Recent Advances In Signal Processing Research Articles

A deep learning approach for suppressing noise in livestream earthquake data from a large seismic network

SUMMARY Detecting and analyzing small earthquakes is important for many seismological studies. Signals of small earthquakes are often obscured by noise. Recent advances in signal processing and deep learning along with available computing resources provide a great opportunity to address this challenge. In this study, we present a time domain method of suppressing noise for processing livestream earthquake data from a large seismic network by applying a deep neural network Real-time Denoiser (RTDenoiser). This neural network is able to attenuate a variety of colored noise and non-earthquake signals and suppress noise in the overlapping frequency bandwidth with signals. Because of its simplicity in time domain without domain transformation and subsequent processing, the method is able to process continuous livestream three-component data from several hundreds of seismic stations simultaneously. We create ‘noise-free’ samples by scaling down the waveforms of relatively large events from ML 3.5–5.0 to ML 1.5–3.0 according to the Richter scaling relationship. We also select noise samples from the same seismic station and add to ‘noise-free’ data to generate samples at different signal-to-noise ratio (SNR) levels. These data samples are randomly split into training, validation, and test sets. We verify the trained network to process data recorded in Sichuan and Yunnan, China from 2013 to 2018. Results show that the RTDenoiser can help improve SNRs from 5 dB to 15 dB in averag. The number of detected small events at magnitude between ML 1.0 and 3.0 has been increased by 58.8 per cent. The method is currently applied in a seismic network of 300 stations in Sichuan and Yunnan, China for continuous processing. It takes about 10 ms on average to process three-component 60-s data from 300 seismic stations on a single GPU.

Recent Advances in Signal Processing for Consumer Technologies

Presents the introductory editorial for this issue of the publication.

The Classification of Heartbeats from Two-Channel ECG Signals Using Layered Hidden Markov Model

Purpose: Cardiac arrhythmia is one of the most common heart diseases that can have serious consequences. Thus, heartbeat arrhythmias classification is very important to help diagnose and treat. To develop the automatic classification of heartbeats, recent advances in signal processing can be employed. The Hidden Markov Model (HMM) is a powerful statistical tool with the ability to learn different dynamics of the real time-series such as cardiac signals.
 Materials and Methods: In this study, a hierarchy of HMMs named Layered HMM (LHMM) was presented to classify heartbeats from the two-channel electrocardiograms. For training in the first layer, the morphology of the heartbeats was used as observations, while observations in the second layer were the inference results of the first layer. The performance of the proposed LHMM was evaluated in classifying three types of heartbeat arrhythmias (Atrial premature beats (A), Escape beats (E), Left bundle branch block beats (L)) using fifteen records of the MIT-BIH arrhythmia database. Furthermore, the obtained results of the proposed model were compared with other HMM generalizations.
 Results: The best average accuracy was achieved 97.10±1.63%. The best sensitivity of 96.8±1.24%, 98.85±0.52%, and 95.64±1.41 were obtained for A, E, and L, respectively. Furthermore, the results of the proposed method were better than other HMM generalizations.
 Conclusion: Extracting information from time-series dynamics by HMM-based methods has good classification results. The proposed model shows that applying a two-layered HMM can lead to better extraction of information from the observations; therefore, the classification performance of cardiac arrhythmias has been improved using LHMM.

Changes in Orientation Behavior due to Extended High-Frequency (5 to 10 kHz) Spatial Cues.

Objectives:Current hearing aids have a limited bandwidth, which limits the intelligibility and quality of their output, and inhibits their uptake. Recent advances in signal processing, as well as novel methods of transduction, allow for a greater useable frequency range. Previous studies have shown a benefit for this extended bandwidth in consonant recognition, talker-sex identification, and separating sound sources. To explore whether there would be any direct spatial benefits to extending bandwidth, we used a dynamic localization method in a realistic situation.Design:Twenty-eight adult participants with minimal hearing loss reoriented themselves as quickly and accurately as comfortable to a new, off-axis near-field talker continuing a story in a background of far-field talkers of the same overall level in a simulated large room with common building materials. All stimuli were low-pass filtered at either 5 or 10 kHz on each trial. To further simulate current hearing aids, participants wore microphones above the pinnae and insert earphones adjusted to provide a linear, zero-gain response.Results:Each individual trajectory was recorded with infra-red motion-tracking and analyzed for accuracy, duration, start time, peak velocity, peak velocity time, complexity, reversals, and misorientations. Results across listeners showed a significant increase in peak velocity and significant decrease in start and peak velocity time with greater (10 kHz) bandwidth.Conclusions:These earlier, swifter orientations demonstrate spatial benefits beyond static localization accuracy in plausible conditions; extended bandwidth without pinna cues provided more salient cues in a realistic mixture of talkers.

