Good Decoding Research Articles

Heterogeneity and imbalance of reading profiles in Autism Spectrum Disorders

This study aimed to identify distinct reading profiles in 4–11-year-old Chinese autistic and neurotypical (NT) children and to examine the unique characteristics of each group. Forty-five autistic children and 45 NT comparisons matched on nonverbal IQ and age were assessed in a comprehensive reading battery including word reading, listening comprehension, and reading-related language skills. Using latent profile analysis, four reading profiles of autistic children emerged: readers with good decoding and listening comprehension skills (GDGL, 33.3 %), good decoders with poor listening comprehension skills (GDPL, 35.7 %), readers with average decoding and listening comprehension skills (ADAL, 20 %), and readers with poor decoding and listening comprehension skills (PDPL, 20 %). Furthermore, the results indicated an imbalanced pattern of reading profiles with over 50 % of autistic children performing well in certain reading abilities but poorly in other skills. The findings highlight the urgent need for designing individualized reading supports for autistic children.

Algorithm of Encrypting Digital Image Using Chaos Neural Network

With the rapid development of digital communication technology, data and image encryption methods will get more and more in-depth research in these aspects. Although the original cipher method can keep the image confidential, because of the storage method of the image data, for the image with too high pixel value, the process of keeping the two-dimensional information confidential and decoding will bring a large amount of computation, and the accuracy of encryption and decryption cannot be guaranteed. This paper presents a visual image encryption algorithm based on the compressed sensing method of the Hopfield chaotic neural network. Different from the existing visual image encryption methods, our method can directly embed the noise like information after encryption into the alpha channel of the carrier image, thus increasing the visual stability of the computer. Finally, through the comparison and analysis of simulation experiments, we find that the image encryption algorithm proposed in this paper has the advantages of wide password space, low risk, good visual stability, good decoding effect, and good robustness.

A Viterbi Decoder under Class A Modeled Noise in Shallow Water

A traditional Viterbi decoder is primarily optimized for additive white Gaussian noise (AWGN). With the AWGN channel, it offers good decoding performance. However, the underwater acoustic communication (UAC) channel is extremely complicated. In addition to white noise, there are a variety of artificial and natural impulse noise that occur suddenly. The traditional Viterbi decoder cannot obtain the optimum performance under this case. In order to solve this problem, this paper introduces a novel Viterbi decoder with the impulsive noise, which is considered to be subjected to Middleton Class A distribution in shallow ocean. Since Middleton Class A noise is very complicated, a simplified model is first introduced. Then, the error analysis of simplified model under various parameters is discussed in detail. The analysis shows that the simplified one just leads to slight error. Hereafter, a novel Veterbi decoder using the simplified model is discussed. Compared to a traditional decoder, a preprocessing is just required. The performance of soft decision-based decoder in the Middleton Class A noise channel (MAIN) and AWGN are further compared. Based on our simulations, the new decoder can significantly improve the performance in comparison with conventional one, which further validates our presented method.

Two chaotic synchronizations in er-doped fiber lasers and their application in two-channel coding secure communications

A dual-ring erbium-doped fiber laser has nonlinear dynamic characteristics based on optical coupling interaction of lasing from the two laser rings and can send chaotic lasing to encode and mask an information to perform secure communication. Two independent groups of single-ring erbium-doped fiber lasers and a dual-ring erbium-doped fiber laser are used to construct two-channel chaos secure communication system while the synchronization’s physical mathematical model is presented by using the “active-passive” chaotic synchronization method, where a dual-ring erbium-doped fiber laser is used as a transmitter and two independent groups of single-ring erbium-doped fiber lasers are used as two receivers. Our numerical simulation shows that these two receivers can synchronize with the transmitter respectively. A two-channel chaotic hidden encoding secure communications with a modulation frequency of 10kHz analog signal and a rate of 20kbit/s digital signal are numerically simulated respectively, which shows to have good decoding quality and strong security. The obtained result shows that the chaotic coding system can be well applied in chaotic two channel secure communications. The research results have an important reference value for optical multi-channel secure communication and network security.

