scholarly journals Radiometric Identification of Signals by Matched Whitening Transform

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8398
Author(s):  
Bijan G. Mobasseri ◽  
Amro Lulu
Keyword(s):  
Euclidean Distance ◽  
Majority Vote ◽  
Identification Algorithm ◽  
Distance Metrics ◽  
Learning Approaches ◽  
White Noise Process ◽  
Data Set ◽  
Mode Function ◽  
Source Signal ◽  
Specific Source

Radiometric identification is the problem of attributing a signal to a specific source. In this work, a radiometric identification algorithm is developed using the whitening transformation. The approach stands out from the more established methods in that it works directly on the raw IQ data and hence is featureless. As such, the commonly used dimensionality reduction algorithms do not apply. The premise of the idea is that a data set is “most white” when projected on its own whitening matrix than on any other. In practice, transformed data are never strictly white since the training and the test data differ. The Förstner-Moonen measure that quantifies the similarity of covariance matrices is used to establish the degree of whiteness. The whitening transform that produces a data set with the minimum Förstner-Moonen distance to a white noise process is the source signal. The source is determined by the output of the mode function operated on the Majority Vote Classifier decisions. Using the Förstner-Moonen measure presents a different perspective compared to maximum likelihood and Euclidean distance metrics. The whitening transform is also contrasted with the more recent deep learning approaches that are still dependent on feature vectors with large dimensions and lengthy training phases. It is shown that the proposed method is simpler to implement, requires no features vectors, needs minimal training and because of its non-iterative structure is faster than existing approaches.

scholarly journals K-Nearest Neighbours Method as a Tool for Failure Rate Prediction

2017 ◽  
Author(s):  
Małgorzata Kutyłowska
Keyword(s):  
Failure Rate ◽  
Euclidean Distance ◽  
Optimal Number ◽  
Distance Metrics ◽  
Optimal Prediction ◽  
Data Set ◽  
Independent Variables ◽  
Rate Prediction ◽  
Nearest Neighbours ◽  
Water Mains

The paper shows the results of failure rate prediction using non-parametric regression algorithm K-nearest neighbours. The whole data set for years 1999-2013 was divided randomly into two groups (learning – 75% and testing – 25%). Besides, data from year 2014 were used for verifying the model. The dependent variable (failure rate) was forecasted on the basis of independent variables (number of installed house connections, total length and number of damages of water mains, distribution pipes and house connections). Four types of distance metric: Euclidean, quadratic Euclidean, Manhattan and Czebyszew were checked and four KNN models were created. Taking into consideration all constraints and assumptions, models using Euclidean and quadratic Euclidean distance metrics gave the most optimal prediction results. The optimal number of K nearest neighbours equalled to 2 and 3 concerning models KNN-E, KNN-E2, KNN-C and KNN-M, respectively. Validation error was the smallest for models KNN-E and KNN-E2 and amounted to 0.0130, for model KNN-M was equal to 0.0152 and for KNN-C to 0.0150.

Deep Learning Approaches for Whiteboard Image Quality Enhancement

2019 ◽  
Vol 2019 (1) ◽  
pp. 360-368
Author(s):  
Mekides Assefa Abebe ◽  
Jon Yngve Hardeberg
Keyword(s):  
Deep Learning ◽  
Image Quality ◽  
Image Data ◽  
Quality Enhancement ◽  
Network Architectures ◽  
Learning Approaches ◽  
Data Set ◽  
Image Quality Enhancement ◽  
Processing Techniques ◽  
White Balancing

Different whiteboard image degradations highly reduce the legibility of pen-stroke content as well as the overall quality of the images. Consequently, different researchers addressed the problem through different image enhancement techniques. Most of the state-of-the-art approaches applied common image processing techniques such as background foreground segmentation, text extraction, contrast and color enhancements and white balancing. However, such types of conventional enhancement methods are incapable of recovering severely degraded pen-stroke contents and produce artifacts in the presence of complex pen-stroke illustrations. In order to surmount such problems, the authors have proposed a deep learning based solution. They have contributed a new whiteboard image data set and adopted two deep convolutional neural network architectures for whiteboard image quality enhancement applications. Their different evaluations of the trained models demonstrated their superior performances over the conventional methods.

Spectroscopy-Based Mapping with Scanning Microwave Impedance Microscopy

2018 ◽  
Author(s):  
Peter De Wolf ◽  
Zhuangqun Huang ◽  
Bede Pittenger
Keyword(s):  
Single Point ◽  
Electrical Characterization ◽  
Principal Component ◽  
High Sensitivity ◽  
Data Cube ◽  
Nanometer Scale ◽  
Learning Approaches ◽  
Data Set ◽  
3D Data ◽  
Higher Dimensional

Abstract Methods are available to measure conductivity, charge, surface potential, carrier density, piezo-electric and other electrical properties with nanometer scale resolution. One of these methods, scanning microwave impedance microscopy (sMIM), has gained interest due to its capability to measure the full impedance (capacitance and resistive part) with high sensitivity and high spatial resolution. This paper introduces a novel data-cube approach that combines sMIM imaging and sMIM point spectroscopy, producing an integrated and complete 3D data set. This approach replaces the subjective approach of guessing locations of interest (for single point spectroscopy) with a big data approach resulting in higher dimensional data that can be sliced along any axis or plane and is conducive to principal component analysis or other machine learning approaches to data reduction. The data-cube approach is also applicable to other AFM-based electrical characterization modes.

