Adaptive Decoding Algorithm Research Articles

A brain machine interface framework for exploring proactive control of smart environments

Brain machine interfaces (BMIs) can substantially improve the quality of life of elderly or disabled people. However, performing complex action sequences with a BMI system is onerous because it requires issuing commands sequentially. Fundamentally different from this, we have designed a BMI system that reads out mental planning activity and issues commands in a proactive manner. To demonstrate this, we recorded brain activity from freely-moving monkeys performing an instructed task and decoded it with an energy-efficient, small and mobile field-programmable gate array hardware decoder triggering real-time action execution on smart devices. Core of this is an adaptive decoding algorithm that can compensate for the day-by-day neuronal signal fluctuations with minimal re-calibration effort. We show that open-loop planning-ahead control is possible using signals from primary and pre-motor areas leading to significant time-gain in the execution of action sequences. This novel approach provides, thus, a stepping stone towards improved and more humane control of different smart environments with mobile brain machine interfaces.

Adaptive HD-sEMG decomposition: towards robust real-time decoding of neural drive.

Objective. Neural interfacing via decomposition of high-density surface electromyography (HD-sEMG) should be robust to signal non-stationarities incurred by changes in joint pose and contraction intensity.Approach. We present an adaptive real-time motor unit decoding algorithm and test it on HD-sEMG collected from the extensor carpi radialis brevis during isometric contractions over a range of wrist angles and contraction intensities. The performance of the algorithm was verified using high-confidence benchmark decompositions derived from concurrently recorded intramuscular electromyography.Main results. In trials where contraction conditions between the initialization and testing data differed, the adaptive decoding algorithm maintained significantly higher decoding accuracies when compared to static decoding methods.Significance. Using "gold standard" verification techniques, we demonstrate the limitations of filter re-use decoding methods and show the necessity of parameter adaptation to achieve robust neural decoding.

Deep-Learning-Based Adaptive Error-Correction Decoding for Spin-Torque Transfer Magnetic Random Access Memory (STT-MRAM)

Spin-torque transfer magnetic random access memory (STT-MRAM) is a promising emerging non-volatile memory (NVM) technology with wide applications. However, the data recovery of STT-MRAM is affected by the diversity of channel raw bit error rate (BER) across different dies caused by process variations, as well as the unknown resistance offset due to temperature change. Therefore, it is critical to develop effective decoding algorithms of error correction codes for STT-MRAM. In this paper, we first propose a neural bit-flipping (BF) decoding algorithm, which can share the same trellis representation as the state-of-the-art neural decoding algorithms, such as the neural belief propagation (NBP) and neural offset min-sum (NOMS) algorithm. Hence a neural network (NN) decoder with a uniform architecture but different NN parameters can realize all these neural decoding algorithms. Based on such a unified NN decoder architecture, we further propose a novel deep learning (DL)-based adaptive decoding algorithm whose decoding complexity can be adjusted according to the change of the channel conditions of STT-MRAM. Extensive experimental evaluation results demonstrate that the proposed neural decoders can greatly improve the performance over the standard decoders, with similar decoding latency and energy consumption. Moreover, the DL-based adaptive decoder can work well over different channel conditions of STT-MRAM irrespective of the unknown resistance offset, with a 50% reduction of the decoding latency and energy consumption compared to the fixed decoder.

Adaptive Decoding Algorithm With Variable Sliding Window for Double SC-LDPC Coding System

In this letter, an adaptive decoding algorithm with a variable sliding window for joint source-channel coding (JSCC) scheme based on double spatially coupled low-density parity-check (DSC-LDPC) codes is investigated. For the traditional sliding window decoding (SWD) algorithm, one or more errors occurring in a decoding window may affect the decoding of the next window, which will trigger error propagation. To mitigate the effects, a window extension algorithm based on average log-likelihood ratios (LLRs) will be proposed, but the implement of extension implies higher decoding complexity. In order to trade-off the error rate performance and complexity, the source and channel threshold that determines the window extension are analyzed. In the simulation, the proposed SWD algorithm with acceptable complexity has better error rate performance in the water fall region and the error floor region.

An adaptive acoustoelectric signal decoding algorithm based on Fourier fitting for brain function imaging.

