树莓派高精度AD_DA扩展板：8通道ADC与2通道DAC详解

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"High-Precision AD_DA Board是一款针对Raspberry Pi系列主板和Jetson Nano平台设计的高性能模拟数字转换器（ADC）与数字模拟转换器（DAC）扩展板。这款板卡的核心特点是采用了8通道24位高精度的ADS1256 ADC，支持4路差分输入，具有高达30ksps的采样速率，能满足精确的数据采集需求。同时，它配备了2通道16位高精度的DAC8532 DAC，提供了丰富的模拟信号处理能力。 该板卡的接口设计十分实用，板载排针封装输入接口支持模拟信号接入，兼容微雪传感器接口标准，方便用户连接各种传感器模块，如温度、压力等。此外，还有接线端子封装的输入输出接口，既可用于模拟信号，也支持数字信号传输，适应多种应用场景。为了便于用户进行实验和调试，板卡内置了AD/DA检测电路，可以直接观察转换效果。 在编程支持方面，High-Precision AD_DA Board提供了C语言和Python示例程序，使得非专业用户也能轻松上手。使用时，需要在树莓派终端通过`sudo raspi-config`命令进入配置界面，选择SPI接口并启用。这样，用户就可以利用树莓派的SPI接口来驱动AD_DA板，并与外部设备进行通信。 这款高精度AD_DA扩展板为Raspberry Pi和Jetson Nano平台提供了强大的模拟信号处理能力，广泛应用于数据采集、信号处理和嵌入式系统开发，是进行实验、原型制作或工业自动化项目的理想选择。"

precision-3630-workstation_users-guide_zh-cn.pdf

2020-06-28 上传
根据提供的文件内容，以下是关于Dell Precision 3630塔式工作站用户手册的详细知识点： 1. 安全使用说明： - 用户在拆装计算机内部组件前需要阅读相关的安全信息。 - 在开始拆装之前，必须断开所有电源，并在拆装...

gmpy2-2.0.8-cp38-cp38-win_amd64.whl.zip

2023-12-14 上传
`gmpy2` 是一个高性能的Python扩展模块，提供了大整数和浮点数的运算功能，它基于GMP (GNU Multiple Precision Arithmetic Library) 和 MPFR (Multiple Precision Floating-Point Reliable) 库。这个库对于需要处理...

torch-1.10.0-cp36-cp36m-linux_aarch64.zip

2021-12-13 上传
2. **自动混合精度训练（Automatic Mixed Precision, AMP）**：AMP可以帮助用户利用半精度浮点运算来加速训练，同时保持模型的精度，这对于资源有限的ARM设备来说尤其重要。 3. **分布式训练的改进**：PyTorch ...

分析一下以下代码：#define ABS(a) (0 - (a)) > 0 ? (-(a)) : (a) #define DYNAMIC_PRECISION 30 /*动态精度*/ /*一个线性移位寄存器，用于过滤高频噪声*/ typedef struct slid_reg{ axis_info_t new_sample; axis_info_t old_sample; }slid_reg_t; static char slid_update(slid_reg_t *slid, axis_info_t *cur_sample) { char res = 0; if (ABS((cur_sample->x - slid->new_sample.x)) > DYNAMIC_PRECISION) { slid->old_sample.x = slid->new_sample.x; slid->new_sample.x = cur_sample->x; res = 1; } else { slid->old_sample.x = slid->new_sample.x; } if (ABS((cur_sample->y - slid->new_sample.y)) > DYNAMIC_PRECISION) { slid->old_sample.y = slid->new_sample.y; slid->new_sample.y = cur_sample->y; res = 1; } else { slid->old_sample.y = slid->new_sample.y; } if (ABS((cur_sample->z - slid->new_sample.z)) > DYNAMIC_PRECISION) { slid->old_sample.z = slid->new_sample.z; slid->new_sample.z = cur_sample->z; res = 1; } else { slid->old_sample.z = slid->new_sample.z; } return res; }

2023-05-19 上传
然后分别对 x、y、z 三个轴上的样本信息进行判断，如果当前样本与新样本之间的差值大于 DYNAMIC_PRECISION，则将旧样本更新为新样本，将新样本更新为当前样本，并将 res 设为 1；否则只更新旧样本。最后返回 res。

ImportError Traceback (most recent call last) <ipython-input-3-b25a42d5a266> in <module>() 8 from sklearn.preprocessing import StandardScaler,PowerTransformer 9 from sklearn.linear_model import LinearRegression,LassoCV,LogisticRegression ---> 10 from sklearn.ensemble import RandomForestClassifier,RandomForestRegressor 11 from sklearn.model_selection import KFold,train_test_split,StratifiedKFold,GridSearchCV,cross_val_score 12 from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score,accuracy_score, precision_score,recall_score, roc_auc_score ~\Anaconda3\lib\site-packages\sklearn\ensemble\__init__.py in <module>() 3 classification, regression and anomaly detection. 4 """ ----> 5 from ._base import BaseEnsemble 6 from ._forest import RandomForestClassifier 7 from ._forest import RandomForestRegressor ~\Anaconda3\lib\site-packages\sklearn\ensemble\_base.py in <module>() 16 from ..base import BaseEstimator 17 from ..base import MetaEstimatorMixin ---> 18 from ..tree import DecisionTreeRegressor, ExtraTreeRegressor 19 from ..utils import Bunch, _print_elapsed_time 20 from ..utils import check_random_state ~\Anaconda3\lib\site-packages\sklearn\tree\__init__.py in <module>() 4 """ 5 ----> 6 from ._classes import BaseDecisionTree 7 from ._classes import DecisionTreeClassifier 8 from ._classes import DecisionTreeRegressor ~\Anaconda3\lib\site-packages\sklearn\tree\_classes.py in <module>() 39 from ..utils.validation import check_is_fitted 40 ---> 41 from ._criterion import Criterion 42 from ._splitter import Splitter 43 from ._tree import DepthFirstTreeBuilder sklearn\tree\_criterion.pyx in init sklearn.tree._criterion() ImportError: DLL load failed: 找不到指定的模块。 怎么改

