Decoding Of Data Research Articles

Poststack Seismic Data Compression Using a Generative Adversarial Network

This work presents a method for volumetric seismic data compression by coupling a 3-D convolution-based autoencoder to a generative adversarial network (GAN). The main challenge of 3-D convolutional autoencoders for data compression is how to fully exploit volumetric redundancy while keeping reasonable latent representation dimensions. Our method is based on a convolutional neural network for seismic data compression called 3DSC. Its encoder and decoder use 3-D convolutions and are connected by a latent representation with the same dimensions as its 2-D network counterparts. Our main hypothesis is that the 3DSC architecture can be improved by adversarial training. We, thus, propose a new 3-D-based seismic data compression method (3DSC-GAN) by coupling the 3DSC network to a GAN. The seismic data decoder is used as a generator of poststack data that are integrated with a discriminator module to better exploit 3-D redundancy. Results show that our method outperforms previous seismic data compression methods for very low target bit rates, increasing the peak signal-to-noise ratio (PSNR) with fairly high visual quality.

NOMA‐enabled D2D adaptive relaying and transmission in cellular networks

AbstractWith the aim of improving the spectrum efficiency, we propose a two‐phase cellular non‐orthogonal and device‐to‐device (D2D) non‐orthogonal (CNDN) transmission scheme, wherein a base station (BS) can transmit data to a near‐user and a far‐user simultaneously assisted by two D2D users. Either the D2D transmitter or the D2D receiver can adaptively forward the BS data to the far‐user according to their decoding statuses. When the D2D transmitter forwards the cellular data, it also transmits its own data to the D2D receiver simultaneously. The superposition coding technique is adopted for the non‐orthogonal transmission and the opportunistic interference cancelation is performed for the data decoding. Taking into account the adaptive operations of cellular and D2D users, we analyze the throughput of both cellular and D2D systems. Extensive simulations show that the proposed CNDN scheme greatly outperforms the cellular orthogonal and D2D non‐orthogonal (CODN) transmission scheme in terms of throughput.

Shared neural codes for visual and semantic information about familiar faces in a common representational space

Processes evoked by seeing a personally familiar face encompass recognition of visual appearance and activation of social and person knowledge. Whereas visual appearance is the same for all viewers, social and person knowledge may be more idiosyncratic. Using between-subject multivariate decoding of hyperaligned functional magnetic resonance imaging data, we investigated whether representations of personally familiar faces in different parts of the distributed neural system for face perception are shared across individuals who know the same people. We found that the identities of both personally familiar and merely visually familiar faces were decoded accurately across brains in the core system for visual processing, but only the identities of personally familiar faces could be decoded across brains in the extended system for processing nonvisual information associated with faces. Our results show that personal interactions with the same individuals lead to shared neural representations of both the seen and unseen features that distinguish their identities.

UbiEi-Edge: Human Big Data Decoding Using Deep Learning on Mobile Edge.

Human big data decoding is of great potential to reveal the complex patterns of human dynamics like physiological and biomechanical signals. In this study, we take special interest in brain visual dynamics, e.g., eye movement signals, and investigate how to leverage eye signal decoding to provide a voice-free communication possibility for ALS patients who lose ability to control their muscles. Due to substantial complexity of visual dynamics, we propose a deep learning framework to decode the visual dynamics when the user performs eye-writing tasks. Further, to enable real-time inference of the eye signals, we design and develop a mobile edge computing platform, called UbiEi-Edge, which can wirelessly receive the eye signals via low-energy Bluetooth, execute the deep learning algorithm, and visualize decoding results. This real word implementation, developed on an Android Phone, aims to provide real-time data streaming and automatic, real-time decoding of brain visual dynamics, thereby enabling a new paradigm for ALS patients to communicate with the external world. Our experiment has demonstrated the feasibility and effectiveness of the proposed novel mobile edge computing prototype. The study, by innovatively bridging AI, edge computing, and mobile health, will greatly advance the brain dynamics decoding-empowered human-centered computing and smart health big data applications.

