Fast Decoding Research Articles

Fast EEG-Based Decoding Of The Directional Focus Of Auditory Attention Using Common Spatial Patterns.

Noise reduction algorithms in current hearing devices lack informationabout the sound source a user attends to when multiple sources are present. To resolve this issue, they can be complemented with auditory attention decoding (AAD) algorithms, which decode the attention using electroencephalography (EEG) sensors. State-of-the-art AAD algorithms employ a stimulus reconstruction approach, in which the envelope of the attended source is reconstructed from the EEG and correlated with the envelopes of the individual sources. This approach, however, performs poorly on short signal segments, whilelonger segments yield impractically long detection delays when the user switches attention. We propose decoding the directional focus of attention using filterbank common spatial pattern filters (FB-CSP) as an alternative AAD paradigm, whichdoes not require access to the clean source envelopes. The proposed FB-CSP approach outperforms both the stimulus reconstruction approach on short signal segments, as well as a convolutional neural network approach on the same task. We achieve a high accuracy (80% for [Formula: see text] windows and 70% for quasi-instantaneous decisions), which is sufficient to reach minimal expected switch durations below [Formula: see text]. We also demonstrate that the decoder can adapt to unlabeled data from anunseen subject and works with only a subset of EEG channels located around the ear to emulate a wearable EEG setup. The proposed FB-CSP method provides fast and accurate decoding of the directional focus of auditory attention. The high accuracy on very short data segments is a major step forward towards practical neuro-steered hearing devices.

Fast Block Sequential Decoding of Polar Codes

A reduced complexity sequential decoding algorithm for polar (sub) codes is described. The proposed approach relies on a decomposition of the polar (sub) code being decoded into a number of outer codes, and on-demand construction of codewords of these codes in the descending order of their probability. Construction of such codewords is implemented by fast decoding algorithms, which are available for many codes arising in the decomposition of polar codes. Further complexity reduction is achieved by taking hard decisions of the intermediate LLRs, and avoiding decoding of some outer codes. Data structures for sequential decoding of polar codes are described. The proposed algorithm can be also used for decoding of polar codes with CRC and short extended BCH codes. It has lower average decoding complexity compared with the existing decoding algorithms for the corresponding codes.

Zigzag-Decodable Reconstruction Codes With Asymptotically Optimal Repair for All Nodes

Zigzag-decodable codes have been proposed for distributed storage systems to achieve fast decoding of uncoded data packets through the iterative decoding of data bits from coded packets. To maintain high data availability, it is critical to minimize the repair bandwidth by downloading the least amount of bits for repairing any lost packet. In this work, we propose zigzag-decodable reconstruction (ZDR) codes which achieve asymptotically minimum repair bandwidth for repairing a single node, while preserving the high computational efficiency due to zigzag decoding. We present two explicit constructions of ZDR codes such that any node of ZDR codes can be repaired with asymptotically minimum repair bandwidth. The first construction is based on the well-designed encoding matrix and a generic transformation, while the second construction is designed by recursively employing the proposed generic transformation for any existing zigzag-decodable code. Moreover, we show that the proposed two classes of ZDR codes can be decoded by the zigzag decoding algorithm and have less computational complexity than the existing codes with asymptotically or exactly minimum repair bandwidth.

Reinforcement Learning Based Fast Self-Recalibrating Decoder for Intracortical Brain-Machine Interface.

Background: For the nonstationarity of neural recordings in intracortical brain–machine interfaces, daily retraining in a supervised manner is always required to maintain the performance of the decoder. This problem can be improved by using a reinforcement learning (RL) based self-recalibrating decoder. However, quickly exploring new knowledge while maintaining a good performance remains a challenge in RL-based decoders. Methods: To solve this problem, we proposed an attention-gated RL-based algorithm combining transfer learning, mini-batch, and weight updating schemes to accelerate the weight updating and avoid over-fitting. The proposed algorithm was tested on intracortical neural data recorded from two monkeys to decode their reaching positions and grasping gestures. Results: The decoding results showed that our proposed algorithm achieved an approximate 20% increase in classification accuracy compared to that obtained by the non-retrained classifier and even achieved better classification accuracy than the daily retraining classifier. Moreover, compared with a conventional RL method, our algorithm improved the accuracy by approximately 10% and the online weight updating speed by approximately 70 times. Conclusions: This paper proposed a self-recalibrating decoder which achieved a good and robust decoding performance with fast weight updating and might facilitate its application in wearable device and clinical practice.

