Autocorrelation Values Research Articles

Sparse array design for maximizing the signal-to-interference-plus-noise-ratio by matrix completion

We consider sparse array beamformer design achieving maximum signal-to interference plus noise ratio (MaxSINR). Both array configuration and weights are attuned to the changing sensing environment. This is accomplished by simultaneously switching among antenna positions and adjusting the corresponding weights. The sparse array optimization design requires estimating the data autocorrelations at all spatial lags across the array aperture. Towards this end, we adopt low rank matrix completion under the semidefinite Toeplitz constraint for interpolating those autocorrelation values corresponding to the missing lags. We compare the performance of matrix completion approach with that of the fully augmentable sparse array design acting on the same objective function. The optimization tool employed is the regularized l1-norm successive convex approximation (SCA). Design examples with simulated data are presented using different operating scenarios, along with performance comparisons among various configurations.

Automation of low-speed bearing fault diagnosis based on autocorrelation of time domain features

This study is focused on the application of automated techniques on low-speed bearing diagnostics. The diagnosis in low-speed conditions is hampered by the long periods between defect-related impulses and the high level of noise relative to the magnitude of the impulses. To detect a localised defect in such conditions, a new approach that uses vibration signals and information on the bearing defect frequencies is proposed. At first, the vibration signal is filtered in a specific frequency range to enable the detection of the impulses hidden in the signal. The filtered signal is then segmented into short time windows, the length of which are selected based on the bearing defect frequencies. Statistical time domain features are calculated from these windows to amplify and compress the impulses inflicted by the defect. Then, a criterion based on the autocorrelation values of specific time lags is calculated. An exhaustive search procedure is used to determine the frequency band for signal filtering and to select the statistical feature, which together maximises the proposed criterion. The highest value of the criterion is finally compared with the corresponding value from the baseline condition to detect the localised defect. The proposed technique is demonstrated on simulated signals, and validated based on the vibration signals from laboratory tests with undamaged, slightly damaged and severely damaged rolling elements in a rolling element bearing. Different conditions with shaft speeds from 20 to 80 rpm were studied in the laboratory tests. The proposed technique was compared with automated envelope spectrum diagnosis approaches based on the peak ratio and peak-to-median indicators and the fast kurtogram. The results reveal that the criterion based on autocorrelation gave defect indications associated with the correct type of defect in various circumstances while the tested envelope spectrum approaches were prone to induce an incorrect conclusion. Moreover, the results indicate that the approach could be used successfully on signals with a length that includes relatively few defect periods or impulses. The approach requires a high sampling rate relative to the defect frequencies, which may limit its suitability for the higher shaft speeds.

Fine Doppler scale estimations for an underwater acoustic CP-OFDM system

In the challenging underwater acoustic (UWA) communication channels, the performance of orthogonal frequency division multiplexing (OFDM) systems is severely affected by the Doppler effect. Hence, the Doppler scale estimation and compensation are critical for UWA OFDM systems. As a commonly adopted solution, the Doppler scale can be estimated by the maximization of auto-correlation between the received cyclic prefix (CP) and OFDM symbol. However, the accuracy of the auto-correlation based Doppler scale estimation is decided by the sampling rate of the signal. To achieve higher estimation accuracy, higher sampling rate is required and which means more computational complexity. To overcome this problem, we propose several fine Doppler scale estimation methods by interpolating the auto-correlation peak values, based on the derived expression of the expected auto-correlation outputs corresponding to a CP-OFDM signal after the UWA channel. Simulation results demonstrate the effectiveness of the proposed methods, and the decoding of experimental data further verifies the superiority of those methods.

Generation of Spreading Codes with Minimum Correlation using Sorting Genetic Algorithm-II

In a code division multiple access (CDMA) system, multiple access interference (MAI) and Inter-symbol interference (ISI) appears if generated spreading codes are not maintained orthogonally and the communication channel is taken as multi-path communication channel. When generated spreading codes are multi-path spread and then channel delay occurs, it shows that ortho-gonality of the spreading codes is not maintained. The effect of MAI can be mitigated by maintaining low cross-correlation values as much as low between the large numbers of spreading codes. The code division multiple technique spreading codes must maintain absolutely impulsive autocorrelation at origin and very low cross correlation other than origin to avoid false synchronisation. i.e autocorrelation must be maximum at origin and cross correlation must be minimum at non origin point. In this paper, we propose multi-objective Genetic Algorithm approach –Genetic Algorithm-II (NSGA-II) to reduce the out-of-phase average mean-square aperiodic autocorrelation and average mean-square aperiodic cross-correlation value of randomly initialized binary spreading code set