Detection of Apnea Bradycardia from ECG Signals of Preterm Infants Using Layered Hidden Markov Model

Apnea-bradycardia (AB) is a common complication in prematurely born infants, which is associated with reduced survival and neurodevelopmental outcomes. Thus, early detection or predication of AB episodes is critical for initiating preventive interventions. To develop automatic real-time operating systems for early detection of AB, recent advances in signal processing can be employed. Hidden Markov Models (HMM) are probabilistic models with the ability of learning different dynamics of the real time-series such as clinical recordings. In this study, a hierarchy of HMMs named as layered HMM was presented to detect AB episodes from pre-processed single-channel Electrocardiography (ECG). For training the hierarchical structure, RR interval, and width of QRS complex were extracted from ECG as observations. The recordings of 32 premature infants with median 31.2 (29.7, 31.9) weeks of gestation were used for this study. The performance of the proposed layered HMM was evaluated in detecting AB. The best average accuracy of 97.14 ± 0.31% with detection delay of -5.05 ± 0.41s was achieved. The results show that layered structure can improve the performance of the detection system in early detecting of AB episodes. Such system can be incorporated for more robust long-term monitoring of preterm infants.

Signal Processing on Directed Graphs: The Role of Edge Directionality When Processing and Learning From Network Data

This article provides an overview of the current landscape of signal processing (SP) on directed graphs (digraphs). Directionality is inherent to many real-world (information, transportation, biological) networks, and it should play an integral role in processing and learning from network data. We thus lay out a comprehensive review of recent advances in SP on digraphs, offering insights through comparisons with results available for undirected graphs, discussing emerging directions, establishing links with related areas in machine learning and causal inference in statistics as well as illustrating their practical relevance to timely applications. To this end, we begin by surveying (orthonormal) signal representations and their graph-frequency interpretations based on novel measurements of signal variation for digraphs. We then move on to filtering, a central component in deriving a comprehensive theory of SP on digraphs. Indeed, through the lens of filter-based generative signal models, we explore a unified framework to study inverse problems (e.g., sampling and deconvolution on networks), the statistical analysis of random signals, and the topology inference of digraphs from nodal observations.

Upper Limb Movement Classification Via Electromyographic Signals and an Enhanced Probabilistic Network.

Few studies in the literature have researched the use of surface electromyography (sEMG) for motor assessment post-stroke due to the complexity of this type of signal. However, recent advances in signal processing and machine learning have provided fresh opportunities for analyzing complex, non-linear, non-stationary signals, such as sEMG. This paper presents a method for identification of the upper limb movements from sEMG signals using a combination of digital signal processing, that is discrete wavelet transform, and the enhanced probabilistic neural network (EPNN). To explore the potential of sEMG signals for monitoring motor rehabilitation progress, this study used sEMG signals from a subset of movements of the Arm Motor Ability Test (AMAT) as inputs into a movement classification algorithm. The importance of a particular frequency domain feature, that is the ratio of the mean absolute values between sub-bands, was discovered in this work. An average classification accuracy of 75.5% was achieved using the proposed approach with a maximum accuracy of 100%. The performance of the proposed method was compared with results obtained using three other classification algorithms: support vector machine (SVM), k-Nearest Neighbors (k-NN), and probabilistic neural network (PNN) in terms of sEMG movement classification. The study demonstrated the capability of using upper limb sEMG signals to identify and distinguish between functional movements used in standard upper limb motor assessments for stroke patients. The classification algorithm used in the proposed method, EPNN, outperformed SVM, k-NN, and PNN.

