bluuebunny/crossref_metadata_2025_split huggingface dataset:
Aptive Image Segmentation based on Saliency Detection
Authors: Linlin Shui 
Published: November 2017

Abstract
in this article, we propose an adaptive image segmentation method based on saliency. First of all, we obtain the saliency map of an image via four bottom-layer feature tunnels, i.e. color, intensity, direction and energy. The energy tunnel helps to describe the outline of objects better in the saliency map. Then, we construct the target detection masks according to the greyness of pixels in the saliency map. Each mask is applied to the original image as the result of pre-segmentation, then corresponding image entropy is calculated. Predict the expected entropy according to maximum entropy criteria and select the optimal segmentation according to the entropies of pre-segmented images and the expected entropy. A large number of experiments have proved the effectiveness and advantages of this algorithm.
New Stereo Matching Method Based on Improved BP Algorithm
Authors: Zhang Qian. , Li Shaomin , Zhang Y. , Wang P. , Huang JF. 
Published: November 2017

Abstract
As stereo matching methods are widely used in computer vision and stereo reconstruction, from the perspective of improving the matching accuracy, this paper focuses on the global optimization algorithm. An improved stereo matching method based on 8- neighbor Belief Propagation method is proposed in this paper, by involving more pixels into information transmission, our method improves the accuracy of stereo matching. The experimental results verify the efficiency and reliability of our method.
A Deployment Algorithm for Mobile Wireless Sensor Networks based on the Electrostatic Field Theory
Authors: Xu Jiang , Qian Huanyan , Ying Wenhao , Zhang Jun 
Published: November 2017

Abstract
This paper proposes a deployment algorithm based on the electrostatic field theory for mobile wireless sensor networks. The nodes and obstacles in the deployment area are taken as the charged particles; and the particles will move due to the Coulomb’s force from other particles or obstacles. Finally, the nodes automatically spread to the whole area by the resultant action and complete the deployment. Four metrics, including coverage, uniformity, deployment time and average displacement distance, are used to evaluate the performance of the algorithm. The simulation results show that the proposed algorithm can give full play to its self-adaptive advantages and achieve the desired deployment effect; it is a kind of deployment algorithm with self-adaptive characteristics.
The Transformer Winding Temperature Monitoring System Based on Fiber Bragg Grating
Authors: Jia Danping , Yao Zuoying , Li Chunhua 
Published: November 2017

Abstract
High temperature is one of the important reasons causing the fire in power system. Therefore, the early warning could be told by monitoring the temperature and effective measures could be taken. The corresponding accidents will be reduced. The traditional transformer winding temperature monitoring methods are electrical signal measurement and infrared measurement. Such electrical signal sensors are vulnerable to the interference of electromagnetic field, so the result of measurement is bad. Multiplexing optical path is unable to be realized by fluorescence thermometry. The optical fiber Bragg grating technology is used on temperature monitoring of transformer winding in this paper. The transformer winding temperature monitoring system based on optical fiber Bragg grating includes three parts: Temperature monitoring program based on the LabVIEW programming language, a fiber Bragg grating wavelength demodulation system and FBG temperature sensor. When the temperature of the transformer windings changes, the central wavelength of the fiber Bragg grating changes accordingly. Broadband ASE light source emits light, which is transmitted from the optical circulator into the fiber Bragg grating. The reflected light gets into the wavelength demodulation module again through the optical circulator. Wavelength demodulation module could transmit the center wavelength of the spectral reflectance information, peak power information to the computer. The computer analyzes, processes the information and displays the data with XY graph by calling the LabVIEW program. The program can get center wavelength values at the same time then get temperature by fitting function and display temperature on the front panel of the program. The fitting line of linearity between center wavelength of optical fiber Bragg grating sensor and temperature is above 0.95. The function reveals a good linearity which fitting the wavelength changes of the optical fiber Bragg grating during this temperature range. The experimental results show that the system has high accurate measurement which meeting high precision requirements of transformer winding temperature.
Intelligent Detection of Facial Expression based on Image
Authors: Zhu Shaoping , Xiao Yongliang 
Published: November 2017

