Fungal keratitis is an inflammation of the cornea that results from infection by fungal organisms. It has a high rate of blindness, which makes the accurate diagnosis of fungal keratitis important. Confocal microscopy is an optical imaging technique that assists doctors in diagnosing fungal keratitis, and cornea images obtained by confocal microscopy can be used to detect hyphae. The current challenges are how to classify normal cornea images with nerves and abnormal cornea images with hyphae and how to detect the hyphae in a complicated background. To address this problem, this paper proposes a novel automatic hyphae detection method that assists doctors in making diagnoses. It includes two primary steps: texture classification of images and hyphae detection. In texture classification step, first, after image enhancement using a subregional contrast stretching algorithm, an adaptive robust binary pattern (ARBP), which is an improvement on the adaptive median binary pattern (AMBP), is proposed and adopted to extract texture features; and a support vector machine model is used to classify the normal and abnormal images. In the hyphae detection step, binarization and a connected domain process are used to further enhance the targets, and a line segment detector algorithm is adopted to detect the hyphae; then, the hyphal density is defined to quantitatively evaluate the infection severity. The contributions of this study include the improvement of the AMBP and the design of a novel framework. ARBP can extract effective texture features of images with relatively bright and small targets. The experimental results demonstrate the effectiveness of the proposed framework.

The gateway selection is an essential issue in hybrid mobile ad hoc networks (MANETs). Current independent and random selections without supporting of routing negotiation protocol may cause links or gateways overloaded when they are selected by multiple nodes simultaneously. Therefore, the network performance may not be in a proper way. Furthermore, the ad hoc nature of MANETs makes the topology change dynamically, so that the gateway selection becomes even more difficult. This paper presents a mathematical model for gateway capability and a novel approach, called bio-inspired gateway selection, where the gateways are selected according to the network status and associated with a cooperative mechanism for optimization. The novelty includes the use of attractor selection model, the self-adaptability and the autonomy of biological system. The performance of the proposed approach is evaluated by simulation with different scenarios and compared with the conventional approaches being currently used in hybrid MANETs. The illustrated numerical results present the performance of the proposed approach in terms of packet delivery ratio, average delivery latency, normalized routing overhead and gateway load balance under different network conditions. Furthermore, the numerical results are also able to demonstrate the significant performance gain compared with the conventional gateway selection approaches.

This paper presents a fast SAR imagery algorithm for Ground Moving Target Imaging (GMTIm) based on the slope detection strategy combined with Time-Frequency Representation (TFR), which is known as Lv’s Distribution (LVD). This fast imagery algorithm focuses on the solution of the ambiguity problems and relevant heavy computing load in SAR imagery. Firstly, according to the relationship between the slope of the range walk trajectory and the cross-track velocity of moving target, a new high-efficiency slope detection strategy based on gradient and level-line angle is presented in the image domain. Then, the Doppler centroid shift induced by cross-track velocity can also be obtained. Secondly, owing to the cross-track velocity estimated before, the Range Walk Migration Correction (RWMC) can be performed to concentrate the echo response of the moving target into a single range cell. Finally, due to the superior performance in representing multi-component Linear Frequency Modulation (LFM) signal, LVD is adopted here to represent the Doppler chirp rate of multiple moving targets in a Doppler Centroid Frequency and Chirp Rate domain (CFCR). The performance of the proposed algorithm is evaluated in terms of superiority and effectiveness using simulations, and the comparison between the proposed algorithm and the other conventional algorithms is also presented.

Purpose: This paper aims to provide a framework of the supervised hyperspectral classification, to study the traditional flowchart of hyperspectral image (HIS) analysis and processing. HSI technology has been proposed for many years, and the applications of this technology were promoted by technical advancements. Design/methodology/approach: First, the properties and current situation of hyperspectral technology are summarized. Then, this paper introduces a series of common classification approaches. In addition, a comparison of different classification approaches on real hyperspectral data is conducted. Finally, this survey presents a discussion on the classification results and points out the classification development tendency. Findings: The core of this survey is to review of the state of the art of the classification for hyperspectral images, to study the performance and efficiency of certain implementation measures and to point out the challenges still exist. Originality value: The study categorized the supervised classification for hyperspectral images, demonstrated the comparisons among these methods and pointed out the challenges that still exist.