Earticle

Grain stored in granary is easily spoiled due to accumulated hear or humidity if not monitored and maintained properly. Supervisory control and data acquisition (SCADA) system is commonly used in granary to provide real-time storage information. Major functionalities in the granary SCADA system is usually provided by an acquisition and control system based on single chip computer installed inside or near the granary. Based on theoretical and commissioning data, the design of the acquisition and control system was introduced in this paper. The acquisition and control system is able to acquire real-time data of temperature, humidity and concentration of CO2 inside the granary. It also provides the functionalities of open/shut the granary windows, as well as turning on/off the high-power roller shutter doors and blowers. Users are able to obtain real-time monitored data and control the system accordingly.

At school there are always first and second degree of education, higher education and training. The question is: the transition from one level to the other should they exist regulatory mechanisms and a prediction crowned by tool for decision support in school and university? National decision-making approach of orientation to accompany the student in the development of his identity and his personal path and vocational is required. We can resume our work by implementing a predictive model making integrated educational project facility through the impact of certain variables of different types of academic success. This is the case of the university sidi mohamed ben abdellah (SMBAU).

According to mass point clouds without explicit topology relation, a parallel fast sort algorithm is proposed in this paper. Morton order is introduced and used to merge one-dimensional data. The mass point clouds of irregular model are generated corresponding address code named Morton code and these points are stored in the octree structure chain. And then a parallel fast sort algorithm based on Euclidean distance is used to sort by CPU and GPU. The k-Nearest Neighbors of point can be located in the chain. The experiment results show that much time is saved and k-Nearest Neighbors of point can be searched directly. This algorithm is simpler than those complex sort methods used on the whole point clouds.

Software Project Management (SPM) is one of the primary factors to software success or failure. SPM has been the bottleneck in software engineering area. We apply an improved Max–Min Ant System algorithm to Software Project Planning to make the appropriate worker-task assignment in a software project so the cost and duration of the project are minimized. Experimental results shows that using this Improved Ant Colony Optimization algorithm can obtain a feasible solution which can help us to get the appropriate PERT Graph and Gantt Chart of the software project, then we can obtain the minimized project duration and cost. So Software project management can been improved.

In the case of the Chungwadae Gallery, both smartphone and tablet computer applications as a digital docent failed to achieve a seamless visit model. Visitors had no interest in interpretation and the usage of mobile applications stayed at very low levels. And technical, practical, contextual and social issues in relation to using mobile applications have been derived from the empirical analysis. More importantly, visitors thought that the applications leave visitors incapable of deep engagement with companions in terms of isolation and intrusiveness. From this point of view, the biggest challenge of managing mobile interpretative device is encouraging visitors to use the mobile experience. It is somewhat skeptical about the value of mobile applications in the research of the Chungwadae Gallery, whereas the usability and the degree of satisfaction of using the application were relatively high in the research of the Leeum. However, several problems such as intrusiveness, isolation, head-down effect and technical problems should be improved to use the mobile application. In the case of the Leeum, a short orientation session as one of marketing efforts is a key component in encouraging visitors to use a mobile experience and in solving the problem of hesitancy.

Soybean is the main food crop and an important economical crop of the world. Proper disease control measures must be undertaken to minimize losses. Techniques of machine vision and image processing were applied mostly to plant protection in recent years. Disease detection and segmentation are very important, but the diseases of soybean are complex in real environment and traditional segmentation methods cannot quickly and accurately obtain segmentation results. This research presented a new method for soybean leaf disease detection based on salient regions. This method used low-level features of luminance and color, combined with multi-scale analysis to determine saliency maps in images, and then K-means algorithm was used. The experimental results show that this method can accurately extract the disease regions from soybean disease leaf images with complex background, and it can provide an excellent foundation for extracting disease feature and identifying the diseases categories.

