Earticle

Currently, mobile applications can be developed with two ways, native or hybrid applications. In this paper, we develop representative mobile application, social network service application, in both of native method and hybrid method to evaluate with the application at the aspect of development cost and performance cost. There are two key factors in developing mobile applications; the one is user interface design, and the other is efficient utilization of device capabilities such as various sensors, cameras, network interfaces. The UI composition time and networking performance are experimented with developed applications. From the results, we identify that the UI for hybrid applications had better to develop with dynamic composition nature, and mobile device capabilities had better to be done aggregately when these are used in hybrid applications.

Single Image Dehazing technology is widely needed in many fields. In order to solve the problem, we propose an improved and modified framework for estimating the optical transmission t in hazy scenes in a given single input image. At first, a novel formulation to the t estimation is presented with the combination of constant albedo and dark channel prior knowledge. Later, we introduce the watershed segmentation methodology into the algorithm to separate the image into some gray level consistent parts based on the original image’s color distribution and feature difference. As a result, we could estimate the atmospheric light A better and avoid the important drawback of artifacts phenomenon. At last, through this effective estimation to t and A, the scene visibility is largely increased and the haze-free scene contrasts can be better recovered. The experimental analysis shows that compared with other state-of-the-art algorithms, our proposed algorithm can provide promising results to dark channel prior and get corresponding reliable estimation value t with the advantage of minimal halo artifacts and fewer unreal details. Our method is more effective and robust.

3-D reconstruction of industrial flame could be used to research for the completeness of combustion and flame temperature field. As the flame flicks and has no regular evident characteristic, 3-D reconstruction of flame is difficult and lack of study. The paper proposed a stereo matching algorithm for flame based on distance ratio constraint (DRCFMA). Taking flame characteristics into account and using the processed SIFT operates to get the ratio constraint of matched feature points, the algorithm is applied on the follow-up 3-D reconstruction of flame. The results shows the validity and feasibility of DRCFMA. Compared with conventional algorithms, the proposed algorithm has a better accuracy and shorter time-taken for industrial flame. The experiments had shown that the follow-up 3-D reconstruction of flame has clear and continuously changing greyscale layers.

In this paper, we present a method for fully automatic facial expression recognition in facial image sequences using feature extracted from tracking of facial landmarks. The facial landmarks at the first frame of the image sequence under examination are initialized using elastic bunch graph matching (EBGM) algorithm and tracked in the consecutive video frame over time. At first, the most discriminative geometric features in terms of triangle are selected using multi-class AdaBoost with extreme learning machine (ELM) classifier. The features for facial expression recognition (FER) are extracted from AdaBoost selected most discriminative set of triangles composed of facial landmarks. Finally, the facial expressions are recognized using support vector machines (SVM) classification. The results on the extended Cohn-Kanade (CK+) and Multimedia Understanding Group (MUG) facial expression database shows a recognition accuracy of 97.80% and 95.50% respectively using proposed facial expression recognition system.

Evolution design takes the advantage of the evolution technology into the art design, and designers can achieve satisfactory products by performing evolutionary operations. It’s a powerful method in the procedure of the art design. However, the traditional evolution running on single site leads to the low efficiency and the less diversity. In addition, the products just represent the aesthetics of the single site. Based on these problems, this paper aims at using distributed evolution technology to complete the collaborative evolution, broadcasting evolutionary operations among distributed sites and executing evolutionary operations on each site. Then, the acquired products could represent the aesthetics of most sites due to its rich diversity. The paper mainly solves the problems of the operation conflicts which come from different sites, builds a new hybrid document model designs several algorithms based on Operation Transformation (OT) to solve the conflicts, and describes the procedure of the consistency maintenance. The proposed solution has been theoretically verified for its correctness in hybrid model.

Wi-Fi positioning system is widely being studied in many fields of smartphones. In an open air environment a GPS receiver is able to determine its position with a high level accuracy. But in inside a building, where the GPS signal is bad or unavailable or unappreciated. The position estimation from the GPS receiver is very erroneous and has no practical use. Wi-Fi triangulation system is applied in indoor location but faced signal coverage error. Therefore, we propose a method of indoor positioning that determines the user's current position with the nearby Wi-Fi routers and cellular network signal strength RSSI values. This Signal strength RSSI estimates the location fingerprints based on at least two wireless Access Points and an alternative Cellular network or three access points. The cellular network is an alternative backup of unreached Wi-Fi Access point where this Access point’s coverage is unknown or sparse. Predefined points are used to measure distance from a user could also generate advance level accuracy. Referring to the floor map, our method adjusts the real-time user's current location. Gravity sensor works on the back end to control Wi-Fi scanning enable or disable considering user movement for energy efficiency. We have implemented the proposed method on Android smart phones and our test results are discussed in this paper.

