bluuebunny/crossref_metadata_2025 huggingface dataset:
Vibration Based Health Monitoring Of Honeycomb Core Sandwich Panels Using Support Vector Machine
Authors: Gupta Saurabh , Satpal Satish B , Banerjee Sauvik , Guha Anirban
Published: February 2018

Honeycomb sandwich structures are extensively used in aerospace, aeronautic, marine and automotive industries due to their high strength-to-weight ratios, high energy absorption capability and effective acoustic insulation. Unfortunately, either presence of disbond along the skin-core interface or emergence of disbond due to repeated loading, aging or an intensive load can jeopardize the integrity and safety of the whole structure The current work presents a new array based technique for health monitoring of these structures using support vector machine (SVM). The proposed technique is first used on simulated mode shape data of the structure and then the technique is validated using experimental mode shape data. The experimental set up has been developed in laboratory and Laser Doppler Vibrometer (LDV) is used to extract the experimental mode shapes. The results have been obtained using both support vector classification and regression analysis and it is found that that the former is better at prediction of debond location
Detection Of Moving Object By Fusion Of Color And Depth Information
Authors: Zhang T. T. , Zhao G. P. , Liu L. J.
Published: February 2018

Moving object detection based on color information is easily affected by illumination changes and shadows in complex scenes. Depth information can provide complementary information. In the paper, a novel method is presented by using color and depth information. Firstly, we improve the codebook algorithm by fusing the depth information as the fourth channel in the code word. Next, a compensation factor algorithm is presented to make the edges accurate. So the final detection result can be obtained by logic operation. Experiments adapt the public datasets, and experimental results show that the proposed method can successfully cope with the limitations of color-based or depth-based detection
Experimental And Theoretical Validation Method For Estimation Of Straightness Deviation And Associated Uncertainty In Cnc-Cmm Measurement
Authors: Ali Salah H.R. , Khalafalla M.A.H. , Naeim Ihab H. , Zahwi Sarwat Z.A.
Published: February 2018

Geometrical deviation in intelligent metrology system is an important factor in precision engineering. Estimating the deviation and associated uncertainty in straightness feature remains a necessary requirement to ensure highly accurate metrology method especially in CNC-CMM measurement. Optimization in straightness measurement using soft algorithm techniques is a widely encountered solution in coordinate metrology. In this work, straightness deviation has been measured precisely in 2D and in 3D using slab surface by CNC-CMM at the Egyptian national metrology institute (NIS). The work has been investigated experimentally and theoretically analyzed. The straightness deviation and its uncertainty results from 2D measurement have been estimated experimentally. The straightness deviation result of the 3D discrete points measurements have been analyzed theoretically using the standard Particle Swarm Optimization (PSO) algorithm. The probability density distribution of the measured straightness was calculated using a Sequential Monte Carlo (SMC) technique. A probability density histogram is obtained with an expanded measurement uncertainty based on coverage factor k equals 2 providing confidence level 95%. The computational results of straightness deviation and expanded uncertainty have been also estimated for 3D discrete point measurements. Comparison with relevant report showed agreement with our result since we used a computationally efficient modified SMC technique and PSO algorithm. The results of the straightness deviations and associated expanded uncertainties for both 2D and 3D measurements have been discussed and compared. They were found to be suitable for the proposed validation method. This work confirms that the developed strategic alternative methodology can be achieved successfully. Systematic acquisition of CNC-CMM data is another contributing factor for improving the required accuracy in measurement. Moreover, the confidence in the proposed hybrid validation method for estimating the straightness deviation with associated uncertainty has been achieved
Errors of Manometric Co2 Sorption Experiments On Coal Caused By Accuracy Of Pressure Sensor
Authors: Renxia Jiang , Hongguan Yu , Li Wang , Lihui Chen
Published: February 2018

The accuracy of supercritical CO2 sorption determination on coal is important for estimating the CO2 sequestration potential of coal bed using CO2 injection into deep unminable coal seams. Based on the CO2 density change and CO2 sorption data of three selected coal samples, this paper investigates the effect of pressure-sensor accuracy on measurements of CO2 adsorption isotherms on coals with manometric equipment. The characteristic density-change of CO2 in proper pressure range determines a greater influence of pressure error on CO2 adsorption data on coal. The sensor accuracy has significant influence on Excess sorption increment of CO2 on coal in medium pressure (7-12 MPa), and on CO2 Excess-sorption isotherm at medium-and high pressure. The medium pressures with a greater CO2 density-change at a constant temperature in the sample cell are probably inevitable. A lower experiment-temperature will results in a greater measurement error of the sorption caused by pressure sensor accuracy. The pressure sensor accuracy may result in the less reproducibility and repeatability of CO2 adsorption on coals at medium- and high pressure, and negative sorption increment and even negative adsorption
Filter Bank Design Using Multiple Prototype Approach For Variable Granularity Bands
Authors: Prema S Chris , K Maneesha
Published: February 2018

