Automatic Signal Research Articles

How time-based alignment of realized acoustic landmarks and predicted landmarks improves analysis of feature cue modification patterns in speech

Acoustic landmarks are abrupt spectral changes that signal the underlying manner features of phonemes (Stevens 2002). Our goal in developing an automatic method to detect these landmarks is to create a robust, knowledge-based approach to phoneme extraction in automatic speech signal processing. One challenge in such an approach is posed by massive reductions, in which many landmarks and other feature cues are missing. Thus, there is a need to hand-label the acoustic landmarks that actually occur in the speech signal and align them with predicted landmarks. However, this often results in a discrepancy between the locations of labels for the automatically generated and hand-labeled landmarks, which leads to an inaccurate analysis of where realized landmarks occur with respect to word and phoneme interval boundaries. We attempted to solve this issue with a time-based alignment method derived from the minimum edit distance algorithm. The result improved alignment of the realized landmark labels with the predicted landmark labels, enabling a more accurate analysis of modifications in the hand-labeled (realized) landmark tiers.

Automatic identification of rockfalls and volcano-tectonic earthquakes at the Piton de la Fournaise volcano using a Random Forest algorithm

Monitoring the endogenous seismicity of volcanoes helps to forecast eruptions and prevent their related risks, and also provides critical information on the eruptive processes. Due the high number of events recorded during pre-eruptive periods by the seismic monitoring networks, cataloging each event can be complex and time-consuming if done by human operators. Automatic seismic signal processing methods are thus essential to build consistent catalogs based on objective criteria. We evaluated the performance of the “Random Forests” (RF) machine-learning algorithm for classifying seismic signals recorded at the Piton de la Fournaise volcano, La Réunion Island (France). We focused on the discrimination of the dominant event types (rockfalls and volcano-tectonic earthquakes) using over 19,000 events covering two time periods: 2009–2011 and 2014–2015. We parametrized the seismic signals using 60 attributes that were then given to RF algorithm. When the RF classifier was given enough training samples, its sensitivity (rate of good identification) exceeded 99%, and its performance remained high (above 90%) even with few training samples. The sensitivity collapsed when using an RF classifier trained with data from 2009 to 2011 to classify data from 2014 to 2015 catalog, because the physical characteristics of the rockfalls and hence their seismic signals had evolved between the two time-periods. The main attribute families (waveform, spectrum, spectrogram or polarization) were all found to be useful for event discrimination. Our work validates the performance of the RF algorithm and suggests it could be implemented at other volcanic observatories to perform automatic, near real-time, classification of seismic events.

HCPCF-based in-line fiber Fabry-Perot refractometer and high sensitivity signal processing method

An in-line fiber Fabry-Perot interferometer (FPI) based on the hollow-core photonic crystal fiber (HCPCF) for refractive index (RI) measurement is proposed in this paper. The FPI is formed by splicing both ends of a short section of the HCPCF to single mode fibers (SMFs) and cleaving the SMF pigtail to a proper length. The RI response of the sensor is analyzed theoretically and demonstrated experimentally. The results show that the FPI sensor has linear response to external RI and good repeatability. The sensitivity calculated from the maximum fringe contrast is –136 dB/RIU. A new spectrum differential integration (SDI) method for signal processing is also presented in this study. In this method, the RI is obtained from the integrated intensity of the absolute difference between the interference spectrum and its smoothed spectrum. The results show that the sensitivity obtained from the integrated intensity is about –1.34×105 dB/RIU. Compared with the maximum fringe contrast method, the new SDI method can provide the higher sensitivity, better linearity, improved reliability, and accuracy, and it’s also convenient for automatic and fast signal processing in real-time monitoring of RI.

Automatic carrier signal track algorithm in all-digital PGC demodulation scheme for optical interferometric sensors

An optimal all-digital phase generated carrier (PGC) demodulation scheme for optical interferometric sensors is presented in order to reduce the effect of frequency and phase difference between the carrier and reference signals. First, the influence on the signal from the PGC demodulation scheme is discussed when the frequency and phase of the carrier signal are inconsistent with the reference signal. It reveals that the different frequencies and phases will cause modulation and fading phenomena in the output signal of the scheme. Second, a carrier signal track algorithm is proposed to lock the frequency and phase of the carrier signal in real time to reduce these influences on the scheme. The frequency and phase of the carrier signal can be tracked by frequency analysis, desampling, and curve fitting. After that, the measurement signal can be demodulated accurately by the scheme from the signal detected by the photodiode in real time. The accuracy and effectiveness of this algorithm is verified by experimental results.

