bluuebunny/crossref_metadata_2025 huggingface dataset:
Spectrum Sensing Algorithm Based on ATSC DTV Signal Structure
Authors: Cong Yuliang , Xing Lijuan , Bi Jiaxing
Published: November 2017

In order to improve spectrum utilization rate, people need a method to sense the spectrum hole. This article puts forward a spectrum sensing algorithm, which uses ATSC DTV signal structural characteristics. First, we should sample the input signal based on structural characteristics. Second, these signal samples should be separated into different sets and accumulate these samples. Third, we execute the correlation operation to accumulation result and synchronization. Finally, decision can be got by comparing the test statistics with threshold. According to the simulation result, we can get the conclusion that signal structural characteristics based on spectrum sensing method can realize spectrum sensing well enough.
Research on Longitudinal Control Algorithm for Flying Wing UAV Based on LQR Technology
Authors: Li Yibo , Chen Chao , Chen Wei
Published: November 2017

Linear Quadratic Regulator (LQR) is widely used in many practical engineering fields due to good stability margin and strong robustness. But there is little literature reports the technology that has been used to control the flying wing unmanned aerial vehicles (UAV). In this paper, aiming at the longitudinal static and dynamic characteristics of the flying wing UAV, LQR technology will be introduced to the flying wing UAV flight control. The longitudinal stability augmentation control law and longitudinal attitude control law are designed. The stability augmentation control law is designed by using output feedback linear quadratic method. It can not only increase the longitudinal static stability, but also improve the dynamic characteristics. The longitudinal attitude control law of the flying wing UAV is designed by using command tracking augmented LQR method. The controller can realize the control and maintain the flight attitude and velocity under the condition without breaking robustness of LQR. It solves the command tracking problems that conventional LQR beyond reach. Considering that some state variables of the system are difficult to obtain directly, a control method that called quasi-command tracking augmented LQR is designed by combing with the reduced order observer, it retains all the features of command tracking augmented LQR and more suitable for the application of practice engineering. Finally, the control laws are simulated under the environment of Matlab/Simulink. The results show that the longitudinal control laws of the flying wing UAV which are designed based on LQR can make the flying wing UAV achieve satisfactory longitudinal flying quality.
Design of Linear-Phase Filter Banks with Multiplier-Less Lattice Structures
Authors: Chen Li , Wang Xiyan , Peng Ronghua , Yang Fu
Published: November 2017

In this paper, a new multiplier-less algorithm is proposed for the design of perfectreconstruction linear-phase (PR LP) filter banks by using multiplier-less lattice structures. The coefficients in the multiplication operations have been replaced with limited number of additions and the computational complexity is reduced significantly. The property of perfection reconstruction, however, is preserved regardless the multiplier-less approximation of lattice structures in the factorization of polyphase matrix. The coefficients in the 2x2 rotation matrices of the lattice structures are expressed as sum-of-powers-of-two (SOPOT) coefficients in the parameterization processes. By using the multiplier-less rotation matrices, the unitary matrices are constructed for the lattice factorization of perfect-reconstruction linear-phase filter banks. Design examples of 5-channel and 8-channel multiplier-less linear-phase filter banks are included to validate the algorithm and implementation.
Analysis of Key Fuel Properties Inside Combination Electronic unit Pump Fuel Pipeline
Authors: Hayat Qaisar , Liyun Fan , Ma Xiuzhen , Bingqi Tian , Farouk Naeim
Published: November 2017

Because of high pressure fluctuations in Combination Electronic Unit Pump (CEUP) system during fuel injection cycles, physical properties of diesel fuel including density, acoustic wave speed and bulk modulus also vary. These physical properties of fuel have significant importance in predicting the fuel injection behavior and its optimization. A 1D viscous damped mathematical model of wave equation has been developed in MATLAB not only to simulate the pressure fluctuations in fuel pipeline at various operating conditions of diesel engine but also to determine and analyze the variations in these physical properties of diesel fuel.
A Climbing Hold With an Integrated Three Dimensional Force Measurement and Wireless Data Acquisition
Authors: Lechner B. , Filzwieser I. , Lieschnegg M. , Sammer P.
Published: November 2017