In this paper, a multiple prototype based filter bank is proposed for variable granularity bands. This method makes use of channel combiners for variable granularity bands from a cosine modulated filter bank. In the proposed method, the filter bank is generated from proper choice of prototype filters and different bandwidth combinations are generated from different prototypes, where M determines the granularity of the uniform bandwidth from individual prototypes. The condition for generation of variable granularity bands from multiple prototype filters is also defined. The method is found to have less complexity and distortions as compared to a single prototype approach using channel combiners. The method allows different combinations of the uniform filter banks to generate the filter banks with variable granularity for integer powers of two. Simulations are done to obtain varying bandwidth channels, and analyzed for different input signals with different fidelity parameters. The results are found to be comparable with existing methods
A Hybrid Fuzzy Morphology And Connected Components Labeling Methods For Vehicle Detection And Counting System
Authors: Fatichah Chastine , Buliali Joko Lianto , Saikhu Ahmad , Tena Silvester
Published: February 2018

A hybrid fuzzy morphology and connected components labeling method is proposed for detecting and counting the number of vehicles in an image taken from a traffic monitoring camera. A fuzzy morphology approach in image segmentation method is used in the system to achieve faster computation time compared to the supervised learning. The connected components labeling method is combined with a fuzzy morphology method to determine the region and number of objects in an image. The processing phases in the proposed system are image preprocessing, image segmentation, and vehicle detection and counting the number of vehicles. Images are captured from the traffic monitoring cameras installed in highways. Results from testing phase using thirty images with varying brightness, contrast, and quality taken from different cameras during daylight showed that the accuracy of the system in counting the number of vehicles is 78.21%.
Image Segmentation Algorithm Based On Color Features: Case Study With Giant Panda
Authors: Wang Hua , Xiao Jiang , Zhang Junguo
Published: February 2018

Color which is one of the basic features of the image is widely used in image processing. The choice of color space is a primary issue for the color image segmentation based on color features. In this paper, giant pandas are chosen as the research objects. In order to achieve good segmentation results, different color spaces and the corresponding algorithms are chosen for image segmentation according to the color characteristics of different background of panda images. There are three kinds of color spaces introduced in detail and its advantages and disadvantages for the giant panda image segmentation are also summarized in this paper
Prediction Of Sewage Quality Based On Fusion Of Bpnetworks
Authors: Wang Lijuan
Published: February 2018

Sewage treatment system is a complicated nonlinear system with multi-variables, chemical reaction, biological process and altered loads, hard to describe mathematically. Thus prediction of the effluent quality parameters of sewage treatment plant has being a challenge. In this paper we adopt fusion of two BP networks to predict sewage quality parameters with a popular process Cyclic Activated Sludge System (CASS). We take use of SVM (support vector machine) to classify the input data into two kinds, and train the corresponding BP networks with the two kinds of data. Before using SVM to classify the input data, PCA (principle component analysis) is used to analyze the correlation between sewage quality parameters. Then we predict the value of sewage quality parameters with the fusion results of the two BP networks. Test results of the case study show that fusion of BP networks not only can improve the stability of BP networks but also can improve the prediction accuracy
An Improved Multi-Objective Evolutionary Optimization Algorithm For Sugar Cane Crystallization
Authors: Meng Yanmei , Li Wenxing , Chen Qingwei , Yu Xian , Zheng Kangyuan , Lu Guancheng
Published: February 2018

The nature of optimization for intermittent sugar cane crystallization process is to obtain ideal crystals. One typical difficulty in crystallization optimization refers to the simultaneous effects of both seeding characters and process variables on the final crystal size distribution (CSD) parameters, including mean size (MA) and coefficient of variation (CV). And the application of traditional multiobjective evolutionary algorithm in crystallization process could not optimize all of them. Therefore, this paper puts forward a different multi-objective framework, and correspondingly, an improved optimization algorithm is applied to intermittent sugar cane crystallization. This method combines the elitist non-dominated sorting genetic algorithm (NSGA-II) with technique for order preference which is similar to an ideal solution (TOPSIS), and it provides a quantitative way to analyze the effect of both seed characteristics and process variables on the trade-off between MA and CV. Furthermore, the proposed algorithm has been adapted here to be compared with the NSGA-II, and the comparing results demonstrate better Pareto-optimal solutions of the novel approach
Random Key Pre-Distribution Scheme Based On Key Updating
Authors: Ling-Zhi Zhu , Rui He , Jun-Ling Zhang
Published: February 2018

