Successive Decoding Research Articles

Asynchronous Code Division Multiplexing-Based Visible Light Positioning and Communication Network Using Successive Interference Cancellation Decoding.

In the evolving landscape of sixth-generation wireless communication, the integration of visible light communication (VLC) and visible light positioning (VLP), known as visible light positioning and communication (VLPC), emerges as a pivotal technology. This study addresses the challenges of asynchronous code division multiplexing (ACDM) in VLPC networks, focusing on the enhancement of data transmission quality and positioning accuracy. Firstly, we propose an orthogonal pseudo-random code (OPRC) for ACDM-based VLP systems. Leveraging its excellent correlation properties, VLP signals preserve orthogonality even amidst asynchronous transmissions, achieving sub-centimeter average positioning errors. Next, by combining OPRC with successive interference cancellation decoding (SICD), we propose an enhanced ACDM-based VLPC system that utilizes OPRC for improved signal orthogonality and SICD for progressive elimination of multiple access interference (MAI) among VLPC signals. The results show substantial improvements in bit-error rate (BER) and positioning error (PE), approaching the performance levels observed in synchronized VLPC systems. Specifically, the SICD-OPRC scheme reduces average BER to 4.3 × 10-4 and average PE to 2.7 cm, demonstrating its robustness and superiority in complex asynchronous scenarios.

Interactive experience of cross-cultural Japanese communication e-learning intelligent system based on computer information security

In the current situation of widespread digital media, e-learning has become the mainstream mode of online learning. Its intelligent and entertaining learning experience can enhance students' interest in learning. This article analyzes the interactive experience of cross-cultural Japanese communication e-learning intelligent system. The e-learning intelligent system can provide network platform support for the learning process, allowing students and teachers to engage in interactive learning through computer systems. At the same time, the system will also recommend learning materials that students are interested in. Adding some encryption techniques to the system in this article is mainly aimed at protecting the security of data. In order to build an intelligent communication system for cross-cultural Japanese, the main parameters of data information are generally used as the basis for experiments, and relevant coding principles are formulated. For some neural networks, the larger the stored values of the original training samples, the higher the sample size level of information parameters after successful decoding. The experimental results indicate that using data security encryption technology in computer information systems can effectively improve the overall performance of cross-cultural Japanese intelligent systems.

Algorithmization and Hardware Implementation of Polar Coding for 5G Telecommunications

Abstract The article explores a recursive algorithm for determining Bhattacharyya parameters, which is a measure of the degree of channel polarization for telecommunications with polar coding. A method is given for determining the positions of information and fixed bits in a polar code. The principles of constructing a polar encoder and a polar decoder successive cancellation are considered. The Field-Programmable Gate Array implementation of the successive cancellation list decoder is considered. Experimental studies of the construction of a polar code using Gaussian approximation have been carried out. The influence of the design signal-to-noise ratio on the bit error rate of telecommunications with polar codes has been studied. The use of multi-position modulation in telecommunications with polar codes has been studied. It is expected that the results obtained will be useful in hardware and software implementation of 5G telecommunications with polar coding.

The Doorway to Deciphering: Methodology and Analysis of Successful Decoding Results in the Voynich Manuscript

The Doorway to Deciphering: Methodology and Analysis of Successful Decoding Results in the Voynich Manuscript

The speech neuroprosthesis.

Loss of speech after paralysis is devastating, but circumventing motor-pathway injury by directly decoding speech from intact cortical activity has the potential to restore naturalcommunication and self-expression. Recent discoveries have defined how key features of speech production are facilitated by the coordinated activity of vocal-tract articulatory and motor-planning cortical representations. In this Review, we highlight such progress and how it has led to successful speech decoding, first in individuals implanted with intracranial electrodes for clinicalepilepsy monitoringand subsequently in individuals with paralysis as part ofearlyfeasibility clinical trials to restore speech. We discuss high-spatiotemporal-resolution neural interfaces and the adaptation of state-of-the-art speech computational algorithms that have driven rapidandsubstantial progress indecoding neural activity into text, audible speech, and facial movements. Although restoring natural speech is a long-term goal, speech neuroprostheses already have performance levels that surpass communication rates offered by current assistive-communication technology. Given this accelerated rate of progress in the field, we propose key evaluation metrics for speed and accuracy, among others, to help standardize across studies. We finish by highlighting several directions to more fully explore the multidimensional feature space of speech and language, which will continue to accelerate progress towards a clinically viable speech neuroprosthesis.

