Spatial Frequency Data Research Articles

Chipless Image-Based System for Spatial-Frequency Data Encoding

Chipless Image-Based System for Spatial-Frequency Data Encoding

Analysis of CYGNSS Soil Moisture Retrieval Based on ANN over a Selected Region in Ethiopia

Soil moisture (SM) plays a vital role in agriculture, ecosystem functioning, water conservation, weather predictions and climate models. High spatial and temporal frequency data of soil moisture is crucial for agricultural and other important applications. Recent advancements have brought attention to the possibility of using GNSS reflectometry (GNSS-R) for applications on land such as snow sensing, soil moisture retrieval, sea surface monitoring and other applications in addition to positioning, navigation, and timing applications of GNSS. Cyclone Global Navigation Satellite System (CYGNSS) is designed to improve hurricane forecasting by studying the interaction between the ocean and the atmosphere within tropical cyclones. However recent studies show the opportunity of this system for high spatio-temporal soil moisture retrieval. This study presents a machine learning-based approach to get SM at a selected region in Ethiopia using CYGNSS data and analysis of the result. Artificial Neural Network (ANN) model is developed and used to predict soil moisture. The Soil Moisture Active Passive (SMAP) global soil moisture data have been used as reference data in the ML algorithm. The proposed approach has achieved a good correlation between predicted values of soil moisture and reference values from SMAP.

Extraction of the stable component of electrical surveys of soils and the consequence on the mapping of their thicknesses

Knowledge of the spatial variability of soil thickness (ST) in agricultural fields is useful for improving crop management. Spatial patterns of ST can be characterized using temporal stability analysis of high spatial density and temporal frequency data, associated with sparse and difficult to obtain data on the target variable. Apparent electrical resistivity measurements of soil at high spatial density have been widely used to infer the spatial variability of soil properties. However, electrical resistivity measurements are sensitive to many factors (e.g. soil water content). Thus, it remains difficult to interpret them according to a characteristic of the soil such as its thickness. The objective of this study was to present a geostatistical approach called automatic factorial cokriging (AFACK), which allows capturing the time-invariant part between the spatio-temporal measurements of a signal, called stable component in the sequel. The stable component achieved by AFACK was then used as a covariate in a multivariate estimation algorithm to map a static soil property pattern. The underlying idea was to highlight that the spatial estimates of a static soil property pattern is well achieved by integrating, in a multivariate estimation algorithm, the stable component over the time of a covariate instead of its single acquisitions. The latter generally show weak and inconsistent relationships with the static properties of the soil such as its thickness. These inconsistent relationships are mainly generated by the temporal variability of the signal measurements carried out under various conditions. Through three surveys of soil resistivity measurements carried out on three dates on the soil of an agricultural plot, results revealed that AFACK allows: (i) reproducing an initial signal deliberately corrupted by a noise, (ii) extracting the time-stable component of two or more soil electrical resistivity surveys. Cluster analysis results also revealed that the time-stable component of the signal exhibits well homogeneous areas in space conversely to surveys considered separately. Results also showed that the time-stable component of the electrical signal is better linked to the thickness of soil (ST). In addition, the mapping of ST by collocated cokriging (CC), using any stable component between two or more electrical resistivity surveys carried out by AFACK, leaded to an invariable map of ST, unlike the maps achieved by CC using, as covariate, soil electrical surveys considered individually. Statistical indicators resulting from leave-one-out cross-validation (LOOCV) showed that CC using any invariant part of the signal outperforms CC using the single acquisitions. Correlation coefficient (r), mean absolute error (MAE) and root mean square error (RMSE) values range between 0.93 and 0.94; 4.33 and 5.03; 0.75 and 0.88, respectively, for estimates using as a covariate an invariant part of the signal. While values of these statistical indicators range between 0.64 and 0.74; 7.20 and 8.69; 1.25 and 1.52, respectively, for estimates by CC using single acquisitions. Values of these indicators are almost invariant regardless of the invariant part used in CC, unlike the scenario using the surveys individually.

