Coherence Estimation Research Articles

Оцінка когерентності тексту на основі графа узгодженості словосполучень для виявлення симптомів шизофренії

Different state-of-the-art methods of the detection of schizophrenia symptoms based on the estimation of text coherence have been analyzed. The analysis of a text at the level of phrases has been suggested. The method based on the graph of the consistency of phrases has been proposed to evaluate the semantic coherence and the cohesion of a text. The semantic coherence, cohesion, and other linguistic features (lexical diversity, lexical density) have been taken into account to form feature vectors for the training of a model-classifier. The training of the classifier has been performed on the set of English-language interviews. According to the retrieved results, the impact of each feature on the output of the model has been analyzed. The results obtained can indicate that the proposed method based on the graph of the consistency of phrases may be used in the different tasks of the detection of mental illness.

Direct estimation of quantum coherence by collective measurements

The recently established resource theory of quantum coherence allows for a quantitative understanding of the superposition principle, with applications reaching from quantum computing to quantum biology. While different quantifiers of coherence have been proposed in the literature, their efficient estimation in today’s experiments remains a challenge. Here, we introduce a collective measurement scheme for estimating the amount of coherence in quantum states, which requires entangled measurements on two copies of the state. As we show by numerical simulations, our scheme outperforms other estimation methods based on tomography or adaptive measurements, leading to a higher precision in a large parameter range for estimating established coherence quantifiers of qubit and qutrit states. We show that our method is accessible with today’s technology by implementing it experimentally with photons, finding a good agreement between experiment and theory.

The effects of aging on the distribution and strength of correlated neural inputs to postural muscles during unperturbed bipedal stance.

The present study investigated the effects of aging on the distribution of common descending neural drives to main postural muscles acting on the ankle, knee, hip, and lower trunk. The presence, distribution, and strength of these drives were assessed using intermuscular coherence estimations at a low-frequency band (0-55Hz). Ten healthy older adults (68.7 ± 3.5years) with no recent history of falls and ten healthy younger adults (26.8 ± 2.7years) performed bipedal stances with eyes either opened or closed. Electromyographic (EMG) signals of six postural muscles were recorded. Estimations of intermuscular coherence were obtained from fifteen muscle pairs and four muscle groups. In general, single-pair and pooled coherence analyzes revealed significant levels of signal synchronization within 1-10Hz. Significant common drives to anterior, posterior, and antagonist muscle groups were observed for both cohorts of participants. However, older participants showed significantly stronger EMG-EMG synchronization in the frequency domain compared to younger participants. It seems that age-related sarcopenia, visual-vestibular-proprioceptive decline, cortical activation increase, presynaptic inhibition modulation decrease, and co-contraction increase had a major impact on strengthening the common drives to the aforementioned muscle groups. Differently from young adults, the absence of visual inputs did not reduce the magnitude of signal synchronization in older adults. These results suggest that the aging central nervous system seems to organize similar arrangements of common drives to postural antagonist muscles at different joints, and to postural muscles pushing the body either forward or backward when visual information is not available.

ANALYSIS OF THE TIME-FREQUENCY CONNECTEDNESS BETWEEN GOLD PRICES, OIL PRICES AND HUNGARIAN FINANCIAL MARKETS

We study the time-frequency connectedness between the global commodities markets (crude oil and gold) and Hungary's equity markets (stock and exchange rate) by using the multivariate generalized autoregressive conditional heteroskedasticity dynamic conditional correlation DCC-GARCH model alongside with the wavelet coherence analysis. Results of this paper highlight the long-term time-varying intercorrelations between the global commodities markets and Hungarian financial markets. Additionally, the estimations of wavelet coherence indicate that the commodities markets have a significant impact on the Hungarian financial markets in the short term, but stock and exchange markets influence crude oil and gold markets in the long run. Our empirical results shed light on the commodity-finance sectors connectedness, which is able to help investors minimize risks to build discretionary portfolios.Keywords: oil price, gold price, stock market, exchange rate, DCC-GARCH, wavelet technique, Hungary.JEL Classifications: G10; G11; C32; E44DOI: https://doi.org/10.32479/ijeep.9230

Grid‐less coherent DOA estimation based on fourth‐order cumulants with Gaussian coloured noise

This study investigates the continuous coherent direction-of-arrival (DOA) estimation, and concentrated on developing grid-less sparsity-based methods to Gaussian coloured noise environment. The noise component is greatly suppressed by applying fourth-order cumulants (FOC) due to its blind property to additive Gaussian noise. Two grid-less sparse models are designed separately. The first sparse representation model is built based on the simplified FOC vector, which would effectively reduce the computational complexity. Then the dual atomic norm minimisation algorithms are applied to solve the basis mismatch problem and improve the estimation accuracy. Additionally, a Toeplitz matrix based on FOC vector is constructed. The second sparse model based on this Toeplitz FOC matrix is proposed to implement array aperture extension, which can break through the restriction of maximum signal number and improve resolution. The proposed methods can handle the coherent signals and do not require the signal number as a prior. Numerical simulations demonstrate the outperformance of the proposed methods in estimation precision, computational cost and robustness to coloured noise.

