Modeling the Pulse Signal by Wave-Shape Function and Analyzing by Synchrosqueezing Transform

PubMed Central

Wang, Chun-Li; Yang, Yueh-Lung; Wu, Wen-Hsiang; Tsai, Tung-Hu; Chang, Hen-Hong

2016-01-01

We apply the recently developed adaptive non-harmonic model based on the wave-shape function, as well as the time-frequency analysis tool called synchrosqueezing transform (SST) to model and analyze oscillatory physiological signals. To demonstrate how the model and algorithm work, we apply them to study the pulse wave signal. By extracting features called the spectral pulse signature, and based on functional regression, we characterize the hemodynamics from the radial pulse wave signals recorded by the sphygmomanometer. Analysis results suggest the potential of the proposed signal processing approach to extract health-related hemodynamics features. PMID:27304979

Modeling the Pulse Signal by Wave-Shape Function and Analyzing by Synchrosqueezing Transform.

PubMed

Wu, Hau-Tieng; Wu, Han-Kuei; Wang, Chun-Li; Yang, Yueh-Lung; Wu, Wen-Hsiang; Tsai, Tung-Hu; Chang, Hen-Hong

2016-01-01

We apply the recently developed adaptive non-harmonic model based on the wave-shape function, as well as the time-frequency analysis tool called synchrosqueezing transform (SST) to model and analyze oscillatory physiological signals. To demonstrate how the model and algorithm work, we apply them to study the pulse wave signal. By extracting features called the spectral pulse signature, and based on functional regression, we characterize the hemodynamics from the radial pulse wave signals recorded by the sphygmomanometer. Analysis results suggest the potential of the proposed signal processing approach to extract health-related hemodynamics features.

An electromagnetic signals monitoring and analysis wireless platform employing personal digital assistants and pattern analysis techniques

NASA Astrophysics Data System (ADS)

Ninos, K.; Georgiadis, P.; Cavouras, D.; Nomicos, C.

2010-05-01

This study presents the design and development of a mobile wireless platform to be used for monitoring and analysis of seismic events and related electromagnetic (EM) signals, employing Personal Digital Assistants (PDAs). A prototype custom-developed application was deployed on a 3G enabled PDA that could connect to the FTP server of the Institute of Geodynamics of the National Observatory of Athens and receive and display EM signals at 4 receiver frequencies (3 KHz (E-W, N-S), 10 KHz (E-W, N-S), 41 MHz and 46 MHz). Signals may originate from any one of the 16 field-stations located around the Greek territory. Employing continuous recordings of EM signals gathered from January 2003 till December 2007, a Support Vector Machines (SVM)-based classification system was designed to distinguish EM precursor signals within noisy background. EM-signals corresponding to recordings preceding major seismic events (Ms≥5R) were segmented, by an experienced scientist, and five features (mean, variance, skewness, kurtosis, and a wavelet based feature), derived from the EM-signals were calculated. These features were used to train the SVM-based classification scheme. The performance of the system was evaluated by the exhaustive search and leave-one-out methods giving 87.2% overall classification accuracy, in correctly identifying EM precursor signals within noisy background employing all calculated features. Due to the insufficient processing power of the PDAs, this task was performed on a typical desktop computer. This optimal trained context of the SVM classifier was then integrated in the PDA based application rendering the platform capable to discriminate between EM precursor signals and noise. System's efficiency was evaluated by an expert who reviewed 1/ multiple EM-signals, up to 18 days prior to corresponding past seismic events, and 2/ the possible EM-activity of a specific region employing the trained SVM classifier. Additionally, the proposed architecture can form a base platform for a future integrated system that will incorporate services such as notifications for field station power failures, disruption of data flow, occurring SEs, and even other types of measurement and analysis processes such as the integration of a special analysis algorithm based on the ratio of short term to long term signal average.

Signal Processing and Interpretation Using Multilevel Signal Abstractions.

DTIC Science & Technology

1986-06-01

mappings expressed in the Fourier domain. Pre- viously proposed causal analysis techniques for diagnosis are based on the analysis of intermediate data ...can be processed either as individual one-dimensional waveforms or as multichannel data 26 I P- - . . . ." " ." h9. for source detection and direction...microphone data . The signal processing for both spectral analysis of microphone signals and direc- * tion determination of acoustic sources involves

Quantifying two-dimensional nonstationary signal with power-law correlations by detrended fluctuation analysis

NASA Astrophysics Data System (ADS)

Fan, Qingju; Wu, Yonghong

2015-08-01

In this paper, we develop a new method for the multifractal characterization of two-dimensional nonstationary signal, which is based on the detrended fluctuation analysis (DFA). By applying to two artificially generated signals of two-component ARFIMA process and binomial multifractal model, we show that the new method can reliably determine the multifractal scaling behavior of two-dimensional signal. We also illustrate the applications of this method in finance and physiology. The analyzing results exhibit that the two-dimensional signals under investigation are power-law correlations, and the electricity market consists of electricity price and trading volume is multifractal, while the two-dimensional EEG signal in sleep recorded for a single patient is weak multifractal. The new method based on the detrended fluctuation analysis may add diagnostic power to existing statistical methods.

Electrical Motor Current Signal Analysis using a Modulation Signal Bispectrum for the Fault Diagnosis of a Gearbox Downstream

NASA Astrophysics Data System (ADS)

Haram, M.; Wang, T.; Gu, F.; Ball, A. D.

2012-05-01

Motor current signal analysis has been an effective way for many years of monitoring electrical machines themselves. However, little work has been carried out in using this technique for monitoring their downstream equipment because of difficulties in extracting small fault components in the measured current signals. This paper investigates the characteristics of electrical current signals for monitoring the faults from a downstream gearbox using a modulation signal bispectrum (MSB), including phase effects in extracting small modulating components in a noisy measurement. An analytical study is firstly performed to understand amplitude, frequency and phase characteristics of current signals due to faults. It then explores the performance of MSB analysis in detecting weak modulating components in current signals. Experimental study based on a 10kw two stage gearbox, driven by a three phase induction motor, shows that MSB peaks at different rotational frequencies can be based to quantify the severity of gear tooth breakage and the degrees of shaft misalignment. In addition, the type and location of a fault can be recognized based on the frequency at which the change of MSB peak is the highest among different frequencies.

Wayside Bearing Fault Diagnosis Based on a Data-Driven Doppler Effect Eliminator and Transient Model Analysis

PubMed Central

Liu, Fang; Shen, Changqing; He, Qingbo; Zhang, Ao; Liu, Yongbin; Kong, Fanrang

2014-01-01

A fault diagnosis strategy based on the wayside acoustic monitoring technique is investigated for locomotive bearing fault diagnosis. Inspired by the transient modeling analysis method based on correlation filtering analysis, a so-called Parametric-Mother-Doppler-Wavelet (PMDW) is constructed with six parameters, including a center characteristic frequency and five kinematic model parameters. A Doppler effect eliminator containing a PMDW generator, a correlation filtering analysis module, and a signal resampler is invented to eliminate the Doppler effect embedded in the acoustic signal of the recorded bearing. Through the Doppler effect eliminator, the five kinematic model parameters can be identified based on the signal itself. Then, the signal resampler is applied to eliminate the Doppler effect using the identified parameters. With the ability to detect early bearing faults, the transient model analysis method is employed to detect localized bearing faults after the embedded Doppler effect is eliminated. The effectiveness of the proposed fault diagnosis strategy is verified via simulation studies and applications to diagnose locomotive roller bearing defects. PMID:24803197

Component isolation for multi-component signal analysis using a non-parametric gaussian latent feature model

NASA Astrophysics Data System (ADS)

Yang, Yang; Peng, Zhike; Dong, Xingjian; Zhang, Wenming; Clifton, David A.

2018-03-01

A challenge in analysing non-stationary multi-component signals is to isolate nonlinearly time-varying signals especially when they are overlapped in time and frequency plane. In this paper, a framework integrating time-frequency analysis-based demodulation and a non-parametric Gaussian latent feature model is proposed to isolate and recover components of such signals. The former aims to remove high-order frequency modulation (FM) such that the latter is able to infer demodulated components while simultaneously discovering the number of the target components. The proposed method is effective in isolating multiple components that have the same FM behavior. In addition, the results show that the proposed method is superior to generalised demodulation with singular-value decomposition-based method, parametric time-frequency analysis with filter-based method and empirical model decomposition base method, in recovering the amplitude and phase of superimposed components.

Effective Use of Multimedia Presentations to Maximize Learning within High School Science Classrooms

ERIC Educational Resources Information Center

Rapp, Eric

2013-01-01

This research used an evidenced-based experimental 2 x 2 factorial design General Linear Model with Repeated Measures Analysis of Covariance (RMANCOVA). For this analysis, time served as the within-subjects factor while treatment group (i.e., static and signaling, dynamic and signaling, static without signaling, and dynamic without signaling)…

Signal analysis techniques for incipient failure detection in turbomachinery

NASA Technical Reports Server (NTRS)

Coffin, T.

1985-01-01

Signal analysis techniques for the detection and classification of incipient mechanical failures in turbomachinery were developed, implemented and evaluated. Signal analysis techniques available to describe dynamic measurement characteristics are reviewed. Time domain and spectral methods are described, and statistical classification in terms of moments is discussed. Several of these waveform analysis techniques were implemented on a computer and applied to dynamic signals. A laboratory evaluation of the methods with respect to signal detection capability is described. Plans for further technique evaluation and data base development to characterize turbopump incipient failure modes from Space Shuttle main engine (SSME) hot firing measurements are outlined.

Embedded DCT and wavelet methods for fine granular scalable video: analysis and comparison

NASA Astrophysics Data System (ADS)

van der Schaar-Mitrea, Mihaela; Chen, Yingwei; Radha, Hayder

2000-04-01

Video transmission over bandwidth-varying networks is becoming increasingly important due to emerging applications such as streaming of video over the Internet. The fundamental obstacle in designing such systems resides in the varying characteristics of the Internet (i.e. bandwidth variations and packet-loss patterns). In MPEG-4, a new SNR scalability scheme, called Fine-Granular-Scalability (FGS), is currently under standardization, which is able to adapt in real-time (i.e. at transmission time) to Internet bandwidth variations. The FGS framework consists of a non-scalable motion-predicted base-layer and an intra-coded fine-granular scalable enhancement layer. For example, the base layer can be coded using a DCT-based MPEG-4 compliant, highly efficient video compression scheme. Subsequently, the difference between the original and decoded base-layer is computed, and the resulting FGS-residual signal is intra-frame coded with an embedded scalable coder. In order to achieve high coding efficiency when compressing the FGS enhancement layer, it is crucial to analyze the nature and characteristics of residual signals common to the SNR scalability framework (including FGS). In this paper, we present a thorough analysis of SNR residual signals by evaluating its statistical properties, compaction efficiency and frequency characteristics. The signal analysis revealed that the energy compaction of the DCT and wavelet transforms is limited and the frequency characteristic of SNR residual signals decay rather slowly. Moreover, the blockiness artifacts of the low bit-rate coded base-layer result in artificial high frequencies in the residual signal. Subsequently, a variety of wavelet and embedded DCT coding techniques applicable to the FGS framework are evaluated and their results are interpreted based on the identified signal properties. As expected from the theoretical signal analysis, the rate-distortion performances of the embedded wavelet and DCT-based coders are very similar. However, improved results can be obtained for the wavelet coder by deblocking the base- layer prior to the FGS residual computation. Based on the theoretical analysis and our measurements, we can conclude that for an optimal complexity versus coding-efficiency trade- off, only limited wavelet decomposition (e.g. 2 stages) needs to be performed for the FGS-residual signal. Also, it was observed that the good rate-distortion performance of a coding technique for a certain image type (e.g. natural still-images) does not necessarily translate into similarly good performance for signals with different visual characteristics and statistical properties.

Signal Quality Improvement Algorithms for MEMS Gyroscope-Based Human Motion Analysis Systems: A Systematic Review.

PubMed

Du, Jiaying; Gerdtman, Christer; Lindén, Maria

2018-04-06

Motion sensors such as MEMS gyroscopes and accelerometers are characterized by a small size, light weight, high sensitivity, and low cost. They are used in an increasing number of applications. However, they are easily influenced by environmental effects such as temperature change, shock, and vibration. Thus, signal processing is essential for minimizing errors and improving signal quality and system stability. The aim of this work is to investigate and present a systematic review of different signal error reduction algorithms that are used for MEMS gyroscope-based motion analysis systems for human motion analysis or have the potential to be used in this area. A systematic search was performed with the search engines/databases of the ACM Digital Library, IEEE Xplore, PubMed, and Scopus. Sixteen papers that focus on MEMS gyroscope-related signal processing and were published in journals or conference proceedings in the past 10 years were found and fully reviewed. Seventeen algorithms were categorized into four main groups: Kalman-filter-based algorithms, adaptive-based algorithms, simple filter algorithms, and compensation-based algorithms. The algorithms were analyzed and presented along with their characteristics such as advantages, disadvantages, and time limitations. A user guide to the most suitable signal processing algorithms within this area is presented.

Signal Quality Improvement Algorithms for MEMS Gyroscope-Based Human Motion Analysis Systems: A Systematic Review

PubMed Central

Gerdtman, Christer

2018-01-01

Motion sensors such as MEMS gyroscopes and accelerometers are characterized by a small size, light weight, high sensitivity, and low cost. They are used in an increasing number of applications. However, they are easily influenced by environmental effects such as temperature change, shock, and vibration. Thus, signal processing is essential for minimizing errors and improving signal quality and system stability. The aim of this work is to investigate and present a systematic review of different signal error reduction algorithms that are used for MEMS gyroscope-based motion analysis systems for human motion analysis or have the potential to be used in this area. A systematic search was performed with the search engines/databases of the ACM Digital Library, IEEE Xplore, PubMed, and Scopus. Sixteen papers that focus on MEMS gyroscope-related signal processing and were published in journals or conference proceedings in the past 10 years were found and fully reviewed. Seventeen algorithms were categorized into four main groups: Kalman-filter-based algorithms, adaptive-based algorithms, simple filter algorithms, and compensation-based algorithms. The algorithms were analyzed and presented along with their characteristics such as advantages, disadvantages, and time limitations. A user guide to the most suitable signal processing algorithms within this area is presented. PMID:29642412

Identification and quantitation of semi-crystalline microplastics using image analysis and differential scanning calorimetry.

PubMed

Rodríguez Chialanza, Mauricio; Sierra, Ignacio; Pérez Parada, Andrés; Fornaro, Laura

2018-06-01

There are several techniques used to analyze microplastics. These are often based on a combination of visual and spectroscopic techniques. Here we introduce an alternative workflow for identification and mass quantitation through a combination of optical microscopy with image analysis (IA) and differential scanning calorimetry (DSC). We studied four synthetic polymers with environmental concern: low and high density polyethylene (LDPE and HDPE, respectively), polypropylene (PP), and polyethylene terephthalate (PET). Selected experiments were conducted to investigate (i) particle characterization and counting procedures based on image analysis with open-source software, (ii) chemical identification of microplastics based on DSC signal processing, (iii) dependence of particle size on DSC signal, and (iv) quantitation of microplastics mass based on DSC signal. We describe the potential and limitations of these techniques to increase reliability for microplastic analysis. Particle size demonstrated to have particular incidence in the qualitative and quantitative performance of DSC signals. Both, identification (based on characteristic onset temperature) and mass quantitation (based on heat flow) showed to be affected by particle size. As a result, a proper sample treatment which includes sieving of suspended particles is particularly required for this analytical approach.

Analysis of digital communication signals and extraction of parameters

NASA Astrophysics Data System (ADS)

Al-Jowder, Anwar

1994-12-01

The signal classification performance of four types of electronics support measure (ESM) communications detection systems is compared from the standpoint of the unintended receiver (interceptor). Typical digital communication signals considered include binary phase shift keying (BPSK), quadrature phase shift keying (QPSK), frequency shift keying (FSK), and on-off keying (OOK). The analysis emphasizes the use of available signal processing software. Detection methods compared include broadband energy detection, FFT-based narrowband energy detection, and two correlation methods which employ the fast Fourier transform (FFT). The correlation methods utilize modified time-frequency distributions, where one of these is based on the Wigner-Ville distribution (WVD). Gaussian white noise is added to the signal to simulate various signal-to-noise ratios (SNR's).

Stationary Wavelet-based Two-directional Two-dimensional Principal Component Analysis for EMG Signal Classification

NASA Astrophysics Data System (ADS)

Ji, Yi; Sun, Shanlin; Xie, Hong-Bo

2017-06-01

Discrete wavelet transform (WT) followed by principal component analysis (PCA) has been a powerful approach for the analysis of biomedical signals. Wavelet coefficients at various scales and channels were usually transformed into a one-dimensional array, causing issues such as the curse of dimensionality dilemma and small sample size problem. In addition, lack of time-shift invariance of WT coefficients can be modeled as noise and degrades the classifier performance. In this study, we present a stationary wavelet-based two-directional two-dimensional principal component analysis (SW2D2PCA) method for the efficient and effective extraction of essential feature information from signals. Time-invariant multi-scale matrices are constructed in the first step. The two-directional two-dimensional principal component analysis then operates on the multi-scale matrices to reduce the dimension, rather than vectors in conventional PCA. Results are presented from an experiment to classify eight hand motions using 4-channel electromyographic (EMG) signals recorded in healthy subjects and amputees, which illustrates the efficiency and effectiveness of the proposed method for biomedical signal analysis.

An evaluation of HEMT potential for millimeter-wave signal sources using interpolation and harmonic balance techniques

NASA Technical Reports Server (NTRS)

Kwon, Youngwoo; Pavlidis, Dimitris; Tutt, Marcel N.

1991-01-01

A large-signal analysis method based on an harmonic balance technique and a 2-D cubic spline interpolation function has been developed and applied to the prediction of InP-based HEMT oscillator performance for frequencies extending up to the submillimeter-wave range. The large-signal analysis method uses a limited number of DC and small-signal S-parameter data and allows the accurate characterization of HEMT large-signal behavior. The method has been validated experimentally using load-pull measurement. Oscillation frequency, power performance, and load requirements are discussed, with an operation capability of 300 GHz predicted using state-of-the-art devices (fmax is approximately equal to 450 GHz).

SVM-Based Spectral Analysis for Heart Rate from Multi-Channel WPPG Sensor Signals.

PubMed

Xiong, Jiping; Cai, Lisang; Wang, Fei; He, Xiaowei

2017-03-03

Although wrist-type photoplethysmographic (hereafter referred to as WPPG) sensor signals can measure heart rate quite conveniently, the subjects' hand movements can cause strong motion artifacts, and then the motion artifacts will heavily contaminate WPPG signals. Hence, it is challenging for us to accurately estimate heart rate from WPPG signals during intense physical activities. The WWPG method has attracted more attention thanks to the popularity of wrist-worn wearable devices. In this paper, a mixed approach called Mix-SVM is proposed, it can use multi-channel WPPG sensor signals and simultaneous acceleration signals to measurement heart rate. Firstly, we combine the principle component analysis and adaptive filter to remove a part of the motion artifacts. Due to the strong relativity between motion artifacts and acceleration signals, the further denoising problem is regarded as a sparse signals reconstruction problem. Then, we use a spectrum subtraction method to eliminate motion artifacts effectively. Finally, the spectral peak corresponding to heart rate is sought by an SVM-based spectral analysis method. Through the public PPG database in the 2015 IEEE Signal Processing Cup, we acquire the experimental results, i.e., the average absolute error was 1.01 beat per minute, and the Pearson correlation was 0.9972. These results also confirm that the proposed Mix-SVM approach has potential for multi-channel WPPG-based heart rate estimation in the presence of intense physical exercise.

Platform for Post-Processing Waveform-Based NDE

NASA Technical Reports Server (NTRS)

Roth, Don J.

2010-01-01

Signal- and image-processing methods are commonly needed to extract information from the waves, improve resolution of, and highlight defects in an image. Since some similarity exists for all waveform-based nondestructive evaluation (NDE) methods, it would seem that a common software platform containing multiple signal- and image-processing techniques to process the waveforms and images makes sense where multiple techniques, scientists, engineers, and organizations are involved. NDE Wave & Image Processor Version 2.0 software provides a single, integrated signal- and image-processing and analysis environment for total NDE data processing and analysis. It brings some of the most useful algorithms developed for NDE over the past 20 years into a commercial-grade product. The software can import signal/spectroscopic data, image data, and image series data. This software offers the user hundreds of basic and advanced signal- and image-processing capabilities including esoteric 1D and 2D wavelet-based de-noising, de-trending, and filtering. Batch processing is included for signal- and image-processing capability so that an optimized sequence of processing operations can be applied to entire folders of signals, spectra, and images. Additionally, an extensive interactive model-based curve-fitting facility has been included to allow fitting of spectroscopy data such as from Raman spectroscopy. An extensive joint-time frequency module is included for analysis of non-stationary or transient data such as that from acoustic emission, vibration, or earthquake data.

Ridge extraction from the time-frequency representation (TFR) of signals based on an image processing approach: application to the analysis of uterine electromyogram AR TFR.

PubMed

Terrien, Jérémy; Marque, Catherine; Germain, Guy

2008-05-01

Time-frequency representations (TFRs) of signals are increasingly being used in biomedical research. Analysis of such representations is sometimes difficult, however, and is often reduced to the extraction of ridges, or local energy maxima. In this paper, we describe a new ridge extraction method based on the image processing technique of active contours or snakes. We have tested our method on several synthetic signals and for the analysis of uterine electromyogram or electrohysterogram (EHG) recorded during gestation in monkeys. We have also evaluated a postprocessing algorithm that is especially suited for EHG analysis. Parameters are evaluated on real EHG signals in different gestational periods. The presented method gives good results when applied to synthetic as well as EHG signals. We have been able to obtain smaller ridge extraction errors when compared to two other methods specially developed for EHG. The gradient vector flow (GVF) snake method, or GVF-snake method, appears to be a good ridge extraction tool, which could be used on TFR of mono or multicomponent signals with good results.

A novel analysis method for near infrared spectroscopy based on Hilbert-Huang transform

NASA Astrophysics Data System (ADS)

Zhou, Zhenyu; Yang, Hongyu; Liu, Yun; Ruan, Zongcai; Luo, Qingming; Gong, Hui; Lu, Zuhong

2007-05-01

Near Infrared Imager (NIRI) has been widely used to access the brain functional activity non-invasively. We use a portable, multi-channel and continuous-wave NIR topography instrument to measure the concentration changes of each hemoglobin species and map cerebral cortex functional activation. By extracting some essential features from the BOLD signals, optical tomography is able to be a new way of neuropsychological studies. Fourier spectral analysis provides a common framework for examining the distribution of global energy in the frequency domain. However, this method assumes that the signal should be stationary, which limits its application in non-stationary system. The hemoglobin species concentration changes are of such kind. In this work we develop a new signal processing method using Hilbert-Huang transform to perform spectral analysis of the functional NIRI signals. Compared with wavelet based multi-resolution analysis (MRA), we demonstrated the extraction of task related signal for observation of activation in the prefrontal cortex (PFC) in vision stimulation experiment. This method provides a new analysis tool for functional NIRI signals. Our experimental results show that the proposed approach provides the unique method for reconstructing target signal without losing original information and enables us to understand the episode of functional NIRI more precisely.

Intensity-based masking: A tool to improve functional connectivity results of resting-state fMRI.

PubMed

Peer, Michael; Abboud, Sami; Hertz, Uri; Amedi, Amir; Arzy, Shahar

2016-07-01

Seed-based functional connectivity (FC) of resting-state functional MRI data is a widely used methodology, enabling the identification of functional brain networks in health and disease. Based on signal correlations across the brain, FC measures are highly sensitive to noise. A somewhat neglected source of noise is the fMRI signal attenuation found in cortical regions in close vicinity to sinuses and air cavities, mainly in the orbitofrontal, anterior frontal and inferior temporal cortices. BOLD signal recorded at these regions suffers from dropout due to susceptibility artifacts, resulting in an attenuated signal with reduced signal-to-noise ratio in as many as 10% of cortical voxels. Nevertheless, signal attenuation is largely overlooked during FC analysis. Here we first demonstrate that signal attenuation can significantly influence FC measures by introducing false functional correlations and diminishing existing correlations between brain regions. We then propose a method for the detection and removal of the attenuated signal ("intensity-based masking") by fitting a Gaussian-based model to the signal intensity distribution and calculating an intensity threshold tailored per subject. Finally, we apply our method on real-world data, showing that it diminishes false correlations caused by signal dropout, and significantly improves the ability to detect functional networks in single subjects. Furthermore, we show that our method increases inter-subject similarity in FC, enabling reliable distinction of different functional networks. We propose to include the intensity-based masking method as a common practice in the pre-processing of seed-based functional connectivity analysis, and provide software tools for the computation of intensity-based masks on fMRI data. Hum Brain Mapp 37:2407-2418, 2016. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Programming an offline-analyzer of motor imagery signals via python language.

PubMed

Alonso-Valerdi, Luz María; Sepulveda, Francisco

2011-01-01

Brain Computer Interface (BCI) systems control the user's environment via his/her brain signals. Brain signals related to motor imagery (MI) have become a widespread method employed by the BCI community. Despite the large number of references describing the MI signal treatment, there is not enough information related to the available programming languages that could be suitable to develop a specific-purpose MI-based BCI. The present paper describes the development of an offline-analysis system based on MI-EEG signals via open-source programming languages, and the assessment of the system using electrical activity recorded from three subjects. The analyzer recognized at least 63% of the MI signals corresponding to three classes. The results of the offline analysis showed a promising performance considering that the subjects have never undergone MI trainings.

Encoder fault analysis system based on Moire fringe error signal

NASA Astrophysics Data System (ADS)

Gao, Xu; Chen, Wei; Wan, Qiu-hua; Lu, Xin-ran; Xie, Chun-yu

2018-02-01

Aiming at the problem of any fault and wrong code in the practical application of photoelectric shaft encoder, a fast and accurate encoder fault analysis system is researched from the aspect of Moire fringe photoelectric signal processing. DSP28335 is selected as the core processor and high speed serial A/D converter acquisition card is used. And temperature measuring circuit using AD7420 is designed. Discrete data of Moire fringe error signal is collected at different temperatures and it is sent to the host computer through wireless transmission. The error signal quality index and fault type is displayed on the host computer based on the error signal identification method. The error signal quality can be used to diagnosis the state of error code through the human-machine interface.

Molecular genetic analysis of activation-tagged transcription factors thought to be involved in photomorphogenesis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Neff, Michael M.

This is a final report for Department of Energy Grant No. DE-FG02-08ER15927 entitled “Molecular Genetic Analysis of Activation-Tagged Transcription Factors Thought to be Involved in Photomorphogenesis”. Based on our preliminary photobiological and genetic analysis of the sob1-D mutant, we hypothesized that OBP3 is a transcription factor involved in both phytochrome and cryptochrome-mediated signal transduction. In addition, we hypothesized that OBP3 is involved in auxin signaling and root development. Based on our preliminary photobiological and genetic analysis of the sob2-D mutant, we also hypothesized that a related gene, LEP, is involved in hormone signaling and seedling development.

Systematic analysis of signaling pathways using an integrative environment.

PubMed

Visvanathan, Mahesh; Breit, Marc; Pfeifer, Bernhard; Baumgartner, Christian; Modre-Osprian, Robert; Tilg, Bernhard

2007-01-01

Understanding the biological processes of signaling pathways as a whole system requires an integrative software environment that has comprehensive capabilities. The environment should include tools for pathway design, visualization, simulation and a knowledge base concerning signaling pathways as one. In this paper we introduce a new integrative environment for the systematic analysis of signaling pathways. This system includes environments for pathway design, visualization, simulation and a knowledge base that combines biological and modeling information concerning signaling pathways that provides the basic understanding of the biological system, its structure and functioning. The system is designed with a client-server architecture. It contains a pathway designing environment and a simulation environment as upper layers with a relational knowledge base as the underlying layer. The TNFa-mediated NF-kB signal trans-duction pathway model was designed and tested using our integrative framework. It was also useful to define the structure of the knowledge base. Sensitivity analysis of this specific pathway was performed providing simulation data. Then the model was extended showing promising initial results. The proposed system offers a holistic view of pathways containing biological and modeling data. It will help us to perform biological interpretation of the simulation results and thus contribute to a better understanding of the biological system for drug identification.

Research on vibration signal of engine based on subband energy method

NASA Astrophysics Data System (ADS)

Wu, Chunmei; Cui, Feng; Zhao, Yong; Fu, Baohong; Ma, Junchi; Yang, Guihua

2017-04-01

Based on the research of DA462 type engine cylinder and cylinder head vibration signal of the surface, the signal measured in the time domain and frequency domain are analyzed in detail, draw the following conclusions: the analysis of vibration signal of the subband energy method is applied to the engine, the concentration response of each of the motivation band can clearly be seen. Through the analysis we can see that the combustion excitation frequency response from 0k to 1K, the vibration influence on the body piston lateral impact force is mainly concentrated in 2K˜5K frequency range of Hz, valve opening and closing the excitation response frequency is mainly concentrated in the 3K˜4K range of Hz, and thus locating the valve clearance fault. This method is simple, accurate and practical for the post processing and analysis of vibration signals.

Comparison of wavelet based denoising schemes for gear condition monitoring: An Artificial Neural Network based Approach

NASA Astrophysics Data System (ADS)

Ahmed, Rounaq; Srinivasa Pai, P.; Sriram, N. S.; Bhat, Vasudeva

2018-02-01

Vibration Analysis has been extensively used in recent past for gear fault diagnosis. The vibration signals extracted is usually contaminated with noise and may lead to wrong interpretation of results. The denoising of extracted vibration signals helps the fault diagnosis by giving meaningful results. Wavelet Transform (WT) increases signal to noise ratio (SNR), reduces root mean square error (RMSE) and is effective to denoise the gear vibration signals. The extracted signals have to be denoised by selecting a proper denoising scheme in order to prevent the loss of signal information along with noise. An approach has been made in this work to show the effectiveness of Principal Component Analysis (PCA) to denoise gear vibration signal. In this regard three selected wavelet based denoising schemes namely PCA, Empirical Mode Decomposition (EMD), Neighcoeff Coefficient (NC), has been compared with Adaptive Threshold (AT) an extensively used wavelet based denoising scheme for gear vibration signal. The vibration signals acquired from a customized gear test rig were denoised by above mentioned four denoising schemes. The fault identification capability as well as SNR, Kurtosis and RMSE for the four denoising schemes have been compared. Features extracted from the denoised signals have been used to train and test artificial neural network (ANN) models. The performances of the four denoising schemes have been evaluated based on the performance of the ANN models. The best denoising scheme has been identified, based on the classification accuracy results. PCA is effective in all the regards as a best denoising scheme.

Non-destructive testing of full-length bonded rock bolts based on HHT signal analysis

NASA Astrophysics Data System (ADS)

Shi, Z. M.; Liu, L.; Peng, M.; Liu, C. C.; Tao, F. J.; Liu, C. S.

2018-04-01

Full-length bonded rock bolts are commonly used in mining, tunneling and slope engineering because of their simple design and resistance to corrosion. However, the length of a rock bolt and grouting quality do not often meet the required design standards in practice because of the concealment and complexity of bolt construction. Non-destructive testing is preferred when testing a rock bolt's quality because of the convenience, low cost and wide detection range. In this paper, a signal analysis method for the non-destructive sound wave testing of full-length bonded rock bolts is presented, which is based on the Hilbert-Huang transform (HHT). First, we introduce the HHT analysis method to calculate the bolt length and identify defect locations based on sound wave reflection test signals, which includes decomposing the test signal via empirical mode decomposition (EMD), selecting the intrinsic mode functions (IMF) using the Pearson Correlation Index (PCI) and calculating the instantaneous phase and frequency via the Hilbert transform (HT). Second, six model tests are conducted using different grouting defects and bolt protruding lengths to verify the effectiveness of the HHT analysis method. Lastly, the influence of the bolt protruding length on the test signal, identification of multiple reflections from defects, bolt end and protruding end, and mode mixing from EMD are discussed. The HHT analysis method can identify the bolt length and grouting defect locations from signals that contain noise at multiple reflected interfaces. The reflection from the long protruding end creates an irregular test signal with many frequency peaks on the spectrum. The reflections from defects barely change the original signal because they are low energy, which cannot be adequately resolved using existing methods. The HHT analysis method can identify reflections from the long protruding end of the bolt and multiple reflections from grouting defects based on mutations in the instantaneous frequency, which makes weak reflections more noticeable. The mode mixing phenomenon is observed in several tests, but this does not markedly affect the identification results due to the simple medium in bolt tests. The mode mixing can be reduced by ensemble EMD (EEMD) or complete ensemble EMD with adaptive noise (CEEMDAN), which are powerful tools to used analyze the test signal in a complex medium and may play an important role in future studies. The HHT bolt signal analysis method is a self-adaptive and automatic process, which can be programed as analysis software and will make bolt tests more convenient in practice.

[Multi-channel in vivo recording techniques: signal processing of action potentials and local field potentials].

PubMed

Xu, Jia-Min; Wang, Ce-Qun; Lin, Long-Nian

2014-06-25

Multi-channel in vivo recording techniques are used to record ensemble neuronal activity and local field potentials (LFP) simultaneously. One of the key points for the technique is how to process these two sets of recorded neural signals properly so that data accuracy can be assured. We intend to introduce data processing approaches for action potentials and LFP based on the original data collected through multi-channel recording system. Action potential signals are high-frequency signals, hence high sampling rate of 40 kHz is normally chosen for recording. Based on waveforms of extracellularly recorded action potentials, tetrode technology combining principal component analysis can be used to discriminate neuronal spiking signals from differently spatially distributed neurons, in order to obtain accurate single neuron spiking activity. LFPs are low-frequency signals (lower than 300 Hz), hence the sampling rate of 1 kHz is used for LFPs. Digital filtering is required for LFP analysis to isolate different frequency oscillations including theta oscillation (4-12 Hz), which is dominant in active exploration and rapid-eye-movement (REM) sleep, gamma oscillation (30-80 Hz), which is accompanied by theta oscillation during cognitive processing, and high frequency ripple oscillation (100-250 Hz) in awake immobility and slow wave sleep (SWS) state in rodent hippocampus. For the obtained signals, common data post-processing methods include inter-spike interval analysis, spike auto-correlation analysis, spike cross-correlation analysis, power spectral density analysis, and spectrogram analysis.

Petri net-based method for the analysis of the dynamics of signal propagation in signaling pathways.

PubMed

Hardy, Simon; Robillard, Pierre N

2008-01-15

Cellular signaling networks are dynamic systems that propagate and process information, and, ultimately, cause phenotypical responses. Understanding the circuitry of the information flow in cells is one of the keys to understanding complex cellular processes. The development of computational quantitative models is a promising avenue for attaining this goal. Not only does the analysis of the simulation data based on the concentration variations of biological compounds yields information about systemic state changes, but it is also very helpful for obtaining information about the dynamics of signal propagation. This article introduces a new method for analyzing the dynamics of signal propagation in signaling pathways using Petri net theory. The method is demonstrated with the Ca(2+)/calmodulin-dependent protein kinase II (CaMKII) regulation network. The results constitute temporal information about signal propagation in the network, a simplified graphical representation of the network and of the signal propagation dynamics and a characterization of some signaling routes as regulation motifs.

Effect of extreme data loss on heart rate signals quantified by entropy analysis

NASA Astrophysics Data System (ADS)

Li, Yu; Wang, Jun; Li, Jin; Liu, Dazhao

2015-02-01

The phenomenon of data loss always occurs in the analysis of large databases. Maintaining the stability of analysis results in the event of data loss is very important. In this paper, we used a segmentation approach to generate a synthetic signal that is randomly wiped from data according to the Gaussian distribution and the exponential distribution of the original signal. Then, the logistic map is used as verification. Finally, two methods of measuring entropy-base-scale entropy and approximate entropy-are comparatively analyzed. Our results show the following: (1) Two key parameters-the percentage and the average length of removed data segments-can change the sequence complexity according to logistic map testing. (2) The calculation results have preferable stability for base-scale entropy analysis, which is not sensitive to data loss. (3) The loss percentage of HRV signals should be controlled below the range (p = 30 %), which can provide useful information in clinical applications.

[Research on Time-frequency Characteristics of Magneto-acoustic Signal of Different Thickness Medium Based on Wave Summing Method].

PubMed

Zhang, Shunqi; Yin, Tao; Ma, Ren; Liu, Zhipeng

2015-08-01

Functional imaging method of biological electrical characteristics based on magneto-acoustic effect gives valuable information of tissue in early tumor diagnosis, therein time and frequency characteristics analysis of magneto-acoustic signal is important in image reconstruction. This paper proposes wave summing method based on Green function solution for acoustic source of magneto-acoustic effect. Simulations and analysis under quasi 1D transmission condition are carried out to time and frequency characteristics of magneto-acoustic signal of models with different thickness. Simulation results of magneto-acoustic signal were verified through experiments. Results of the simulation with different thickness showed that time-frequency characteristics of magneto-acoustic signal reflected thickness of sample. Thin sample, which is less than one wavelength of pulse, and thick sample, which is larger than one wavelength, showed different summed waveform and frequency characteristics, due to difference of summing thickness. Experimental results verified theoretical analysis and simulation results. This research has laid a foundation for acoustic source and conductivity reconstruction to the medium with different thickness in magneto-acoustic imaging.

Separation of Intercepted Multi-Radar Signals Based on Parameterized Time-Frequency Analysis

NASA Astrophysics Data System (ADS)

Lu, W. L.; Xie, J. W.; Wang, H. M.; Sheng, C.

2016-09-01

Modern radars use complex waveforms to obtain high detection performance and low probabilities of interception and identification. Signals intercepted from multiple radars overlap considerably in both the time and frequency domains and are difficult to separate with primary time parameters. Time-frequency analysis (TFA), as a key signal-processing tool, can provide better insight into the signal than conventional methods. In particular, among the various types of TFA, parameterized time-frequency analysis (PTFA) has shown great potential to investigate the time-frequency features of such non-stationary signals. In this paper, we propose a procedure for PTFA to separate overlapped radar signals; it includes five steps: initiation, parameterized time-frequency analysis, demodulating the signal of interest, adaptive filtering and recovering the signal. The effectiveness of the method was verified with simulated data and an intercepted radar signal received in a microwave laboratory. The results show that the proposed method has good performance and has potential in electronic reconnaissance applications, such as electronic intelligence, electronic warfare support measures, and radar warning.

Multi-Modal Intelligent Traffic Signal Systems (MMITSS) impacts assessment.

DOT National Transportation Integrated Search

2015-08-01

The study evaluates the potential network-wide impacts of the Multi-Modal Intelligent Transportation Signal System (MMITSS) based on a field data analysis utilizing data collected from a MMITSS prototype and a simulation analysis. The Intelligent Tra...

Introduction to Radar Signal and Data Processing: The Opportunity

DTIC Science & Technology

2006-09-01

SpA) Director of Analysis of Integrated Systems Group Via Tiburtina Km. 12.400 00131 Rome ITALY e.mail: afarina@selex-si.com Key words: radar...signal processing, data processing, adaptivity, space-time adaptive processing, knowledge based systems , CFAR. 1. SUMMARY This paper introduces to...the lecture series dedicated to the knowledge-based radar signal and data processing. Knowledge-based expert system (KBS) is in the realm of

DOE Office of Scientific and Technical Information (OSTI.GOV)

Espinosa-Paredes, Gilberto; Prieto-Guerrero, Alfonso; Nunez-Carrera, Alejandro

This paper introduces a wavelet-based method to analyze instability events in a boiling water reactor (BWR) during transient phenomena. The methodology to analyze BWR signals includes the following: (a) the short-time Fourier transform (STFT) analysis, (b) decomposition using the continuous wavelet transform (CWT), and (c) application of multiresolution analysis (MRA) using discrete wavelet transform (DWT). STFT analysis permits the study, in time, of the spectral content of analyzed signals. The CWT provides information about ruptures, discontinuities, and fractal behavior. To detect these important features in the signal, a mother wavelet has to be chosen and applied at several scales tomore » obtain optimum results. MRA allows fast implementation of the DWT. Features like important frequencies, discontinuities, and transients can be detected with analysis at different levels of detail coefficients. The STFT was used to provide a comparison between a classic method and the wavelet-based method. The damping ratio, which is an important stability parameter, was calculated as a function of time. The transient behavior can be detected by analyzing the maximum contained in detail coefficients at different levels in the signal decomposition. This method allows analysis of both stationary signals and highly nonstationary signals in the timescale plane. This methodology has been tested with the benchmark power instability event of Laguna Verde nuclear power plant (NPP) Unit 1, which is a BWR-5 NPP.« less

Frequency hopping signal detection based on wavelet decomposition and Hilbert-Huang transform

NASA Astrophysics Data System (ADS)

Zheng, Yang; Chen, Xihao; Zhu, Rui

2017-07-01

Frequency hopping (FH) signal is widely adopted by military communications as a kind of low probability interception signal. Therefore, it is very important to research the FH signal detection algorithm. The existing detection algorithm of FH signals based on the time-frequency analysis cannot satisfy the time and frequency resolution requirement at the same time due to the influence of window function. In order to solve this problem, an algorithm based on wavelet decomposition and Hilbert-Huang transform (HHT) was proposed. The proposed algorithm removes the noise of the received signals by wavelet decomposition and detects the FH signals by Hilbert-Huang transform. Simulation results show the proposed algorithm takes into account both the time resolution and the frequency resolution. Correspondingly, the accuracy of FH signals detection can be improved.

Flow analysis system and method

NASA Technical Reports Server (NTRS)

Hill, Wayne S. (Inventor); Barck, Bruce N. (Inventor)

1998-01-01

A non-invasive flow analysis system and method wherein a sensor, such as an acoustic sensor, is coupled to a conduit for transmitting a signal which varies depending on the characteristics of the flow in the conduit. The signal is amplified and there is a filter, responsive to the sensor signal, and tuned to pass a narrow band of frequencies proximate the resonant frequency of the sensor. A demodulator generates an amplitude envelope of the filtered signal and a number of flow indicator quantities are calculated based on variations in amplitude of the amplitude envelope. A neural network, or its equivalent, is then used to determine the flow rate of the flow in the conduit based on the flow indicator quantities.

Design of microcontroller-based EMG and the analysis of EMG signals.

PubMed

Güler, Nihal Fatma; Hardalaç, Firat

2002-04-01

In this work, a microcontroller-based EMG designed and tested on 40 patients. When the patients are in rest, the fast Fourier transform (FFT) analysis was applied to EMG signals recorded from right leg peroneal region. The histograms are constructed from the results of the FFT analysis. The analysis results shows that the amplitude of fibrillation potential of the muscle fiber of 30 patients measured from peroneal region is low and the duration is short. This is the reason why the motor nerves degenerated and 10 patients were found to be healthy.

A method for environmental acoustic analysis improvement based on individual evaluation of common sources in urban areas.

PubMed

López-Pacheco, María G; Sánchez-Fernández, Luis P; Molina-Lozano, Herón

2014-01-15

Noise levels of common sources such as vehicles, whistles, sirens, car horns and crowd sounds are mixed in urban soundscapes. Nowadays, environmental acoustic analysis is performed based on mixture signals recorded by monitoring systems. These mixed signals make it difficult for individual analysis which is useful in taking actions to reduce and control environmental noise. This paper aims at separating, individually, the noise source from recorded mixtures in order to evaluate the noise level of each estimated source. A method based on blind deconvolution and blind source separation in the wavelet domain is proposed. This approach provides a basis to improve results obtained in monitoring and analysis of common noise sources in urban areas. The method validation is through experiments based on knowledge of the predominant noise sources in urban soundscapes. Actual recordings of common noise sources are used to acquire mixture signals using a microphone array in semi-controlled environments. The developed method has demonstrated great performance improvements in identification, analysis and evaluation of common urban sources. © 2013 Elsevier B.V. All rights reserved.

SU-E-J-261: Statistical Analysis and Chaotic Dynamics of Respiratory Signal of Patients in BodyFix

DOE Office of Scientific and Technical Information (OSTI.GOV)

Michalski, D; Huq, M; Bednarz, G

Purpose: To quantify respiratory signal of patients in BodyFix undergoing 4DCT scan with and without immobilization cover. Methods: 20 pairs of respiratory tracks recorded with RPM system during 4DCT scan were analyzed. Descriptive statistic was applied to selected parameters of exhale-inhale decomposition. Standardized signals were used with the delay method to build orbits in embedded space. Nonlinear behavior was tested with surrogate data. Sample entropy SE, Lempel-Ziv complexity LZC and the largest Lyapunov exponents LLE were compared. Results: Statistical tests show difference between scans for inspiration time and its variability, which is bigger for scans without cover. The same ismore » for variability of the end of exhalation and inhalation. Other parameters fail to show the difference. For both scans respiratory signals show determinism and nonlinear stationarity. Statistical test on surrogate data reveals their nonlinearity. LLEs show signals chaotic nature and its correlation with breathing period and its embedding delay time. SE, LZC and LLE measure respiratory signal complexity. Nonlinear characteristics do not differ between scans. Conclusion: Contrary to expectation cover applied to patients in BodyFix appears to have limited effect on signal parameters. Analysis based on trajectories of delay vectors shows respiratory system nonlinear character and its sensitive dependence on initial conditions. Reproducibility of respiratory signal can be evaluated with measures of signal complexity and its predictability window. Longer respiratory period is conducive for signal reproducibility as shown by these gauges. Statistical independence of the exhale and inhale times is also supported by the magnitude of LLE. The nonlinear parameters seem more appropriate to gauge respiratory signal complexity since its deterministic chaotic nature. It contrasts with measures based on harmonic analysis that are blind for nonlinear features. Dynamics of breathing, so crucial for 4D-based clinical technologies, can be better controlled if nonlinear-based methodology, which reflects respiration characteristic, is applied. Funding provided by Varian Medical Systems via Investigator Initiated Research Project.« less

Trellis coding with Continuous Phase Modulation (CPM) for satellite-based land-mobile communications

NASA Technical Reports Server (NTRS)

1989-01-01

This volume of the final report summarizes the results of our studies on the satellite-based mobile communications project. It includes: a detailed analysis, design, and simulations of trellis coded, full/partial response CPM signals with/without interleaving over various Rician fading channels; analysis and simulation of computational cutoff rates for coherent, noncoherent, and differential detection of CPM signals; optimization of the complete transmission system; analysis and simulation of power spectrum of the CPM signals; design and development of a class of Doppler frequency shift estimators; design and development of a symbol timing recovery circuit; and breadboard implementation of the transmission system. Studies prove the suitability of the CPM system for mobile communications.

Application of Petri net based analysis techniques to signal transduction pathways.

PubMed

Sackmann, Andrea; Heiner, Monika; Koch, Ina

2006-11-02

Signal transduction pathways are usually modelled using classical quantitative methods, which are based on ordinary differential equations (ODEs). However, some difficulties are inherent in this approach. On the one hand, the kinetic parameters involved are often unknown and have to be estimated. With increasing size and complexity of signal transduction pathways, the estimation of missing kinetic data is not possible. On the other hand, ODEs based models do not support any explicit insights into possible (signal-) flows within the network. Moreover, a huge amount of qualitative data is available due to high-throughput techniques. In order to get information on the systems behaviour, qualitative analysis techniques have been developed. Applications of the known qualitative analysis methods concern mainly metabolic networks. Petri net theory provides a variety of established analysis techniques, which are also applicable to signal transduction models. In this context special properties have to be considered and new dedicated techniques have to be designed. We apply Petri net theory to model and analyse signal transduction pathways first qualitatively before continuing with quantitative analyses. This paper demonstrates how to build systematically a discrete model, which reflects provably the qualitative biological behaviour without any knowledge of kinetic parameters. The mating pheromone response pathway in Saccharomyces cerevisiae serves as case study. We propose an approach for model validation of signal transduction pathways based on the network structure only. For this purpose, we introduce the new notion of feasible t-invariants, which represent minimal self-contained subnets being active under a given input situation. Each of these subnets stands for a signal flow in the system. We define maximal common transition sets (MCT-sets), which can be used for t-invariant examination and net decomposition into smallest biologically meaningful functional units. The paper demonstrates how Petri net analysis techniques can promote a deeper understanding of signal transduction pathways. The new concepts of feasible t-invariants and MCT-sets have been proven to be useful for model validation and the interpretation of the biological system behaviour. Whereas MCT-sets provide a decomposition of the net into disjunctive subnets, feasible t-invariants describe subnets, which generally overlap. This work contributes to qualitative modelling and to the analysis of large biological networks by their fully automatic decomposition into biologically meaningful modules.

Application of Petri net based analysis techniques to signal transduction pathways

PubMed Central

Sackmann, Andrea; Heiner, Monika; Koch, Ina

2006-01-01

Background Signal transduction pathways are usually modelled using classical quantitative methods, which are based on ordinary differential equations (ODEs). However, some difficulties are inherent in this approach. On the one hand, the kinetic parameters involved are often unknown and have to be estimated. With increasing size and complexity of signal transduction pathways, the estimation of missing kinetic data is not possible. On the other hand, ODEs based models do not support any explicit insights into possible (signal-) flows within the network. Moreover, a huge amount of qualitative data is available due to high-throughput techniques. In order to get information on the systems behaviour, qualitative analysis techniques have been developed. Applications of the known qualitative analysis methods concern mainly metabolic networks. Petri net theory provides a variety of established analysis techniques, which are also applicable to signal transduction models. In this context special properties have to be considered and new dedicated techniques have to be designed. Methods We apply Petri net theory to model and analyse signal transduction pathways first qualitatively before continuing with quantitative analyses. This paper demonstrates how to build systematically a discrete model, which reflects provably the qualitative biological behaviour without any knowledge of kinetic parameters. The mating pheromone response pathway in Saccharomyces cerevisiae serves as case study. Results We propose an approach for model validation of signal transduction pathways based on the network structure only. For this purpose, we introduce the new notion of feasible t-invariants, which represent minimal self-contained subnets being active under a given input situation. Each of these subnets stands for a signal flow in the system. We define maximal common transition sets (MCT-sets), which can be used for t-invariant examination and net decomposition into smallest biologically meaningful functional units. Conclusion The paper demonstrates how Petri net analysis techniques can promote a deeper understanding of signal transduction pathways. The new concepts of feasible t-invariants and MCT-sets have been proven to be useful for model validation and the interpretation of the biological system behaviour. Whereas MCT-sets provide a decomposition of the net into disjunctive subnets, feasible t-invariants describe subnets, which generally overlap. This work contributes to qualitative modelling and to the analysis of large biological networks by their fully automatic decomposition into biologically meaningful modules. PMID:17081284

Gear wear monitoring by modulation signal bispectrum based on motor current signal analysis

NASA Astrophysics Data System (ADS)

Zhang, Ruiliang; Gu, Fengshou; Mansaf, Haram; Wang, Tie; Ball, Andrew D.

2017-09-01

Gears are important mechanical components for power transmissions. Tooth wear is one of the most common failure modes, which can present throughout a gear's lifetime. It is significant to accurately monitor gear wear progression in order to take timely predictive maintenances. Motor current signature analysis (MCSA) is an effective and non-intrusive approach which is able to monitor faults from both electrical and mechanical systems. However, little research has been reported in monitoring the gear wear and estimating its severity based on MCSA. This paper presents a novel gear wear monitoring method through a modulation signal bispectrum based motor current signal analysis (MSB-MCSA). For a steady gear transmission, it is inevitable to exist load and speed oscillations due to various errors including wears. These oscillations can induce small modulations in the current signals of the driving motor. MSB is particularly effective in characterising such small modulation signals. Based on these understandings, the monitoring process was implemented based on the current signals from a run-to-failure test of an industrial two stages helical gearbox under a moderate accelerated fatigue process. At the initial operation of the test, MSB analysis results showed that the peak values at the bifrequencies of gear rotations and the power supply can be effective monitoring features for identifying faulty gears and wear severity as they exhibit agreeable changes with gear loads. A monotonically increasing trend established by these features allows a clear indication of the gear wear progression. The dismantle inspection at 477 h of operation, made when one of the monitored features is about 123% higher than its baseline, has found that there are severe scuffing wear marks on a number of tooth surfaces on the driving gear, showing that the gear endures a gradual wear process during its long test operation. Therefore, it is affirmed that the MSB-MSCA approach proposed is reliable and accurate for monitoring gear wear deterioration.

Global phosphorylation analysis of beta-arrestin-mediated signaling downstream of a seven transmembrane receptor (7TMR).

PubMed

Xiao, Kunhong; Sun, Jinpeng; Kim, Jihee; Rajagopal, Sudarshan; Zhai, Bo; Villén, Judit; Haas, Wilhelm; Kovacs, Jeffrey J; Shukla, Arun K; Hara, Makoto R; Hernandez, Marylens; Lachmann, Alexander; Zhao, Shan; Lin, Yuan; Cheng, Yishan; Mizuno, Kensaku; Ma'ayan, Avi; Gygi, Steven P; Lefkowitz, Robert J

2010-08-24

beta-Arrestin-mediated signaling downstream of seven transmembrane receptors (7TMRs) is a relatively new paradigm for signaling by these receptors. We examined changes in protein phosphorylation occurring when HEK293 cells expressing the angiotensin II type 1A receptor (AT1aR) were stimulated with the beta-arrestin-biased ligand Sar(1), Ile(4), Ile(8)-angiotensin (SII), a ligand previously found to signal through beta-arrestin-dependent, G protein-independent mechanisms. Using a phospho-antibody array containing 46 antibodies against signaling molecules, we found that phosphorylation of 35 proteins increased upon SII stimulation. These SII-mediated phosphorylation events were abrogated after depletion of beta-arrestin 2 through siRNA-mediated knockdown. We also performed an MS-based quantitative phosphoproteome analysis after SII stimulation using a strategy of stable isotope labeling of amino acids in cell culture (SILAC). We identified 1,555 phosphoproteins (4,552 unique phosphopeptides), of which 171 proteins (222 phosphopeptides) showed increased phosphorylation, and 53 (66 phosphopeptides) showed decreased phosphorylation upon SII stimulation of the AT1aR. This study identified 38 protein kinases and three phosphatases whose phosphorylation status changed upon SII treatment. Using computational approaches, we performed system-based analyses examining the beta-arrestin-mediated phosphoproteome including construction of a kinase-substrate network for beta-arrestin-mediated AT1aR signaling. Our analysis demonstrates that beta-arrestin-dependent signaling processes are more diverse than previously appreciated. Notably, our analysis identifies an AT1aR-mediated cytoskeletal reorganization network whereby beta-arrestin regulates phosphorylation of several key proteins, including cofilin and slingshot. This study provides a system-based view of beta-arrestin-mediated phosphorylation events downstream of a 7TMR and opens avenues for research in a rapidly evolving area of 7TMR signaling.

Use of Multiscale Entropy to Facilitate Artifact Detection in Electroencephalographic Signals

PubMed Central

Mariani, Sara; Borges, Ana F. T.; Henriques, Teresa; Goldberger, Ary L.; Costa, Madalena D.

2016-01-01

Electroencephalographic (EEG) signals present a myriad of challenges to analysis, beginning with the detection of artifacts. Prior approaches to noise detection have utilized multiple techniques, including visual methods, independent component analysis and wavelets. However, no single method is broadly accepted, inviting alternative ways to address this problem. Here, we introduce a novel approach based on a statistical physics method, multiscale entropy (MSE) analysis, which quantifies the complexity of a signal. We postulate that noise corrupted EEG signals have lower information content, and, therefore, reduced complexity compared with their noise free counterparts. We test the new method on an open-access database of EEG signals with and without added artifacts due to electrode motion. PMID:26738116

[Local Regression Algorithm Based on Net Analyte Signal and Its Application in Near Infrared Spectral Analysis].

PubMed

Zhang, Hong-guang; Lu, Jian-gang

2016-02-01

Abstract To overcome the problems of significant difference among samples and nonlinearity between the property and spectra of samples in spectral quantitative analysis, a local regression algorithm is proposed in this paper. In this algorithm, net signal analysis method(NAS) was firstly used to obtain the net analyte signal of the calibration samples and unknown samples, then the Euclidean distance between net analyte signal of the sample and net analyte signal of calibration samples was calculated and utilized as similarity index. According to the defined similarity index, the local calibration sets were individually selected for each unknown sample. Finally, a local PLS regression model was built on each local calibration sets for each unknown sample. The proposed method was applied to a set of near infrared spectra of meat samples. The results demonstrate that the prediction precision and model complexity of the proposed method are superior to global PLS regression method and conventional local regression algorithm based on spectral Euclidean distance.

Diagnosis of insomnia sleep disorder using short time frequency analysis of PSD approach applied on EEG signal using channel ROC-LOC.

PubMed

Siddiqui, Mohd Maroof; Srivastava, Geetika; Saeed, Syed Hasan

2016-01-01

Insomnia is a sleep disorder in which the subject encounters problems in sleeping. The aim of this study is to identify insomnia events from normal or effected person using time frequency analysis of PSD approach applied on EEG signals using channel ROC-LOC. In this research article, attributes and waveform of EEG signals of Human being are examined. The aim of this study is to draw the result in the form of signal spectral analysis of the changes in the domain of different stages of sleep. The analysis and calculation is performed in all stages of sleep of PSD of each EEG segment. Results indicate the possibility of recognizing insomnia events based on delta, theta, alpha and beta segments of EEG signals.

Nonlinear wave-wave interactions in the subauroral ionosphere on the basis of ISIS-2 satellite observations of Siple station VLF signals

NASA Technical Reports Server (NTRS)

Ohnami, S.; Hayakawa, M.; Bell, T. F.; Ondoh, T.

1993-01-01

Nonlinear wave-wave interaction between signals from a ground-based VLF transmitter and narrow-band ELF emissions in the subauroral ionosphere is studied by means of the bispectrum and bicoherence analysis. A bicoherence analysis has indicated that the sideband structures around the Siple transmitter signal received onboard the ISIS satellite are due to the nonlinear interaction between the Siple VLF signal and the pre-existing ELF emission.

Fourier analysis and signal processing by use of the Moebius inversion formula

NASA Technical Reports Server (NTRS)

Reed, Irving S.; Yu, Xiaoli; Shih, Ming-Tang; Tufts, Donald W.; Truong, T. K.

1990-01-01

A novel Fourier technique for digital signal processing is developed. This approach to Fourier analysis is based on the number-theoretic method of the Moebius inversion of series. The Fourier transform method developed is shown also to yield the convolution of two signals. A computer simulation shows that this method for finding Fourier coefficients is quite suitable for digital signal processing. It competes with the classical FFT (fast Fourier transform) approach in terms of accuracy, complexity, and speed.

Analysis and iterative equalization of transient and adiabatic chirp effects in DML-based OFDM transmission systems.

PubMed

Wei, Chia-Chien

2012-11-05

This work theoretically studies the transmission performance of a DML-based OFDM system by small-signal approximation, and the model considers both the transient and adiabatic chirps. The dispersion-induced distortion is modeled as subcarrier-to-subcarrier intermixing interference (SSII), and the theoretical SSII agrees with the distortion obtained from large-signal simulation statistically and deterministically. The analysis shows that the presence of the adiabatic chirp will ease power fading or even provide gain, but will increase the SSII to deteriorate OFDM signals after dispersive transmission. Furthermore, this work also proposes a novel iterative equalization to eliminate the SSII. From the simulation, the distortion could be effectively mitigated by the proposed equalization such that the maximum transmission distance of the DML-based OFDM signal is significantly improved. For instance, the transmission distance of a 30-Gbps DML-based OFDM signal can be extended from 10 km to more than 100 km. Besides, since the dispersion-induced distortion could be effectively mitigated by the equalization, negative power penalties are observed at some distances due to chirp-induced power gain.

Sensor, signal, and image informatics - state of the art and current topics.

PubMed

Lehmann, T M; Aach, T; Witte, H

2006-01-01

The number of articles published annually in the fields of biomedical signal and image acquisition and processing is increasing. Based on selected examples, this survey aims at comprehensively demonstrating the recent trends and developments. Four articles are selected for biomedical data acquisition covering topics such as dose saving in CT, C-arm X-ray imaging systems for volume imaging, and the replacement of dose-intensive CT-based diagnostic with harmonic ultrasound imaging. Regarding biomedical signal analysis (BSA), the four selected articles discuss the equivalence of different time-frequency approaches for signal analysis, an application to Cochlea implants, where time-frequency analysis is applied for controlling the replacement system, recent trends for fusion of different modalities, and the role of BSA as part of a brain machine interfaces. To cover the broad spectrum of publications in the field of biomedical image processing, six papers are focused. Important topics are content-based image retrieval in medical applications, automatic classification of tongue photographs from traditional Chinese medicine, brain perfusion analysis in single photon emission computed tomography (SPECT), model-based visualization of vascular trees, and virtual surgery, where enhanced visualization and haptic feedback techniques are combined with a sphere-filled model of the organ. The selected papers emphasize the five fields forming the chain of biomedical data processing: (1) data acquisition, (2) data reconstruction and pre-processing, (3) data handling, (4) data analysis, and (5) data visualization. Fields 1 and 2 form the sensor informatics, while fields 2 to 5 form signal or image informatics with respect to the nature of the data considered. Biomedical data acquisition and pre-processing, as well as data handling, analysis and visualization aims at providing reliable tools for decision support that improve the quality of health care. Comprehensive evaluation of the processing methods and their reliable integration in routine applications are future challenges in the field of sensor, signal and image informatics.

Characterization of electrically-active defects in ultraviolet light-emitting diodes with laser-based failure analysis techniques

DOE Office of Scientific and Technical Information (OSTI.GOV)

Miller, Mary A.; Tangyunyong, Paiboon; Cole, Edward I.

2016-01-14

Laser-based failure analysis techniques demonstrate the ability to quickly and non-intrusively screen deep ultraviolet light-emitting diodes (LEDs) for electrically-active defects. In particular, two laser-based techniques, light-induced voltage alteration and thermally-induced voltage alteration, generate applied voltage maps (AVMs) that provide information on electrically-active defect behavior including turn-on bias, density, and spatial location. Here, multiple commercial LEDs were examined and found to have dark defect signals in the AVM indicating a site of reduced resistance or leakage through the diode. The existence of the dark defect signals in the AVM correlates strongly with an increased forward-bias leakage current. This increased leakage ismore » not present in devices without AVM signals. Transmission electron microscopy analysis of a dark defect signal site revealed a dislocation cluster through the pn junction. The cluster included an open core dislocation. Even though LEDs with few dark AVM defect signals did not correlate strongly with power loss, direct association between increased open core dislocation densities and reduced LED device performance has been presented elsewhere [M. W. Moseley et al., J. Appl. Phys. 117, 095301 (2015)].« less

49 CFR 236.923 - Task analysis and basic requirements.

Code of Federal Regulations, 2011 CFR

2011-10-01

... classroom, simulator, computer-based, hands-on, or other formally structured training and testing, except... for Processor-Based Signal and Train Control Systems § 236.923 Task analysis and basic requirements...) Based on a formal task analysis, identify the installation, maintenance, repair, modification...

Efficient Analysis of Mass Spectrometry Data Using the Isotope Wavelet

NASA Astrophysics Data System (ADS)

Hussong, Rene; Tholey, Andreas; Hildebrandt, Andreas

2007-09-01

Mass spectrometry (MS) has become today's de-facto standard for high-throughput analysis in proteomics research. Its applications range from toxicity analysis to MS-based diagnostics. Often, the time spent on the MS experiment itself is significantly less than the time necessary to interpret the measured signals, since the amount of data can easily exceed several gigabytes. In addition, automated analysis is hampered by baseline artifacts, chemical as well as electrical noise, and an irregular spacing of data points. Thus, filtering techniques originating from signal and image analysis are commonly employed to address these problems. Unfortunately, smoothing, base-line reduction, and in particular a resampling of data points can affect important characteristics of the experimental signal. To overcome these problems, we propose a new family of wavelet functions based on the isotope wavelet, which is hand-tailored for the analysis of mass spectrometry data. The resulting technique is theoretically well-founded and compares very well with standard peak picking tools, since it is highly robust against noise spoiling the data, but at the same time sufficiently sensitive to detect even low-abundant peptides.

Generalized sample entropy analysis for traffic signals based on similarity measure

NASA Astrophysics Data System (ADS)

Shang, Du; Xu, Mengjia; Shang, Pengjian

2017-05-01

Sample entropy is a prevailing method used to quantify the complexity of a time series. In this paper a modified method of generalized sample entropy and surrogate data analysis is proposed as a new measure to assess the complexity of a complex dynamical system such as traffic signals. The method based on similarity distance presents a different way of signals patterns match showing distinct behaviors of complexity. Simulations are conducted over synthetic data and traffic signals for providing the comparative study, which is provided to show the power of the new method. Compared with previous sample entropy and surrogate data analysis, the new method has two main advantages. The first one is that it overcomes the limitation about the relationship between the dimension parameter and the length of series. The second one is that the modified sample entropy functions can be used to quantitatively distinguish time series from different complex systems by the similar measure.

Improving resolution of crosswell seismic section based on time-frequency analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Luo, H.; Li, Y.

1994-12-31

According to signal theory, to improve resolution of seismic section is to extend high-frequency band of seismic signal. In cross-well section, sonic log can be regarded as a reliable source providing high-frequency information to the trace near the borehole. In such case, what to do is to introduce this high-frequency information into the whole section. However, neither traditional deconvolution algorithms nor some new inversion methods such as BCI (Broad Constraint Inversion) are satisfied because of high-frequency noise and nonuniqueness of inversion results respectively. To overcome their disadvantages, this paper presents a new algorithm based on Time-Frequency Analysis (TFA) technology whichmore » has been increasingly received much attention as an useful signal analysis too. Practical applications show that the new method is a stable scheme to improve resolution of cross-well seismic section greatly without decreasing Signal to Noise Ratio (SNR).« less

Multi- and monofractal indices of short-term heart rate variability.

PubMed

Fischer, R; Akay, M; Castiglioni, P; Di Rienzo, M

2003-09-01

Indices of heart rate variability (HRV) based on fractal signal models have recently been shown to possess value as predictors of mortality in specific patient populations. To develop more powerful clinical indices of HRV based on a fractal signal model, the study investigated two HRV indices based on a monofractal signal model called fractional Brownian motion and an index based on a multifractal signal model called multifractional Brownian motion. The performance of the indices was compared with an HRV index in common clinical use. To compare the indices, 18 normal subjects were subjected to postural changes, and the indices were compared on their ability to respond to the resulting autonomic events in HRV recordings. The magnitude of the response to postural change (normalised by the measurement variability) was assessed by analysis of variance and multiple comparison testing. Four HRV indices were investigated for this study: the standard deviation of all normal R-R intervals; an HRV index commonly used in the clinic; detrended fluctuation analysis, an HRV index found to be the most powerful predictor of mortality in a study of patients with depressed left ventricular function; an HRV index developed using the maximum likelihood estimation (MLE) technique for a monofractal signal model; and an HRV index developed for the analysis of multifractional Brownian motion signals. The HRV index based on the MLE technique was found to respond most strongly to the induced postural changes (95% CI). The magnitude of its response (normalised by the measurement variability) was at least 25% greater than any of the other indices tested.

Correlation dynamics and enhanced signals for the identification of serial biomolecules and DNA bases.