Phase-Based Displacement Sensor With Improved Spatial Frequency Estimation and Data Fusion Strategy

Phase-based vibration measurement has attra- cted a lot of attention due to its advantages of wireless, non-contact, and full-field measurement capabilities. The phase shifts across the different video frames correspond to the motion of the structure, which makes it possible to measure the vibration with the phase. However, the decoding of the phase shifts and the presence of the phase singularities make the phase-based method noise-sensitive and non-robust when concerning vibration measurement. Within this paper, an improved framework is proposed to enhance the performance of the phase-based technique for vibration measurement. A double filtering approach combined with the O’Shea refinement is introduced to decrease the phase noise to obtain accurate spatial frequency estimates. Then a confidence measure index is constructed to evaluate the reliability of phase responses to avoid the outliers induced by the phase singularities. Finally, an information fusion strategy over multiple scales is developed to enhance the accuracy and robustness of the motion estimation. Both simulated and experimental results verify the effectiveness and accuracy of the proposed framework.

Through the Wall Imaging Using Ultrawideband Frequency Domain Sampling Method

In this paper, a novel ultrawideband (UWB) time reversal technique for through-the-wall imaging (TWI) is introduced. The proposed algorithm is based on utilization of spatial and UWB frequency data acquired by antenna array. Scattering data are obtained by transmitting a short pulse from the central transmitter and recording the received signal at each array element. Individual multistatic scattering data matrix according to each antenna is formed by casting the fine and coarse frequency samples of the scattering information. The resulting matrix is calibrated based on transmitter signal and fed into the DORT (French acronym for decomposition of the time reversal operator) algorithm. The performance of the proposed method is investigated numerically by applying it to discrete scatterers embedded in homogeneous and continuously random medium. Numerical results are presented to show the effectiveness and high efficiency of the proposed algorithm for TWI.

Fast Cardiac CINE MRI by Iterative Truncation of Small Transformed Coefficients

Cardiovascular MRI with high spatial and temporal resolutions is essential to diagnosis; however, the collection of large data within a limited time is challenging, even with the latest high-end hardware (1). Many approaches have been tried to increase the rate of data acquisition with improved hardware, e.g., a high intensity and high slew-rate gradient system (2), and parallel acquisition systems with array coils (3, 4). Fast switching of large gradient fields, however, increases eddy currents and acoustic noise, and induces peripheral nerve stimulation (5, 6). Parallel imaging using the parallel acquisition system has been used for various clinical applications including cardiac MRI to increase the acquisition rate (7). Alternatively, cardiovascular MRI with less data without much loss of image quality has been investigated, for example view sharing (8), to skip high spatial frequency data This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/ by-nc/3.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. Received: March 4, 2015 Revised: March 20, 2015 Accepted: March 20, 2015

Genetic algorithm and pure random search for exosensor distribution optimisation

The positioning, amount(s) and field of view(s) of exosensors are a fundamental characteristic of a smart home environment. Contemporary smart home sensor distribution is aligned to either a a total coverage approach b a human assessment approach. These methods for sensor arrangement are not data driven strategies, are unempirical, and frequently irrational. Little research has been conducted in relation to optimal resource allocation in smart homes environments. This study aimed to generate globally optimal sensor distributions for a smart home replica-kitchen using two distinct methodologies, namely a genetic algorithm (GA) and a pure random search algorithm (PRS), to ascertain which method is appropriate for this task. GA outperformed PRS consistently, with a coverage percentage that encapsulated an average of 43.6% more inhabitant spatial frequency data. The results of this study indicate that GA provides more optimal solutions than PRS for exosensor distributions in a smart home environment.

Pure random search for ambient sensor distribution optimisation in a smart home environment

Smart homes are living spaces facilitated with technology to allow individuals to remain in their own homes for longer, rather than be institutionalised. Sensors are the fundamental physical layer with any smart home, as the data they generate is used to inform decision support systems, facilitating appropriate actuator actions. Positioning of sensors is therefore a fundamental characteristic of a smart home. Contemporary smart home sensor distribution is aligned to either a) a total coverage approach; b) a human assessment approach. These methods for sensor arrangement are not data driven strategies, are unempirical and frequently irrational. This Study hypothesised that sensor deployment directed by an optimisation method that utilises inhabitants' spatial frequency data as the search space, would produce more optimal sensor distributions vs. the current method of sensor deployment by engineers. Seven human engineers were tasked to create sensor distributions based on perceived utility for 9 deployment scenarios. A Pure Random Search (PRS) algorithm was then tasked to create matched sensor distributions. The PRS method produced superior distributions in 98.4% of test cases (n=64) against human engineer instructed deployments when the engineers had no access to the spatial frequency data, and in 92.0% of test cases (n=64) when engineers had full access to these data. These results thus confirmed the hypothesis.

