Data Mining - Concepts and Techniques.

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On robust estimation of the location parameter

International Journal of Computer and Applications

Using a wireless sensor network, the authors developed an online microclimate monitoring and control system for greenhouses. They field-tested the system in a greenhouse in Punjab, India, evaluating its measurement capabilities and network performance in real time.

A Wireless Sensor Network for Greenhouse Climate Control

Artificial neural networks have been getting popularity for predicting various performance parameters of microstrip antennas due to their learning and generalization features. In this letter, a neural-networks-based synthesis model is presented for predicting the “slot-size” on the radiating patch and inserted “air-gap” between the ground plane and the substrate sheet, simultaneously. Different performance parameters like resonance frequencies, gains, directivities, antenna efficiencies, and radiation efficiencies for dual resonance are observed by varying the dimensions of slot and inserted air-gap. For validation, a prototype of microstrip antenna is fabricated using Roger's substrate, and its performance parameters are measured. Measured results show a very good agreement to their predicted and simulated values.

Prediction of Slot-Size and Inserted Air-Gap for Improving the Performance of Rectangular Microstrip Antennas Using Artificial Neural Networks

One of the most important and useful feature of autonomous mobile robots is their ability to adopt themselves to operate in unstructured environment Today robots are performing autonomously in industrial floor, office environments, as well as in crowded public places where the robots need to maintain their localization and mapping parametersThe basic requirement of an intelligent mobile robot is to develop and maintain localization and mapping parameters to complete the complex missions In such situations, several difficulties arise in due to the inaccuracies and uncertainties in sensor measurements Various techniques are there to handle such noises where the multisensor data fusion is not the exceptional oneFrom the last two decades, multisensor data fusions in mobile robots become a dominant paradigm  due to its potential advantages like reduction in uncertainty, increase in accuracy and reliability and reduction of costThis paper presents the reviews of autonomous mobile robots and role of multisenosr data fusion

Multisensor Data Fusion and Integration for Mobile Robots: A Review

One of the major drawbacks of the block-based DCT compression methods is that it may result in visible artifacts at block boundaries due to coarse quantization of the coefficients. This paper proposes a new adaptive post-filtering algorithm to remove coding artifacts in block-based DCT compressed images. Presence of blocking artifacts is detected using block activity based on human visual system and block statistics. The boundary regions between the blocks are identified as smooth, non-smooth or intermediate regions. The blocking artifact in the smooth and non-smooth regions are removed by modifying a few DCT coefficients while an edge preserving smoothing filter is applied to the intermediate region. Experimental results illustrating the performance of proposed method on the basis of PSNR, MOS, MSSIM, SF, and BBM indices are presented and evaluated. Compared with other methods, the proposed one achieves better detail preservation and artifact removal performance with lower computational complexity.

A signal adaptive filter for blocking effect reduction of JPEG compressed images

Function Point Analysis (FPA) is one of the most reliable methods for measuring the size of computer software. It is extensively being used as Industry standard for sizing. It is also tremendously useful in estimating projects, managing change in requirements, measuring efficiency and communicating functional requirements. International Standard bodies like International Function Point User Group (IFPUG) has been maintaining a repository of data based projects with measures like data functions, transactional functions, function size, work effort, delivery date, project duration, max team size, development technique and platform etc. This paper presents the results of analysis that were performed on IFPUG data using a Statistical Package. Modelling techniques like Multivariate Adaptive Regression Splines (MAR Splines), Support Vector Machine (SVM), Automated Neural Network (ANN), k-Nearest Neighborhood (kNN) were used to estimate the function points from the IFPUG data set repository The training and validation data was randomly selected from the data repository. The performance of the various models was analyzed by comparing the observed and predicted function points. The comparison of results on the validation data set of 100 projects indicate that MAR Splines gives the best predicted values for function points as it has the lowest Mean Relative Error (MRE) and highest correlation coefficient. SVM and ANN also gave good results as compared to kNN. MAR Splines is thus a competitive predictive model for estimating function points. Thus, these machine learning and regression models can be used as approximation tool for function point metric.

Estimating function points: Using machine learning and regression models

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