Signal-3L 2.0: A Hierarchical Mixture Model for Enhancing Protein Signal Peptide Prediction by Incorporating Residue-Domain Cross-Level Features.

Signal peptides play key roles in targeting and translocation of integral membrane proteins and secretory proteins. However, signal peptides present several challenges for automatic prediction methods. One challenge is that it is difficult to discriminate signal peptides from transmembrane helices, as both the H-region of the peptides and the transmembrane helices are hydrophobic. Another is that it is difficult to identify the cleavage site between signal peptides and mature proteins, as cleavage motifs or patterns are still unclear for most proteins. To solve these problems and further enhance automatic signal peptide recognition, we report a new Signal-3L 2.0 predictor. Our new model is constructed with a hierarchical protocol, where it first determines the existence of a signal peptide. For this, we propose a new residue-domain cross-level feature-driven approach, and we demonstrate that protein functional domain information is particularly useful for discriminating between the transmembrane helices and signal peptides as they perform different functions. Next, in order to accurately identify the unique signal peptide cleavage sites along the sequence, we designed a top-down approach where a subset of potential cleavage sites are screened using statistical learning rules, and then a final unique site is selected according to its evolution conservation score. Because this mixed approach utilizes both statistical learning and evolution analysis, it shows a strong capacity for recognizing cleavage sites. Signal-3L 2.0 has been benchmarked on multiple data sets, and the experimental results have demonstrated its accuracy. The online server is available at www.csbio.sjtu.edu.cn/bioinf/Signal-3L/ .

FMCW Signal Detection and Parameter Extraction by Cross Wigner–Hough Transform

The combination of Wigner–Ville distribution (WVD) and Hough transform (HT) has been successfully used in detection and parameter extraction of frequency modulated continuous waveform (FMCW) signals. In this paper, a combination of Cross–Wigner–Ville and HT [(Cross Wigner–Hough transform (XWHT)] is proposed for detection and parameter extraction of FMCW signals with a novel methodology. The XWHT method makes use of the cross-terms created by WVD instead of trying to suppress them. Utilization of the properties of the cross-terms to detect and unveil the parameters of FMCW signals on HT space is a new approach. The performance of the method is compared with other Wigner–Hough transform-based methods in terms of transform speed, parameter extraction, and detection performance. As a result, this study proposes that the XWHT is a candidate method to be used in digital electronic support receivers’ automatic signal detection and analysis capabilities due to its speed and performance.

Fault-tolerant cooperative control in an offshore wind farm using model-free and model-based fault detection and diagnosis approaches

Given the importance of reliability and availability in wind farms, this paper focuses on the development of fault diagnosis and fault-tolerant control schemes in a cooperative framework (referred to as active fault-tolerant cooperative control) at the wind farm level against the decreasing power generation caused by turbine blade erosion and debris build-up on the blades over time. In more details, the paper presents a novel integrated fault detection and diagnosis and fault-tolerant control approach oriented to the design and development of two active fault-tolerant cooperative control schemes for an offshore wind farm. Each of the schemes employs a fault detection and diagnosis system to provide accurate and timely diagnosis information to be used in an appropriate automatic signal correction algorithm for accommodation of faults in the wind farm. The effectiveness and performance of the proposed schemes are evaluated and compared using different simulations on a high-fidelity offshore wind farm benchmark model in the presence of wind turbulences, measurement noises and realistic fault scenarios.

