Pin Assignment Optimization for Large-Scale High-Pin-Count BGA Packages Using Simulated Annealing

Li, Zhuoyue; Zhang, Mu-Shui; Long, Yunliang

doi:10.1109/tcpmt.2016.2605695

Cited by 9 publications

(3 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…• Continuous Space Optimization(CSO): The algorithm of continuous space cooperative localization followed by projection into the feasible solution space, as described in Section III. • Simulated Annealing (SA) [31]: A probabilistic approach often used for optimization in a large discrete space(e.g., assignment problem) [32], [33]. SA mimics annealing processes in metallurgy, which is a technique involving heating and controlled cooling of a material.…”

Section: Experiments Resultsmentioning

confidence: 99%

Three-dimensional Cooperative Localization of Commercial-Off-The-Shelf Sensors

Wang¹,

Li²,

Ni³

et al. 2021

Preprint

View full text Add to dashboard Cite

Many location-based services use Received Signal Strength (RSS) measurements due to their universal availability. In this paper, we study the association of a large number of low-cost Internet-of-Things (IoT) sensors and their possible installation locations, which can enable various sensing and automation-related applications. We propose an efficient approach to solve the corresponding permutation combinatorial optimization problem, which integrates continuous space cooperative localization and permutation space likelihood ascent search. A convex relaxation-based optimization is designed to estimate the coarse locations of blindfolded devices in continuous 3D spaces, which are then projected to the feasible permutation space. An efficient Cramér-Rao Lower Bound based likelihood ascent search algorithm is proposed to refine the solution. Extensive experiments were conducted to evaluate the performance of the proposed approach, which show that the proposed approach significantly outperforms state-ofthe-art combinatorial optimization algorithms and achieves close-to-100% accuracy with affordable execution time.

show abstract

Section: Experiments Resultsmentioning

confidence: 99%

Three-dimensional Cooperative Localization of Commercial-Off-The-Shelf Sensors

Wang¹,

Li²,

Ni³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…initially applied to the solution of combinatorial problems. Other reported applications of SA, besides combinatorial problems, include pin assignment optimization [20], image segmentation [21] and image reconstruction in Electrical Impedance tomography [22]. The compressed samples locations determination problem is indeed a combinatorial one as M locations out of N (the number of samples in a frame) must be found according to a suitable MSE criterion.…”

Section: The Simulated Annealing Algorithmmentioning

confidence: 99%

“…The compressed samples locations determination problem is indeed a combinatorial one as M locations out of N (the number of samples in a frame) must be found according to a suitable MSE criterion. In [20][21][22] Simulated Annealing optimization is applied only once. In the present applications, however, SA shall be applied for each signal segment.…”

Section: The Simulated Annealing Algorithmmentioning

confidence: 99%

A Novel Approach for Supporting Approximate Representation of Linear Prediction Residuals in Pattern Recognition Tools

Cuzzocrea

Mumolo

2017

Computational Science and Its Applications – ICCSA 2017

View full text Add to dashboard Cite

The goal of this work is to describe an optimization approach for selecting a reduced number of samples of the linear prediction residual. Sample determination is a combinatorial problem. Our approach addresses the combinatorial problem with simulated annealing based optimization. We show that better results than that obtained by a standard approximation approach, namely the multipulse algorithm, are obtained with our approach. Multipulse selects pulse locations by a sequential, sub-optimal, algorithm and computes the pulses amplitudes according to an optimization criteria. Our approach finds the optimal residual samples by means of an optimization algorithm approach without amplitudes optimization. The compressed residual is fed to an all-pole model of speech obtaining better results than standard Multipulse modeling. We believe that this algorithm could be used as an alternative to other algorithms for medium-rate coding of speech in low complexity embedded devices. In this paper we also discuss performance and complexity issues of the described algorithm.

show abstract