Mode-shape-based mass detection scheme using mechanically diverse, indirectly coupled microresonator arrays

2015 ◽  
Vol 117 (5) ◽  
pp. 054505 ◽  
Author(s):  
Aldo A. Glean ◽  
John A. Judge ◽  
Joseph F. Vignola ◽  
Teresa J. Ryan
Keyword(s):  
Mode Shape ◽  
Mass Detection ◽  
Detection Scheme

Inverse Eigenmode Method for Identifying and Locating Added Mass in Mechanically Diverse Coupled Microresonantor Arrays

2011 ◽  
Author(s):  
Aldo A. J. Glean ◽  
John A. Judge ◽  
Joseph F. Vignola
Keyword(s):  
Resonance Frequency ◽  
Frequency Shift ◽  
Mode Shape ◽  
Added Mass ◽  
Inverse Eigenvalue Problem ◽  
Single Frequency ◽  
Mass Detection ◽  
Detection Scheme ◽  
Shift Method ◽  
Inverse Eigenvalue

This paper summarizes a numerical analysis of an eigenmode-based approach for ultrasensitive mass detection via coupled microcantilevers. Mass detection using microcantilevers typically entails the observation of shifts in resonance frequency. Recently, detection systems have been proposed in which multiple cantilever sensors are coupled, either directly or by attachment to a single shuttle mass. Once sensors are coupled, however, mass adsorption on a single sensor alters all eigenmodes of the system. Thus, one disadvantage of the frequency-shift method in such cases is the need for strong mode localization, such that the shift of a single frequency can be associated with a mass change on a specific sensor. The consequent requirement for weak coupling limits the number of microcantilevers that can occupy a specific frequency band. The proposed eigenmode-based detection scheme involves solving the inverse eigenvalue problem to identify added mass, and can be used in cases where more than one eigenfrequency has shifted significantly. The method requires a single measured mode shape and corresponding natural frequency, selected from among those where a shift was observed. The fidelity of the identification of added mass and its location depends on the ability to accurately measure the mode shape, and on the amplitude with which each cantilever vibrates in the chosen mode (in modes without strong localization, multiple cantilevers respond with significant amplitude). Simulation results are presented that quantify, as a function of measurement noise, the ability of the method to accurately identify the cantilever(s) where mass adheres. In cases in which the resonance frequency-shift method is inappropriate due to non-localized modes, the inverse eigenvalue method proposed here can be used to identify both the amount and location of the added mass.

Mass Detection With Nonlinear Nanomechanical Resonators

2008 ◽  
Author(s):  
Eyal Buks
Keyword(s):  
Output Signal ◽  
Nonlinear Oscillations ◽  
Small Mass ◽  
High Mass ◽  
Mass Detection ◽  
Detection Scheme ◽  
Nanomechanical Resonator ◽  
Nanomechanical Resonators ◽  
Mass Sensitivity ◽  
Damping Rate

Nanomechanical resonators having small mass, high resonance frequency and low damping rate are widely employed as mass detectors. We study the performance of such a detector when the resonator is driven into a region of nonlinear oscillations [1]. We predict theoretically that the mass sensitivity of the device in this region may exceed the upper bound imposed by thermo-mechanical noise upon the sensitivity when operating in the linear region. On the other hand, we find that the high mass sensitivity is accompanied by a slow response of the system to a change in the mass. For experimental demonstration we employ homodyne detection (see Fig. 1) for readout of the output signal of an optical displacement detector, which monitors the motion of a doubly clamped nanomechanical resonator made of Pd-Au [2, 3]. The nanomechanical resonator is driven into the region of nonlinear oscillations (see Fig. 2) and the region of bistability is identified (see Fig. 3). As expected theoretically [1] we find that when operating close to the edge of the bistability region the device exhibits strong intermodulation amplification [2] (see Fig. 3). Moreover, strong noise squeezing in the output signal of the homodyne detector is observed in this region [3] (see Fig. 4). An alternative mass detection scheme, in which the resonator is driven into a stochastic resonance, will also be discussed [4].

scholarly journals Optimization of Network Topology in Computer-Aided Detection Schemes Using Phased Searching with NEAT in a Time-Scaled Framework

2014 ◽  
Vol 13s1 ◽  
pp. CIN.S13885
Author(s):  
Maxine Tan ◽  
Jiantao Pu ◽  
Bin Zheng
Keyword(s):  
Selection Process ◽  
Learning Task ◽  
Classification Performance ◽  
Optimal Topology ◽  
Support Vector ◽  
Mass Detection ◽  
Detection Scheme ◽  
Linear Discriminant ◽  
Computer Aided ◽  
Fixed Topology