This paper focuses on audio event detection in tennis match. A new audio event detection method is proposed my making use of contextual information. This method detects the sounds of ball hit and line judge’s shout by the grunts of players when they hit the ball. To model for audio events, the proposed method adopts unsupervised learning to use the information of grunts. Compared with current methods, the proposed method possesses two advantages. First, it does not need any labeled data which is appeal in real applications. Second, this method can reduce the mismatch between training and testing to further improve the performance. Experimental results show that the proposed method can improve the performance of audio event detection substantially.

In order to improve the channel capacity of the multiband impulse radio ultra-wide-band (MB-IR-UWB) system, the multiple input and multiple output (MIMO) technology is introduced to MB-IR-UWB system, and we get a new MB-IR-UWB system based on MIMO. But the introduction of MIMO technology brings multi-stream interference (MSI) and same frequency interference (SFI). Meanwhile, there may be inter-symbol interference (ISI) over the multipath channel proposed by IEEE 802.15.3a. To eliminate these interferences, it is necessary to add an equalization technique which is a technique used for increasing the energy of the desired signal and reducing the energy of the undesired signal. In this system, we introduce time reversal (TR) equalization and frequency domain equalization (FDE) to the new system and compare the performance of the two equalizations. In this paper, by MATLAB simulation, we compare the robustness to channel and the anti-interference performance of two different equalizations in the MB-IR-UWB system based on MIMO.

A new type of digital signal method is presented for embedded systems including mobile handheld devices. A digital communication method is suggested for mobile phone and embedded devices using screen light signals that contain data such as game item, payment information, personal information, and so on. The new method provides an economic and efficient solution to mobile networking environment. We studied the solutions focusing on payment system by updating embedded software without need of additional hardware modules to mobile devices. The solutions can be used also for digital item movement between mobile phones or game devices.

Crowded pedestrian detection and density estimation are very useful and important under transportation environment. In this paper, we present a novel method for crowded pedestrian detection and density estimation through a weighting scheme of bag of visual words model which characterizes both the weight and the relative spatial arrangement aspects of visual words in depicting an image. Firstly, we analyze the visual words generation process. We give each visual word a weight by counting the number of images through which each visual word is clustered and computing the cluster radius of each visual word. To be more specifically, the co-occurrences of visual words are computed with respect to spatial predicates over a hierarchical spatial partitioning of an image. We validate this method using a challenging ground truth pedestrian dataset Pascal VOC 2007. Our approach is shown to be more accuracy than a non-weighting bag-of-visual-words one. The algorithm’s cost is also more efficient than the competing pairs.

In recent years, with the increasing popularity of diverse online social network sites, such as Facebook, Twitter, Blogger, YouTube, LinkedIn, and MySpace, a massive amount of data has become available. Applying data mining techniques to social media can yield interesting perspectives to understanding individual and human behavior, detecting hot issues and topics, or discovering a group and community. This study aims to address the question of how people use Twitter and to assess the power of Twitter in terms of creating consumer interest in commercials based on an analysis of Twitter messages about various Super Bowl ads. Instead of relying on the audience’s responses, this study adopts a hybrid approach, combining a data-mining approach to collect, filter and analyze a massive amount of tweets with manual content analysis.

The high false alarm rate appears during the traditional spam recognition method processing the large-scale unbalanced data. A method which transforms the unbalanced issue into the balanced issue is proposed, when the K-means clustering algorithm is improved based on the support vector machine classification model, to obtain the balanced training set. Firstly, the improved K-means clustering algorithm clusters spam and extracts the typical spam，then the training set consists of the typical spam and legitimate messages, and finally the goal of the filtration of spam is realized by trained SVM classification model. Comparing the K-SVM filtration method to standard SVM method through the experiment, the result indicates that the K-SVM filtration method in large-scale unbalance data set can obtain high classified efficiency and the generalization performance.