A three dimensional force measurement system was integrated in a climbing hold. The force measurement system consisted of three orthogonally mounted platform load cells. Data acquisition and power supply of the load cells was performed by a wireless data acquisition system ViFDAQ. Data was transferred by ViFDAQ to a nearby laptop by a wireless Bluetooth connection. Measurements were done during a regional climbing competition. The instrumented climbing hold was mounted in one of the climbing routes. The forces the athletes exerted on the climbing hold to reach the top were measured. Force data was analyzed and compared to the actual ranking of the athlete in this competition.
A Study of X-Ray Machine Image Local Semantic Features Extraction Model based on bag-of-words for Airport Security
Authors: Zhang Ning , Zhu Jinfu
Published: November 2017

The aviation security at the airport has been faced with increasingly severe situations since the 9-11 event. It’s of utmost importance to train airport X-ray machine screener’s image recognition competency. So they can prevent terrorists from bringing dangerous articles in their carry-on or checked bags. However, usually the luggages are placed in different positions and the density & volume of articles differ greatly. As a result, dangerous articles show a variety of X-ray image features. It’s easy for the confused screeners to miss or incorrectly detect dangerous articles. This has been a hidden danger for civil aviation safety. For image recognition improvement, the researcher analyzed the visual semantics of dangerous goods images and applied a local semantic features extraction method. After classification and summarization, the method was used to train the screeners for particular image recognition. The comparison showed the improved accuracy and efficiency of image recognition for the screeners and demonstrated a satisfactory effect.
Survey of Semantic Similarity Measures in Pervasive Computing
Authors: Guessoum Djamel , Miraoui Moeiz , Tadj Chakib
Published: November 2017

Semantic similarity measures usage is prevalent in pervasive computing with the following aims: 1) to compare the components of an application; 2) to recommend and rank services by degree of relevance; 3) to identify services by matching the description of a query with the available services; 5) to compare the current context with already known contexts. The existing works that apply semantic similarity measures to pervasive computing focus on one particular issue. Furthermore, surveys in this domain are limited to the recommendation or discovery of context-aware services. In this article, we therefore present a survey of context-aware semantic similarity measures used in various areas of pervasive computing.
Tracking of Moving Target Based on Video Motion Nuclear Algorithm
Authors: Xiaojun Wang , Feng Pan , Weihong Wang
Published: November 2017

Moving target detection and tracking algorithm research content is very broad and complex applications, without and different target features directly affects the detection of selected tracking algorithm. So far still does not exist a universal algorithm for perfect can be suitable for various applications, so the detection and tracking of moving targets is still a valuable research subject of. The research work in this paper is in the field, the moving target detection spatiotemporal correlation and difference contour tracking algorithm based on a fixed background. The algorithm in the background under the condition of fixed to pay a smaller time complexity, the target detection and tracking has a good effect, so it has higher application value. Based on solving the detection and location of moving target tracking in real-time and accuracy requirements, a new moving target detection spatiotemporal correlation and difference contour tracking scheme based on the practical implementation, at the same time analysis and the experimental results are given. In the moving target tracking, tracking method is mainly traditional correlation method target based on template matching. The matching process is time consuming, so the actual use of more of the improved algorithm of correlation method, the improved algorithm attempts to improve the efficiency of feature matching and search range, and also achieved a certain effect, the some excellent tracking algorithm. This paper presents an improved active contour model tracking algorithm, improve the tracking efficiency and quality, the algorithm first from the frame difference detection results to find the moving target coarse contour, and then the convergence of coarse contour by using improved Snake algorithm, the right edge to get the target in the course of the campaign, in order to achieve the tracking of moving objects.
Characterization of a Needle-Type Giant Magnetoresistance Sensor for Detection of Escherichia Coli’S Magnetic Marker
Authors: Shirzadfar Hamidreza , Nadi Mustapha , Kourtiche Djilali , Yamada Sotoshi , Shahabi Payman
Published: November 2017

In the recent years, the introduction and development of simple and portable sensors has been the focus of researchers in nearly all scientific domains, particularly in the biomedical settings. Giant magnetoresistance (GMR) provides a cutting-edge sensor technology. The GMR-based sensors are capable to affordably and sensitively detect and quantify micro- and nano-magnetic particles, even in very weak magnetic fields.