Improved functional connectivity network estimation for brain networks using multivariate partial coherence

Objective. Graphical networks and network metrics are widely used to understand and characterise brain networks and brain function. These methods can be applied to a range of electrophysiological data including electroencephalography, local field potential and single unit recordings. Functional networks are often constructed using pair-wise correlation between variables. The objective of this study is to demonstrate that functional networks can be more accurately estimated using partial correlation than with pair-wise correlation. Approach. We compared network metrics derived from unconditional and conditional graphical networks, obtained using coherence and multivariate partial coherence (MVPC), respectively. Graphical networks were constructed using coherence and MVPC estimates, and binary and weighted network metrics derived from these: node degree, path length, clustering coefficients and small-world index. Main results. Network metrics were applied to simulated and experimental single unit spike train data. Simulated data used a 10x10 grid of simulated cortical neurons with centre-surround connectivity. Conditional network metrics gave a more accurate representation of the known connectivity: Numbers of excitatory connections had range 3–11, unconditional binary node degree had range 6–80, conditional node degree had range 2–13. Experimental data used multi-electrode array recording with 19 single-units from left and right hippocampal brain areas in a rat model for epilepsy. Conditional network analysis showed similar trends to simulated data, with lower binary node degree and longer binary path lengths compared to unconditional networks. Significance. We conclude that conditional networks, where common dependencies are removed through partial coherence analysis, give a more accurate representation of the interactions in a graphical network model. These results have important implications for graphical network analyses of brain networks and suggest that functional networks should be derived using partial correlation, based on MVPC estimates, as opposed to the common approach of pair-wise correlation.

Phase enhancement model based on supervised convolutional neural network for coherent DOA estimation

When the elevation of targets is smaller than beamwidth, the coherent multi-path signals will significantly degrade the direction of arrival (DOA) estimation accuracy of existing methods for a very-high-frequency (VHF) radar system. Through detailed theoretical analysis, we demonstrate that the phase distortion is the key factor of degrading the accuracy of DOA estimation. Hence, a novel phase enhancement model based on supervised convolutional neural network (CNN) for coherent DOA estimation is proposed to mitigate the phase distortion and improve estimation accuracy. The results of simulation experiments and real data have demonstrated the superiority of proposed method in DOA estimation accuracy and resolution compared to classic physics-driven methods. Moreover, the proposed scheme is suitable for the coherent DOA estimation compared with existing data-driven methods.

Chasing the evidence: the influence of data segmentation on estimates of dynamic cerebral autoregulation

Objective: Transfer function analysis (TFA) of dynamic cerebral autoregulation (dCA) requires smoothing of spectral estimates using segmentation of the data (SD). Systematic studies are required to elucidate the potential influence of SD on dCA parameters. Approach: Healthy subjects (HS, n = 237) and acute ischaemic stroke patients (AIS, n = 98) were included. Cerebral blood flow velocity (CBFV, transcranial Doppler ultrasound) was recorded supine at rest with continuous arterial blood pressure (BP, Finometer) for a minimum of 5 min. TFA was performed with durations SD = 100, 50 or 25 s and 50% superposition to derive estimates of coherence, gain and phase for the BP–CBFV relationship. The autoregulation index (ARI) was estimated from the CBFV step response. Intrasubject reproducibility was expressed by the intraclass correlation coefficient (ICC). Main results: In HS, the ARI, coherence, gain, and phase (low frequency) were influenced by SD, but in AIS, phase (very low frequency) and ARI were not affected. ICC was excellent (>0.75) for all parameters, for both HS and AIS. For SD = 100 s, ARI was different between HS and AIS (mean ± sdev: 5.70 ± 1.61 vs 5.1 ± 2.0; p < 0.01) and the significance of this difference was maintained for SD = 50 s and 25 s. Using SD = 100 s as reference, the rate of misclassification, based on a threshold of ARI ⩽ 4, was 6.3% for SD = 50 s and 8.1% for SD = 25 s in HS, with corresponding values of 11.7% and 8.2% in AIS patients, respectively. Significance: Further studies are warranted with SD values lower than the recommended standard of SD = 100 s, to explore possibilities of improving the reproducibility, sensitivity and prognostic value of TFA parameters used as metrics of dCA.