A random key pre-distribution scheme based on key updating (RKKU) was proposed, which is effective in wireless sensor networks. Firstly, the base station will randomly distribute some keys, a hash function and some code slices to each node. Furthermore, the RKKU scheme compares with the information of some random key to find the same key, and computes the communication key between two sensor nodes with one-way hash function. Since the one-way hash function can ensure that the attacker cannot use the obtained communication key to decipher the source key, it affects only two nodes communicate with each other. To assure the communication security, the key updating was designed based on code segment. The analysis shows that the proposed scheme can meet the security requirement of key management, and it also has less computation cost and storage cost than the existing schemes
Three-Step Registration And Multi-Thread Processing Based Image Mosaic For Unmanned Aerial Vehicle Applications
Authors: Li Hongguang , Ding Wenrui , Wang Yufeng
Published: February 2018

In the area of image mosaic for unmanned aerial vehicle (UAV) applications, the problems of precision and time consumption have drawn many scholars’ attention. To address above two problems, a novel algorithm based on three-step registration and multi-thread processing is proposed in this paper. This method divides the image registration into three steps to improve the precision. Firstly, based on the SIFT features, the fast index mechanism k-d tree and the Euclidean distance are utilized to determine the common points between two adjacent images; then, the linear slope constraint model is used to filter the mismatching point-pairs; finally, the RANSAC algorithm is adopted to remove outer points from the common points to ensure matching precision of inter frames. To accommodate the real time requirement of UAV application, a parallel data processing pattern is presented. The multi-core resources and multi-thread computing method of computers are employed adequately in the new pattern to speed up the whole algorithm. Extensive experiments on precision and time consumption show the superior performance of the proposed algorithm
Design Of A Non-Contact Infrared Thermometer
Authors: Long Guangli
Published: February 2018

In order to realize the human body temperature fast and non-contact measurement, an infrared thermometer is designed. The infrared human body temperature sensor is mainly used to convert the human body’s infrared into voltage signal, an operational amplifier to amplify the signal, filter circuit to filter the signal, the analog signal into digital signal by the A/D conversion circuit, data processing by the MCU, LCD display and voice reporting body temperature and time, so the human body non-contact measurement is realized. The experimental results show that: the device can realize the temperature and time of acquisition, the measurement error is not more than 0.5 °Ç voice broadcast and liquid crystal display the temperature and time, overrun alarm and other functions
Measuring Lung Abnormalities In Images-Based Ct
Authors: Lay-Ekuakille A. , Vergallo P. , Jabłoński I. , Casciaro S. , Conversano F.
Published: February 2018

Diagnosis by imaging is one of the most important findings in biomedical imaging because it allows not only the diagnosing of a specific pathology but to perform online and offline surgical operations using imaging as it is noticed in interventional radiology. This paper illustrates the use Hough transform in identifying pathological structures included in CT (Computer Tomography) and HRCT (High Resolution Computer Tomography) images related to patients suffering from lung disease. These abnormal areas appear as bulges of the trophic vessels and they are similar to circular structures with level of lighter gray near to white. Circular Hough transform (CHT) identifies regions with a circular shape. However, a metrics is defined in order to understand if the pointed out area has a pathological morphology. CHT is used here for helping to detect possible events of indolent tumors or undetermined significance pathologies for lung apparatus. For this aim, we use entropy approach with CHT because it measures the scatter of the directional elements in an image. In fact a high entropy value is related to areas with a strong contrast in grayscale, and abnormalities in the image are present as a set of points with more lighter than the dark background. The results have shown, by means of an accuracy true table, rendering a comparison between clinicians’diagnosis and CHT detection, it is possible to indicate, with a better accuracy, potential areas of undetermined significance pathologies. Finally, a receiver operational curve (ROC) is used as an accuracy index for evaluating the positive impact of entropy on diagnosis
Recognition of the Stacked Objects for Bin Picking
Authors: Hikizu M. , Mikami S. , Seki H.
Published: February 2018

In this paper, we propose recognition method of the stacked objects for pick-and-place motion. The situation that the objects are stacked miscellaneously in the home is assumed. In the home, the equipment to arrange the objects doesn’t exist. Therefore it’s necessary to recognize the stacked objects respectively. In this paper, Information on the objects are measured by a laser range finder (LRF). Those information is used as 3-D point cloud, and the objects are recognized by model-base. A local minimum problem exists in recognition of the objects. We propose the method to recognize the stacked objects statistically using multiple recognition result. Avoidance of the local minimum problem and the segmentation of each objects are performed by recognizing statistically.
User Behaviour Monitoring Using Mobile Phones to Improve 5G Services and Performance
Authors: Cui Zhihao , Yan Jize
Published: February 2018

4G has been widely commercialised, and 5G is currently under development. The expected data bandwidth for 5G is 100 times faster than 4G and 500 times faster than 3G; however, the evolution of telecommunication technologies involves both a boost in speed and the enhancement of user experience. The key word used to describe 5G is ‘user-centric’, rather than ‘service-centric’ for 4G, and thus user behaviours of mobile data usage should be further investigated. On the other hand, the testing equipment currently being used for base stations is limited to hardware devices, such as spectrum analysers and power meters. These testing methods do not include the considerable potential variations in data demands due to changes in user behaviours, which could be resolved by presuming that all data resources could be dynamically allocated by real-time events.