In this paper, we introduce a highly sensitive needle-type GMR-based sensor, designed for the identification and quantification of Escherichia coli O157:H7 bacteria covered by superparamagnetic beads, Dynabeads® MAX E.coli O157. The sensor characteristics, measurement system setup and the properties of the magnetic marker solution are discussed in detail.
Robust Visual Tracking Based on Support Vector Machine and Weighted Sampling Method
Authors: Xiaoxing Gao , Feng Liu
Published: November 2017

Visual tracking algorithm based on binary classification has become the research hot issue. The tracking algorithm firstly constructs a binary classifier between object and background, then to determine the object’s location by the probability of the classifier. However, such binary classification may not fully handle the outliers, which may cause drifting. To improve the robustness of these tracking methods, a novel object tracking algorithm is proposed based on support vector machine (SVM) and weighted multi-sample sampling method. Our method constructs a classifier by sampling positive and negative samples and then to find the best candidate that has the largest response using SVM classifier. What’s more, the proposed method integrates weighted multi-instance sampling method, which can consider the sample importance by the different weights. The experimental results on many sequences show the robustness and accuracy of the improved method. The proposed target tracking algorithm in video target tracking with a variety of classic popular tracking algorithm, better able to achieve robust target tracking, but also in the infrared video, the infrared target tracking is also has the advantages of stable and accurate.
A Comprehensive Analysis on Sizing of Solar Energy Harvester Elements for Wireless Sensor Motes
Authors: Hemalatha R. , Ramaprabha R. , Radha S.
Published: November 2017

Wireless sensor networks (WSN) have limited lifetime due to on board battery. The lifetime can be improved by energy harvesting solutions. In this paper the solar energy harvester requirements for TelosB mote has been analyzed and calculated. Photovoltaic (PV) panel and battery sizing requirements are calculated by assuming that the mote follows SMAC and TDMA-MAC schedule for image communication. The calculations are validated by comparing it with the parameters calculated from the real time current consumption measurement of the mote. Lifetime has been predicted with the physical design of energy harvester. The analysis confirms that the lifetime of the network can be increased to a greater extent, by proper sizing of the harvester and efficient utilization of the available energy.
Review on Modeling and Controller Design of Hydraulic Actuator Systems
Authors: Salleh S. , Rahmat M.F. , Othman S.M. , Danapalasingam K. A.
Published: November 2017

The purpose of this paper is to review the literature on modeling and previous control strategies of the hydraulic actuator system proposed by most of the researchers around the world. Before comes to the main discussion, some background information related to hydraulic actuator will be presented. This review includes a short summary and conclusion for hydraulic actuator system. The repercussion of this review is for future inventions of a better and robust hydraulic actuator system.
Recommender Algorithms Based on Boosting Ensemble Learning
Authors: Lili Cheng
Published: November 2017

This article introduces ensemble learning algorithms in recommender systems, and in boosting algorithm framework of this article, shows how to filter the basic recommendation algorithm according to the characteristics of boosting algorithm. By comparing the rational choice of the two recommended boosting algorithm is applied to the frame. And then it determines the main parameters of the algorithm through the experiments, ultimately to obtain a more effective integration of the recommendation algorithm. Experimental results on Netflix validate the effectiveness of the proposed algorithm.
Iris-Face Fusion and Security Analysis Based on Fisher Discriminant
Authors: Wang Qihui , Zhu Bo , Liu Yu , Xie Lijun , Zheng Yao
Published: November 2017

