Multichannel Data Research Articles

Inkjet-printed fully customizable and low-cost electrodes matrix for gesture recognition

The use of surface electromyography (sEMG) is rapidly spreading, from robotic prostheses and muscle computer interfaces to rehabilitation devices controlled by residual muscular activities. In this context, sEMG-based gesture recognition plays an enabling role in controlling prosthetics and devices in real-life settings. Our work aimed at developing a low-cost, print-and-play platform to acquire and analyse sEMG signals that can be arranged in a fully customized way, depending on the application and the users’ needs. We produced 8-channel sEMG matrices to measure the muscular activity of the forearm using innovative nanoparticle-based inks to print the sensors embedded into each matrix using a commercial inkjet printer. Then, we acquired the multi-channel sEMG data from 12 participants while repeatedly performing twelve standard finger movements (six extensions and six flexions). Our results showed that inkjet printing-based sEMG signals ensured significant similarity values across repetitions in every participant, a large enough difference between movements (dissimilarity index above 0.2), and an overall classification accuracy of 93–95% for flexion and extension, respectively.

Lattice-based multi-channel sampling theorem for linear canonical transform

The multi-channel sampling theorem has flourished as one of the nicest alternatives to the classical Shanon's sampling theorem which relies on non-uniform or multi-channel data acquisition. Although, a primary attempt to study the multi-channel sampling theorem for the multi-dimensional linear canonical transform (LCT) was made by D. Wei and Y. Li (2014) [16], however, it lacks the inherent multi-dimensional nature in the sense that the associated kernel is a product of N-copies of the usual one dimensional kernel, restricting the degrees of freedom only to three. The goal of this paper is to fill this gap by studying the lattice-based multi-channel sampling theorem for the multi-dimensional LCT with N(2N+1) degrees of freedom. The idea is accomplished in two steps: firstly, an elegant convolution structure is presented for constructing the multiplicative filters; secondly, the sampling lattices are constructed via general separable and non-separable matrices which often provide more flexibility and better performance in the context of multi-dimensional signal analysis. In the sequel, the Shanon's sampling theorem for the multi-dimensional LCT is deduced as a particular case of the proposed multi-channel sampling theorem. Finally, some potential applications including signal reconstruction and image super-resolution are presented to validate the obtained results.

Do not waste your electrodes—principles of optimal electrode geometry for spike sorting

Objective. This study examines how the geometrical arrangement of electrodes influences spike sorting efficiency, and attempts to formalise principles for the design of electrode systems enabling optimal spike sorting performance. Approach. The clustering performance of KlustaKwik, a popular toolbox, was evaluated using semi-artificial multi-channel data, generated from a library of real spike waveforms recorded in the CA1 region of mouse Hippocampus in vivo. Main results. Based on spike sorting results under various channel configurations and signal levels, a simple model was established to describe the efficiency of different electrode geometries. Model parameters can be inferred from existing spike waveform recordings, which allowed quantifying both the cooperative effect between channels and the noise dependence of clustering performance. Significance. Based on the model, analytical and numerical results can be derived for the optimal spacing and arrangement of electrodes for one- and two-dimensional electrode systems, targeting specific brain areas.

Determination of sky status by ground based radiometric data analysis

Objectives: To predict meteorological phenomena such as rain events from sky status during rainy and non-rainy periods. Methods: The method used here is based on brightness temperature ratio measurement at two different frequencies, namely 23.8 GHz and 30 GHz respectively, using ground based dual frequency radiometric data. The ratios of brightness temperature readings obtained by the dual-frequency radiometer at the two above mentioned frequencies are calculated for each simultaneously-taken pair of measurements. Data obtained by the authors for the year 2009 at Cachoeira Paulista in Brazil has been used for analysis. Findings: The major results obtained from the analysis of data collected over a continuous period of seven months are used to construct corresponding histograms and cumulative count graphs of brightness temperature ratios. The histograms and graphs clearly show three peak values that could be interpreted as thresholds between clear sky, cloudy sky and rainy sky conditions respectively. Novelty: The study implements detection of rain events from sky status during rainy and non-rainy periods using peak brightness temperature values obtained from graphs generated using the observation data. The outlined technique can therefore be used to clearly determine sky conditions and accurately predict rain phenomena. The ratio of brightness temperatures at the two frequencies is a unique parameter which is critical to the successful estimation of rain from sky status. The results agree well with multi-channel radiometric data obtained by other researchers at lower frequencies. Keywords: sky status; microwave; millimeter wave; dual frequency radiometer; brightness temperature; rainfall

Core Imaging Library - Part II: multichannel reconstruction for dynamic and spectral tomography.

