“有意味”的视觉符号设计——重庆火锅品牌的logo设计研究

重庆火锅因其独特的味感成为川东饮食文化最为代表的符号系统且形成了相应的"品牌效应"。从某种意义上,重庆火锅传递着特定地域饮食文化与生活生产发展的文化信码,而火锅品牌的logo则是具有特定指向的"有意味"的视觉符号设计。因此,对其视觉符号系统的"有意味"性的诠释主要涵盖三个方面:采集典型火锅品牌的logo设计及其应用范畴、分析不同品牌logo设计的异同及存在的问题、通过对典型案例的综合分析进而思考区域特色品牌logo设计并提出相应的建议。     

浅析安卓计算器的开发

随着安卓智能平台的逐步完善,安卓已经成为目前最主流的智能平台。该文描述了安卓的系统构架和环境的搭建,通过使用安卓平台推出的SDK进行计算器的开发,在模拟器上仿真实现了数字的加减乘除等功能,最后分析了安卓应用开发的优缺点。     

浅析计算机网络故障的原因及对策

随着计算机网络技术渗透到人们生活中的方面,在使用网络的同时,每个融入到网络中的使用者都在使用过程中遇到各种各样的网络故障,由于这些网络故障而给人们带了各式的烦恼。该文则对这些网络故障产生的原因作总结和解决问题的对策。     

大学计算机基础课程教学模式改革的研究

大学计算机基础课程是高校非计算机专业的一门公共基础课。它着力培养学生运用计算机技术分析问题、解决问题的意识和能力;培养学生的系统思维能力,提高大学生的信息素养。该文分析了目前大学计算机基础课程教学的存在的主要问题,针对问题,提出了一些改革性的建议。     

面向生态监测的IPv6无线传感器网络研制及应用

环境监测是无线传感器网络应用的重要领域,特别是在流域生态学的研究中,发挥着非常重要的作用。为了帮助生态学家在恶劣的环境中收集科学数据,针对黑河流域生态研究的需要,我们提出了一套 IPv6 无线传感器网络(IPv6WSN) 的系统设计方案。本文重点介绍了方案中的三个关键技术:硬件平台的设计,IPv6 网络协议的集成以及 IPv6 无线传感网络(WSN) 的元数据建模。该系统已经在黑河流域研究计划(HiWater) 进行了实际部署和使用,为参与该研究计划的各相关领域科学家提供了大量的第一手数据,同时也为自主开发的 IPv6 无线传感网络在生态环境监测中的有效使用奠定了坚实的基础。     

M2M通信技术及其在智能电网中的应用

M2M通信是近年来随着物联网的兴起和发展而得到长足发展的下一代无线通信技术。该技术的发展和普及为智能电网功能的扩展和实现奠定了基础。文章介绍了M2M技术的发展历史和技术特色,探索了M2M通信在智能电网,特别是家庭区域网络的应用前景,同时指出了M2M通信技术目前所面临的问题和挑战。     

改良救护车急救系统 提高危重病人存活率——救护车增设便携式气管插管系统和呼吸机势在必行

在抢救急危重症病人生命、应对自然灾害和突发事件中,救护车发挥着极为重要的作用。然而我国救护车车载装 备仍然比较落后,只能提供最基本的抢救处理。本文从改良急救车装备角度出发,提出在救护车上增设便携式气管插管 系统和呼吸机,为呼吸困难的危重病人快速建立人工气道,最大限度赢得进一步救治的时间和机会,大大提高危重病人 存活率。     

Generative face alignment through 2.5D active appearance models

This work addresses the matching of a 3D deformable face model to 2D images through a 2.5D Active Appearance Models (AAM). We propose a 2.5D AAM that combines a 3D metric Point Distribution Model (PDM) and a 2D appearance model whose control points are defined by a full perspective projection of the PDM. The advantage is that, assuming a calibrated camera, 3D metric shapes can be retrieved from single view images. Two model fitting algorithms and their computational efficient approximations are proposed: the Simultaneous Forwards Additive (SFA) and the Normalization Forwards Additive (NFA), both based on the Lucas–Kanade framework. The SFA algorithm searches for shape and appearance parameters simultaneously whereas the NFA projects out the appearance from the error image and searches only for the shape parameters. SFA is therefore more accurate. Robust solutions for the SFA and NFA are also proposed in order to take into account the self-occlusion or partial occlusion of the face. Several performance evaluations for the SFA, NFA and theirs efficient approximations were performed. The experiments include evaluating the frequency of converge, the fitting performance in unseen data and the tracking performance in the FGNET Talking Face sequence. All results show that the 2.5D AAM can outperform both the 2D + 3D combined models and the 2D standard methods. The robust extensions to occlusion were tested on a synthetic sequence showing that the model can deal efficiently with large head rotation.     

Single machine batch scheduling with release times and delivery costs

We study single machine batch scheduling with release times. Our goal is to minimize the sum of weighted flow times (or completion times) and delivery costs. Since the problem is strongly $\mathcal{NP}$ -hard even with no delivery cost and identical weights for all jobs, an approximation algorithm is presented for the problem with identical weights. This uses the polynomial time solution we give for the preemptive version of the problem. We also present an evolutionary metaheuristic algorithm for the general case. Computational results show very small gaps between the results of the metaheuristic and the lower bound.     

ELCA evaluation for keyword search on probabilistic XML data

As probabilistic data management is becoming one of the main research focuses and keyword search is turning into a more popular query means, it is natural to think how to support keyword queries on probabilistic XML data. With regards to keyword query on deterministic XML documents, ELCA (Exclusive Lowest Common Ancestor) semantics allows more relevant fragments rooted at the ELCAs to appear as results and is more popular compared with other keyword query result semantics (such as SLCAs). In this paper, we investigate how to evaluate ELCA results for keyword queries on probabilistic XML documents. After defining probabilistic ELCA semantics in terms of possible world semantics, we propose an approach to compute ELCA probabilities without generating possible worlds. Then we develop an efficient stack-based algorithm that can find all probabilistic ELCA results and their ELCA probabilities for a given keyword query on a probabilistic XML document. Finally, we experimentally evaluate the proposed ELCA algorithm and compare it with its SLCA counterpart in aspects of result probability, time and space efficiency, and scalability.