Autocorrelation Sequence Research Articles

Covariance recovery for one-bit sampled stationary signals with time-varying sampling thresholds

One-bit quantization, which relies on comparing the signals of interest with given threshold levels, has attracted considerable attention in signal processing for communications and sensing. A useful tool for covariance recovery in such settings is the arcsine law, that estimates the normalized covariance matrix of zero-mean stationary input signals. This relation, however, only considers a zero sampling threshold, which can cause a remarkable information loss. In this paper, the idea of the arcsine law is extended to the case where one-bit analog-to-digital converters (ADCs) apply time-varying thresholds. Specifically, three distinct approaches are proposed, investigated, and compared, to recover the autocorrelation sequence of the stationary signals of interest. Additionally, we will study a modification of the Bussgang law, a famous relation facilitating the recovery of the cross-correlation between the one-bit sampled data and the zero-mean stationary input signal. Similar to the case of the arcsine law, the Bussgang law only considers a zero sampling threshold. This relation is also extended to accommodate the more general case of time-varying thresholds for the stationary input signals.

Performance Enhancement of W-Band RoF System Using 4D Trellis Coded Modulation OFDM With Precoding

In the paper, a four-dimensional trellis-coded modulation 16-quadrature amplitude modulation-orthogonal frequency division multiplexing (4D TCM 16QAM-OFDM) combined with precoding scheme is proposed and experimentally demonstrated in W-band radio-over-fiber (RoF) system. In W-band RoF system, frequency selective fading can cause unbalanced signal-to-noise ratio (SNR) distribution on the sub-carriers of the 4D TCM 16QAM-OFDM signals. Orthogonal circulant matrix transform (OCT) precoding, discrete Fourier transform (DFT) precoding, and constant amplitude zero autocorrelation sequence (CAZAC) precoding can be used to balance the SNR over the data subcarriers of 4D TCM 16QAM-OFDM signals. Moreover, DFT precoding and CAZAC precoding can reduce the peak-to-average power ratio (PAPR) of 4D TCM 16QAM-OFDM signals. After 50km single-mode fiber (SMF) and 5m free space transmission, the experimental results show that, at the bit error rate (BER) of 3.8×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−3</sup> , compared with 4D TCM 16QAM-OFDM using OCT precoding and DFT precoding, 4D TCM 16QAM-OFDM using CAZAC precoding can achieve the receiver sensitivity improvement of 2.33 dB and 0.48 dB respectively. In addition, compared with 4D TCM 16QAM-OFDM without precoding, it can achieve the receiver sensitivity improvement of 3 dB by using CAZAC precoding. Furthermore, 4D TCM 16QAM-OFDM has higher spectral efficiency than 8QAM-OFDM at the same symbol rate. The receiver sensitivity for 4D TCM 16QAM-OFDM using CAZAC precoding is enhanced about 1.1 dB, compared with 8QAM-OFDM using CAZAC precoding.

Preamble-Based Symbol Timing Algorithms in OFDM Systems

In orthogonal frequency division multiplexing (OFDM) systems, a preamble is added at the start of the OFDM data frame in order to find the correct frame starting point. In the receiver, the symbol starting point is estimated by a correlation-based timing metric that uses the periodic properties of the preamble. In this study, a novel preamble structure using the constant amplitude zero autocorrelation (CAZAC) sequence is proposed. The performance of the timing synchronization algorithms has been compared with the proposed method in terms of mean squared error (MSE). The simulations are performed under additive white Gaussian noise (AWGN) and Rayleigh multipath fading channels. The simulation results indicate that our proposed method provides better timing MSE performance for the low signal-to-noise ratio (SNR) values.

Robust Dynamic Indoor Visible Light Positioning Method Based on CMOS Image Sensor

A real-time imaging recognition and positioning method based on visible light communication flat light source is proposed. This method images the visible light communication flat light source through the rolling shutter effect of the complementary metal-oxide semiconductor imaging sensor and obtains the rectangular area outline of the light source. The light and dark stripe information of image with the digital image processing method realizes light source matching recognition by defining the concept, the autocorrelation sequence, which can be used to obtain the identity of the light source, and the rectangular vertex coordinate information of flat light source achieves high-precision vision positioning on the basis of inertial measurement unit attitude sensor-assisted imaging. Simultaneously, the corresponding positioning module is developed for positioning testing. The test results indicate that the plane positioning error is less than 4.5 cm, and the positioning frequency is greater than 10 Hz, which provides a high-precision visual positioning solution for indoor positioning.

