Search Results (10)

This paper presents a near real-time implementation of the Karhunen–Loève Transform (KLT) for Radio Frequency Interference (RFI) mitigation in microwave radiometry. KLT is a powerful, data-adaptive technique capable of adjusting to various signal types by estimating the covariance matrix of the incoming signal and segmenting its eigenvectors to form an effective RFI basis. In this paper, the KLT is evaluated with real signals in laboratory conditions, aiming to characterize its performance in realistic conditions. To that effect, the dual Rx/Tx capability of a Pluto SDR is used to generate and capture RFI. The main mitigation metrics are computed for the KLT and other commonly used mitigation methods. In addition, while previous studies have shown the effectiveness of offline processing of recorded I/Q data, real-time mitigation is often necessary. Given the computational cost of eigendecomposition, this work introduces a low-complexity solution using the “economy covariance” approach alongside asynchronous covariance decomposition. The proposed implementation, realized within the GNU Radio framework, demonstrates the practical feasibility of real-time KLT-based mitigation and underscores its potential for improving signal integrity in digital radiometers operating under dynamic RFI conditions. Full article

The ATLAZUL project is an Interreg effort among 18 partners from Spain and Portugal along the Atlantic Iberian coasts. One of its objectives is the development of new methods and data processing for oceanic information to produce useful products for private and public stakeholders. This study proposes a new insight on the sea surface dynamic of the ATLAZUL area based on almost two years of multiscale high resolution sea surface temperature imagery. The use of techniques such as the Karhunen–Loève transform (Empirical Orthogonal Function) and the Maximum Entropy Spectral Analysis were applied to study long- and short-term features in the sea surface temperature imagery. Mathematical Morphology and the Geometrical Theory of Measure are utilized to compute the Medial Axis Transform and the Hausdorff dimension. The results can be summarized as follows: (i) the tow upwelling areas are identified along the Galician–Portugal coast as indicated in the second and third modes of KLT/EOF analysis, and they are partially affected by wind; (ii) the tow warm water outflows from the Bay of Cádiz to the Gulf of Cádiz are identified as the second and third modes of KLT/EOF analysis, which are also influenced by wind; (iii) the skeletons of the surface signature of the upwelling and of the warmer water outflow, along with their fractal dimensions, indicate a chaotic pattern of spatial distribution and (iv) the harmonic prediction model should be combined with the wind prediction. Full article

Two key hurdles to the adoption of Machine Learning (ML) techniques in hyperspectral data compression are computational complexity and scalability for large numbers of bands. These are due to the limited computing capacity available in remote sensing platforms and the high computational cost of compression algorithms for hyperspectral data, especially when the number of bands is large. To address these issues, a channel clusterisation strategy is proposed, which reduces the computational demands of learned compression methods for real scenarios and is scalable for different sources of data with varying numbers of bands. The proposed method is compatible with an embedded implementation for state-of-the-art on board hardware, a first for a ML hyperspectral data compression method. In terms of coding performance, our proposal surpasses established lossy methods such as JPEG 2000 preceded by a spectral Karhunen-Loève Transform (KLT), in clusters of 3 to 7 bands, achieving a PSNR improvement of, on average, 9 dB for AVIRIS and 3 dB for Hyperion images. Full article

The design and usage of the addressed combined fiber-optic sensors (ACFOSs) and the multisensory control systems of the greenhouse gas concentration on their basis are investigated herein. The main development trend of the combined fiber-optic sensors (CFOSs), which consists of the fiber Bragg grating (FBG) and the Fabry–Perot resonator (FPR), which are successively formed at the optical fiber end, is highlighted. The use of the addressed fiber Bragg structures (AFBSs) instead of the FBG in the CFOSs not only leads to the significant cheapening of the sensor system due to microwave photonics interrogating methods, but also increasing its metrological characteristics. The structural scheme of the multisensory gas concentration monitoring system is suggested. The suggested scheme allows detecting four types of greenhouse gases (CO₂, NO₂, CH₄ and O_x) depending on the material and thickness of the polymer film, which is the FPR sensitive element. The usage of the Karhunen–Loève transform (KLT), which allows separating each component contribution to the reflected spectrum according to its efficiency, is proposed. In the future, this allows determining the gas concentration at the AFBS address frequencies. The estimations show that the ACFOS design in the multisensory system allows measuring the environment temperature in the range of −60…+300 °C with an accuracy of 0.1–0.01 °C, and the gas concentration in the range of 10…90% with an accuracy of 0.1–0.5%. Full article

Optical fiber ball resonators based on single-mode fibers in the infrared range are an emerging technology for refractive index sensing and biosensing. These devices are easy and rapid to fabricate using a CO₂ laser splicer and yield a very low finesse reflection spectrum with a quasi-random pattern. In addition, they can be functionalized for biosensing by using a thin-film sputtering method. A common problem of this type of device is that the spectral response is substantially unknown, and poorly correlated with the size and shape of the spherical device. In this work, we propose a detection method based on Karhunen−Loeve transform (KLT), applied to the undersampled spectrum measured by an optical backscatter reflectometer. We show that this method correctly detects the response of the ball resonator in any working condition, without prior knowledge of the sensor under interrogation. First, this method for refractive index sensing of a gold-coated resonator is applied, showing 1594 RIU⁻¹ sensitivity; then, this concept is extended to a biofunctionalized ball resonator, detecting CD44 cancer biomarker concentration with a picomolar-level limit of detection (19.7 pM) and high specificity (30–41%). Full article

