Miracle-dz/UniFAR-FTUs huggingface dataset:
Design and Memory Optimization of Motion Estimation Module for H.264/AVC Encoder

H.264/AVC standard has been widely used in video compression at various kinds of application domain. Motion estimation takes the most calculation workload of H.264/AVC encoder. Memory optimization has played an even more important role in encoder design. Firstly, dependency relation between motion vectors was analyzed and removed at a little cost of estimation accuracy decrement, and then a 3-stage macro-block level pipeline architecture was proposed to increase parallel process ability of motion estimation. Then an optimized memory organization strategy of reference frame data was put forward, aiming at avoiding row changing frequently in SDRAM access. Finally, based on the 3-stage pipeline structure, a shared cyclic search window memory was proposed: 1) data relativity between adjacent macro-block was analyzed, 2) and search window memory size was elaborated, 3) and then a slice based structure and the work process were discussed. Analysis and experiment result show that 50% of on chip memory resource and cycles for off chip SDRAM access can be saved. The whole design was implemented with Verilog HDL and integrated into a H.264 encoder, which can demo 1280*720@30 video successfully at frequency of 120MHz under a cyclone III FPGA development board.
Learner-Interface Interaction in Distance Education: An Extension of Contemporary Models and Strategies for Practitioners

Abstract Most treatments of the concept of interaction in distance education have been based on Moore's (1989) discussion of three types of interaction: learner‐content, learner‐instructor, and learner‐learner. However, these previous discussions have failed to consider the interaction that occurs between the learner and the technologies used to deliver instruction. This article presents the concept of learner‐interface interaction and recommends instructional design strategies that will facilitate students' acquisition of the skills needed to participate effectively in the electronic classroom.
Development of the Power Load Modeling System with Denoising and Parameter Identification

The power load modeling system is designed with denoising and parameter identification. This system consists of signal acquisition, signal preprocessing, parameter identification, different load modeling methods such as ZIP model and Dynamic modeling. Original signal can be read from Excel file, which is the simulated signal or measurement signal. Then some kinds of denoising methods can be selected, which are mean filtering, medial filtering and wavelet denoising. After being denoised, the load signal is suitable for the parameter identification process. ZIP model is used to simulate the static load model, and the dynamic model is used to simulate the dynamic load model which is changeable during different periods. With the parameter identification and simulation process, measurement power load signal is used in the experiment, the dynamic model is more suitable for the variable load voltage features description.
Text Segmentation Model Based on Multiple Discriminant Analysis

This paper proposes a new domain-independent statistical model. In this model, four multiple discriminant analysis (MDA) criterion functions are defined and used to achieve global optimization in finding the best segmentation by means of the smallest within-segment distance, the largest between-segment distance and segment length. To alleviate the high computational complexity problem introduced by the new model, genetic algorithms (GAs) are used. Comparative experimental results show that the methods based on MDA criterion functions have achieved higher Pµ than that of TextTiling and Dotplotting algorithms.
Wavelet image modeling and its application in model-based image coding

This paper describes an image modeling scheme based on wavelet signal decomposition and statistical texture analysis, and its application in model-based image coding. In this approach, the image being considered is first decomposed into octal signal bands which contains different spectral components of the images. Based on the property of the signal decomposition statistical texture analysis techniques are hierarchically employed to separate the image into different categories with distinct modeling parameters. Coding is performed based on the texture image model.
Design consideration of a static memory cell

Describes design criteria for high-density low-power static RAM cells with a four-transistor two-resistor configuration. The states of the cell latch are expressed by a DC stability factor introduced from transfer curves of the inverters in the cell. The criteria use only static conditions for read/write/retain operations. The designed cell, considering mask-misalignment, measured 22.8/spl times/27.6 /spl mu/m with 2.5 /spl mu/m layout rules. From the evaluation of dynamic characteristics, it was shown that the 16K RAM using the cell had a sufficient operating margin.
The Application of Multimedia and Network in Promoting English Learning and Communication

Communication is the ultimate goal of learning a language. Traditional way of English learning is teacher-centered and there is lack of communication for the students to practice English. This paper aims at promoting English learning and communication with the help of advanced multimedia and network technology. It analyzes the advantages of multimedia and network applied in English learning and communication and explains many activities about the application in English learning and communication.
A Method of Roads Extraction from Aerial City Images Using D-S Theory of Evidence