With the development of society and science technology, the information security becomes more and more important for people and nation. In this paper, we focus on the technology of iris-face fusion in multi-mode sense in order to find an algorithm to get a better recognition performance. Firstly, we introduce the feature extraction of iris and face, and fusion the features using different algorithms. By simulation, we find out the recognition performance with different fusion methods, which provide some new conclusions. In order to improve the performance of recognition system, we classify these features with fisher discriminant analysis again. Then we analyze the security of recognition system from the aspect of influence of leak in biology features. Finally, experiments were designed to demonstrate the effectiveness of the proposed algorithms.
Aptive Image Segmentation based on Saliency Detection
Authors: Linlin Shui
Published: November 2017

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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.
A Novel Knowledge-Compatibility Benchmarker for Semantic Segmentation
Authors: Dewanto Vektor , Aprinaldi  , Ian Zulfikar , Jatmiko Wisnu
Published: November 2017

The quality of a semantic annotation is typically measured with its averaged class-accuracy value, whose computation requires scarce ground-truth annotations. We observe that humans accumulate knowledge through their vision and believe that the quality of a semantic annotation is proportionally related to its compatibility with the vision-based knowledge. We propose a knowledge-compatibility benchmarker, whose backbone is a regression machine. It takes as input a semantic annotation and the vision-based knowledge, then outputs an estimate of the corresponding averaged class-accuracy value. The knowledge encodes three kinds of information, namely: cooccurrence statistics, scene properties and relative positions. We introduce three types of feature vectors for regression. Each specifies the characteristics of a probability vector that captures the compatibility between an annotation and each kind of the knowledge. Experiment results show that the Gradient Boosting regression outperforms the ν -Support Vector regression. It achieves best performance at an R2-score of 0.737 and an MSE of 0.034. This indicates not only that the vision-based knowledge resembles humans’ common sense but also that the feature vector for regression is justifiable.
A Study on Optimization Methods of X-Ray Machine Recognition for Aviation Security System
Authors: Zhang Ning
Published: November 2017

Traditional X-ray machine image recognition methods for airport security system have difficulties in recognition and are prone to result in recognition errors due to the impact of placing angle, density and volume of detected objects. This paper accurately describes the image features of X-ray machine visual image, carries out SVM classification after a visual dictionary is formed and enhances the accuracy of image discrimination by means of robust acceleration. The experimental results indicate that both identification efficiency and accuracy are improved to some extent.
Performance Bound Analysis on Transmit Antennas Selection Systems Considering Erroneous Channel Information
Authors: Zhuang Mingjie
Published: November 2017

We consider multiple-inputs-single-output (MISO) systems equipped with N transmit and single receive antennas, and concentrate on transmit antenna selection (TAS) for flat Rayleigh fading channels. We assume perfect channel state information (CSI) at receiver and imperfect CSIs through an feedback link at transmitter. The metric of TAS are those with the largest instantaneous signal-to- noise ratio (SNR), the best 2 out of N available transmit antennas always are selected. We use Alamouti encoding scheme and derive the probability density function (PDF) of Frobenius norm of column vector of the channel matrix. Using the known PDF we can derive the joint PDF of order statistics channel for the subseti, j. Assuming that the transmitted signals employ Mary phase-shift keying (MPSK) constellation, we consider the impact of imperfect antenna selection subsets on system performance, and explicitly derive a closed-form BER expression of Chernoff upper bounds (CUB). For two special cases with 2 out of N =3 and 4 possible transmit antennas, we analyses asymptotic performance of selected subsets. Numerical and simulating results show that we can achieve full spatial diversity with antenna selection in the presence of imperfect CSI and the largest ordinal number antenna selected as if all the transmit antennas were used; there may be some loss in the diversity order without the best transmit antenna.
Design of an Intelligent Car for Searching Track and Avoiding Obstacles
Authors: Yang Degang , Liu Tingya , Hu Chunyan
Published: November 2017

With the rapid development of informational and intelligent technology, the mobile robot has become an important branch of robot. In this paper, through UP-InnoSTARTM robot kits for the chassis and MultiFLEXTM 2-PXA270 controller for the control core, we design an intelligent car. It uses infrared proximity sensors to detect obstacles and gray-scale sensors to search the track. And the main functions are: autonomously recognizing the trajectory, searching it steadily, making quick judgment and timely responses when meeting obstacles. In addition, a simple laboratory experiments are also proposed to illustrate the practicability of our design.
Development of Wireless Sensor Network using Bluetooth Low Energy (BLE) for Construction Noise Monitoring
Authors: Hughes Josie , Yan Jize , Soga Kenichi
Published: November 2017