Object tracking is important and challenging task in many computer vision applications such as surveillance, vehicle navigation, and autonomous robot navigation. Video surveillance in a dynamic environment, especially for humans and vehicles, is one of the current challenging research topics in computer vision. It is a key technology to fight against terrorism, crime, public safety and for efficient management of traffic. In this paper, in order To get an accurate description of the trajectory points, regression analysis technique is used. This technique has the ability to summarize the collection of trajectory points by fitting it to mathematical models which will accurately describe these points and consequently describe object behavior. The regression analysis technique uses the least square method to obtain the best fit of equations for the given set of trajectory points. The least square method assumes that the best fit curve has the minimal sum of the deviations squared error from the given set of data. In this paper, propose new method to deal with the trajectory by converting the trajectory points into 3D approximation function using best fit plane after interpolation the time factor this method offers high flexibility as well as statistical tools for the analysis behavior of object. Planar regression calculates the best fit plane through a group of 3 or more data points. The plane is calculated by minimizing the residuals (or errors) between the plane and the original points using least squares minimization. The objective of this paper was to develop methods for optimization of least square best fit geometry for planes.

Internet is changing customers’ consumption patterns and the manufactures’ sale model. With the development of the computer network technology and the electronic commerce, more and more firms establish the electronic sale channel and get great profits. The huge supply chain network is established through the new idea and the technology. In this paper, we propose an improved local search algorithm- clustering local search algorithm (CLSA) to solve the hybrid location problem. We apply this method to distribution center location model and get the optimal solution. Result shows that this method can avoid the exponential explosion and get a good solution. In numerical analysis, we compare this method with simulated annealing method and ant searching algorithm. The numerical analysis demonstrates that this CLSA method not only classifies simply and flexibly, but also has the characteristic of fast search speed and small search space.

On the basis of empirical study on the characteristics of Jiuzhai Valley and tourist satisfaction, sample data which can reflect tourist satisfaction is obtained through questionnaires. With the help of SPSS 16.0, the data is processed and analyzed, and the result indicates that among all the factors that influent tourist satisfaction, satisfaction in varied dimensions differed greatly and each factor’s correspondent satisfaction in each dimension differed distinctively as well. Specifically, satisfaction of the scenic spot’s environment, infrastructure, management and service are higher than that of tourist scale, accommodation and catering. Correlation analysis and one-way ANOVA are adopted to analyze the aspects with low tourist satisfaction and put forward workable proposals.

The objective of this paper is to evolve a system, which automatically mines the raaga of an Indian Classical Music. In the first step Note transcription is applied on a given audio file in order to generate the sequence of notes which are used to play the song. In the next step, the features related to Arohana – Avarohana are extracted. The features of two/three songs are then selected in random and given as input to the training system. Totally songs of 72 melakartha raagas and 45 janya raagas are considered. Subsequently, work testing is done by extracting features of one or two songs of each raaga, which are given as inputs in the training part. The generated output indicates the identification of each raaga. Unique labeling has been done for each raaga, for the system to identify the set of trained raagas. In this work 7 instruments namely Veena, Saxophone, Violin, Nadaswaram, Mandolin, Flute and Piano are used. The database generated is trained and tested by using (1) Gaussian Mixed Model (2) Hidden Markov Model (3) K-Nearest Neighbor using Cosine distance and Earth Mover Distance to draw appropriate conclusions.

As the revolution of Web 2.0 technology, more and more novel service industries, such as social network, web of things and mobile internet emerge. The data of Web explosive growth is called the “big data”, which is hottest. Because of the great value of big data of Web, how to achieve the Web data and how to mine and utilize it, the two are paid attention by an increasing number of researchers. Under the big data circumstance, the Web data is characterized by huge scale, various kinds and high-speed bitstream. Therefore, we can investigate, further, the Web data mining, integration, interpretation and analysis. Simultaneously, Web data mining and integration still confront challenges consist of data scale, data variety, data timeliness and protection of privacy.