A complete system has been designed and implemented in this study to investigate current user behaviours regarding mobile data usage. The system consists of three individual parts, including a user iOS application, a web server and an administrative iOS application. Ten devices were tested within the two-month data collection period. Although the sample size was too small to produce any statistical results, it was found that data usage behaviours differ from user to user, with the exception of using more than 10 times the Wi-Fi over WWAN data at all times. The data also proved that some of the usage case families, which are described in the NGMN 5G white paper, do have strong demands, which could not be fulfilled using current telecommunication technologies due to technological gaps.

This paper shows that the system proposed is a feasible method to investigate user behaviours of mobile data usage. If the sample size of users involved could be increased in the future, it would be possible to develop a model for real-time simulations of mobile users in specific areas so that limited connection resources could be dynamically allocated. Moreover, the basic communication infra-structures, such as base stations, should be well-planned and developed in advance to fulfill the potential 5G demand.
Middleware for Smart Heterogeneous Critical Infrastructure Networks Intercommunication
Authors: Okathe Titus , Heydari Shahram Shah , Sood Vijay , Cole Orane , El-Khatib Khalil
Published: February 2018

Critical Infrastructures (CIs) are physical assets and organizations responsible for the production and distribution of society’s vital goods and services. The increasing interconnection of CIs has resulted in interdependencies which might lead to propagation of failure from one infrastructure to another. Most of current critical infrastructures are equipped with data collection and communication capabilities that can be used to inform and warn other CIs about such events and alarms. In this paper, a publish/subscribe-based communication system among dissimilar (heterogeneous) CIs is presented. The proposed system improves the manageability of CIs by providing an exchange medium for status information and alerts. It achieves this via a uniform architecture, within and across infrastructure boundaries, that maintains data restrictions that reflect real life organizational, administrative, and policy boundaries. Finally, the proposed system is modeled using the OMNET++ simulation framework, and a network performance study investigating scalability is presented. Simulation results showed that system scalability depends on service time per packet, subscription density, and number of clients per router.
PGSA-Based Localization Algorithm for Wireless Sensor Network
Authors: Qin Yuqiang , Ying Hui
Published: February 2018

This paper proposes a novel localization algorithm for wireless sensor network (WSN). Accurate localization is very important for WSN. WSN localization problem is sometimes regarded as an optimization problem. Plant growth simulation algorithm (PGSA) is a kind of new intelligent optimization algorithm, which is intelligent simulation of plant growth in natural way. In addition to the common characteristics of intelligent algorithms, PGSA show robustness and provides a global optimal solution, etc. In this paper, further enhancement of the algorithm by adding the plant root of adaptive backlight function to effectively improve the computing speed and localization precision has been reported. Comparing this algorithm with simulated annealing algorithm (SAA), simulation results show that this algorithm has a higher and more consistent localization precision and faster computational speed.
Testbed Evaluation of Self-Adaptive Trust Model for Cooperative Geographic Routing in Wireless Sensor Networks
Authors: RaghuVamsi P. , Kant Krishna
Published: February 2018

Wireless Sensor Networks (WSNs) are often deployed in remote and hostile environments to monitor the mission critical tasks. In such environments, cooperation among nodes plays a vital role for successful execution of protocol operations. However, node cooperation may not be guaranteed when malicious activities are present in the WSNs. Trust management based security has received considerable attention to use along with cryptography based security to improve the cooperation among nodes in the presence of malicious activities in the network. This paper proposes the self-adaptive trust model for cooperative geographic routing in WSNs. Unlike existing trust models, nodes running the proposed trust model systematically observe the behavior of their neighboring nodes and evaluate the trust values with adaptive weight assessment. The proposed method has been integrated with conventional Greedy Perimeter Stateless Routing (GPSR) protocol. Further, it has been coded with nesC programming in TinyOS environment and evaluated on a WSN testbed consisting of 15 Telosb sensor nodes. The experimental results show that the proposed method significantly improved the packet delivery ratio in the presence of packet dropping and modification attacks.
Visualization Technology of the Microstructure of Reservoir Core
Authors: Wenjie Niu , Qingqing Xu , Ying Xu , Guohui Xu , Xiaobin Wang , Shipo Zheng , He Liu
Published: February 2018

