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In allusion to the prediction of the stock return rate, this paper has proposed nonlinear combined prediction method based on EMD (Empirical Mode Decomposition) and SVMs (Support Vector Machines). The method has divided stock return rate series into several components of different frequencies using EMD technology, getting three new sequences by grouping superposition of each component according to the frequency, which represents items of market volatility, major events, and trend respectively. Based on these three sequences, prediction is made by constructing different SVMs models to obtain the predicted value of each sequence. With SVMs, a combined model is built on the basis of predictive value of each sequence to obtain the final predicted value of stock return rate. Using CSI 300 Index, the validity of this method is verified, and the results show that the proposed model is better than the other models presented in this paper on forecasting CSI 300 Index.

In this study, we have developed an Indonesian WordNet through four main phases: synonym set extraction (synset) as the smallest entity of lexical database from a natural language, semantic relation establishment between synsets (hypernym-hyponym and holonym-meronym), gloss extraction for synset collection, and the visual editor creation. The Semi-automatic term refers to the three initial phases which are automatically done using a number of machine learning approaches, while using visual editor to collaboratively complement the results collected from the previous phases. A number of raw data used on synset acquisition, semantic relations and glosses come from Kamus Besar Bahasa Indonesia (Great Dictionary of the Indonesian Language, abbreviated as KBBI) and Tesaurus Bahasa Indonesia (Indonesian Language Thesaurus), large collection of web pages from search engines, Wikipedia, and even Princeton WordNet for mapping purpose. This study shows that the proposed system successfully achieve 37,485 synsets, 24,256 hypernym-hyponym relations, 11,044 holonym-meronym relations and 6,520 gloss synsets. Similar approach is believed to accelerate lexical database development like WordNet for other languages.

The characteristics of remote sensing image is not obvious and can not reflect the be reconstructed the detail characters of the objects. For the sparse points in multi images fusion results, texture selection and it is relatively difficult problems, for accurate reconstruction of remote sensing image details, this paper presents CMVS-PMVS(cluster multi view stereo- patch based multi view stereo) combining with region growing for computing dense matching points. First, select some seed points by region growing algorithm, and find the matching relationship between the seed points, followed by the matching relation from the seed point to spread until to the entire image. Then the image set are clustered by CMVS in order to reduce the amount of data in the process of reconstruction, and the operation rate and reconstruction accuracy can be improved. PMVS reconstruction method is used to complete the reconstruction task by matching, expanding and filtering three steps. The experimental results showed that the point cloud is dense enough which are reconstructed by the 3d reconstruction algorithm based on regional growth combining CMVS-PMVS and well expressed the practical model of object reconstruction, the reconstruction of objects in remote sensing images has very strong practicability.

Aiming at the visual effect and the fusion quality is poor of the traditional image fusion algorithm, the new fusion algorithm is adopted that combining the Canny operator with taking big window contrast. The algorithm uses wavelet transform to the original images are decomposed firstly, the process will be based on the frequency characteristics of the image information to form two pieces of information, low frequency and high frequency information. Low frequency subband uses weighted average fusion algorithm, while high frequency subband uses the algorithm which combines Canny operator with taking big window contrast, the last fusion image is got from the wavelet reconstruction in contrast to the principle of wavelet decomposition . The corresponding simulation analysis was carried out on the fusion algorithm, and the visual effect is more clear. Also the object evaluation standard increased significantly.

The ab-initio Maxwell's equation was introduced using the Schrödinger equation and the Legendre polynomial. The efficiency, bandwidth and size of the transmitting loop was resolved theoretically to enhance the transmission of successive pulses of radio waves to illuminate objects. The radiation characteristics the resonant loop pattern of the loop antenna have been improved via the introduction of the ground plane. However, the perfect results have not been obtained. Further simulation of a dummy experimentation revealed the possibility of a high efficiency in the transmitting loop antenna via the introduction of angular loop displacement along a well defined axis. The results were confirmed experimentally. When the angular loop displacement is pi/2, we expect to see an improved uplink task under either bad or good weather.

