Decrease Of Error Probabilities Research Articles

DNA Computing-Based Multi-Source Data Storage Model in Digital Twins

The work aims to study the application of Deoxyribonucleic Acid (DNA) multi-source data storage in Digital Twins (DT) . Through the investigation of the research status of DT and DNA computing, the work puts forward the concept of DNA multi-source data storage for DT. Raptor code is improved from the design direction of degree distribution function, and six degree function distribution schemes are proposed in turn in the process of describing the research method. Additionally, a quaternary dynamic Huffman coding method is applied in DNA data storage, combined with the improved concatenated code as the error correction code. Considering the content of cytosine deoxynucleotide (C) and guanine deoxynucleotide Guanine (G) and the distribution of homopolymer in DNA storage, the work proposes and verifies an improved concatenated code algorithm Deoxyribonucleic Acid-Improved Concatenated code (DNA-ICC) . The results show that while the Signal-to-Noise Ratio (SNR) increases, the Bit Error Rate (BER) decreases gradually and the trend is similar. But the anti-interference ability of the degree distribution function optimized by the probability transfer method is better. The BER of DNA-ICC scheme decreases with the decrease of error probability, which is stronger than other error correction codes. Compared with the original concatenated code, it saves at least 1.65 s, and has a good control effect on homopolymer. When the size of homopolymer exceeds 4 nt, the probability of homopolymer is only 0.44%. The proposed Quaternary dynamic Huffman code and concatenated error correction code have excellent performance.

Error-related pupil dilation is sensitive to the evaluation of different error types

Adjusting behavior following errors is essential for successful goal-directed performance. Error-related pupil dilation indicates increased autonomic arousal and has been shown to predict adaptive adjustments of post-error behavior. Because different types of errors may require different behavioral adjustments, we investigated whether this process is also sensitive to the evaluation of different types of errors. We used a four-choice flanker task where errors occur either by pressing a button associated with the distractors (flanker errors), or by pressing a button not associated with the stimulus at all (nonflanker errors). Flanker errors imply suboptimal selective attention to the target and are therefore of increased significance for successful performance. Pupil dilation was larger for flanker errors than nonflanker errors, and only pupil dilation on flanker errors predicted a decrease of error probability on the next trial. Moreover, the error-related negativity, an electrophysiological marker of early error monitoring in the medial frontal cortex, was larger on flanker errors anticipating the effect of error type on pupil dilation. These results show that error-related pupil dilation is sensitive to the type and significance of errors and correlates with adaptive behavioral adjustments accordingly. This suggests that mechanisms underlying error-related pupil dilation receive inputs from error evaluation mechanisms in the medial frontal cortex.

Noise Correlation Effect on Detection: Signals in Equicorrelated or Autoregressive(1) Gaussian.

In this letter, we consider the effect of noise correlation on the error performance of binary hypothesis signal detection, when one of two deterministic signals is received in correlated Gaussian noise. For the likelihood ratio detection scheme, analytical performance results are derived for equicorrelated and autoregressive order one models. Although it is known previously that the best signal lies in the direction of eigenvector corresponding to the minimum eigenvalue of the noise covariance matrix, our investigation of the variation of mean signal-to-noise power ratio as a function of correlation parameter (i) shows how correlation leads to increased probability of error up to a point, beyond which monotonic decrease in error probability with increasing correlation is possible and (ii) provides a max-min signal design solution for the unknown correlation parameter case. Numerical results are also included for some specific signals.

QUANTUM STATE ESTIMATION AND LARGE DEVIATIONS

In this paper we propose a method to estimate the density matrix ρ of a d-level quantum system by measurements on the N-fold system in the joint state ρ⊗N. The scheme is based on covariant observables and representation theory of unitary groups and it extends previous results concerning pure states and the estimation of the spectrum of ρ. We show that it is consistent (i.e. the original input state ρ is recovered with certainty if N → ∞), analyze its large deviation behavior, and calculate explicitly the corresponding rate function which describes the exponential decrease of error probabilities in the limit N → ∞. Finally, we discuss the question whether the proposed scheme provides the fastest possible decay of error probabilities.

On Large Deviations in Testing Ornstein-Uhlenbeck Type Models with Delay

We obtain an explicit form of fine large deviation theorems for the log-likelihood ratio in testing models with observed Ornstein-Uhlenbeck processes and get explicit rates of decrease for error probabilities of Neyman-Pearson, Bayes, and minimax tests. We also give expressions for the rates of decrease of error probabilities of Neyman-Pearson tests in models with observed processes solving affine stochastic delay differential equations.

Rejection of PSK interference in DS-SS/QPSK systemusing complexadaptive filter and nonlinear correlation receiver

The rejection of PSK interference in a DS-SS/QPSK system by using a complex adaptive filter and a nonlinear correlation receiver with soft-limiter is discussed. This system leads to a decrease in error probability of several orders of magnitude compared to a system which uses a complex adaptive filter and a linear receiver.

Rejection of PSK interference in DS-SS/QPSK systemusing complex adaptive filter and nonlinear correlation receiver

The authors investigate the rejection of PSK interference in a DS-SS/QPSK system using a complex adaptive filter and a nonlinear correlation receiver with soft-limiter. This system leads to a decrease in error probability for several orders of magnitude compared to the system which uses a complex adaptive filter and a linear receiver.

Robust large deviations performance analysis for large sample detectors

Large deviations theory is used to analyze the exponential rate of decrease of error probabilities for a sequence of decisions based on a test statistics sequence (T/sub n/). It is assumed that (for a given statistical hypothesis) the distributions of T/sub n/ are determined by some unknown member of a class of probability distributions. The worst case, or least favorably exponential rate of error probability decrease over this class, is sought. It is shown that the Legendre-Fenchel transform of the maximized cumulant function yields a lower bound for the minimized large deviations rate function, and that in many cases this bound is tight. Application of the result is illustrated by a detailed consideration of i.i.d memoryless detection with an epsilon -contamination distribution family. >

A nonlocal approach for asymptotic memoryless detection theory (Corresp.)

Previous literature on detector design has presented many tractable results using efficacy as a performance functional, which is a small signal or local approach. Some nonlocal issues of memoryless detection are considered. We argue that the asymptotic exponential rate of decrease of error probabilities, or Bayes risk, provides the appropriate asymptotic measure of performance. A main contribution is the development of an approximation for nonlocal design that retains the tractability of the efficacy approach. We have proved the local consistency of our approximation. Also, as a consequence of the analysis, new proofs for Pitman-Noether-like theorems have been obtained.

Dual Control Strategies for Discrete State Markov Processes Part I

ABSTRACT This paper deals with the optimal on-line control of a stationary repetitive single-stage discrete state Markov process whose statistical description is initially unknown. In the presence of uncertainty concerning the optimal control policy, the controller must perform the dual functions of estimation and control, Evidently it is necessary in the course of learning the process parameters to probe the system with inputs which appear in retrospect to be non-optimal from the control standpoint. The algorithm which orders these non-optimal decisions is termed a strategy. The ideal dual strategy is defined as that which extracts the maximum information from the process for a given cost, the term ‘information’ here denoting a decrease in error probability. An examination of the foregoing definition reveals the close relationship between the estimation procedure and the system cost function. It is shown that this relationship can be expressed as a simple equality constraint which is readily implemented as an on-line control strategy. Numerical results are presented which confirm the effectiveness of the strategy.