Linear Processing Methods Research Articles

The Influence of Random Internal Waves on the Characteristics of a Horizontal Antenna in a Shallow Sea

The effect of random internal waves on the gain and pattern of a horizontal antenna array in a shallow sea has been investigated. An algorithm for calculating the correlation matrix of the point source field at the grating aperture is proposed. The antenna gain is analyzed for different spatial processing methods: the beam forming method, the optimal linear processing method, and the optimal quadratic processing method. The effect of internal waves on the directivity pattern is described by the dispersion of the angular response of the grating. Numerical simulation results are presented for a model summer-type waveguide and an exponential Brunt–Väisälä frequency profile. The empirical internal wave spectrum proposed earlier from the SWARM95 experiment is used. The dependences of the antenna array characteristics on the number of its elements, orientation with respect to the source, radiation frequency, and the acoustic characteristics of the bottom are analyzed.

Beamforming Techniques for Passive Radar: An Overview.

Passive radar is an interesting approach in the context of non-cooperative target detection. Because the signal source takes advantage of the so-called illuminator of opportunity (IoO), the deployed system is silent, allowing the operator cheap, portable, and practically undetectable deployments. These systems match perfectly with the use of antenna arrays to take advantage of the additional gains provided by the coherent combination of the signals received at each element. To obtain these benefits, linear processing methods are required to enhance the system's performance. In this work, we summarize the main beamforming methods in the literature to provide a clear picture of the current state of the art. Next, we perform an analysis of the benefits and drawbacks and explore the chance of increasing the number of antenna elements. Finally, we identify the major challenges to be addressed by researchers in the future.

Profiles of cognitive ability domains in children with and without ADHD symptoms

Studies to date have most often compared the mean scores of an ADHD and control group to see if there are differences between them. Various cluster or linear data processing methods have also been used in studies to group children into certain subgroups according to various characteristics, but so far we have not found any study that has succeeded in dividing both ADHD and control group children into such subgroups. Results vary from study to study. Therefore, the aim of this study was to investigate and compare the heterogeneity of different cognitive ability domains in children with and without ADHD symptoms with qualitative data analysis methods - creating unique profiles of cognitive ability domains for each child. In this study participated 76 children aged 8–13 and were divided into two groups: ADHD group – 46 children (M = 10.08; SD = 1.67), control group – 30 children (M = 9.41; SD = 1.60). Four methods were used to calculate cognitive ability domain scores: Stroop’s Word and Color Test, Symbol Digit Modalities Test, Digit Span Test, and Continuous Performance Test. The analysis of the profiles revealed a wide heterogeneity in both groups. Also, it was observed in these profiles that children with ADHD experience had more pronounced difficulties in cognitive ability domains compared to the control group. At the same time, it can be concluded that not all children with ADHD experience them. Comparing profiles of children from either of these two groups it can be concluded that some of the children with ADHD symptoms have profiles similar to typically developing children.

Linear Axial Error Signal Measurement and Processing Method of a Machine Tool for Accuracy Compensation Improvement

Linear Axial Error Signal Measurement and Processing Method of a Machine Tool for Accuracy Compensation Improvement

Multipair Relaying With Space-Constrained Large-Scale MIMO Arrays: Spectral and Energy Efficiency Analysis With Incomplete CSI

In this paper, we study a multi-pair two-way large-scale multiple-input multiple-output (MIMO) decode-and-forward relay system. Multiple single-antenna user pairs exchange information via a shared relay working at half-duplex. The proposed scenario considers a practical case where an increasing number of antennas is deployed in a fixed physical space, giving rise to a trade-off between antenna gain and spatial correlation. The channel is assumed imperfectly known, and the relay employs linear processing methods. We study the large-scale approximations of the sum spectral efficiency (SE) and investigate the energy efficiency (EE) with a practical power consumption model when the number of relay antennas becomes large. We demonstrate the impact of the relay antenna number and spatial correlation with reducing inter-antenna distance on the EE performance. We exploit the increasing spatial correlation to allow an incomplete channel state information (CSI) acquisition where explicit CSI is acquired only for a subset of antennas. Our analytical derivations and numerical results show that applying the incomplete CSI strategy in the proposed system can improve the EE against complete CSI systems while maintaining the average SE performance.

