Decoding System Research Articles

The Leaf-Style Axial Field Variable Reluctance Resolver With an Efficient Decoding System

The Leaf-Style Axial Field Variable Reluctance Resolver With an Efficient Decoding System

Estimation of the Impulse Response of the AWGN Channel with ISI within an Iterative Equalization and Decoding System That Uses LDPC Codes.

In this paper, new schemes have been proposed for the estimation of the additive white Gaussian noise (AWGN) channel with intersymbol interference (ISI) in an iterative equalization and decoding system using low-density parity check (LDPC) codes. This article explores the use of the least squares algorithm in various scenarios. For example, the impulse response of the AWGN channel h was initially estimated using a training sequence. Subsequently, the impulse response was calculated based on the training sequence and then re-estimated once using the sequence estimated from the output of the LDPC decoder. Lastly, the impulse response was calculated based on the training sequence and re-estimated twice using the sequence estimated from the output of the LDPC decoder. Comparisons were made between the performances of the three mentioned situations, with the situation in which a perfect estimate of the impulse response of the channel is assumed. The performance analysis focused on how the bit error rate changes in relation to the signal-to-noise ratio. The BER performance comes close to the scenario of having a perfect estimate of the impulse response when the estimation is performed based on the training sequence and then re-estimated twice from the sequence obtained from the output of the LDPC decoder.

A high security coding and anti-counterfeiting method based on the nonlinear magnetization response of superparamagnetic nanomaterials

Traditional coding methods based on graphics and digital or magnetic labels have gradually decreased their anti-counterfeiting because of market popularity. This paper presents a new magnetic anti-counterfeiting coding method. This method uses a high-performance coding material, which, along with small changes to the material itself and the particle size of the superparamagnetic nanomaterials, results in a large difference in the nonlinear magnetization response. This method, which adopts 12-site coding and establishes a screening model by measuring the voltage amplitude of 12-site variables, can code different kinds of products, establishing long-term stable coding and decoding means. Through the anti-counterfeiting experiment of wine, the experiment results show that the authenticity of the coded products can be verified using the self-developed magnetic encoding and decoding system. The new coding technology can verify the anti-counterfeiting of 9000 products, with a single detection accuracy of 97% and a detection time of less than one minute. Moreover, this coding method completely depends on the production batch of the superparamagnetic nanomaterials, which is difficult to imitate, and it provides a new coding anti-counterfeiting technology for related industries with a wide range of potential applications.

TFormer: A time–frequency Transformer with batch normalization for driver fatigue recognition

Within the framework of the advanced human-cybernetic interfaces (HCI), Cross-subject electroencephalogram (EEG)-based driver fatigue recognition is emerging as a pivotal application in the paradigm of Industry 5.0. Recognizing the importance of ensuring driver safety through proactive monitoring, it is essential to offer a general EEG decoding system to improve road safety. This work investigated the use of Transformers for the challenging cross-subject EEG decoding task due to the great success the Transformers have achieved in various applications. Previous research focused on using Transformers to capture global temporal information, but less work targeted global frequency-domain patterns. Furthermore, in order to leverage a standard Transformer architecture grounded in natural language processing for EEG decoding, it is imperative to account for inherent characteristics in EEG and make pertinent adjustments accordingly. In this work, we proposed a time–frequency Transformer (TFormer) that can automatically learn the global time–frequency patterns from raw EEG data. TFormer consisted of three components: convolutional stems for input embedding, time–frequency multi-head cross-attention (TF-MCA) for integrating time-domain patterns into frequency points, and self-attention to further learn global time-frequency patterns. Moreover, we analyzed TFormer’s internal settings and found batch normalization (BN) more suitable for cross-subject EEG decoding than layer normalization (LN). The experiment results demonstrated the superiority of the proposed model compared to existing methods. Overall, our work contributes to the development of Transformer models in EEG decoding and illustrates a different way to leverage Transformers for decoding raw EEG data.

Tamil Content Generation Using Transformer[Yazhi

This paper presents Yazhi, a transformation model specially designed for Tamil, known for its robustness and unique language features Yazhi combines advanced transformer architecture with reinforcement learning, encoder -decoder system for Yazhi traditional model moves highly effective therefore , improving comprehensibility and generation of advanced Tamil text Represents a significant improvement in natural language processing, and offers robust solutions to computer understanding and translation challenges using available resources mastery and learning to quickly understand subtle nuances of language. Through his seminal work, Yazhi sets a new benchmark for business research on Tamil content generation and translation.