PubMed

Ahmed, Towfiq; Haraldsen, Jason T; Rehr, John J; Di Ventra, Massimiliano; Schuller, Ivan; Balatsky, Alexander V

2014-03-28

Nanopore-based sequencing has demonstrated a significant potential for the development of fast, accurate, and cost-efficient fingerprinting techniques for next generation molecular detection and sequencing. We propose a specific multilayered graphene-based nanopore device architecture for the recognition of single biomolecules. Molecular detection and analysis can be accomplished through the detection of transverse currents as the molecule or DNA base translocates through the nanopore. To increase the overall signal-to-noise ratio and the accuracy, we implement a new 'multi-point cross-correlation' technique for identification of DNA bases or other molecules on the single molecular level. We demonstrate that the cross-correlations between each nanopore will greatly enhance the transverse current signal for each molecule. We implement first-principles transport calculations for DNA bases surveyed across a multilayered graphene nanopore system to illustrate the advantages of the proposed geometry. A time-series analysis of the cross-correlation functions illustrates the potential of this method for enhancing the signal-to-noise ratio. This work constitutes a significant step forward in facilitating fingerprinting of single biomolecules using solid state technology.

Laplace Transform Based Radiative Transfer Studies

NASA Astrophysics Data System (ADS)

Hu, Y.; Lin, B.; Ng, T.; Yang, P.; Wiscombe, W.; Herath, J.; Duffy, D.

2006-12-01

Multiple scattering is the major uncertainty for data analysis of space-based lidar measurements. Until now, accurate quantitative lidar data analysis has been limited to very thin objects that are dominated by single scattering, where photons from the laser beam only scatter a single time with particles in the atmosphere before reaching the receiver, and simple linear relationship between physical property and lidar signal exists. In reality, multiple scattering is always a factor in space-based lidar measurement and it dominates space- based lidar returns from clouds, dust aerosols, vegetation canopy and phytoplankton. While multiple scattering are clear signals, the lack of a fast-enough lidar multiple scattering computation tool forces us to treat the signal as unwanted "noise" and use simple multiple scattering correction scheme to remove them. Such multiple scattering treatments waste the multiple scattering signals and may cause orders of magnitude errors in retrieved physical properties. Thus the lack of fast and accurate time-dependent radiative transfer tools significantly limits lidar remote sensing capabilities. Analyzing lidar multiple scattering signals requires fast and accurate time-dependent radiative transfer computations. Currently, multiple scattering is done with Monte Carlo simulations. Monte Carlo simulations take minutes to hours and are too slow for interactive satellite data analysis processes and can only be used to help system / algorithm design and error assessment. We present an innovative physics approach to solve the time-dependent radiative transfer problem. The technique utilizes FPGA based reconfigurable computing hardware. The approach is as following, 1. Physics solution: Perform Laplace transform on the time and spatial dimensions and Fourier transform on the viewing azimuth dimension, and convert the radiative transfer differential equation solving into a fast matrix inversion problem. The majority of the radiative transfer computation goes to matrix inversion processes, FFT and inverse Laplace transforms. 2. Hardware solutions: Perform the well-defined matrix inversion, FFT and Laplace transforms on highly parallel, reconfigurable computing hardware. This physics-based computational tool leads to accurate quantitative analysis of space-based lidar signals and improves data quality of current lidar mission such as CALIPSO. This presentation will introduce the basic idea of this approach, preliminary results based on SRC's FPGA-based Mapstation, and how we may apply it to CALIPSO data analysis.

Analysis of Digital Communication Signals and Extraction of Parameters.

DTIC Science & Technology

1994-12-01

Fast Fourier Transform (FFT). The correlation methods utilize modified time-frequency distributions , where one of these is based on the Wigner - Ville ... Distribution ( WVD ). Gaussian white noise is added to the signal to simulate various signal-to-noise ratios (SNRs).

Logic-Based Models for the Analysis of Cell Signaling Networks†

PubMed Central

2010-01-01

Computational models are increasingly used to analyze the operation of complex biochemical networks, including those involved in cell signaling networks. Here we review recent advances in applying logic-based modeling to mammalian cell biology. Logic-based models represent biomolecular networks in a simple and intuitive manner without describing the detailed biochemistry of each interaction. A brief description of several logic-based modeling methods is followed by six case studies that demonstrate biological questions recently addressed using logic-based models and point to potential advances in model formalisms and training procedures that promise to enhance the utility of logic-based methods for studying the relationship between environmental inputs and phenotypic or signaling state outputs of complex signaling networks. PMID:20225868

Adventitious sounds identification and extraction using temporal-spectral dominance-based features.

PubMed

Jin, Feng; Krishnan, Sridhar Sri; Sattar, Farook

2011-11-01

Respiratory sound (RS) signals carry significant information about the underlying functioning of the pulmonary system by the presence of adventitious sounds (ASs). Although many studies have addressed the problem of pathological RS classification, only a limited number of scientific works have focused on the analysis of the evolution of symptom-related signal components in joint time-frequency (TF) plane. This paper proposes a new signal identification and extraction method for various ASs based on instantaneous frequency (IF) analysis. The presented TF decomposition method produces a noise-resistant high definition TF representation of RS signals as compared to the conventional linear TF analysis methods, yet preserving the low computational complexity as compared to those quadratic TF analysis methods. The discarded phase information in conventional spectrogram has been adopted for the estimation of IF and group delay, and a temporal-spectral dominance spectrogram has subsequently been constructed by investigating the TF spreads of the computed time-corrected IF components. The proposed dominance measure enables the extraction of signal components correspond to ASs from noisy RS signal at high noise level. A new set of TF features has also been proposed to quantify the shapes of the obtained TF contours, and therefore strongly, enhances the identification of multicomponents signals such as polyphonic wheezes. An overall accuracy of 92.4±2.9% for the classification of real RS recordings shows the promising performance of the presented method.

LWT Based Sensor Node Signal Processing in Vehicle Surveillance Distributed Sensor Network

NASA Astrophysics Data System (ADS)

Cha, Daehyun; Hwang, Chansik

Previous vehicle surveillance researches on distributed sensor network focused on overcoming power limitation and communication bandwidth constraints in sensor node. In spite of this constraints, vehicle surveillance sensor node must have signal compression, feature extraction, target localization, noise cancellation and collaborative signal processing with low computation and communication energy dissipation. In this paper, we introduce an algorithm for light-weight wireless sensor node signal processing based on lifting scheme wavelet analysis feature extraction in distributed sensor network.

49 CFR 236.923 - Task analysis and basic requirements.

Code of Federal Regulations, 2013 CFR

2013-10-01

..., INSPECTION, MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Standards for Processor-Based Signal and Train Control Systems § 236.923 Task analysis and basic requirements..., inspection, testing, and operating tasks that must be performed on a railroad's products. This includes the...

49 CFR 236.923 - Task analysis and basic requirements.

Code of Federal Regulations, 2014 CFR

2014-10-01

..., INSPECTION, MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Standards for Processor-Based Signal and Train Control Systems § 236.923 Task analysis and basic requirements..., inspection, testing, and operating tasks that must be performed on a railroad's products. This includes the...

49 CFR 236.923 - Task analysis and basic requirements.

Code of Federal Regulations, 2010 CFR

2010-10-01

..., INSPECTION, MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Standards for Processor-Based Signal and Train Control Systems § 236.923 Task analysis and basic requirements..., inspection, testing, and operating tasks that must be performed on a railroad's products. This includes the...

49 CFR 236.923 - Task analysis and basic requirements.

Code of Federal Regulations, 2012 CFR

2012-10-01

..., INSPECTION, MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Standards for Processor-Based Signal and Train Control Systems § 236.923 Task analysis and basic requirements..., inspection, testing, and operating tasks that must be performed on a railroad's products. This includes the...

Novel Signal Noise Reduction Method through Cluster Analysis, Applied to Photoplethysmography.

PubMed

Waugh, William; Allen, John; Wightman, James; Sims, Andrew J; Beale, Thomas A W

2018-01-01

Physiological signals can often become contaminated by noise from a variety of origins. In this paper, an algorithm is described for the reduction of sporadic noise from a continuous periodic signal. The design can be used where a sample of a periodic signal is required, for example, when an average pulse is needed for pulse wave analysis and characterization. The algorithm is based on cluster analysis for selecting similar repetitions or pulses from a periodic single. This method selects individual pulses without noise, returns a clean pulse signal, and terminates when a sufficiently clean and representative signal is received. The algorithm is designed to be sufficiently compact to be implemented on a microcontroller embedded within a medical device. It has been validated through the removal of noise from an exemplar photoplethysmography (PPG) signal, showing increasing benefit as the noise contamination of the signal increases. The algorithm design is generalised to be applicable for a wide range of physiological (physical) signals.

Non-stationary signal analysis based on general parameterized time-frequency transform and its application in the feature extraction of a rotary machine

NASA Astrophysics Data System (ADS)

Zhou, Peng; Peng, Zhike; Chen, Shiqian; Yang, Yang; Zhang, Wenming

2018-06-01

With the development of large rotary machines for faster and more integrated performance, the condition monitoring and fault diagnosis for them are becoming more challenging. Since the time-frequency (TF) pattern of the vibration signal from the rotary machine often contains condition information and fault feature, the methods based on TF analysis have been widely-used to solve these two problems in the industrial community. This article introduces an effective non-stationary signal analysis method based on the general parameterized time-frequency transform (GPTFT). The GPTFT is achieved by inserting a rotation operator and a shift operator in the short-time Fourier transform. This method can produce a high-concentrated TF pattern with a general kernel. A multi-component instantaneous frequency (IF) extraction method is proposed based on it. The estimation for the IF of every component is accomplished by defining a spectrum concentration index (SCI). Moreover, such an IF estimation process is iteratively operated until all the components are extracted. The tests on three simulation examples and a real vibration signal demonstrate the effectiveness and superiority of our method.

Fetal source extraction from magnetocardiographic recordings by dependent component analysis

NASA Astrophysics Data System (ADS)

de Araujo, Draulio B.; Kardec Barros, Allan; Estombelo-Montesco, Carlos; Zhao, Hui; Roque da Silva Filho, A. C.; Baffa, Oswaldo; Wakai, Ronald; Ohnishi, Noboru

2005-10-01

Fetal magnetocardiography (fMCG) has been extensively reported in the literature as a non-invasive, prenatal technique that can be used to monitor various functions of the fetal heart. However, fMCG signals often have low signal-to-noise ratio (SNR) and are contaminated by strong interference from the mother's magnetocardiogram signal. A promising, efficient tool for extracting signals, even under low SNR conditions, is blind source separation (BSS), or independent component analysis (ICA). Herein we propose an algorithm based on a variation of ICA, where the signal of interest is extracted using a time delay obtained from an autocorrelation analysis. We model the system using autoregression, and identify the signal component of interest from the poles of the autocorrelation function. We show that the method is effective in removing the maternal signal, and is computationally efficient. We also compare our results to more established ICA methods, such as FastICA.

Partial correlation-based functional connectivity analysis for functional near-infrared spectroscopy signals

NASA Astrophysics Data System (ADS)

Akın, Ata

2017-12-01

A theoretical framework, a partial correlation-based functional connectivity (PC-FC) analysis to functional near-infrared spectroscopy (fNIRS) data, is proposed. This is based on generating a common background signal from a high passed version of fNIRS data averaged over all channels as the regressor in computing the PC between pairs of channels. This approach has been employed to real data collected during a Stroop task. The results show a strong significance in the global efficiency (GE) metric computed by the PC-FC analysis for neutral, congruent, and incongruent stimuli (NS, CS, IcS; GEN=0.10±0.009, GEC=0.11±0.01, GEIC=0.13±0.015, p=0.0073). A positive correlation (r=0.729 and p=0.0259) is observed between the interference of reaction times (incongruent-neutral) and interference of GE values (GEIC-GEN) computed from [HbO] signals.

Research on the fault diagnosis of bearing based on wavelet and demodulation

NASA Astrophysics Data System (ADS)

Li, Jiapeng; Yuan, Yu

2017-05-01

As a most commonly-used machine part, antifriction bearing is extensively used in mechanical equipment. Vibration signal analysis is one of the methods to monitor and diagnose the running status of antifriction bearings. Therefore, using wavelet analysis for demising is of great importance in the engineering practice. This paper firstly presented the basic theory of wavelet analysis to study the transformation, decomposition and reconstruction of wavelet. In addition, edition software LabVIEW was adopted to conduct wavelet and demodulation upon the vibration signal of antifriction bearing collected. With the combination of Hilbert envelop demodulation analysis, the fault character frequencies of the demised signal were extracted to conduct fault diagnosis analysis, which serves as a reference for the wavelet and demodulation of the vibration signal in engineering practice.

Classification using NMR-based metabolomics of Sophora flavescens grown in Japan and China.

PubMed

Suzuki, Ryuichiro; Ikeda, Yuriko; Yamamoto, Akari; Saima, Toyoe; Fujita, Tatsuya; Fukuda, Tatsuo; Fukuda, Eriko; Baba, Masaki; Okada, Yoshihito; Shirataki, Yoshiaki

2012-11-01

We demonstrate that NMR-based metabolomics can be used to identify the country of growth (Japan or China) of Sophora flavescens plants. Principle Component Analysis (PCA) conducted on extracts of S. flavescens grown in China provided data distinct from that of extracts of plants grown in Japan. Loading plot analysis showed signals characteristic of Japanese S. flavescens. NMR analyses showed these signals to be due to kurarinol (1) and kushenol H (2). These compounds were confirmed by HPLC analysis to be distinctive markers for Japanese S. flavescens.

Audio-guided audiovisual data segmentation, indexing, and retrieval

NASA Astrophysics Data System (ADS)

Zhang, Tong; Kuo, C.-C. Jay

1998-12-01

While current approaches for video segmentation and indexing are mostly focused on visual information, audio signals may actually play a primary role in video content parsing. In this paper, we present an approach for automatic segmentation, indexing, and retrieval of audiovisual data, based on audio content analysis. The accompanying audio signal of audiovisual data is first segmented and classified into basic types, i.e., speech, music, environmental sound, and silence. This coarse-level segmentation and indexing step is based upon morphological and statistical analysis of several short-term features of the audio signals. Then, environmental sounds are classified into finer classes, such as applause, explosions, bird sounds, etc. This fine-level classification and indexing step is based upon time- frequency analysis of audio signals and the use of the hidden Markov model as the classifier. On top of this archiving scheme, an audiovisual data retrieval system is proposed. Experimental results show that the proposed approach has an accuracy rate higher than 90 percent for the coarse-level classification, and higher than 85 percent for the fine-level classification. Examples of audiovisual data segmentation and retrieval are also provided.

Real time automatic detection of bearing fault in induction machine using kurtogram analysis.

PubMed

Tafinine, Farid; Mokrani, Karim

2012-11-01

A proposed signal processing technique for incipient real time bearing fault detection based on kurtogram analysis is presented in this paper. The kurtogram is a fourth-order spectral analysis tool introduced for detecting and characterizing non-stationarities in a signal. This technique starts from investigating the resonance signatures over selected frequency bands to extract the representative features. The traditional spectral analysis is not appropriate for non-stationary vibration signal and for real time diagnosis. The performance of the proposed technique is examined by a series of experimental tests corresponding to different bearing conditions. Test results show that this signal processing technique is an effective bearing fault automatic detection method and gives a good basis for an integrated induction machine condition monitor.

Radar mechanocardiography: a novel analysis of the mechanical behavior of the heart.

PubMed

Tavakolian, Kouhyar; Zadeh, Faranak M; Chuo, Yindar; Siu, Tiffany; Vaseghi, Ali; Kaminska, Bozena

2008-01-01

In this paper a novel system for detection of the mechanical movement of heart, mechanocardiography (MCG), with no connection to the subject's body is presented. This signal is based on radar technology. The acquired signal is highly correlated to the acceleration-based ballistocardiograph signal (BCG) recorded directly from the sternum. It is shown that the heart and breathing rates can be reliably detected using this system.

A node-wise analysis of the uterine muscle networks for pregnancy monitoring.

PubMed

Nader, N; Hassan, M; Falou, W; Marque, C; Khalil, M

2016-08-01

The recent past years have seen a noticeable increase of interest in the correlation analysis of electrohysterographic (EHG) signals in the perspective of improving the pregnancy monitoring. Here we propose a new approach based on the functional connectivity between multichannel (4×4 matrix) EHG signals recorded from the women's abdomen. The proposed pipeline includes i) the computation of the statistical couplings between the multichannel EHG signals, ii) the characterization of the connectivity matrices, computed by using the imaginary part of the coherence, based on the graph-theory analysis and iii) the use of these measures for pregnancy monitoring. The method was evaluated on a dataset of EHGs, in order to track the correlation between EHGs collected by each electrode of the matrix (called `node-wise' analysis) and follow their evolution along weeks before labor. Results showed that the strength of each node significantly increases from pregnancy to labor. Electrodes located on the median vertical axis of the uterus seemed to be the more discriminant. We speculate that the network-based analysis can be a very promising tool to improve pregnancy monitoring.

An alternative approach to measure similarity between two deterministic transient signals

NASA Astrophysics Data System (ADS)

Shin, Kihong

2016-06-01

In many practical engineering applications, it is often required to measure the similarity of two signals to gain insight into the conditions of a system. For example, an application that monitors machinery can regularly measure the signal of the vibration and compare it to a healthy reference signal in order to monitor whether or not any fault symptom is developing. Also in modal analysis, a frequency response function (FRF) from a finite element model (FEM) is often compared with an FRF from experimental modal analysis. Many different similarity measures are applicable in such cases, and correlation-based similarity measures may be most frequently used among these such as in the case where the correlation coefficient in the time domain and the frequency response assurance criterion (FRAC) in the frequency domain are used. Although correlation-based similarity measures may be particularly useful for random signals because they are based on probability and statistics, we frequently deal with signals that are largely deterministic and transient. Thus, it may be useful to develop another similarity measure that takes the characteristics of the deterministic transient signal properly into account. In this paper, an alternative approach to measure the similarity between two deterministic transient signals is proposed. This newly proposed similarity measure is based on the fictitious system frequency response function, and it consists of the magnitude similarity and the shape similarity. Finally, a few examples are presented to demonstrate the use of the proposed similarity measure.

Diagnosis of multiple sclerosis from EEG signals using nonlinear methods.

PubMed

Torabi, Ali; Daliri, Mohammad Reza; Sabzposhan, Seyyed Hojjat

2017-12-01

EEG signals have essential and important information about the brain and neural diseases. The main purpose of this study is classifying two groups of healthy volunteers and Multiple Sclerosis (MS) patients using nonlinear features of EEG signals while performing cognitive tasks. EEG signals were recorded when users were doing two different attentional tasks. One of the tasks was based on detecting a desired change in color luminance and the other task was based on detecting a desired change in direction of motion. EEG signals were analyzed in two ways: EEG signals analysis without rhythms decomposition and EEG sub-bands analysis. After recording and preprocessing, time delay embedding method was used for state space reconstruction; embedding parameters were determined for original signals and their sub-bands. Afterwards nonlinear methods were used in feature extraction phase. To reduce the feature dimension, scalar feature selections were done by using T-test and Bhattacharyya criteria. Then, the data were classified using linear support vector machines (SVM) and k-nearest neighbor (KNN) method. The best combination of the criteria and classifiers was determined for each task by comparing performances. For both tasks, the best results were achieved by using T-test criterion and SVM classifier. For the direction-based and the color-luminance-based tasks, maximum classification performances were 93.08 and 79.79% respectively which were reached by using optimal set of features. Our results show that the nonlinear dynamic features of EEG signals seem to be useful and effective in MS diseases diagnosis.

[An EMD based time-frequency distribution and its application in EEG analysis].

PubMed

Li, Xiaobing; Chu, Meng; Qiu, Tianshuang; Bao, Haiping

2007-10-01

Hilbert-Huang transform (HHT) is a new time-frequency analytic method to analyze the nonlinear and the non-stationary signals. The key step of this method is the empirical mode decomposition (EMD), with which any complicated signal can be decomposed into a finite and small number of intrinsic mode functions (IMF). In this paper, a new EMD based method for suppressing the cross-term of Wigner-Ville distribution (WVD) is developed and is applied to analyze the epileptic EEG signals. The simulation data and analysis results show that the new method suppresses the cross-term of the WVD effectively with an excellent resolution.

Time-Frequency Analysis And Pattern Recognition Using Singular Value Decomposition Of The Wigner-Ville Distribution

NASA Astrophysics Data System (ADS)

Boashash, Boualem; Lovell, Brian; White, Langford

1988-01-01

Time-Frequency analysis based on the Wigner-Ville Distribution (WVD) is shown to be optimal for a class of signals where the variation of instantaneous frequency is the dominant characteristic. Spectral resolution and instantaneous frequency tracking is substantially improved by using a Modified WVD (MWVD) based on an Autoregressive spectral estimator. Enhanced signal-to-noise ratio may be achieved by using 2D windowing in the Time-Frequency domain. The WVD provides a tool for deriving descriptors of signals which highlight their FM characteristics. These descriptors may be used for pattern recognition and data clustering using the methods presented in this paper.

Directional dual-tree rational-dilation complex wavelet transform.

PubMed

Serbes, Gorkem; Gulcur, Halil Ozcan; Aydin, Nizamettin

2014-01-01

Dyadic discrete wavelet transform (DWT) has been used successfully in processing signals having non-oscillatory transient behaviour. However, due to the low Q-factor property of their wavelet atoms, the dyadic DWT is less effective in processing oscillatory signals such as embolic signals (ESs). ESs are extracted from quadrature Doppler signals, which are the output of Doppler ultrasound systems. In order to process ESs, firstly, a pre-processing operation known as phase filtering for obtaining directional signals from quadrature Doppler signals must be employed. Only then, wavelet based methods can be applied to these directional signals for further analysis. In this study, a directional dual-tree rational-dilation complex wavelet transform, which can be applied directly to quadrature signals and has the ability of extracting directional information during analysis, is introduced.

Methods for automatically analyzing humpback song units.

PubMed

Rickwood, Peter; Taylor, Andrew

2008-03-01

This paper presents mathematical techniques for automatically extracting and analyzing bioacoustic signals. Automatic techniques are described for isolation of target signals from background noise, extraction of features from target signals and unsupervised classification (clustering) of the target signals based on these features. The only user-provided inputs, other than raw sound, is an initial set of signal processing and control parameters. Of particular note is that the number of signal categories is determined automatically. The techniques, applied to hydrophone recordings of humpback whales (Megaptera novaeangliae), produce promising initial results, suggesting that they may be of use in automated analysis of not only humpbacks, but possibly also in other bioacoustic settings where automated analysis is desirable.

An Optimization-Driven Analysis Pipeline to Uncover Biomarkers and Signaling Paths: Cervix Cancer.

PubMed

Lorenzo, Enery; Camacho-Caceres, Katia; Ropelewski, Alexander J; Rosas, Juan; Ortiz-Mojer, Michael; Perez-Marty, Lynn; Irizarry, Juan; Gonzalez, Valerie; Rodríguez, Jesús A; Cabrera-Rios, Mauricio; Isaza, Clara

2015-06-01

Establishing how a series of potentially important genes might relate to each other is relevant to understand the origin and evolution of illnesses, such as cancer. High-throughput biological experiments have played a critical role in providing information in this regard. A special challenge, however, is that of trying to conciliate information from separate microarray experiments to build a potential genetic signaling path. This work proposes a two-step analysis pipeline, based on optimization, to approach meta-analysis aiming to build a proxy for a genetic signaling path.

Analysis of a spatial tracking subsystem for optical communications

NASA Technical Reports Server (NTRS)

Win, Moe Z.; Chen, CHIEN-C.

1992-01-01

Spatial tracking plays a very critical role in designing optical communication systems because of the small angular beamwidth associated with the optical signal. One possible solution for spatial tracking is to use a nutating mirror which dithers the incoming beam at a rate much higher than the mechanical disturbances. A power detector then senses the change in detected power as the signal is reflected off the nutating mirror. This signal is then correlated with the nutator driver signals to obtain estimates of the azimuth and elevation tracking signals to control the fast scanning mirrors. A theoretical analysis is performed for a spatial tracking system using a nutator disturbed by shot noise and mechanical vibrations. Contributions of shot noise and mechanical vibrations to the total tracking error variance are derived. Given the vibration spectrum and the expected signal power, there exists an optimal amplitude for the nutation which optimizes the receiver performance. The expected performance of a nutator based system is estimated based on the choice of nutation amplitude.

Embedding Dimension Selection for Adaptive Singular Spectrum Analysis of EEG Signal.

PubMed

Xu, Shanzhi; Hu, Hai; Ji, Linhong; Wang, Peng

2018-02-26

The recorded electroencephalography (EEG) signal is often contaminated with different kinds of artifacts and noise. Singular spectrum analysis (SSA) is a powerful tool for extracting the brain rhythm from a noisy EEG signal. By analyzing the frequency characteristics of the reconstructed component (RC) and the change rate in the trace of the Toeplitz matrix, it is demonstrated that the embedding dimension is related to the frequency bandwidth of each reconstructed component, in consistence with the component mixing in the singular value decomposition step. A method for selecting the embedding dimension is thereby proposed and verified by simulated EEG signal based on the Markov Process Amplitude (MPA) EEG Model. Real EEG signal is also collected from the experimental subjects under both eyes-open and eyes-closed conditions. The experimental results show that based on the embedding dimension selection method, the alpha rhythm can be extracted from the real EEG signal by the adaptive SSA, which can be effectively utilized to distinguish between the eyes-open and eyes-closed states.

Augmenting the decomposition of EMG signals using supervised feature extraction techniques.

PubMed

Parsaei, Hossein; Gangeh, Mehrdad J; Stashuk, Daniel W; Kamel, Mohamed S

2012-01-01

Electromyographic (EMG) signal decomposition is the process of resolving an EMG signal into its constituent motor unit potential trains (MUPTs). In this work, the possibility of improving the decomposing results using two supervised feature extraction methods, i.e., Fisher discriminant analysis (FDA) and supervised principal component analysis (SPCA), is explored. Using the MUP labels provided by a decomposition-based quantitative EMG system as a training data for FDA and SPCA, the MUPs are transformed into a new feature space such that the MUPs of a single MU become as close as possible to each other while those created by different MUs become as far as possible. The MUPs are then reclassified using a certainty-based classification algorithm. Evaluation results using 10 simulated EMG signals comprised of 3-11 MUPTs demonstrate that FDA and SPCA on average improve the decomposition accuracy by 6%. The improvement for the most difficult-to-decompose signal is about 12%, which shows the proposed approach is most beneficial in the decomposition of more complex signals.

Noncontact measurement of heart rate using facial video illuminated under natural light and signal weighted analysis.

PubMed

Yan, Yonggang; Ma, Xiang; Yao, Lifeng; Ouyang, Jianfei

2015-01-01

Non-contact and remote measurements of vital physical signals are important for reliable and comfortable physiological self-assessment. We presented a novel optical imaging-based method to measure the vital physical signals. Using a digital camera and ambient light, the cardiovascular pulse waves were extracted better from human color facial videos correctly. And the vital physiological parameters like heart rate were measured using a proposed signal-weighted analysis method. The measured HRs consistent with those measured simultaneously with reference technologies (r=0.94, p<0.001 for HR). The results show that the imaging-based method is suitable for measuring the physiological parameters, and provide a reliable and comfortable measurement mode. The study lays a physical foundation for measuring multi-physiological parameters of human noninvasively.

[Quantitative Analysis of Heavy Metals in Water with LIBS Based on Signal-to-Background Ratio].

PubMed

Hu, Li; Zhao, Nan-jing; Liu, Wen-qing; Fang, Li; Zhang, Da-hai; Wang, Yin; Meng, De Shuo; Yu, Yang; Ma, Ming-jun

2015-07-01

There are many influence factors in the precision and accuracy of the quantitative analysis with LIBS technology. According to approximately the same characteristics trend of background spectrum and characteristic spectrum along with the change of temperature through in-depth analysis, signal-to-background ratio (S/B) measurement and regression analysis could compensate the spectral line intensity changes caused by system parameters such as laser power, spectral efficiency of receiving. Because the measurement dates were limited and nonlinear, we used support vector machine (SVM) for regression algorithm. The experimental results showed that the method could improve the stability and the accuracy of quantitative analysis of LIBS, and the relative standard deviation and average relative error of test set respectively were 4.7% and 9.5%. Data fitting method based on signal-to-background ratio(S/B) is Less susceptible to matrix elements and background spectrum etc, and provides data processing reference for real-time online LIBS quantitative analysis technology.

Knee Joint Vibration Signal Analysis with Matching Pursuit Decomposition and Dynamic Weighted Classifier Fusion

PubMed Central

Cai, Suxian; Yang, Shanshan; Zheng, Fang; Lu, Meng; Wu, Yunfeng; Krishnan, Sridhar

2013-01-01

Analysis of knee joint vibration (VAG) signals can provide quantitative indices for detection of knee joint pathology at an early stage. In addition to the statistical features developed in the related previous studies, we extracted two separable features, that is, the number of atoms derived from the wavelet matching pursuit decomposition and the number of significant signal turns detected with the fixed threshold in the time domain. To perform a better classification over the data set of 89 VAG signals, we applied a novel classifier fusion system based on the dynamic weighted fusion (DWF) method to ameliorate the classification performance. For comparison, a single leastsquares support vector machine (LS-SVM) and the Bagging ensemble were used for the classification task as well. The results in terms of overall accuracy in percentage and area under the receiver operating characteristic curve obtained with the DWF-based classifier fusion method reached 88.76% and 0.9515, respectively, which demonstrated the effectiveness and superiority of the DWF method with two distinct features for the VAG signal analysis. PMID:23573175

A MATLAB-based graphical user interface for the identification of muscular activations from surface electromyography signals.

PubMed

Mengarelli, Alessandro; Cardarelli, Stefano; Verdini, Federica; Burattini, Laura; Fioretti, Sandro; Di Nardo, Francesco

2016-08-01

In this paper a graphical user interface (GUI) built in MATLAB® environment is presented. This interactive tool has been developed for the analysis of superficial electromyography (sEMG) signals and in particular for the assessment of the muscle activation time intervals. After the signal import, the tool performs a first analysis in a totally user independent way, providing a reliable computation of the muscular activation sequences. Furthermore, the user has the opportunity to modify each parameter of the on/off identification algorithm implemented in the presented tool. The presence of an user-friendly GUI allows the immediate evaluation of the effects that the modification of every single parameter has on the activation intervals recognition, through the real-time updating and visualization of the muscular activation/deactivation sequences. The possibility to accept the initial signal analysis or to modify the on/off identification with respect to each considered signal, with a real-time visual feedback, makes this GUI-based tool a valuable instrument in clinical, research applications and also in an educational perspective.

Gear fault diagnosis based on the structured sparsity time-frequency analysis

NASA Astrophysics Data System (ADS)

Sun, Ruobin; Yang, Zhibo; Chen, Xuefeng; Tian, Shaohua; Xie, Yong

2018-03-01

Over the last decade, sparse representation has become a powerful paradigm in mechanical fault diagnosis due to its excellent capability and the high flexibility for complex signal description. The structured sparsity time-frequency analysis (SSTFA) is a novel signal processing method, which utilizes mixed-norm priors on time-frequency coefficients to obtain a fine match for the structure of signals. In order to extract the transient feature from gear vibration signals, a gear fault diagnosis method based on SSTFA is proposed in this work. The steady modulation components and impulsive components of the defective gear vibration signals can be extracted simultaneously by choosing different time-frequency neighborhood and generalized thresholding operators. Besides, the time-frequency distribution with high resolution is obtained by piling different components in the same diagram. The diagnostic conclusion can be made according to the envelope spectrum of the impulsive components or by the periodicity of impulses. The effectiveness of the method is verified by numerical simulations, and the vibration signals registered from a gearbox fault simulator and a wind turbine. To validate the efficiency of the presented methodology, comparisons are made among some state-of-the-art vibration separation methods and the traditional time-frequency analysis methods. The comparisons show that the proposed method possesses advantages in separating feature signals under strong noise and accounting for the inner time-frequency structure of the gear vibration signals.

Visible spectrum-based non-contact HRV and dPTT for stress detection

NASA Astrophysics Data System (ADS)

Kaur, Balvinder; Hutchinson, J. Andrew; Ikonomidou, Vasiliki N.

2017-05-01

Stress is a major health concern that not only compromises our quality of life, but also affects our physical health and well-being. Despite its importance, our ability to objectively detect and quantify it in a real-time, non-invasive manner is very limited. This capability would have a wide variety of medical, military, and security applications. We have developed a pipeline of image and signal processing algorithms to make such a system practical, which includes remote cardiac pulse detection based on visible spectrum videos and physiological stress detection based on the variability in the remotely detected cardiac signals. First, to determine a reliable cardiac pulse, principal component analysis (PCA) was applied for noise reduction and independent component analysis (ICA) was applied for source selection. To determine accurate cardiac timing for heart rate variability (HRV) analysis, a blind source separation method based least squares (LS) estimate was used to determine signal peaks that were closely related to R-peaks of the electrocardiogram (ECG) signal. A new metric, differential pulse transit time (dPTT), defined as the difference in arrival time of the remotely acquired cardiac signal at two separate distal locations, was derived. It was demonstrated that the remotely acquired metrics, HRV and dPTT, have potential for remote stress detection. The developed algorithms were tested against human subject data collected under two physiological conditions using the modified Trier Social Stress Test (TSST) and the Affective Stress Response Test (ASRT). This research provides evidence that the variability in remotely-acquired blood wave (BW) signals can be used for stress (high and mild) detection, and as a guide for further development of a real-time remote stress detection system based on remote HRV and dPTT.

Multiscale Characterization of PM2.5 in Southern Taiwan based on Noise-assisted Multivariate Empirical Mode Decomposition and Time-dependent Intrinsic Correlation

NASA Astrophysics Data System (ADS)

Hsiao, Y. R.; Tsai, C.

2017-12-01

As the WHO Air Quality Guideline indicates, ambient air pollution exposes world populations under threat of fatal symptoms (e.g. heart disease, lung cancer, asthma etc.), raising concerns of air pollution sources and relative factors. This study presents a novel approach to investigating the multiscale variations of PM2.5 in southern Taiwan over the past decade, with four meteorological influencing factors (Temperature, relative humidity, precipitation and wind speed),based on Noise-assisted Multivariate Empirical Mode Decomposition(NAMEMD) algorithm, Hilbert Spectral Analysis(HSA) and Time-dependent Intrinsic Correlation(TDIC) method. NAMEMD algorithm is a fully data-driven approach designed for nonlinear and nonstationary multivariate signals, and is performed to decompose multivariate signals into a collection of channels of Intrinsic Mode Functions (IMFs). TDIC method is an EMD-based method using a set of sliding window sizes to quantify localized correlation coefficients for multiscale signals. With the alignment property and quasi-dyadic filter bank of NAMEMD algorithm, one is able to produce same number of IMFs for all variables and estimates the cross correlation in a more accurate way. The performance of spectral representation of NAMEMD-HSA method is compared with Complementary Empirical Mode Decomposition/ Hilbert Spectral Analysis (CEEMD-HSA) and Wavelet Analysis. The nature of NAMAMD-based TDICC analysis is then compared with CEEMD-based TDIC analysis and the traditional correlation analysis.

A new multiscale noise tuning stochastic resonance for enhanced fault diagnosis in wind turbine drivetrains

NASA Astrophysics Data System (ADS)

Hu, Bingbing; Li, Bing

2016-02-01

It is very difficult to detect weak fault signatures due to the large amount of noise in a wind turbine system. Multiscale noise tuning stochastic resonance (MSTSR) has proved to be an effective way to extract weak signals buried in strong noise. However, the MSTSR method originally based on discrete wavelet transform (DWT) has disadvantages such as shift variance and the aliasing effects in engineering application. In this paper, the dual-tree complex wavelet transform (DTCWT) is introduced into the MSTSR method, which makes it possible to further improve the system output signal-to-noise ratio and the accuracy of fault diagnosis by the merits of DTCWT (nearly shift invariant and reduced aliasing effects). Moreover, this method utilizes the relationship between the two dual-tree wavelet basis functions, instead of matching the single wavelet basis function to the signal being analyzed, which may speed up the signal processing and be employed in on-line engineering monitoring. The proposed method is applied to the analysis of bearing outer ring and shaft coupling vibration signals carrying fault information. The results confirm that the method performs better in extracting the fault features than the original DWT-based MSTSR, the wavelet transform with post spectral analysis, and EMD-based spectral analysis methods.

The signaling petri net-based simulator: a non-parametric strategy for characterizing the dynamics of cell-specific signaling networks.

PubMed

Ruths, Derek; Muller, Melissa; Tseng, Jen-Te; Nakhleh, Luay; Ram, Prahlad T

2008-02-29

Reconstructing cellular signaling networks and understanding how they work are major endeavors in cell biology. The scale and complexity of these networks, however, render their analysis using experimental biology approaches alone very challenging. As a result, computational methods have been developed and combined with experimental biology approaches, producing powerful tools for the analysis of these networks. These computational methods mostly fall on either end of a spectrum of model parameterization. On one end is a class of structural network analysis methods; these typically use the network connectivity alone to generate hypotheses about global properties. On the other end is a class of dynamic network analysis methods; these use, in addition to the connectivity, kinetic parameters of the biochemical reactions to predict the network's dynamic behavior. These predictions provide detailed insights into the properties that determine aspects of the network's structure and behavior. However, the difficulty of obtaining numerical values of kinetic parameters is widely recognized to limit the applicability of this latter class of methods. Several researchers have observed that the connectivity of a network alone can provide significant insights into its dynamics. Motivated by this fundamental observation, we present the signaling Petri net, a non-parametric model of cellular signaling networks, and the signaling Petri net-based simulator, a Petri net execution strategy for characterizing the dynamics of signal flow through a signaling network using token distribution and sampling. The result is a very fast method, which can analyze large-scale networks, and provide insights into the trends of molecules' activity-levels in response to an external stimulus, based solely on the network's connectivity. We have implemented the signaling Petri net-based simulator in the PathwayOracle toolkit, which is publicly available at http://bioinfo.cs.rice.edu/pathwayoracle. Using this method, we studied a MAPK1,2 and AKT signaling network downstream from EGFR in two breast tumor cell lines. We analyzed, both experimentally and computationally, the activity level of several molecules in response to a targeted manipulation of TSC2 and mTOR-Raptor. The results from our method agreed with experimental results in greater than 90% of the cases considered, and in those where they did not agree, our approach provided valuable insights into discrepancies between known network connectivities and experimental observations.

The Signaling Petri Net-Based Simulator: A Non-Parametric Strategy for Characterizing the Dynamics of Cell-Specific Signaling Networks

PubMed Central

Ruths, Derek; Muller, Melissa; Tseng, Jen-Te; Nakhleh, Luay; Ram, Prahlad T.

2008-01-01

Reconstructing cellular signaling networks and understanding how they work are major endeavors in cell biology. The scale and complexity of these networks, however, render their analysis using experimental biology approaches alone very challenging. As a result, computational methods have been developed and combined with experimental biology approaches, producing powerful tools for the analysis of these networks. These computational methods mostly fall on either end of a spectrum of model parameterization. On one end is a class of structural network analysis methods; these typically use the network connectivity alone to generate hypotheses about global properties. On the other end is a class of dynamic network analysis methods; these use, in addition to the connectivity, kinetic parameters of the biochemical reactions to predict the network's dynamic behavior. These predictions provide detailed insights into the properties that determine aspects of the network's structure and behavior. However, the difficulty of obtaining numerical values of kinetic parameters is widely recognized to limit the applicability of this latter class of methods. Several researchers have observed that the connectivity of a network alone can provide significant insights into its dynamics. Motivated by this fundamental observation, we present the signaling Petri net, a non-parametric model of cellular signaling networks, and the signaling Petri net-based simulator, a Petri net execution strategy for characterizing the dynamics of signal flow through a signaling network using token distribution and sampling. The result is a very fast method, which can analyze large-scale networks, and provide insights into the trends of molecules' activity-levels in response to an external stimulus, based solely on the network's connectivity. We have implemented the signaling Petri net-based simulator in the PathwayOracle toolkit, which is publicly available at http://bioinfo.cs.rice.edu/pathwayoracle. Using this method, we studied a MAPK1,2 and AKT signaling network downstream from EGFR in two breast tumor cell lines. We analyzed, both experimentally and computationally, the activity level of several molecules in response to a targeted manipulation of TSC2 and mTOR-Raptor. The results from our method agreed with experimental results in greater than 90% of the cases considered, and in those where they did not agree, our approach provided valuable insights into discrepancies between known network connectivities and experimental observations. PMID:18463702

Joint Estimation of Time-Frequency Signature and DOA Based on STFD for Multicomponent Chirp Signals

PubMed Central

Zhao, Ziyue; Liu, Congfeng

2014-01-01

In the study of the joint estimation of time-frequency signature and direction of arrival (DOA) for multicomponent chirp signals, an estimation method based on spatial time-frequency distributions (STFDs) is proposed in this paper. Firstly, array signal model for multicomponent chirp signals is presented and then array processing is applied in time-frequency analysis to mitigate cross-terms. According to the results of the array processing, Hough transform is performed and the estimation of time-frequency signature is obtained. Subsequently, subspace method for DOA estimation based on STFD matrix is achieved. Simulation results demonstrate the validity of the proposed method. PMID:27382610

Joint Estimation of Time-Frequency Signature and DOA Based on STFD for Multicomponent Chirp Signals.

PubMed

Zhao, Ziyue; Liu, Congfeng

2014-01-01

In the study of the joint estimation of time-frequency signature and direction of arrival (DOA) for multicomponent chirp signals, an estimation method based on spatial time-frequency distributions (STFDs) is proposed in this paper. Firstly, array signal model for multicomponent chirp signals is presented and then array processing is applied in time-frequency analysis to mitigate cross-terms. According to the results of the array processing, Hough transform is performed and the estimation of time-frequency signature is obtained. Subsequently, subspace method for DOA estimation based on STFD matrix is achieved. Simulation results demonstrate the validity of the proposed method.

Digital signal processing algorithms for automatic voice recognition

NASA Technical Reports Server (NTRS)

Botros, Nazeih M.

1987-01-01

The current digital signal analysis algorithms are investigated that are implemented in automatic voice recognition algorithms. Automatic voice recognition means, the capability of a computer to recognize and interact with verbal commands. The digital signal is focused on, rather than the linguistic, analysis of speech signal. Several digital signal processing algorithms are available for voice recognition. Some of these algorithms are: Linear Predictive Coding (LPC), Short-time Fourier Analysis, and Cepstrum Analysis. Among these algorithms, the LPC is the most widely used. This algorithm has short execution time and do not require large memory storage. However, it has several limitations due to the assumptions used to develop it. The other 2 algorithms are frequency domain algorithms with not many assumptions, but they are not widely implemented or investigated. However, with the recent advances in the digital technology, namely signal processors, these 2 frequency domain algorithms may be investigated in order to implement them in voice recognition. This research is concerned with real time, microprocessor based recognition algorithms.

Energy Analysis of Decoders for Rakeness-Based Compressed Sensing of ECG Signals.

PubMed

Pareschi, Fabio; Mangia, Mauro; Bortolotti, Daniele; Bartolini, Andrea; Benini, Luca; Rovatti, Riccardo; Setti, Gianluca

2017-12-01

In recent years, compressed sensing (CS) has proved to be effective in lowering the power consumption of sensing nodes in biomedical signal processing devices. This is due to the fact the CS is capable of reducing the amount of data to be transmitted to ensure correct reconstruction of the acquired waveforms. Rakeness-based CS has been introduced to further reduce the amount of transmitted data by exploiting the uneven distribution to the sensed signal energy. Yet, so far no thorough analysis exists on the impact of its adoption on CS decoder performance. The latter point is of great importance, since body-area sensor network architectures may include intermediate gateway nodes that receive and reconstruct signals to provide local services before relaying data to a remote server. In this paper, we fill this gap by showing that rakeness-based design also improves reconstruction performance. We quantify these findings in the case of ECG signals and when a variety of reconstruction algorithms are used either in a low-power microcontroller or a heterogeneous mobile computing platform.

Epinephrine-Induced and Antiapoptotic Signaling in Prostate Cancer

DTIC Science & Technology

2009-05-01

Phosphorylation of ectopically expressed HA-BAD mirrors endogenous BAD phosphorylation, thus analysis of HA-BAD allows adequately interpret modifications...subjected to emotional stress (Fig.2 A). However, more extensive analysis of CREB phosphorylation in C42Luc xenografts showed that in some cases...data indicate that assignment of tumor samples for analysis of signaling pathways should be done based on blood epinephrine measurements. Given

A model-based analysis of extinction ratio effects on phase-OTDR distributed acoustic sensing system performance

NASA Astrophysics Data System (ADS)

Aktas, Metin; Maral, Hakan; Akgun, Toygar

2018-02-01

Extinction ratio is an inherent limiting factor that has a direct effect on the detection performance of phase-OTDR based distributed acoustics sensing systems. In this work we present a model based analysis of Rayleigh scattering to simulate the effects of extinction ratio on the received signal under varying signal acquisition scenarios and system parameters. These signal acquisition scenarios are constructed to represent typically observed cases such as multiple vibration sources cluttered around the target vibration source to be detected, continuous wave light sources with center frequency drift, varying fiber optic cable lengths and varying ADC bit resolutions. Results show that an insufficient ER can result in high optical noise floor and effectively hide the effects of elaborate system improvement efforts.

Energy spectrum analysis - A model of echolocation processing. [in animals

NASA Technical Reports Server (NTRS)

Johnson, R. A.; Titlebaum, E. L.

1976-01-01

The paper proposes a frequency domain approach based on energy spectrum analysis of the combination of a signal and its echoes as the processing mechanism for the echolocation process used by bats and other animals. The mechanism is a generalized wide-band one and can account for the large diversity of wide-band signals used for orientation. The coherency in the spectrum of the signal-echo combination is shown to be equivalent to correlation.

Real-Time Cameraless Measurement System Based on Bioelectrical Ventilatory Signals to Evaluate Fear and Anxiety.

PubMed

Soh, Zu; Matsuno, Motoki; Yoshida, Masayuki; Tsuji, Toshio

2018-04-01

Fear and anxiety in fish are generally evaluated by video-based behavioral analysis. However, it is difficult to distinguish the psychological state of fish exclusively through video analysis, particularly whether the fish are freezing, which represents typical fear behavior, or merely resting. We propose a system that can measure bioelectrical signals called ventilatory signals and simultaneously analyze swimming behavior in real time. Experimental results comparing the behavioral analysis of the proposed system and the camera system showed a low error level with an average absolute position error of 9.75 ± 3.12 mm (about one-third of the body length) and a correlation between swimming speeds of r = 0.93 ± 0.07 (p < 0.01). We also exposed the fish to zebrafish skin extracts containing alarm substances that induce fear and anxiety responses to evaluate their emotional changes. The results confirmed that this solution significantly changed all behavioral and ventilatory signal indices obtained by the proposed system (p < 0.01). By combining the behavioral and ventilatory signal indices, we could detect fear and anxiety with a discrimination rate of 83.3% ± 16.7%. Furthermore, we found that the decreasing fear and anxiety over time could be detected according to the peak frequency of the ventilatory signals, which cannot be measured through video analysis.

[Computers in biomedical research: I. Analysis of bioelectrical signals].

PubMed

Vivaldi, E A; Maldonado, P

2001-08-01

A personal computer equipped with an analog-to-digital conversion card is able to input, store and display signals of biomedical interest. These signals can additionally be submitted to ad-hoc software for analysis and diagnosis. Data acquisition is based on the sampling of a signal at a given rate and amplitude resolution. The automation of signal processing conveys syntactic aspects (data transduction, conditioning and reduction); and semantic aspects (feature extraction to describe and characterize the signal and diagnostic classification). The analytical approach that is at the basis of computer programming allows for the successful resolution of apparently complex tasks. Two basic principles involved are the definition of simple fundamental functions that are then iterated and the modular subdivision of tasks. These two principles are illustrated, respectively, by presenting the algorithm that detects relevant elements for the analysis of a polysomnogram, and the task flow in systems that automate electrocardiographic reports.

Photoacoustic detection of blood in dental pulp by using short-time Fourier transform

NASA Astrophysics Data System (ADS)

Yamada, Azusa; Kakino, Satoko; Matsuura, Yuji

2016-03-01

A method based on photoacoustic analysis is proposed to diagnose dental pulp vitality. Photoacoustic analysis enables to get signal from deeper tissues than other optical analyses and therefore, signal detection from root canal of thick dental tissues such as molar teeth is expected. As a light source for excitation of photoacoustic waves, a microchip Q-switched YAG laser with a wavelength of 1064 nm was used and owing to large penetration depth of the near infrared laser, photoacoustic signals from dental root were successfully obtained. It was found that the photoacoustic signals from the teeth containing hemoglobin solution in the pulp cavity provide vibration in high frequency region. It was also shown that the intensities of the high frequency component have correlation with the hemoglobin concentration of solution. We applied short-time Fourier transform for evaluation of photoacoustic signals and this analysis clearly showed photoacoustic signals from dental root.

A decomposition model and voxel selection framework for fMRI analysis to predict neural response of visual stimuli.

PubMed

Raut, Savita V; Yadav, Dinkar M

2018-03-28

This paper presents an fMRI signal analysis methodology using geometric mean curve decomposition (GMCD) and mutual information-based voxel selection framework. Previously, the fMRI signal analysis has been conducted using empirical mean curve decomposition (EMCD) model and voxel selection on raw fMRI signal. The erstwhile methodology loses frequency component, while the latter methodology suffers from signal redundancy. Both challenges are addressed by our methodology in which the frequency component is considered by decomposing the raw fMRI signal using geometric mean rather than arithmetic mean and the voxels are selected from EMCD signal using GMCD components, rather than raw fMRI signal. The proposed methodologies are adopted for predicting the neural response. Experimentations are conducted in the openly available fMRI data of six subjects, and comparisons are made with existing decomposition models and voxel selection frameworks. Subsequently, the effect of degree of selected voxels and the selection constraints are analyzed. The comparative results and the analysis demonstrate the superiority and the reliability of the proposed methodology.

Noise-aware dictionary-learning-based sparse representation framework for detection and removal of single and combined noises from ECG signal

PubMed Central

Ramkumar, Barathram; Sabarimalai Manikandan, M.

2017-01-01

Automatic electrocardiogram (ECG) signal enhancement has become a crucial pre-processing step in most ECG signal analysis applications. In this Letter, the authors propose an automated noise-aware dictionary learning-based generalised ECG signal enhancement framework which can automatically learn the dictionaries based on the ECG noise type for effective representation of ECG signal and noises, and can reduce the computational load of sparse representation-based ECG enhancement system. The proposed framework consists of noise detection and identification, noise-aware dictionary learning, sparse signal decomposition and reconstruction. The noise detection and identification is performed based on the moving average filter, first-order difference, and temporal features such as number of turning points, maximum absolute amplitude, zerocrossings, and autocorrelation features. The representation dictionary is learned based on the type of noise identified in the previous stage. The proposed framework is evaluated using noise-free and noisy ECG signals. Results demonstrate that the proposed method can significantly reduce computational load as compared with conventional dictionary learning-based ECG denoising approaches. Further, comparative results show that the method outperforms existing methods in automatically removing noises such as baseline wanders, power-line interference, muscle artefacts and their combinations without distorting the morphological content of local waves of ECG signal. PMID:28529758

Noise-aware dictionary-learning-based sparse representation framework for detection and removal of single and combined noises from ECG signal.

PubMed

Satija, Udit; Ramkumar, Barathram; Sabarimalai Manikandan, M

2017-02-01

Automatic electrocardiogram (ECG) signal enhancement has become a crucial pre-processing step in most ECG signal analysis applications. In this Letter, the authors propose an automated noise-aware dictionary learning-based generalised ECG signal enhancement framework which can automatically learn the dictionaries based on the ECG noise type for effective representation of ECG signal and noises, and can reduce the computational load of sparse representation-based ECG enhancement system. The proposed framework consists of noise detection and identification, noise-aware dictionary learning, sparse signal decomposition and reconstruction. The noise detection and identification is performed based on the moving average filter, first-order difference, and temporal features such as number of turning points, maximum absolute amplitude, zerocrossings, and autocorrelation features. The representation dictionary is learned based on the type of noise identified in the previous stage. The proposed framework is evaluated using noise-free and noisy ECG signals. Results demonstrate that the proposed method can significantly reduce computational load as compared with conventional dictionary learning-based ECG denoising approaches. Further, comparative results show that the method outperforms existing methods in automatically removing noises such as baseline wanders, power-line interference, muscle artefacts and their combinations without distorting the morphological content of local waves of ECG signal.

Power-law statistics of neurophysiological processes analyzed using short signals

NASA Astrophysics Data System (ADS)

Pavlova, Olga N.; Runnova, Anastasiya E.; Pavlov, Alexey N.

2018-04-01

We discuss the problem of quantifying power-law statistics of complex processes from short signals. Based on the analysis of electroencephalograms (EEG) we compare three interrelated approaches which enable characterization of the power spectral density (PSD) and show that an application of the detrended fluctuation analysis (DFA) or the wavelet-transform modulus maxima (WTMM) method represents a useful way of indirect characterization of the PSD features from short data sets. We conclude that despite DFA- and WTMM-based measures can be obtained from the estimated PSD, these tools outperform the standard spectral analysis when characterization of the analyzed regime should be provided based on a very limited amount of data.

Synthesis, Analysis, and Processing of Fractal Signals

DTIC Science & Technology

1991-10-01

coordinator in hockey, squash, volleyball, and softball, but also for reminding me periodically that 1/f noise can exist outside a computer. More...similar signals as Fourier-based representations are for stationary and periodic signals. Furthermore, because wave- let transformations can be...and periodic signals. Furthermore, just as the discovery of fast Fourier transform (FFT) algorithms dramatically increased the viability the Fourier

[Analysis and experimental verification of sensitivity and SNR of laser warning receiver].

PubMed

Zhang, Ji-Long; Wang, Ming; Tian, Er-Ming; Li, Xiao; Wang, Zhi-Bin; Zhang, Yue

2009-01-01

In order to countermeasure increasingly serious threat from hostile laser in modern war, it is urgent to do research on laser warning technology and system, and the sensitivity and signal to noise ratio (SNR) are two important performance parameters in laser warning system. In the present paper, based on the signal statistical detection theory, a method for calculation of the sensitivity and SNR in coherent detection laser warning receiver (LWR) has been proposed. Firstly, the probabilities of the laser signal and receiver noise were analyzed. Secondly, based on the threshold detection theory and Neyman-Pearson criteria, the signal current equation was established by introducing detection probability factor and false alarm rate factor, then, the mathematical expressions of sensitivity and SNR were deduced. Finally, by using method, the sensitivity and SNR of the sinusoidal grating laser warning receiver developed by our group were analyzed, and the theoretic calculation and experimental results indicate that the SNR analysis method is feasible, and can be used in performance analysis of LWR.

The Emotion Recognition System Based on Autoregressive Model and Sequential Forward Feature Selection of Electroencephalogram Signals

PubMed Central

Hatamikia, Sepideh; Maghooli, Keivan; Nasrabadi, Ali Motie

2014-01-01

Electroencephalogram (EEG) is one of the useful biological signals to distinguish different brain diseases and mental states. In recent years, detecting different emotional states from biological signals has been merged more attention by researchers and several feature extraction methods and classifiers are suggested to recognize emotions from EEG signals. In this research, we introduce an emotion recognition system using autoregressive (AR) model, sequential forward feature selection (SFS) and K-nearest neighbor (KNN) classifier using EEG signals during emotional audio-visual inductions. The main purpose of this paper is to investigate the performance of AR features in the classification of emotional states. To achieve this goal, a distinguished AR method (Burg's method) based on Levinson-Durbin's recursive algorithm is used and AR coefficients are extracted as feature vectors. In the next step, two different feature selection methods based on SFS algorithm and Davies–Bouldin index are used in order to decrease the complexity of computing and redundancy of features; then, three different classifiers include KNN, quadratic discriminant analysis and linear discriminant analysis are used to discriminate two and three different classes of valence and arousal levels. The proposed method is evaluated with EEG signals of available database for emotion analysis using physiological signals, which are recorded from 32 participants during 40 1 min audio visual inductions. According to the results, AR features are efficient to recognize emotional states from EEG signals, and KNN performs better than two other classifiers in discriminating of both two and three valence/arousal classes. The results also show that SFS method improves accuracies by almost 10-15% as compared to Davies–Bouldin based feature selection. The best accuracies are %72.33 and %74.20 for two classes of valence and arousal and %61.10 and %65.16 for three classes, respectively. PMID:25298928

MRTD: man versus machine

NASA Astrophysics Data System (ADS)

van Rheenen, Arthur D.; Taule, Petter; Thomassen, Jan Brede; Madsen, Eirik Blix

2018-04-01

We present Minimum-Resolvable Temperature Difference (MRTD) curves obtained by letting an ensemble of observers judge how many of the six four-bar patterns they can "see" in a set of images taken with different bar-to-background contrasts. The same images are analyzed using elemental signal analysis algorithms and machine-analysis based MRTD curves are obtained. We show that by adjusting the minimum required signal-to-noise ratio the machine-based MRTDs are very similar to the ones obtained with the help of the human observers.

Identification of a xanthine oxidase-inhibitory component from Sophora flavescens using NMR-based metabolomics.

PubMed

Suzuki, Ryuichiro; Hasuike, Yuka; Hirabayashi, Moeka; Fukuda, Tatsuo; Okada, Yoshihito; Shirataki, Yoshiaki

2013-10-01

We demonstrate that NMR-based metabolomics studies can be used to identify xanthine oxidase-inhibitory compounds in the diethyl ether soluble fraction prepared from a methanolic extract of Sophora flavescens. Loading plot analysis, accompanied by direct comparison of 1H NMR spectraexhibiting characteristic signals, identified compounds exhibiting inhibitory activity. NMR analysis indicated that these characteristic signals were attributed to flavanones such as sophoraflavanone G and kurarinone. Sophoraflavanone G showed inhibitory activity towards xanthine oxidase in an in vitro assay.

A canonical correlation analysis based EMG classification algorithm for eliminating electrode shift effect.

PubMed

Zhe Fan; Zhong Wang; Guanglin Li; Ruomei Wang

2016-08-01

Motion classification system based on surface Electromyography (sEMG) pattern recognition has achieved good results in experimental condition. But it is still a challenge for clinical implement and practical application. Many factors contribute to the difficulty of clinical use of the EMG based dexterous control. The most obvious and important is the noise in the EMG signal caused by electrode shift, muscle fatigue, motion artifact, inherent instability of signal and biological signals such as Electrocardiogram. In this paper, a novel method based on Canonical Correlation Analysis (CCA) was developed to eliminate the reduction of classification accuracy caused by electrode shift. The average classification accuracy of our method were above 95% for the healthy subjects. In the process, we validated the influence of electrode shift on motion classification accuracy and discovered the strong correlation with correlation coefficient of >0.9 between shift position data and normal position data.

A Small Leak Detection Method Based on VMD Adaptive De-Noising and Ambiguity Correlation Classification Intended for Natural Gas Pipelines.

PubMed

Xiao, Qiyang; Li, Jian; Bai, Zhiliang; Sun, Jiedi; Zhou, Nan; Zeng, Zhoumo

2016-12-13

In this study, a small leak detection method based on variational mode decomposition (VMD) and ambiguity correlation classification (ACC) is proposed. The signals acquired from sensors were decomposed using the VMD, and numerous components were obtained. According to the probability density function (PDF), an adaptive de-noising algorithm based on VMD is proposed for noise component processing and de-noised components reconstruction. Furthermore, the ambiguity function image was employed for analysis of the reconstructed signals. Based on the correlation coefficient, ACC is proposed to detect the small leak of pipeline. The analysis of pipeline leakage signals, using 1 mm and 2 mm leaks, has shown that proposed detection method can detect a small leak accurately and effectively. Moreover, the experimental results have shown that the proposed method achieved better performances than support vector machine (SVM) and back propagation neural network (BP) methods.

A Small Leak Detection Method Based on VMD Adaptive De-Noising and Ambiguity Correlation Classification Intended for Natural Gas Pipelines

PubMed Central

Xiao, Qiyang; Li, Jian; Bai, Zhiliang; Sun, Jiedi; Zhou, Nan; Zeng, Zhoumo

2016-01-01

In this study, a small leak detection method based on variational mode decomposition (VMD) and ambiguity correlation classification (ACC) is proposed. The signals acquired from sensors were decomposed using the VMD, and numerous components were obtained. According to the probability density function (PDF), an adaptive de-noising algorithm based on VMD is proposed for noise component processing and de-noised components reconstruction. Furthermore, the ambiguity function image was employed for analysis of the reconstructed signals. Based on the correlation coefficient, ACC is proposed to detect the small leak of pipeline. The analysis of pipeline leakage signals, using 1 mm and 2 mm leaks, has shown that proposed detection method can detect a small leak accurately and effectively. Moreover, the experimental results have shown that the proposed method achieved better performances than support vector machine (SVM) and back propagation neural network (BP) methods. PMID:27983577

The Researches on Damage Detection Method for Truss Structures

NASA Astrophysics Data System (ADS)

Wang, Meng Hong; Cao, Xiao Nan

2018-06-01

This paper presents an effective method to detect damage in truss structures. Numerical simulation and experimental analysis were carried out on a damaged truss structure under instantaneous excitation. The ideal excitation point and appropriate hammering method were determined to extract time domain signals under two working conditions. The frequency response function and principal component analysis were used for data processing, and the angle between the frequency response function vectors was selected as a damage index to ascertain the location of a damaged bar in the truss structure. In the numerical simulation, the time domain signal of all nodes was extracted to determine the location of the damaged bar. In the experimental analysis, the time domain signal of a portion of the nodes was extracted on the basis of an optimal sensor placement method based on the node strain energy coefficient. The results of the numerical simulation and experimental analysis showed that the damage detection method based on the frequency response function and principal component analysis could locate the damaged bar accurately.

Object Classification Based on Analysis of Spectral Characteristics of Seismic Signal Envelopes

NASA Astrophysics Data System (ADS)

Morozov, Yu. V.; Spektor, A. A.

2017-11-01

A method for classifying moving objects having a seismic effect on the ground surface is proposed which is based on statistical analysis of the envelopes of received signals. The values of the components of the amplitude spectrum of the envelopes obtained applying Hilbert and Fourier transforms are used as classification criteria. Examples illustrating the statistical properties of spectra and the operation of the seismic classifier are given for an ensemble of objects of four classes (person, group of people, large animal, vehicle). It is shown that the computational procedures for processing seismic signals are quite simple and can therefore be used in real-time systems with modest requirements for computational resources.

An Analysis of Periodic Components in BL Lac Object S5 0716 +714 with MUSIC Method

NASA Astrophysics Data System (ADS)

Tang, J.

2012-01-01

Multiple signal classification (MUSIC) algorithms are introduced to the estimation of the period of variation of BL Lac objects.The principle of MUSIC spectral analysis method and theoretical analysis of the resolution of frequency spectrum using analog signals are included. From a lot of literatures, we have collected a lot of effective observation data of BL Lac object S5 0716 + 714 in V, R, I bands from 1994 to 2008. The light variation periods of S5 0716 +714 are obtained by means of the MUSIC spectral analysis method and periodogram spectral analysis method. There exist two major periods: (3.33±0.08) years and (1.24±0.01) years for all bands. The estimation of the period of variation of the algorithm based on the MUSIC spectral analysis method is compared with that of the algorithm based on the periodogram spectral analysis method. It is a super-resolution algorithm with small data length, and could be used to detect the period of variation of weak signals.

Time-frequency analysis based on ensemble local mean decomposition and fast kurtogram for rotating machinery fault diagnosis

NASA Astrophysics Data System (ADS)

Wang, Lei; Liu, Zhiwen; Miao, Qiang; Zhang, Xin

2018-03-01

A time-frequency analysis method based on ensemble local mean decomposition (ELMD) and fast kurtogram (FK) is proposed for rotating machinery fault diagnosis. Local mean decomposition (LMD), as an adaptive non-stationary and nonlinear signal processing method, provides the capability to decompose multicomponent modulation signal into a series of demodulated mono-components. However, the occurring mode mixing is a serious drawback. To alleviate this, ELMD based on noise-assisted method was developed. Still, the existing environmental noise in the raw signal remains in corresponding PF with the component of interest. FK has good performance in impulse detection while strong environmental noise exists. But it is susceptible to non-Gaussian noise. The proposed method combines the merits of ELMD and FK to detect the fault for rotating machinery. Primarily, by applying ELMD the raw signal is decomposed into a set of product functions (PFs). Then, the PF which mostly characterizes fault information is selected according to kurtosis index. Finally, the selected PF signal is further filtered by an optimal band-pass filter based on FK to extract impulse signal. Fault identification can be deduced by the appearance of fault characteristic frequencies in the squared envelope spectrum of the filtered signal. The advantages of ELMD over LMD and EEMD are illustrated in the simulation analyses. Furthermore, the efficiency of the proposed method in fault diagnosis for rotating machinery is demonstrated on gearbox case and rolling bearing case analyses.

Intelligent detection and identification in fiber-optical perimeter intrusion monitoring system based on the FBG sensor network

NASA Astrophysics Data System (ADS)

Wu, Huijuan; Qian, Ya; Zhang, Wei; Li, Hanyu; Xie, Xin

2015-12-01

A real-time intelligent fiber-optic perimeter intrusion detection system (PIDS) based on the fiber Bragg grating (FBG) sensor network is presented in this paper. To distinguish the effects of different intrusion events, a novel real-time behavior impact classification method is proposed based on the essential statistical characteristics of signal's profile in the time domain. The features are extracted by the principal component analysis (PCA), which are then used to identify the event with a K-nearest neighbor classifier. Simulation and field tests are both carried out to validate its effectiveness. The average identification rate (IR) for five sample signals in the simulation test is as high as 96.67%, and the recognition rate for eight typical signals in the field test can also be achieved up to 96.52%, which includes both the fence-mounted and the ground-buried sensing signals. Besides, critically high detection rate (DR) and low false alarm rate (FAR) can be simultaneously obtained based on the autocorrelation characteristics analysis and a hierarchical detection and identification flow.

The Statistical Meaning of Kurtosis and Its New Application to Identification of Persons Based on Seismic Signals

PubMed Central

Liang, Zhiqiang; Wei, Jianming; Zhao, Junyu; Liu, Haitao; Li, Baoqing; Shen, Jie; Zheng, Chunlei

2008-01-01

This paper presents a new algorithm making use of kurtosis, which is a statistical parameter, to distinguish the seismic signal generated by a person's footsteps from other signals. It is adaptive to any environment and needs no machine study or training. As persons or other targets moving on the ground generate continuous signals in the form of seismic waves, we can separate different targets based on the seismic waves they generate. The parameter of kurtosis is sensitive to impulsive signals, so it's much more sensitive to the signal generated by person footsteps than other signals generated by vehicles, winds, noise, etc. The parameter of kurtosis is usually employed in the financial analysis, but rarely used in other fields. In this paper, we make use of kurtosis to distinguish person from other targets based on its different sensitivity to different signals. Simulation and application results show that this algorithm is very effective in distinguishing person from other targets. PMID:27873804

Slow feature analysis: unsupervised learning of invariances.