General framework for constructing fast and near-optimal machine-learning-based decoder of the topological stabilizer codes

Quantum error correction is an essential technique for constructing a scalable quantum computer. In order to implement quantum error correction with near-term quantum devices, a fast and near-optimal decoding method is demanded. A decoder based on machine learning is considered as one of the most viable solutions for this purpose, since its prediction is fast once training has been done, and it is applicable to any quantum error correcting code and any noise model. So far, various formulations of the decoding problem as the task of machine learning have been proposed. Here, we discuss general constructions of machine-learning-based decoders. We found several conditions to achieve near-optimal performance, and proposed a criterion which should be optimized when a size of training data set is limited. We also discuss preferable constructions of neural networks, and proposed a decoder using spatial structures of topological codes using a convolutional neural network. We numerically show that our method can improve the performance of machine-learning-based decoders in various topological codes and noise models.

Distributed color QR code with high-capability and fast decoding

Considering incompatibility between the color QR code and the standard QR code decoder and insecurity of information storage in the open network environment, this paper proposes a distributed color QR code by analyzing the decoding process of the QR code, which employs the Shamir’s (k, n) secret sharing algorithm to ensure the security of the secret information and sets threshold to achieve compatibility with the standard decoder. Furthermore, we analyze the influence of threshold values on the performance of the proposed scheme, and determine the valid threshold value interval of the algorithm. Experimental results show that the proposed scheme is feasible with a fast reading speed and high secret storage capacity within the threshold interval.

Syllable based Hindi speech recognition

In this paper, one of the acoustic units of speech, the syllable, is used for the development of a continuous Hindi speech recognition system. The syllable is a larger acoustic unit that overcomes the contextual effects and requires fewer training samples in comparison to triphone based and word-based models. Other acoustic units such as phoneme-based suffer from contextual influences, and context-dependent triphones suffer due to the non-availability of triphone patterns with a large memory storage for numerous models. Earlier research works related to Hindi speech recognition were performed using the word, phoneme, and context-dependent models. The authors proposed a syllable based Hindi speech recognition system in this study due to different advantages of syllable units such as longer acoustic units, fast decoding, reducing contextual effects, and reduction of irregularities due to phonemes. The continuous Hindi speech recognition system was developed utilizing syllable based acoustic units. Hindi is widely spoken in India and other parts of the world also. The experiments are performed on Continuous Hindi speech by using a widely known Hidden Markov Model (HMM) with perceptual linear predictive coefficients(PLPs). The research outcomes reveal that by using syllables, the performance of the system was increased by 27% than phoneme and 20% than triphones. Research findings indicate that by selecting an appropriate acoustic unit for Hindi, the performance of the speech recognition system may be improved. Further, the study also provides useful insights to develop a syllable based pronunciation dictionary that may be used in speech recognition, speaker identification, and text to speech conversion systems.

Symbol positions‐based Slepian–Wolf coding with application to distributed video coding

In this study, the authors will show that coding the positions of the symbols, instead of their values, can be a good way to implement efficient Slepian–Wolf (SW) coding and can reduce the complexity of both the encoder and the decoder. The authors will also propose a practical distributed video coding (DVC) system that exploits this idea. This system will use binary maps to indicate the positions of the most probable symbols, instead of separating them into bitplanes. Simulations show that this position-based SW coding allows a simple and more efficient DVC system with improved rate-distortion performance, compared to the bitplane-based DVC system that uses the same side information. The memory requirements at the encoder are reduced by about 50% and the number of channel coding operations is also reduced. This DVC system also allows to use an easy way to control quantisation, reduces the decoding latency, and allows fast parallel decoding.

