scholarly journals Optimal Power Constrained Distributed Detection over a Noisy Multiaccess Channel

2015 ◽  
Vol 2015 ◽  
pp. 1-7
Author(s):  
Zhiwen Hu ◽  
Zhenhua Xu
Keyword(s):  
Signal To Noise Ratio ◽  
Closed Form Solution ◽  
Form Solution ◽  
Detection Performance ◽  
Distributed Detection ◽  
New Method ◽  
Fusion System ◽  
Robust Detection ◽  
Optimal Power ◽  
Multiaccess Channel

The problem of optimal power constrained distributed detection over a noisy multiaccess channel (MAC) is addressed. Under local power constraints, we define the transformation function for sensor to realize the mapping from local decision to transmitted waveform. The deflection coefficient maximization (DCM) is used to optimize the performance of power constrained fusion system. Using optimality conditions, we derive the closed-form solution to the considered problem. Monte Carlo simulations are carried out to evaluate the performance of the proposed new method. Simulation results show that the proposed method could significantly improve the detection performance of the fusion system with low signal-to-noise ratio (SNR). We also show that the proposed new method has a robust detection performance for broad SNR region.

Coherent Distributed Detection Using Inhomogeneous Sensors Under Local Power Constraint

2015 ◽  
Vol 743 ◽  
pp. 792-795
Author(s):  
Z.H. Xu ◽  
J.Y. Li ◽  
J. Wang
Keyword(s):  
Signal To Noise Ratio ◽  
Closed Form Solution ◽  
Form Solution ◽  
Distributed Detection ◽  
Fusion System ◽  
Local Power ◽  
Power Constraint ◽  
Deflection Coefficient ◽  
Access Channel ◽  
Multi Access

In this paper, we consider the problem of local power constrained coherent distributed detection over fading multi-access channel (MAC). The deflection coefficient maximization (DCM) is used to optimize the performance of fusion system under local power constraint of sensors. We use the statistical information of channel gain to obtain the deflection coefficient of the detection statistics at the fusion center. We derive the closed-form solution to the considered optimization problem. Monte-Carlo simulations are carried out to verify the performance of the proposed method. Simulation results show that the proposed method could significantly improve the detection performance of the fusion system under local power constraint and low signal-to-noise ratio (SNR).

scholarly journals Deep learning for a space-variant deconvolution in galaxy surveys

2020 ◽  
Vol 641 ◽  
pp. A67
Author(s):  
F. Sureau ◽  
A. Lechat ◽  
J.-L. Starck
Keyword(s):  
Deep Learning ◽  
Network Architecture ◽  
Signal To Noise Ratio ◽  
Closed Form Solution ◽  
Computation Time ◽  
Form Solution ◽  
High Signal ◽  
Galaxy Surveys ◽  
Point Spread ◽  
Space Variant

The deconvolution of large survey images with millions of galaxies requires developing a new generation of methods that can take a space-variant point spread function into account. These methods have also to be accurate and fast. We investigate how deep learning might be used to perform this task. We employed a U-net deep neural network architecture to learn parameters that were adapted for galaxy image processing in a supervised setting and studied two deconvolution strategies. The first approach is a post-processing of a mere Tikhonov deconvolution with closed-form solution, and the second approach is an iterative deconvolution framework based on the alternating direction method of multipliers (ADMM). Our numerical results based on GREAT3 simulations with realistic galaxy images and point spread functions show that our two approaches outperform standard techniques that are based on convex optimization, whether assessed in galaxy image reconstruction or shape recovery. The approach based on a Tikhonov deconvolution leads to the most accurate results, except for ellipticity errors at high signal-to-noise ratio. The ADMM approach performs slightly better in this case. Considering that the Tikhonov approach is also more computation-time efficient in processing a large number of galaxies, we recommend this approach in this scenario.

EEG SOURCE LOCALIZATION: A COMPARATIVE STUDY OF CLASSICAL AND NEURAL NETWORK METHODS

2001 ◽  
Vol 11 (04) ◽  
pp. 349-359 ◽  
Author(s):  
UDANTHA R. ABEYRATNE ◽  
G. ZHANG ◽  
P. SARATCHANDRAN
Keyword(s):  
Neural Network ◽  
Inverse Problem ◽  
Signal To Noise Ratio ◽  
Closed Form Solution ◽  
Form Solution ◽  
Hybrid Technique ◽  
Eeg Signals ◽  
Rbf Network ◽  
Network Methods ◽  
Lm Algorithm

We address the problem of estimating biopotential sources within the brain, based on EEG signals observed on the scalp. This problem, known as the inverse problem of electrophysiology, has no closed-form solution, and requires iterative techniques such as the Levenberg – Marquardt (LM) algorithm. Considering the nonlinear nature of the inverse problem, and the low signal to noise ratio inherent in EEG signals, a backpropagation neural network (BPN) has been recently proposed as a solution. The technique has not been properly compared with classical techniques such as the LM method, or with more recent neural network techniques such as the Radial Basis Function (RBF) network. In this paper, we provide improved strategies based on BPN and consider RBF networks in solving the inverse problem. We compare the performances of BPN, RBF and a hybrid technique with that of the classical LM method.

