Browsing by Subject "Constrained optimization"

Now showing 1 - 10 of 10

Open Access
Access pattern-based code compression for memory-constrained systems
(Association for Computing Machinery, 2008-09) Ozturk, O.; Kandemir, M.; Chen, G.
As compared to a large spectrum of performance optimizations, relatively less effort has been dedicated to optimize other aspects of embedded applications such as memory space requirements, power, real-time predictability, and reliability. In particular, many modern embedded systems operate under tight memory space constraints. One way of addressing this constraint is to compress executable code and data as much as possible. While researchers on code compression have studied efficient hardware and software based code compression strategies, many of these techniques do not take application behavior into account; that is, the same compression/decompression strategy is used irrespective of the application being optimized. This article presents an application-sensitive code compression strategy based on control flow graph (CFG) representation of the embedded program. The idea is to start with a memory image wherein all basic blocks of the application are compressed, and decompress only the blocks that are predicted to be needed in the near future. When the current access to a basic block is over, our approach also decides the point at which the block could be compressed. We propose and evaluate several compression and decompression strategies that try to reduce memory requirements without excessively increasing the original instruction cycle counts. Some of our strategies make use of profile data, whereas others are fully automatic. Our experimental evaluation using seven applications from the MediaBench suite and three large embedded applications reveals that the proposed code compression strategy is very successful in practice. Our results also indicate that working at a basic block granularity, as opposed to a procedure granularity, is important for maximizing memory space savings. © 2008 ACM.
Open Access
Atomistic structure simulation of silicon nanocrystals driven with suboxide penalty energies
(American Scientific Publishers, 2008) Yılmaz, Dündar E.; Bulutay, Ceyhun; Çağın, T.
The structural control of silicon nanocrystals embedded in amorphous oxide is currently an important technological problem. In this work, an approach is presented to simulate the structural behavior of silicon nanocrystals embedded in amorphous oxide matrix based on simple valence force fields as described by Keating-type potentials. After generating an amorphous silicon-rich-oxide, its evolution towards an embedded nanocrystal is driven by the oxygen diffusion process implemented in the form of a Metropolis algorithm based on the suboxide penalty energies. However, it is observed that such an approach cannot satisfactorily reproduce the shape of annealed nanocrystals. As a remedy, the asphericity and surface-to-volume minimization constraints are imposed. With the aid of such a multilevel approach, realistic-sized silicon nanocrystals can be simulated. Prediction for the nanocrystal size at a chosen oxygen molar fraction matches reasonably well with the experimental data when the interface region is also accounted. The necessity for additional shape constraints suggests the use of more involved force fields including long-range forces as well as accommodating different chemical environments such as the double bonds.
Open Access
Characterizing finite-dimensional quantum behavior
(American Physical Society, 2015) Navascués, M.; Feix, A.; Araújo, M.; Vértesi, T.
We study and extend the semidefinite programming (SDP) hierarchies introduced in Navascués and Vértesi [Phys. Rev. Lett. 115, 020501 (2015)PRLTAO0031-900710.1103/PhysRevLett.115.020501] for the characterization of the statistical correlations arising from finite-dimensional quantum systems. First, we introduce the dimension-constrained noncommutative polynomial optimization (NPO) paradigm, where a number of polynomial inequalities are defined and optimization is conducted over all feasible operator representations of bounded dimensionality. Important problems in device-independent and semi-device-independent quantum information science can be formulated (or almost formulated) in this framework. We present effective SDP hierarchies to attack the general dimension-constrained NPO problem (and related ones) and prove their asymptotic convergence. To illustrate the power of these relaxations, we use them to derive a number of dimension witnesses for temporal and Bell-type correlation scenarios, and also to bound the probability of success of quantum random access codes. © 2015 American Physical Society.
Open Access
Computational homogenization of soft matter friction: Isogeometric framework and elastic boundary layers
(John Wiley and Sons Ltd, 2014) Temizer, I.
SUMMARY: A computational contact homogenization framework is established for the modeling and simulation of soft matter friction. The main challenges toward the realization of the framework are (1) the establishment of a frictional contact algorithm that displays an optimal combination of accuracy, efficiency, and robustness and plays a central role in (2) the construction of a micromechanical contact test within which samples of arbitrary size may be embedded and which is not restricted to a single deformable body. The former challenge is addressed through the extension of mixed variational formulations of contact mechanics to a mortar-based isogeometric setting where the augmented Lagrangian approach serves as the constraint enforcement method. The latter challenge is addressed through the concept of periodic embedding, with which a periodically replicated C1-continuous interface topography is realized across which not only pending but also ensuing contact among simulation cells will be automatically captured. Two-dimensional and three-dimensional investigations with unilateral/bilateral periodic/random roughness on two elastic micromechanical samples demonstrate the overall framework and the nature of the macroscopic frictional response. © 2014 John Wiley & Sons, Ltd.
Open Access
Detection of compound structures using a gaussian mixture model with spectral and spatial constraints
(Institute of Electrical and Electronics Engineers Inc., 2014) Arı, C.; Aksoy, S.
