基于无线传感器网络的动态手势识别算法及动态演示

与语言文字一样,手势是人类沟通交流的重要方式。在计算机技术高速发展的现在,手势识别技术的出现,能大大提高用户与机器设备、计算机部件之间的交流效率。现在采用摄像头跟踪进行手势识别的技术已经使用一段时间,但是其对硬件的要求高、分析的数据大,使得其产品相对昂贵或者识别能力不够强。与摄像头跟踪技术相比,基于无线传感器网络的手势识别速度更快,价格更便宜并且实用。通过对移动终端设计一套手势识别的算法,利用无线传感器网络实现用手机终端操作计算机的终端的一系列复杂命令。实验证明,该算法适用于大型游戏操作和平台展示,使用户不用通过购买相关硬件直接安装使用。     

《计算机组装与办公设备维护》课程改革探讨

《计算机组装与办公设备维护》是高职院校计算机应用技术专业一门实践性很强的核心课程,随着办公自动化的普及．不少用人单位对计算机与各种办公设备实操能力的要求越来越高,所以熟练操作、维护计算机和办公设备,提高实践操作技能．是培养学生的目标。从实际教学出发,探讨课程教学过程中存在的问题和难点,结合人才培养方案、课程目标、学生和课程特点,提出讲练结合等多种改革方法,以达到更好的教学效果。     

电子教室在高职计算机实践教学中的应用

计算机实践教学是高职教育的重要环节,传统的多媒体教学手段已经无法满足现代计算机实践教学的需求。电子教室是现代信息技术的产物,也是现代计算机实践教学过程中重要的工具,倡导电子教室与传统教学手段相结合,既能提升学生获取知识的便捷性,又能激发学生的学习兴趣。     

案例教学法在《大学计算机基础》课程教学中的应用

通过对《大学计算机基础》教学现状及存在的问题进行分析,探讨一种与传统教学方法不同的新型教学方法,即案例教学法。重点研究如何有效地把案例教学方法应用到教学中,从而提高教学效率。实践证明案例教学法是一种有效的教学方法．有利于培养学生的学习兴趣和动手能力,能取得良好的教学效果。     

一种基于移动P2P平台的无线传感器网络体系结构

借助P2P思想,构建一个基于移动网络基础设施提供对等服务的无线传感器网络体系结构。移动网络充当移动P2P平台,提供一种普适的传感器组网。代表传感器网络的网关节点隐藏了其实现细节,在P2P模式工作下向用户提供更加优质的个人服务。此外,还讨论了服务平台构建过程中的一般性问题。     

Visual learning and classification of human epithelial type 2 cell images through spontaneous activity patterns

Identifying the presence of anti-nuclear antibody (ANA) in human epithelial type 2 (HEp-2) cells via the indirect immunofluorescence (IIF) protocol is commonly used to diagnose various connective tissue diseases in clinical pathology tests. As it is a labour and time intensive diagnostic process, several computer aided diagnostic (CAD) systems have been proposed. However, the existing CAD systems suffer from numerous shortcomings due to the selection of features, which is commonly based on expert experience. Such a choice of features may not work well when the CAD systems are retasked to another dataset. To address this, in our previous work, we proposed a novel approach that learns a set of filters from HEp-2 cell images. It is inspired by the receptive fields in the mammalian's vision system, since the receptive fields can be thought as a set of filters for similar shapes. We obtain robust filters for HEp-2 cell classification by employing the independent component analysis (ICA) framework. Although, this approach may be held back due to one particular problem; ICA learning requires a sufficiently large volume of training data which is not always available. In this paper, we demonstrate a biologically inspired solution to address this issue via the use of spontaneous activity patterns (SAP). The spontaneous activity patterns, which are related to the spontaneous neural activities initialised by the chemical release in the brain, are found as the typical stimuli for the visual cell development of newborn animals. In the classification system for HEp-2 cells, we propose to model SAP as a set of small image patches containing randomly positioned Gaussian spots. The SAP image patches are generated and mixed with the training images in order to learn filters via the ICA framework. The obtained filters are adopted to extract the set of responses from a HEp-2 cell image. We then employ regions from this set of responses and stack them into “cubic regions”, and apply a classification based on the correlation information of the features. We show that applying the additional SAP leads to a better classification performance on HEp-2 cell images compared to using only the existing patterns for training ICA filters. The improvement on classification is particularly significant when there are not enough specimen images available in the training set, as SAP adds more variations to the existing data that makes the learned ICA model more robust. We show that the proposed approach consistently outperforms three recently proposed CAD systems on two publicly available datasets: ICPR HEp-2 contest and SNPHEp-2.     

Importance Sampling Microfacet‐Based BSDFs using the Distribution of Visible Normals

We present a new approach to microfacet‐based BSDF importance sampling. Previously proposed sampling schemes for popular analytic BSDFs typically begin by choosing a microfacet normal at random in a way that is independent of direction of incident light. To sample the full BSDF using these normals requires arbitrarily large sample weights leading to possible fireflies. Additionally, at grazing angles nearly half of the sampled normals face away from the incident ray and must be rejected, making the sampling scheme inefficient. Instead, we show how to use the distribution of visible normals directly to generate samples, where normals are weighted by their projection factor toward the incident direction. In this way, no backfacing normals are sampled and the sample weights contain only the shadowing factor of outgoing rays (and additionally a Fresnel term for conductors). Arbitrarily large sample weights are avoided and variance is reduced. Since the BSDF depends on the microsurface model, we describe our sampling algorithm for two models: the V‐cavity and the Smith models. We demonstrate results for both isotropic and anisotropic rough conductors and dielectrics with Beckmann and GGX distributions.     

3D Motion Completion in Crowded Scenes

Crowded motions refer to multiple objects moving around and interacting such as crowds, pedestrians and etc. We capture crowded scenes using a depth scanner at video frame rates. Thus, our input is a set of depth frames which sample the scene over time. Processing such data is challenging as it is highly unorganized, with large spatio‐temporal holes due to many occlusions. As no correspondence is given, locally tracking 3D points across frames is hard due to noise and missing regions. Furthermore global segmentation and motion completion in presence of large occlusions is ambiguous and hard to predict. Our algorithm utilizes Gestalt principles of common fate and good continuity to compute motion tracking and completion respectively. Our technique does not assume any pre‐given markers or motion template priors. Our key‐idea is to reduce the motion completion problem to a 1D curve fitting and matching problem which can be solved efficiently using a global optimization scheme. We demonstrate our segmentation and completion method on a variety of synthetic and real world crowded scanned scenes.     

3D Shape Segmentation and Labeling via Extreme Learning Machine

We propose a fast method for 3D shape segmentation and labeling via Extreme Learning Machine (ELM). Given a set of example shapes with labeled segmentation, we train an ELM classifier and use it to produce initial segmentation for test shapes. Based on the initial segmentation, we compute the final smooth segmentation through a graph‐cut optimization constrained by the super‐face boundaries obtained by over‐segmentation and the active contours computed from ELM segmentation. Experimental results show that our method achieves comparable results against the state‐of‐the‐arts, but reduces the training time by approximately two orders of magnitude, both for face‐level and super‐face‐level, making it scale well for large datasets. Based on such notable improvement, we demonstrate the application of our method for fast online sequential learning for 3D shape segmentation at face level, as well as realtime sequential learning at super‐face level.