Ensemble Learning Method for the Continuous Decoding of Hand Joint Angles.

Human-machine interface technology is fundamentally constrained by the dexterity of motion decoding. Simultaneous and proportional control can greatly improve the flexibility and dexterity of smart prostheses. In this research, a new model using ensemble learning to solve the angle decoding problem is proposed. Ultimately, seven models for angle decoding from surface electromyography (sEMG) signals are designed. The kinematics of five angles of the metacarpophalangeal (MCP) joints are estimated using the sEMG recorded during functional tasks. The estimation performance was evaluated through the Pearson correlation coefficient (CC). In this research, the comprehensive model, which combines CatBoost and LightGBM, is the best model for this task, whose average CC value and RMSE are 0.897 and 7.09. The mean of the CC and the mean of the RMSE for all the test scenarios of the subjects' dataset outperform the results of the Gaussian process model, with significant differences. Moreover, the research proposed a whole pipeline that uses ensemble learning to build a high-performance angle decoding system for the hand motion recognition task. Researchers or engineers in this field can quickly find the most suitable ensemble learning model for angle decoding through this process, with fewer parameters and fewer training data requirements than traditional deep learning models. In conclusion, the proposed ensemble learning approach has the potential for simultaneous and proportional control (SPC) of future hand prostheses.

Labeling-Based Recipient Identification with Low-Order Modulation

Labeling-Based Recipient Identification (LABRID) brings the possibility of representing the destination station address in a Bit-Interleaved Coded Modulation with Iterative Decoding (BICM-ID) system by a signal labeling rule. Low-order modulations, such as BPSK or QPSK, pose a general problem for BICM-ID due to a limited convergence of iterative decoding. In the context of LABRID, they have one more drawback—a small number of different labeling rules in general; the number of the optimal ones, which exhibit the maximal asymptotic coding gain, is reduced even further. Meanwhile, LABRID needs a sizable collection of different optimal labeling rules to serve many users in large wireless networks. In this paper, the author suggests the use of hypercube BPSK or QPSK labeling to overcome all these challenges. By means of the Reactive Tabu Search (RTS) algorithm, more than 1500 equivalent optimal hypercube labeling rules are found. Analytical error bounds of the system are developed and supported by simulation experiments. Then, the focus is moved to the criterion to determine the frame destination at the LABRID receiver; a simple threshold-based method is proposed to keep the incorrect decision probability below 10−5. Finally, it is shown that LABRID outperforms a reference BICM-ID system in terms of computational complexity.

Supervised Contrastive Learning-Based Domain Generalization Network for Cross-Subject Motor Decoding.

Developing an electroencephalogram (EEG)-based motor imagery and motor execution (MI/ME) decoding system that is both highly accurate and calibration-free for cross-subject applications remains challenging due to domain shift problem inherent in such scenario. Recent research has increasingly embraced transfer learning strategies, especially domain adaptation techniques. However, domain adaptation becomes impractical when the target subject data is either difficult to obtain or unknown. To address this issue, we propose a supervised contrastive learning-based domain generalization network (SCLDGN) for cross-subject MI/ME decoding. Firstly, the feature encoder is purposefully designed to learn the EEG discriminative feature representations. Secondly, the domain alignment based on deep correlation alignment constrains the representations distance across various domains to learn domain-invariant features. In addition, the class regularization block is proposed, where the supervised contrastive learning with domain-agnostic mixup is established to learn the class-relevant features and achieve class-level alignment. Finally, in the latent space, clusters of domain-agnostic representations from the same class are mapped closer together. Consequently, SCLDGN is capable of learning domain-invariant and class-relevant discriminative representations, which are essential for effective cross-subject decoding. Extensive experiments conducted on six MI/ME datasets demonstrate the effectiveness of the proposed method in comparison with other state-of-the-art approaches. Furthermore, ablation study and visualization analyses explain the generalization mechanism of the proposed method and also show neurophysiologically meaningful patterns related to MI/ME.