A NEW METHOD FOR OBTAINING THE AUTOCOVARIANCE OF AN ARMA MODEL: AN EXACT FORM SOLUTION

1998 ◽  
Vol 14 (5) ◽  
pp. 622-640 ◽  
Author(s):  
M. Karanasos
Keyword(s):  
Moving Average ◽  
Closed Form Solution ◽  
Arma Model ◽  
Form Solution ◽  
New Method ◽  
Autoregressive Moving Average ◽  
Average Model ◽  
Autoregressive Moving Average Model ◽  
Moving Average Model ◽  
Model Redundancy

In this article we present a new method for computing the theoretical autocovariance function of an autoregressive moving average model. The importance of our theorem is that it yields two interesting results: First, a closed-form solution is derived in terms of the roots of the autoregressive polynomial and the parameters of the moving average part. Second, a sufficient condition for the lack of model redundancy is obtained.

scholarly journals Generalized analog thresholding for spike acquisition at ultralow sampling rates

2015 ◽  
Vol 114 (1) ◽  
pp. 746-760 ◽  
Author(s):  
Bryan D. He ◽  
Alex Wein ◽  
Lav R. Varshney ◽  
Julius Kusuma ◽  
Andrew G. Richardson ◽  
...  
Keyword(s):  
Compressive Sensing ◽  
Large Scale ◽  
Closed Form Solution ◽  
Form Solution ◽  
New Method ◽  
Multielectrode Arrays ◽  
Base Method ◽  
Order Of Magnitude ◽  
Signal Processing Methods ◽  
Spike Waveforms

Efficient spike acquisition techniques are needed to bridge the divide from creating large multielectrode arrays (MEA) to achieving whole-cortex electrophysiology. In this paper, we introduce generalized analog thresholding (gAT), which achieves millisecond temporal resolution with sampling rates as low as 10 Hz. Consider the torrent of data from a single 1,000-channel MEA, which would generate more than 3 GB/min using standard 30-kHz Nyquist sampling. Recent neural signal processing methods based on compressive sensing still require Nyquist sampling as a first step and use iterative methods to reconstruct spikes. Analog thresholding (AT) remains the best existing alternative, where spike waveforms are passed through an analog comparator and sampled at 1 kHz, with instant spike reconstruction. By generalizing AT, the new method reduces sampling rates another order of magnitude, detects more than one spike per interval, and reconstructs spike width. Unlike compressive sensing, the new method reveals a simple closed-form solution to achieve instant (noniterative) spike reconstruction. The base method is already robust to hardware nonidealities, including realistic quantization error and integration noise. Because it achieves these considerable specifications using hardware-friendly components like integrators and comparators, generalized AT could translate large-scale MEAs into implantable devices for scientific investigation and medical technology.

scholarly journals Network Enhancement: a general method to denoise weighted biological networks

2018 ◽  
Author(s):  
Bo Wang ◽  
Armin Pourshafeie ◽  
Marinka Zitnik ◽  
Junjie Zhu ◽  
Carlos D. Bustamante ◽  
...  
Keyword(s):  
Biological Networks ◽  
Signal To Noise Ratio ◽  
Closed Form Solution ◽  
Form Solution ◽  
Fine Grained ◽  
Contact Maps ◽  
Novel Method ◽  
Alternative Approaches ◽  
Downstream Analysis ◽  
General Method

Networks are ubiquitous in biology where they encode connectivity patterns at all scales of organization, from molecular to the biome. However, biological networks are noisy due to the limitations of technology used to generate them as well as inherent variation within samples. The presence of high levels of noise can hamper discovery of patterns and dynamics encapsulated by these networks. Here we propose Network Enhancement (NE), a novel method for improving the signal-to-noise ratio of undirected, weighted networks, and thereby improving the performance of downstream analysis. NE applies a novel operator that induces sparsity and leverages higher-order network structures to remove weak edges and enhance real connections. This iterative approach has a closed-form solution at convergence with desirable performance properties. We demonstrate the effectiveness of NE in denoising biological networks for several challenging yet important problems. Our experiments show that NE improves gene function prediction by denoising interaction networks from 22 human tissues. Further, we use NE to interpret noisy Hi-C contact maps from the human genome and demonstrate its utility across varying degrees of data quality. Finally, when applied to fine-grained species identification, NE outperforms alternative approaches by a significant margin. Taken together, our results indicate that NE is widely applicable for denoising weighted biological networks, especially when they contain high levels of noise.

scholarly journals An Adiabatic Approximation to the Tissue Homogeneity Model for Water Exchange in the Brain: I. Theoretical Derivation

