Bhattacharyya distance as a contrast parameter for statistical processing of noisy optical images

In many imaging applications, the measured optical images are perturbed by strong fluctuations or boise. This can be the case, for example, for coherent-active or low-flux imagery. In such cases, the noise is not Gaussian additive and the definition of a contrast parameter between two regions in the image is not always a straightforward task. We show that for noncorrelated noise, the Bhattacharyya distance can be an efficient candidate for contrast definition when one uses statistical algorithms for detection, location, or segmentation. We demonstrate with numerical simulations that different images with the same Bhattacharyya distance lead to equivalent values of the performance criterion for a large number of probability laws. The Bhattacharyya distance can thus be used to compare different noisy situations and to simplify the analysis and the specification of optical imaging systems.     

Automatic algorithm for inverse synthetic aperture radar images recognition and classification

The authors propose an algorithm for automatic aircraft categories that is models classification from inverse synthetic aperture radar (ISAR) images that use pulse reflection shape and Doppler shifts of parts of aircraft that are in any maneuver that introduces rotation to the target. The authors artificially generated five different categories of ISAR aircraft using computer simulations and tested these simulated ISAR aircraft images of the airplanes defined by size and shape that are flying in a prescribed holding pattern. The authors investigate in what parts of the holding pattern the ISAR reflections provide information that makes it possible to identify to which of the five categories an aircraft in the holding pattern belongs. The obtained results show that it is possible in most parts of the holding pattern to successfully classify the various aircraft targets.     

Virtual images inspired consolidate collaborative representation-based classification method for face recognition

Abstract

The collaborative representation-based classification method performs well in the field of classification of high-dimensional images such as face recognition. It utilizes training samples from all classes to represent a test sample and assigns a class label to the test sample using the representation residuals. However, this method still suffers from the problem that limited number of training sample influences the classification accuracy when applied to image classification. In this paper, we propose a modified collaborative representation-based classification method (MCRC), which exploits novel virtual images and can obtain high classification accuracy. The procedure to produce virtual images is very simple but the use of them can bring surprising performance improvement. The virtual images can sufficiently denote the features of original face images in some case. Extensive experimental results doubtlessly demonstrate that the proposed method can effectively improve the classification accuracy. This is mainly attributed to the integration of the collaborative representation and the proposed feature-information dominated virtual images.     

Improved cuckoo search with particle swarm optimization for classification of compressed images

The need for a general purpose Content Based Image Retrieval (CBIR) system for huge image databases has attracted information-technology researchers and institutions for CBIR techniques development. These techniques include image feature extraction, segmentation, feature mapping, representation, semantics, indexing and storage, image similarity-distance measurement and retrieval making CBIR system development a challenge. Since medical images are large in size running to megabits of data they are compressed to reduce their size for storage and transmission. This paper investigates medical image retrieval problem for compressed images. An improved image classification algorithm for CBIR is proposed. In the proposed method, RAW images are compressed using Haar wavelet. Features are extracted using Gabor filter and Sobel edge detector. The extracted features are classified using Partial Recurrent Neural Network (PRNN). Since training parameters in Neural Network are NP hard, a hybrid Particle Swarm Optimization (PSO) – Cuckoo Search algorithm (CS) is proposed to optimize the learning rate of the neural network.     

Photostable luminescent nanoparticles as biological label for cell recognition of system lupus erythematosus patients

In this article, we report a method for cell recognition of system lupus erythematosus (SLE) patients that uses photostable luminescent nanoparticles as biological labels. The luminescent silica nanoparticles are prepared with a water-in-oil microemulsion (W/O) technique. The silica network is produced by the controlled hydrolysis of tetraethylorthosilicate (TEOS) in water nanodroplets with the initiation of ammonia (NH3.H2O). A luminescent compound, tris(2,2'-bipyridyl)dichlororuthenium(II)hexahydrate [Ru(II)(bpy)3]2+, is doped inside as a luminescent signaling element, and the most appropriate dye concentration for the preparation of the nanoparticles with a size of 28 +/- 4 nm has been determined. The luminescent silica nanoparticles are covalently immobilized with goat anti-human immunoglobulin G (IgG), which can recognize SmIgG+ B lymphocytes. We have used antibody-labeled nanoparticles to recognize target SmIgG+ B lymphocytes isolated from the circulating blood of SLE patients. It has been observed that a bioassay based on fluorescent nanoparticles can identify target cells selectively and efficiently. And fluorescent nanoparticle labels also exhibit high photostability. The experiment results have shown that this cell recognition method was an effective one as further proof of the diagnosis of SLE.     