Considering Neural Connectivity in Point Process Decoder for Brain-Machine Interface.

Brain machine interface (BMI) can translate neural activity into digital commands to control prostheses. The decoder in BMI models the mechanism relating to neural activity and intents in brain. In our brain, single neuronal tuning property and neural connectivity contribute to encoding the intents together. These properties may change, a phenomenon which is named neural adaptation during using BMIs. Neural adaptation requires the decoder to consider the two factors at the same time and has the potential to follow their changes. However, in the previous work, the class of neural network and clustering decoder can consider the neural connectivity regardless of the single neuronal tuning property. On the other hand, point process methods can model the single neuronal tuning property but fail to address the neural connectivity. In this paper, we propose a new point process decoder with the information of neural connectivity named NCPP. We derive the neural connectivity component from the point process method by Bayes' rule and use a clustering decoder to represent the neural connectivity. This method can consider the neural connectivity and the single neuronal tuning property at the same time. We validate the method on simulation data where the point process method cannot achieve a good decoding performance and compare it with sequential Monte Carlo point process method (SMCPP). The results show our method outperforms the pure point process method which indicates our method can model the neural connectivity and single neuronal tuning property at the same time.Clinical Relevance-This paper proposes a decoder that can model the neural connectivity and the single neuronal tuning property at the same time, which is potential to explain the neural adaptation computationally.

A Novel Convolutional Neural Network Classification Approach of Motor-Imagery EEG Recording Based on Deep Learning

Recently, Electroencephalography (EEG) motor imagery (MI) signals have received increasing attention because it became possible to use these signals to encode a person’s intention to perform an action. Researchers have used MI signals to help people with partial or total paralysis, control devices such as exoskeletons, wheelchairs, prostheses, and even independent driving. Therefore, classifying the motor imagery tasks of these signals is important for a Brain-Computer Interface (BCI) system. Classifying the MI tasks from EEG signals is difficult to offer a good decoder due to the dynamic nature of the signal, its low signal-to-noise ratio, complexity, and dependence on the sensor positions. In this paper, we investigate five multilayer methods for classifying MI tasks: proposed methods based on Artificial Neural Network, Convolutional Neural Network 1 (CNN1), CNN2, CNN1 with CNN2 merged, and the modified CNN1 with CNN2 merged. These proposed methods use different spatial and temporal characteristics extracted from raw EEG data. We demonstrate that our proposed CNN1-based method outperforms state-of-the-art machine/deep learning techniques for EEG classification by an accuracy value of 68.77% and use spatial and frequency characteristics on the BCI Competition IV-2a dataset, which includes nine subjects performing four MI tasks (left/right hand, feet, and tongue). The experimental results demonstrate the feasibility of this proposed method for the classification of MI-EEG signals and can be applied successfully to BCI systems where the amount of data is large due to daily recording.

Flexible soft-output decoding of polar codes

In this research, we study soft-output decoding of polar codes. Two representative soft-output decoding algorithms are belief propagation (BP) and soft cancellation (SCAN). The BP algorithm has low latency but suffers from high computational complexity. On the other hand, the SCAN algorithm, which is proposed for reduced complexity of soft-output decoding, achieves good decoding performance but suffers from long latency. These two algorithms are suitable only for two extreme cases that need very low latency (but with high complexity) or very low complexity (but with high latency). However, many practical systems may need to work for the moderate cases (i.e., not too high latency and not too high complexity) rather than two extremes. To adapt to the various needs of the systems, we propose a very flexible soft-output decoding framework of polar codes. Depending on which system requirement is most crucial, the proposed scheme can adapt to the systems by controlling the level of parallelism. Numerical results demonstrate that the proposed scheme can effectively adapt to various system requirements by changing the level of parallelism.

