Using deep transfer learning for image-based plant disease identification

Prognostics and remaining useful life (RUL) estimation for lithium-ion batteries play an important role in intelligent battery management systems (BMS). The capacity is often used as the fade indicator for estimating the remaining cycle life of a lithium-ion battery. For spacecraft requiring high reliability and long lifetime, in-orbit RUL estimation and reliability verification on ground should be carefully addressed. However, it is quite challenging to monitor and estimate the capacity of a lithium-ion battery on-line in satellite applications. In this work, a novel health indicator (HI) is extracted from the operating parameters of a lithium-ion battery to quantify battery degradation. Moreover, the Grey Correlation Analysis (GCA) is utilized to evaluate the similarities between the extracted HI and the battery’s capacity. The result illustrates the effectiveness of using this new HI for fading indication. Furthermore, we propose an optimized ensemble monotonic echo state networks (En_MONESN) algorithm, in which the monotonic constraint is introduced to improve the adaptivity of degradation trend estimation, and ensemble learning is integrated to achieve high stability and precision of RUL prediction. Experiments with actual testing data show the efficiency of our proposed method in RUL estimation and degradation modeling for the satellite lithium-ion battery application.

/pdf/satellite-lithium-ion-battery-remaining-cycle-life-3z59217w51.pdf

Satellite Lithium-Ion Battery Remaining Cycle Life Prediction with Novel Indirect Health Indicator Extraction

Plant leaf disease identification using exponential spider monkey optimization

Identification of nine Iranian wheat seed varieties by textural analysis with image processing

http://www.cs.rug.nl/~george/articles/PRL2013.pdf

Automatic detection of vascular bifurcations in segmented retinal images using trainable COSFIRE filters

Steganography is the science of hiding information in a media such as video, image or audio files. On the other hand, the aim of steganalysis is to detect the presence of embedded data in a given media. In this paper, a steganalysis method is presented for the colored joint photographic experts group images in which the statistical moments of contourlet transform coefficients are used as the features. In this way, binary particle swarm optimization algorithm is also employed as a closed-loop feature selection method to select the efficient features in tandem with improvement of the detection rate. Nonlinear support vector machine and two variants of radial basis neural networks, i.e., radial basis function and probabilistic neural network, are used as the classification tools and their performance is compared in detecting the stego and clean images. Experimental results show that even for low embedding rates, the detection accuracy of the proposed method is more than 80% along with 30% reduction in the size of feature set.

Improved contourlet-based steganalysis using binary particle swarm optimization and radial basis neural networks

Boosting Approach for Score Level Fusion in Multimodal Biometrics Based on AUC Maximization.

In this paper, we present a tracking technique utilizing a simple saliency visual descriptor. Initially, we define a visual descriptor named local similarity pattern that mimics the famous texture operator local binary patterns. The key difference is that it assigns each pixel a code based on the similarity to the neighbouring pixels. Later, we simplify this descriptor to a local saliency operator which counts the number of similar pixels in a neighbourhood. We name this operator local similarity number (LSN). We apply the local similarity number operator to measure the amount of saliency in a target patch and model the target. The proposed tracking algorithm uses a joint saliency-colour histogram to represent the target in a mean-shift tracking framework. We will show that the proposed saliency-colour target representation outperforms texture-colour where texture modelled by local binary patterns and colour target representation techniques are used.

Local Similarity Number and its Application to Object Tracking

A blind color image steganalyzer is proposed, in which the features are extracted from Contourlet domain. Statistical features of Contourlet coefficients and cooccurrence metrics of subband images are used as features. For evaluating the proposed steganalysis method, some popular steganography methods such as OutGuess, JPHS, Model-based and Jsteg are used with payloads of 10% to 25%. To reduce the number of features, Analysis of Variance (ANOVA) method is used and the selected features are fed to nonlinear Support Vector Machine (SVM) for classification into stego and clean images. Empirical results show high sensitivity of Contourlet and co-occurrence matrix features to data hiding.

Blind image steganalysis via joint co-occurrence matrix and statistical moments of contourlet transform

A texture based object tracking algorithm is presented. The algorithm is an extension to famous mean-shift tracking method. It does not rely on color histogram. It incorporates both color histogram and texture histogram information to model tracking target.

M. Shahram Moin

Papers

Improved contourlet-based steganalysis using binary particle swarm optimization and radial basis neural networks

Boosting Approach for Score Level Fusion in Multimodal Biometrics Based on AUC Maximization.

Local Similarity Number and its Application to Object Tracking

Blind image steganalysis via joint co-occurrence matrix and statistical moments of contourlet transform

Mean-shift video tracking using color-LSN histogram