High-Speed Visual Target Identification for Low-Cost Wearable Brain-Computer Interfaces

Non-invasive brain-computer interfaces (BCI) have received a great deal of attention due to recent advances in signal processing. Two types of electroencephalograms (EEG), P300 and steady-state visual evoked potential (SSVEP), have been widely used to enable paralyzed patients to communicate with others. Although there have been many signal processing algorithms focusing on target identification accuracies such as power spectral density analysis (PSDA) and canonical correlation analysis (CCA), their high computational complexity drives up the cost of such systems. In the proposed lightweight target identification algorithm, we have focused on developing an improved information transfer rate (ITR) for high-quality communication and reducing overall implementation cost. The proposed algorithm, CCA-Lite, includes two algorithmic optimizations–signal binarization and on-the-fly covariance matrix calculation–which have enabled the development of a low-cost, single-channel, and wearable BCI system using SSVEP. The prototypical BCI system makes use of an ARM Cortex-M3-based low-cost microcontroller unit (MCU), which has been built for 1.5s SSVEP recordings. Compared to the state-of-the-art CCA-based target identification algorithm, CCA-Lite exhibits 25% better ITR and has reduced memory requirements by 92% and single-target identification cycle time by 26%.

Spatiotemporal Mapping of Interictal Spike Propagation: A Novel Methodology Applied to Pediatric Intracranial EEG Recordings.

Synchronized cortical activity is implicated in both normative cognitive functioning and many neurologic disorders. For epilepsy patients with intractable seizures, irregular synchronization within the epileptogenic zone (EZ) is believed to provide the network substrate through which seizures initiate and propagate. Mapping the EZ prior to epilepsy surgery is critical for detecting seizure networks in order to achieve postsurgical seizure control. However, automated techniques for characterizing epileptic networks have yet to gain traction in the clinical setting. Recent advances in signal processing and spike detection have made it possible to examine the spatiotemporal propagation of interictal spike discharges across the epileptic cortex. In this study, we present a novel methodology for detecting, extracting, and visualizing spike propagation and demonstrate its potential utility as a biomarker for the EZ. Eighteen presurgical intracranial EEG recordings were obtained from pediatric patients ultimately experiencing favorable (i.e., seizure-free, n = 9) or unfavorable (i.e., seizure-persistent, n = 9) surgical outcomes. Novel algorithms were applied to extract multichannel spike discharges and visualize their spatiotemporal propagation. Quantitative analysis of spike propagation was performed using trajectory clustering and spatial autocorrelation techniques. Comparison of interictal propagation patterns revealed an increase in trajectory organization (i.e., spatial autocorrelation) among Sz-Free patients compared with Sz-Persist patients. The pathophysiological basis and clinical implications of these findings are considered.

Recent advances in resonant ultrasound spectroscopy to measure bone stiffness tensor

Resonant Ultrasound Spectroscopy (RUS) is a method to measure the elasticity tensor of a material. RUS is particularly advantageous to measure small samples of anisotropic materials. In RUS, resonant frequencies of a sample are measured and computed frequencies of a numerical model of the sample are fitted, yielding the stiffness tensor. RUS was developed in the 1990s, but until recently, it was in practice limited to measure materials with a high quality factor. We have adapted the method to measure bone whose quality factor is about 25. Our strategy combines Bayesian methods to retrieve overlapped resonant peaks in the RUS spectra and to solve the inverse problem using a limited number of resonant frequencies. The method allows a quasi-automated processing of RUS spectra where it is not necessary to know a priori the pairing between measured and computed frequencies. In the last years we have extensively used RUS to document the anisotropic elastic properties of human cortical bone and to investigate the determinants of elastic properties. We present in this talk 1) recent advances in signal processing for RUS; 2) a discussion on the precision of bone elasticity measurement; and 3) a new perspective regarding cortical bone elasticity determinants.

Spectral Transfer Learning Using Information Geometry for a User-Independent Brain-Computer Interface.

Recent advances in signal processing and machine learning techniques have enabled the application of Brain-Computer Interface (BCI) technologies to fields such as medicine, industry, and recreation; however, BCIs still suffer from the requirement of frequent calibration sessions due to the intra- and inter-individual variability of brain-signals, which makes calibration suppression through transfer learning an area of increasing interest for the development of practical BCI systems. In this paper, we present an unsupervised transfer method (spectral transfer using information geometry, STIG), which ranks and combines unlabeled predictions from an ensemble of information geometry classifiers built on data from individual training subjects. The STIG method is validated in both off-line and real-time feedback analysis during a rapid serial visual presentation task (RSVP). For detection of single-trial, event-related potentials (ERPs), the proposed method can significantly outperform existing calibration-free techniques as well as outperform traditional within-subject calibration techniques when limited data is available. This method demonstrates that unsupervised transfer learning for single-trial detection in ERP-based BCIs can be achieved without the requirement of costly training data, representing a step-forward in the overall goal of achieving a practical user-independent BCI system.

