资源列表
TMS320LF2812
- TMS320LF2812原理图，pdf格式-The TMS320LF2812 schematic, pdf format
exp05_int
- dsp的外部中断程序，与建立的工程，适用于初学者-DSP external interrupt program, with the establishment of project, it is suitable for beginners
delay
- To illustrate real-time digital signal processing using C6713 To demonstrate implementation of signal processing systems described by convolution To demonstrate implementation of signal processing systems described by difference equations -To illus
4
- To study the frequency response of real-time LTI systems using C6713 DSK 2. To demonstrate discrete LTI systems as frequency selective filters (e.g., digital resonators, notch filters, etc.) 3. To study real-time digital filtering of audio sign
fir_lp
- 1. To design FIR filters in MATLAB to achieve various types of frequency selectivity, e.g., lowpass, highpass，bandpass, etc 2. To verify the frequency response of the designed filters in MATLAB 3. To implement the designed FIR filters using C67
6
- 1. To design IIR filters in MATLAB to achieve various types of frequency selectivity, e.g.,lowpass, highpass, bandpass, etc 2. To implement the designed IIR filters in different architectural forms (e.g., direct form vssecond-order cascade form) u
fixed16_lp
- 1. To study the effects of quantization noise in digital signal processing systems 2. To investigate the effects of quantized filter coefficients on frequency response 3.To compare the effects of quantization on different implementation structure
8
- 1. To study representation of digital images as two dimensional (2D) signals 2. To apply signal processing techniques for digital image processing and 3. To implement and perform image processing, tasks such as image softening, sharpening, and
6f66f7997e2b
- face recognition using adaboost classifier is done here.it trains the weak classifier and converts to strong classifier
65f33c80aa2c
- adaboost training for weak classifier.Simple example for classifier and training is done
410d5453ed77
- minimization of error using adaboost classifier for a program.
face_recognition_adaBoost_M2
- face recognition is done here for a dataset by sequence of series of images and weak classifiers are trained to get back strong classifier