Performance analysis of neural networks for classification of medical images with wavelets as a feature extractor

In this article, we analyze the performance of artificial neural network, in classification of medical images using wavelets as feature extractor. This work classifies the mammographic image, MRI images, CT images, and ultrasound images as either normal or abnormal. We have tested the proposed approach using 50 mammogram images (13 normal and 37 abnormal), 24 MRI brain images (9 normal and 15 abnormal), 33 CT images (11 normal and 22 abnormal), and 20 ultrasound images (6 normal and 14 abnormal). Four kind of neural network models such as BPN (Back Propagation Network), Hopfield, RBF (Radial Basis Function), and PNN (Probabilistic neural network) were chosen for study. To improve diagnostic accuracy, the feature extracted using wavelets such as Harr, Daubechies (db2, db4, and db8), Biorthogonal and Coiflet wavelets are given as input to the neural network models. Good classification percentage of 96% was achieved using the RBF when Daubechies (db4) wavelet based feature extraction was used. We observed that the classification rate is almost high under the RBF neural network for all the dataset considered. © 2015 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 25, 33–40, 2015     

Digital unit for statistical processing of measuring information represented as a voltage

Conclusion A digital device has been developed for statistical processing of information in the form of voltages, where measurement and conversion of the information are combined with the execution of the necessary arithmetic and logical operations, whereupon the need to store the information is eliminated. The possibilities provided, i.e., execution of the v/c, m_X, D_X, w(), r _xy ^* , modes, are realized by a sufficiently simple control, which makes the device simple in construction, reliable, and convenient for servicing.Translated from Izmeritel'naya Tekhnika, No. 8, pp. 32–34, August, 1972.     

Nanostructured hybrid materials as precursors for synthesis of nanocoposites in Si-O-C-N-Zr system

Nanocomposites built from silicon oxycarbonitride amorphous materials and ZrO₂ (t) nanocrystals were obtained by pyrolysis of sol-gel derived nanostructured hybrids in the Si-O-C-N-Zr system. The structure of the hybrid materials before and after pyrolysis was investigated by XRD, SEM, FTIR, ²⁹Si MAS NMR. Based on the data of the analyses pointed out it has been proposed a most probable cross-linking mechanism in the derived gels. The results from the preliminary experiments carried out motivate our research team to continue the study in direction of deposition of the obtained materials on metal substrates in order to improve their resistance toward oxidation and corrosion or to modify their surface properties.     

An automated methodology for the classification of focal and nonfocal EEG signals using a hybrid classification approach

The uncertainty in human brain leads to the formation of epilepsy disease in human. The automatic detection and severity analysis of epilepsy disease is proposed in this article using a hybrid classification algorithm. The proposed method consists of decomposition stage, feature extraction, and classification stages. The electroencephalogram (EEG) signals are decomposed using dual-tree complex wavelet transform and then features are extracted from these coefficients. These features are then classified using the neural network classification approach in order to classify the EEG signals into either focal or nonfocal EEG signals. Furthermore, severity of the focal EEG signal is analyzed using an adaptive neuro-fuzzy inference system classification approach. The proposed hybrid classification method for the classification of focal signals and nonfocal signals achieved 98.6% of sensitivity, 99.1% of specificity, and 99.4% of accuracy. The average detection rate for both focal and nonfocal dataset is about 98.5%.     