Serendipitous Internal Wave Signals in Deep Argo Data

AbstractSerendipitous measurements of deep internal wave signatures are evident in oscillatory variations around the background descent rates reported by one model of Deep Argo float. For the 10,045 profiles analyzed here, the average root‐mean‐square of vertical velocity variances, , from 1,000 m to the seafloor, is 0.0045 m s−1, with a 5%–95% range of 0.0028–0.0067 m s−1. Dominant vertical wavelengths, λz, estimated from the integrals of lagged autocorrelation sequences have an average value of 757 m, with a 5%–95% range of 493–1,108 m. Both and λz exhibit regional variations among and within some deep ocean basins, with generally larger and shorter λz in regions of rougher bathymetry or stronger deep currents. These correlations are both expected, since larger and shorter λz should be found near internal wave generation regions.

On Properties of Periodic Binary Sequences in the Presence of System Non-Linearities

Phase-modulated continuous-wave (PMCW) radar systems possess inherent interference mitigation capabilities and can be useful for many civilian applications including autonomous driving. For PMCW radar systems, binary sequences with good autocorrelation properties are used as probing sequences due to their low cost to generate in practical hardware systems. However, the non-linearity (NL) suffered by practical PMCW radar systems can produce undesirable cross-products and distort the receiver matched filter outputs. The purpose of this paper is to investigate the properties of the well-known periodic binary almost perfect autocorrelation sequences (APAS) under NL conditions, including even-order and odd-order NL conditions. We also consider designing long binary periodic sequences with low autocorrelation sidelobes within a low correlation zone (LCZ) using a computational algorithm. The computationally optimized sequences (COS) do not suffer from sequence length restrictions and offer much higher diversity than their APAS counterparts. We compare COS with APAS under NL conditions. We show that both types of sequences can be used to avoid ghost peaks in the range profiles under NL conditions.

Carrier frequency offset estimation for MIMO single-carrier FDMA system in wireless communication

Multiple-Input Multiple-Output (MIMO) Orthogonal Frequency-Division Multiplexing (OFDM) systems and Single-Carrier Frequency-Division Multiple Access (SC-FDMA) are so susceptible to carrier frequency offset (CFO) between transceivers. In this paper, a novel sequence called CAZBAR is proposed. The proposed CAZBAR sequence is based on constant amplitude zero autocorrelation (CAZAC) sequence and barker code. CAZBAR sequence shows better autocorrelation with higher peak at zero shift without ripples, which facilitates estimating carrier frequency offset in communication systems compared to other compensation methods in terms of mean square error (MSE). By applying CAZBAR sequence in MIMO SC-FDMA system 2 × 2 and 4×4, the bit error rate (BER) performance has improved significantly. At a BER=10-2, a SNR reduction of 11 dB is achieved for 2 × 2 system and 7 dB reduction for 4 × 4 system compared to other sequences. Which improves the performance of the whole system MIMO SC-FDMA. The CAZBAR sequence can be applied in 5G techniques, MIMO-OFDM, and MIMO SC-FDMA systems.

An Algorithm for Estimating the Signal Frequency at the Output of a Channel with a Controlled Information Flow under Phase Noise Conditions

Research in the field of efficient frequency estimation algorithms is of great interest. The reason for this is the redistribution of the role of additive and phase noise in many modern radio-engineering applications. An example is the area of measuring radio devices, which usually operate at high signal-to-noise ratios (SNR). The estimation error is largely determined not by the broadband noise, but by the frequency and phase noise of the local oscillators of the receiving and transmitting devices. In particular, earlier works \\cite{Nikiforov} proposed an efficient computational algorithm for estimating the frequency of a quasi-harmonic signal based on the iterative calculation of the autocorrelation sequence (ACS). In \\cite{Volkov}, this algorithm was improved and its proximity to the Rao-Cramer boundary was shown (the sources of this noise are master oscillators and frequency synthesizers). Possibilities of frequency estimation in radio channels make it possible to significantly expand the functionality of the entire radio network. This can include, for example, the problem of adaptive distribution of information flows of a radio network. This also includes the tasks of synchronization and coherent signal processing. For these reasons, more research is needed on this algorithm, the calculation of theoretical boundaries and their comparison with the simulation results.