Measuring image similarity is important for a number of image processing applications. The goal of research in objective image similarity assessment is to develop quantitative measures that can automatically predict perceived image similarity. In this paper, we propose a new objective approach of measuring image similarity based on shape context. We take the geometric structures of objects into account during measuring the image similarity by virtue of shape context which is a robust and compact, yet highly discriminative descriptor. Firstly we find visual salient regions of images by virtue of a regional contrast based saliency extraction algorithm and employ shape context to describe the shape of visual salient region. Then we detect shape deformations of visual salient regions between two images through estimating shape context distances, and accordingly compute the image similarity values. Real data have been used to test the proposed approach and very good results have been achieved, validating it.

This research is to investigate a structural model of an integrated information system for health promoting behaviors after radical prostectomy. The subjects of this paper were 142 patients who had visited a general hospital located in Metropolitan area. The pairwise t-test was done to compare the before and after application effect of health practice rate after radical prostatectomy. The results of this study are as follows. First, the response rate(29.6%) of experimental group was statistically significantly higher than the response rate(26.8%) in the 50 to 59 years old(χ²=10.27, p<.05). Second, for soybean intake, positive change of health behaviors behaviors diminished the progression rate of prostate cancer(31.24±0.35, t=-0.57, p=.000). Third, this paper found that the health promoting behaviors in prostate cancer patients was increased by 69.2-73.6% compared with the previous status. This study will contribute to reducing prostate cancer patients from prostatitis to prostate cancer in the future.

Near Field Communication (NFC) technology provides a more natural and intuitive way for users to interact with intelligent environments within close proximity. It can be used in many scenarios including ticketing, advertising, controlled access, etc. However, NFC applications, especially those installed to the Secure Element (SE) for secure usage, have not yet been deployed widely enough in commercial environment. One reason is that the market is emerging and NFC-enabled smartphones are still very limited. Another reason is the life-cycle management of NFC applications on the SE is very complicated for application providers since the NFC business ecosystems have not yet taken off. In this paper, in order to establish a secure operating environment for third-party NFC applications, we propose a trusted NFC application management framework, which provides secure application provisioning and life-cycle management services. With the framework, NFC smartphone users can find and download interested NFC applications easily, and application providers can deploy NFC applications on users' SEs in a simple but secure way.

With the rapid development of china railway, ZPW-2000A track circuit has been widely used. Tuning area, which is an important part of the ZPW-2000A track circuit, is not only the relationship between the signal transmission quality, and determine the effect of electrical insulation between adjacent sections. Therefore, the study of fault diagnosis aspects for tuning area is urgent and significant. In this paper, using the theory of transmission line a model of track circuit is built, the comparison of the actual data and experimental data of the track surface voltage envelope curve shows the correctness of this model. Owning to the good time-frequency characteristics of wavelet and the nonlinear mapping features of neural network, a fault diagnosis of ZPW-2000A tuning area based on the wavelet neural network(WNN) is proposed. Combined with the practical failure situation of railway site, the fault diagnosis method in this paper can accurately identify failure modes of tuning area.

Opportunistic networks are a kind of Ad Hoc networks that make use of meeting opportunities from mobile nodes to realize the network communication which doesn't need a full path between the source nodes and destination nodes. It’s tolerant in latency and network division. It’s a great challenge to design routing protocols in opportunistic networks in that the movements of nodes are uncertain leading to intermittent connection which make it hard to transmit message to its destination. However, the problems have also attracted more and more attention from researchers. Firstly, in this paper, we introduced the basic concept of opportunistic networks. Secondly we classified and interpreted routing protocols according to the infrastructure, scenario, forwarding mechanism etc. Thirdly, we carried out the computer simulation and analysis for opportunistic network routing protocols in aspect of message delivery ratio, network latency, routing overhead and the number of lost packets. Then we summarized the advantages and disadvantages of routing protocol and discussed its applicability. Finally we looked ahead the hotspot of recent routing protocols under opportunistic networks.