Methylcellulose cell culture as a new cytotoxicity test system for biomaterials

The cytotoxicity of biomaterials can be testedin vitro using various culture systems. Liquid culture systems may detect cytotoxicity of a material either by culture of cells with extracts or with the material itself. In the latter instance, renewing the medium will remove possible released cytotoxic products. The agar-overlay test is a short term semi-solid culture system in which the possible cytotoxicity of biomaterials is identified only by the presence of cell free zones. The aim of this study was to develop a more sensitive cytotoxicity test system for biomaterials, using methylcellulose as a culture gel, mixed with human fibroblasts. The main advantage of the test system is the possibility of evaluating cytotoxicity for a period of up to seven days without renewal of the culture gel. Furthermore it is possible to both quantitatively evaluate by counting absolute cell numbers and to qualitatively evaluate by studying cell morphology with light- and/or electron microscopy. Processed dermal sheep collagen was selected as test material, since contradictory results concerning the cytotoxicity of its extracts have been reported by others [2, 15, 18, 19]. Using our test system, both primary and secondary cytotoxic effects were found. Primary cytotoxicity is due to direct leakage of products from the material, detected by testing, extracts of the collagen or the collagen itself. Secondary cytotoxicity is due to release of cytotoxic products resulting from cell-biomaterial interactions. We conclude that our test system is extremely useful to test materials which are suspected of primary and/or secondary cytotoxicity, either with slow release of cytotoxic products or release of products with late cytotoxic effects.     

Integrating GWTM and BAT algorithm for face recognition in low-resolution images

In biometrics, face recognition is one of the important identification methods with various applications such as, video surveillance, defence, human/computer interactions and many more. The current face recognition systems perform well using the frontal images with high resolution. In contrast, the utilisation of low-resolution (LR) images degrades the performance of face recognition systems. Hence, this paper integrates the Gabor filter?+?wavelet?+?texture (GWTM) operator and the BAT algorithm to increase the performance, while deploying the LR images. The proposed algorithm integrates the uniqueness of Gabor features, the robustness of local features and the wavelet features to handle the inter-person and intra-person variations. This paper utilises the spherical SVM classifier to enhance the recognition performance. Finally, the proposed GWTM operator is compared with other existing algorithms such as, GOM, LBP and LGP based on the parameters of accuracy, FAR and FRR. The proposed GWTM operator attains the highest accuracy of 95% and a minimum FAR of 5%. The results prove that the proposed GWTM yields a performance improvement of 5, 3, 4 and 15% over the GOM, LBP, LGP and GWTM, respectively, in the absence of the BAT algorithm.     

Automated diagnosis system for age‐related macular degeneration using hybrid features set from fundus images

Impairment to macula can cause loss of central vision. There are various macular disorders that can affect macular region and if not treated at an early stage can cause irreversible central vision loss. Age‐related macular degeneration (AMD) disorder is one of the most threading macular disorder. Bright lesion, drusens presence in macular region is known as the hallmark of AMD disorder. This bright lesion differentiation from other bright lesion like exudates is important for accurate diagnosis of AMD. Focus of this article is automated diagnosis of affected macular region by applying a hybrid features set containing textural, color, and structural/shape features for more accurate detection of AMD at an early stage using fundus images. These features also help to distinguish drusens from exudates. The proposed algorithm at first stage, detect macular region from input fundus image and then perform features extraction based on textural pattern, edge, and structural properties of macular region to classify abnormal macula from normal macula. For classification, we have used support vector machine (SVM), K‐nearest neighbor and neural networks but SVM classifier achieves high accuracy. The proposed algorithm is tested on publicly available STARE and locally available AFIO datasets. Attained sensitivity, specificity, and accuracy of our proposed system are 97.5%, 95% and 95.45%, respectively, when applied on STARE dataset. When we have applied our proposed system on AFIO dataset, we have attained sensitivity, specificity, and accuracy of 93.3%, 92% and 92.34%, respectively.     

Employee substitutability as a tool to improve the robustness in personnel scheduling

OR Spectrum - Organisations usually construct personnel rosters under the assumption of a deterministic operating environment. In the short term, however, organisations operate in a stochastic...     

Methods for multidimensional event classification: a case study using images from a Cherenkov gamma-ray telescope

We present results from a case study comparing different multivariate classification methods. The input is a set of Monte Carlo data, generated and approximately triggered and pre-processed for an imaging gamma-ray Cherenkov telescope. Such data belong to two classes, originating either from incident gamma rays or caused by hadronic showers. There is only a weak discrimination between signal (gamma) and background (hadrons), making the data an excellent proving ground for classification techniques.The data and methods are described, and a comparison of the results is made. Several methods give results comparable in quality within small fluctuations, suggesting that they perform at or close to the Bayesian limit of achievable separation. Other methods give clearly inferior or inconclusive results. Some problems that this study can not address are also discussed.     

