MATLAB和SIMU金宝appLINK用于信号处理
分析信号和时间序列数据。模型,设计和模拟信号处理系统。
信号处理工程师使用MATLAB®和Sim金宝appulink®在开发的各个阶段 - 从分析信号和探索算法到评估设计实现实时信号处理系统的设计实施权衡。MATLAB和SIMU金宝appLINK提供:
- 内置功能和应用程序,用于分析和预处理时间序列数据,光谱和时频分析以及信号测量值
- 从基本的FIR和IIR过滤器到自适应,多阶段和多阶段设计,用于设计,分析和实施数字过滤器(FIR和IIR)的应用程序和算法
- 与程序和框图组合结合的信号处理系统建模和模拟信号处理系统的环境
- 建模功能fixed-point behavior并自动生成用于部署嵌入式处理器,FPGA和ASIC的C/C ++或HDL代码
- Tools for developing predictive models on signals and sensor data using machine learning and deep learning workflows
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阅读电子书信号分析和测量
使用MATLAB和Si金宝appmulink帮助您分析信号built-in apps for visualizing and preprocessing signals in time, frequency, and time-frequency domains to detect patterns and trends without having to manually write code. You can characterize signals and signal processing systems using domain-specific algorithms across different applications such as communications, radar, audio, medical devices, and IoT.
Filter Design and Analysis
设计和分析数字过滤器,从基本的单速率低通或高通到更先进的FIR和IIR设计,包括多阶段,多阶段和自适应过滤器。您可以可视化大小,相位,组延迟和脉冲响应,并评估滤波器性能,包括稳定性和相位线性。可以分析和模拟滤波器设计,以评估不同内部结构和固定点数据类型的效果。他们还可以生成嵌入式软件或硬件实现。对于高级和特定应用程序的用例,您可以利用预先设计的过滤器和过滤器库,例如基于小波的滤波器库,感知间隔的过滤器库或渠道化器。
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基于模型的信号处理设计
设计信号处理系统时,您可以使用框图和基于语言的编程的组合。您可以使用Simulin金宝appk将基于模型的设计应用于信号处理系统,以建模,仿真,早期验证和代码生成。您可以将块的库带有特定于应用程序的算法,以用于基线信号处理,音频,模拟混合信号和RF,有线和无线通信以及雷达系统。您可以使用虚拟范围(包括频谱和逻辑分析仪,星座和眼图)在模拟过程中可视化实时信号。
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Embedded Code Generation
You can automatically generate C and C++ code from signal processing algorithms and bit-accurate system models using MATLAB Coder™ and Simulink Coder™. The generated code can be used for simulation acceleration, rapid prototyping, and embedded implementation of your system. You can also generate optimized C code for targeting embedded hardware processors such as ARM®Cortex®-A or Cortex-M.
You can also generate portable, synthesizable Verilog®和VHDL®来自MATLAB函数和Simulink模型的代码。金宝app生成的HDL代码可用于FPGA编程或ASIC设计。
Machine and Deep Learning
With MATLAB, you can build predictive models for signal processing applications. You can exploit built-in signal processing algorithms to extract features for machine learning systems as well as work with large datasets for ingesting, augmenting, and annotating signals when developing deep learning applications.