scholarly journals Fast Feature-Preserving Approach to Carpal Bone Surface Denoising

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2379
Author(s):  
Ibrahim Salim ◽  
A. Hamza
Keyword(s):  
Objective Function ◽  
Linear Equations ◽  
Noise Model ◽  
Carpal Bone ◽  
Geometric Framework ◽  
Sparse System ◽  
Adaptive Kernel ◽  
Feature Preserving ◽  
Data Adaptive ◽  
Matrix Balancing

We present a geometric framework for surface denoising using graph signal processing, which is an emerging field that aims to develop new tools for processing and analyzing graph-structured data. The proposed approach is formulated as a constrained optimization problem whose objective function consists of a fidelity term specified by a noise model and a regularization term associated with prior data. Both terms are weighted by a normalized mesh Laplacian, which is defined in terms of a data-adaptive kernel similarity matrix in conjunction with matrix balancing. Minimizing the objective function reduces it to iteratively solve a sparse system of linear equations via the conjugate gradient method. Extensive experiments on noisy carpal bone surfaces demonstrate the effectiveness of our approach in comparison with existing methods. We perform both qualitative and quantitative comparisons using various evaluation metrics.

scholarly journals Function Estimation Using Data-Adaptive Kernel Smoothers—How Much Smoothing?

1994 ◽  
Vol 8 (4) ◽  
pp. 402 ◽  
Author(s):  
K. S. Riedel ◽  
A. Sidorenko ◽  
James R. Matey
Keyword(s):  
Function Estimation ◽  
Adaptive Kernel ◽  
Data Adaptive ◽  
Using Data ◽  
Kernel Smoothers

MULTIDIMENSIONAL SCALING WITH CITY‐BLOCK DISTANCES BASED ON COMBINATORIAL OPTIMIZATION AND SYSTEMS OF LINEAR EQUATIONS

2009 ◽  
Vol 14 (2) ◽  
pp. 259-270 ◽  
Author(s):  
Julius Žilinskas
Keyword(s):  
Combinatorial Optimization ◽  
Multidimensional Scaling ◽  
Objective Function ◽  
Linear Equations ◽  
Exploratory Analysis ◽  
Multidimensional Data ◽  
City Block ◽  
Multimodal Function ◽  
Systems Of Linear Equations

Multidimensional scaling is a technique for exploratory analysis of multidimensional data. The essential part of the technique is minimization of a multimodal function with unfavorable properties like invariants and non‐differentiability. In this paper a two‐level optimization based on combinatorial optimization and systems of linear equations is proposed exploiting piecewise quadratic structure of the objective function with city‐block distances. The approach is tested experimentally and improvement directions are identified.

ANALISIS OPTIMALISASI PENJUALAN FURNITURE MELALUI LINIER PROGAMMING DENGAN METODE SIMPLEKS PADA PT CAHAYA SAKTI MULTI INTRACO BOGOR

2019 ◽  
Vol 8 (02) ◽  
pp. 81
Author(s):  
Syarief Gerald Prasetya
Keyword(s):  
Linear Programming ◽  
Objective Function ◽  
Linear Function ◽  
Linear Equations ◽  
Maximum Capacity ◽  
Trading Company ◽  
The Core ◽  
Maximum Value ◽  
Small Branch ◽  
Total Sales

PT. Cahaya Sakti Multi Intraco is a trading company engaged in the procurement of furniture has a variety of types and a variety of brands. In the course of his business face several obstacles in planning the allocation handling products. From one brand alone was encountered problems in the combination of the products offered. Olympic brand is the core product to penetrate the domestic furniture market. Fluctuations in demand for goods erratic from one period to another period resulted in a shortage or oversupplyProducts PT Cahaya Sakti Multi Intraco ie Kitchen Set ( KS ), Cabinet Decorative ( LH ), Wardrobe ( LP ), Desk Study ( MB ) and other furniture products ( OT ). There are several obstacles in the selling of each of these products are: 1 ). The type of vehicle ( truck ) is used to fit furniture consisting of a capacity of 5,500 kgs ( Ants ) , 10,500 kgs ( Elephant ) , 13,500 kgs ( Container 20 ' feet ) , 15,000 kgs ( Double ankle ) , and 27,000 kgs ( Container 40 " feet / Tronton ) . 2 ) Maximum capacity warehouse Branch consisting of : A maximum value of 700 million ( Small Branch ) . 1,000 million ( Branch Intermediate ) , 1.600 million ( Big Branch ) . 3 ). Request for furniture products : KS as much as 4,200 units , as many as 2,600 units LH , LP as many as 11,500 units , as many as 5,200 units of MB and OT . as many as 7,000 units . Discussion of the results obtained by a linear function which aims to maximize sales ie sales = 363 X1 + 830 X2 + X3 604 + 347 + 366 X4 and X5 linear equations of the constraints that exist , namely : Freight truck type vehicle Ants = 23X1 + 8x2 + 24X3 + 22X4 +50 x5 ≤ 5,500,000 grams , Elephant = 44X1 + 15X2 + 46X3 + 43X4 +95 x5 ≤ 10,500,000 grams , Container 20 " = 56X1 + 19X2 + 59X3 + 55X4 +122 x5 ≤ 13,500,000 grams , Double ankle = 63X1 + 21X2 + 66X3 + 61X4 +135 x5 ≤ 15,000,000 grams , Container 40 " = 113X1 + 38X2 + 119X3 + 110X4 +244 x5 ≤ 27,000,000 grams and warehouse capacity Capacity Big Branch = 617X1 + 173X2 + 954X3 + 784X4 +1.0499 X5 ≤ 1,600,000,000 smu , Branch Intermediate = 386X1 + 108X2 + 596X3 + 490X4 +656 x5 ≤ 1,000,000,000 smu , and Small Branch = 270X1 + 76X2 + 417X3 + 343X4 +459 700 000 000 X5 ≤ smu , furniture Request KS ( X1 ) = ≤ 4,200 units , LH ( X2 ) = ≤ 2,600 units , LP ( X3 ) = ≤ 11,500 units , MB ( X4 ) = ≤ 5.200 , and OT ( X5 ) = ≤ 7,000 units units Total sales of Olympic brand furniture acquired PT Cahaya Sakti Multi Intraco per month is Rp . Rp 14,995,000.00 or a year . 179,940,000.00 assuming the acquisition of sales in accordance with the objective function and constraint functions remain.Keywords : optimization , sales , linear programming 

