Multi-channel Waveforms Research Articles

A high-precision fusion bathymetry of multi-channel waveform curvature for bathymetric LiDAR systems

Airborne LiDAR bathymetry (ALB) system is an attractive and efficient method for nearshore bathymetry and underwater topography mapping. To ensure and improve the measurement accuracy and reliability in various water environments, different receivers using a segmented field of view (FOV) have been designed and are implemented in ALB. These are used to obtain various echo data of multiple channels and perform bathymetry at various water depths. However, in echo waveform processing, detailed information about the water body described by the different echo waveforms is lacking, making it difficult to overcome severe waveform superposition in extremely shallow areas or to separate the weak echo of the water bottom from the noise in extremely deep areas. Therefore, we employed a novel method of multi-channel waveform fusion bathymetry (MWFB) to achieve information complementarity, gain multi-channel waveforms, and detect the robustness of water and bottom echo signals. This method uses an adaptive signal waveform extraction of a multichannel fusion mechanism to eliminate systematic and random noise, and the waveform curvature is used to reconstruct the energy–waveform curves. Iterative decomposition based on waveform curvature and energy curves was conducted to improve the correctness of waveform decomposition and the reliability of waveform components. Furthermore, the water surface and bottom peaks were detected based on the multichannel waveform curvature, which enabled acquisition of the accurate temporal positions of the peaks and achieve high-accuracy bathymetry. According to the experimental results and a comparison with reference datasets, the highest bathymetric accuracy of the MWFB method reached an RMSE of 0.22. For the different study areas, the bathymetric accuracy of the slope reached 95%, 92%, and 97% at Ganquan Island, Lingyang Reef, and Bei Island, respectively. Furthermore, the bathymetric point numbers were effectively increased and bathymetric accuracy was also improved in shallow and deep water, which illustrates the superior bathymetric performance of the MWFB method and its ability to provide a high-efficiency waveform dataset through multi-channel data fusion. The novel LiDAR bathymetric method proposed in this study can effectively achieve high-accuracy nearshore bathymetry and seafloor topographical mapping.

Microseismic event waveform classification using CNN-based transfer learning models

The efficient processing of large amounts of data collected by the microseismic monitoring system (MMS), especially the rapid identification of microseismic events in explosions and noise, is essential for mine disaster prevention. Currently, this work is primarily performed by skilled technicians, which results in severe workloads and inefficiency. In this paper, CNN-based transfer learning combined with computer vision technology was used to achieve automatic recognition and classification of multi-channel microseismic signal waveforms. First, data collected by MMS was generated into 6-channel original waveforms based on events. After that, sample data sets of microseismic events, blasts, drillings, and noises were established through manual identification. These datasets were split into training sets and test sets according to a certain proportion, and transfer learning was performed on AlexNet, GoogLeNet, and ResNet50 pre-training network models, respectively. After training and tuning, optimal models were retained and compared with support vector machine classification. Results show that transfer learning models perform well on different test sets. Overall, GoogLeNet performed best, with a recognition accuracy of 99.8%. Finally, the possible effects of the number of training sets and the imbalance of different types of sample data on the accuracy and effectiveness of classification models were discussed.

MVCNN: A Deep Learning-Based Ocean–Land Waveform Classification Network for Single-Wavelength LiDAR Bathymetry

Ocean–land waveform classification (OLWC) is crucial in airborne LiDAR bathymetry (ALB) data processing and can be used for ocean–land discrimination and waterline extraction. However, the accuracy of OLWC for single-wavelength ALB systems is low given the nature of the green laser waveform in complex environments. Thus, in this article, a deep learning-based OLWC method called the multichannel voting convolutional neural network (MVCNN) is proposed based on the comprehensive utilization of multichannel green laser waveforms. First, multiple green laser waveforms collected in deep and shallow channels are input into a multichannel input module. Second, a one-dimensional (1-D) convolutional neural network (CNN) structure is proposed to handle each green channel waveform. Finally, a multichannel voting module is introduced to perform majority voting on the predicted categories derived by each 1-D CNN model and output the final waveform category (i.e., ocean or land waveforms). The proposed MVCNN is evaluated using the raw green laser waveforms collected by Optech coastal zone mapping and imaging LiDAR (CZMIL). Results show that the overall accuracy, kappa coefficient, and standard deviation of the overall accuracy for the OLWC utilizing green laser waveforms based on MVCNN can reach 99.41%, 0.9800, and 0.03%, respectively. Results further show that the classification accuracy of the MVCNN is improved gradually with the increase in the number of laser channels. The multichannel voting module can select the correct waveform category from the deep and shallow channels. The proposed MVCNN is highly accurate and robust, and it is slightly affected by aquaculture rafts and the merging effect of green laser waveform in very shallow waters. Thus, the use of MVCNN in OLWC for single-wavelength ALB systems is recommended. In addition, this article explores the relationships between green deep and shallow channel waveforms based on the analysis of CZMIL waveform data.