Abstract
Human facial expressions detection plays a central role in pervasive health care and it is an active research field in computer vision. In this paper, a novel method for facial expression detection from dynamic facial images is proposed, which includes two stages of feature extraction and facial expression detection. Firstly, Active Shape Model (ASM) is used to extract the local texture feature, and optical flow technique is determined facial velocity information, which is used to characterize facial expression. Then, fusing the local texture feature and facial velocity information get the hybrid characteristics using Bag of Words. Finally, Multi-Instance Boosting model is used to recognize facial expression from video sequences. In order to be learned quickly and complete the detection, the class label information is used for the learning of the Multi-Instance Boosting model. Experiments were performed on a facial expression dataset built by ourselves and on the JAFFE database to evaluate the proposed method. The proposed method shows substantially higher accuracy at facial expression detection than has been previously achieved and gets a detection accuracy of 95.3%, which validates its effectiveness and meets the requirements of stable, reliable, high precision and anti-interference ability etc.
Travel Time Prediction Based on Pattern Matching Method
Authors: Zhao Jiandong , Xu Feifei , Liu Wenhui , Bai Jigen , Luo Xiaoling 
Published: November 2017

Abstract
The microwave detection technology has become an effective tool for monitoring highway traffic flow in China. The cross section traffic data collected provides models opportunity for travel time prediction. However, the sparseness of data somewhat constrains the prediction accuracy. To tackle this problem, the paper presents a highway travel time prediction algorithm based on pattern matching method. First, a pattern library is established by choosing traffic volume and speed as its certain state components and time as its uncertain state component. Then, the space-time two-dimensional linear interpolation method is used to calculate the mean speed and subsequently the travel time. Finally, similar patterns are obtained using K Nearest Neighbor approach and predicted travel time is calculated by the Weighted Average method. The case study shows that the pattern matching method for travel time prediction based on microwave detection data produces sufficient accuracy, which solves the problem of sparse detectors effectively.
Object Tracking Based on Machine Vision and improved SVDD Algorithm
Authors: Wang Yongqing , Zhang Yanzhou 
Published: November 2017

Abstract
Object tracking is an important research topic in the applications of machine vision, and has made great progress in the past decades, among which the technique based on classification is a very efficient way to solve the tracking problem. The classifier classifies the objects and background into two different classes, where the tracking drift caused by noisy background can be effectively handled by one-class SVM. But the time and space complexities of traditional one-class SVM methods tend to be high, which makes it do not scale well with the number of training sample, and limits its wide applications. Based on the idea proposed by Support Vector Data Description, we present an improved SVDD algorithm to handle object tracking efficiently. The experimental results on synthetic data, tracking results on car and plane demonstrate the validity of the proposed algorithm.
Automated Telehealth System for Fetal Growth Detection and Approximation of Ultrasound Images
Authors: Jatmiko Wisnu , Habibie Ikhsanul , Ma’sum M. Anwar , Rahmatullah Robeth , Satwika I Putu 
Published: November 2017

Abstract
One of the most profound use of ultrasound imaging is fetal growth monitoring. Conventionally, physicians will perform manual measurements of several parameters of the ultrasound images to draw some conclusion of the fetal condition by manually annotating the fetal images on the ultrasound device interface. However, performing manual annotation of fetal images will require significant amount of time considering the number of patients an obstetrician can have. In this paper, an integrated automatic system for fetal growth monitoring and detection is proposed. This system will be able to automatically measuring the parameters of fetal head, abdomen, femur, and humerus. In addition to automated image detection, we also propose an integrated telehealth monitoring system to provide better access of ultrasound monitoring for patients that lives in rural areas. A new approach of fetal image detection is also proposed by using AdaBoost.MH boosting algorithm that is combined with an improved efficient Hough Transform for detecting ellipse-like organs such as head and abdomen. Experiments of the method are tested on real ultrasound image dataset. The detection was applied on 2D ultrasound images to perform fetal object measurement to approximate the Head Circumference (HC) and Biparietal Diameter (BPD), Femur Length (FL), and Humerus Length (HL).
Construction of Traceability System for Maintenance of Quality and Safety of Beef
Authors: Zhang Wanli , Yang Xiaoying , Song Qixiang 
Published: November 2017

Abstract
In order to allow consumers to trace to the whole process of beef breeding, production and processing after purchasing beef, to achieve smart, fast and practical goals for quality and safety of beef traceability system, B / S (Browser / Server) mode architecture is used to build quality beef traceability system based on RFID technology in this paper. First, the system is analyzed, the structure and database system are designed and the key technologies to achieve traceability system is explored, including the individual identification of cattle, data encoding, data synchronization technology between the business and the central database and multi-platform tracing techniques. Through the use of RFID technology, all aspects of information for each head of beef cattle from breeding, slaughtering, processing, cold chain storage and transportation can be tracked and traced, and a quality retrospective file which can be queried on the Internet network for each head of beef cattle is formed. Solve the problem which the origin of beef production can be quality controlled and is traceability. The system implements safety warning function for cattle breeding epidemic and product quality, so it can ensure the safety of beef products from all aspects of production management. Finally, the renderings of system implementation are gained.
WSN Based Thermal Modeling: A New Indoor Energy Efficient Solution
Authors: Zhao Yi , Gies Valentin , Ginoux Jean-Marc 
Published: November 2017