Cubic metric reduction for repetitive CAZAC sequences in frequency domain

In NR-based Access to Unlicensed Spectrum (NR-U) of 5G system, to satisfy the rules of Occupied Channel Bandwidth (OCB) of unlicensed spectrum, the channels of PRACH and PUCCH have to use some sequence repetition mechanisms in frequency domain. These repetition mechanisms will cause serious cubic metric(CM) problems for these channels, although these two types of channels are composed of Constant Amplitude Zero Auto-correlation(CAZAC) sequences.. Based on the characteristics of CAZAC sequences, which are used for PRACH and PUCCH (refer to PUCCH format 0 and format 1) in 5G NR, in this paper, we propose some new mechanisms of CM reduction for these two types of channels considering the design principles to ensure the sequence performance of the auto-correlation and cross-correlation. Then the proposed CM schemes are evaluated and the optimized parameters are further provided considering CM performance and the complexity.

Prediction of Protein-protein Interactions in Arabidopsis thaliana Using Partial Training Samples in a Machine Learning Framework

Background: Protein-protein interactions (PPI) play a vital role in a wide range of biological processes starting from cell-cell interactions to developmental control in all organisms. However, experimental identification of PPI is often laborious, time-consuming and costly compared to computational prediction. There are several computational prediction models in the literature based on complete training samples, but none of them dealt with the partial training samples. Objective: The objective of this work was to develop an effective PPI prediction model for Arabidopsis Thaliana using partial training samples in a machine learning framework. Methods: We proposed an effective computational PPI prediction model by combining random forest (RF) classifier and autocorrelation (AC) sequence encoding features with 1:2 ratio of positive- PPI and unknown-PPI samples. Results: We observed that the proposed prediction model produces the highest average performance scores of sensitivity (94.62%), AUC (0.92) and pAUC (0.189) with the training datasets and sensitivity (88.14%), AUC (0.89) and pAUC (0.176) with the test datasets of 5-fold crossvalidation compared to other candidate predictors based on LDA, LOGI, ADA, NB, KNN &amp; SVM classifiers. It also computed the highest performance scores of TPR (91.82%) and pAUC (0.174) at FPR= 20% with AUC (0.948) compared to other candidate predictors. Conclusion: Overall performance of the developed model revealed that our proposed predictor might be useful to elucidate the biological function of unseen PPIs from a large number of candidate proteins in Arabidopsis thaliana.

SERDP: Signature‐based and energy‐efficient relay discovery protocol for Internet of Things in cellular networks

SummaryWith the proliferation of Internet of Things (IoT), a large number of devices are expected to connect to the Internet over different networks due to various demands and characteristics of IoT devices (IoTDs). One of the networks that will connect IoTDs to the Internet is cellular networks. The 3rd Generation Partnership Project (3GPP) proposes a connectivity model for cellular networks in which IoTDs connect to the network via relay devices. In this model, discovering the relay devices is a challenging task due to energy budget constraints, mobility of devices, and collision of discovery messages. Since IoTDs are constrained devices, it is important that they perform discovery with the minimum energy consumption by intermittently sleeping and waking up. In this paper, we propose signature‐based and energy‐efficient relay discovery protocol (SERDP) using Zadoff–Chu sequences. Since Zadoff–Chu is a Constant Amplitude Zero Autocorrelation (CAZAC) sequence, we use the sequence for pointing the active slots of the devices within the proposed three‐stage original period structure. The probabilistic analysis and simulation results show that SERDP discovers more devices with less energy consumption as well as preventing collisions, as compared with the existing protocols.