Cortical Pyramidal and Parvalbumin Cells Exhibit Distinct Spatiotemporal Extracellular Electric Potentials.

Brain circuits are composed of diverse cell types with distinct morphologies, connections, and distributions of ion channels. Modeling suggests that the spatial distribution of the extracellular voltage during a spike depends on cellular morphology, connectivity, and identity. However, experimental evidence from the intact brain is lacking. Here, we combined high-density recordings from hippocampal region CA1 and neocortex of freely moving mice with optogenetic tagging of parvalbumin-immunoreactive (PV) cells. We used ground truth tagging of the recorded pyramidal cells (PYR) and PV cells to construct binary classification models. Features derived from single-channel waveforms or from spike timing alone allowed near-perfect classification of PYR and PV cells. To determine whether there is unique information in the spatial distribution of the extracellular potentials, we removed all single-channel waveform information from the multichannel waveforms using an event-based delta-transformation. We found that spatiotemporal features derived from the transformed waveforms yield accurate classification. The extracellular analog of the spatial distribution of the initial depolarization phase provided the highest contribution to the spatially based prediction. Compared with PV cell spikes, PYR spikes exhibited higher spatial synchrony at the beginning of the extracellular spike and lower synchrony at the trough. The successful classification of PYR and PV cells based on purely spatial features provides direct experimental evidence that spikes of distinct cell types are associated with distinct spatial distributions of extracellular potentials.

An effective method for laboratory acoustic emission detection and location using template matching

In this paper, a template matching and location method, which has been rapidly adopted in microseismic research in recent years, is applied to laboratory acoustic emission (AE) monitoring. First, we used traditional methods to detect P-wave first motions and locate AE hypocenters in three dimensions. In addition, we selected events located with sufficient accuracy (normally corresponding AE events of relatively larger energy, showing clear P-wave first motion and a higher signal-to-noise ratio in most channels) as template events. Then, the template events were used to scan and match other poorly located events in triggered event records or weak events in continuous records. Through cross-correlation of the multi-channel waveforms between the template and the event to be detected, the weak signal was detected and located using a grid-searching algorithm (with the grid centered at the template hypocenter). In order to examine the performance of the approach, we calibrated the proposed method using experimental data of different rocks and different types of experiments. The results show that the proposed method can significantly improve the detection capability and location accuracy, and can be applied to various laboratory and in situ experiments, which use multi-channel AE monitoring with waveforms recorded in either triggering or continuous mode.

Multichannel Speech Enhancement in Vehicle Environment Based on Interchannel Attention Mechanism

Speech enhancement in a vehicle environment remains a challenging task for the complex noise. The paper presents a feature extraction method that we use interchannel attention mechanism frame by frame for learning spatial features directly from the multichannel speech waveforms. The spatial features of the individual signals learned through the proposed method are provided as an input so that the two-stage BiLSTM network is trained to perform adaptive spatial filtering as time-domain filters spanning signal channels. The two-stage BiLSTM network is capable of local and global features extracting and reaches competitive results. Using scenarios and data based on car cockpit simulations, in contrast to other methods that extract the feature from multichannel data, the results show the proposed method has a significant performance in terms of all SDR, SI-SNR, PESQ, and STOI.

Characteristics of Cases with Poor Transcranial Motor-evoked Potentials Baseline Waveform Derivation in Spine Surgery: A Prospective Multicenter Study of the Monitoring Committee of the Japanese Society for Spine Surgery and Related Research.