To solve the problem in conventional technologies of analysis and integration in micro scale of reservoir core, virtual reality and visualization technology can be used in the research of reservoir core microstructure. Integrated with practical research and application works, the modeling method of reservoir core and the database store method of microstructure model are analyzed and discussed. A skeleton extraction algorithm based on maximum circumsphere method is proposed to achieve the skeleton model construction of reservoir core. And the visualization of reservoir core microstructure which includes the visualizations of voxel model, surface model, solid model and skeleton model of reservoir core are realized based on OpenGL graphics library. Taking a carbonate as a case study, we develop a set of software named “visualization system of reservoir core microstructure”, which is an effective evidence in the application to scientific research of reservoir core microstructure.
Valve Barrel Position Control Based on Self-Tuning Fuzzy Pid with Particle Swarm Optimization
Authors: Haiyan Zhang , Lepeng Song , Zhiming Dong
Published: February 2018

This paper introduced the self-tuning fuzzy PID controller based on particle swarm optimization which aims to gain more precise control over the position of pneumatic proportional valve barrel, where particle swarm works to optimize the membership function, fuzzy rule and PID parameter in fuzzy control. The study fruits also include online optimization of the self-tuning fuzzy PID controller parameters. Comparing to the conventional control methodology, The self-tuning fuzzy PID controller with PSO optimization is proven to show better precision, dynamic performance index and more rapid tracking performance and robustness. To this end, step response was used to compare and analyze the results from the PSO algorithm optimization, providing a pragmatic method of better comprehensive performance for PID parameter optimization.
Secure Data Storage Mechanism for Integration of Wireless Sensor Networks and Mobile Cloud
Authors: Hu Chengwei
Published: February 2018

Together with an explosive growth of the mobile applications and emerging of cloud computing concept, mobile cloud computing (MCC) has been introduced to be a potential technology for mobile services. Wireless Sensor Networks (WSN) is the technology that connects the virtual world and the physical world where nodes can autonomously communicate among each other and with intelligent systems. This paper describes the concept of wireless sensor networks and mobile cloud computing. Recently, much research has proposed to integrate wireless sensor networks (WSNs) with mobile cloud computing, so that powerful cloud computing can be exploited to process the sensory data accumulated by WSNs and provide these date to the mobile users on demand. The current WSN-MCC integration schemes have several drawbacks. This paper proposes a data processing framework, which aims at transmitting desired data to the mobile users in a rapid, reliable and even more secure manner. The proposed framework decreases the storage requirements for sensor nodes and networks gateway. And it minimizes the traffic overhead and bandwidth requirement for sensor networks. Additionally, the framework can predict the future trend of sensory data and provide security for this sensory data. This framework ensures the mobile users obtain their desired data faster.
Calculation and Simulation of Electromagnetic Wave Propagation Path Loss Based on Matlab
Authors: Yongxian Song , xianjin Zhang
Published: February 2018

In order to reliable data transmission of wireless sensor network (WSN) in indoor environment, the indoor field intensity distribution and transmission characteristics of electromagnetic wave were researched. First of all, the 3D model in specific indoor environment was built by the finite difference time domain method (FDTD).Then, layout of room, different furniture, position of field source and field source frequency had an influence on indoor field intensity distribution that were studied, and the field intensity distribution was simulated by MATALAB. According to simulation of three dimensional field intensity distribution, and it had directly shown that various factors had an influence on the indoor field intensity distribution, thus indoor wireless sensor network nodes can be reasonable deployed by it, the packet loss rate of WSN transmission was reduced from information source, and it provided theoretical basis for further improving WSN information transmission reliability.
Hyperspectral Data Feature Extraction Using Deep Belief Network
Authors: Xinhua Jiang , Heru Xue , Lina Zhang , Yanqing Zhou
Published: February 2018

Hyperspectral data has rich spectrum information, strong correlation between bands and high data redundancy. Feature band extraction of hyperspectral data is a prerequisite and an important basis for the subsequent study of classification and target recognition. Deep belief network is a kind of deep learning model, the paper proposed a deep belief network to realize the characteristics band extraction of hyperspectral data, and use the advantages of unsupervised and supervised learning of deep belief network, and to extract feature bands of spectral data from low level to high-level gradually. The extracted feature band has a stronger discriminant performance, so that it can better to classify hyperspectral data. Finally, the AVIRIS data is used to extract the feature band, and the SVM classifier is used to classify the data, which verifies the effectiveness of the method.
Modeling and Optimal Supervision of Multi Sensors and Multi-States Discrete-Event Systems with Structured Languages
Authors: Ji Shunping
Published: February 2018

In a discrete-event system, more than one sensor will create more than one event. Several events may happen at the same time parallelly. Events in different parallel branches are irrelevant and it will lead states explosions if mixing them together and enumerating all possible arrangements. A system with parallel sequences has multi-threads and can be dealt with as a whole. In this paper, after analyzing the process of discrete-event systems, the new model is established as a 5-tuple set, which is called a generator with multi-threads. The new generator will generate structured languages which are in accordance with the sequences of the process. The controllability of the structured languages is defined and the theorem about the existence of the supervisor for given languages is presented and proved. For given specifications, the method to solve the supervisor is presented. With the new model, the states of the total system will not explode when two or more systems synchronizing. From two examples shown in Section VI, the new model and supervisor theory for multi-sensors and multi-states discrete-event system are more convenient and natural than that of the traditional theories.
Deterministic Deployment for Directional Sensor Nodes
Authors: Yutong Lei , jian Wen , Xuan Zhao , Jianyu Li , Junguo Zhang
Published: February 2018