燃料电池汽车并行复合制动系统开发

通过对复合制动系统功能需求的分析,提出了基于反比例溢流控制的并行复合制动系统方案及其控制系统结构。通过对再生制动控制算法约束条件的分析,提出了基于再生制动系统外特性的再生制动控制算法及其ECE法规修正方法。通过试验获取了控制算法过程控制参数,采用硬件在环的方式对系统的控制效果和制动能量回收效率进行了仿真测试。结果表明,液压制动力能够良好地跟随再生制动控制算法分配的结果,在一次制动过程中,改进后的系统和算法能够提高能量回收效率6.3%。     

卫星遥感图像中类圆形油库的自动识别方法

油库是典型军事目标之一,对其识别是卫星图像判读的一项重要内容,传统的方法是通过判读员进行人工判读,工作量非常大是其缺点之一。为了克服这一缺点,本文提出了一种类圆形油库的自动识别方法。首先利用Kapur熵法对图像进行阈值分割,得到二值图像：然后对二值图像中的白像素进行最近邻聚类形成团块,并计算其面积以及体态比和矩形度等形状参数;最后利用油库近似圆形和成群分布的特点对油库群进行识别和定位。实验结果表明该方法对于高分辨率卫星遥感图像中的类圆形油库的识别是很有效的。     

Relative entropy as a measure of the degree of mixing in noisy systems

A new concept of the relative metric entropy is introduced that makes possible quantitative evaluation of the chaotic mixing in a dynamical system under the action of an external noise. It is shown that, in the absence of noise, the relative metric entropy represents an estimate from below for the Kolmogorov entropy of the given dynamical system.     

A novel classifier model for mass classification using BI-RADS category in ultrasound images based on Type-2 fuzzy inference system

Ultrasound imaging is an imaging technique for early detection of breast cancer. Breast Imaging Reporting and Data System (BI-RADS) lexicon, developed by The American College of Radiology, provides a standard for expert doctors to interpret the ultrasound images of breast cancer. This standard describes the features to classify the tumour as benign or malignant and it also categorizes the biopsy requirement as a percentage. Biopsy is an invasive method that doctors use for diagnosis of breast cancer. Computer-aided detection (CAD)/diagnosis systems that are designed to include the feature standards used in benign/malignant classification help the doctors in diagnosis but they do not provide enough information about the BI-RADS category of the mass. These systems classify the benign tumours with 90% biopsy possibility (BI-RADS-4) and with 2% biopsy possibility (BI-RADS-2) in the same category. There are some studies in the literature that make category classification via commonly used classifier methods but their success rates are low. In this study, a two-layer, high-success-rate classifier model based on Type-2 fuzzy inference is developed, which classifies the tumour as benign or malignant with its BI-RADS category by incorporating the opinions of the expert doctors. A 99.34% success rate in benign/malignant classification and a 92% success rate in category classification (BI-RADS 2, 3, 4, 5) were obtained in the accuracy tests. These results indicate that the CAD system is valuable as a means of providing a second diagnostic opinion when radiologists carry out mass diagnosis.     

Improved current density with Al2O3 coated ZnO nanorod in hybrid solar cell

Heterojunction photovoltaic devices consisting of hybrid p-type organic Cu-phthalocyanine and inorganic n-type Al2O3 nanoparticle-coated aligned ZnO nanorods were fabricated. With microwave treatment, an interaction occurred between the Al2O3 and ZnO, as evidenced from TEM image. This interaction shifts the absorption peak of the aligned nanorods from the UV region to visible light and subsequently causes more charge generation. For 5 mol% Al2O3 nanoparticle-coated aligned ZnO nanorods treated with microwaves of 600 W for 300 sec, the maximum incident photon to electron conversion and energy conversion efficiencies under simulated sunlight of AM1.5G (10 mW/cm2) are 0.036 mA and 1.32%, respectively.