In this work is introduced one new hierarchical decomposition for cubical tensor of size 2ⁿ, based on the well-known orthogonal transforms Principal Component Analysis and Karhunen–Loeve Transform. The decomposition is called 3D Frequency-Ordered Hierarchical KLT (3D-FOHKLT). It is separable, and its calculation is based on the one-dimensional Frequency-Ordered Hierarchical KLT (1D-FOHKLT) applied on a sequence of matrices. The transform matrix is the product of n sparse matrices, symmetrical at the point of their main diagonal. In particular, for the case in which the angles which define the transform coefficients for the couples of matrices in each hierarchical level of 1D-FOHKLT are equal to π/4, the transform coincides with this of the frequency-ordered 1D Walsh–Hadamard. Compared to the hierarchical decompositions of Tucker (H-Tucker) and the Tensor-Train (TT), the offered approach does not ensure full decorrelation between its components, but is close to the maximum. On the other hand, the evaluation of the computational complexity (CC) of the new decomposition proves that it is lower than that of the above-mentioned similar approaches. In correspondence with the comparison results for H-Tucker and TT, the CC decreases fast together with the increase of the hierarchical levels’ number, n. An additional advantage of 3D-FOHKLT is that it is based on the use of operations of low complexity, while the similar famous decompositions need large numbers of iterations to achieve the coveted accuracy. Full article

A novel scheme is presented for image compression using a compatible form called Chimera. This form represents a new transformation for the image pixels. The compression methods generally look for image division to obtain small parts of an image called blocks. These blocks contain limited predicted patterns such as flat area, simple slope, and single edge inside images. The block content of these images represent a special form of data which be reformed using simple masks to obtain a compressed representation. The compression representation is different according to the type of transform function which represents the preprocessing operation prior the coding step. The cost of any image transformation is represented by two main parameters which are the size of compressed block and the error in reconstructed block. Our proposed Chimera Transform (CT) shows a robustness against other transform such as Discrete Cosine Transform (DCT), Wavelet Transform (WT) and Karhunen-Loeve Transform (KLT). The suggested approach is designed to compress a specific data type which are the images, and this represents the first powerful characteristic of this transform. Additionally, the reconstructed image using Chimera transform has a small size with low error which could be considered as the second characteristic of the suggested approach. Our results show a Peak Signal to Noise Ratio (PSNR) enhancement of $2.0272$ for DCT, $1.179$ for WT and $4.301$ for KLT. In addition, a Structural Similarity Index Measure (SSIM) enhancement of $0.1108$ for DCT, $0.051$ for WT and $0.175$ for KLT. Full article

This paper presents a fragile watermarking algorithm`m for the protection of the integrity of color images with alpha channel. The system is able to identify modified areas with very high probability, even with small color or transparency changes. The main characteristic of the algorithm is the embedding of the watermark by modifying the alpha channel, leaving the color channels untouched and introducing a very small error with respect to the host image. As a consequence, the resulting watermarked images have a very high peak signal-to-noise ratio. The security of the algorithm is based on a secret key defining the embedding space in which the watermark is inserted by means of the Karhunen–Loève transform (KLT) and a genetic algorithm (GA). Its high sensitivity to modifications is shown, proving the security of the whole system. Full article

In this paper, we apply the quaternion framework for color images to a fragile watermarking algorithm with the objective of multimedia integrity protection (Quaternion Karhunen-Loève Transform Fragile Watermarking (QKLT-FW)). The use of quaternions to represent pixels allows to consider the color information in a holistic and integrated fashion. We stress out that, by taking advantage of the host image quaternion representation, we extract complex features that are able to improve the embedding and verification of fragile watermarks. The algorithm, based on the Quaternion Karhunen-Loève Transform (QKLT), embeds a binary watermark into some QKLT coefficients representing a host image in a secret frequency space: the QKLT basis images are computed from a secret color image used as a symmetric key. A computational intelligence technique (i.e., genetic algorithm) is employed to modify the host image pixels in such a way that the watermark is contained in the protected image. The sensitivity to image modifications is then tested, showing very good performance. Full article

The Karhunen-Loeve Transform (KLT) is applied to accurate detection of optical fiber sensors in the spectral domain. By processing an optical spectrum, although coarsely sampled, through the KLT, and subsequently processing the obtained eigenvalues, it is possible to decode a plurality of optical sensor results. The KLT returns higher accuracy than other demodulation techniques, despite coarse sampling, and exhibits higher resilience to noise. Three case studies of KLT-based processing are presented, representing most of the current challenges in optical fiber sensing: (1) demodulation of individual sensors, such as Fiber Bragg Gratings (FBGs) and Fabry-Perot Interferometers (FPIs); (2) demodulation of dual (FBG/FPI) sensors; (3) application of reverse KLT to isolate different sensors operating on the same spectrum. A simulative outline is provided to demonstrate the KLT operation and estimate performance; a brief experimental section is also provided to validate accurate FBG and FPI decoding. Full article