Combined adaptive robust Kalman filter algorithm

The precise positioning of dynamic and static objects such as vehicles and pedestrians is a key technology. A global navigation satellite system signal is the primary signal source required to achieve precise positioning, and the optimal estimation method used in precise positioning is Kalman filtering (KF). Standard KF can only achieve optimal estimation results under the conditions that the mathematical model has been determined and the noise characteristics are known. However, when there are measurement outliers and prior noise information deviations, the accuracy of KF cannot be guaranteed. To effectively solve the above problems, a combined adaptive robust Kalman filter (CARKF) algorithm is proposed. First, the influence of measurement outliers on KF accuracy is resisted using the robust Kalman filter method. Then, the influence of an anomalous innovation on its autocorrelation sequence and cross-correlation sequence is resisted by the robust estimator for correlated observations based on the IGGIII method, to ensure the correct estimation of the correlation function sequence of the filtered innovation statistics. Finally, the autocovariance least-squares method is used to eliminate the coupling effect between noise covariances and accurately estimate the unknown noise covariance information. Moreover, an iterative strategy is adopted to eliminate the effect of errors caused by prior noise covariance deviations. The results of the three experiments show that the CARKF method can not only effectively resist the influence of measurement outliers on filtering accuracy and noise covariance valuation but also overcome the influence of errors caused by prior noise deviations and accurately simultaneously estimate two kinds of unknown noise covariance information.

Temperature trend analysis and extreme high temperature prediction based on weighted Markov Model in Lanzhou

In this study, temporal trend analysis was conducted on the annual and quarterly meteorological variables of Lanzhou from 1951 to 2016, and a weighted Markov model for extremely high temperature prediction was constructed. Several non-parametric methods were used to analyse the trend of meteorological variables. Considering that sequence autocorrelation may affect the accuracy of the trend test, we performed an autocorrelation test and carried out trend analysis for sequences with autocorrelation after removing correlation. The results show that the maximum temperature, minimum temperature and average temperature in Lanzhou all have a significant upward trend and show different performances in each season. In detail, the trend of maximum temperature in the summer is not significant, while the upward trend of minimum temperature in the winter is the most significant, which leads to more and more “warm winter” phenomenon. Finally, we construct a weighted Markov prediction model for extremely high temperature and obtain the conclusion that the prediction results by the model are consistent with the actual situation.

Generalized Subspace Snoring Signal Enhancement Based on Noise Covariance Matrix Estimation

Acoustical properties of snoring signal have been widely studied as a potentially cost-effective and reliable alternative to diagnosing obstructive sleep apnea hypopnea syndrome, with a common recognition that the diagnostic accuracy depends heavily on the snoring signal quality. In the paper, generalized subspace noise reduction based on noise covariance matrix estimate is proposed. The noise covariance matrix is the Toeplitz matrix of the unbiased autocorrelation sequence which is estimated by recursive averaging its past value adjusted by a time-varying smoothing parameter controlled by the snoring signal presence probability, and the signal presence is determined by the ratio of temporal frame autocorrelation value to its minimum absolute value. The proposed method has a better estimate of noise covariance matrix, and the results of objective quality measurements and spectrograms of snoring signal show obvious improvement in terms of noise reduction and signal distortion under different non-stationary noise environments compared with conventional subspace enhancement algorithm.

Why and How Should Cognitive Science Care about Aesthetics?

Empirical aesthetics has found its way into mainstream cognitive science. Until now, most research has focused either on identifying the internal processes that underlie a perceiver's aesthetic experience or on identifying the stimulus features that lead to a specific type of aesthetic experience. To progress, empirical aesthetics must integrate these approaches into a unified paradigm that encourages researchers to think in terms of temporal dynamics and interactions between: (i) the stimulus and the perceiver; (ii) different systems within the perceiver; and (iii) different layers of the stimulus. At this critical moment, empirical aesthetics must also clearly identify and define its key concepts, sketch out its agenda, and specify its approach to grow into a coherent and distinct discipline.

Optimal Signal Design for a New Physically Motivated Clutter Model With Applications to Ultrasonic Testing.

In this article, we will explore the optimal signal design problem for a new physically motivated clutter model. The reason to pursue the optimal signal design is that the model, even though it was simple, led to a robust detector that worked well on real ultrasonic data. Moreover, by using the model, an analytical solution for the optimal signal is obtained. In addition, the optimal waveform gives a new insight into the signal design problem that can be valuable for many applications. New proof has been provided for a finite data record that is lacking in the literature. We have found that the optimal signal is an impulse, and as a result, it has an impulsive autocorrelation sequence (ACS). Since an impulsive ACS signal is not realizable, in practice, a study for an alternative waveform is conducted. Waveforms from four different categories have been explored: linear frequency-modulated (LFM), nonlinear frequency-modulated (NLFM), phase-coded modulated (PCM) signals, and, finally, what we called other signals. Comparison analysis between these waveforms themselves and the most commonly used transmitted pulse in practice, which is the Gaussian amplitude-modulated sinusoid (GAMS) pulse, has been implemented. We show that the LFM pulse has a large advantage over the GAMS pulse in terms of detectability. In addition, a comparison between the LFM and the GAMS signals under a deviation from the single scatterer assumption, indicating a more complex target, using simulated noise is performed.