PEMILIHAN INPUT MODEL ADAPTIVE FUZZY INFERENCE SYSTEM (ANFIS) BERBASIS LAGRANGE MULTIPLIER TEST DILENGKAPI GUI MATLAB (Aplikasi pada Data Harga Beras Kualitas Rendah di Indonesia Periode Januari 2013 – Februari 2019)

Adaptive Neuro Fuzzy Inference System (ANFIS) is a method that uses artificial neural networks to implement fuzzy inference systems. The optimum ANFIS model is influenced by the selection of inputs, number of membership and rules. In general, the selection of ANFIS input is based on Autoregressive (AR) unit as a result of ARIMA preprocessing. Thus it requires several assumptions. In this research, an alternative selection of ANFIS input based on Lagrange Multiplier Test (LM Test) is used to test hypothesis for the addition of one input. Preprocessing is conducted to obtain the value of partial autocorrelation against Zt. The input lag variable which has the highest partial autocorrelation is the first input ANFIS. The next input selection is selected based on LM test for adding one variable. To test the performance of LM Test, an empirical study of two groups of generated data and low quality rice prices is conducted as a case study. Generating data with stationary and non-stationary criteria has a good performance because it has very good forecasting ability with MAPE out sample for each characteristic are 5.6785% and 9.4547%. In the case study using LM Test, the selected input are and with the number of membership 2. The chosen model has very good forecasting ability with MAPE outsampel 6.4018%. Keywords : ANFIS, ANFIS Input, LM-Test, Low Quality Rice Prices, Forecasting

Incorporating the temporal autocorrelation of demographic rates into structured population models

Population dynamics are typically temporally autocorrelated: population sizes are positively or negatively correlated with past population sizes. Previous studies have found that positive temporal autocorrelation increases the risk of extinction due to ‘inertia’ that prolongs downward fluctuations in population size. However, temporal autocorrelation has not yet been analyzed at the level of life cycle transitions. We developed an R package, colorednoise, which creates stochastic matrix population projections with distinct temporal autocorrelation values for each matrix element. We used it to analyze long‐term demographic data on 25 populations from the COMADRE and COMPADRE databases and simulate their stochastic dynamics. We found a broad range of temporal autocorrelation across species, populations and life cycle stages. The number of stage‐classes in the matrix strongly affected the temporal autocorrelation of the growth rate. In the plant populations, reproduction transitions had more negative temporal autocorrelation than survival transitions, and matrices dominated by positive temporal autocorrelation had higher extinction risk, while in animal populations transition type was not associated with noise color. Our results indicate that temporal autocorrelation varies across life cycle transitions, even among populations of the same species. We present the colorednoise package for researchers to analyze the temporal autocorrelation of structured demographic rates.

Binary periodic sequences with 2-level autocorrelation values

Binary sequences with low autocorrelation values have important applications in cryptography and communications. In this paper, we study binary periodic sequences with 2-level autocorrelation values. For n≡1(mod4), we prove some cases of Schmidt’s Conjecture for perfect binary sequences (Des. Codes Cryptogr. 78 (2016), 237-267). For n≡2(mod4), Jungnickel and Pott (1999) left the existence of four perfect binary sequences as an open question and we solve three of them. For n≡3(mod4), we present nonexistence of some binary sequences whose nontrivial autocorrelation values are all equal to 3. For n≡0(mod4), we give two binary sequences with d=4 for n=8,40, and also show that there do not exist such binary sequences for all other values of n.

Making reference solar forecasts with climatology, persistence, and their optimal convex combination

Climatology and persistence are the two most popular naïve reference methods for benchmarking deterministic solar forecasts. Depending on the forecast horizon, the preferred reference methods differ. This brief note derives the general relationship among climatology, persistence, and their optimal convex combination. The value of the lag-h autocorrelation is shown to be the main indicator of choice for the standard of reference.

Autocorrelation Values of Generalized Cyclotomic Sequences with Period pn+1

Recently Edemskiy proposed a method for computing the linear complexity of generalized cyclotomic binary sequences of period p n + 1 , where p = d R + 1 is an odd prime, d , R are two non-negative integers, and n > 0 is a positive integer. In this paper we determine the exact values of autocorrelation of these sequences of period p n + 1 ( n ≥ 0 ) with special subsets. The method is based on certain identities involving character sums. Our results on the autocorrelation values include those of Legendre sequences, prime-square sequences, and prime cube sequences.