The commonly used accumulated error correction of strip stereo models connection need a number of GCPs(Ground Control Points), which is unsuitable for low-altitude photogrammetry with a large number of images. In this paper, a novel approach of accumulated error correction of strip stereo models connection without GCPs was proposed. The accumulated error is adjusted by changing reference image and nonlinear polynomial method. Firstly the middle image of a strip is used as reference image. Then the accumulated error of baseline parameters are adjusted by inverse distance weighted method. Finally the rotation angles of relative orientation are adjusted by polynomial formula between the fitted angles and ideal statistics. Experimental results show that the approach is effective and practical, and can significantly reduce the distortion and accumulated error of model connection, may be suitable to implement strip aerial triangulation in low-altitude photogrammetry.

This research motivated by the number of SME that growth rapidly year by year in Indonesia. As one of the developing country, Indonesia SMEs, contribute a huge portion of Indonesian Gross Domestic Product (GDP). In order to support and empower the SMEs, Indonesian SMEs need the touch of technology, the aim of this paper is to know clearly about the right requirement and the behavior of prospective users in Indonesia SMEs. The result of this research is the business model of Indonesia SMEs Rating and review portal and applications development using the use case diagram modeling and Entity relationship diagram to explain the database development regarding the applications. Method used to develop the business model in this paper is business model canvas by Alexander Otserwalder and Yves Pigneur and modeled using Use case Diagram.

A number of studies have shown that the prevention and cure of malignant tumors may become much easier if growth mechanisms, such as the role of cleavage furrow formation in tumor cell division, could be discovered and understood in the earlier avascular phase. Although it is well known that metazoan mitotic spindle which is one of the most important substances during the cytokinesis of a cell division determines the position of the cleavage furrow formation, we are still on the verge of solving the puzzle of what molecules and how they control the formation of the cleavage furrow. In this paper, we propose a cleavage furrow formation approach based on finite element method, which combines nodes selection, mesh partition and matrix assembly in a total energy equation, for modeling the mechanisms of avascular tumor growth. We also perform several experiments. Results show that, by using our proposed approach for the avascular tumor growth, the positions of cleavage furrows are decided not only chemically but also physically and tumor cells are not symmetric, which demonstrates the effectiveness of our proposed approach.

We propose ensemble-based modeling for classifying streaming data with concept drift. The concept drift is a phenomenon in which the distribution of streaming data changes. In this paper, the types of the concept drift are categorized into the change of data distribution and the change of class distribution. The proposed ensemble modeling generates a meta-ensemble which consists of ensembles of classifiers. Whenever a change of class distribution occurs in streaming data, our modeling builds a new classifier of an existing ensemble and whenever a change of data distribution occurs, it builds a new ensemble which consists of an only one classifier. In our approach, new classifiers of a meta-ensemble on streaming data will be generated dynamically according to the estimated distribution of streaming data. We compared the results of our approach and of the chunk-based ensemble approach, which builds new classifiers of an ensemble periodically. In experiments with 13 benchmark data sets, our approach produced an average of 21.95% higher classification accuracy generating an average of 61.7% fewer new classifiers of an ensemble than the chunk-based ensemble method using partially labeled samples. We also examine that the time points when our approach builds new classifiers are appropriate for maintaining performance of an ensemble.

For the coordination and control of robot behavior, behavior decision is the foundation and the determinant of robot intelligence. Based on autonomous underwater vehicle (AUV), through the analysis of domestic anti-surge technology research status and inrush current model, a control technology with the use of behavioural decision upper and lower piecewise integration control between PID and FPID(Fuzzy PID) is designed. Then, according to the characteristics of underwater robotics and inrush current optimal path planning, AUV can get information and plan a different anti-surge behavioural decision, which makes underwater robot better complete the underwater operations. Finally, the simulation of anti-surge behaviour underwater robot is done. The results show that it achieves a good control performance, and the environmental adaptability of AUV has been improved.

Globally, coronary heart diseases are one of the most common diseases and regarded as a cause of deaths. Prediction and management of such diseases with high mortality as well as occurrence rate (e.g., coronary heart diseases) are particularly critical. Often, coronary heart disease patients accompany depression symptoms hence, further accurate prediction and continuing management are warranted. Improper therapeutic treatments and failure of early detection of depression patients with coronary heart diseases may result serious clinical outcomes. Data mining, utilizing database, has been shown to aid for finding effective therapeutic patterns thereby pursuing qualitative improvement of medical treatments through diagnosis based on the dataset. In the current study therefore, we compared prediction models of coronary heart disease utilizing data-mining of depression patients data in order to develop the prediction model for coronary heart diseases of depression patients. In results, we demonstrated that the neural networks model predicted most accurately thus results herein may provide a basis of prediction model for coronary heart diseases in depression patients and be effective for the establishment of effective therapeutic treatments and management plans.