In this paper the development of a Wireless Sensor Network (WSN) for construction noise identification and sound locating is investigated using the novel application of Bluetooth Low Energy (BLE). Three WSNs using different system-on-chip (SoC) devices and networking protocols have been prototyped using a Raspberry Pi as the gateway in the network. The functionality of the system has been demonstrated with data logging experiments and comparisons has been made between the different WSN systems developed to identify the relative advantages of BLE. Experiments using the WSN for vehicle noise identification and sound location have further demonstrated the potential of the system. This paper demonstrates the versatility of a BLE WSNs and the low power consumption that is achievable with BLE devices for noise detection applications.x
Intelligent Neural Network Control Strategy of Hydraulic System Driven by Servo Motor
Authors: Yu Ma
Published: November 2017

A novel intelligent neural network control scheme which integrates the merits of fuzzy inference, neural network adaptivity and simple PID method is presented in this paper. This control method overcomes the defects existed in the traditional variable frequency induction motor driven hydraulic source, such as slow response, poor control precision, easy to overshoot. Permanent magnet synchronous motor driven constant pump hydraulic system is designed instead of common motor, energy saving, fast response and easy to realize closed loop control. System uses the structure of the combination of neural network control and RBF network online identification. The parameters of the controller are optimized by PSO algorithm offline and error back propagation (BP) algorithm offline, and a RBF network is built to identify the system online. The hydraulic power system’s control simulation experiments are conducted, and the experimental results at the typical working conditions of the hydraulic source show that the controller and its optimization algorithm can effectively improve the system performance, and the system has no steady-state error, good dynamic performance and good robustness, superior to conventional fuzzy controller and PID controller.
Compensation of Capacitive Differential Pressure Sensor using Multi Layer Perceptron Neural Network
Authors: Moallem Payman , Abdollahi Mohammad Ali , Hashemi S. Mehdi
Published: November 2017

Capacitive differential pressure sensor (CPS), which converts an input differential pressure to an output current, is extremely used in different industries. Since the accuracy of CPS is limited due to ambient temperature variations and nonlinear dependency of input and output, compensation is necessary in industries that are sensitive to pressure measurement. This paper proposes a framework for designing of CPS compensation system based on Multi Layer Perceptron (MLP) neural network. Firstly, a test bench for a sample popular CPS is designed and implemented for data acquisition in a real environment. Then, the gathered data are used to train different MLPs as CPS compensation system which inputs are the output current of CPS and temperature value, and the output is compensated current or computed pressure. The experimental results for an ATP3100 smart capacitive pressure transmitter show the trained three layers MLP with Levenberg- Marquardt learning algorithm could effectively compensate the output against variation of temperature as well as nonlinear effects, and reduce the pressure measurement error to about 0.1% FS (Full Scale) , over the temperature range of 5 ~ 60 ° C.
Automatic Recognition of Facial Expression Based on Computer Vision
Authors: Zhu Shaoping
Published: November 2017

Automatic facial expression recognition from video sequence is an essential research area in the field of computer vision. In this paper, a novel method for recognition facial expressions is proposed, which includes two stages of facial expression feature extraction and facial expression recognition. Firstly, in order to exact robust facial expression features, we use Active Appearance Model (AAM) to extract the global texture feature and optical flow technique to characterize facial expression which is determined facial velocity information. Then, these two features are integrated and converted to visual words using “bag-of-words” models, and facial expression is represented by a number of visual words. Secondly, the Latent Dirichlet Allocation (LDA) model are utilized to classify different facial expressions such as “anger”, “disgust”, “fear”, “happiness”, “neutral”, “sadness”, and “surprise”. The experimental results show that our proposed method not only performs stably and robustly and improves the recognition rate efficiently, but also needs the least dimension when achieves the highest recognition rate , which demonstrates that our proposed method is superior to others.