1998 ◽  
Vol 18 (12) ◽  
pp. 1365-1377 ◽  
Author(s):  
Keith S. St. Lawrence ◽  
Ting-Yim Lee
Keyword(s):  
Adiabatic Approximation ◽  
Signal To Noise Ratio ◽  
Closed Form Solution ◽  
Form Solution ◽  
Model Parameters ◽  
Theoretical Derivation ◽  
Extraction Fraction ◽  
Tissue Changes ◽  
Clearance Rate Constant ◽  
The Brain

Using the adiabatic approximation, which assumes that the tracer concentration in parenchymal tissue changes slowly relative to that in capillaries, we derived a time-domain, closed-form solution of the tissue homogeneity model. This solution, which is called the adiabatic solution, is similar in form to those of two-compartment models, Owing to its simplicity, the adiabatic solution can be used in CBF experiments in which kinetic data with only limited time resolution or signal-to-noise ratio, or both, are obtained. Using computer simulations, we investigated the accuracy and the precision of the parameters in the adiabatic solution for values that reflect 2H-labeled water (D2O) clearance from the brain (see Part II). It was determined that of the three model parameters, (1) the vascular volume ( Vi), (2) the product of extraction fraction and blood flow ( EF), and (3) the clearance rate constant ( kadb), only the last one could be determined accurately, and therefore CBF must be determined from this parameter only. From the error analysis of the adiabatic solution, it was concluded that for the D2O clearance experiments described in Part II, the coefficient of variation of CBF was approximately 7% in gray matter and 22% in white matter.

scholarly journals Generalized Block-Diagonalization Schemes for MIMO Relay Broadcasting Systems

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Xianan Wang ◽  
Xiaoxiang Wang ◽  
Wenrong Gong ◽  
Zijia Huang
Keyword(s):  
Mean Squared Error ◽  
Multiple Input Multiple Output ◽  
Closed Form Solution ◽  
Base Station ◽  
Form Solution ◽  
Block Diagonalization ◽  
Mimo Relay ◽  
Minimum Mean Squared Error ◽  
Optimal Power ◽  
Power Loading

We propose two generalized block-diagonalization (BD) schemes for multiple-input multiple-output (MIMO) relay broadcasting systems with no channel state information (CSI) at base station. We first introduce a generalized zero forcing (ZF) scheme that reduces the complexity of the traditional BD scheme. Then the optimal power loading matrix for the proposed scheme is analyzed and the closed-form solution is derived. Furthermore, an enhanced scheme is proposed by employing the minimum-mean-squared-error (MMSE) criterion. Simulation results show that the proposed generalized MMSE scheme outperforms the other schemes and the optimal power loading scheme improves the sum-rate performance efficiently.

Closed-Form Solution of Inverse Kinematics for a Minimally Invasive Surgical Robot Slave Manipulator Similar to Da Vinci Robot System

2020 ◽  
Vol 3 (2) ◽  
Author(s):  
Wenjie Wang ◽  
Yuting Cao ◽  
Xiaohua Wang ◽  
Lingtao Yu
Keyword(s):  
Minimally Invasive ◽  
Closed Form ◽  
Inverse Kinematics ◽  
Robot Control ◽  
Da Vinci ◽  
Closed Form Solution ◽  
Form Solution ◽  
Surgical Robot ◽  
New Method ◽  
Minimally Invasive Surgical

Abstract Closed-form solution inverse kinematics has a unique advantage in robot control; it is quite difficult to be obtained through traditional methods as no effective analytic method has been identified so far, when the robot's joint configuration does not conform to the “Pieper Criterion.” In this paper, a new modeling method named extended Denavit-Hartenberg (DH) method was presented for solving this problem. And the conditions of robots' configuration that conform to the method have been given for different joints combinations. The precise closed-form solution to a minimally invasive surgical robot slave manipulator was obtained through this new method. The correctness of the new method was verified through simulation analyses; this study enriched robot kinematic modeling and the closed-form solution to inverse kinematics of Da Vinci surgical robot, and will help to obtain a fast, accurate, and general method of closed-form solution for the same kind of robots and provide the precondition for robot control and trajectory planning.

A New Method for Analyzing Thick Walled Shape Memory Alloy Cylinders Subjected to Internal Pressure

2009 ◽  
Author(s):  
Reza Mirzaeifar
Keyword(s):  
Shape Memory Alloy ◽  
Internal Pressure ◽  
Shape Memory ◽  
Constitutive Relations ◽  
Three Dimensional ◽  
Closed Form Solution ◽  
Form Solution ◽  
New Method ◽  
Plane Stress Condition ◽  
Three Dimensional Finite Element

In this paper, a new method is proposed for analyzing thick walled shape memory alloy cylinders subjected to internal pressure. The plane stress condition is assumed for the cylinder and three-dimensional phenomenological macroscopic SMA constitutive model presented by Boyd and Lagoudas is simplified to obtain the required two-dimensional constitutive relations. The cylinder is divided to some narrow annular regions and appropriate assumptions are made in order to find a closed-form solution for the equilibrium equation in terms of radial displacements in each region. Numerical examples are presented for demonstrating the performance of the proposed method and the results are compared with three-dimensional finite element method.

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