The Automatic Nerve Signal Transmission and Conscious Primacy Hypothesis

This paper proposes a hypothesis about automatic nerve signal transmission and conscious primacy. If stimulated suddenly, people are initially unaware of the stimulation. Without conscious guidance, nerve signals are automatically transmitted via the strongest neural connections, thereby producing fast and involuntary behavior. Upon awareness of the stimulation, conscious primacy plays a role, and nerve signals are transmitted in consciously guided directions, thereby generating cognition and voluntary behaviors. When we repeat an action in a special environment, it will become a habit. This is because when nerve signals are repeatedly transmitted to action cells in a specific environment, neural connections between the specific environment and the action are strengthened. Upon return to this specific environment, nerve signals will be automatically transmitted, producing a stereotyped habit behavior without conscious thought. Facial expression, for example, is controlled by both emotion and cognition. Prior to emotional awareness of sudden stimulation, nerve signals are automatically transmitted, producing microexpressions consistent with emotion. Upon awareness, control switches from emotion to cognition, producing flexible facial expressions consistent with consciousness. The automatic nerve signal transmission and conscious primacy hypothesis suggests a new mechanism for producing different behaviors, reveals the essential difference and mutual transformation of stereotyped and flexible behaviors, and opens a new field for ethological study.

Signal processing methods to improve the Signal-to-noise ratio (SNR) in ultrasonic non-destructive testing of wind turbine blade

Ultrasonic non-destructive testing (NDT) methods are being used quite effectively nowadays, but the multilayered structure of composite materials results in the serious problem in the detection of defects/flaws. The resulting ultrasonic signal is often noisy and denoising of this signal is necessary in order to extract useful information so that faults can be detected, located and sized. Currently, there is a high demand for automatic ultrasonic signal processing techniques to not only remove the need for manual flaw detection and assessment, but also increase the accuracy, reliability and repeatability of the non-destructive evaluation. There are various signal processing techniques which can be used in ultrasonic measurements and selection of appropriate method is one of the major key factors in the field of ultrasonic testing of composite materials. In the presented work, the sample of wind turbine blade (WTB) manufactured using glass fiber reinforced plastic (GFRP) was investigated using ultrasonic NDT in order to estimate the artificially made disbond type defects of 15 mm and 25 mm diameter on the trailing edge. The transmitting and receiving transducers were fixed on movable panel at distance of 50 mm and guided waves (GW) were received at each one millimeter step along the scanning distance of 500 mm. The measurement is performed using low-frequency (LF) ultrasonic system which was developed by Ultrasound Institute of Kaunas University of Technology. Various signal processing techniques were applied to overcome the structural noise and/or extract the information about the defects. The three most promising signal processing techniques: cross-correlation methods, wavelet transform (WT) and Hilbert-Huang (HHT) transform were discussed and compared in the process of defects estimation.

13th European Workshop on Advanced Control and Diagnosis (ACD 2016)

13th European Workshop on Advanced Control and DiagnosisThe 13th European Workshop on Advanced Control and Diagnosis (ACD 2016), www.acd2016.eu took place at Hautes Etrudes d’Ingénieur (HEI), Lille, France, on November 17-18, 2016.The annual European Workshop on Advanced Control and Diagnosis has been organized since 2003 by Control Engineering departments of several European universities in Germany, France, the UK, Poland, Italy, Hungary, Denmark and Czech Republic, to bring together senior and junior academics and engineers from diverse fields of automatic control, fault detection, and signal processing. The workshop provides an opportunity for researchers and developers to present their recent theoretical developments, practical applications, or even open problems. It also offers a great opportunity for industrial partners to express their needs and priorities and to review the current activities in the fields.A total of 100 papers have been submitted for ACD 2016. Based on the peer reviews 71 papers were accepted for oral presentation, 4 papers withdrawn, 3 papers not presented. The accepted papers covered areas of Fault Diagnosis, Estimation and Observation, Fault Tolerant Control, Predictive Control, Robust Control, Control Theory, fractional order systems, Modeling and Data °Processing, Software tools and code generation. In addition, four excellent plenary lectures were delivered by experts on their domain: Ir. Serge Legonidec from the company Airbus Safran Launchers, France : An overview of connections between scientific automatic topics and their applications in the propulsive systems, Dr. Ir. Jan-Willem Van Wingerden, Technical University of Delft, Nederland : Wind Energy Control Research, Prof. Sarah Spurgeon, University College London, UK: On discontinuous Observers: From Basic Properties to a Robust Fault Detection and Condition Monitoring Tool, Dr. Joseph-Julien Yame, University of Lorraine, Nancy, France : Data Driven Fault Tolerant Control: a behavioral approach.The ACD 2016 has been co-sponsored technically by the International Federation of Automatic Control (IFAC).On behalf of the ACD 2016 organizing committee, we would like to thank all those who prepared and submitted papers, participated in the peer review process, supported, and attended the workshop.Abdel AITOUCHE - General ChairJosef KORBICZ - IPC ChairVincent COCQUEMPOT - IPC Co-Chair

