pseudorandom

简明释义

英[ˌsjuːdəʊˈrændəm]美[ˌsʊdoˈrændəm]

n. 伪随机；假散乱

adj. 伪随机的

英英释义

单词用法

同义词

反义词

例句

1.This article deals with error bounds for linear congruential sequences and vectors which are used as pseudorandom Numbers and vectors of uniform distribution.

采用关于网格理论的方法，对线性同余序列及向量列在其作为伪随机序列模拟均匀分布时的偏差加以讨论并给出估计。

2.This paper presents a method of layout plan of large area pseudorandom sequence diffusers to cut down the diffuse peaks and to meet user's demand.

为了消除这些旁瓣，并得到符合使用者要求的扩散声压响应曲线，本文提出一种实现伪随机序列扩散体群大面积最佳布置方案的布置方法。

3.As a result of experiment, the relative pseudorandom exciting method is applicable in the dynamic experiment of machine tool structure, with higher precision and efficiency.

研究结果表明，相对伪随机激振法适用于机床结构的动态测试，具有较高的测试精度和效率。

4.In order to solve the above problems, we develop ultrasonic range-finder which is based on pseudorandom code.

为了解决上述问题，我们研制了基于伪随机码调制的超声波测距。

5.Investigation shows that the scheme is provable security under the blockwise adaptive attack model if the underlying block cipher is a pseudorandom permutation.

结果表明，在所用分组密码是伪随机置换的条件下，方案在分块适应性攻击模型下是可证明安全的。

6.In this paper, a new class of binary pseudorandom sequences is proposed, which is called NPC sequences.

本文提出了一类新的多址序列的设计称为NPC序列。

7.The uniqueness of pseudorandom binary array pairs and pseudorandom binary sequence pairs are proved as the specific cases of binary array pairs with two-level autocorrelation.

提出并证明了二值自相关二进阵列偶的唯一性问题，将伪随机二进阵列偶和伪随机二进序列偶作为其特例证明也满足唯一性。

8.Based on the interleave method, a new class of binary pseudorandom sequences is constructed, which is called GMW Phase Controlled sequence (or GMW PC sequence).

基于交错方法构造出了一类新的伪随机序列，称为GMW相控序列。

9.In cryptography, pseudorandom 伪随机 number generators are essential for secure key generation.

在密码学中，伪随机 伪随机 数字生成器对于安全密钥生成至关重要。

10.The computer generated a sequence of pseudorandom 伪随机 numbers for the simulation.

计算机为模拟生成了一系列伪随机 伪随机 数字。

11.The algorithm uses a pseudorandom 伪随机 number sequence to ensure fairness in the game.

该算法使用伪随机 伪随机 数字序列以确保游戏的公平性。

12.For testing purposes, we need a pseudorandom 伪随机 data set that mimics real-world conditions.

出于测试目的，我们需要一个伪随机 伪随机 数据集，以模拟现实世界的条件。

13.The pseudorandom 伪随机 generator was implemented in the software to enhance performance.