Node deployment is the key problem of wireless sensor network technology in application. The existing study on deployment of deterministic perceived nodes is simplified to the randomly deployment. In this paper, we take the effective coverage, connectivity and probability threshold as the evaluation indices to analyze the different deployment models. Experimental results demonstrate that the effective coverage area of the triangle deployment is the largest when using the same number of nodes; and if the probability is less than limits, tile deployment does not need to increase the node to ensure coverage. The research results of this paper provide an important reference for the deployment of the directional sensor networks with the given parameters.
Road Damage Identification and Degree Assessment Based on UGV
Authors: Song J. H. , Gao H. W. , Liu Y. J. , Yu Y.
Published: February 2018

Aiming at the problem of automatic identification and evaluation of road damage degree, the road damage identification and degree assessment algorithms based on unmanned vehicles experimental platform are studied. The road crack segmentation extraction method based on adaptive sliding window is studied. On this basis, the road damage crack classifies and identifies according to the crack geometry information and the principle of template matching. The road damage degree assessment algorithm based on fuzzy decision is proposed based on the quantitative analysis of the road crack and the corresponding parameters information. The experimental results demonstrate that the road damage identification and degree assessment algorithms proposed in this paper are effective and stable.
Image Fusion Based on Joint Nonsubsampled Contourlet and Overcomplete Brushlet Transforms
Authors: Pai Zhang
Published: February 2018

This paper proposed an image fusion method based on a novel scheme with joint nonsubsampled contourlet and overcomplete brushlet transform. And an improved region energy operator is employed as the fusion strategy, which can take full advantage of the anisotropic texture information and multidimensional singular information in the new multiresolution domain. Experimental results shows that the proposed method improved the fusion results not only in visual effects but also in objective evaluating parameters.
Optimization of Modified Rotameter Using Hall Probe Sensor with Respect to Liquid Density and its Calibration Using Artificial Neural Network
Authors: Sinha Sunita , Mandal Nirupama
Published: February 2018

Rotameter is one of the most commonly used local indicating type flow measuring instrument. For remote indication and control a secondary transducer like Hall Probe sensor, LVDT etc. is incorporated with the conventional rotameter. In this paper, a modified rotameter with Hall Probe sensor is used as a measuring instrument. The output hall voltage is proportional to the flow rate of the fluid and the change in fluid density may also vary the hall voltage. So this kind of variation shows incorrect flow rate if the density of the float is not taken to a very high value compared to the density of the fluid. But the density float may affect the flow rate measurement and introduce error. In this respect firstly the variation of Hall voltage with respect to liquid density is analyzed and then the measuring system is calibrated using ANN. The ANN calculates the correction factor with respect to the change in liquid density, which results in obtaining the output close to the desired output. The simulation results show that the calibration technique is efficient.
Unsupervised Grouping of Moving Objects Based on Agglomerative Hierarchical Clustering
Authors: Fujinami Kaori
Published: February 2018

In this article, we present a method to identify a grouping of sensor nodes that show similar movement patterns in an ad-hoc manner. The motivation behind the ad-hoc grouping is to allow a system to monitor complex and concrete situations of people and/or devices such as “who is/are utilizing what object(s)” and “what objects are carried together” without any supervision of human before and at the time of interaction. An agglomerative hierarchical clustering algorithm was applied to a data stream to find the group members as a set of clusters within a certain height. A threshold was also determined in an unsupervised way based on simple statistics obtained from the previous clustering results. An off-line analysis was conducted on data collected in realistic situations. Although grouping two of the same but unrelated activities proved to be difficult, the proposed algorithm performed well in other relaxed cases such as walking with a bag vs. pushing a platform hand truck. Furthermore, we confirmed the effectiveness of clustering-based grouping in comparison with simple distance-based grouping.
Description of a New Anguinid Nematode, Nothotylenchus phoenixae
n. sp. (Nematoda: Anguinidae) Associated with Palm Date Trees and Its Phylogenetic Relations within the Family Anguinidae
Authors: Esmaeili Mehrab , Heydari Ramin , Ye Weimin
Published: February 2018

