A new linearly constrained minimum variance beamformer for reconstructing EEG sparse sources

Brain source imaging based on EEG aims to reconstruct the neural activities producing the scalp potentials. This includes solving the forward and inverse problems. The aim of the inverse problem is to estimate the activity of the brain sources based on the measured data and leadfield matrix computed in the forward step. Spatial filtering, also known as beamforming, is an inverse method that reconstructs the time course of the source at a particular location by weighting and linearly combining the sensor data. In this paper, we considered a temporal assumption related to the time course of the source, namely sparsity, in the Linearly Constrained Minimum Variance (LCMV) beamformer. This assumption sounds reasonable since not all brain sources are active all the time such as epileptic spikes and also some experimental protocols such as electrical stimulations of a peripheral nerve can be sparse in time. Developing the sparse beamformer is done by incorporating L1-norm regularization of the beamformer output in the relevant cost function while obtaining the filter weights. We called this new beamformer SParse LCMV (SP-LCMV). We compared the performance of the SP-LCMV with that of LCMV for both superficial and deep sources with different amplitudes using synthetic EEG signals. Also, we compared them in localization and reconstruction of sources underlying electric median nerve stimulation. Results show that the proposed sparse beamformer can enhance reconstruction of sparse sources especially in the case of sources with high amplitude spikes.     

基于机器学习识别与标记分水岭分割的盲道图像定位

视觉导盲仪是一种旨在解决盲人出行困难的导盲设备，为了实现视觉导盲仪诱导盲人找到盲道并沿盲道行走，提出了一种基于机器学习识别与标记分水岭分割的盲道图像定位算法，通过离线训练与在线的识别、分割来定位盲道区域。首先对盲道图像进行视角变换的预处理，根据盲道的地面方程将变化的倾斜视角转换为固定的俯视视角，消除射影变换带来的失真；然后利用局部二进制模式描述子提取鸟瞰图的纹理特征，以自适应增强学习算法离线训练盲道识别分类器；进而利用分类器对鸟瞰图像进行在线识别，粗略确定盲道区域；最后将识别结果进行形态学处理后作为标记，利用标记分水岭算法得到精确分割的盲道区域并定位盲道中心线。在研制的视觉导盲仪上进行验证，结果表明盲道定位准确率达到了95.44%，速度平均每秒8帧，具有高准确率的同时达到实时性要求，为盲道的准确三维定位提供了必要条件。     

基于量子秘密共享的盲签名方案

提出了一种基于量子秘密共享的盲签名方案。其中量子秘密共享中用到了Bell纠缠和诱骗光子；盲签名使用的是异或操作和Hash函数。Bell纠缠是纠缠态中最简单的纠缠，而异或操作也是简单易操作；诱骗光子和Hash函数保证了安全性，将这些结合的本方案简单安全易实现，同时还保证了信息的盲性、签名消息的不可否认和不可伪造性。     

基于DSP的电子行走辅助系统的设计与实现

为帮助视障者独立行走，基于DSP设计一套电子行走辅助系统。介绍系统的功能和基本结构，详细描述各模块的硬件设计和系统软件工作流程。该系统以实时图像处理为基础，能够完全脱离 PC机实现视频图像的采集、处理和语音提示等功能，帮助视障碍者独立行走。该系统具有小巧、便携、使用方便的特点，与传统助盲设备相比提高了导盲精度，与大型电子盲道或全球定位系统相比节省了成本，实验验证了其有效性。     

Extracting Low-Frequency Spatio-Temporal Patterns in Ambient Power System Data Using Blind Source Separation

Wide-area techniques provide a powerful tool to extract spatio-temporal patterns from high-dimensional datasets and can be used for event detection and visualization, data fusion, stability assessment, and coherency analysis. In this paper, a novel blind source separation-based approach for extracting low-frequency spatio-temporal patterns from measured ambient power system data is proposed and a spatio-temporal visualization index is also suggested. This methodology combines a nonlinear hierarchical neural network with a Blind Source Separation (BSS) technique. The neural network allows reducing noise and removing the nonlinear relations among data (preserve dynamic features of interest), while the BSS technique permits extracting spatial and temporal patterns. In addition, the proposed approach takes advantage of the latest techniques in nonlinear estimation of non-stationary time series. Finally, application examples of the proposed framework on real test cases recorded from an actual power system by Phasor Measurement Units (PMUs) are presented. The obtained results show that the temporal patterns can be used for extracting and identifying the low-frequency oscillation modes and the spatial patterns can be used for identifying modes with the most contribution in original data. Compared to other BSS approaches, the proposed method has shown to be better for the analysis of real ambient data.     

