Evolution-Operator-Based Single-Step Method for Image Processing

This work proposes an evolution-operator-based single-time-step method for image and signal processing. The key component of the proposed method is a local spectral evolution kernel (LSEK) that analytically integrates a class of evolution partial differential equations (PDEs). From the point of view PDEs, the LSEK provides the analytical solution in a single time step, and is of spectral accuracy, free of instability constraint. From the point of image/signal processing, the LSEK gives rise to a family of lowpass filters. These filters contain controllable time delay and amplitude scaling. The new evolution operator-based method is constructed by pointwise adaptation of anisotropy to the coefficients of the LSEK. The Perona-Malik-type of anisotropic diffusion schemes is incorporated in the LSEK for image denoising. A forward-backward diffusion process is adopted to the LSEK for image deblurring or sharpening. A coupled PDE system is modified for image edge detection. The resulting image edge is utilized for image enhancement. Extensive computer experiments are carried out to demonstrate the performance of the proposed method. The major advantages of the proposed method are its single-step solution and readiness for multidimensional data analysis

An On-demand Photonic Ising Machine with Simplified Hamiltonian Calculation by Phase-encoding and Intensity Detection

Photonic Ising machine is a new paradigm of optical computing, which is based on the characteristics of light wave propagation, parallel processing and low loss transmission. Thus, the process of solving the combinatorial optimization problems can be accelerated through photonic/optoelectronic devices. In this work, we have proposed and demonstrated the so-called Phase-Encoding and Intensity Detection Ising Annealer (PEIDIA) to solve arbitrary Ising problems on demand. The PEIDIA is based on the simulated annealing algorithm and requires only one step of optical linear transformation with simplified Hamiltonian calculation. With PEIDIA, the Ising spins are encoded on the phase term of the optical field and only intensity detection is required during the solving process. As a proof of principle, several 20 and 30-dimensional Ising problems have been solved with high ground state probability

Aggressive shadowing of a low-dimensional model of atmospheric dynamics

Predictions of the future state of the Earth's atmosphere suffer from the consequences of chaos: numerical weather forecast models quickly diverge from observations as uncertainty in the initial state is amplified by nonlinearity. One measure of the utility of a forecast is its shadowing time, informally given by the period of time for which the forecast is a reasonable description of reality. The present work uses the Lorenz 096 coupled system, a simplified nonlinear model of atmospheric dynamics, to extend a recently developed technique for lengthening the shadowing time of a dynamical system. Ensemble forecasting is used to make forecasts with and without inflation, a method whereby the ensemble is regularly expanded artificially along dimensions whose uncertainty is contracting. The first goal of this work is to compare model forecasts, with and without inflation, to a true trajectory created by integrating a modified version of the same model. The second goal is to establish whether inflation can increase the maximum shadowing time for a single optimal member of the ensemble. In the second experiment the true trajectory is known a priori, and only the closest ensemble members are retained at each time step, a technique known as stalking. Finally, a targeted inflation is introduced to both techniques to reduce the number of instances in which inflation occurs in directions likely to be incommensurate with the true trajectory. Results varied for inflation, with success dependent upon the experimental design parameters (e.g. size of state space, inflation amount). However, a more targeted inflation successfully reduced the number of forecast degradations without significantly reducing the number of forecast improvements. Utilized appropriately, inflation has the potential to improve predictions of the future state of atmospheric phenomena, as well as other physical systems.Comment: 14 pages, 16 figure

Constrained pre-equalization accounting for multi-path fading emulated using large RC networks: applications to wireless and photonics communications