PubMed

Wiskott, Laurenz; Sejnowski, Terrence J

2002-04-01

Invariant features of temporally varying signals are useful for analysis and classification. Slow feature analysis (SFA) is a new method for learning invariant or slowly varying features from a vectorial input signal. It is based on a nonlinear expansion of the input signal and application of principal component analysis to this expanded signal and its time derivative. It is guaranteed to find the optimal solution within a family of functions directly and can learn to extract a large number of decorrelated features, which are ordered by their degree of invariance. SFA can be applied hierarchically to process high-dimensional input signals and extract complex features. SFA is applied first to complex cell tuning properties based on simple cell output, including disparity and motion. Then more complicated input-output functions are learned by repeated application of SFA. Finally, a hierarchical network of SFA modules is presented as a simple model of the visual system. The same unstructured network can learn translation, size, rotation, contrast, or, to a lesser degree, illumination invariance for one-dimensional objects, depending on only the training stimulus. Surprisingly, only a few training objects suffice to achieve good generalization to new objects. The generated representation is suitable for object recognition. Performance degrades if the network is trained to learn multiple invariances simultaneously.

Analysis of radiometric signal in sedimentating suspension flow in open channel

NASA Astrophysics Data System (ADS)

Zych, Marcin; Hanus, Robert; Petryka, Leszek; Świsulski, Dariusz; Doktor, Marek; Mastej, Wojciech

2015-05-01

The article discusses issues related to the estimation of the sedimentating solid particles average flow velocity in an open channel using radiometric methods. Due to the composition of the compound, which formed water and diatomite, received data have a very weak signal to noise ratio. In the process analysis the known determining of the solid phase transportation time delay the classical cross-correlation function is the most reliable method. The use of advanced frequency analysis based on mutual spectral density function and wavelet transform of recorded signals allows a reduction of the noise contribution.

Bayesian Inference for Signal-Based Seismic Monitoring

NASA Astrophysics Data System (ADS)

Moore, D.

2015-12-01

Traditional seismic monitoring systems rely on discrete detections produced by station processing software, discarding significant information present in the original recorded signal. SIG-VISA (Signal-based Vertically Integrated Seismic Analysis) is a system for global seismic monitoring through Bayesian inference on seismic signals. By modeling signals directly, our forward model is able to incorporate a rich representation of the physics underlying the signal generation process, including source mechanisms, wave propagation, and station response. This allows inference in the model to recover the qualitative behavior of recent geophysical methods including waveform matching and double-differencing, all as part of a unified Bayesian monitoring system that simultaneously detects and locates events from a global network of stations. We demonstrate recent progress in scaling up SIG-VISA to efficiently process the data stream of global signals recorded by the International Monitoring System (IMS), including comparisons against existing processing methods that show increased sensitivity from our signal-based model and in particular the ability to locate events (including aftershock sequences that can tax analyst processing) precisely from waveform correlation effects. We also provide a Bayesian analysis of an alleged low-magnitude event near the DPRK test site in May 2010 [1] [2], investigating whether such an event could plausibly be detected through automated processing in a signal-based monitoring system. [1] Zhang, Miao and Wen, Lianxing. "Seismological Evidence for a Low-Yield Nuclear Test on 12 May 2010 in North Korea". Seismological Research Letters, January/February 2015. [2] Richards, Paul. "A Seismic Event in North Korea on 12 May 2010". CTBTO SnT 2015 oral presentation, video at https://video-archive.ctbto.org/index.php/kmc/preview/partner_id/103/uiconf_id/4421629/entry_id/0_ymmtpps0/delivery/http

Monitoring Seismo-volcanic and Infrasonic Signals at Volcanoes: Mt. Etna Case Study

NASA Astrophysics Data System (ADS)

Cannata, Andrea; Di Grazia, Giuseppe; Aliotta, Marco; Cassisi, Carmelo; Montalto, Placido; Patanè, Domenico

2013-11-01

Volcanoes generate a broad range of seismo-volcanic and infrasonic signals, whose features and variations are often closely related to volcanic activity. The study of these signals is hence very useful in the monitoring and investigation of volcano dynamics. The analysis of seismo-volcanic and infrasonic signals requires specifically developed techniques due to their unique characteristics, which are generally quite distinct compared with tectonic and volcano-tectonic earthquakes. In this work, we describe analysis methods used to detect and locate seismo-volcanic and infrasonic signals at Mt. Etna. Volcanic tremor sources are located using a method based on spatial seismic amplitude distribution, assuming propagation in a homogeneous medium. The tremor source is found by calculating the goodness of the linear regression fit ( R 2) of the log-linearized equation of the seismic amplitude decay with distance. The location method for long-period events is based on the joint computation of semblance and R 2 values, and the location method of very long-period events is based on the application of radial semblance. Infrasonic events and tremor are located by semblance-brightness- and semblance-based methods, respectively. The techniques described here can also be applied to other volcanoes and do not require particular network geometries (such as arrays) but rather simple sparse networks. Using the source locations of all the considered signals, we were able to reconstruct the shallow plumbing system (above sea level) during 2011.

Astrophysical data analysis with information field theory

NASA Astrophysics Data System (ADS)

Enßlin, Torsten

2014-12-01

Non-parametric imaging and data analysis in astrophysics and cosmology can be addressed by information field theory (IFT), a means of Bayesian, data based inference on spatially distributed signal fields. IFT is a statistical field theory, which permits the construction of optimal signal recovery algorithms. It exploits spatial correlations of the signal fields even for nonlinear and non-Gaussian signal inference problems. The alleviation of a perception threshold for recovering signals of unknown correlation structure by using IFT will be discussed in particular as well as a novel improvement on instrumental self-calibration schemes. IFT can be applied to many areas. Here, applications in in cosmology (cosmic microwave background, large-scale structure) and astrophysics (galactic magnetism, radio interferometry) are presented.

Dynamic decomposition of spatiotemporal neural signals

PubMed Central

2017-01-01

Neural signals are characterized by rich temporal and spatiotemporal dynamics that reflect the organization of cortical networks. Theoretical research has shown how neural networks can operate at different dynamic ranges that correspond to specific types of information processing. Here we present a data analysis framework that uses a linearized model of these dynamic states in order to decompose the measured neural signal into a series of components that capture both rhythmic and non-rhythmic neural activity. The method is based on stochastic differential equations and Gaussian process regression. Through computer simulations and analysis of magnetoencephalographic data, we demonstrate the efficacy of the method in identifying meaningful modulations of oscillatory signals corrupted by structured temporal and spatiotemporal noise. These results suggest that the method is particularly suitable for the analysis and interpretation of complex temporal and spatiotemporal neural signals. PMID:28558039

Acoustic impact testing and waveform analysis for damage detection in glued laminated timber

Treesearch

Feng Xu; Xiping Wang; Marko Teder; Yunfei Liu

2017-01-01

Delamination and decay are common structural defects in old glued laminated timber (glulam) buildings, which, if left undetected, could cause severe structural damage. This paper presents a new damage detection method for glulam inspection based on moment analysis and wavelet transform (WT) of impact acoustic signals. Acoustic signals were collected from a glulam arch...

Biosensor method and system based on feature vector extraction

DOEpatents

Greenbaum, Elias; Rodriguez, Jr., Miguel; Qi, Hairong; Wang, Xiaoling

2013-07-02

A system for biosensor-based detection of toxins includes providing at least one time-dependent control signal generated by a biosensor in a gas or liquid medium, and obtaining a time-dependent biosensor signal from the biosensor in the gas or liquid medium to be monitored or analyzed for the presence of one or more toxins selected from chemical, biological or radiological agents. The time-dependent biosensor signal is processed to obtain a plurality of feature vectors using at least one of amplitude statistics and a time-frequency analysis. At least one parameter relating to toxicity of the gas or liquid medium is then determined from the feature vectors based on reference to the control signal.

SIG-VISA: Signal-based Vertically Integrated Seismic Monitoring

NASA Astrophysics Data System (ADS)

Moore, D.; Mayeda, K. M.; Myers, S. C.; Russell, S.

2013-12-01

Traditional seismic monitoring systems rely on discrete detections produced by station processing software; however, while such detections may constitute a useful summary of station activity, they discard large amounts of information present in the original recorded signal. We present SIG-VISA (Signal-based Vertically Integrated Seismic Analysis), a system for seismic monitoring through Bayesian inference on seismic signals. By directly modeling the recorded signal, our approach incorporates additional information unavailable to detection-based methods, enabling higher sensitivity and more accurate localization using techniques such as waveform matching. SIG-VISA's Bayesian forward model of seismic signal envelopes includes physically-derived models of travel times and source characteristics as well as Gaussian process (kriging) statistical models of signal properties that combine interpolation of historical data with extrapolation of learned physical trends. Applying Bayesian inference, we evaluate the model on earthquakes as well as the 2009 DPRK test event, demonstrating a waveform matching effect as part of the probabilistic inference, along with results on event localization and sensitivity. In particular, we demonstrate increased sensitivity from signal-based modeling, in which the SIGVISA signal model finds statistical evidence for arrivals even at stations for which the IMS station processing failed to register any detection.

AOD furnace splash soft-sensor in the smelting process based on improved BP neural network

NASA Astrophysics Data System (ADS)

Ma, Haitao; Wang, Shanshan; Wu, Libin; Yu, Ying

2017-11-01

In view of argon oxygen refining low carbon ferrochrome production process, in the splash of smelting process as the research object, based on splash mechanism analysis in the smelting process , using multi-sensor information fusion and BP neural network modeling techniques is proposed in this paper, using the vibration signal, the audio signal and the flame image signal in the furnace as the characteristic signal of splash, the vibration signal, the audio signal and the flame image signal in the furnace integration and modeling, and reconstruct splash signal, realize the splash soft measurement in the smelting process, the simulation results show that the method can accurately forecast splash type in the smelting process, provide a new method of measurement for forecast splash in the smelting process, provide more accurate information to control splash.

High-efficiency (6 + 1) × 1 pump-signal combiner based on low-deformation and high-precision alignment fabrication

NASA Astrophysics Data System (ADS)

Zou, Shuzhen; Chen, Han; Yu, Haijuan; Sun, Jing; Zhao, Pengfei; Lin, Xuechun

2017-12-01

We demonstrate a new method for fabricating a (6 + 1) × 1 pump-signal combiner based on the reduction of signal fiber diameter by corrosion. This method avoids the mismatch loss of the splice between the signal fiber and the output fiber caused by the signal fiber taper processing. The optimum radius of the corroded signal fiber was calculated according to the analysis of the influence of the cladding thickness on the laser propagating in the fiber core. Besides, we also developed a two-step splicing method to complete the high-precision alignment between the signal fiber core and the output fiber core. A high-efficiency (6 + 1) × 1 pump-signal combiner was produced with an average pump power transmission efficiency of 98.0% and a signal power transmission efficiency of 97.7%, which is well suitable for application to high-power fiber laser system.

Monitoring of electric-cardio signals based on DSP

NASA Astrophysics Data System (ADS)

Yan, Yi-xin; Sun, Hui-nan; Lv, Shuang

2008-10-01

Monitoring of electric-cardio signals is the most direct method of discovering heart diseases. This article presents an electric-cardio signal acquisition and processing system based on DSP. According to the features of electric-cardio signals, the proposed system uses the AgCl electrode as electric-cardio signals sensor, and acquires analog signals with AD620 as the prepositional amplifier, and the digital system equipped is with TMS320LF2407A DSP. The design of digital filter and the analysis of heart rate variation are realized by programming in the DSP. Finally the ECG is obtained with P and T waves along with obvious QRS multi-wave characteristics. The system has low power dissipation, low cost and high precision, which meets the requirements for medical instruments.

B Cell Receptor Signaling-Based Index as a Biomarker for the Loss of Peripheral Immune Tolerance in Autoreactive B Cells in Rheumatoid Arthritis

PubMed Central

Lyubchenko, Taras; Zerbe, Gary O.

2014-01-01

This study examines the loss of peripherally induced B cell immune tolerance in Rheumatoid arthritis (RA) and establishes a novel signaling-based measure of activation in a subset of autoreactive B cells - the Induced tolerance status index (ITSI). Naturally occurring naïve autoreactive B cells can escape the “classical” tolerogenic mechanisms of clonal deletion and receptor editing, but remain peripherally tolerized through B cell receptor (BCR) signaling inhibition (postdevelopmental “receptor tuning” or anergy). ITSI is a statistical index that numerically determines the level of homology between activation patterns of BCR signaling intermediaries in B cells that are either tolerized or activated by auto antigen exposure, and thus quantifies the level of peripheral immune tolerance. The index is based on the logistic regression analysis of phosphorylation levels in a panel of BCR signaling proteins. Our results demonstrate a new approach to identifying autoreactive B cells based on their BCR signaling features. PMID:25057856

Spatially distributed modal signals of free shallow membrane shell structronic system

NASA Astrophysics Data System (ADS)

Yue, H. H.; Deng, Z. Q.; Tzou, H. S.

2008-11-01

Based on the smart material and structronics technology, distributed sensor and control of shell structures have been rapidly developed for the last 20 years. This emerging technology has been utilized in aerospace, telecommunication, micro-electromechanical systems and other engineering applications. However, distributed monitoring technique and its resulting global spatially distributed sensing signals of shallow paraboloidal membrane shells are not clearly understood. In this paper, modeling of free flexible paraboloidal shell with spatially distributed sensor, micro-sensing signal characteristics, and location of distributed piezoelectric sensor patches are investigated based on a new set of assumed mode shape functions. Parametric analysis indicates that the signal generation depends on modal membrane strains in the meridional and circumferential directions in which the latter is more significant than the former, when all bending strains vanish in membrane shells. This study provides a modeling and analysis technique for distributed sensors laminated on lightweight paraboloidal flexible structures and identifies critical components and regions that generate significant signals.

Spatial Signal Characteristics of Shallow Paraboloidal Shell Structronic Systems

NASA Astrophysics Data System (ADS)

Yue, H. H.; Deng, Z. Q.; Tzou, H. S.

Based on the smart material and structronics technology, distributed sensor and control of shell structures have been rapidly developed for the last twenty years. This emerging technology has been utilized in aerospace, telecommunication, micro-electromechanical systems and other engineering applications. However, distributed monitoring technique and its resulting global spatially distributed sensing signals of thin flexible membrane shells are not clearly understood. In this paper, modeling of free thin paraboloidal shell with spatially distributed sensor, micro-sensing signal characteristics, and location of distributed piezoelectric sensor patches are investigated based on a new set of assumed mode shape functions. Parametric analysis indicates that the signal generation depends on modal membrane strains in the meridional and circumferential directions in which the latter is more significant than the former, when all bending strains vanish in membrane shells. This study provides a modeling and analysis technique for distributed sensors laminated on lightweight paraboloidal flexible structures and identifies critical components and regions that generate significant signals.

Fractal-Based Analysis of the Influence of Music on Human Respiration

NASA Astrophysics Data System (ADS)

Reza Namazi, H.

An important challenge in respiration related studies is to investigate the influence of external stimuli on human respiration. Auditory stimulus is an important type of stimuli that influences human respiration. However, no one discovered any trend, which relates the characteristics of the auditory stimuli to the characteristics of the respiratory signal. In this paper, we investigate the correlation between auditory stimuli and respiratory signal from fractal point of view. We found out that the fractal structure of respiratory signal is correlated with the fractal structure of the applied music. Based on the obtained results, the music with greater fractal dimension will result in respiratory signal with smaller fractal dimension. In order to verify this result, we benefit from approximate entropy. The results show the respiratory signal will have smaller approximate entropy by choosing the music with smaller approximate entropy. The method of analysis could be further investigated to analyze the variations of different physiological time series due to the various types of stimuli when the complexity is the main concern.

Systems analysis of arrestin pathway functions.

PubMed

Maudsley, Stuart; Siddiqui, Sana; Martin, Bronwen

2013-01-01

To fully appreciate the diversity and specificity of complex cellular signaling events, such as arrestin-mediated signaling from G protein-coupled receptor activation, a complex systems-level investigation currently appears to be the best option. A rational combination of transcriptomics, proteomics, and interactomics, all coherently integrated with applied next-generation bioinformatics, is vital for the future understanding of the development, translation, and expression of GPCR-mediated arrestin signaling events in physiological contexts. Through a more nuanced, systems-level appreciation of arrestin-mediated signaling, the creation of arrestin-specific molecular response "signatures" should be made simple and ultimately amenable to drug discovery processes. Arrestin-based signaling paradigms possess important aspects, such as its specific temporal kinetics and ability to strongly affect transcriptional activity, that make it an ideal test bed for next-generation of drug discovery bioinformatic approaches such as multi-parallel dose-response analysis, data texturization, and latent semantic indexing-based natural language data processing and feature extraction. Copyright © 2013 Elsevier Inc. All rights reserved.

Tissue artifact removal from respiratory signals based on empirical mode decomposition.

PubMed

Liu, Shaopeng; Gao, Robert X; John, Dinesh; Staudenmayer, John; Freedson, Patty

2013-05-01

On-line measurement of respiration plays an important role in monitoring human physical activities. Such measurement commonly employs sensing belts secured around the rib cage and abdomen of the test object. Affected by the movement of body tissues, respiratory signals typically have a low signal-to-noise ratio. Removing tissue artifacts therefore is critical to ensuring effective respiration analysis. This paper presents a signal decomposition technique for tissue artifact removal from respiratory signals, based on the empirical mode decomposition (EMD). An algorithm based on the mutual information and power criteria was devised to automatically select appropriate intrinsic mode functions for tissue artifact removal and respiratory signal reconstruction. Performance of the EMD-algorithm was evaluated through simulations and real-life experiments (N = 105). Comparison with low-pass filtering that has been conventionally applied confirmed the effectiveness of the technique in tissue artifacts removal.

Analysis of two potential long-distance signaling molecules, LjCLE-RS1/2 and jasmonic acid, in a hypernodulating mutant too much love.

PubMed

Magori, Shimpei; Kawaguchi, Masayoshi

2010-04-01

Legume plants tightly control the number and development of root nodules. This is partly regulated by a long-distance signaling known as auto-regulation of nodulation (AON). AON signaling involves at least two potential long-distance signals: root-derived signal and shoot-derived signal. However, their molecular characteristics and the mode of action remain unclear. In our recent study, we isolated a novel Lotus japonicus hypernodulating mutant too much love (tml). Based on several grafting experiments, we concluded that its causative gene TML functions as a receptor of the shoot-derived signal. This finding prompted us to ask how the candidates of the long-distance signal molecules, LjCLE-RS1/2 and jasmonic acid (JA), are affected in tml mutants. Expression analysis revealed that rapid induction of LjCLE-RS1/2 upon rhizobial inoculation is still intact in tml, supporting that TML plays a role in reception of the shoot-derived signal but not in generation of the root-derived signal. Furthermore, physiological analysis showed that JA, a candidate of the shoot-derived signal, can suppress tml hypernodulation. Therefore, contrary to the previous report, JA might not be a component of AON signaling.

Improving MEG source localizations: an automated method for complete artifact removal based on independent component analysis.

PubMed

Mantini, D; Franciotti, R; Romani, G L; Pizzella, V

2008-03-01

The major limitation for the acquisition of high-quality magnetoencephalography (MEG) recordings is the presence of disturbances of physiological and technical origins: eye movements, cardiac signals, muscular contractions, and environmental noise are serious problems for MEG signal analysis. In the last years, multi-channel MEG systems have undergone rapid technological developments in terms of noise reduction, and many processing methods have been proposed for artifact rejection. Independent component analysis (ICA) has already shown to be an effective and generally applicable technique for concurrently removing artifacts and noise from the MEG recordings. However, no standardized automated system based on ICA has become available so far, because of the intrinsic difficulty in the reliable categorization of the source signals obtained with this technique. In this work, approximate entropy (ApEn), a measure of data regularity, is successfully used for the classification of the signals produced by ICA, allowing for an automated artifact rejection. The proposed method has been tested using MEG data sets collected during somatosensory, auditory and visual stimulation. It was demonstrated to be effective in attenuating both biological artifacts and environmental noise, in order to reconstruct clear signals that can be used for improving brain source localizations.

Develop Advanced Nonlinear Signal Analysis Topographical Mapping System

NASA Technical Reports Server (NTRS)

Jong, Jen-Yi

1997-01-01

During the development of the SSME, a hierarchy of advanced signal analysis techniques for mechanical signature analysis has been developed by NASA and AI Signal Research Inc. (ASRI) to improve the safety and reliability for Space Shuttle operations. These techniques can process and identify intelligent information hidden in a measured signal which is often unidentifiable using conventional signal analysis methods. Currently, due to the highly interactive processing requirements and the volume of dynamic data involved, detailed diagnostic analysis is being performed manually which requires immense man-hours with extensive human interface. To overcome this manual process, NASA implemented this program to develop an Advanced nonlinear signal Analysis Topographical Mapping System (ATMS) to provide automatic/unsupervised engine diagnostic capabilities. The ATMS will utilize a rule-based Clips expert system to supervise a hierarchy of diagnostic signature analysis techniques in the Advanced Signal Analysis Library (ASAL). ASAL will perform automatic signal processing, archiving, and anomaly detection/identification tasks in order to provide an intelligent and fully automated engine diagnostic capability. The ATMS has been successfully developed under this contract. In summary, the program objectives to design, develop, test and conduct performance evaluation for an automated engine diagnostic system have been successfully achieved. Software implementation of the entire ATMS system on MSFC's OISPS computer has been completed. The significance of the ATMS developed under this program is attributed to the fully automated coherence analysis capability for anomaly detection and identification which can greatly enhance the power and reliability of engine diagnostic evaluation. The results have demonstrated that ATMS can significantly save time and man-hours in performing engine test/flight data analysis and performance evaluation of large volumes of dynamic test data.

Interaction of Herbal Compounds with Biological Targets: A Case Study with Berberine

PubMed Central

Chen, Xiao-Wu; Di, Yuan Ming; Zhang, Jian; Zhou, Zhi-Wei; Li, Chun Guang; Zhou, Shu-Feng

2012-01-01

Berberine is one of the main alkaloids found in the Chinese herb Huang lian (Rhizoma Coptidis), which has been reported to have multiple pharmacological activities. This study aimed to analyze the molecular targets of berberine based on literature data followed by a pathway analysis using the PANTHER program. PANTHER analysis of berberine targets showed that the most classes of molecular functions include receptor binding, kinase activity, protein binding, transcription activity, DNA binding, and kinase regulator activity. Based on the biological process classification of in vitro berberine targets, those targets related to signal transduction, intracellular signalling cascade, cell surface receptor-linked signal transduction, cell motion, cell cycle control, immunity system process, and protein metabolic process are most frequently involved. In addition, berberine was found to interact with a mixture of biological pathways, such as Alzheimer's disease-presenilin and -secretase pathways, angiogenesis, apoptosis signalling pathway, FAS signalling pathway, Hungtington disease, inflammation mediated by chemokine and cytokine signalling pathways, interleukin signalling pathway, and p53 pathways. We also explored the possible mechanism of action for the anti-diabetic effect of berberine. Further studies are warranted to elucidate the mechanisms of action of berberine using systems biology approach. PMID:23213296

Develop advanced nonlinear signal analysis topographical mapping system

NASA Technical Reports Server (NTRS)

1994-01-01

The Space Shuttle Main Engine (SSME) has been undergoing extensive flight certification and developmental testing, which involves some 250 health monitoring measurements. Under the severe temperature, pressure, and dynamic environments sustained during operation, numerous major component failures have occurred, resulting in extensive engine hardware damage and scheduling losses. To enhance SSME safety and reliability, detailed analysis and evaluation of the measurements signal are mandatory to assess its dynamic characteristics and operational condition. Efficient and reliable signal detection techniques will reduce catastrophic system failure risks and expedite the evaluation of both flight and ground test data, and thereby reduce launch turn-around time. The basic objective of this contract are threefold: (1) develop and validate a hierarchy of innovative signal analysis techniques for nonlinear and nonstationary time-frequency analysis. Performance evaluation will be carried out through detailed analysis of extensive SSME static firing and flight data. These techniques will be incorporated into a fully automated system; (2) develop an advanced nonlinear signal analysis topographical mapping system (ATMS) to generate a Compressed SSME TOPO Data Base (CSTDB). This ATMS system will convert tremendous amount of complex vibration signals from the entire SSME test history into a bank of succinct image-like patterns while retaining all respective phase information. High compression ratio can be achieved to allow minimal storage requirement, while providing fast signature retrieval, pattern comparison, and identification capabilities; and (3) integrate the nonlinear correlation techniques into the CSTDB data base with compatible TOPO input data format. Such integrated ATMS system will provide the large test archives necessary for quick signature comparison. This study will provide timely assessment of SSME component operational status, identify probable causes of malfunction, and indicate feasible engineering solutions. The final result of this program will yield an ATMS system of nonlinear and nonstationary spectral analysis software package integrated with the Compressed SSME TOPO Data Base (CSTDB) on the same platform. This system will allow NASA engineers to retrieve any unique defect signatures and trends associated with different failure modes and anomalous phenomena over the entire SSME test history across turbo pump families.

Develop advanced nonlinear signal analysis topographical mapping system

NASA Technical Reports Server (NTRS)

Jong, Jen-Yi

1993-01-01

The SSME has been undergoing extensive flight certification and developmental testing, which involves some 250 health monitoring measurements. Under the severe temperature pressure, and dynamic environments sustained during operation, numerous major component failures have occurred, resulting in extensive engine hardware damage and scheduling losses. To enhance SSME safety and reliability, detailed analysis and evaluation of the measurements signal are mandatory to assess its dynamic characteristics and operational condition. Efficient and reliable signal detection techniques will reduce catastrophic system failure risks and expedite the evaluation of both flight and ground test data, and thereby reduce launch turn-around time. The basic objective of this contract are threefold: (1) Develop and validate a hierarchy of innovative signal analysis techniques for nonlinear and nonstationary time-frequency analysis. Performance evaluation will be carried out through detailed analysis of extensive SSME static firing and flight data. These techniques will be incorporated into a fully automated system. (2) Develop an advanced nonlinear signal analysis topographical mapping system (ATMS) to generate a Compressed SSME TOPO Data Base (CSTDB). This ATMS system will convert tremendous amounts of complex vibration signals from the entire SSME test history into a bank of succinct image-like patterns while retaining all respective phase information. A high compression ratio can be achieved to allow the minimal storage requirement, while providing fast signature retrieval, pattern comparison, and identification capabilities. (3) Integrate the nonlinear correlation techniques into the CSTDB data base with compatible TOPO input data format. Such integrated ATMS system will provide the large test archives necessary for a quick signature comparison. This study will provide timely assessment of SSME component operational status, identify probable causes of malfunction, and indicate feasible engineering solutions. The final result of this program will yield an ATMS system of nonlinear and nonstationary spectral analysis software package integrated with the Compressed SSME TOPO Data Base (CSTDB) on the same platform. This system will allow NASA engineers to retrieve any unique defect signatures and trends associated with different failure modes and anomalous phenomena over the entire SSME test history across turbo pump families.

An accuracy aware low power wireless EEG unit with information content based adaptive data compression.

PubMed

Tolbert, Jeremy R; Kabali, Pratik; Brar, Simeranjit; Mukhopadhyay, Saibal

2009-01-01

We present a digital system for adaptive data compression for low power wireless transmission of Electroencephalography (EEG) data. The proposed system acts as a base-band processor between the EEG analog-to-digital front-end and RF transceiver. It performs a real-time accuracy energy trade-off for multi-channel EEG signal transmission by controlling the volume of transmitted data. We propose a multi-core digital signal processor for on-chip processing of EEG signals, to detect signal information of each channel and perform real-time adaptive compression. Our analysis shows that the proposed approach can provide significant savings in transmitter power with minimal impact on the overall signal accuracy.

A real-time stress classification system based on arousal analysis of the nervous system by an F-state machine.

PubMed

Martinez, R; Irigoyen, E; Arruti, A; Martin, J I; Muguerza, J

2017-09-01

Detection and labelling of an increment in the human stress level is a contribution focused principally on improving the quality of life of people. This work is aimed to develop a biophysical real-time stress identification and classification system, analysing two noninvasive signals, the galvanic skin response and the heart rate variability. An experimental procedure was designed and configured in order to elicit a stressful situation that is similar to those found in real cases. A total of 166 subjects participated in this experimental stage. The set of registered signals of each subject was considered as one experiment. A preliminary qualitative analysis of the signals collected was made, based on previous counselling received from neurophysiologists and psychologists. This study revealed a relationship between changes in the temporal signals and the induced stress states in each subject. To identify and classify such states, a subsequent quantitative analysis was performed in order to determine specific numerical information related to the above mentioned relationship. This second analysis gives the particular details to design the finally proposed classification algorithm, based on a Finite State Machine. The proposed system is able to classify the detected stress stages at three levels: low, medium, and high. Furthermore, the system identifies persistent stress situations or momentary alerts, depending on the subject's arousal. The system reaches an F 1 score of 0.984 in the case of high level, an F 1 score of 0.970 for medium level, and an F 1 score of 0.943 for low level. The resulting system is able to detect and classify different stress stages only based on two non invasive signals. These signals can be collected in people during their monitoring and be processed in a real-time sense, as the system can be previously preconfigured. Therefore, it could easily be implemented in a wearable prototype that could be worn by end users without feeling to be monitored. Besides, due to its low computational, the computation of the signals slopes is easy to do and its deployment in real-time applications is feasible. Copyright © 2017 Elsevier B.V. All rights reserved.

Optical coherence tomography angiography-based capillary velocimetry

NASA Astrophysics Data System (ADS)

Wang, Ruikang K.; Zhang, Qinqin; Li, Yuandong; Song, Shaozhen

2017-06-01

Challenge persists in the field of optical coherence tomography (OCT) when it is required to quantify capillary blood flow within tissue beds in vivo. We propose a useful approach to statistically estimate the mean capillary flow velocity using a model-based statistical method of eigendecomposition (ED) analysis of the complex OCT signals obtained with the OCT angiography (OCTA) scanning protocol. ED-based analysis is achieved by the covariance matrix of the ensemble complex OCT signals, upon which the eigenvalues and eigenvectors that represent the subsets of the signal makeup are calculated. From this analysis, the signals due to moving particles can be isolated by employing an adaptive regression filter to remove the eigencomponents that represent static tissue signals. The mean frequency (MF) of moving particles can be estimated by the first lag-one autocorrelation of the corresponding eigenvectors. Three important parameters are introduced, including the blood flow signal power representing the presence of blood flow (i.e., OCTA signals), the MF indicating the mean velocity of blood flow, and the frequency bandwidth describing the temporal flow heterogeneity within a scanned tissue volume. The proposed approach is tested using scattering phantoms, in which microfluidic channels are used to simulate the functional capillary vessels that are perfused with the scattering intralipid solution. The results indicate a linear relationship between the MF and mean flow velocity. In vivo animal experiments are also conducted by imaging mouse brain with distal middle cerebral artery ligation to test the capability of the method to image the changes in capillary flows in response to an ischemic insult, demonstrating the practical usefulness of the proposed method for providing important quantifiable information about capillary tissue beds in the investigations of neurological conditions in vivo.

CalQuo: automated, simultaneous single-cell and population-level quantification of global intracellular Ca2+ responses.

PubMed

Fritzsche, Marco; Fernandes, Ricardo A; Colin-York, Huw; Santos, Ana M; Lee, Steven F; Lagerholm, B Christoffer; Davis, Simon J; Eggeling, Christian

2015-11-13

Detecting intracellular calcium signaling with fluorescent calcium indicator dyes is often coupled with microscopy techniques to follow the activation state of non-excitable cells, including lymphocytes. However, the analysis of global intracellular calcium responses both at the single-cell level and in large ensembles simultaneously has yet to be automated. Here, we present a new software package, CalQuo (Calcium Quantification), which allows the automated analysis and simultaneous monitoring of global fluorescent calcium reporter-based signaling responses in up to 1000 single cells per experiment, at temporal resolutions of sub-seconds to seconds. CalQuo quantifies the number and fraction of responding cells, the temporal dependence of calcium signaling and provides global and individual calcium-reporter fluorescence intensity profiles. We demonstrate the utility of the new method by comparing the calcium-based signaling responses of genetically manipulated human lymphocytic cell lines.

Characterization of electrically-active defects in ultraviolet light-emitting diodes with laser-based failure analysis techniques

DOE PAGES

Miller, Mary A.; Tangyunyong, Paiboon; Edward I. Cole, Jr.

2016-01-12

In this study, laser-based failure analysis techniques demonstrate the ability to quickly and non-intrusively screen deep ultraviolet light-emitting diodes(LEDs) for electrically-active defects. In particular, two laser-based techniques, light-induced voltage alteration and thermally-induced voltage alteration, generate applied voltage maps (AVMs) that provide information on electrically-active defect behavior including turn-on bias, density, and spatial location. Here, multiple commercial LEDs were examined and found to have dark defect signals in the AVM indicating a site of reduced resistance or leakage through the diode. The existence of the dark defect signals in the AVM correlates strongly with an increased forward-bias leakage current. This increasedmore » leakage is not present in devices without AVM signals. Transmission electron microscopyanalysis of a dark defect signal site revealed a dislocation cluster through the pn junction. The cluster included an open core dislocation. Even though LEDs with few dark AVM defect signals did not correlate strongly with power loss, direct association between increased open core dislocation densities and reduced LED device performance has been presented elsewhere [M. W. Moseley et al., J. Appl. Phys. 117, 095301 (2015)].« less

Improving Remote Health Monitoring: A Low-Complexity ECG Compression Approach

PubMed Central

Al-Ali, Abdulla; Mohamed, Amr; Ward, Rabab

2018-01-01

Recent advances in mobile technology have created a shift towards using battery-driven devices in remote monitoring settings and smart homes. Clinicians are carrying out diagnostic and screening procedures based on the electrocardiogram (ECG) signals collected remotely for outpatients who need continuous monitoring. High-speed transmission and analysis of large recorded ECG signals are essential, especially with the increased use of battery-powered devices. Exploring low-power alternative compression methodologies that have high efficiency and that enable ECG signal collection, transmission, and analysis in a smart home or remote location is required. Compression algorithms based on adaptive linear predictors and decimation by a factor B/K are evaluated based on compression ratio (CR), percentage root-mean-square difference (PRD), and heartbeat detection accuracy of the reconstructed ECG signal. With two databases (153 subjects), the new algorithm demonstrates the highest compression performance (CR=6 and PRD=1.88) and overall detection accuracy (99.90% sensitivity, 99.56% positive predictivity) over both databases. The proposed algorithm presents an advantage for the real-time transmission of ECG signals using a faster and more efficient method, which meets the growing demand for more efficient remote health monitoring. PMID:29337892

Improving Remote Health Monitoring: A Low-Complexity ECG Compression Approach.

PubMed

Elgendi, Mohamed; Al-Ali, Abdulla; Mohamed, Amr; Ward, Rabab

2018-01-16

Recent advances in mobile technology have created a shift towards using battery-driven devices in remote monitoring settings and smart homes. Clinicians are carrying out diagnostic and screening procedures based on the electrocardiogram (ECG) signals collected remotely for outpatients who need continuous monitoring. High-speed transmission and analysis of large recorded ECG signals are essential, especially with the increased use of battery-powered devices. Exploring low-power alternative compression methodologies that have high efficiency and that enable ECG signal collection, transmission, and analysis in a smart home or remote location is required. Compression algorithms based on adaptive linear predictors and decimation by a factor B / K are evaluated based on compression ratio (CR), percentage root-mean-square difference (PRD), and heartbeat detection accuracy of the reconstructed ECG signal. With two databases (153 subjects), the new algorithm demonstrates the highest compression performance ( CR = 6 and PRD = 1.88 ) and overall detection accuracy (99.90% sensitivity, 99.56% positive predictivity) over both databases. The proposed algorithm presents an advantage for the real-time transmission of ECG signals using a faster and more efficient method, which meets the growing demand for more efficient remote health monitoring.

Evaluation of different time domain peak models using extreme learning machine-based peak detection for EEG signal.

PubMed

Adam, Asrul; Ibrahim, Zuwairie; Mokhtar, Norrima; Shapiai, Mohd Ibrahim; Cumming, Paul; Mubin, Marizan

2016-01-01

Various peak models have been introduced to detect and analyze peaks in the time domain analysis of electroencephalogram (EEG) signals. In general, peak model in the time domain analysis consists of a set of signal parameters, such as amplitude, width, and slope. Models including those proposed by Dumpala, Acir, Liu, and Dingle are routinely used to detect peaks in EEG signals acquired in clinical studies of epilepsy or eye blink. The optimal peak model is the most reliable peak detection performance in a particular application. A fair measure of performance of different models requires a common and unbiased platform. In this study, we evaluate the performance of the four different peak models using the extreme learning machine (ELM)-based peak detection algorithm. We found that the Dingle model gave the best performance, with 72 % accuracy in the analysis of real EEG data. Statistical analysis conferred that the Dingle model afforded significantly better mean testing accuracy than did the Acir and Liu models, which were in the range 37-52 %. Meanwhile, the Dingle model has no significant difference compared to Dumpala model.

[Analysis of scatterer microstructure feature based on Chirp-Z transform cepstrum].

PubMed

Guo, Jianzhong; Lin, Shuyu

2007-12-01

The fundamental research field of medical ultrasound has been the characterization of tissue scatterers. The signal processing method is widely used in this research field. A new method of Chirp-Z Transform Cepstrum for mean spacing estimation of tissue scatterers using ultrasonic scattered signals has been developed. By using this method together with conventional AR cepstrum method, we processed the backscattered signals of mimic tissue and pig liver in vitro. The results illustrated that the Chirp-Z Transform Cepstrum method is effective for signal analysis of ultrasonic scattering and characterization of tissue scatterers, and it can improve the resolution for mean spacing estimation of tissue scatterers.

Infrasound Signals from Ground-Motion Sources

DTIC Science & Technology

2008-09-01

signals as a basis for discriminants between underground nuclear tests ( UGT ) and earthquakes (EQ). In an earlier program, infrasound signals from... UGTs and EQs were collected at ranges of a few hundred kilometers, in the far-field. Analysis of these data revealed two parameters that had potential...well. To study the near-field signals, we are using computational techniques based on modeled ground motions from UGTs and EQs. One is the closed

The Analysis of Surface EMG Signals with the Wavelet-Based Correlation Dimension Method

PubMed Central

Zhang, Yanyan; Wang, Jue

2014-01-01

Many attempts have been made to effectively improve a prosthetic system controlled by the classification of surface electromyographic (SEMG) signals. Recently, the development of methodologies to extract the effective features still remains a primary challenge. Previous studies have demonstrated that the SEMG signals have nonlinear characteristics. In this study, by combining the nonlinear time series analysis and the time-frequency domain methods, we proposed the wavelet-based correlation dimension method to extract the effective features of SEMG signals. The SEMG signals were firstly analyzed by the wavelet transform and the correlation dimension was calculated to obtain the features of the SEMG signals. Then, these features were used as the input vectors of a Gustafson-Kessel clustering classifier to discriminate four types of forearm movements. Our results showed that there are four separate clusters corresponding to different forearm movements at the third resolution level and the resulting classification accuracy was 100%, when two channels of SEMG signals were used. This indicates that the proposed approach can provide important insight into the nonlinear characteristics and the time-frequency domain features of SEMG signals and is suitable for classifying different types of forearm movements. By comparing with other existing methods, the proposed method exhibited more robustness and higher classification accuracy. PMID:24868240

Transcriptome analysis supports viral infection and fluoride toxicity as contributors to chronic kidney disease of unknown etiology (CKDu) in Sri Lanka.

PubMed

Sayanthooran, Saravanabavan; Gunerathne, Lishanthe; Abeysekera, Tilak D J; Magana-Arachchi, Dhammika N

2018-05-28

Chronic kidney disease of unknown etiology (CKDu), having epidemic characteristics, is being diagnosed increasingly in certain tropical regions of the world, mainly Latin America and Sri Lanka. They have been observed primarily in farming communities and current hypotheses point toward many environmental and occupational triggers. CKDu does not have common etiologies of chronic kidney disease (CKD) such as hypertension, diabetes, or autoimmune disease. We aimed to understand the molecular processes underlying CKDu in Sri Lanka using transcriptome analysis. RNA extracted from whole blood was reverse transcribed and used for microarray analysis using the Human HT-12 v.4 array (Illumina). Pathway analysis was carried out using ingenuity pathway analysis (IPA-Qiagen). Microarray results were validated using real-time PCR of five selected genes. Pathways related to innate immune response, including interferon signaling, inflammasome signaling and TREM1 signaling had the most significant positive activation z scores, where as EIF2 signaling and mTOR signaling had the most significant negative activation z scores. Pathways previously linked to fluoride toxicity; G-protein activation, Cdc42 signaling, Rac signaling and RhoA signaling were activated in CKDu patients. The most significantly activated biological functions were cell death, cell movement and antimicrobial response. Significant toxicological functions were mitochondrial dysfunction, oxidative stress and apoptosis. Based on the molecular pathway analysis in CKDu patients and review of literature, viral infections and fluoride toxicity appear to be contributing to the molecular mechanisms underlying CKDu.

DHS Internship Paper

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dreyer, J

2007-09-18

During my internship at Lawrence Livermore National Laboratory I worked with microcalorimeter gamma-ray and fast-neutron detectors based on superconducting Transition Edge Sensors (TESs). These instruments are being developed for fundamental science and nuclear non-proliferation applications because of their extremely high energy resolution; however, this comes at the expense of a small pixel size and slow decay times. The small pixel sizes are being addressed by developing detector arrays while the low count rate is being addressed by developing Digital Signal Processors (DSPs) that allow higher throughput than traditional pulse processing algorithms. Traditionally, low-temperature microcalorimeter pulses have been processed off-line withmore » optimum filtering routines based on the measured spectral characteristics of the signal and the noise. These optimum filters rely on the spectral content of the signal being identical for all events, and therefore require capturing the entire pulse signal without pile-up. In contrast, the DSP algorithm being developed is based on differences in signal levels before and after a trigger event, and therefore does not require the waveform to fully decay, or even the signal level to be close to the base line. The readout system allows for real time data acquisition and analysis at count rates exceeding 100 Hz for pulses with several {approx}ms decay times with minimal loss of energy resolution. Originally developed for gamma-ray analysis with HPGe detectors we have modified the hardware and firmware of the system to accommodate the slower TES signals and optimized the parameters of the filtering algorithm to maximize either resolution or throughput. The following presents an overview of the digital signal processing hardware and discusses the results of characterization measurements made to determine the systems performance.« less

Waveform Similarity Analysis: A Simple Template Comparing Approach for Detecting and Quantifying Noisy Evoked Compound Action Potentials.

PubMed

Potas, Jason Robert; de Castro, Newton Gonçalves; Maddess, Ted; de Souza, Marcio Nogueira

2015-01-01

Experimental electrophysiological assessment of evoked responses from regenerating nerves is challenging due to the typical complex response of events dispersed over various latencies and poor signal-to-noise ratio. Our objective was to automate the detection of compound action potential events and derive their latencies and magnitudes using a simple cross-correlation template comparison approach. For this, we developed an algorithm called Waveform Similarity Analysis. To test the algorithm, challenging signals were generated in vivo by stimulating sural and sciatic nerves, whilst recording evoked potentials at the sciatic nerve and tibialis anterior muscle, respectively, in animals recovering from sciatic nerve transection. Our template for the algorithm was generated based on responses evoked from the intact side. We also simulated noisy signals and examined the output of the Waveform Similarity Analysis algorithm with imperfect templates. Signals were detected and quantified using Waveform Similarity Analysis, which was compared to event detection, latency and magnitude measurements of the same signals performed by a trained observer, a process we called Trained Eye Analysis. The Waveform Similarity Analysis algorithm could successfully detect and quantify simple or complex responses from nerve and muscle compound action potentials of intact or regenerated nerves. Incorrectly specifying the template outperformed Trained Eye Analysis for predicting signal amplitude, but produced consistent latency errors for the simulated signals examined. Compared to the trained eye, Waveform Similarity Analysis is automatic, objective, does not rely on the observer to identify and/or measure peaks, and can detect small clustered events even when signal-to-noise ratio is poor. Waveform Similarity Analysis provides a simple, reliable and convenient approach to quantify latencies and magnitudes of complex waveforms and therefore serves as a useful tool for studying evoked compound action potentials in neural regeneration studies.

Waveform Similarity Analysis: A Simple Template Comparing Approach for Detecting and Quantifying Noisy Evoked Compound Action Potentials

PubMed Central

Potas, Jason Robert; de Castro, Newton Gonçalves; Maddess, Ted; de Souza, Marcio Nogueira

2015-01-01

Experimental electrophysiological assessment of evoked responses from regenerating nerves is challenging due to the typical complex response of events dispersed over various latencies and poor signal-to-noise ratio. Our objective was to automate the detection of compound action potential events and derive their latencies and magnitudes using a simple cross-correlation template comparison approach. For this, we developed an algorithm called Waveform Similarity Analysis. To test the algorithm, challenging signals were generated in vivo by stimulating sural and sciatic nerves, whilst recording evoked potentials at the sciatic nerve and tibialis anterior muscle, respectively, in animals recovering from sciatic nerve transection. Our template for the algorithm was generated based on responses evoked from the intact side. We also simulated noisy signals and examined the output of the Waveform Similarity Analysis algorithm with imperfect templates. Signals were detected and quantified using Waveform Similarity Analysis, which was compared to event detection, latency and magnitude measurements of the same signals performed by a trained observer, a process we called Trained Eye Analysis. The Waveform Similarity Analysis algorithm could successfully detect and quantify simple or complex responses from nerve and muscle compound action potentials of intact or regenerated nerves. Incorrectly specifying the template outperformed Trained Eye Analysis for predicting signal amplitude, but produced consistent latency errors for the simulated signals examined. Compared to the trained eye, Waveform Similarity Analysis is automatic, objective, does not rely on the observer to identify and/or measure peaks, and can detect small clustered events even when signal-to-noise ratio is poor. Waveform Similarity Analysis provides a simple, reliable and convenient approach to quantify latencies and magnitudes of complex waveforms and therefore serves as a useful tool for studying evoked compound action potentials in neural regeneration studies. PMID:26325291

A Hybrid Wavelet-Based Method for the Peak Detection of Photoplethysmography Signals.

PubMed

Li, Suyi; Jiang, Shanqing; Jiang, Shan; Wu, Jiang; Xiong, Wenji; Diao, Shu

2017-01-01

The noninvasive peripheral oxygen saturation (SpO 2 ) and the pulse rate can be extracted from photoplethysmography (PPG) signals. However, the accuracy of the extraction is directly affected by the quality of the signal obtained and the peak of the signal identified; therefore, a hybrid wavelet-based method is proposed in this study. Firstly, we suppressed the partial motion artifacts and corrected the baseline drift by using a wavelet method based on the principle of wavelet multiresolution. And then, we designed a quadratic spline wavelet modulus maximum algorithm to identify the PPG peaks automatically. To evaluate this hybrid method, a reflective pulse oximeter was used to acquire ten subjects' PPG signals under sitting, raising hand, and gently walking postures, and the peak recognition results on the raw signal and on the corrected signal were compared, respectively. The results showed that the hybrid method not only corrected the morphologies of the signal well but also optimized the peaks identification quality, subsequently elevating the measurement accuracy of SpO 2 and the pulse rate. As a result, our hybrid wavelet-based method profoundly optimized the evaluation of respiratory function and heart rate variability analysis.

A Hybrid Wavelet-Based Method for the Peak Detection of Photoplethysmography Signals

PubMed Central

Jiang, Shanqing; Jiang, Shan; Wu, Jiang; Xiong, Wenji

2017-01-01

The noninvasive peripheral oxygen saturation (SpO2) and the pulse rate can be extracted from photoplethysmography (PPG) signals. However, the accuracy of the extraction is directly affected by the quality of the signal obtained and the peak of the signal identified; therefore, a hybrid wavelet-based method is proposed in this study. Firstly, we suppressed the partial motion artifacts and corrected the baseline drift by using a wavelet method based on the principle of wavelet multiresolution. And then, we designed a quadratic spline wavelet modulus maximum algorithm to identify the PPG peaks automatically. To evaluate this hybrid method, a reflective pulse oximeter was used to acquire ten subjects' PPG signals under sitting, raising hand, and gently walking postures, and the peak recognition results on the raw signal and on the corrected signal were compared, respectively. The results showed that the hybrid method not only corrected the morphologies of the signal well but also optimized the peaks identification quality, subsequently elevating the measurement accuracy of SpO2 and the pulse rate. As a result, our hybrid wavelet-based method profoundly optimized the evaluation of respiratory function and heart rate variability analysis. PMID:29250135

Miniature Tunable Laser Spectrometer for Detection of a Trace Gas

NASA Technical Reports Server (NTRS)

Christensen, Lance E. (Inventor)

2017-01-01

An open-path laser spectrometer (OPLS) for measuring a concentration of a trace gas, the OPLS including an open-path multi-pass analysis region including a first mirror, a second mirror at a distance and orientation from the first mirror, and a support structure for locating the mirrors, a laser coupled to the analysis region and configured to emit light of a wavelength range and to enable a plurality of reflections of the emitted light between the mirrors, a detector coupled to the analysis region and configured to detect a portion of the emitted light impinging on the detector and to generate a corresponding signal, and an electronic system coupled to the laser and the detector, and configured to adjust the wavelength range of the emitted light from the laser based on the generated signal, and to measure the concentration of the trace gas based on the generated signal.

On analysis of electroencephalogram by multiresolution-based energetic approach

NASA Astrophysics Data System (ADS)

Sevindir, Hulya Kodal; Yazici, Cuneyt; Siddiqi, A. H.; Aslan, Zafer

2013-10-01

Epilepsy is a common brain disorder where the normal neuronal activity gets affected. Electroencephalography (EEG) is the recording of electrical activity along the scalp produced by the firing of neurons within the brain. The main application of EEG is in the case of epilepsy. On a standard EEG some abnormalities indicate epileptic activity. EEG signals like many biomedical signals are highly non-stationary by their nature. For the investigation of biomedical signals, in particular EEG signals, wavelet analysis have found prominent position in the study for their ability to analyze such signals. Wavelet transform is capable of separating the signal energy among different frequency scales and a good compromise between temporal and frequency resolution is obtained. The present study is an attempt for better understanding of the mechanism causing the epileptic disorder and accurate prediction of occurrence of seizures. In the present paper following Magosso's work [12], we identify typical patterns of energy redistribution before and during the seizure using multiresolution wavelet analysis on Kocaeli University's Medical School's data.

Time-frequency analysis of phonocardiogram signals using wavelet transform: a comparative study.

PubMed

Ergen, Burhan; Tatar, Yetkin; Gulcur, Halil Ozcan

2012-01-01

Analysis of phonocardiogram (PCG) signals provides a non-invasive means to determine the abnormalities caused by cardiovascular system pathology. In general, time-frequency representation (TFR) methods are used to study the PCG signal because it is one of the non-stationary bio-signals. The continuous wavelet transform (CWT) is especially suitable for the analysis of non-stationary signals and to obtain the TFR, due to its high resolution, both in time and in frequency and has recently become a favourite tool. It decomposes a signal in terms of elementary contributions called wavelets, which are shifted and dilated copies of a fixed mother wavelet function, and yields a joint TFR. Although the basic characteristics of the wavelets are similar, each type of the wavelets produces a different TFR. In this study, eight real types of the most known wavelets are examined on typical PCG signals indicating heart abnormalities in order to determine the best wavelet to obtain a reliable TFR. For this purpose, the wavelet energy and frequency spectrum estimations based on the CWT and the spectra of the chosen wavelets were compared with the energy distribution and the autoregressive frequency spectra in order to determine the most suitable wavelet. The results show that Morlet wavelet is the most reliable wavelet for the time-frequency analysis of PCG signals.

Digital signal processing in microwave radiometers

NASA Technical Reports Server (NTRS)

Lawrence, R. W.; Stanley, W. D.; Harrington, R. F.

1980-01-01

A microprocessor based digital signal processing unit has been proposed to replace analog sections of a microwave radiometer. A brief introduction to the radiometer system involved and a description of problems encountered in the use of digital techniques in radiometer design are discussed. An analysis of the digital signal processor as part of the radiometer is then presented.

Layover and shadow detection based on distributed spaceborne single-baseline InSAR

NASA Astrophysics Data System (ADS)

Huanxin, Zou; Bin, Cai; Changzhou, Fan; Yun, Ren

2014-03-01

Distributed spaceborne single-baseline InSAR is an effective technique to get high quality Digital Elevation Model. Layover and Shadow are ubiquitous phenomenon in SAR images because of geometric relation of SAR imaging. In the signal processing of single-baseline InSAR, the phase singularity of Layover and Shadow leads to the phase difficult to filtering and unwrapping. This paper analyzed the geometric and signal model of the Layover and Shadow fields. Based on the interferometric signal autocorrelation matrix, the paper proposed the signal number estimation method based on information theoretic criteria, to distinguish Layover and Shadow from normal InSAR fields. The effectiveness and practicability of the method proposed in the paper are validated in the simulation experiments and theoretical analysis.

Biosensor method and system based on feature vector extraction

DOEpatents

Greenbaum, Elias [Knoxville, TN; Rodriguez, Jr., Miguel; Qi, Hairong [Knoxville, TN; Wang, Xiaoling [San Jose, CA

2012-04-17

A method of biosensor-based detection of toxins comprises the steps of providing at least one time-dependent control signal generated by a biosensor in a gas or liquid medium, and obtaining a time-dependent biosensor signal from the biosensor in the gas or liquid medium to be monitored or analyzed for the presence of one or more toxins selected from chemical, biological or radiological agents. The time-dependent biosensor signal is processed to obtain a plurality of feature vectors using at least one of amplitude statistics and a time-frequency analysis. At least one parameter relating to toxicity of the gas or liquid medium is then determined from the feature vectors based on reference to the control signal.

Intelligent approach for analysis of respiratory signals and oxygen saturation in the sleep apnea/hypopnea syndrome.

PubMed

Moret-Bonillo, Vicente; Alvarez-Estévez, Diego; Fernández-Leal, Angel; Hernández-Pereira, Elena

2014-01-01

This work deals with the development of an intelligent approach for clinical decision making in the diagnosis of the Sleep Apnea/Hypopnea Syndrome, SAHS, from the analysis of respiratory signals and oxygen saturation in arterial blood, SaO2. In order to accomplish the task the proposed approach makes use of different artificial intelligence techniques and reasoning processes being able to deal with imprecise data. These reasoning processes are based on fuzzy logic and on temporal analysis of the information. The developed approach also takes into account the possibility of artifacts in the monitored signals. Detection and characterization of signal artifacts allows detection of false positives. Identification of relevant diagnostic patterns and temporal correlation of events is performed through the implementation of temporal constraints.

Development of pixellated Ir-TESs

NASA Astrophysics Data System (ADS)

Zen, Nobuyuki; Takahashi, Hiroyuki; Kunieda, Yuichi; Damayanthi, Rathnayaka M. T.; Mori, Fumiakira; Fujita, Kaoru; Nakazawa, Masaharu; Fukuda, Daiji; Ohkubo, Masataka

2006-04-01

We have been developing Ir-based pixellated superconducting transition edge sensors (TESs). In the area of material or astronomical applications, the sensor with few eV energy resolution and over 1000 pixels imaging property is desired. In order to achieve this goal, we have been analyzing signals from pixellated TESs. In the case of a 20 pixel array of Ir-TESs, with 45 μm×45 μm pixel sizes, the incident X-ray signals have been classified into 16 groups. We have applied numerical signal analysis. On the one hand, the energy resolution of our pixellated TES is strongly degraded. However, using pulse shape analysis, we can dramatically improve the resolution. Thus, we consider that the pulse signal analysis will lead this device to be used as a practical photon incident position identifying TES.

Intelligent Approach for Analysis of Respiratory Signals and Oxygen Saturation in the Sleep Apnea/Hypopnea Syndrome

PubMed Central

Moret-Bonillo, Vicente; Alvarez-Estévez, Diego; Fernández-Leal, Angel; Hernández-Pereira, Elena

2014-01-01

This work deals with the development of an intelligent approach for clinical decision making in the diagnosis of the Sleep Apnea/Hypopnea Syndrome, SAHS, from the analysis of respiratory signals and oxygen saturation in arterial blood, SaO2. In order to accomplish the task the proposed approach makes use of different artificial intelligence techniques and reasoning processes being able to deal with imprecise data. These reasoning processes are based on fuzzy logic and on temporal analysis of the information. The developed approach also takes into account the possibility of artifacts in the monitored signals. Detection and characterization of signal artifacts allows detection of false positives. Identification of relevant diagnostic patterns and temporal correlation of events is performed through the implementation of temporal constraints. PMID:25035712

LED traffic signal replacement schedules : facilitating smooth freight flows.

DOT National Transportation Integrated Search

2011-11-01

This research details a field study of LED traffic signals in Missouri and develops a replacement schedule based on key findings. : Rates of degradation were statistically analyzed using Analysis of Variance (ANOVA). Results of this research will pro...

Independent component analysis for the extraction of reliable protein signal profiles from MALDI-TOF mass spectra.

PubMed

Mantini, Dante; Petrucci, Francesca; Del Boccio, Piero; Pieragostino, Damiana; Di Nicola, Marta; Lugaresi, Alessandra; Federici, Giorgio; Sacchetta, Paolo; Di Ilio, Carmine; Urbani, Andrea

2008-01-01

Independent component analysis (ICA) is a signal processing technique that can be utilized to recover independent signals from a set of their linear mixtures. We propose ICA for the analysis of signals obtained from large proteomics investigations such as clinical multi-subject studies based on MALDI-TOF MS profiling. The method is validated on simulated and experimental data for demonstrating its capability of correctly extracting protein profiles from MALDI-TOF mass spectra. The comparison on peak detection with an open-source and two commercial methods shows its superior reliability in reducing the false discovery rate of protein peak masses. Moreover, the integration of ICA and statistical tests for detecting the differences in peak intensities between experimental groups allows to identify protein peaks that could be indicators of a diseased state. This data-driven approach demonstrates to be a promising tool for biomarker-discovery studies based on MALDI-TOF MS technology. The MATLAB implementation of the method described in the article and both simulated and experimental data are freely available at http://www.unich.it/proteomica/bioinf/.

A Quantitative Analysis of Pulsed Signals Emitted by Wild Bottlenose Dolphins.

PubMed

Luís, Ana Rita; Couchinho, Miguel N; Dos Santos, Manuel E

2016-01-01

Common bottlenose dolphins (Tursiops truncatus), produce a wide variety of vocal emissions for communication and echolocation, of which the pulsed repertoire has been the most difficult to categorize. Packets of high repetition, broadband pulses are still largely reported under a general designation of burst-pulses, and traditional attempts to classify these emissions rely mainly in their aural characteristics and in graphical aspects of spectrograms. Here, we present a quantitative analysis of pulsed signals emitted by wild bottlenose dolphins, in the Sado estuary, Portugal (2011-2014), and test the reliability of a traditional classification approach. Acoustic parameters (minimum frequency, maximum frequency, peak frequency, duration, repetition rate and inter-click-interval) were extracted from 930 pulsed signals, previously categorized using a traditional approach. Discriminant function analysis revealed a high reliability of the traditional classification approach (93.5% of pulsed signals were consistently assigned to their aurally based categories). According to the discriminant function analysis (Wilk's Λ = 0.11, F3, 2.41 = 282.75, P < 0.001), repetition rate is the feature that best enables the discrimination of different pulsed signals (structure coefficient = 0.98). Classification using hierarchical cluster analysis led to a similar categorization pattern: two main signal types with distinct magnitudes of repetition rate were clustered into five groups. The pulsed signals, here described, present significant differences in their time-frequency features, especially repetition rate (P < 0.001), inter-click-interval (P < 0.001) and duration (P < 0.001). We document the occurrence of a distinct signal type-short burst-pulses, and highlight the existence of a diverse repertoire of pulsed vocalizations emitted in graded sequences. The use of quantitative analysis of pulsed signals is essential to improve classifications and to better assess the contexts of emission, geographic variation and the functional significance of pulsed signals.

A Pseudorange Measurement Scheme Based on Snapshot for Base Station Positioning Receivers.

PubMed

Mo, Jun; Deng, Zhongliang; Jia, Buyun; Bian, Xinmei

2017-12-01

Digital multimedia broadcasting signal is promised to be a wireless positioning signal. This paper mainly studies a multimedia broadcasting technology, named China mobile multimedia broadcasting (CMMB), in the context of positioning. Theoretical and practical analysis on the CMMB signal suggests that the existing CMMB signal does not have the meter positioning capability. So, the CMMB system has been modified to achieve meter positioning capability by multiplexing the CMMB signal and pseudo codes in the same frequency band. The time difference of arrival (TDOA) estimation method is used in base station positioning receivers. Due to the influence of a complex fading channel and the limited bandwidth of receivers, the regular tracking method based on pseudo code ranging is difficult to provide continuous and accurate TDOA estimations. A pseudorange measurement scheme based on snapshot is proposed to solve the problem. This algorithm extracts the TDOA estimation from the stored signal fragments, and utilizes the Taylor expansion of the autocorrelation function to improve the TDOA estimation accuracy. Monte Carlo simulations and real data tests show that the proposed algorithm can significantly reduce the TDOA estimation error for base station positioning receivers, and then the modified CMMB system achieves meter positioning accuracy.

[Detection of Weak Speech Signals from Strong Noise Background Based on Adaptive Stochastic Resonance].

PubMed

Lu, Huanhuan; Wang, Fuzhong; Zhang, Huichun

2016-04-01

Traditional speech detection methods regard the noise as a jamming signal to filter,but under the strong noise background,these methods lost part of the original speech signal while eliminating noise.Stochastic resonance can use noise energy to amplify the weak signal and suppress the noise.According to stochastic resonance theory,a new method based on adaptive stochastic resonance to extract weak speech signals is proposed.This method,combined with twice sampling,realizes the detection of weak speech signals from strong noise.The parameters of the systema,b are adjusted adaptively by evaluating the signal-to-noise ratio of the output signal,and then the weak speech signal is optimally detected.Experimental simulation analysis showed that under the background of strong noise,the output signal-to-noise ratio increased from the initial value-7dB to about 0.86 dB,with the gain of signalto-noise ratio is 7.86 dB.This method obviously raises the signal-to-noise ratio of the output speech signals,which gives a new idea to detect the weak speech signals in strong noise environment.

Data Unfolding with Wiener-SVD Method

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tang, W.; Li, X.; Qian, X.

Here, data unfolding is a common analysis technique used in HEP data analysis. Inspired by the deconvolution technique in the digital signal processing, a new unfolding technique based on the SVD technique and the well-known Wiener filter is introduced. The Wiener-SVD unfolding approach achieves the unfolding by maximizing the signal to noise ratios in the effective frequency domain given expectations of signal and noise and is free from regularization parameter. Through a couple examples, the pros and cons of the Wiener-SVD approach as well as the nature of the unfolded results are discussed.

Data Unfolding with Wiener-SVD Method

DOE PAGES

Tang, W.; Li, X.; Qian, X.; ...

2017-10-04

Here, data unfolding is a common analysis technique used in HEP data analysis. Inspired by the deconvolution technique in the digital signal processing, a new unfolding technique based on the SVD technique and the well-known Wiener filter is introduced. The Wiener-SVD unfolding approach achieves the unfolding by maximizing the signal to noise ratios in the effective frequency domain given expectations of signal and noise and is free from regularization parameter. Through a couple examples, the pros and cons of the Wiener-SVD approach as well as the nature of the unfolded results are discussed.

Examining the effectiveness of discriminant function analysis and cluster analysis in species identification of male field crickets based on their calling songs.

PubMed

Jaiswara, Ranjana; Nandi, Diptarup; Balakrishnan, Rohini

2013-01-01

Traditional taxonomy based on morphology has often failed in accurate species identification owing to the occurrence of cryptic species, which are reproductively isolated but morphologically identical. Molecular data have thus been used to complement morphology in species identification. The sexual advertisement calls in several groups of acoustically communicating animals are species-specific and can thus complement molecular data as non-invasive tools for identification. Several statistical tools and automated identifier algorithms have been used to investigate the efficiency of acoustic signals in species identification. Despite a plethora of such methods, there is a general lack of knowledge regarding the appropriate usage of these methods in specific taxa. In this study, we investigated the performance of two commonly used statistical methods, discriminant function analysis (DFA) and cluster analysis, in identification and classification based on acoustic signals of field cricket species belonging to the subfamily Gryllinae. Using a comparative approach we evaluated the optimal number of species and calling song characteristics for both the methods that lead to most accurate classification and identification. The accuracy of classification using DFA was high and was not affected by the number of taxa used. However, a constraint in using discriminant function analysis is the need for a priori classification of songs. Accuracy of classification using cluster analysis, which does not require a priori knowledge, was maximum for 6-7 taxa and decreased significantly when more than ten taxa were analysed together. We also investigated the efficacy of two novel derived acoustic features in improving the accuracy of identification. Our results show that DFA is a reliable statistical tool for species identification using acoustic signals. Our results also show that cluster analysis of acoustic signals in crickets works effectively for species classification and identification.

Denoising of chaotic signal using independent component analysis and empirical mode decomposition with circulate translating

NASA Astrophysics Data System (ADS)

Wen-Bo, Wang; Xiao-Dong, Zhang; Yuchan, Chang; Xiang-Li, Wang; Zhao, Wang; Xi, Chen; Lei, Zheng

2016-01-01

In this paper, a new method to reduce noises within chaotic signals based on ICA (independent component analysis) and EMD (empirical mode decomposition) is proposed. The basic idea is decomposing chaotic signals and constructing multidimensional input vectors, firstly, on the base of EMD and its translation invariance. Secondly, it makes the independent component analysis on the input vectors, which means that a self adapting denoising is carried out for the intrinsic mode functions (IMFs) of chaotic signals. Finally, all IMFs compose the new denoised chaotic signal. Experiments on the Lorenz chaotic signal composed of different Gaussian noises and the monthly observed chaotic sequence on sunspots were put into practice. The results proved that the method proposed in this paper is effective in denoising of chaotic signals. Moreover, it can correct the center point in the phase space effectively, which makes it approach the real track of the chaotic attractor. Project supported by the National Science and Technology, China (Grant No. 2012BAJ15B04), the National Natural Science Foundation of China (Grant Nos. 41071270 and 61473213), the Natural Science Foundation of Hubei Province, China (Grant No. 2015CFB424), the State Key Laboratory Foundation of Satellite Ocean Environment Dynamics, China (Grant No. SOED1405), the Hubei Provincial Key Laboratory Foundation of Metallurgical Industry Process System Science, China (Grant No. Z201303), and the Hubei Key Laboratory Foundation of Transportation Internet of Things, Wuhan University of Technology, China (Grant No.2015III015-B02).

Spectral and correlation analysis with applications to middle-atmosphere radars

NASA Technical Reports Server (NTRS)

Rastogi, Prabhat K.

1989-01-01

The correlation and spectral analysis methods for uniformly sampled stationary random signals, estimation of their spectral moments, and problems arising due to nonstationary are reviewed. Some of these methods are already in routine use in atmospheric radar experiments. Other methods based on the maximum entropy principle and time series models have been used in analyzing data, but are just beginning to receive attention in the analysis of radar signals. These methods are also briefly discussed.