The newly developed core imaging library (CIL) is a flexible plug and play library for tomographic imaging with a specific focus on iterative reconstruction. CIL provides building blocks for tailored regularized reconstruction algorithms and explicitly supports multichannel tomographic data. In the first part of this two-part publication, we introduced the fundamentals of CIL. This paper focuses on applications of CIL for multichannel data, e.g. dynamic and spectral. We formalize different optimization problems for colour processing, dynamic and hyperspectral tomography and demonstrate CIL’s capabilities for designing state-of-the-art reconstruction methods through case studies and code snapshots.This article is part of the theme issue ‘Synergistic tomographic image reconstruction: part 2’.

Core Imaging Library - Part I: a versatile Python framework for tomographic imaging

We present the Core Imaging Library (CIL), an open-source Python framework for tomographic imaging with particular emphasis on reconstruction of challenging datasets. Conventional filtered back-projection reconstruction tends to be insufficient for highly noisy, incomplete, non-standard or multi-channel data arising for example in dynamic, spectral and in situ tomography. CIL provides an extensive modular optimization framework for prototyping reconstruction methods including sparsity and total variation regularization, as well as tools for loading, preprocessing and visualizing tomographic data. The capabilities of CIL are demonstrated on a synchrotron example dataset and three challenging cases spanning golden-ratio neutron tomography, cone-beam X-ray laminography and positron emission tomography.This article is part of the theme issue ‘Synergistic tomographic image reconstruction: part 2’.

Investigating the pulse short electric arc milling of Ti6Al4V alloy

In this study, the machining characteristics of pulse short arc milling (SEAM) Ti6Al4V alloy were studied. In this regard, the influence of SEAM parameters on the material removal rate (MRR), relative electrode wear ratio (REWR), and surface integrity of Ti6Al4V is studied. A wide variety of processing parameters, including different voltages, frequencies, duty cycles, flushing pressures, electrode rotation speeds, and electrode feed rates, are considered in the investigation. Scanning electron microscope (SEM), energy dispersive spectrograph (EDS), and micro-hardness analysis are applied to analyze results. Moreover, a multichannel data acquisition system is used to measure gap voltage and gap current. Obtained results cover the variation of MRR, REWR, surface roughness (Ra), average resolidified layer thickness, and average heat-affected zone thickness with different processing parameters. Furthermore, the electrode surface morphology and chemical composition of the negative growth of REWR are studied. Based on the obtained results, the microstructure of the resolidified layer and heat-affected zone of the workpiece cross-section is confirmed. This study provides a basis for the high-quality pulse SEAM technology of difficult-to-machine (DTM) materials to enter the semi-finishing field.

Transient processes and cross talk in an O-band bismuth-doped fibre amplifier

This paper reports on transient processes and cross talk (cross modulation) in an O-band (1260 – 1360 nm) bismuth-doped phosphosilicate fibre amplifier. Times characterising dynamic parameters of the amplifier at a pump wavelength of 1.23 μm have been determined experimentally. Data have been obtained on the effect of the modulation frequency Ω of one of the channels in the range 10 Hz to 1 MHz on the cross-talk intensity in another channel. The data, in combination with numerical calculation results, show that the cross talk in the Bi-doped fibre amplifier is insignificant at modulation frequencies Ω above 100 kHz. Our results demonstrate that O-band Bi-doped fibre amplifiers are potentially attractive for practical application in multichannel high-speed data transmission systems.