A modified spatial smoothing-based Nystrom approach for coherent target in bistatic MIMO radar

ABSTRACT The performance of most angle estimation system degrades when the signal attributes coherency coupled with the high computational burden of the preprocessing technique. This paper addresses the issue of the computational burden of the coherent angle estimation for the bistatic multiple-input multiple-output (MIMO) radar. A modified spatial smoothing technique is initially developed by ensuring the generation of a lower rank kernel matrix of the decorrelated signal via a Nyström approach. Then the formed data matrix is subjected to the shift-invariance technique where the automatic pairing of the angles is estimated without loss of the virtual aperture. Hence, in addition to significantly reducing the computational complexity, the proposed method presents an improved performance against existing techniques verifiable by simulation results.

ICARE: An R package to build, validate and apply absolute risk models.

This report describes an R package, called the Individualized Coherent Absolute Risk Estimator (iCARE) tool, that allows researchers to build and evaluate models for absolute risk and apply them to estimate an individual’s risk of developing disease during a specified time interval based on a set of user defined input parameters. An attractive feature of the software is that it gives users flexibility to update models rapidly based on new knowledge on risk factors and tailor models to different populations by specifying three input arguments: a model for relative risk, an age-specific disease incidence rate and the distribution of risk factors for the population of interest. The tool can handle missing information on risk factors for individuals for whom risks are to be predicted using a coherent approach where all estimates are derived from a single model after appropriate model averaging. The software allows single nucleotide polymorphisms (SNPs) to be incorporated into the model using published odds ratios and allele frequencies. The validation component of the software implements the methods for evaluation of model calibration, discrimination and risk-stratification based on independent validation datasets. We provide an illustration of the utility of iCARE for building, validating and applying absolute risk models using breast cancer as an example.

Cloud Detection Based on High Resolution Stereo Pairs of the Geostationary Meteosat Images

Due to the considerable impact of clouds on the energy balance in the atmosphere and on the earth surface, they are of great importance for various applications in meteorology or remote sensing. An important aspect of the cloud research studies is the detection of cloudy pixels from the processing of satellite images. In this research, we investigated a stereographic method on a new set of Meteosat images, namely the combination of the high resolution visible (HRV) channel of the Meteosat-8 Indian Ocean Data Coverage (IODC) as a stereo pair with the HRV channel of the Meteosat Second Generation (MSG) Meteosat-10 image at 0° E. In addition, an approach based on the outputs from stereo analysis was proposed to detect cloudy pixels. This approach is introduced with a 2D-scatterplot based on the parallax value and the minimum intersection distance. The mentioned scatterplot was applied to determine/detect cloudy pixels in various image subsets with different amounts of cloud cover. Apart from the general advantage of the applied stereography method, which only depends on geometric relationships, the cloud detection results are also improved because: (1) The stereo pair is the HRV bands of the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) sensor, with the highest spatial resolution available from the Meteosat geostationary platform; and (2) the time difference between the image pairs is nearly 5 s, which improves the matching results and also decreases the effect of cloud movements. In order to prove this improvement, the results of this stereo-based approach were compared with three different reflectance-based target detection techniques, including the adaptive coherent estimator (ACE), constrained energy minimization (CEM), and matched filter (MF). The comparison of the receiver operating characteristics (ROC) detection curves and the area under these curves (AUC) showed better detection results with the proposed method. The AUC value was 0.79, 0.90, 0.90, and 0.93 respectively for ACE, CEM, MF, and the proposed stereo-based detection approach. The results of this research shall enable a more realistic modelling of down-welling solar irradiance in the future.

Interaural Coherence Estimation for Speech Processing in Reverberant Environment

Interaural coherence is used to quantify the effects of reverberation on speech, and previous studies applied the conventional method using all previous time data in the form of an infinite impulse response filter to estimate interaural coherence. To consider a characteristic of speech that continuously changes over time, this paper proposes a new method of estimating interaural coherence using time data within a finite length of speech, which is called the quasi-steady interval. The length of the quasi-steady interval is determined with various frequency bands, reverberation times, and short-time Fourier transform (STFT) variables through numerical experiment, and it decreased as reverberation time decreased and the frequency increased. In this interval, a diffuse speech, which is an infinite sum of reflected speeches of different propagating paths, is uncorrelated between two microphones apart from each other; thus, the coherence is close to zero. However, a direct speech measured at the two microphones has steady amplitude and phase difference in this internal; thus, the coherence is close to one. Moreover, the new method is the form of a finite impulse response filter that has a linear phase delay or zero phase delay with respect to speech to frequency; thus, the same or zero time delay for each frequency is applied to the power spectral density. Therefore, the coherence estimation of the new method is closer to the ideal value than the conventional one, and the coherence is accurately estimated at the time–frequency bins of direct speech, which is time-varying according to speech variation.