P6541Wrist band photoplethysmography pulse morphology-based autocorrelation analysis enables atrial fibrillation detection without the need of pulse detection

Abstract Background Atrial fibrillation (AF) is often asymptomatic and intermittent making its detection a major clinical challenge. A photoplethysmography (PPG) wrist band with algorithm-based detection of AF provides a promising solution for screening of AF. However, the shapes of individual pulse waveforms vary in AF decreasing pulse detection accuracy. Purpose The purpose of this study was to evaluate the utility of PPG wrist band pulse morphology in detection of AF. Methods A 5-minute PPG was recorded with a PPG wrist band from patients with AF or sinus rhythm. A simultaneously registered ECG served as the golden standard for the rhythm analysis and was interpreted by two cardiologists. In addition to using the inter-beat-interval (IBI) based AFEvidence algorithm in comparison, we extracted a feature straight from the PPG signal, without the need of pulse detection. This feature was calculated as the average of absolute autocorrelation values over different lags. The feature describes the regularity of the PPG signal and is decreased if the shape and periodicity of pulse waves vary. The performance of this PPG morphology-based method in detection of AF was evaluated and compared to the AFEvidence. Results The study population consisted of 213 patients (106 AF, 107 sinus rhythm). The sensitivity and specificity of PPG morphology-based autocorrelation AF detection method were 98.1% and 94.4%. For AFEvidence, the sensitivity and specificity were 96.2% and 98.1%, respectively (p=0.146 between the methods, McNemar test). PPG samples of AF and sinus rhythm Conclusions The PPG morphology-based autocorrelation method detects AF with good accuracy without the need of pulse detection. The method seems promising in detection of AF and should be studied further. Acknowledgement/Funding None

Fallowing temporal patterns assessment in rainfed agricultural areas based on NDVI time series autocorrelation values

Fallowing is a common practice in Mediterranean areas where water scarcity becomes a limiting factor, affecting soil productivity, crop yield and biodiversity. In mainland Spain, fallow lands expand across three million hectares every year, constituting around 30% of rainfed arable lands and 6% of the national surface. There is a need of monitoring fallow lands to better map land use intensity and therefore achieve a sustainable expansion and intensification of agriculture. However, most of current land use classification systems do not include lands under fallowing practices as a specific class. In this research, a new and highly operative methodology based on NDVI time series autocorrelation values to assess fallowing temporal patterns across rainfed agricultural areas is proposed. This approach was tested in mainland Spain, using the autocorrelation function of MODIS NDVI time series from 2001 to 2012 at 250 m spatial resolution. The field observational database from the Spanish Ministry of Agriculture, Fisheries and Food was used for validation purposes. The dataset used includes 338 pixels with annual information about the cultivated and fallowed surface within the entire study period. It was demonstrated that specific autocorrelation values at lags corresponding to one, two, and three years contained relevant information to identify lands under fallowing practices and assess their temporal pattern. Integrating autocorrelation variables in a random forest model made it possible to improve the assessment. The classification results were in agreement with the field dataset with an overall accuracy higher than 80%. Results revealed that approximately half of rainfed agricultural areas were regularly cultivated and distributed mainly in the northwestern Spain. The other half mainly located across northeast, center and south of Spain, showed crop-fallow rotation patterns. This methodology is a promising technique to map land management intensity using the entire time series in a highly operative manner. It is expected that in the near future the availability of remote sensing time series with better spatial resolution will make it possible to improve the assessment of agricultural intensification.

Reducing the variability of inter-departure times of a single-server queueing system – The effects of skewness

A critical performance measure in serial production lines is the variability of inter-departure times of a single-server queue. Increasing upstream inter-departure time variability generates greater downstream variability, diminishing overall line performance. Theory suggests that the variability of inter-departure times of a single-server queue is reduced by decreasing the variance of inter-arrival and service times. This study investigates the effects of the skewness of inter-arrival and service time distributions on the variability of inter-departure times. Contrary to previous results suggesting that mean waiting times of a GI/G/1 queue can be reduced by increasing inter-arrival time skewness, this experimental study of a GI/G/1 queue with triangular inter-arrival and service times shows that the inter-departure time coefficient of variation is reduced through a combination of negative inter-arrival time skewness and positive service time skewness. These results also suggest that the absolute value of the negative autocorrelation between consecutive departures is reduced by the same combination of negative inter-arrival time skewness and positive service time skewness for low values of server’s utilization, while positive skewness for both inter-arrival and service times reduces this value for high values of server’s utilization. Finally, it was found that queue capacity constraints increase the coefficient of variation of inter-departure times, as has been previously suggested, as well as the skewness and the absolute correlation values of the inter-departure time distribution.

