Accuracy, Precision, Resolution & Sensitivity

Instrument manufacturers usually supply specifications for their equipment that define its accuracy, precision, resolution and sensitivity. Unfortunately, not all of these specifications are uniform from one to another or expressed in the same terms. Moreover, even when they are given, do you know how they apply to your system and to the variables you are measuring? Some specifications are given as worst-case values, while others take into consideration your actual measurements.

Accuracy can be defined as the amount of uncertainty in a measurement with respect to an absolute standard. Accuracy specifications usually contain the effect of errors due to gain and offset parameters. Offset errors can be given as a unit of measurement such as volts or ohms and are independent of the magnitude of the input signal being measured. An example might be given as ±1.0 millivolt (mV) offset error, regardless of the range or gain settings. In contrast, gain errors do depend on the magnitude of the input signal and are expressed as a percentage of the reading, such as ±0.1%. Total accuracy is therefore equal to the sum of the two: ±(0.1% of input +1.0 mV). An example of this is illustrated in Table 1.

Table 1. Readings as a function of accuracy


Input Voltage


Range of Readings within the Accuracy Specification


0V


-1 mV to +1 mV


5V


4.994V to 5.006V (±6 mV)


10V


9.989V to 10.011V (±11 mV)

conditions: input 0-10V, Accuracy = ±(0.1% of input + 1mV)

Precision describes the reproducibility of the measurement. For example, measure a steady state signal many times. In this case if the values are close together then it has a high degree of precision or repeatability. The values do not have to be the true values just grouped together. Take the average of the measurements and the difference is between it and the true value is accuracy.

Resolution can be expressed in two ways:

1. It is the ratio between the maximum signal measured to the smallest part that can be resolved - usually with an analog-to-digital (A/D) converter.

2. It is the degree to which a change can be theoretically detected, usually expressed as a number of bits. This relates the number of bits of resolution to the actual voltage measurements.

In order to determine the resolution of a system in terms of voltage, we have to make a few calculations. First, assume a measurement system capable of making measurements across a ±10V range (20Vspan) using a 16-bits A/D converter. Next, determine the smallest possible increment we can detect at 16 bits. That is, 216 = 65,536, or 1 part in 65,536, so 20V÷65536 = 305 microvolt (uV) per A/D count. Therefore, the smallest theoretical change we can detect is 305 uV.

Unfortunately, other factors enter the equation to diminish the theoretical number of bits that can be used, such as noise. A data acquisition system specified to have a 16-bit resolution may also contain 16 counts of noise. Considering this noise, the 16 counts equal 4 bits (24 = 16); therefore the 16 bits of resolution specified for the measurement system is diminished by four bits, so the A/D converter actually resolves only 12 bits, not 16 bits.

A technique called averaging can improve the resolution, but it sacrifices speed. Averaging reduces the noise by the square root of the number of samples, therefore it requires multiple readings to be added together and then divided by the total number of samples. For example, in a system with three bits of noise, 23 = 8 , that is, eight counts of noise averaging 64 samples would reduce the noise contribution to one count, √64 = 8: 8÷8 = 1. However, this technique cannot reduce the affects of non-linearity, and the noise must have a Gaussian distribution. 

Sensitivity is an absolute quantity, the smallest absolute amount of change that can be detected by a measurement. Consider a measurement device that has a ±1.0 volt input range and ±4 counts of noise, if the A/D converter resolution is 212 the peak-to-peak sensitivity will be ±4 counts x (2 ÷ 4096) or ±1.9mV p-p. This will dictate how the sensor responds. For example, take a sensor that is rated for 1000 units with an output voltage of 0-1 volts (V). This means that at 1 volt the equivalent measurement is 1000 units or 1mV equals one unit. However the sensitivity is 1.9mV p-p so it will take two units before the input detects a change.

Measurement Computing‘s USB-1608G Series Example

Let’s use the USB-1608G and determine its resolution, accuracy, and sensitivity. (Refer to Table 2 and Table 3, below, for its specifications.) Consider a sensor that outputs a signal between 0 and 3 volts and is connected to the USB-1608G‘s analog input. We will determine the accuracy at two conditions: Condition No. 1 when the sensor output is 200 mV and Condition No. 2 when it is 3.0 volts.

Accuracy: The USB-1608G uses a 16 bit A/D converter

Condition No. 1: 200 mV measurement on a ±1 volt single-ended range

  • Temperature = 25º C
  • Resolution = 2V ÷ 216 = 30.5 uV
  • Sensitivity = 30.5 uV  × 1.36 LSB rms = 41.5 uV rms
  • Gain Error:  0.024% × 200mV = ±48uV
  • Offset Error = ±245uV
  • Linearity Error = 0.0076% of range  = 760uV
  • Total Error = 48uV + 245uV + 760uV = 1053uV

Therefore a 200 mV reading could fall within a range of 198.947 mV to 201.053 mV.

Condition No. 2: 3.0 V measurement on a ±5 volt single-ended range

  • Temperature = 25º C
  • Resolution =10 volts ÷ 216 = 152.6uV
  • Sensitivity = 152.6 uV  × 0.91 LSB rms= 138.8 uV rms
  • Gain Error:  0.024% × 3.0V = 720uV
  • Offset Error = 686uV
  • Linearity error = 0.0076% of range = 380uV
  • Total Error = 720uV + 686uV + 380uV = 1.786mV

Therefore, a 3.0V reading could fall within a range of 2.9982 mV to 3.0018 mV.