Nothotylenchus phoenixae n. sp. is described and illustrated from soil samples of palm trees in Kermanshah Province, western Iran. The new species is characterized by a body length of 784 (663 to 925) mm in females and 677 to 715 mm in males; a delicate stylet 6 (5 to 7) mm long and six lines in the lateral field; median bulb of pharynx fusiform, nonmuscular, and nonvalvate; isthmus elongate, slender ending to a pyriform basal pharyngeal bulb not overlapping intestine; postvulval uterine sac well developed, 15 (14 to 17) mm long, female tail elongate-conoid with pointed terminus; and male with adanal bursa and spicules 21 to 22 mm long (n = 2). The new species comes close in morphology and morphometrics to five known species of the genus, namely N. affinis, N. hexaglyphus, N. persicus, N. taylori, and N. uniformis. Molecular analyses of the partial 18S, D2/D3 expansion segments of the partial 28S and internal transcribed spacer (ITS) revealed this as a new species. The sequences of the partial 18S and 28S D2/D3 regions confirmed the close phylogenetic relationship between N. phoenixae n. sp. and other anguinids, but Nothotylenchus is clearly separated from Ditylenchus species and should be considered as a valid genus.
Influence of Root Exudates and Soil on Attachment of Pasteuria penetrans to Meloidogyne arenaria
Authors: Liu Chang , Timper Patricia , Ji Pingsheng , Mekete Tesfamariam , Joseph Soumi
Published: February 2018

The bacterium Pasteuria penetrans is a parasite of root-knot nematodes (Meloidogyne spp.). Endospores of P. penetrans attach to the cuticle of second-stage juveniles (J2) and subsequently sterilize infected females. When encumbered by large numbers of spores, juveniles are less mobile and their ability to infect roots is reduced. This study looked at different factors that influence spore attachment of P. penetrans to the root-knot nematode Meloidogyne arenaria. Pretreatment of J2 with root exudates of eggplant (Solanum melongena cv. Black beauty) reduced spore attachment compared with pretreatment with phosphate-buffered saline (PBS), suggesting that the nematode surface coat was altered or the spore recognition domains on the nematode surface were blocked. Spore attachment was equally reduced following exposure to root exudates from both host and nonhost plants for M. arenaria, indicating a common signal that affects spore attachment. Although phytohormones have been shown to influence the lipophilicity of the nematode surface coat, auxins and kinetins did not affect spore attachment compared with PBS. Root exudates reduced spore attachment more in sterilized soil than in natural soil. Sterilization may have eliminated microbes that consume root exudates, or altered the chemical components of the soil solution or root exudates. Root exudates caused a greater decrease in spore attachment in loamy sand than in a sandy loam soil. The sandy loam had higher clay content than the loamy sand, which may have resulted in more adsorption of compounds in the root exudates that affect spore attachment. The components of the root exudates could have also been modified by soil type. The results of this study demonstrate that root exudates can decrease the attachment of P. penetrans endospores to root-knot nematodes, indicating that when these nematodes enter the root zone their susceptibility to spore attachment may decrease.
Vertical Distribution of Pasteuria penetrans Parasitizing Meloidogyne incognita on Pittosporum tobira in Florida
Authors: Baidoo Richard , Mengistu Tesfamariam Mekete , Brito Janete A. , Mcsorley Robert , Stamps Robert H. , Crow William T.
Published: February 2018

Pasteuria penetrans is considered as the primary agent responsible for soil suppressiveness to root-knot nematodes widely distributed in many agricultural fields. A preliminary survey on a Pittosporum tobira field where the grower had experienced a continuous decline in productivity caused by Meloidogyne incognita showed that the nematode was infected with Pasteuria penetrans. For effective control of the nematode, the bacterium and the host must coexist in the same root zone. The vertical distribution of Pasteuria penetrans and its relationship with the nematode host in the soil was investigated to identify (i) the vertical distribution of
P. penetrans endospores in an irrigated P. tobira field and (ii) the relationship among P. penetrans endospore density, M. incognita J2 population density, and host plant root distribution over time. Soil bioassays revealed that endospore density was greater in the upper 18 cm of the top soil compared with the underlying depths. A correlation analysis showed that the endospore density was positively related to the J2 population density and host plant root distribution. Thus, the vertical distribution of P. penetrans was largely dependent on its nematode host which in turn was determined by the distribution of the host plant roots. The Pasteuria was predominant mostly in the upper layers of the soil where their nematode host and the plant host roots are abundant, a factor which may be a critical consideration when using P. penetrans as a nematode biological control agent.
Superparasitism and Population Regulation of the Mosquito-Parasitic Mermithid Nematodes Romanomermis iyengari and Strelkovimermis spiculatus
Authors: Sanad Manar , Sun Jennifer S. , Shamseldean Muhammad S. M. , Wang Yi , gaugler Randy
Published: February 2018

