Visualizing for the Non‐Visual: Enabling the Visually Impaired to Use Visualization

The majority of visualizations on the web are still stored as raster images, making them inaccessible to visually impaired users. We propose a deep‐neural‐network‐based approach that automatically recognizes key elements in a visualization, including a visualization type, graphical elements, labels, legends, and most importantly, the original data conveyed in the visualization. We leverage such extracted information to provide visually impaired people with the reading of the extracted information. Based on interviews with visually impaired users, we built a Google Chrome extension designed to work with screen reader software to automatically decode charts on a webpage using our pipeline. We compared the performance of the back‐end algorithm with existing methods and evaluated the utility using qualitative feedback from visually impaired users.     

基于CCS和DSK5402板的音频采集和回放程序设计

为了给程序设计作下基础,本文首先介绍了TI公司的TMS320VC5402和AIC(模拟接口电路)芯片TLC320AD50C的特点,最后着重介绍了利用DSK板上的TMS320C5402和TLC320AD50C实现音频采集并实时回放的软件设计过程,并利用CCS进行了模拟.     

利用Matlab和Simulink对DSP进行系统级的设计方法

本文介绍了利用Matlab和Simulink中Developer‘s Kit for TI DSP工具对DSP进行系统级设计的方法。     

A proof system for communicating processes with value-passing

A proof system for a version of CCS with value-passing is proposed in which the reasoning about data is factored out from that about the structure of processes. The system is shown to be sound and complete for finite terms with respect to a denotational semantics based on Acceptance Trees.     

面向对象方法在7号信令软件设计中的应用

随着交换技术的飞速发展，程控交换软件的规模越来越大，改动也越来越频繁，如何使代码对数据的改动具有相对的稳定性成为程控软件设计的一个首要问题。本文以ＣＣＳ７（７号公共信道信令）为例，从对象模型、动态模型、功能模型３个方面阐述了ＯＯ（面向对象）方法在ＣＣＳ７软件设计中的应用。这一方法，也可应用于程控软件的设计。     

中国煤电生命周期二氧化碳和大气污染物排放相互影响建模分析

煤电在中国电力供应结构中占据主导地位,其环境影响是研究热点之一。建立中国煤电生命周期二氧化碳和大气污染物排放分析模型,基于文献调研构建参数数据库,测算中国煤电的单位发电量排放。结果表明,近年来中国煤电生命周期单位发电量的CO₂、SO₂、NO_x和PM_2.5排放分别为838.6 g/（kW·h）、0.34 g/（kW·h）、0.32 g/（kW·h）和0.08 g/（kW·h）。其中煤电单位发电量大气污染物排放,比实施超低排放改造前,下降幅度超过90%。研究发现,增大单机机组规模和进行超低排放改造能够有效降低煤电发电过程的大气污染物排放,采用煤电燃烧后碳捕集和存储(carbon capture and storage, CCS)处理技术能够使煤电CO₂排放下降到144 g/（kW·h）,助力碳中和目标实现。如果不采用更加严格的大气污染物排放标准和处理方式,CCS技术可能会使煤电大气污染物排放强度上升30%~40%,这与碳捕集过程使用的技术有关。     

CO2混合物热物性在CCS研究中的作用:实验数据、理论模型和典型应用

二氧化碳捕集与封存（CCS）各工艺过程的设计、运行都依赖于对CO₂及其混合物热物理性质的深入理解。同时，CCS的规模化发展和商业化进程，对CO₂混合物及其热物性的准确性提出了更高的要求。本文从实验数据、理论模型和典型应用3个方面综述了CO₂及其混合物热物性的发展现状，并尝试对发展趋势进行归纳。在实验研究方面，CO₂混合体系的研究进展视组分不同，差异较大，其中CO₂-N₂、CO₂-CH₄、CO₂-H₂O和CO₂-H₂二元体系已形成较完善的物性数据库，而CO₂-NH₃、CO₂-NO _x 和CO₂-CO体系的物性数据还比较欠缺；在物性估算方面，面向CCS的物性估算模型研究自2008年开始活跃，基于不同理论构架，目前已逐步形成面向CCS的多元化的物性估算体系。物性研究在CCS中的应用主要体现在物性是支撑CCS过程研究的基础，其不准确性在过程模拟或计算中会被“放大”，从而影响过程评估的准确性，本文从物性在循环构建和能效分析中的作用以及CO₂水合物的形成3个方面入手做了说明。文章最后对面向CCS的物性研究趋势进行了梳理，对分子模拟技术、通用性强的物性估算模型和物性在过程设计和循环分析中的角色进行了展望。     

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.