Average Log-likelihood Ratio Research Articles

Novel intelligent blind information reconciliation for LDPC codes in quantum key distribution systems

Information reconciliation is an important step in quantum key distribution (QKD). As a good channel coding technology, LDPC (low-density parity-check) code perform well in information reconciliation. In this paper, we investigate a new approach, namely blind information reconciliation. In our approach, after the state distribution is carried out by Alice and the measurements are taken by Bob, Alice can select an appropriate LDPC encoder (parity-check matrix) from the predefined candidate pool according to the estimated QBER (quantum bit-error-rate). Bob can use the expectation–maximization (EM) estimator and the maximum average log-likelihood ratio (LLR) detector to blindly identify parity-check matrices associated with not only different code-rates but also different codeword-lengths Alice employed. Moreover, puncturing and shortening can also be invoked to adjust the code-rate slightly to improve the error-correction performance. Monte Carlo simulations have demonstrated that our proposed new blind information-reconciliation scheme can successfully identify the parity-check matrix even under high QBER conditions, reduce the interactive communication-rounds, and improve the reconciliation efficiency in comparison with the conventional rate-adaptive LDPC coding scheme without QBER estimation.

Speech frame selection for spoofing detection with an application to partially spoofed audio-data

In this paper, we introduce a frame selection strategy for improved detection of spoofed speech. A countermeasure (CM) system typically uses a Gaussian mixture model (GMM) based classifier for computing the log-likelihood scores. The average log-likelihood ratio for all speech frames of a test utterance is calculated as the score for the decision making. As opposed to this standard approach, we propose to use selected speech frames of the test utterance for scoring. We present two simple and computationally efficient frame selection strategies based on the log-likelihood ratios of the individual frames. The performance is evaluated with constant-Q cepstral coefficients as front-end feature extraction and two-class GMM as a back-end classifier. We conduct the experiments using the speech corpora from ASVspoof 2015, 2017, and 2019 challenges. The experimental results show that the proposed scoring techniques substantially outperform the conventional scoring technique for both the development and evaluation data set of ASVspoof 2015 corpus. We did not observe noticeable performance gain in ASVspoof 2017 and ASVspoof 2019 corpus. We further conducted experiments with partially spoofed data where spoofed data is created by augmenting natural and spoofed speech. In this scenario, the proposed methods demonstrate considerable performance improvement over baseline.

Blind Data Detection With Unknown Channel Coding

Blind data detection is widely investigated since it finds many crucial applications in the interference cancelation, cognitive radio, and military interception, etc. In this letter, we propose a blind iterative algorithm that simultaneously improves the performance of channel coding identification and data detection in the communication system with the unknown channel coding and the unknown fading channels. First, the likelihood-based channel estimator and the soft detector and regenerator are adopted in an iterative manner, which estimates the unknown parameters and detects the information bits. Then, an average log-likelihood ratio (LLR) classifier is adopted to identify the unknown channel coding and determine the information bits. Numerical results show that the proposed algorithm outperforms the existing scheme in terms of correct identification and data detection.

Improved Bit-Flipping Algorithm for Successive Cancellation Decoding of Polar Codes

The interest in polar codes has been increasing significantly since their adoption for use in the 5th generation wireless systems standard. Successive cancellation (SC) decoding algorithm has low implementation complexity, but yields mediocre error-correction performance at the code lengths of interest. SC-Flip algorithm improves the error-correction performance of SC by identifying possibly erroneous decisions made by SC and re-iterates after flipping one bit. It was recently shown that only a portion of bit-channels are most likely to be in error. In this paper, we investigate the average log-likelihood ratio (LLR) values and their distribution related to the erroneous bit-channels, and develop the Thresholded SC-Flip (TSCF) decoding algorithm. We also replace the LLR selection and sorting of SC-Flip with a comparator to reduce the implementation complexity. Simulation results demonstrate that for practical code lengths and a wide range of rates, TSCF shows negligible loss compared with the error-correction performance obtained when all single-errors are corrected. At matching maximum iterations, TSCF has an error-correction performance gain of up to 0.45 dB compared with SC-Flip decoding. At matching error-correction performance, the computational complexity of TSCF is reduced by up to 40% on average and requires up to $5\times $ lower maximum number of iterations.