The Interpretable Fast Multi-Scale Deep Decoder for the Standard HEVC Bitstreams

It is a research hotspot to restore decoded videos with existing bitstreams by applying deep neural network to improve compression efficiency at decoder-end. Existing research has verified that the utilization of redundancy at decoder-end, which is underused by the encoder, can bring an increase of compression efficiency. However, most existing research neglects the abundant multi-scale information among video frames as a typical type of such redundancy. It remains an interesting yet challenging topic how to build an effective, interpretable and fast deep neural network for the purpose of using the multi-scale similarity at decoder-end and further enhancing compression efficiency. To this end, this paper considers the use of underused inter multi-scale information and proposes the Fast Multi-Scale Deep Decoder (Fast MSDD) for the state-of-the-art video coding standard HEVC. The advantages of Fast MSDD are three-fold. First, it achieves a higher coding efficiency without modifying any encoding algorithm. Second, Fast MSDD is interpretable based on the framework of using the underused redundancy. Third, it guarantees the model's inference speed while fully using the multi-scale similarity among video frames. Extensive experimental results verify Fast MSDD's effectiveness, interpretability, and computational efficiency. Fast MSDD obtains averagely 14.3%, 10.8%, 8.5% and 7.6% BD gains for AI, LP, LB and RA respectively. Compared with our previous work MSDD, Fast MSDD achieves increases of 59.3%, 49.1%, 61.0% and 29.3%. Meanwhile, 16.9%, 11.2%, 9.2% and 8.3% BD gains are observed on videos with scale changes, which validate the interpretability of the proposed method. Furthermore, Fast MSDD can save at most 56.3% time compared to MSDD.

FPT-spike: a flexible precise-time-dependent single-spike neuromorphic computing architecture

Modern Artificial Neural networks (ANNs) like Convolutional Neural Network (CNN), have found broad applications in real-world cognitive tasks. One challenging faced by these models is their tremendous memory and computing resource requirement. This also greatly hinders their adoptions from resource-constraint platforms, such as drone, mobile phone and IoT devices. Recently the brain-inspired spiking neural network (SNN) has been demonstrated as a promising solution for delivering more impressive computing and power efficiency. For SNNs, a large body of prior work were conducted on the spiking system design with a focus on using the spike firing rate (or rate-coded) for fulfilling the practical cognitive tasks. Such rate-based designs can underestimate the energy efficiency, throughput and system flexibility of SNNs. On the other hand, the potentials of time-based SNN are not fully unleashed in real applications due to lack of efficient coding and practical learning schemes in temporal domain. In this work, we make an early attempt to fill this gap: that said, a flexible precise-time-dependent single-spike neuromorphic computing architecture, namely “FPT-Spike”, is developed. “FPT-spike” relies on three hardware-favorable components: precise ultra-sparse spike temporal encoding, efficient supervised temporal learning and fast asymmetric decoding, to realize flexible spatial–temporal information trade-off for neural network size reduction without scarifying data processing capability. Extensive experimental results show that “FPT-Spike” outperforms rate-based SNN and ANN significantly in three aspects: network size, processing speed and power consumption, demonstrating great potentials for its deployment in edge devices.

Fast linear motion deblurring for 2D barcode

Barcode image deblurring has been an important task in image processing domain. On production lines, the demand of using 2D barcode to identify moving products requires scanners to decode the blurred images efficiently. Therefore, a fast and robust deblurring method is needed. However, most of state-of-the-art methods are not suitable for the fast decoding of 2D barcode images due to their being time-consuming. In this paper, we propose an efficient deblurring method for 2D barcode image focusing on linear motion blur in manufacturing situations. We first deconvolute image with Wiener Filter. Then we form a metrics which is based on the binary property of 2D barcode image to evaluate the quality of each deblurred image. With the guide of this metrics, we adopt a line-search method to reduce the time spent on searching the best blur kernel. We compare our method with the existing methods for 2D barcode regarding efficiency and effectiveness. The experiment results show that our method is much faster on recovering information from motion-blurred 2D barcode.

Enhanced Diversity-Achieving Quadrature Spatial Modulation With Fast Decodability

In this paper, based on the architecture of diversity-achieving quadrature spatial modulation (DA-QSM) transmission scheme, we propose an Enhanced DA-QSM (EDA-QSM) scheme. In the proposed EDA-QSM scheme, by using Kronecker product extension to the eight dispersion matrices of Sezginer-Sari-Biglieri (SSB) code, the size of the dispersion matrices set is doubled compared with DA-QSM. Consequently, EDA-QSM is shown to have higher spectral efficiency as well as nonvanishing determinant (NVD) property without requiring any parameter optimization. Furthermore, EDA-QSM with an appropriate ordering of the dispersion matrices is a block-orthogonal space-time code (BOSTC), which then makes the four real variables corresponding to the same block are independent and orthogonal. By utilizing this feature, for the detection of EDA-QSM we propose a state codewords matched block-by-block sphere decoding (SD) algorithm, which significantly decreases the decoding complexity of ML decoder. Simulation results of bit error rate (BER) performance show that the new EDA-QSM scheme outperforms some newly presented diversity-achieving SM schemes.

