Feature Evaluation and Comparison in Radar Emitter Recognition Based on SAHP

In the field of radar emitter recognition, with the wide application of modern radar, the traditional recognition method based on typical five feature parameters cannot achieve satisfactory recognition results in a complex electromagnetic environment. Currently, many new feature extraction methods are presented, but few approaches have been applied for feature evaluation or performance comparison. To deal with this problem, a feature evaluation and selection method was proposed based on set pair analysis (SPA) theory and analytic hierarchy process (AHP). The main idea of this method is to use SPA theory to solve problems regarding the construction of the decision matrix based on AHP, as it relies heavily on expert’s subjective experience. The aim was to improve the objectivity of the evaluation. To check the effectiveness of the proposed method, six feature parameters were selected for a comprehensive performance evaluation. Then, the convolutional neural network (CNN) was introduced to validate the recognition capability based on the evaluation results. Simulation results demonstrated that the proposed method could achieve the feature analysis and evaluation more reasonably and objectively.

Feature evaluation and selection for condition monitoring using a self-organizing map and spatial statistics

This paper presents a novel approach to sensor-based feature evaluation and selection using a self-organizing map and spatial statistics as a combined technique applied to tool condition monitoring of the turning process. This approach takes advantage of the unique features of unsupervised neural networks combined with spatial statistics to perform analyses into the contributions of the different sensor-based features, carrying large quantities of noise, to achieve a classification of tool wear and a quantitative measure of each feature's suitability. This method does not assume a prior direct correlation between features avoiding misconstructions inherent to common approaches that assume that only obviously correlated features should be considered for condition monitoring. Instead, and taking advantage of neural networks ability to perform non-linear modeling, it has allowed a prior modeling of the process and then analyzed each feature's contribution toward classification. It was found that some of the commonly used features have proven to have a significant contribution to the classification of cutting tool wear, whereas others adversely affect classification performance. Further, it is demonstrated that the proposed combined technique can be used extensively to quantitatively evaluate the contribution of different features toward system monitoring in the presence of noisy data.

Image Analysis and Understanding Techniques for Breast Cancer Detection from Digital Mammograms

In this chapter, an overview of recent developments in image analysis and understanding techniques for automated detection of breast cancer from digital mammograms is presented. The various steps in the design of an automated system (i.e. Computer Aided Detection [CADe] and Computer Aided Diagnostics (CADx)] include preparation of image database for classification, image pre-processing, mammogram image enhancement and restoration, segmentation of Region Of Interest (ROI) for cancer detection, feature extraction of selected ROIs, feature evaluation and selection, and classification of selected mammogram images in to benign, malignant, and normal. In this chapter, a detailed overview of the various methods developed in recent years for each stage required in the design of an automated system for breast cancer detection is discussed. Further, the design, implementation and performance analysis of a CAD tool is also presented. The various types of features extracted for classification purpose in the proposed tool include histogram features, texture features, geometric features, wavelet features, and Gabor features. The proposed CAD tool uses fuzzy c-means segmentation algorithm, the feature selection algorithm based on the concepts of genetic algorithm which uses mutual information as a fitness function, and linear support vector machine as a classifier.