scholarly journals New convolutional code constructions and a class of asymptotically good time-varying codes

1973 ◽  
Vol 19 (2) ◽  
pp. 220-225 ◽  
Author(s):  
J. Justesen
Keyword(s):  
Convolutional Code ◽  
Good Time ◽  
Time Varying ◽  
Code Constructions

scholarly journals Learning Midlevel Auditory Codes from Natural Sound Statistics

2018 ◽  
Vol 30 (3) ◽  
pp. 631-669 ◽  
Author(s):  
Wiktor Młynarski ◽  
Josh H. McDermott
Keyword(s):  
Convolutional Code ◽  
Complex Structures ◽  
Time Varying ◽  
Sensory Coding ◽  
Natural Stimulus ◽  
Activation Patterns ◽  
Natural Sound ◽  
Neuronal Processing ◽  
Neuronal Computation ◽  
Insight Into

Interaction with the world requires an organism to transform sensory signals into representations in which behaviorally meaningful properties of the environment are made explicit. These representations are derived through cascades of neuronal processing stages in which neurons at each stage recode the output of preceding stages. Explanations of sensory coding may thus involve understanding how low-level patterns are combined into more complex structures. To gain insight into such midlevel representations for sound, we designed a hierarchical generative model of natural sounds that learns combinations of spectrotemporal features from natural stimulus statistics. In the first layer, the model forms a sparse convolutional code of spectrograms using a dictionary of learned spectrotemporal kernels. To generalize from specific kernel activation patterns, the second layer encodes patterns of time-varying magnitude of multiple first-layer coefficients. When trained on corpora of speech and environmental sounds, some second-layer units learned to group similar spectrotemporal features. Others instantiate opponency between distinct sets of features. Such groupings might be instantiated by neurons in the auditory cortex, providing a hypothesis for midlevel neuronal computation.

scholarly journals The Error-Correcting Coding in Information Storage Modules with Increased Radiation Resistance

2018 ◽  
Vol 7 (4.7) ◽  
pp. 180
Author(s):  
Matveev V.M ◽  
Litvinenko R.S
Keyword(s):  
Convolutional Code ◽  
Ldpc Code ◽  
Analysis Data ◽  
Information Storage ◽  
Code Design ◽  
Memory Architecture ◽  
Memory Errors ◽  
Decoding Algorithms ◽  
Low Energy Consumption ◽  
Code Constructions

This article is devoted to the study and analysis of various noise-resistant code structures, which are designed for use in miniature memory drives on spacecrafts. Error-correcting coding is aimed for correcting memory errors that occur due to ionizing radiation. The first part of the article provides information about the general memory architecture using error-correcting coding. The second part considers linear code constructions, such as Hamming code, convolutional code, PC and LDPC code, as well as nonlinear code constructions, which are promising means of correcting memory errors (Vasiliev code, Phelps code, switching code, AMD-code). Based on the research and analysis data, the conclusion is made about the most suitable code design for the development of the information storage module. It should be noted that the determining requirement for choosing the code for the drive used on the spacecraft is the presence of simple decoding algorithms that allow high decoding speed and low energy consumption.  

scholarly journals Signal Analysis In The Ambiguity Domain

2021 ◽  
Author(s):  
Lakshmi Sugavaneswaran
Keyword(s):  
Signal Analysis ◽  
Real Life ◽  
Automated Analysis ◽  
Good Time ◽  
Time Varying ◽  
Frequency Distributions ◽  
Speech Detection ◽  
Time Frequency ◽  
Analysis Scheme

Time-Frequency Distributions (TFDs) are accounted to be one of the powerful tools for analysis of time-varying signals. Although a variety of TFDs have been proposed, most of their designs were targeted towards obtaining good visualization and limited work is available for characterization applications. In this work, the characteristics of the ambiguity domain (AD) is suitably exploited to obtain a novel automated analysis scheme that preserves the inherent TF connection during Non-Stationary (NS) signal processing. Following this, an energy-based discriminative set of feature vectors for facilitating efficient characterization of the given time-varying input has been proposed. This scheme is motivated by the fact that, although, the interfering (or cross-) terms plague the representation, they carry important signal interaction information, which could be investigated for usability for time-varying signal analysis. Once having assessed the suitability of this domain for NS signal analysis, a new formulation for obtaining AD transformation is introduced. The number theory concepts, specifically the even-ordered Ramanujan Sums (RS) are used to obtain the proposed transform function. A detailed investigation and comparison to the classical approach, on this novel class of functions reveals the many benefits of the RS-modified AD functions: inherent sparsity in representation, dimensionality reduction, and robustness to noise. The next contribution in this work, is the proposal of kernel modifications in AD for obtaining high resolution (and good time localization) distribution. This is motivated by the existing trade-off between TF resolution and interfering term reduction in TF distributions. Here, certain variants of TF kernels are proposed in the AD. In addition, kernels that are derived from the concept of learning machines are introduced for discriminative characterization of NS signals. Following this, two novel AD-based schemes for neurological disorder discrimination using gait and pathological speech detection are introduced. The performance evaluation of these AD-based schemes, using a linear classifier, resulted in a maximum overall classification accuracy of 93.1% and 97.5% for gait and pathological speech applications respectively. The accuracies were obtained after a rigorous leave-one-out technique validation strategy.These results further confirm the potential of the proposed schemes for efficient information extraction for real-life signals.

scholarly journals Signal Analysis In The Ambiguity Domain

2021 ◽  
Author(s):  
Lakshmi Sugavaneswaran
Keyword(s):  
Signal Analysis ◽  
Real Life ◽  
Automated Analysis ◽  
Good Time ◽  
Time Varying ◽  
Frequency Distributions ◽  
Speech Detection ◽  
Time Frequency ◽  
Analysis Scheme

Time-Frequency Distributions (TFDs) are accounted to be one of the powerful tools for analysis of time-varying signals. Although a variety of TFDs have been proposed, most of their designs were targeted towards obtaining good visualization and limited work is available for characterization applications. In this work, the characteristics of the ambiguity domain (AD) is suitably exploited to obtain a novel automated analysis scheme that preserves the inherent TF connection during Non-Stationary (NS) signal processing. Following this, an energy-based discriminative set of feature vectors for facilitating efficient characterization of the given time-varying input has been proposed. This scheme is motivated by the fact that, although, the interfering (or cross-) terms plague the representation, they carry important signal interaction information, which could be investigated for usability for time-varying signal analysis. Once having assessed the suitability of this domain for NS signal analysis, a new formulation for obtaining AD transformation is introduced. The number theory concepts, specifically the even-ordered Ramanujan Sums (RS) are used to obtain the proposed transform function. A detailed investigation and comparison to the classical approach, on this novel class of functions reveals the many benefits of the RS-modified AD functions: inherent sparsity in representation, dimensionality reduction, and robustness to noise. The next contribution in this work, is the proposal of kernel modifications in AD for obtaining high resolution (and good time localization) distribution. This is motivated by the existing trade-off between TF resolution and interfering term reduction in TF distributions. Here, certain variants of TF kernels are proposed in the AD. In addition, kernels that are derived from the concept of learning machines are introduced for discriminative characterization of NS signals. Following this, two novel AD-based schemes for neurological disorder discrimination using gait and pathological speech detection are introduced. The performance evaluation of these AD-based schemes, using a linear classifier, resulted in a maximum overall classification accuracy of 93.1% and 97.5% for gait and pathological speech applications respectively. The accuracies were obtained after a rigorous leave-one-out technique validation strategy.These results further confirm the potential of the proposed schemes for efficient information extraction for real-life signals.

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