A zero power harmonic transponder sensor for ubiquitous wireless μL liquid-volume monitoring

NASA Astrophysics Data System (ADS)

Huang, Haiyu; Chen, Pai-Yen; Hung, Cheng-Hsien; Gharpurey, Ranjit; Akinwande, Deji

2016-01-01

Autonomous liquid-volume monitoring is crucial in ubiquitous healthcare. However, conventional approach is based on either human visual observation or expensive detectors, which are costly for future pervasive monitoring. Here we introduce a novel approach based on passive harmonic transponder antenna sensor and frequency hopping spread spectrum (FHSS) pattern analysis, to provide a very low cost wireless μL-resolution liquid-volume monitoring without battery or digital circuits. In our conceptual demonstration, the harmonic transponder comprises of a passive nonlinear frequency multiplier connected to a metamaterial-inspired 3-D antenna designed to be highly sensitive to the liquid-volume within a confined region. The transponder first receives some FHSS signal from an interrogator, then converts such signal to its harmonic band and re-radiates through the antenna sensor. The harmonic signal is picked up by a sniffer receiver and decoded through pattern analysis of the high dimensional FHSS signal strength data. A robust, zero power, absolute accuracy wireless liquid-volume monitoring is realized in the presence of strong direct coupling, background scatters, distance variance as well as near-field human-body interference. The concepts of passive harmonic transponder sensor, metamaterial-inspired antenna sensor, and FHSS pattern analysis based sensor decoding may help establishing cost-effective, energy-efficient and intelligent wireless pervasive healthcare monitoring platforms.

A zero power harmonic transponder sensor for ubiquitous wireless μL liquid-volume monitoring.

PubMed

Huang, Haiyu; Chen, Pai-Yen; Hung, Cheng-Hsien; Gharpurey, Ranjit; Akinwande, Deji

2016-01-06

Autonomous liquid-volume monitoring is crucial in ubiquitous healthcare. However, conventional approach is based on either human visual observation or expensive detectors, which are costly for future pervasive monitoring. Here we introduce a novel approach based on passive harmonic transponder antenna sensor and frequency hopping spread spectrum (FHSS) pattern analysis, to provide a very low cost wireless μL-resolution liquid-volume monitoring without battery or digital circuits. In our conceptual demonstration, the harmonic transponder comprises of a passive nonlinear frequency multiplier connected to a metamaterial-inspired 3-D antenna designed to be highly sensitive to the liquid-volume within a confined region. The transponder first receives some FHSS signal from an interrogator, then converts such signal to its harmonic band and re-radiates through the antenna sensor. The harmonic signal is picked up by a sniffer receiver and decoded through pattern analysis of the high dimensional FHSS signal strength data. A robust, zero power, absolute accuracy wireless liquid-volume monitoring is realized in the presence of strong direct coupling, background scatters, distance variance as well as near-field human-body interference. The concepts of passive harmonic transponder sensor, metamaterial-inspired antenna sensor, and FHSS pattern analysis based sensor decoding may help establishing cost-effective, energy-efficient and intelligent wireless pervasive healthcare monitoring platforms.

A zero power harmonic transponder sensor for ubiquitous wireless μL liquid-volume monitoring

PubMed Central

Huang, Haiyu; Chen, Pai-Yen; Hung, Cheng-Hsien; Gharpurey, Ranjit; Akinwande, Deji

2016-01-01

Autonomous liquid-volume monitoring is crucial in ubiquitous healthcare. However, conventional approach is based on either human visual observation or expensive detectors, which are costly for future pervasive monitoring. Here we introduce a novel approach based on passive harmonic transponder antenna sensor and frequency hopping spread spectrum (FHSS) pattern analysis, to provide a very low cost wireless μL-resolution liquid-volume monitoring without battery or digital circuits. In our conceptual demonstration, the harmonic transponder comprises of a passive nonlinear frequency multiplier connected to a metamaterial-inspired 3-D antenna designed to be highly sensitive to the liquid-volume within a confined region. The transponder first receives some FHSS signal from an interrogator, then converts such signal to its harmonic band and re-radiates through the antenna sensor. The harmonic signal is picked up by a sniffer receiver and decoded through pattern analysis of the high dimensional FHSS signal strength data. A robust, zero power, absolute accuracy wireless liquid-volume monitoring is realized in the presence of strong direct coupling, background scatters, distance variance as well as near-field human-body interference. The concepts of passive harmonic transponder sensor, metamaterial-inspired antenna sensor, and FHSS pattern analysis based sensor decoding may help establishing cost-effective, energy-efficient and intelligent wireless pervasive healthcare monitoring platforms. PMID:26732251

Effect of signal intensity and camera quantization on laser speckle contrast analysis

PubMed Central

Song, Lipei; Elson, Daniel S.

2012-01-01

Laser speckle contrast analysis (LASCA) is limited to being a qualitative method for the measurement of blood flow and tissue perfusion as it is sensitive to the measurement configuration. The signal intensity is one of the parameters that can affect the contrast values due to the quantization of the signals by the camera and analog-to-digital converter (ADC). In this paper we deduce the theoretical relationship between signal intensity and contrast values based on the probability density function (PDF) of the speckle pattern and simplify it to a rational function. A simple method to correct this contrast error is suggested. The experimental results demonstrate that this relationship can effectively compensate the bias in contrast values induced by the quantized signal intensity and correct for bias induced by signal intensity variations across the field of view. PMID:23304650

PAM-4 Signaling over VCSELs with 0.13µm CMOS Chip Technology

NASA Astrophysics Data System (ADS)

Cunningham, J. E.; Beckman, D.; Zheng, Xuezhe; Huang, Dawei; Sze, T.; Krishnamoorthy, A. V.

2006-12-01

We present results for VCSEL based links operating PAM-4 signaling using a commercial 0.13µm CMOS technology. We perform a complete link analysis of the Bit Error Rate, Q factor, random and deterministic jitter by measuring waterfall curves versus margins in time and amplitude. We demonstrate that VCSEL based PAM 4 can match or even improve performance over binary signaling under conditions of a bandwidth limited, 100meter multi-mode optical link at 5Gbps. We present the first sensitivity measurements for optical PAM-4 and compare it with binary signaling. Measured benefits are reconciled with information theory predictions.

PAM-4 Signaling over VCSELs with 0.13microm CMOS Chip Technology.

PubMed

Cunningham, J E; Beckman, D; Zheng, Xuezhe; Huang, Dawei; Sze, T; Krishnamoorthy, A V

2006-12-11

We present results for VCSEL based links operating PAM-4 signaling using a commercial 0.13microm CMOS technology. We perform a complete link analysis of the Bit Error Rate, Q factor, random and deterministic jitter by measuring waterfall curves versus margins in time and amplitude. We demonstrate that VCSEL based PAM-4 can match or even improve performance over binary signaling under conditions of a bandwidth limited, 100meter multi-mode optical link at 5Gbps. We present the first sensitivity measurements for optical PAM-4 and compare it with binary signaling. Measured benefits are reconciled with information theory predictions.

Intelligent diagnosis of short hydraulic signal based on improved EEMD and SVM with few low-dimensional training samples

NASA Astrophysics Data System (ADS)

Zhang, Meijun; Tang, Jian; Zhang, Xiaoming; Zhang, Jiaojiao

2016-03-01

The high accurate classification ability of an intelligent diagnosis method often needs a large amount of training samples with high-dimensional eigenvectors, however the characteristics of the signal need to be extracted accurately. Although the existing EMD(empirical mode decomposition) and EEMD(ensemble empirical mode decomposition) are suitable for processing non-stationary and non-linear signals, but when a short signal, such as a hydraulic impact signal, is concerned, their decomposition accuracy become very poor. An improve EEMD is proposed specifically for short hydraulic impact signals. The improvements of this new EEMD are mainly reflected in four aspects, including self-adaptive de-noising based on EEMD, signal extension based on SVM(support vector machine), extreme center fitting based on cubic spline interpolation, and pseudo component exclusion based on cross-correlation analysis. After the energy eigenvector is extracted from the result of the improved EEMD, the fault pattern recognition based on SVM with small amount of low-dimensional training samples is studied. At last, the diagnosis ability of improved EEMD+SVM method is compared with the EEMD+SVM and EMD+SVM methods, and its diagnosis accuracy is distinctly higher than the other two methods no matter the dimension of the eigenvectors are low or high. The improved EEMD is very propitious for the decomposition of short signal, such as hydraulic impact signal, and its combination with SVM has high ability for the diagnosis of hydraulic impact faults.

Double Fourier analysis for Emotion Identification in Voiced Speech

NASA Astrophysics Data System (ADS)

Sierra-Sosa, D.; Bastidas, M.; Ortiz P., D.; Quintero, O. L.

2016-04-01

We propose a novel analysis alternative, based on two Fourier Transforms for emotion recognition from speech. Fourier analysis allows for display and synthesizes different signals, in terms of power spectral density distributions. A spectrogram of the voice signal is obtained performing a short time Fourier Transform with Gaussian windows, this spectrogram portraits frequency related features, such as vocal tract resonances and quasi-periodic excitations during voiced sounds. Emotions induce such characteristics in speech, which become apparent in spectrogram time-frequency distributions. Later, the signal time-frequency representation from spectrogram is considered an image, and processed through a 2-dimensional Fourier Transform in order to perform the spatial Fourier analysis from it. Finally features related with emotions in voiced speech are extracted and presented.

Applications of Advanced, Waveform Based AE Techniques for Testing Composite Materials

NASA Technical Reports Server (NTRS)

Prosser, William H.

1996-01-01

Advanced, waveform based acoustic emission (AE) techniques have been previously used to evaluate damage progression in laboratory tests of composite coupons. In these tests, broad band, high fidelity acoustic sensors were used to detect signals which were then digitized and stored for analysis. Analysis techniques were based on plate mode wave propagation characteristics. This approach, more recently referred to as Modal AE, provides an enhanced capability to discriminate and eliminate noise signals from those generated by damage mechanisms. This technique also allows much more precise source location than conventional, threshold crossing arrival time determination techniques. To apply Modal AE concepts to the interpretation of AE on larger composite structures, the effects of wave propagation over larger distances and through structural complexities must be well characterized and understood. In this research, measurements were made of the attenuation of the extensional and flexural plate mode components of broad band simulated AE signals in large composite panels. As these materials have applications in a cryogenic environment, the effects of cryogenic insulation on the attenuation of plate mode AE signals were also documented.

Enhanced automatic artifact detection based on independent component analysis and Renyi's entropy.

PubMed

Mammone, Nadia; Morabito, Francesco Carlo

2008-09-01

Artifacts are disturbances that may occur during signal acquisition and may affect their processing. The aim of this paper is to propose a technique for automatically detecting artifacts from the electroencephalographic (EEG) recordings. In particular, a technique based on both Independent Component Analysis (ICA) to extract artifactual signals and on Renyi's entropy to automatically detect them is presented. This technique is compared to the widely known approach based on ICA and the joint use of kurtosis and Shannon's entropy. The novel processing technique is shown to detect on average 92.6% of the artifactual signals against the average 68.7% of the previous technique on the studied available database. Moreover, Renyi's entropy is shown to be able to detect muscle and very low frequency activity as well as to discriminate them from other kinds of artifacts. In order to achieve an efficient rejection of the artifacts while minimizing the information loss, future efforts will be devoted to the improvement of blind artifact separation from EEG in order to ensure a very efficient isolation of the artifactual activity from any signals deriving from other brain tasks.

A novel rail defect detection method based on undecimated lifting wavelet packet transform and Shannon entropy-improved adaptive line enhancer

NASA Astrophysics Data System (ADS)

Hao, Qiushi; Zhang, Xin; Wang, Yan; Shen, Yi; Makis, Viliam

2018-07-01

Acoustic emission (AE) technology is sensitive to subliminal rail defects, however strong wheel-rail contact rolling noise under high-speed condition has gravely impeded detecting of rail defects using traditional denoising methods. In this context, the paper develops an adaptive detection method for rail cracks, which combines multiresolution analysis with an improved adaptive line enhancer (ALE). To obtain elaborate multiresolution information of transient crack signals with low computational cost, lifting scheme-based undecimated wavelet packet transform is adopted. In order to feature the impulsive property of crack signals, a Shannon entropy-improved ALE is proposed as a signal enhancing approach, where Shannon entropy is introduced to improve the cost function. Then a rail defect detection plan based on the proposed method for high-speed condition is put forward. From theoretical analysis and experimental verification, it is demonstrated that the proposed method has superior performance in enhancing the rail defect AE signal and reducing the strong background noise, offering an effective multiresolution approach for rail defect detection under high-speed and strong-noise condition.

Timescale analysis of rule-based biochemical reaction networks

PubMed Central

Klinke, David J.; Finley, Stacey D.

2012-01-01

The flow of information within a cell is governed by a series of protein-protein interactions that can be described as a reaction network. Mathematical models of biochemical reaction networks can be constructed by repetitively applying specific rules that define how reactants interact and what new species are formed upon reaction. To aid in understanding the underlying biochemistry, timescale analysis is one method developed to prune the size of the reaction network. In this work, we extend the methods associated with timescale analysis to reaction rules instead of the species contained within the network. To illustrate this approach, we applied timescale analysis to a simple receptor-ligand binding model and a rule-based model of Interleukin-12 (IL-12) signaling in näive CD4+ T cells. The IL-12 signaling pathway includes multiple protein-protein interactions that collectively transmit information; however, the level of mechanistic detail sufficient to capture the observed dynamics has not been justified based upon the available data. The analysis correctly predicted that reactions associated with JAK2 and TYK2 binding to their corresponding receptor exist at a pseudo-equilibrium. In contrast, reactions associated with ligand binding and receptor turnover regulate cellular response to IL-12. An empirical Bayesian approach was used to estimate the uncertainty in the timescales. This approach complements existing rank- and flux-based methods that can be used to interrogate complex reaction networks. Ultimately, timescale analysis of rule-based models is a computational tool that can be used to reveal the biochemical steps that regulate signaling dynamics. PMID:21954150

Signal Strength-Based Global Navigation Satellite System Performance Assessment in the Space Service Volume

NASA Technical Reports Server (NTRS)

Welch, Bryan W.

2016-01-01

NASA is participating in the International Committee on Global Navigation Satellite Systems (GNSS) (ICG)'s efforts towards demonstrating the benefits to the space user in the Space Service Volume (SSV) when a multi-GNSS solution space approach is utilized. The ICG Working Group: Enhancement of GNSS Performance, New Services and Capabilities has started a three phase analysis initiative as an outcome of recommendations at the ICG-10 meeting, in preparation for the ICG-11 meeting. The second phase of that increasing complexity and fidelity analysis initiative is based on augmenting the Phase 1 pure geometrical approach with signal strength-based limitations to determine if access is valid. The second phase of analysis has been completed, and the results are documented in this paper.

EEG feature selection method based on decision tree.

PubMed

Duan, Lijuan; Ge, Hui; Ma, Wei; Miao, Jun

2015-01-01

This paper aims to solve automated feature selection problem in brain computer interface (BCI). In order to automate feature selection process, we proposed a novel EEG feature selection method based on decision tree (DT). During the electroencephalogram (EEG) signal processing, a feature extraction method based on principle component analysis (PCA) was used, and the selection process based on decision tree was performed by searching the feature space and automatically selecting optimal features. Considering that EEG signals are a series of non-linear signals, a generalized linear classifier named support vector machine (SVM) was chosen. In order to test the validity of the proposed method, we applied the EEG feature selection method based on decision tree to BCI Competition II datasets Ia, and the experiment showed encouraging results.

From data towards knowledge: revealing the architecture of signaling systems by unifying knowledge mining and data mining of systematic perturbation data.

PubMed

Lu, Songjian; Jin, Bo; Cowart, L Ashley; Lu, Xinghua

2013-01-01

Genetic and pharmacological perturbation experiments, such as deleting a gene and monitoring gene expression responses, are powerful tools for studying cellular signal transduction pathways. However, it remains a challenge to automatically derive knowledge of a cellular signaling system at a conceptual level from systematic perturbation-response data. In this study, we explored a framework that unifies knowledge mining and data mining towards the goal. The framework consists of the following automated processes: 1) applying an ontology-driven knowledge mining approach to identify functional modules among the genes responding to a perturbation in order to reveal potential signals affected by the perturbation; 2) applying a graph-based data mining approach to search for perturbations that affect a common signal; and 3) revealing the architecture of a signaling system by organizing signaling units into a hierarchy based on their relationships. Applying this framework to a compendium of yeast perturbation-response data, we have successfully recovered many well-known signal transduction pathways; in addition, our analysis has led to many new hypotheses regarding the yeast signal transduction system; finally, our analysis automatically organized perturbed genes as a graph reflecting the architecture of the yeast signaling system. Importantly, this framework transformed molecular findings from a gene level to a conceptual level, which can be readily translated into computable knowledge in the form of rules regarding the yeast signaling system, such as "if genes involved in the MAPK signaling are perturbed, genes involved in pheromone responses will be differentially expressed."

Comparing model-based and model-free analysis methods for QUASAR arterial spin labeling perfusion quantification.

PubMed

Chappell, Michael A; Woolrich, Mark W; Petersen, Esben T; Golay, Xavier; Payne, Stephen J

2013-05-01

Amongst the various implementations of arterial spin labeling MRI methods for quantifying cerebral perfusion, the QUASAR method is unique. By using a combination of labeling with and without flow suppression gradients, the QUASAR method offers the separation of macrovascular and tissue signals. This permits local arterial input functions to be defined and "model-free" analysis, using numerical deconvolution, to be used. However, it remains unclear whether arterial spin labeling data are best treated using model-free or model-based analysis. This work provides a critical comparison of these two approaches for QUASAR arterial spin labeling in the healthy brain. An existing two-component (arterial and tissue) model was extended to the mixed flow suppression scheme of QUASAR to provide an optimal model-based analysis. The model-based analysis was extended to incorporate dispersion of the labeled bolus, generally regarded as the major source of discrepancy between the two analysis approaches. Model-free and model-based analyses were compared for perfusion quantification including absolute measurements, uncertainty estimation, and spatial variation in cerebral blood flow estimates. Major sources of discrepancies between model-free and model-based analysis were attributed to the effects of dispersion and the degree to which the two methods can separate macrovascular and tissue signal. Copyright © 2012 Wiley Periodicals, Inc.

Element analysis: a wavelet-based method for analysing time-localized events in noisy time series.

PubMed

Lilly, Jonathan M

2017-04-01

A method is derived for the quantitative analysis of signals that are composed of superpositions of isolated, time-localized 'events'. Here, these events are taken to be well represented as rescaled and phase-rotated versions of generalized Morse wavelets, a broad family of continuous analytic functions. Analysing a signal composed of replicates of such a function using another Morse wavelet allows one to directly estimate the properties of events from the values of the wavelet transform at its own maxima. The distribution of events in general power-law noise is determined in order to establish significance based on an expected false detection rate. Finally, an expression for an event's 'region of influence' within the wavelet transform permits the formation of a criterion for rejecting spurious maxima due to numerical artefacts or other unsuitable events. Signals can then be reconstructed based on a small number of isolated points on the time/scale plane. This method, termed element analysis , is applied to the identification of long-lived eddy structures in ocean currents as observed by along-track measurements of sea surface elevation from satellite altimetry.

A Subject-Independent Method for Automatically Grading Electromyographic Features During a Fatiguing Contraction

PubMed Central

Jesunathadas, Mark; Poston, Brach; Santello, Marco; Ye, Jieping; Panchanathan, Sethuraman

2014-01-01

Many studies have attempted to monitor fatigue from electromyogram (EMG) signals. However, fatigue affects EMG in a subject-specific manner. We present here a subject-independent framework for monitoring the changes in EMG features that accompany muscle fatigue based on principal component analysis and factor analysis. The proposed framework is based on several time- and frequency-domain features, unlike most of the existing work, which is based on two to three features. Results show that latent factors obtained from factor analysis on these features provide a robust and unified framework. This framework learns a model from EMG signals of multiple subjects, that form a reference group, and monitors the changes in EMG features during a sustained submaximal contraction on a test subject on a scale from zero to one. The framework was tested on EMG signals collected from 12 muscles of eight healthy subjects. The distribution of factor scores of the test subject, when mapped onto the framework was similar for both the subject-specific and subject-independent cases. PMID:22498666

Ultrasonic test of resistance spot welds based on wavelet package analysis.

PubMed

Liu, Jing; Xu, Guocheng; Gu, Xiaopeng; Zhou, Guanghao

2015-02-01

In this paper, ultrasonic test of spot welds for stainless steel sheets has been studied. It is indicated that traditional ultrasonic signal analysis in either time domain or frequency domain remains inadequate to evaluate the nugget diameter of spot welds. However, the method based on wavelet package analysis in time-frequency domain can easily distinguish the nugget from the corona bond by extracting high-frequency signals in different positions of spot welds, thereby quantitatively evaluating the nugget diameter. The results of ultrasonic test fit the actual measured value well. Mean value of normal distribution of error statistics is 0.00187, and the standard deviation is 0.1392. Furthermore, the quality of spot welds was evaluated, and it is showed ultrasonic nondestructive test based on wavelet packet analysis can be used to evaluate the quality of spot welds, and it is more reliable than single tensile destructive test. Copyright © 2014 Elsevier B.V. All rights reserved.

[Design of a Front-end Device of Heart Rate Variability Analysis System Based on Photoplethysmography].

PubMed

Shi, Lei; Sun, Peng; Pang, Yu; Luo, Zhiyong; Wang, Wei; Wang, Yanxiang

2016-02-01

Heart rate variability (HRV) is the difference between the successive changes in the heartbeat cycle, and it is produced in the autonomic nervous system modulation of the sinus node of the heart. The HRV is a valuable indicator in predicting the sudden cardiac death and arrhythmic events. Traditional analysis of HRV is based on a multielectrocardiogram (ECG), but the ECG signal acquisition is complex, so we have designed an HRV analysis system based on photoplethysmography (PPG). PPG signal is collected by a microcontroller from human's finger, and it is sent to the terminal via USB-Serial module. The terminal software not only collects the data and plot waveforms, but also stores the data for future HRV analysis. The system is small in size, low in power consumption, and easy for operation. It is suitable for daily care no matter whether it is used at home or in a hospital.

Non-contact FBG sensing based steam turbine rotor dynamic balance vibration detection system

NASA Astrophysics Data System (ADS)

Li, Tianliang; Tan, Yuegang; Cai, Lin

2015-10-01

This paper has proposed a non-contact vibration sensor based on fiber Bragg grating sensing, and applied to detect vibration of steam turbine rotor dynamic balance experimental platform. The principle of the sensor has been introduced, as well as the experimental analysis; performance of non-contact FBG vibration sensor has been analyzed in the experiment; in addition, turbine rotor dynamic vibration detection system based on eddy current displacement sensor and non-contact FBG vibration sensor have built; finally, compared with results of signals under analysis of the time domain and frequency domain. The analysis of experimental data contrast shows that: the vibration signal analysis of non-contact FBG vibration sensor is basically the same as the result of eddy current displacement sensor; it verified that the sensor can be used for non-contact measurement of steam turbine rotor dynamic balance vibration.

Wavelength Selection Method Based on Differential Evolution for Precise Quantitative Analysis Using Terahertz Time-Domain Spectroscopy.

PubMed

Li, Zhi; Chen, Weidong; Lian, Feiyu; Ge, Hongyi; Guan, Aihong

2017-12-01

Quantitative analysis of component mixtures is an important application of terahertz time-domain spectroscopy (THz-TDS) and has attracted broad interest in recent research. Although the accuracy of quantitative analysis using THz-TDS is affected by a host of factors, wavelength selection from the sample's THz absorption spectrum is the most crucial component. The raw spectrum consists of signals from the sample and scattering and other random disturbances that can critically influence the quantitative accuracy. For precise quantitative analysis using THz-TDS, the signal from the sample needs to be retained while the scattering and other noise sources are eliminated. In this paper, a novel wavelength selection method based on differential evolution (DE) is investigated. By performing quantitative experiments on a series of binary amino acid mixtures using THz-TDS, we demonstrate the efficacy of the DE-based wavelength selection method, which yields an error rate below 5%.

Algorithm based on the short-term Rényi entropy and IF estimation for noisy EEG signals analysis.

PubMed

Lerga, Jonatan; Saulig, Nicoletta; Mozetič, Vladimir

2017-01-01

Stochastic electroencephalogram (EEG) signals are known to be nonstationary and often multicomponential. Detecting and extracting their components may help clinicians to localize brain neurological dysfunctionalities for patients with motor control disorders due to the fact that movement-related cortical activities are reflected in spectral EEG changes. A new algorithm for EEG signal components detection from its time-frequency distribution (TFD) has been proposed in this paper. The algorithm utilizes the modification of the Rényi entropy-based technique for number of components estimation, called short-term Rényi entropy (STRE), and upgraded by an iterative algorithm which was shown to enhance existing approaches. Combined with instantaneous frequency (IF) estimation, the proposed method was applied to EEG signal analysis both in noise-free and noisy environments for limb movements EEG signals, and was shown to be an efficient technique providing spectral description of brain activities at each electrode location up to moderate additive noise levels. Furthermore, the obtained information concerning the number of EEG signal components and their IFs show potentials to enhance diagnostics and treatment of neurological disorders for patients with motor control illnesses. Copyright © 2016 Elsevier Ltd. All rights reserved.

Sparse electrocardiogram signals recovery based on solving a row echelon-like form of system.

PubMed

Cai, Pingmei; Wang, Guinan; Yu, Shiwei; Zhang, Hongjuan; Ding, Shuxue; Wu, Zikai

2016-02-01

The study of biology and medicine in a noise environment is an evolving direction in biological data analysis. Among these studies, analysis of electrocardiogram (ECG) signals in a noise environment is a challenging direction in personalized medicine. Due to its periodic characteristic, ECG signal can be roughly regarded as sparse biomedical signals. This study proposes a two-stage recovery algorithm for sparse biomedical signals in time domain. In the first stage, the concentration subspaces are found in advance. Then by exploiting these subspaces, the mixing matrix is estimated accurately. In the second stage, based on the number of active sources at each time point, the time points are divided into different layers. Next, by constructing some transformation matrices, these time points form a row echelon-like system. After that, the sources at each layer can be solved out explicitly by corresponding matrix operations. It is noting that all these operations are conducted under a weak sparse condition that the number of active sources is less than the number of observations. Experimental results show that the proposed method has a better performance for sparse ECG signal recovery problem.

Polarization-insensitive techniques for optical signal processing

NASA Astrophysics Data System (ADS)

Salem, Reza

2006-12-01

This thesis investigates polarization-insensitive methods for optical signal processing. Two signal processing techniques are studied: clock recovery based on two-photon absorption in silicon and demultiplexing based on cross-phase modulation in highly nonlinear fiber. The clock recovery system is tested at an 80 Gb/s data rate for both back-to-back and transmission experiments. The demultiplexer is tested at a 160 Gb/s data rate in a back-to-back experiment. We experimentally demonstrate methods for eliminating polarization dependence in both systems. Our experimental results are confirmed by theoretical and numerical analysis.

Essential Limitations of the Standard THz TDS Method for Substance Detection and Identification and a Way of Overcoming Them

PubMed Central

Trofimov, Vyacheslav A.; Varentsova, Svetlana A.

2016-01-01

Low efficiency of the standard THz TDS method of the detection and identification of substances based on a comparison of the spectrum for the signal under investigation with a standard signal spectrum is demonstrated using the physical experiments conducted under real conditions with a thick paper bag as well as with Si-based semiconductors under laboratory conditions. In fact, standard THz spectroscopy leads to false detection of hazardous substances in neutral samples, which do not contain them. This disadvantage of the THz TDS method can be overcome by using time-dependent THz pulse spectrum analysis. For a quality assessment of the standard substance spectral features presence in the signal under analysis, one may use time-dependent integral correlation criteria. PMID:27070617

Essential Limitations of the Standard THz TDS Method for Substance Detection and Identification and a Way of Overcoming Them.

PubMed

Trofimov, Vyacheslav A; Varentsova, Svetlana A

2016-04-08

Low efficiency of the standard THz TDS method of the detection and identification of substances based on a comparison of the spectrum for the signal under investigation with a standard signal spectrum is demonstrated using the physical experiments conducted under real conditions with a thick paper bag as well as with Si-based semiconductors under laboratory conditions. In fact, standard THz spectroscopy leads to false detection of hazardous substances in neutral samples, which do not contain them. This disadvantage of the THz TDS method can be overcome by using time-dependent THz pulse spectrum analysis. For a quality assessment of the standard substance spectral features presence in the signal under analysis, one may use time-dependent integral correlation criteria.

The Removal of EOG Artifacts From EEG Signals Using Independent Component Analysis and Multivariate Empirical Mode Decomposition.

PubMed

Wang, Gang; Teng, Chaolin; Li, Kuo; Zhang, Zhonglin; Yan, Xiangguo

2016-09-01

The recorded electroencephalography (EEG) signals are usually contaminated by electrooculography (EOG) artifacts. In this paper, by using independent component analysis (ICA) and multivariate empirical mode decomposition (MEMD), the ICA-based MEMD method was proposed to remove EOG artifacts (EOAs) from multichannel EEG signals. First, the EEG signals were decomposed by the MEMD into multiple multivariate intrinsic mode functions (MIMFs). The EOG-related components were then extracted by reconstructing the MIMFs corresponding to EOAs. After performing the ICA of EOG-related signals, the EOG-linked independent components were distinguished and rejected. Finally, the clean EEG signals were reconstructed by implementing the inverse transform of ICA and MEMD. The results of simulated and real data suggested that the proposed method could successfully eliminate EOAs from EEG signals and preserve useful EEG information with little loss. By comparing with other existing techniques, the proposed method achieved much improvement in terms of the increase of signal-to-noise and the decrease of mean square error after removing EOAs.

Department of Cybernetic Acoustics

NASA Astrophysics Data System (ADS)

The development of the theory, instrumentation and applications of methods and systems for the measurement, analysis, processing and synthesis of acoustic signals within the audio frequency range, particularly of the speech signal and the vibro-acoustic signal emitted by technical and industrial equipments treated as noise and vibration sources was discussed. The research work, both theoretical and experimental, aims at applications in various branches of science, and medicine, such as: acoustical diagnostics and phoniatric rehabilitation of pathological and postoperative states of the speech organ; bilateral ""man-machine'' speech communication based on the analysis, recognition and synthesis of the speech signal; vibro-acoustical diagnostics and continuous monitoring of the state of machines, technical equipments and technological processes.

An internal reference model-based PRF temperature mapping method with Cramer-Rao lower bound noise performance analysis.

PubMed

Li, Cheng; Pan, Xinyi; Ying, Kui; Zhang, Qiang; An, Jing; Weng, Dehe; Qin, Wen; Li, Kuncheng

2009-11-01

The conventional phase difference method for MR thermometry suffers from disturbances caused by the presence of lipid protons, motion-induced error, and field drift. A signal model is presented with multi-echo gradient echo (GRE) sequence using a fat signal as an internal reference to overcome these problems. The internal reference signal model is fit to the water and fat signals by the extended Prony algorithm and the Levenberg-Marquardt algorithm to estimate the chemical shifts between water and fat which contain temperature information. A noise analysis of the signal model was conducted using the Cramer-Rao lower bound to evaluate the noise performance of various algorithms, the effects of imaging parameters, and the influence of the water:fat signal ratio in a sample on the temperature estimate. Comparison of the calculated temperature map and thermocouple temperature measurements shows that the maximum temperature estimation error is 0.614 degrees C, with a standard deviation of 0.06 degrees C, confirming the feasibility of this model-based temperature mapping method. The influence of sample water:fat signal ratio on the accuracy of the temperature estimate is evaluated in a water-fat mixed phantom experiment with an optimal ratio of approximately 0.66:1. (c) 2009 Wiley-Liss, Inc.

Filtration of human EEG recordings from physiological artifacts with empirical mode method

NASA Astrophysics Data System (ADS)

Grubov, Vadim V.; Runnova, Anastasiya E.; Khramova, Marina V.

2017-03-01

In the paper we propose the new method for dealing with noise and physiological artifacts in experimental human EEG recordings. The method is based on analysis of EEG signals with empirical mode decomposition (Hilbert-Huang transform). We consider noises and physiological artifacts on EEG as specific oscillatory patterns that cause problems during EEG analysis and can be detected with additional signals recorded simultaneously with EEG (ECG, EMG, EOG, etc.) We introduce the algorithm of the method with following steps: empirical mode decomposition of EEG signal, choosing of empirical modes with artifacts, removing empirical modes with artifacts, reconstruction of the initial EEG signal. We test the method on filtration of experimental human EEG signals from eye-moving artifacts and show high efficiency of the method.

Multisensor signal denoising based on matching synchrosqueezing wavelet transform for mechanical fault condition assessment

NASA Astrophysics Data System (ADS)

Yi, Cancan; Lv, Yong; Xiao, Han; Huang, Tao; You, Guanghui

2018-04-01

Since it is difficult to obtain the accurate running status of mechanical equipment with only one sensor, multisensor measurement technology has attracted extensive attention. In the field of mechanical fault diagnosis and condition assessment based on vibration signal analysis, multisensor signal denoising has emerged as an important tool to improve the reliability of the measurement result. A reassignment technique termed the synchrosqueezing wavelet transform (SWT) has obvious superiority in slow time-varying signal representation and denoising for fault diagnosis applications. The SWT uses the time-frequency reassignment scheme, which can provide signal properties in 2D domains (time and frequency). However, when the measured signal contains strong noise components and fast varying instantaneous frequency, the performance of SWT-based analysis still depends on the accuracy of instantaneous frequency estimation. In this paper, a matching synchrosqueezing wavelet transform (MSWT) is investigated as a potential candidate to replace the conventional synchrosqueezing transform for the applications of denoising and fault feature extraction. The improved technology utilizes the comprehensive instantaneous frequency estimation by chirp rate estimation to achieve a highly concentrated time-frequency representation so that the signal resolution can be significantly improved. To exploit inter-channel dependencies, the multisensor denoising strategy is performed by using a modulated multivariate oscillation model to partition the time-frequency domain; then, the common characteristics of the multivariate data can be effectively identified. Furthermore, a modified universal threshold is utilized to remove noise components, while the signal components of interest can be retained. Thus, a novel MSWT-based multisensor signal denoising algorithm is proposed in this paper. The validity of this method is verified by numerical simulation, and experiments including a rolling bearing system and a gear system. The results show that the proposed multisensor matching synchronous squeezing wavelet transform (MMSWT) is superior to existing methods.

Right-\\0xADturn traffic volume adjustment in traffic signal warrant analysis : final report.

DOT National Transportation Integrated Search

2016-05-06

This report was based on the research project, Right-Turn Traffic Volume Adjustment in : Traffic Signal Warrants, sponsored by the Nevada Department of Transportation (NDOT) : and SOLARIS. Right-turn traffic does not affect intersection performance i...

Right-­turn traffic volume adjustment in traffic signal warrant analysis : final report.

DOT National Transportation Integrated Search

2016-05-06

This report was based on the research project, Right-Turn Traffic Volume Adjustment in Traffic Signal Warrants, sponsored by the Nevada Department of Transportation (NDOT) and SOLARIS. Right-turn traffic does not affect intersection performance in th...

Life expectancy evaluation and development of a replacement schedule for LED traffic signals.

DOT National Transportation Integrated Search

2011-03-01

This research details a field study of LED traffic signals in Missouri and develops a replacement schedule : based on key findings. Rates of degradation were statistically analyzed using Analysis of Variance : (ANOVA). Results of this research will p...

Multiple velocity encoding in the phase of an MRI signal

NASA Astrophysics Data System (ADS)

Benitez-Read, E. E.

2017-01-01

The measurement of fluid velocity by encoding it in the phase of a magnetic resonance imaging (MRI) signal could allow the discrimination of the stationary spins signals from those of moving spins. This results in a wide variety of applications i.e. in medicine, in order to obtain more than angiograms, blood velocity images of veins, arteries and other vessels without having static tissue perturbing the signal of fluid in motion. The work presented in this paper is a theoretical analysis of some novel methods for multiple fluid velocity encoding in the phase of an MRI signal. These methods are based on a tripolar gradient (TPG) and can be an alternative to the conventional methods based on a bipolar gradient (BPG) and could be more suitable for multiple velocity encoding in the phase of an MRI signal.

Two-dimensional wavelet analysis based classification of gas chromatogram differential mobility spectrometry signals.

PubMed

Zhao, Weixiang; Sankaran, Shankar; Ibáñez, Ana M; Dandekar, Abhaya M; Davis, Cristina E

2009-08-04

This study introduces two-dimensional (2-D) wavelet analysis to the classification of gas chromatogram differential mobility spectrometry (GC/DMS) data which are composed of retention time, compensation voltage, and corresponding intensities. One reported method to process such large data sets is to convert 2-D signals to 1-D signals by summing intensities either across retention time or compensation voltage, but it can lose important signal information in one data dimension. A 2-D wavelet analysis approach keeps the 2-D structure of original signals, while significantly reducing data size. We applied this feature extraction method to 2-D GC/DMS signals measured from control and disordered fruit and then employed two typical classification algorithms to testify the effects of the resultant features on chemical pattern recognition. Yielding a 93.3% accuracy of separating data from control and disordered fruit samples, 2-D wavelet analysis not only proves its feasibility to extract feature from original 2-D signals but also shows its superiority over the conventional feature extraction methods including converting 2-D to 1-D and selecting distinguishable pixels from training set. Furthermore, this process does not require coupling with specific pattern recognition methods, which may help ensure wide applications of this method to 2-D spectrometry data.

Analysis of human serum proteins by liquid phase isoelectric focusing and matrix-assisted laser desorption/ionization-mass spectrometry.

PubMed

Wang, Michael Z; Howard, Brandon; Campa, Michael J; Patz, Edward F; Fitzgerald, Michael C

2003-09-01

Direct matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS) analysis of human serum yielded ion signals from only a fraction of the total number of peptides and proteins expected to be in the sample. We increased the number of peptide and protein ion signals observed in the MALDI-TOF mass spectra analysis of human serum by using a prefractionation protocol based on liquid phase isoelectric focusing electrophoresis. This pre-fractionation technique facilitated the MALDI-TOF MS detection of as many as 262 different peptide and protein ion signals from human serum. The results obtained from three replicate fractionation experiments on the same serum sample indicated that 148 different peptide and protein ion signals were reproducibly detected using our isoelectric focusing and MALDI-TOF MS protocol.

High-performance wavelet engine

NASA Astrophysics Data System (ADS)

Taylor, Fred J.; Mellot, Jonathon D.; Strom, Erik; Koren, Iztok; Lewis, Michael P.

1993-11-01

Wavelet processing has shown great promise for a variety of image and signal processing applications. Wavelets are also among the most computationally expensive techniques in signal processing. It is demonstrated that a wavelet engine constructed with residue number system arithmetic elements offers significant advantages over commercially available wavelet accelerators based upon conventional arithmetic elements. Analysis is presented predicting the dynamic range requirements of the reported residue number system based wavelet accelerator.

Poisson pre-processing of nonstationary photonic signals: Signals with equality between mean and variance.

PubMed

Poplová, Michaela; Sovka, Pavel; Cifra, Michal

2017-01-01

Photonic signals are broadly exploited in communication and sensing and they typically exhibit Poisson-like statistics. In a common scenario where the intensity of the photonic signals is low and one needs to remove a nonstationary trend of the signals for any further analysis, one faces an obstacle: due to the dependence between the mean and variance typical for a Poisson-like process, information about the trend remains in the variance even after the trend has been subtracted, possibly yielding artifactual results in further analyses. Commonly available detrending or normalizing methods cannot cope with this issue. To alleviate this issue we developed a suitable pre-processing method for the signals that originate from a Poisson-like process. In this paper, a Poisson pre-processing method for nonstationary time series with Poisson distribution is developed and tested on computer-generated model data and experimental data of chemiluminescence from human neutrophils and mung seeds. The presented method transforms a nonstationary Poisson signal into a stationary signal with a Poisson distribution while preserving the type of photocount distribution and phase-space structure of the signal. The importance of the suggested pre-processing method is shown in Fano factor and Hurst exponent analysis of both computer-generated model signals and experimental photonic signals. It is demonstrated that our pre-processing method is superior to standard detrending-based methods whenever further signal analysis is sensitive to variance of the signal.

Poisson pre-processing of nonstationary photonic signals: Signals with equality between mean and variance

PubMed Central

Poplová, Michaela; Sovka, Pavel

2017-01-01

Photonic signals are broadly exploited in communication and sensing and they typically exhibit Poisson-like statistics. In a common scenario where the intensity of the photonic signals is low and one needs to remove a nonstationary trend of the signals for any further analysis, one faces an obstacle: due to the dependence between the mean and variance typical for a Poisson-like process, information about the trend remains in the variance even after the trend has been subtracted, possibly yielding artifactual results in further analyses. Commonly available detrending or normalizing methods cannot cope with this issue. To alleviate this issue we developed a suitable pre-processing method for the signals that originate from a Poisson-like process. In this paper, a Poisson pre-processing method for nonstationary time series with Poisson distribution is developed and tested on computer-generated model data and experimental data of chemiluminescence from human neutrophils and mung seeds. The presented method transforms a nonstationary Poisson signal into a stationary signal with a Poisson distribution while preserving the type of photocount distribution and phase-space structure of the signal. The importance of the suggested pre-processing method is shown in Fano factor and Hurst exponent analysis of both computer-generated model signals and experimental photonic signals. It is demonstrated that our pre-processing method is superior to standard detrending-based methods whenever further signal analysis is sensitive to variance of the signal. PMID:29216207

Advanced methods in NDE using machine learning approaches

NASA Astrophysics Data System (ADS)

Wunderlich, Christian; Tschöpe, Constanze; Duckhorn, Frank

2018-04-01

Machine learning (ML) methods and algorithms have been applied recently with great success in quality control and predictive maintenance. Its goal to build new and/or leverage existing algorithms to learn from training data and give accurate predictions, or to find patterns, particularly with new and unseen similar data, fits perfectly to Non-Destructive Evaluation. The advantages of ML in NDE are obvious in such tasks as pattern recognition in acoustic signals or automated processing of images from X-ray, Ultrasonics or optical methods. Fraunhofer IKTS is using machine learning algorithms in acoustic signal analysis. The approach had been applied to such a variety of tasks in quality assessment. The principal approach is based on acoustic signal processing with a primary and secondary analysis step followed by a cognitive system to create model data. Already in the second analysis steps unsupervised learning algorithms as principal component analysis are used to simplify data structures. In the cognitive part of the software further unsupervised and supervised learning algorithms will be trained. Later the sensor signals from unknown samples can be recognized and classified automatically by the algorithms trained before. Recently the IKTS team was able to transfer the software for signal processing and pattern recognition to a small printed circuit board (PCB). Still, algorithms will be trained on an ordinary PC; however, trained algorithms run on the Digital Signal Processor and the FPGA chip. The identical approach will be used for pattern recognition in image analysis of OCT pictures. Some key requirements have to be fulfilled, however. A sufficiently large set of training data, a high signal-to-noise ratio, and an optimized and exact fixation of components are required. The automated testing can be done subsequently by the machine. By integrating the test data of many components along the value chain further optimization including lifetime and durability prediction based on big data becomes possible, even if components are used in different versions or configurations. This is the promise behind German Industry 4.0.

Dictionary-based image reconstruction for superresolution in integrated circuit imaging.

PubMed

Cilingiroglu, T Berkin; Uyar, Aydan; Tuysuzoglu, Ahmet; Karl, W Clem; Konrad, Janusz; Goldberg, Bennett B; Ünlü, M Selim

2015-06-01

Resolution improvement through signal processing techniques for integrated circuit imaging is becoming more crucial as the rapid decrease in integrated circuit dimensions continues. Although there is a significant effort to push the limits of optical resolution for backside fault analysis through the use of solid immersion lenses, higher order laser beams, and beam apodization, signal processing techniques are required for additional improvement. In this work, we propose a sparse image reconstruction framework which couples overcomplete dictionary-based representation with a physics-based forward model to improve resolution and localization accuracy in high numerical aperture confocal microscopy systems for backside optical integrated circuit analysis. The effectiveness of the framework is demonstrated on experimental data.

CyTOF workflow: differential discovery in high-throughput high-dimensional cytometry datasets

PubMed Central

Nowicka, Malgorzata; Krieg, Carsten; Weber, Lukas M.; Hartmann, Felix J.; Guglietta, Silvia; Becher, Burkhard; Levesque, Mitchell P.; Robinson, Mark D.

2017-01-01

High dimensional mass and flow cytometry (HDCyto) experiments have become a method of choice for high throughput interrogation and characterization of cell populations.Here, we present an R-based pipeline for differential analyses of HDCyto data, largely based on Bioconductor packages. We computationally define cell populations using FlowSOM clustering, and facilitate an optional but reproducible strategy for manual merging of algorithm-generated clusters. Our workflow offers different analysis paths, including association of cell type abundance with a phenotype or changes in signaling markers within specific subpopulations, or differential analyses of aggregated signals. Importantly, the differential analyses we show are based on regression frameworks where the HDCyto data is the response; thus, we are able to model arbitrary experimental designs, such as those with batch effects, paired designs and so on. In particular, we apply generalized linear mixed models to analyses of cell population abundance or cell-population-specific analyses of signaling markers, allowing overdispersion in cell count or aggregated signals across samples to be appropriately modeled. To support the formal statistical analyses, we encourage exploratory data analysis at every step, including quality control (e.g. multi-dimensional scaling plots), reporting of clustering results (dimensionality reduction, heatmaps with dendrograms) and differential analyses (e.g. plots of aggregated signals). PMID:28663787

A Phonocardiographic-Based Fiber-Optic Sensor and Adaptive Filtering System for Noninvasive Continuous Fetal Heart Rate Monitoring.

PubMed

Martinek, Radek; Nedoma, Jan; Fajkus, Marcel; Kahankova, Radana; Konecny, Jaromir; Janku, Petr; Kepak, Stanislav; Bilik, Petr; Nazeran, Homer

2017-04-18

This paper focuses on the design, realization, and verification of a novel phonocardiographic- based fiber-optic sensor and adaptive signal processing system for noninvasive continuous fetal heart rate (fHR) monitoring. Our proposed system utilizes two Mach-Zehnder interferometeric sensors. Based on the analysis of real measurement data, we developed a simplified dynamic model for the generation and distribution of heart sounds throughout the human body. Building on this signal model, we then designed, implemented, and verified our adaptive signal processing system by implementing two stochastic gradient-based algorithms: the Least Mean Square Algorithm (LMS), and the Normalized Least Mean Square (NLMS) Algorithm. With this system we were able to extract the fHR information from high quality fetal phonocardiograms (fPCGs), filtered from abdominal maternal phonocardiograms (mPCGs) by performing fPCG signal peak detection. Common signal processing methods such as linear filtering, signal subtraction, and others could not be used for this purpose as fPCG and mPCG signals share overlapping frequency spectra. The performance of the adaptive system was evaluated by using both qualitative (gynecological studies) and quantitative measures such as: Signal-to-Noise Ratio-SNR, Root Mean Square Error-RMSE, Sensitivity-S+, and Positive Predictive Value-PPV.

A Phonocardiographic-Based Fiber-Optic Sensor and Adaptive Filtering System for Noninvasive Continuous Fetal Heart Rate Monitoring

PubMed Central

Martinek, Radek; Nedoma, Jan; Fajkus, Marcel; Kahankova, Radana; Konecny, Jaromir; Janku, Petr; Kepak, Stanislav; Bilik, Petr; Nazeran, Homer

2017-01-01

This paper focuses on the design, realization, and verification of a novel phonocardiographic- based fiber-optic sensor and adaptive signal processing system for noninvasive continuous fetal heart rate (fHR) monitoring. Our proposed system utilizes two Mach-Zehnder interferometeric sensors. Based on the analysis of real measurement data, we developed a simplified dynamic model for the generation and distribution of heart sounds throughout the human body. Building on this signal model, we then designed, implemented, and verified our adaptive signal processing system by implementing two stochastic gradient-based algorithms: the Least Mean Square Algorithm (LMS), and the Normalized Least Mean Square (NLMS) Algorithm. With this system we were able to extract the fHR information from high quality fetal phonocardiograms (fPCGs), filtered from abdominal maternal phonocardiograms (mPCGs) by performing fPCG signal peak detection. Common signal processing methods such as linear filtering, signal subtraction, and others could not be used for this purpose as fPCG and mPCG signals share overlapping frequency spectra. The performance of the adaptive system was evaluated by using both qualitative (gynecological studies) and quantitative measures such as: Signal-to-Noise Ratio—SNR, Root Mean Square Error—RMSE, Sensitivity—S+, and Positive Predictive Value—PPV. PMID:28420215

A Variance Distribution Model of Surface EMG Signals Based on Inverse Gamma Distribution.

PubMed

Hayashi, Hideaki; Furui, Akira; Kurita, Yuichi; Tsuji, Toshio

2017-11-01

Objective: This paper describes the formulation of a surface electromyogram (EMG) model capable of representing the variance distribution of EMG signals. Methods: In the model, EMG signals are handled based on a Gaussian white noise process with a mean of zero for each variance value. EMG signal variance is taken as a random variable that follows inverse gamma distribution, allowing the representation of noise superimposed onto this variance. Variance distribution estimation based on marginal likelihood maximization is also outlined in this paper. The procedure can be approximated using rectified and smoothed EMG signals, thereby allowing the determination of distribution parameters in real time at low computational cost. Results: A simulation experiment was performed to evaluate the accuracy of distribution estimation using artificially generated EMG signals, with results demonstrating that the proposed model's accuracy is higher than that of maximum-likelihood-based estimation. Analysis of variance distribution using real EMG data also suggested a relationship between variance distribution and signal-dependent noise. Conclusion: The study reported here was conducted to examine the performance of a proposed surface EMG model capable of representing variance distribution and a related distribution parameter estimation method. Experiments using artificial and real EMG data demonstrated the validity of the model. Significance: Variance distribution estimated using the proposed model exhibits potential in the estimation of muscle force. Objective: This paper describes the formulation of a surface electromyogram (EMG) model capable of representing the variance distribution of EMG signals. Methods: In the model, EMG signals are handled based on a Gaussian white noise process with a mean of zero for each variance value. EMG signal variance is taken as a random variable that follows inverse gamma distribution, allowing the representation of noise superimposed onto this variance. Variance distribution estimation based on marginal likelihood maximization is also outlined in this paper. The procedure can be approximated using rectified and smoothed EMG signals, thereby allowing the determination of distribution parameters in real time at low computational cost. Results: A simulation experiment was performed to evaluate the accuracy of distribution estimation using artificially generated EMG signals, with results demonstrating that the proposed model's accuracy is higher than that of maximum-likelihood-based estimation. Analysis of variance distribution using real EMG data also suggested a relationship between variance distribution and signal-dependent noise. Conclusion: The study reported here was conducted to examine the performance of a proposed surface EMG model capable of representing variance distribution and a related distribution parameter estimation method. Experiments using artificial and real EMG data demonstrated the validity of the model. Significance: Variance distribution estimated using the proposed model exhibits potential in the estimation of muscle force.

Finite Element Analysis of Lamb Waves Acting within a Thin Aluminum Plate

DTIC Science & Technology

2007-09-01

signal to avoid time aliasing % LambWaveMode % lamb wave mode to simulate; use proper phase velocity curve % thickness % thickness of...analysis of the simulated signal response data demonstrated that elevated temperatures delay wave propagation, although the delays are minimal at the...Echo Techniques Ultrasonic NDE techniques are based on the propagation and reflection of elastic waves , with the assumption that damage in the

Robust Multipoint Water-Fat Separation Using Fat Likelihood Analysis

PubMed Central

Yu, Huanzhou; Reeder, Scott B.; Shimakawa, Ann; McKenzie, Charles A.; Brittain, Jean H.

2016-01-01

Fat suppression is an essential part of routine MRI scanning. Multiecho chemical-shift based water-fat separation methods estimate and correct for Bo field inhomogeneity. However, they must contend with the intrinsic challenge of water-fat ambiguity that can result in water-fat swapping. This problem arises because the signals from two chemical species, when both are modeled as a single discrete spectral peak, may appear indistinguishable in the presence of Bo off-resonance. In conventional methods, the water-fat ambiguity is typically removed by enforcing field map smoothness using region growing based algorithms. In reality, the fat spectrum has multiple spectral peaks. Using this spectral complexity, we introduce a novel concept that identifies water and fat for multiecho acquisitions by exploiting the spectral differences between water and fat. A fat likelihood map is produced to indicate if a pixel is likely to be water-dominant or fat-dominant by comparing the fitting residuals of two different signal models. The fat likelihood analysis and field map smoothness provide complementary information, and we designed an algorithm (Fat Likelihood Analysis for Multiecho Signals) to exploit both mechanisms. It is demonstrated in a wide variety of data that the Fat Likelihood Analysis for Multiecho Signals algorithm offers highly robust water-fat separation for 6-echo acquisitions, particularly in some previously challenging applications. PMID:21842498

A new statistical PCA-ICA algorithm for location of R-peaks in ECG.

PubMed

Chawla, M P S; Verma, H K; Kumar, Vinod

2008-09-16

The success of ICA to separate the independent components from the mixture depends on the properties of the electrocardiogram (ECG) recordings. This paper discusses some of the conditions of independent component analysis (ICA) that could affect the reliability of the separation and evaluation of issues related to the properties of the signals and number of sources. Principal component analysis (PCA) scatter plots are plotted to indicate the diagnostic features in the presence and absence of base-line wander in interpreting the ECG signals. In this analysis, a newly developed statistical algorithm by authors, based on the use of combined PCA-ICA for two correlated channels of 12-channel ECG data is proposed. ICA technique has been successfully implemented in identifying and removal of noise and artifacts from ECG signals. Cleaned ECG signals are obtained using statistical measures like kurtosis and variance of variance after ICA processing. This analysis also paper deals with the detection of QRS complexes in electrocardiograms using combined PCA-ICA algorithm. The efficacy of the combined PCA-ICA algorithm lies in the fact that the location of the R-peaks is bounded from above and below by the location of the cross-over points, hence none of the peaks are ignored or missed.

Data Analysis for the E and B EXperiment and Instrumentation Development for Cosmic Microwave Background Polarimetry

NASA Astrophysics Data System (ADS)

Araujo, Derek C.

The E and B EXperiment (EBEX) was a balloon-borne instrument designed to measure the polarization of the cosmic microwave background (CMB) while simultaneously characterizing Galactic dust emission. The instrument was based on a two-mirror ambient temperature Gregorian-Dragone telescope coupled with cooled refractive optics to a kilo-pixel array of transition edge sensor (TES) bolometeric detectors. To achieve sensitivity to both the CMB signal and Galactic foregrounds, EBEX observed in three signal bands centered on 150, 250, and 410 GHz. Polarimetry was achieved via a stationary wire-grid polarizer and a continuously rotating achromatic half-wave plate (HWP) based on a superconducting magnetic bearing (SMB). EBEX launched from McMurdo station, Antarctica on December 29, 2012 and collected 1.3 TB of data during 11 days of observation. This thesis is presented in two Parts. Part I reviews the data analysis we performed to transform the raw EBEX data into maps of temperature and polarization sky signals, with a particular focus on post-flight pointing reconstruction; time stream cleaning and map making; the generation of model sky maps of the expected signal for each of the three EBEX signal bands; removal of the HWP-synchronous signal from the detector time streams; and our attempts to identify, characterize, and correct for non-linear detector responsivity. In Part II we present recent developments in instrumentation for the next generation of CMB polarimeters. The developments we describe, including advances in lumped-element kinetic inductance detector (LEKID) technology and the development of a hollow-shaft SMB-based motor for use in HWP polarimetry, were motivated in part by the design for a prospective ground-based CMB polarimeter based in Greenland.

Gene expression profile in cardiovascular disease and preeclampsia: a meta-analysis of the transcriptome based on raw data from human studies deposited in Gene Expression Omnibus.

PubMed

Sitras, V; Fenton, C; Acharya, G

2015-02-01

Cardiovascular disease (CVD) and preeclampsia (PE) share common clinical features. We aimed to identify common transcriptomic signatures involved in CVD and PE in humans. Meta-analysis of individual raw microarray data deposited in GEO, obtained from blood samples of patients with CVD versus controls and placental samples from women with PE versus healthy women with uncomplicated pregnancies. Annotation of cases versus control samples was taken directly from the microarray documentation. Genes that showed a significant differential expression in the majority of experiments were selected for subsequent analysis. Hypergeometric gene list analysis was performed using Bioconductor GOstats package. Bioinformatic analysis was performed in PANTHER. Seven studies in CVD and 5 studies in PE were eligible for meta-analysis. A total of 181 genes were found to be differentially expressed in microarray studies investigating gene expression in blood samples obtained from patients with CVD compared to controls and 925 genes were differentially expressed between preeclamptic and healthy placentas. Among these differentially expressed genes, 22 were common between CVD and PE. Bioinformatic analysis of these genes revealed oxidative stress, p-53 pathway feedback, inflammation mediated by chemokines and cytokines, interleukin signaling, B-cell activation, PDGF signaling, Wnt signaling, integrin signaling and Alzheimer disease pathways to be involved in the pathophysiology of both CVD and PE. Metabolism, development, response to stimulus, immune response and cell communication were the associated biologic processes in both conditions. Gene set enrichment analysis showed the following overlapping pathways between CVD and PE: TGF-β-signaling, apoptosis, graft-versus-host disease, allograft rejection, chemokine signaling, steroid hormone synthesis, type I and II diabetes mellitus, VEGF signaling, pathways in cancer, GNRH signaling, Huntingtons disease and Notch signaling. CVD and PE share same common traits in their gene expression profile indicating common pathways in their pathophysiology. Copyright © 2014 Elsevier Ltd. All rights reserved.

G-CNV: A GPU-Based Tool for Preparing Data to Detect CNVs with Read-Depth Methods.

PubMed

Manconi, Andrea; Manca, Emanuele; Moscatelli, Marco; Gnocchi, Matteo; Orro, Alessandro; Armano, Giuliano; Milanesi, Luciano

2015-01-01

Copy number variations (CNVs) are the most prevalent types of structural variations (SVs) in the human genome and are involved in a wide range of common human diseases. Different computational methods have been devised to detect this type of SVs and to study how they are implicated in human diseases. Recently, computational methods based on high-throughput sequencing (HTS) are increasingly used. The majority of these methods focus on mapping short-read sequences generated from a donor against a reference genome to detect signatures distinctive of CNVs. In particular, read-depth based methods detect CNVs by analyzing genomic regions with significantly different read-depth from the other ones. The pipeline analysis of these methods consists of four main stages: (i) data preparation, (ii) data normalization, (iii) CNV regions identification, and (iv) copy number estimation. However, available tools do not support most of the operations required at the first two stages of this pipeline. Typically, they start the analysis by building the read-depth signal from pre-processed alignments. Therefore, third-party tools must be used to perform most of the preliminary operations required to build the read-depth signal. These data-intensive operations can be efficiently parallelized on graphics processing units (GPUs). In this article, we present G-CNV, a GPU-based tool devised to perform the common operations required at the first two stages of the analysis pipeline. G-CNV is able to filter low-quality read sequences, to mask low-quality nucleotides, to remove adapter sequences, to remove duplicated read sequences, to map the short-reads, to resolve multiple mapping ambiguities, to build the read-depth signal, and to normalize it. G-CNV can be efficiently used as a third-party tool able to prepare data for the subsequent read-depth signal generation and analysis. Moreover, it can also be integrated in CNV detection tools to generate read-depth signals.

ECG-based gating in ultra high field cardiovascular magnetic resonance using an independent component analysis approach.

PubMed

Krug, Johannes W; Rose, Georg; Clifford, Gari D; Oster, Julien

2013-11-19

In Cardiovascular Magnetic Resonance (CMR), the synchronization of image acquisition with heart motion is performed in clinical practice by processing the electrocardiogram (ECG). The ECG-based synchronization is well established for MR scanners with magnetic fields up to 3 T. However, this technique is prone to errors in ultra high field environments, e.g. in 7 T MR scanners as used in research applications. The high magnetic fields cause severe magnetohydrodynamic (MHD) effects which disturb the ECG signal. Image synchronization is thus less reliable and yields artefacts in CMR images. A strategy based on Independent Component Analysis (ICA) was pursued in this work to enhance the ECG contribution and attenuate the MHD effect. ICA was applied to 12-lead ECG signals recorded inside a 7 T MR scanner. An automatic source identification procedure was proposed to identify an independent component (IC) dominated by the ECG signal. The identified IC was then used for detecting the R-peaks. The presented ICA-based method was compared to other R-peak detection methods using 1) the raw ECG signal, 2) the raw vectorcardiogram (VCG), 3) the state-of-the-art gating technique based on the VCG, 4) an updated version of the VCG-based approach and 5) the ICA of the VCG. ECG signals from eight volunteers were recorded inside the MR scanner. Recordings with an overall length of 87 min accounting for 5457 QRS complexes were available for the analysis. The records were divided into a training and a test dataset. In terms of R-peak detection within the test dataset, the proposed ICA-based algorithm achieved a detection performance with an average sensitivity (Se) of 99.2%, a positive predictive value (+P) of 99.1%, with an average trigger delay and jitter of 5.8 ms and 5.0 ms, respectively. Long term stability of the demixing matrix was shown based on two measurements of the same subject, each being separated by one year, whereas an averaged detection performance of Se = 99.4% and +P = 99.7% was achieved.Compared to the state-of-the-art VCG-based gating technique at 7 T, the proposed method increased the sensitivity and positive predictive value within the test dataset by 27.1% and 42.7%, respectively. The presented ICA-based method allows the estimation and identification of an IC dominated by the ECG signal. R-peak detection based on this IC outperforms the state-of-the-art VCG-based technique in a 7 T MR scanner environment.

New method for enhanced efficiency in detection of gravitational waves from supernovae using coherent network of detectors

NASA Astrophysics Data System (ADS)

Mukherjee, S.; Salazar, L.; Mittelstaedt, J.; Valdez, O.

2017-11-01

Supernovae in our universe are potential sources of gravitational waves (GW) that could be detected in a network of GW detectors like LIGO and Virgo. Core-collapse supernovae are rare, but the associated gravitational radiation is likely to carry profuse information about the underlying processes driving the supernovae. Calculations based on analytic models predict GW energies within the detection range of the Advanced LIGO detectors, out to tens of Mpc for certain types of signals e.g. coalescing binary neutron stars. For supernovae however, the corresponding distances are much less. Thus, methods that can improve the sensitivity of searches for GW signals from supernovae are desirable, especially in the advanced detector era. Several methods have been proposed based on various likelihood-based regulators that work on data from a network of detectors to detect burst-like signals (as is the case for signals from supernovae) from potential GW sources. To address this problem, we have developed an analysis pipeline based on a method of noise reduction known as the harmonic regeneration noise reduction (HRNR) algorithm. To demonstrate the method, sixteen supernova waveforms from the Murphy et al. 2009 catalog have been used in presence of LIGO science data. A comparative analysis is presented to show detection statistics for a standard network analysis as commonly used in GW pipelines and the same by implementing the new method in conjunction with the network. The result shows significant improvement in detection statistics.

A novel non-contact radar sensor for affective and interactive analysis.

PubMed

Lin, Hong-Dun; Lee, Yen-Shien; Shih, Hsiang-Lan; Chuang, Bor-Nian

2013-01-01

Currently, many physiological signal sensing techniques have been applied for affective analysis in Human-Computer Interaction applications. Most known maturely developed sensing methods (EEG/ECG/EMG/Temperature/BP etc. al.) replied on contact way to obtain desired physiological information for further data analysis. However, those methods might cause some inconvenient and uncomfortable problems, and not easy to be used for affective analysis in interactive performing. To improve this issue, a novel technology based on low power radar technology (Nanosecond Pulse Near-field Sensing, NPNS) with 300 MHz radio-frequency was proposed to detect humans' pulse signal by the non-contact way for heartbeat signal extraction. In this paper, a modified nonlinear HRV calculated algorithm was also developed and applied on analyzing affective status using extracted Peak-to-Peak Interval (PPI) information from detected pulse signal. The proposed new affective analysis method is designed to continuously collect the humans' physiological signal, and validated in a preliminary experiment with sound, light and motion interactive performance. As a result, the mean bias between PPI (from NPNS) and RRI (from ECG) shows less than 1ms, and the correlation is over than 0.88, respectively.

Radar signal analysis of ballistic missile with micro-motion based on time-frequency distribution

NASA Astrophysics Data System (ADS)

Wang, Jianming; Liu, Lihua; Yu, Hua

2015-12-01

The micro-motion of ballistic missile targets induces micro-Doppler modulation on the radar return signal, which is a unique feature for the warhead discrimination during flight. In order to extract the micro-Doppler feature of ballistic missile targets, time-frequency analysis is employed to process the micro-Doppler modulated time-varying radar signal. The images of time-frequency distribution (TFD) reveal the micro-Doppler modulation characteristic very well. However, there are many existing time-frequency analysis methods to generate the time-frequency distribution images, including the short-time Fourier transform (STFT), Wigner distribution (WD) and Cohen class distribution, etc. Under the background of ballistic missile defence, the paper aims at working out an effective time-frequency analysis method for ballistic missile warhead discrimination from the decoys.

The multiple complex exponential model and its application to EEG analysis

NASA Astrophysics Data System (ADS)

Chen, Dao-Mu; Petzold, J.

The paper presents a novel approach to the analysis of the EEG signal, which is based on a multiple complex exponential (MCE) model. Parameters of the model are estimated using a nonharmonic Fourier expansion algorithm. The central idea of the algorithm is outlined, and the results, estimated on the basis of simulated data, are presented and compared with those obtained by the conventional methods of signal analysis. Preliminary work on various application possibilities of the MCE model in EEG data analysis is described. It is shown that the parameters of the MCE model reflect the essential information contained in an EEG segment. These parameters characterize the EEG signal in a more objective way because they are closer to the recent supposition of the nonlinear character of the brain's dynamic behavior.

SETI at the Nancay radiotelescope.

PubMed

Biraud, F

1983-11-01

The Nancay (France) radiotelescope has been used in June, 1981, to search for artificial monochromatic signals from 102 nearby stars, without success. A different approach to SETI is also considered based on the properties of wide band signals. A detection procedure, through Karhunen-Loeve analysis, is suggested.

Numerical Investigation of the Microscopic Heat Current Inside a Nanofluid System Based on Molecular Dynamics Simulation and Wavelet Analysis.

PubMed

Jia, Tao; Gao, Di

2018-04-03

Molecular dynamics simulation is employed to investigate the microscopic heat current inside an argon-copper nanofluid. Wavelet analysis of the microscopic heat current inside the nanofluid system is conducted. The signal of the microscopic heat current is decomposed into two parts: one is the approximation part; the other is the detail part. The approximation part is associated with the low-frequency part of the signal, and the detail part is associated with the high-frequency part of the signal. Both the probability distributions of the high-frequency and the low-frequency parts of the signals demonstrate Gaussian-like characteristics. The curves fit to data of the probability distribution of the microscopic heat current are established, and the parameters including the mean value and the standard deviation in the mathematical formulas of the curves show dramatic changes for the cases before and after adding copper nanoparticles into the argon base fluid.

Detection of motion artifact patterns in photoplethysmographic signals based on time and period domain analysis.

PubMed

Couceiro, R; Carvalho, P; Paiva, R P; Henriques, J; Muehlsteff, J

2014-12-01

The presence of motion artifacts in photoplethysmographic (PPG) signals is one of the major obstacles in the extraction of reliable cardiovascular parameters in continuous monitoring applications. In the current paper we present an algorithm for motion artifact detection based on the analysis of the variations in the time and the period domain characteristics of the PPG signal. The extracted features are ranked using a normalized mutual information feature selection algorithm and the best features are used in a support vector machine classification model to distinguish between clean and corrupted sections of the PPG signal. The proposed method has been tested in healthy and cardiovascular diseased volunteers, considering 11 different motion artifact sources. The results achieved by the current algorithm (sensitivity--SE: 84.3%, specificity--SP: 91.5% and accuracy--ACC: 88.5%) show that the current methodology is able to identify both corrupted and clean PPG sections with high accuracy in both healthy (ACC: 87.5%) and cardiovascular diseases (ACC: 89.5%) context.