An application of 8-APSK modulation for the uplink using SVD-SCMA

In recent years SCMA has been proposed as a strong candidate to perform as access technique for fifth-generation mobile communications systems; usually for the uplink, bit pairs are mapped directly to code words and MPA is used in the receiver. In this work, we proposed to encode three bits to increase the capacity of data transmission in the uplink using singular value decomposition of the initial mother constellation to generate the codewords. In the receiver, the detection and decoding of data is more complex than its predecessors with two bits, since the symbols in the IQ constellation are closer to each other, so a receiver based on neural networks is proposed to perform these tasks. The experimental performance of this proposal was evaluated by implementing a software defined radio platform. The results show that, even under unfavorable SNR conditions, it is possible to use 8-APSK as a modulation technique with low error rate in the detection and decoding of SCMA signals.

Bridge Design between AXI Lite and AHB Bus Protocol

Architecture of bridge model between AXI Lite and AHB for this paper were simulated using Synopsys VCS and DC in Verilog HDL. Bridge structure mainly comprises of arbitration techniques, control signals, multiplexing techniques for writing data signals and Decoder for reading data section. In this work, bridge model between AHB and AXI lite was simulated and characterized. The proposed model of bridge design provides efficient communication between on chip bus protocols like AXI and AHB on chip in the era of deep sub-micron technology where channel side is reduced as much as 5 nm.

Low-rank, Orthogonally Decomposable Tensor Regression With Application to Visual Stimulus Decoding of fMRI Data

We consider the problem of fitting a generalized linear model with a three-dimensional image covariate, such as one obtained by functional magnetic resonance imaging (fMRI). A major challenge for fitting such a model is that the image is a multidimensional array, called a tensor, containing tens of thousands of elements, called voxels. Because there is a parameter associated with each voxel, fitting the model entails estimating tens of thousands of parameters with a typical sample size on the order of hundreds of subjects. We propose to reduce the dimensionality of the problem by imposing a low-rank assumption on the parameter tensor, which not only reduces the number of parameters to estimate, but also exploits the implicit spatial information in fMRI data. In addition, we assume the parameter tensor is orthogonally decomposable, enabling us to penalize the so-called tensor “singular values” to obtain a low-rank estimate. We develop algorithms based on projected gradient descent and the proximal gradient method to estimate the model. In simulation, the proposed methods yielded estimates with smaller squared errors than an existing method based on alternating minimization. For visual stimulus decoding of a real fMRI dataset, the proposed methods resulted in better classification accuracies than the existing method. Visualization of the estimates revealed compact clusters of voxels with relatively large values, potentially representing brain regions relevant to the task. Supplementary files for this article are available online.

Leaping through Hurdles: Adaptability among Female Athletes

Background: Basketball is a popular team sport played mostly by men. However, female athletes are quite daunting to play. Aims: This study was designed to explore and unveil the adaptability among female athletes in playing basketball. Settings and Design: Using the qualitative and interpretive orientation research, the study employed a phenomenological approach with ladies' varsity and athletes in the Davao Oriental State College of Science and Technology. Methods and Material: The researchers utilized a validated content and conducted interview guide schedule prior to obtain the investigated data from the informants through in-depth interviews and focus group discussion techniques. Statistical analysis used: Thorough transcription, careful translation, and appropriate decoding of data were conducted before the generation of the interview results operating the NVivo 12 software. Results: The findings of the study indicated that adaptability occurred when there was a need for change. Time-management, focus, conflicts among teammates and coaches, and physical qualities were recognized as problems and challenges by the informants. Identified limitations brought negative interconnected effects from their academics, athletics, and social relationships in which athletes self-assess for obtaining poor performance. Focus and adjusting are a few of the essential elements as a resolve to overcome hurdles. Conclusions: Finally, adaptability varies depending on the perception and the range of the experience of the athletes. Few of the informants have been now not all successful in overcoming playing basketball. Moreover, the authors recommend the RID Adaptation Framework as a guide for the athletes to play and future researchers for further investigation and improvement of this context.