An approach is presented to extract roads from aerial city images based on the Dempster-Shafer evidence theory. A road model is a priori constructed. Aerial images are divided into sub-blocks from which regions consisting of 8-connected pixels with similar gray scales are obtained, and regions with relatively big areas are selected as candidate road segments. Then Dempster rule is applied to compute the fusion of basic probability assignment functions (BPAF) defined respectively on the features extracted from the candidate road segments and on the original road model. Finally, the BPAF fusion is utilized to find the conclusive road segments and these road segments are connected and pruned to form the de facto roads. Experimental results demonstrate the ability of the D-S evidence theory based approach to accurately extract roads from aerial city images.
Re‐invigorating openness at The Open University: the role of Open Educational Resources

This paper describes the internal motivations and external drivers that led The Open University UK to enter the field of Open Educational Resources through its institution‐wide OpenLearn initiative (www.open.ac.uk/openlearn). It also describes some of the emerging evidence of the impacts inside and outside the university. Through the rapid implementation and operation of the OpenLearn website, The Open University UK has been able to better understand and promote openness through open and distance learning.
Model-based segmentation of the brain from 3-D MRI using active surfaces

Traditional segmentation approaches have proven inadequate when faced with the anatomical complexity and variability exhibited by biological structures such as the brain. A 3-D extension to the 'snakes' algorithm has been implemented and used to segment the brain surface from MRI image volumes of the head in an effort to investigate model-based, top-down segmentation strategies. These active surfaces allow closed surfaces of complex objects to be recovered using a priori knowledge in the form of initial conditions and applied external forces. Preliminary results suggest that active surfaces may be initialized according to a preconceived model and adaptively deformed by image data to recover the desired object surface. <<ETX>>
Aurora Arterial Modeler – A Macroscopic Tool for Urban Traffic Signal Control

Abstract This paper describes a macroscopic simulation model for urban signal control based on the Aurora Road Network Modeler (RNM) software package. The paper first presents the implementation of pre-timed and actuated signal controllers in a cell transmission model based simulator. Then a case study with wireless sensor traffic data is carried out to test the performance of the model. The results show that the macroscopic model produces good estimates of arterial travel time, as compared to predictions of the Highway Capacity Manual. The actuated controller produces significantly higher delay than its pre-timed counterpart, which agrees with our previous findings using micro-simulation. This research should contribute to the area of arterial traffic signal simulation and control as the proposed model is more computationally efficient than the more widely used microscopic simulation models.
Cartographic symbol library considering symbol relations based on anti-aliasing graphic library

Cartographic visualization represents geographic information with a map form, which enables us retrieve useful geospatial information. In digital environment, cartographic symbol library is the base of cartographic visualization and is an essential component of Geographic Information System as well. Existing cartographic symbol libraries have two flaws. One is the display quality and the other one is relations adjusting. Statistic data presented in this paper indicate that the aliasing problem is a major factor on the symbol display quality on graphic display devices. So, effective graphic anti-aliasing methods based on a new anti-aliasing algorithm are presented and encapsulated in an anti-aliasing graphic library with the form of Component Object Model. Furthermore, cartographic visualization should represent feature relation in the way of correctly adjusting symbol relations besides displaying an individual feature. But current cartographic symbol libraries don't have this capability. This paper creates a cartographic symbol design model to implement symbol relations adjusting. Consequently the cartographic symbol library based on this design model can provide cartographic visualization with relations adjusting capability. The anti-aliasing graphic library and the cartographic symbol library are sampled and the results prove that the two libraries both have better efficiency and effect.
A Novel Video Detection System on Traffic Flow Inspection

A prototype of video detection system applied to traffic flow inspection is developed, which uses CMOS linear image sensor with high resolution 2K pixels and wide dynamic range as the core of imaging device. It combines FPGA with DSP as the core of acquisition and processing of massive image data. Moreover, a novel multiscale and hierarchical clustering algorithm for image segmentation is presented. Based on the theory of graph spectral, the algorithm can construct a new graph by analyzing the feature of an original image at different clustering scales, so that image segmentation can be accomplished easily to segment the image. The simulation results show that the row scan speed of this system can reach to 1000 lines per second, the resolution being 2048 pixels.
Study to detect and block leakage of personal information : Android-platform environment