Shared-ACS Binary Code Pair Search Exploiting Coupled Symparity

Binary code negation and reversal are known to leave the autocorrelation sequence unchanged. However, for some code lengths, there exist code pairs for which the mechanism behind shared autocorrelation is not so simple; these are called shared-autocorrelation (or shared-ACS) binary code pairs. They are useful for, among other things, constructing binary and quad-phase Golay pairs of longer lengths. We define a symparity vector for any given binary code. For every shared-ACS binary code pairs up to length 39, the two code have the same symparity, or coupled symparity. Conditions are identified for which shared-ACS binary code pairs have coupled symparity. The coupled-symparity condition is used to develop an algorithm to find shared-ACS binary code pairs, with reduced complexity relative to more brute-force exhaustive searches. Although the algorithm assumes extra structure in the shared-ACS code pairs, it rediscovered all the sets of binary shared-ACS code pairs for lengths up to 20. It is then run for lengths 40 to 44, returning all the shared-ACS binary code pairs for each length. In particular, the algorithm results establish that there exist shared-ACS code pairs for the prime lengths 41 and 43.

Spectrally compatible aperiodic sequence set design with low cross- and auto-correlation PSL

It is known that unimodular sequence set with low cross- and auto-correlation peak sidelobe level (C/A-PSL) can improve the detection ability of the signal-of-interest in the presence of multipath propagation and/or clutters, while that with specified spectral power null/notch in certain frequency bands can prevent mutual interference of the systems that are operating in the same frequency bands. However, it is exceedingly difficult to design such a unimodular sequence set because nonsmooth quadratic objective function as well as nonlinear unimodular and spectral constraints are involved. In this paper, under the unimodular and spectral constraints, we formulate the sequence set design problem based on minimum C/A-PSL. Then, we apply block successive upper-bound minimization to solve the corresponding NP-hard problem and use the relation of the sequence auto-correlation and its fast Fourier transform (FFT) and inverse FFT to devise an efficient algorithm. Numerical examples show that the proposed method can effectively produce spectrally constrained sequence set with low C/A-PSL.

Robust Adaptive Beamforming Based on Low-Complexity Discrete Fourier Transform Spatial Sampling

In this paper, a novel and robust algorithm is proposed for adaptive beamforming based on the idea of reconstructing the autocorrelation sequence (ACS) of a random process from a set of measured data. This is obtained from the first column and the first row of the sample covariance matrix (SCM) after averaging along its diagonals. Then, the power spectrum of the correlation sequence is estimated using the discrete Fourier transform (DFT). The DFT coefficients corresponding to the angles within the noise-plus-interference region are used to reconstruct the noise-plus-interference covariance matrix (NPICM), while the desired signal covariance matrix (DSCM) is estimated by identifying and removing the noise-plus-interference component from the SCM. In particular, the spatial power spectrum of the estimated received signal is utilized to compute the correlation sequence corresponding to the noise-plus-interference in which the dominant DFT coefficient of the noise-plus-interference is captured. A key advantage of the proposed adaptive beamforming is that only little prior information is required. Specifically, an imprecise knowledge of the array geometry and of the angular sectors in which the interferences are located is needed. Simulation results demonstrate that compared with previous reconstruction-based beamformers, the proposed approach can achieve better overall performance in the case of multiple mismatches over a very large range of input signal-to-noise ratios.

DOA estimation in the autocorrelation domain for coprime array

In this paper a new algorithm for direction of arrival (DOA) estimation in coprime arrays based on the autocorrelation domain (AD) method is proposed. The spatial autocorrelation sequence of the virtual uniform linear array (ULA) associated with the coprime array is built by averaging the diagonals of the spatially smoothed covariance matrix. The AD method estimates the DOAs by solving a generalized eigenvalue problem and refining the obtained solution. The main advantage of the introduced method is a spatial spectrum without false peaks. Simulation results indicate that the proposed approach is better than existing methods, especially for small number of snapshots.