Spectral kurtosis for fault detection, diagnosis and prognostics of rotating machines: A review with applications

For pt. I see ibid., vol. 35, no. 7, p. 90, 1997. In Part I of this tutorial, the major elements that contribute to fading and their effects in a communication channel were characterized. In Part II, these phenomena are briefly summarized, and emphasis is then placed on methods to cope with these degradation effects. Two particular mitigation techniques are examined: the Viterbi equalizer implemented in the Global System for Mobile Communication (GSM), and the RAKE receiver used in CDMA systems built to meet Interim Standard 95 (IS-95).

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Rayleigh Fading Channels in Mobile Digital Communication Systems Part II: Mitigation

Skin lesion classification with ensembles of deep convolutional neural networks.

In this paper, we present a novel framework for dermoscopy image recognition via both a deep learning method and a local descriptor encoding strategy. Specifically, deep representations of a rescaled dermoscopy image are first extracted via a very deep residual neural network pretrained on a large natural image dataset. Then these local deep descriptors are aggregated by orderless visual statistic features based on Fisher vector (FV) encoding to build a global image representation. Finally, the FV encoded representations are used to classify melanoma images using a support vector machine with a Chi-squared kernel. Our proposed method is capable of generating more discriminative features to deal with large variations within melanoma classes, as well as small variations between melanoma and nonmelanoma classes with limited training data. Extensive experiments are performed to demonstrate the effectiveness of our proposed method. Comparisons with state-of-the-art methods show the superiority of our method using the publicly available ISBI 2016 Skin lesion challenge dataset.

Melanoma Recognition in Dermoscopy Images via Aggregated Deep Convolutional Features

The ABCD (asymmetry, border irregularity, colour and dermoscopic structure) rule of dermoscopy is a scoring method used by dermatologists to quantify dermoscopy findings and effectively separate melanoma from benign lesions. Automatic detection of the ABCD features and separation of benign lesions from melanoma could enable earlier detection of melanoma. In this study, automatic ABCD scoring of dermoscopy lesions is implemented. Pre-processing enables automatic detection of hair using Gabor filters and lesion boundaries using geodesic active contours. Algorithms are implemented to extract the characteristics of ABCD attributes. Methods used here combine existing methods with novel methods to detect colour asymmetry and dermoscopic structures. To classify lesions as melanoma or benign nevus, the total dermoscopy score is calculated. The experimental results, using 200 dermoscopic images, where 80 are malignant melanomas and 120 benign lesions, show that the algorithm achieves 91.25% sensitivity of 91.25 and 95.83% specificity. This is comparable to the 92.8% sensitivity and 90.3% specificity reported for human implementation of the ABCD rule. The experimental results show that the extracted features can be used to build a promising classifier for melanoma detection.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-ipr.2015.0385

Classification of malignant melanoma and benign skin lesions: implementation of automatic ABCD rule

Improved Fuzzy C-Means based Particle Swarm Optimization (PSO) initialization and outlier rejection with level set methods for MR brain image segmentation

BACKGROUND/PURPOSE Computer-aided diagnosis of skin cancer requires accurate lesion segmentation, which must overcome noise such as hair, skin color variations, and ambient light variability. METHODS A biologically inspired geodesic active contour (GAC) technique is used for lesion segmentation. The algorithm presented here employs automatic contour initialization close to the actual lesion boundary, overcoming the 'sticking' at minimum local energy spots caused by noise artifacts such as hair. The border is significantly smoothed to mimic natural lesions. In addition, features that mimic biological parameters include spectral image subtraction and removal of peninsulas and inlets. Multiple boundary choices borders are created by parameter options used at different steps. These choices can allow future improvement over the basic default border. RESULTS The basic GAC algorithm was tested on 100 images (30 melanomas and 70 benign lesions), yielding a median XOR border error of 6.7%, comparable to the median inter-dermatologist XOR border error (7.4%), and lower than the gradient vector flow snake median XOR error of 14.2% on the same image set. On a difficult low-contrast border set of 1238 images, which included 350 non-melanocytic lesions, a median XOR error of 23.9% is obtained. CONCLUSION GAC techniques show promise in attaining the goal of automatic skin lesion segmentation.

Biologically inspired skin lesion segmentation using a geodesic active contour technique.

In this paper, we propose combining between the transformed hand-crafted and deep features using PCA to recognize the six-basic facial expressions from static images. To evaluate our approach, we use three popular databases (CK+, CASIA and MMI). We introduce the use of the Pyramid Multi Level (PML) face representation for facial expression recognition. The hand-crafted features are obtained with such representations. Initially, we determine the optimal level of the PML features of three hand-crafted descriptors (HOG, LPQ and BSIF) using CK+, CASIA and MMI databases. After the optimal level of the PML is found for each descriptor, we combine them together with the transformed final VGG-face layers (FC6 and FC7) in order to get a compact image descriptor. In within-database experiments, our approach achieved higher accuracy than the state-of-art methods on both the CK+ and CASIA databases, and competitive result on the MMI database. Likewise, our approach outperformed the static methods in all six experiments of cross-databases. (C) 2020 Published by Elsevier Ltd.

Fusing Transformed Deep and Shallow features (FTDS) for image-based facial expression recognition

This work presents the application of the Hilbert-Huang transform and its marginal spectrum, for the analysis of the stator current signals for bearing faults diagnosis in asynchronous machines. Firstly, the current signals are decomposed into several intrinsic mode functions (IMFs) using the empirical mode decomposition (EMD). The Hilbert Huang spectrum for each IMF is an energy representation in the time-frequency domain using the instantaneous frequency. The marginal spectrum of each IMF can then be obtained. Secondly, the IMFs that includes dominant fault information are modeled using an autoregressive (AR) model. Finally, the AR model parameters serve as the input fault feature vectors to support vector machine (SVM) classifiers. Experimental studies show that the marginal spectrum of the second IMF can be used for the detection and classification of bearing faults. The proposed approach provides a viable signal processing tool for an online machine health status monitoring.

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Karim Mokrani

Papers

Classification of malignant melanoma and benign skin lesions: implementation of automatic ABCD rule

Improved Fuzzy C-Means based Particle Swarm Optimization (PSO) initialization and outlier rejection with level set methods for MR brain image segmentation

Biologically inspired skin lesion segmentation using a geodesic active contour technique.

Fusing Transformed Deep and Shallow features (FTDS) for image-based facial expression recognition

Bearing fault diagnosis using Hilbert-Huang transform (HHT) and support vector machine (SVM)