediction of Air Quality using Neural Network

Air Quality monitoring is very important in today s world. There are many harmful pollutants present in the air which are very harmful for human health. Prolonged consumption of such air may lead to severe death and harmful diseases. It is also harmful for crops as well as animals which may damage natural environment. There are several pollutants which are present in the air that decreases the quality of air such as sulfur oxide, nitrogen dioxide, carbon monoxide and dioxide, and particulate matter. Neural Network can be used for prediction of population for short term as well as long term using a deep learning technologies. Neural network specify two types of predictive models. the first one is the a temporal which is for short-term forecast of the pollutants in the air for the short coming or nearest days and the second one is a spatial forecast of atmospheric pollution index in any point of city. The artificial neural networks takes initial information and consider the hidden dependencies are used to improve the efficiency and accuracy of the ecology management decisions. In this paper the forecasting of atmospheric air pollution index in industrial cities based on the neural network model has proposed.

Performance Analysis of Virtual Human Bodies with Clothing and Hair from Images to Animation

Virtual human bodies, clothing, and hair are widely used in a number of scenarios such as 3D animated movies, gaming, and online fashion. Machine learning can be used to construct data-driven 3D human bodies, clothing, and hair. In this thesis, we provide a solution to 3D shape and pose estimation under the most challenging situation where only a single image is available and the image is captured in a natural environment with unknown camera calibration. We also demonstrate that a simplified 2D clothing model helps to increase the accuracy of 2D body shape estimation significantly.

Face Spoof Detection System Based on Genetic Algorithm and Artificial Intelligence Technique: A Review

Face recognition is an alternative means to authenticate a person in different applications for access control. Instead of many improvements, this method is prone to various attacks like photos, 3D masks and video replay attack. Due to these attacks, system should require a face spoof detection system. A face spoof detection systems have an ability to identify whether a face is from a real person or a fake image. Face spoofing effect the image by adding deformation in it and also degrades the image pattern quality. Face spoofing detection system automatically identifies the human face is a true face or a fake face. In today's era, face recognition method is widely used to authenticate the face (like for unlocking mobile phones etc.) and providing access to the services or facilities but some intruders use various trick to crack the authentication system by presenting the false face in front of the authentication system, so it become necessity to prevent our face authentication system from face spoofing attack. So the choice of the technique to detect the face spoofing attack should be accurate and highly efficient.

COMPUTATIONAL BIOLOGY

Computation is an integral part of a larger revolution that will affect how science is conducted. Computational biology is an important emerging field of biology which is uniquely enabled by computation. It involves using computers to model biological problems and interpret data, especially problems in evolutionary and molecular biology. The application of computational tools to all areas of biology is producing excitements and insights into biological problems too complex for conventional approaches. This paper provides a brief introduction on computational biology.

EXPERT SYSTEMS: A PRIMER

Expert systems are one of the most exciting and promising applications of computers. An expert system (ES) is an intelligent computer system that contains an organized body of knowledge and emulates an expert problem solving skills. It is designed to emulate the decision-making ability of a human expert. This paper provides a primer on expert systems, their features, applications, benefits, and challenges.

Conversion of Website Users to Customers-The Black Hat SEO Technique

Search Engine Optimization (SEO) is a technique which helps search engines to find and rank one site over another in response to a search query. SEO thus helps site owners to get traffic from search engines. Although the basic principle of operation of all search engines is the same, the minor differences between them lead to major changes in results relevancy. Choosing the right keywords to optimize for is thus the first and most crucial step to a successful SEO campaign. In the context of SEO, keyword density can be used as a factor in determining whether a webpage is relevant to a specified keyword or keyword phrase. SEO is known for its contribution as a process that affects the online visibility of a website or a webpage in a web search engine's results. In general, the earlier (or higher ranked on the search results page), and more frequently a website appears in the search results list, the more visitors it will receive from the search engine's users; these visitors can then be converted into customers. It is the objective of this paper to re-present black hat SEO technique as an unprofessional but profitable method of converting website users to customers. Having studied and understood white hat SEO, black hat SEO, gray hat SEO, crawling, indexing, processing and retrieving methods used by search engines as a web software program or web based script to search for documents and files for keywords over the internet to return the list of results containing those keywords; it would be seen that proper application of SEO gives website a better user experience, SEO helps build brand awareness through high rankings, SEO helps circumvent competition, and SEO gives room for high increased return on investment.