Feedback Modulates Audio-Visual Spatial Recalibration.

In an ever-changing environment, crossmodal recalibration is crucial to maintain precise and coherent spatial estimates across different sensory modalities. Accordingly, it has been found that perceived auditory space is recalibrated toward vision after consistent exposure to spatially misaligned audio-visual stimuli (VS). While this so-called ventriloquism aftereffect (VAE) yields internal consistency between vision and audition, it does not necessarily lead to consistency between the perceptual representation of space and the actual environment. For this purpose, feedback about the true state of the external world might be necessary. Here, we tested whether the size of the VAE is modulated by external feedback and reward. During adaptation audio-VS with a fixed spatial discrepancy were presented. Participants had to localize the sound and received feedback about the magnitude of their localization error. In half of the sessions the feedback was based on the position of the VS and in the other half it was based on the position of the auditory stimulus. An additional monetary reward was given if the localization error fell below a certain threshold that was based on participants’ performance in the pretest. As expected, when error feedback was based on the position of the VS, auditory localization during adaptation trials shifted toward the position of the VS. Conversely, feedback based on the position of the auditory stimuli reduced the visual influence on auditory localization (i.e., the ventriloquism effect) and improved sound localization accuracy. After adaptation with error feedback based on the VS position, a typical auditory VAE (but no visual aftereffect) was observed in subsequent unimodal localization tests. By contrast, when feedback was based on the position of the auditory stimuli during adaptation, no auditory VAE was observed in subsequent unimodal auditory trials. Importantly, in this situation no visual aftereffect was found either. As feedback did not change the physical attributes of the audio-visual stimulation during adaptation, the present findings suggest that crossmodal recalibration is subject to top–down influences. Such top–down influences might help prevent miscalibration of audition toward conflicting visual stimulation in situations in which external feedback indicates that visual information is inaccurate.

Capon–Bartlett cross-spectrum and a perspective on robust adaptive filtering

Adaptive filtering / beamforming (ABF) optimized for maximum signal-to-interference-plus-noise ratio (SINR) results in filter weights that depend on the data covariance and the signal array response vector. The effectiveness of practical application of such solutions, however, is limited by (i) extent of data stationarity and (ii) imperfect knowledge of the true signal array response vector. Robust ABF methods attempt to address these two critical issues via a slight reformulation of the ABF problem statement. Many robust ABF solutions result in some form of diagonal loading of the data covariance, and can be interpreted as a hybrid beamformer that engages the tradespace between data adaptive beamforming and conventional beamforming (CBF). In view of this interpretation, an exact joint probability distribution is derived for 1) the data adaptive Capon minimum variance distortionless response (MVDR) spectral estimator, and 2) the conventional Bartlett spectral estimator (a.k.a. the smoothed periodogram) when based on the same data covariance estimate. The resulting joint distribution motivates (i) proposal of a robust adaptive filtering design that constrains filter weight cross coherence (a metric quantifying the statistical coupling between the Capon and Bartlett spectral statistics), and (ii) introduction of a coherence estimate called the Capon–Bartlett cross spectrum.

Coherent wide-band signals DOA estimation by the new CTOPS algorithm

Direction of arrival estimation is one of the most important issues in array signals processing. In this paper, a new method is presented for direction estimation of coherent wide-band signals. First of all, signal eigenvalues which have information of the direction of coherent signals, are extracted as primary vectors in each frequency bin. Afterwards, by constructing the innovative matrix H from signal eigenvalues, the de-correlation process is performed and the linear independent vectors of sources are extracted from SVD decomposition. Finally, by choosing transfer matrix, the signal subspace of the reference frequency is transferred to other frequency bins and the direction estimation process is performed by using TOPS algorithm. The proposed method does not need any knowledge of the number of sources and initial estimation of arrival angles. According to the simulation results, the CTOPS new method has a better performance than TOPS method in the presence of correlated sources.

