scholarly journals Detecting Ca2+ sparks on stationary and varying baselines

2011 ◽  
Vol 301 (3) ◽  
pp. C717-C728 ◽  
Author(s):  
Peter Bankhead ◽  
C. Norman Scholfield ◽  
Tim M. Curtis ◽  
J. Graham McGeown
Keyword(s):  
Automated Detection ◽  
Noise Model ◽  
Detection Methods ◽  
Data Sets ◽  
Baseline Subtraction ◽  
Variance Stabilization ◽  
Reliable Detection ◽  
Large Populations ◽  
Microscopy Images ◽  
Straightforward Approach

Studies concerning the physiological significance of Ca2+ sparks often depend on the detection and measurement of large populations of events in noisy microscopy images. Automated detection methods have been developed to quickly and objectively distinguish potential sparks from noise artifacts. However, previously described algorithms are not suited to the reliable detection of sparks in images where the local baseline fluorescence and noise properties can vary significantly, and risk introducing additional bias when applied to such data sets. Here, we describe a new, conceptually straightforward approach to spark detection in linescans that addresses this issue by combining variance stabilization with local baseline subtraction. We also show that in addition to greatly increasing the range of images in which sparks can be automatically detected, the use of a more accurate noise model enables our algorithm to achieve similar detection sensitivities with fewer false positives than previous approaches when applied both to synthetic and experimental data sets. We propose, therefore, that it might be a useful tool for improving the reliability and objectivity of spark analysis in general, and describe how it might be further optimized for specific applications.

Automated Detection of Pin Defects on Counterfeit Microelectronics

2018 ◽  
Author(s):  
Pallabi Ghosh ◽  
Domenic Forte ◽  
Damon L. Woodard ◽  
Rajat Subhra Chakraborty
Keyword(s):  
Integrated Circuits ◽  
Depth Map ◽  
Optical Microscope ◽  
Automated Detection ◽  
Detection Methods ◽  
Global Supply Chains ◽  
Subject Matter Experts ◽  
Security Breaches ◽  
The Past ◽  
Skilled Subject

Abstract Counterfeit electronics constitute a fast-growing threat to global supply chains as well as national security. With rapid globalization, the supply chain is growing more and more complex with components coming from a diverse set of suppliers. Counterfeiters are taking advantage of this complexity and replacing original parts with fake ones. Moreover, counterfeit integrated circuits (ICs) may contain circuit modifications that cause security breaches. Out of all types of counterfeit ICs, recycled and remarked ICs are the most common. Over the past few years, a plethora of counterfeit IC detection methods have been created; however, most of these methods are manual and require highly-skilled subject matter experts (SME). In this paper, an automated bent and corroded pin detection methodology using image processing is proposed to identify recycled ICs. Here, depth map of images acquired using an optical microscope are used to detect bent pins, and segmented side view pin images are used to detect corroded pins.

scholarly journals Comparison of Novelty Detection Methods for Detection of Various Rotary Machinery Faults

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3536
Author(s):  
Jakub Górski ◽  
Adam Jabłoński ◽  
Mateusz Heesch ◽  
Michał Dziendzikowski ◽  
Ziemowit Dworakowski
Keyword(s):  
Novelty Detection ◽  
Feature Space ◽  
Detection Methods ◽  
Data Sets ◽  
Similar Distribution ◽  
Detection Algorithms ◽  
Rotary Machinery ◽  
Detection Approach ◽  
Input Reconstruction ◽  
Quantitative Indicators

Condition monitoring is an indispensable element related to the operation of rotating machinery. In this article, the monitoring system for the parallel gearbox was proposed. The novelty detection approach is used to develop the condition assessment support system, which requires data collection for a healthy structure. The measured signals were processed to extract quantitative indicators sensitive to the type of damage occurring in this type of structure. The indicator’s values were used for the development of four different novelty detection algorithms. Presented novelty detection models operate on three principles: feature space distance, probability distribution, and input reconstruction. One of the distance-based models is adaptive, adjusting to new data flowing in the form of a stream. The authors test the developed algorithms on experimental and simulation data with a similar distribution, using the training set consisting mainly of samples generated by the simulator. Presented in the article results demonstrate the effectiveness of the trained models on both data sets.