Singular value decomposition based feature extraction technique for physiological signal analysis.

PubMed

Chang, Cheng-Ding; Wang, Chien-Chih; Jiang, Bernard C

2012-06-01

Multiscale entropy (MSE) is one of the popular techniques to calculate and describe the complexity of the physiological signal. Many studies use this approach to detect changes in the physiological conditions in the human body. However, MSE results are easily affected by noise and trends, leading to incorrect estimation of MSE values. In this paper, singular value decomposition (SVD) is adopted to replace MSE to extract the features of physiological signals, and adopt the support vector machine (SVM) to classify the different physiological states. A test data set based on the PhysioNet website was used, and the classification results showed that using SVD to extract features of the physiological signal could attain a classification accuracy rate of 89.157%, which is higher than that using the MSE value (71.084%). The results show the proposed analysis procedure is effective and appropriate for distinguishing different physiological states. This promising result could be used as a reference for doctors in diagnosis of congestive heart failure (CHF) disease.

Wavelet analysis of poorly-focused ultrasonic signal of pressure tube inspection in nuclear industry

NASA Astrophysics Data System (ADS)

Zhao, Huan; Gachagan, Anthony; Dobie, Gordon; Lardner, Timothy

2018-04-01

Pressure tube fabrication and installment challenges combined with natural sagging over time can produce issues with probe alignment for pressure tube inspection of the primary circuit of CANDU reactors. The ability to extract accurate defect depth information from poorly focused ultrasonic signals would reduce additional inspection procedures, which leads to a significant time and cost saving. Currently, the defect depth measurement protocol is to simply calculate the time difference between the peaks of the echo signals from the tube surface and the defect from a single element probe focused at the back-wall depth. When alignment issues are present, incorrect focusing results in interference within the returning echo signal. This paper proposes a novel wavelet analysis method that employs the Haar wavelet to decompose the original poorly focused A-scan signal and reconstruct detailed information based on a selected high frequency component range within the bandwidth of the transducer. Compared to the original signal, the wavelet analysis method provides additional characteristic defect information and an improved estimate of defect depth with errors less than 5%.

Fine tuning breath-hold-based cerebrovascular reactivity analysis models.

PubMed

van Niftrik, Christiaan Hendrik Bas; Piccirelli, Marco; Bozinov, Oliver; Pangalu, Athina; Valavanis, Antonios; Regli, Luca; Fierstra, Jorn

2016-02-01

We elaborate on existing analysis methods for breath-hold (BH)-derived cerebrovascular reactivity (CVR) measurements and describe novel insights and models toward more exact CVR interpretation. Five blood-oxygen-level-dependent (BOLD) fMRI datasets of neurovascular patients with unilateral hemispheric hemodynamic impairment were used to test various BH CVR analysis methods. Temporal lag (phase), percent BOLD signal change (CVR), and explained variance (coherence) maps were calculated using three different sine models and two novel "Optimal Signal" model-free methods based on the unaffected hemisphere and the sagittal sinus fMRI signal time series, respectively. All models showed significant differences in CVR and coherence between the affected-hemodynamic impaired-and unaffected hemisphere. Voxel-wise phase determination significantly increases CVR (0.60 ± 0.18 vs. 0.82 ± 0.27; P < 0.05). Incorporating different durations of breath hold and resting period in one sine model (two-task) did increase coherence in the unaffected hemisphere, as well as eliminating negative phase commonly obtained by one-task frequency models. The novel model-free "optimal signal" methods both explained the BOLD MR data similar to the two task sine model. Our CVR analysis demonstrates an improved CVR and coherence after implementation of voxel-wise phase and frequency adjustment. The novel "optimal signal" methods provide a robust and feasible alternative to the sine models, as both are model-free and independent of compliance. Here, the sagittal sinus model may be advantageous, as it is independent of hemispheric CVR impairment.

Gearbox Tooth Cut Fault Diagnostics Using Acoustic Emission and Vibration Sensors — A Comparative Study

PubMed Central

Qu, Yongzhi; He, David; Yoon, Jae; Van Hecke, Brandon; Bechhoefer, Eric; Zhu, Junda

2014-01-01

In recent years, acoustic emission (AE) sensors and AE-based techniques have been developed and tested for gearbox fault diagnosis. In general, AE-based techniques require much higher sampling rates than vibration analysis-based techniques for gearbox fault diagnosis. Therefore, it is questionable whether an AE-based technique would give a better or at least the same performance as the vibration analysis-based techniques using the same sampling rate. To answer the question, this paper presents a comparative study for gearbox tooth damage level diagnostics using AE and vibration measurements, the first known attempt to compare the gearbox fault diagnostic performance of AE- and vibration analysis-based approaches using the same sampling rate. Partial tooth cut faults are seeded in a gearbox test rig and experimentally tested in a laboratory. Results have shown that the AE-based approach has the potential to differentiate gear tooth damage levels in comparison with the vibration-based approach. While vibration signals are easily affected by mechanical resonance, the AE signals show more stable performance. PMID:24424467

Study and application of acoustic emission testing in fault diagnosis of low-speed heavy-duty gears.

PubMed

Gao, Lixin; Zai, Fenlou; Su, Shanbin; Wang, Huaqing; Chen, Peng; Liu, Limei

2011-01-01

Most present studies on the acoustic emission signals of rotating machinery are experiment-oriented, while few of them involve on-spot applications. In this study, a method of redundant second generation wavelet transform based on the principle of interpolated subdivision was developed. With this method, subdivision was not needed during the decomposition. The lengths of approximation signals and detail signals were the same as those of original ones, so the data volume was twice that of original signals; besides, the data redundancy characteristic also guaranteed the excellent analysis effect of the method. The analysis of the acoustic emission data from the faults of on-spot low-speed heavy-duty gears validated the redundant second generation wavelet transform in the processing and denoising of acoustic emission signals. Furthermore, the analysis illustrated that the acoustic emission testing could be used in the fault diagnosis of on-spot low-speed heavy-duty gears and could be a significant supplement to vibration testing diagnosis.

Study and Application of Acoustic Emission Testing in Fault Diagnosis of Low-Speed Heavy-Duty Gears

PubMed Central

Gao, Lixin; Zai, Fenlou; Su, Shanbin; Wang, Huaqing; Chen, Peng; Liu, Limei

2011-01-01

Most present studies on the acoustic emission signals of rotating machinery are experiment-oriented, while few of them involve on-spot applications. In this study, a method of redundant second generation wavelet transform based on the principle of interpolated subdivision was developed. With this method, subdivision was not needed during the decomposition. The lengths of approximation signals and detail signals were the same as those of original ones, so the data volume was twice that of original signals; besides, the data redundancy characteristic also guaranteed the excellent analysis effect of the method. The analysis of the acoustic emission data from the faults of on-spot low-speed heavy-duty gears validated the redundant second generation wavelet transform in the processing and denoising of acoustic emission signals. Furthermore, the analysis illustrated that the acoustic emission testing could be used in the fault diagnosis of on-spot low-speed heavy-duty gears and could be a significant supplement to vibration testing diagnosis. PMID:22346592

Monitoring the RNA distribution in human embryonic stem cells using Raman micro-spectroscopy and fluorescence imaging

NASA Astrophysics Data System (ADS)

Falamas, A.; Kalra, S.; Chis, V.; Notingher, I.

2013-11-01

The aim of this study was to monitor the intracellular distribution of nucleic acids in human embryonic stem cells. Raman micro-spectroscopy and fluorescence imaging investigations were employed to obtain high-spatial resolution maps of nucleic acids. The DNA Raman signal was identified based on the 782 cm-1 band, while the RNA characteristic signal was detected based on the 813 cm-1 fingerprint band assigned to O-P-O symmetric stretching vibrations. Additionally, principal components analysis was performed and nucleic acids characteristic Raman signals were identified in the data set, which were plotted at each position in the cells. In this manner, high intensity RNA signal was identified in the cells nucleolus and cytoplasm, while the nucleus presented a much lower signal.

Fault feature analysis of cracked gear based on LOD and analytical-FE method

NASA Astrophysics Data System (ADS)

Wu, Jiateng; Yang, Yu; Yang, Xingkai; Cheng, Junsheng

2018-01-01

At present, there are two main ideas for gear fault diagnosis. One is the model-based gear dynamic analysis; the other is signal-based gear vibration diagnosis. In this paper, a method for fault feature analysis of gear crack is presented, which combines the advantages of dynamic modeling and signal processing. Firstly, a new time-frequency analysis method called local oscillatory-characteristic decomposition (LOD) is proposed, which has the attractive feature of extracting fault characteristic efficiently and accurately. Secondly, an analytical-finite element (analytical-FE) method which is called assist-stress intensity factor (assist-SIF) gear contact model, is put forward to calculate the time-varying mesh stiffness (TVMS) under different crack states. Based on the dynamic model of the gear system with 6 degrees of freedom, the dynamic simulation response was obtained for different tooth crack depths. For the dynamic model, the corresponding relation between the characteristic parameters and the degree of the tooth crack is established under a specific condition. On the basis of the methods mentioned above, a novel gear tooth root crack diagnosis method which combines the LOD with the analytical-FE is proposed. Furthermore, empirical mode decomposition (EMD) and ensemble empirical mode decomposition (EEMD) are contrasted with the LOD by gear crack fault vibration signals. The analysis results indicate that the proposed method performs effectively and feasibility for the tooth crack stiffness calculation and the gear tooth crack fault diagnosis.

Performance Analysis of Control Signal Transmission Technique for Cognitive Radios in Dynamic Spectrum Access Networks

NASA Astrophysics Data System (ADS)

Sakata, Ren; Tomioka, Tazuko; Kobayashi, Takahiro

When cognitive radio (CR) systems dynamically use the frequency band, a control signal is necessary to indicate which carrier frequencies are currently available in the network. In order to keep efficient spectrum utilization, this control signal also should be transmitted based on the channel conditions. If transmitters dynamically select carrier frequencies, receivers have to receive control signals without knowledge of their carrier frequencies. To enable such transmission and reception, this paper proposes a novel scheme called DCPT (Differential Code Parallel Transmission). With DCPT, receivers can receive low-rate information with no knowledge of the carrier frequencies. The transmitter transmits two signals whose carrier frequencies are spaced by a predefined value. The absolute values of the carrier frequencies can be varied. When the receiver acquires the DCPT signal, it multiplies the signal by a frequency-shifted version of the signal; this yields a DC component that represents the data signal which is then demodulated. The performance was evaluated by means of numerical analysis and computer simulation. We confirmed that DCPT operates successfully even under severe interference if its parameters are appropriately configured.

Temporal Noise Analysis of Charge-Domain Sampling Readout Circuits for CMOS Image Sensors.

PubMed

Ge, Xiaoliang; Theuwissen, Albert J P

2018-02-27

This paper presents a temporal noise analysis of charge-domain sampling readout circuits for Complementary Metal-Oxide Semiconductor (CMOS) image sensors. In order to address the trade-off between the low input-referred noise and high dynamic range, a Gm-cell-based pixel together with a charge-domain correlated-double sampling (CDS) technique has been proposed to provide a way to efficiently embed a tunable conversion gain along the read-out path. Such readout topology, however, operates in a non-stationery large-signal behavior, and the statistical properties of its temporal noise are a function of time. Conventional noise analysis methods for CMOS image sensors are based on steady-state signal models, and therefore cannot be readily applied for Gm-cell-based pixels. In this paper, we develop analysis models for both thermal noise and flicker noise in Gm-cell-based pixels by employing the time-domain linear analysis approach and the non-stationary noise analysis theory, which help to quantitatively evaluate the temporal noise characteristic of Gm-cell-based pixels. Both models were numerically computed in MATLAB using design parameters of a prototype chip, and compared with both simulation and experimental results. The good agreement between the theoretical and measurement results verifies the effectiveness of the proposed noise analysis models.

Temporal Noise Analysis of Charge-Domain Sampling Readout Circuits for CMOS Image Sensors †

PubMed Central

Theuwissen, Albert J. P.

2018-01-01

This paper presents a temporal noise analysis of charge-domain sampling readout circuits for Complementary Metal-Oxide Semiconductor (CMOS) image sensors. In order to address the trade-off between the low input-referred noise and high dynamic range, a Gm-cell-based pixel together with a charge-domain correlated-double sampling (CDS) technique has been proposed to provide a way to efficiently embed a tunable conversion gain along the read-out path. Such readout topology, however, operates in a non-stationery large-signal behavior, and the statistical properties of its temporal noise are a function of time. Conventional noise analysis methods for CMOS image sensors are based on steady-state signal models, and therefore cannot be readily applied for Gm-cell-based pixels. In this paper, we develop analysis models for both thermal noise and flicker noise in Gm-cell-based pixels by employing the time-domain linear analysis approach and the non-stationary noise analysis theory, which help to quantitatively evaluate the temporal noise characteristic of Gm-cell-based pixels. Both models were numerically computed in MATLAB using design parameters of a prototype chip, and compared with both simulation and experimental results. The good agreement between the theoretical and measurement results verifies the effectiveness of the proposed noise analysis models. PMID:29495496

Quadrature demodulation based circuit implementation of pulse stream for ultrasonic signal FRI sparse sampling

NASA Astrophysics Data System (ADS)

Shoupeng, Song; Zhou, Jiang

2017-03-01

Converting ultrasonic signal to ultrasonic pulse stream is the key step of finite rate of innovation (FRI) sparse sampling. At present, ultrasonic pulse-stream-forming techniques are mainly based on digital algorithms. No hardware circuit that can achieve it has been reported. This paper proposes a new quadrature demodulation (QD) based circuit implementation method for forming an ultrasonic pulse stream. Elaborating on FRI sparse sampling theory, the process of ultrasonic signal is explained, followed by a discussion and analysis of ultrasonic pulse-stream-forming methods. In contrast to ultrasonic signal envelope extracting techniques, a quadrature demodulation method (QDM) is proposed. Simulation experiments were performed to determine its performance at various signal-to-noise ratios (SNRs). The circuit was then designed, with mixing module, oscillator, low pass filter (LPF), and root of square sum module. Finally, application experiments were carried out on pipeline sample ultrasonic flaw testing. The experimental results indicate that the QDM can accurately convert ultrasonic signal to ultrasonic pulse stream, and reverse the original signal information, such as pulse width, amplitude, and time of arrival. This technique lays the foundation for ultrasonic signal FRI sparse sampling directly with hardware circuitry.

Real-Time EEG Signal Enhancement Using Canonical Correlation Analysis and Gaussian Mixture Clustering

PubMed Central

Huang, Chih-Sheng; Yang, Wen-Yu; Chuang, Chun-Hsiang; Wang, Yu-Kai

2018-01-01

Electroencephalogram (EEG) signals are usually contaminated with various artifacts, such as signal associated with muscle activity, eye movement, and body motion, which have a noncerebral origin. The amplitude of such artifacts is larger than that of the electrical activity of the brain, so they mask the cortical signals of interest, resulting in biased analysis and interpretation. Several blind source separation methods have been developed to remove artifacts from the EEG recordings. However, the iterative process for measuring separation within multichannel recordings is computationally intractable. Moreover, manually excluding the artifact components requires a time-consuming offline process. This work proposes a real-time artifact removal algorithm that is based on canonical correlation analysis (CCA), feature extraction, and the Gaussian mixture model (GMM) to improve the quality of EEG signals. The CCA was used to decompose EEG signals into components followed by feature extraction to extract representative features and GMM to cluster these features into groups to recognize and remove artifacts. The feasibility of the proposed algorithm was demonstrated by effectively removing artifacts caused by blinks, head/body movement, and chewing from EEG recordings while preserving the temporal and spectral characteristics of the signals that are important to cognitive research. PMID:29599950

pyAudioAnalysis: An Open-Source Python Library for Audio Signal Analysis.

PubMed

Giannakopoulos, Theodoros

2015-01-01

Audio information plays a rather important role in the increasing digital content that is available today, resulting in a need for methodologies that automatically analyze such content: audio event recognition for home automations and surveillance systems, speech recognition, music information retrieval, multimodal analysis (e.g. audio-visual analysis of online videos for content-based recommendation), etc. This paper presents pyAudioAnalysis, an open-source Python library that provides a wide range of audio analysis procedures including: feature extraction, classification of audio signals, supervised and unsupervised segmentation and content visualization. pyAudioAnalysis is licensed under the Apache License and is available at GitHub (https://github.com/tyiannak/pyAudioAnalysis/). Here we present the theoretical background behind the wide range of the implemented methodologies, along with evaluation metrics for some of the methods. pyAudioAnalysis has been already used in several audio analysis research applications: smart-home functionalities through audio event detection, speech emotion recognition, depression classification based on audio-visual features, music segmentation, multimodal content-based movie recommendation and health applications (e.g. monitoring eating habits). The feedback provided from all these particular audio applications has led to practical enhancement of the library.

pyAudioAnalysis: An Open-Source Python Library for Audio Signal Analysis

PubMed Central

Giannakopoulos, Theodoros

2015-01-01

Audio information plays a rather important role in the increasing digital content that is available today, resulting in a need for methodologies that automatically analyze such content: audio event recognition for home automations and surveillance systems, speech recognition, music information retrieval, multimodal analysis (e.g. audio-visual analysis of online videos for content-based recommendation), etc. This paper presents pyAudioAnalysis, an open-source Python library that provides a wide range of audio analysis procedures including: feature extraction, classification of audio signals, supervised and unsupervised segmentation and content visualization. pyAudioAnalysis is licensed under the Apache License and is available at GitHub (https://github.com/tyiannak/pyAudioAnalysis/). Here we present the theoretical background behind the wide range of the implemented methodologies, along with evaluation metrics for some of the methods. pyAudioAnalysis has been already used in several audio analysis research applications: smart-home functionalities through audio event detection, speech emotion recognition, depression classification based on audio-visual features, music segmentation, multimodal content-based movie recommendation and health applications (e.g. monitoring eating habits). The feedback provided from all these particular audio applications has led to practical enhancement of the library. PMID:26656189

Characterization of real-world vibration sources with a view toward optimal energy harvesting architectures

NASA Astrophysics Data System (ADS)

Rantz, Robert; Roundy, Shad

2016-04-01

A tremendous amount of research has been performed on the design and analysis of vibration energy harvester architectures with the goal of optimizing power output; most studies assume idealized input vibrations without paying much attention to whether such idealizations are broadly representative of real sources. These "idealized input signals" are typically derived from the expected nature of the vibrations produced from a given source. Little work has been done on corroborating these expectations by virtue of compiling a comprehensive list of vibration signals organized by detailed classifications. Vibration data representing 333 signals were collected from the NiPS Laboratory "Real Vibration" database, processed, and categorized according to the source of the signal (e.g. animal, machine, etc.), the number of dominant frequencies, the nature of the dominant frequencies (e.g. stationary, band-limited noise, etc.), and other metrics. By categorizing signals in this way, the set of idealized vibration inputs commonly assumed for harvester input can be corroborated and refined, and heretofore overlooked vibration input types have motivation for investigation. An initial qualitative analysis of vibration signals has been undertaken with the goal of determining how often a standard linear oscillator based harvester is likely the optimal architecture, and how often a nonlinear harvester with a cubic stiffness function might provide improvement. Although preliminary, the analysis indicates that in at least 23% of cases, a linear harvester is likely optimal and in no more than 53% of cases would a nonlinear cubic stiffness based harvester provide improvement.

Cerebral capillary velocimetry based on temporal OCT speckle contrast.

PubMed

Choi, Woo June; Li, Yuandong; Qin, Wan; Wang, Ruikang K

2016-12-01

We propose a new optical coherence tomography (OCT) based method to measure red blood cell (RBC) velocities of single capillaries in the cortex of rodent brain. This OCT capillary velocimetry exploits quantitative laser speckle contrast analysis to estimate speckle decorrelation rate from the measured temporal OCT speckle signals, which is related to microcirculatory flow velocity. We hypothesize that OCT signal due to sub-surface capillary flow can be treated as the speckle signal in the single scattering regime and thus its time scale of speckle fluctuations can be subjected to single scattering laser speckle contrast analysis to derive characteristic decorrelation time. To validate this hypothesis, OCT measurements are conducted on a single capillary flow phantom operating at preset velocities, in which M-mode B-frames are acquired using a high-speed OCT system. Analysis is then performed on the time-varying OCT signals extracted at the capillary flow, exhibiting a typical inverse relationship between the estimated decorrelation time and absolute RBC velocity, which is then used to deduce the capillary velocities. We apply the method to in vivo measurements of mouse brain, demonstrating that the proposed approach provides additional useful information in the quantitative assessment of capillary hemodynamics, complementary to that of OCT angiography.

The prediction of a pathogenesis-related secretome of Puccinia helianthi through high-throughput transcriptome analysis.

PubMed

Jing, Lan; Guo, Dandan; Hu, Wenjie; Niu, Xiaofan

2017-03-11

Many plant pathogen secretory proteins are known to be elicitors or pathogenic factors,which play an important role in the host-pathogen interaction process. Bioinformatics approaches make possible the large scale prediction and analysis of secretory proteins from the Puccinia helianthi transcriptome. The internet-based software SignalP v4.1, TargetP v1.01, Big-PI predictor, TMHMM v2.0 and ProtComp v9.0 were utilized to predict the signal peptides and the signal peptide-dependent secreted proteins among the 35,286 ORFs of the P. helianthi transcriptome. 908 ORFs (accounting for 2.6% of the total proteins) were identified as putative secretory proteins containing signal peptides. The length of the majority of proteins ranged from 51 to 300 amino acids (aa), while the signal peptides were from 18 to 20 aa long. Signal peptidase I (SpI) cleavage sites were found in 463 of these putative secretory signal peptides. 55 proteins contained the lipoprotein signal peptide recognition site of signal peptidase II (SpII). Out of 908 secretory proteins, 581 (63.8%) have functions related to signal recognition and transduction, metabolism, transport and catabolism. Additionally, 143 putative secretory proteins were categorized into 27 functional groups based on Gene Ontology terms, including 14 groups in biological process, seven in cellular component, and six in molecular function. Gene ontology analysis of the secretory proteins revealed an enrichment of hydrolase activity. Pathway associations were established for 82 (9.0%) secretory proteins. A number of cell wall degrading enzymes and three homologous proteins specific to Phytophthora sojae effectors were also identified, which may be involved in the pathogenicity of the sunflower rust pathogen. This investigation proposes a new approach for identifying elicitors and pathogenic factors. The eventual identification and characterization of 908 extracellularly secreted proteins will advance our understanding of the molecular mechanisms of interactions between sunflower and rust pathogen and will enhance our ability to intervene in disease states.

Defect induced guided waves mode conversion

NASA Astrophysics Data System (ADS)

Wandowski, Tomasz; Kudela, Pawel; Malinowski, Pawel; Ostachowicz, Wieslaw

2016-04-01

This paper deals with analysis of guided waves mode conversion phenomenon in fiber reinforced composite materials. Mode conversion phenomenon may take place when propagating elastic guided waves interact with discontinuities in the composite waveguide. The examples of such discontinuities are sudden thickness change or delamination between layers in composite material. In this paper, analysis of mode conversion phenomenon is based on full wave-field signals. In the full wave-field approach signals representing propagation of elastic waves are gathered from dense mesh of points that span over investigated area of composite part. This allow to animate the guided wave propagation. The reported analysis is based on signals resulting from numerical calculations and experimental measurements. In both cases defect in the form of delamination is considered. In the case of numerical research, Spectral Element Method (SEM) is utilized, in which a mesh is composed of 3D elements. Numerical model includes also piezoelectric transducer. Full wave-field experimental measurements are conducted by using piezoelectric transducer for guided wave excitation and Scanning Laser Doppler Vibrometer (SLDV) for sensing.

Artifacts and noise removal in electrocardiograms using independent component analysis.

PubMed

Chawla, M P S; Verma, H K; Kumar, Vinod

2008-09-26

Independent component analysis (ICA) is a novel technique capable of separating independent components from electrocardiogram (ECG) complex signals. The purpose of this analysis is to evaluate the effectiveness of ICA in removing artifacts and noise from ECG recordings. ICA is applied to remove artifacts and noise in ECG segments of either an individual ECG CSE data base file or all files. The reconstructed ECGs are compared with the original ECG signal. For the four special cases discussed, the R-Peak magnitudes of the CSE data base ECG waveforms before and after applying ICA are also found. In the results, it is shown that in most of the cases, the percentage error in reconstruction is very small. The results show that there is a significant improvement in signal quality, i.e. SNR. All the ECG recording cases dealt showed an improved ECG appearance after the use of ICA. This establishes the efficacy of ICA in elimination of noise and artifacts in electrocardiograms.

Fiber optic sensor for continuous health monitoring in CFRP composite materials

NASA Astrophysics Data System (ADS)

Rippert, Laurent; Papy, Jean-Michel; Wevers, Martine; Van Huffel, Sabine

2002-07-01

An intensity modulated sensor, based on the microbending concept, has been incorporated in laminates produced from a C/epoxy prepreg. Pencil lead break tests (Hsu-Neilsen sources) and tensile tests have been performed on this material. In this research study, fibre optic sensors will be proven to offer an alternative for the robust piezoelectric transducers used for Acoustic Emission (AE) monitoring. The main emphasis has been put on the use of advanced signal processing techniques based on time-frequency analysis. The signal Short Time Fourier Transform (STFT) has been computed and several robust noise reduction algorithms, such as Wiener adaptive filtering, improved spectral subtraction filtering, and Singular Value Decomposition (SVD) -based filtering, have been applied. An energy and frequency -based detection criterion is put forward to detect transient signals that can be correlated with Modal Acoustic Emission (MAE) results and thus damage in the composite material. There is a strong indication that time-frequency analysis and the Hankel Total Least Squares (HTLS) method can also be used for damage characterization. This study shows that the signal from a quite simple microbend optical sensor contains information on the elastic energy released whenever damage is being introduced in the host material by mechanical loading. Robust algorithms can be used to retrieve and analyze this information.

Unsupervised pattern recognition methods in ciders profiling based on GCE voltammetric signals.

PubMed

Jakubowska, Małgorzata; Sordoń, Wanda; Ciepiela, Filip

2016-07-15

This work presents a complete methodology of distinguishing between different brands of cider and ageing degrees, based on voltammetric signals, utilizing dedicated data preprocessing procedures and unsupervised multivariate analysis. It was demonstrated that voltammograms recorded on glassy carbon electrode in Britton-Robinson buffer at pH 2 are reproducible for each brand. By application of clustering algorithms and principal component analysis visible homogenous clusters were obtained. Advanced signal processing strategy which included automatic baseline correction, interval scaling and continuous wavelet transform with dedicated mother wavelet, was a key step in the correct recognition of the objects. The results show that voltammetry combined with optimized univariate and multivariate data processing is a sufficient tool to distinguish between ciders from various brands and to evaluate their freshness. Copyright © 2016 Elsevier Ltd. All rights reserved.

Time-Frequency Learning Machines for Nonstationarity Detection Using Surrogates

NASA Astrophysics Data System (ADS)

Borgnat, Pierre; Flandrin, Patrick; Richard, Cédric; Ferrari, André; Amoud, Hassan; Honeine, Paul

2012-03-01

Time-frequency representations provide a powerful tool for nonstationary signal analysis and classification, supporting a wide range of applications [12]. As opposed to conventional Fourier analysis, these techniques reveal the evolution in time of the spectral content of signals. In Ref. [7,38], time-frequency analysis is used to test stationarity of any signal. The proposed method consists of a comparison between global and local time-frequency features. The originality is to make use of a family of stationary surrogate signals for defining the null hypothesis of stationarity and, based upon this information, to derive statistical tests. An open question remains, however, about how to choose relevant time-frequency features. Over the last decade, a number of new pattern recognition methods based on reproducing kernels have been introduced. These learning machines have gained popularity due to their conceptual simplicity and their outstanding performance [30]. Initiated by Vapnik’s support vector machines (SVM) [35], they offer now a wide class of supervised and unsupervised learning algorithms. In Ref. [17-19], the authors have shown how the most effective and innovative learning machines can be tuned to operate in the time-frequency domain. This chapter follows this line of research by taking advantage of learning machines to test and quantify stationarity. Based on one-class SVM, our approach uses the entire time-frequency representation and does not require arbitrary feature extraction. Applied to a set of surrogates, it provides the domain boundary that includes most of these stationarized signals. This allows us to test the stationarity of the signal under investigation. This chapter is organized as follows. In Section 22.2, we introduce the surrogate data method to generate stationarized signals, namely, the null hypothesis of stationarity. The concept of time-frequency learning machines is presented in Section 22.3, and applied to one-class SVM in order to derive a stationarity test in Section 22.4. The relevance of the latter is illustrated by simulation results in Section 22.5.

Planetary Transmission Diagnostics

NASA Technical Reports Server (NTRS)

Lewicki, David G. (Technical Monitor); Samuel, Paul D.; Conroy, Joseph K.; Pines, Darryll J.

2004-01-01

This report presents a methodology for detecting and diagnosing gear faults in the planetary stage of a helicopter transmission. This diagnostic technique is based on the constrained adaptive lifting algorithm. The lifting scheme, developed by Wim Sweldens of Bell Labs, is a time domain, prediction-error realization of the wavelet transform that allows for greater flexibility in the construction of wavelet bases. Classic lifting analyzes a given signal using wavelets derived from a single fundamental basis function. A number of researchers have proposed techniques for adding adaptivity to the lifting scheme, allowing the transform to choose from a set of fundamental bases the basis that best fits the signal. This characteristic is desirable for gear diagnostics as it allows the technique to tailor itself to a specific transmission by selecting a set of wavelets that best represent vibration signals obtained while the gearbox is operating under healthy-state conditions. However, constraints on certain basis characteristics are necessary to enhance the detection of local wave-form changes caused by certain types of gear damage. The proposed methodology analyzes individual tooth-mesh waveforms from a healthy-state gearbox vibration signal that was generated using the vibration separation (synchronous signal-averaging) algorithm. Each waveform is separated into analysis domains using zeros of its slope and curvature. The bases selected in each analysis domain are chosen to minimize the prediction error, and constrained to have the same-sign local slope and curvature as the original signal. The resulting set of bases is used to analyze future-state vibration signals and the lifting prediction error is inspected. The constraints allow the transform to effectively adapt to global amplitude changes, yielding small prediction errors. However, local wave-form changes associated with certain types of gear damage are poorly adapted, causing a significant change in the prediction error. The constrained adaptive lifting diagnostic algorithm is validated using data collected from the University of Maryland Transmission Test Rig and the results are discussed.

Prony Ringdown GUI (CERTS Prony Ringdown, part of the DSI Tool Box)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tuffner, Francis; Marinovici, PNNL Laurentiu; Hauer, PNNL John

2014-02-21

The PNNL Prony Ringdown graphical user interface is one analysis tool included in the Dynamic System Identification toolbox (DSI Toolbox). The Dynamic System Identification toolbox is a MATLAB-based collection of tools for parsing and analyzing phasor measurement unit data, especially in regards to small signal stability. It includes tools to read the data, preprocess it, and perform small signal analysis. 5. Method of Solution: The Dynamic System Identification Toolbox (DSI Toolbox) is designed to provide a research environment for examining phasor measurement unit data and performing small signal stability analysis. The software uses a series of text-driven menus to helpmore » guide users and organize the toolbox features. Methods for reading in populate phasor measurement unit data are provided, with appropriate preprocessing options for small-signal-stability analysis. The toolbox includes the Prony Ringdown GUI and basic algorithms to estimate information on oscillatory modes of the system, such as modal frequency and damping ratio.« less

Brain-computer interface using wavelet transformation and naïve bayes classifier.

PubMed

Bassani, Thiago; Nievola, Julio Cesar

2010-01-01

The main purpose of this work is to establish an exploratory approach using electroencephalographic (EEG) signal, analyzing the patterns in the time-frequency plane. This work also aims to optimize the EEG signal analysis through the improvement of classifiers and, eventually, of the BCI performance. In this paper a novel exploratory approach for data mining of EEG signal based on continuous wavelet transformation (CWT) and wavelet coherence (WC) statistical analysis is introduced and applied. The CWT allows the representation of time-frequency patterns of the signal's information content by WC qualiatative analysis. Results suggest that the proposed methodology is capable of identifying regions in time-frequency spectrum during the specified task of BCI. Furthermore, an example of a region is identified, and the patterns are classified using a Naïve Bayes Classifier (NBC). This innovative characteristic of the process justifies the feasibility of the proposed approach to other data mining applications. It can open new physiologic researches in this field and on non stationary time series analysis.

Improved Reconstruction of Radio Holographic Signal for Forward Scatter Radar Imaging

PubMed Central

Hu, Cheng; Liu, Changjiang; Wang, Rui; Zeng, Tao

2016-01-01

Forward scatter radar (FSR), as a specially configured bistatic radar, is provided with the capabilities of target recognition and classification by the Shadow Inverse Synthetic Aperture Radar (SISAR) imaging technology. This paper mainly discusses the reconstruction of radio holographic signal (RHS), which is an important procedure in the signal processing of FSR SISAR imaging. Based on the analysis of signal characteristics, the method for RHS reconstruction is improved in two parts: the segmental Hilbert transformation and the reconstruction of mainlobe RHS. In addition, a quantitative analysis of the method’s applicability is presented by distinguishing between the near field and far field in forward scattering. Simulation results validated the method’s advantages in improving the accuracy of RHS reconstruction and imaging. PMID:27164114

Large-Signal Lyapunov-Based Stability Analysis of DC/AC Inverters and Inverter-Based Microgrids

NASA Astrophysics Data System (ADS)

Kabalan, Mahmoud

Microgrid stability studies have been largely based on small-signal linearization techniques. However, the validity and magnitude of the linearization domain is limited to small perturbations. Thus, there is a need to examine microgrids with large-signal nonlinear techniques to fully understand and examine their stability. Large-signal stability analysis can be accomplished by Lyapunov-based mathematical methods. These Lyapunov methods estimate the domain of asymptotic stability of the studied system. A survey of Lyapunov-based large-signal stability studies showed that few large-signal studies have been completed on either individual systems (dc/ac inverters, dc/dc rectifiers, etc.) or microgrids. The research presented in this thesis addresses the large-signal stability of droop-controlled dc/ac inverters and inverter-based microgrids. Dc/ac power electronic inverters allow microgrids to be technically feasible. Thus, as a prelude to examining the stability of microgrids, the research presented in Chapter 3 analyzes the stability of inverters. First, the 13 th order large-signal nonlinear model of a droop-controlled dc/ac inverter connected to an infinite bus is presented. The singular perturbation method is used to decompose the nonlinear model into 11th, 9th, 7th, 5th, 3rd and 1st order models. Each model ignores certain control or structural components of the full order model. The aim of the study is to understand the accuracy and validity of the reduced order models in replicating the performance of the full order nonlinear model. The performance of each model is studied in three different areas: time domain simulations, Lyapunov's indirect method and domain of attraction estimation. The work aims to present the best model to use in each of the three domains of study. Results show that certain reduced order models are capable of accurately reproducing the performance of the full order model while others can be used to gain insights into those three areas of study. This will enable future studies to save computational effort and produce the most accurate results according to the needs of the study being performed. Moreover, the effect of grid (line) impedance on the accuracy of droop control is explored using the 5th order model. Simulation results show that traditional droop control is valid up to R/X line impedance value of 2. Furthermore, the 3rd order nonlinear model improves the currently available inverter-infinite bus models by accounting for grid impedance, active power-frequency droop and reactive power-voltage droop. Results show the 3rd order model's ability to account for voltage and reactive power changes during a transient event. Finally, the large-signal Lyapunov-based stability analysis is completed for a 3 bus microgrid system (made up of 2 inverters and 1 linear load). The thesis provides a systematic state space large-signal nonlinear mathematical modeling method of inverter-based microgrids. The inverters include the dc-side dynamics associated with dc sources. The mathematical model is then used to estimate the domain of asymptotic stability of the 3 bus microgrid. The three bus microgrid system was used as a case study to highlight the design and optimization capability of a large-signal-based approach. The study explores the effect of system component sizing, load transient and generation variations on the asymptotic stability of the microgrid. Essentially, this advancement gives microgrid designers and engineers the ability to manipulate the domain of asymptotic stability depending on performance requirements. Especially important, this research was able to couple the domain of asymptotic stability of the ac microgrid with that of the dc side voltage source. Time domain simulations were used to demonstrate the mathematical nonlinear analysis results.

Improved neutron-gamma discrimination for a {sup 6}Li-glass neutron detector using digital signal analysis methods

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, C. L., E-mail: wangc@ornl.gov; Riedel, R. A.

2016-01-15

A {sup 6}Li-glass scintillator (GS20) based neutron Anger camera was developed for time-of-flight single-crystal diffraction instruments at Spallation Neutron Source. Traditional Pulse-Height Analysis (PHA) for Neutron-Gamma Discrimination (NGD) resulted in the neutron-gamma efficiency ratio (defined as NGD ratio) on the order of 10{sup 4}. The NGD ratios of Anger cameras need to be improved for broader applications including neutron reflectometers. For this purpose, six digital signal analysis methods of individual waveforms acquired from photomultiplier tubes were proposed using (i) charge integration, (ii) pulse-amplitude histograms, (iii) power spectrum analysis combined with the maximum pulse-amplitude, (iv) two event parameters (a{sub 1}, b{submore » 0}) obtained from a Wiener filter, (v) an effective amplitude (m) obtained from an adaptive least-mean-square filter, and (vi) a cross-correlation coefficient between individual and reference waveforms. The NGD ratios are about 70 times those from the traditional PHA method. Our results indicate the NGD capabilities of neutron Anger cameras based on GS20 scintillators can be significantly improved with digital signal analysis methods.« less

Psychophysical Models for Signal Detection with Time Varying Uncertainty. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Gai, E.

1975-01-01

Psychophysical models for the behavior of the human operator in detection tasks which include change in detectability, correlation between observations and deferred decisions are developed. Classical Signal Detection Theory (SDT) is discussed and its emphasis on the sensory processes is contrasted to decision strategies. The analysis of decision strategies utilizes detection tasks with time varying signal strength. The classical theory is modified to include such tasks and several optimal decision strategies are explored. Two methods of classifying strategies are suggested. The first method is similar to the analysis of ROC curves, while the second is based on the relation between the criterion level (CL) and the detectability. Experiments to verify the analysis of tasks with changes of signal strength are designed. The results show that subjects are aware of changes in detectability and tend to use strategies that involve changes in the CL's.

Characterizing scaling properties of complex signals with missed data segments using the multifractal analysis.

PubMed

Pavlov, A N; Pavlova, O N; Abdurashitov, A S; Sindeeva, O A; Semyachkina-Glushkovskaya, O V; Kurths, J

2018-01-01

The scaling properties of complex processes may be highly influenced by the presence of various artifacts in experimental recordings. Their removal produces changes in the singularity spectra and the Hölder exponents as compared with the original artifacts-free data, and these changes are significantly different for positively correlated and anti-correlated signals. While signals with power-law correlations are nearly insensitive to the loss of significant parts of data, the removal of fragments of anti-correlated signals is more crucial for further data analysis. In this work, we study the ability of characterizing scaling features of chaotic and stochastic processes with distinct correlation properties using a wavelet-based multifractal analysis, and discuss differences between the effect of missed data for synchronous and asynchronous oscillatory regimes. We show that even an extreme data loss allows characterizing physiological processes such as the cerebral blood flow dynamics.

Fault diagnosis of motor bearing with speed fluctuation via angular resampling of transient sound signals

NASA Astrophysics Data System (ADS)

Lu, Siliang; Wang, Xiaoxian; He, Qingbo; Liu, Fang; Liu, Yongbin

2016-12-01

Transient signal analysis (TSA) has been proven an effective tool for motor bearing fault diagnosis, but has yet to be applied in processing bearing fault signals with variable rotating speed. In this study, a new TSA-based angular resampling (TSAAR) method is proposed for fault diagnosis under speed fluctuation condition via sound signal analysis. By applying the TSAAR method, the frequency smearing phenomenon is eliminated and the fault characteristic frequency is exposed in the envelope spectrum for bearing fault recognition. The TSAAR method can accurately estimate the phase information of the fault-induced impulses using neither complicated time-frequency analysis techniques nor external speed sensors, and hence it provides a simple, flexible, and data-driven approach that realizes variable-speed motor bearing fault diagnosis. The effectiveness and efficiency of the proposed TSAAR method are verified through a series of simulated and experimental case studies.

Characterizing scaling properties of complex signals with missed data segments using the multifractal analysis

NASA Astrophysics Data System (ADS)

Pavlov, A. N.; Pavlova, O. N.; Abdurashitov, A. S.; Sindeeva, O. A.; Semyachkina-Glushkovskaya, O. V.; Kurths, J.

2018-01-01

The scaling properties of complex processes may be highly influenced by the presence of various artifacts in experimental recordings. Their removal produces changes in the singularity spectra and the Hölder exponents as compared with the original artifacts-free data, and these changes are significantly different for positively correlated and anti-correlated signals. While signals with power-law correlations are nearly insensitive to the loss of significant parts of data, the removal of fragments of anti-correlated signals is more crucial for further data analysis. In this work, we study the ability of characterizing scaling features of chaotic and stochastic processes with distinct correlation properties using a wavelet-based multifractal analysis, and discuss differences between the effect of missed data for synchronous and asynchronous oscillatory regimes. We show that even an extreme data loss allows characterizing physiological processes such as the cerebral blood flow dynamics.

EnvironmentalWaveletTool: Continuous and discrete wavelet analysis and filtering for environmental time series

NASA Astrophysics Data System (ADS)

Galiana-Merino, J. J.; Pla, C.; Fernandez-Cortes, A.; Cuezva, S.; Ortiz, J.; Benavente, D.

2014-10-01

A MATLAB-based computer code has been developed for the simultaneous wavelet analysis and filtering of several environmental time series, particularly focused on the analyses of cave monitoring data. The continuous wavelet transform, the discrete wavelet transform and the discrete wavelet packet transform have been implemented to provide a fast and precise time-period examination of the time series at different period bands. Moreover, statistic methods to examine the relation between two signals have been included. Finally, the entropy of curves and splines based methods have also been developed for segmenting and modeling the analyzed time series. All these methods together provide a user-friendly and fast program for the environmental signal analysis, with useful, practical and understandable results.

Genomic Analysis of ATP Efflux in Saccharomyces cerevisiae

PubMed Central

Peters, Theodore W.; Miller, Aaron W.; Tourette, Cendrine; Agren, Hannah; Hubbard, Alan; Hughes, Robert E.

2015-01-01

Adenosine triphosphate (ATP) plays an important role as a primary molecule for the transfer of chemical energy to drive biological processes. ATP also functions as an extracellular signaling molecule in a diverse array of eukaryotic taxa in a conserved process known as purinergic signaling. Given the important roles of extracellular ATP in cell signaling, we sought to comprehensively elucidate the pathways and mechanisms governing ATP efflux from eukaryotic cells. Here, we present results of a genomic analysis of ATP efflux from Saccharomyces cerevisiae by measuring extracellular ATP levels in cultures of 4609 deletion mutants. This screen revealed key cellular processes that regulate extracellular ATP levels, including mitochondrial translation and vesicle sorting in the late endosome, indicating that ATP production and transport through vesicles are required for efflux. We also observed evidence for altered ATP efflux in strains deleted for genes involved in amino acid signaling, and mitochondrial retrograde signaling. Based on these results, we propose a model in which the retrograde signaling pathway potentiates amino acid signaling to promote mitochondrial respiration. This study advances our understanding of the mechanism of ATP secretion in eukaryotes and implicates TOR complex 1 (TORC1) and nutrient signaling pathways in the regulation of ATP efflux. These results will facilitate analysis of ATP efflux mechanisms in higher eukaryotes. PMID:26585826

Wavelength modulation spectroscopy--digital detection of gas absorption harmonics based on Fourier analysis.

PubMed

Mei, Liang; Svanberg, Sune

2015-03-20

This work presents a detailed study of the theoretical aspects of the Fourier analysis method, which has been utilized for gas absorption harmonic detection in wavelength modulation spectroscopy (WMS). The lock-in detection of the harmonic signal is accomplished by studying the phase term of the inverse Fourier transform of the Fourier spectrum that corresponds to the harmonic signal. The mathematics and the corresponding simulation results are given for each procedure when applying the Fourier analysis method. The present work provides a detailed view of the WMS technique when applying the Fourier analysis method.

Using Pattern Recognition and Discriminance Analysis to Predict Critical Events in Large Signal Databases

NASA Astrophysics Data System (ADS)

Feller, Jens; Feller, Sebastian; Mauersberg, Bernhard; Mergenthaler, Wolfgang

2009-09-01

Many applications in plant management require close monitoring of equipment performance, in particular with the objective to prevent certain critical events. At each point in time, the information available to classify the criticality of the process, is represented through the historic signal database as well as the actual measurement. This paper presents an approach to detect and predict critical events, based on pattern recognition and discriminance analysis.

A data-driven method to enhance vibration signal decomposition for rolling bearing fault analysis

NASA Astrophysics Data System (ADS)

Grasso, M.; Chatterton, S.; Pennacchi, P.; Colosimo, B. M.

2016-12-01

Health condition analysis and diagnostics of rotating machinery requires the capability of properly characterizing the information content of sensor signals in order to detect and identify possible fault features. Time-frequency analysis plays a fundamental role, as it allows determining both the existence and the causes of a fault. The separation of components belonging to different time-frequency scales, either associated to healthy or faulty conditions, represents a challenge that motivates the development of effective methodologies for multi-scale signal decomposition. In this framework, the Empirical Mode Decomposition (EMD) is a flexible tool, thanks to its data-driven and adaptive nature. However, the EMD usually yields an over-decomposition of the original signals into a large number of intrinsic mode functions (IMFs). The selection of most relevant IMFs is a challenging task, and the reference literature lacks automated methods to achieve a synthetic decomposition into few physically meaningful modes by avoiding the generation of spurious or meaningless modes. The paper proposes a novel automated approach aimed at generating a decomposition into a minimal number of relevant modes, called Combined Mode Functions (CMFs), each consisting in a sum of adjacent IMFs that share similar properties. The final number of CMFs is selected in a fully data driven way, leading to an enhanced characterization of the signal content without any information loss. A novel criterion to assess the dissimilarity between adjacent CMFs is proposed, based on probability density functions of frequency spectra. The method is suitable to analyze vibration signals that may be periodically acquired within the operating life of rotating machineries. A rolling element bearing fault analysis based on experimental data is presented to demonstrate the performances of the method and the provided benefits.

Wavelet-based polarimetry analysis

NASA Astrophysics Data System (ADS)

Ezekiel, Soundararajan; Harrity, Kyle; Farag, Waleed; Alford, Mark; Ferris, David; Blasch, Erik

2014-06-01

Wavelet transformation has become a cutting edge and promising approach in the field of image and signal processing. A wavelet is a waveform of effectively limited duration that has an average value of zero. Wavelet analysis is done by breaking up the signal into shifted and scaled versions of the original signal. The key advantage of a wavelet is that it is capable of revealing smaller changes, trends, and breakdown points that are not revealed by other techniques such as Fourier analysis. The phenomenon of polarization has been studied for quite some time and is a very useful tool for target detection and tracking. Long Wave Infrared (LWIR) polarization is beneficial for detecting camouflaged objects and is a useful approach when identifying and distinguishing manmade objects from natural clutter. In addition, the Stokes Polarization Parameters, which are calculated from 0°, 45°, 90°, 135° right circular, and left circular intensity measurements, provide spatial orientations of target features and suppress natural features. In this paper, we propose a wavelet-based polarimetry analysis (WPA) method to analyze Long Wave Infrared Polarimetry Imagery to discriminate targets such as dismounts and vehicles from background clutter. These parameters can be used for image thresholding and segmentation. Experimental results show the wavelet-based polarimetry analysis is efficient and can be used in a wide range of applications such as change detection, shape extraction, target recognition, and feature-aided tracking.

Inflammable Gas Mixture Detection with a Single Catalytic Sensor Based on the Electric Field Effect

PubMed Central

Tong, Ziyuan; Tong, Min-Ming; Meng, Wen; Li, Meng

2014-01-01

This paper introduces a new way to analyze mixtures of inflammable gases with a single catalytic sensor. The analysis technology was based on a new finding that an electric field on the catalytic sensor can change the output sensitivity of the sensor. The analysis of mixed inflammable gases results from processing the output signals obtained by adjusting the electric field parameter of the catalytic sensor. For the signal process, we designed a group of equations based on the heat balance of catalytic sensor expressing the relationship between the output signals and the concentration of gases. With these equations and the outputs of different electric fields, the gas concentration in a mixture could be calculated. In experiments, a mixture of methane, butane and ethane was analyzed by this new method, and the results showed that the concentration of each gas in the mixture could be detected with a single catalytic sensor, and the maximum relative error was less than 5%. PMID:24717635

Network-Based Analysis of Nutraceuticals in Human Hepatocellular Carcinomas Reveals Mechanisms of Chemopreventive Action

PubMed Central

Michailidou, M; Melas, IN; Messinis, DE; Klamt, S; Alexopoulos, LG; Kolisis, FN; Loutrari, H

2015-01-01

Chronic inflammation is associated with the development of human hepatocellular carcinoma (HCC), an essentially incurable cancer. Anti-inflammatory nutraceuticals have emerged as promising candidates against HCC, yet the mechanisms through which they influence the cell signaling machinery to impose phenotypic changes remain unresolved. Herein we implemented a systems biology approach in HCC cells, based on the integration of cytokine release and phospoproteomic data from high-throughput xMAP Luminex assays to elucidate the action mode of prominent nutraceuticals in terms of topology alterations of HCC-specific signaling networks. An optimization algorithm based on SigNetTrainer, an Integer Linear Programming formulation, was applied to construct networks linking signal transduction to cytokine secretion by combining prior knowledge of protein connectivity with proteomic data. Our analysis identified the most probable target phosphoproteins of interrogated compounds and predicted translational control as a new mechanism underlying their anticytokine action. Induced alterations corroborated with inhibition of HCC-driven angiogenesis and metastasis. PMID:26225263

Applied Chaos Level Test for Validation of Signal Conditions Underlying Optimal Performance of Voice Classification Methods.

PubMed

Liu, Boquan; Polce, Evan; Sprott, Julien C; Jiang, Jack J

2018-05-17

The purpose of this study is to introduce a chaos level test to evaluate linear and nonlinear voice type classification method performances under varying signal chaos conditions without subjective impression. Voice signals were constructed with differing degrees of noise to model signal chaos. Within each noise power, 100 Monte Carlo experiments were applied to analyze the output of jitter, shimmer, correlation dimension, and spectrum convergence ratio. The computational output of the 4 classifiers was then plotted against signal chaos level to investigate the performance of these acoustic analysis methods under varying degrees of signal chaos. A diffusive behavior detection-based chaos level test was used to investigate the performances of different voice classification methods. Voice signals were constructed by varying the signal-to-noise ratio to establish differing signal chaos conditions. Chaos level increased sigmoidally with increasing noise power. Jitter and shimmer performed optimally when the chaos level was less than or equal to 0.01, whereas correlation dimension was capable of analyzing signals with chaos levels of less than or equal to 0.0179. Spectrum convergence ratio demonstrated proficiency in analyzing voice signals with all chaos levels investigated in this study. The results of this study corroborate the performance relationships observed in previous studies and, therefore, demonstrate the validity of the validation test method. The presented chaos level validation test could be broadly utilized to evaluate acoustic analysis methods and establish the most appropriate methodology for objective voice analysis in clinical practice.

The Hilbert-Huang Transform-Based Denoising Method for the TEM Response of a PRBS Source Signal

NASA Astrophysics Data System (ADS)

Hai, Li; Guo-qiang, Xue; Pan, Zhao; Hua-sen, Zhong; Khan, Muhammad Younis

2016-08-01

The denoising process is critical in processing transient electromagnetic (TEM) sounding data. For the full waveform pseudo-random binary sequences (PRBS) response, an inadequate noise estimation may result in an erroneous interpretation. We consider the Hilbert-Huang transform (HHT) and its application to suppress the noise in the PRBS response. The focus is on the thresholding scheme to suppress the noise and the analysis of the signal based on its Hilbert time-frequency representation. The method first decomposes the signal into the intrinsic mode function, and then, inspired by the thresholding scheme in wavelet analysis; an adaptive and interval thresholding is conducted to set to zero all the components in intrinsic mode function which are lower than a threshold related to the noise level. The algorithm is based on the characteristic of the PRBS response. The HHT-based denoising scheme is tested on the synthetic and field data with the different noise levels. The result shows that the proposed method has a good capability in denoising and detail preservation.

AccuTyping: new algorithms for automated analysis of data from high-throughput genotyping with oligonucleotide microarrays

PubMed Central

Hu, Guohong; Wang, Hui-Yun; Greenawalt, Danielle M.; Azaro, Marco A.; Luo, Minjie; Tereshchenko, Irina V.; Cui, Xiangfeng; Yang, Qifeng; Gao, Richeng; Shen, Li; Li, Honghua

2006-01-01

Microarray-based analysis of single nucleotide polymorphisms (SNPs) has many applications in large-scale genetic studies. To minimize the influence of experimental variation, microarray data usually need to be processed in different aspects including background subtraction, normalization and low-signal filtering before genotype determination. Although many algorithms are sophisticated for these purposes, biases are still present. In the present paper, new algorithms for SNP microarray data analysis and the software, AccuTyping, developed based on these algorithms are described. The algorithms take advantage of a large number of SNPs included in each assay, and the fact that the top and bottom 20% of SNPs can be safely treated as homozygous after sorting based on their ratios between the signal intensities. These SNPs are then used as controls for color channel normalization and background subtraction. Genotype calls are made based on the logarithms of signal intensity ratios using two cutoff values, which were determined after training the program with a dataset of ∼160 000 genotypes and validated by non-microarray methods. AccuTyping was used to determine >300 000 genotypes of DNA and sperm samples. The accuracy was shown to be >99%. AccuTyping can be downloaded from . PMID:16982644

A web-based quantitative signal detection system on adverse drug reaction in China.

PubMed

Li, Chanjuan; Xia, Jielai; Deng, Jianxiong; Chen, Wenge; Wang, Suzhen; Jiang, Jing; Chen, Guanquan

2009-07-01

To establish a web-based quantitative signal detection system for adverse drug reactions (ADRs) based on spontaneous reporting to the Guangdong province drug-monitoring database in China. Using Microsoft Visual Basic and Active Server Pages programming languages and SQL Server 2000, a web-based system with three software modules was programmed to perform data preparation and association detection, and to generate reports. Information component (IC), the internationally recognized measure of disproportionality for quantitative signal detection, was integrated into the system, and its capacity for signal detection was tested with ADR reports collected from 1 January 2002 to 30 June 2007 in Guangdong. A total of 2,496 associations including known signals were mined from the test database. Signals (e.g., cefradine-induced hematuria) were found early by using the IC analysis. In addition, 291 drug-ADR associations were alerted for the first time in the second quarter of 2007. The system can be used for the detection of significant associations from the Guangdong drug-monitoring database and could be an extremely useful adjunct to the expert assessment of very large numbers of spontaneously reported ADRs for the first time in China.

Research and Implementation of Heart Sound Denoising

NASA Astrophysics Data System (ADS)

Liu, Feng; Wang, Yutai; Wang, Yanxiang

Heart sound is one of the most important signals. However, the process of getting heart sound signal can be interfered with many factors outside. Heart sound is weak electric signal and even weak external noise may lead to the misjudgment of pathological and physiological information in this signal, thus causing the misjudgment of disease diagnosis. As a result, it is a key to remove the noise which is mixed with heart sound. In this paper, a more systematic research and analysis which is involved in heart sound denoising based on matlab has been made. The study of heart sound denoising based on matlab firstly use the powerful image processing function of matlab to transform heart sound signals with noise into the wavelet domain through wavelet transform and decomposition these signals in muli-level. Then for the detail coefficient, soft thresholding is made using wavelet transform thresholding to eliminate noise, so that a signal denoising is significantly improved. The reconstructed signals are gained with stepwise coefficient reconstruction for the processed detail coefficient. Lastly, 50HZ power frequency and 35 Hz mechanical and electrical interference signals are eliminated using a notch filter.

Integrated Formulation of Beacon-Based Exception Analysis for Multimissions

NASA Technical Reports Server (NTRS)

Mackey, Ryan; James, Mark; Park, Han; Zak, Mickail

2003-01-01

Further work on beacon-based exception analysis for multimissions (BEAM), a method of real-time, automated diagnosis of a complex electromechanical systems, has greatly expanded its capability and suitability of application. This expanded formulation, which fully integrates physical models and symbolic analysis, is described. The new formulation of BEAM expands upon previous advanced techniques for analysis of signal data, utilizing mathematical modeling of the system physics, and expert-system reasoning,

Develop advanced nonlinear signal analysis topographical mapping system

NASA Technical Reports Server (NTRS)

Jong, Jen-Yi

1993-01-01

This study will provide timely assessment of SSME component operational status, identify probable causes of malfunction, and indicate feasible engineering solutions. The final result of this program will yield an advanced nonlinear signal analysis topographical mapping system (ATMS) of nonlinear and nonstationary spectral analysis software package integrated with the Compressed SSME TOPO Data Base (CSTDB) on the same platform. This system will allow NASA engineers to retrieve any unique defect signatures and trends associated with different failure modes and anomalous phenomena over the entire SSME test history across turbopump families.

Microprocessor-based cardiotachometer

NASA Technical Reports Server (NTRS)

Crosier, W. G.; Donaldson, J. A.

1981-01-01

Instrument operates reliably even with stress-test electrocardiogram (ECG) signals subject to noise, baseline wandering, and amplitude change. It records heart rate from preamplified, single-lead ECG input signal and produces digital and analog heart-rate outputs which are fed elsewhere. Analog hardware processes ECG input signal, producing 10-ms pulse for each heartbeat. Microprocessor analyzes resulting pulse train, identifying irregular heartbeats and maintaining stable output during lead switching. Easily modified computer program provides analysis.

Capacity Estimation Model for Signalized Intersections under the Impact of Access Point

PubMed Central

Zhao, Jing; Li, Peng; Zhou, Xizhao

2016-01-01

Highway Capacity Manual 2010 provides various factors to adjust the base saturation flow rate for the capacity analysis of signalized intersections. No factors, however, is considered for the potential change of signalized intersections capacity caused by the access point closeing to the signalized intersection. This paper presented a theoretical model to estimate the lane group capacity at signalized intersections with the consideration of the effects of access points. Two scenarios of access point locations, upstream or downstream of the signalized intersection, and impacts of six types of access traffic flow are taken into account. The proposed capacity model was validated based on VISSIM simulation. Results of extensive numerical analysis reveal the substantial impact of access point on the capacity, which has an inverse correlation with both the number of major street lanes and the distance between the intersection and access point. Moreover, among the six types of access traffic flows, the access traffic flow 1 (right-turning traffic from major street), flow 4 (left-turning traffic from access point), and flow 5 (left-turning traffic from major street) cause a more significant effect on lane group capacity than others. Some guidance on the mitigation of the negative effect is provided for practitioners. PMID:26726998

Investigation to realize a computationally efficient implementation of the high-order instantaneous-moments-based fringe analysis method

NASA Astrophysics Data System (ADS)

Gorthi, Sai Siva; Rajshekhar, Gannavarpu; Rastogi, Pramod

2010-06-01

Recently, a high-order instantaneous moments (HIM)-operator-based method was proposed for accurate phase estimation in digital holographic interferometry. The method relies on piece-wise polynomial approximation of phase and subsequent evaluation of the polynomial coefficients from the HIM operator using single-tone frequency estimation. The work presents a comparative analysis of the performance of different single-tone frequency estimation techniques, like Fourier transform followed by optimization, estimation of signal parameters by rotational invariance technique (ESPRIT), multiple signal classification (MUSIC), and iterative frequency estimation by interpolation on Fourier coefficients (IFEIF) in HIM-operator-based methods for phase estimation. Simulation and experimental results demonstrate the potential of the IFEIF technique with respect to computational efficiency and estimation accuracy.

IQM: An Extensible and Portable Open Source Application for Image and Signal Analysis in Java

PubMed Central

Kainz, Philipp; Mayrhofer-Reinhartshuber, Michael; Ahammer, Helmut

2015-01-01

Image and signal analysis applications are substantial in scientific research. Both open source and commercial packages provide a wide range of functions for image and signal analysis, which are sometimes supported very well by the communities in the corresponding fields. Commercial software packages have the major drawback of being expensive and having undisclosed source code, which hampers extending the functionality if there is no plugin interface or similar option available. However, both variants cannot cover all possible use cases and sometimes custom developments are unavoidable, requiring open source applications. In this paper we describe IQM, a completely free, portable and open source (GNU GPLv3) image and signal analysis application written in pure Java. IQM does not depend on any natively installed libraries and is therefore runnable out-of-the-box. Currently, a continuously growing repertoire of 50 image and 16 signal analysis algorithms is provided. The modular functional architecture based on the three-tier model is described along the most important functionality. Extensibility is achieved using operator plugins, and the development of more complex workflows is provided by a Groovy script interface to the JVM. We demonstrate IQM’s image and signal processing capabilities in a proof-of-principle analysis and provide example implementations to illustrate the plugin framework and the scripting interface. IQM integrates with the popular ImageJ image processing software and is aiming at complementing functionality rather than competing with existing open source software. Machine learning can be integrated into more complex algorithms via the WEKA software package as well, enabling the development of transparent and robust methods for image and signal analysis. PMID:25612319

IQM: an extensible and portable open source application for image and signal analysis in Java.

PubMed

Kainz, Philipp; Mayrhofer-Reinhartshuber, Michael; Ahammer, Helmut

2015-01-01

Image and signal analysis applications are substantial in scientific research. Both open source and commercial packages provide a wide range of functions for image and signal analysis, which are sometimes supported very well by the communities in the corresponding fields. Commercial software packages have the major drawback of being expensive and having undisclosed source code, which hampers extending the functionality if there is no plugin interface or similar option available. However, both variants cannot cover all possible use cases and sometimes custom developments are unavoidable, requiring open source applications. In this paper we describe IQM, a completely free, portable and open source (GNU GPLv3) image and signal analysis application written in pure Java. IQM does not depend on any natively installed libraries and is therefore runnable out-of-the-box. Currently, a continuously growing repertoire of 50 image and 16 signal analysis algorithms is provided. The modular functional architecture based on the three-tier model is described along the most important functionality. Extensibility is achieved using operator plugins, and the development of more complex workflows is provided by a Groovy script interface to the JVM. We demonstrate IQM's image and signal processing capabilities in a proof-of-principle analysis and provide example implementations to illustrate the plugin framework and the scripting interface. IQM integrates with the popular ImageJ image processing software and is aiming at complementing functionality rather than competing with existing open source software. Machine learning can be integrated into more complex algorithms via the WEKA software package as well, enabling the development of transparent and robust methods for image and signal analysis.

Operation, analysis, and design of signalized intersections : a module for the introductory course in transportation engineering.

DOT National Transportation Integrated Search

2014-02-01

This report presents materials that can be used as the basis for a module on signalized intersections in the introductory : course in transportation engineering. The materials were developed based on studies of the work of students who took : this in...

Model accuracy impact through rescaled observations in hydrological data assimilation studies

USDA-ARS?s Scientific Manuscript database

Signal and noise time-series variability of soil moisture datasets (e.g. satellite-, model-, station-based) vary greatly. Optimality of the analysis obtained after observations are assimilated into the model depends on the degree that the differences between the signal variances of model and observa...

Impact of model relative accuracy in framework of rescaling observations in hydrological data assimilation studies

USDA-ARS?s Scientific Manuscript database

Soil moisture datasets (e.g. satellite-, model-, station-based) vary greatly with respect to their signal, noise, and/or combined time-series variability. Minimizing differences in signal variances is particularly important in data assimilation techniques to optimize the accuracy of the analysis obt...

Data Mining of the Public Version of the FDA Adverse Event Reporting System

PubMed Central

Sakaeda, Toshiyuki; Tamon, Akiko; Kadoyama, Kaori; Okuno, Yasushi

2013-01-01

The US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS, formerly AERS) is a database that contains information on adverse event and medication error reports submitted to the FDA. Besides those from manufacturers, reports can be submitted from health care professionals and the public. The original system was started in 1969, but since the last major revision in 1997, reporting has markedly increased. Data mining algorithms have been developed for the quantitative detection of signals from such a large database, where a signal means a statistical association between a drug and an adverse event or a drug-associated adverse event, including the proportional reporting ratio (PRR), the reporting odds ratio (ROR), the information component (IC), and the empirical Bayes geometric mean (EBGM). A survey of our previous reports suggested that the ROR provided the highest number of signals, and the EBGM the lowest. Additionally, an analysis of warfarin-, aspirin- and clopidogrel-associated adverse events suggested that all EBGM-based signals were included in the PRR-based signals, and also in the IC- or ROR-based ones, and that the PRR- and IC-based signals were in the ROR-based ones. In this article, the latest information on this area is summarized for future pharmacoepidemiological studies and/or pharmacovigilance analyses. PMID:23794943

Detection of epileptiform activity in EEG signals based on time-frequency and non-linear analysis

PubMed Central

Gajic, Dragoljub; Djurovic, Zeljko; Gligorijevic, Jovan; Di Gennaro, Stefano; Savic-Gajic, Ivana

2015-01-01

We present a new technique for detection of epileptiform activity in EEG signals. After preprocessing of EEG signals we extract representative features in time, frequency and time-frequency domain as well as using non-linear analysis. The features are extracted in a few frequency sub-bands of clinical interest since these sub-bands showed much better discriminatory characteristics compared with the whole frequency band. Then we optimally reduce the dimension of feature space to two using scatter matrices. A decision about the presence of epileptiform activity in EEG signals is made by quadratic classifiers designed in the reduced two-dimensional feature space. The accuracy of the technique was tested on three sets of electroencephalographic (EEG) signals recorded at the University Hospital Bonn: surface EEG signals from healthy volunteers, intracranial EEG signals from the epilepsy patients during the seizure free interval from within the seizure focus and intracranial EEG signals of epileptic seizures also from within the seizure focus. An overall detection accuracy of 98.7% was achieved. PMID:25852534

Digital Signal Processing Methods for Ultrasonic Echoes.

PubMed

Sinding, Kyle; Drapaca, Corina; Tittmann, Bernhard

2016-04-28

Digital signal processing has become an important component of data analysis needed in industrial applications. In particular, for ultrasonic thickness measurements the signal to noise ratio plays a major role in the accurate calculation of the arrival time. For this application a band pass filter is not sufficient since the noise level cannot be significantly decreased such that a reliable thickness measurement can be performed. This paper demonstrates the abilities of two regularization methods - total variation and Tikhonov - to filter acoustic and ultrasonic signals. Both of these methods are compared to a frequency based filtering for digitally produced signals as well as signals produced by ultrasonic transducers. This paper demonstrates the ability of the total variation and Tikhonov filters to accurately recover signals from noisy acoustic signals faster than a band pass filter. Furthermore, the total variation filter has been shown to reduce the noise of a signal significantly for signals with clear ultrasonic echoes. Signal to noise ratios have been increased over 400% by using a simple parameter optimization. While frequency based filtering is efficient for specific applications, this paper shows that the reduction of noise in ultrasonic systems can be much more efficient with regularization methods.