Prospective multicenter study. The purpose of the study is to examine cases with poor baseline waveform derivation for all muscles in multichannel monitoring of transcranial motor-evoked potentials (Tc-MEPs) in spine surgery. Intraoperative neuromonitoring (IONM) is useful for identifying neurologic deterioration during spinal surgery. Tc-MEPs are widely used for IONM, but some cases have poor waveform derivation, even in multichannel Tc-MEP monitoring. The subjects were 3625 patients (mean age 60.1 years, range 4-95; 1886 females, 1739 males) who underwent Tc-MEP monitoring during spinal surgery at 16 spine centers between April 2017 and March 2020. Baseline Tc-MEPs were recorded from the deltoid, abductor pollicis brevis, adductor longus, quadriceps femoris, hamstrings, tibialis anterior, gastrocnemius, and abductor hallucis (AH) muscles after surgical exposure of the spine. The 3625 cases included cervical, thoracic, and lumbar lesions (50%, 33% and 17%, respectively) and had preoperative motor status of no motor deficit, and motor deficit with manual muscle testing (MMT) ≥3 and MMT <3 (70%, 24% and 6%, respectively). High-risk surgery was performed in 1540 cases (43%). There were 73 cases with poor baseline waveform derivation (2%), and this was significantly associated with higher body weight, body mass index, thoracic lesions, motor deficit of MMT <3, high-risk surgery (42/1540 [2.7%] vs. 31/2085 [1.5%], P < 0.05), and surgery for ossification of the posterior longitudinal ligament (OPLL). Intraoperative waveform derivation occurred in 25 poor derivation cases (34%) and the AH had the highest rate. The rate of poor baseline waveform derivation in spine surgery was 2% in our series. This was significantly more likely in high-risk surgery for thoracic lesions and OPLL, and in cases with preoperative severe motor deficit. In such cases, it may be preferable to use multiple modalities for IONM to derive multichannel waveforms from distal limb muscles, including the AH.Level of Evidence: 3.

Application of Impact-Echo Method to 3D SIBIE Procedure for Damage Detection in Concrete

In this study, to visualize damage and defects, such as cracks and voids in concrete, the SIBIE (stack imaging of spectral amplitudes based on impact echo) procedure is applied and numerically improved to construct a three-dimensional (3D) model of elastic wave propagation behavior. A unit of arrayed accelerometers is installed to detect multi-channel signal waveforms in the frequency domain. The resonant frequencies due to reflections at each node in 3D lattice nodes are computed by using the distances from elastic wave input to multiple output locations. The amplitudes corresponding to the resonant frequencies in the spectrum are summed up as the reflection intensity of elastic wave at each node. The reflection intensity distribution is visualized finally in the targeted area three-dimensionally. Case studies are carried out on the proposal of the improved 3D-SIBIE procedure, applied to a concrete specimen with simulated-damage as well as in-situ highway RC (Reinforced Concrete) slabs in service. As for the signal detection, a non-contact elastic wave detecting system using a laser doppler vibrometer is also introduced to consider and validate the promising remote sensing and inspection technique for damage evaluation in concrete with the 3D SIBIE procedure.

Method of inter‐turn fault detection for next‐generation smart transformers based on deep learning algorithm

In this study, an inter-turn fault diagnosis method is proposed based on deep learning algorithm. 12-channel data is obtained in MATLAB/Simulink as the time-domain monitoring signals and labelled with 16 different fault tags, including both primary and secondary voltage and current waveforms. An auto-encoder is presented to classify the fault type of the abundant and comprehensive fault waveforms. The overall waveforms compose a two-dimension data matrix and the auto-encoder is trained to extract the features in the multi-channel waveforms. The selected features are convoluted with the original data, generating a one-dimensional vector as the input to the softmax classifier. Variables such as type, activation function and depth of auto-encoder, sparsity of sparse auto-encoder, number of features and pooling strategies are studied, which gives an intuitive process to train a proper learning model. The overall recognition accuracy reaches 99.5%. Signal characteristics such as channel selection, time span of the input signal and signal sampling frequency are studied to find the best solution for the inter-turn fault detection of the three-phase transformer. The proposed method under deep learning framework increases the accuracy and robustness in transformer fault diagnosis, indicating its potential and prospect in the next-generation smart transformers.