Deep Learning-Based Approaches for Decoding Motor Intent From Peripheral Nerve Signals.

Previous literature shows that deep learning is an effective tool to decode the motor intent from neural signals obtained from different parts of the nervous system. However, deep neural networks are often computationally complex and not feasible to work in real-time. Here we investigate different approaches' advantages and disadvantages to enhance the deep learning-based motor decoding paradigm's efficiency and inform its future implementation in real-time. Our data are recorded from the amputee's residual peripheral nerves. While the primary analysis is offline, the nerve data is cut using a sliding window to create a “pseudo-online” dataset that resembles the conditions in a real-time paradigm. First, a comprehensive collection of feature extraction techniques is applied to reduce the input data dimensionality, which later helps substantially lower the motor decoder's complexity, making it feasible for translation to a real-time paradigm. Next, we investigate two different strategies for deploying deep learning models: a one-step (1S) approach when big input data are available and a two-step (2S) when input data are limited. This research predicts five individual finger movements and four combinations of the fingers. The 1S approach using a recurrent neural network (RNN) to concurrently predict all fingers' trajectories generally gives better prediction results than all the machine learning algorithms that do the same task. This result reaffirms that deep learning is more advantageous than classic machine learning methods for handling a large dataset. However, when training on a smaller input data set in the 2S approach, which includes a classification stage to identify active fingers before predicting their trajectories, machine learning techniques offer a simpler implementation while ensuring comparably good decoding outcomes to the deep learning ones. In the classification step, either machine learning or deep learning models achieve the accuracy and F1 score of 0.99. Thanks to the classification step, in the regression step, both types of models result in a comparable mean squared error (MSE) and variance accounted for (VAF) scores as those of the 1S approach. Our study outlines the trade-offs to inform the future implementation of real-time, low-latency, and high accuracy deep learning-based motor decoder for clinical applications.

Erasure decoding of convolutional codes using first-order representations

It is well known that there is a correspondence between convolutional codes and discrete-time linear systems over finite fields. In this paper, we employ the linear systems representation of a convolutional code to develop a decoding algorithm for convolutional codes over the erasure channel. In this kind of channel, which is important due to its use for data transmission over the Internet, the receiver knows if a received symbol is correct. We study the decoding problem using the state space description of a convolutional code, and this provides in a natural way additional information. With respect to previously known decoding algorithms, our new algorithm has the advantage that it is able to reduce the decoding delay as well as the computational effort in the erasure recovery process. We describe which properties a convolutional code should have in order to obtain a good decoding performance and illustrate it with an example.

Synchronous playback technology of airborne network video based on RTP

In the flight test, the airborne video data is an important part of the flight test data. In order to solve the problem of synchronous playback of multiple channels of airborne network video data, a synchronous playback scheme of multiple channels of airborne network video data based on the RTP/RTCP protocol is proposed. The network video data streaming and preprocessing mechanism are used in this paper. Firstly, by identifying the data packets, the network video data packets are extracted from the data recorded on the airborne. Then, after unpacking and time calculation of the RTP-encapsulated H.264 video data, it uses FFmpeg’s good decoding capabilities to decode a frame of image, and uses SDL’s excellent video performance to display YUV data. The synchronization mechanism based on timestamp is adopted to realize the fast synchronized playback of multiple airborne network videos. The software adopts modular and multi-threaded design ideas to improve processing efficiency and ensure functional scalability. At present, the system has been successfully applied to the simultaneous playback of multi-channel airborne network video. The actual application effect shows that the system works stably and can effectively improve the efficiency of model flight tests.