2D radar images of template objects

A method for calculation of 2D radar images of template objects based on aperture synthesis and application of known rigorous solutions to diffraction problems is proposed. All parameters of the algorithm for the synthesis of 2D radar images from the spatial-frequency data are determined. An expression relating the given frequency tuning band of the probing signal and the width of the interval of synthesis angles implementing the achievable resolution is obtained. Two-dimensional radar images of a sphere and a disk with different wave dimensions are calculated for the case of backward reflection.

A consideration on motion‐image‐quality improvement of LCDs and video systems

Abstract— The motion image quality of video systems with hold‐type displays, such as LCDs or OLEDs, were studied with regard to dynamic spatial frequency response and data from subjective evaluations on motion blur. The system parameters of motion image quality, or frame rate (F) and temporal aperture (At), were investigated and their required values were derived. A smaller temporal aperture and/or higher frame rate can improve the dynamic response and motion image quality, but the parameters required in order to maintain a good dynamic response for high motion image velocity seems very difficult to implement, such as a frame rate of 900 Hz. Therefore, the performance goal of video systems is set on “limit of acceptance” for motion image quality, as a compromise. An equation or the relational expression between motion image velocity and required parameter values is derived based on dynamic response and data from subjective evaluations found in published studies. Possible examples of parameter sets are obtained from the equation. Those are (F = 300 Hz, At = 5/6), (F = 240 Hz, At = 2/3), (F = 120 Hz, At = 1/3), and (F > 360 Hz, At = 1).

Spatial-frequency data acquisition using rotational invariant pattern matching in smart environments

This article details the development and testing of an empirical data capture system with the ability to collect spatial-frequency statistics relating to the movement behaviour of a smart home inhabitant. This is achieved using a greyscale normalised cross-correlation pattern matching algorithm. Environmental obstructions on the floor space can also be inferred from a visual representation of the accumulated data. Whilst this methodology itself is not novel, its application to person tracking specifically within a smart home environment does not appear in the literature and is considered a novel approach. The results of tests performed on the pattern matching technique show a tracking competency rate of 94.45% with a standard deviation of 0.009027, indicating high fidelity across a wide variety of environmental factors.

UWB Through-Wall Imaging Based on Compressive Sensing

To achieve high-resolution 2-D images, through-wall imaging (TWI) radar with ultra-wideband and long antenna arrays faces considerable technical challenges such as a prolonged data collection time, a huge amount of data, and a high hardware complexity. This paper presents a novel data acquisition scheme and an imaging algorithm for TWI radar based on compressive sensing (CS), which states that a signal having a sparse representation can be reconstructed from a small number of nonadaptive randomized projections by solving a tractable convex program. Instead of measuring all spatial-frequency data, a few samples, by employing an overcomplete dictionary, are sufficient to obtain reliable target space images even at high noise levels. Preliminary simulated and experimental results show that the proposed algorithm outperforms the conventional delay-and-sum beamforming method even though many fewer CS measurements are used.

ホールド型ディスプレイを用いる映像システムの動画質改善に関する一検討

The motion image quality of video systems with hold-type displays (such as LCDs) is studied with dynamic spatial frequency response and data from image quality evaluations on motion blur. The system parameters of motion image quality, or frame rate and temporal aperture, are discussed and their required values are speculated. A smaller temporal aperture and/or higher frame rate can improve the dynamic response and motion image quality, but the parameter values required to maintain a good dynamic response for a high motion image velocity, seem difficult to implement. Therefore, the target of video systems is set on a limit of acceptance of motion image quality, as a compromise. An equation for the relation between motion image velocity and required system parameter values is derived based on dynamic response and data from image quality evaluations found in published studies. Possible two examples of parameter sets are obtained from the equation.