Motor Imagery Classification Based on a Recurrent-Convolutional Architecture to Control a Hexapod Robot

Advances in the field of Brain-Computer Interfaces (BCIs) aim, among other applications, to improve the movement capacities of people suffering from the loss of motor skills. The main challenge in this area is to achieve real-time and accurate bio-signal processing for pattern recognition, especially in Motor Imagery (MI). The significant interaction between brain signals and controllable machines requires instantaneous brain data decoding. In this study, an embedded BCI system based on fist MI signals is developed. It uses an Emotiv EPOC+ Brainwear®, an Altera SoCKit® development board, and a hexapod robot for testing locomotion imagery commands. The system is tested to detect the imagined movements of closing and opening the left and right hand to control the robot locomotion. Electroencephalogram (EEG) signals associated with the motion tasks are sensed on the human sensorimotor cortex. Next, the SoCKit processes the data to identify the commands allowing the controlled robot locomotion. The classification of MI-EEG signals from the F3, F4, FC5, and FC6 sensors is performed using a hybrid architecture of Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. This method takes advantage of the deep learning recognition model to develop a real-time embedded BCI system, where signal processing must be seamless and precise. The proposed method is evaluated using k-fold cross-validation on both created and public Scientific-Data datasets. Our dataset is comprised of 2400 trials obtained from four test subjects, lasting three seconds of closing and opening fist movement imagination. The recognition tasks reach 84.69% and 79.2% accuracy using our data and a state-of-the-art dataset, respectively. Numerical results support that the motor imagery EEG signals can be successfully applied in BCI systems to control mobile robots and related applications such as intelligent vehicles.

On Trading the Spreading Gain With the Coding Rate and Its Application to GNSS Data Component Design

The ubiquity of global navigation satellite system (GNSS)-based positioning and timing services is often frustrated by the necessity to operate in harsh environments, where the carrier-to-noise ratio is low, and hence, decoding of navigation data and even tracking of an acquired symbol are difficult. We consider the possibility of improving the decoding performance of the GNSS data component by trading the spreading gain against the coding rate. The rationale is that spreading codes can be seen as a form of repetition coding that can be (at least partially) replaced by more robust coding forms to improve robustness to (any form of) noise. This is true both for the classical additive white Gaussian noise channel case and for more realistic GNSS channel formats severely degraded by multiple access interference and near-far effects. By bringing results on finite-block-length channel capacity and coding rates from information/communication theory to the GNSS domain, we are able to establish and discuss the expected performance gap, as well as the limits of such tradeoff.

Overlapping neural representations for the position of visible and imagined objects

Humans can covertly track the position of an object, even if the object is temporarily occluded. What are the neural mechanisms underlying our capacity to track moving objects when there is no physical stimulus for the brain to track? One possibility is that the brain ‘fills-in’ information about imagined objects using internally generated representations similar to those generated by feed-forward perceptual mechanisms. Alternatively, the brain might deploy a higher order mechanism, for example using an object tracking model that integrates visual signals and motion dynamics. In the present study, we used EEG and time-resolved multivariate pattern analyses to investigate the spatial processing of visible and imagined objects. Participants tracked an object that moved in discrete steps around fixation, occupying six consecutive locations. They were asked to imagine that the object continued on the same trajectory after it disappeared and move their attention to the corresponding positions. Time-resolved decoding of EEG data revealed that the location of the visible stimuli could be decoded shortly after image onset, consistent with early retinotopic visual processes. For processing of unseen/imagined positions, the patterns of neural activity resembled stimulus-driven mid-level visual processes, but were detected earlier than perceptual mechanisms, implicating an anticipatory and more variable tracking mechanism. Encoding models revealed that spatial representations were much weaker for imagined than visible stimuli. Monitoring the position of imagined objects thus utilises similar perceptual and attentional processes as monitoring objects that are actually present, but with different temporal dynamics. These results indicate that internally generated representations rely on top-down processes, and their timing is influenced by the predictability of the stimulus.