Low Back Pain Assessment Based on Alpha Oscillation Changes in Spontaneous Electroencephalogram (EEG)

Objectively and accurately assessing pain in clinical settings is challenging. Previous studies showed that alpha oscillations of electroencephalogram data are correlated with subjective perceived pain. Based on this finding, this study is aimed at assessing chronic low back pain based on alpha oscillations. Multichannel electroencephalogram data were recorded from 27 subjects with chronic low back pain under the simple conditions of closing eyes or opening eyes. Spectral analyses were conducted to extract the alpha band responses, and the alpha powers were calculated for the two conditions, respectively. Normalized alpha power was calculated by subtracting the alpha power in the eyes-open condition from that in the eyes-closed condition. The correlation between the alpha power and the subjective pain intensity was evaluated in frontal, central, and posterior regions. The normalized alpha power in the central region was negatively correlated with the subjective pain intensity (R = −0.50, P = 0.01), with the strongest correlation occurring at the Cz electrode (R = −0.59, P = 0.04). The correlation analysis results demonstrated the possibility of using the differences of alpha spectral power between eyes-closed and eyes-open conditions as a measure for assessing chronic low back pain. The findings suggest that the normalized alpha power in the central region may be used as a measurable and quantitative indicator of chronic pain for clinical applications.

Seismic Reflection Images of Possible Mantle-Fluid Conduits and Basal Erosion in the 2011 Tohoku Earthquake Rupture Area

Multi-channel seismic reflection and sub-bottom profiling data reveal landward-dipping normal faults as potential conduits for mantle-derived fluids in the coseismic slip area of the 2011 Tohoku earthquake (Mw9.0). Normal faults below the helium isotope anomaly sites appear to develop through the forearc crust (i.e., the seafloor sedimentary section and Cretaceous basement) and to evolve to lower dip angles as extension progresses deeper, potentially extending down to the mantle wedge, despite their intermittently continuous reflections. The faults are characterized by high-amplitude, reverse-polarity reflections within the Cretaceous basement. Moreover, deep extension of the faults connecting to a low-velocity region spreading from the Cretaceous basement into the mantle wedge across the forearc Moho suggests that the faults are overpressured by local filling with mantle-derived fluids. The locations of the normal faults are roughly consistent with aftershocks of the 2011 Tohoku earthquake, which show normal-faulting focal mechanisms. The 2011 Tohoku mainshock and subsequent aftershocks can lead the pre-existing normal faults to be reactive and more permeable so that locally trapped mantle fluids can migrate up to the seafloor through fault fracture zones. The reactivated normal faults may be an indicator of shallow coseismic slip to the trench. Locally elevated fluid pressures can decrease the effective normal stress for the fault plane, facilitating easier slip along the fault and local tsunami. The landward-dipping normal faults developing from the seafloor down into the Cretaceous basement are predominant in the middle slope region of the forearc. A possible shear zone with high-amplitude, reverse-polarity reflections above the plate interface, which is almost localized to the middle slope region, suggests more intense basal erosion of the overlying plate in that region.

Temporal convolutional network with soft thresholding and attention mechanism for machinery prognostics

Remaining useful life (RUL) prediction is a challenging task for prognostics and health management (PHM). Due to the complexity physics involved for precisely modeling the machine degradation process, learning-based data-driven methods, which learn the degradation pattern solely from the historical data without referring to physical models, have become promising alternatives to model-based prognostic methods. In this paper, a new temporal convolutional neural network (TCN) with soft threshold and attention mechanism is proposed for machinery prognostics. Multi-channel sensor data are directly used as inputs to the prognostic network without feature extraction as a pre-processing step. A soft thresholding mechanism is embedded in the network, serving as a flexible activation function for certain layers to preserve useful features. The threshold value is adaptively learned by a subnetwork trained with the attention mechanism instead of assigning a deterministic value to the threshold. As a result, each feature map is assigned a customized threshold value such that the network training process can focus on features that are more critical to RUL prediction. To verify the generalization ability of the proposed method, three benchmark datasets related to rolling bearings and cutting tools are tested, and the performance of the developed method is compared with several state-of-the-art prognostic approaches. The results show that for all the three case studies, the developed method has produced accurate RUL prediction with good robustness and generalization ability.