Contrast-enhanced MR angiography with frequency-dependent mask subtraction

One of the main challenges for high-resolution contrast-enhanced magnetic resonance angiography (CE-MRA) is the limited signal-to-noise ratio (SNR). In conventional CE-MRA, spoiled gradient-recalled echo sequence is typically used to acquire raw data before and after contrast material injection, followed by mask subtraction to improve vessel contrast at the cost of increased image noise. We reported here on a frequency-dependent mask subtraction technique where only low spatial frequency data were subject to mask subtraction to improve image contrast and reduce motion artifact while high spatial frequency data were not subtracted to preserve image noise level. The feasibility of this technique was demonstrated through phantom, animal, and human volunteer studies. The lowest half, quarter, 1/8 and 1/16 of all spatial frequencies were subject to mask subtraction, respectively. These partial subtraction techniques were compared with conventional mask subtraction in terms of SNR and artifact power. An SNR gain up to 37% was achieved without significant artifact power increase for quarter-subtraction where only the central one quarter k-space data were subject to mask subtraction.

Space–Frequency Ultrawideband Time-Reversal Imaging

We introduce time-reversal ultrawideband (UWB) imaging functionals based on the simultaneous utilization of spatial and UWB frequency data acquired by limited-aspect antenna arrays. The targets are discrete scatterers embedded in homogeneous or continuous random inhomogeneous media. Singular value decomposition is applied to space-frequency multistatic scattering data matrices indexed by sensor location and frequency data, and the resulting singular values and vectors are employed to construct time-domain excitation signals for UWB imaging of the embedded scatterer(s) via synthetic backpropagation (reverse migration). Spatial information needed for focusing on the embedded scatterer(s) is provided by either the left singular vectors or the eigenvectors of the space-space multistatic data matrices. The resulting UWB imaging functionals can yield statistical stability in random media.

Methods of visual acuity determination with the spatial frequency sweep visual evoked potential

The spatial frequency sweep visual evoked potential (sVEP) is used to rapidly determine visual acuity in children or non-responsive patients. Two techniques have been used to separate signal from noise: (1) the 95% confidence interval for the signal amplitude (95% CI) or (2) the amplitude of a Fourier frequency adjacent to 2x the signal frequency (DFT). The purpose of this study is to determine if there is a significant difference in acuity estimates with these techniques. Ten normal subjects (approximately 0.00 logMAR acuity) and 11 patients with decreased visual acuity took part in this project. Stimulus production and data analysis were done with an Enfant 4010 (Neuroscientific Corp). Standard VEP recording techniques were employed. The stimulus was a horizontal-oriented, sine wave grating that swept up the spatial frequency spectrum (contrast 80%, temporal reversal rate 7.5 Hz). Sweeps were repeated until the confidence intervals for the data were no longer decreasing. The Bailey Lovie logMAR chart was used to determine visual acuity. A line was fit to the high spatial frequency data using either the 95% CI or the DFT as the noise estimate. By using these linear equations, acuity estimates were obtained at 0, 1, and 2 microV signal amplitudes. The average logMAR acuity for the subjects with normal acuity was -0.06 +/- 0.070 (SD). The sVEP acuity estimates were 0.08 +/- 0.098, 0.18 +/- 0.092, and 0.33 +/- 0.195 (0, 1, and 2 microV extrapolations) with the 95% CI used as noise and 0.07 +/- 0.100, 0.18 +/- 0.103, and 0.33 +/- 0.202 (0, 1, and 2 microV extrapolations) with the DFT used as noise. By using the average noise from the Fourier frequency as the extrapolation level, the acuity was 0.10 +/- 0.098 logMAR. The average logMAR acuity for the subjects with decreased visual acuity was 0.67 +/- 0.306 (SD). The sVEP acuity estimates were 0.53 +/- 0.175, 0.66 +/- 0.171, and 0.88 +/- 0.295 (0, 1, and 2 microV extrapolations) with the 95% CI used as noise and 0.53 +/- 0.179, 0.65 +/- 0.176, and 0.86 +/- 0.268 (0, 1, and 2 microV extrapolations) with the DFT used as noise. By using the average noise from the Fourier frequency as the extrapolation level, the acuity was 0.57 +/- 0.186 logMAR. No significant difference was found between the two acuity estimate techniques for all of the subjects (repeated measures ANOVA, p = 0.16, F20 = 2.131). The sVEP estimates of acuity to the 0 microV and noise levels were not significantly different from the logMAR acuity (paired t-test, all p values > 0.05). The results indicate that the sVEP acuity does not depend on the noise estimation technique. In agreement with prior studies, the sVEP acuity underestimates the logMAR acuity in normally sighted individuals by about an octave.

