一、 什么是马克思主义 二、 马克思主义的产生和发展 三、 马克思主义的科学性与革命性的统一 四、 努力学习和自觉运用马克思主义

1.世界的物质性 2.社会生活本质上是实践的 3.事物的普遍联系和永恒发展 4.对立统一规律是宇宙的根本规律 5.唯物辩证法是根本方法 6.自然规律和社会规律 7.主观能动性与客观规律性的辩证统一

马克思主义新闻观 新闻学理论学习什么内容 如何学习新闻学 新闻学是一门跨学科学科 主要分支 新闻学理论导论 新闻学与传播学的区别与联系 几个相关的概念 新闻学研究的特点与趋势 概念的借用 传播学 吸收的成果 新闻学对传播学和三论的借鉴 研究方法 三论

§2.1 Discrete-Time Signals: Time-Domain Representation §2.2 Operations on Sequences §2.3 Basic Sequences §2.4 The Sampling Process §2.5 Discrete-Time Systems §2.6 Time-Domain Characterization of LTI Discrete-Time System §2.7 Classification of LTI Discrete-Time Systems §2.8 Correlation of Signals

§4.1 LTI Discrete-Time Systems in the Transform Domain §4.2 The Frequency Response §4.3 Frequency Response Computation Using MATLAB §4.4 The Concept of Filtering §4.5 Phase and Group Delays §4.6 Frequency Response of the LTI Discrete-Time System §4.7 The Transfer Function §4.8 The Transfer Function §4.9 Frequency Response from Transfer Function §4.10 Types of Transfer Functions §4.11 Linear-Phase FIR Transfer Functions §4.12 Allpass Transfer Function §4.13 Minimum-Phase and Maximum-Phase Transfer Functions

§7.1 Digital Filter Specifications §7.2 Selection of Filter Type §7.3 Digital Filter Design: Basic Approaches §7.4 IIR Digital Filter Design: Bilinear Transformation Method §7.5 IIR Highpass, Bandpass, and Bandstop Digital Filter Design §7.6 Fixed Window Functions §7.7 FIR Filter Design Example

§5.1 Digital Processing of Continuous-Time Signals §5.2 Sampling of Continuous-time Signals §5.3 Effect of Sampling in the Frequency Domain §5.4 Recovery of the Analog Signal §5.5 Implication of the Sampling Process §5.6 Sampling of Bandpass Signals §5.7 Analog Lowpass Filter Specifications §5.8 Analog Lowpass Filter Design

• 第一节 骨连结概述 • 第二节 躯干骨的连结 • 第三节 头骨的连结 • 第四节 前肢骨的连结 • 第五节 后肢骨的连结

◆ Color Fundamentals ◆ Color Models ◆ Pseudocolor Image Processing ◆ Basics of Full-Color Image Processing ◆ Color Transformations ◆ Smoothing and Sharpening ◆ Image Segmentation Based on Color ◆ Noise in Color Images ◆ Color Image Compression

◆ A Model of the Image Degradation/Restoration Process ◆ Noise Models ◆ Restoration in the Presence of Noise Only-Spatial Filtering ◆ Periodic Noise Reduction by Frequency Domain Filtering ◆ Linear, Position-Invariant Degradations ◆ Estimating the Degradation Function ◆ Inverse Filtering ◆ Minimum Mean Square Error (Wiener) Filtering ◆ Constrained Least Squares Filtering ◆ Geometric Mean Filter ◆ Image Reconstruction from Projections*