Autocorrelation Algorithm Research Articles

A cascade autocorrelation model of pitch perception

Autocorrelation algorithms, in combination with computational models of the auditory periphery, have been successfully used to predict the pitch of a wide range of complex stimuli. However, new stimuli are frequently offered as counterexamples to the viability of this approach. This study addresses the issue of whether in the light of these challenges the predictive power of autocorrelation can be preserved by changes to the peripheral model and the computational algorithm. An existing model is extended by the addition of a low-pass filter of the summary integration of the individual within-channel autocorrelations. Other recent developments are also incorporated, including nonlinear processing on the basilar membrane and the use of integration time constants that are proportional to the autocorrelation lags. The modified and extended model predicts with reasonable success the pitches of a range of stimuli that have proved problematic for earlier implementations of the autocorrelation principle. The evaluation stimuli include short tone sequences, click trains consisting of alternating interclick intervals, click trains consisting of mixtures of regular and irregular intervals, shuffled click trains, and transposed tones.

Simulating the precedence effect by means of autocorrelation.

A number of algorithms have been developed to simulate the robust localization performance of humans in reverberant conditions, the most successful of which are based on (contralateral) inhibition. While these models can demonstrate the precedence effect in general, their nonlinear behavior makes it difficult to optimize their settings. A linear algorithm has now been developed to overcome this limitation. An autocorrelation algorithm determines the delay between lead and lag and their amplitude ratio for both channels. An inverse filter is then used to eliminate the lag signal before it is localized with a standard localization algorithm. Interestingly, the filter contains both inhibitory and excitatory elements, and the filter’s impulse response looks somewhat similar to the response of a chopper cell. The algorithm operates robustly on top of a model of the auditory periphery (gammatone filterbank and halfwave rectification). Due to its linear nature, the model performs better if the full waveform is reconstructed by subtracting a delayed version of the halfwave‐rectified signal, with a delay time that corresponds to half the period of each frequency band’s center frequency. The model is able to simulate a number of experiments with ongoing stimuli and performs robustly with onset‐truncated and interaural‐level‐difference based stimuli.

Estimation of grain-size distributions and associated parameters from digital images of sediment

A new technique to estimate the grain-size distribution (GSD) from a digital image of sediment is proposed, advancing the applicability of a suite of sedimentary ‘look-up-catalogue’ approaches originated by Rubin [Rubin, D.M., 2004. A simple autocorrelation algorithm for determining grain size from digital images of sediment. Journal of Sedimentary Research, 74(1): 160–165]. The outputs of an automated procedure to estimate the GSD from digital images of sediment are examined with reference to the distributions obtained from both manually sieving the corresponding sediment samples, and axial measurements made on the grains in the images. Measures of grain-size obtained from the imaging procedure correlate very well with grain-size measures derived from both the number-frequency and mass-frequency curve. As expected the GSD obtained from the new automated approach, based on kernel density, compared better with point counts because of a shared two-rather than three-dimensionality. The GSD shape is not always mimicked exactly, however the percentiles obtained from the cumulative GSD compare well with those from sieved distributions, which allow for the first time computation of graphical sorting and skewness from digital images of sediment which are accurate reflections of those measures obtained for point-count and sieved samples. The new approach allows for realistic GSDs from which the residual can be computed, thus a numerical criterion upon which the grain-size distribution from an image can be accepted or rejected. Finally, a method is presented whereby two-dimensional autocorrelogram fields are derived from image power spectra. Ellipsoids are fitted to isolines of autocorrelation coefficients, and from this the dominant axial lengths and orientation of these isolines which could facilitate computation of major and minor axis lengths of grains in sample images, as well as their dominant orientation. In turn, this could allow for parameterisation of axial ratios for parameterisation of 2D shape. Such analysis is completely automated, rapid and non-intrusive.

A Class of Adaptive Cyclostationary Beamforming Algorithms

One of the main benefits of the cyclostationary beamforming algorithms is their ability to extract signals from co-channel interference with only a knowledge of the cycle frequency. In this paper, we study the popular cyclostationary beamformers, and propose five new algorithms, namely, the adaptive cyclic adaptive beamforming (ACAB), adaptive cross-SCORE (ACS), constrained least-squares (CLS), adaptive phase-SCORE (APS), and maximal constrained autocorrelation (MCA) algorithms. All these algorithms are adaptive and have a computational complexity of O(n 2 ) complex multiplications, where n is the number of array elements. A comparative study of these algorithms is made based on numerical simulations. Each of these algorithms has specific application scenarios. The ACS and the APS algorithms are particularly suited for very adverse signal environments. The ACAB, MCA and cyclic adaptive beamforming (CAB, from the work of Wu and Wong) algorithms can pro- vide good performance in the case of medium or weak interference, while the CLS algorithm is especially suitable for weak interference. The CAB algorithm is shown to be a special case of the least-square self-coherent restoral (LS-SCORE) algorithm. Some insights as to how one can assign carrier frequency and symbol rate during digital modulation are also suggested. The proposed adaptive algorithms are easy to implement, and thus are very promising for applications in wireless and mobile communications.

