A new experiment is described to detect a permanent electric dipole moment of the proton with a sensitivity of 10−29 e ⋅ cm by using polarized “magic” momentum 0.7 GeV/c protons in an all-electric storage ring. Systematic errors relevant to the experiment are discussed and techniques to address them are presented. The measurement is sensitive to new physics beyond the standard model at the scale of 3000 TeV.

A pixel‐level fusion technique for RGB representation of multispectral images is proposed. The technique results in highly correlated RGB components, a fact which occurs in natural colour images and is strictly related to the colour perception attributes of the human eye. Accordingly, specific properties for the covariance matrix of the final RGB image are demanded. Mutual information is employed as an objective criterion for quality refinement. The method provides dimensionality reduction, while the resulting RGB colour image is perceptually of high quality. Comparisons with existing techniques are carried out using both subjective and objective measures.

Feature extraction plays a pivotal role in processing remote sensing datasets, especially in the realm of fully polarimetric data. This review investigates a variety of polarimetric decomposition techniques aimed at extracting comprehensive information from polarimetric imagery. These techniques are categorized as coherent and non-coherent methods, depending on their assumptions about the distribution of information among polarimetric cells. The review explores well-established and innovative approaches in polarimetric decomposition within both categories. It begins with a thorough examination of the foundational Pauli decomposition, a key algorithm in this field. Within the coherent category, the Cameron target decomposition is extensively explored, shedding light on its underlying principles. Transitioning to the non-coherent domain, the review investigates the Freeman–Durden decomposition and its extension, the Yamaguchi’s approach. Additionally, the widely recognized eigenvector–eigenvalue decomposition introduced by Cloude and Pottier is scrutinized. Furthermore, each method undergoes experimental testing on the benchmark dataset of the broader Vancouver area, offering a robust analysis of their efficacy. The primary objective of this review is to systematically present well-established polarimetric decomposition algorithms, elucidating the underlying mathematical foundations of each. The aim is to facilitate a profound understanding of these approaches, coupled with insights into potential combinations for diverse applications.

A high-resolution image is reconstructed from a sequence of subpixel shifted, aliased low-resolution frames, by means of stochastic regularized super-resolution (SR) image reconstruction. The Tukey (T), Lorentzian (L), and Huber (H) cost functions are employed for the data-fidelity term. The performance of the particular error norms, in SR image reconstruction, is presented. Actually, their employment in SR reconstruction is preceded by dilating and scaling their influence functions to make them as similar as possible. Thus, the direct comparison of these norms in rejecting outliers takes place. The bilateral total variation (BTV) regularization is incorporated as a priori knowledge about the solution. The outliers effect is significantly reduced, and the high-frequency edge structures of the reconstructed image are preserved. The proposed TTV, LTV, and HTV methods are directly compared with a former SR method that employs the $L1$-norm in the data-fidelity term for synthesized and real sequences of frames. In the simulated experiments, noiseless frames as well as frames corrupted by salt-and-pepper noise are employed. Experimental results verify the robust statistics theory. Thus, the Tukey method performs best, while the $L1$-norm technique performs inferiorly to the proposed techniques.

In the ever-evolving landscape of tomographic imaging algorithms, this literature review explores a diverse array of themes shaping the field’s progress. It encompasses foundational principles, special innovative approaches, tomographic implementation algorithms, and applications of tomography in medicine, natural sciences, remote sensing, and seismology. This choice is to show off the diversity of tomographic applications and simultaneously the new trends in tomography in recent years. Accordingly, the evaluation of backprojection methods for breast tomographic reconstruction is highlighted. After that, multi-slice fusion takes center stage, promising real-time insights into dynamic processes and advanced diagnosis. Computational efficiency, especially in methods for accelerating tomographic reconstruction algorithms on commodity PC graphics hardware, is also presented. In geophysics, a deep learning-based approach to ground-penetrating radar (GPR) data inversion propels us into the future of geological and environmental sciences. We venture into Earth sciences with global seismic tomography: the inverse problem and beyond, understanding the Earth’s subsurface through advanced inverse problem solutions and pushing boundaries. Lastly, optical coherence tomography is reviewed in basic applications for revealing tiny biological tissue structures. This review presents the main categories of applications of tomography, providing a deep insight into the methods and algorithms that have been developed so far so that the reader who wants to deal with the subject is fully informed.