Independent Component Analysis-motivated Approach to Classificatory Decomposition of Cortical Evoked Potentials

PubMed Central

Smolinski, Tomasz G; Buchanan, Roger; Boratyn, Grzegorz M; Milanova, Mariofanna; Prinz, Astrid A

2006-01-01

Background Independent Component Analysis (ICA) proves to be useful in the analysis of neural activity, as it allows for identification of distinct sources of activity. Applied to measurements registered in a controlled setting and under exposure to an external stimulus, it can facilitate analysis of the impact of the stimulus on those sources. The link between the stimulus and a given source can be verified by a classifier that is able to "predict" the condition a given signal was registered under, solely based on the components. However, the ICA's assumption about statistical independence of sources is often unrealistic and turns out to be insufficient to build an accurate classifier. Therefore, we propose to utilize a novel method, based on hybridization of ICA, multi-objective evolutionary algorithms (MOEA), and rough sets (RS), that attempts to improve the effectiveness of signal decomposition techniques by providing them with "classification-awareness." Results The preliminary results described here are very promising and further investigation of other MOEAs and/or RS-based classification accuracy measures should be pursued. Even a quick visual analysis of those results can provide an interesting insight into the problem of neural activity analysis. Conclusion We present a methodology of classificatory decomposition of signals. One of the main advantages of our approach is the fact that rather than solely relying on often unrealistic assumptions about statistical independence of sources, components are generated in the light of a underlying classification problem itself. PMID:17118151

Objective research of auscultation signals in Traditional Chinese Medicine based on wavelet packet energy and support vector machine.

PubMed

Yan, Jianjun; Shen, Xiaojing; Wang, Yiqin; Li, Fufeng; Xia, Chunming; Guo, Rui; Chen, Chunfeng; Shen, Qingwei

2010-01-01

This study aims at utilising Wavelet Packet Transform (WPT) and Support Vector Machine (SVM) algorithm to make objective analysis and quantitative research for the auscultation in Traditional Chinese Medicine (TCM) diagnosis. First, Wavelet Packet Decomposition (WPD) at level 6 was employed to split more elaborate frequency bands of the auscultation signals. Then statistic analysis was made based on the extracted Wavelet Packet Energy (WPE) features from WPD coefficients. Furthermore, the pattern recognition was used to distinguish mixed subjects' statistical feature values of sample groups through SVM. Finally, the experimental results showed that the classification accuracies were at a high level.

Robustness of Representative Signals Relative to Data Loss Using Atlas-Based Parcellations.

PubMed

Gajdoš, Martin; Výtvarová, Eva; Fousek, Jan; Lamoš, Martin; Mikl, Michal

2018-04-24

Parcellation-based approaches are an important part of functional magnetic resonance imaging data analysis. They are a necessary processing step for sorting data in structurally or functionally homogenous regions. Real functional magnetic resonance imaging datasets usually do not cover the atlas template completely; they are often spatially constrained due to the physical limitations of MR sequence settings, the inter-individual variability in brain shape, etc. When using a parcellation template, many regions are not completely covered by actual data. This paper addresses the issue of the area coverage required in real data in order to reliably estimate the representative signal and the influence of this kind of data loss on network analysis metrics. We demonstrate this issue on four datasets using four different widely used parcellation templates. We used two erosion approaches to simulate data loss on the whole-brain level and the ROI-specific level. Our results show that changes in ROI coverage have a systematic influence on network measures. Based on the results of our analysis, we recommend controlling the ROI coverage and retaining at least 60% of the area in order to ensure at least 80% of explained variance of the original signal.

Element analysis: a wavelet-based method for analysing time-localized events in noisy time series

PubMed Central

2017-01-01

A method is derived for the quantitative analysis of signals that are composed of superpositions of isolated, time-localized ‘events’. Here, these events are taken to be well represented as rescaled and phase-rotated versions of generalized Morse wavelets, a broad family of continuous analytic functions. Analysing a signal composed of replicates of such a function using another Morse wavelet allows one to directly estimate the properties of events from the values of the wavelet transform at its own maxima. The distribution of events in general power-law noise is determined in order to establish significance based on an expected false detection rate. Finally, an expression for an event’s ‘region of influence’ within the wavelet transform permits the formation of a criterion for rejecting spurious maxima due to numerical artefacts or other unsuitable events. Signals can then be reconstructed based on a small number of isolated points on the time/scale plane. This method, termed element analysis, is applied to the identification of long-lived eddy structures in ocean currents as observed by along-track measurements of sea surface elevation from satellite altimetry. PMID:28484325

Fetal heart rate deceleration detection using a discrete cosine transform implementation of singular spectrum analysis.

PubMed

Warrick, P A; Precup, D; Hamilton, E F; Kearney, R E

2007-01-01

To develop a singular-spectrum analysis (SSA) based change-point detection algorithm applicable to fetal heart rate (FHR) monitoring to improve the detection of deceleration events. We present a method for decomposing a signal into near-orthogonal components via the discrete cosine transform (DCT) and apply this in a novel online manner to change-point detection based on SSA. The SSA technique forms models of the underlying signal that can be compared over time; models that are sufficiently different indicate signal change points. To adapt the algorithm to deceleration detection where many successive similar change events can occur, we modify the standard SSA algorithm to hold the reference model constant under such conditions, an approach that we term "base-hold SSA". The algorithm is applied to a database of 15 FHR tracings that have been preprocessed to locate candidate decelerations and is compared to the markings of an expert obstetrician. Of the 528 true and 1285 false decelerations presented to the algorithm, the base-hold approach improved on standard SSA, reducing the number of missed decelerations from 64 to 49 (21.9%) while maintaining the same reduction in false-positives (278). The standard SSA assumption that changes are infrequent does not apply to FHR analysis where decelerations can occur successively and in close proximity; our base-hold SSA modification improves detection of these types of event series.

Error analysis and algorithm implementation for an improved optical-electric tracking device based on MEMS

NASA Astrophysics Data System (ADS)

Sun, Hong; Wu, Qian-zhong

2013-09-01

In order to improve the precision of optical-electric tracking device, proposing a kind of improved optical-electric tracking device based on MEMS, in allusion to the tracking error of gyroscope senor and the random drift, According to the principles of time series analysis of random sequence, establish AR model of gyro random error based on Kalman filter algorithm, then the output signals of gyro are multiple filtered with Kalman filter. And use ARM as micro controller servo motor is controlled by fuzzy PID full closed loop control algorithm, and add advanced correction and feed-forward links to improve response lag of angle input, Free-forward can make output perfectly follow input. The function of lead compensation link is to shorten the response of input signals, so as to reduce errors. Use the wireless video monitor module and remote monitoring software (Visual Basic 6.0) to monitor servo motor state in real time, the video monitor module gathers video signals, and the wireless video module will sent these signals to upper computer, so that show the motor running state in the window of Visual Basic 6.0. At the same time, take a detailed analysis to the main error source. Through the quantitative analysis of the errors from bandwidth and gyro sensor, it makes the proportion of each error in the whole error more intuitive, consequently, decrease the error of the system. Through the simulation and experiment results shows the system has good following characteristic, and it is very valuable for engineering application.

Analysis of an Interplanetary Coronal Mass Ejection by a Spacecraft Radio Signal: A Case Study

NASA Astrophysics Data System (ADS)

Molera Calvés, G.; Kallio, E.; Cimo, G.; Quick, J.; Duev, D. A.; Bocanegra Bahamón, T.; Nickola, M.; Kharinov, M. A.; Mikhailov, A. G.

2017-11-01

Tracking radio communication signals from planetary spacecraft with ground-based telescopes offers the possibility to study the electron density and the interplanetary scintillation of the solar wind. Observations of the telemetry link of planetary spacecraft have been conducted regularly with ground antennae from the European Very Long Baseline Interferometry Network, aiming to study the propagation of radio signals in the solar wind at different solar elongations and distances from the Sun. We have analyzed the Mars Express spacecraft radio signal phase fluctuations while, based on a 3-D heliosphere plasma simulation, an interplanetary coronal mass ejection (ICME) crossed the radio path during one of our observations on 6 April 2015. Our measurements showed that the phase scintillation indices increased by a factor of 4 during the passage of the ICME. The method presented here confirms that the phase scintillation technique based on spacecraft signals provides information of the properties and propagation of the ICMEs in the heliosphere.

A MUSIC-based method for SSVEP signal processing.

PubMed

Chen, Kun; Liu, Quan; Ai, Qingsong; Zhou, Zude; Xie, Sheng Quan; Meng, Wei

2016-03-01

The research on brain computer interfaces (BCIs) has become a hotspot in recent years because it offers benefit to disabled people to communicate with the outside world. Steady state visual evoked potential (SSVEP)-based BCIs are more widely used because of higher signal to noise ratio and greater information transfer rate compared with other BCI techniques. In this paper, a multiple signal classification based method was proposed for multi-dimensional SSVEP feature extraction. 2-second data epochs from four electrodes achieved excellent accuracy rates including idle state detection. In some asynchronous mode experiments, the recognition accuracy reached up to 100%. The experimental results showed that the proposed method attained good frequency resolution. In most situations, the recognition accuracy was higher than canonical correlation analysis, which is a typical method for multi-channel SSVEP signal processing. Also, a virtual keyboard was successfully controlled by different subjects in an unshielded environment, which proved the feasibility of the proposed method for multi-dimensional SSVEP signal processing in practical applications.

Selective Listening Point Audio Based on Blind Signal Separation and Stereophonic Technology

NASA Astrophysics Data System (ADS)

Niwa, Kenta; Nishino, Takanori; Takeda, Kazuya

A sound field reproduction method is proposed that uses blind source separation and a head-related transfer function. In the proposed system, multichannel acoustic signals captured at distant microphones are decomposed to a set of location/signal pairs of virtual sound sources based on frequency-domain independent component analysis. After estimating the locations and the signals of the virtual sources by convolving the controlled acoustic transfer functions with each signal, the spatial sound is constructed at the selected point. In experiments, a sound field made by six sound sources is captured using 48 distant microphones and decomposed into sets of virtual sound sources. Since subjective evaluation shows no significant difference between natural and reconstructed sound when six virtual sources and are used, the effectiveness of the decomposing algorithm as well as the virtual source representation are confirmed.

Compaction of rolling circle amplification products increases signal integrity and signal-to-noise ratio

PubMed Central

Clausson, Carl-Magnus; Arngården, Linda; Ishaq, Omer; Klaesson, Axel; Kühnemund, Malte; Grannas, Karin; Koos, Björn; Qian, Xiaoyan; Ranefall, Petter; Krzywkowski, Tomasz; Brismar, Hjalmar; Nilsson, Mats; Wählby, Carolina; Söderberg, Ola

2015-01-01

Rolling circle amplification (RCA) for generation of distinct fluorescent signals in situ relies upon the self-collapsing properties of single-stranded DNA in commonly used RCA-based methods. By introducing a cross-hybridizing DNA oligonucleotide during rolling circle amplification, we demonstrate that the fluorophore-labeled RCA products (RCPs) become smaller. The reduced size of RCPs increases the local concentration of fluorophores and as a result, the signal intensity increases together with the signal-to-noise ratio. Furthermore, we have found that RCPs sometimes tend to disintegrate and may be recorded as several RCPs, a trait that is prevented with our cross-hybridizing DNA oligonucleotide. These effects generated by compaction of RCPs improve accuracy of visual as well as automated in situ analysis for RCA based methods, such as proximity ligation assays (PLA) and padlock probes. PMID:26202090

Average BER analysis of SCM-based free-space optical systems by considering the effect of IM3 with OSSB signals under turbulence channels.

PubMed

Lim, Wansu; Cho, Tae-Sik; Yun, Changho; Kim, Kiseon

2009-11-09

In this paper, we derive the average bit error rate (BER) of subcarrier multiplexing (SCM)-based free space optics (FSO) systems using a dual-drive Mach-Zehnder modulator (DD-MZM) for optical single-sideband (OSSB) signals under atmospheric turbulence channels. In particular, we consider the third-order intermodulation (IM3), a significant performance degradation factor, in the case of high input signal power systems. The derived average BER, as a function of the input signal power and the scintillation index, is employed to determine the optimum number of SCM users upon the designing FSO systems. For instance, when the user number doubles, the input signal power decreases by almost 2 dBm under the log-normal and exponential turbulence channels at a given average BER.

Classification of communication signals of the little brown bat

NASA Astrophysics Data System (ADS)

Melendez, Karla V.; Jones, Douglas L.; Feng, Albert S.

2005-09-01

Little brown bats, Myotis lucifugus, are known for their ability to echolocate and utilize their echolocation system to navigate, locate, and identify prey. Their echolocation signals have been characterized in detail, but their communication signals are poorly understood despite their widespread use during the social interactions. The goal of this study was to characterize the communication signals of little brown bats. Sound recordings were made overnight on five individual bats (housed separately from a large group of captive bats) for 7 nights, using a Pettersson ultrasound detector D240x bat detector and Nagra ARES-BB digital recorder. The spectral and temporal characteristics of recorded sounds were first analyzed using BATSOUND software from Pettersson. Sounds were first classified by visual observation of calls' temporal pattern and spectral composition, and later using an automatic classification scheme based on multivariate statistical parameters in MATLAB. Human- and machine-based analysis revealed five discrete classes of bat's communication signals: downward frequency-modulated calls, constant frequency calls, broadband noise bursts, broadband chirps, and broadband click trains. Future studies will focus on analysis of calls' spectrotemporal modulations to discriminate any subclasses that may exist. [Research supported by Grant R01-DC-04998 from the National Institute for Deafness and Communication Disorders.

Pathological speech signal analysis and classification using empirical mode decomposition.

PubMed

Kaleem, Muhammad; Ghoraani, Behnaz; Guergachi, Aziz; Krishnan, Sridhar

2013-07-01

Automated classification of normal and pathological speech signals can provide an objective and accurate mechanism for pathological speech diagnosis, and is an active area of research. A large part of this research is based on analysis of acoustic measures extracted from sustained vowels. However, sustained vowels do not reflect real-world attributes of voice as effectively as continuous speech, which can take into account important attributes of speech such as rapid voice onset and termination, changes in voice frequency and amplitude, and sudden discontinuities in speech. This paper presents a methodology based on empirical mode decomposition (EMD) for classification of continuous normal and pathological speech signals obtained from a well-known database. EMD is used to decompose randomly chosen portions of speech signals into intrinsic mode functions, which are then analyzed to extract meaningful temporal and spectral features, including true instantaneous features which can capture discriminative information in signals hidden at local time-scales. A total of six features are extracted, and a linear classifier is used with the feature vector to classify continuous speech portions obtained from a database consisting of 51 normal and 161 pathological speakers. A classification accuracy of 95.7 % is obtained, thus demonstrating the effectiveness of the methodology.

Examining the Effectiveness of Discriminant Function Analysis and Cluster Analysis in Species Identification of Male Field Crickets Based on Their Calling Songs

PubMed Central

Jaiswara, Ranjana; Nandi, Diptarup; Balakrishnan, Rohini

2013-01-01

Traditional taxonomy based on morphology has often failed in accurate species identification owing to the occurrence of cryptic species, which are reproductively isolated but morphologically identical. Molecular data have thus been used to complement morphology in species identification. The sexual advertisement calls in several groups of acoustically communicating animals are species-specific and can thus complement molecular data as non-invasive tools for identification. Several statistical tools and automated identifier algorithms have been used to investigate the efficiency of acoustic signals in species identification. Despite a plethora of such methods, there is a general lack of knowledge regarding the appropriate usage of these methods in specific taxa. In this study, we investigated the performance of two commonly used statistical methods, discriminant function analysis (DFA) and cluster analysis, in identification and classification based on acoustic signals of field cricket species belonging to the subfamily Gryllinae. Using a comparative approach we evaluated the optimal number of species and calling song characteristics for both the methods that lead to most accurate classification and identification. The accuracy of classification using DFA was high and was not affected by the number of taxa used. However, a constraint in using discriminant function analysis is the need for a priori classification of songs. Accuracy of classification using cluster analysis, which does not require a priori knowledge, was maximum for 6–7 taxa and decreased significantly when more than ten taxa were analysed together. We also investigated the efficacy of two novel derived acoustic features in improving the accuracy of identification. Our results show that DFA is a reliable statistical tool for species identification using acoustic signals. Our results also show that cluster analysis of acoustic signals in crickets works effectively for species classification and identification. PMID:24086666

Review of Vibration-Based Helicopters Health and Usage Monitoring Methods

DTIC Science & Technology

2001-04-05

FM4, NA4, NA4*, NB4 and NB48* (Polyshchuk et al., 1998). The Wigner - Ville distribution ( WVD ) is a joint time-frequency signal analysis. The WVD is one...signal processing methodologies that are of relevance to vibration based damage detection (e.g., Wavelet Transform and Wigner - Ville distribution ) will be...operation cost, reduce maintenance flights, and increase flight safety. Key Words: HUMS; Wavelet Transform; Wigner - Ville distribution ; O&S; Machinery

Sparse representation of whole-brain fMRI signals for identification of functional networks.

PubMed

Lv, Jinglei; Jiang, Xi; Li, Xiang; Zhu, Dajiang; Chen, Hanbo; Zhang, Tuo; Zhang, Shu; Hu, Xintao; Han, Junwei; Huang, Heng; Zhang, Jing; Guo, Lei; Liu, Tianming

2015-02-01

There have been several recent studies that used sparse representation for fMRI signal analysis and activation detection based on the assumption that each voxel's fMRI signal is linearly composed of sparse components. Previous studies have employed sparse coding to model functional networks in various modalities and scales. These prior contributions inspired the exploration of whether/how sparse representation can be used to identify functional networks in a voxel-wise way and on the whole brain scale. This paper presents a novel, alternative methodology of identifying multiple functional networks via sparse representation of whole-brain task-based fMRI signals. Our basic idea is that all fMRI signals within the whole brain of one subject are aggregated into a big data matrix, which is then factorized into an over-complete dictionary basis matrix and a reference weight matrix via an effective online dictionary learning algorithm. Our extensive experimental results have shown that this novel methodology can uncover multiple functional networks that can be well characterized and interpreted in spatial, temporal and frequency domains based on current brain science knowledge. Importantly, these well-characterized functional network components are quite reproducible in different brains. In general, our methods offer a novel, effective and unified solution to multiple fMRI data analysis tasks including activation detection, de-activation detection, and functional network identification. Copyright © 2014 Elsevier B.V. All rights reserved.

Identification and classification of upper limb motions using PCA.

PubMed

Veer, Karan; Vig, Renu

2018-03-28

This paper describes the utility of principal component analysis (PCA) in classifying upper limb signals. PCA is a powerful tool for analyzing data of high dimension. Here, two different input strategies were explored. The first method uses upper arm dual-position-based myoelectric signal acquisition and the other solely uses PCA for classifying surface electromyogram (SEMG) signals. SEMG data from the biceps and the triceps brachii muscles and four independent muscle activities of the upper arm were measured in seven subjects (total dataset=56). The datasets used for the analysis are rotated by class-specific principal component matrices to decorrelate the measured data prior to feature extraction.

Data acquisition instrument for EEG based on embedded system

NASA Astrophysics Data System (ADS)

Toresano, La Ode Husein Z.; Wijaya, Sastra Kusuma; Prawito, Sudarmaji, Arief; Syakura, Abdan; Badri, Cholid

2017-02-01

An electroencephalogram (EEG) is a device for measuring and recording the electrical activity of brain. The EEG data of signal can be used as a source of analysis for human brain function. The purpose of this study was to design a portable multichannel EEG based on embedded system and ADS1299. The ADS1299 is an analog front-end to be used as an Analog to Digital Converter (ADC) to convert analog signal of electrical activity of brain, a filter of electrical signal to reduce the noise on low-frequency band and a data communication to the microcontroller. The system has been tested to capture brain signal within a range of 1-20 Hz using the NETECH EEG simulator 330. The developed system was relatively high accuracy of more than 82.5%. The EEG Instrument has been successfully implemented to acquire the brain signal activity using a PC (Personal Computer) connection for displaying the recorded data. The final result of data acquisition has been processed using OpenBCI GUI (Graphical User Interface) based through real-time process for 8-channel signal acquisition, brain-mapping and power spectral decomposition signal using the standard FFT (Fast Fourier Transform) algorithm.

Automatic event detection in low SNR microseismic signals based on multi-scale permutation entropy and a support vector machine

NASA Astrophysics Data System (ADS)

Jia, Rui-Sheng; Sun, Hong-Mei; Peng, Yan-Jun; Liang, Yong-Quan; Lu, Xin-Ming

2017-07-01

Microseismic monitoring is an effective means for providing early warning of rock or coal dynamical disasters, and its first step is microseismic event detection, although low SNR microseismic signals often cannot effectively be detected by routine methods. To solve this problem, this paper presents permutation entropy and a support vector machine to detect low SNR microseismic events. First, an extraction method of signal features based on multi-scale permutation entropy is proposed by studying the influence of the scale factor on the signal permutation entropy. Second, the detection model of low SNR microseismic events based on the least squares support vector machine is built by performing a multi-scale permutation entropy calculation for the collected vibration signals, constructing a feature vector set of signals. Finally, a comparative analysis of the microseismic events and noise signals in the experiment proves that the different characteristics of the two can be fully expressed by using multi-scale permutation entropy. The detection model of microseismic events combined with the support vector machine, which has the features of high classification accuracy and fast real-time algorithms, can meet the requirements of online, real-time extractions of microseismic events.

Time-frequency techniques in biomedical signal analysis. a tutorial review of similarities and differences.

PubMed

Wacker, M; Witte, H

2013-01-01

This review outlines the methodological fundamentals of the most frequently used non-parametric time-frequency analysis techniques in biomedicine and their main properties, as well as providing decision aids concerning their applications. The short-term Fourier transform (STFT), the Gabor transform (GT), the S-transform (ST), the continuous Morlet wavelet transform (CMWT), and the Hilbert transform (HT) are introduced as linear transforms by using a unified concept of the time-frequency representation which is based on a standardized analytic signal. The Wigner-Ville distribution (WVD) serves as an example of the 'quadratic transforms' class. The combination of WVD and GT with the matching pursuit (MP) decomposition and that of the HT with the empirical mode decomposition (EMD) are explained; these belong to the class of signal-adaptive approaches. Similarities between linear transforms are demonstrated and differences with regard to the time-frequency resolution and interference (cross) terms are presented in detail. By means of simulated signals the effects of different time-frequency resolutions of the GT, CMWT, and WVD as well as the resolution-related properties of the interference (cross) terms are shown. The method-inherent drawbacks and their consequences for the application of the time-frequency techniques are demonstrated by instantaneous amplitude, frequency and phase measures and related time-frequency representations (spectrogram, scalogram, time-frequency distribution, phase-locking maps) of measured magnetoencephalographic (MEG) signals. The appropriate selection of a method and its parameter settings will ensure readability of the time-frequency representations and reliability of results. When the time-frequency characteristics of a signal strongly correspond with the time-frequency resolution of the analysis then a method may be considered 'optimal'. The MP-based signal-adaptive approaches are preferred as these provide an appropriate time-frequency resolution for all frequencies while simultaneously reducing interference (cross) terms.

Ultrasonic data compression via parameter estimation.

PubMed

Cardoso, Guilherme; Saniie, Jafar

2005-02-01

Ultrasonic imaging in medical and industrial applications often requires a large amount of data collection. Consequently, it is desirable to use data compression techniques to reduce data and to facilitate the analysis and remote access of ultrasonic information. The precise data representation is paramount to the accurate analysis of the shape, size, and orientation of ultrasonic reflectors, as well as to the determination of the properties of the propagation path. In this study, a successive parameter estimation algorithm based on a modified version of the continuous wavelet transform (CWT) to compress and denoise ultrasonic signals is presented. It has been shown analytically that the CWT (i.e., time x frequency representation) yields an exact solution for the time-of-arrival and a biased solution for the center frequency. Consequently, a modified CWT (MCWT) based on the Gabor-Helstrom transform is introduced as a means to exactly estimate both time-of-arrival and center frequency of ultrasonic echoes. Furthermore, the MCWT also has been used to generate a phase x bandwidth representation of the ultrasonic echo. This representation allows the exact estimation of the phase and the bandwidth. The performance of this algorithm for data compression and signal analysis is studied using simulated and experimental ultrasonic signals. The successive parameter estimation algorithm achieves a data compression ratio of (1-5N/J), where J is the number of samples and N is the number of echoes in the signal. For a signal with 10 echoes and 2048 samples, a compression ratio of 96% is achieved with a signal-to-noise ratio (SNR) improvement above 20 dB. Furthermore, this algorithm performs robustly, yields accurate echo estimation, and results in SNR enhancements ranging from 10 to 60 dB for composite signals having SNR as low as -10 dB.

Multi-Scale Morphological Analysis of Conductance Signals in Vertical Upward Gas-Liquid Two-Phase Flow

NASA Astrophysics Data System (ADS)

Lian, Enyang; Ren, Yingyu; Han, Yunfeng; Liu, Weixin; Jin, Ningde; Zhao, Junying

2016-11-01

The multi-scale analysis is an important method for detecting nonlinear systems. In this study, we carry out experiments and measure the fluctuation signals from a rotating electric field conductance sensor with eight electrodes. We first use a recurrence plot to recognise flow patterns in vertical upward gas-liquid two-phase pipe flow from measured signals. Then we apply a multi-scale morphological analysis based on the first-order difference scatter plot to investigate the signals captured from the vertical upward gas-liquid two-phase flow loop test. We find that the invariant scaling exponent extracted from the multi-scale first-order difference scatter plot with the bisector of the second-fourth quadrant as the reference line is sensitive to the inhomogeneous distribution characteristics of the flow structure, and the variation trend of the exponent is helpful to understand the process of breakup and coalescence of the gas phase. In addition, we explore the dynamic mechanism influencing the inhomogeneous distribution of the gas phase in terms of adaptive optimal kernel time-frequency representation. The research indicates that the system energy is a factor influencing the distribution of the gas phase and the multi-scale morphological analysis based on the first-order difference scatter plot is an effective method for indicating the inhomogeneous distribution of the gas phase in gas-liquid two-phase flow.

Security region-based small signal stability analysis of power systems with FSIG based wind farm

NASA Astrophysics Data System (ADS)

Qin, Chao; Zeng, Yuan; Yang, Yang; Cui, Xiaodan; Xu, Xialing; Li, Yong

2018-02-01

Based on the Security Region approach, the impact of fixed-speed induction generator based wind farm on the small signal stability of power systems is analyzed. Firstly, the key factors of wind farm on the small signal stability of power systems are analyzed and the parameter space for small signal stability region is formed. Secondly, the small signal stability region of power systems with wind power is established. Thirdly, the corresponding relation between the boundary of SSSR and the dominant oscillation mode is further studied. Results show that the integration of fixed-speed induction generator based wind farm will cause the low frequency oscillation stability of the power system deteriorate. When the output of wind power is high, the oscillation stability of the power system is mainly concerned with the inter-area oscillation mode caused by the integration of the wind farm. Both the active power output and the capacity of reactive power compensation of the wind farm have a significant influence on the SSSR. To improve the oscillation stability of power systems with wind power, it is suggested to reasonably set the reactive power compensation capacity for the wind farm through SSSR.

Bearing faults identification and resonant band demodulation based on wavelet de-noising methods and envelope analysis

NASA Astrophysics Data System (ADS)

Abdelrhman, Ahmed M.; Sei Kien, Yong; Salman Leong, M.; Meng Hee, Lim; Al-Obaidi, Salah M. Ali

2017-07-01

The vibration signals produced by rotating machinery contain useful information for condition monitoring and fault diagnosis. Fault severities assessment is a challenging task. Wavelet Transform (WT) as a multivariate analysis tool is able to compromise between the time and frequency information in the signals and served as a de-noising method. The CWT scaling function gives different resolutions to the discretely signals such as very fine resolution at lower scale but coarser resolution at a higher scale. However, the computational cost increased as it needs to produce different signal resolutions. DWT has better low computation cost as the dilation function allowed the signals to be decomposed through a tree of low and high pass filters and no further analysing the high-frequency components. In this paper, a method for bearing faults identification is presented by combing Continuous Wavelet Transform (CWT) and Discrete Wavelet Transform (DWT) with envelope analysis for bearing fault diagnosis. The experimental data was sampled by Case Western Reserve University. The analysis result showed that the proposed method is effective in bearing faults detection, identify the exact fault’s location and severity assessment especially for the inner race and outer race faults.

APEX_SCOPE: A graphical user interface for visualization of multi-modal data in inter-disciplinary studies.

PubMed

Kanbar, Lara J; Shalish, Wissam; Precup, Doina; Brown, Karen; Sant'Anna, Guilherme M; Kearney, Robert E

2017-07-01

In multi-disciplinary studies, different forms of data are often collected for analysis. For example, APEX, a study on the automated prediction of extubation readiness in extremely preterm infants, collects clinical parameters and cardiorespiratory signals. A variety of cardiorespiratory metrics are computed from these signals and used to assign a cardiorespiratory pattern at each time. In such a situation, exploratory analysis requires a visualization tool capable of displaying these different types of acquired and computed signals in an integrated environment. Thus, we developed APEX_SCOPE, a graphical tool for the visualization of multi-modal data comprising cardiorespiratory signals, automated cardiorespiratory metrics, automated respiratory patterns, manually classified respiratory patterns, and manual annotations by clinicians during data acquisition. This MATLAB-based application provides a means for collaborators to view combinations of signals to promote discussion, generate hypotheses and develop features.

Fusion of spectral models for dynamic modeling of sEMG and skeletal muscle force.

PubMed

Potluri, Chandrasekhar; Anugolu, Madhavi; Chiu, Steve; Urfer, Alex; Schoen, Marco P; Naidu, D Subbaram

2012-01-01

In this paper, we present a method of combining spectral models using a Kullback Information Criterion (KIC) data fusion algorithm. Surface Electromyographic (sEMG) signals and their corresponding skeletal muscle force signals are acquired from three sensors and pre-processed using a Half-Gaussian filter and a Chebyshev Type- II filter, respectively. Spectral models - Spectral Analysis (SPA), Empirical Transfer Function Estimate (ETFE), Spectral Analysis with Frequency Dependent Resolution (SPFRD) - are extracted from sEMG signals as input and skeletal muscle force as output signal. These signals are then employed in a System Identification (SI) routine to establish the dynamic models relating the input and output. After the individual models are extracted, the models are fused by a probability based KIC fusion algorithm. The results show that the SPFRD spectral models perform better than SPA and ETFE models in modeling the frequency content of the sEMG/skeletal muscle force data.

Analysis of cardiac signals using spatial filling index and time-frequency domain

PubMed Central

Faust, Oliver; Acharya U, Rajendra; Krishnan, SM; Min, Lim Choo

2004-01-01

Background Analysis of heart rate variation (HRV) has become a popular noninvasive tool for assessing the activities of the autonomic nervous system (ANS). HRV analysis is based on the concept that fast fluctuations may specifically reflect changes of sympathetic and vagal activity. It shows that the structure generating the signal is not simply linear, but also involves nonlinear contributions. These signals are essentially non-stationary; may contain indicators of current disease, or even warnings about impending diseases. The indicators may be present at all times or may occur at random in the time scale. However, to study and pinpoint abnormalities in voluminous data collected over several hours is strenuous and time consuming. Methods This paper presents the spatial filling index and time-frequency analysis of heart rate variability signal for disease identification. Renyi's entropy is evaluated for the signal in the Wigner-Ville and Continuous Wavelet Transformation (CWT) domain. Results This Renyi's entropy gives lower 'p' value for scalogram than Wigner-Ville distribution and also, the contours of scalogram visually show the features of the diseases. And in the time-frequency analysis, the Renyi's entropy gives better result for scalogram than the Wigner-Ville distribution. Conclusion Spatial filling index and Renyi's entropy has distinct regions for various diseases with an accuracy of more than 95%. PMID:15361254

Heart Rate Variability and Wavelet-based Studies on ECG Signals from Smokers and Non-smokers

NASA Astrophysics Data System (ADS)

Pal, K.; Goel, R.; Champaty, B.; Samantray, S.; Tibarewala, D. N.

2013-12-01

The current study deals with the heart rate variability (HRV) and wavelet-based ECG signal analysis of smokers and non-smokers. The results of HRV indicated dominance towards the sympathetic nervous system activity in smokers. The heart rate was found to be higher in case of smokers as compared to non-smokers ( p < 0.05). The frequency domain analysis showed an increase in the LF and LF/HF components with a subsequent decrease in the HF component. The HRV features were analyzed for classification of the smokers from the non-smokers. The results indicated that when RMSSD, SD1 and RR-mean features were used concurrently a classification efficiency of > 90 % was achieved. The wavelet decomposition of the ECG signal was done using the Daubechies (db 6) wavelet family. No difference was observed between the smokers and non-smokers which apparently suggested that smoking does not affect the conduction pathway of heart.

Method and apparatus for digitally based high speed x-ray spectrometer

DOEpatents

Warburton, W.K.; Hubbard, B.

1997-11-04

A high speed, digitally based, signal processing system which accepts input data from a detector-preamplifier and produces a spectral analysis of the x-rays illuminating the detector. The system achieves high throughputs at low cost by dividing the required digital processing steps between a ``hardwired`` processor implemented in combinatorial digital logic, which detects the presence of the x-ray signals in the digitized data stream and extracts filtered estimates of their amplitudes, and a programmable digital signal processing computer, which refines the filtered amplitude estimates and bins them to produce the desired spectral analysis. One set of algorithms allow this hybrid system to match the resolution of analog systems while operating at much higher data rates. A second set of algorithms implemented in the processor allow the system to be self calibrating as well. The same processor also handles the interface to an external control computer. 19 figs.

Method and apparatus for digitally based high speed x-ray spectrometer

DOEpatents

Warburton, William K.; Hubbard, Bradley

1997-01-01

A high speed, digitally based, signal processing system which accepts input data from a detector-preamplifier and produces a spectral analysis of the x-rays illuminating the detector. The system achieves high throughputs at low cost by dividing the required digital processing steps between a "hardwired" processor implemented in combinatorial digital logic, which detects the presence of the x-ray signals in the digitized data stream and extracts filtered estimates of their amplitudes, and a programmable digital signal processing computer, which refines the filtered amplitude estimates and bins them to produce the desired spectral analysis. One set of algorithms allow this hybrid system to match the resolution of analog systems while operating at much higher data rates. A second set of algorithms implemented in the processor allow the system to be self calibrating as well. The same processor also handles the interface to an external control computer.

Precession missile feature extraction using sparse component analysis of radar measurements

NASA Astrophysics Data System (ADS)

Liu, Lihua; Du, Xiaoyong; Ghogho, Mounir; Hu, Weidong; McLernon, Des

2012-12-01

According to the working mode of the ballistic missile warning radar (BMWR), the radar return from the BMWR is usually sparse. To recognize and identify the warhead, it is necessary to extract the precession frequency and the locations of the scattering centers of the missile. This article first analyzes the radar signal model of the precessing conical missile during flight and develops the sparse dictionary which is parameterized by the unknown precession frequency. Based on the sparse dictionary, the sparse signal model is then established. A nonlinear least square estimation is first applied to roughly extract the precession frequency in the sparse dictionary. Based on the time segmented radar signal, a sparse component analysis method using the orthogonal matching pursuit algorithm is then proposed to jointly estimate the precession frequency and the scattering centers of the missile. Simulation results illustrate the validity of the proposed method.

Vital physical signals measurements using a webcam

NASA Astrophysics Data System (ADS)

Ouyang, Jianfei; Yan, Yonggang; Yao, Lifeng

2013-10-01

Non-contact and remote measurements of vital physical signals are important for reliable and comfortable physiological self-assessment. In this paper, we provide a new video-based methodology for remote and fast measurements of vital physical signals such as cardiac pulse and breathing rate. A webcam is used to track color video of a human face or wrist, and a Photoplethysmography (PPG) technique is applied to perform the measurements of the vital signals. A novel sequential blind signal extraction methodology is applied to the color video under normal lighting conditions, based on correlation analysis between the green trace and the source signals. The approach is successfully applied in the measurement of vital signals under the condition of different illuminating in which the target signal can also be found out accurately. To assess the advantages, the measuring time of a large number of cases is recorded correctly. The experimental results show that it only takes less than 30 seconds to measure the vital physical signals using presented technique. The study indicates the proposed approach is feasible for PPG technique, which provides a way to study the relationship of the signal for different ROI in future research.

Automatic detection of muscle activity from mechanomyogram signals: a comparison of amplitude and wavelet-based methods.

PubMed

Alves, Natasha; Chau, Tom

2010-04-01

Knowledge of muscle activity timing is critical to many clinical applications, such as the assessment of muscle coordination and the prescription of muscle-activated switches for individuals with disabilities. In this study, we introduce a continuous wavelet transform (CWT) algorithm for the detection of muscle activity via mechanomyogram (MMG) signals. CWT coefficients of the MMG signal were compared to scale-specific thresholds derived from the baseline signal to estimate the timing of muscle activity. Test signals were recorded from the flexor carpi radialis muscles of 15 able-bodied participants as they squeezed and released a hand dynamometer. Using the dynamometer signal as a reference, the proposed CWT detection algorithm was compared against a global-threshold CWT detector as well as amplitude-based event detection for sensitivity and specificity to voluntary contractions. The scale-specific CWT-based algorithm exhibited superior detection performance over the other detectors. CWT detection also showed good muscle selectivity during hand movement, particularly when a given muscle was the primary facilitator of the contraction. This may suggest that, during contraction, the compound MMG signal has a recurring morphological pattern that is not prevalent in the baseline signal. The ability of CWT analysis to be implemented in real time makes it a candidate for muscle-activity detection in clinical applications.

Automatic detection of sleep macrostructure based on a sensorized T-shirt.

PubMed

Bianchi, Anna M; Mendez, Martin O

2010-01-01

In the present work we apply a fully automatic procedure to the analysis of signal coming from a sensorized T-shit, worn during the night, for sleep evaluation. The goodness and reliability of the signals recorded trough the T-shirt was previously tested, while the employed algorithms for feature extraction and sleep classification were previously developed on standard ECG recordings and the obtained classification was compared to the standard clinical practice based on polysomnography (PSG). In the present work we combined T-shirt recordings and automatic classification and could obtain reliable sleep profiles, i.e. the sleep classification in WAKE, REM (rapid eye movement) and NREM stages, based on heart rate variability (HRV), respiration and movement signals.

Nonnegative Matrix Factorization for identification of unknown number of sources emitting delayed signals

PubMed Central

Iliev, Filip L.; Stanev, Valentin G.; Vesselinov, Velimir V.

2018-01-01

Factor analysis is broadly used as a powerful unsupervised machine learning tool for reconstruction of hidden features in recorded mixtures of signals. In the case of a linear approximation, the mixtures can be decomposed by a variety of model-free Blind Source Separation (BSS) algorithms. Most of the available BSS algorithms consider an instantaneous mixing of signals, while the case when the mixtures are linear combinations of signals with delays is less explored. Especially difficult is the case when the number of sources of the signals with delays is unknown and has to be determined from the data as well. To address this problem, in this paper, we present a new method based on Nonnegative Matrix Factorization (NMF) that is capable of identifying: (a) the unknown number of the sources, (b) the delays and speed of propagation of the signals, and (c) the locations of the sources. Our method can be used to decompose records of mixtures of signals with delays emitted by an unknown number of sources in a nondispersive medium, based only on recorded data. This is the case, for example, when electromagnetic signals from multiple antennas are received asynchronously; or mixtures of acoustic or seismic signals recorded by sensors located at different positions; or when a shift in frequency is induced by the Doppler effect. By applying our method to synthetic datasets, we demonstrate its ability to identify the unknown number of sources as well as the waveforms, the delays, and the strengths of the signals. Using Bayesian analysis, we also evaluate estimation uncertainties and identify the region of likelihood where the positions of the sources can be found. PMID:29518126

Nonnegative Matrix Factorization for identification of unknown number of sources emitting delayed signals.

PubMed

Iliev, Filip L; Stanev, Valentin G; Vesselinov, Velimir V; Alexandrov, Boian S

2018-01-01

Factor analysis is broadly used as a powerful unsupervised machine learning tool for reconstruction of hidden features in recorded mixtures of signals. In the case of a linear approximation, the mixtures can be decomposed by a variety of model-free Blind Source Separation (BSS) algorithms. Most of the available BSS algorithms consider an instantaneous mixing of signals, while the case when the mixtures are linear combinations of signals with delays is less explored. Especially difficult is the case when the number of sources of the signals with delays is unknown and has to be determined from the data as well. To address this problem, in this paper, we present a new method based on Nonnegative Matrix Factorization (NMF) that is capable of identifying: (a) the unknown number of the sources, (b) the delays and speed of propagation of the signals, and (c) the locations of the sources. Our method can be used to decompose records of mixtures of signals with delays emitted by an unknown number of sources in a nondispersive medium, based only on recorded data. This is the case, for example, when electromagnetic signals from multiple antennas are received asynchronously; or mixtures of acoustic or seismic signals recorded by sensors located at different positions; or when a shift in frequency is induced by the Doppler effect. By applying our method to synthetic datasets, we demonstrate its ability to identify the unknown number of sources as well as the waveforms, the delays, and the strengths of the signals. Using Bayesian analysis, we also evaluate estimation uncertainties and identify the region of likelihood where the positions of the sources can be found.

Feature extraction in MFL signals of machined defects in steel tubes

NASA Astrophysics Data System (ADS)

Perazzo, R.; Pignotti, A.; Reich, S.; Stickar, P.

2001-04-01

Thirty defects of various shapes were machined on the external and internal wall surfaces of a 177 mm diameter ferromagnetic steel pipe. MFL signals were digitized and recorded at a frequency of 4 Khz. Various magnetizing currents and relative tube-probe velocities of the order of 2m/s were used. The identification of the location of the defect by a principal component/neural network analysis of the signal is shown to be more effective than the standard procedure of classification based on the average signal frequency.

Single-base-pair discrimination of terminal mismatches by using oligonucleotide microarrays and neural network analyses

NASA Technical Reports Server (NTRS)

Urakawa, Hidetoshi; Noble, Peter A.; El Fantroussi, Said; Kelly, John J.; Stahl, David A.

2002-01-01

The effects of single-base-pair near-terminal and terminal mismatches on the dissociation temperature (T(d)) and signal intensity of short DNA duplexes were determined by using oligonucleotide microarrays and neural network (NN) analyses. Two perfect-match probes and 29 probes having a single-base-pair mismatch at positions 1 to 5 from the 5' terminus of the probe were designed to target one of two short sequences representing 16S rRNA. Nonequilibrium dissociation rates (i.e., melting profiles) of all probe-target duplexes were determined simultaneously. Analysis of variance revealed that position of the mismatch, type of mismatch, and formamide concentration significantly affected the T(d) and signal intensity. Increasing the concentration of formamide in the washing buffer decreased the T(d) and signal intensity, and it decreased the variability of the signal. Although T(d)s of probe-target duplexes with mismatches in the first or second position were not significantly different from one another, duplexes with mismatches in the third to fifth positions had significantly lower T(d)s than those with mismatches in the first or second position. The trained NNs predicted the T(d) with high accuracies (R(2) = 0.93). However, the NNs predicted the signal intensity only moderately accurately (R(2) = 0.67), presumably due to increased noise in the signal intensity at low formamide concentrations. Sensitivity analysis revealed that the concentration of formamide explained most (75%) of the variability in T(d)s, followed by position of the mismatch (19%) and type of mismatch (6%). The results suggest that position of the mismatch at or near the 5' terminus plays a greater role in determining the T(d) and signal intensity of duplexes than the type of mismatch.

Effects of iterative learning based signal control strategies on macroscopic fundamental diagrams of urban road networks

NASA Astrophysics Data System (ADS)

Yan, Fei; Tian, Fuli; Shi, Zhongke

2016-10-01

Urban traffic flows are inherently repeated on a daily or weekly basis. This repeatability can help improve the traffic conditions if it is used properly by the control system. In this paper, we propose a novel iterative learning control (ILC) strategy for traffic signals of urban road networks using the repeatability feature of traffic flow. To improve the control robustness, the ILC strategy is further integrated with an error feedback control law in a complementary manner. Theoretical analysis indicates that the ILC-based traffic signal control methods can guarantee the asymptotic learning convergence, despite the presence of modeling uncertainties and exogenous disturbances. Finally, the impacts of the ILC-based signal control strategies on the network macroscopic fundamental diagram (MFD) are examined. The results show that the proposed ILC-based control strategies can homogenously distribute the network accumulation by controlling the vehicle numbers in each link to the desired levels under different traffic demands, which can result in the network with high capacity and mobility.

Analysis of photonic Doppler velocimetry data based on the continuous wavelet transform

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liu Shouxian; Wang Detian; Li Tao

2011-02-15

The short time Fourier transform (STFT) cannot resolve rapid velocity changes in most photonic Doppler velocimetry (PDV) data. A practical analysis method based on the continuous wavelet transform (CWT) was presented to overcome this difficulty. The adaptability of the wavelet family predicates that the continuous wavelet transform uses an adaptive time window to estimate the instantaneous frequency of signals. The local frequencies of signal are accurately determined by finding the ridge in the spectrogram of the CWT and then are converted to target velocity according to the Doppler effects. A performance comparison between the CWT and STFT is demonstrated bymore » a plate-impact experiment data. The results illustrate that the new method is automatic and adequate for analysis of PDV data.« less

Polyspectral signal analysis techniques for condition based maintenance of helicopter drive-train system

NASA Astrophysics Data System (ADS)

Hassan Mohammed, Mohammed Ahmed

For an efficient maintenance of a diverse fleet of air- and rotorcraft, effective condition based maintenance (CBM) must be established based on rotating components monitored vibration signals. In this dissertation, we present theory and applications of polyspectral signal processing techniques for condition monitoring of critical components in the AH-64D helicopter tail rotor drive train system. Currently available vibration-monitoring tools are mostly built around auto- and cross-power spectral analysis which have limited performance in detecting frequency correlations higher than second order. Studying higher order correlations and their Fourier transforms, higher order spectra, provides more information about the vibration signals which helps in building more accurate diagnostic models of the mechanical system. Based on higher order spectral analysis, different signal processing techniques are developed to assess health conditions of different critical rotating-components in the AH-64D helicopter drive-train. Based on cross-bispectrum, quadratic nonlinear transfer function is presented to model second order nonlinearity in a drive-shaft running between the two hanger bearings. Then, quadratic-nonlinearity coupling coefficient between frequency harmonics of the rotating shaft is used as condition metric to study different seeded shaft faults compared to baseline case, namely: shaft misalignment, shaft imbalance, and combination of shaft misalignment and imbalance. The proposed quadratic-nonlinearity metric shows better capabilities in distinguishing the four studied shaft settings than the conventional linear coupling based on cross-power spectrum. We also develop a new concept of Quadratic-Nonlinearity Power-Index spectrum, QNLPI(f), that can be used in signal detection and classification, based on bicoherence spectrum. The proposed QNLPI(f) is derived as a projection of the three-dimensional bicoherence spectrum into two-dimensional spectrum that quantitatively describes how much of the mean square power at certain frequency f is generated due to nonlinear quadratic interaction between different frequency components. The proposed index, QNLPI(f), can be used to simplify the study of bispectrum and bicoherence signal spectra. It also inherits useful characteristics from the bicoherence such as high immunity to additive Gaussian noise, high capability of nonlinear-systems identifications, and amplification invariance. The quadratic-nonlinear power spectral density PQNL(f) and percentage of quadratic nonlinear power PQNLP are also introduced based on the QNLPI(f). Concept of the proposed indices and their computational considerations are discussed first using computer generated data, and then applied to real-world vibration data to assess health conditions of different rotating components in the drive train including drive-shaft, gearbox, and hanger bearing faults. The QNLPI(f) spectrum enables us to gain more details about nonlinear harmonic generation patterns that can be used to distinguish between different cases of mechanical faults, which in turn helps to gaining more diagnostic/prognostic capabilities.

Using multi-scale entropy and principal component analysis to monitor gears degradation via the motor current signature analysis

NASA Astrophysics Data System (ADS)

Aouabdi, Salim; Taibi, Mahmoud; Bouras, Slimane; Boutasseta, Nadir

2017-06-01

This paper describes an approach for identifying localized gear tooth defects, such as pitting, using phase currents measured from an induction machine driving the gearbox. A new tool of anomaly detection based on multi-scale entropy (MSE) algorithm SampEn which allows correlations in signals to be identified over multiple time scales. The motor current signature analysis (MCSA) in conjunction with principal component analysis (PCA) and the comparison of observed values with those predicted from a model built using nominally healthy data. The Simulation results show that the proposed method is able to detect gear tooth pitting in current signals.

Time-frequency analysis of human motion during rhythmic exercises.

PubMed

Omkar, S N; Vyas, Khushi; Vikranth, H N

2011-01-01

Biomechanical signals due to human movements during exercise are represented in time-frequency domain using Wigner Distribution Function (WDF). Analysis based on WDF reveals instantaneous spectral and power changes during a rhythmic exercise. Investigations were carried out on 11 healthy subjects who performed 5 cycles of sun salutation, with a body-mounted Inertial Measurement Unit (IMU) as a motion sensor. Variance of Instantaneous Frequency (I.F) and Instantaneous Power (I.P) for performance analysis of the subject is estimated using one-way ANOVA model. Results reveal that joint Time-Frequency analysis of biomechanical signals during motion facilitates a better understanding of grace and consistency during rhythmic exercise.

Eventogram: A Visual Representation of Main Events in Biomedical Signals.

PubMed

Elgendi, Mohamed

2016-09-22

Biomedical signals carry valuable physiological information and many researchers have difficulty interpreting and analyzing long-term, one-dimensional, quasi-periodic biomedical signals. Traditionally, biomedical signals are analyzed and visualized using periodogram, spectrogram, and wavelet methods. However, these methods do not offer an informative visualization of main events within the processed signal. This paper attempts to provide an event-related framework to overcome the drawbacks of the traditional visualization methods and describe the main events within the biomedical signal in terms of duration and morphology. Electrocardiogram and photoplethysmogram signals are used in the analysis to demonstrate the differences between the traditional visualization methods, and their performance is compared against the proposed method, referred to as the " eventogram " in this paper. The proposed method is based on two event-related moving averages that visualizes the main time-domain events in the processed biomedical signals. The traditional visualization methods were unable to find dominant events in processed signals while the eventogram was able to visualize dominant events in signals in terms of duration and morphology. Moreover, eventogram -based detection algorithms succeeded with detecting main events in different biomedical signals with a sensitivity and positive predictivity >95%. The output of the eventogram captured unique patterns and signatures of physiological events, which could be used to visualize and identify abnormal waveforms in any quasi-periodic signal.

Conceptualization of the Complex Outcomes of Sexual Abuse: A Signal Detection Analysis

ERIC Educational Resources Information Center

Pechtel, Pia; Evans, Ian M.; Podd, John V.

2011-01-01

Eighty-five New Zealand based practitioners experienced in treating adults with a history of child sexual abuse participated in an online judgment study of child sexual abuse outcomes using signal detection theory methodology. Participants' level of sensitivity was assessed independent of their degree of response bias when discriminating (a) known…

Supporting Multimedia Learning with Visual Signalling and Animated Pedagogical Agent: Moderating Effects of Prior Knowledge

ERIC Educational Resources Information Center

Johnson, A. M.; Ozogul, G.; Reisslein, M.

2015-01-01

An experiment examined the effects of visual signalling to relevant information in multiple external representations and the visual presence of an animated pedagogical agent (APA). Students learned electric circuit analysis using a computer-based learning environment that included Cartesian graphs, equations and electric circuit diagrams. The…

Investigating Strength and Frequency Effects in Recognition Memory Using Type-2 Signal Detection Theory

ERIC Educational Resources Information Center

Higham, Philip A.; Perfect, Timothy J.; Bruno, Davide

2009-01-01

Criterion- versus distribution-shift accounts of frequency and strength effects in recognition memory were investigated with Type-2 signal detection receiver operating characteristic (ROC) analysis, which provides a measure of metacognitive monitoring. Experiment 1 demonstrated a frequency-based mirror effect, with a higher hit rate and lower…

The research of single intersection sensor signal control based on section data

NASA Astrophysics Data System (ADS)

Liu, Yunxiang; Huang, Yue; Wang, Hao

2016-12-01

Propose a sensing signal intersection control design electronic license based on the design by setting the intersection readers to interact with active electronic tags equipped vehicles, vehicle information obtained on the road section. In the vehicle detection sensor may control the green density as evaluation criteria are extended when the vehicle is higher than the threshold, the green density continuity, whereas the switching phases. Induction showed improved control strategy can achieve real-time traffic signal control effectively in high saturation intersection, to overcome the traditional sensor control failure at high saturation drawbacks and improve the utilization of urban Intersection comparative analysis by simulation.

The technique of entropy optimization in motor current signature analysis and its application in the fault diagnosis of gear transmission

NASA Astrophysics Data System (ADS)

Chen, Xiaoguang; Liang, Lin; Liu, Fei; Xu, Guanghua; Luo, Ailing; Zhang, Sicong

2012-05-01

Nowadays, Motor Current Signature Analysis (MCSA) is widely used in the fault diagnosis and condition monitoring of machine tools. However, although the current signal has lower SNR (Signal Noise Ratio), it is difficult to identify the feature frequencies of machine tools from complex current spectrum that the feature frequencies are often dense and overlapping by traditional signal processing method such as FFT transformation. With the study in the Motor Current Signature Analysis (MCSA), it is found that the entropy is of importance for frequency identification, which is associated with the probability distribution of any random variable. Therefore, it plays an important role in the signal processing. In order to solve the problem that the feature frequencies are difficult to be identified, an entropy optimization technique based on motor current signal is presented in this paper for extracting the typical feature frequencies of machine tools which can effectively suppress the disturbances. Some simulated current signals were made by MATLAB, and a current signal was obtained from a complex gearbox of an iron works made in Luxembourg. In diagnosis the MCSA is combined with entropy optimization. Both simulated and experimental results show that this technique is efficient, accurate and reliable enough to extract the feature frequencies of current signal, which provides a new strategy for the fault diagnosis and the condition monitoring of machine tools.

Unconstrained and Noninvasive Measurement of Swimming Behavior of Small Fish Based on Ventilatory Signals

NASA Astrophysics Data System (ADS)

Kitayama, Shigehisa; Soh, Zu; Hirano, Akira; Tsuji, Toshio; Takiguchi, Noboru; Ohtake, Hisao

Ventilatory signal is a kind of bioelectric signals reflecting the ventilatory conditions of fish, and has received recent attention as an indicator for assessment of water quality, since breathing is adjusted by the respiratory center according to changes in the underwater environment surrounding the fish. The signals are thus beginning to be used in bioassay systems for water examination. Other than ventilatory conditions, swimming behavior also contains important information for water examination. The conventional bioassay systems, however, only measure either ventilatory signals or swimming behavior. This paper proposes a new unconstrained and noninvasive measurement method that is capable of conducting ventilatory signal measurement and behavioral analysis of fish at the same time. The proposed method estimates the position and the velocity of a fish in free-swimming conditions using power spectrum distribution of measured ventilatory signals from multiple electrodes. This allowed the system to avoid using a camera system which requires light sources. In order to validate estimation accuracy, the position and the velocity estimated by the proposed method were compared to those obtained from video analysis. The results confirmed that the estimated error of the fish positions was within the size of fish, and the correlation coefficient between the velocities was 0.906. The proposed method thus not only can measure the ventilatory signals, but also performs behavioral analysis as accurate as using a video camera.

Automated eddy current analysis of materials

NASA Technical Reports Server (NTRS)

Workman, Gary L.

1990-01-01

This research effort focused on the use of eddy current techniques for characterizing flaws in graphite-based filament-wound cylindrical structures. A major emphasis was on incorporating artificial intelligence techniques into the signal analysis portion of the inspection process. Developing an eddy current scanning system using a commercial robot for inspecting graphite structures (and others) has been a goal in the overall concept and is essential for the final implementation for expert system interpretation. Manual scans, as performed in the preliminary work here, do not provide sufficiently reproducible eddy current signatures to be easily built into a real time expert system. The expert systems approach to eddy current signal analysis requires that a suitable knowledge base exist in which correct decisions as to the nature of the flaw can be performed. In eddy current or any other expert systems used to analyze signals in real time in a production environment, it is important to simplify computational procedures as much as possible. For that reason, we have chosen to use the measured resistance and reactance values for the preliminary aspects of this work. A simple computation, such as phase angle of the signal, is certainly within the real time processing capability of the computer system. In the work described here, there is a balance between physical measurements and finite element calculations of those measurements. The goal is to evolve into the most cost effective procedures for maintaining the correctness of the knowledge base.

Concrete Condition Assessment Using Impact-Echo Method and Extreme Learning Machines

PubMed Central

Zhang, Jing-Kui; Yan, Weizhong; Cui, De-Mi

2016-01-01

The impact-echo (IE) method is a popular non-destructive testing (NDT) technique widely used for measuring the thickness of plate-like structures and for detecting certain defects inside concrete elements or structures. However, the IE method is not effective for full condition assessment (i.e., defect detection, defect diagnosis, defect sizing and location), because the simple frequency spectrum analysis involved in the existing IE method is not sufficient to capture the IE signal patterns associated with different conditions. In this paper, we attempt to enhance the IE technique and enable it for full condition assessment of concrete elements by introducing advanced machine learning techniques for performing comprehensive analysis and pattern recognition of IE signals. Specifically, we use wavelet decomposition for extracting signatures or features out of the raw IE signals and apply extreme learning machine, one of the recently developed machine learning techniques, as classification models for full condition assessment. To validate the capabilities of the proposed method, we build a number of specimens with various types, sizes, and locations of defects and perform IE testing on these specimens in a lab environment. Based on analysis of the collected IE signals using the proposed machine learning based IE method, we demonstrate that the proposed method is effective in performing full condition assessment of concrete elements or structures. PMID:27023563

Multi-Fault Detection of Rolling Element Bearings under Harsh Working Condition Using IMF-Based Adaptive Envelope Order Analysis

PubMed Central

Zhao, Ming; Lin, Jing; Xu, Xiaoqiang; Li, Xuejun

2014-01-01

When operating under harsh condition (e.g., time-varying speed and load, large shocks), the vibration signals of rolling element bearings are always manifested as low signal noise ratio, non-stationary statistical parameters, which cause difficulties for current diagnostic methods. As such, an IMF-based adaptive envelope order analysis (IMF-AEOA) is proposed for bearing fault detection under such conditions. This approach is established through combining the ensemble empirical mode decomposition (EEMD), envelope order tracking and fault sensitive analysis. In this scheme, EEMD provides an effective way to adaptively decompose the raw vibration signal into IMFs with different frequency bands. The envelope order tracking is further employed to transform the envelope of each IMF to angular domain to eliminate the spectral smearing induced by speed variation, which makes the bearing characteristic frequencies more clear and discernible in the envelope order spectrum. Finally, a fault sensitive matrix is established to select the optimal IMF containing the richest diagnostic information for final decision making. The effectiveness of IMF-AEOA is validated by simulated signal and experimental data from locomotive bearings. The result shows that IMF-AEOA could accurately identify both single and multiple faults of bearing even under time-varying rotating speed and large extraneous shocks. PMID:25353982

Computer-aided mathematical analysis of probability of intercept for ground-based communication intercept system

NASA Astrophysics Data System (ADS)

Park, Sang Chul

1989-09-01

We develop a mathematical analysis model to calculate the probability of intercept (POI) for the ground-based communication intercept (COMINT) system. The POI is a measure of the effectiveness of the intercept system. We define the POI as the product of the probability of detection and the probability of coincidence. The probability of detection is a measure of the receiver's capability to detect a signal in the presence of noise. The probability of coincidence is the probability that an intercept system is available, actively listening in the proper frequency band, in the right direction and at the same time that the signal is received. We investigate the behavior of the POI with respect to the observation time, the separation distance, antenna elevations, the frequency of the signal, and the receiver bandwidths. We observe that the coincidence characteristic between the receiver scanning parameters and the signal parameters is the key factor to determine the time to obtain a given POI. This model can be used to find the optimal parameter combination to maximize the POI in a given scenario. We expand this model to a multiple system. This analysis is conducted on a personal computer to provide the portability. The model is also flexible and can be easily implemented under different situations.

A wavelet-based statistical analysis of FMRI data: I. motivation and data distribution modeling.

PubMed

Dinov, Ivo D; Boscardin, John W; Mega, Michael S; Sowell, Elizabeth L; Toga, Arthur W

2005-01-01

We propose a new method for statistical analysis of functional magnetic resonance imaging (fMRI) data. The discrete wavelet transformation is employed as a tool for efficient and robust signal representation. We use structural magnetic resonance imaging (MRI) and fMRI to empirically estimate the distribution of the wavelet coefficients of the data both across individuals and spatial locations. An anatomical subvolume probabilistic atlas is used to tessellate the structural and functional signals into smaller regions each of which is processed separately. A frequency-adaptive wavelet shrinkage scheme is employed to obtain essentially optimal estimations of the signals in the wavelet space. The empirical distributions of the signals on all the regions are computed in a compressed wavelet space. These are modeled by heavy-tail distributions because their histograms exhibit slower tail decay than the Gaussian. We discovered that the Cauchy, Bessel K Forms, and Pareto distributions provide the most accurate asymptotic models for the distribution of the wavelet coefficients of the data. Finally, we propose a new model for statistical analysis of functional MRI data using this atlas-based wavelet space representation. In the second part of our investigation, we will apply this technique to analyze a large fMRI dataset involving repeated presentation of sensory-motor response stimuli in young, elderly, and demented subjects.

Modal parameter identification based on combining transmissibility functions and blind source separation techniques

NASA Astrophysics Data System (ADS)

Araújo, Iván Gómez; Sánchez, Jesús Antonio García; Andersen, Palle

2018-05-01

Transmissibility-based operational modal analysis is a recent and alternative approach used to identify the modal parameters of structures under operational conditions. This approach is advantageous compared with traditional operational modal analysis because it does not make any assumptions about the excitation spectrum (i.e., white noise with a flat spectrum). However, common methodologies do not include a procedure to extract closely spaced modes with low signal-to-noise ratios. This issue is relevant when considering that engineering structures generally have closely spaced modes and that their measured responses present high levels of noise. Therefore, to overcome these problems, a new combined method for modal parameter identification is proposed in this work. The proposed method combines blind source separation (BSS) techniques and transmissibility-based methods. Here, BSS techniques were used to recover source signals, and transmissibility-based methods were applied to estimate modal information from the recovered source signals. To achieve this combination, a new method to define a transmissibility function was proposed. The suggested transmissibility function is based on the relationship between the power spectral density (PSD) of mixed signals and the PSD of signals from a single source. The numerical responses of a truss structure with high levels of added noise and very closely spaced modes were processed using the proposed combined method to evaluate its ability to identify modal parameters in these conditions. Colored and white noise excitations were used for the numerical example. The proposed combined method was also used to evaluate the modal parameters of an experimental test on a structure containing closely spaced modes. The results showed that the proposed combined method is capable of identifying very closely spaced modes in the presence of noise and, thus, may be potentially applied to improve the identification of damping ratios.

Method and apparatus for frequency spectrum analysis

NASA Technical Reports Server (NTRS)

Cole, Steven W. (Inventor)

1992-01-01

A method for frequency spectrum analysis of an unknown signal in real-time is discussed. The method is based upon integration of 1-bit samples of signal voltage amplitude corresponding to sine or cosine phases of a controlled center frequency clock which is changed after each integration interval to sweep the frequency range of interest in steps. Integration of samples during each interval is carried out over a number of cycles of the center frequency clock spanning a number of cycles of an input signal to be analyzed. The invention may be used to detect the frequency of at least two signals simultaneously. By using a reference signal of known frequency and voltage amplitude (added to the two signals for parallel processing in the same way, but in a different channel with a sampling at the known frequency and phases of the reference signal), the absolute voltage amplitude of the other two signals may be determined by squaring the sine and cosine integrals of each channel and summing the squares to obtain relative power measurements in all three channels and, from the known voltage amplitude of the reference signal, obtaining an absolute voltage measurement for the other two signals by multiplying the known voltage of the reference signal with the ratio of the relative power of each of the other two signals to the relative power of the reference signal.

Extraction and analysis of neuron firing signals from deep cortical video microscopy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kerekes, Ryan A; Blundon, Jay

We introduce a method for extracting and analyzing neuronal activity time signals from video of the cortex of a live animal. The signals correspond to the firing activity of individual cortical neurons. Activity signals are based on the changing fluorescence of calcium indicators in the cells over time. We propose a cell segmentation method that relies on a user-specified center point, from which the signal extraction method proceeds. A stabilization approach is used to reduce tissue motion in the video. The extracted signal is then processed to flatten the baseline and detect action potentials. We show results from applying themore » method to a cortical video of a live mouse.« less

Topics in the Detection of Gravitational Waves from Compact Binary Inspirals

NASA Astrophysics Data System (ADS)

Kapadia, Shasvath Jagat

Orbiting compact binaries - such as binary black holes, binary neutron stars and neutron star-black hole binaries - are among the most promising sources of gravitational waves observable by ground-based interferometric detectors. Despite numerous sophisticated engineering techniques, the gravitational wave signals will be buried deep within noise generated by various instrumental and environmental processes, and need to be extracted via a signal processing technique referred to as matched filtering. Matched filtering requires large banks of signal templates that are faithful representations of the true gravitational waveforms produced by astrophysical binaries. The accurate and efficient production of templates is thus crucial to the success of signal processing and data analysis. To that end, the dissertation presents a numerical technique that calibrates existing analytical (Post-Newtonian) waveforms, which are relatively inexpensive, to more accurate fiducial waveforms that are computationally expensive to generate. The resulting waveform family is significantly more accurate than the analytical waveforms, without incurring additional computational costs of production. Certain kinds of transient background noise artefacts, called "glitches'', can masquerade as gravitational wave signals for short durations and throw-off the matched-filter algorithm. Identifying glitches from true gravitational wave signals is a highly non-trivial exercise in data analysis which has been attempted with varying degrees of success. We present here a machine-learning based approach that exploits the various attributes of glitches and signals within detector data to provide a classification scheme that is a significant improvement over previous methods. The dissertation concludes by investigating the possibility of detecting a non-linear DC imprint, called the Christodoulou memory, produced in the arms of ground-based interferometers by the recently detected gravitational waves. The memory, which is even smaller in amplitude than the primary (detected) gravitational waves, will almost certainly not be seen in the current detection event. Nevertheless, future space-based detectors will likely be sensitive enough to observe the memory.

Structures of the recurrence plot of heart rate variability signal as a tool for predicting the onset of paroxysmal atrial fibrillation.

PubMed

Mohebbi, Maryam; Ghassemian, Hassan; Asl, Babak Mohammadzadeh

2011-05-01

This paper aims to propose an effective paroxysmal atrial fibrillation (PAF) predictor which is based on the analysis of the heart rate variability (HRV) signal. Predicting the onset of PAF, based on non-invasive techniques, is clinically important and can be invaluable in order to avoid useless therapeutic interventions and to minimize the risks for the patients. This method consists of four steps: Preprocessing, feature extraction, feature reduction, and classification. In the first step, the QRS complexes are detected from the electrocardiogram (ECG) signal and then the HRV signal is extracted. In the next step, the recurrence plot (RP) of HRV signal is obtained and six features are extracted to characterize the basic patterns of the RP. These features consist of length of longest diagonal segments, average length of the diagonal lines, entropy, trapping time, length of longest vertical line, and recurrence trend. In the third step, these features are reduced to three features by the linear discriminant analysis (LDA) technique. Using LDA not only reduces the number of the input features, but also increases the classification accuracy by selecting the most discriminating features. Finally, a support vector machine-based classifier is used to classify the HRV signals. The performance of the proposed method in prediction of PAF episodes was evaluated using the Atrial Fibrillation Prediction Database which consists of both 30-minutes ECG recordings end just prior to the onset of PAF and segments at least 45 min distant from any PAF events. The obtained sensitivity, specificity, and positive predictivity were 96.55%, 100%, and 100%, respectively.