Acousticelectric brain imaging (ABI), which is based on the acoustoelectric (AE) effect, is a potential brain function imaging method for mapping brain electrical activity with high temporal and spatial resolution. To further enhance the quality of the decoded signal and the resolution of the ABI, the decoding accuracy of the AE signal is essential. An adaptive decoding algorithm based on Fourier fitting (aDAF) is suggested to increase the AE signal decoding precision. The envelope of the AE signal is first split into a number of harmonics by Fourier fitting in the suggested aDAF. The least square method is then utilized to adaptively select the greatest harmonic component. Several phantom experiments are implemented to assess the performance of the aDAF, including 1-source with various frequencies, multiple-source with various frequencies and amplitudes, and multiple-source with various distributions. Imaging resolution and decoded signal quality are quantitatively evaluated. According to the results of the decoding experiments, the decoded signal amplitude accuracy has risen by 11.39% when compared to the decoding algorithm with envelope (DAE). The correlation coefficient between the source signal and the decoded timing signal of aDAF is, on average, 34.76% better than it was for DAE. Finally, the results of the imaging experiment show that aDAF has superior imaging quality than DAE, with signal-to noise ratio (SNR) improved by 23.32% and spatial resolution increased by 50%. According to the experiments, the proposed aDAF increased AE signal decoding accuracy, which is vital for future research and applications related to ABI.

Improved encoding structure and decoding algorithms for spinal codes

To improve the error correction performance, an innovative encoding structure with tail-biting for spinal codes is designed. Furthermore, an adaptive forward stack decoding (A-FSD) algorithm with lower complexity for spinal codes is proposed. In the A-FSD algorithm, a flexible threshold parameter is set by a variable channel state to narrow the scale of nodes accessed. On this basis, a new decoding method of AFSD with early termination (AFSD-ET) is further proposed. The AFSD-ET decoder not only has the ability of dynamically modifying the number of stored nodes, but also adopts the early termination criterion to curtail complexity. The complexity and related parameters are verified through a series of simulations. The simulation results show that the proposed spinal codes with tail-biting and the AFSD-ET decoding algorithms can reduce the complexity and improve the decoding rate without sacrificing correct decoding performance.

Adaptive power line communication for intelligent separate layer production with single-core cables

Power line communication (PLC) techniques have been proven to be efficient for the intelligent separate layer production (ISLP) system with single-core cables. However, the permanent mode of the ISLP may cause a continuous change of the resistance and capacitance of the single-core cables due to the long-term effect of high temperature, high pressure as well as the vibration of the bad downhole conditions, which will have great influence on the data transmission efficiency. In this paper, we present an adaptive PLC approach for the data transmission of ISLP with single-core cables. Instead of the precise slide rheostat, program-controlled potentiometer is used to achieve an adaptive decoding algorithm for fast initialization of the ISLP transmission. Moreover, a long-term adaptive method is also proposed to adjust the gain and filter coefficients in real time according to the bit error rate of data transmission systems. Finally, experiments were conducted and the results demonstrate the effeciveness of the proposed adaptive PLC data transmission for the improvement of the efficiency and stability of the ISLP system.

Adaptive Threshold-Aided K-Best Sphere Decoding for Large MIMO Systems

This paper proposes an adaptive threshold-aided K-best sphere decoding (AKSD) algorithm for large multiple-input multiple-output systems. In the proposed scheme, to reduce the average number of visited nodes compared to the conventional K-best sphere decoding (KSD), the threshold for retaining the nodes is adaptively determined at each layer of the tree. Specifically, we calculate the adaptive threshold based on the signal-to-noise ratio and index of the layer. The ratio between the first and second smallest accumulated path metrics at each layer is also exploited to determine the threshold value. In each layer, in addition to the K paths associated with the smallest path metrics, we also retain the paths whose path metrics are within the threshold from the Kth smallest path metric. The simulation results show that the proposed AKSD provides nearly the same bit error rate performance as the conventional KSD scheme while achieving a significant reduction in the average number of visited nodes, especially at high signal-to-noise ratios.