scholarly journals Deep Recurrent Neural Networks for Automatic Detection of Sleep Apnea from Single Channel Respiration Signals

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5037
Author(s):  
Hisham ElMoaqet ◽  
Mohammad Eid ◽  
Martin Glos ◽  
Mutaz Ryalat ◽  
Thomas Penzel
Keyword(s):  
Feature Extraction ◽  
Sleep Apnea ◽  
Short Term Memory ◽  
Single Channel ◽  
Automated Detection ◽  
Detection Methods ◽  
Short Term ◽  
Term Memory ◽  
Deep Recurrent Neural Network ◽  
Long Short Term Memory

Sleep apnea is a common sleep disorder that causes repeated breathing interruption during sleep. The performance of automated apnea detection methods based on respiratory signals depend on the signals considered and feature extraction methods. Moreover, feature engineering techniques are highly dependent on the experts’ experience and their prior knowledge about different physiological signals and conditions of the subjects. To overcome these problems, a novel deep recurrent neural network (RNN) framework is developed for automated feature extraction and detection of apnea events from single respiratory channel inputs. Long short-term memory (LSTM) and bidirectional long short-term memory (BiLSTM) are investigated to develop the proposed deep RNN model. The proposed framework is evaluated over three respiration signals: Oronasal thermal airflow (FlowTh), nasal pressure (NPRE), and abdominal respiratory inductance plethysmography (ABD). To demonstrate our results, we use polysomnography (PSG) data of 17 patients with obstructive, central, and mixed apnea events. Our results indicate the effectiveness of the proposed framework in automatic extraction for temporal features and automated detection of apneic events over the different respiratory signals considered in this study. Using a deep BiLSTM-based detection model, the NPRE signal achieved the highest overall detection results with true positive rate (sensitivity) = 90.3%, true negative rate (specificity) = 83.7%, and area under receiver operator characteristic curve = 92.4%. The present results contribute a new deep learning approach for automated detection of sleep apnea events from single channel respiration signals that can potentially serve as a helpful and alternative tool for the traditional PSG method.

scholarly journals Uncertainty-Aware Deep Classifiers Using Generative Models

2020 ◽  
Vol 34 (04) ◽  
pp. 5620-5627 ◽  
Author(s):  
Murat Sensoy ◽  
Lance Kaplan ◽  
Federico Cerutti ◽  
Maryam Saleki
Keyword(s):  
Neural Networks ◽  
Epistemic Uncertainty ◽  
Feature Space ◽  
Generative Models ◽  
Detection Methods ◽  
Generative Adversarial Networks ◽  
Data Sets ◽  
Bayesian Approaches ◽  
Data Set ◽  
Auxiliary Data

Deep neural networks are often ignorant about what they do not know and overconfident when they make uninformed predictions. Some recent approaches quantify classification uncertainty directly by training the model to output high uncertainty for the data samples close to class boundaries or from the outside of the training distribution. These approaches use an auxiliary data set during training to represent out-of-distribution samples. However, selection or creation of such an auxiliary data set is non-trivial, especially for high dimensional data such as images. In this work we develop a novel neural network model that is able to express both aleatoric and epistemic uncertainty to distinguish decision boundary and out-of-distribution regions of the feature space. To this end, variational autoencoders and generative adversarial networks are incorporated to automatically generate out-of-distribution exemplars for training. Through extensive analysis, we demonstrate that the proposed approach provides better estimates of uncertainty for in- and out-of-distribution samples, and adversarial examples on well-known data sets against state-of-the-art approaches including recent Bayesian approaches for neural networks and anomaly detection methods.