Virtual resources have the duality of hardware and software, which brings new challenges to virtual resources management and manipulation. There is urgent need for formally describing virtual resources in order to effectively reason about virtual resources allocation, scheduling and management. This paper describes the virtual resources and their dynamic behavior. We first give the formal definition of the resources pool and the virtual machine (VM). Then, we describe the dynamic behavior of VMs. In particular, we present a formal description of how virtual resources are used by VMs. This paper reveals the logical laws of virtual resources and their dynamic behaviors and makes the virtualization theory system more rigorous.

Many methods have been applied in image compression and Non-negative matrix factorization (NMF) is one of some approach which could be applied in image compression. Non-Negative Matrix Factorization (NMF) was a realtively new approach to decompose data into two factors with non-negative entries. This paper shows that the NMF method can be applied in image compression using a model of 2x2 pixels weighted matrix rather than using the model of 200x200 pixels, 10x10 pixels and 4x4 pixels weighted matrix. This paper also shows that the 2x2 Weighted matrix model example of ((65536, 1), (1, 65536)) was the best weighted matrix for NMF image compression. Finally, this research proved that by using the weighted matrix with higher determinant value could gain smaller size compressed image.

Head pose estimation is recently a more popular area of research. Challenging conditions, such as extreme pose, lighting, and occlusion, has historically hampered traditional, model-based methods. This paper presents a proposal of an integrated method for head pose estimation based on face detection and tracking. This method first locates certain facial features and based on their relative locations determine the head pose, the head pose estimated using coordinates of both eyes and a mouth relative to the nose as the origin of the coordinate system. The nose position is set up as the origin. The coordinates of the other parts defined from the origin, the distance between the face parts normalized so that the coordinates are independent of the image size. For facial feature detection from the detected face region, Haar-like feature utilized along with AdaBoost learning, the Adaboost learning algorithm used for creating optimized learning data. From the experiments, the proposed approach shows robustness in face and facial feature detection and eventually produces better results in estimating head pose rather than simply using Haar-like feature for both face and facial feature detection. The computational cost is low because it uses only those three points.

In this paper, an e-PBL model and problems for an effective computer education were developed and applied for information literacy education of computer in elementary schools. In order to progress an effective e-PBL class, a support learning system including an e-mentoring and a web discussion function has to be built. In this paper, we developed the e-PBL teaching model for information literacy education of computer in elementary schools and inspected its effectiveness and validity. In order to resolve the research questions, class elements were extracted from an analysis of the prior research in the e-PBL. The class elements in the e-PBL were analyzed to six elements of the ‘unstructured practical problem’, ‘learner-centered problem’, ‘process reflected problem’, ‘cooperative learning reflected problem’, ‘on-line learning support system’, and ‘e-mentoring’. The e-PBL model was designed based on the extracted elements. The model developed by the present research added an on-line element and an e-mentoring element to the conventional PBL model. The main learning steps were given by 6 steps and it was characterized that the e-PAS system (Learning support system) and e-mentoring were used. The main steps include an introducing step, a problem proposing step, a problem solving plan step, a problem solving step, a result proposing and presenting step, a class finishing step, and an evaluation step. For the successful e-PBL teaching in elementary education of computer, the learning system that has the various learning strategies and support systems and interacts teachers with learners frequently will ought to be built.

This paper introduces the basic concept, development process, key technology of RFID and related theory. And in view of the key technology of RFID, anti collision algorithm is analyzed. Tag collision algorithm is to solve the reading and writing implement effective communications within the scope of multiple tags at the same time, speaking, reading and writing for communication problems. Under the condition of keep a certain complexity and cost, minimize the search time, improve the efficiency of recognition, is the direction and trend of algorithms. Through the comparison of several kinds of algorithm for collision, clear the necessity of the collision algorithm is improved. The author select dynamic frame ALOHA algorithm, introduces in detail the improved algorithm, and apply it to the system, to improve the recognition efficiency.