Hybrid Beamforming/Combining for Millimeter Wave MIMO: A Machine Learning Approach

Hybrid beamforming (HB) has emerged as a promising technology to support ultra high transmission capacity and with low complexity for Millimeter Wave (mmWave) multiple-input and multiple-output (MIMO) system. However, the design of digital and analog beamformer is a challenge task with non-convex optimization, especially for the multi-user scenario. Recently, the blooming of deep learning research provides a new vision for the signal processing of communication system. In this work, we propose a deep neural network based HB for the multi-User mmWave massive MIMO system, referred as DNHB. The HB system is formulated as an autoencoder neural network, which is trained in a style of end-to-end self-supervised learning. With the strong representation capability of deep neural network, the proposed DNHB exhibits superior performance than the traditional linear processing methods. According to the simulation results, DNHB outperforms about 2 dB in terms of bit error rate (BER) performance compared with existing methods.

One-Bit LFMCW Radar: Spectrum Analysis and Target Detection

One-bit radar, performing signal sampling and quantization by a 1-bit analog-to-digital converter, is a promising technology for many civilian applications due to its low-cost and low-power consumptions. In this article, problems encountered by a 1-bit linear-frequency-modulated continuous-wave (LFMCW) radar are studied, and a two-stage target detection method termed as the dimension-reduced generalized approximate message passing (DR-GAMP) approach is proposed. First, the spectrum of 1-bit quantized signals in a scenario with multiple targets is analyzed. It is indicated that high-order harmonics may result in false alarms and cannot be neglected. Second, based on the spectrum analysis, the DR-GAMP approach is proposed to carry out target detection. Specifically, linear preprocessing methods and target predetection are first adopted to perform the dimension reduction, and then, the generalized approximate message passing algorithm is utilized to suppress high-order harmonics and recover true targets. Finally, numerical simulations are conducted to evaluate the performance of the 1-bit LFMCW radar under typical parameters. It is shown that compared to the conventional radar applying linear processing methods, the 1-bit LFMCW radar has about 1.3-dB performance gain when the input signal-to-noise ratios of targets are low. In the presence of a strong target, it has about 1.0-dB performance loss.

FastICA and total variation algorithm for geochemical anomaly extraction

Because the ore-forming system in the crust of the Earth is a highly nonlinear system, geochemical anomaly classification is very important for improving the accuracy of metallogenic prediction. Further, since the distribution of the geochemical elements usually presents nonlinear characteristics due to the complexity and uncertainty of geology factors, the traditional linear data processing method has limited applications for an ore-forming system. The FastICA algorithm is applied to preprocessed geochemical data to reduce the interference information between elements. On the basis of obtaining the separated geochemical elements, the continuity of the spatial distribution of geochemical elements is considered, and combined with the application of the total variation (TV) in image processing; thus the total variation is introduced when processing geochemical data for anomaly analysis to eliminate the influence of singular geochemical data values. To measure the spatial distribution of geochemical elements, assays of the 1:10000 soil geochemical data in the area of Dachaidan in the Qinghai province of China are processed. The elemental anomaly zoning sequences are divided into three levels of anomaly: 85%, 90% and 95%. The anomaly isograms of Au and Cu processed by FastICA and the total variation algorithm predict the geological background of the study area better than the traditional cumulative frequency method. These results indicate that the application of the FastICA algorithm and the total variation algorithm to process geochemical data processing is valid and effective.

Compensation of the baseline temperature fluctuations for autonomous CE–C4D instrument working in harsh environments

One of the main problems of the remote complex sample analysis instrumentation is that such systems are susceptible to temperature fluctuations. Temperature regulation is energetically ineffective, and it is not used in most of the field portable analytical systems. Separations performed in a changing temperature environment provide electropherograms with considerable baseline fluctuations, resulting in significant errors in detection and integration of the peaks. This paper describes electropherogram baseline compensation that is suitable for the capillary electrophoresis-contactless conductivity detection analytical method. The baseline compensation utilizes linear or polynomial data processing methods, and can be programmed in-line using simple microcontroller, or on-line and off-line in data acquisition software. This method is targeted for field portable and autonomous analytical systems that are utilized in a fluctuating environment.