Low Power Wake-Up Receivers for Underwater Acoustic Wireless Sensor Networks

This article reports two low power wake-up receiver (WuRx) circuits (Type A and B) for underwater acoustic wireless sensor networks. The proposed circuits realize the same novel method to extract the wake-up call from the acoustic signals received by a sensor node using different circuit topologies. This is accomplished by comparing the amplitude and the duration of the received pulses to a predefined code. A combination of these two parameters provides a safe and robust decoding system. The values and duration of the predefined code can be adjusted by tuning the biasing voltages in the circuits. The main advantages of the presented designs are modularity and tunability. To verify the operation of the proposed system, an ASIC has been realized. The WuRxs have been characterized in terms of tuning, duty cycle, and power consumption. Both WuRxs can consume less than 0.5. Type A occupies an area of mode = text0.058mm2, has a data rate of mode = text250bit/s, and presents the lowest power consumption reported, consuming 265nW and 61nW in active and sleep modes, respectively.

Utilizing EEG Signal Data and Motion to Aid in Prosthetic Hand Motion

Current prosthetic arm technologies are often difficult to use intuitively by amputees, require invasive surgical procedures, and can be extremely costly with prices ranging from $20,000 to $80,000. To address these challenges, the engineering goal of this project aims to design a smart, low-cost, mind-controlled transhumeral prosthesis by integrating the brain-interfacing capabilities of electroencephalography (EEG), the economical means of 3D printing technology, and gesture-detecting attributes of an accelerometer-gyroscope. Single-channel brain signals are transmitted through Bluetooth to be interpreted by a novel EEG decoding algorithm and head gestures from the inertial measurement unit actuate movement within the arm. Force sensitive resistors were employed to regulate force control in real-time to optimize grasp type. An LCD screen is integrated within the arm’s design to display the type of touch it is exerting on an object. The arm itself was printed with an original design utilizing PLA plastic filament making it extremely durable and lightweight. After thoroughly testing the prosthesis, the novel EEG decoding system boasts an accuracy of 94.7% and a user cognition to machine delay of 1.64 + - 0.37 seconds. The inertial measurement unit system recognizes user gestures with a delay of .136 seconds. With a bill of materials approximately $375 USD and ability to be moved in 4 degrees of motion, this novel upper limb prosthesis serves as a promising alternative to existing units on the market. The algorithms developed within this project have a wide range of use within other brain control interface projects.

Word-level reading skills of Brazilian children with developmental language disorder

ABSTRACT Children with Developmental Language Disorder (DLD) show a wide range of impairments, including poor pre-reading skills and decoding difficulties due to phonological deficits and such difficulties have significant repercussions on the acquisition of written language. However, evidence about reading processes and development is mainly available for English-speaking children with DLD, limiting our understanding of this process in a cross-linguistic manner. The orthographic characteristics of languages significantly influence the learning process of written code. Unlike English, Brazilian Portuguese has a transparent decoding system and its orthography presents a set of consistent, univocal grapheme-phoneme relations. The present study investigated whether the challenges reported in decoding for children with DLD in opaque languages hold for children with DLD who are speakers of Brazilian Portuguese and whether the length and type of stimuli influence the decoding skills of children with DLD differently than when compared to children with typical language development (TLD). Sixteen children with DLD between seven and ten years of age who are monolingual speakers of Brazilian Portuguese and 64 controls with TLD matched by gender, age, and socioeconomic status, with the children with DLD in a 4:1 ratio; participated in the study. All children performed a computerized task where they were asked to decode a linguistically balanced list of words and nonwords designed according to Brazilian Portuguese decoding rules. The present study provides substantial evidence that children with DLD who are speakers of Brazilian Portuguese have deficits in the acquisition of decoding and that the decoding profile of children with DLD is subject to multiple influences, not only with relation to the length and type of stimuli but also characteristics of the languages these children are being literate in, highlighting the multifactorial nature involved in the development of decoding.

An estimation of triaxial forces from normal stress tactile sensor arrays

This work proposes a new method to find an optimal solution of the inverse problem of the Boussinesq equation for ill-posed problems, applicable in the reconstruction of triaxial forces for processing contact data in tactile decoding systems. This paper focuses on the estimation of triaxial forces from normal stress tactile sensor arrays on flat non-deformable surfaces for single static contacts. This method estimates a triaxial force vector on the sensor coverage surface for each stress value from the relation between the stress data and the applied normal and tangential contact forces. The main contribution of this work consists of a new procedure for estimating contact forces simplifying both arithmetic operations and the optimization process used. Because of this, obtaining a force estimation has a predictable computation time, which makes this new method an attractive solution for implementation in hardware-based real-time tactile sensing systems. The verification process was conducted using a Finite Element Analysis (FEA) as a reference. When testing tactile sensors in a range of sizes and resolutions for piezoelectric and piezoresistive technologies, a maximum estimation error of 10.93% was obtained, including errors due to the array discretization.