2023-07-14 上传
您好！根据您提供的错误信息，看起来是缺少了某个模块导致的错误。有两种可能的解决方案： 1. 检查是否安装了所需的模块。在这种情况下，您需要确保已经正确安装了 scikit-learn 库。您可以使用以下命令来安装最新...

def init_datasets(self): """ Init self.dataset_train, self.dataset_train_iter, self.dataset_val. """ dataset_parameters = dict(base_folder=self.local_base_folder, image_size=self.image_size, image_spacing=self.image_spacing, normalize_zero_mean_unit_variance=False, cv=self.cv, heatmap_sigma=3.0, generate_spine_heatmap=True, use_variable_image_size=True, valid_output_sizes_x=[32, 64, 96, 128], valid_output_sizes_y=[32, 64, 96, 128], valid_output_sizes_z=[32, 64, 96, 128], output_image_type=np.float16 if self.use_mixed_precision else np.float32, data_format=self.data_format, save_debug_images=self.save_debug_images)

2023-07-09 上传
- `output_image_type`：输出图像的数据类型，根据`self.use_mixed_precision`来选择是`np.float16`还是`np.float32`。 - `data_format`：数据格式，指定了图像和张量的轴向顺序。 - `save_debug_images`：是否保存...

linux下载postgresql报错gcc -Wall -Wmissing-prototypes -Wpointer-arith -Wdeclaration-after-statement -Werror=vla -Wendif-labels -Wmissing-format-attribute -Wimplicit-fallthrough=3 -Wcast-function-type -Wformat-security -fno-strict-aliasing -fwrapv -fexcess-precision=standard -Wno-format-truncation -Wno-stringop-truncation -O2 -I../../../../src/include -D_GNU_SOURCE -c -o signalfuncs.o signalfuncs.c

2023-05-26 上传
这个错误提示是编译 PostgreSQL 的时候出现的，大概意思是缺少某些头文件或者依赖库。可能需要安装一些相关的开发库来解决这个问题。你可以尝试安装以下的依赖项： ``` sudo apt-get install build-essential ...

pc@pc-Precision-3650-Tower:~$ bash meshlab/scripts/Linux/0_setup_env_ubuntu.sh bash: meshlab/scripts/Linux/0_setup_env_ubuntu.sh: 没有那个文件或目录

2023-05-18 上传
这个错误可能是因为该文件不存在于您的计算机上。请确保您已经正确地克隆了 MeshLab 仓库并且已经切换到了正确的目录。您可以尝试使用以下命令来查找该文件的位置： ``` find / -name 0_setup_env_ubuntu.sh ...

set COMMANDLINE_ARGS=--lowvram --precision full --no-half --skip-torch-cuda-test

2023-03-31 上传
As an AI language model, I do not have access to your command line environment, but this command sets the value of the environment variable COMMANDLINE_ARGS to the string "--lowvram --precision full -...

修改一下这段代码在pycharm中的实现，import pandas as pd import numpy as np from sklearn.model_selection import train_test_split import torch import torch.nn as nn import torch.nn.functional as F import torch.optim as optim #from torchvision import datasets,transforms import torch.utils.data as data #from torch .nn:utils import weight_norm import matplotlib.pyplot as plt from sklearn.metrics import precision_score from sklearn.metrics import recall_score from sklearn.metrics import f1_score from sklearn.metrics import cohen_kappa_score data_ = pd.read_csv(open(r"C:\Users\zhangjinyue\Desktop\rice.csv"),header=None) data_ = np.array(data_).astype('float64') train_data =data_[:,:520] train_Data =np.array(train_data).astype('float64') train_labels=data_[:,520] train_labels=np.array(train_data).astype('float64') train_data,train_data,train_labels,train_labels=train_test_split(train_data,train_labels,test_size=0.33333) train_data=torch.Tensor(train_data) train_data=torch.LongTensor(train_labels) train_data=train_data.reshape(-1,1,20,26) train_data=torch.Tensor(train_data) train_data=torch.LongTensor(train_labels) train_data=train_data.reshape(-1,1,20,26) start_epoch=1 num_epoch=1 BATCH_SIZE=70 Ir=0.001 classes=('0','1','2','3','4','5') device=torch.device("cuda"if torch.cuda.is_available()else"cpu") torch.backends.cudnn.benchmark=True best_acc=0.0 train_dataset=data.TensorDataset(train_data,train_labels) test_dataset=data.TensorDataset(train_data,train_labels) train_loader=torch.utills.data.DataLoader(dtaset=train_dataset,batch_size=BATCH_SIZE,shuffle=True) test_loader=torch.utills.data.DataLoader(dtaset=train_dataset,batch_size=BATCH_SIZE,shuffle=True)

2023-07-12 上传
from sklearn.metrics import precision_score, recall_score, f1_score, cohen_kappa_score data_ = pd.read_csv(r"C:\Users\zhangjinyue\Desktop\rice.csv", header=None) data_ = np.array(data_).astype('float...
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