Function-Aware Network Coding for Dynamic Vision Sensor Networks

Research on new multimedia stream coding, and transmission mechanism and transceiver structure design has been motivated by the increasing demand for low-latency applications, as well as the development of new multimedia devices, such as dynamic vision sensors (DVS). We pose a concept of function awareness in the context of packet-level network coding in this letter, and a function-aware network coding approach with a parallel coding transceiver structure for DVS video streams from the perspective of real-time applications is proposed. The posed function awareness concept is used for determining a DVS data selection criterion in order to minimise the transmission and decoding latency of the selected data. The proposed parallel coding transceiver design for different DVS data streams achieves fast encoding and decoding, and simulation results illustrate that the proposed approach outperforms the conventional multilevel network coding schemes.

Randomized symmetric McEliece cryptosystem based on generalized Reed-Solomon codes

One of the actual problems of modern cryptography is the design of practical post-quantum cryptosystems whose security is based on the complexity of solving one computationally challenging problem, in the same way as the security of the RSA cryptosystem is based on the complexity of integer factorization. A promising class of such cryptosystems is formed by code-based cryptosystems the first asymmetric of which is the McEliece cryptosystem. The purpose of this work is to design and research a symmetric version of the McEliece cryptosystem based on generalized Reed-Solomon codes. These codes were chosen because they exist for all natural values of the parameters (the length and dimension of the code) and they are maximal distance separable allowing a wide range to change the characteristics of the relevant cryptosystems. In addition, very fast decoding algorithms are known for these codes. Asymmetric cryptosystems based on the generalized Reed-Solomon codes are not secure because for them there are efficient algorithms for recovering private keys from public keys.A symmetric code cryptosystem is proposed that is more efficient (in terms of the length of the secret key for given security requirements) compared to the LPN-C cryptosystem. An estimate of the security of the proposed cryptosystem relative to an attack with the chosen plaintext is obtained and an algorithm for selecting parameters for constructing this cryptosystem is proposed. The proposed cryptosystem is compared with the LPN-C cryptosystem along the key length for a given lower limit of security to the attack in question.

Latent-Variable Non-Autoregressive Neural Machine Translation with Deterministic Inference Using a Delta Posterior

Although neural machine translation models reached high translation quality, the autoregressive nature makes inference difficult to parallelize and leads to high translation latency. Inspired by recent refinement-based approaches, we propose LaNMT, a latent-variable non-autoregressive model with continuous latent variables and deterministic inference procedure. In contrast to existing approaches, we use a deterministic inference algorithm to find the target sequence that maximizes the lowerbound to the log-probability. During inference, the length of translation automatically adapts itself. Our experiments show that the lowerbound can be greatly increased by running the inference algorithm, resulting in significantly improved translation quality. Our proposed model closes the performance gap between non-autoregressive and autoregressive approaches on ASPEC Ja-En dataset with 8.6x faster decoding. On WMT'14 En-De dataset, our model narrows the gap with autoregressive baseline to 2.0 BLEU points with 12.5x speedup. By decoding multiple initial latent variables in parallel and rescore using a teacher model, the proposed model further brings the gap down to 1.0 BLEU point on WMT'14 En-De task with 6.8x speedup.

A decoding method based on RNN for OvTDM

Overlapped X domain multiplexing (OvXDM) is a promising encoding technique to obtain high spectral efficiency by utilizing Inter-Symbol Interference (ISI). However, the computational complexity of Maximum Likelihood Sequence Detection (MLSD) increases exponentially with the growth of spectral efficiency in OvXDM, which is unbearable for practical implementations. This paper proposes an OvTDM decoding method based on Recurrent Neural Network (RNN) to realize fast decoding of OvTDM system, which has lower decoding complexity than the traditional fast decoding method. The paper derives the mathematical model of the OvTDM decoder based on RNN and constructs the decoder model. And we compare the performance of the proposed decoding method with the MLSD algorithm and the Fano algorithm. It's verified that the proposed decoding method exhibits a higher performance than the traditional fast decoding algorithm, especially for the scenarios of a high overlapped multiplexing coefficient.