An all-passive Si3N4 optical row decoder circuit for addressable optical RAM memories

In this work we experimentally demonstrate a Si 3 N 4 photonic integrated circuit which offers row decoding and RAM addressing functionalities. The passive integrated structure comprises a MRR-based wavelength filtering bank scheme in a 2 × 4 configuration, which reveals a suppression ratio in the range of 12–25 dB. The performance of the optical circuit has been evaluated in a system-level testbed, where successful addressing in one RAM row has been achieved. Error-free operation has been accomplished for all cases under study, with the whole row decoder system’s performance to offer a total power penalty of 2.5 dB.

Design and Implementation of a Highly Efficient Quasi-Cyclic Low-Density Parity-Check Transceiving System Using an Overlapping Decoder.

The traditional LDPC encoding and decoding system is characterized by low throughput and high resource consumption, making it unsuitable for use in cost-efficient, energy-saving sensor networks. Aiming to optimize coding complexity and throughput, this paper proposes a combined design of a novel LDPC code structure and the corresponding overlapping decoding strategies. With regard to structure of LDPC code, a CCSDS-like quasi-cyclic parity check matrix (PCM) with uniform distribution of submatrices is constructed to maximize overlap depth and adapt the parallel decoding. In terms of reception decoding strategies, we use a modified 2-bit Min-Sum algorithm (MSA) that achieves a coding gain of 5 dB at a bit error rate of 10-6 compared to an uncoded BPSK, further mitigating resource consumption, and which only incurs a slight loss compared to the standard MSA. Moreover, a shift-register-based memory scheduling strategy is presented to fully utilize the quasi-cyclic characteristic and shorten the read/write latency. With proper overlap scheduling, the time consumption can be reduced by one third per iteration compared to the non-overlap algorithm. Simulation and implementation results demonstrate that our decoder can achieve a throughput up to 7.76 Gbps at a frequency of 156.25 MHz operating eight iterations, with a two-thirds resource consumption saving.

Prediction of wave-induced ship motions based on integrated neural network system and spatiotemporal wave-field data

This study introduces an artificial neural network system for ship motion prediction in seaways. To consider the physical characteristics of wave-induced ship motions, neural networks based on a Long Short-Term Memory (LSTM) encoder and decoder, and a convolutional neural network (CNN) are integrated. The LSTM encoder computes the state vector representing the memory effects of motion-induced radiated waves based on past motion records, that is, a sequence-to-one model. In the LSTM decoder, the motion time series is predicted using the encoded initial state vector and foreseen information on the ocean wave field around a vessel, that is, a sequence-to-sequence model. In addition, a CNN is adopted to compress the wave data into a vector sequence. Particularly, the present CNN uses spatiotemporal wave-field data, not a wave signal at single location. To validate the proposed system, a database for training the integrated system was constructed using a physics-based seakeeping program for various sea states. By applying the trained model, deterministic predictions were performed for a new ocean environment, and the accuracy and reliability of the testing results are investigated according to the input data and neural network structures. From the simulation results, it was confirmed that the present encoder–decoder system can conduct ship motion forecasting by effectively considering the motion memory effects and wave excitations as in the ship hydrodynamic model. In addition, excitations and resulting motion responses by short-crested waves can be considered through CNN-based wave-field data processing. Finally, the present machine-learning model also showed the capability of extracting ship operation information (maneuvering quantities) from the given wave-field data.

Resilient control for networked control systems with dynamic quantization and DoS attacks

AbstractIn this article, the resilient control for networked control systems in the presence of denial‐of‐service (DoS) attacks is investigated in a sampled‐data and dynamic quantization scheme. A novel dynamic quantization strategy is designed for signal transmissions from encoding systems to decoding systems, in which the quantized states are transmitted through networks with a risk of DoS attacks. An estimator is introduced to the design of control laws. Some sufficient conditions in terms of quantization levels, DoS attack duration and frequencies are given for the asymptotic stability of networked control systems. Furthermore, an event‐triggered communication scheme is designed for signal transmissions in control channels to reduce network resource consumption. The Zeno behavior is excluded in the designed event‐triggered communication scheme. The quantization levels can be adaptively adjusted according to real‐time situations. Finally, the effectiveness of the proposed strategy is illustrated by simulations.