In the field of computer-aided mammographic mass detection, many different features and classifiers have been tested. Frequently, the relevant features and optimal topology for the artificial neural network (ANN)-based approaches at the classification stage are unknown, and thus determined by trial-and-error experiments. In this study, we analyzed a classifier that evolves ANNs using genetic algorithms (GAs), which combines feature selection with the learning task. The classifier named “Phased Searching with NEAT in a Time-Scaled Framework” was analyzed using a dataset with 800 malignant and 800 normal tissue regions in a 10-fold cross-validation framework. The classification performance measured by the area under a receiver operating characteristic (ROC) curve was 0.856 ± 0.029. The result was also compared with four other well-established classifiers that include fixed-topology ANNs, support vector machines (SVMs), linear discriminant analysis (LDA), and bagged decision trees. The results show that Phased Searching outperformed the LDA and bagged decision tree classifiers, and was only significantly outperformed by SVM. Furthermore, the Phased Searching method required fewer features and discarded superfluous structure or topology, thus incurring a lower feature computational and training and validation time requirement. Analyses performed on the network complexities evolved by Phased Searching indicate that it can evolve optimal network topologies based on its complexification and simplification parameter selection process. From the results, the study also concluded that the three classifiers – SVM, fixed-topology ANN, and Phased Searching with NeuroEvolution of Augmenting Topologies (NEAT) in a Time-Scaled Framework – are performing comparably well in our mammographic mass detection scheme.

Performance and Reproducibility of a Computerized Mass Detection Scheme for Digitized Mammography Using Rotated and Resampled Images: An Assessment

2005 ◽  
Vol 185 (1) ◽  
pp. 194-198 ◽  
Author(s):  
Bin Zheng ◽  
Glenn S. Maitz ◽  
Marie A. Ganott ◽  
Gordon Abrams ◽  
Joseph K. Leader ◽  
...  
Keyword(s):  
Mass Detection ◽  
Detection Scheme

scholarly journals Optical-Thermally Excited Graphene Resonant Mass Detection: A Molecular Dynamics Analysis

Nanomaterials ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1924
Author(s):  
Xing Xiao ◽  
Shang-Chun Fan ◽  
Cheng Li ◽  
Yu-Jian Liu
Keyword(s):  
Molecular Dynamics ◽  
Quality Factor ◽  
Graphene Sheet ◽  
Central Area ◽  
Mass Detection ◽  
Mass Sensing ◽  
Detection Scheme ◽  
Thermal Actuation ◽  
Molecular Dynamics Calculations ◽  
Adsorbed Mass

In consideration of the presented optical-thermally excited resonant mass detection scheme, molecular dynamics calculations are performed to investigate the thermal actuation and resonant mass sensing mechanism. The simulation results indicate that an extremely high temperature exists in a 6% central area of the graphene sheet exposed to the exciting laser. Therefore, constraining the laser driving power and enlarging the laser spot radius are essential to weaken the overheating in the middle of the graphene sheet, thus avoiding being burned through. Moreover, molecular dynamics calculations demonstrate a mass sensitivity of 214 kHz/zg for the graphene resonator with a pre-stress of 1 GPa. However, the adsorbed mass would degrade the resonant quality factor from 236 to 193. In comparison, the sensitivity and quality factor could rise by 1.3 and 4 times, respectively, for the graphene sheet with a pre-stress of 5 GPa, thus revealing the availability of enlarging pre-stress for better mass sensing performance.

A PYRAMID-BASED MASS DETECTION SCHEME BASED ON TEXTURE ANALYSIS AND NEURAL CLASSIFIER FOR DIGITAL MAMMOGRAMS

2014 ◽  
Vol 26 (04) ◽  
pp. 1440001
Author(s):  
Guo-Shiang Lin ◽  
Yu-Cheng Chang ◽  
Wei-Cheng Yeh ◽  
Kai-Che Liu ◽  
Chia-Hung Yeh
Keyword(s):  
Feature Extraction ◽  
Computational Complexity ◽  
Texture Analysis ◽  
Detection Method ◽  
Texture Features ◽  
Recall Rate ◽  
Experimental Result ◽  
Mass Detection ◽  
Detection Scheme ◽  
Neural Classifier

In the paper, we proposed a pyramid-based mass detection method based on texture analysis and neural classifier for digital mammograms. The proposed mass detection method is composed of four parts: pyramid decomposition, region of interest (ROI) selection, feature extraction and neural classifier. Based on pyramid decomposition, a coarse-to-fine approach was utilized to achieve mass detection for reducing computational complexity in the proposed scheme. For decreasing computational complexity, ROI selection where a thresholding algorithm and polynomial function fitting were to find the breast area is also exploited to remove nonbreast regions in the proposed scheme. In the texture analysis, the intensity and texture information extracted from spatial and wavelet domains are utilized to analyze each pixel within the ROI. After feature extraction, these extracted texture features are combined with a supervised neural network to detect masses in the ROI. To evaluate the performance of the proposed scheme, the mammograms of 19 patients captured in Taiwan are used for testing. The experimental result shows that ROI selection can localize breast regions well for further analysis. In addition, the average recall rate of our proposed scheme is more than 86%. Therefore, these experimental results demonstrate that the proposed pyramid-based scheme can achieve mass detection.

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