Multi-Channel Data Stream Transmission Method of Internet of Things in Power Systems (IOTIPS) Based on Big Data Analysis

To solve the problems of frequent network link jitter and high bit error rate is the development direction of power grid communication technology. Therefore, a multi-channel data stream transmission method of Internet of things in power systems based on big data analysis is proposed. The data stream matching method based on big data stability mechanism is constructed by using data stream matching method to match the data stream to be transmitted and improve the anti-noise performance of the transmission process; the multichannel model of data stream transmission is constructed, and the matched data stream is transmitted by the multi-channel model; the big data analysis technology is used to process the data stream transmission process and improve the transmission performance of the model; the adaptive multi-channel equalization control method of sampling decision is used to realize the equalization design of data stream transmission channel, optimize the model transmission process, and reduce the bit error rate of transmission. Experimental results show that this method has better channel equalization performance; the link jitter frequency of this method is low, and it has better transmission stability; the lowest bit error rate can reach 0%, and the reliability of data stream transmission is high.

Design and characterization of Kerberos: a 48-channel analog pulse processing and data acquisition platform

A multi-channel data processing and acquisition system based on an analog ASIC (SFERA) has been designed and realized. The platform, called Kerberos, is suitable for the readout of large arrays of Silicon Drift Detector (SDD) for X-ray, γ-ray and electron spectroscopy applications. Each one of its 48 inputs is equipped with a 9th order semi-Gaussian shaping amplifier with programmable peaking time (0.5, 1, 2, 3, 4 and 6 ). The pulse amplitudes are multiplexed into three 16-bit high linearity SAR ADCs and digitized into an Artix-7 FPGA module. Kerberos will be used for the characterization of monolithic SDD matrices for the TRISTAN project. Many different readout strategies can be selected on Kerberos Graphic User Interface: for TRISTAN it has been decided to use a full detector readout strategy, with maximum input throughput of about 166 kcount/s. This work presents a full characterization of this scalable platform and its use with several detectors types (SDD, micro-strips) in X-ray, gamma and beta spectroscopy.

Intelligent fault recognition framework by using deep reinforcement learning with one dimension convolution and improved actor-critic algorithm

The quality of fault recognition part is one of the key factors affecting the efficiency of intelligent manufacturing. Many excellent achievements in deep learning (DL) have been realized recently as methods of fault recognition. However, DL models have inherent shortcomings. In particular, the phenomenon of over-fitting or degradation suggests that such an intelligent algorithm cannot fully use its feature perception ability. Researchers have mainly adapted the network architecture for fault diagnosis, but the above limitations are not taken into account. In this study, we propose a novel deep reinforcement learning method that combines the perception of DL with the decision-making ability of reinforcement learning. This method enhances the classification accuracy of the DL module to autonomously learn much more knowledge hidden in raw data. The proposed method based on the convolutional neural network (CNN) also adopts an improved actor-critic algorithm for fault recognition. The important parts in standard actor-critic algorithm, such as environment, neural network, reward, and loss functions, have been fully considered in improved actor-critic algorithm. Additionally, to fully distinguish compound faults under heavy background noise, multi-channel signals are first stacked synchronously and then input into the model in the end-to-end training mode. The diagnostic results on the compound fault of the bearing and tool in the machine tool experimental system show that compared with other methods, the proposed network structure has more accurate results. These findings demonstrate that under the guidance of the improved actor-critic algorithm and processing method for multi-channel data, the proposed method thus has stronger exploration performance.

Integrated analysis of magnetic, gravity and multichannel seismic reflection data along a transect southeast of Sri Lanka, Bay of Bengal: New constraints