Increasing spectral and spatial resolution of new-generation remotely sensed images necessitate the joint use of both types of information for detection and classification tasks. This paper describes a new approach for detecting heterogeneous compound structures such as different types of residential, agricultural, commercial, and industrial areas that are comprised of spatial arrangements of primitive objects such as buildings, roads, and trees. The proposed approach uses Gaussian mixture models (GMMs), in which the individual Gaussian components model the spectral and shape characteristics of the individual primitives and an associated layout model is used to model their spatial arrangements. We propose a novel expectation-maximization (EM) algorithm that solves the detection problem using constrained optimization. The input is an example structure of interest that is used to estimate a reference GMM and construct spectral and spatial constraints. Then, the EM algorithm fits a new GMM to the target image data so that the pixels with high likelihoods of being similar to the Gaussian object models while satisfying the spatial layout constraints are identified without any requirement for region segmentation. Experiments using WorldView-2 images show that the proposed method can detect high-level structures that cannot be modeled using traditional techniques. © 1980-2012 IEEE.
Open Access
Efficient parameter mapping for magnetic resonance imaging
(2019-07) Keskin, Kübra
Balanced steady-state free precession (bSSFP) is a magnetic resonance imaging (MRI) sequence that enables high signal-to-noise ratios in short scan times. However, it has elevated sensitivity to main eld inhomogeneity, which leads to banding artifacts near regions of relatively large o -resonance shifts. To suppress these artifacts, multiple bSSFP images of the same anatomy are commonly acquired with a set of di erent RF phase-cycling increments. Joint processing of phase-cycled acquisitions has long been employed to eliminate banding artifacts due to eld inhomogeneity. Multiple bSSFP acquisitions can be further used for parameter mapping by exploiting the signal model of phase-cycled bSSFP. While model based approaches for mapping are e ective, they often need a large number of acquisitions, inherently limiting scan e ciency. In this thesis, we propose a new method for parameter mapping with improved e ciency and accuracy in phasecycled bSSFP MRI. The proposed method is based on the elliptical signal model framework for complex bSSFP signals; and introduces an observation about the signal's geometry to the constrained parameter mapping problem, such that the number of unknowns and thereby the required number of acquisitions can be reduced. It also leverages dictionary-based identi cation to improve estimation accuracy. Simulated, phantom and in vivo experiments demonstrate that the proposed method enables improved parameter mapping with fewer acquisitions.
Open Access
Image reconstruction for Magnetic Particle Imaging using an Augmented Lagrangian Method
(IEEE, 2017) Ilbey S.; Top C.B.; Çukur, Tolga; Sarıtaş, Emine Ülkü; Guven H.E.
Magnetic particle imaging (MPI) is a relatively new imaging modality that images the spatial distribution of superparamagnetic iron oxide nanoparticles administered to the body. In this study, we use a new method based on Alternating Direction Method of Multipliers (a subset of Augmented Lagrangian Methods, ADMM) with total variation and l1 norm minimization, to reconstruct MPI images. We demonstrate this method on data simulated for a field free line MPI system, and compare its performance against the conventional Algebraic Reconstruction Technique. The ADMM improves image quality as indicated by a higher structural similarity, for low signal-to-noise ratio datasets, and it significantly reduces computation time. © 2017 IEEE.
Open Access
Signal processing problems and algorithms in display side of 3DTV
(IEEE, 2006-10) Ulusoy, E.; Esmer, Gökhan Bora; Özaktaş, Haldun M.; Onural, Levent; Gotchev, A.; Uzunov, V.
Two important signal processing problems in the display side of a holographic 3DTV are the computation of the diffraction field of a 3D object from its abstract representation, and determination of the best display configuration to synthesize some intended light distribution. To solve the former problem, we worked on the computation of ID diffraction patterns from discrete data distributed over 2D space. The problem is solved using matrix pseudo-inversion which dominates the computational complexity. Then, the light field synthesis problem by a deflectable mirror array device (DMAD) is posed as a constrained linear optimization problem. The formulation makes direct application of common optimization algorithms quite easy. The simulations indicate that developed methods are promising. ©2006 IEEE.
Open Access
A synthesis-based approach to compressive multi-contrast magnetic resonance imaging
(IEEE, 2017) Güngör, A.; Kopanoğlu, E.; Çukur, Tolga; Güven, H. E.
In this study, we deal with the problem of image reconstruction from compressive measurements of multi-contrast magnetic resonance imaging (MRI). We propose a synthesis based approach for image reconstruction to better exploit mutual information across contrasts, while retaining individual features of each contrast image. For fast recovery, we propose an augmented Lagrangian based algorithm, using Alternating Direction Method of Multipliers (ADMM). We then compare the proposed algorithm to the state-of-the-art Compressive Sensing-MRI algorithms, and show that the proposed method results in better quality images in shorter computation time.
Open Access
Trajectory control of a quadrotor using a control allocation approach
(IEEE, 2017) Zaki, H.; Ünel, M.; Yıldız, Yıldıray
A quadrotor is an underactuated unmanned aerial vehicle with four inputs to control the dynamics. Trajectory control of a quadrotor is a challenging task and usually tackled in a hierarchical framework where desired/reference attitude angles are analytically determined from the desired command signals, i.e. virtual controls, that control the positional dynamics of the quadrotor and the desired yaw angle is set to some constant value. Although this method is relatively straightforward, it may produce large and nonsmooth reference angles which must be saturated and low-pass filtered. In this work, we show that the determination of desired attitude angles from virtual controls can be viewed as a control allocation problem and it can be solved numerically using nonlinear optimization where certain magnitude and rate constraints can be imposed on the desired attitude angles and the yaw angle need not be constant. Simulation results for both analytical and numerical methods have been presented and compared. Results for constrained optimization show that the flight performance is quite satisfactory.