A scalable eigenspace-based fuzzy c-means for topic detection

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Hendri Murfi
Keyword(s):  
Representation Learning ◽  
Detection Methods ◽  
Data Sets ◽  
Topic Detection ◽  
Data Set ◽  
Content Type ◽  
Running Time ◽  
Fuzzy C Means ◽  
Coherence Score ◽  
Value Decomposition

PurposeThe aim of this research is to develop an eigenspace-based fuzzy c-means method for scalable topic detection.Design/methodology/approachThe eigenspace-based fuzzy c-means (EFCM) combines representation learning and clustering. The textual data are transformed into a lower-dimensional eigenspace using truncated singular value decomposition. Fuzzy c-means is performed on the eigenspace to identify the centroids of each cluster. The topics are provided by transforming back the centroids into the nonnegative subspace of the original space. In this paper, we extend the EFCM method for scalability by using the two approaches, i.e. single-pass and online. We call the developed topic detection methods as oEFCM and spEFCM.FindingsOur simulation shows that both oEFCM and spEFCM methods provide faster running times than EFCM for data sets that do not fit in memory. However, there is a decrease in the average coherence score. For both data sets that fit and do not fit into memory, the oEFCM method provides a tradeoff between running time and coherence score, which is better than spEFCM.Originality/valueThis research produces a scalable topic detection method. Besides this scalability capability, the developed method also provides a faster running time for the data set that fits in memory.

scholarly journals Outlier Detection Methods for Uncovering of Critical Events in Historical Phasor Measurement Records

2018 ◽  
Vol 64 ◽  
pp. 08006 ◽  
Author(s):  
Kummerow André ◽  
Nicolai Steffen ◽  
Bretschneider Peter
Keyword(s):  
Power Systems ◽  
Outlier Detection ◽  
Training Data ◽  
Detection Methods ◽  
Data Sets ◽  
Critical Events ◽  
Failure Patterns ◽  
Detection Algorithms ◽  
Reduction Techniques ◽  
Dimension Reduction Techniques

The scope of this survey is the uncovering of potential critical events from mixed PMU data sets. An unsupervised procedure is introduced with the use of different outlier detection methods. For that, different techniques for signal analysis are used to generate features in time and frequency domain as well as linear and non-linear dimension reduction techniques. That approach enables the exploration of critical grid dynamics in power systems without prior knowledge about existing failure patterns. Furthermore new failure patterns can be extracted for the creation of training data sets used for online detection algorithms.

Freely Accessible Internet Resources for Nanoscience and Nanotechnology Education and Research at Portland State University's Research Servers

2006 ◽  
Vol 931 ◽  
Author(s):  
Peter Moeck ◽  
Bjoern Seipel ◽  
Girish Upreti ◽  
Morgan Harvey ◽  
William Garrick
Keyword(s):  
Three Dimensions ◽  
Data Sets ◽  
Structured Materials ◽  
Nanoscience And Nanotechnology ◽  
Internet Resources ◽  
Crystallographic Databases ◽  
Transmission Electron ◽  
Nanotechnology Education ◽  
Crystallography Open Database ◽  
Microscopy Images

ABSTRACTBecause a great deal of nanoscience and nanotechnology relies on crystalline nanometer sized or nanometer structured materials, crystallographers have to provide their specific contributions to the National Nanotechnology Initiative. Here we review two open access internet-based crystallographic databases, the Crystallography Open Database (COD) and the Nano-Crystallography Database (NCD), that store information in the Crystallographic Information File (CIF) format. Having more than ten thousand crystallographic data sets available on the internet in a standardized format allows for many kinds of internet-based crystallographic calculations and visualizations. Examples for this that are dealt with in this paper are interactive crystal structure visualizations in three dimensions (3D) and calculations of theoretical lattice-fringe fingerprint plots for the identification of unknown nanocrystals from their atomic-resolution transmission electron microscopy images.