Integrated analysis of high quality magnetic, gravity, bathymetry and multichannel seismic reflection data along a 1300 km long transect SK101 between the DSDP218/IODP1455 and ODP718 sites reveals the structure and evolutionary history of the underlying sedimentary load and basement. The smooth southward deepening bathymetry is pierced by several topographic highs of varying dimensions. Gravity does not mimic the bathymetry in general, and is as low as −75 mgal over a deep basement region. Magnetic anomalies depict significant variation along the transect, with chron A34 identified at its either end.Multichannel seismic reflection data reveal sediment thickness up to 3 s two-way travel time (TWT), but minimal (<0.1 s TWT) over protruding basement highs. Intense folding/faulting of sediment and basement producing low amplitude, long wavelength topographic highs is observed towards south along the transect. Flower structures seen within the sedimentary load suggest intermittent strike-slip faulting. The basement highs lying at the southern end of NW-SE trending fracture zones may have formed as the spreading center rotated clockwise during the Middle Cretaceous period. An arcuate gravity low is due to the presence of a deep and old basement, overlain by thick sediments. A prominent step in the basement coinciding with an abrupt change in magnetic anomaly pattern probably indicates the concurrent presence of oceanic crust of different ages, as a consequence of the Middle Cretaceous major plate reorganization.

Recency of Faulting and Subsurface Architecture of the San Diego Bay Pull-Apart Basin, California, USA

In Southern California, plate boundary motion between the North American and Pacific plates is distributed across several sub-parallel fault systems. The offshore faults of the California Continental Borderland (CCB) are thought to accommodate ∼10–15% of the total plate boundary motion, but the exact distribution of slip and the mechanics of slip partitioning remain uncertain. The Newport-Inglewood-Rose Canyon fault is the easternmost fault within the CCB whose southern segment splays out into a complex network of faults beneath San Diego Bay. A pull-apart basin model between the Rose Canyon and the offshore Descanso fault has been used to explain prominent fault orientations and subsidence beneath San Diego Bay; however, this model does not account for faults in the southern portion of the bay or faulting east of the bay. To investigate the characteristics of faulting and stratigraphic architecture beneath San Diego Bay, we combined a suite of reprocessed legacy airgun multi-channel seismic profiles and high-resolution Chirp data, with age and lithology controls from geotechnical boreholes and shallow sub-surface vibracores. This combined dataset is used to create gridded horizon surfaces, fault maps, and perform a kinematic fault analysis. The structure beneath San Diego Bay is dominated by down-to-the-east motion on normal faults that can be separated into two distinct groups. The strikes of these two fault groups can be explained with a double pull-apart basin model for San Diego Bay. In our conceptual model, the western portion of San Diego Bay is controlled by a right-step between the Rose Canyon and Descanso faults, which matches both observations and predictions from laboratory models. The eastern portion of San Diego Bay appears to be controlled by an inferred step-over between the Rose Canyon and San Miguel-Vallecitos faults and displays distinct fault strike orientations, which kinematic analysis indicates should have a significant component of strike-slip partitioning that is not detectable in the seismic data. The potential of a Rose Canyon-San Miguel-Vallecitos fault connection would effectively cut the stepover distance in half and have important implications for the seismic hazard of the San Diego-Tijuana metropolitan area (population ∼3 million people).

Air quality monitoring platform with multiple data source support

ABSTRACT Worldwide, various air quality monitoring systems collect a huge amount of relevant data for the local air quality. The aggregation and further analysis of such data allow to better understand air quality-related problems, to find anomalies and to identify new trends and correlations. In order to exploit the full potential of data, the analysis should utilize integrated datasets from multiple sources. However, most of the current air quality platforms support data acquisition only from a single source. At the same time, the diversity of air quality data and the specifics of time series data itself, bring additional challenges related to data pre-processing, analytics and visualization. The development of more comprehensive services, which fully utilize the variability of air quality data, requires a harmonized, multi-channel data acquisition and its seamless aggregation. This paper addresses these challenges and needs by proposing an air quality platform for monitoring, collecting and aggregating data from multiple sources and in different formats. The platform is able to compare measurements of air quality parameters from different data sources. Furthermore, it supports monitoring of sensors themselves, validation of their measurements and ignoring those in which data anomalies are spotted. The paper presents a detailed overview of the platform design, including its multi-layered architecture, data model, functional design and according logical packages. The key implementation points are also introduced, such as the three data collectors realized for the integration of external APIs, provided by Luftdaten.info, WAQI.info and Openweathermap.org. The extensive validation of the platform that is also presented in the paper employs vast amount of diverse data collected from two cities through over 1.2 million measurements, including 3.5 million data points obtained by 880 sensors from 500 locations.