In design of the object-based storage system, cache design of the server becomes an important guarantee for improving service quality. This paper presents a cache design of load balancing system for object-based storage, which consists of three main aspects. First, the cache update policy is proposed. The strategy considers the overall cache as a pool, consistency and accuracy of updating is ensured through the locking mechanism, while using Bloom Filter combined with the updated time series to achieve updating simplification. Secondly, the cache replacement strategy is introduced which achieving the cache replacement policy using cache spanning tree combined with cache stack. Finally, cache design model is proposed based on energy consumption. Experimental results show that the proposed cache design can improve the efficiency of cache operation in object-based storage system, while reducing energy consumption of cache operation.

Fuzzy Cube Granule Structure (FCGS) for image segmentation is proposed in the paper. Firstly, the atomic cube granule is represented as the vector including the YCbCr values of pixel of color image and radii 0. Secondly, the join operation between two cube granules is designed to obtain the larger cube granule. Thirdly, the FCGS is formed by the fuzzy inclusion measure defined by join operation and the user-defined granularity threshold . Global Consistency Error (GCE), Variation of Information (VI), Rand Index (RI) are used to evaluate the segmentations. Images selected from BSD300 are used to verify the feasibility of FCGS.

Tourism product is not business of a single service but it consists of complementary products including: products of tourism, travel services and tourist hotel. Today web technology has been used for improving the promotion and marketing of the tourism products in Indonesia, but it has not complementary supported promotional and marketing activities optimally yet. This study aims to design a model of the provincial tourism office websites, travel agencies and tours, hotels, and tourist destinations in the perspective of integrated marketing and promotion, technical development, and website management involving the government through the Department of Tourism as the facilitator of business activities. The first research method used was Entity Relationship (ER) to develop a collaboration model. The second was Object Oriented Analysis Design (OOAD) to analyze the information needs based on the modeling approach. The obtained model was proposed as a basis for development of the integrated website. It is hoped that the integrated webs increase promotion and marketing productivity.

A novel digital camouflage generation scheme based on color quantization is proposed. First, a color similarity measurement in RGB color space is presented by exploiting the similarity of two color vectors. Then main colors in the background of the host image are extracted by combining color similarity and color quantization. Finally, the camouflage image is generated by replacing target pixels with main background colors. Simulated experiments prove that the proposed algorithm can achieve full fusion of the target and the background, and the generated camouflage image has satisfactory visual quality.

In this paper, we present a practical identification approach of video fragment for digital video files. Before analyzing the video content, we must decode it based on its encoding format first. In order to effectively identify the format of a fragment, a format classification is performed before the format identification. The methods of format classification and identification are discriminative subspace clustering (DiSC) and the K-nearest neighbor (KNN).Because of losing the meta-information, we add a maximum similar header (MSH) to the front of the fragment to recover the video content. We adopt a simple key frame detection method using standard deviation and mean value. Motion vectors of macro blocks are utilized to classify the video features for effectively identifying the video. Several edges of frames are accumulated and compose a video feature. The experimental results show the evaluations of the video format classification and identification, fragment recovery, and content identification.

In this paper, resource allocation for a relay-based multi-user Orthogonal Frequency-Division Multiple Access (OFDMA) uplink system is studied. All the relays adopt the decode-and-forward protocol and assist the transmission from the source to destination. In current wireless networks, the terminal depends more on battery power. Given that, battery life is limited, prolonging the service life of equipment is a key problem in ensuring the information transmission and in reducing the financial burden of the batteries. We aim to maximize the survival time of the relay system and to reduce the rate of loss. Therefore, we formulate the problem in which the subcarriers select the optimal relay based on both the channel gain and the residual capacity of the relays. We also consider a weighting factor that reflects the residual capacity of different relays. Simulation results show that the proposed algorithms can achieve longer survival time of the relay systems.

