Foreword In actual AD acquisition scenarios, there will be effects of periodic changes and occasional pulse fluctuation interference on AD acquisition. Here a combination of median average filtering + recursive average filtering is used Refer to the code framework written by predecessors, and also refer to the blog below by blogger GuYH_, and make slight […]
Tag: sampling
Digital signal processing experiment 2: time domain sampling and frequency domain sampling
Experimental content Experimental purpose Time domain sampling theory and frequency domain sampling theory are important theories in digital signal processing. It is required to master the changes in the spectrum of analog signals before and after sampling, and how to choose the sampling frequency so that the sampled signal does not lose information; it is […]
Python Bayesian MCMC: Metropolis-Hastings, Gibbs Sampling, Hierarchical Models, Convergence Assessment
Full text link: https://tecdat.cn/?p=33961 In a conventional Markov chain model, we are usually interested in finding an equilibrium distribution(Click “Read the original text” at the end of the article to get the completecode data ). Related videos MCMC thinks the other way around – we fix the equilibrium distribution as the posterior distribution: And find […]
The most detailed image pyramid principle and implementation of downsampling
Table of Contents What is an image pyramid? principle Downsampling Parameter analysis Code Advantages and Disadvantages Advantages of downsampling Disadvantages of downsampling What is an image pyramid? Image pyramid is a multi-scale expression of an image. Arranging the images at each scale from small to large in resolution from top to bottom will form a […]
Point cloud processing [3] (point cloud downsampling)
Point cloud downsampling Chapter 1 Point Cloud Data Collection Chapter 2 Point Cloud Filtering Chapter 3 Point Cloud Downsampling Chapter 4 Point Cloud Key Point Detection Chapter 5 Point Cloud Feature Extraction Chapter 6 Point Cloud Segmentation Chapter 7 Point Cloud Registration 1. Why downsampling? The amount of data we obtain is large, especially point […]
Resampling calculation of ffmpeg
Recently I was looking at the resampling calculation logic of ffmpeg, and there was a sentence that I didn’t quite understand: dst_nb_samples = av_rescale_rnd(swr_get_delay(swr_ctx, src_rate) + src_nb_samples, dst_rate, src_rate, AV_ROUND_UP);. After various consultations, The records are as follows. Total number of samples after resampling Why include delays in the resampling process? During the audio resampling […]
Big data analysis practice | pandas data filtering and sampling
Article directory related functions Data reading and writing: read_csv and to_csv Data filtering: pandas.DataFrame.loc Sampling: pandas.DataFrame.sample Delete missing data: dropha Add element: append groupby and anonymous functions Case python basics | Pandas basics Related functions Data reading and writing: read_csv and to_csv Detailed explanation of each parameter of the pandas series read_csv and to_csv methods […]
Dubbo Diagnosis and Tuning-Request Time-consuming Sampling
Function Description The performance sampling function can detect the time consumption of various parts of the Dubbo processing link. When a timeout occurs, ( usageTime / timeout > profilerWarnPercent * 100) records the time consumption of the call through the log. This function is divided into two modes: simple profiler and detail profiler. Among them, […]
Generative AI New World | Code Practice Sampling of Diffusion Model Principles
In the previous article, we explored fine-tuning the Falcon 40B large language model using quantization techniques such as QLoRA on Amazon SageMaker Studio. Starting from this issue, we will try to explore the hot new knowledge field of generative AI in a deeper knowledge dimension. The Amazon Cloud Technology Developer Community provides developers with global […]
Random sampling consensus algorithm RANSAC
In the field of computer vision, various sampling consistency parameter estimation algorithms are widely used to eliminate erroneous samples. Different samples have different applications, such as eliminating erroneous registration point pairs, segmenting point sets originating from the model, and PCL. With the random sampling consensus algorithm (RANSAC) as the core, it also implements five random […]