scholarly journals A New Generalized Projection and Its Application to Acceleration of Audio Declipping

Axioms ◽  
2019 ◽  
Vol 8 (3) ◽  
pp. 105
Author(s):  
Pavel Rajmic ◽  
Pavel Záviška ◽  
Vítězslav Veselý ◽  
Ondřej Mokrý
Keyword(s):  
Signal Processing ◽  
Convex Optimization ◽  
Linear Operator ◽  
Explicit Formula ◽  
Convex Sets ◽  
State Of The Art ◽  
Computational Cost ◽  
The Other ◽  
Speed Up ◽  
Low Computational Cost

In convex optimization, it is often inevitable to work with projectors onto convex sets composed with a linear operator. Such a need arises from both the theory and applications, with signal processing being a prominent and broad field where convex optimization has been used recently. In this article, a novel projector is presented, which generalizes previous results in that it admits to work with a broader family of linear transforms when compared with the state of the art but, on the other hand, it is limited to box-type convex sets in the transformed domain. The new projector is described by an explicit formula, which makes it simple to implement and requires a low computational cost. The projector is interpreted within the framework of the so-called proximal splitting theory. The convenience of the new projector is demonstrated on an example from signal processing, where it was possible to speed up the convergence of a signal declipping algorithm by a factor of more than two.

scholarly journals New Generalized Projection Speeds Up Audio Declipping

2019 ◽  
Author(s):  
Pavel Rajmic ◽  
Pavel Záviška ◽  
Vítězslav Veselý ◽  
Ondřej Mokrý
Keyword(s):  
Signal Processing ◽  
Explicit Formula ◽  
Convex Sets ◽  
The Other ◽  
Generalized Projection ◽  
Other Hand ◽  
Linear Transforms ◽  
Speed Up ◽  
Linear Transform

In theory and applications, it is often inevitable to work with projectors onto convex sets, where a linear transform is involved. In this article, a novel projector is presented, which generalizes previous results in that it admits a broader family of linear transforms, but on the other hand it is limited to box-type convex sets in the transformed domain. The new projector has an explicit formula and it can be interpreted within the framework of proximal optimization. The benefit of the new projector is demonstrated on an example from signal processing, where it was possible to speed up the convergence of a signal declipping algorithm by a factor of more than two.

scholarly journals Fast Image Registration for Spacecraft Autonomous Navigation Using Natural Landmarks

2018 ◽  
Vol 2018 ◽  
pp. 1-12
Author(s):  
Yun-Hua Wu ◽  
Lin-Lin Ge ◽  
Feng Wang ◽  
Bing Hua ◽  
Zhi-Ming Chen ◽  
...  
Keyword(s):  
Image Registration ◽  
Autonomous Navigation ◽  
Computational Cost ◽  
Segmentation Algorithm ◽  
Speeded Up Robust Features ◽  
Speed Up ◽  
Random Sample Consensus ◽  
Invariant Properties ◽  
Low Computational Cost ◽  
Ransac Algorithm

In order to satisfy the real-time requirement of spacecraft autonomous navigation using natural landmarks, a novel algorithm called CSA-SURF (chessboard segmentation algorithm and speeded up robust features) is proposed to improve the speed without loss of repeatability performance of image registration progress. It is a combination of chessboard segmentation algorithm and SURF. Here, SURF is used to extract the features from satellite images because of its scale- and rotation-invariant properties and low computational cost. CSA is based on image segmentation technology, aiming to find representative blocks, which will be allocated to different tasks to speed up the image registration progress. To illustrate the advantages of the proposed algorithm, PCA-SURF, which is the combination of principle component analysis and SURF, is also analyzed in this paper for comparison. Furthermore, random sample consensus (RANSAC) algorithm is applied to eliminate the false matches for further accuracy improvement. The simulation results show that the proposed strategy obtains good results, especially in scaling and rotation variation. Besides, CSA-SURF decreased 50% of the time in extraction and 90% of the time in matching without losing the repeatability performance by comparing with SURF algorithm. The proposed method has been demonstrated as an alternative way for image registration of spacecraft autonomous navigation using natural landmarks.