Performance evaluation for MOTIFSIM

BackgroundPrevious studies show various results obtained from different motif finders for an identical dataset. This is largely due to the fact that these tools use different strategies and possess unique features for discovering the motifs. Hence, using multiple tools and methods has been suggested because the motifs commonly reported by them are more likely to be biologically significant.ResultsThe common significant motifs from multiple tools can be obtained by using MOTIFSIM tool. In this work, we evaluated the performance of MOTIFSIM in three aspects. First, we compared the pair-wise comparison technique of MOTIFSIM with the un-gapped Smith-Waterman algorithm and four common distance metrics: average Kullback-Leibler, average log-likelihood ratio, Chi-Square distance, and Pearson Correlation Coefficient. Second, we compared the performance of MOTIFSIM with RSAT Matrix-clustering tool for motif clustering. Lastly, we evaluated the performances of nineteen motif finders and the reliability of MOTIFSIM for identifying the common significant motifs from multiple tools.ConclusionsThe pair-wise comparison results reveal that MOTIFSIM attains better performance than the un-gapped Smith-Waterman algorithm and four distance metrics. The clustering results also demonstrate that MOTIFSIM achieves similar or even better performance than RSAT Matrix-clustering. Furthermore, the findings indicate if the motif detection does not require a special tool for detecting a specific type of motif then using multiple motif finders and combining with MOTIFSIM for obtaining the common significant motifs, it improved the results for DNA motif detection.

GA Optimized HMM Error Model for OFDM

Finite-state HMM error models are an established powerful tool for capturing temporal characteristics of fading channels. They make network simulations much more practical and fast while developing and testing higher layer wireless system protocols and designing interleaving and FEC schemes. This paper has introduced two new Genetic Algorithm (GA) based approaches, namely, GA-S and GA-B as an alternative to HMM error models, for the accurate learning of error statistics of autocorrelation function (ACF) and error-run distributions. Validity comparisons of fit obtained show that the GA method is better than both the conventional Baum–Welch algorithm (BWA) and Semi-hidden Markov model (SHMM) methods, even when the target sequences are as short as 1000 in length. The independent elements of the [A] and [B] parameters of BWA used as chromosomes of the population space are used for error generation within the Genetic Algorithm. Mean square error of statistical properties of the error sequences is used to determine fitness of the chromosomes unlike other works using average log-likelihood ratio. Applicability has been tested by numerical simulations using error sequences of different lengths as well as target sequences of fixed length from an OFDM transceiver system under different fading rates. A T s spaced time-delay model of the propagation channel with a fixed power delay profile has been used. For slow faded channels or when long sequences of error free intervals poses difficulties for BWA training, the GA method proves to be an excellent alternative for fast and accurate modeling of the error bursts. Unlike the computationally cumbersome BWA or the simplified SHMM approach, the GA model is capable of arriving at desired levels of accuracy with two to three states, in contrast to the Markov models needing a much higher number of states.

On Blind Recognition of Channel Codes Within a Candidate Set

Existing schemes for blind recognition of channel codes make use of the average log-likelihood ratio (LLR) of each code’s parity checks. There are difficulties in setting necessary thresholds and in theoretical analysis of the method. This letter proposes to use the average likelihood difference (LD) of the parity checks. The computational complexity is reduced while the recognition performance is kept comparable to that of the LLR method. Moreover, an optimum decoding-based blind recognition is also described. Simulations show that, for rate 1/2 convolutional codes, this new method notably improves the performance.

