Related Research Articles
Mathematical optimization or mathematical programming is the selection of a best element, with regard to some criterion, from some set of available alternatives. Optimization problems of sorts arise in all quantitative disciplines from computer science and engineering to operations research and economics, and the development of solution methods has been of interest in mathematics for centuries.
Reinforcement learning (RL) is an area of machine learning concerned with how intelligent agents ought to take actions in an environment in order to maximize the notion of cumulative reward. Reinforcement learning is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning.
Simulated annealing (SA) is a probabilistic technique for approximating the global optimum of a given function. Specifically, it is a metaheuristic to approximate global optimization in a large search space for an optimization problem. It is often used when the search space is discrete. For problems where finding an approximate global optimum is more important than finding a precise local optimum in a fixed amount of time, simulated annealing may be preferable to exact algorithms such as gradient descent or branch and bound.
Multi-disciplinary design optimization (MDO) is a field of engineering that uses optimization methods to solve design problems incorporating a number of disciplines. It is also known as multidisciplinary system design optimization (MSDO), and Multidisciplinary Design Analysis and Optimization (MDAO).
Optical coherence tomography (OCT) is an imaging technique that uses low-coherence light to capture micrometer-resolution, two- and three-dimensional images from within optical scattering media. It is used for medical imaging and industrial nondestructive testing (NDT). Optical coherence tomography is based on low-coherence interferometry, typically employing near-infrared light. The use of relatively long wavelength light allows it to penetrate into the scattering medium. Confocal microscopy, another optical technique, typically penetrates less deeply into the sample but with higher resolution.
In computer science and mathematical optimization, a metaheuristic is a higher-level procedure or heuristic designed to find, generate, or select a heuristic that may provide a sufficiently good solution to an optimization problem, especially with incomplete or imperfect information or limited computation capacity. Metaheuristics sample a subset of solutions which is otherwise too large to be completely enumerated or otherwise explored. Metaheuristics may make relatively few assumptions about the optimization problem being solved and so may be usable for a variety of problems.
Medical optical imaging is the use of light as an investigational imaging technique for medical applications. Examples include optical microscopy, spectroscopy, endoscopy, scanning laser ophthalmoscopy, laser Doppler imaging, and optical coherence tomography. Because light is an electromagnetic wave, similar phenomena occur in X-rays, microwaves, and radio waves.
A surrogate model is an engineering method used when an outcome of interest cannot be easily measured or computed, so a model of the outcome is used instead. Most engineering design problems require experiments and/or simulations to evaluate design objective and constraint functions as a function of design variables. For example, in order to find the optimal airfoil shape for an aircraft wing, an engineer simulates the airflow around the wing for different shape variables. For many real-world problems, however, a single simulation can take many minutes, hours, or even days to complete. As a result, routine tasks such as design optimization, design space exploration, sensitivity analysis and what-if analysis become impossible since they require thousands or even millions of simulation evaluations.
As applied in the field of computer vision, graph cut optimization can be employed to efficiently solve a wide variety of low-level computer vision problems, such as image smoothing, the stereo correspondence problem, image segmentation, object co-segmentation, and many other computer vision problems that can be formulated in terms of energy minimization. Many of these energy minimization problems can be approximated by solving a maximum flow problem in a graph. Under most formulations of such problems in computer vision, the minimum energy solution corresponds to the maximum a posteriori estimate of a solution. Although many computer vision algorithms involve cutting a graph, the term "graph cuts" is applied specifically to those models which employ a max-flow/min-cut optimization.
Fitness approximation aims to approximate the objective or fitness functions in evolutionary optimization by building up machine learning models based on data collected from numerical simulations or physical experiments. The machine learning models for fitness approximation are also known as meta-models or surrogates, and evolutionary optimization based on approximated fitness evaluations are also known as surrogate-assisted evolutionary approximation. Fitness approximation in evolutionary optimization can be seen as a sub-area of data-driven evolutionary optimization.
Optical projection tomography is a form of tomography involving optical microscopy. The OPT technique is sometimes referred to as Optical Computed Tomography (optical-CT) and Optical Emission Computed Tomography (optical-ECT) in the literature, to address the fact that the technique bears similarity to X-ray computed tomography (CT) and single-photon emission computed tomography (SPECT).
