Autocorrelation Transformation Research Articles

Multimodality-aided Multicarrier Waveform Recognition in low SNR Regimes based on Denoised Cyclic Autocorrelation Transformation

Wireless signal recognition driven by artificial intelligence (AI) plays a pivotal role in 6G ultra-reliable wireless communications, facilitating spectrum surveillance to impede illegal radio interference. As a promising wireless technique, multicarrier waveform recognition (MWR) has been explored to enhance the reliability of wireless data transmission. However, existing works fail to achieve reliable recognition accuracy under strong noise. It remains a daunting task for MWR in signal-to-noise ratio (SNR) regimes. To deal with this issue, we propose a denoised cyclic autocorrelation-based multimodality fusion network (DCA-MFNet). Specifically, we first leverage the cyclic autocorrelation transformation to convert intercepted signals into cyclic autocorrelation (CA) features in cyclic frequency domains, which have the robust property of being insensitive to low SNR. Next, the singular value decomposition (SVD) method is employed to weaken the strong noise effects on useful CA peak values. Based on the denoised CA matrix (DCAM), the projection accumulation strategy is proposed to generate the time delay accumulation vector (TDV) and cyclic frequency accumulation vector (CFV), which can enlarge the discrimination among multicarrier signals. Finally, we fed the multimodality features of DCAM, TDV, and CFV into the developed DCA-MFNet to perform hierarchical learning, feature aggregation training, and multicarrier type prediction. Experimental results demonstrate that the proposed DCA-MFNet obtains better recognition performance than existing algorithms. Moreover, DCA-MFNet can effectively identify six multicarrier signals with a recognition accuracy of 100% at a low SNR of even -2 dB.

Living on the margins: Socio-spatial characterization of residential and water deprivations in Lagos informal settlements, Nigeria

The third world countries are generally noted for ubiquitous informal settlements (slum communities) where a large number of urban residents live on the margins of precarious poverty, infrastructural decay and social deprivation. However, existing studies have largely glossed over individual community deprivations and allowed spatial heterogeneity to be masked by gross generalization. Using geospatial information systems (GIS) and slum deprivation index (SDI) this study described the residential and water deprivations of 15 selected slum communities in Lagos Nigeria. Based on residential conditions we found that majority of the residents lived in one-room apartments and in neighbourhoods with about 20 persons per dwelling. The residents were without access to improved drinking water, and lived in dwellings surrounded by solid waste piles. The slum deprivation index (SDI) indicated that residents experienced critical residential and environmental deprivation and water, sanitation and hygiene deprivation. The spatial autocorrelation transformation of these slum deprivation indices evidently showed that social deprivation was wide spread in Lagos slums with over two-third of the communities being highly deprived. We therefore suggest that urban stakeholders should be more focused on the highly deprived communities. The study has critical planning implications for urban management and sustainability.

Clustering district heat exchange stations using smart meter consumption data

Contrary to electricity smart meter data analysis, little research regarding district heat smart meter data has been published. Previous papers on smart meter data analytics have not investigated autocorrelation in smart meter data. This paper examines district heat smart meter data from the largest district heat supplier in Denmark and autocorrelation is identified in the data. The K-Means algorithm is not able to take autocorrelation into account when clustering. We propose different data transformation methods to enable K-Means to account for this autocorrelation information in the data by using wavelet transformation and autocorrelation features. Our results show that the K-Means yield acceptable clustering results for district heat data when clustering normalized data, inclusion of autocorrelation improves the clustering. The clusters on normalized data are similar to the wavelet transformed clusters, where the autocorrelation has been accounted for. The clustering achieved with the autocorrelation transformation yields finer clusters through accounting for autocorrelation. We are not able to statistically show a difference between the transformations. All transformations result in shadowing clusters, but the autocorrelation transformation generates fewer shadow clusters and reduce the number of dimensions from 744 to 24, resulting in a dramatic reduction in K-Means runtime.

Search prefilters for library matching of infrared spectra in the PDQ database using the autocorrelation transformation

The feasibility of using the autocorrelation transformation in combination with pattern recognition methods to develop search prefilters to facilitate infrared spectral library searching of the PDQ database has been investigated. The use of the autocorrelation transformation addresses many problems encountered when transferring classification models between spectrometers. The autocorrelation transformation is also sensitive at distinguishing subtle but significant features in spectral data such as minor peaks, shoulders, and peaks with unique shapes. Previous workers have shown that peak shifts and related spectral alignment problems can be obviated by using the autocorrelation transformation. The autocorrelation transformation produces a histogram for each IR spectrum, which can be a more useful representation of spectra for pattern recognition involving data collected on different spectrometers. Instead of directly sampling points from the histograms using classification techniques such as SIMCA to identify the informative region, each histogram was first deconvolved to better capture the signal using Coiflet wavelets with the informative wavelet coefficients identified using a genetic algorithm for pattern recognition and feature selection.

