Logo detection with extendibility and discrimination

Logos are specially designed marks that identify goods, services, and organizations using distinguished characters, graphs, signals, and colors. Identifying logos can facilitate scene understanding, intelligent navigation, and object recognition. Although numerous logo recognition methods have been proposed for printed logos, a few methods have been specifically designed for logos in photos. Furthermore, most recognition methods use codebook-based approaches for the logos in photos. A codebook-based method is concerned with the generation of visual words for all the logo models. When new logos are added, the codebook reconstruction is required if effectiveness is a crucial factor. Moreover, logo detection in natural scenes is difficult because of perspective tilt and non-rigid deformation. Therefore, this study develops an extendable, but discriminating, model-based logo detection method. The proposed logo detection method is based on a support vector machine (SVM) using edge-based histograms of oriented gradient (HOGE) as features through multi-scale sliding window scanning. Thereafter, anti-distortion affine scale invariant feature transform (ASIFT) is used for logo verification with constraints on the ASIFT matching pairs and neighbors. The experimental results using the public Flickr-Logo database confirm that the proposed method has a higher retrieval and precision accuracy compared to existing model-based methods.

     

An efficient mode decision algorithm for H.264/AVC intra prediction

Multimedia Tools and Applications - Rate distortion optimization technique is adopted by H.264/AVC to select the best intra and inter prediction modes. It achieves remarkable improvement in...     

Dynamics modeling and analysis of a flexible-base space robot for capturing large flexible spacecraft

Space robots are in huge demand due to the rapid growth of their service targets, i.e., spacecraft. There are generally large flexible components on spacecraft, such as antenna reflectors and solar paddles. Due to the vibratility of their structure, it is challenging for a space robot with flexible appendages (the base is then called flexible-base) to capture and repair the large flexible spacecraft. After capturing, the two spacecraft with flexible appendages are connected by a space manipulator, and a compounded system is formed. In this paper, we developed a dynamic model and a closed-loop simulation system, to provide a means to verify path planning and control algorithms. Initially, the dynamic characteristics of different capturing stages (preimpact and post-impact) were analyzed. The topologies of a flexible-base space robot and the compounded system were described based on incidence and channel matrices. Secondly, the recursive dynamics was formulated and resolved by an effective numerical method. The modeling was verified by Adams’ model. Thirdly, we implemented a dynamics calculation block in Matlab/Simulink environment using the S-function package for the C program, and developed a closed-loop simulation system, which was composed of the Planning and Controller, the Multibody Dynamic, and the 3D Display modules. Finally, based on the simulation system, two typical missions—target berthing and on-orbital manipulation of the target along a circle, were simulated and evaluated. Dynamics analysis results presented some useful rules for the path planning and control to suppress the vibration of the flexible structure.     

Discovering small-world in association link networks for association learning

Association Link Network (ALN) is a kind of Semantic Link Network built by mining the association relations among multimedia Web resources for effectively supporting Web intelligent application such as Web-based learning, and semantic search. This paper explores the Small-World properties of ALN to provide theoretical support for association learning (i.e., a simple idea of “learning from Web resources”). First, a filtering algorithm of ALN is proposed to generate the filtered status of ALN, aiming to observe the Small-World properties of ALN at given network size and filtering parameter. Comparison of the Small-World properties between ALN and random graph shows that ALN reveals prominent Small-World characteristic. Then, we investigate the evolution of Small-World properties over time at several incremental network sizes. The average path length of ALN scales with the network size, while clustering coefficient of ALN is independent of the network size. And we find that ALN has smaller average path length and higher clustering coefficient than WWW at the same network size and network average degree. After that, based on the Small-World characteristic of ALN, we present an Association Learning Model (ALM), which can efficiently provide association learning of Web resources in breadth or depth for learners.     

A hybrid recommendation algorithm adapted in e-learning environments

With the rapid development of online learning technology, a huge amount of e-learning materials have been generated which are highly heterogeneous and in various media formats. Besides, e-learning environments are highly dynamic with the ever increasing number of learning resources that are naturally distributed over the network. On the other hand, in the online learning scenario, it is very difficult for users without sufficient background knowledge to choose suitable resources for their learning. In this paper, a hybrid recommender system is proposed to recommend learning items in users’ learning processes. The proposed method consists of two steps: (1) discovering content-related item sets using item-based collaborative filtering (CF), and (2) applying the item sets to sequential pattern mining (SPM) algorithm to filter items according to common learning sequences. The two approaches are combined to recommend potentially useful learning items to guide users in their current learning processes. We also apply the proposed approach to a peer-to-peer learning environment for resource pre-fetching where a central directory of learning items is not available. Experiments are conducted in a centralized and a P2P online learning systems for the evaluation of the proposed method and the results show good performance of it.     

数字化煤场与配煤掺烧技术的实施要点初论

优化配置不同种类、不同性质的煤炭,将其掺配成在综合性能上呈最佳状态的新煤种,满足锅炉在不同条件下的各种需要;开发数字化煤场,实现煤场的数字化动态管理。主要分析、研究了数字化煤场和配煤掺烧技术的实施要点。     

电厂电气运行中常见故障分析及其应对措施探讨

为了保证电厂的生产效率,必须要分析电气的运行特征,确定电气故障发生的原因,综合考虑多方面因素,选择切实可行的措施解决问题。简要分析了电厂电气运行常见故障发生原因,并提出了相应的解决措施。     

无线通信基站建设中电磁辐射缓解技术应用实践

无线通信基站电磁辐射缓解技术包括增加发射天线架设高度、增加天线增益、降低发射器功率、减小VRP下倾角度和改变垂直辐射图VRP等。结合案例分析了无线通信基站建设过程中电磁辐射缓解技术的应用实践。     

基于RFID的电动车防盗系统开发

针对目前市面上普通电动车防盗器不能满足广大车主新的防盗要求,为了弥补普通防盗器功能的不足,本防盗系统整合了RFID技术和GSM模块,在电动车被盗时,第一时间通知车主,实现了车主对车辆的24小时监控。系统具有自动感应外接电源电压,如果外电被剪或是无电,均会向车主进行报警。     

基于CDIO的一体化课程建设探索与实践

近几年CDIO教育模式应用在工科类课程教学中显现出很好的效果。该模式以学生为中心,以"构思—设计—实施—运作"为主线,培养学生的实践能力、自学能力和团队交流能力。将这一模式引入到高等职业院校的局域网安全与运维课程教学设计与实践中,教学实践表明,该模式可较大提升学生的动手能力和自学能力,促进教学质量的大幅度提高。