An Evaluation of the Semi-Coupled Scheme for the Analysis of Floating Production Systems

2014 ◽  
Author(s):  
Aldo Roberto Cruces Girón ◽  
Fabrício Nogueira Corrêa ◽  
Breno Pinheiro Jacob
Keyword(s):  
Deep Water ◽  
Production Systems ◽  
Computational Cost ◽  
The Other ◽  
Coupled Analysis ◽  
Computational Costs ◽  
Analysis Techniques ◽  
Early Design Stages ◽  
Accuracy Of Results ◽  
Low Computational Cost

Analysis techniques and numerical formulations are available in a variety for mooring and riser designers. They are applied in the different stages of the design processes of floating production systems (FPS) by taking advantage of both the accuracy of results and the computational costs. In early design stages, the low computational cost is more valued with the aim of obtaining fast results and taking decisions. So in these stages it is common to use uncoupled analysis. On the other hand, in more advanced design stages, the accuracy of results is more valued, for which the use of coupled analysis is adequate. However, it can lead to excessive computing times. To overcome such high computational costs, new formulations have been proposed with the aim of obtaining results similar to a coupled analysis, but with low computational costs. One of these formulations is referred as the semi-coupled scheme (S-C). Its main characteristic is that it combines the advantages of uncoupled and coupled analysis techniques. In this way, analyses can be performed with very fast execution times and results are superior to those obtained by the classical uncoupled analysis. This work presents an evaluation of the S-C scheme. The evaluation is made by comparing their results with the results of coupled analyses. Both type of analysis were applied in a representative deep water platform. The results show that the S-C scheme have the potentially to provide results with appropriate precision with very low computational times. In this way, the S-C scheme represents an attractive procedure to be applied in early and intermediate stages of the design process of FPS.

scholarly journals Risk Measurement by G-Expected Shortfall

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Ziting Pei ◽  
Xuhui Wang ◽  
Xingye Yue
Keyword(s):  
Explicit Formula ◽  
Empirical Analysis ◽  
Risk Measure ◽  
Computational Cost ◽  
Expected Shortfall ◽  
Risk Measurement ◽  
Worst Case ◽  
New Type ◽  
The Empirical Analysis ◽  
Low Computational Cost

G-expected shortfall (G-ES), which is a new type of worst-case expected shortfall (ES), is defined as measuring risk under infinite distributions induced by volatility uncertainty. Compared with extant notions of the worst-case ES, the G-ES can be computed using an explicit formula with low computational cost. We also conduct backtests for the G-ES. The empirical analysis demonstrates that the G-ES is a reliable risk measure.

scholarly journals Circumcentering approximate reflections for solving the convex feasibility problem

2022 ◽  
Vol 2022 (1) ◽  
Author(s):  
G. H. M. Araújo ◽  
R. Arefidamghani ◽  
R. Behling ◽  
Y. Bello-Cruz ◽  
A. Iusem ◽  
...  
Keyword(s):  
Convex Sets ◽  
Classical Method ◽  
Computational Cost ◽  
Linear Convergence ◽  
Feasibility Problem ◽  
Reflection Method ◽  
Convex Feasibility ◽  
Method Of Alternating Projections ◽  
Low Computational Cost ◽  
Approximate Version

AbstractThe circumcentered-reflection method (CRM) has been applied for solving convex feasibility problems. CRM iterates by computing a circumcenter upon a composition of reflections with respect to convex sets. Since reflections are based on exact projections, their computation might be costly. In this regard, we introduce the circumcentered approximate-reflection method (CARM), whose reflections rely on outer-approximate projections. The appeal of CARM is that, in rather general situations, the approximate projections we employ are available under low computational cost. We derive convergence of CARM and linear convergence under an error bound condition. We also present successful theoretical and numerical comparisons of CARM to the original CRM, to the classical method of alternating projections (MAP), and to a correspondent outer-approximate version of MAP, referred to as MAAP. Along with our results and numerical experiments, we present a couple of illustrative examples.