Abstract
In this paper, we proposed to use the Efficient Indoor Thermal Time Constant (EITTC) to characterize the indoor thermal response in old buildings. Accordingly, a low cost, energy-efficient, wide-applicable indoor thermal modeling solution is developed by combining Wireless Sensor Network (WSN) and Artificial Neural Network (ANN). Experiments on both prototype and building room showed consistent results that the combination of WSN and ANN can provide accurate indoor thermal models. A linear approximation of these models makes it possible to estimate the EITTC of building room. Statistical computations confirmed these estimations by showing a strong correlation between the model’s predicted EITTC and measured data. Thus the indoor thermal response under different indoor/outdoor conditions can be characterized. Finally, a model based adaptive heating Start/Shut control method is proposed and tested, with which, direct energy saving is achieved.
An Advanced Nonlinear Signal Model to Analyze Pulsation-Derived Photoplethysmogram Signals
Authors: Ozaki H. , Omura Y. 
Published: November 2017

Abstract
This paper proposes an advanced nonlinear signal model to handle the intrinsic nonlinearity of the pulsation of photoplethysmogram (PPG) signals and so permit their assessment. This model consists of three-different sinusoidal source signals and it successfully reproduces the primary harmonic, the second harmonic, the respiratory and the other spectra stemming from the interaction of the three source signals. We demonstrate the potential of the model in various experiments on PPG signals. It is discovered that the model has a great performance in that it characterizes PPG signals captured under various measurement conditions if nonlinearity is small. It is also demonstrated that the model successfully characterizes the post-alcohol-intake state and the post-physical-exercise state using the behavior of the nonlinear factors, which should be very useful for alcohol-intake detection. The remaining issues of the model are also addressed.
Building a Parallel Decision-Making System Based on Rule-Based Classifiers in Molecular Robotics
Authors: Adi Wibowo , Sekiyama Kosuke 
Published: November 2017

Abstract
Controlled drug delivery based on cellular components can be achieved by exploiting diseasespecific properties, but these require a rapid, sensitive, and selective method of detection in a biomolecular system. We propose a parallel decision-making system for disease detection and classification based on the fact that DNA computing along with biomolecular systems can be subjected to massively parallel processing. We designed and programmed a DNA strand displacement reaction to implement rule-based classifiers from a binary tree classification as a decision-making system. In our framework for molecular robot development, the system components of molecular robots and simple classifier rules were used to alleviate the computational burden. The design consists of a basic model that generates rule-based classifier gates in several binary tree and cancer classifications based on micro (mi)RNA expression. Simulation results showed that detection and classification were rapid using this system. Moreover, experiments using the synthetic miRNA hsa-miR-21 demonstrated that our model could be a feasible decision-making system for drug delivery.
Automatic Segmentation of Brain Tumor Magnetic Resonance Imaging Based on Multi-Constrains and Dynamic Prior
Authors: Erlin Liu , Meng Wang , Jianfeng Teng , Jianjian Li 
Published: November 2017

Abstract
The most difficult and challenging problem in medical image analysis is image segmentation. Due to the limited imaging capability of magnetic resonance (MR), the sampled magnetic resonance images from clinic always suffer from noise, bias filed (also known as intensity non-uniformity), partial volume effects and motive artifacts. In additional, for the complex shape boundary and topology of brain tissues and structures, segmenting magnetic resonance image of brain tumor fast, accurately and robustly is very difficult. In this paper, we propose an image segmentation algorithm based on multiconstrains and dynamic prior. Through introducing a novel big scale constrain into Markov random filed model from magnetic resonance image we realize automatic segmentation under the principle of maximum a Posterior and a modified expectation-maximization algorithm according to the Bayesian frame. Finally, a set of human body detection and tracking experiments are designed to demonstrate the effectiveness of the proposed algorithms.
Target Recognition Based on Rough Set with Multi-Source Information
Authors: Zengping Cheng 
Published: November 2017

Abstract
As the attributes provided by multi-source information can be used to distinguish between the different species of targets, attributes recognition becomes the most important work in target recognition. In this paper, a new method for attributes recognition was proposed with rough set theory. It used a new way to described the target with a information system consisting of four elements, reduced the attribute value according to the mission requirements, valuated the attribute based on the degree of importance, and recognized targets by the sum of valuation. Finally, a set of experiments were designed to demonstrate the effectiveness of the proposed method. Furthermore, the factors that would affect the performance of the recognition system were discussed.
Enhanced Iris Recognition based on Image Match and Hamming Distance
Authors: Xiaoxing Gao , Sumin Feng , Han Cui 
Published: November 2017