To achieve the rapid detection of soybean straw component, the key lies in establishing a quantitative analysis model with higher prediction accuracy which is rapid, stable and reliable. In order to establish the optimal Near-infrared (NIR) analysis model of cellulose and hemicellulose content in soybean straw, this paper uses NIR transmission technology by applying interval Partial Least Squares (iPLS) on the optimization of characteristic spectrum range of cellulose and hemicellulose spectrum. During the optimized characteristic spectrum range, prediction models of Partial Least Squares Regression (PLSR) and the Back Propagation Neural Network (BPNN) are built in the cellulose and hemicellulose contents respectively. The results show that the best modeling band of the Cellulose content is 5615-5731cm-1, and the optimal coefficient of determination of prediction model, PredictionR2(P-R2) reaches 0.9179266; And the best modeling band of the hemicellulose content is 5615-5731cm-1 ,the P-R2 is 0.920407. After the selection of iPLS optimal band, the quantitative analysis model of cellulose and hemicelluloses established by adopting the PLSR and BP Neural Network is more concise and has higher prediction accuracy and faster data computing speed. It also provides a theoretical basis for the optimization of characteristic spectrum range for the design of small dedicated NIR analytical instruments.

The objective of this study is to propose a new model for hiding text in an image. The proposed model use logical connective in propositional logic to calculate a new binary number of secret message. This model can produce a low computational complexity of steganography because of the simplicity of the proposed algorithm. Moreover, the using of simple operator but very efficient is expected to be able to hide high capacity of a secret message.

Aiming at the characteristics of high dimension and small samples in microarray data, this paper proposes a selective ensemble method to classify microarray data. Firstly, kruskal-wallis test is used to filter irrelevant genes with classification task and to obtain a set of genes, and then a reduced training set is produced from original training set according to gene subset obtained. Secondly, multiple gene subsets are generated by using neighborhood rough set model with different radius and used to construct training subsets on above reduced training set. Thirdly, every constructed training subset is used to train a classifier by using SVM algorithm, and then multiple classifiers are produced as base classifiers. Finally, a set of base classifiers are selected by using teaching-learning-based optimization and build an ensemble classifier by weighted voting. Five benchmarks tumor microarray datasets are applied to evaluate performance of our proposed method. Experimental results indicate our proposed method is very effective and efficient for classifying microarray data, and it improves not only classification accuracy, but also decrease memory costs and computation times.

We present a particle-based method for realistically modeling the interaction of incompressible fluids with arbitrarily shaped solid boundaries. When dealing with static solids, previous meshless Lagrangian approaches either require uniform sampling of solid boundaries or suffer from unexpected side effects. Our method resolves these issues using a new boundary handling algorithm that represents the solid surface as a single layer of boundary particles with different influence factors. This technique can more efficiently handle arbitrarily shaped boundaries without the side effects of existing approaches, and produces more realistic simulation results. We further propose a new form of weighting kernel that corrects the fluid density estimations and avoids negative pressures at the free surface. We have demonstrated the robustness and effectiveness of our method with a range of scenarios.

In this paper, a spinning effect is studied and applied in existing imageset. Some examples of spinning effect are provided and their assessed performance results are shown in visual and objective performance metric. It is found that applied spinning effect in original images could cause unwanted artifacts. However, basically spinning operation is reversible operation. Two parameters, θ and ρ, are used for the system, and these parameters are transformed into polar coordinate, and final θ and ρ values are obtained by processing D and K parameters. After spinning operation, pixels in polar coordinate are re-transformed into Cartesian coordinate. Simulation results section provides performance comparison and discovered results.

In this paper, transmit antenna selection is performed to improve the energy efficiency of multiuser large scale MIMO systems, which is necessary for the energy efficiency maximization of large scale MIMO, because the RF chains which are connected with antennas are very expensive and consume a lot of power. However, the consumed power of the large scale MIMO systems should include not only transmit power but also the fundamental power for operating the circuit at the transmitter, because the effect of circuit power consumption is more serious when the transmitter is equipped with massive number of antennas. We evaluate the performance of the energy efficiency in the multiuser case where zero-forcing precoding is used to reduce the inter-user interference. The theoretical analysis indicates that there exists an optimal number of selected antennas to maximize the energy efficiency, and we derive optimal antenna number for different number of users. All the analytical results are verified through both mathematical analysis and numerical simulations.