Intelligent robot not only can realize reservation on conduct and actions, but also can be understand the characteristics of the unknown environment and adapt to changes in the environment through their own "sensory system", robots get outside information mainly by vision, this paper take the joint robot of binocular robot vision system as control plant, pointing on the problem of the uncertainties that existed in the dynamic model of binocular vision robot may cause instability. This paper has proposed a sliding model control scheme with RBF neural network adaptive control strategy based on sub-block approximation algorithm, in this control method, sliding model control was used to control trajectory of the joints of robot, and utilize the RBF neural network to approximate the each uncertain in the dynamic model of robot. The simulation results show that compared with the RBF neural network adaptive control strategy based on integral approximation for uncertainness, the proposed control method has features with good position tracking.

Thanks to their multiple sensors, the Android smartphones have drawn significant attention for supporting vehicular sensing apps. These apps usually require continual sampling of sensors to collect the events of driving. However, the standard garbage collection (GC) of the Dalvik virtual machine blocks these apps from collecting sensing information for hundreds of milliseconds, which leads to the failure of detecting vehicular events. To alleviate this problem, we present a scheme that proactively invokes the app-driven GC to reduce the duration of blocking based on free heap memory ratio or periodic calling. The experimental results show that we can significantly reduce 75% of the blocking time.

To satisfy green communication needs, energy efficiency is regraded as one of the most important indicators to evaluate the effectiveness of a communication system. In this paper, we establish the energy efficiency optimization framework for uplink multiuser mutliple-input multiple-output system. Taking the circuit power, the antenna link power and signal processing power into consideration, an analytical expression of the achieveable energy efficiency is firstly established, which can better model the practical communication process. Furthermore, we simplify the objective function under some mild conditions. After demonstrating the existence of a unique golbally optimal energy efficiency, an adaptive water-filling power allocation algorithm is proposed to further improve the energey efficiency of the system. Finally some simulation results are given to verify the performance of our scheme. Our work can provide a fundamental principles for uplink mutiluser multiple-input multiple-output systems design.

The Smooth of the shuttle vehicle speed control system is the guarantee of the safe operation of the car body, the current shuttle vehicle parking in the start acceleration and deceleration will happen when the shaking phenomenon, It reduces the shuttle car shelf system security and stability. The S-curve to add and subtract in the simplified method is applied to speed regulation system of shuttle car, can reduce the occurrence of this phenomenon. But the S-curve algorithm without considering the bodies weight and load change on the result of speed regulation. In this paper, we improve the S-curve and speed reduction algorithm. Considering the factor of the weight of the vehicle and the load factor, realize the smooth control of shuttle car. This paper introduces the method and principle of the speed control of shuttle car. An improved method for calculating the S curve acceleration and deceleration is presented. And Matlab simulation is carried out. Results of simulation and actual equipment show that the improved algorithm can control the smooth running of the shuttle vehicle.

Nowadays, the progress of information and communications technology and information of society has made wire/wireless-based communications become a popular approach, being a widely used methodology for delivering various educational programs and organizational business situations, such as continuous education, online academic lecture, distant private trainings in companies, and other similar cases. Graduate students and company employees are continuously in need to learn more advanced knowledge, but oftentimes have limited opportunities due to time, location, and cost limitations involved in traditional learning. Distance education by means of a Mobile Learning Environment (MLE) is strongly requested to solve these problems. Such systems could help in organizing a meeting comfortably via the network environment. This highly technological innovation is used to link multiple users to join from any place to meet in a virtual environment. It also brings change in education-based communication by reducing the operational administrative costs and can make educational content competition stronger. Occasionally, the social communication demand for internationalized educational program is continuously increasing. Hence, the role of universities is to change the direction of the learning environment from the traditional classroom into wire/wireless-based communications for learning. Accordingly, these higher educational institutions are expected to spearhead that change direction with distant learning, or distant lecture technologies. In this paper, we present the policy and target of higher educational models using STEAM.