The Malicious code that targets Android is growing dramatically as the number of Android users are increasing. Most of the malicious code have an intention of leaking personal information. Recently in Korea, a malicious code `chest` has appeared and generated monetary damages by using malicious code to leak personal information and try to make small purchases. A variety of techniques to detect personal information leaks have been proposed on Android platform. However, the existing techniques are hard to apply to the user`s smart-phone due to the characteristics of Android security model. This paper proposed a technique that detects and blocks file approaches and internet connections that are not allowed access to personal information by using the system call hooking in the kernel and white-list based approach policy. In addition, this paper proved the possibility of a real application on smart-phone through the implementation.
Learning from Video Games

Playing video games stimulates affective, cognitive, and communicational processes, thus facilitating the emergence of knowledge. In order to support this idea, this chapter first historically contextualizes the evolution of educational video games and provides some basic classification of genres. Then it identifies the major theoretical currents that may inspire teachers and designers to develop learning scenarios adapted to educational video games. It also describes some interesting examples of the educational usage of video games by the general public and of video games specifically created for a particular pedagogical context. Several arguments are presented to stimulate discussions around motivation, evalution, and learning aspects of educational video games usage and design. With the intention of supporting an approach that responds to user needs, some models identifying user profiles are also described. Finally, this chapter presents general design specifications for successful development of educational video games projects. DOI: 10.4018/978-1-60960-495-0.ch004
The application of data source authentication protocol to video multicast

An efficient packet-injection resistant data source authentication protocol for group communication(EPJRSA) was proposed to authenticate data source. It can recognize which packets are injected by attacker and delete them. But it cannot recover the lost packets. So an advanced protocol based on EPJRSA protocol is proposed to solve this problem. This advanced EPJRSA protocol is used to build a video broadcast system. The system has the ability of packet loss and injection resistant. And it can guarantee the security of both video data and authentication information at the same time. Experiments are set to verify the correctness of the system. Keywordsdata source authentication; application on video multicast; packet-lost; packet-injection
The Librarian as Video Game Player

Despite media coverage that is usually negative or non-existent, digital (video) game playing is a major form of entertainment for significant numbers of people in developed countries. This paper begins with a brief outline of what these games are, who plays them and why there is interest within the library community. We then move through several examples of the current direct impact of digital games on libraries. This is followed by more abstract examples of how video games could, and do, influence library practise and technology. Attributes of online games are discussed, focusing on one particular game, Second Life. This game is used widely within the library community, with many librarians spending significant time developing avatars, open services and infrastructure. The paper concludes with a summary of the ten attributes of video game players, found across the research literature body, that are of relevance to digital information and library services.
The P/Q channel in human disease: untangling the genetics and physiology

Several neurological disorders are associated with mutations in CACNA1A, the gene which encodes the core subunit of P/Q calcium channels. Although there is overlap between the various phenotypes, particularly with respect to cerebellar involvement, different disorders are increasingly being associated with different aspects of P/Q-channel function. A preliminary genotype–phenotype relationship indicates that while almost any type of P/Q dysfunction can lead to progressive ataxia, mutations that increase channel function and those that remove arginines from S4 voltage sensors are most associated with migraine. In contrast, episodic ataxia and, to a lesser extent, epilepsy may be caused by mutations that decrease channel function. This dissection of channelopathies associated with P/Q channels is a case study of how rare experiments of nature may inform us about the different functions of widely expressed genes that play multiple roles in neurons. WIREs Membr Transp Signal 2012,1:311–320. doi: 10.1002/wmts.22 For further resources related to this article, please visit the WIREs website.
BARTIN: minimising Bayes risk and incorporating priors using supervised learning networks

BARTIN (BAyesian Real-Time Network) is a general structure for learning Bayesian minimum risk decision schemes. It comprises two user-specified supervised learning nets (an observer and a utility network) and associated elements. This two stage structure allows separate minimisation of risk and compensation for changes in prior probabilities. It is able to learn Bayesian minimum risk decision schemes accurately from training data and priors alone. The design provides a new mechanism (the prior compensator) for correcting for discrepancies between class probabilities in training and recall. The same mechanism can be adapted to bias output decisions. The general structure of BARTIN is described together with its enumerative and Gaussian forms. The enumerative form of BARTIN is applied to a visual inspection problem and compared with the MLP. The value of taking both priors and risk into account is demonstrated.
RF low-noise amplifiers

