sampled data system

简明释义

取样数据系统

英英释义

例句

1.In a digital signal processing application, the sampled data system 采样数据系统 converts analog signals into discrete values for analysis.

在数字信号处理应用中，采样数据系统 采样数据系统 将模拟信号转换为离散值以进行分析。

2.The performance of the sampled data system 采样数据系统 can greatly affect the quality of the reconstructed signal.

采样数据系统的性能会极大影响重建信号的质量。

3.Engineers often use a sampled data system 采样数据系统 to simulate real-time processes in control systems.

工程师们经常使用采样数据系统 采样数据系统 来模拟控制系统中的实时过程。

4.The sampled data system 采样数据系统 must be carefully designed to avoid aliasing effects.

必须仔细设计采样数据系统 采样数据系统 以避免混叠效应。

5.In telecommunications, a sampled data system 采样数据系统 is essential for encoding and transmitting voice signals.

在电信中，采样数据系统 采样数据系统 对于编码和传输语音信号至关重要。

作文

In the field of digital signal processing, a crucial concept is the sampled data system. This system plays a significant role in how we process and analyze signals that are inherently continuous in nature. To understand this better, we first need to grasp what sampling means. Sampling is the process of converting a continuous signal into a discrete one by taking measurements at specific intervals. This conversion is essential because digital systems, such as computers, can only process discrete data. Therefore, the sampled data system is designed to handle these discrete samples effectively.The importance of a sampled data system cannot be overstated. It allows us to represent real-world signals, such as audio and video, in a format that can be manipulated and stored digitally. For instance, when we record music, the analog sound waves are sampled at regular intervals to create a digital representation. The quality of this representation depends on the sampling rate, which is the number of samples taken per second. A higher sampling rate results in better fidelity to the original signal, but it also requires more storage space and processing power.Moreover, a sampled data system involves not just the act of sampling but also the subsequent processing of these samples. Once the data is sampled, various algorithms can be applied to analyze it, filter noise, or even compress it for efficient storage. The design of these systems must consider factors such as aliasing, which occurs when the sampling rate is too low to accurately capture the signal's variations. To avoid this, the Nyquist-Shannon sampling theorem states that the sampling frequency must be at least twice the highest frequency present in the signal.In practical applications, sampled data systems are ubiquitous. They are used in telecommunications, audio engineering, medical imaging, and many other fields. For example, in telecommunications, voice signals are sampled and transmitted over networks, allowing for clear communication. In medical imaging, techniques such as MRI rely on sampled data systems to create detailed images of the human body, providing critical information for diagnosis and treatment.Furthermore, understanding the principles behind sampled data systems is vital for engineers and technicians working in these fields. They must be adept at selecting appropriate sampling rates, designing filters, and implementing algorithms to ensure the integrity of the data being processed. As technology advances, the capabilities of sampled data systems continue to evolve, enabling higher-quality data capture and processing, which opens up new possibilities in various domains.In conclusion, the sampled data system is an essential component of modern digital technology. By transforming continuous signals into discrete samples, it allows for the effective processing and analysis of data across numerous applications. As we continue to innovate and improve these systems, our ability to capture and understand the world around us will only grow stronger. Therefore, mastering the concepts related to sampled data systems is crucial for anyone looking to excel in fields related to digital signal processing and beyond.

在数字信号处理领域，一个关键概念是采样数据系统。这个系统在我们处理和分析本质上是连续的信号时起着重要作用。为了更好地理解这一点，我们首先需要掌握采样的含义。采样是将连续信号转换为离散信号的过程，通过在特定间隔内进行测量。这种转换是必需的，因为数字系统，如计算机，只能处理离散数据。因此，采样数据系统旨在有效处理这些离散样本。采样数据系统的重要性不容小觑。它使我们能够以可以操作和存储的数字格式表示现实世界的信号，例如音频和视频。例如，当我们录制音乐时，模拟声波会在规则的时间间隔内被采样，以创建数字表示。这种表示的质量取决于采样率，即每秒采样的次数。较高的采样率会导致对原始信号的更好保真度，但这也需要更多的存储空间和处理能力。此外，采样数据系统不仅涉及采样的行为，还包括对这些样本的后续处理。一旦数据被采样，可以应用各种算法来分析、过滤噪声或甚至压缩以便于存储。这些系统的设计必须考虑到诸如混叠等因素，当采样率太低而无法准确捕捉信号的变化时，就会发生混叠。为了避免这种情况，Nyquist-Shannon采样定理指出，采样频率必须至少是信号中存在的最高频率的两倍。在实际应用中，采样数据系统无处不在。它们被用于电信、音频工程、医学成像和许多其他领域。例如，在电信中，语音信号被采样并通过网络传输，从而实现清晰的通信。在医学成像中，MRI等技术依赖于采样数据系统来创建人体的详细图像，为诊断和治疗提供重要信息。此外，了解与采样数据系统相关的原理对于在这些领域工作的工程师和技术人员至关重要。他们必须熟练选择适当的采样率、设计滤波器以及实施算法，以确保所处理数据的完整性。随着技术的进步，采样数据系统的能力不断发展，使得更高质量的数据捕获和处理成为可能，这为各个领域开辟了新的可能性。总之，采样数据系统是现代数字技术的重要组成部分。通过将连续信号转换为离散样本，它使得在众多应用中有效处理和分析数据成为可能。随着我们继续创新和改善这些系统，我们捕捉和理解周围世界的能力只会越来越强。因此，掌握与采样数据系统相关的概念对于任何希望在与数字信号处理及其相关领域中取得成功的人来说都是至关重要的。