With the emergence of cloud computing, many data owners outsource their local data to cloud server so as to enjoy high-quality data storage services. For the protection of data privacy, sensitive data has to be encrypted before outsourcing, which makes effective data utilization a challenging task. Although existing searchable encryption technologies enable data users to conduct secure search over encrypted data, the functionality of these schemes need to be further improved. In this paper, we construct a secure and efficient multi-keyword ranked search scheme which supports both the semantic extension search and the multi-keyword ranked search. The semantic extension is achieved through the mutual information statistical analysis of keywords. And the multi-keyword ranked search is achieved through a balanced binary tree whose nodes are the vectors of term frequency (TF) values. The splitting operation and secure transformation are utilized to encrypt the vectors of index and query. Note that, the encrypted vectors can be well used to calculate accurate relevance scores. Phantom terms are added to the index vector to blind the search results to resist statistical attacks. Due to the use of tree-based index structure, the proposed scheme can achieve the sub-linear search time. Finally, the experiments are conducted to demonstrate the efficiency of the proposed scheme.

In this paper, we propose an efficient technique for an iterative soft input soft output (SISO) decoding algorithm of block turbo codes with constituent Hamming codes. Pyndiah’s iterative decoding algorithm for block turbo codes supports various code rates by using concatenation, and shows high bit error rate (BER) performance in an additive white Gaussian noise channel (AWGN) environment as the iterative decoding progresses. However, one disadvantage of this code is that the delay increases during decoding with an increasing number of iterations. By employing syndrome-based iterative SISO decoding and iterative SISO decoding using an alpha value, we could obtain high BER performance. In addition, we propose a SISO-hybrid maximum likelihood decoding (SISO-H-MLD) algorithm which achieves almost the same performance as SISO decoding while reducing the number of iterations.

At present, V2G(vehicle to grid) technique is received more and more attention due to developing of renewable energy sources. So, development process of EV(electric vehicle) technology is introduced in this paper and relative difficulties in V2G is analyzed. The main difficulties including the service life of battery, control strategy of current and the EVs paralleling into the grid. Battery charger is one of the key technologies of V2G.The charger consist of a filter, an AC/DC converter for grid interface and multiple bidirectional DC/DC converters for EV battery management, and a direct-current control mechanism is employed for ac system bus voltage and dc-link voltage control. Simulation results show that the AC/DC converter and the DC/DC converters have the function of electric power flowing with two-ways. Finally it points out the development direction of V2G technology.

Due to presence of foggy wounds in a retinal image, tracking down of blood vessels become very difficult. In this paper we are going to present the segmentation of blood vessels using line operator. Changes in blood vessels tells us about some serious diseases like cardiovascular disease and stroke which can help us to detect some diseases in patients at very early stage and it can help abundantly in medical field and will give birth to a whole new era of examining diseases. DRIVE and STARE databases are used for parting of blood vessels. MATLAB has been used for detection of blood vessels in retina using line operator technique.

Task scheduling and resource scheduling are the core issue in cloud computing. Pointing at the premature problem in the scheduling algorithm of particle swarm, we propose a scheduling algorithm of cloud task particle swarm based on “fission” mechanism in this paper. The particle in traditional particle swarm algorithm gets “fission” by the new algorithm in appropriate place, to get more kinds of the particles, contributing to the particle swarm diversity, avoiding premature convergence of the swarm. As the experimental result shows that, the algorithm in this paper has faster scheduling efficiency than the FIFO and the PSO, also solves the premature problem in PSO.

The digital music market has been growing significantly in the past years. In music streaming services, music recommendation plays a key role, but Korean users’ recognition about their music service is not high because the service’s recommendation accuracy is not good. Therefore, this paper suggests technique to predict the user’s musical taste. This technique proceeds through a four-step process; data collection, data pre-processing, feature extraction, and machine learning. Collection of data was taken from TOP 100 chart in ‘Melon’, the number one music service provider in Korea from December 2013 to March 2015. Then, collected MP3 file format is converted into WAV file format. In the stage of feature extraction, we classify the genre from the music’s metadata and extract factors that can be taken using STFT’s ZCR, Spectral Rolloff, Spectral Flux. In the stage of machine learning, we produce a prediction model in a variety of classification techniques. To measure the performance of the created prediction model, 456 data were used for training dataset and 130 data were used for validation dataset. Since the results of experiment show an average of 78% of accuracy, the proposed technique seems to be effective.