Circularly-symmetric complex normal ratio distribution for scalar transmissibility functions. Part I: Fundamentals

Recent advances in signal processing and structural dynamics have spurred the adoption of transmissibility functions in academia and industry alike. Due to the inherent randomness of measurement and variability of environmental conditions, uncertainty impacts its applications. This study is focused on statistical inference for raw scalar transmissibility functions modeled as complex ratio random variables. The goal is achieved through companion papers. This paper (Part I) is dedicated to dealing with a formal mathematical proof. New theorems on multivariate circularly-symmetric complex normal ratio distribution are proved on the basis of principle of probabilistic transformation of continuous random vectors. The closed-form distributional formulas for multivariate ratios of correlated circularly-symmetric complex normal random variables are analytically derived. Afterwards, several properties are deduced as corollaries and lemmas to the new theorems. Monte Carlo simulation (MCS) is utilized to verify the accuracy of some representative cases. This work lays the mathematical groundwork to find probabilistic models for raw scalar transmissibility functions, which are to be expounded in detail in Part II of this study.

Relating simple drivers to snow instability

Snow layers form during and after accumulation due to the interaction of meteorological and physical processes. It is known that the vertical structure and also the lateral continuity of layers depend on these processes and the boundaries set by the terrain. This study addresses the variations seen among vertical penetration resistance profiles and investigates possible forcings at the basin scale. In the past years we acquired a unique dataset with 613 snow micro-penetrometer (SMP) resistance measurements covering a variety of dry-snow conditions. With recent advances in signal processing all snow layer properties required for snow instability modeling are extracted from a SMP signal so that quantitative metrics of the propensity to failure initiation and crack propagation can be calculated. The modeled values of instability corresponded well with field test results obtained during the measurement campaigns and the verified, local danger. We then analyzed whether snow instability was related to simple drivers such as slope aspect, snow depth, and slope angle. In general, aspect was the most prominent driver as on all field days we found associations of our measures of snow instability with aspect. For ‘old’ slab layers the relation between aspect and snow instability was more pronounced than for recently deposited slab layers. However, the relationships between drivers and our measures of snow instability varied depending on whether we analyzed the single field days separately or jointly. Considering all field days jointly, which reflects mean trends over varying snowpack conditions, slope angle was weakly related to the failure initiation propensity and snow depth to the crack propagation propensity. Our findings suggest that with SMP field measurements differences in snow conditions can be resolved which relate to the failure initiation and crack propagation propensity relevant for snow instability assessment. Our analysis of terrain and snow depth data showed that readily and widely available simple drivers have the potential to enhance snow instability predictions from point measurements at the basin scale.

Power-Controlled Medium Access Control Protocol for Full-Duplex WiFi Networks

Recent advances in signal processing have demonstrated in-band full-duplex capability at WiFi ranges. In addition to simultaneous two-way exchange between two nodes, full-duplex access points can potentially support simultaneous uplink and downlink flows. However, the atomic three-node topology, which allows simultaneous uplink and downlink, leads to inter-client interference. In this paper, we propose a random-access medium access control protocol using distributed power control to manage inter-client interference in wireless networks with full-duplex-capable access points that serve half-duplex clients. Our key contributions are two-fold. First, we identify the regimes in which power control provides sum throughput gains for the three-node atomic topology, with one uplink flow and one downlink flow. Second, we develop and benchmark PoCMAC, a full 802.11-based protocol that allows distributed selection of a three-node topology. The proposed MAC protocol is shown to achieve higher capacity as compared to an equivalent half-duplex counterpart, while maintaining similar fairness characteristics in single contention domain networks. We carried out extensive simulations and software-defined radio-based experiments to evaluate the performance of the proposed MAC protocol, which is shown to achieve a significant improvement over its half-duplex counterpart in terms of throughput performance.

