Deep inelastic scattering and parton models.

Date

1976

Author

Balantekin, A. Baha

Metadata

Show full item record

Item Usage Stats

51
views

0
downloads

URI

https://hdl.handle.net/11511/3418

Collections

Graduate School of Natural and Applied Sciences, Thesis

Suggestions

OpenMETU
Core

Deep learning-based reconstruction methods for near-field MIMO radar imaging Manisalı, İrfan; Öktem, Sevinç Figen; Department of Electrical and Electronics Engineering (2022-6-29) Near-field multiple-input multiple-output (MIMO) radar imaging systems are of interest in diverse fields such as medicine, through-wall imaging, airport security, and surveillance. These computational imaging systems reconstruct the three-dimensional scene reflectivity distribution from the radar data. Hence their imaging performance largely depends on the image reconstruction method. The analytical reconstruction methods suffer from either low image quality or high computational cost. In fact, sparsity-bas...
Deep convolutional neural networks for airport detection in remote sensing images Budak, Umit; Sengur, Abdulkadir; Halıcı, Uğur (2018-05-05) This study investigated the use of deep convolutional neural networks (CNNs) in providing a solution for the problem of airport detection in remote sensing images (RSIs). In recent years, Deep CNNs have gained much attention with numerous applications having been undertaken in the area of computer vision. Researchers generally approach airport detection as a pattern recognition problem, in which first various distinctive features are extracted, and then a classifier is adopted to detect airports. CNNs not o...
Deep Metric Learning With Alternating Projections Onto Feasible Sets Can, Oğul; Gürbüz, Yeti Z.; Alatan, Abdullah Aydın (2021-01-01) Minimizers of the typical distance metric learning loss functions can be considered as "feasible points" satisfying a set of constraints imposed by the training data. We reformulate distance metric learning problem as finding a feasible point of a constraint set where the embedding vectors of the training data satisfy desired intra-class and inter-class proximity. The feasible set induced by the constraint set is expressed as the intersection of the relaxed feasible sets which enforce the proximity constrai...
Multiple target tracking with Gaussian mixture PHD filter using passive acoustic Doppler-only measurements Guldogan, Mehmet B.; Lindgren, David; Gustafsson, Fredrik; Habberstad, Hans; Orguner, Umut (null; 2012-09-12) In this paper, we present the performance of the Gaussian mixture probability hypothesis density (GM-PHD) filter in tracking multiple ground targets using a passive acoustic-sensor network. For this purpose, an experimental setup consisting of a network of microphones and a loudspeaker was prepared. Non-cooperative transmissions from a loudspeaker (i.e. illuminator of opportunity) are exploited by non-directional separately located microphones (i.e. Doppler measuring sensors). Experimental proof-of-concept ...
Dense depth map estimation for multiple view coding Ozkalayci, Burak; Alatan, Abdullah Aydın (2006-01-01) In this paper the basics of a proposed method that handles the stereo and especially multiple view coding problem in a geometrical way, are explained. For this purpose, estimation of the depth maps of the multiple views, captured by fully calibrated cameras, are done. In depth map estimation problem Markov Random Field modelling is used to have a depth map in a desired smoothness and in an efficient coding fashion. The geometric structure which is acquired by the depth map estimation, is used to reconstruct...

Citation Formats

A. B. Balantekin, “Deep inelastic scattering and parton models.,” Middle East Technical University, 1976.