Vehicle Driving Wandering Automatic System Based on Laser Ranging

Laser ranging has so many advantages, such as rapid response, high accuracy, and excellent reliability and so on, it is widely used in numerous engineering test field. To use laser range on the measurements of vehicle driving wandering can simplify the structure of vehicle driving wandering test system and improve its usability. With lucubrating the compose and theory, test procedure and data processing method of vehicle driving wandering system which is based on laser ranging, it has developed a new set of vehicle driving wandering system which is based on laser ranging. Making use of the symmetric method of laser ranging can eliminate the effect of auto vibration; Vehicle driving wandering test system based on the lab view platform will achieve automatic control, signal transmission, data acquisition and processing, test result’s sharing, inquiring and so on.

Hybrid control of aggregated thermostatically controlled loads: step rule, parameter optimisation, parallel and cascade structures

This study proposes hybrid control strategies for aggregated thermostatically controlled loads (TCLs) in order to provide ancillary service. The on/off control strategy and the setpoint-regulation strategy are two typical control strategies. In order to reduce the tracking errors of the two control strategies, two improved methods were developed. The first method dispatches the aggregated loads to the automatic generation control signal following a step rule based on the state of charge. The second method optimises the controller parameters of the setpoint-regulation strategy by the Powell optimisation algorithm. Based on these two improved methods, the hybrid control strategies with parallel and cascade control structures were established by dividing the TCLs into two clusters, and the optimal allocations of the loads, the reference signal and the tracking error between the two clusters were obtained by the particle swarm optimisation algorithm. The simulation results demonstrate that the hybrid control strategies can reduce the errors in load following.

Model-based Active Fault-tolerant Cooperative Control in an Offshore Wind Farm

Abstract Given the importance of reliability and availability in wind farms, this paper focuses on the development of an integrated fault diagnosis and fault-tolerant control scheme in a cooperative framework at wind farm level against the decreased power generation caused by turbine blade erosion and debris build-up on the blades over time. More precisely, the paper presents a novel integrated fault detection and diagnosis (FDD) and fault-tolerant control (FTC) approach oriented to the design and development of an active fault-tolerant cooperative control (AFTCC)scheme for an offshore wind farm. The scheme employs a fault detection and diagnosis system to provide accurate and timely diagnosis information to be used in an appropriate automatic signal correction algorithm for accommodation of faults in the farm. The effectiveness and performance of the proposed scheme are evaluated and analyzed by different simulations on a high-fidelity offshore wind farm benchmark model in the presence of wind turbulences, measurement noises and realistic fault scenarios.

V2X 시스템 기반 교차로 네트워크 자동 신호시스템 개발에 관한 연구

V2X 시스템 기반 교차로 네트워크 자동 신호시스템 개발에 관한 연구

Multipurpose UAV for search and rescue operations in mountain avalanche events

ABSTRACTThis paper presents a multipurpose UAV (unmanned aerial vehicle) for mountain rescue operations. The multi-rotors based flying platform and its embedded avionics are designed to meet environmental requirements for mountainous terrain such as low temperatures, high altitude and strong winds, assuring the capability of carrying different payloads (separately or together) such as: avalanche beacon (ARTVA) with automatic signal recognition and path following algorithms for the rapid location of snow-covered body; camera (visible and thermal) for search and rescue of missing persons on snow and in woods during the day or night; payload deployment to drop emergency kits or specific explosive cartridge for controlled avalanche detachment. The resulting small (less than 5 kg) UAV is capable of full autonomous flight (including take-off and landing) of a pre-programmed, or easily configurable, custom mission. Furthermore, the autopilot manages the sensors measurements (i.e. beacons or cameras) to update the flying mission automatically in flight. Specific functionalities such as terrain following were developed and implemented. Ground station programming of the UAV is not needed, except compulsory monitoring, as the rescue mission can be accomplished in a full automatic mode.