Sparse Sensing for Distributed Detection

An offline sampling design problem for distributed detection is considered in this paper. To reduce the sensing, storage, transmission, and processing costs, the natural choice for the sampler is the sparsest one that results in a desired global error probability. Since the numerical optimization of the error probabilities is difficult, we adopt simpler costs related to distance measures between the conditional distributions of the sensor observations. We design sparse samplers for the Bayesian as well as the Neyman-Pearson setting. The developed theory can be applied to sensor placement/selection, sample selection, and fully decentralized data compression. For conditionally independent observations, we give an explicit solution, which is optimal in terms of the error exponents. More specifically, the best subset of sensors is the one with the smallest local average root-likelihood ratio and largest local average log-likelihood ratio in the Bayesian and Neyman-Pearson setting, respectively. We supplement the proposed framework with a thorough analysis for Gaussian observations, including the case when the sensors are conditionally dependent, and also provide examples for other observation distributions. One of the results shows that, for nonidentical Gaussian sensor observations with uncommon means and common covariances under both hypotheses, the number of sensors required to achieve a desired detection performance reduces significantly as the sensors become more coherent.

Detection of rayleigh targets using adjacent matched filter samples

The matched filter maximizes signal-to-noise ratio (SNR) at its output. To determine the presence of a target, traditional radar detection compares the observed SNR at samples of the matched filter output to a specified threshold. However, sample rates often used in practice result in two or more adjacent samples with target energy. Therefore, traditional radar detection ignores a key piece of information - the correlation between samples. Using an explicit model of adjacent matched filter samples and the functional form of the matched filter response, the average log likelihood ratio test (ALLRT) is derived assuming a Rayleigh target with pulse-to-pulse fluctuations. Furthermore, key features relevant to the design of a detector are investigated. In particular, the ALLRT detector provides robustness against the negative effects of range bin straddling, and in some cases outperforms traditional detectors that use oversampled matched filter outputs.

Novel Blind Identification of LDPC Codes Using Average LLR of Syndrome a Posteriori Probability

Blind signal processing methods have been very popular recently since they can play crucial roles in the prevalent cognitive radio research. Blind encoder identification has drawn research interest lately. In this paper, we would like to tackle the blind identification of binary low-density parity-check (LDPC) codes for binary phase-shift keying (BPSK) signals. We propose a novel blind identification system which consists of three components, namely expectation-maximization (EM) estimator for signal amplitude and noise variance, log-likelihood ratio (LLR) estimator for syndrome a posteriori probabilities, and maximum average LLR detector. Monte Carlo simulation results demonstrate that our proposed blind LDPC encoder identification scheme is very promising even for low signal-to-noise ratio conditions.

The strong deviation theorem for discrete-time and continuous-state nonhomogeneous Markov chains

In this paper, the notion of asymptotic average log-likelihood ratio, as a measure of the difference between the sequence of random variables and Markov chains, is introduced, and by constructing a nonnegative martingale, the strong deviation theorem for discrete-time and continuous-state nonhomogeneous Markov chains is established.

Strong deviation theorems for functionals of the nonnegative continuous random variables

By using the approach of Laplace transform, we establish the strong deviation theorems represented by inequalities and described by asymptotic average log-likelihood ratio for functionals of the nonnegative continuous random variables. As corollaries, we obtain several strong laws of large numbers for the random variable functionals. The results of this paper extend and improve the corresponding results of some current literatures.

Improved Decision-Feedback Detection Schemes for STBC over Time-Selective Fading Channels

This letter proposes two efficient decision-feedback (DF) detection schemes for space-time block code (STBC) over time-selective fading channels. The existing DF detection causes error propagation when the first symbol is not detected correctly. However, the proposed detection schemes provide two candidates according to a channel gain or an average log-likelihood ratio (LLR) based selection rule and choose a better candidate for the first symbol. Simulation results show that the proposed detection schemes reduce error propagation and yield significant signal-to-noise ratio (SNR) gain with moderate complexity, compared to the existing DF detection scheme.