A generalised eigenvalue reweighting covariance matrix estimation algorithm for airborne STAP radar in complex environment

To improve the space-time adaptive processing (STAP) performance of airborne radar in complex environment, a generalised eigenvalue reweighting covariance matrix estimation algorithm called GERCM is proposed here. First, the interference plus noise (IPN) covariance matrix of cell under test (CUT) data is estimated by the selected target-free training samples around the CUT with the sample covariance matrix method. Then, with the component decompositions of the selected training samples and the assumption of approximately equal subspace, the IPN covariance matrix of CUT data is reformulated by the eigenvector matrix, eigenvalue matrix, and the eigenvalue reweighting vector. Subsequently, based on the modified covariance matching estimation criterion, the eigenvalue reweighting vector is estimated by solving the redesigned convex optimisation problem with the Lagrange dual method. Finally, the STAP weight vector is calculated to process the CUT data. The proposed algorithm can obtain a relatively accurate IPN covariance matrix of CUT data by sufficiently utilising the non-homogeneous training samples and can effectively protect the moving targets in CUT data, which can be applied to airborne radar with arbitrary array structure and antenna configuration. Simulation results and performance analyses based on the multi-channel airborne radar measurement data demonstrate the effectiveness of the proposed GERCM algorithm.

Modeling and analysis of priority andrange‐based‐deterministic and synchronous multichannel extension‐guaranteed time slot allocation inIEEE802.15.4emedium access controlprotocol

AbstractInternet of Things (IoT) enabled technology in the current applications such as smart grids, smart cities, and smart health sectors utilize wireless sensor networks (WSN) to establish stable and reliable communication between the nodes. Moreover, the primary requirement of WSN in IoT applications is to provide low‐rate data communication. The IEEE 802.15.4e standard‐based medium access control (MAC) protocol is found suitable for low‐rate data communication in WSN. The IEEE 802.15.4e standard adopts a deterministic and synchronous multichannel extension (DSME) MAC protocol which employs a multisuperframe structure with multichannel data transmission during the contention free period. However, the guaranteed time slot (GTS) allocation process in DSME employed by the standard may not be suitable for an adaptive traffic network scenario that has a direct impact on the throughput and bandwidth utilization. In this article, we develop a priority and range‐based DSME‐GTS allocation algorithm based on the priorities of end nodes and range estimation. Furthermore, the proposed algorithm is developed for the two types of network scenarios encountered in a star topology‐enabled WSN. In scenario‐1, the slots allocation process is carried out based on the priorities of end nodes. Next, in scenario‐2, the slots allocation process is carried out based on the range estimation of end nodes. Detailed mathematical modeling of the proposed methodology is provided for the various performance metrics. The model is validated using the MATLAB 2017a programming tool. Simulation results show that the proposed methodology outperforms the existing DSME‐GTS approaches in terms of performance metrics such as packet delivery ratio, end‐to‐end delay, packet loss ratio, throughput, delay, bandwidth utilization, and the energy consumption.

BeamLearning: An end-to-end deep learning approach for the angular localization of sound sources using raw multichannel acoustic pressure data.

Sound source localization using multichannel signal processing has been a subject of active research for decades. In recent years, the use of deep learning in audio signal processing has significantly improved the performances for machine hearing. This has motivated the scientific community to also develop machine learning strategies for source localization applications. This paper presents BeamLearning, a multiresolution deep learning approach that allows the encoding of relevant information contained in unprocessed time-domain acoustic signals captured by microphone arrays. The use of raw data aims at avoiding the simplifying hypothesis that most traditional model-based localization methods rely on. Benefits of its use are shown for real-time sound source two-dimensional localization tasks in reverberating and noisy environments. Since supervised machine learning approaches require large-sized, physically realistic, precisely labelled datasets, a fast graphics processing unit-based computation of room impulse responses was developed using fractional delays for image source models. A thorough analysis of the network representation and extensive performance tests are carried out using the BeamLearning network with synthetic and experimental datasets. Obtained results demonstrate that the BeamLearning approach significantly outperforms the wideband MUSIC and steered response power-phase transform methods in terms of localization accuracy and computational efficiency in the presence of heavy measurement noise and reverberation.