Joint Identification and Channel Estimation for Fault Detection in Industrial IoT With Correlated Sensors

As industrial plants increase the number of wirelessly connected sensors for fault detection, a key problem is to identify and obtain data from the sensors. Due to the large number of sensors, random access protocols exploiting non-orthogonal multiple access (NOMA) are a natural approach. In this paper, we develop new algorithms based on approximate message passing for sensor identification and channel estimation accounting for correlation in the activity probability of each sensor and observations of physical variables (e.g., temperature) by the access point. These algorithms form the basis for data decoding, while also identifying faulty machines and estimating local values of the temperature, which can lead to a reduction in the amount of data required to be transmitted. Numerical results show that for an expected activity probability of 0.35, our algorithms improve the normalized mean-square error of the channel estimate by up to 5dB and reduce the rate of sensor identification errors by a factor of four.

Assessment of the Ecological Condition of Soil Cover Based on Remote Sensing Data: Erosional Aspect

Soil erosion by water is the most important global environmental problem. A modern system for assessing and monitoring soil erosional degradation should be based on the use of remote sensing data. This raises the issue of correct data decoding. The article proposes a method for visual interpretation of eroded soils according to the Sentinel image obtained in the visible range. The authors give some combinations of decoding signs to determine the manifestations of linear and surface water erosion from images. The article shows possible errors in decoding the manifestations of water erosion and gives an example of assessing the erosion of the soil cover based on the results of decoding the Sentinel-2 satellite image. Moderately and heavily eroded soils are reliably distinguished, the area of which, according to the interpretation data, was 2.4% of the area of arable land in the studied territory. In the future, the obtained sample of spectral images of eroded soils can be used to develop an automated method of interpretation based on the principle of "computer vision".

Decoding Optical Data with Machine Learning.

Optical spectroscopy and imaging techniques play important roles in many fields such as disease diagnosis, biological study, information technology, optical science, and materials science. Over the past decade, machine learning (ML) has proved promising in decoding complex data, enabling rapid and accurate analysis of optical spectra and images. This review aims to shed light on various ML algorithms for optical data analysis with a focus on their applications in a wide range of fields. The goal of this work is to sketch the validity of ML-based optical data decoding. The review concludes with an outlook on unaddressed problems and opportunities in this emerging subject that interfaces optics, data science and ML.

Quantum-Enhanced Barcode Decoding and Pattern Recognition

Quantum hypothesis testing is one of the most fundamental problems in quantum information theory, with crucial implications in areas like quantum sensing, where it has been used to prove quantum advantage in a series of binary photonic protocols, e.g., for target detection or memory cell readout. In this work, we generalize this theoretical model to the multi-partite setting of barcode decoding and pattern recognition. We start by defining a digital image as an array or grid of pixels, each pixel corresponding to an ensemble of quantum channels. Specializing each pixel to a black and white alphabet, we naturally define an optical model of barcode. In this scenario, we show that the use of quantum entangled sources, combined with suitable measurements and data processing, greatly outperforms classical coherent-state strategies for the tasks of barcode data decoding and classification of black and white patterns. Moreover, introducing relevant bounds, we show that the problem of pattern recognition is significantly simpler than barcode decoding, as long as the minimum Hamming distance between images from different classes is large enough. Finally, we theoretically demonstrate the advantage of using quantum sensors for pattern recognition with the nearest neighbor classifier, a supervised learning algorithm, and numerically verify this prediction for handwritten digit classification.

Stability Region and Delay Analysis of a SWIPT-Based Two-Way Relay Network With Opportunistic Network Coding

In this article, we consider a buffer-aided two-way relay network in which a wireless-powered relay acts as an intermediate node for exchanging the data between two sources. The relay applies a time-switching (TS) policy to switch between data decoding (DD) and energy harvesting (EH) modes. Due to the presence of data queues at the relay, the relay is capable of employing opportunistic network coding (ONC) for transmission of the packets. Based on some realistic assumptions, we derive the stability region of such a network through designing TS and ONC policies. It is proved that the stability region is characterized by the TS policy and zero-wait ONC policy. Moreover, we derive the throughput-optimal and power-optimal policies and show that these policies provide the maximum coding opportunities. Also, we derive the average delay of an applied TS-ONC policy at the relay through modeling the data queues and energy buffer of the relay by two inter-related quasi-birth-death processes. We show that although waiting in ONC does not contribute to throughput-optimal and power-optimal policies, it can reduce the average delay. Finally, through simulation, we confirm our analytical results.