Neural ensemble decoding for topological quantum error-correcting codes

Topological quantum error-correcting codes are a promising candidate for building fault-tolerant quantum computers. Decoding topological codes optimally, however, is known to be a computationally hard problem. Various decoders have been proposed that achieve approximately optimal error thresholds. Due to practical constraints, it is not known if there exists an obvious choice for a decoder. In this paper, we introduce a framework which can combine arbitrary decoders for any given code to significantly reduce the logical error rates. We rely on the crucial observation that two different decoding techniques, while possibly having similar logical error rates, can perform differently on the same error syndrome. We use machine learning techniques to assign a given error syndrome to the decoder which is likely to decode it correctly. We apply our framework to an ensemble of Minimum-Weight Perfect Matching (MWPM) and Hard-Decision Re-normalization Group (HDRG) decoders for the surface code in the depolarizing noise model. Our simulations show an improvement of 38.4%, 14.6%, and 7.1% over the pseudo-threshold of MWPM in the instance of distance 5, 7, and 9 codes, respectively. Lastly, we discuss the advantages and limitations of our framework and applicability to other error-correcting codes. Our framework can provide a significant boost to error correction by combining the strengths of various decoders. In particular, it may allow for combining very fast decoders with moderate error-correcting capability to create a very fast ensemble decoder with high error-correcting capability.

Complete grid pattern decoding method for a one-shot structured light system.

Structured light 3D reconstruction methods using a De Bruijn sequence-based color grid pattern have an impressive advantage of fast and accurate decoding, which leads to fast 3D reconstruction. They are especially suitable for capturing moving objects. However, the drawback of these methods is their high false decoding rate while dealing with feature points at the object's boundaries, and objects can be prone to becoming deformed by the uneven structure of the dynamic scene. To solve this problem, we present an efficient opened-grid-point detector and a complete grid pattern decoding method. Specifically, a new, to the best of our knowledge, color grid pattern is designed to reduce the influence of color noise and increase the density of 3D cloud points. In addition, a LCD screen projected with the proposed pattern is utilized to calibrate the camera-projector system. The experiments, conducted in a laboratory without a light curtain, demonstrate that the proposed method can fully satisfy the requirements of real applications.

Single Bimetallic Lanthanide-Based Metal-Organic Frameworks for Visual Decoding of a Broad Spectrum of Molecules.

Distinguishing the delicate structural differences among molecules is a critical and challenging task in biological/chemical analysis. A molecular decoding strategy has recently become promising to differentiate similar molecules, which is advantageous over the common sensing methods mostly used for detecting a single target. However, the design of an ideal molecular decoder is still strictly hindered by the tailored preparation of probes for particular molecules and the severe lack of widespread feasibility. We herein for the first time proposed to use single bimetallic lanthanide-based metal-organic frameworks (Ln-MOFs) as a powerful, versatile probe for fast and facile decoding of homologues, isomers, enantiomers, and even deuterated isotopomers, based on the unique host-guest interaction of a specific target with the Ln-MOF which could provide an according visual output based on the modulated energy transfer process.

Nonresonant Metasurface for Fast Decoding in Acoustic Communications

Acoustic communication is crucial in underwater exploration, where sound is the dominant information carrier, with significantly less loss and scattering than that of electromagnetic waves. However, the capacity of acoustic communication channels is limited due to the intrinsically low speed of sound relative to that of electromagnetic waves and because the attenuation of acoustic waves underwater increases with frequency. Recently, orbital angular momentum (OAM) has emerged as an alternative multiplexing degree of freedom to encode data onto vortex beams for increasing the capacity of acoustic communication. For information retrieval from the multiplexed acoustic vortices, an active scanning method and a passive resonant method are explored. Time-consuming scanning and complex postprocessing significantly restrict the data-transmission speed, while the large amount of resonant cascaded devices in the passive technique intrinsically results in a low efficiency and bulky volume of the system. Here, we propose and experimentally demonstrate a passive and nonresonant approach with the ability to separate different OAM states of multiplexed acoustic vortex beams in parallel using a parabolic-phased metasurface. The metasurface converts the spiral-phase patterns of vortex beams carrying various angular momenta into plane waves with different in-plane linear momenta. Our approach is compatible with multiplexing technologies, significantly enhancing the speed in acoustic communication.