Among new services recently introduced on the Internet, the real time broadcasting service that is provided by AfreecaTV and USTREAM is particularly eye catching. This paper introduces a design of a real time broadcasting system that provides Picture by Picture (PBP), Picture in Picture (PIP), caption, synthesis of videos, background music, flexible cue sheet manipulation, saving the video file at a server via FTP, and many other services. The existing real time broadcasting system shows two fixed windows: one for background image and the other for caster's video image. With PBP, we can flexibly divide the window into several small windows in order to display multiple images. With PIP and Chroma Key, we can obtain more natural video display from the background image and the real time video. In the existing real time broadcasting system, the number of background images is limited to one. It synthesizes it with the video from the webcam. We can improve this feature so that we can have many background images and choose any one of them during broadcast. We can even use a PowerPoint file for the background images in order to broadcast a real time lecture naturally. Furthermore, even another video can be used as the background image. This paper describes our design of such system in detail.

Mobile data traffic has been rising rapidly caused by spreads and development of smart devices As a result, it brought a lot of changes throughout our life and usage pattern for mobile users, as well as mobile technology advance and market alternation after data bandwidth growth and decline of mobile rate for data. Recently, the “Unlimited mobile tariff” has been released due to the severe competition between domestic mobile carriers, thus, the user of unlimited mobile tariff system increases while users have mobile tariff cost relief. Also, the various devices are connected to each other everywhere in our life, not only past mobile phone and tablet. It is overly connected in one IoT era, then users of its services increases sharply, so that the data traffic of IoT devices are regarded as significant element to find out next mobile data traffic usage and pattern study. The Goal of this study is to analyze the traffic of Korean mobile and wired line internet statistics by user group upon the network development and mobile tariff progress. And it is required to find out IoT devices market state and influence for mobile market based on empirical data and statistics in Korea.

This paper provides A performance analysis and implementation between Proxy Mobile IPv6 (PMIPv6) and partially distributed mobility management (DMM) network architecture. The simulation results indicated DMM networks perform better benefits than PMIPv6 network by analyzing packet delivery ratio (PDR) and CPU point-to-point utilization.

In recent years, lots of studies have been in progress around the world to solve the problem of global warming. The reduction of CO2 would be important to solve the global warming problem. Probably the production and conservation of energy might be thought to be highly crucial. Also it would be important to produce new energy with high efficiency. In addition, the efficient use of the current energy might be also an important issue. Therefore, this paper focused on the development of devices for reducing electricity as a primary energy source for mobile cloud computing, which can be utilized in homes, shops, buildings, factories and so on. Moreover, we should further emphasize to develop the inference engine as core component. As a result, this paper proposes an efficient electricity saving system for reducing the electricity consumption using the mobile cloud computing technique and SARIMA model. Performance evaluation is carried out for showing the effectiveness of the proposed system.

A novel feature representation method based on AdaBoost algorithm is put forward for action recognition in this paper. The method can not only adequately describe action in complex scenarios, but also select the most discriminative sample subset from a large amount of raw features of training data. So it can realize a double result, that is, reduce the recognition computational complexity and achieve a good recognition accuracy. The pyramid histogram of oriented gradient feature (PHOG) descriptor is utilized to represent raw feature data. In order to select most discriminative samples subset, AdaBoost algorithm is used to extract the raw feature data. The nearest neighbor classifier algorithm is utilized to test the proposed method on the UCF Sports database. Experiment results show that the method not only achieve the better recognition rate but also greatly improve the speed of recognition.