A deep learning approach to source localization and seabed classification using pressure time-series from a vertical array

Source localization in a shallow ocean environment has historically been done using optimization techniques such as matched-field processing. However, such optimizations depend on the parameterization of the ocean environment. Due to the complexity of this physical system, some researchers are currently applying machine and deep learning techniques to source localization problems. We propose a convolution neural network (CNN) to better predict the source localization and seabed classification simultaneously using pressure time series waveforms from a vertical line array. Building on research using a CNN to classify the source locale and seabed type using waveforms from only one hydrophone, the method has been extended to a 16-element vertical line array. The additional hydrophones add more physical information from the system as well as simply more features for a CNN to learn source range, depth, and seabed type. The synthetic data were generated using a range independent normal-mode model for multiple ocean environments. Modifications to the CNN are made to exploit the multi-channel waveforms. Ocean acoustic applications require this accurate classification of source locale and seabed environment. In future work we will extend our technique and CNN model to work with real world data.

Phase Modulated Radio-Over-Fiber for Efficient 5G Fronthaul Uplink

Analog radio-over-fiber technology is gaining interest as a potential candidate for radio signal transport over the future fronthaul section of the 5th generation (and beyond) radio access network. In this paper, we propose a radio-over-fiber fronthaul with intensity modulation in the downlink and phase modulation with interferometric detection in the uplink, for simplified and power efficient remote units. We conduct an experimental investigation and verification of theoretical and simulation models of the performance of the phase-modulated uplink and demonstrate the ability of such an architecture to transport single-channel and multi-channel 5G-type radio waveforms. Experimentally verified data rates of 4.3 Gbps and simulation-based predictions, using a well matched-to-measurements model of the uplink, of 12.4 Gbps are presented, with error-vector magnitude performance well within relevant standard specifications for 64-QAM.

Automatic recognition and classification of multi-channel microseismic waveform based on DCNN and SVM

The recognition and classification of the multi-channel microseismic waveform are important for mine hazard prediction. It is widely used to design the corresponding waveform feature for recognition and classification of the microseismic waveform by hand. The process of designing features manually is arduous and the results of recognition and classification are not ideal. In this paper, we propose a method combining Deep Convolutional Neural Networks (DCNN) with Support Vector Machine (SVM) for identifying the microseismic waveform automatically. We constructed a DCNN structure to train the optimal weight model named the DCNN-Model. The DCNN-Model is used as a tool for extracting features from multi-channel waveforms. After combining the extracted features, we used SVM to classify multi-channel waveforms. We compared the outputs of other classifiers, such as Random Forest and k-Nearest Neighbor (KNN). To extend the dataset of DCNN training and extract the essential characteristics of waveform images more accurately, we pre-process the raw data by means of filtering and de-nosing. The experiment shows that the recognition and classification method is of practical value, and the accuracy rate can reach as high as 98.18%.

Automatic classification of multi-channel microseismic waveform based on DCNN-SPP

The recognition of multi-channel microseismic waveform is important for hazard prediction. This method is widely used to design the corresponding waveform feature of microseismic waveform recognition by hand. However, manual design process is challenging with unsatisfactory classification effect. Thus, this study presents a method with deep convolutional neural network and Spatial Pyramid Pooling (DCNN-SPP) to identify automatically microseismic waveform. We constructed DCNN-SPP to classify multi-channel microseismic waveform. Given the inconsistent channel number of each receiving microseismic signal, we used spatial pyramid pooling to fix the features of multi-channel waveforms. This signal was extracted from the last convolution layer, which can achieve end-to-end training. To extend the dataset, we pre-process raw data by filtering and de-nosing. The experiment shows the practical value of the recognition method with an accuracy rate of up to 91.13%.