Multi-path propagation is modelled assuming a multi-layer RC network with randomly allocated resistors and capacitors to represent the transmission medium. Due to frequency-selective attenuation, the waveforms associated with each propagation path incur path-dependent distortion. A pre-equalization procedure that takes into account the capabilities of the transmission source as well as the transmission properties of the medium is developed. The problem is cast within a Mixed Integer Linear Programming optimization framework that uses the developed nominal RC network model, with the excitation waveform customized to optimize signal fidelity from the transmitter to the receiver. The objective is to match a Gaussian pulse input accounting for frequency regions where there would be pronounced fading. Simulations are carried out with different network realizations in order to evaluate the sensitivity of the solution with respect to changes in the transmission medium mimicking the multi-path propagation. The proposed approach is of relevance where equalization techniques are difficult to implement. Applications are discussed within the context of emergent communication modalities across the EM spectrum such as light percolation as well as emergent indoor communications assuming various modulation protocols or UWB schemes as well as within the context of space division multiplexing

Quantum sensing

"Quantum sensing" describes the use of a quantum system, quantum properties or quantum phenomena to perform a measurement of a physical quantity. Historical examples of quantum sensors include magnetometers based on superconducting quantum interference devices and atomic vapors, or atomic clocks. More recently, quantum sensing has become a distinct and rapidly growing branch of research within the area of quantum science and technology, with the most common platforms being spin qubits, trapped ions and flux qubits. The field is expected to provide new opportunities - especially with regard to high sensitivity and precision - in applied physics and other areas of science. In this review, we provide an introduction to the basic principles, methods and concepts of quantum sensing from the viewpoint of the interested experimentalist.Comment: 45 pages, 13 figures. Submitted to Rev. Mod. Phy

Quantum channels and their entropic characteristics

One of the major achievements of the recently emerged quantum information theory is the introduction and thorough investigation of the notion of quantum channel which is a basic building block of any data-transmitting or data-processing system. This development resulted in an elaborated structural theory and was accompanied by the discovery of a whole spectrum of entropic quantities, notably the channel capacities, characterizing information-processing performance of the channels. This paper gives a survey of the main properties of quantum channels and of their entropic characterization, with a variety of examples for finite dimensional quantum systems. We also touch upon the "continuous-variables" case, which provides an arena for quantum Gaussian systems. Most of the practical realizations of quantum information processing were implemented in such systems, in particular based on principles of quantum optics. Several important entropic quantities are introduced and used to describe the basic channel capacity formulas. The remarkable role of the specific quantum correlations - entanglement - as a novel communication resource, is stressed.Comment: review article, 60 pages, 5 figures, 194 references; Rep. Prog. Phys. (in press

Hyperspectral Imaging of a Turbine Engine Exhaust Plume to Determine Radiance, Temperature, and Concentration Spatial Distributions

The usefulness of imaging Fourier transform spectroscopy (IFTS) when looking at a rapidly varying turbine engine exhaust scene was explored by characterizing the scene change artifacts (SCAs) present in the plume and the effect they have on the calibrated spectra using the Telops, Inc.-manufactured Field-portable Imaging Radiometric Spectrometer Technology, Midwave Extended (FIRST-MWE). It was determined that IFTS technology can be applied to the problem of a rapidly varying turbine engine exhaust plume due to the zero mean, stochastic nature of the SCAs, through the use of temporal averaging. The FIRST-MWE produced radiometrically calibrated hyperspectral datacubes, with calibration uncertainty of 35% in the 1800 - 2500 cm-1 (4 - 5.5 µm) spectral region for pixels with signal-to-noise ratio (SNR) greater than 1.5; the large uncertainty was due to the presence of SCAs. Spatial distributions of temperature and chemical species concentration pathlengths for CO2, CO, and H2O were extracted from the radiometrically calibrated hyperspectral datacubes using a simple radiative transfer model for diesel and kerosene fuels, each with fuel flow rates of 300 cm3/min and 225 cm3/min. The temperatures were found to be, on average, within 212 K of in situ measurements, the difference attributed to the simplicity of the model. Although no in situ concentration measurements were made, the concentrations of CO2 and CO were found to be within expected limits set by the ambient atmospheric parameters and the calculated products of the turbine engine, on the order of 1015 and 1017 molecules/cm3, respectively