A performance enhancement of physical layer at Wi-MAX system with RLDPC code

<span>In present wireless communication network, the error correction codes plays the major role for efficient data transmission in noisy environments. To get minimum BER and PAPR has been the main aim towards the field in forward error control coding. Majority of the researchers has considered turbo codes at specific SNR over AWGN channel but have complexity issues with the iterative output decoder and causes degradation in the Wi-Max network system. In this paper, the author presents and evaluates WiMAX physical layer performance with using MIMO technologies, where a Robust-LDPC technique of coding and decoding in OFDM based WiMAX system is consider. The decoding method of RLDPC has processed by Belief Propagation at the logarithmic domain in an iterative manner, the proposed methodology shows good decoding outcome for RLDPC codes at Rician and Rayleigh channel. Moreover, the applicability of our proposed model channel codes is defined under IEEE Wi-MAX standard and the results analysis is done under different code-rate and modulation schemes.</span>

Decoding and interpreting cortical signals with a compact convolutional neural network

Objective. Brain–computer interfaces (BCIs) decode information from neural activity and send it to external devices. The use of Deep Learning approaches for decoding allows for automatic feature engineering within the specific decoding task. Physiologically plausible interpretation of the network parameters ensures the robustness of the learned decision rules and opens the exciting opportunity for automatic knowledge discovery. Approach. We describe a compact convolutional network-based architecture for adaptive decoding of electrocorticographic (ECoG) data into finger kinematics. We also propose a novel theoretically justified approach to interpreting the spatial and temporal weights in the architectures that combine adaptation in both space and time. The obtained spatial and frequency patterns characterizing the neuronal populations pivotal to the specific decoding task can then be interpreted by fitting appropriate spatial and dynamical models. Main results. We first tested our solution using realistic Monte-Carlo simulations. Then, when applied to the ECoG data from Berlin BCI competition IV dataset, our architecture performed comparably to the competition winners without requiring explicit feature engineering. Using the proposed approach to the network weights interpretation we could unravel the spatial and the spectral patterns of the neuronal processes underlying the successful decoding of finger kinematics from an ECoG dataset. Finally we have also applied the entire pipeline to the analysis of a 32-channel EEG motor-imagery dataset and observed physiologically plausible patterns specific to the task. Significance. We described a compact and interpretable CNN architecture derived from the basic principles and encompassing the knowledge in the field of neural electrophysiology. For the first time in the context of such multibranch architectures with factorized spatial and temporal processing we presented theoretically justified weights interpretation rules. We verified our recipes using simulations and real data and demonstrated that the proposed solution offers a good decoder and a tool for investigating motor control neural mechanisms.

Use of morphological and contextual cues in children\u2019s lexical inferencing in L1 and L2

In an experimental design, we investigated how fifth-grade readers use morphological and contextual information to infer the meaning of unknown words, and to what extent this is related to their cognitive and linguistic skills. A group of 166 fifth-grade Dutch children (59 L1, 107 L2) performed a lexical inferencing task in which the availability of morphological and contextual information was manipulated. Readers used both morphological and contextual information in lexical inferencing. Good decoding skill was related to more use of morphological information. Reading comprehension skill was associated with the use of morphological and contextual cues. L1 and L2 readers did not differ with respect to the use of morphological information. L2 readers used contextual information less in their inferences than L1 readers did. This difference was driven by L2 readers with weak vocabulary. The use of contextual information was especially high in L1 readers with good reading comprehension skills, and especially low in L2 readers with low vocabulary. Results indicate that to access morphological information, decoding is crucial, whereas for contextual inferencing, a minimum of linguistic competence is needed, which makes it more challenging for L2 readers.