Generalized additive models fitting with autocorrelation for sea surface temperature anomaly data

Climatic conditions in Sumatra Island are affected by Sea Surface Temperature Anomaly (SSTA) in the Indian Ocean or more commonly referred to as Indian Ocean Dipole (IOD). Extreme climate events also closely related to SSTA. Several climate features that affect SSTA such as air temperature, precipitation rain, relative humidity, wind speed, and solar radiation. SSTA is an increase or decrease in the mean of Sea Surface Temperature so required the analysis to assess extreme climatic events to the risk due to the occurrence of anomalies. Generalized Additive Models (GAM) with autocorrelation can be used to model this phenomenon. GAM method accommodates the nonlinear influence between response variables and predictor variables. The data used in this research is the time series data, daily data from 2006-2017 and there are gaps in it, where there is an autocorrelation value. The purpose of this research is to get a representative model and to know the factors that influence to SSTA. The results show that GAM’s best model with autocorrelation is GAM model by including month and year variables with the monthly autocorrelation structure. Factors that affecting SSTA are the air temperature, month and year as time covariates.

A 14-Bit 500-MS/s Time-Interleaved ADC With Autocorrelation-Based Time Skew Calibration

This brief presents a two-channel 14-bit 500-MS/s time-interleaved analog-to-digital converter (ADC) with background time skew calibration fabricated in a 0.18- ${\mu }\text{m}$ CMOS process. Time skew is detected with an improved autocorrelation-based strategy and corrected with digitally controlled delay line. Different from traditional strategy, the proposed strategy doesn’t align all the channel ADCs with the reference ADC, but aligns channel ADCs with each other and remains a time interval with the reference ADC. The time interval is acceptable in a wide range. It can enhance the sensitivity of time skew detection. By using only the maximum significant bit of channel ADC output to calculate the autocorrelation value, hardware overhead can be further reduced. After time skew calibration, measurement results show that signal-to-noise and distortion ratio and interleaving spur are improved to 68.5 dB and −90.7 dBc from 68.0 dB and −76.0 dBc, respectively, at low input frequency, and to 66.1 dB and −77.9 dBc from 58.7 dB and −59.5 dBc, respectively, at Nyquist frequency. At Nyquist frequency, the proposed ADC achieves a Walden FOM of 0.9 pJ/conv.-step.

The analysis of partial autocorrelation function in predicting maximum wind speed

The stationary and non-stationary testing of the data set can be done using a plot analysis of the Partial Autocorrelation Function of the data, by viewing the maximum number of the expected value of Partial Autocorrelation. The Autocorrelation Function (ACF) is a function that shows the correlation between the observation of the t-time and the observation at the previous time. The autocorrelation function shows the autocorrelation coefficient, which is the correlation measurement of the observations at different times. Data taken from Statistics Indonesia -known in Indonesia as BPS (Badan Pusat Statistik)- contains the information about the maximum wind speed by month at the Paotere station in 2008 - 2017 in Makassar City. By using the data, the maximum wind speed for the next 12 months will be estimated.ie from January 2017 to December 2018. The results obtained, forecasting is done with 12 leads period ahead with 95% confidence interval.

Linear complexity and autocorrelation of a new class of binary generalized cyclotomic sequences of order two and length pqr

Cyclotomic sequences have good cryptographic properties and are closely related to difference sets. This paper proposes a new class of binary generalized cyclotomic sequences of order two and length pqr. Its linear complexity, minimal polynomial, and autocorrelation are investigated. The results show that these sequences have a large linear complexity when 2 e D 1 , which means they can resist the Berlekamp-Massey attack. Furthermore, the autocorrelation values are close to 0 with a probability of approximately 1 — 1/r. Therefore, when r is a big prime, the new sequence has a good autocorrelation.

Auditory Brainstem Representation of the Voice Pitch Contours in the Resolved and Unresolved Components of Mandarin Tones.