An exoskeleton controlled by an epidural wireless brain–machine interface in a tetraplegic patient: a proof-of-concept demonstration

Approximately 20% of traumatic cervical spinal cord injuries result in tetraplegia. Neuroprosthetics are being developed to manage this condition and thus improve the lives of patients. We aimed to test the feasibility of a semi-invasive technique that uses brain signals to drive an exoskeleton. We recruited two participants at Clinatec research centre, associated with Grenoble University Hospital, Grenoble, France, into our ongoing clinical trial. Inclusion criteria were age 18-45 years, stability of neurological deficits, a need for additional mobility expressed by the patient, ambulatory or hospitalised monitoring, registration in the French social security system, and signed informed consent. The exclusion criteria were previous brain surgery, anticoagulant treatments, neuropsychological sequelae, depression, substance dependence or misuse, and contraindications to magnetoencephalography (MEG), EEG, or MRI. One participant was excluded because of a technical problem with the implants. The remaining participant was a 28-year-old man, who had tetraplegia following a C4-C5 spinal cord injury. Two bilateral wireless epidural recorders, each with 64 electrodes, were implanted over the upper limb sensorimotor areas of the brain. Epidural electrocorticographic (ECoG) signals were processed online by an adaptive decoding algorithm to send commands to effectors (virtual avatar or exoskeleton). Throughout the 24 months of the study, the patient did various mental tasks to progressively increase the number of degrees of freedom. Between June 12, 2017, and July 21, 2019, the patient cortically controlled a programme that simulated walking and made bimanual, multi-joint, upper-limb movements with eight degrees of freedom during various reach-and-touch tasks and wrist rotations, using a virtual avatar at home (64·0% [SD 5·1] success) or an exoskeleton in the laboratory (70·9% [11·6] success). Compared with microelectrodes, epidural ECoG is semi-invasive and has similar efficiency. The decoding models were reusable for up to approximately 7 weeks without recalibration. These results showed long-term (24-month) activation of a four-limb neuroprosthetic exoskeleton by a complete brain-machine interface system using continuous, online epidural ECoG to decode brain activity in a tetraplegic patient. Up to eight degrees of freedom could be simultaneously controlled using a unique model, which was reusable without recalibration for up to about 7 weeks. French Atomic Energy Commission, French Ministry of Health, Edmond J Safra Philanthropic Foundation, Fondation Motrice, Fondation Nanosciences, Institut Carnot, Fonds de Dotation Clinatec.

Adaptive 2-D Scheduling-Based Nonbinary Majority-Logic Decoding for NAND Flash Memory

This brief presents a nonbinary adaptive 2D scheduling-based majority logic decoding (NB-ATS-MLGD) algorithm for NAND flash memory. The proposed algorithms provide considerable tradeoff between error-correcting capability and decoding complexity, and make the NB-MLGD decoding more attractive for practical purposes in the multi-level cells (MLC) NAND flash memory. The most significant feature of the proposed NB-ATS-MLGD algorithm is the 2D layered scheduling strategy, where the decoding orders of check nodes (CNs) and variable nodes (VNs) are both adaptive. By leveraging on the MLC flash memory bit error patterns, an early-correcting (EC) criterion is incorporated into the NB-ATS-MLGD algorithm. Furthermore, the simplification and parallelization of proposed algorithms make them more practical in NAND flash memory. Simulation results show that the proposed algorithms increase the lifetime of MLC flash memory up to 3000 program-and-erase (PE) cycles and have desirable convergence speed compared with the conventional non-binary MLGD algorithm. When at low PE cycles, the NB-EC-ATS-MLGD algorithm improves frame error rate (FER) performance by more than 3 order of magnitudes compared with the conventional non-binary MLGD algorithm.

Analysing correlated noise on the surface code using adaptive decoding algorithms

Laboratory hardware is rapidly progressing towards a state where quantum error-correcting codes can be realised. As such, we must learn how to deal with the complex nature of the noise that may occur in real physical systems. Single qubit Pauli errors are commonly used to study the behaviour of error-correcting codes, but in general we might expect the environment to introduce correlated errors to a system. Given some knowledge of structures that errors commonly take, it may be possible to adapt the error-correction procedure to compensate for this noise, but performing full state tomography on a physical system to analyse this structure quickly becomes impossible as the size increases beyond a few qubits. Here we develop and test new methods to analyse blue a particular class of spatially correlated errors by making use of parametrised families of decoding algorithms. We demonstrate our method numerically using a diffusive noise model. We show that information can be learnt about the parameters of the noise model, and additionally that the logical error rates can be improved. We conclude by discussing how our method could be utilised in a practical setting blue and propose extensions of our work to study more general error models.