Field test comparison of an autocorrelation technique for determining grain size using a digital ‘beachball’ camera versus traditional methods

This extensive field test of an autocorrelation technique for determining grain size from digital images was conducted using a digital bed-sediment camera, or ‘beachball’ camera. Using 205 sediment samples and >1200 images from a variety of beaches on the west coast of the US, grain size ranging from sand to granules was measured from field samples using both the autocorrelation technique developed by Rubin [Rubin, D.M., 2004. A simple autocorrelation algorithm for determining grain size from digital images of sediment. Journal of Sedimentary Research, 74(1): 160–165.] and traditional methods (i.e. settling tube analysis, sieving, and point counts). To test the accuracy of the digital-image grain size algorithm, we compared results with manual point counts of an extensive image data set in the Santa Barbara littoral cell. Grain sizes calculated using the autocorrelation algorithm were highly correlated with the point counts of the same images ( r 2 = 0.93; n = 79) and had an error of only 1%. Comparisons of calculated grain sizes and grain sizes measured from grab samples demonstrated that the autocorrelation technique works well on high-energy dissipative beaches with well-sorted sediment such as in the Pacific Northwest ( r 2 ≥ 0.92; n = 115). On less dissipative, more poorly sorted beaches such as Ocean Beach in San Francisco, results were not as good ( r 2 ≥ 0.70; n = 67; within 3% accuracy). Because the algorithm works well compared with point counts of the same image, the poorer correlation with grab samples must be a result of actual spatial and vertical variability of sediment in the field; closer agreement between grain size in the images and grain size of grab samples can be achieved by increasing the sampling volume of the images (taking more images, distributed over a volume comparable to that of a grab sample). In all field tests the autocorrelation method was able to predict the mean and median grain size with ∼96% accuracy, which is more than adequate for the majority of sedimentological applications, especially considering that the autocorrelation technique is estimated to be at least 100 times faster than traditional methods.

A distributed algorithm for multi-tau autocorrelation

Network data-transfer times in distributed simulation environments can be reduced by performing data analysis at the remote source, if the analytical technique does not require the entire set of data at once. This novel multi-tau autocorrelation algorithm allows time-domain data records to be processed in discrete, distributed segments and combined at a later point in time. The new approach agrees with autocorrelation results performed by concatenating the discrete segments before correlation, but it operates with significantly shortened processing times. The multi-tau algorithm also benefits from reduced memory requirements since it does not require access to the entire data record at once, and from improved scalability since the multi-tau algorithm has order O(N), while fast Fourier transform autocorrelation algorithms have order O(N log N). This distributed algorithm has particular utility in simulations of fluorescence correlation spectroscopy or photon correlation spectroscopy.

TU‐FF‐A4‐03: Post‐Processing Dead Pixel Evaluation for Digital Detectors

Purpose: To investigate and determine a viable method for determining the absolute number of dead pixels from a diagnostic x‐ray digital acquisition output. Method and Materials: In a digital receptor, dead pixels are averaged over surrounding pixels to present an image without artifacts. The absolute number of dead pixels is in general not provided by the manufacturer, but a relative number of pixels from the last check may be reported. Due to the relative difficulty of gaining access to the raw image values, we focus on the least processed images obtainable from a given unit and employ post‐processing techniques to evaluate the pixel information. We use statistical and autocorrelation algorithms to evaluate the likelihood that a given pixel point or line of pixels is aberrant from the expected random noise resulting from several uniform exposures from a digital acquisition detector. Results: We are able to determine that both algorithms provide indications for the line and individual (or grouped) point pixel modifications which are not consistent with random noise. The statistical method (employing both a local and global statistical analysis of noise variations) shows both geometries (point and line defects) but requires statistical averaging over several images to provide relevant point geometry data (we use a minimum of three images to average). The autocorrelation method provides results without the need for averaging. However, by employing the criteria that each of the three images must meet the same correlation requirements, we increase the confidence of the final evaluation results through redundancy. Conclusion: We have developed two independent methods for determining the overall dead pixel information from a digital acquisition detector given three uniform 2223 2006 AAPM Meeting Program 2223 Medical Physics, Vol. 33, No. 6, June 2006 exposures. Both methods provide data for point and line geometries and can be used to provide initial and time logged progression of absolute pixel drop out with time.