グラフ信号処理のすゝめ

グラフ信号処理のすゝめ

Considerations in designing piezocomposite transducers and arrays for broadband sonar systems

The term broadband is commonly used to refer to the capability of a sonar system. Recent advances in signal processing and system electronics have re-defined capabilities for these systems and introduced new complex broadband pulse requirements for the sonar transducer. Advances in 1-3 piezocomposite technology have addressed this need through optimization of materials and innovative electroacoustic designs to provide high performance broadband solutions. Taking full advantage of this transducer technology requires careful consideration of the total system performance including dynamic range of receiver electronics, available transmit voltage/current/power, directivity and array configuration. This generalized study considers the design options available for optimizing transducers and arrays for broadband operation. Several Langevin or sandwich style Piezocomposite transducer configurations will be presented for both monostatic (two-way) and bistatic (separate receive and transmit) transducer systems and ...

Maximum Achievable Throughput in Multiband Multiantenna Wireless Mesh Networks

We have recently witnessed a rapidly increasing demand for, and hence, a shortage of, wireless network bandwidth due to rapidly growing wireless services and applications. It is, therefore, important to develop an efficient way of utilizing this limited bandwidth resource. Fortunately, recent technological advances have enabled software-defined radios (SDRs) to switch from one frequency band to another at minimum cost, thereby making dynamic multiband access and sharing possible. On the other hand, recent advances in signal processing combined with those in antenna technology provide multiple-input multiple-output (MIMO) capabilities, thereby creating opportunities for enhancing the throughput of wireless networks. Both SDRs and MIMO together enable next-generation wireless networks, such as mesh networks, to support dynamic and adaptive bandwidth sharing along time, frequency, and space. In this paper, we develop a new framework that 1) identifies the limits and potential of SDRs and MIMO in terms of achievable network throughput and 2) provides guidelines for designers to determine the optimal parameters of wireless mesh networks equipped with multiband and multiantenna capabilities.

MO-A-201C-01: Modern US Equipment and US Physics

Diagnostic ultrasound imaging has advance substantially over the past 25 years and this has been accelerated by the progressive move toward digital systems. Modern ultrasound systems rely heavily on multi-element array transducers, image reconstruction, and signal processing to produce the clinical images seen today. In addition, there has been a significant effort to make ultrasound more portable and less expensive while trying to maintain image quality and performance. Those involved in the operation and evaluation of ultrasound systems need to understand the technology and approaches being used in ultrasound imaging today. This lecture will provide the background understanding of ultrasound physics and its application in imaging systems and the evolution of imaging toward the modern digital devices being used today. Learning Objectives: 1. Understand fundamental physics and operation of b-mode and Doppler. 2. Understand recent advances in signal processing including harmonic imaging, coded excitation, automated optimization methods, compound imaging and speckle reduction. 3. Understand the methods for image improvements by the processing methods used in ultrasound imaging.

Circadian variation in dominant atrial fibrillation frequency in persistent atrial fibrillation

Circadian variation in atrial fibrillation (AF) frequency is explored in this paper by employing recent advances in signal processing. Once the AF frequency has been estimated and tracked by a hidden Markov model approach, the resulting trend is analyzed for the purpose of detecting and characterizing the presence of circadian variation. With cosinor analysis, the results show that the short-term variations in the AF frequency exceed the variation that may be attributed to circadian. Using the autocorrelation method, circadian variation was found in 13 of 18 ambulatory ECG recordings (Holter) acquired from patients with long-standing persistent AF. Using the ensemble correlation method, the highest AF frequency usually occurred during the afternoon, whereas the lowest usually occurred during late night. It is concluded that circadian variation is present in most patients with long-standing persistent AF though the short-term variation in the AF frequency is considerable and should be taken into account.

Constraint Design and Throughput Evaluation in Multi-Band Wireless Networks Using Multiple-Input Multiple-Output Links

Recent advances in radio technology enabled software-defined radios (SDRs), which can switch from one frequency band to another at minimum cost, are making dynamic multi-band access and sharing possible. Also, recent advances in signal processing combined with those in antenna technology, enabled MIMO (Multiple-Input Multiple-Output) capabilities, which created great opportunities for enhancing the throughput of wireless networks. Together SDRs and MIMO empower the next-generation wireless networks, such as mesh networks, with dynamic and adaptive bandwidth sharing and use along time, frequency, and space. In this paper, we design and model the radio and interference constraints on multi-hop wireless networks, when they are both SDR-capable and MIMO-equipped. The developed constraint models were then used to formulate and solve multi-hop routing problems in wireless networks with MIMO and multi-band capabilities. We evaluated and analyzed the throughput that multi-hop wireless networks can achieve, when they are both allowed to communicate on multiple bands and equipped with MIMO links.