Artificial blood circulatory and special Ultrasound Doppler probes for detecting and sizing gaseous embolism

Abstract This paper presents a special designed artificial blood circulatory (ABC) for studying gaseous embolism based on detection of gaseous emboli and their sizing using ultrasound (US) spectral Doppler. Blood mimicking fluid (BMF) was used in the circulatory to get valid results without using human blood. The additional necessary degassing circulatory shows a promising effect of decontaminating the BMF from air bubbles. This offers the base for detecting and sizing microemboli using special algorithms and finally leads to reliable calculations of dangerous embolism and its air volume. Standard US probes at an integrated tissue model and a new 8-MHz central catheter ultrasound (CCUS) probe inside a superior vena cava model (SVCM) are used and deliver the Doppler spectrogram as input for automatic emboli detection and further signal analysis. First results using the newly developed 8-MHz CCUS probe inside the SVCM and its Doppler spectrogram characteristics show promising results but need more detailed studies.

Using an infrasonic method to monitor the destruction of glaciers in Arctic conditions

We study the application of an infrasonic method to detect infrasonic acoustic emission caused by the destruction of glaciers in the Arctic. We consider the main approaches and methods for automatic signal detection from the data of infrasonic microarrays from the viewpoint of their practical use in conditions of frequent and significant variations in the noise level characteristics of the Arctic coast. We propose a novel method for the automatic detection of infrasonic events based on representation of a plane wave signal and adaptive estimation of the noise level. The method makes it possible to detect signals with a small number of sensors (up to three) in the specific conditions of the Arctic coast. We present the results of infrasonic monitoring of the destruction of Icefjord outlet glaciers (Spitsbergen archipelago) carried out by the Kola Branch of the RAS Geophysical Survey in 2011–2012.

Automatic detection and mapping of double potentials in intracardiac electrograms

Abstract Catheter ablation has become a very efficient strategy to terminate sustained cardiac arrhythmias like atrial flutter (AFlut). Identification of the optimal ablation spot, however, often proves difficult when scar from previous ablations is present. Although the application of electro-anatomical mapping systems allows to record thousands of intracardiac electrograms (EGMs) from each atrium, state-of-the-art techniques provide limited options for automatic signal processing. Goal of the presented research was the development of an algorithm to detect EGMs that present double potentials (DPs), as these often indicate functional or anatomical lines of block for cardiac excitation. Using an annotated database, we developed several features based on the morphological descriptors of DPs. These were used to train a binary decision tree which was able to detect DPs with a correct rate of over 90%.

Expiratory and Inspiratory Cries Detection Using Different Signals' Decomposition Techniques

SummaryThis paper addresses the problem of automatic cry signal segmentation for the purposes of infant cry analysis. The main goal is to automatically detect expiratory and inspiratory phases from recorded cry signals. The approach used in this paper is made up of three stages: signal decomposition, features extraction, and classification. In the first stage, short-time Fourier transform, empirical mode decomposition (EMD), and wavelet packet transform have been considered. In the second stage, various set of features have been extracted, and in the third stage, two supervised learning methods, Gaussian mixture models and hidden Markov models, with four and five states, have been discussed as well. The main goal of this work is to investigate the EMD performance and to compare it with the other standard decomposition techniques. A combination of two and three intrinsic mode functions (IMFs) that resulted from EMD has been used to represent cry signal. The performance of nine different segmentation systems has been evaluated. The experiments for each system have been repeated several times with different training and testing datasets, randomly chosen using a 10-fold cross-validation procedure. The lowest global classification error rates of around 8.9% and 11.06% have been achieved using a Gaussian mixture models classifier and a hidden Markov models classifier, respectively. Among all IMF combinations, the winner combination is IMF3+IMF4+IMF5.

A constant false alarm rate frame detector for satellite‐based automatic identification system

SummaryFrame detection is an important step in satellite‐based automatic identification system for its contributions in verifying the presence of automatic identification system signal before frame synchronization. In this paper, a constant false alarm rate frame detector is proposed, which exploits the feature implied in the training sequence, to realize frame detection in the presence of additive white Gaussian noise and frequency offset. False alarm probability is related with a threshold, which is independent of the signal and noise. For fixed false alarm probability, the relationship between detection performance and Eb/N0 is analyzed. Simulations prove that the proposed detector outperforms the detector based on cyclic autocorrelation when message collisions exist. Copyright © 2016 John Wiley & Sons, Ltd.