Quantitative imaging of cells with multi-isotope imaging mass spectrometry (MIMS)—Nanoautography with stable isotope tracers

We describe some technical aspects of the application of multi-isotope imaging mass spectrometry (MIMS) to biological research, particularly the use of isotopic tags to localize and measure their incorporation into intracellular compartments. We touch on sample preparation, on image formation, on drift correction and on extraction of quantitative data from isotope ratio imaging. We insist on the wide variety of sample types that can be used, ranging from whole cells prepared directly on Si supports, to thin sections of cells and tissues on Si supports, to ultrathin TEM sections on carbon-coated grid. We attempt to dispel the myth of difficulties in sample preparation, which we view as a needless deterrent to the application of MIMS to the general biological community. We present protocols for the extraction of isotope ratio data from mass images. We illustrate the benefits of using sequential image plane acquisition followed by the application of an autocorrelation algorithm (nanotracking) to remove the effects of specimen drift. We insist on the advantages to display the isotope ratios as hue saturation intensity images.

Remapping Auditory-Motor Representations in Voice Production

Evidence regarding visually guided limb movements suggests that the motor system learns and maintains neural maps between motor commands and sensory feedback. Such systems are hypothesized to be used in a feed-forward control strategy that permits precision and stability without the delays of direct feedback control. Human vocalizations involve precise control over vocal and respiratory muscles. However, little is known about the sensorimotor representations underlying speech production. Here, we manipulated the heard fundamental frequency of the voice during speech to demonstrate learning of auditory-motor maps. Mandarin speakers repeatedly produced words with specific pitch patterns (tone categories). On each successive utterance, the frequency of their auditory feedback was increased by 1/100 of a semitone until they heard their feedback one full semitone above their true pitch. Subjects automatically compensated for these changes by lowering their vocal pitch. When feedback was unexpectedly returned to normal, speakers significantly increased the pitch of their productions beyond their initial baseline frequency. This adaptation was found to generalize to the production of another tone category. However, results indicate that a more robust adaptation was produced for the tone that was spoken during feedback alteration. The immediate aftereffects suggest a global remapping of the auditory-motor relationship after an extremely brief training period. However, this learning does not represent a complete transformation of the mapping; rather, it is in part target dependent.

Sensorless tool failure monitoring system for drilling machines

It is well known that on-line tool condition monitoring has great significance in modern manufacturing processes. In order to prevent possible damages to the workpiece or the machine tool, reliable techniques are required providing an on-line response to an unexpected tool failure. Drilling is one of the most fundamental machining operations and two of the most crucial issues related to it are tool wear and fracture. During the spindle process, the motor driver current is related to the drill condition: power consumption is higher for a worn drill in comparison to a sharp drill for the same process. This difference in power consumption can be self-correlated to obtain the resulting waveform variance to provide a merit figure for tool condition. This paper describes a driver current signal analysis to estimate the tool condition by using the discrete Wavelet Transform in order to extract the information from the original cutting force, and through an autocorrelation algorithm evaluate the tool wear in the form of an asymmetry weighting function. The current is monitored from the motor driver to give a sensorless approach. Experimental results are presented to show the algorithm performance, a complete sensorless tool failure system which allows the detection of tool failure as a function of spindle current in real time.

Encoding of pitch in the human brainstem is sensitive to language experience

Neural processes underlying pitch perception at the level of the cerebral cortex are influenced by language experience. We investigated whether early, pre-attentive stages of pitch processing at the level of the human brainstem may also be influenced by language experience. The human frequency following response (FFR), reflecting sustained phase-locked activity in a population of neural elements, was used to measure activity within the rostral brainstem. FFRs elicited by four Mandarin tones were recorded from native speakers of Mandarin Chinese and English. Pitch strength (reflecting robustness of neural phase-locking at the pitch periods) and accuracy of pitch tracking were extracted from the FFRs using autocorrelation algorithms. These measures revealed that the Chinese group exhibits stronger pitch representation and smoother pitch tracking than the English group. Consistent with the pitch data, FFR spectral data showed that the Chinese group exhibits stronger representation of the second harmonic relative to the English group across all four tones. These results cannot be explained by a temporal pitch encoding scheme which simply extracts the dominant interspike interval. Rather, these results support the possibility of neural plasticity at the brainstem level that is induced by language experience that may be enhancing or priming linguistically relevant features of the speech input.