An analysis based on rainfall rate characteristics has been carried out to estimate flood occurance. In this paper, we analyzed the rain gauge data for 5 different rain gauge stations. 14 days acquired data covering events before, during and after the flood tragedy in Malaysia. The analysis of the rain gauge data was processed on precipitation phenomena observed in year 2014 in Kota Bharu, Kelantan (Malaysia) from 13 December until 26 December. The data was acquired from the Malaysian Drainage and Irrigation Department (DID). The objective of the research is to derive the tropical flood estimation model using rain gauge data in Malaysia. Among the preliminary result shows that the average rainfall rate at kota bharu is 204.5 mm/hr during the flood tragedy.

With the rapid development of modern information technology, network information technology has become an important means of application in mathematics teaching; certain extent prompted the change of mathematics teaching content, form and concept. In this paper, the author analyzes the multimedia teaching platform application in mathematics teaching, and makes comparative experimental analysis. The result shows that the test scores is significant difference between control class and experimental class, the scores of the experimental class were significantly higher than those students in control group(t=-10.986, P=0.000<0.001). Obviously, the mathematics teaching strategy based on the three-dimensional goal can not only improve the students' cognitive performance, but also promote the students with poor chemical learning foundation.

TFRC (TCP-Friendly Rate Control Protocol) has been recognized as a relatively good mechanism for TCP friendly congestion control on cable network. However, TFRC is sensitive to the network status and cannot actually distinguish congestion losses and non-congestion losses well in wireless networks. The high error rates and large delay of wireless networks result in significant performance degradation and poor quality of the streaming media transmission service in transmission. To solve this problem, this paper proposes an Explicit Congestion Notification TCP (ECN-TCP) based TCP Friendly Wireless streaming Media Rate Control (TF-WMRC) congestion control mechanism. This mechanism improves TFRC and conjunctions with ECN and RED (Random Early Detection) methods, differentiating correctly congestion losses and wireless error losses and effectively alleviating the throughput degradation caused by wireless links. Performance evaluation of network efficiency, TCP friendliness and streaming media stability in NS2 simulation platform has shown that it is a good congestion control mechanism for streaming media transmission in wireless environment.

Activity theory presents a broad and conceptual framework of analysis for HCI (Human Computer Interaction). As media art matures and becomes accepted as an art form, a theoretical and conceptual framework for its analysis and evaluation is needed. Activity theory studies the level of connection between the individual, community and society in a broad sense, and the connection's process of development is studied through different human practices as a philosophical base. As a branch of structuralism, activity theory analyzes the connotations of meaning in human activity and not the activity itself. Activity theory comes to researchers of interactivity, intuitively. The reason for this is that activity theory provides a common language in comprehending the delivery of meaning from an interactive art work, in the cultural and societal sense. In order to understand the activity of the participant of the artwork through activity theory is to clarify the purpose of the activity involved, pinpoint the elements of influences in the activity systems; the artist/designer can thoroughly plan the interactive art elements, and ultimately comprehend the apprehension of the participants. The pragmatics of the system transcend its uses into artist meaning and message through the participant own history of cultural and societal experiences.

Wireless Sensors Networks (WSN) have gained a lot of significance because of potential applications such as Marine engineering, Human body tissue study and animal life study. The scope of WSN is limited to some extent due to tiny battery inside the node, thus limiting the scope of applicability where replacing a battery is infeasible. A research to solve this issue has been proposed in this paper. The motivation behind this work is the LEACH protocol which is considered as the oldest technique for Energy Conservation in WSN. But with the advancement in the fabrication of nodes, this protocol needs to be altered to cater the needs. Analysis proved that Intelligent LEACH (I-LEACH) is better than MOD-LEACH approach (A variation to LEACH) in terms of increased lifetime. Results depict 54% increase in the lifetime of the network as compared to MOD-LEACH, which ensures longer connectivity and reliability.

With regard to the cigarette manufacturing’s demand for modern management, to protect the integrity and reliability of the cigarette production processing data, the proposed RFID application in cigarette production system, building on the basis of RFID security controlled digital information system platform, the system covers the entire process of cigarette production and can realize the control and management of cigarette production and optimize the improvement on the basis of binary ALOHA anti-collision algorithm. It can also improve RFID’s recognition accuracy to achieve the basic information for cigarette products in the process of digitization accuracy.