Superparasitism is a common phenomenon in mosquito-parasitic mermithid nematodes. Multiple nematodes are needed in a single host to produce males. Host selection behavior and intraspecific competition among Romanomermis iyengari and Strelkovimermis spiculatus were investigated against their host, Culex pipiens pipiens in laboratory experiments. In a choice assay between previously infected and uninfected host larvae, infectious preparasites of both nematode species could distinguish not only between infected and uninfected hosts, but even between different parasite loads in showing a strong preference for uninfected hosts or hosts with a low parasite load. Host heart rate declined briefly immediately after parasitism. Superparasitism resulted in increased parasite mortality. Scramble competition within mosquito larvae for limited host nutrients, coupled with a skewed sex ratio favoring males, is assumed to lead to parasite population decline and subsequently toward host-parasite population equilibrium. The ability of mermithid preparasites to accurately assess parasite load likely plays an important role in host population dynamics and regulation.
An exploration of the experience of parents with children with autism spectrum disorder after diagnosis and intervention
Authors: Milbourn Ben , Falkmer Marita , Black Melissa H. , Girdler Sonya , Falkmer Torbjorn , Horlin Chiara
Published: February 2018

Delays and difficulties in both diagnosis and access to services can compound existing stressors experienced by families with children with autism spectrum disorder. Early and accurate diagnosis and appropriate intervention may not only improve child-specific outcomes but may also mitigate some of the stressors impacting family relationships and quality of life. We aimed to understand the experience of over 500 families that had sought autism spectrum disorder diagnosis and intervention, their perceptions of the efficacy of these services, and the impact that this process had on their family life. Parents overwhelmingly described frustration with access to a timely diagnosis, specialized intervention services, and funding that impacted their family life and relationships. However, parents simultaneously reported positive perceptions of change as a consequence of diagnosis and effective intervention.
The Inter-Country Comparison of the Cost of Children Maintenance Using Housing Expenditure1
Authors: Kalbarczyk Małgorzata , Miazga Agata , Nicińska Anna
Published: February 2018

It is interesting to compare maintenance costs of children between countries with similar yet different family policy regimes because this could yield valuable lessons for researchers and policy-makers and also for the sake of methodological development.

In this study, we aim to conduct a comparative analysis of the equivalence scales in Austria, Italy, Poland and France taking into account the age of children. To this end, we use data from the European Income and Living Condition (EU-SILC) to calculate equivalence scales for mono- and duo-parental households for the first and second child. The four countries share common European cultural context, yet differ with respect to social environment, in particular to family policy. We apply the Engel estimation method proposing the share of housing spending in total expenditures as a tool to obtain commodity-specific equivalence scales.

Our results are consistent with other studies showing that the cost of a first child is higher than that of a later child. The scale values are not the same across all the countries concerned, with the highest cost observed in Italy and the lowest in Poland.
Improvement of Fuzzy Mortality Models by Means of Algebraic Methods
Authors: Szymański Andrzej , Rossa Agnieszka
Published: February 2018

The forecasting of mortality is of fundamental importance in many areas, such as the funding of public and private pensions, the care of the elderly, and the provision of health service. The first studies on mortality models date back to the 19th century, but it was only in the last 30 years that the methodology started to develop at a fast rate. Mortality models presented in the literature form two categories (see, e.g. Tabeau et al., 2001, Booth, 2006) consisting of the so-called static or stationary models and dynamic models, respectively. Models contained in the first, bigger group contains models use a real or fuzzy variable function with some estimated parameters to represent death probabilities or specific mortality rates. The dynamic models in the second group express death probabilities or mortality rates by means of the solutions of stochastic differential equations, etc.

The well-known Lee-Carter model (1992), which is widely used today, is considered to belong to the first group, similarly as its fuzzy version published by Koissi and Shapiro (2006). In the paper we propose a new class of fuzzy mortality models based on a fuzzy version of the Lee-Carter model. Theoretical backgrounds are based on the algebraic approach to fuzzy numbers (Ishikawa, 1997a, Kosiński, Prokopowicz and Ślęzak, 2003, Rossa, Socha and Szymański, 2015, Szymański and Rossa, 2014). The essential idea in our approach focuses on representing a membership function of a fuzzy number as an element of C*-Banach algebra. If the membership function μ(z) of a fuzzy number is strictly monotonic on two disjoint intervals, then it can be decomposed into strictly decreasing and strictly increasing functions Φ(z), Ψ(z), and the inverse functions fu)=Φ−1(u) and g(u)=Ψ−1(u), u ∈ [0, 1] can be found.

Ishikawa (1997a) proposed foundations of the fuzzy measurement theory, which is a general measurement theory for classical and quantum systems. We have applied this approach, termed C*-measurement, as the theoretical foundation of the mortality model. Ishikawa (1997b) introduced also the notions of objective and subjective C*-measurement called real and imaginary C*-measurements. In our proposal of the mortality model the function f is treated as an objective C*-measurement and the function g as an subjective C*-measurement, and the membership function μ(z) is represented by means of a complex-valued function f(u)+ig(u), where i is the imaginary unit. We use the Hilbert space of quaternion algebra as an introduction to the mortality models.