Implementation of real time autocorrelation method employing the DS87C550 microcontroller

In this work, the implementation of Real Time Autocorrelation method proposed by Takeuchi and Hogaki, is exposed. The purpose is to obtain the frequency of the fetal cardiac beats, originated from a Fetal Detector, using a wide-benefits DS87C550 microcontroller. The value of frequency is showed in a LCD. The autocorrelation algorithm was simulated in a PC to know its dynamic behavior from selected signals. A criterion to carry out the recognition and the selection of the first conformed crest, was obtained. By means of this criterion, the period of the processed signal is determined. Consequently, an optimized and original algorithm was created and implemented in the microcontroller, where the value of the fetal cardiac frequency (given in pulses per minute, ppm.) is obtained each 1.2 sec. In that time, two calculated values of frequency are averaged.

Acoustic correlates of non-modal phonation in telephone speech

Non-modal phonation conveys both linguistic and paralinguistic information, and is distinguished by acoustic source and filter features. Detecting non-modal phonation in speech requires reliable F0 analysis, a problem for telephone-band speech, where F0 analysis frequently fails. An approach is demonstrated to the detection of creaky phonation in telephone speech based on robust F0 and spectral analysis. The F0 analysis relies on an autocorrelation algorithm applied to the inverse-filtered speech signal and succeeds in regions of non-modal phonation where the non-filtered F0 analysis typically fails. In addition to the extracted F0 values, spectral amplitude is measured at the first two harmonics (H1, H2) and the first three formants (A1, A2, A3). F0 and spectral tilt are measured from 300 samples of modal and creaky voice vowels, selected from Switchboard telephone speech using auditory and visual criteria. Results show successful F0 detection in creaky voice regions, with distinctive low F0, and statistically significant differences between modal and creaky voice in measures of spectral amplitude, especially for measures based on H1. Our current work develops methods for the automatic detection of creaky voicing in spontaneous speech based on the analysis technique shown here. [Work supported by NSF.]

Signal path optimization in software-defined radio systems

Growing requirements set upon communication transceivers lead to high sensitivity to nonidealities of analog components, especially in case of software-defined radio (SDR) systems. This paper deals with disturbances, mismatches, and the rejection of images that are caused by the front-end processing. A hybrid architecture comprehending advantages of homodyne and heterodyne receivers is proposed for an SDR system. Theoretical outcomes are discussed and backed by simulations. A fast Fourier transform (FFT)-based algorithm, the phase increment Kay algorithm, and two autocorrelation algorithms, i.e., Morelli-Mengali and Crozier-Moreland, used for frequency offset correction, are compared. The FFT algorithm turns out to be the best solution, both in terms of performance and ease of implementation. DC correction is then discussed and in-phase/quadrature imbalance compensation by means of a blind-source separation (BSS) algorithm and a hard-decision algorithm is performed. The latter algorithm displays implementation advantages, while BSS performs better.

A Simple Autocorrelation Algorithm for Determining Grain Size from Digital Images of Sediment

ABSTRACT Autocorrelation between pixels in digital images of sediment can be used to measure average grain size of sediment on the bed, grain-size distribution of bed sediment, and vertical profiles in grain size in a cross-sectional image through a bed. The technique is less sensitive than traditional laboratory analyses to tails of a grain-size distribution, but it offers substantial other advantages: it is 100 times as fast; it is ideal for sampling surficial sediment (the part that interacts with a flow); it can determine vertical profiles in grain size on a scale finer than can be sampled physically; and it can be used in the field to provide almost real-time grain-size analysis. The technique can be applied to digital images obtained using any source with sufficient resolution, including digital cameras, digital video, or underwater digital microscopes (for real-time grain-size mapping of the bed).