Adaptive beamforming for uniformly-spaced linear hydrophone array using temporal convolutional neural networks

In oceanic remote sensing, large discrete linear hydrophone arrays are usually utilized to enhance the signal-to-noise ratio (SNR) by means of beamforming that attenuates noise coming from the directions outside the target direction. Although the widely adopted delay-and-sum (DS) and filter-and-sum (FS) beamforming techniques can improve the performance of linear hydrophone arrays in some scenarios, both DS and FS methods have limited adaptability to the changing oceanic environments especially with spatially correlated noise. Moreover, these beamforming techniques aims to optimize the SNR, which is not completely consistent with some objectives such as target recognition. In this work, a neural network adaptive beamforming framework for an uniformly-spaced linear hydrophone array is proposed to make use of the large-scale array signals and address the above issues. In particular, an architecture consists of temporal convolutional neural networks is designed to predict the beamforming filter coefficients in time domain by taking the raw multi-channel waveforms of sound pressure as the input. The filter prediction networks are also jointly trained with a convolutional neural network (CNN) based classification model with the purpose of increasing the target recognition accuracy. The proposed approach is validated by experiments carried out in the south coastal waters of China.

Source localization using deep neural networks in a shallow water environment.

Deep neural networks (DNNs) are advantageous for representing complex nonlinear relationships. This paper applies DNNs to source localization in a shallow water environment. Two methods are proposed to estimate the range and depth of a broadband source through different neural network architectures. The first adopts the classical two-stage scheme, in which feature extraction and DNN analysis are independent steps. The eigenvectors associated with the modal signal space are extracted as the input feature. Then, the time delay neural network is exploited to model the long term feature representation and constructs the regression model. The second concerns a convolutional neural network-feed-forward neural network (CNN-FNN) architecture, which trains the network directly by taking the raw multi-channel waveforms as input. The CNNs are expected to perform spatial filtering for multi-channel signals, in an operation analogous to time domain filters. The outputs of CNNs are summed as the input to FNN. Several experiments are conducted on the simulated and experimental data to evaluate the performance of the proposed methods. The results demonstrate that DNNs are effective for source localization in complex and varied water environments, especially when there is little precise environmental information.

NOVEL METHOD TO FIND THE PARAMETER FOR NOISE REMOVAL FROM MULTI-CHANNEL ECG WAVEFORMS

In general, electrocardiogram (ECG) waveforms are aby noise and artifacts and it is essential to remove the noise in order to support any decision making for specialist. It is v ery difficult to remove the noise from 12 channel E CG waveforms using standard noise removal methodologies. Removal of the noise f rom ECG waveforms is majorly classified into two ty pes in signal processing namely Digital filters and Analog filters. Digital filters are more accurate than analog filters becau se analog filters introduce nonlinear phase shift. Most advanced research digit al filters are FIR and IIR.FIR filters are stable a s they have non-recursive structure. They give the exact linear phase and eff iciently realizable in hardware. The filter respons e is finite duration. Thus noise removal using FIR digital filter is better option iwith IIR digital filter. But it is very difficult to find the cut-off frequency parameter for dynamic multi-channel ECG waveforms u sing existing traditional methods. So, in this research, newly introduced Multi-Swarm Optimization (MSO) methodology for automatically id entifying the cut-off frequency parameter of multichannel ECG waveforms f or low-pass filtering is inspecting. Generally, the spectrums of the ECG waveforms are extracted from four classes: normal srhythm, atria fibrillation, arrhythmia and sup raventricular. Baseline wander is removed using the Moving Median Filter. A datase t of the extracted features of the ECG spectrums is used to train the MSO. The performance of the MSO with various parameters is i nvestigated. Finally, the MSO-identified cut-off fr equency parameter, it’s applied to a Finite Impulse Response (FIR) filter. The resu lting signal is evaluated against the original clea n and conventional filtered ECG signal.

Flexible multielectrodes can resolve multiple muscles in an insect appendage

Research into the neuromechanical basis of behavior, either in biomechanics, neuroethology, or neuroscience, is frequently limited by methods of data collection. Two of the most pressing needs are for methods with which to (1) record from multiple neurons or muscles simultaneously and (2) perform this recording in intact, behaving animals. In this paper we present the fabrication and testing of flexible multielectrode arrays (fMEAs) that move us significantly towards these goals. The fMEAs were used to record the activity of several distinct units in the coxa of the cockroach Blaberus discoidalis. The devices fabricated here address the first goal in two ways: (1) their flexibility allows them to be inserted into an animal and guided through internal tissues in order to access distinct groups of neurons and muscles and (2) their recording site geometry has been tuned to suit the anatomy under study, yielding multichannel spike waveforms that are easily separable under conditions of spike overlap. The flexible nature of the devices simultaneously addresses the second goal, in that it is less likely to interfere with the natural movement of the animal.