Accurate perception of voice pitch plays a vital role in speech understanding, especially for tonal languages such as Mandarin. Lexical tones are primarily distinguished by the fundamental frequency (F0) contour of the acoustic waveform. It has been shown that the auditory system could extract the F0 from the resolved and unresolved harmonics, and the tone identification performance of resolved harmonics was better than unresolved harmonics. To evaluate the neural response to the resolved and unresolved components of Mandarin tones in quiet and in speech-shaped noise, we recorded the frequency-following response. In this study, four types of stimuli were used: speech with either only-resolved harmonics or only-unresolved harmonics, both in quiet and in speech-shaped noise. Frequency-following responses (FFRs) were recorded to alternating-polarity stimuli and were added or subtracted to enhance the neural response to the envelope (FFRENV) or fine structure (FFRTFS), respectively. The neural representation of the F0 strength reflected by the FFRENV was evaluated by the peak autocorrelation value in the temporal domain and the peak phase-locking value (PLV) at F0 in the spectral domain. Both evaluation methods showed that the FFRENV F0 strength in quiet was significantly stronger than in noise for speech including unresolved harmonics, but not for speech including resolved harmonics. The neural representation of the temporal fine structure reflected by the FFRTFS was assessed by the PLV at the harmonic near to F1 (4th of F0). The PLV at harmonic near to F1 (4th of F0) of FFRTFS to resolved harmonics was significantly larger than to unresolved harmonics. Spearman's correlation showed that the FFRENV F0 strength to unresolved harmonics was correlated with tone identification performance in noise (0 dB SNR). These results showed that the FFRENV F0 strength to speech sounds with resolved harmonics was not affected by noise. In contrast, the response to speech sounds with unresolved harmonics, which were significantly smaller in noise compared to quiet. Our results suggest that coding resolved harmonics was more important than coding envelope for tone identification performance in noise.

Chaos-Based transmitted-reference ultra-wideband communications

ABSTRACTTransmitted-reference (TR) ultra-wideband (UWB) communication systems have gained increasing popularity for the usage in the low data rate application, due to its non-coherent receiver structure. In conventional TR system, non-coherency at the receiver is achieved by sending reference pulses prior to the data-bearing pulses. Then, at the receiver side, reference pulses are used as template signals for correlation with data-bearing pulses. Therefore, the orthogonality between reference and data pulses is obtained in time division multiple access (TDMA) fashion. However, the implementation of a wideband delay line is very difficult in the current low power integrated circuits. In this paper, a TR method called Chaos-Based TR (CB-TR) is proposed. In the proposed method, chaotic sequences are used to separate the reference and data pulses. Such approach exploits the benefits of chaotic signals, such as non-periodicity, easy-to-generate, impulse-like autocorrelation value and low cross-correlation value. Furthermore, in order to decrease the influence of some negative properties of conventional chaotic maps, a modified chaotic generator (MCS) is proposed. Simulation results over the IEEE 802.15.4a channel model show comparable bit error rate performance to other TR methods.

Improvement of single photon emission from InGaN QDs embedded in porous micropillars

In many InGaN/GaN single photon emitting structures, significant contamination of the single photon stream by background emission is observed. Here, utilizing InGaN/GaN quantum dots incorporated in mesoporous distributed Bragg reflectors (DBRs) within micropillars, we demonstrate methods for the reduction of this contamination. Using the resulting devices, autocorrelation measurements were performed using a Hanbury Brown and Twiss set-up, and thus, we report a working quantum dot device in the III-nitride system utilizing mesoporous DBRs. Uncorrected g(2)(0) autocorrelation values are shown to be significantly improved when excited with a laser at longer wavelengths and lower powers. Through this optimization, we report a g(2)(0) value from a blue-emitting InGaN/GaN quantum dot of 0.126 ± 0.003 without any form of background correction.

Langmuir adsortion isotherm with regular and irregular autoregressive error structures

The Langmuir isotherm is a nonlinear regression model, being one of the most applied in adsorption studies. In this type of study, the data are collected over time, which can provide correlated errors; in addition, the collection is not always done in an equidistant way, which may influence the estimation of model parameters. One way of modelling the dependent errors in a regression model is to use an autoregressive process that assumes that the observations are performed at equidistant intervals. However, the definition of the independent variable is often performed at irregular intervals, causing a reduction of information obtained from the dataset. One possible improvement in the adjustment quality of these models is the use of the irregular autoregressive process. The objective of this work was to compare the estimates of isotherm parameters with different irregular and regular autoregressive error structures, considering the positive autocorrelation in different sample sizes, error autocorrelation values and positioning of non-equidistant observations. It was found that there is a need to respect the assumptions of the model. The irregular autoregressive model is more appropriate because it is mostly more precise and accurate, especially when non-equidistance occurs in the initial third.