易道

生物识别技术主要是指通过可测量的身体或行为等生物特征进行身份认证的一种技术；而生物特征是指唯一的可以测量或可自动识别和验证的生理特征或行为方式。生物特征分为身体特征和行为特征两类。身体特征包括：指纹、掌型、视网膜、虹膜、人体气味、脸型、手的血管和DNA等；行为特征包括：签名、语音、行走步态等。目前部分学者将视网膜识别、虹膜识别和指纹识别等归为高级生物识别技术；将掌型识别、脸型识别、语音识别和签名识别等归为次级生物识别技术；将血管纹理识别、人体气味识别、DNA识别等归为“深奥的”生物识别技术。

Course Objectives This course will provide an in-depth analysis of the origin of the extra-ordinary sensitivity, fundamental limits, and operating principles of modern nanobiosensors. The primary focus will be the physics of biomolecule detection in terms of three elementary concepts: response time, sensitivity, and selectivity. And, we will use potentiometric, amperometric, and cantilever-based mass sensors to illustrate the application of these concepts to specific sensor technologies. Students of this course will not learn how to fabricate a sensor, but will be able to decide what sensor to make, appreciate their design principles, interpret measured results, and spot emerging research trends. Who Should Take the Course Engineers, chemists, physicists, technology developers, and product managers who have an interest in the emerging field of nanobiosensing. Prerequisites Freshman/sophomore level preparation in physics, chemistry, biology, and mathematics. Course Outline Unit 1: Introduction to Nanobiosensors/ Settling Time L1.1: What are Nanobiosensors, Anyway? L1.2: Basic Concepts: Biomolecules, Analyte Density, Diffusion Distances L1.3: Basic Concepts: Types of Biosensors, Geometry of Biosensing L2.1: Shape of a Surface L2.2: Classical Sensors I L2.3: Classical Sensors II Unit 2: Setting Time L2.4: Sensors with Complex Geometry L2.5: Beating the Limits – Barcode Sensors L2.6: Beating the Limits – Droplet Evaporation L2.7: Beating the Diffusion Limit – Enhanced Diffusion and Fluid Flow L2.8: First Passage and Narrow Escape Time I L2.9: First Passage and Narrow Escape Time II Unit 3: Sensitivity L3.1: Nanobiosensors Sensitivity and Types of Biosensors L3.2: Potentiometric Sensors: Charge Screening for a Planar Sensor L3.3: Potentiometric Sensors: Charge Screening for Cylindrical Sensors L3.4: Potentiometric Sensors: ISFET as a pH-Meter L3.5: Potentiometric Sensors: Why are Biomolecules Charged? L3.6: How to Beat Screening Unit 4: Selectivity L3.7: Amperometric Sensors – Glucose Sensor I L3.8: Amperometric Sensors: Glucose Sensors II L3.9: Amperometric Sensors: Beating the Diffusion Limit by Nanogap Amperometry L3.10: Cantilever-based Sensors: BasicOperation L3.11: Cantilever-based Sensors: Static Response L3.12: Cantilever-based Sensors: Nonlinear Sensing – Flexure FET Unit 5: Putting the Pieces Together L4.1: Introduction and Molecular Recognition L4.2: Physics of Sequential Adsorption L4.3: When all else fails, tag, filer and amplify L4.4: Noise Tranducers L5.1: Genome Sequencer I L5.2: Genome Sequencer II L5.3: Genome Sequencer III L5.4: Concluding Thoughts

神经元网络入门视频——来源于Youtube 包括： 视频1——Data and Architecture 视频2——Forward Propagation 视频3——Gradient Descent 视频4——Backpropagation 视频5——Numerical Gradient Checking 视频6——Training 视频7—— Overfitting, Testing, and Regularization

自己写的.net compact framework下的实时绘图控件，只要往控件的pointlist中放入点，重新invalidate一下窗口，就可以看到动态的绘图效果，解决了闪烁问题。 但这一版功能还比较简单，望大家多多指教

MSDN培训的PPT,有源码 个人感觉丰常不错的PPT 对学习C#或讲授C#都很有帮助

第三章 WinCE开发工具 介绍WinCE开发所所需的软硬件环境

第二章 WinCE系统架构 根据《Windows CE嵌入式系统》教材PPT改编

第一章 操作系统概述及WinCE 根据《Windows CE嵌入式系统》教材PPT改编