An FPGA-Based LDPC Decoder With Ultra-Long Codes for Continuous-Variable Quantum Key Distribution

In this paper, we propose a good decoding performance, low-complexity, and high-speed decoder architecture for ultra-long quasi-cyclic LDPC codes by using the layered sum-product decoding scheme. To reduce implementation complexity and hardware resource consumption, the messages in the iteration process are uniformly quantified and the function Ψ(x) is approximated with second-order functions. The decoder architecture improves the decoding throughput by using partial parallel and pipeline structures. A modified construction method of parity check matrices was applied to prevent read&write conflicts and achieve high-speed pipeline structure. The simulation results show that our decoder architecture has good performance at signal-to-noise ratios (SNRs) as low as -0.6 dB. We have implemented our decoder architecture on a Virtex-7 XC7VX690T field programmable gate array (FPGA) device. The implementation results show that the FPGA-based LDPC decoder can achieve throughputs of 108.64 Mb/s and 70.32 Mb/s at SNR of 1.0 dB when the code length is 262,144 and 349,952, respectively. The decoder can find useful applications in those scenarios that require very low SNRs and high throughputs, such as the information reconciliation of continuous-variable quantum key distribution.

Tracking Fast Neural Adaptation by Globally Adaptive Point Process Estimation for Brain-Machine Interface.

Brain-machine interfaces (BMIs) help the disabled restore body functions by translating neural activity into digital commands to control external devices. Neural adaptation, where the brain signals change in response to external stimuli or movements, plays an important role in BMIs. When subjects purely use neural activity to brain-control a prosthesis, some neurons will actively explore a new tuning property to accomplish the movement task. The prediction of this neural tuning property can help subjects adapt more efficiently to brain control and maintain a good decoding performance. Existing prediction methods track the slow change of the tuning property in the manual control, which is not suitable for the fast neural adaptation in brain control. In order to identify the active neurons in brain control and track their tuning property changes, we propose a globally adaptive point process method (GaPP) to estimate the neural modulation state from spike trains, decompose the states into the hyper preferred direction and reconstruct the kinematics in a dual-model framework. We implement the method on real data from rats performing a two-lever discrimination task under manual control and brain control. The results show our method successfully predicts the neural modulation state and identifies the neurons that become active in brain control. Compared to the existing method, ours tracks the fast changes of the hyper preferred direction from manual control to brain control more accurately and efficiently and reconstructs the kinematics better and faster.

Symmetries for a high-level neural decoder on the toric code

Surface codes are a promising method of quantum error correction and the basis of many proposed quantum computation implementations. However, their efficient decoding is still not fully explored. Recently, approaches based on machine learning techniques have been proposed by Torlai and Melko as well as Varsamopoulos et al. In these approaches, a so called high level decoder is used to post-correct an underlying decoder by correcting logical errors. A significant problem is that these methods require large amounts of training data even for relatively small code distances. The above-mentioned methods were tested on the rotated surface code which encodes one logical qubit. Here, we show that they are viable even for the toric surface code which encodes two logical qubits. Furthermore, we explain how symmetries of the toric code can be exploited to reduce the amount of training data that is required to obtain good decoding results. Finally, we compare different underlying decoders and show that the accuracy of high level decoding noticeably depends on the quality of the underlying decoder in the realistic case of imperfect training.

Concatenated LDPC/2-D-Marker Codes and Non-Iterative Detection/Decoding for Recovering Position Errors in Racetrack Memories

Racetrack memories (RMs) are prone to alignment faults called position errors (PEs), which manifest as insertions and deletions of stored data bits. Conventional coding schemes for PEs demonstrate the promising results by employing low-density parity-check (LDPC) codes with an iterative detection and decoding algorithm. However, the computational complexity of detection is relatively high. In this article, for channels with PEs, we present a new coding scheme that can effectively decode corrupted data, even if the detection is executed only once (i.e., a non-iterative detection and decoding scenario). The proposed code consists of a concatenation of an inner 2-D marker code, which is specialized for PEs to mitigate the effect of insertion and deletion (ID) errors, and an outer irregular LDPC code. We also provide tractable design methodologies for these constituent codes. First, we identify the 2-D-marker code structures that offer higher achievable information rates in a non-iterative scenario and then optimize irregular LDPC codes to ensure good decoding properties. Through asymptotic-performance analysis and finite-length simulation, we confirm the effectiveness of the proposed coding scheme. Ultimately, the proposed coding scheme has the capability to reduce code rate loss and to provide excellent decoding performance under a non-iterative scenario, which also helps in understanding the reliability of RM when a low-complexity decoding algorithm is used to correct ID errors caused by PEs.