Polar Codes for Spin-Torque Transfer Magnetic Random Access Memory

Spin-torque transfer magnetic random access memory (STT-MRAM) is a promising non-volatile memory (NVM) technology due to its superior performance in terms of power consumption, write/read speed, endurance, and scalability. However, the reliability of STT-MRAM is affected by the process variation and thermal fluctuation, leading to both the write errors and read errors. Hence, it is important to develop error correction coding schemes to correct the memory cell errors and improve the system reliability. In this paper, we propose, for the first time, the design and optimization of polar codes for the STT-MRAM channel. In particular, as the STT-MRAM channel is asymmetric by nature, we first adopt an approach to symmetrize the channel so as to facilitate the design of effective polar codes. We then apply the density evolution method to construct polar codes and optimize their performance for the STT-MRAM channel. Furthermore, in order to mitigate the raw bit error rate diversity of STT-MRAM cells caused by process variations, we propose a rate-adaptive polar coding scheme in conjunction with an adaptive decoding algorithm. Simulation results and the decoder complexity analysis show that the constructed polar codes outperform both the Bose–Chaudhuri–Hoquenghem codes and low-density parity-check codes with lower decoding complexity, thus demonstrating the great potential of polar codes for improving the reliability of emerging NVMs.

An optimal decoding algorithm for Molecular Communications systems with noise, memory, and pulse width

Molecular Communications (MC) is a promising paradigm to achieve message exchange between nano-machines. Due to the specific characteristics of MC systems, the channel noise and memory significantly influence the MC system performance. Aiming to mitigate the impact of these two factors, an adaptive decoding algorithm is proposed by optimising the symbol determination threshold. In this paper, this novel decoding scheme is deployed onto a concentration-based MC system with the transmitter emission process considered. To evaluate the performance, an information theoretical approach is developed to derive the Bit Error Rate (BER) and the channel capacity. Simulations are also carried out to verify the accuracy of these formulations, to compare the performance difference against other decoding schemes, and to illustrate the performance deviation caused by different designing of relevant parameters. Furthermore, the performance of MC systems with the distance unknown is also analysed. Comparisons between distance-pre-known and distance-unknown systems are provided.

Convergence Analysis of Iterative Decoding for Binary CEO Problem

Estimation of a binary source using multiple observers, a variant of the so called Chief Executive Officer (CEO) problem, is considered. A low-complexity Distributed Joint Source Channel Coding (D-JSCC) based on the Parallel Concatenated Convolutional Codes (PCCC) is implemented in a cluster of sensors in a distributed fashion. Convergence of the iterative decoder is analyzed by utilizing EXtrinsic Information Transfer (EXIT) chart technique to determine the convergence region in terms of the sensors? observation accuracy and channel SNR, where the iterative decoder outperforms the non-iterative one. This leads to design of a bi-modal decoder that adaptively switches between the iterative and non-iterative modes in order to avoid inefficient iterative information exchange without compromising the resulting Bit Error Rate (BER). This adaptive decoding algorithm saves the computational power and decoding time by a factor of about 10 by avoiding unnecessary iterations.

Adaptive decoding algorithm based on multiplicity of candidate sequences for Block Turbo Codes

It is known that Block Turbo Codes (BTC) can be nearly optimally decoded by Chase-II algorithm, in which the Least Reliable Bits (LRBs) are chosen empirically to keep the size of the test patterns (sequences) relatively small and to reduce the decoding complexity. While there are also other adaptive techniques, where the decoder's LRBs adapt to the external parameter of the decoder like SNR (Signal Noise Ratio) level, a novel adaptive algorithm for BTC based on the statistics of an internal variable of the decoder itself is proposed in this paper. Different from the previous reported results, it collects the statistics of the multiplicity of the candidate sequences, i.e., the number of the same candidate sequences with the same minimum squared Euclidean distance resulted from the decoding of test sequences. It is shown by Monte Carlo simulations that the proposed adaptive algorithm has only about 0.02dB coding loss but the average complexity of the proposed algorithm is about 42% less compared with Pyndiah's iterative decoding algorithm using the fixed LRBs parameter.

