Characteristics of tiger nut milk: effects of milling

A standard method for the extraction of tiger nut milk has been introduced. It has been shown that, although milling duration improves the yield of tiger nut milk solids and its nutrient composition, there is a quantifiable loss of nutrient in the pressing residue during milk extraction. Milling duration improved the colloidal stability of the milk against creaming during 16 h of storage. A higher milling intensity resulted in the aggregation of biological polymers which resulted in colloidal destabilisation. Milling improved the lightness and stability and reduced browning rate of the tiger nut milk during storage. This report is important for the production of tiger nut milk of consistent and comparable characteristics. Milling has been introduced as a processing method for the qualitative and quantitative modulation of the properties of tiger nut milk. It is recommended to develop further strategies to improve the colloidal stability of tiger nut milk as a beverage.     

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.     

水提法提取—电感耦合等离子体质谱法测定婴幼儿配方奶粉中的碘

用水提取婴幼儿奶粉中的碘,亚铁氰化钾和乙酸锌盐析法沉淀蛋白,过滤后可以得到澄清处理液。用电感耦合等离子体质谱对提取样液进行测定,130Te作为内标物进行定量分析。该方法简便快捷,检出限为0.10 mg/kg加标回收率为90.8%~109%,相关系数0.999,精密度试验RSD2%。     

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.     

Road Extraction from High Resolution Remote Sensing Image based on Shape Analysis with Gestalt Theory

Simulating the psychological experience of human vision，a road extraction model based on the format tower is proposed to extract the road in the high resolution remote sensing image from the perspective of morphology.Firstly，based on the spectral and texture information，the suspected road targets are extracted by using segmentation technology.Then these targets are classified according to their reliability and extract the road targets for each category.Finally，three types of identified road information are verified and merged，and the continuous smooth road extraction results are obtained.Experiments on real high resolution images show that the results are consistent with the visual perception of the human eye，and the overall classification accuracy is higher，indicating that the algorithm is effective and feasible and has good use value.     

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.     

细节决定采输卤的成败

通过认真学习《细节决定成败》，结合井矿盐开采的实际，分别以生产过程中的三个细节为主，讨论了在采输卤生产技术控制中的细节的重要性。     

高导电性原油电脱盐工艺研究

南阳原油中金属含量较高,掺有部分3次采油采出原油,加工时易造成电脱盐装置原油乳化,电导率高,脱盐难度较大。装置投产十几年来脱后含盐一直不合格,经过改造仍然超标,常压塔顶腐蚀严重。为此,对原油进行了电脱盐工艺条件及破乳剂研究,研制出了高效电脱盐设备和生物复合脱盐剂及降电流剂,采用平流弓形电极进行改造,解决了该原油的电脱盐问题,使脱后含盐达到3 mg/L。     

Extracting Light from Polymer Light‐Emitting Diodes Using Stamped Bragg Gratings

In this paper, we describe a method for increasing the external efficiency of polymer light‐emitting diodes (LEDs) by coupling out waveguided light with Bragg gratings. We numerically model the waveguide modes in a typical LED structure and demonstrate how optimizing layer thicknesses and reducing waveguide absorption can enhance the grating outcoupling. The gratings were created by a soft‐lithography technique that minimizes changes to the conventional LED structure. Using one‐dimensional and two‐dimensional gratings, we were able to increase the forward‐directed emission by 47 % and 70 %, respectively, and the external quantum efficiency by 15 % and 25 %.     

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