scholarly journals Multi-targets localization based on ARMA and GA in WSNs

2018 ◽  
Vol 189 ◽  
pp. 04015
Author(s):  
Heng Zhang ◽  
Zhongming Pan
Keyword(s):  
Objective Function ◽  
Moving Average ◽  
Linear Equations ◽  
Optimal Solution ◽  
Arma Model ◽  
Target Localization ◽  
Autoregressive Moving Average ◽  
Arma Process ◽  
Popular Subject ◽  
Noise Sequence

Multi-target localization methods for locating of the movingtarget in interested area monitored by Wireless Sensor Networks (WSNs) are nowadays a popular subject of study. The methods can be classified into two categories: range-free algorithm and range-based algorithm. In this work, we propose a novel multi-target localization method, which belongs to the category of range-based algorithm, by using a genetic algorithm (GA) for searching optimal solution of the objective function of multi-target localization. The objective function is only a group of linear equations with independent variables of acoustic energies calculated at each sensor-node in a WSN. However, application of the method, the accuracy of multi-target localization is sensitive to the SNR of the measured sound signals at each node, thus a denoising strategy should be inserted into the method. It turned out that the measured sound noise, comparing intrinsic sensor noise and environmental noise, may be considered as an Autoregressive Moving Average (ARMA) process. Thus, by building the ARMA model, the noise sequence commingled with the target signals can be predicted. As a consequence, the power of the noises can be subtracted from the measured sound signals for revealing the target signal's power. The results in present work demonstrate the advantage of the proposed method.

scholarly journals Efficient LED-SAC Sparse Estimator Using Fast Sequential Adaptive Coordinate-Wise Optimization (LED-2SAC)

2014 ◽  
Vol 2014 ◽  
pp. 1-8
Author(s):  
T. Yousefi Rezaii ◽  
S. Beheshti ◽  
M. A. Tinati
Keyword(s):  
Objective Function ◽  
Linear Equations ◽  
Optimization Criterion ◽  
Reconstruction Method ◽  
Solution Path ◽  
System Of Linear Equations ◽  
Processing Application ◽  
Underdetermined System ◽  
Sequential Solution ◽  
Better Than

Solving the underdetermined system of linear equations is of great interest in signal processing application, particularly when the underlying signal to be estimated is sparse. Recently, a new sparsity encouraging penalty function is introduced as Linearized Exponentially Decaying penalty, LED, which results in the sparsest solution for an underdetermined system of equations subject to the minimization of the least squares loss function. A sequential solution is available for LED-based objective function, which is denoted by LED-SAC algorithm. This solution, which aims to sequentially solve the LED-based objective function, ignores the sparsity of the solution. In this paper, we present a new sparse solution. The new method benefits from the sparsity of the signal both in the optimization criterion (LED) and its solution path, denoted by Sparse SAC (2SAC). The new reconstruction method denoted by LED-2SAC (LED-Sparse SAC) is consequently more efficient and considerably fast compared to the LED-SAC algorithm, in terms of adaptability and convergence rate. In addition, the computational complexity of both LED-SAC and LED-2SAC is shown to be of order𝒪d2, which is better than the other batch solutions like LARS. LARS algorithm has complexity of order𝒪d3+nd2, wheredis the dimension of the sparse signal andnis the number of observations.

scholarly journals Laser Welding Quality Monitoring via Graph Support Vector Machine With Data Adaptive Kernel

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 93108-93122 ◽  
Author(s):  
Sergey A. Shevchik ◽  
Tri Le-Quang ◽  
Farzad Vakili Farahani ◽  
Neige Faivre ◽  
Bastian Meylan ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Laser Welding ◽  
Quality Monitoring ◽  
Support Vector ◽  
Welding Quality ◽  
Adaptive Kernel ◽  
Data Adaptive
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