19th Ave New York, NY 95822, USA

# 物理代写|传感器代写Sensor代考|E-545 Signals and Information

my-assignmentexpert™提供最专业的一站式服务：Essay代写，Dissertation代写，Assignment代写，Paper代写，Proposal代写，Proposal代写，Literature Review代写，Online Course，Exam代考等等。my-assignmentexpert™专注为留学生提供Essay代写服务，拥有各个专业的博硕教师团队帮您代写，免费修改及辅导，保证成果完成的效率和质量。同时有多家检测平台帐号，包括Turnitin高级账户，检测论文不会留痕，写好后检测修改，放心可靠，经得起任何考验！

## 物理代写|传感器代写Sensor代考|Signals and Information

To illustrate the role of the information in the sensing process, we will use examples. In Fig. 1.4A is shown a heartbeat detector. The sensor’s main task is to detect the number of beats in a given period of an ECG signal using a decision threshold. We informally link this to the “information” necessary for our application to understand the concept. The three signal examples of Fig. 1.4A are taken from a set of all possible ECG waveforms in the same time period, and we refer this to as samples in the signal space. In the first two cases, the system counts 8 beats, while in the last one, only 7 . Therefore, we associate the result in a measurable space, referred to as information space. In other words, we say that samples in the signal space could be mapped in points in the information space. The acquisition process of a sensor is a correspondence between these two spaces. We will always refer to discrete information space.

In the second example of Fig. 1.4B, the sensing system should detect the number of circles/squares in images. Even in this case, the four sampled images belong to a very large signal space, for example, composed of all possible images of $\mathrm{N} \times \mathrm{M}$ black-andwhite pixels. However, the “information” is relatively smaller than the signal space and could be organized in a two-dimensional space where the variables are the number of circles and the number of squares, respectively.

In these two examples, it is easy for human perception to identify the information in the signal space at first sight and check if the sensor system has correctly detected our task. However, there are other cases in which the information is more hidden than previous examples, and machines could outperform human perception. For example, in the case of Fig. 1.4C, the signal is composed of five measured microwave impedance spectra related to a material having different water content (humidity). The idea is to use these spectra to implement a microwave humidity sensor, where the information is the percent humidity. It is hard to see any regular or monotonic behavior in spectra or in parts of them with respect to the stimulus (humidity). Our intuition concludes that there is no clear relationship between the humidity of the material and the spectra. In other words, it is not easy to see any significant information in the signal itself. However, suppose the signal is treated by suitable mathematical processing. In that case, we can set up a linear predictive model to detect the humidity based on microwave spectra so that signals can be mapped into distinguishable and ordered levels in the information space. The latter example shows that the information could be very hidden in signals, even beyond human capabilities to distinguish them in raw data. For this reason, in these cases, the information to be extracted is often referred to as latent variables.

The preceding examples are related to cases of different complexity of the task and require different processing resources to extract the information.
To summarize:

• The sensing process should be defined by a task, which qualifies the kind of information that should be measured. Thus, the application (task) determines the characteristics of the information space.
• Signals are functions representing states of the sensed environment carrying information. All the possible configurations of signals define the signal space.
• The information space has smaller dimensions of the signal space, and it is discrete. This means that the multiple elements of the signal space may have the same element in the information space.
• The sensing process is a function implying that each sample of the signal space has a correspondence in the information space.

## 物理代写|传感器代写Sensor代考|The Simplest Case of an Analog-to-Digital Interface

The previous section identified a distinction between signals and their information content, mapped into the information space. However, if we refer to the simple analogto-digital (A/D) conversion of a signal in the “analog domain,” we can match more easily the two spaces since the analog value itself encodes the information. We can better understand this with the cases illustrated in Fig. 1.5. In Fig. 1.5A is shown a time-varying biopotential signal that is monitored by an $\mathrm{A} / \mathrm{D}$ interface. Our task is to know the biopotential value evolution with respect to time, and thus it is precisely the information that we need. The $\mathrm{A} / \mathrm{D}$ converter associates a specific analog value of the signal full scale with a binary-encoded discrete value. Therefore, the discrete values of the A/D converter are easily represented in the information space. The correspondence is made by associating an analog value with the converter’s closest discrete value. The case of Fig. 1.5B is even more straightforward: the information is the static analog value of a weight sensor. Therefore, each measure (sample) is directly mapped in the information space. As before, multiple analog values may be mapped into the same coded value by the converter.
In summary:

• In the simple case of an $\mathrm{A} / \mathrm{D}$ interface, the association between information and signal is closer because the signal value itself represents the information that we need to detect.
• The correspondence is made by associating an analog value with the closest discrete level of the A/D converter.

.信号与信息

## Matlab代写

MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中，其中问题和解决方案以熟悉的数学符号表示。典型用途包括：数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发，包括图形用户界面构建MATLAB 是一个交互式系统，其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题，尤其是那些具有矩阵和向量公式的问题，而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问，这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展，得到了许多用户的投入。在大学环境中，它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域，MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要，工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数（M 文件）的综合集合，可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。