Linear Processing for Intercarrier Interference in OFDM Index Modulation Based on Capacity Maximization

In this letter, we study the linear processing method to alleviate the intercarrier interference (ICI) for the orthogonal frequency division multiplexing (OFDM) index modulation (IM) in the rapidly time-varying (RTV) channel. Concretely, the proposed linear processing scheme is implemented by the joint design of the transmit precoding and receive postprocessing matrices. First, a lower bound of the capacity of OFDM-IM in the RTV channel is derived. Then, the precoding and postprocessing matrices are designed to maximize this capacity lower bound through utilizing the particle swarm optimization algorithm. Computer simulations show that the proposed scheme can alleviate the ICI effectively and has better performance than the conventional ICI self-cancellation scheme.

Spectral efficiency of the in‐band full‐duplex massive multi‐user multiple‐input multiple‐output system

In this study, the authors propose an in-band full-duplex massive multi-user multiple-input multiple-output system which exploits the separate-antenna arrays at base station and the shared-antenna at users. First, the channel state information of the two antenna arrays is estimated using the minimum mean-square-error method according to the channel reciprocity, and then the matched-filter and zero-forcing linear processing methods are adopted to analyse the system spectral efficiency (SE). The authors derive the lower and upper bounds of the uplink and downlink achievable rates in succinct forms to measure the system performance. The selection of full/half-duplex mode is also discussed with respect to the number of users. Numerical and simulation results show that the optimal downlink SE can be achieved with the increase of active users, and the full/half-duplex mode selection can help improve the system SE with the variation of traffic loads.

Self-interference suppression with imperfect channel estimation in a shared-antenna full-duplex massive MU-MIMO system

In this paper, we consider a shared-antenna full-duplex massive MU-MIMO system and prove that the self-interference (SI) at the base station (BS) can be suppressed, though the direct-path SI exists and the signal/SI channel is imperfectly estimated. The BS is assumed to employ zero-forcing (ZF) or maximal-ratio transmission/maximal-ratio combining (MRT/MRC) method to linearly process signals. We propose a precoded SI channel training scheme by employing orthogonal sequences and downlink precoding, so the SI channel can be estimated like the signal channel with a much lower dimension. Based on the channel estimation, we further analyze the SI suppression by combining the SI removal and the large-scale antenna linear processing (LALP) method. Numerical results show that an additional 36 dB SI can be suppressed by combining the SI removal and the LALP suppression. We derive the tight closed-form lower bounds of the achievable rates for the combined suppression. Finally, we maximize the system spectral efficiency and energy efficiency based on the simulation and closed-form approximations.

Self‐interference suppression for the full‐duplex wireless communication with large‐scale antenna

AbstractIn this paper, we proposed a shared‐antenna full‐duplex framework for the multiuser multiple‐input multiple‐output (MIMO) system with a large‐scale antenna array exploited at the base station to transmit and receive signals simultaneously. It has the merits of both the full‐duplex system and the time‐division duplex massive MIMO system, that is, the high spectral efficiency and the channel reciprocity. We focus on the zero‐forcing and the maximal‐ratio transmission/maximal‐ratio combining linear processing methods, which are commonly used in the massive MIMO system. As the main finding, we prove that the direct‐path and reflected‐path self‐interferences can both be suppressed by the large‐scale antenna array in the entirely dependent channels as well as independent channels. Monte‐Carlo simulations show that the self‐interference decreases below the white noise level if the scale of antenna array is large enough, and our full‐duplex large‐scale antenna system significantly outperforms the half‐duplex system in terms of spectral efficiency. Copyright © 2016 John Wiley & Sons, Ltd.