Magnetic Field Sensing Based on Bi-Tapered Optical Fibers Using Spectral Phase Analysis.

PubMed

Herrera-Piad, Luis A; Haus, Joseph W; Jauregui-Vazquez, Daniel; Sierra-Hernandez, Juan M; Estudillo-Ayala, Julian M; Lopez-Dieguez, Yanelis; Rojas-Laguna, Roberto

2017-10-20

A compact, magnetic field sensor system based on a short, bi-tapered optical fiber (BTOF) span lying on a magnetic tape was designed, fabricated, and characterized. We monitored the transmission spectrum from a broadband light source, which displayed a strong interference signal. After data collection, we applied a phase analysis of the interference optical spectrum. We here report the results on two fabricated, BTOFs with different interference spectrum characteristics; we analyzed the signal based on the interference between a high-order modal component and the core fiber mode. The sensor exhibited a linear response for magnetic field increments, and we achieved a phase sensitivity of around 0.28 rad/mT. The sensing setup presented remote sensing operation and low-cost transducer magnetic material.

Magnetic Field Sensing Based on Bi-Tapered Optical Fibers Using Spectral Phase Analysis

PubMed Central

Herrera-Piad, Luis A.; Jauregui-Vazquez, Daniel; Sierra-Hernandez, Juan M.; Lopez-Dieguez, Yanelis

2017-01-01

A compact, magnetic field sensor system based on a short, bi-tapered optical fiber (BTOF) span lying on a magnetic tape was designed, fabricated, and characterized. We monitored the transmission spectrum from a broadband light source, which displayed a strong interference signal. After data collection, we applied a phase analysis of the interference optical spectrum. We here report the results on two fabricated, BTOFs with different interference spectrum characteristics; we analyzed the signal based on the interference between a high-order modal component and the core fiber mode. The sensor exhibited a linear response for magnetic field increments, and we achieved a phase sensitivity of around 0.28 rad/mT. The sensing setup presented remote sensing operation and low-cost transducer magnetic material. PMID:29053570

FLIM-FRET image analysis of tryptophan in prostate cancer cells

NASA Astrophysics Data System (ADS)

Periasamy, Ammasi; Alam, Shagufta R.; Svindrych, Zdenek; Wallrabe, Horst

2017-07-01

A region of interest (ROI) based quantitative FLIM-FRET image analysis is developed to quantitate the autofluorescence signals of the essential amino acid tryptophan as a biomarker to investigate the metabolism in prostate cancer cells.

Enhanced correlation of received power-signal fluctuations in bidirectional optical links

NASA Astrophysics Data System (ADS)

Minet, Jean; Vorontsov, Mikhail A.; Polnau, Ernst; Dolfi, Daniel

2013-02-01

A study of the correlation between the power signals received at both ends of bidirectional free-space optical links is presented. By use of the quasi-optical approximation, we show that an ideal (theoretically 100%) power-signal correlation can be achieved in optical links with specially designed monostatic transceivers based on single-mode fiber collimators. The theoretical prediction of enhanced correlation is supported both by experiments conducted over a 7 km atmospheric path and wave optics numerical analysis of the corresponding bidirectional optical link. In the numerical simulations, we also compare correlation properties of received power signals for different atmospheric conditions and for optical links with monostatic and bistatic geometries based on single-mode fiber collimator and on power-in-the-bucket transceiver types. Applications of the observed phenomena for signal fading mitigation and turbulence-enhanced communication link security in free-space laser communication links are discussed.

SSVEP recognition using common feature analysis in brain-computer interface.

PubMed

Zhang, Yu; Zhou, Guoxu; Jin, Jing; Wang, Xingyu; Cichocki, Andrzej

2015-04-15

Canonical correlation analysis (CCA) has been successfully applied to steady-state visual evoked potential (SSVEP) recognition for brain-computer interface (BCI) application. Although the CCA method outperforms the traditional power spectral density analysis through multi-channel detection, it requires additionally pre-constructed reference signals of sine-cosine waves. It is likely to encounter overfitting in using a short time window since the reference signals include no features from training data. We consider that a group of electroencephalogram (EEG) data trials recorded at a certain stimulus frequency on a same subject should share some common features that may bear the real SSVEP characteristics. This study therefore proposes a common feature analysis (CFA)-based method to exploit the latent common features as natural reference signals in using correlation analysis for SSVEP recognition. Good performance of the CFA method for SSVEP recognition is validated with EEG data recorded from ten healthy subjects, in contrast to CCA and a multiway extension of CCA (MCCA). Experimental results indicate that the CFA method significantly outperformed the CCA and the MCCA methods for SSVEP recognition in using a short time window (i.e., less than 1s). The superiority of the proposed CFA method suggests it is promising for the development of a real-time SSVEP-based BCI. Copyright © 2014 Elsevier B.V. All rights reserved.

Mining featured biomarkers associated with prostatic carcinoma based on bioinformatics.

PubMed

Piao, Guanying; Wu, Jiarui

2013-11-01

To analyze the differentially expressed genes and identify featured biomarkers from prostatic carcinoma. The software "Significance Analysis of Microarray" (SAM) was used to identify the differentially coexpressed genes (DCGs). The DCGs existed in two datasets were analyzed by GO (Gene Ontology) functional annotation. A total of 389 DCGs were obtained. By GO analysis, we found these DCGs were closely related with the acinus development, TGF-β receptor and signal transduction pathways. Furthermore, five featured biomarkers were discovered by interaction analysis. These important signal pathways and oncogenes may provide potential therapeutic targets for prostatic carcinoma.

Detecting the crankshaft torsional vibration of diesel engines for combustion related diagnosis

NASA Astrophysics Data System (ADS)

Charles, P.; Sinha, Jyoti K.; Gu, F.; Lidstone, L.; Ball, A. D.

2009-04-01

Early fault detection and diagnosis for medium-speed diesel engines is important to ensure reliable operation throughout the course of their service. This work presents an investigation of the diesel engine combustion related fault detection capability of crankshaft torsional vibration. The encoder signal, often used for shaft speed measurement, has been used to construct the instantaneous angular speed (IAS) waveform, which actually represents the signature of the torsional vibration. Earlier studies have shown that the IAS signal and its fast Fourier transform (FFT) analysis are effective for monitoring engines with less than eight cylinders. The applicability to medium-speed engines, however, is strongly contested due to the high number of cylinders and large moment of inertia. Therefore the effectiveness of the FFT-based approach has further been enhanced by improving the signal processing to determine the IAS signal and subsequently tested on a 16-cylinder engine. In addition, a novel method of presentation, based on the polar coordinate system of the IAS signal, has also been introduced; to improve the discrimination features of the faults compared to the FFT-based approach of the IAS signal. The paper discusses two typical experimental studies on 16- and 20-cylinder engines, with and without faults, and the diagnosis results by the proposed polar presentation method. The results were also compared with the earlier FFT-based method of the IAS signal.

Structural analysis of eyespots: dynamics of morphogenic signals that govern elemental positions in butterfly wings

PubMed Central

2012-01-01

Background To explain eyespot colour-pattern determination in butterfly wings, the induction model has been discussed based on colour-pattern analyses of various butterfly eyespots. However, a detailed structural analysis of eyespots that can serve as a foundation for future studies is still lacking. In this study, fundamental structural rules related to butterfly eyespots are proposed, and the induction model is elaborated in terms of the possible dynamics of morphogenic signals involved in the development of eyespots and parafocal elements (PFEs) based on colour-pattern analysis of the nymphalid butterfly Junonia almana. Results In a well-developed eyespot, the inner black core ring is much wider than the outer black ring; this is termed the inside-wide rule. It appears that signals are wider near the focus of the eyespot and become narrower as they expand. Although fundamental signal dynamics are likely to be based on a reaction-diffusion mechanism, they were described well mathematically as a type of simple uniformly decelerated motion in which signals associated with the outer and inner black rings of eyespots and PFEs are released at different time points, durations, intervals, and initial velocities into a two-dimensional field of fundamentally uniform or graded resistance; this produces eyespots and PFEs that are diverse in size and structure. The inside-wide rule, eyespot distortion, structural differences between small and large eyespots, and structural changes in eyespots and PFEs in response to physiological treatments were explained well using mathematical simulations. Natural colour patterns and previous experimental findings that are not easily explained by the conventional gradient model were also explained reasonably well by the formal mathematical simulations performed in this study. Conclusions In a mode free from speculative molecular interactions, the present study clarifies fundamental structural rules related to butterfly eyespots, delineates a theoretical basis for the induction model, and proposes a mathematically simple mode of long-range signalling that may reflect developmental mechanisms associated with butterfly eyespots. PMID:22409965

A robust functional-data-analysis method for data recovery in multichannel sensor systems.

PubMed

Sun, Jian; Liao, Haitao; Upadhyaya, Belle R

2014-08-01

Multichannel sensor systems are widely used in condition monitoring for effective failure prevention of critical equipment or processes. However, loss of sensor readings due to malfunctions of sensors and/or communication has long been a hurdle to reliable operations of such integrated systems. Moreover, asynchronous data sampling and/or limited data transmission are usually seen in multiple sensor channels. To reliably perform fault diagnosis and prognosis in such operating environments, a data recovery method based on functional principal component analysis (FPCA) can be utilized. However, traditional FPCA methods are not robust to outliers and their capabilities are limited in recovering signals with strongly skewed distributions (i.e., lack of symmetry). This paper provides a robust data-recovery method based on functional data analysis to enhance the reliability of multichannel sensor systems. The method not only considers the possibly skewed distribution of each channel of signal trajectories, but is also capable of recovering missing data for both individual and correlated sensor channels with asynchronous data that may be sparse as well. In particular, grand median functions, rather than classical grand mean functions, are utilized for robust smoothing of sensor signals. Furthermore, the relationship between the functional scores of two correlated signals is modeled using multivariate functional regression to enhance the overall data-recovery capability. An experimental flow-control loop that mimics the operation of coolant-flow loop in a multimodular integral pressurized water reactor is used to demonstrate the effectiveness and adaptability of the proposed data-recovery method. The computational results illustrate that the proposed method is robust to outliers and more capable than the existing FPCA-based method in terms of the accuracy in recovering strongly skewed signals. In addition, turbofan engine data are also analyzed to verify the capability of the proposed method in recovering non-skewed signals.

Structural analysis of eyespots: dynamics of morphogenic signals that govern elemental positions in butterfly wings.

PubMed

Otaki, Joji M

2012-03-13

To explain eyespot colour-pattern determination in butterfly wings, the induction model has been discussed based on colour-pattern analyses of various butterfly eyespots. However, a detailed structural analysis of eyespots that can serve as a foundation for future studies is still lacking. In this study, fundamental structural rules related to butterfly eyespots are proposed, and the induction model is elaborated in terms of the possible dynamics of morphogenic signals involved in the development of eyespots and parafocal elements (PFEs) based on colour-pattern analysis of the nymphalid butterfly Junonia almana. In a well-developed eyespot, the inner black core ring is much wider than the outer black ring; this is termed the inside-wide rule. It appears that signals are wider near the focus of the eyespot and become narrower as they expand. Although fundamental signal dynamics are likely to be based on a reaction-diffusion mechanism, they were described well mathematically as a type of simple uniformly decelerated motion in which signals associated with the outer and inner black rings of eyespots and PFEs are released at different time points, durations, intervals, and initial velocities into a two-dimensional field of fundamentally uniform or graded resistance; this produces eyespots and PFEs that are diverse in size and structure. The inside-wide rule, eyespot distortion, structural differences between small and large eyespots, and structural changes in eyespots and PFEs in response to physiological treatments were explained well using mathematical simulations. Natural colour patterns and previous experimental findings that are not easily explained by the conventional gradient model were also explained reasonably well by the formal mathematical simulations performed in this study. In a mode free from speculative molecular interactions, the present study clarifies fundamental structural rules related to butterfly eyespots, delineates a theoretical basis for the induction model, and proposes a mathematically simple mode of long-range signalling that may reflect developmental mechanisms associated with butterfly eyespots.

Flow-based analysis using microfluidics-chemiluminescence systems.

PubMed

Al Lawati, Haider A J

2013-01-01

This review will discuss various approaches and techniques in which analysis using microfluidics-chemiluminescence systems (MF-CL) has been reported. A variety of applications is examined, including environmental, pharmaceutical, biological, food and herbal analysis. Reported uses of CL reagents, sample introduction techniques, sample pretreatment methods, CL signal enhancement and detection systems are discussed. A hydrodynamic pumping system is predominately used for these applications. However, several reports are available in which electro-osmotic (EO) pumping has been implemented. Various sample pretreatment methods have been used, including liquid-liquid extraction, solid-phase extraction and molecularly imprinted polymers. A wide range of innovative techniques has been reported for CL signal enhancement. Most of these techniques are based on enhancement of the mixing process in the microfluidics channels, which leads to enhancement of the CL signal. However, other techniques are also reported, such as mirror reaction, liquid core waveguide, on-line pre-derivatization and the use of an opaque white chip with a thin transparent seal. Photodetectors are the most commonly used detectors; however, other detection systems have also been used, including integrated electrochemiluminescence (ECL) and organic photodiodes (OPDs). Copyright © 2012 John Wiley & Sons, Ltd.

Electrophysiological signal analysis and visualization using Cloudwave for epilepsy clinical research.

PubMed

Jayapandian, Catherine P; Chen, Chien-Hung; Bozorgi, Alireza; Lhatoo, Samden D; Zhang, Guo-Qiang; Sahoo, Satya S

2013-01-01

Epilepsy is the most common serious neurological disorder affecting 50-60 million persons worldwide. Electrophysiological data recordings, such as electroencephalogram (EEG), are the gold standard for diagnosis and pre-surgical evaluation in epilepsy patients. The increasing trend towards multi-center clinical studies require signal visualization and analysis tools to support real time interaction with signal data in a collaborative environment, which cannot be supported by traditional desktop-based standalone applications. As part of the Prevention and Risk Identification of SUDEP Mortality (PRISM) project, we have developed a Web-based electrophysiology data visualization and analysis platform called Cloudwave using highly scalable open source cloud computing infrastructure. Cloudwave is integrated with the PRISM patient cohort identification tool called MEDCIS (Multi-modality Epilepsy Data Capture and Integration System). The Epilepsy and Seizure Ontology (EpSO) underpins both Cloudwave and MEDCIS to support query composition and result retrieval. Cloudwave is being used by clinicians and research staff at the University Hospital - Case Medical Center (UH-CMC) Epilepsy Monitoring Unit (EMU) and will be progressively deployed at four EMUs in the United States and the United Kingdomas part of the PRISM project.

SPICODYN: A Toolbox for the Analysis of Neuronal Network Dynamics and Connectivity from Multi-Site Spike Signal Recordings.

PubMed

Pastore, Vito Paolo; Godjoski, Aleksandar; Martinoia, Sergio; Massobrio, Paolo

2018-01-01

We implemented an automated and efficient open-source software for the analysis of multi-site neuronal spike signals. The software package, named SPICODYN, has been developed as a standalone windows GUI application, using C# programming language with Microsoft Visual Studio based on .NET framework 4.5 development environment. Accepted input data formats are HDF5, level 5 MAT and text files, containing recorded or generated time series spike signals data. SPICODYN processes such electrophysiological signals focusing on: spiking and bursting dynamics and functional-effective connectivity analysis. In particular, for inferring network connectivity, a new implementation of the transfer entropy method is presented dealing with multiple time delays (temporal extension) and with multiple binary patterns (high order extension). SPICODYN is specifically tailored to process data coming from different Multi-Electrode Arrays setups, guarantying, in those specific cases, automated processing. The optimized implementation of the Delayed Transfer Entropy and the High-Order Transfer Entropy algorithms, allows performing accurate and rapid analysis on multiple spike trains from thousands of electrodes.

The use of SESK as a trend parameter for localized bearing fault diagnosis in induction machines.

PubMed

Saidi, Lotfi; Ben Ali, Jaouher; Benbouzid, Mohamed; Bechhoefer, Eric

2016-07-01

A critical work of bearing fault diagnosis is locating the optimum frequency band that contains faulty bearing signal, which is usually buried in the noise background. Now, envelope analysis is commonly used to obtain the bearing defect harmonics from the envelope signal spectrum analysis and has shown fine results in identifying incipient failures occurring in the different parts of a bearing. However, the main step in implementing envelope analysis is to determine a frequency band that contains faulty bearing signal component with the highest signal noise level. Conventionally, the choice of the band is made by manual spectrum comparison via identifying the resonance frequency where the largest change occurred. In this paper, we present a squared envelope based spectral kurtosis method to determine optimum envelope analysis parameters including the filtering band and center frequency through a short time Fourier transform. We have verified the potential of the spectral kurtosis diagnostic strategy in performance improvements for single-defect diagnosis using real laboratory-collected vibration data sets. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Signal Processing in Periodically Forced Gradient Frequency Neural Networks

PubMed Central

Kim, Ji Chul; Large, Edward W.

2015-01-01

Oscillatory instability at the Hopf bifurcation is a dynamical phenomenon that has been suggested to characterize active non-linear processes observed in the auditory system. Networks of oscillators poised near Hopf bifurcation points and tuned to tonotopically distributed frequencies have been used as models of auditory processing at various levels, but systematic investigation of the dynamical properties of such oscillatory networks is still lacking. Here we provide a dynamical systems analysis of a canonical model for gradient frequency neural networks driven by a periodic signal. We use linear stability analysis to identify various driven behaviors of canonical oscillators for all possible ranges of model and forcing parameters. The analysis shows that canonical oscillators exhibit qualitatively different sets of driven states and transitions for different regimes of model parameters. We classify the parameter regimes into four main categories based on their distinct signal processing capabilities. This analysis will lead to deeper understanding of the diverse behaviors of neural systems under periodic forcing and can inform the design of oscillatory network models of auditory signal processing. PMID:26733858

SILAC-Based Quantitative Proteomic Analysis of Human Lung Cell Response to Copper Oxide Nanoparticles

PubMed Central

Edelmann, Mariola J.; Shack, Leslie A.; Naske, Caitlin D.; Walters, Keisha B.; Nanduri, Bindu

2014-01-01

Copper (II) oxide (CuO) nanoparticles (NP) are widely used in industry and medicine. In our study we evaluated the response of BEAS-2B human lung cells to CuO NP, using Stable isotope labeling by amino acids in cell culture (SILAC)-based proteomics and phosphoproteomics. Pathway modeling of the protein differential expression showed that CuO NP affect proteins relevant in cellular function and maintenance, protein synthesis, cell death and survival, cell cycle and cell morphology. Some of the signaling pathways represented by BEAS-2B proteins responsive to the NP included mTOR signaling, protein ubiquitination pathway, actin cytoskeleton signaling and epithelial adherens junction signaling. Follow-up experiments showed that CuO NP altered actin cytoskeleton, protein phosphorylation and protein ubiquitination level. PMID:25470785

Kurtosis-Based Blind Source Extraction of Complex Non-Circular Signals with Application in EEG Artifact Removal in Real-Time

PubMed Central

Javidi, Soroush; Mandic, Danilo P.; Took, Clive Cheong; Cichocki, Andrzej

2011-01-01

A new class of complex domain blind source extraction algorithms suitable for the extraction of both circular and non-circular complex signals is proposed. This is achieved through sequential extraction based on the degree of kurtosis and in the presence of non-circular measurement noise. The existence and uniqueness analysis of the solution is followed by a study of fast converging variants of the algorithm. The performance is first assessed through simulations on well understood benchmark signals, followed by a case study on real-time artifact removal from EEG signals, verified using both qualitative and quantitative metrics. The results illustrate the power of the proposed approach in real-time blind extraction of general complex-valued sources. PMID:22319461

Complex signal-based optical coherence tomography angiography enables in vivo visualization of choriocapillaris in human choroid

NASA Astrophysics Data System (ADS)

Chu, Zhongdi; Chen, Chieh-Li; Zhang, Qinqin; Pepple, Kathryn; Durbin, Mary; Gregori, Giovanni; Wang, Ruikang K.

2017-12-01

The choriocapillaris (CC) plays an essential role in maintaining the normal functions of the human eye. There is increasing interest in the community to develop an imaging technique for visualizing the CC, yet this remains underexplored due to technical limitations. We propose an approach for the visualization of the CC in humans via a complex signal-based optical microangiography (OMAG) algorithm, based on commercially available spectral domain optical coherence tomography (SD-OCT). We show that the complex signal-based OMAG was superior to both the phase and amplitude signal-based approaches in detailing the vascular lobules previously seen with histological analysis. With this improved ability to visualize the lobular vascular networks, it is possible to identify the feeding arterioles and draining venules around the lobules, which is important in understanding the role of the CC in the pathogenesis of ocular diseases. With built-in FastTrac™ and montage scanning capabilities, we also demonstrate wide-field SD-OCT angiograms of the CC with a field of view at 9×11 mm2.

Satellite single-axis attitude determination based on Automatic Dependent Surveillance - Broadcast signals

NASA Astrophysics Data System (ADS)

Zhou, Kaixing; Sun, Xiucong; Huang, Hai; Wang, Xinsheng; Ren, Guangwei

2017-10-01

The space-based Automatic Dependent Surveillance - Broadcast (ADS-B) is a new technology for air traffic management. The satellite equipped with spaceborne ADS-B system receives the broadcast signals from aircraft and transfers the message to ground stations, so as to extend the coverage area of terrestrial-based ADS-B. In this work, a novel satellite single-axis attitude determination solution based on the ADS-B receiving system is proposed. This solution utilizes the signal-to-noise ratio (SNR) measurement of the broadcast signals from aircraft to determine the boresight orientation of the ADS-B receiving antenna fixed on the satellite. The basic principle of this solution is described. The feasibility study of this new attitude determination solution is implemented, including the link budget and the access analysis. On this basis, the nonlinear least squares estimation based on the Levenberg-Marquardt method is applied to estimate the single-axis orientation. A full digital simulation has been carried out to verify the effectiveness and performance of this solution. Finally, the corresponding results are processed and presented minutely.

Analysis of Coherent Phonon Signals by Sparsity-promoting Dynamic Mode Decomposition

NASA Astrophysics Data System (ADS)

Murata, Shin; Aihara, Shingo; Tokuda, Satoru; Iwamitsu, Kazunori; Mizoguchi, Kohji; Akai, Ichiro; Okada, Masato

2018-05-01

We propose a method to decompose normal modes in a coherent phonon (CP) signal by sparsity-promoting dynamic mode decomposition. While the CP signals can be modeled as the sum of finite number of damped oscillators, the conventional method such as Fourier transform adopts continuous bases in a frequency domain. Thus, the uncertainty of frequency appears and it is difficult to estimate the initial phase. Moreover, measurement artifacts are imposed on the CP signal and deforms the Fourier spectrum. In contrast, the proposed method can separate the signal from the artifact precisely and can successfully estimate physical properties of the normal modes.

Sensorless position estimation and control of permanent-magnet synchronous motors using a saturation model

NASA Astrophysics Data System (ADS)

Kassem Jebai, Al; Malrait, François; Martin, Philippe; Rouchon, Pierre

2016-03-01

Sensorless control of permanent-magnet synchronous motors at low velocity remains a challenging task. A now well-established method consists of injecting a high-frequency signal and using the rotor saliency, both geometric and magnetic-saturation induced. This paper proposes a clear and original analysis based on second-order averaging of how to recover the position information from signal injection; this analysis blends well with a general model of magnetic saturation. It also proposes a simple parametric model of the saturated motor, based on an energy function which simply encompasses saturation and cross-saturation effects. Experimental results on a surface-mounted motor and an interior magnet motor illustrate the relevance of the approach.

[A new method to extract time-frequency characteristics of hypertension pathological signal based on pseudo Wigner-Ville distribution].

PubMed

Zhang, Xun; Zhang, Miaomiao; Du, Panpan

2012-02-01

Hypertension, as one of diseases with the highest incidence in the world at present, is an important cause of stroke, coronary heart disease, renal insufficiency and other serious diseases. Based on pseudo Wigner-Ville distribution, this paper makes an analysis on the relevant pulse characteristics by measuring time range of the energy concentration circle. In view of the present situation, that is, about half of the high-normal blood pressure persons are likely to develop hypertension, we explored the pulse characteristics to find the pathological changes in subjects with prehypertension, in order to solve the problems that there are no obvious clinical significant features in prehypertension. The results showed that the duration of high energy circle in signal with hypertension pathological changes was shorter than the duration of healthy signal. Hence, healthy signal and hypertension pathological signal can be effectively distinguished by this method, and this provides a new basis to identify the lesion signal when blood pressure is in critical period.

On-line Tool Wear Detection on DCMT070204 Carbide Tool Tip Based on Noise Cutting Audio Signal using Artificial Neural Network

NASA Astrophysics Data System (ADS)

Prasetyo, T.; Amar, S.; Arendra, A.; Zam Zami, M. K.

2018-01-01

This study develops an on-line detection system to predict the wear of DCMT070204 tool tip during the cutting process of the workpiece. The machine used in this research is CNC ProTurn 9000 to cut ST42 steel cylinder. The audio signal has been captured using the microphone placed in the tool post and recorded in Matlab. The signal is recorded at the sampling rate of 44.1 kHz, and the sampling size of 1024. The recorded signal is 110 data derived from the audio signal while cutting using a normal chisel and a worn chisel. And then perform signal feature extraction in the frequency domain using Fast Fourier Transform. Feature selection is done based on correlation analysis. And tool wear classification was performed using artificial neural networks with 33 input features selected. This artificial neural network is trained with back propagation method. Classification performance testing yields an accuracy of 74%.

Acid-base equilibrium in aqueous solutions of 1,3-dimethylbarbituric acid as studied by 13C NMR spectroscopy

NASA Astrophysics Data System (ADS)

Gryff-Keller, A.; Kraska-Dziadecka, A.

2011-12-01

13C NMR spectra of 1,3-dimethylbarbituric acid in aqueous solutions of various acidities and for various solute concentrations have been recorded and interpreted. The spectra recorded at pH = 2 and below contain the signals of the neutral solute molecule exclusively, while the ones recorded at pH = 7 and above only the signals of the appropriate anion, which has been confirmed by theoretical GIAO-DFT calculations. The signals in the spectra recorded for solutions of pH < 7 show dynamic broadenings. The lineshape analysis of these signals has provided information on the kinetics of the processes running in the dynamic acid-base equilibrium. The kinetic data determined this way have been used to clarify the mechanisms of these processes. The numerical analysis has shown that under the investigated conditions deprotonation of the neutral solute molecules undergoes not only via a simple transfer of the C-H proton to water molecules but also through a process with participation of the barbiturate anions. Moreover, the importance of tautomerism, or association, or both these phenomena for the kinetics of the acid-base transformations in the investigated system has been shown. Qualitatively similar changes of 13C NMR spectra with the solution pH variation have been observed for the parent barbituric acid.

Performance analysis of gamma ray spectrometric parameters on digital signal and analog signal processing based MCA systems using NaI(Tl) detector.

PubMed

Kukreti, B M; Sharma, G K

2012-05-01

Accurate and speedy estimations of ppm range uranium and thorium in the geological and rock samples are most useful towards ongoing uranium investigations and identification of favorable radioactive zones in the exploration field areas. In this study with the existing 5 in. × 4 in. NaI(Tl) detector setup, prevailing background and time constraints, an enhanced geometrical setup has been worked out to improve the minimum detection limits for primordial radioelements K(40), U(238) and Th(232). This geometrical setup has been integrated with the newly introduced, digital signal processing based MCA system for the routine spectrometric analysis of low concentration rock samples. Stability performance, during the long counting hours, for digital signal processing MCA system and its predecessor NIM bin based MCA system has been monitored, using the concept of statistical process control. Monitored results, over a time span of few months, have been quantified in terms of spectrometer's parameters such as Compton striping constants and Channel sensitivities, used for evaluating primordial radio element concentrations (K(40), U(238) and Th(232)) in geological samples. Results indicate stable dMCA performance, with a tendency of higher relative variance, about mean, particularly for Compton stripping constants. Copyright © 2012 Elsevier Ltd. All rights reserved.

Fractal Analysis of Brain Blood Oxygenation Level Dependent (BOLD) Signals from Children with Mild Traumatic Brain Injury (mTBI).

PubMed

Dona, Olga; Noseworthy, Michael D; DeMatteo, Carol; Connolly, John F

2017-01-01

Conventional imaging techniques are unable to detect abnormalities in the brain following mild traumatic brain injury (mTBI). Yet patients with mTBI typically show delayed response on neuropsychological evaluation. Because fractal geometry represents complexity, we explored its utility in measuring temporal fluctuations of brain resting state blood oxygen level dependent (rs-BOLD) signal. We hypothesized that there could be a detectable difference in rs-BOLD signal complexity between healthy subjects and mTBI patients based on previous studies that associated reduction in signal complexity with disease. Fifteen subjects (13.4 ± 2.3 y/o) and 56 age-matched (13.5 ± 2.34 y/o) healthy controls were scanned using a GE Discovery MR750 3T MRI and 32-channel RF-coil. Axial FSPGR-3D images were used to prescribe rs-BOLD (TE/TR = 35/2000ms), acquired over 6 minutes. Motion correction was performed and anatomical and functional images were aligned and spatially warped to the N27 standard atlas. Fractal analysis, performed on grey matter, was done by estimating the Hurst exponent using de-trended fluctuation analysis and signal summation conversion methods. Voxel-wise fractal dimension (FD) was calculated for every subject in the control group to generate mean and standard deviation maps for regional Z-score analysis. Voxel-wise validation of FD normality across controls was confirmed, and non-Gaussian voxels (3.05% over the brain) were eliminated from subsequent analysis. For each mTBI patient, regions where Z-score values were at least 2 standard deviations away from the mean (i.e. where |Z| > 2.0) were identified. In individual patients the frequently affected regions were amygdala (p = 0.02), vermis(p = 0.03), caudate head (p = 0.04), hippocampus(p = 0.03), and hypothalamus(p = 0.04), all previously reported as dysfunctional after mTBI, but based on group analysis. It is well known that the brain is best modeled as a complex system. Therefore a measure of complexity using rs-BOLD signal FD could provide an additional method to grade and monitor mTBI. Furthermore, this approach can be personalized thus providing unique patient specific assessment.

SPECIAL ISSUE ON OPTICAL PROCESSING OF INFORMATION: Semiconductor-laser Fourier processors of electric signals

NASA Astrophysics Data System (ADS)

Blok, A. S.; Bukhenskii, A. F.; Krupitskii, É. I.; Morozov, S. V.; Pelevin, V. Yu; Sergeenko, T. N.; Yakovlev, V. I.

1995-10-01

An investigation is reported of acousto-optical and fibre-optic Fourier processors of electric signals, based on semiconductor lasers. A description is given of practical acousto-optical processors with an analysis band 120 MHz wide, a resolution of 200 kHz, and 7 cm × 8 cm × 18 cm dimensions. Fibre-optic Fourier processors are considered: they represent a new class of devices which are promising for the processing of gigahertz signals.

Cross Time-Frequency Analysis for Combining Information of Several Sources: Application to Estimation of Spontaneous Respiratory Rate from Photoplethysmography

PubMed Central

Peláez-Coca, M. D.; Orini, M.; Lázaro, J.; Bailón, R.; Gil, E.

2013-01-01

A methodology that combines information from several nonstationary biological signals is presented. This methodology is based on time-frequency coherence, that quantifies the similarity of two signals in the time-frequency domain. A cross time-frequency analysis method, based on quadratic time-frequency distribution, has been used for combining information of several nonstationary biomedical signals. In order to evaluate this methodology, the respiratory rate from the photoplethysmographic (PPG) signal is estimated. The respiration provokes simultaneous changes in the pulse interval, amplitude, and width of the PPG signal. This suggests that the combination of information from these sources will improve the accuracy of the estimation of the respiratory rate. Another target of this paper is to implement an algorithm which provides a robust estimation. Therefore, respiratory rate was estimated only in those intervals where the features extracted from the PPG signals are linearly coupled. In 38 spontaneous breathing subjects, among which 7 were characterized by a respiratory rate lower than 0.15 Hz, this methodology provided accurate estimates, with the median error {0.00; 0.98} mHz ({0.00; 0.31}%) and the interquartile range error {4.88; 6.59} mHz ({1.60; 1.92}%). The estimation error of the presented methodology was largely lower than the estimation error obtained without combining different PPG features related to respiration. PMID:24363777

Improved analytical methods for microarray-based genome-composition analysis

PubMed Central

Kim, Charles C; Joyce, Elizabeth A; Chan, Kaman; Falkow, Stanley

2002-01-01

Background Whereas genome sequencing has given us high-resolution pictures of many different species of bacteria, microarrays provide a means of obtaining information on genome composition for many strains of a given species. Genome-composition analysis using microarrays, or 'genomotyping', can be used to categorize genes into 'present' and 'divergent' categories based on the level of hybridization signal. This typically involves selecting a signal value that is used as a cutoff to discriminate present (high signal) and divergent (low signal) genes. Current methodology uses empirical determination of cutoffs for classification into these categories, but this methodology is subject to several problems that can result in the misclassification of many genes. Results We describe a method that depends on the shape of the signal-ratio distribution and does not require empirical determination of a cutoff. Moreover, the cutoff is determined on an array-to-array basis, accounting for variation in strain composition and hybridization quality. The algorithm also provides an estimate of the probability that any given gene is present, which provides a measure of confidence in the categorical assignments. Conclusions Many genes previously classified as present using static methods are in fact divergent on the basis of microarray signal; this is corrected by our algorithm. We have reassigned hundreds of genes from previous genomotyping studies of Helicobacter pylori and Campylobacter jejuni strains, and expect that the algorithm should be widely applicable to genomotyping data. PMID:12429064

In silico study of protein to protein interaction analysis of AMP-activated protein kinase and mitochondrial activity in three different farm animal species

NASA Astrophysics Data System (ADS)

Prastowo, S.; Widyas, N.

2018-03-01

AMP-activated protein kinase (AMPK) is cellular energy censor which works based on ATP and AMP concentration. This protein interacts with mitochondria in determine its activity to generate energy for cell metabolism purposes. For that, this paper aims to compare the protein to protein interaction of AMPK and mitochondrial activity genes in the metabolism of known animal farm (domesticated) that are cattle (Bos taurus), pig (Sus scrofa) and chicken (Gallus gallus). In silico study was done using STRING V.10 as prominent protein interaction database, followed with biological function comparison in KEGG PATHWAY database. Set of genes (12 in total) were used as input analysis that are PRKAA1, PRKAA2, PRKAB1, PRKAB2, PRKAG1, PRKAG2, PRKAG3, PPARGC1, ACC, CPT1B, NRF2 and SOD. The first 7 genes belong to gene in AMPK family, while the last 5 belong to mitochondrial activity genes. The protein interaction result shows 11, 8 and 5 metabolism pathways in Bos taurus, Sus scrofa and Gallus gallus, respectively. The top pathway in Bos taurus is AMPK signaling pathway (10 genes), Sus scrofa is Adipocytokine signaling pathway (8 genes) and Gallus gallus is FoxO signaling pathway (5 genes). Moreover, the common pathways found in those 3 species are Adipocytokine signaling pathway, Insulin signaling pathway and FoxO signaling pathway. Genes clustered in Adipocytokine and Insulin signaling pathway are PRKAA2, PPARGC1A, PRKAB1 and PRKAG2. While, in FoxO signaling pathway are PRKAA2, PRKAB1, PRKAG2. According to that, we found PRKAA2, PRKAB1 and PRKAG2 are the common genes. Based on the bioinformatics analysis, we can demonstrate that protein to protein interaction shows distinct different of metabolism in different species. However, further validation is needed to give a clear explanation.

On the Design of an Efficient Cardiac Health Monitoring System Through Combined Analysis of ECG and SCG Signals.

PubMed

Sahoo, Prasan Kumar; Thakkar, Hiren Kumar; Lin, Wen-Yen; Chang, Po-Cheng; Lee, Ming-Yih

2018-01-28

Cardiovascular disease (CVD) is a major public concern and socioeconomic problem across the globe. The popular high-end cardiac health monitoring systems such as magnetic resonance imaging (MRI), computerized tomography scan (CT scan), and echocardiography (Echo) are highly expensive and do not support long-term continuous monitoring of patients without disrupting their activities of daily living (ADL). In this paper, the continuous and non-invasive cardiac health monitoring using unobtrusive sensors is explored aiming to provide a feasible and low-cost alternative to foresee possible cardiac anomalies in an early stage. It is learned that cardiac health monitoring based on sole usage of electrocardiogram (ECG) signals may not provide powerful insights as ECG provides shallow information on various cardiac activities in the form of electrical impulses only. Hence, a novel low-cost, non-invasive seismocardiogram (SCG) signal along with ECG signals are jointly investigated for the robust cardiac health monitoring. For this purpose, the in-laboratory data collection model is designed for simultaneous acquisition of ECG and SCG signals followed by mechanisms for the automatic delineation of relevant feature points in acquired ECG and SCG signals. In addition, separate feature points based novel approach is adopted to distinguish between normal and abnormal morphology in each ECG and SCG cardiac cycle. Finally, a combined analysis of ECG and SCG is carried out by designing a Naïve Bayes conditional probability model. Experiments on Institutional Review Board (IRB) approved licensed ECG/SCG signals acquired from real subjects containing 12,000 cardiac cycles show that the proposed feature point delineation mechanisms and abnormal morphology detection methods consistently perform well and give promising results. In addition, experimental results show that the combined analysis of ECG and SCG signals provide more reliable cardiac health monitoring compared to the standalone use of ECG and SCG.

On the Design of an Efficient Cardiac Health Monitoring System Through Combined Analysis of ECG and SCG Signals

PubMed Central

Lin, Wen-Yen; Chang, Po-Cheng

2018-01-01

Cardiovascular disease (CVD) is a major public concern and socioeconomic problem across the globe. The popular high-end cardiac health monitoring systems such as magnetic resonance imaging (MRI), computerized tomography scan (CT scan), and echocardiography (Echo) are highly expensive and do not support long-term continuous monitoring of patients without disrupting their activities of daily living (ADL). In this paper, the continuous and non-invasive cardiac health monitoring using unobtrusive sensors is explored aiming to provide a feasible and low-cost alternative to foresee possible cardiac anomalies in an early stage. It is learned that cardiac health monitoring based on sole usage of electrocardiogram (ECG) signals may not provide powerful insights as ECG provides shallow information on various cardiac activities in the form of electrical impulses only. Hence, a novel low-cost, non-invasive seismocardiogram (SCG) signal along with ECG signals are jointly investigated for the robust cardiac health monitoring. For this purpose, the in-laboratory data collection model is designed for simultaneous acquisition of ECG and SCG signals followed by mechanisms for the automatic delineation of relevant feature points in acquired ECG and SCG signals. In addition, separate feature points based novel approach is adopted to distinguish between normal and abnormal morphology in each ECG and SCG cardiac cycle. Finally, a combined analysis of ECG and SCG is carried out by designing a Naïve Bayes conditional probability model. Experiments on Institutional Review Board (IRB) approved licensed ECG/SCG signals acquired from real subjects containing 12,000 cardiac cycles show that the proposed feature point delineation mechanisms and abnormal morphology detection methods consistently perform well and give promising results. In addition, experimental results show that the combined analysis of ECG and SCG signals provide more reliable cardiac health monitoring compared to the standalone use of ECG and SCG. PMID:29382098

Phase synchronization of instrumental music signals

NASA Astrophysics Data System (ADS)

Mukherjee, Sayan; Palit, Sanjay Kumar; Banerjee, Santo; Ariffin, M. R. K.; Bhattacharya, D. K.

2014-06-01

Signal analysis is one of the finest scientific techniques in communication theory. Some quantitative and qualitative measures describe the pattern of a music signal, vary from one to another. Same musical recital, when played by different instrumentalists, generates different types of music patterns. The reason behind various patterns is the psycho-acoustic measures - Dynamics, Timber, Tonality and Rhythm, varies in each time. However, the psycho-acoustic study of the music signals does not reveal any idea about the similarity between the signals. For such cases, study of synchronization of long-term nonlinear dynamics may provide effective results. In this context, phase synchronization (PS) is one of the measures to show synchronization between two non-identical signals. In fact, it is very critical to investigate any other kind of synchronization for experimental condition, because those are completely non identical signals. Also, there exists equivalence between the phases and the distances of the diagonal line in Recurrence plot (RP) of the signals, which is quantifiable by the recurrence quantification measure τ-recurrence rate. This paper considers two nonlinear music signals based on same raga played by two eminent sitar instrumentalists as two non-identical sources. The psycho-acoustic study shows how the Dynamics, Timber, Tonality and Rhythm vary for the two music signals. Then, long term analysis in the form of phase space reconstruction is performed, which reveals the chaotic phase spaces for both the signals. From the RP of both the phase spaces, τ-recurrence rate is calculated. Finally by the correlation of normalized tau-recurrence rate of their 3D phase spaces and the PS of the two music signals has been established. The numerical results well support the analysis.

Acoustic analysis of trill sounds.

PubMed

Dhananjaya, N; Yegnanarayana, B; Bhaskararao, Peri

2012-04-01

In this paper, the acoustic-phonetic characteristics of steady apical trills--trill sounds produced by the periodic vibration of the apex of the tongue--are studied. Signal processing methods, namely, zero-frequency filtering and zero-time liftering of speech signals, are used to analyze the excitation source and the resonance characteristics of the vocal tract system, respectively. Although it is natural to expect the effect of trilling on the resonances of the vocal tract system, it is interesting to note that trilling influences the glottal source of excitation as well. The excitation characteristics derived using zero-frequency filtering of speech signals are glottal epochs, strength of impulses at the glottal epochs, and instantaneous fundamental frequency of the glottal vibration. Analysis based on zero-time liftering of speech signals is used to study the dynamic resonance characteristics of vocal tract system during the production of trill sounds. Qualitative analysis of trill sounds in different vowel contexts, and the acoustic cues that may help spotting trills in continuous speech are discussed.

Analysis of photogenerated random telegraph signal in single electron detector (photo-SET).

PubMed

Troudi, M; Sghaier, Na; Kalboussi, A; Souifi, A

2010-01-04

In this paper, we analyzed slow single traps, situated inside the tunnel oxide of small area single electron photo-detector (photo-SET or nanopixel). The relationship between excitation signal (photons) and random-telegraph-signal (RTS) was evidenced. We demonstrated that photoinduced RTS observed on a photo-detector is due to the interaction between single photogenerated charges that tunnel from dot to dot and current path. Based on RTS analysis for various temperatures, gate bias and optical power we determined the characteristics of these single photogenerated traps: the energy position within the silicon bandgap, capture cross section and the position within the Si/SiO(x = 1.5) interfaces.

Singularity detection by wavelet approach: application to electrocardiogram signal

NASA Astrophysics Data System (ADS)

Jalil, Bushra; Beya, Ouadi; Fauvet, Eric; Laligant, Olivier

2010-01-01

In signal processing, the region of abrupt changes contains the most of the useful information about the nature of the signal. The region or the points where these changes occurred are often termed as singular point or singular region. The singularity is considered to be an important character of the signal, as it refers to the discontinuity and interruption present in the signal and the main purpose of the detection of such singular point is to identify the existence, location and size of those singularities. Electrocardiogram (ECG) signal is used to analyze the cardiovascular activity in the human body. However the presence of noise due to several reasons limits the doctor's decision and prevents accurate identification of different pathologies. In this work we attempt to analyze the ECG signal with energy based approach and some heuristic methods to segment and identify different signatures inside the signal. ECG signal has been initially denoised by empirical wavelet shrinkage approach based on Steins Unbiased Risk Estimate (SURE). At the second stage, the ECG signal has been analyzed by Mallat approach based on modulus maximas and Lipschitz exponent computation. The results from both approaches has been discussed and important aspects has been highlighted. In order to evaluate the algorithm, the analysis has been done on MIT-BIH Arrhythmia database; a set of ECG data records sampled at a rate of 360 Hz with 11 bit resolution over a 10mv range. The results have been examined and approved by medical doctors.

[An Extraction and Recognition Method of the Distributed Optical Fiber Vibration Signal Based on EMD-AWPP and HOSA-SVM Algorithm].

PubMed

Zhang, Yanjun; Liu, Wen-zhe; Fu, Xing-hu; Bi, Wei-hong

2016-02-01

Given that the traditional signal processing methods can not effectively distinguish the different vibration intrusion signal, a feature extraction and recognition method of the vibration information is proposed based on EMD-AWPP and HOSA-SVM, using for high precision signal recognition of distributed fiber optic intrusion detection system. When dealing with different types of vibration, the method firstly utilizes the adaptive wavelet processing algorithm based on empirical mode decomposition effect to reduce the abnormal value influence of sensing signal and improve the accuracy of signal feature extraction. Not only the low frequency part of the signal is decomposed, but also the high frequency part the details of the signal disposed better by time-frequency localization process. Secondly, it uses the bispectrum and bicoherence spectrum to accurately extract the feature vector which contains different types of intrusion vibration. Finally, based on the BPNN reference model, the recognition parameters of SVM after the implementation of the particle swarm optimization can distinguish signals of different intrusion vibration, which endows the identification model stronger adaptive and self-learning ability. It overcomes the shortcomings, such as easy to fall into local optimum. The simulation experiment results showed that this new method can effectively extract the feature vector of sensing information, eliminate the influence of random noise and reduce the effects of outliers for different types of invasion source. The predicted category identifies with the output category and the accurate rate of vibration identification can reach above 95%. So it is better than BPNN recognition algorithm and improves the accuracy of the information analysis effectively.

Doped Graphene for DNA Analysis: the Electrochemical Signal is Strongly Influenced by the Kind of Dopant and the Nucleobase Structure

PubMed Central

Tian, Huidi; Wang, Lu; Sofer, Zdenek; Pumera, Martin; Bonanni, Alessandra

2016-01-01

Doping graphene with heteroatoms can alter the electronic and electrochemical properties of the starting material. Contrasting properties should be expected when the doping is carried out with electron donating species (n-type dopants) or with electron withdrawing species (p-type dopants). This in turn can have a profound influence on the electroanalytical performance of the doped material being used for the detection of specific probes. Here we investigate the electrochemical oxidation of DNA bases adenine, guanine, thymine and cytosine on two heteroatom-doped graphene platforms namely boron-doped graphene (p-type dopant) and nitrogen-doped graphene (n-type dopant). We found that overall, boron–doped graphene provided the best response in terms of electrochemical signal sensitivity for all bases. This is due to the electron deficiency of boron-doped graphene, which can promote the oxidation of DNA bases, as opposed to nitrogen-doped graphene which possesses an excess of electrons. Moreover, also the structure of the nucleobase was found to have significant influence on the obtained signal. Our study may open new frontiers in the electrochemical detection of DNA bases which is the first step for label-free DNA analysis. PMID:27623951

A Timing Estimation Method Based-on Skewness Analysis in Vehicular Wireless Networks.

PubMed

Cui, Xuerong; Li, Juan; Wu, Chunlei; Liu, Jian-Hang

2015-11-13

Vehicle positioning technology has drawn more and more attention in vehicular wireless networks to reduce transportation time and traffic accidents. Nowadays, global navigation satellite systems (GNSS) are widely used in land vehicle positioning, but most of them are lack precision and reliability in situations where their signals are blocked. Positioning systems base-on short range wireless communication are another effective way that can be used in vehicle positioning or vehicle ranging. IEEE 802.11p is a new real-time short range wireless communication standard for vehicles, so a new method is proposed to estimate the time delay or ranges between vehicles based on the IEEE 802.11p standard which includes three main steps: cross-correlation between the received signal and the short preamble, summing up the correlated results in groups, and finding the maximum peak using a dynamic threshold based on the skewness analysis. With the range between each vehicle or road-side infrastructure, the position of neighboring vehicles can be estimated correctly. Simulation results were presented in the International Telecommunications Union (ITU) vehicular multipath channel, which show that the proposed method provides better precision than some well-known timing estimation techniques, especially in low signal to noise ratio (SNR) environments.

Tile-Based Fisher Ratio Analysis of Comprehensive Two-Dimensional Gas Chromatography Time-of-Flight Mass Spectrometry (GC × GC – TOFMS) Data using a Null Distribution Approach

DOE Office of Scientific and Technical Information (OSTI.GOV)

Parsons, Brendon A.; Marney, Luke C.; Siegler, William C.

Multi-dimensional chromatographic instrumentation produces information-rich, and chemically complex data containing meaningful chemical signals and/or chemical patterns. Two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC × GC – TOFMS) is a prominent instrumental platform that has been applied extensively for discovery-based experimentation, where samples are sufficiently volatile or amenable to derivatization. Use of GC × GC – TOFMS and associated data analysis strategies aim to uncover meaningful chemical signals or chemical patterns. However, for complex samples, meaningful chemical information is often buried in a background of less meaningful chemical signal and noise. In this report, we utilize the tile-based F-ratiomore » software in concert with the standard addition method by spiking non-native chemicals into a diesel fuel matrix at low concentrations. While the previous work studied the concentration range of 100-1000 ppm, the current study focuses on the 0 ppm to 100 ppm analyte spike range. This study demonstrates the sensitivity and selectivity of the tile-based F-ratio software for discovery of true positives in the non-targeted analysis of a chemically complex and analytically challenging sample matrix. By exploring the low concentration spike levels, we gain a better understanding of the limit of detection (LOD) of the tile-based F-ratio software with GC × GC – TOFMS data.« less

Noise Reduction Effect of Multiple-Sampling-Based Signal-Readout Circuits for Ultra-Low Noise CMOS Image Sensors.

PubMed

Kawahito, Shoji; Seo, Min-Woong

2016-11-06

This paper discusses the noise reduction effect of multiple-sampling-based signal readout circuits for implementing ultra-low-noise image sensors. The correlated multiple sampling (CMS) technique has recently become an important technology for high-gain column readout circuits in low-noise CMOS image sensors (CISs). This paper reveals how the column CMS circuits, together with a pixel having a high-conversion-gain charge detector and low-noise transistor, realizes deep sub-electron read noise levels based on the analysis of noise components in the signal readout chain from a pixel to the column analog-to-digital converter (ADC). The noise measurement results of experimental CISs are compared with the noise analysis and the effect of noise reduction to the sampling number is discussed at the deep sub-electron level. Images taken with three CMS gains of two, 16, and 128 show distinct advantage of image contrast for the gain of 128 (noise(median): 0.29 e - rms ) when compared with the CMS gain of two (2.4 e - rms ), or 16 (1.1 e - rms ).

Noise Reduction Effect of Multiple-Sampling-Based Signal-Readout Circuits for Ultra-Low Noise CMOS Image Sensors

PubMed Central

Kawahito, Shoji; Seo, Min-Woong

2016-01-01

This paper discusses the noise reduction effect of multiple-sampling-based signal readout circuits for implementing ultra-low-noise image sensors. The correlated multiple sampling (CMS) technique has recently become an important technology for high-gain column readout circuits in low-noise CMOS image sensors (CISs). This paper reveals how the column CMS circuits, together with a pixel having a high-conversion-gain charge detector and low-noise transistor, realizes deep sub-electron read noise levels based on the analysis of noise components in the signal readout chain from a pixel to the column analog-to-digital converter (ADC). The noise measurement results of experimental CISs are compared with the noise analysis and the effect of noise reduction to the sampling number is discussed at the deep sub-electron level. Images taken with three CMS gains of two, 16, and 128 show distinct advantage of image contrast for the gain of 128 (noise(median): 0.29 e−rms) when compared with the CMS gain of two (2.4 e−rms), or 16 (1.1 e−rms). PMID:27827972

Analysis of MLS Based Surveillance System (MLSS) Concepts

DOT National Transportation Integrated Search

1989-04-01

This report examines a number of surveillance system concepts to support safe independent runway approaches and converging runways under weather conditons. All surveillance conepts are based on the use of MLS signals. The resultin surveillance is ava...

A fault diagnosis scheme for rolling bearing based on local mean decomposition and improved multiscale fuzzy entropy

NASA Astrophysics Data System (ADS)

Li, Yongbo; Xu, Minqiang; Wang, Rixin; Huang, Wenhu

2016-01-01

This paper presents a new rolling bearing fault diagnosis method based on local mean decomposition (LMD), improved multiscale fuzzy entropy (IMFE), Laplacian score (LS) and improved support vector machine based binary tree (ISVM-BT). When the fault occurs in rolling bearings, the measured vibration signal is a multi-component amplitude-modulated and frequency-modulated (AM-FM) signal. LMD, a new self-adaptive time-frequency analysis method can decompose any complicated signal into a series of product functions (PFs), each of which is exactly a mono-component AM-FM signal. Hence, LMD is introduced to preprocess the vibration signal. Furthermore, IMFE that is designed to avoid the inaccurate estimation of fuzzy entropy can be utilized to quantify the complexity and self-similarity of time series for a range of scales based on fuzzy entropy. Besides, the LS approach is introduced to refine the fault features by sorting the scale factors. Subsequently, the obtained features are fed into the multi-fault classifier ISVM-BT to automatically fulfill the fault pattern identifications. The experimental results validate the effectiveness of the methodology and demonstrate that proposed algorithm can be applied to recognize the different categories and severities of rolling bearings.

Comparative study of signalling methods for high-speed backplane transceiver

NASA Astrophysics Data System (ADS)

Wu, Kejun

2017-11-01

A combined analysis of transient simulation and statistical method is proposed for comparative study of signalling methods applied to high-speed backplane transceivers. This method enables fast and accurate signal-to-noise ratio and symbol error rate estimation of a serial link based on a four-dimension design space, including channel characteristics, noise scenarios, equalisation schemes, and signalling methods. The proposed combined analysis method chooses an efficient sampling size for performance evaluation. A comparative study of non-return-to-zero (NRZ), PAM-4, and four-phase shifted sinusoid symbol (PSS-4) using parameterised behaviour-level simulation shows PAM-4 and PSS-4 has substantial advantages over conventional NRZ in most of the cases. A comparison between PAM-4 and PSS-4 shows PAM-4 gets significant bit error rate degradation when noise level is enhanced.

Genomic signal analysis of pathogen variability

NASA Astrophysics Data System (ADS)

Cristea, Paul Dan

2006-02-01

The paper presents results in the study of pathogen variability by using genomic signals. The conversion of symbolic nucleotide sequences into digital signals offers the possibility to apply signal processing methods to the analysis of genomic data. The method is particularly well suited to characterize small size genomic sequences, such as those found in viruses and bacteria, being a promising tool in tracking the variability of pathogens, especially in the context of developing drug resistance. The paper is based on data downloaded from GenBank [32], and comprises results on the variability of the eight segments of the influenza type A, subtype H5N1, virus genome, and of the Hemagglutinin (HA) gene, for the H1, H2, H3, H4, H5 and H16 types. Data from human and avian virus isolates are used.

A Compound Fault Diagnosis for Rolling Bearings Method Based on Blind Source Separation and Ensemble Empirical Mode Decomposition

PubMed Central

Wang, Huaqing; Li, Ruitong; Tang, Gang; Yuan, Hongfang; Zhao, Qingliang; Cao, Xi

2014-01-01

A Compound fault signal usually contains multiple characteristic signals and strong confusion noise, which makes it difficult to separate week fault signals from them through conventional ways, such as FFT-based envelope detection, wavelet transform or empirical mode decomposition individually. In order to improve the compound faults diagnose of rolling bearings via signals’ separation, the present paper proposes a new method to identify compound faults from measured mixed-signals, which is based on ensemble empirical mode decomposition (EEMD) method and independent component analysis (ICA) technique. With the approach, a vibration signal is firstly decomposed into intrinsic mode functions (IMF) by EEMD method to obtain multichannel signals. Then, according to a cross correlation criterion, the corresponding IMF is selected as the input matrix of ICA. Finally, the compound faults can be separated effectively by executing ICA method, which makes the fault features more easily extracted and more clearly identified. Experimental results validate the effectiveness of the proposed method in compound fault separating, which works not only for the outer race defect, but also for the rollers defect and the unbalance fault of the experimental system. PMID:25289644

Mathematical Justification of Expression-Based Pathway Activation Scoring (PAS).

PubMed

Aliper, Alexander M; Korzinkin, Michael B; Kuzmina, Natalia B; Zenin, Alexander A; Venkova, Larisa S; Smirnov, Philip Yu; Zhavoronkov, Alex A; Buzdin, Anton A; Borisov, Nikolay M

2017-01-01

Although modeling of activation kinetics for various cell signaling pathways has reached a high grade of sophistication and thoroughness, most such kinetic models still remain of rather limited practical value for biomedicine. Nevertheless, recent advancements have been made in application of signaling pathway science for real needs of prescription of the most effective drugs for individual patients. The methods for such prescription evaluate the degree of pathological changes in the signaling machinery based on two types of data: first, on the results of high-throughput gene expression profiling, and second, on the molecular pathway graphs that reflect interactions between the pathway members. For example, our algorithm OncoFinder evaluates the activation of molecular pathways on the basis of gene/protein expression data in the objects of the interest.Yet, the question of assessment of the relative importance for each gene product in a molecular pathway remains unclear unless one call for the methods of parameter sensitivity /stiffness analysis in the interactomic kinetic models of signaling pathway activation in terms of total concentrations of each gene product.Here we show two principal points: 1. First, the importance coefficients for each gene in pathways that were obtained using the extremely time- and labor-consuming stiffness analysis of full-scaled kinetic models generally differ from much easier-to-calculate expression-based pathway activation score (PAS) not more than by 30%, so the concept of PAS is kinetically justified. 2. Second, the use of pathway-based approach instead of distinct gene analysis, due to the law of large numbers, allows restoring the correlation between the similar samples that were examined using different transcriptome investigation techniques.

Comprehensive gene- and pathway-based analysis of depressive symptoms in older adults.

PubMed

Nho, Kwangsik; Ramanan, Vijay K; Horgusluoglu, Emrin; Kim, Sungeun; Inlow, Mark H; Risacher, Shannon L; McDonald, Brenna C; Farlow, Martin R; Foroud, Tatiana M; Gao, Sujuan; Callahan, Christopher M; Hendrie, Hugh C; Niculescu, Alexander B; Saykin, Andrew J

2015-01-01

Depressive symptoms are common in older adults and are particularly prevalent in those with or at elevated risk for dementia. Although the heritability of depression is estimated to be substantial, single nucleotide polymorphism-based genome-wide association studies of depressive symptoms have had limited success. In this study, we performed genome-wide gene- and pathway-based analyses of depressive symptom burden. Study participants included non-Hispanic Caucasian subjects (n = 6,884) from three independent cohorts, the Alzheimer's Disease Neuroimaging Initiative (ADNI), the Health and Retirement Study (HRS), and the Indiana Memory and Aging Study (IMAS). Gene-based meta-analysis identified genome-wide significant associations (ANGPT4 and FAM110A, q-value = 0.026; GRM7-AS3 and LRFN5, q-value = 0.042). Pathway analysis revealed enrichment of association in 105 pathways, including multiple pathways related to ERK/MAPK signaling, GSK3 signaling in bipolar disorder, cell development, and immune activation and inflammation. GRM7, ANGPT4, and LRFN5 have been previously implicated in psychiatric disorders, including the GRM7 region displaying association with major depressive disorder. The ERK/MAPK signaling pathway is a known target of antidepressant drugs and has important roles in neuronal plasticity, and GSK3 signaling has been previously implicated in Alzheimer's disease and as a promising therapeutic target for depression. Our results warrant further investigation in independent and larger cohorts and add to the growing understanding of the genetics and pathobiology of depressive symptoms in aging and neurodegenerative disorders. In particular, the genes and pathways demonstrating association with depressive symptoms may be potential therapeutic targets for these symptoms in older adults.

Bi-spectrum based-EMD applied to the non-stationary vibration signals for bearing faults diagnosis.

PubMed

Saidi, Lotfi; Ali, Jaouher Ben; Fnaiech, Farhat

2014-09-01

Empirical mode decomposition (EMD) has been widely applied to analyze vibration signals behavior for bearing failures detection. Vibration signals are almost always non-stationary since bearings are inherently dynamic (e.g., speed and load condition change over time). By using EMD, the complicated non-stationary vibration signal is decomposed into a number of stationary intrinsic mode functions (IMFs) based on the local characteristic time scale of the signal. Bi-spectrum, a third-order statistic, helps to identify phase coupling effects, the bi-spectrum is theoretically zero for Gaussian noise and it is flat for non-Gaussian white noise, consequently the bi-spectrum analysis is insensitive to random noise, which are useful for detecting faults in induction machines. Utilizing the advantages of EMD and bi-spectrum, this article proposes a joint method for detecting such faults, called bi-spectrum based EMD (BSEMD). First, original vibration signals collected from accelerometers are decomposed by EMD and a set of IMFs is produced. Then, the IMF signals are analyzed via bi-spectrum to detect outer race bearing defects. The procedure is illustrated with the experimental bearing vibration data. The experimental results show that BSEMD techniques can effectively diagnosis bearing failures. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Forecasting infectious disease emergence subject to seasonal forcing.

PubMed

Miller, Paige B; O'Dea, Eamon B; Rohani, Pejman; Drake, John M

2017-09-06

Despite high vaccination coverage, many childhood infections pose a growing threat to human populations. Accurate disease forecasting would be of tremendous value to public health. Forecasting disease emergence using early warning signals (EWS) is possible in non-seasonal models of infectious diseases. Here, we assessed whether EWS also anticipate disease emergence in seasonal models. We simulated the dynamics of an immunizing infectious pathogen approaching the tipping point to disease endemicity. To explore the effect of seasonality on the reliability of early warning statistics, we varied the amplitude of fluctuations around the average transmission. We proposed and analyzed two new early warning signals based on the wavelet spectrum. We measured the reliability of the early warning signals depending on the strength of their trend preceding the tipping point and then calculated the Area Under the Curve (AUC) statistic. Early warning signals were reliable when disease transmission was subject to seasonal forcing. Wavelet-based early warning signals were as reliable as other conventional early warning signals. We found that removing seasonal trends, prior to analysis, did not improve early warning statistics uniformly. Early warning signals anticipate the onset of critical transitions for infectious diseases which are subject to seasonal forcing. Wavelet-based early warning statistics can also be used to forecast infectious disease.

Short-time fractional Fourier methods for the time-frequency representation of chirp signals.

PubMed

Capus, Chris; Brown, Keith

2003-06-01

The fractional Fourier transform (FrFT) provides a valuable tool for the analysis of linear chirp signals. This paper develops two short-time FrFT variants which are suited to the analysis of multicomponent and nonlinear chirp signals. Outputs have similar properties to the short-time Fourier transform (STFT) but show improved time-frequency resolution. The FrFT is a parameterized transform with parameter, a, related to chirp rate. The two short-time implementations differ in how the value of a is chosen. In the first, a global optimization procedure selects one value of a with reference to the entire signal. In the second, a values are selected independently for each windowed section. Comparative variance measures based on the Gaussian function are given and are shown to be consistent with the uncertainty principle in fractional domains. For appropriately chosen FrFT orders, the derived fractional domain uncertainty relationship is minimized for Gaussian windowed linear chirp signals. The two short-time FrFT algorithms have complementary strengths demonstrated by time-frequency representations for a multicomponent bat chirp, a highly nonlinear quadratic chirp, and an output pulse from a finite-difference sonar model with dispersive change. These representations illustrate the improvements obtained in using FrFT based algorithms compared to the STFT.

A Combined Methodology to Eliminate Artifacts in Multichannel Electrogastrogram Based on Independent Component Analysis and Ensemble Empirical Mode Decomposition.

PubMed

Sengottuvel, S; Khan, Pathan Fayaz; Mariyappa, N; Patel, Rajesh; Saipriya, S; Gireesan, K

2018-06-01

Cutaneous measurements of electrogastrogram (EGG) signals are heavily contaminated by artifacts due to cardiac activity, breathing, motion artifacts, and electrode drifts whose effective elimination remains an open problem. A common methodology is proposed by combining independent component analysis (ICA) and ensemble empirical mode decomposition (EEMD) to denoise gastric slow-wave signals in multichannel EGG data. Sixteen electrodes are fixed over the upper abdomen to measure the EGG signals under three gastric conditions, namely, preprandial, postprandial immediately, and postprandial 2 h after food for three healthy subjects and a subject with a gastric disorder. Instantaneous frequencies of intrinsic mode functions that are obtained by applying the EEMD technique are analyzed to individually identify and remove each of the artifacts. A critical investigation on the proposed ICA-EEMD method reveals its ability to provide a higher attenuation of artifacts and lower distortion than those obtained by the ICA-EMD method and conventional techniques, like bandpass and adaptive filtering. Characteristic changes in the slow-wave frequencies across the three gastric conditions could be determined from the denoised signals for all the cases. The results therefore encourage the use of the EEMD-based technique for denoising gastric signals to be used in clinical practice.

Text Signals Influence Second Language Expository Text Comprehension: Knowledge Structure Analysis

ERIC Educational Resources Information Center

Kim, Kyung; Clariana, Roy B.

2017-01-01

This quasi-experimental investigation describes the influence of text signals on second language expository science text comprehension. In two course sections, mixed proficiency Korean English language learners (n = 88) read one of two print-based English expository text passage versions. Participants in one section (n = 44) were given a version…

Single photon detection and signal analysis for high sensitivity dosimetry based on optically stimulated luminescence with beryllium oxide

NASA Astrophysics Data System (ADS)

Radtke, J.; Sponner, J.; Jakobi, C.; Schneider, J.; Sommer, M.; Teichmann, T.; Ullrich, W.; Henniger, J.; Kormoll, T.

2018-01-01

Single photon detection applied to optically stimulated luminescence (OSL) dosimetry is a promising approach due to the low level of luminescence light and the known statistical behavior of single photon events. Time resolved detection allows to apply a variety of different and independent data analysis methods. Furthermore, using amplitude modulated stimulation impresses time- and frequency information into the OSL light and therefore allows for additional means of analysis. Considering the impressed frequency information, data analysis by using Fourier transform algorithms or other digital filters can be used for separating the OSL signal from unwanted light or events generated by other phenomena. This potentially lowers the detection limits of low dose measurements and might improve the reproducibility and stability of obtained data. In this work, an OSL system based on a single photon detector, a fast and accurate stimulation unit and an FPGA is presented. Different analysis algorithms which are applied to the single photon data are discussed.

Theory and simulations of covariance mapping in multiple dimensions for data analysis in high-event-rate experiments

NASA Astrophysics Data System (ADS)

Zhaunerchyk, V.; Frasinski, L. J.; Eland, J. H. D.; Feifel, R.

2014-05-01

Multidimensional covariance analysis and its validity for correlation of processes leading to multiple products are investigated from a theoretical point of view. The need to correct for false correlations induced by experimental parameters which fluctuate from shot to shot, such as the intensity of self-amplified spontaneous emission x-ray free-electron laser pulses, is emphasized. Threefold covariance analysis based on simple extension of the two-variable formulation is shown to be valid for variables exhibiting Poisson statistics. In this case, false correlations arising from fluctuations in an unstable experimental parameter that scale linearly with signals can be eliminated by threefold partial covariance analysis, as defined here. Fourfold covariance based on the same simple extension is found to be invalid in general. Where fluctuations in an unstable parameter induce nonlinear signal variations, a technique of contingent covariance analysis is proposed here to suppress false correlations. In this paper we also show a method to eliminate false correlations associated with fluctuations of several unstable experimental parameters.