In this paper two Low-Noise Amplifiers (LNA) are presented. The first topology is a two-stage LNA that is ac coupled to the mixer, while the second one is a single stage LNA that can be dc coupled to the mixer. These circuits have small sensitivity to temperature variations and special biasing techniques allow us to reduce the effects of the process tolerances. Preliminary experimental results obtained from prototype, not properly packaged for high frequency applications, have shown that the proposed technique works properly up to 250 MHz. These circuits have been fabricated in an 8-GHz 1.2 /spl mu/m BiCMOS technology.
Automatically Transforming Legacy XML Documents Into OWL Ontology

It is a challenge to transform legacy XML-based data into ontology for applications of the semantic web, such as semantic-based integrations, intelligent web searching, and internet based knowledge reasoning. We propose a new technique to transform XML data into ontology data automatically by modeling XML documents semantically. Firstly, we provide the XML a semantically interpretation by developing a graph-based formal language, w-graph. Then, the result of the interpretation can be automatically mapped into OWL web ontology language with semantics preserved. The proof of semantics preserved also been considered, and automatic mapping tool has been developed.
Flexible Protocol Reconfiguration for Emergency Communication Systems

The necessity for services of emergency communication systems to be uninterrupted and reliable has brought forward strict requirements for express software upgrades of base stations. Based on reconfigurability technology, this paper presents a protocol reconfiguration scheme for emergency communication base stations. By introducing the concept of ‘local safe state,’ the proposed scheme could make the updating and replacement of the protocol software flexible and maintain interactions during the updating procedure to achieve seamless system upgrades. Further, taking TETRA protocol stack and VxWorks operating system as an example, specific processes and realization methods of reconfiguration are proposed in this paper, along with the measurements of the factors impacting on the system performance. Simulation results show that, compared with traditional technology, the method proposed significantly improves the system efficiency and does not interrupt system services.
QoS estimation for cellular packet data networks

There is a lot of effort in the industry for defining adequate and realistic end-to-end QoS indicators for cellular data services, which might be applicable for estimating delivered QoS and for specifying the QoS control mechanisms for the underline transport networks. The paper proposes a set of measurements and a procedure for mapping the results to key quality and performance indicators. The procedure is independent of the network architecture and technology. It is a top down approach for directing the network optimization process, starting from the end user perspective.
Research Online: Digital Commons as a Publishing Platform at the University of Wollongong, Australia

Abstract Since 2006, Research Online, the University of Wollongong's open access institutional repository has utilised Bepress' Digital Commons software to make available published research outputs and digital theses. This article discusses the outcomes of recent academic demand for its use as a publishing tool of university journals and conference proceedings. The Journal of University Teaching and Learning Practice is provided as an example. Digital Commons includes the editorial management software, EdiKit, which assists in managing submissions, editorial functions, and peer review. Also considered are changes to scholarly communication patterns arising out of the new open access, electronic only, publication regimes.
Image segmentation using random features

This paper presents a novel algorithm for selecting random features via compressed sensing to improve the performance of Normalized Cuts in image segmentation. Normalized Cuts is a clustering algorithm that has been widely applied to segmenting images, using features such as brightness, intervening contours and Gabor filter responses. Some drawbacks of Normalized Cuts are that computation times and memory usage can be excessive, and the obtained segmentations are often poor. This paper addresses the need to improve the processing time of Normalized Cuts while improving the segmentations. A significant proportion of the time in calculating Normalized Cuts is spent computing an affinity matrix. A new algorithm has been developed that selects random features using compressed sensing techniques to reduce the computation needed for the affinity matrix. The new algorithm, when compared to the standard implementation of Normalized Cuts for segmenting images from the BSDS500, produces better segmentations in significantly less time.
A Clustering Algorithm towards Microblogs based on Vector Space Model

Weibos have become wildly popular in China in recent years, and state media reports that there are more than 300 million registered users. The “Real Name” Policy[1] requires all users on Chinese weibo websites to register with the name that corresponds with their government issued ID card. With the rapid development of the web, the research of consensus encounters new problems and challenges. Is a practical method for large-scale text clustering, instant messaging, text content analysis features, and find or track the social hot topics. Unlike the file, which is not suitable for very common clustering algorithm? A new method is proposed of the named MVSM synthesis microblogging dialogue, but also enriched the words of the vector is not included in the text of the blog, but existing content is closely related. Extended vector space this MVSM perform the dialogue, k-means clustering. Experiments on public datasets show better, MVSM than traditional k-means and kmeans algorithm into two. Keywordsmicroblogs clustering, k-means, vector space model