Effect of linear image processing on the depth of correlation in micro PIV

This work investigates the effect of selected linear image processing methods on the depth of correlation (DoC) in micro particle image velocimetry using a single camera. In practice, band-pass and high-pass filters (background subtraction) are commonly applied to micro particle image velocimetry images. This work provides analytical models describing the effect of the parameters of low-, high-, and band-pass filters on the DoC and verifies the models by experiments. Furthermore, we propose a scheme that allows computing the weighting function and DoC for more complicated cases numerically.

ErgoVar: A Toolbox for Exposure Variation Analysis

Ergonomists suggest that increasing exposure variation at work may prevent development of work related musculoskeletal disorder in the jobs which are characterized by repetitive pattern of exposure. However, different methodological approaches could be adopted to quantify exposure variation. ErgoVar provides the implementation of relevant computational methods for analysis of exposure variation. It consists of a set of linear and nonlinear processing methods relevant to exposure variation analysis. It is free for downloading and sharing. ErgoVar aim is to facilitate the research focusing on exposure variation.

Optimal Orthogonal Precoding for Power Leakage Suppression in DFT-Based Systems

A solution to the power leakage minimization problem in discrete Fourier transform (DFT) based communication systems is presented. In a conventional DFT based system, modulated subcarriers exhibit high sidelobe levels, which leads to significant out-of-band power leakage. Existing techniques found in the literature either do not achieve sufficient sidelobe suppression or suffer from significant spectral efficiency loss. Precoding can be seen as a general linear processing method for power leakage reduction, however, how to design the optimal linear precoder is still an open problem. In this paper, the power leakage suppression is first treated as a matrix Frobenius norm minimization problem, and then the optimal orthogonal precoding matrix design for the power leakage suppression is proposed based on singular value decomposition (SVD). By further exploiting the extra degrees of freedom in the precoding matrix, two kinds of optimized precoding matrices, one with multi-carrier property and the other with single-carrier property, are developed to take the advantages of orthogonal frequency division multiplexing (OFDM) and single carrier frequency division multiple access (SC-FDMA), respectively. Simulation results show that both the multi-carrier and the single-carrier precoding schemes achieve significant power leakage suppression, and have similar peak-to-average power ratio (PAPR) and bit-error-rate (BER) to those of OFDM and SC-FDMA systems, respectively.

Multiuser MIMO Downlink Transmission Schemes over Time-Varying Channels

The performance of multiuser multiple-input-multiple-output (MIMO) downlink systems with block diagonalization (BD) depends on the accuracy of the channel state information (CSI) available at the transmitter and the receiver. In time-varying channels, the CSI available at the transmitter (CSIT) is always outdated due to an inherent time delay between the uplink channel estimation and the downlink data transmission in time division duplexing (TDD) systems. This leads to a drastic degradation of system capacity. This paper first analyzes the effect of the outdated CSIT on multiuser MIMO downlink systems using the BD method and then proposes two linear processing methods, BD precoding with user selection and scheduling at the transmitter and total minimum mean squared error (MMSE) decoding at the receiver (TBDUSS-RTMMSE) and BD precoding at the transmitter with partial MMSE decoding at the receiver (TBD-RPMMSE), to mitigate the interference among data streams and users. Analysis and simulation results show that these methods can effectively reduce the impairment of the outdated CSIT to increase the system sum capacity in a suitable time delay region of the CSIT.

Wafer Defect Detection Using Directional Morphological Gradient Techniques

Accurate detection and classification of wafer defects constitute an important component of the IC production process because together they can immediately improve the yield and also provide information needed for future process improvements. One class of inspection procedures involves analyzing surface images. Because of the characteristics of the design patterns and the irregular size and shape of the defects, linear processing methods, such as Fourier transform domain filtering or Sobel edge detection, are not as well suited as morphological methods for detecting these defects. In this paper, a newly developed morphological gradient technique using directional components is applied to the detection and isolation of wafer defects. The new methods are computationally efficient and do not rely on a priori knowledge of the specific design pattern to detect particles, scratches, stains, or missing pattern areas. The directional components of the morphological gradient technique allow direction specific edge suppression and reduce the noise sensitivity. Theoretical analysis and several examples are used to demonstrate the performance of the directional morphological gradient methods.