Abstract
Iris recognition and favor because of its high recognition rate, noninvasive and simple algorithm and other advantages, in a variety of biometric identification technology is very prominent. The iris texture feature extraction is the core of the iris recognition algorithm. Fractal geometry theory provides new ideas and methods to express nonlinear image information, the fractal dimension is an important parameter of fractal geometry, is a measure of complexity of irregular change, covering blanket dimension can better reflect the graphics changes in different resolution characteristics; missing is the fractal dimension and independent statistics, is a supplement to the fractal dimension, overcome the different texture characteristics may have the same fractal dimension of the problem. This paper presents a blanket and missing items based on the combination of texture feature extraction algorithm, can make full use of radiation in different resolution iris texture information and texture, classification ability make feature matrix has a better. Iris matching is the key of iris recognition. How to effectively carry out the matching of the iris code matrix is the decisive step in iris recognition. In this paper based on the normalized correlation classifier, the matching method of cyclic shift, eliminated from the same eyes of different iris image due to differences in rotation caused, improve matching accuracy.
Research on Camera-Based Human Body Tracking using Improved CAM-SHIFT Algorithm
Authors: Li Jiude 
Published: November 2017

Abstract
Camera-based human body detection and tracking is an important research subject of computer vision, which has a widely used in the field of military and civil. In this paper, we focus on the technology of human body tracking based on improved camshaft algorithm. Firstly, we introduce some common image noise reduction algorithms. By combination the frame difference and background subtraction methods, an improved moving target detection algorithm is proposed, by which the whole region of target can be detected. Then, with the analysis of particle filtering and traditional Cam-shift algorithm, we introduce a new human body tracking method that is able to choose the target automatically due to the detection result. On the basis of the detection and tracking results, the algorithm of motion parameter estimation is analyzed. Finally, a set of human body detection and tracking experiments are designed to demonstrate the effectiveness of the proposed algorithms.
Investigation on Photoelectric Theodolite data Processing and Random Errors Model
Authors: Hua Xiang , Zhang Jinjin , Lei Bin 
Published: November 2017

Abstract
Measure error ofphotoelectric theodolite would influence result precision when tracking flight target. This paper researches error problem of testing. It analyses all the causes of error forming, divides them to several sorts: system error, random error and outlier error, then provides resolution method to each sort. Especially for random error, it builds an error model, analyses the properties of unbiased, equal variance and uncorrelated, conducts best error estimate, discuss the relationship between choosing and effect of kernel function and smooth parameter. While it researches measure theory of coplanar intersection and dis-coplanar intersection of photoelectric theodolite, derives a series of measure formulas, builds random error model respectively, and analyses the relationship of effect actors. By comparing simulation of model with experiment measurement, the result shows the error model and processing method is correct.
Novel Multi-Class SVM Algorithm for Multiple Object Recognition
Authors: Wang Yongqing , Zhang Yanzhou 
Published: November 2017

Abstract
Object recognition is a fundamental task in applications of computer vision, which aims at detecting and locating the interested objects out of the backgrounds in images or videos, and can be originally formulated as a binary classification problem that can be effectively handled by binary SVM. Although the binary technique can be naturally extended to solve the multiple object recognition, which are known as one-vs.-one and one-vs.-all techniques, but the scalability of traditional methods tend to be poor, and limits the wide applications. Inspired by the idea presented by Multi-class Core Vector Machine, we propose a novel Multi-class SVM algorithm, which achieves excellent performance on dealing with multiple object recognition. The simulation results on synthetic numerical data and recognition results on real-world pictures demonstrate the validity of the proposed algorithm.
Breath Acetone-Based Non-Invasive Detection of Blood Glucose Levels
Authors: Thati Anand , Biswas Arunangshu , Chowdhury Shubhajit Roy , Sau Tapan Kumar 
Published: November 2017

Abstract
There has been a constant demand for the development of non-invasive, sensitive glucose sensor system that offers fast and real-time electronic readout of blood glucose levels. In this article, we propose a new system for detecting blood glucose levels by estimating the concentration of acetone in the exhaled breath. A TGS822 tin oxide (SnO2) sensor has been used to detect the concentration of acetone in the exhaled air. Acetone in exhaled breath showed a correlation with the blood glucose levels. Effects of pressure, temperature and humidity have been considered. Artificial Neural Network (ANN) has been used to extract features from the output waveform of the sensors. The system has been trained and tested with patient data in the blood glucose ranges from 80 mg/dl to 180 mg/dl. Using the proposed system, the blood glucose concentration has been estimated within an error limit of ±7.5 mg/dl.