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関連する概念動画

Random Sampling Method01:09

Random Sampling Method

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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
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Random and Systematic Errors01:20

Random and Systematic Errors

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Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
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Random Variables01:09

Random Variables

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A random variable is a single numerical value that indicates the outcome of a procedure. The concept of random variables is fundamental to the probability theory and was introduced by a Russian mathematician, Pafnuty Chebyshev, in the mid-nineteenth century.
Uppercase letters such as X or Y denote a random variable. Lowercase letters like x or y denote the value of a random variable. If X is a random variable, then X is written in words, and x is given as a number.
For example, let X = the...
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Randomized Experiments01:13

Randomized Experiments

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The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
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Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

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An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
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Random Error01:04

Random Error

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Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...
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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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機械学習支援の安全なランダム通信システム

Areeb Ahmed1, Zoran Bosnić1

  • 1University of Ljubljana, Faculty of Computer and Information Science, Večna pot 113, 1000 Ljubljana, Slovenia.

Entropy (Basel, Switzerland)
|August 28, 2025
PubMed
まとめ

機械学習支援のランダム通信システム (ML-RCS) を導入し 物理層セキュリティ (PLS) を強化します このシステムは,意思決定ツリー受信機とアルファ安定ノイズを使用して,高いデータ速度で安全なデータ送信を行います.

科学分野:

  • 通信システム工学
  • 機械学習アプリケーション
  • 情報セキュリティ

背景:

  • 機械学習 (ML) は,通信システムにおける物理層セキュリティ (PLS) を大幅に向上させます.
  • 現代の通信ネットワークの性能とセキュリティを最適化することは,依然として重要な課題です.

研究 の 目的:

  • 最初の機械学習支援ランダム通信システム (ML-RCS) を提案する.
  • MLと非従来のノイズキャリアを使用する通信システムのセキュリティとデータレートを向上させる.

主な方法:

  • ランダムなノイズ信号からバイナリ情報を抽出するために,事前に訓練された意思決定ツリー (DT) 受信機を開発した.
  • バイナリビットをエンコードするための安全なランダムキャリアとして,歪んだアルファ安定 (α-stable) ノイズを使用します.
  • 既定のキー (パルス長) とDTモデルを使用して,正当な受信者が安全な解読を行います.

主要な成果:

  • 10-3のビットエラー率 (BER) を達成し,安全な通信が成功したことを確認しました.
  • 既存のランダム通信システムと比較して データの速度が増加しました
  • 盗聴機が情報解読に失敗した (50.2%の偽陰性率) 鍵とデータセットなしで
キーワード:
秘密のコミュニケーション意思決定ツリー機械学習ランダム通信システムα 安定分布

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結論:

  • ML-RCSは,より高いデータレートで安全な通信を効果的に確立します.
  • システムのセキュリティは 盗聴の試みに対する抵抗によって検証されます
  • 非従来のML-RCSは,統合されたPLSを備えた安全な次世代通信デバイスの開発に希望を示しています.