MicroRNA sequence motifs reveal asymmetry between the stem arms

The processing of micro RNAs (miRNAs) from their stemloop precursor have revealed asymmetry in the processing of the mature and its star sequence. Furthermore, the miRNA processing system between organism differ. To assess this at the sequence level we have investigated mature miRNAs in their genomic contexts. We have compared profiles of mature miRNAs within their genomic context of the 5 ′ and 3 ′ stemloop precursor arms and we find asymmetry between mature sequences of the 5 ′ and 3 ′ stemloop precursor arms. The main observation is that vertebrate organisms have a characteristic motif on the 5 ′ arm which is in contrast to the 3 ′ arm motif which mainly show the conserved U at the position of the mature start. Also the vertebrate 5 ′ arm motif show a semi-conserved G 13 nucleotides upstream from the first position. We compared the 5 ′ and 3 ′ arm profiles using the average log likelihood ratio (ALLR) score, as defined by Wang and Stormo (2003) [Wang T., Stormo, G.D., 2003. Combining phylogenetic data with co-regulated genes to identify regulatory motifs. Bioinformatics 2369–2380.] and computing a p-value we find that the two profiles differs significantly in their 3 ′ end where the 5 ′ arm motif (in contrast to the 3 ′ arm motif) has a semi-conserved GU rich region. Similar findings are also obtained for other organisms, such as fly, worm and plants. The observed similarities and differences between closely and distantly related organisms are discussed and related to current knowledge of miRNA processing.

5531897 Dynamic bed reactor: Stormo Keith E, Moscow, ID, United States assigned to Idaho Research Foundation Inc

The processing of micro RNAs (miRNAs) from their stemloop precursor have revealed asymmetry in the processing of the mature and its star sequence. Furthermore, the miRNA processing system between organism differ. To assess this at the sequence level we have investigated mature miRNAs in their genomic contexts. We have compared profiles of mature miRNAs within their genomic context of the 5′ and 3′ stemloop precursor arms and we find asymmetry between mature sequences of the 5′ and 3′ stemloop precursor arms. The main observation is that vertebrate organisms have a characteristic motif on the 5′ arm which is in contrast to the 3′ arm motif which mainly show the conserved U at the position of the mature start. Also the vertebrate 5′ arm motif show a semi-conserved G 13 nucleotides upstream from the first position. We compared the 5′ and 3′ arm profiles using the average log likelihood ratio (ALLR) score, as defined by Wang and Stormo (2003) [Wang T., Stormo, G.D., 2003. Combining phylogenetic data with co-regulated genes to identify regulatory motifs. Bioinformatics 2369–2380.] and computing a p-value we find that the two profiles differs significantly in their 3′ end where the 5′ arm motif (in contrast to the 3′ arm motif) has a semi-conserved GU rich region. Similar findings are also obtained for other organisms, such as fly, worm and plants. The observed similarities and differences between closely and distantly related organisms are discussed and related to current knowledge of miRNA processing.

5531809 Pretreatment layer for CO-VSA: Golden Timothy C; Webley Paul A; Auvil Steven; Katz Wilbur Allentown, PA, United States assigned to Air Products and Chemicals Inc

The processing of micro RNAs (miRNAs) from their stemloop precursor have revealed asymmetry in the processing of the mature and its star sequence. Furthermore, the miRNA processing system between organism differ. To assess this at the sequence level we have investigated mature miRNAs in their genomic contexts. We have compared profiles of mature miRNAs within their genomic context of the 5′ and 3′ stemloop precursor arms and we find asymmetry between mature sequences of the 5′ and 3′ stemloop precursor arms. The main observation is that vertebrate organisms have a characteristic motif on the 5′ arm which is in contrast to the 3′ arm motif which mainly show the conserved U at the position of the mature start. Also the vertebrate 5′ arm motif show a semi-conserved G 13 nucleotides upstream from the first position. We compared the 5′ and 3′ arm profiles using the average log likelihood ratio (ALLR) score, as defined by Wang and Stormo (2003) [Wang T., Stormo, G.D., 2003. Combining phylogenetic data with co-regulated genes to identify regulatory motifs. Bioinformatics 2369–2380.] and computing a p-value we find that the two profiles differs significantly in their 3′ end where the 5′ arm motif (in contrast to the 3′ arm motif) has a semi-conserved GU rich region. Similar findings are also obtained for other organisms, such as fly, worm and plants. The observed similarities and differences between closely and distantly related organisms are discussed and related to current knowledge of miRNA processing.