超大电解槽清槽方法优化与运用

电积过程中周期管理至关重要,然而,电解槽的清槽作业效果直接影响电积作业能否正常运行,超大电解槽清槽作业比小型电解槽清槽作业难度更大,本文将阐述如何将大型电解槽清槽作业工业化和成功运用于生产且取得良好的效果     

感官代偿在药品盲用无障碍包装设计中的应用研究

目的 针对盲人难以精准、便捷、安全服药的问题，基于感官代偿原理，研究如何利用触觉、听觉等感官代偿的潜在优势设计“适盲性”药品无障碍包装，辅助盲人实现科学、合理、精准用药。方法 基于盲人的生理和心理特点，分析药品包装在适应盲人使用时的“痛点”，在把握盲人感官代偿机理的基础上，结合具体案例，研究药品包装适盲性无障碍设计的途径和方法。结论 在基于盲人特殊的感官代偿机制设计盲用无障碍药品包装时，要理性、深度地思考如何最大限度地激发触觉和听觉的代偿潜能来解决盲人的服药障碍问题。通过简洁规范的盲文标注设计、科学合理的适盲性结构设计、智能交互的语音指令设计等方式，改善药品包装的核心信息呈现方式，提升药品信息传递的便捷性和准确性，使盲人能够独立自主地识药、取药和服药。     

基于面元素重排的三维网格模型数据隐藏算法

目的 为了解决目前三维数据隐藏算法不能兼顾无失真和盲提取的问题，提出一种新的完全无失真的三维网格模型数据隐藏盲算法。方法 首先使用混沌逻辑映射选择嵌入与提取模式，保证数据的安全性。然后利用面元素重排，完全不会造成三维模型失真的性质，通过不同嵌入模式规则对三角面元素进行重排，以嵌入秘密数据。接收端则可根据相应的提取模式规则提取秘密数据。结果 仿真结果与分析表明，该算法不会对三维模型造成任何失真，嵌入容量为每顶点2比特，且能抵抗仿射变换攻击、噪声攻击和平滑攻击等。结论 这种三维数据隐藏盲算法无失真，容量大、安全性高、鲁棒性强，适用于三维载体不容修改的情形，如军事、医学、秘密通信和版权保护等。     

大断面注浆硐室围岩稳定性分析及控制技术

为解决大断面注浆硐室围岩稳定性控制难题,采用FLAC3D软件对硐室围岩变形破坏特征进行了模拟分析。数值模拟结果表明硐室围岩变形控制的关键部位为硐室顶底板的边角部位和硐壁中部。硐室开挖采用"溜井出渣、分层开挖、及时支护"的思路,尽量减小开挖扰动对围岩的破坏。硐顶与硐壁支护方法为"锚网索喷"联合支护,临时"初喷"支护及时封闭围岩,二次"锚网索喷"支护充分调动了围岩的自承载能力。硐底部分采用锚索和钢筋混凝土衬砌支护,避免硐底边角部位产生剪切破坏。监测结果表明支护结构能够较好地满足了硐室长期稳定性和防渗要求。     

盲谷水库防渗处理——云南五里冲水库工程简介

云南五里冲水库是我国在岩溶地区利用盲谷采用以灌浆为主,结合堵、截进行防渗处理,首次建成的水深超百米的无坝中型水库.帷幕灌浆工程量21.6×104m,单层帷幕深160m,地下溶洞内建造了高100.4m,长50m,厚2.0～2.5m的钢筋混凝土防渗墙;在岩溶塌陷体内用高压灌浆建造帷幕.