Polar-coded channel polarization for reducing complexity of soft-decision forward error correction

To reduce the computational complexity of soft-decision (SD) forward error correction (FEC), we propose a polar coding method with a low-complexity successive cancellation decoder. Polar coding induces channel polarization in which two bit-channels with lower and higher reliabilities are polarized. Only the less-reliable bit-channels are protected by SD-FEC, whereas the more-reliable bit-channels are offloaded, reducing the complexity of SD-FEC decoding. The degradation of the bit error ratio (BER) performance can be suppressed by designing the polar encoder structures for the successive cancellation decoder. We numerically demonstrate that the proposed method manages to both reduce the computational complexity by half and suppress the BER performance degradation by less than 0.6 dB, compared with the conventional method using only the SD-FEC.

A Deterministic Algorithm for Computing the Weight Distribution of Polar Code

In this work, we present a deterministic algorithm for computing the entire weight distribution of polar codes. As the first step, we derive an efficient recursive procedure to compute the weight distribution that arises in successive cancellation decoding of polar codes along any decoding path. This solves the open problem recently posed by Polyanskaya, Davletshin, and Polyanskii. Using this recursive procedure, at code length <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> , we can compute the weight distribution of any <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">polar cosets</i> in time <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</i> ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ). We show that any polar code can be represented as a disjoint union of such polar cosets; moreover, this representation extends to polar codes with dynamically frozen bits. However, the number of polar cosets in such representation scales exponentially with a parameter introduced herein, which we call the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mixing factor</i> . To upper bound the complexity of our algorithm for polar codes being decreasing monomial codes, we study the range of their mixing factors. We prove that among all decreasing monomial codes with rates at most 1/2, self-dual Reed-Muller codes have the largest mixing factors. To further reduce the complexity of our algorithm, we make use of the fact that, as decreasing monomial codes, polar codes have a large automorphism group. That automorphism group includes the block lower-triangular affine group (BLTA), which in turn contains the lower-triangular affine group (LTA). We prove that a subgroup of LTA acts transitively on certain subsets of decreasing monomial codes, thereby drastically reducing the number of polar cosets that we need to evaluate. This complexity reduction makes it possible to compute the weight distribution of polar codes at length <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> = 128.

A learnable continuous wavelet-based multi-branch attentive convolutional neural network for spatio–spectral–temporal EEG signal decoding

Deep learning methods, particularly convolutional neural networks (CNNs), have advanced the field of motor imagery (MI) by effectively extracting spatio-spectral–temporal (SST) features from electroencephalography (EEG) signals. Image-based CNNs using time–frequency analysis such as wavelet transform have outperformed signal-based CNNs in MI-EEG classification. However, the spatial characteristics of EEG signals can be neglected due to the high dimensionality of spectral–temporal feature space. Moreover, the performance hinges on whether the predefined transformation can capture the class-discriminative spectral–temporal representations of EEG signals well. Despite attempts to alleviate these challenges by selecting EEG electrodes or restricting spectral–temporal feature space, large inter-subject variabilities have impeded successful MI-EEG decoding. In this paper, we propose a learnable continuous wavelet-based multi-branch attentive CNN framework for decoding MI-EEG signals. Our method is designed to automatically generate class-discriminative SST representations of EEG signals within subject-specific sub-bands by utilizing learnable wavelet-based convolutions. Additionally, it employs multi-branch attentive CNNs for the effective and efficient extraction of local and global SST features. Our comprehensive evaluation on two public datasets demonstrates that the proposed method significantly outperformed state-of-the-art methods. We also conduct an ablation study under various configuration settings within the proposed framework to demonstrate the effectiveness of our method. The proposed method provides a powerful and innovative approach to MI-EEG signal decoding, with implications for personalized EEG decoding.

Performance analysis of multi-folded pipelined successive cancellation decoder architecture for polar code

Performance analysis of multi-folded pipelined successive cancellation decoder architecture for polar code

All-optical 2-bit decoder based on a silicon waveguide device for BPSK-modulated signals

Optical computing technology has gained attention as a solution to address the computational latency caused by the resistance-capacitance (RC) delay in processors based on complementary metal-oxide-semiconductor (CMOS). However, many optical computing technologies tend to rely on nonlinear effects, resulting in an increase in the device length and input light intensity to enhance nonlinear efficiency. This study proposed what we believe is a new optical decoder device based on linear effects. The device was composed of two cascaded delay-line interferometers (DLIs) made of a silicon waveguide. Targeting 2-bit binary phase-shift keying (BPSK) signals, it outputs ON state for a specific bit pattern by setting different phase conditions. The experimental results confirmed the functionality of the device, including measurements of the impulse response, evaluation of the phase-shift conditions, and successful decoding operations for a signal at 10 Gbps. The proposed decoder, which does not rely on nonlinear effects, offers advantages in terms of low latency and power consumption.