Summary Analysis:

Accuracy: Consider Condition No. 1. The total accuracy is 369 uV ÷ 2 V  ×  100 = 0.0184%

Accuracy: Consider Condition No. 2. The total accuracy is 1.786 mV ÷ 10 V  × 100 = 0.0177%

Effective Resolution: The USB-1608G has a specification of 16 bits of theoretical resolution. However the effective resolution is the ratio between the maximum signal being measured and the smallest voltage that can be resolved, i.e. the sensitivity. For example...if we consider Condition No. 2, divide the sensitivity value by the measured signal value or (138.5uV ÷ 3.0 V) = 46.5e-6 and then converting to the equivalent bit value produces (1V ÷ 46.5e-6) = 21660 or 214.4 bits of effective resolution. To further improve on the effective resolution, consider averaging the values as previously discussed.

Sensitivity: The most sensitive measurement is made on the ±1 volt range where the noise is only 41.5uV rms whereas the sensitivity of the 5 volt range is 138.8uV rms. In general, when selecting a sensor, set the equipment to capture the highest output with the best sensitivity. For example, if the output signal is 0-3 volts select the 5 volt range instead of the 10 volt.

Table 2. 


Table 3. 

时间: 2024-08-02 08:53:00

Accuracy, Precision, Resolution & Sensitivity的相关文章

Accuracy and precision

From Wikipedia!! :Accuracy is the proximity of measurement results to the true value; precision, the repeatability, or reproducibility of the measurement In the fields of science, engineering, industry, and statistics, the accuracy of ameasurement sy

目标检测的评价标准mAP, Precision, Recall, Accuracy

目录 metrics 评价方法 TP , FP , TN , FN 概念 计算流程 Accuracy , Precision ,Recall Average Precision PR曲线 AP计算 Average Precision mAP 参考资料 metrics 评价方法 注意,在多分类问题中,评价方法是逐个类计算的,不是所有类一起算,是只针对一个类算,每个类别有自己的指标值! TP , FP , TN , FN 概念 TP = 预测为positive 且ground-truth和预测一致

评估指标:ROC,AUC,Precision、Recall、F1-score

一.ROC,AUC ROC(Receiver Operating Characteristic)曲线和AUC常被用来评价一个二值分类器(binary classifier)的优劣 . ROC曲线一般的横轴是FPR,纵轴是FPR.AUC为曲线下面的面积,作为评估指标,AUC值越大,说明模型越好.如下图: 二.Precision.Recall.F1-score Terminology and derivationsfrom a confusion matrix true positive (TP)

分类器模型评价指标

Spark mllib 自带了许多机器学习算法,它能够用来进行模型的训练和预测.当使用这些算法来构建模型的时候,我们需要一些指标来评估这些模型的性能,这取决于应用和和其要求的性能.Spark mllib 也提供一套指标用来评估这些机器学习模型. 具体的机器学习算法归入更广泛类型的机器学习应用,例如:分类,回归,聚类等等,每一种类型都很好的建立了性能评估指标.本节主要分享分类器模型评价指标. ROC曲线 ROC曲线指受试者工作特征曲线 / 接收器操作特性曲线(receiver operating

机器学习评判指标

0.背景 机器学习通常评判一个算法的好坏,是基于不同场景下采用不同的指标的.通常来说,有: [x] 准确度:PR (Precision Recall): [x] F测量: [ ] MCC: [ ] BM: [ ] MK: [ ] Gini系数: [x] ROC: [ ] Z score: [x] AUC : [ ] Cost Curve: [ ] BLEU: [ ] Matthews correlation coefficient: [ ] METEOR: [ ] Brier score: [

STM32 F4 DAC DMA Waveform Generator

STM32 F4 DAC DMA Waveform Generator Goal: generating an arbitrary periodic waveform using a DAC with DMA and TIM6 as a trigger. Agenda: Modeling a waveform in MATLAB and getting the waveform data Studying the DAC, DMA, and TIM6 to see how it can be u

聚类模型性能评价指标

有监督的分类算法的评价指标通常是accuracy, precision, recall, etc:由于聚类算法是无监督的学习算法,评价指标则没有那么简单了.因为聚类算法得到的类别实际上不能说明任何问题,除非这些类别的分布和样本的真实类别分布相似,或者聚类的结果满足某种假设,即同一类别中样本间的相似性高于不同类别间样本的相似性.聚类模型的评价指标如下: 1. Adjusted Rand Index(兰德指数): 若已知样本的真实类别标签labels_true ,和聚类算法得到的标签labels_p

3D Mapping with an RGB-D Camera(RGBD SLAM V2 )论文笔记

__ 这篇文章即是Felix Endres等人12年完成的RGB-D SLAM V2,是最早的为kinect风格传感器设计的SLAM系统之一 在Github上可找到开源代码,工程配置与运行参考http://www.cnblogs.com/voyagee/p/6898278.html 系统流程: 系统分为前后端.前端就是视觉里程记.从每一帧的RGB图像提取特征,计算描述符,RANSAC+ICP计算两帧之间的motion estimation, 并提出了一个EMM(Environment Measu

模型的性能评估(二) 用sklearn进行模型评估

在sklearn当中,可以在三个地方进行模型的评估 1:各个模型的均有提供的score方法来进行评估. 这种方法对于每一种学习器来说都是根据学习器本身的特点定制的,不可改变,这种方法比较简单.这种方法受模型的影响, 2:用交叉验证cross_val_score,或者参数调试GridSearchCV,它们都依赖scoring参数传入一个性能度量函数.这种方法就是我们下面讨论的使用scoring进行模型的性能评估. 3:Metric方法,Metric有为各种问题提供的评估方法.这些问题包括分类.聚类