Optimus is a Process Integration and Design Optimization (PIDO) platform developed by Noesis Solutions. Noesis Solutions takes part in key research projects, such as MEGaFIT, JTI CleanSky Green Rotorcraft and iProd.
Endomicroscopy is a technique for obtaining histology-like images from inside the human body in real-time, a process known as ‘optical biopsy’. It generally refers to fluorescence confocal microscopy, although multi-photon microscopy and optical coherence tomography have also been adapted for endoscopic use. Commercially available clinical and pre-clinical endomicroscopes can achieve a resolution on the order of a micrometre, have a field-of-view of several hundred µm, and are compatible with fluorophores which are excitable using 488 nm laser light. The main clinical applications are currently in imaging of the tumour margins of the brain and gastro-intestinal tract, particularly for the diagnosis and characterisation of Barrett’s Esophagus, pancreatic cysts and colorectal lesions. A number of pre-clinical and transnational applications have been developed for endomicroscopy as it enables researchers to perform live animal imaging. Major pre-clinical applications are in gastro-intestinal tract, toumour margin detection, uterine complications, ischaemia, live imaging of cartilage and tendon and organoid imaging.
pSeven is a design space exploration software platform developed by DATADVANCE, extending design, simulation and analysis capabilities and assisting in smarter and faster design decisions. It provides a seamless integration with third party CAD and CAE software tools, powerful multi-objective and robust optimization algorithms, data analysis and uncertainty quantification tools.
Simulation-based optimization integrates optimization techniques into simulation modeling and analysis. Because of the complexity of the simulation, the objective function may become difficult and expensive to evaluate. Usually, the underlying simulation model is stochastic, so that the objective function must be estimated using statistical estimation techniques.
In machine learning, hyperparameter optimization or tuning is the problem of choosing a set of optimal hyperparameters for a learning algorithm. A hyperparameter is a parameter whose value is used to control the learning process. By contrast, the values of other parameters are learned.
Computational imaging is the process of indirectly forming images from measurements using algorithms that rely on a significant amount of computing. In contrast to traditional imaging, computational imaging systems involve a tight integration of the sensing system and the computation in order to form the images of interest. The ubiquitous availability of fast computing platforms, the advances in algorithms and modern sensing hardware is resulting in imaging systems with significantly enhanced capabilities. Computational Imaging systems cover a broad range of applications include computational microscopy, tomographic imaging, MRI, ultrasound imaging, computational photography, Synthetic Aperture Radar (SAR), seismic imaging etc. The integration of the sensing and the computation in computational imaging systems allows for accessing information which was otherwise not possible. For example:
Speckle variance optical coherence tomography (SV-OCT) is an imaging algorithm for functional optical imaging. Optical coherence tomography is an imaging modality that uses low-coherence interferometry to obtain high resolution, depth-resolved volumetric images. OCT can be used to capture functional images of blood flow, a technique known as optical coherence tomography angiography (OCT-A). SV-OCT is one method for OCT-A that uses the variance of consecutively acquired images to detect flow at the micron scale. SV-OCT can be used to measure the microvasculature of tissue. In particular, it is useful in ophthalmology for visualizing blood flow in retinal and choroidal regions of the eye, which can provide information on the pathophysiology of diseases.
References
- ↑ Hans R. G. W. Verstraete, Sander Wahls, Jeroen Kalkman, Michel Verhaegen: Model-based sensor-less wavefront aberration correction in optical coherence tomography, Opt. Lett. 40, 5722-5725 (2015)
- ↑ Ali Rahimi, Benjamin Recht: Random features for large-scale kernel machines, Advances in neural information processing systems, pp. 1177-1184 (2007)
- ↑ Dean Wilding, Paolo Pozzi, Oleg Soloviev, Gleb Vdovin, Colin J. Sheppard, Michel Verhaegen: Pupil filters for extending the field-of-view in light-sheet microscopy, Optics letters 41, no. 6 (2016): 1205-1208
 | This algorithms or data structures-related article is a stub. You can help Wikipedia by expanding it. |