Ancient Iraq was the home of a major urban civilization which developed during 4000-3000 BCE. The Sumerians, who lived in Mesopotamia in southern Iraq, invented the cuneiform system of writing, which was an essential element of Sumerians culture. The translation of cuneiform is a highly complicated process. It is only in comparatively recent years that the grammar has been scientifically established, while the lexical problems are still numerous and far from resolved. Furthermore, most of the Sumerians tablets lost only few old images left, some of it saved in a special collection or worldwide museums. In this paper, we present a novel method used to obtain the cuneiform text from old Sumerian clay tablets, proposed method based on automatically select wavelet bases which it is essential and critical issues for wavelet algorithm implementation. Our procedure offers the archaeological and Cuneiformest an easy, fast and active method for extracting the cuneiform sentences. Experimental results of sample images show that the proposed system has superior result.

Image retrieval methods have been significantly developed in the last decade. The BOW (Bag-of-words) model lacks spatial information. Some methods stem from BOW approach which is recently extended to a vector aggregation model. Most of them are either too strict or too loose so that they are only effective in limited cases. In this study, we present a novel feature extraction method for image retrieval. We acquire the gradients features from the p.d.f (Probability density function) because of essentially representing the image. We construct the features by the histogram of the oriented p.d.f gradients via aggregation of the orientation codes. Then, we adopt the PCA (Principal component analysis) method to reduce the dimensionality of BOW. Furthermore, we introduce a novel and robust re-ranking method with the k-nearest neighbors. We estimate our method using various datasets. In the experiments on scene retrieval, the proposed method is efficient, and exhibits superior performances compared to the other existing methods.

The release of various mobile game engines and other easy-to-use game engines have opened up an era wherein anyone from beginner to expert can easily develop games. Currently launched smart phones have large screens, clear and high-definition displays, and high-performance chipsets. This allows 3D Role Playing Games, previously only available on the PC, on a mobile phone. The Unreal Development Kit (UDK) is highly suitable for designing high-quality games and provides the perfect environment to design games on mobile devices. This paper explains an easy method for intuitive development of 3D RPG games. In order to design the game intuitively, 3D geographic models and characters were designed firsthand using Zbrush and 3D MAX, and cinematic pictures were inserted by actively using the Kismet provided by UDK to allow the viewers to watch movie-like event images during the game.

As the further development of banking business for personalization and diversification, the bank information system is capable of dynamically adjusting business process and has set higher requirements for the flexible mechanism. This paper constructs a model of the business process based on Event-driven Process Chains (EPC), puts forward the concrete specification in the process of construction. A case study was implemented to validate the model using Pi-calculus. The results show that this model can be a powerful tool for the creation of flexible or customized business process.

Vehicle horn is a kind of electro acoustic device. The energy transfer process were analyzed through the energetic coil current, magnetic force, and displacement curves which had gotten before, and the process was happened in stage of the action, transaction and reciprocating vibration. The reasonable criterion for checking reciprocating vibration and sounding of horn was proposed, and the criterion was proved by changed the design parameters. The conclusions are of great value for further optimizing of reliability of the electro acoustic device such as electromagnetic horn.

In decision making, crisp ranking is not possible when the entire attribute characteristics and their degree of importance are known precisely. In real world situations decision making takes place in an environment where the goals, the constraints, and the consequences of possible actions are not known precisely. Thus the best condition for classic decision making problem may not be satisfied when the situation involves both fuzzy and crisp data. Site specific soil fertility and seasonal crop selection data are characterized by high degree of fuzziness and uncertainty. In our model, intuitionistic fuzzy rough set establishes a close connection between the concepts of similarity and dissimilarity thereby providing an excellent framework for ranking soil fertility. Further fuzzy Bayesian incorporates both fuzzy and uncertainty in the probability model yielding more realistic seasonal paddy variety selection. The decision model introduced in this paper is suitable for both data rich and data poor environment. The results illustrate that the soil fertility ranking and successive paddy variety selection can help to sustain the soil fertility in subsequent rotations and minimize the loss of nutrients from the sites.