Numerical Simulation of AA2024-T3 Friction Stir Welding (FSW) Process: Sensitivity Analyses and Influence of Decisions during Modelling Stages

2015 ◽  
Vol 651-653 ◽  
pp. 919-924 ◽  
Author(s):  
Rui M.F. Paulo ◽  
Pierpaolo Carlone ◽  
Robertt A.F. Valente ◽  
Filipe Teixeira-Dias ◽  
Gaetano S. Palazzo
Keyword(s):  
Friction Stir Welding ◽  
Good Accuracy ◽  
Computational Cost ◽  
The Other ◽  
Sensitivity Analyses ◽  
Element Analysis ◽  
Shell Elements ◽  
Friction Stir ◽  
The One ◽  
Low Computational Cost

The main objective of the present work is to assess the influence of several parameters relevant for Finite Element Analysis (FEA) in modelling Friction Stir Welding (FSW) processes on AA2024-T3 plates. Several tests were performed including variations on the type of shell elements, number of integration points across thickness direction and mesh refinement levels, aiming for good accuracy and low computational cost. On the one hand, several setups of the mechanical boundary conditions, modelling the clamping systems, were also tested, leading to the conclusion that the results, in terms of longitudinal residual stresses, are significantly affected by this factor. On the other hand, variations on the heat input distribution showed a reduced effect, or almost null, on the final results.

scholarly journals Speeding up Very Fast Decision Tree with Low Computational Cost

2020 ◽  
Author(s):  
Jian Sun ◽  
Hongyu Jia ◽  
Bo Hu ◽  
Xiao Huang ◽  
Hao Zhang ◽  
...  
Keyword(s):  
Decision Tree ◽  
Classification Accuracy ◽  
State Of The Art ◽  
Computational Cost ◽  
Incremental Algorithm ◽  
Whole Process ◽  
High Classification Accuracy ◽  
Very Fast Decision Tree ◽  
Low Computational Cost ◽  
Fast Decision

Very Fast Decision Tree (VFDT) is one of the most widely used online decision tree induction algorithms, and it provides high classification accuracy with theoretical guarantees. In VFDT, the split-attempt operation is essential for leaf-split. It is computation-intensive since it computes the heuristic measure of all attributes of a leaf. To reduce split-attempts, VFDT tries to split at constant intervals (for example, every 200 examples). However, this mechanism introduces split-delay for split can only happen at fixed intervals, which slows down the growth of VFDT and finally lowers accuracy. To address this problem, we first devise an online incremental algorithm that computes the heuristic measure of an attribute with a much lower computational cost. Then a subset of attributes is carefully selected to find a potential split timing using this algorithm. A split-attempt will be carried out once the timing is verified. By the whole process, computational cost and split-delay are lowered significantly. Comprehensive experiments are conducted using multiple synthetic and real datasets. Compared with state-of-the-art algorithms, our method reduces split-attempts by about 5 to 10 times on average with much lower split-delay, which makes our algorithm run faster and more accurate.

scholarly journals Designing Compact Convolutional Filters for Lightweight Human Pose Estimation

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Shili Niu ◽  
Weihua Ou ◽  
Shihua Feng ◽  
Jianping Gou ◽  
Fei Long ◽  
...  
Keyword(s):  
Pose Estimation ◽  
State Of The Art ◽  
Computational Cost ◽  
Estimation Accuracy ◽  
Human Pose Estimation ◽  
Model Parameters ◽  
Resource Limited ◽  
Benchmark Datasets ◽  
Human Pose ◽  
Low Computational Cost

Existing methods for human pose estimation usually use a large intermediate tensor, leading to a high computational load, which is detrimental to resource-limited devices. To solve this problem, we propose a low computational cost pose estimation network, MobilePoseNet, which includes encoder, decoder, and parallel nonmaximum suppression operation. Specifically, we design a lightweight upsampling block instead of transposing the convolution as the decoder and use the lightweight network as our downsampling part. Then, we choose the high-resolution features as the input for upsampling to reduce the number of model parameters. Finally, we propose a parallel OKS-NMS, which significantly outperforms the conventional NMS in terms of accuracy and speed. Experimental results on the benchmark datasets show that MobilePoseNet obtains almost comparable results to state-of-the-art methods with a low compilation load. Compared to SimpleBaseline, the parameter of MobilePoseNet is only 4%, while the estimation accuracy reaches 98%.