A novel ultrasound instrument for investigation of arterial mechanics

The study of arterial mechanics concerns functional characteristics depending on wall elasticity and flow profile. Wall elasticity can be investigated through the estimation of parameters like the arterial distensibility, which is of high clinical interest because of its known correlation not only with the advanced atherosclerotic disease, but also with aging and major risk factors for cardiovascular disease. The flow velocity profile is also clinically relevant, because it modulates endothelial function and can be responsible for the development and distribution of atherosclerotic plaques. A clinically relevant variable extracted from the blood velocity profile is the wall shear rate (WSR), which represents the spatial velocity gradient near the vessel wall. This paper describes an integrated ultrasound system, capable of detecting both the velocity profile and the wall movements in human arteries. It basically consists of a PC add-on board including a single high-speed digital signal processor. This is dedicated to the analysis of echo-signals backscattered from 128 range cells located along the axis of the interrogating ultrasound (US) beam. Echoes generated from the walls (characterized by high amplitudes and low Doppler frequencies) and from red blood cells (characterized by low amplitudes and relatively high Doppler frequencies) are independently processed in real-time. Wall velocity is detected through the autocorrelation algorithm, while blood velocity is investigated through a complete spectral analysis of all signals backscattered by erythrocytes and WSR is extracted from the estimated velocity profile. Preliminary applications of the new system, including the simultaneous analysis of blood flow and arterial wall movement in healthy volunteers and in a diseased patient, are discussed, and first results are presented.

FPGA based on-line tool breakage detection system for CNC milling machines

On-line tool breakage detection is one of the most important mechatronics problems in automated CNC machines. In this paper an on-line tool breakage detector for milling machines is described. The detector employs the cutting forces on the movement axis to compute the discrete wavelet transform of the resultant force. The novelty of this work is to apply the discrete wavelet transform of the resultant cutting force an autocorrelation algorithm to detect tool breakage in the form of an asymmetry weighting function and to implement the algorithm by using hardware signal processing techniques. The cutting force signals are taken from the current at the axis motor drivers for a sensorless instrumentation approach. The system has three parts: the signal conditioners, the data acquisition system and the hardware signal processing unit. The hardware signal processing unit was designed by following the system on-a-chip approach and it includes three application specific digital signal processing units, general synchronization, interface and control, implemented in a single field programmable gate array and a coefficient read-only memory. On-line detection and low-cost are guaranteed by following this approach. The detector system is a stand-alone unit and does not require microprocessors or computers to perform its task. Experimental results that show the overall system performance are presented.

Spectroscopy of DNS update traffic

We study attempts to dynamically update DNS records for private (RFC1918) addresses, by analyzing the frequency spectrum of updates observed at an authoritative nameserver for these addresses. Using a discrete autocorrelation algorithm we found that updates series have periods of 60 or 75 minutes, which we identified as default settings of out-of-the-box Microsoft Windows 2000 and XP DNS software.

REAL-TIME CONDITION MONITORING BY SIGNIFICANT AND NATURAL FREQUENCIES ANALYSIS OF VIBRATION SIGNAL WITH WAVELET FILTER AND AUTOCORRELATION ENHANCEMENT

Vibration signals from machining processes contain very useful information and offer excellent possibilities for in-process condition monitoring. Significant and natural frequencies in vibration signals reflect the inherent property of the system. Any change in the condition will result in the change in these frequencies. The time–frequency localization of the wavelet transform makes it very useful in signal processing, so that it can be used in filter design. In this paper, the wavelet filter with very narrow frequency-band and autocorrelation enhancement is proposed as a means of monitoring the natural frequencies in real-time. With the efficient, reliable and flexible wavelet filters, any number of natural frequencies in either the time or the frequency domain can be observed at the same time. After filtering, the signal with a single central frequency is enhanced by an autocorrelation algorithm in the time domain. This can provide real-time monitoring based on the changes of significant and natural frequencies, corresponding to the change in conditions.

Psychophysical evidence against the autocorrelation theory of auditory temporal processing.

Nowadays, it is widely believed that the temporal structure of the auditory nerve fibers' response to sound stimuli plays an important role in auditory perception. An influential hypothesis is that information is extracted from this temporal structure by neural operations akin to an autocorrelation algorithm. The goal of the present work was to test this hypothesis. The stimuli consisted of sequences of unipolar clicks that were high-pass filtered and mixed with low-pass noise so as to exclude spectral cues. In experiment 1, "interfering" clicks were inserted in an otherwise periodic (isochronous) click sequence. Each click belonging to the periodic sequence was followed, after a random portion of the period, by one interfering click. This disrupted the detection of temporal regularity, even when the interfering clicks were 5 dB less intense than the periodic clicks. Experiments 2-4 used click sequences that showed a single peak in their autocorrelation functions. For some sequences, this peak originated from "first-order" temporal regularities, that is from the temporal relations between consecutive clicks. For other sequences, the peak originated instead from "second-order" regularities, relative to nonconsecutive clicks. The detection of second-order regularities appeared to be much more difficult than the detection of comparable first-order regularities. Overall, these results do not tally with the current autocorrelation models of temporal processing. They suggest that the extraction of temporal information from a group of closely spaced spectral components makes no use of time intervals between nonconsecutive peaks of the amplitude envelope.