BATMAN: Bayesian Technique for Multi-image Analysis

NASA Astrophysics Data System (ADS)

Casado, J.; Ascasibar, Y.; García-Benito, R.; Guidi, G.; Choudhury, O. S.; Bellocchi, E.; Sánchez, S. F.; Díaz, A. I.

2017-04-01

This paper describes the Bayesian Technique for Multi-image Analysis (BATMAN), a novel image-segmentation technique based on Bayesian statistics that characterizes any astronomical data set containing spatial information and performs a tessellation based on the measurements and errors provided as input. The algorithm iteratively merges spatial elements as long as they are statistically consistent with carrying the same information (I.e. identical signal within the errors). We illustrate its operation and performance with a set of test cases including both synthetic and real integral-field spectroscopic data. The output segmentations adapt to the underlying spatial structure, regardless of its morphology and/or the statistical properties of the noise. The quality of the recovered signal represents an improvement with respect to the input, especially in regions with low signal-to-noise ratio. However, the algorithm may be sensitive to small-scale random fluctuations, and its performance in presence of spatial gradients is limited. Due to these effects, errors may be underestimated by as much as a factor of 2. Our analysis reveals that the algorithm prioritizes conservation of all the statistically significant information over noise reduction, and that the precise choice of the input data has a crucial impact on the results. Hence, the philosophy of BaTMAn is not to be used as a 'black box' to improve the signal-to-noise ratio, but as a new approach to characterize spatially resolved data prior to its analysis. The source code is publicly available at http://astro.ft.uam.es/SELGIFS/BaTMAn.

Deterring watermark collusion attacks using signal processing techniques

NASA Astrophysics Data System (ADS)

Lemma, Aweke N.; van der Veen, Michiel

2007-02-01

Collusion attack is a malicious watermark removal attack in which the hacker has access to multiple copies of the same content with different watermarks and tries to remove the watermark using averaging. In the literature, several solutions to collusion attacks have been reported. The main stream solutions aim at designing watermark codes that are inherently resistant to collusion attacks. The other approaches propose signal processing based solutions that aim at modifying the watermarked signals in such a way that averaging multiple copies of the content leads to a significant degradation of the content quality. In this paper, we present signal processing based technique that may be deployed for deterring collusion attacks. We formulate the problem in the context of electronic music distribution where the content is generally available in the compressed domain. Thus, we first extend the collusion resistance principles to bit stream signals and secondly present experimental based analysis to estimate a bound on the maximum number of modified versions of a content that satisfy good perceptibility requirement on one hand and destructive averaging property on the other hand.

Genome-wide analysis of a Wnt1-regulated transcriptional network implicates neurodegenerative pathways.

PubMed

Wexler, Eric M; Rosen, Ezra; Lu, Daning; Osborn, Gregory E; Martin, Elizabeth; Raybould, Helen; Geschwind, Daniel H

2011-10-04

Wnt proteins are critical to mammalian brain development and function. The canonical Wnt signaling pathway involves the stabilization and nuclear translocation of β-catenin; however, Wnt also signals through alternative, noncanonical pathways. To gain a systems-level, genome-wide view of Wnt signaling, we analyzed Wnt1-stimulated changes in gene expression by transcriptional microarray analysis in cultured human neural progenitor (hNP) cells at multiple time points over a 72-hour time course. We observed a widespread oscillatory-like pattern of changes in gene expression, involving components of both the canonical and the noncanonical Wnt signaling pathways. A higher-order, systems-level analysis that combined independent component analysis, waveform analysis, and mutual information-based network construction revealed effects on pathways related to cell death and neurodegenerative disease. Wnt effectors were tightly clustered with presenilin1 (PSEN1) and granulin (GRN), which cause dominantly inherited forms of Alzheimer's disease and frontotemporal dementia (FTD), respectively. We further explored a potential link between Wnt1 and GRN and found that Wnt1 decreased GRN expression by hNPs. Conversely, GRN knockdown increased WNT1 expression, demonstrating that Wnt and GRN reciprocally regulate each other. Finally, we provided in vivo validation of the in vitro findings by analyzing gene expression data from individuals with FTD. These unbiased and genome-wide analyses provide evidence for a connection between Wnt signaling and the transcriptional regulation of neurodegenerative disease genes.

Computational Analysis and Simulation of Empathic Behaviors: a Survey of Empathy Modeling with Behavioral Signal Processing Framework.

PubMed

Xiao, Bo; Imel, Zac E; Georgiou, Panayiotis; Atkins, David C; Narayanan, Shrikanth S

2016-05-01

Empathy is an important psychological process that facilitates human communication and interaction. Enhancement of empathy has profound significance in a range of applications. In this paper, we review emerging directions of research on computational analysis of empathy expression and perception as well as empathic interactions, including their simulation. We summarize the work on empathic expression analysis by the targeted signal modalities (e.g., text, audio, and facial expressions). We categorize empathy simulation studies into theory-based emotion space modeling or application-driven user and context modeling. We summarize challenges in computational study of empathy including conceptual framing and understanding of empathy, data availability, appropriate use and validation of machine learning techniques, and behavior signal processing. Finally, we propose a unified view of empathy computation and offer a series of open problems for future research.

Resonance-Based Sparse Signal Decomposition and its Application in Mechanical Fault Diagnosis: A Review.

PubMed

Huang, Wentao; Sun, Hongjian; Wang, Weijie

2017-06-03

Mechanical equipment is the heart of industry. For this reason, mechanical fault diagnosis has drawn considerable attention. In terms of the rich information hidden in fault vibration signals, the processing and analysis techniques of vibration signals have become a crucial research issue in the field of mechanical fault diagnosis. Based on the theory of sparse decomposition, Selesnick proposed a novel nonlinear signal processing method: resonance-based sparse signal decomposition (RSSD). Since being put forward, RSSD has become widely recognized, and many RSSD-based methods have been developed to guide mechanical fault diagnosis. This paper attempts to summarize and review the theoretical developments and application advances of RSSD in mechanical fault diagnosis, and to provide a more comprehensive reference for those interested in RSSD and mechanical fault diagnosis. Followed by a brief introduction of RSSD's theoretical foundation, based on different optimization directions, applications of RSSD in mechanical fault diagnosis are categorized into five aspects: original RSSD, parameter optimized RSSD, subband optimized RSSD, integrated optimized RSSD, and RSSD combined with other methods. On this basis, outstanding issues in current RSSD study are also pointed out, as well as corresponding instructional solutions. We hope this review will provide an insightful reference for researchers and readers who are interested in RSSD and mechanical fault diagnosis.

Resonance-Based Sparse Signal Decomposition and Its Application in Mechanical Fault Diagnosis: A Review

PubMed Central

Huang, Wentao; Sun, Hongjian; Wang, Weijie

2017-01-01

Mechanical equipment is the heart of industry. For this reason, mechanical fault diagnosis has drawn considerable attention. In terms of the rich information hidden in fault vibration signals, the processing and analysis techniques of vibration signals have become a crucial research issue in the field of mechanical fault diagnosis. Based on the theory of sparse decomposition, Selesnick proposed a novel nonlinear signal processing method: resonance-based sparse signal decomposition (RSSD). Since being put forward, RSSD has become widely recognized, and many RSSD-based methods have been developed to guide mechanical fault diagnosis. This paper attempts to summarize and review the theoretical developments and application advances of RSSD in mechanical fault diagnosis, and to provide a more comprehensive reference for those interested in RSSD and mechanical fault diagnosis. Followed by a brief introduction of RSSD’s theoretical foundation, based on different optimization directions, applications of RSSD in mechanical fault diagnosis are categorized into five aspects: original RSSD, parameter optimized RSSD, subband optimized RSSD, integrated optimized RSSD, and RSSD combined with other methods. On this basis, outstanding issues in current RSSD study are also pointed out, as well as corresponding instructional solutions. We hope this review will provide an insightful reference for researchers and readers who are interested in RSSD and mechanical fault diagnosis. PMID:28587198

Integrated analysis reveals that STAT3 is central to the crosstalk between HER/ErbB receptor signaling pathways in human mammary epithelial cells

DOE PAGES

Gong, Chunhong; Zhang, Yi; Shankaran, Harish; ...

2014-10-02

Human epidermal growth factor receptors (HER, also known as ErbB) drive cellular proliferation, pro-survival and stress responses by activating several downstream kinases, in particular ERK, p38, JNK (SAPK), the PI3K/AKT, as well as various transcriptional regulators such as STAT3. When co-expressed, first three members of HER family (HER1-3) can form homo- and hetero-dimers. Based on the considerable evidence which suggest that every receptor dimer activates intracellular signaling pathways differentially, we hypothesized that the HER dimerization pattern is a better predictor of downstream signaling than the total receptor activation levels. We validated our hypothesis using a combination of model-based analysis tomore » quantify the HER dimerization patterns and multi-factorial experiments where HER dimerization patterns and signaling crosstalk were rationally perturbed. We have measured the activation of HER1-3 receptors and of the sentinel signaling proteins ERK, AKT, p38, JNK, STAT3 as a function of time in a panel of human mammary epithelial (HME) cells expressing different levels of HER1-3 stimulated with various ligand combinations. Our analysis using multiple ways of clustering the activation data has confirmed that the HER receptor dimer is a better predictor of the signaling through p38, ERK and AKT pathways than the total HER receptor expression and activation levels. Targeted inhibition studies to identify the causal effects allowed us to obtain a consensus regulatory interaction model, which revealed that STAT3 occupies a central role in the crosstalk between the studied pathways.« less

Artifact removal from EEG data with empirical mode decomposition

NASA Astrophysics Data System (ADS)

Grubov, Vadim V.; Runnova, Anastasiya E.; Efremova, Tatyana Yu.; Hramov, Alexander E.

2017-03-01

In the paper we propose the novel method for dealing with the physiological artifacts caused by intensive activity of facial and neck muscles and other movements in experimental human EEG recordings. The method is based on analysis of EEG signals with empirical mode decomposition (Hilbert-Huang transform). We introduce the mathematical algorithm of the method with following steps: empirical mode decomposition of EEG signal, choosing of empirical modes with artifacts, removing empirical modes with artifacts, reconstruction of the initial EEG signal. We test the method on filtration of experimental human EEG signals from movement artifacts and show high efficiency of the method.

Comparison of Signals from Gravitational Wave Detectors with Instantaneous Time-Frequency Maps

NASA Technical Reports Server (NTRS)

Stroeer, A.; Blackburn, L.; Camp, J.

2011-01-01

Gravitational wave astronomy relies on the use of multiple detectors, so that coincident detections may distinguish real signals from instrumental artifacts, and also so that relative timing of signals can provide the sky position of sources. We show that the comparison of instantaneous time-frequency and time-amplitude maps provided by the Hilbert-Huang Transform (HHT) can be used effectively for relative signal timing of common signals, to discriminate between the case of identical coincident signals and random noise coincidences and to provide a classification of signals based on their time-frequency trajectories. The comparison is done with a X(sup 2) goodness-offit method which includes contributions from both the instantaneous amplitude and frequency components of the HHT to match two signals in the time domain. This approach naturally allows the analysis of waveforms with strong frequency modulation.

Directional dual-tree complex wavelet packet transforms for processing quadrature signals.

PubMed

Serbes, Gorkem; Gulcur, Halil Ozcan; Aydin, Nizamettin

2016-03-01

Quadrature signals containing in-phase and quadrature-phase components are used in many signal processing applications in every field of science and engineering. Specifically, Doppler ultrasound systems used to evaluate cardiovascular disorders noninvasively also result in quadrature format signals. In order to obtain directional blood flow information, the quadrature outputs have to be preprocessed using methods such as asymmetrical and symmetrical phasing filter techniques. These resultant directional signals can be employed in order to detect asymptomatic embolic signals caused by small emboli, which are indicators of a possible future stroke, in the cerebral circulation. Various transform-based methods such as Fourier and wavelet were frequently used in processing embolic signals. However, most of the times, the Fourier and discrete wavelet transforms are not appropriate for the analysis of embolic signals due to their non-stationary time-frequency behavior. Alternatively, discrete wavelet packet transform can perform an adaptive decomposition of the time-frequency axis. In this study, directional discrete wavelet packet transforms, which have the ability to map directional information while processing quadrature signals and have less computational complexity than the existing wavelet packet-based methods, are introduced. The performances of proposed methods are examined in detail by using single-frequency, synthetic narrow-band, and embolic quadrature signals.

Signaling Role of Fructose Mediated by FINS1/FBP in Arabidopsis thaliana

PubMed Central

Cho, Young-Hee; Yoo, Sang-Dong

2011-01-01

Sugars are evolutionarily conserved signaling molecules that regulate the growth and development of both unicellular and multicellular organisms. As sugar-producing photosynthetic organisms, plants utilize glucose as one of their major signaling molecules. However, the details of other sugar signaling molecules and their regulatory factors have remained elusive, due to the complexity of the metabolite and hormone interactions that control physiological and developmental programs in plants. We combined information from a gain-of-function cell-based screen and a loss-of-function reverse-genetic analysis to demonstrate that fructose acts as a signaling molecule in Arabidopsis thaliana. Fructose signaling induced seedling developmental arrest and interacted with plant stress hormone signaling in a manner similar to that of glucose. For fructose signaling responses, the plant glucose sensor HEXOKINASE1 (HXK1) was dispensable, while FRUCTOSE INSENSITIVE1 (FINS1), a putative FRUCTOSE-1,6-BISPHOSPHATASE, played a crucial role. Interestingly, FINS1 function in fructose signaling appeared to be independent of its catalytic activity in sugar metabolism. Genetic analysis further indicated that FINS1–dependent fructose signaling may act downstream of the abscisic acid pathway, in spite of the fact that HXK1–dependent glucose signaling works upstream of hormone synthesis. Our findings revealed that multiple layers of controls by fructose, glucose, and abscisic acid finely tune the plant autotrophic transition and modulate early seedling establishment after seed germination. PMID:21253566

Linear Array Ultrasonic Testing Of A Thick Concrete Specimens For Non-Destructive Evaluation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Clayton, Dwight A.; Khazanovich, Lev; Zammerachi, Mattia

The University of Minnesota and Oak Ridge National Laboratory are collaborating on the design and construction of a concrete specimen with sufficient reinforcement density and cross-sectional size to represent a light water reactor (LWR) containment wall with various defects. The preliminary analysis of the collected data using extended synthetic aperture focussin technique (SAFT) reconstruction indicated a great potential of the ultrasound array technology for locating relatively shallow distresses. However, the resolution and reliability of the analysis is inversely proportional to the defect depth and the amount of reinforcement between the measurement point and the defect location. The objective of thismore » round of testing is to evaluate repeatability of the obtained reconstructions from measurements with different frequencies as well as to examine the effect of the duration of the sending ultrasound signal on the resulting reconstructions. Two series of testing are performed in this study. The objective of the first series is to evaluate repeatability of the measurements and resulting reconstructed images. The measurements use three center frequencies. Five measurements are performed at each location with and without lifting the device. The analysis of the collected data suggested that a linear array ultrasound system can produce reliably repeatable reconstructions using 50 kHz signals for relatively shallow depths (less than 0.5 m). However, for reconstructions at the greater depths the use of lower frequency and/or signal filtering to reduce the effect of signal noise may be required. The objective of the second series of testing is to obtain measurements with various impulse signal durations. The entire grid on the smooth surface is tested with four different various impulse signal durations. An analysis of the resulting extended SAFT reconstructions suggested that Kirchhoff-based migration leads to easier interpreting reconstructions when shorter duration impulse is used. Longer duration impulses may provide useful information for model-based reconstructions.« less

Analysis and Modeling of Echolocation Signals Emitted by Mediterranean Bottlenose Dolphins

NASA Astrophysics Data System (ADS)

Greco, Maria; Gini, Fulvio

2006-12-01

We analyzed the echolocation sounds emitted by Mediterranean bottlenose dolphins. We extracted the click trains by visual inspection of the data files recorded along the coast of the Tuscany with the collaboration of the CETUS Research Center. We modeled the extracted sonar clicks as Gaussian or exponential multicomponent signals, we estimated the characteristic parameters and compared the data with the reconstructed signals based on the estimates. Results about the estimation and the data fitting are largely shown in the paper.

Application of Mathematical Signal Processing Techniques to Mission Systems. (l’Application des techniques mathematiques du traitement du signal aux systemes de conduite des missions)

DTIC Science & Technology

1999-11-01

represents the linear time invariant (LTI) response of the combined analysis /synthesis system while the second repre- sents the aliasing introduced into...effectively to implement voice scrambling systems based on time - frequency permutation . The most general form of such a system is shown in Fig. 22 where...92201 NEUILLY-SUR-SEINE CEDEX, FRANCE RTO LECTURE SERIES 216 Application of Mathematical Signal Processing Techniques to Mission Systems (1

Solid-state circularly polarized luminescence measurements: Theoretical analysis

NASA Astrophysics Data System (ADS)

Harada, Takunori; Kuroda, Reiko; Moriyama, Hiroshi

2012-03-01

Because a circularly polarized luminescence (CPL) spectrophotometer is a polarization-modulation instrument, artifacts resulting from optical anisotropies that are unique to the solid state necessarily accompany CPL signals. A set of procedures for obtaining the true CPL signal has been derived based on the Stokes-Mueller matrix method. Experiments on chiral fluorophore single crystals of benzil with larger and smaller optical anisotropies have shown that our method can eliminate parasitic artifacts to obtain the true CPL signal, even in cases where optical anisotropies are substantial.

A Novel Range Compression Algorithm for Resolution Enhancement in GNSS-SARs.

PubMed

Zheng, Yu; Yang, Yang; Chen, Wu

2017-06-25

In this paper, a novel range compression algorithm for enhancing range resolutions of a passive Global Navigation Satellite System-based Synthetic Aperture Radar (GNSS-SAR) is proposed. In the proposed algorithm, within each azimuth bin, firstly range compression is carried out by correlating a reflected GNSS intermediate frequency (IF) signal with a synchronized direct GNSS base-band signal in the range domain. Thereafter, spectrum equalization is applied to the compressed results for suppressing side lobes to obtain a final range-compressed signal. Both theoretical analysis and simulation results have demonstrated that significant range resolution improvement in GNSS-SAR images can be achieved by the proposed range compression algorithm, compared to the conventional range compression algorithm.

Compensation based on linearized analysis for a six degree of freedom motion simulator

NASA Technical Reports Server (NTRS)

Parrish, R. V.; Dieudonne, J. E.; Martin, D. J., Jr.; Copeland, J. L.

1973-01-01

The inertial response characteristics of a synergistic, six-degree-of-freedom motion base are presented in terms of amplitude ratio and phase lag as functions of frequency data for the frequency range of interest (0 to 2 Hz) in real time, digital, flight simulators. The notch filters which smooth the digital-drive signals to continuous drive signals are presented, and appropriate compensation, based on the inertial response data, is suggested. The existence of an inverse transformation that converts actuator extensions into inertial positions makes it possible to gather the response data in the inertial axis system.

Magnetic Flux Leakage Sensing and Artificial Neural Network Pattern Recognition-Based Automated Damage Detection and Quantification for Wire Rope Non-Destructive Evaluation.

PubMed

Kim, Ju-Won; Park, Seunghee

2018-01-02

In this study, a magnetic flux leakage (MFL) method, known to be a suitable non-destructive evaluation (NDE) method for continuum ferromagnetic structures, was used to detect local damage when inspecting steel wire ropes. To demonstrate the proposed damage detection method through experiments, a multi-channel MFL sensor head was fabricated using a Hall sensor array and magnetic yokes to adapt to the wire rope. To prepare the damaged wire-rope specimens, several different amounts of artificial damages were inflicted on wire ropes. The MFL sensor head was used to scan the damaged specimens to measure the magnetic flux signals. After obtaining the signals, a series of signal processing steps, including the enveloping process based on the Hilbert transform (HT), was performed to better recognize the MFL signals by reducing the unexpected noise. The enveloped signals were then analyzed for objective damage detection by comparing them with a threshold that was established based on the generalized extreme value (GEV) distribution. The detected MFL signals that exceed the threshold were analyzed quantitatively by extracting the magnetic features from the MFL signals. To improve the quantitative analysis, damage indexes based on the relationship between the enveloped MFL signal and the threshold value were also utilized, along with a general damage index for the MFL method. The detected MFL signals for each damage type were quantified by using the proposed damage indexes and the general damage indexes for the MFL method. Finally, an artificial neural network (ANN) based multi-stage pattern recognition method using extracted multi-scale damage indexes was implemented to automatically estimate the severity of the damage. To analyze the reliability of the MFL-based automated wire rope NDE method, the accuracy and reliability were evaluated by comparing the repeatedly estimated damage size and the actual damage size.

Vibration based condition monitoring of a multistage epicyclic gearbox in lifting cranes

NASA Astrophysics Data System (ADS)

Assaad, Bassel; Eltabach, Mario; Antoni, Jérôme

2014-01-01

This paper proposes a model-based technique for detecting wear in a multistage planetary gearbox used by lifting cranes. The proposed method establishes a vibration signal model which deals with cyclostationary and autoregressive models. First-order cyclostationarity is addressed by the analysis of the time synchronous average (TSA) of the angular resampled vibration signal. Then an autoregressive model (AR) is applied to the TSA part in order to extract a residual signal containing pertinent fault signatures. The paper also explores a number of methods commonly used in vibration monitoring of planetary gearboxes, in order to make comparisons. In the experimental part of this study, these techniques are applied to accelerated lifetime test bench data for the lifting winch. After processing raw signals recorded with an accelerometer mounted on the outside of the gearbox, a number of condition indicators (CIs) are derived from the TSA signal, the residual autoregressive signal and other signals derived using standard signal processing methods. The goal is to check the evolution of the CIs during the accelerated lifetime test (ALT). Clarity and fluctuation level of the historical trends are finally considered as a criteria for comparing between the extracted CIs.

Integrating physically based simulators with Event Detection Systems: Multi-site detection approach.

PubMed

Housh, Mashor; Ohar, Ziv

2017-03-01

The Fault Detection (FD) Problem in control theory concerns of monitoring a system to identify when a fault has occurred. Two approaches can be distinguished for the FD: Signal processing based FD and Model-based FD. The former concerns of developing algorithms to directly infer faults from sensors' readings, while the latter uses a simulation model of the real-system to analyze the discrepancy between sensors' readings and expected values from the simulation model. Most contamination Event Detection Systems (EDSs) for water distribution systems have followed the signal processing based FD, which relies on analyzing the signals from monitoring stations independently of each other, rather than evaluating all stations simultaneously within an integrated network. In this study, we show that a model-based EDS which utilizes a physically based water quality and hydraulics simulation models, can outperform the signal processing based EDS. We also show that the model-based EDS can facilitate the development of a Multi-Site EDS (MSEDS), which analyzes the data from all the monitoring stations simultaneously within an integrated network. The advantage of the joint analysis in the MSEDS is expressed by increased detection accuracy (higher true positive alarms and fewer false alarms) and shorter detection time. Copyright © 2016 Elsevier Ltd. All rights reserved.

Architecture of Eph receptor clusters

DOE Office of Scientific and Technical Information (OSTI.GOV)

Himanen, Juha P.; Yermekbayeva, Laila; Janes, Peter W.

2010-10-04

Eph receptor tyrosine kinases and their ephrin ligands regulate cell navigation during normal and oncogenic development. Signaling of Ephs is initiated in a multistep process leading to the assembly of higher-order signaling clusters that set off bidirectional signaling in interacting cells. However, the structural and mechanistic details of this assembly remained undefined. Here we present high-resolution structures of the complete EphA2 ectodomain and complexes with ephrin-A1 and A5 as the base unit of an Eph cluster. The structures reveal an elongated architecture with novel Eph/Eph interactions, both within and outside of the Eph ligand-binding domain, that suggest the molecular mechanismmore » underlying Eph/ephrin clustering. Structure-function analysis, by using site-directed mutagenesis and cell-based signaling assays, confirms the importance of the identified oligomerization interfaces for Eph clustering.« less

Electroencephalography Based Fusion Two-Dimensional (2D)-Convolution Neural Networks (CNN) Model for Emotion Recognition System.

PubMed

Kwon, Yea-Hoon; Shin, Sae-Byuk; Kim, Shin-Dug

2018-04-30

The purpose of this study is to improve human emotional classification accuracy using a convolution neural networks (CNN) model and to suggest an overall method to classify emotion based on multimodal data. We improved classification performance by combining electroencephalogram (EEG) and galvanic skin response (GSR) signals. GSR signals are preprocessed using by the zero-crossing rate. Sufficient EEG feature extraction can be obtained through CNN. Therefore, we propose a suitable CNN model for feature extraction by tuning hyper parameters in convolution filters. The EEG signal is preprocessed prior to convolution by a wavelet transform while considering time and frequency simultaneously. We use a database for emotion analysis using the physiological signals open dataset to verify the proposed process, achieving 73.4% accuracy, showing significant performance improvement over the current best practice models.

Small-Signal Dynamic Analysis of LCC-HVDC with STATCOM at the Inverter Busbar

NASA Astrophysics Data System (ADS)

Liu, Dong; Jiang, Wen; Guo, Chunyi; Rehman, Atiq Ur; Zhao, Chengyong

2018-01-01

This paper develops a linearized small-signal dynamic model of a Line-Commutated-Converter based HVDC (LCC-HVDC) system with STATCOM at the inverter busbar, and validates its accuracy by comparing time-domain responses from small-signal model and PSCAD-based simulation results. Considering the potential impact of Phase-Locked-Loop (PLL) parameters on the study system and the close connection of STATCOM and LCC inverter station at AC busbar, this paper investigates the impact of PLL gains and AC voltage control parameters of STATCOM on the system small-signal stability. The studies show that (i) the PLL gain has highly impact on the study system and smaller PLL gains are preferable; (ii) larger values of both the proportional gain and the integral gain of AC voltage controller of STATCOM could result in oscillation/instability of the system.

A New Method for Suppressing Periodic Narrowband Interference Based on the Chaotic van der Pol Oscillator

NASA Astrophysics Data System (ADS)

Lu, Jia; Zhang, Xiaoxing; Xiong, Hao

The chaotic van der Pol oscillator is a powerful tool for detecting defects in electric systems by using online partial discharge (PD) monitoring. This paper focuses on realizing weak PD signal detection in the strong periodic narrowband interference by using high sensitivity to the periodic narrowband interference signals and immunity to white noise and PD signals of chaotic systems. A new approach to removing the periodic narrowband interference by using a van der Pol chaotic oscillator is described by analyzing the motion characteristic of the chaotic oscillator on the basis of the van der Pol equation. Furthermore, the Floquet index for measuring the amplitude of periodic narrowband signals is redefined. The denoising signal processed by the chaotic van der Pol oscillators is further processed by wavelet analysis. Finally, the denoising results verify that the periodic narrowband and white noise interference can be removed efficiently by combining the theory of the chaotic van der Pol oscillator and wavelet analysis.

Wind Turbine Diagnosis under Variable Speed Conditions Using a Single Sensor Based on the Synchrosqueezing Transform Method.

PubMed

Guo, Yanjie; Chen, Xuefeng; Wang, Shibin; Sun, Ruobin; Zhao, Zhibin

2017-05-18

The gearbox is one of the key components in wind turbines. Gearbox fault signals are usually nonstationary and highly contaminated with noise. The presence of amplitude-modulated and frequency-modulated (AM-FM) characteristics compound the difficulty of precise fault diagnosis of wind turbines, therefore, it is crucial to develop an effective fault diagnosis method for such equipment. This paper presents an improved diagnosis method for wind turbines via the combination of synchrosqueezing transform and local mean decomposition. Compared to the conventional time-frequency analysis techniques, the improved method which is performed in non-real-time can effectively reduce the noise pollution of the signals and preserve the signal characteristics, and hence is suitable for the analysis of nonstationary signals with high noise. This method is further validated by simulated signals and practical vibration data measured from a 1.5 MW wind turbine. The results confirm that the proposed method can simultaneously control the noise and increase the accuracy of time-frequency representation.

Wind Turbine Diagnosis under Variable Speed Conditions Using a Single Sensor Based on the Synchrosqueezing Transform Method

PubMed Central

Guo, Yanjie; Chen, Xuefeng; Wang, Shibin; Sun, Ruobin; Zhao, Zhibin

2017-01-01

The gearbox is one of the key components in wind turbines. Gearbox fault signals are usually nonstationary and highly contaminated with noise. The presence of amplitude-modulated and frequency-modulated (AM-FM) characteristics compound the difficulty of precise fault diagnosis of wind turbines, therefore, it is crucial to develop an effective fault diagnosis method for such equipment. This paper presents an improved diagnosis method for wind turbines via the combination of synchrosqueezing transform and local mean decomposition. Compared to the conventional time-frequency analysis techniques, the improved method which is performed in non-real-time can effectively reduce the noise pollution of the signals and preserve the signal characteristics, and hence is suitable for the analysis of nonstationary signals with high noise. This method is further validated by simulated signals and practical vibration data measured from a 1.5 MW wind turbine. The results confirm that the proposed method can simultaneously control the noise and increase the accuracy of time-frequency representation. PMID:28524090

Null stream analysis of Pulsar Timing Array data: localisation of resolvable gravitational wave sources

NASA Astrophysics Data System (ADS)

Goldstein, Janna; Veitch, John; Sesana, Alberto; Vecchio, Alberto

2018-04-01

Super-massive black hole binaries are expected to produce a gravitational wave (GW) signal in the nano-Hertz frequency band which may be detected by pulsar timing arrays (PTAs) in the coming years. The signal is composed of both stochastic and individually resolvable components. Here we develop a generic Bayesian method for the analysis of resolvable sources based on the construction of `null-streams' which cancel the part of the signal held in common for each pulsar (the Earth-term). For an array of N pulsars there are N - 2 independent null-streams that cancel the GW signal from a particular sky location. This method is applied to the localisation of quasi-circular binaries undergoing adiabatic inspiral. We carry out a systematic investigation of the scaling of the localisation accuracy with signal strength and number of pulsars in the PTA. Additionally, we find that source sky localisation with the International PTA data release one is vastly superior than what is achieved by its constituent regional PTAs.

Using independent component analysis for electrical impedance tomography

NASA Astrophysics Data System (ADS)

Yan, Peimin; Mo, Yulong

2004-05-01

Independent component analysis (ICA) is a way to resolve signals into independent components based on the statistical characteristics of the signals. It is a method for factoring probability densities of measured signals into a set of densities that are as statistically independent as possible under the assumptions of a linear model. Electrical impedance tomography (EIT) is used to detect variations of the electric conductivity of the human body. Because there are variations of the conductivity distributions inside the body, EIT presents multi-channel data. In order to get all information contained in different location of tissue it is necessary to image the individual conductivity distribution. In this paper we consider to apply ICA to EIT on the signal subspace (individual conductivity distribution). Using ICA the signal subspace will then be decomposed into statistically independent components. The individual conductivity distribution can be reconstructed by the sensitivity theorem in this paper. Compute simulations show that the full information contained in the multi-conductivity distribution will be obtained by this method.

Uniform, optimal signal processing of mapped deep-sequencing data.

PubMed

Kumar, Vibhor; Muratani, Masafumi; Rayan, Nirmala Arul; Kraus, Petra; Lufkin, Thomas; Ng, Huck Hui; Prabhakar, Shyam

2013-07-01

Despite their apparent diversity, many problems in the analysis of high-throughput sequencing data are merely special cases of two general problems, signal detection and signal estimation. Here we adapt formally optimal solutions from signal processing theory to analyze signals of DNA sequence reads mapped to a genome. We describe DFilter, a detection algorithm that identifies regulatory features in ChIP-seq, DNase-seq and FAIRE-seq data more accurately than assay-specific algorithms. We also describe EFilter, an estimation algorithm that accurately predicts mRNA levels from as few as 1-2 histone profiles (R ∼0.9). Notably, the presence of regulatory motifs in promoters correlates more with histone modifications than with mRNA levels, suggesting that histone profiles are more predictive of cis-regulatory mechanisms. We show by applying DFilter and EFilter to embryonic forebrain ChIP-seq data that regulatory protein identification and functional annotation are feasible despite tissue heterogeneity. The mathematical formalism underlying our tools facilitates integrative analysis of data from virtually any sequencing-based functional profile.

Independent component analysis based digital signal processing in coherent optical fiber communication systems

NASA Astrophysics Data System (ADS)

Li, Xiang; Luo, Ming; Qiu, Ying; Alphones, Arokiaswami; Zhong, Wen-De; Yu, Changyuan; Yang, Qi

2018-02-01

In this paper, channel equalization techniques for coherent optical fiber transmission systems based on independent component analysis (ICA) are reviewed. The principle of ICA for blind source separation is introduced. The ICA based channel equalization after both single-mode fiber and few-mode fiber transmission for single-carrier and orthogonal frequency division multiplexing (OFDM) modulation formats are investigated, respectively. The performance comparisons with conventional channel equalization techniques are discussed.

Overrepresentation of glutamate signaling in Alzheimer's disease: network-based pathway enrichment using meta-analysis of genome-wide association studies.

PubMed

Pérez-Palma, Eduardo; Bustos, Bernabé I; Villamán, Camilo F; Alarcón, Marcelo A; Avila, Miguel E; Ugarte, Giorgia D; Reyes, Ariel E; Opazo, Carlos; De Ferrari, Giancarlo V

2014-01-01

Genome-wide association studies (GWAS) have successfully identified several risk loci for Alzheimer's disease (AD). Nonetheless, these loci do not explain the entire susceptibility of the disease, suggesting that other genetic contributions remain to be identified. Here, we performed a meta-analysis combining data of 4,569 individuals (2,540 cases and 2,029 healthy controls) derived from three publicly available GWAS in AD and replicated a broad genomic region (>248,000 bp) associated with the disease near the APOE/TOMM40 locus in chromosome 19. To detect minor effect size contributions that could help to explain the remaining genetic risk, we conducted network-based pathway analyses either by extracting gene-wise p-values (GW), defined as the single strongest association signal within a gene, or calculated a more stringent gene-based association p-value using the extended Simes (GATES) procedure. Comparison of these strategies revealed that ontological sub-networks (SNs) involved in glutamate signaling were significantly overrepresented in AD (p<2.7×10(-11), p<1.9×10(-11); GW and GATES, respectively). Notably, glutamate signaling SNs were also found to be significantly overrepresented (p<5.1×10(-8)) in the Alzheimer's disease Neuroimaging Initiative (ADNI) study, which was used as a targeted replication sample. Interestingly, components of the glutamate signaling SNs are coordinately expressed in disease-related tissues, which are tightly related to known pathological hallmarks of AD. Our findings suggest that genetic variation within glutamate signaling contributes to the remaining genetic risk of AD and support the notion that functional biological networks should be targeted in future therapies aimed to prevent or treat this devastating neurological disorder.

Overrepresentation of Glutamate Signaling in Alzheimer's Disease: Network-Based Pathway Enrichment Using Meta-Analysis of Genome-Wide Association Studies

PubMed Central

Villamán, Camilo F.; Alarcón, Marcelo A.; Avila, Miguel E.; Ugarte, Giorgia D.; Reyes, Ariel E.; Opazo, Carlos; De Ferrari, Giancarlo V.

2014-01-01

Genome-wide association studies (GWAS) have successfully identified several risk loci for Alzheimer's disease (AD). Nonetheless, these loci do not explain the entire susceptibility of the disease, suggesting that other genetic contributions remain to be identified. Here, we performed a meta-analysis combining data of 4,569 individuals (2,540 cases and 2,029 healthy controls) derived from three publicly available GWAS in AD and replicated a broad genomic region (>248,000 bp) associated with the disease near the APOE/TOMM40 locus in chromosome 19. To detect minor effect size contributions that could help to explain the remaining genetic risk, we conducted network-based pathway analyses either by extracting gene-wise p-values (GW), defined as the single strongest association signal within a gene, or calculated a more stringent gene-based association p-value using the extended Simes (GATES) procedure. Comparison of these strategies revealed that ontological sub-networks (SNs) involved in glutamate signaling were significantly overrepresented in AD (p<2.7×10−11, p<1.9×10−11; GW and GATES, respectively). Notably, glutamate signaling SNs were also found to be significantly overrepresented (p<5.1×10−8) in the Alzheimer's disease Neuroimaging Initiative (ADNI) study, which was used as a targeted replication sample. Interestingly, components of the glutamate signaling SNs are coordinately expressed in disease-related tissues, which are tightly related to known pathological hallmarks of AD. Our findings suggest that genetic variation within glutamate signaling contributes to the remaining genetic risk of AD and support the notion that functional biological networks should be targeted in future therapies aimed to prevent or treat this devastating neurological disorder. PMID:24755620

Structures of the Recurrence Plot of Heart Rate Variability Signal as a Tool for Predicting the Onset of Paroxysmal Atrial Fibrillation

PubMed Central

Mohebbi, Maryam; Ghassemian, Hassan; Asl, Babak Mohammadzadeh

2011-01-01

This paper aims to propose an effective paroxysmal atrial fibrillation (PAF) predictor which is based on the analysis of the heart rate variability (HRV) signal. Predicting the onset of PAF, based on non-invasive techniques, is clinically important and can be invaluable in order to avoid useless therapeutic interventions and to minimize the risks for the patients. This method consists of four steps: Preprocessing, feature extraction, feature reduction, and classification. In the first step, the QRS complexes are detected from the electrocardiogram (ECG) signal and then the HRV signal is extracted. In the next step, the recurrence plot (RP) of HRV signal is obtained and six features are extracted to characterize the basic patterns of the RP. These features consist of length of longest diagonal segments, average length of the diagonal lines, entropy, trapping time, length of longest vertical line, and recurrence trend. In the third step, these features are reduced to three features by the linear discriminant analysis (LDA) technique. Using LDA not only reduces the number of the input features, but also increases the classification accuracy by selecting the most discriminating features. Finally, a support vector machine-based classifier is used to classify the HRV signals. The performance of the proposed method in prediction of PAF episodes was evaluated using the Atrial Fibrillation Prediction Database which consists of both 30-minutes ECG recordings end just prior to the onset of PAF and segments at least 45 min distant from any PAF events. The obtained sensitivity, specificity, and positive predictivity were 96.55%, 100%, and 100%, respectively. PMID:22606666

PyMVPA: A python toolbox for multivariate pattern analysis of fMRI data.

PubMed

Hanke, Michael; Halchenko, Yaroslav O; Sederberg, Per B; Hanson, Stephen José; Haxby, James V; Pollmann, Stefan

2009-01-01

Decoding patterns of neural activity onto cognitive states is one of the central goals of functional brain imaging. Standard univariate fMRI analysis methods, which correlate cognitive and perceptual function with the blood oxygenation-level dependent (BOLD) signal, have proven successful in identifying anatomical regions based on signal increases during cognitive and perceptual tasks. Recently, researchers have begun to explore new multivariate techniques that have proven to be more flexible, more reliable, and more sensitive than standard univariate analysis. Drawing on the field of statistical learning theory, these new classifier-based analysis techniques possess explanatory power that could provide new insights into the functional properties of the brain. However, unlike the wealth of software packages for univariate analyses, there are few packages that facilitate multivariate pattern classification analyses of fMRI data. Here we introduce a Python-based, cross-platform, and open-source software toolbox, called PyMVPA, for the application of classifier-based analysis techniques to fMRI datasets. PyMVPA makes use of Python's ability to access libraries written in a large variety of programming languages and computing environments to interface with the wealth of existing machine learning packages. We present the framework in this paper and provide illustrative examples on its usage, features, and programmability.

PyMVPA: A Python toolbox for multivariate pattern analysis of fMRI data

PubMed Central

Hanke, Michael; Halchenko, Yaroslav O.; Sederberg, Per B.; Hanson, Stephen José; Haxby, James V.; Pollmann, Stefan

2009-01-01

Decoding patterns of neural activity onto cognitive states is one of the central goals of functional brain imaging. Standard univariate fMRI analysis methods, which correlate cognitive and perceptual function with the blood oxygenation-level dependent (BOLD) signal, have proven successful in identifying anatomical regions based on signal increases during cognitive and perceptual tasks. Recently, researchers have begun to explore new multivariate techniques that have proven to be more flexible, more reliable, and more sensitive than standard univariate analysis. Drawing on the field of statistical learning theory, these new classifier-based analysis techniques possess explanatory power that could provide new insights into the functional properties of the brain. However, unlike the wealth of software packages for univariate analyses, there are few packages that facilitate multivariate pattern classification analyses of fMRI data. Here we introduce a Python-based, cross-platform, and open-source software toolbox, called PyMVPA, for the application of classifier-based analysis techniques to fMRI datasets. PyMVPA makes use of Python's ability to access libraries written in a large variety of programming languages and computing environments to interface with the wealth of existing machine-learning packages. We present the framework in this paper and provide illustrative examples on its usage, features, and programmability. PMID:19184561

Study on Unified Chaotic System-Based Wind Turbine Blade Fault Diagnostic System

NASA Astrophysics Data System (ADS)

Kuo, Ying-Che; Hsieh, Chin-Tsung; Yau, Her-Terng; Li, Yu-Chung

At present, vibration signals are processed and analyzed mostly in the frequency domain. The spectrum clearly shows the signal structure and the specific characteristic frequency band is analyzed, but the number of calculations required is huge, resulting in delays. Therefore, this study uses the characteristics of a nonlinear system to load the complete vibration signal to the unified chaotic system, applying the dynamic error to analyze the wind turbine vibration signal, and adopting extenics theory for artificial intelligent fault diagnosis of the analysis signal. Hence, a fault diagnostor has been developed for wind turbine rotating blades. This study simulates three wind turbine blade states, namely stress rupture, screw loosening and blade loss, and validates the methods. The experimental results prove that the unified chaotic system used in this paper has a significant effect on vibration signal analysis. Thus, the operating conditions of wind turbines can be quickly known from this fault diagnostic system, and the maintenance schedule can be arranged before the faults worsen, making the management and implementation of wind turbines smoother, so as to reduce many unnecessary costs.

Radial artery pulse waveform analysis based on curve fitting using discrete Fourier series.

PubMed

Jiang, Zhixing; Zhang, David; Lu, Guangming

2018-04-19

Radial artery pulse diagnosis has been playing an important role in traditional Chinese medicine (TCM). For its non-invasion and convenience, the pulse diagnosis has great significance in diseases analysis of modern medicine. The practitioners sense the pulse waveforms in patients' wrist to make diagnoses based on their non-objective personal experience. With the researches of pulse acquisition platforms and computerized analysis methods, the objective study on pulse diagnosis can help the TCM to keep up with the development of modern medicine. In this paper, we propose a new method to extract feature from pulse waveform based on discrete Fourier series (DFS). It regards the waveform as one kind of signal that consists of a series of sub-components represented by sine and cosine (SC) signals with different frequencies and amplitudes. After the pulse signals are collected and preprocessed, we fit the average waveform for each sample using discrete Fourier series by least squares. The feature vector is comprised by the coefficients of discrete Fourier series function. Compared with the fitting method using Gaussian mixture function, the fitting errors of proposed method are smaller, which indicate that our method can represent the original signal better. The classification performance of proposed feature is superior to the other features extracted from waveform, liking auto-regression model and Gaussian mixture model. The coefficients of optimized DFS function, who is used to fit the arterial pressure waveforms, can obtain better performance in modeling the waveforms and holds more potential information for distinguishing different psychological states. Copyright © 2018 Elsevier B.V. All rights reserved.

Large-scale Proteomics Analysis of the Human Kinome

PubMed Central

Oppermann, Felix S.; Gnad, Florian; Olsen, Jesper V.; Hornberger, Renate; Greff, Zoltán; Kéri, György; Mann, Matthias; Daub, Henrik

2009-01-01

Members of the human protein kinase superfamily are the major regulatory enzymes involved in the activity control of eukaryotic signal transduction pathways. As protein kinases reside at the nodes of phosphorylation-based signal transmission, comprehensive analysis of their cellular expression and site-specific phosphorylation can provide important insights into the architecture and functionality of signaling networks. However, in global proteome studies, low cellular abundance of protein kinases often results in rather minor peptide species that are occluded by a vast excess of peptides from other cellular proteins. These analytical limitations create a rationale for kinome-wide enrichment of protein kinases prior to mass spectrometry analysis. Here, we employed stable isotope labeling by amino acids in cell culture (SILAC) to compare the binding characteristics of three kinase-selective affinity resins by quantitative mass spectrometry. The evaluated pre-fractionation tools possessed pyrido[2,3-d]pyrimidine-based kinase inhibitors as immobilized capture ligands and retained considerable subsets of the human kinome. Based on these results, an affinity resin displaying the broadly selective kinase ligand VI16832 was employed to quantify the relative expression of more than 170 protein kinases across three different, SILAC-encoded cancer cell lines. These experiments demonstrated the feasibility of comparative kinome profiling in a compact experimental format. Interestingly, we found high levels of cytoplasmic and low levels of receptor tyrosine kinases in MV4–11 leukemia cells compared with the adherent cancer lines HCT116 and MDA-MB-435S. The VI16832 resin was further exploited to pre-fractionate kinases for targeted phosphoproteomics analysis, which revealed about 1200 distinct phosphorylation sites on more than 200 protein kinases. This hitherto largest survey of site-specific phosphorylation across the kinome significantly expands the basis for functional follow-up studies on protein kinase regulation. In conclusion, the straightforward experimental procedures described here enable different implementations of kinase-selective proteomics with considerable potential for future signal transduction and kinase drug target analysis. PMID:19369195

Improved quantitative analysis of spectra using a new method of obtaining derivative spectra based on a singular perturbation technique.

PubMed

Li, Zhigang; Wang, Qiaoyun; Lv, Jiangtao; Ma, Zhenhe; Yang, Linjuan

2015-06-01

Spectroscopy is often applied when a rapid quantitative analysis is required, but one challenge is the translation of raw spectra into a final analysis. Derivative spectra are often used as a preliminary preprocessing step to resolve overlapping signals, enhance signal properties, and suppress unwanted spectral features that arise due to non-ideal instrument and sample properties. In this study, to improve quantitative analysis of near-infrared spectra, derivatives of noisy raw spectral data need to be estimated with high accuracy. A new spectral estimator based on singular perturbation technique, called the singular perturbation spectra estimator (SPSE), is presented, and the stability analysis of the estimator is given. Theoretical analysis and simulation experimental results confirm that the derivatives can be estimated with high accuracy using this estimator. Furthermore, the effectiveness of the estimator for processing noisy infrared spectra is evaluated using the analysis of beer spectra. The derivative spectra of the beer and the marzipan are used to build the calibration model using partial least squares (PLS) modeling. The results show that the PLS based on the new estimator can achieve better performance compared with the Savitzky-Golay algorithm and can serve as an alternative choice for quantitative analytical applications.

Prediction of paroxysmal atrial fibrillation using recurrence plot-based features of the RR-interval signal.

PubMed

Mohebbi, Maryam; Ghassemian, Hassan

2011-08-01

Atrial fibrillation (AF) is the most common cardiac arrhythmia and increases the risk of stroke. Predicting the onset of paroxysmal AF (PAF), based on noninvasive techniques, is clinically important and can be invaluable in order to avoid useless therapeutic intervention and to minimize risks for the patients. In this paper, we propose an effective PAF predictor which is based on the analysis of the RR-interval signal. This method consists of three steps: preprocessing, feature extraction and classification. In the first step, the QRS complexes are detected from the electrocardiogram (ECG) signal and then the RR-interval signal is extracted. In the next step, the recurrence plot (RP) of the RR-interval signal is obtained and five statistically significant features are extracted to characterize the basic patterns of the RP. These features consist of the recurrence rate, length of longest diagonal segments (L(max )), average length of the diagonal lines (L(mean)), entropy, and trapping time. Recurrence quantification analysis can reveal subtle aspects of dynamics not easily appreciated by other methods and exhibits characteristic patterns which are caused by the typical dynamical behavior. In the final step, a support vector machine (SVM)-based classifier is used for PAF prediction. The performance of the proposed method in prediction of PAF episodes was evaluated using the Atrial Fibrillation Prediction Database (AFPDB) which consists of both 30 min ECG recordings that end just prior to the onset of PAF and segments at least 45 min distant from any PAF events. The obtained sensitivity, specificity, positive predictivity and negative predictivity were 97%, 100%, 100%, and 96%, respectively. The proposed methodology presents better results than other existing approaches.

Adaptive synchrosqueezing based on a quilted short-time Fourier transform

NASA Astrophysics Data System (ADS)

Berrian, Alexander; Saito, Naoki

2017-08-01

In recent years, the synchrosqueezing transform (SST) has gained popularity as a method for the analysis of signals that can be broken down into multiple components determined by instantaneous amplitudes and phases. One such version of SST, based on the short-time Fourier transform (STFT), enables the sharpening of instantaneous frequency (IF) information derived from the STFT, as well as the separation of amplitude-phase components corresponding to distinct IF curves. However, this SST is limited by the time-frequency resolution of the underlying window function, and may not resolve signals exhibiting diverse time-frequency behaviors with sufficient accuracy. In this work, we develop a framework for an SST based on a "quilted" short-time Fourier transform (SST-QSTFT), which allows adaptation to signal behavior in separate time-frequency regions through the use of multiple windows. This motivates us to introduce a discrete reassignment frequency formula based on a finite difference of the phase spectrum, ensuring computational accuracy for a wider variety of windows. We develop a theoretical framework for the SST-QSTFT in both the continuous and the discrete settings, and describe an algorithm for the automatic selection of optimal windows depending on the region of interest. Using synthetic data, we demonstrate the superior numerical performance of SST-QSTFT relative to other SST methods in a noisy context. Finally, we apply SST-QSTFT to audio recordings of animal calls to demonstrate the potential of our method for the analysis of real bioacoustic signals.

Analysis of embolic signals with directional dual tree rational dilation wavelet transform.

PubMed

Serbes, Gorkem; Aydin, Nizamettin

2016-08-01

The dyadic discrete wavelet transform (dyadic-DWT), which is based on fixed integer sampling factor, has been used before for processing piecewise smooth biomedical signals. However, the dyadic-DWT has poor frequency resolution due to the low-oscillatory nature of its wavelet bases and therefore, it is less effective in processing embolic signals (ESs). To process ESs more effectively, a wavelet transform having better frequency resolution than the dyadic-DWT is needed. Therefore, in this study two ESs, containing micro-emboli and artifact waveforms, are analyzed with the Directional Dual Tree Rational-Dilation Wavelet Transform (DDT-RADWT). The DDT-RADWT, which can be directly applied to quadrature signals, is based on rational dilation factors and has adjustable frequency resolution. The analyses are done for both low and high Q-factors. It is proved that, when high Q-factor filters are employed in the DDT-RADWT, clearer representations of ESs can be attained in decomposed sub-bands and artifacts can be successfully separated.

Tacholess order-tracking approach for wind turbine gearbox fault detection

NASA Astrophysics Data System (ADS)

Wang, Yi; Xie, Yong; Xu, Guanghua; Zhang, Sicong; Hou, Chenggang

2017-09-01

Monitoring of wind turbines under variable-speed operating conditions has become an important issue in recent years. The gearbox of a wind turbine is the most important transmission unit; it generally exhibits complex vibration signatures due to random variations in operating conditions. Spectral analysis is one of the main approaches in vibration signal processing. However, spectral analysis is based on a stationary assumption and thus inapplicable to the fault diagnosis of wind turbines under variable-speed operating conditions. This constraint limits the application of spectral analysis to wind turbine diagnosis in industrial applications. Although order-tracking methods have been proposed for wind turbine fault detection in recent years, current methods are only applicable to cases in which the instantaneous shaft phase is available. For wind turbines with limited structural spaces, collecting phase signals with tachometers or encoders is difficult. In this study, a tacholess order-tracking method for wind turbines is proposed to overcome the limitations of traditional techniques. The proposed method extracts the instantaneous phase from the vibration signal, resamples the signal at equiangular increments, and calculates the order spectrum for wind turbine fault identification. The effectiveness of the proposed method is experimentally validated with the vibration signals of wind turbines.

Analysis of degree of nonlinearity and stochastic nature of HRV signal during meditation using delay vector variance method.

PubMed

Reddy, L Ram Gopal; Kuntamalla, Srinivas

2011-01-01

Heart rate variability analysis is fast gaining acceptance as a potential non-invasive means of autonomic nervous system assessment in research as well as clinical domains. In this study, a new nonlinear analysis method is used to detect the degree of nonlinearity and stochastic nature of heart rate variability signals during two forms of meditation (Chi and Kundalini). The data obtained from an online and widely used public database (i.e., MIT/BIH physionet database), is used in this study. The method used is the delay vector variance (DVV) method, which is a unified method for detecting the presence of determinism and nonlinearity in a time series and is based upon the examination of local predictability of a signal. From the results it is clear that there is a significant change in the nonlinearity and stochastic nature of the signal before and during the meditation (p value > 0.01). During Chi meditation there is a increase in stochastic nature and decrease in nonlinear nature of the signal. There is a significant decrease in the degree of nonlinearity and stochastic nature during Kundalini meditation.

Noise reduction in functional near-infrared spectroscopy signals by independent component analysis

NASA Astrophysics Data System (ADS)

Santosa, Hendrik; Jiyoun Hong, Melissa; Kim, Sung-Phil; Hong, Keum-Shik

2013-07-01

Functional near-infrared spectroscopy (fNIRS) is used to detect concentration changes of oxy-hemoglobin and deoxy-hemoglobin in the human brain. The main difficulty entailed in the analysis of fNIRS signals is the fact that the hemodynamic response to a specific neuronal activation is contaminated by physiological and instrument noises, motion artifacts, and other interferences. This paper proposes independent component analysis (ICA) as a means of identifying the original hemodynamic response in the presence of noises. The original hemodynamic response was reconstructed using the primary independent component (IC) and other, less-weighting-coefficient ICs. In order to generate experimental brain stimuli, arithmetic tasks were administered to eight volunteer subjects. The t-value of the reconstructed hemodynamic response was improved by using the ICs found in the measured data. The best t-value out of 16 low-pass-filtered signals was 37, and that of the reconstructed one was 51. Also, the average t-value of the eight subjects' reconstructed signals was 40, whereas that of all of their low-pass-filtered signals was only 20. Overall, the results showed the applicability of the ICA-based method to noise-contamination reduction in brain mapping.

Machine fault feature extraction based on intrinsic mode functions

NASA Astrophysics Data System (ADS)

Fan, Xianfeng; Zuo, Ming J.

2008-04-01

This work employs empirical mode decomposition (EMD) to decompose raw vibration signals into intrinsic mode functions (IMFs) that represent the oscillatory modes generated by the components that make up the mechanical systems generating the vibration signals. The motivation here is to develop vibration signal analysis programs that are self-adaptive and that can detect machine faults at the earliest onset of deterioration. The change in velocity of the amplitude of some IMFs over a particular unit time will increase when the vibration is stimulated by a component fault. Therefore, the amplitude acceleration energy in the intrinsic mode functions is proposed as an indicator of the impulsive features that are often associated with mechanical component faults. The periodicity of the amplitude acceleration energy for each IMF is extracted by spectrum analysis. A spectrum amplitude index is introduced as a method to select the optimal result. A comparison study of the method proposed here and some well-established techniques for detecting machinery faults is conducted through the analysis of both gear and bearing vibration signals. The results indicate that the proposed method has superior capability to extract machine fault features from vibration signals.

Detection of low-amplitude in vivo intrinsic signals from an optical imager of retinal function

NASA Astrophysics Data System (ADS)

Barriga, Eduardo S.; T'so, Dan; Pattichis, Marios; Kwon, Young; Kardon, Randy; Abramoff, Michael; Soliz, Peter

2006-02-01

In the early stages of some retinal diseases, such as glaucoma, loss of retinal activity may be difficult to detect with today's clinical instruments. Many of today's instruments focus on detecting changes in anatomical structures, such as the nerve fiber layer. Our device, which is based on a modified fundus camera, seeks to detect changes in optical signals that reflect functional changes in the retina. The functional imager uses a patterned stimulus at wavelength of 535nm. An intrinsic functional signal is collected at a near infrared wavelength. Measured changes in reflectance in response to the visual stimulus are on the order of 0.1% to 1% of the total reflected intensity level, which makes the functional signal difficult to detect by standard methods because it is masked by other physiological signals and by imaging system noise. In this paper, we analyze the video sequences from a set of 60 experiments with different patterned stimuli from cats. Using a set of statistical techniques known as Independent Component Analysis (ICA), we estimate the signals present in the videos. Through controlled simulation experiments, we quantify the limits of signal strength in order to detect the physiological signal of interest. The results of the analysis show that, in principle, signal levels of 0.1% (-30dB) can be detected. The study found that in 86% of the animal experiments the patterned stimuli effects on the retina can be detected and extracted. The analysis of the different responses extracted from the videos can give an insight of the functional processes present during the stimulation of the retina.

Bivariate empirical mode decomposition for ECG-based biometric identification with emotional data.

PubMed

Ferdinando, Hany; Seppanen, Tapio; Alasaarela, Esko

2017-07-01

Emotions modulate ECG signals such that they might affect ECG-based biometric identification in real life application. It motivated in finding good feature extraction methods where the emotional state of the subjects has minimum impacts. This paper evaluates feature extraction based on bivariate empirical mode decomposition (BEMD) for biometric identification when emotion is considered. Using the ECG signal from the Mahnob-HCI database for affect recognition, the features were statistical distributions of dominant frequency after applying BEMD analysis to ECG signals. The achieved accuracy was 99.5% with high consistency using kNN classifier in 10-fold cross validation to identify 26 subjects when the emotional states of the subjects were ignored. When the emotional states of the subject were considered, the proposed method also delivered high accuracy, around 99.4%. We concluded that the proposed method offers emotion-independent features for ECG-based biometric identification. The proposed method needs more evaluation related to testing with other classifier and variation in ECG signals, e.g. normal ECG vs. ECG with arrhythmias, ECG from various ages, and ECG from other affective databases.

A novel LabVIEW-based multi-channel non-invasive abdominal maternal-fetal electrocardiogram signal generator.

PubMed

Martinek, Radek; Kelnar, Michal; Koudelka, Petr; Vanus, Jan; Bilik, Petr; Janku, Petr; Nazeran, Homer; Zidek, Jan

2016-02-01

This paper describes the design, construction, and testing of a multi-channel fetal electrocardiogram (fECG) signal generator based on LabVIEW. Special attention is paid to the fetal heart development in relation to the fetus' anatomy, physiology, and pathology. The non-invasive signal generator enables many parameters to be set, including fetal heart rate (FHR), maternal heart rate (MHR), gestational age (GA), fECG interferences (biological and technical artifacts), as well as other fECG signal characteristics. Furthermore, based on the change in the FHR and in the T wave-to-QRS complex ratio (T/QRS), the generator enables manifestations of hypoxic states (hypoxemia, hypoxia, and asphyxia) to be monitored while complying with clinical recommendations for classifications in cardiotocography (CTG) and fECG ST segment analysis (STAN). The generator can also produce synthetic signals with defined properties for 6 input leads (4 abdominal and 2 thoracic). Such signals are well suited to the testing of new and existing methods of fECG processing and are effective in suppressing maternal ECG while non-invasively monitoring abdominal fECG. They may also contribute to the development of a new diagnostic method, which may be referred to as non-invasive trans-abdominal CTG +  STAN. The functional prototype is based on virtual instrumentation using the LabVIEW developmental environment and its associated data acquisition measurement cards (DAQmx). The generator also makes it possible to create synthetic signals and measure actual fetal and maternal ECGs by means of bioelectrodes.

Kmeans-ICA based automatic method for ocular artifacts removal in a motorimagery classification.

PubMed

Bou Assi, Elie; Rihana, Sandy; Sawan, Mohamad

2014-01-01

Electroencephalogram (EEG) recordings aroused as inputs of a motor imagery based BCI system. Eye blinks contaminate the spectral frequency of the EEG signals. Independent Component Analysis (ICA) has been already proved for removing these artifacts whose frequency band overlap with the EEG of interest. However, already ICA developed methods, use a reference lead such as the ElectroOculoGram (EOG) to identify the ocular artifact components. In this study, artifactual components were identified using an adaptive thresholding by means of Kmeans clustering. The denoised EEG signals have been fed into a feature extraction algorithm extracting the band power, the coherence and the phase locking value and inserted into a linear discriminant analysis classifier for a motor imagery classification.

Method and algorithm of automatic estimation of road surface type for variable damping control

NASA Astrophysics Data System (ADS)

Dąbrowski, K.; Ślaski, G.

2016-09-01

In this paper authors presented an idea of road surface estimation (recognition) on a base of suspension dynamic response signals statistical analysis. For preliminary analysis cumulated distribution function (CDF) was used, and some conclusion that various roads have responses values in a different ranges of limits for the same percentage of samples or for the same limits different percentages of samples are located within the range between limit values. That was the base for developed and presented algorithm which was tested using suspension response signals recorded during road test riding over various surfaces. Proposed algorithm can be essential part of adaptive damping control algorithm for a vehicle suspension or adaptive control strategy for suspension damping control.

Speech transformations based on a sinusoidal representation

NASA Astrophysics Data System (ADS)

Quatieri, T. E.; McAulay, R. J.

1986-05-01

A new speech analysis/synthesis technique is presented which provides the basis for a general class of speech transformation including time-scale modification, frequency scaling, and pitch modification. These modifications can be performed with a time-varying change, permitting continuous adjustment of a speaker's fundamental frequency and rate of articulation. The method is based on a sinusoidal representation of the speech production mechanism that has been shown to produce synthetic speech that preserves the waveform shape and is essentially perceptually indistinguishable from the original. Although the analysis/synthesis system originally was designed for single-speaker signals, it is equally capable of recovering and modifying nonspeech signals such as music; multiple speakers, marine biologic sounds, and speakers in the presence of interferences such as noise and musical backgrounds.

Exploring G protein-coupled receptor signaling networks using SILAC-based phosphoproteomics

PubMed Central

Williams, Grace R.; Bethard, Jennifer R.; Berkaw, Mary N.; Nagel, Alexis K.; Luttrell, Louis M.; Ball, Lauren E.

2015-01-01

The type 1 parathyroid hormone receptor (PTH1R) is a key regulator of calcium homeostasis and bone turnover. Here, we employed SILAC-based quantitative mass spectrometry combined with bioinformatic pathways analysis to examine global changes in protein phosphorylation following short-term stimulation of endogenously expressed PTH1R in osteoblastic cells in vitro. Following 5 min exposure to the conventional agonist, PTH(1-34), we detected significant changes in the phosphorylation of 224 distinct proteins. Kinase substrate motif enrichment demonstrated that consensus motifs for PKA and CAMK2 were the most heavily upregulated within the phosphoproteome, while consensus motifs for mitogen-activated protein kinases were strongly downregulated. Signaling pathways analysis identified ERK1/2 and AKT as important nodal kinases in the downstream network and revealed strong regulation of small GTPases involved in cytoskeletal rearrangement, cell motility, and focal adhesion complex signaling. Our data illustrate the utility of quantitative mass spectrometry in measuring dynamic changes in protein phosphorylation following GPCR activation. PMID:26160508

Time-Gated Raman Spectroscopy for Quantitative Determination of Solid-State Forms of Fluorescent Pharmaceuticals.

PubMed

Lipiäinen, Tiina; Pessi, Jenni; Movahedi, Parisa; Koivistoinen, Juha; Kurki, Lauri; Tenhunen, Mari; Yliruusi, Jouko; Juppo, Anne M; Heikkonen, Jukka; Pahikkala, Tapio; Strachan, Clare J

2018-04-03

Raman spectroscopy is widely used for quantitative pharmaceutical analysis, but a common obstacle to its use is sample fluorescence masking the Raman signal. Time-gating provides an instrument-based method for rejecting fluorescence through temporal resolution of the spectral signal and allows Raman spectra of fluorescent materials to be obtained. An additional practical advantage is that analysis is possible in ambient lighting. This study assesses the efficacy of time-gated Raman spectroscopy for the quantitative measurement of fluorescent pharmaceuticals. Time-gated Raman spectroscopy with a 128 × (2) × 4 CMOS SPAD detector was applied for quantitative analysis of ternary mixtures of solid-state forms of the model drug, piroxicam (PRX). Partial least-squares (PLS) regression allowed quantification, with Raman-active time domain selection (based on visual inspection) improving performance. Model performance was further improved by using kernel-based regularized least-squares (RLS) regression with greedy feature selection in which the data use in both the Raman shift and time dimensions was statistically optimized. Overall, time-gated Raman spectroscopy, especially with optimized data analysis in both the spectral and time dimensions, shows potential for sensitive and relatively routine quantitative analysis of photoluminescent pharmaceuticals during drug development and manufacturing.

Elements of an integrated health monitoring framework

NASA Astrophysics Data System (ADS)

Fraser, Michael; Elgamal, Ahmed; Conte, Joel P.; Masri, Sami; Fountain, Tony; Gupta, Amarnath; Trivedi, Mohan; El Zarki, Magda

2003-07-01

Internet technologies are increasingly facilitating real-time monitoring of Bridges and Highways. The advances in wireless communications for instance, are allowing practical deployments for large extended systems. Sensor data, including video signals, can be used for long-term condition assessment, traffic-load regulation, emergency response, and seismic safety applications. Computer-based automated signal-analysis algorithms routinely process the incoming data and determine anomalies based on pre-defined response thresholds and more involved signal analysis techniques. Upon authentication, appropriate action may be authorized for maintenance, early warning, and/or emergency response. In such a strategy, data from thousands of sensors can be analyzed with near real-time and long-term assessment and decision-making implications. Addressing the above, a flexible and scalable (e.g., for an entire Highway system, or portfolio of Networked Civil Infrastructure) software architecture/framework is being developed and implemented. This framework will network and integrate real-time heterogeneous sensor data, database and archiving systems, computer vision, data analysis and interpretation, physics-based numerical simulation of complex structural systems, visualization, reliability & risk analysis, and rational statistical decision-making procedures. Thus, within this framework, data is converted into information, information into knowledge, and knowledge into decision at the end of the pipeline. Such a decision-support system contributes to the vitality of our economy, as rehabilitation, renewal, replacement, and/or maintenance of this infrastructure are estimated to require expenditures in the Trillion-dollar range nationwide, including issues of Homeland security and natural disaster mitigation. A pilot website (http://bridge.ucsd.edu/compositedeck.html) currently depicts some basic elements of the envisioned integrated health monitoring analysis framework.

Precisely and Accurately Inferring Single-Molecule Rate Constants

PubMed Central

Kinz-Thompson, Colin D.; Bailey, Nevette A.; Gonzalez, Ruben L.

2017-01-01

The kinetics of biomolecular systems can be quantified by calculating the stochastic rate constants that govern the biomolecular state versus time trajectories (i.e., state trajectories) of individual biomolecules. To do so, the experimental signal versus time trajectories (i.e., signal trajectories) obtained from observing individual biomolecules are often idealized to generate state trajectories by methods such as thresholding or hidden Markov modeling. Here, we discuss approaches for idealizing signal trajectories and calculating stochastic rate constants from the resulting state trajectories. Importantly, we provide an analysis of how the finite length of signal trajectories restrict the precision of these approaches, and demonstrate how Bayesian inference-based versions of these approaches allow rigorous determination of this precision. Similarly, we provide an analysis of how the finite lengths and limited time resolutions of signal trajectories restrict the accuracy of these approaches, and describe methods that, by accounting for the effects of the finite length and limited time resolution of signal trajectories, substantially improve this accuracy. Collectively, therefore, the methods we consider here enable a rigorous assessment of the precision, and a significant enhancement of the accuracy, with which stochastic rate constants can be calculated from single-molecule signal trajectories. PMID:27793280