Relating periodicity of nucleosome organization and gene regulation

The relationship between nucleosome positioning and gene regulation is fundamental yet complex. Previous studies on genomic nucleosome positions have revealed a correlation between nucleosome occupancy on promoters and gene expression levels. Many of these studies focused on individual nucleosomes, especially those proximal to transcription start sites. To study the collective effect of multiple nucleosomes on the gene expression, we developed a mathematical approach based on autocorrelation to relate genomic nucleosome organization to gene regulation. We found that nucleosome organization in gene promoters can be well described by autocorrelation transformation. Some promoters show obvious periods in their nucleosome organization, while others have no clear periodicity. The genes with periodic nucleosome organization in promoters tend to be lower expressed than the genes without periodic nucleosome organization. These suggest that regular organization of nucleosomes plays a critical role in gene regulation. To quantitatively associate nucleosome organization and gene expression, we predicted gene expression solely based on nucleosome status and found that nucleosome status accounts for approximately 25% of the observed gene expression variability. Furthermore, we explored the underlying forces that maintain the periodicity in nucleosome organization, namely intrinsic (i.e. DNA sequence) and extrinsic forces (i.e. chromatin remodeling factors). We found that the extrinsic factors play a critical role in maintaining the periodic nucleosome organization.

Invariant 2D object recognition using KRA and GRA

Computer vision has been extensively adopted in industry for the last two decades. It enhances productivity and quality management, and is flexibility, efficient, fast, inexpensive, reliable and robust. This study presents a new translation, rotation and scaling-free object recognition method for 2D objects. The proposed method comprises two parts: KRA feature extractor and GRA classifier. The KRA feature extractor employs K-curvature, re-sampling, and autocorrelation transformation to extract unique features of objects, and then gray relational analysis (GRA) classifies the extracted invariant features. The boundary of the digital object was first represented as the form of the K-curvature over a given region of support, and was then re-sampled and transformed with autocorrelation function. After that, the extracted features own the unique property that is invariant to translation, rotation and scaling. To verify and validate the proposed method, 50 synthetic and 50 real objects were digitized as standard patterns, and 10 extra images of each object (test images) which were taken at different positions, orientations and scales, were acquired and compared with the standard patterns. The experimental results reveal that the proposed method with either GRA or MD methods is effective and reliable for part recognition.

Invariant 2D object recognition using eigenvalues of covariance matrices, re-sampling and autocorrelation

This study presents a novel invariant object recognition method for two-dimensional object. The proposed method employs the eigenvalues of covariance matrix, re-sampling, and autocorrelation transformation to extract unique features from boundary information, and then use minimum Euclidean distance method (MD) and backpropagation neural networks (BPN) for classification. The boundary of the binary digital part is first extracted and represented as the sequence of the smaller eigenvalues of covariance matrix over a given region of support. Then the sequence is re-sampled into a pre-determined number, and transformed using autocorrelation function. The experimental results reveal that the proposed method successfully derives translation, rotation, and scaling invariant features which can be classified easily with MD and BPN.

Transforming the error-components model for estimation with general ARMA disturbances

Baltagi and Li (1991) give a transformation which may be applied to certain autocorrelated disturbances in an error-components model to yield spherical disturbances. They derive the transformation for AR(1) and AR(2) cases. We show that the results of Galbraith and Zinde-Walsh (1992) on the general ARMA form of the transformation matrix can be applied to allow estimation of the error-components model with disturbances which follow any ARMA ( p, q) process. We also show that, for models with a cross-sectional dimension which is large relative to the time dimension, it is possible to use this method to perform a general autocorrelation transformation without specifying a particular ARMA process or estimating ARMA parameters.

Digital One-Third Octave Spectral Analysis

An economical approach is described for estimating power spectra from sampled data through the application of Z -transform theory. The method consists of computing a weighting sequence whose frequency characteristics approximate a one-third octave bandwidth filter, and applying these coefficients recursively to the digitized data record. Filtering is followed by a variance calculation and a division by the appropriate filter bandwidth. A specific example of power spectra computed in the usual manner (Fourier transformation of the autocorrelation function) and power spectra computed by the method in this paper demonstrates the practicability of the technique. The most significant advantage is the economical aspect. It is shown that owing to the variable bandwidth and the small number of filtering coefficients, the savings that may be realized by the employment of this technique, in lieu of the autocorrelation transformation approach, may be quite considerable, depending on the record length and the number of lag products.