RGB-D Hand-Held Ob ject Recognition Based on Heterogeneous Feature Fusion

Ob ject recognition has many applications in human-machine interaction and multimedia retrieval. However, due to large intra-class variability and inter-class similarity, accurate recognition relying o...     

一种基于FPGA的实时NPR系统

在进行非真实感渲染(NPR)处理时需要大量的计算,这对高帧率、高分辨率的视频做实时的NPR渲染是一个难题。根据FPGA以并行运算为主的特点,对传统的NPR算法做了改进和简化,最终设计了一种基于FPGA的实时NPR系统。实验结果表明,该系统对高帧率、高分辨率的视频做实时NPR处理具有运算速度快、系统稳定等特点,同时还具有体积小、便于携带等优点。     

三氨基胍与乙二醛反应相互作用能的理论研究

采用量子化学方法详细计算了三氨基胍与乙二醛反应中各分子的结构,反应相互作用能。计算结果表明,反应过程中A分子中2个亚氨基上的氮原子分别与B分子中的2个碳原子结合形成一个咪唑烷五员环,同时,原来B分子中的碳原子由sp~2杂化转变为sp~3杂化。反应过程中A和B分子由于结构变化造成的分子能量增量之和小于A和B分子相互作用(吸引)能的绝对值,因此,A和B分子可以结合生成C分子。     

Coverage-enhancing approach in multimedia directional sensor networks for smart transportation

Smart transportation has a significantly impact on city management and city planning, which has received extensive attentions from academic and industrial communities. Different from omni-directional sensing system, as a directional sensing system, the multimedia-directional sensor network holds the special coverage scheme, which is usually used for smart cities, smart transportation, and harsh environment surveillance, for instance, nuclear-pollution regions where are inhospitable for people. This paper advances Virtual Stream Artificial Fish-swarm based Coverage-Enhancing Algorithm (VSAFCEA) as a coverage-enhancing means in multimedia directional sensor networks. Firstly, a concept of virtual streams, based on traditional artificial fish-swarm algorithm, is proposed. Then, the traditional behaviors of fishes in artificial fish-swarm algorithm are modified and expanded with several new behaviors. Finally, the presented VSAFCEA is adopted for coverage-enhancing issue in the situation of directional sensor networks with rotational direction-adjustable model. With a sequence of steps of artificial fishes in virtual stream, the presented VSAFCEA can figure out the approximation to the highest area coverage rate. Based on comparison of these simulation results (results of presented VSAFCEA and that of other typical coverage-enhancing ways in directional sensor networks), the conclusion can be drawn that VSAFCEA could attain higher area coverage rate of directional sensor networks with fewer iterative computing times.

     

Corpus based part-of-speech tagging

In natural language processing, a crucial subsystem in a wide range of applications is a part-of-speech (POS) tagger, which labels (or classifies) unannotated words of natural language with POS labels corresponding to categories such as noun, verb or adjective. Mainstream approaches are generally corpus-based: a POS tagger learns from a corpus of pre-annotated data how to correctly tag unlabeled data. Presented here is a brief state-of-the-art account on POS tagging. POS tagging approaches make use of labeled corpus to train computational trained models. Several typical models of three kings of tagging are introduced in this article: rule-based tagging, statistical approaches and evolution algorithms. The advantages and the pitfalls of each typical tagging are discussed and analyzed. Some rule-based and stochastic methods have been successfully achieved accuracies of 93–96 %, while that of some evolution algorithms are about 96–97 %.     

Delay-dependent <Emphasis Type="Italic">H</Emphasis><Subscript>∞</Subscript> control for a class of uncertain time-delay singular Markovian jump systems via hybrid impulsive control

This paper deals with the problem of robust normalization and delay-dependent H _∞ control for a class of singular Markovian jump systems with norm-bounded parameter uncertainties and time delay. A new impulsive and proportional-derivative control strategy with memory is presented, which results in a novel class of hybrid impulsive systems. Sufficient conditions are developed to guarantee that the resultant closed-loop system is not only robust normal and stochastically stable, but also satisfies a prescribed H _∞ performance level for all delays no larger than a given upper bound. In addition, the explicit expression of the desired impulsive control gains is also given together with the design approach. Finally, two numerical examples are provided to illustrate the effectiveness of the proposed methods.     

Traffic Signal Timing via Deep Reinforcement Learning

In this paper, we propose a set of algorithms to design signal timing plans via deep reinforcement learning. The core idea of this approach is to set up a deep neural network (DNN) to learn the Q-function of reinforcement learning from the sampled traffic state/control inputs and the corresponding traffic system performance output. Based on the obtained DNN, we can find the appropriate signal timing policies by implicitly modeling the control actions and the change of system states. We explain the possible benefits and implementation tricks of this new approach. The relationships between this new approach and some existing approaches are also carefully discussed.      

信息安全本科毕业生质量分析

为进一步了解信息安全本科毕业生的就业情况、用人单位和毕业生对信息安全人才培养的意见和建议,调查了杭州电子科技大学2008-2012届信息安全本科毕业生的就业状况,对用人单位和部分毕业生进行了专访和调查,并对调查结果进行了分析和研究。总结了企业对毕业生能力的要求、总体评价和在人才培养方面的建议,也包括毕业生对学校教学工作的评价、人才培养的意见和建议。文章提出信息安全本科专业在人才培养目标、知识和能力结构、基础理论与专业教学、培养途径和就业指导方面的观点的建议。     

基于C#的DES加密算法的应用

信息安全问题伴随着信息技术和产业的急速发展越来越突出,为了防止信息的泄漏,对信息的加密保护迫在眉睫.本文运用C#语言对DES这种复杂度高、保护性强的算法进行了算法实现,并在数据库信息的加密中加以应用.