Partially Coherent Compressive Phase Retrieval for Millimeter-Wave Massive MIMO Channel Estimation

Channel estimation is challenging for millimeter-wave (mmWave) massive MIMO systems. Leveraging the spatial sparsity of mmWave channels, compressive sensing (CS) based channel estimation been extensively studied. However, the mmWave hardware imperfections may introduce random phase distortions to the received pilots, which makes the conventional CS methods fail to estimate the channel. The compressive phase retrieval (CPR) method can be employed to solve this challenging problem. In this paper, we exploit the partial coherence in hybrid mmWave systems, i.e., the pilots sent from different radio frequency (RF) chains share the same phase distortion in the same time frame, while the phase distortions are different across different time frames. Based on this property, we propose an on-grid partially coherent CPR (PC-CPR) algorithm for mmWave channel estimation in the presence of severe phase distortions. Unlike the existing coherent channel estimation schemes that require perfect phase information or the noncoherent channel estimation schemes that ignore the phases of measurements, the proposed on-grid PC-CPR algorithm exploits the partial coherence property to estimate the sparse angle-domain channel vector. Furthermore, to solve the resolution limitation of the on-grid PC-CPR algorithm, we propose an off-grid PC-CPR algorithm that directly estimates the parameters of channel paths. The proposed partially coherent channel estimation framework subsumes the existing coherent and noncoherent channel estimation methods as special cases. Simulation results show that under the presence of random phase distortions, the proposed PC-CPR algorithms outperform noncoherent channel estimation methods with higher reliability and lower pilot overhead by leveraging the partial coherence.

Number of Coherent Nodes Estimation Based on Multiple Feature Extraction in Wireless Sensor Network

Knowing number of nodes is the precondition of their locating and estimation of other parameters in wireless sensor network, actually it can be evaluated according to the signal received by the sensor array. The popular algorithms are only appropriate for the uncorrelated signals and circumstance of Gaussian white noise, the estimation precision will deteriorate at some harsh environments. Hence a new algorithm for determining coherent node number based on multiple feature extraction in wireless sensor network is presented in this article. First, perform spatial difference smoothing to the array data. Then the eigenvalues and eigenvectors can be acquired by eigen-decomposition, consequently multiple features which are used for training are constructed. Finally, back propagation neural network and particle swarm optimization are exploited for calculating node number. Simulation results demonstrate that the algorithms has good performances under the background of colored noise and small samples.

Nonlinear Iterative Hard Thresholding for Inverse Scattering

We consider the inverse scattering problem for sparse scatterers. An image reconstruction algorithm is proposed that is based on a nonlinear generalization of iterative hard thresholding. The convergence and error of the method was analyzed by means of coherence estimates and compared to numerical simulations.

A Generalized Version of ACE and Performance Analysis

This paper considers the problem of detecting a signal that belongs to an unknown one dimensional subspace of $\mathbb {C}^{N \times 1}$ in additive interference-plus-noise whose covariance matrix is unknown. The interference-plus-noise is assumed to be modeled as a complex multivariate zero-mean random vector whose covariance matrix ${\bf R}$ , is estimated from signal-free training vectors. The hypothesis test, labeled the generalized Adaptive Coherence Estimator (GACE) involves two test vectors, both of which contain the unknown signal. The test statistic reduces to the ACE test statistic as the signal-to-interference-plus-noise ratio of any one of the test vectors increases without limit. In the limit of large number of training samples the GACE test statistic reduces to the magnitude square of the inner-product of a signal vector in additive statistically independent white noise vectors. Analytical expressions for the probability of false alarm and the probability of detection of the GACE test are derived and the test is shown to have the constant false alarm rate (CFAR) property. Sample results to illustrate the performance of the detector are provided and compared with the performance of the generalized likelihood ratio test (GLRT) for the specific problem, along with results on the sequential application of the GLRT and GACE.

High-Dimensional Metric Combining for Non-Coherent Molecular Signal Detection

In emerging Internet-of-Nano-Thing (IoNT), information will be embedded and conveyed in the form of molecules through complex and diffusive medias. One main challenge lies in the long-tail nature of the channel response causing inter-symbol-interference (ISI), which deteriorates the detection performance. If the channel is unknown, we cannot easily achieve traditional coherent channel estimation and cancellation, and the impact of ISI will be more severe. In this paper, we develop a novel high-dimensional non-coherent scheme for blind detection of molecular signals. We achieve this in a higher-dimensional metric space by combining different non-coherent metrics that exploit the transient features of the signals. By deducing the theoretical bit error rate (BER) for any constructed high-dimensional non-coherent metric, we prove that, higher dimensionality always achieves a lower BER in the same sample space. Then, we design a generalised blind detection algorithm that utilizes the Parzen approximation and its probabilistic neural network (Parzen-PNN) to detect information bits. Taking advantages of its fast convergence and parallel implementation, our proposed scheme can meet the needs of detection accuracy and real-time computing. Numerical simulations demonstrate that our proposed scheme can gain 10dB BER compared with other state of the art methods.