Noise reduction in MR angiography with nonlinear anisotropic filtering.

To evaluate three-dimensional nonlinear anisotropic filtering in suppressing image noise in high spatial resolution magnetic resonance angiograms (MRA) acquired with hybrid undersampled projection reconstruction and phase contrast vastly undersampled isotropic projection reconstruction (PC-VIPR). Three-dimensional nonlinear anisotropic filtering was quantitatively analyzed and evaluated through the measurement of contrast to noise ratio (CNR) in PC-VIPR images and contrast enhanced peripheral MRA images. To filter MRA images with ultra-high spatial resolution and poor CNR, a spatial frequency dependent nonlinear anisotropic filtering algorithm was proposed that uses two-step processing to filter the whole spatial frequency data. Three-dimensional nonlinear anisotropic filtering was shown to be effective in suppressing noise and improving CNR in MRA with isotropic spatial resolution. Higher CNR was achieved using spatial frequency dependent nonlinear anisotropic filtering. A typical CNR gain of between 50-100% was shown in our studies. Three-dimensional nonlinear anisotropic filtering significantly improved CNR in MRA images with isotropic spatial resolution. Spatial frequency dependent nonlinear anisotropic filtering further improved CNR for MRA images with ultra-high spatial resolution and low CNR.

Fine Structure of Parvocellular Receptive Fields in the Primate Fovea Revealed by Laser Interferometry

Optical blurring in the eye prevents conventional physiological techniques from revealing the fine structure of the small parvocellular receptive fields in the primate fovea in vivo. We explored the organization of receptive fields in macaque parvocellular lateral geniculate nucleus cells by using sinusoidal interference fringes formed directly on the retina to measure spatial frequency tuning at different orientations. Most parvocellular cells in and near the fovea respond reliably to spatial frequencies up to and beyond 100 cycles/ degrees of visual angle, implying center input arising mainly from a single cone. Temporal frequency and contrast response characteristics were also measured at spatial frequencies up to 130 cycles/degrees. We compared our spatial frequency data with the frequency responses of model receptive fields that estimate the number, configuration, and weights of cones that feed the center and surround. On the basis of these comparisons, we infer possible underlying circuits. Most cells had irregular spatial frequency-response curves that imply center input from more than one cone. The measured responses are consistent with a single cone center together with weak input from nearby cones. By exposing a fine structure that cannot be discerned by conventional techniques, interferometry allows functional measurements of the early neural mechanisms in spatial vision.

A new image distortion measure based on a data-driven multisensor organization

This paper describes a visual model that gives a perceptual distortion measure between an input image and that of reference based on a human-image representational model. We study an approach in which once a few active recognizers tuned to significant orientation and spatial-frequency components of the reference spectrum are obtained, any input image to be compared with the reference one is passed through an operator designated to compare its excitation levels given by the active recognizers, to the corresponding excitation levels for the reference image. Hence, the distortion between a pair of complex images is measured as the weighted sum of the distortion in each filter of a bank of strongly responding recognizers, each tuned to a certain 2D spatial-frequency data in the reference picture, with the weighting of each filter modulating its amplitude response.

Representation of spatial frequency and orientation in the visual cortex.

Knowledge of the response of the primary visual cortex to the various spatial frequencies and orientations in the visual scene should help us understand the principles by which the brain recognizes patterns. Current information about the cortical layout of spatial frequency response is still incomplete because of difficulties in recording and interpreting adequate data. Here, we report results from a study of the cat primary visual cortex in which we employed a new image-analysis method that allows improved separation of signal from noise and that we used to examine the neurooptical response of the primary visual cortex to drifting sine gratings over a range of orientations and spatial frequencies. We found that (i) the optical responses to all orientations and spatial frequencies were well approximated by weighted sums of only two pairs of basis pictures, one pair for orientation and a different pair for spatial frequency; (ii) the weightings of the two pictures in each pair were approximately in quadrature (1/4 cycle apart); and (iii) our spatial frequency data revealed a cortical map that continuously assigns different optimal spatial frequency responses to different cortical locations over the entire spatial frequency range.