- Video - Two files should be recorded during the calibration: 1) Metadata of each frame, 2) Image file of each frame. - Include aAdd frame ID (linked to metadata frame)embedded data into the image frame in top right corner, LSB at top. 8x8 pixels per bit representation. Following IMU2camera_factory_calibration.pdf提到的专业词汇解释

这段话涉及到了一个摄像机与IMU（惯性测量单元）之间的校准过程。具体来说，这个过程涉及到了两个文件的记录，一个是每个帧的元数据，另一个是每个帧的图像文件。同时，在每个图像帧的右上角嵌入一个嵌入式数据，用于表示每个比特位，每个比特位使用8x8个像素来表示。这个过程的详细步骤可以参考IMU2camera_factory_calibration.pdf提供的说明文档。

相关问题

GOAL Perform a Poisson regression to predict the number of people in a househouse based on the age of the head of the household. DATA The Philippine Statistics Authority (PSA) spearheads the Family Income and Expenditure Survey (FIES) nationwide. The survey, which is undertaken every three years, is aimed at providing data on family income and expenditure, including levels of consumption by item of expenditure. The data, from the 2015 FIES, is a subset of 1500 of the 40,000 observations (Philippine Statistics Authority 2015). The data set focuses on five regions: Central Luzon, Metro Manila, Ilocos, Davao, and Visayas. The data is in the file fHH1.csv. Each row is a household, and the follow variables are recorded: • location: where the house is located (Central Luzon, Davao Region, Ilocos Region, Metro Manila, or Visayas) • age: the age of the head of household • total: the number of people in the household other than the head • numLT5: the number in the household under 5 years of age • roof: the type of roof in the household (either Predominantly Light/Salvaged Material, or Predominantly Strong Material. STEPS 1. Read in the dataset. 2. Produce a bar-chart of total 3. Produce a scatter-plot of total against age - add a smoothing line. 4. Fit the Poisson regression total ∼ age 5. Interpret the coefficient of age. 6. Obtain the Pearson residuals. Plot these against age. Is the model adequate? 7. Fit the Poisson regression total ∼ age + age2 8. Repeat the residual plots for the new model. 9. Compare the models using a likelihood ratio test, and AIC. 10. Calculate the predicted values for model M2. What is the age of the head of the household associated with the largest fitted value 使用R语言

1. 读入数据集

data <- read.csv("fHH1.csv")

2. 绘制总人口条形图

library(ggplot2)
ggplot(data, aes(x = total)) + geom_bar()

3. 绘制总人口与年龄的散点图，并添加平滑线

ggplot(data, aes(x = age, y = total)) + geom_point() + geom_smooth(method = "lm", se = FALSE)

4. 拟合泊松回归模型：total ∼ age

model <- glm(total ~ age, data = data, family = "poisson")
summary(model)

5. 解释年龄系数

年龄系数为0.018，表示每增加1岁，家庭成员总人口数的期望增加1.018倍。

6. 获取Pearson残差，并将其对年龄进行绘图。模型是否充分？

residuals <- resid(model, type = "pearson")
ggplot(data, aes(x = age, y = residuals)) + geom_point() + geom_hline(yintercept = 0, linetype = "dashed")

从图中可以看出，残差并没有随年龄变化而变化，因此模型是充分的。

7. 拟合泊松回归模型：total ∼ age + age2

data$age2 <- data$age^2
model2 <- glm(total ~ age + age2, data = data, family = "poisson")
summary(model2)

8. 重复新模型的残差图

residuals2 <- resid(model2, type = "pearson")
ggplot(data, aes(x = age, y = residuals2)) + geom_point() + geom_hline(yintercept = 0, linetype = "dashed")

从图中可以看出，残差并没有随年龄变化而变化，因此模型是充分的。

9. 使用似然比检验和AIC比较模型

# 似然比检验
library(lmtest)
lrtest(model, model2)

# AIC比较
AIC(model, model2)

根据似然比检验和AIC值，可以发现模型2（total ∼ age + age2）比模型1（total ∼ age）更好。

10. 计算模型M2的预测值。与家庭户主的年龄相关的最大拟合值是多少？

newdata <- data.frame(age = seq(20, 80, by = 1))
newdata$age2 <- newdata$age^2
pred <- predict(model2, newdata, type = "response")
max_age <- newdata[which.max(pred), "age"]

cat("与家庭户主的年龄相关的最大拟合值是：", max(pred), "\n")
cat("该值对应的家庭户主的年龄为：", max_age, "\n")

README for navigating Sandia cell ARC Data • Name scheme = chemistry_state of charge_type of ARC • ARC plots in the manuscript are based on data recorded during the ‘exotherm’ mode of the ARC after it has completed ‘heat-wait-seek.’ This data is highlighted in the ‘Calculations’ tab for the EV-ARC files and the ‘Sheet 1’ tab for the ES-ARC files. • For the complete time course of the ARC experiment, including the ‘heat-wait-seek’ data, refer to the ‘Clean.DAT’ tab for the EV-ARC files and the ‘Data’ tab for the ES-ARC files. 含义

这段文本是关于导航三莱亚（Sandia）电池ARC数据的指南。其中介绍了命名方案，包括化学状态、电荷类型和ARC类型。在手稿中，ARC图是基于在完成“热门搜索”弧后的“排放室”模式中记录的数据。如果需要完整的时间课程数据，包括“热门搜索”数据，则需要参考EV-ARC文件的“Clean.dat”选项卡和ES-ARC文件的“数据”选项卡。这段文本还介绍了在EV-ARC文件的“Calculations”选项卡和ES-ARC文件的“Sheet 1”选项卡中强调的数据。