A robust workflow for performing joint impedance inversion and its applications

2020 ◽  
Vol 8 (1) ◽  
pp. T141-T149
Author(s):  
Ritesh Kumar Sharma ◽  
Satinder Chopra ◽  
Larry R. Lines
Keyword(s):  
Time Domain ◽  
Seismic Data ◽  
Sedimentary Basin ◽  
Vertical Component ◽  
Data Sets ◽  
Data Set ◽  
Matching Process ◽  
Multicomponent Seismic ◽  
Impedance Inversion ◽  
Straightforward Approach

Multicomponent seismic data offer several advantages for characterizing reservoirs with the use of the vertical component (PP) and mode-converted (PS) data. Joint impedance inversion inverts both of these data sets simultaneously; hence, it is considered superior to simultaneous impedance inversion. However, the success of joint impedance inversion depends on how accurately the PS data are mapped on the PP time domain. Normally, this is attempted by performing well-to-seismic ties for PP and PS data sets and matching different horizons picked on PP and PS data. Although it seems to be a straightforward approach, there are a few issues associated with it. One of them is the lower resolution of the PS data compared with the PP data that presents difficulties in the correlation of the equivalent reflection events on both the data sets. Even after a few consistent horizons get tracked, the horizon matching process introduces some artifacts on the PS data when mapped into PP time. We have evaluated such challenges using a data set from the Western Canadian Sedimentary Basin and then develop a novel workflow for addressing them. The importance of our workflow was determined by comparing data examples generated with and without its adoption.

scholarly journals A Strategy for Removing the Bias in the Graphical Analysis Method

2001 ◽  
Vol 21 (3) ◽  
pp. 307-320 ◽  
Author(s):  
Jean Logan ◽  
Joanna S. Fowler ◽  
Nora D. Volkow ◽  
Yu Shin Ding ◽  
Gene-Jack Wang ◽  
...  
Keyword(s):  
Least Squares ◽  
Graphical Method ◽  
Graphical Analysis ◽  
Distribution Volume ◽  
Noise Model ◽  
Data Sets ◽  
Multiple Time ◽  
Analysis Method ◽  
Reference Tissue ◽  
The One

The graphical analysis method, which transforms multiple time measurements of plasma and tissue uptake data into a linear plot, is a useful tool for rapidly obtaining information about the binding of radioligands used in PET studies. The strength of the method is that it does not require a particular model structure. However, a bias is introduced in the case of noisy data resulting in the underestimation of the distribution volume (DV), the slope obtained from the graphical method. To remove the bias, a modification of the method developed by Feng et al. (1993) , the generalized linear least squares (GLLS) method, which provides unbiased estimates for compartment models was used. The one compartment GLLS method has a relatively simple form, which was used to estimate the DV directly and as a smoothing technique for more general classes of model structures. In the latter case, the GLLS method was applied to the data in two parts, that is, one set of parameters was determined for times 0 to T1 and a second set from T1 to the end time. The curve generated from these two sets of parameters then was used as input to the graphical method. This has been tested using simulations of data similar to that of the PET ligand [11C]- d-threo-methylphenidate (MP, DV = 35 mL/mL) and 11C raclopride (RAC, DV = 1.92 mL/mL) and compared with two examples from image data with the same tracers. The noise model was based on counting statistics through the half-life of the isotope and the scanning time. Five hundred data sets at each noise level were analyzed. Results (DV) for the graphical analysis (DVg), the nonlinear least squares (NLS) method (DVnls), the one-tissue compartment GLLS method (DVf), and the two part GLLS followed by graphical analysis (DVfg) were compared. DVFG was found to increase somewhat with increasing noise and in some data sets at high noise levels no estimate could be obtained. However, at intermediate levels it provided a good estimation of the true DV. This method was extended to use a reference tissue in place of the input function to generate the distribution volume ratio (DVR) to the reference region. A linearized form of the simplified reference tissue method of Lammertsma and Hume (1996) was used. The DVR generated directly from the model (DVRfl) was compared with DVRfg (determined from a “smoothed” uptake curve as for DVfg) using the graphical method.

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