Rational thoughts in neural codes.

Complex behaviors are often driven by an internal model, which integrates sensory information over time and facilitates long-term planning to reach subjective goals. A fundamental challenge in neuroscience is, How can we use behavior and neural activity to understand this internal model and its dynamic latent variables? Here we interpret behavioral data by assuming an agent behaves rationally—that is, it takes actions that optimize its subjective reward according to its understanding of the task and its relevant causal variables. We apply a method, inverse rational control (IRC), to learn an agent’s internal model and reward function by maximizing the likelihood of its measured sensory observations and actions. This thereby extracts rational and interpretable thoughts of the agent from its behavior. We also provide a framework for interpreting encoding, recoding, and decoding of neural data in light of this rational model for behavior. When applied to behavioral and neural data from simulated agents performing suboptimally on a naturalistic foraging task, this method successfully recovers their internal model and reward function, as well as the Markovian computational dynamics within the neural manifold that represent the task. This work lays a foundation for discovering how the brain represents and computes with dynamic latent variables.

Pulsewidth Modulation-Based Algorithm for Spike Phase Encoding and Decoding of Time-Dependent Analog Data.

This article proposes a new spike encoding and decoding algorithm for analog data. The algorithm uses the pulsewidth modulation principles to achieve a high reconstruction accuracy of the signal, along with a high level of data compression. Two benchmark data sets are used to illustrate the method: stock index time series and human voice data. Applications of the method for spiking neural network (SNN) modeling and neuromorphic implementations are discussed. The proposed method would allow the development of new applications of SNNs as regression techniques for predictive time-series modeling.

Polar Coding-Based Selective Mapping for PAPR Reduction Without Redundant Information Transmission

High peak-to-average power ratio (PAPR) has been major drawback of orthogonal frequency-division multiplexing (OFDM). To reduce the PAPR in the polar-coded OFDM systems, we propose a polar coding-based selective mapping (SLM) which does not transmit redundant information. Based on a set of independent frozen data blocks, we construct coset codes of a linear polar code to efficiently generate multiple OFDM signals, and selectively transmit the one with the lowest PAPR. Since the proposed SLM does not transmit either the side information or PAPR control labels, we can avoid the data rate loss. We also present an adaptive trial-based blind receiver to perform not only decoding of the unfrozen data block but also detection of the selected signal (or frozen data block). Simulation results show that the PAPR performance of the proposed SLM can approach to the optimal, and the blind receiver can support the proposed SLM with high accuracy.

PolarScout: Wi-Fi Interference-Resilient ZigBee Communication via Shell-Shaping

The prosperity of IEEE 802.11-based Wi-Fi networks aggravates cross-technology interference to IEEE 802.15.4-enabled ZigBee networks widely deployed to enable various Internet-of-Things applications. To make ZigBee communication reliable and robust even in a dense Wi-Fi environment, taming Wi-Fi interference in ZigBee networks especially from the perspective of physical layer is of paramount importance. In this context, this work takes aim to design a novel Wi-Fi interference-resilient ZigBee decoder called PolarScout, which separates collided ZigBee signal samples out of Wi-Fi interference to bootstrap ZigBee data decoding. Unlike several existing solutions which need clear signal preamble, tremendous signal strength difference between ZigBee and Wi-Fi, and Wi-Fi interference recognition in prior to ZigBee decoding, PolarScout aims at direct ZigBee decoding in a more generic and challenging case where Wi-Fi interference features a wide range of power levels and arises within a ZigBee packet at an arbitrary position. At the heart of PolarScout lies a subtle shell-shaping technique which harnesses a customized sample sequence to smooth the shell of corrupted signal samples. PolarScout then refers to the resulting shell to recover each contaminated ZigBee sample. Experimental results validate the superiority of PolarScout and its resilience to a wide range of Wi-Fi interference types.