A micromachined silicon multielectrode for multiunit recording

A 16-channel multielectrode was used to record propagating action potentials from multiple units in the ventral nerve cord of the cricket Gryllus bimaculatus. The multielectrode was fabricated using photolithographic and bulk silicon etching techniques. The fabrication differs from standard methods in its use of deep reactive ion etching (DRIE) to form the bulk electrode structure. This technique enables the fabrication of relatively thick (>100 μm), rigid structures whose top surface can have any form of thin film electronics. The multielectrode tested in this paper consists of 16 narrow silicon bridges, 150 μm wide and 350 μm tall, spaced evenly over a centimeter, with passive rectangular gold recording sites on the top surface. The nerve cord was placed perpendicularly across the bridges. In this geometry, the nerve spans a 350 μm deep, 450 μm wide trench between each recording site, permitting adequate isolation of recording sites from each other and a platinum ground plane. Spike templates for eight neurons were formed using principle component analysis and clustering of the concatenated multichannel waveforms. Clean templates were generated from a 40 s recording of stimulus evoked activity. Conduction velocities ranged from 2.59±0.05 to 4.99±0.12 m/s. Two limitations of extracellular electrode arrays are the resolution of overlapping spikes and relation of discriminated units to known anatomy. The high density, precise positioning, and controlled impedance of recording sites achievable in microfabricated devices such as this one will aid in overcoming these limitations. The rigid devices fabricated using this process offer stable positioning of recording sites over relatively large distances (several millimeters) and are suitable for clamping or squeezing of nerve cords.

Similarities in the pattern of regional brain dysfunction in negative schizophrenia and unipolar depression: a single photon emission-computed tomography and auditory evoked potentials study

1. Negative schizophrenic and unipolar depressive patients were clinically assessed. In addition to this SANS and HRSD tests were administered. 2. SPECT and AEP measurements were provided. SPECT resulted in quantified brain blood perfusion, by means of average “count/pixel“ values in the brain regions of interest. AEPs resulted in stored multichannel signal waveforms. 3. Statistical analyses of blood perfusion measurement data revealed an overall similarity between these two disorders in the majority of brain regions. An exception to this are the regions: inferior temporalis, inferior occipitalis, hippocampus and the anterior basal ganglia. Both diagnostic groups manifested hypofrontality. In general, hypoperfusion of the left hemisphere was found, albeit displaying different patterns in the two groups investigated. 4. AEP lantencies were prolonged and found to be similar in both diagnostic groups, whilst AEP amplitudes were smaller in schizophrenics compared to depressives.

Multichannel waveforms and topographic mapping of the auditory brainstem response under common stimulus and recording conditions

Topographic representation of brain electrical activity may be employed to provide information about excitation patterns and symmetry of responses to sensory stimulation. The present investigation describes the waveforms and topographic distribution of the auditory brainstem response (ABR) in normal-hearing and neurologically-normal women during several stimulus and recording conditions using a multiple electrode array. Acoustic stimuli were rarefaction clicks presented monaurally at five intensities. Latencies and amplitudes for the sagittal and coronal electrodes as well as the topographies of waves I, III, and V were described. Wave I, in an ipsilateral inverting reference condition, exhibited smallest amplitudes at the temporal electrode ipsilateral to the stimulated ear and maximum voltage at electrode sites contralateral to the stimulated ear. Amplitude and latency patterns of wave III were perculiar to level of stimulation, ear of stimulation and selection of inverting electrode site. Wave III maximum voltage was detected at fronto-central scalp areas contralateral to the stimulated ear. Wave V latencies and amplitudes varied across electrode site and inverting electrode position. Its topography revealed maximum positive voltage at fronto-central electrodes, slightly asymmetric in some instances. Analysis of amplitude and topographic patterns of ABR components may provide useful information to supplement that provided by conventional displays of waveforms.