Aerodynamic prediction of a projectile fitted with fins

2018 ◽  
Vol 28 (5) ◽  
pp. 1218-1236 ◽  
Author(s):  
Cédric Decrocq ◽  
Bastien Martinez ◽  
Marie Albisser ◽  
Simona Dobre ◽  
Patrick Gnemmi ◽  
...  
Keyword(s):  
Long Range ◽  
Computational Cost ◽  
Preliminary Design ◽  
Cfd Simulations ◽  
Content Type ◽  
Speed Up ◽  
Computational Fluid Dynamics Cfd ◽  
Lifting Surfaces ◽  
Time Accuracy ◽  
Low Computational Cost

Purpose The present paper deals with weapon aerodynamics and aims to describe preliminary studies that were conducted for developing the next generation of long-range guided ammunition. Over history, ballistic research scientists were constantly investigating new artillery systems capable of overcoming limitations of range, accuracy and manoeuvrability. While futuristic technologies are increasingly under development, numerous issues concerning current powdered systems still need to be addressed. In this context, the present work deals with the design and the optimization of a new concept of long-range projectile with regard to multidisciplinary fields, including flight scenario, steering strategy, mechanical actuators or size of payload. Design/methodology/approach Investigations are conducted for configurations that combine existing full calibre 155 mm guided artillery shell with a set of lifting surfaces. As the capability of the ammunition highly depends on lifting surfaces in terms of number, shape or position, a parametric study has to be conducted for determining the best aerodynamic architecture. To speed-up this process, initial estimations are conducted thanks to low computational cost methods suitable for preliminary design requirements, in terms of time, accuracy and flexibility. The WASP code (Wing-Aerodynamic-eStimation-for-Projectiles) has been developed for rapidly predicting aerodynamic coefficients (static and dynamic) of a set of lifting surfaces fitted on a projectile fuselage, as a function of geometry and flight conditions, up to transonic velocities. Findings In the present study, WASP predictions at Mach 0.7 of both normal force and pitching moment coefficients are assessed for two configurations. Originality/value Analysis is conducted by gathering results from WASP, computational-fluid-dynamics (CFD) simulations, wind-tunnel experiments and free-flight tests. Obtained results demonstrate the ability of WASP code to be used for preliminary design steps.

scholarly journals A Layer Decomposition-Recomposition Framework for Neuron Pruning towards Accurate Lightweight Networks

2019 ◽  
Vol 33 ◽  
pp. 3355-3362 ◽  
Author(s):  
Weijie Chen ◽  
Yuan Zhang ◽  
Di Xie ◽  
Shiliang Pu
Keyword(s):  
State Of The Art ◽  
Computational Cost ◽  
Fine Tuning ◽  
Layer By Layer ◽  
Storage Overhead ◽  
Speed Up ◽  
Output Neurons ◽  
End To End ◽  
Output Information ◽  
And Storage

Neuron pruning is an efficient method to compress the network into a slimmer one for reducing the computational cost and storage overhead. Most of state-of-the-art results are obtained in a layer-by-layer optimization mode. It discards the unimportant input neurons and uses the survived ones to reconstruct the output neurons approaching to the original ones in a layer-by-layer manner. However, an unnoticed problem arises that the information loss is accumulated as layer increases since the survived neurons still do not encode the entire information as before. A better alternative is to propagate the entire useful information to reconstruct the pruned layer instead of directly discarding the less important neurons. To this end, we propose a novel Layer DecompositionRecomposition Framework (LDRF) for neuron pruning, by which each layer’s output information is recovered in an embedding space and then propagated to reconstruct the following pruned layers with useful information preserved. We mainly conduct our experiments on ILSVRC-12 benchmark with VGG-16 and ResNet-50. What should be emphasized is that our results before end-to-end fine-tuning are significantly superior owing to the information-preserving property of our proposed framework. With end-to-end fine-tuning, we achieve state-of-the-art results of 5.13× and 3× speed-up with only 0.5% and 0.65% top-5 accuracy drop respectively, which outperform the existing neuron pruning methods.

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