QoE-Oriented Adaptive SVC Decoding in DVB-T2

Associating DVB-T2 and Scalable Video Coding (SVC) constitutes an efficient way for broadcasting added-value video services, such as HDTV and 3D TV, to end-users. The ultimate objective of this new approach of broadcasting video services is to ensure high Quality of Experience (QoE) for end users. Whilst Quality of Service (QoS) is the collective effect of performance that determines the degree of satisfaction of a user of a service, QoE reflects more accurately the user experience, as it is based on human perception when evaluating the video quality. Maximizing user QoE is thus becoming a crucial requirement when deploying new broadcast platforms for the provisioning of high quality video services. The contributions of this paper are two-fold. At first, we introduce a reference-less QoE measurement tool dedicated to SVC coding. Based on a learning function, this tool is able to learn the non-linear relationship between parameters affecting video quality and perceived user QoE. According to several experiments carried out using this tool, we demonstrate that decoding all SVC layers is not always efficient to achieve high user QoE, mostly when SVC enhanced layers experience packet losses. For the sake of maintaining a good QoE, it is worthwhile withdrawing enhanced layers experiencing packet losses and not displaying them to end-users. Based on this observation, we propose a QoE-Based Adaptive SVC Decoding (QoE-BASD) algorithm that assists a video receiver to select the appropriate SVC layers for video decoding in order to maximize QoE. We evaluate the performance of the proposed solution: (i) analytically, by using discrete Markov Chains to model the proposed solution; and (ii) via OPNET-based computer simulations. The obtained results are encouraging, and illustrate the gain achieved by QoE-BASD when compared to the conventional approach.

Improving Long Term Myoelectric Decoding, Using an Adaptive Classifier with Label Correction.

This study presents a novel adaptive myoelectric decoding algorithm for control of upper limb prosthesis. Myoelectric decoding algorithms are inherently subject to decay in decoding accuracy over time, which is caused by the changes occurring in the muscle signals. The proposed algorithm relies on an unsupervised and on demand update of the training set, and has been designed to adapt to both the slow and fast changes that occur in myoelectric signals. An update in the training data is used to counter the slow changes, whereas an update with label correction addresses the fast changes in the signals. We collected myoelectric data from an able bodied user for over four and a half hours, while the user performed repetitions of eight wrist movements. The major benefit of the proposed algorithm is the lower rate of decay in accuracy; it has a decay rate of 0.2 per hour as opposed to 3.3 for the non adaptive classifier. The results show that, long term decoding accuracy in EMG signals can be maintained over time, improving the performance and reliability of myoelectric prosthesis.

Performance of adaptive low-density parity check codes in a low-cost spectrum-sliced wavelength division multiplex network

Regular low-density parity-check (LDPC) codes have been applied for the first time to a low-cost spectrum-sliced wavelength division multiplex system. The Gaussian approximation of the noise in such system has been commonly used in previous studies, and there is a standard LDPC for symmetric additive white Gaussian noise channels. However, in the system considered here, the actual noise character is asymmetric and is chi-square distributed. Owing to the specific channel property, an adaptive decoding algorithm based on the precise chi-square noise statistics has been proposed and proved to outperform decoding with conventional Gaussian approximations. Simulations are conducted for various code rates. The results show that such a forward error correction scheme provides significant coding gain for a dedicated system in terms of improving the overall transmission capacity and available power budget.

Adaptive Decoding Algorithms for Low-Density Parity-Check Codes over the Binary Erasure Channel

Two decoding procedures combined with a belief-propagation (BP) decoding algorithm for low-density parity-check codes over the binary erasure channel are presented. These algorithms continue a decoding procedure after the BP decoding algorithm terminates. We derive a condition that our decoding algorithms can correct an erased bit which is uncorrectable by the BP decoding algorithm. We show by simulation results that the performance of our decoding algorithms is enhanced compared with that of the BP decoding algorithm with little increase of the decoding complexity.

Adaptive Viterbi decoding of convolutional codes over memoryless channels

In this paper, an adaptive decoding algorithm for convolutional codes, which is a modification of the Viterbi algorithm (VA) is presented. For a given code, the proposed algorithm yields nearly the same error performance as the VA while requiring a substantially smaller average number of computations. Unlike most of the other suboptimum algorithms, this algorithm is self-synchronizing. If the transmitted path is discarded, the adaptive Viterbi algorithm (AVA) can recover the state corresponding to the transmitted path after a few trellis depths. Using computer simulations over hard and soft 3-bit quantized additive white Gaussian noise channels, it is shown that codes with a constraint length K up to 11 can be used to improve the bit-error performance over the VA with K=7 while maintaining a similar average number of computations. Although a small variability of the computational effort is present with our algorithm, this variability is exponentially distributed, leading to a modest size of the input buffer and, hence, a small probability of overflow.