Informatic Capabilities of Translation and Its Implications for the Origins of Life.

The ability to encode and convert heritable information into molecular function is a defining feature of life as we know it. The conversion of information into molecular function is performed by the translation process, in which triplets of nucleotides in a nucleic acid polymer (mRNA) encode specific amino acids in a protein polymer that folds into a three-dimensional structure. The folded protein then performs one or more molecular activities, often as one part of a complex and coordinated physiological network. Prebiotic systems, lacking the ability to explicitly translate information between genotype and phenotype, would have depended upon either chemosynthetic pathways to generate its components-constraining its complexity and evolvability- or on the ambivalence of RNA as both carrier of information and of catalytic functions-a possibility which is still supported by a very limited set of catalytic RNAs. Thus, the emergence of translation during early evolutionary history may have allowed life to unmoor from the setting of its origin.The originof translation machineryalso represents an entirely novel and distinct threshold of behavior for which there is no abiotic counterpart-it could be the only known example of computing that emerged naturally at the chemical level. Here we describetranslation machinery's decoding system asthe basis of cellular translation's information-processing capabilities, and the four operation types that find parallels in computer systems engineering that this biological machineryexhibits.

Deep Spread Multiplexing and Study of Training Methods for DNN-Based Encoder and Decoder.

We propose a deep spread multiplexing (DSM) scheme using a DNN-based encoder and decoder and we investigate training procedures for a DNN-based encoder and decoder system. Multiplexing for multiple orthogonal resources is designed with an autoencoder structure, which originates from the deep learning technique. Furthermore, we investigate training methods that can leverage the performance in terms of various aspects such as channel models, training signal-to-noise (SNR) level and noise types. The performance of these factors is evaluated by training the DNN-based encoder and decoder and verified with simulation results.

Thickness and fluorescence-based dual-encoded suspension array and corresponding decoding system for multiplexed detection

In response to the urgent requirement for high-throughput multiplexed detection, a novel dual-encoded suspension array (SA) based on thickness and digital fluorescent peak is proposed, along with the corresponding optical system. The SAs are prepared by thickness encoded micro-glass chips (MGCs) with electrostatically adsorbed quantum dots (QDs). Using the thickness characteristics of microcarrier itself and digital fluorescence peak (DFP) as the encoded signals ensure the high encoding stability with simple fabrication process at a low cost. A spectral-domain phase and fluorescence spectroscopy microscopy (SDPFSM) system is designed for the first time to decode the thickness information and DFP with ultrahigh accuracy. The high selectivity and sensitivity of the dual-encoded SAs are verified by the multiplexed immunoassays. The quantitative detection of anti-human IgG, anti-mouse IgG, and anti-rabbit IgG was realized with the limits of detection (LOD) of 0.19, 0.35 and 0.31 pM/mL and the sensitivity of 713, 470 and 456 a.u./(pM/mL), respectively. The coding number increases by a factor of 458 compared with the traditional fluorescence coding method due to the additional use of the thickness information of the MGCs itself as a coding channel. The aforementioned properties demonstrate that the dual-encoded SA and the corresponding decoding platform are promising and robust in multiplexed detection.

Hardware-Accelerated Data Decoding and Reconstruction for Automotive LiDAR Sensors

The automotive industry is facing an unprecedented technological transformation towards fully autonomous vehicles. Optimists predict that, by 2030, cars will be sufficiently reliable, affordable, and common to displace most current human driving tasks. To cope with these trends, autonomous vehicles require reliable perception systems to hear and see all the surroundings, being light detection and ranging (LiDAR) sensors a key instrument for recreating a 3D visualization of the world. However, for a reliable operation, such systems require LiDAR sensors to provide high-resolution 3D representations of the car's vicinity, which results in millions of data points to be processed in real-time. With this article we propose the ALFA-Pi, a data packet decoder and reconstruction system fully deployed on an embedded reconfigurable hardware platform. By resorting to field-programmable gate array (FPGA) technology, ALFA-Pi is able to interface different LiDAR sensors at the same time, while providing custom representation outputs to high-level perception systems. By accelerating the LiDAR interface, the proposed system outperforms current software-only approaches, achieving lower latency in the data acquisition and data decoding tasks while reaching high performance ratios.