Fabricable Unobtrusive 3D‐QR‐Codes with Directional Light

AbstractQR code is a 2D matrix barcode widely used for product tracking, identification, document management and general marketing. Recently, there have been various attempts to utilize QR codes in 3D manufacturing by carving QR codes on the surface of the printed 3D shape. Nevertheless, significant shape editing and modulation may be required to allow readability of the embedded 3D‐QR‐codes with good decoding accuracy. In this paper, we introduce a novel QR code 3D fabrication framework aimed at unobtrusive embedding of 3D‐QR‐codes in the shape hence introducing minimal shape modulation. Essentially, our method computes bi‐directional carvings in the 3D shape surface to obtain the black‐and‐white QR pattern. By using a directional light source, the black‐and‐white QR pattern emerges as lighted and shadow casted blocks on the shape respectively. To account for minimal modulation and elusiveness, we optimize the QR code carving w.r.t. shape geometry, visual disparity and light source position. Our technique employs a simulation of lighting phenomena through carved modules on the shape to ensure adequate contrast of the printed 3D‐QR‐code.

Neural Representation of Articulable and Inarticulable Novel Sound Contrasts: The Role of the Dorsal Stream.

The extent that articulatory information embedded in incoming speech contributes to the formation of new perceptual categories for speech sounds has been a matter of discourse for decades. It has been theorized that the acquisition of new speech sound categories requires a network of sensory and speech motor cortical areas (the “dorsal stream”) to successfully integrate auditory and articulatory information. However, it is possible that these brain regions are not sensitive specifically to articulatory information, but instead are sensitive to the abstract phonological categories being learned. We tested this hypothesis by training participants over the course of several days on an articulable non-native speech contrast and acoustically matched inarticulable nonspeech analogues. After reaching comparable levels of proficiency with the two sets of stimuli, activation was measured in fMRI as participants passively listened to both sound types. Decoding of category membership for the articulable speech contrast alone revealed a series of left and right hemisphere regions outside of the dorsal stream that have previously been implicated in the emergence of non-native speech sound categories, while no regions could successfully decode the inarticulable nonspeech contrast. Although activation patterns in the left inferior frontal gyrus, the middle temporal gyrus, and the supplementary motor area provided better information for decoding articulable (speech) sounds compared to the inarticulable (sine wave) sounds, the finding that dorsal stream regions do not emerge as good decoders of the articulable contrast alone suggests that other factors, including the strength and structure of the emerging speech categories are more likely drivers of dorsal stream activation for novel sound learning.

Vision and Locomotion Combine to Drive Path Integration Sequences in Mouse Retrosplenial Cortex.

The retrosplenial cortex (RSC) is involved in a broad range of cognitive functions, integrating rich sensory, motor, and spatial signals from multiple brain areas, including the hippocampal system. RSC neurons show hippocampus-dependent activity reminiscent of place cell sequences. Using cellular calcium imaging in a virtual reality (VR)-based locomotion task, we investigate how the integration of visual and locomotor inputs may give rise to such activity in RSC. A substantial population shows neural sequences that track position in the VR environment. This activity is driven by the conjunction of visual stimuli sequences and active movement, which is suggestive of path integration. The activity is anchored to a reference point and predominantly follows the VR upon manipulations of optic flow against locomotion. Thus, locomotion-gated optic flow, combined with the presence of contextual cues at the start of each trial, is sufficient to drive the sequential activity. A subpopulation shows landmark-related visual responses that are modulated by animal's position in the VR. Thus, rather than fragmenting the spatial representationinto equivalent locomotion-based ensemble versus optic-flow-based ensemble, in RSC, optic flow appears to override locomotion signals coherently in the population, when the gain betweenthe two signals is altered.