Specific emitter identification for satellite communication using probabilistic neural networks

Electromagnetic signal emitted by satellite communication (satcom) transmitters are used to identify specific individual uplink satcom terminals sharing the common transponder in real environment, which is known as specific emitter identification (SEI) that allows for early indications and warning (I&W) of the targets carrying satcom furnishment and furthermore the real time electromagnetic situation awareness in military operations. In this paper, the authors are the first to propose the identification of specific transmitters of satcom by using probabilistic neural networks (PNN) to reach the goal of target recognition. We have been devoted to the examination by exploring the feasibility of utilizing the Hilbert transform to signal preprocessing, applying the discrete wavelet transform to feature extraction, and employing the PNN to perform the classification of stationary signals. There are a total of 1000 sampling time series with binary phase shift keying (BPSK) modulation originated by five types of satcom transmitters in the test. The established PNNs classifier implements the data testing and finally yields satisfactory accuracy at 8 dB(±1 dB) carrier to noise ratio, which indicates the feasibility of our method, and even the keen insight of its application in military.     

A hybrid multiple feature construction approach for classification using Genetic Programming

The purpose of feature construction is to create new higher-level features from original ones. Genetic Programming (GP) was usually employed to perform feature construction tasks due to its flexible representation. Filter-based approach and wrapper-based approach are two commonly used feature construction approaches according to their different evaluation functions. In this paper, we propose a hybrid feature construction approach using genetic programming (Hybrid-GPFC) that combines filter’s fitness function and wrapper’s fitness function, and propose a multiple feature construction method that stores top excellent individuals during a single GP run. Experiments on ten datasets show that our proposed multiple feature construction method (Fcm) can achieve better (or equivalent) classification performance than the single feature construction method (Fcs), and our Hybrid-GPFC can obtain better classification performance than filter-based feature construction approaches (Filter-GPFC) and wrapper-based feature construction approaches (Wrapper-GPFC) in most cases. Further investigations on combinations of constructed features and original features show that constructed features augmented with original features do not improve the classification performance comparing with constructed features only. The comparisons with three state-of-art methods show that in majority of cases, our proposed hybrid multiple feature construction approach can achieve better classification performance.     

Retinal Vessel Extraction Framework Using Modified Adaboost Extreme Learning Machine

An explicit extraction of the retinal vessel is a standout amongst the most significant errands in the field of medical imaging to analyze both the ophthalmological infections, for example, Glaucoma, Diabetic Retinopathy (DR), Retinopathy of Prematurity (ROP), Age-Related Macular Degeneration (AMD) as well as non retinal sickness such as stroke, hypertension and cardiovascular diseases. The state of the retinal vasculature is a significant indicative element in the field of ophthalmology. Retinal vessel extraction in fundus imaging is a difficult task because of varying size vessels, moderately low distinction, and presence of pathologies such as hemorrhages, microaneurysms etc. Manual vessel extraction is a challenging task due to the complicated nature of the retinal vessel structure, which also needs strong skill set and training. In this paper, a supervised technique for blood vessel extraction in retinal images using Modified Adaboost Extreme Learning Machine (MAD-ELM) is proposed. Firstly, the fundus image preprocessing is done for contrast enhancement and in-homogeneity correction. Then, a set of core features is extracted, and the best features are selected using “minimal Redundancy-maximum Relevance (mRmR).” Later, using MAD-ELM method vessels and non vessels are classified. DRIVE and DR-HAGIS datasets are used for the evaluation of the proposed method. The algorithm’s performance is assessed based on accuracy, sensitivity and specificity. The proposed technique attains accuracy of 0.9619 on the DRIVE database and 0.9519 on DR-HAGIS database, which contains pathological images. Our results show that, in addition to healthy retinal images, the proposed method performs well in extracting blood vessels from pathological images and is therefore comparable with state of the art methods.     

Food recognition using neural network classifier and multiple hypotheses image segmentation

ABSTRACT

This paper proposes the multiple-hypotheses image segmentation and feed-forward neural network classifier for food recognition to improve the performance. Initially, the food or meal image is given as input. Then, the segmentation is applied to identify the regions, where a particular food item is located using salient region detection, multi-scale segmentation, and fast rejection. Then, the features of every food item are extracted by the global feature and local feature extraction. After the features are obtained, the classification is performed for each segmented region using a feed-forward neural network model. Finally, the calorie value is computed with the aid of (i) food volume and (ii) calorie and nutrition measure based on mass value. The experimental results and performance evaluation are validated. The outcome of the proposed method attains 0.947 for Macro Average Accuracy (MAA) and 0.959 for Standard Accuracy (SA), which provides better classification performance.     

当前腐败现象的特点和对策分析

论述了当前腐败现象的表现和特点,分析了产生腐败现象的主要原因,提出了治理腐败现象的对策措施: 建立有效的教育机制、严格的用人机制、规范的决策机制、健全的管理机制、完善的监督机制和有力的责任追究机制,建立科学的法人治理结构并实行纪检监察机构派驻制。     

利用信息融合方法的三维特征提取

本文提出了一种基于信息融合的物体三维特征的提取方法，该方法利用两幅互相配准的三维测距图像和灰度图像，来提取多面体的三维特征。首先，通过分析灰度图像中的灰度变化及测距图像中的测距值变化，分别求取各自图像中物体的特征点及特征边；然后，利用两配准图像之间的对应关系，求得所有特征点、面与多边形在三维测距图像中的三维表示；接着，通过分析三维测距图像中所测得的各候选平面上特定点与边处的曲率及法向，验证候选平面     

用金字塔图法匹配边界图象的三眼立体视觉方法

本文在分析了双眼立体视觉面临的本质困难之后，提出了用金字塔图法匹配特征基元的三眼立体视觉方法，这种方法由于建筑在透视投影几何原理的稳固基础上，无须任何先验限制，也不以图象相似性为基础，故可较好地解决精度与匹配难度的矛盾，且运用金字塔图法进行匹配传播使在保证精度的基础上，匹配速度大大提高，对计算机人工图象的匹配结果表明，误匹配率几乎为零，且有较高的匹配速度和精度。     

双相活齿泵的工作原理

在活齿传动理论及齿轮泵工作原理相结合的基础上，介绍了双相活齿泵的结构及其工作原理，研究结果表明，双相活齿泵具有流量大、流量均匀性好、噪声低以及可变量等优点。     

自旋式脱接器在临盘油田的应用

为保障临盘采油厂的大泵井正常生产 ,购进了自旋式脱接器。这种脱接器由上体和下体两部分组成 ,上体包括轨道管和轨道套 ;下体包括接头、挡圈、弹簧、滑套和中心杆。自旋式脱接器可在任意位置和角度对接和脱开 ,对接和脱开时只需下放和上提抽油杆柱即可完成。当泵砂卡时 ,左旋抽油杆柱即可脱开 ,解决了砂卡井脱接器不能脱开的难题。该脱接器可重复对接12 0次以上 ,耐疲劳寿命 7× 10 7次以上     

低压电、汽两用节能加热锅炉的结构特点

通过对电、汽两用节能加热锅炉的描述,指出该设备具有投资少,无污染,能源转化率高,起停速度快,负荷调节范围大,结构简单,安全性能好并可充分利用电能等特点.