Improving Data Delivery in Unreliable Networks Using Network Coding and Ant-Colony Optimization

Wireless Sensor Networks (WSNs) comprise interconnected wireless nodes that receive and transmit data across applications and platforms. This paper addressed the problem of link failures in WSNs that potentially could lead to the loss of data packets while still in transit. This was achieved through the use of network coding which is known to address capacity bottleneck problems in WSNs. In particular, a technique called Ant Agent-Assisted Network Coding (AAANC) is proposed that employs the ant colony optimization technique in addition to network coding operations. The main aim of AAANC is to facilitate the successful delivery and decoding of coded data packets in the presence of link failures. AAANC employs a packet route selection technique that is inspired by the social behavior of natural ants. For natural ants, a strong pheromone trail along a path indicates a promising route to a food source, and this is analogous to a reliable communication link for routing data packets in this paper. Through simulations, AAANC was compared to diagonal pseudorandom network coding (DNC) and triangular pseudorandom network coding (TNC), and it proved to have a superior performance in terms of packet delivery ratio and number of decoded packets. Significant performance gain can be achieved if AAANC algorithm is made to dynamically adapt the ant colony and network coding parameters in response to traffic changes.

Throughput and delay analysis of cognitive M2M communications

In this paper, we analyze throughput and delay performance of clustered Machine Type Communication (MTC) devices which access an eNodeB utilizing a primary spectrum in underlay mode. We assume that the MTC devices form two clusters and there is an optimal preamble allocation between the two clusters to maximize the throughput. We further investigate the impact of the tolerable interference threshold on throughput, successful preamble decoding probability, and delay. Then, the impact of the preamble partition factor and the access barring factor on throughput and delay is analyzed. Finally, we evaluate the impact of the number of devices, retransmission requests, and preamble partitions on the delay.

Decoding crystallization behavior of aluminoborosilicate glasses: From structural descriptors to Quantitative Structure – Property Relationship (QSPR) based predictive models

Successful decoding of structural descriptors controlling the crystallization in multicomponent functional glasses can pave the way for the transition from the trial-and-error approach and empirical modeling for glass/glass-ceramic composition design toward more rational and scientifically rigorous Quantitative Structure-Property Relationship (QSPR) based models. However, due to the compositional and structural complexity of multicomponent glasses and the longer time and length scales associated with nucleation, the development and validation of QSPR models are still in it's infancy. The work presented in the article is an attempt to leap forward in this pursuit by combining the strengths of experimental and computational materials science to decode the chemo-structural drivers that promote or suppress nucleation and crystal growth in alkali/alkaline-earth aluminoborosilicate glasses leading to the development of a QSPR-based model (powered by MD simulations). The results reveal the following two descriptors that govern the nucleation and crystallization of a particular aluminosilicate phase in the functional glasses: (1) degree of mixing between the SiO4 and AlO4 units, i.e., Si–O–Al linkages, and (2) difference/similarity between the short-to-intermediate range ordering in the glass structure to that of the structure of corresponding crystalline phase. Based on the established composition–structure–crystallization behavior relationships, a cluster analysis based QSPR model has been developed (and tested) to predict the propensity of nepheline (and anorthite) crystallization in the investigated glasses. The model has been tested on several compositions from the present and previous studies and has successfully predicted the crystallization propensity of all glass compositions, even in cases where previous empirical and semi-empirical models were unsuccessful.

Fast Successive Cancellation Decoding of Polar Codes with Large Kernels

Fast Successive Cancellation Decoding of Polar Codes with Large Kernels

Adaptive successive cancellation list decoding for Polar-coded Probabilistic amplitude shaping

Adaptive successive cancellation list decoding for Polar-coded Probabilistic amplitude shaping

Novel Wearable Device for Mindful Sensorimotor Training: Integrating Motor Decoding and Somatosensory Stimulation for Neurorehabilitation.

Sensorimotor impairment is a prevalent condition requiring effective rehabilitation strategies. This study introduces a novel wearable device for Mindful Sensorimotor Training (MiSMT) designed for sensory and motor rehabilitation. Our MiSMT device combines motor training using myoelectric pattern recognition along sensory training using two tactile displays. This device offers a comprehensive solution, integrating electromyography and haptic feedback, lacking in existing devices. The device features eight electromyography channels, a rechargeable battery, and wireless Bluetooth or Wi-Fi connectivity for seamless communication with a computer or mobile device. Its flexible material allows for adaptability to various body parts, ensuring ease of use in diverse patients. The two tactile displays, with 16 electromagnetic actuators each, provide touch and vibration sensations up to 250 Hz. In this proof-of-concept study, we show improved two-point discrimination after 5 training sessions in participants with intact limbs (p=0.047). We also demonstrated successful acquisition, processing, and decoding of myoelectric signals in offline and online evaluations. In conclusion, the MiSMT device presents a promising tool for sensorimotor rehabilitation by combining motor execution and sensory training benefits. Further studies are required to assess its effectiveness in individuals with sensorimotor impairments. Integrating mindful sensory and motor training with innovative technology can enhance rehabilitation outcomes and improve the quality of life for those with sensorimotor impairments.