软件中实现了伪随机 伪随机 生成器，以提高性能。

作文

In the realm of computer science and mathematics, the term pseudorandom refers to a sequence of numbers that appears to be random but is generated by a deterministic process. This concept plays a crucial role in various applications, including cryptography, simulations, and gaming. Understanding pseudorandom sequences is vital for both theoretical studies and practical implementations. To delve deeper into the significance of pseudorandom numbers, we must first distinguish them from truly random numbers. True randomness is derived from unpredictable physical processes, such as radioactive decay or atmospheric noise. In contrast, pseudorandom numbers are produced by algorithms known as pseudorandom number generators (PRNGs). These algorithms use initial values, known as seeds, to generate sequences that mimic the properties of randomness. One of the most common PRNG algorithms is the Linear Congruential Generator (LCG), which produces a sequence of numbers based on a linear equation. The output of an LCG may seem random at first glance; however, the sequence will eventually repeat itself due to its deterministic nature. This repetition is a significant characteristic of pseudorandom sequences, making them less suitable for applications requiring high levels of security, such as cryptography. In cryptographic contexts, the quality of pseudorandom numbers is paramount. If an attacker can predict the sequence generated by a PRNG, they can potentially compromise the security of encrypted data. To address this issue, cryptographically secure pseudorandom number generators (CSPRNGs) have been developed. These generators incorporate additional complexity and entropy sources to ensure that their outputs are as unpredictable as possible, thereby enhancing security. Beyond cryptography, pseudorandom numbers are widely used in simulations, particularly in Monte Carlo methods. These techniques rely on generating large quantities of random samples to estimate mathematical functions or simulate complex systems. For instance, financial analysts might use pseudorandom numbers to model stock market behaviors and assess risk. By leveraging these sequences, researchers can gain insights into phenomena that would be difficult to analyze using deterministic methods alone. Additionally, gaming industries utilize pseudorandom number generators to create unpredictable outcomes in video games, ensuring that players have a fair and engaging experience. Whether it’s rolling dice, shuffling cards, or spawning enemies, the use of pseudorandom sequences adds an element of surprise and excitement to gameplay.In conclusion, the concept of pseudorandom numbers is fundamental to various fields, including cryptography, simulations, and gaming. While they may not possess true randomness, their ability to approximate random behavior makes them invaluable tools in modern technology. As we continue to advance in computational power and algorithm design, the importance of understanding and effectively utilizing pseudorandom sequences will only grow. The challenge lies in ensuring that these sequences remain secure and unpredictable, especially in applications where stakes are high. Thus, the study of pseudorandom numbers is not merely an academic pursuit; it is a critical component of our digital infrastructure that impacts everyday life.

在计算机科学和数学领域，术语伪随机指的是一种看似随机但由确定性过程生成的数字序列。这个概念在各种应用中发挥着至关重要的作用，包括密码学、模拟和游戏。理解伪随机序列对于理论研究和实际实施都至关重要。要深入了解伪随机数字的重要性，我们必须首先区分它们与真正随机数字之间的差异。真正的随机性源于不可预测的物理过程，例如放射性衰变或大气噪声。相比之下，伪随机数字是由称为伪随机数生成器（PRNG）的算法生成的。这些算法使用初始值，称为种子，来生成模仿随机性特性的序列。最常见的PRNG算法之一是线性同余生成器（LCG），它基于线性方程产生数字序列。LCG的输出在初看时可能显得随机；然而，由于其确定性特性，该序列最终会重复。这个重复是伪随机序列的一个重要特征，使其在需要高安全性应用中不太适用，例如密码学。在密码学环境中，伪随机数字的质量至关重要。如果攻击者能够预测PRNG生成的序列，他们可能会破坏加密数据的安全性。为了解决这个问题，开发了密码学安全伪随机数生成器（CSPRNG）。这些生成器结合了额外的复杂性和熵源，以确保其输出尽可能不可预测，从而增强安全性。除了密码学，伪随机数字在模拟中也被广泛使用，尤其是在蒙特卡洛方法中。这些技术依赖于生成大量随机样本来估计数学函数或模拟复杂系统。例如，金融分析师可能使用伪随机数字来模拟股市行为并评估风险。通过利用这些序列，研究人员可以获得难以仅通过确定性方法分析的现象的洞察。此外，游戏行业利用伪随机数生成器来创建视频游戏中的不可预测结果，确保玩家有一个公平且引人入胜的体验。无论是掷骰子、洗牌还是生成敌人，使用伪随机序列为游戏玩法增加了惊喜和兴奋的元素。总之，伪随机数字的概念是多个领域的基础，包括密码学、模拟和游戏。虽然它们可能不具备真正的随机性，但它们近似随机行为的能力使它们成为现代技术中不可或缺的工具。随着我们在计算能力和算法设计上的不断进步，理解和有效利用伪随机序列的重要性只会增加。挑战在于确保这些序列在需要高风险的应用中保持安全和不可预测。因此，研究伪随机数字不仅仅是一项学术追求；它是影响日常生活的数字基础设施的关键组成部分。