危险废物贮存库防渗层网络化实时监测系统

该文以两检测电极间充满渗出液时电阻发生突变的特点作为HDPE膜渗漏的判据,结合渗出液的化学成分来判断渗漏的种类,利用TC35I模块通过GSM网进入INTERNET进行数据的交换,实现了危险物贮存库渗漏的网络化监测,为各级环保部门的管理提供了科学的依据。利用HDPE膜的绝缘性,根据贮存库中电场分布特点,提出了偶极子的漏洞定位方法,成功地解决了漏洞定位的问题。并指出检测电压随着漏洞的大小、偶极子的间距、贮存库液体的电阻率、供电电压的增加而增加,随着偶极子距HDPE膜的距离、偶极子的移动方向到漏洞的距离、贮存库液体的深度的增加而减小。该方法为填埋场HDPE膜铺设过程中的施工验收提供了参考。     

基于xPC目标的实时I／O接口技术研究

使用xPC目标可以将一台PC兼容机制转变为一个实时系统,来实现控制系统的快速原型化、硬件在回路中的测试,与传统方法相比方便、快捷很多。在工程应用中,负责与目标硬件进行实时通信的I／O接口是由I／O板卡及相应的设备驱动程序构成,这也是xPC目标实现的基础。在对比、论述xPC目标所支持的PCI和ISA两种主要总线类型I／O接口技术的基础上,对基于PCI总线的DAQ和多串口设备驱动进行了实际测试。     

供热自动控制中的室温采集电话的设计与实现

应用于供暖自动控制中的室温采集电话除了具有普通电话机的功能外还具有采集室温的功能.室温采集电话通过公共电话网络,接收到室温采集基站的请求后将温度上传至PC机,构成远程温度采集系统.本文基于普通电话的通话电路用HT95C30P微控制器和温度芯片LM92设计和实现室温采集电话.     

Panorama light‐field imaging

We present a novel approach to recording and computing panorama light fields. In contrast to previous methods that estimate panorama light fields from focal stacks or naive multi‐perspective image stitching, our approach is the first that processes ray entries directly and does not require depth reconstruction or matching of image features. Arbitrarily complex scenes can therefore be captured while preserving correct occlusion boundaries, anisotropic reflections, refractions, and other light effects that go beyond diffuse reflections of Lambertian surfaces.     

On‐the‐fly generation and rendering of infinite cities on the GPU

In this paper, we present a new approach for shape‐grammar‐based generation and rendering of huge cities in real‐time on the graphics processing unit (GPU). Traditional approaches rely on evaluating a shape grammar and storing the geometry produced as a preprocessing step. During rendering, the pregenerated data is then streamed to the GPU. By interweaving generation and rendering, we overcome the problems and limitations of streaming pregenerated data. Using our methods of visibility pruning and adaptive level of detail, we are able to dynamically generate only the geometry needed to render the current view in real‐time directly on the GPU. We also present a robust and efficient way to dynamically update a scene's derivation tree and geometry, enabling us to exploit frame‐to‐frame coherence. Our combined generation and rendering is significantly faster than all previous work. For detailed scenes, we are capable of generating geometry more rapidly than even just copying pregenerated data from main memory, enabling us to render cities with thousands of buildings at up to 100 frames per second, even with the camera moving at supersonic speed.     

Bilinear Accelerated Filter Approximation

Our method approximates exact texture filtering for arbitrary scales and translations of an image while taking into account the performance characteristics of modern GPUs. Our algorithm is fast because it accesses textures with a high degree of spatial locality. Using bilinear samples guarantees that the texels we read are in a regular pattern and that we use a hardware accelerated path. We control the texel weights by manipulating the u, v parameters of each sample and the blend factor between the samples. Our method is similar in quality to Cardinality‐Constrained Texture Filtering [ MS13 ] but runs two times faster.     

Feature‐Preserving Surface Completion Using Four Points

We present a user‐guided, semi‐automatic approach to completing large holes in a mesh. The reconstruction of the missing features in such holes is usually ambiguous. Thus, unsupervised methods may produce unsatisfactory results. To overcome this problem, we let the user indicate constraints by providing merely four points per important feature curve on the mesh. Our algorithm regards this input as an indication of an important broken feature curve. Our completion is formulated as a global energy minimization problem, with user‐defined spatial‐coherence constraints, allows for completion that adheres to the existing features. We demonstrate the method on example problems that are not handled satisfactorily by fully automatic methods.     

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.