Understanding and Text Properties: Investigating Readers’ Sense-making Processes

Literary reading and the comprehension of literary text(s) has long been a key part of education. While reading comprehension in general has received a fair amount of attention, empirical research on comprehension processes of literary texts is still relatively rare; and yet, it is advisable to gain a thorough understanding of comprehension processes along with potential difficulties and hurdles to understanding specifically of literary texts. To address this issue, we analyse such comprehension processes in a group of university students of English as a second language, drawing on a test based on Shakespeare’s sonnet 43 which employs standardised, open-ended questions. Our research has two goals: firstly, to analyse readers’ approaches that result in a more or less successful decoding of the text they are presented with, and, secondly, to explore whether different textual phenomena help or hinder understanding. We find that strong and weak readers employ similar reading strategies, they do differ, however, with regard to their literary response, with weak readers more likely to draw on irrelevant associations not warranted by the text. In addition, we are able to show that some textual phenomena are more difficult to understand than others. We discuss possible implications of our findings for teaching.

ConTraNet: A hybrid network for improving the classification of EEG and EMG signals with limited training data

ObjectiveBio-Signals such as electroencephalography (EEG) and electromyography (EMG) are widely used for the rehabilitation of physically disabled people and for the characterization of cognitive impairments. Successful decoding of these bio-signals is however non-trivial because of the time-varying and non-stationary characteristics. Furthermore, existence of short- and long-range dependencies in these time-series signal makes the decoding even more challenging. State-of-the-art studies proposed Convolutional Neural Networks (CNNs) based architectures for the classification of these bio-signals, which are proven useful to learn spatial representations. However, CNNs because of the fixed size convolutional kernels and shared weights pay only uniform attention and are also suboptimal in learning short-long term dependencies, simultaneously, which could be pivotal in decoding EEG and EMG signals. Therefore, it is important to address these limitations of CNNs. To learn short- and long-range dependencies simultaneously and to pay more attention to more relevant part of the input signal, Transformer neural network-based architectures can play a significant role. Nonetheless, it requires a large corpus of training data. However, EEG and EMG decoding studies produce limited amount of the data. Therefore, using standalone transformers neural networks produce ordinary results. In this study, we ask a question whether we can fix the limitations of CNN and transformer neural networks and provide a robust and generalized model that can simultaneously learn spatial patterns, long-short term dependencies, pay variable amount of attention to time-varying non-stationary input signal with limited training data. ApproachIn this work, we introduce a novel single hybrid model called ConTraNet, which is based on CNN and Transformer architectures that contains the strengths of both CNN and Transformer neural networks. ConTraNet uses a CNN block to introduce inductive bias in the model and learn local dependencies, whereas the Transformer block uses the self-attention mechanism to learn the short- and long-range or global dependencies in the signal and learn to pay different attention to different parts of the signals. Main resultsWe evaluated and compared the ConTraNet with state-of-the-art methods on four publicly available datasets (BCI Competition IV dataset 2b, Physionet MI-EEG dataset, Mendeley sEMG dataset, Mendeley sEMG V1 dataset) which belong to EEG-HMI and EMG-HMI paradigms. ConTraNet outperformed its counterparts in all the different category tasks (2-class, 3-class, 4-class, 7-class, and 10-class decoding tasks). SignificanceWith limited training data ConTraNet significantly improves classification performance on four publicly available datasets for 2, 3, 4, 7, and 10-classes compared to its counterparts.

Cyclic redundancy check-aided successive cancellation-based polar decoders

&lt;span&gt;Research on channel coding for network transmission using polar codes has produced excellent results. By removing error redundancy from the decoding process, cyclic redundancy check (CRC) is frequently used by researchers to increase a system’s performance. In prior research, the application of decoder algorithms for polar codes was examined but not thoroughly compared. For the general capabilities of the previously proposed algorithms to be ascertained, it is crucial to analyze the employment of polar decoders especially successive cancellation (SC)-based polar decoders and the use of CRC in additive white gaussian noise (AWGN). Hence, this paper analyzes the performance of CRC with SC-based polar decoders in AWGN. In the simulation setup, (256,128) polar codes and CRCs with three-bit sizes (6, 8 and 11) were utilized. SC-based polar decoders, such as SC, soft-output cancellation, SC list (SCL) and simplified SC, were applied at the decoder part. The outcomes show that CRC-aided SC-based polar decoders reduced redundancy error. Among all the decoders, the SCL decoder with 11-bits CRC performed well when the normalized signal-to-noise ratio was high. Based on the analysis, removing errors at the highest level is possible using a suitable CRC size for SC-based polar decoders.&lt;/span&gt;

Scalable Polar Code Construction for Successive Cancellation List Decoding: A Graph Neural Network-Based Approach

Scalable Polar Code Construction for Successive Cancellation List Decoding: A Graph Neural Network-Based Approach