The performances of 130 W (Solara PV) and 100 W (Sunworth PV) solar modules are evaluated using a single diode equivalent circuit. The equivalent circuit is able to simulate both the I–V and P–V characteristic curves, and is used to study the effect of the operating temperature, diode ideality factor, series resistance, and solar irradiance level on the model performance. The results of the PV characteristics curves are compared with the parameters from the manufacturing companies for each model. Afterwards, the Solara PV model is tested under different irradiance levels. The relationship between the model power versus its current under different irradiance levels is plotted, such that if the solar power meter (pyrheliometer) does not exist, the irradiance-current (G–I) curve can be used to measure solar radiation power without using the solar power meter. The measurement is achieved by moving the solar panel by a certain angle toward the solar radiation, and then measuring the corresponding current.

The present paper discusses the implementation of direction of arrival estimation using Incoherent Wideband Music algorithm. The direction of arrival of an audio source is estimated using two microphones plugged in “Line-in” input of a DSP development board. A solution to the problem of fluctuating estimation of the angle of arrival has been proposed. The solution consists on adding an audio activity detector before going on processing. Only voiced sound frames are considered as they fulfill the theoretical constraint of the used estimation technique. Furthermore, the latter operation is followed by integration over few frames. Only two sensors are used. For such a reduced number of sensors, the obtained results are promising.

Artificial Neural networks are often used as a powerful discriminating classifier for tasks in medical diagnosis for early detection of diseases. They have several advantages over parametric classifiers such as discriminate analysis. The objective of this paper is to diagnose kidney stone disease by using three different neural network algorithms which have different architecture and characteristics. The aim of this work is to compare the performance of all three neural networks on the basis of its accuracy, time taken to build model, and training data set size. We will use Learning vector quantization (LVQ), two layers feed forward perceptron trained with back propagation training algorithm and Radial basis function (RBF) networks for diagnosis of kidney stone disease. In this work we used Waikato Environment for Knowledge Analysis (WEKA) version 3.7.5 as simulation tool which is an open source tool. The data set we used for diagnosis is real world data with 1000 instances and 8 attributes. In the end part we check the performance comparison of different algorithms to propose the best algorithm for kidney stone diagnosis. So this will helps in early identification of kidney stone in patients and reduces the diagnosis time.

The research of Boolean index sets of graphs is one of the important graph theory in the graph theory. Boolean index sets of graphs are to use the vertex sets and the edge sets of graphs to study the characteristics of various graphs and their inherent characteristics through corresponding the mapping function to Z_2. Its theory can be applied to information engineering, communication networks, computer science, economic management, medicine, etc. The edge-balance index set is an important issue in Boolean index set. In this paper, we defined edge-friendly labeling of the graph, edge-balance index set of the graph and the graph C_(n^m) × P_(m_n) (n=4,m≥2). We completely determine the edge-balance index sets of the graph C_(4^m) × P_(m_4) (m=0(mod3)) and solve formula proof and graphic tectonic methods.

As databases become widely used, there is a growing need to translate information between multiple databases. Semantic interoperability and integration has been a long standing challenge for the database community and has now become a prominent area of database research. In this paper, we aim to answer the question how semantic interoperability between two databases can be achieved by using Formal Concept Analysis (FCA for short) and Information Flow (IF for short) theories. For our purposes, firstly we discover knowledge from different databases by using FCA, and then align what is discovered by using IF and FCA. The development of FCA has led to some software systems such as TOSCANA and TUPLEWARE, which can be used as a tool for discovering knowledge in databases. A prototype based on the IF and FCA has been developed. Our method is tested and verified by using this prototype and TUPLEWARE.

In the field of Machine learning & Data Mining, lot of work had been done to construct new classification techniques/ classifiers and lot of research is going on to construct further new classifiers with the help of nature inspired technique such as Genetic Algorithm, Ant Colony Optimization, Bee Colony Optimization, Neural Network, Particle Swarm Optimization etc. Many researchers provided comparative study/ analysis of classification techniques. But this paper deals with another form of analysis of classification techniques i.e. parametric and non parametric classifiers analysis. This paper identifies parametric & non parametric classifiers that are used in classification process and provides tree representation of these classifiers. For the analysis purpose, four classifiers are used in which two of them are parametric and rest of are non-parametric in nature.

This paper aims to define an adaptive guidance for software process modeling. The proposed guidance approach is based on development’s profile context (actor’s role in the process, actor’s qualification and related activities in progress). We introduce new guidance concepts through adaptive guidance meta-model (AGM) allowing specific assistance interventions (corrective, constructive and automatic guidance). We illustrate our guidance approach using SPEM formalism extended with these new guidance concepts.

This paper is to address the problem of environmental health monitoring system facing Nigeria as a whole. Environment and the factors that are associated with it are the root causes of many epidemic diseases both in the developed and developing nations. In Nigeria, environmental health problems arise from population pressure on housing, poor environmental sanitation, coupled with lack of safe drink water and basic housing facilities. Despite the deplorable state of environmental health (lack of clean and safe drinking water, bad housing condition, and so on), there is no reliable and timely means of surveillance or any monitoring system. The result of this research makes it possible for environmental health workers to capture environmental health situation of any house in Nigeria real time while on the field. In conclusion, this paper presents result of a research which developed a web based environmental health tracking system for Nigeria.