scholarly journals Deep Neural Network for Gender-Based Violence Detection on Twitter Messages

Mathematics ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 807
Author(s):  
Carlos M. Castorena ◽  
Itzel M. Abundez ◽  
Roberto Alejo ◽  
Everardo E. Granda-Gutiérrez ◽  
Eréndira Rendón ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Gender Violence ◽  
Strategic Decision ◽  
Learning Approaches ◽  
Gender Based Violence ◽  
Data Set ◽  
Violence Detection ◽  
Governmental Institutions ◽  
Gender Based

The problem of gender-based violence in Mexico has been increased considerably. Many social associations and governmental institutions have addressed this problem in different ways. In the context of computer science, some effort has been developed to deal with this problem through the use of machine learning approaches to strengthen the strategic decision making. In this work, a deep learning neural network application to identify gender-based violence on Twitter messages is presented. A total of 1,857,450 messages (generated in Mexico) were downloaded from Twitter: 61,604 of them were manually tagged by human volunteers as negative, positive or neutral messages, to serve as training and test data sets. Results presented in this paper show the effectiveness of deep neural network (about 80% of the area under the receiver operating characteristic) in detection of gender violence on Twitter messages. The main contribution of this investigation is that the data set was minimally pre-processed (as a difference versus most state-of-the-art approaches). Thus, the original messages were converted into a numerical vector in accordance to the frequency of word’s appearance and only adverbs, conjunctions and prepositions were deleted (which occur very frequently in text and we think that these words do not contribute to discriminatory messages on Twitter). Finally, this work contributes to dealing with gender violence in Mexico, which is an issue that needs to be faced immediately.

Machine learning identifies an immunological pattern associated with multiple juvenile idiopathic arthritis subtypes

2019 ◽  
Vol 78 (5) ◽  
pp. 617-628 ◽  
Author(s):  
Erika Van Nieuwenhove ◽  
Vasiliki Lagou ◽  
Lien Van Eyck ◽  
James Dooley ◽  
Ulrich Bodenhofer ◽  
...  
Keyword(s):  
Machine Learning ◽  
Juvenile Idiopathic Arthritis ◽  
Large Scale ◽  
Inflammatory Diseases ◽  
Adaptive Immune System ◽  
Healthy Children ◽  
Learning Approaches ◽  
Data Set ◽  
Immune Signature ◽  
Systemic Jia

ObjectivesJuvenile idiopathic arthritis (JIA) is the most common class of childhood rheumatic diseases, with distinct disease subsets that may have diverging pathophysiological origins. Both adaptive and innate immune processes have been proposed as primary drivers, which may account for the observed clinical heterogeneity, but few high-depth studies have been performed.MethodsHere we profiled the adaptive immune system of 85 patients with JIA and 43 age-matched controls with indepth flow cytometry and machine learning approaches.ResultsImmune profiling identified immunological changes in patients with JIA. This immune signature was shared across a broad spectrum of childhood inflammatory diseases. The immune signature was identified in clinically distinct subsets of JIA, but was accentuated in patients with systemic JIA and those patients with active disease. Despite the extensive overlap in the immunological spectrum exhibited by healthy children and patients with JIA, machine learning analysis of the data set proved capable of discriminating patients with JIA from healthy controls with ~90% accuracy.ConclusionsThese results pave the way for large-scale immune phenotyping longitudinal studies of JIA. The ability to discriminate between patients with JIA and healthy individuals provides proof of principle for the use of machine learning to identify immune signatures that are predictive to treatment response group.

scholarly journals Modified Energy Statistic for Unsupervised Anomaly Detection

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Rupam Mukherjee
Keyword(s):  
Anomaly Detection ◽  
Real Life ◽  
Industrial Applications ◽  
Test Point ◽  
Distance Metrics ◽  
Data Set ◽  
Energy Distance ◽  
New Energy ◽  
Statistical Distance ◽  
Computational Simplicity

For prognostics in industrial applications, the degree of anomaly of a test point from a baseline cluster is estimated using a statistical distance metric. Among different statistical distance metrics, energy distance is an interesting concept based on Newton’s Law of Gravitation, promising simpler computation than classical distance metrics. In this paper, we review the state of the art formulations of energy distance and point out several reasons why they are not directly applicable to the anomaly-detection problem. Thereby, we propose a new energy-based metric called the P-statistic which addresses these issues, is applicable to anomaly detection and retains the computational simplicity of the energy distance. We also demonstrate its effectiveness on a real-life data-set.

scholarly journals Batch Decorrelation for Active Metric Learning

2020 ◽  
Author(s):  
Priyadarshini Kumari ◽  
Ritesh Goru ◽  
Siddhartha Chaudhuri ◽  
Subhasis Chaudhuri
Keyword(s):  
Active Learning ◽  
Learning Strategy ◽  
Metric Learning ◽  
Parametric Models ◽  
Distance Metrics ◽  
Learning Approaches ◽  
Fundamental Goal ◽  
Novel Method ◽  
Active Learning Strategy ◽  
Perceptual Metrics

We present an active learning strategy for training parametric models of distance metrics, given triplet-based similarity assessments: object $x_i$ is more similar to object $x_j$ than to $x_k$. In contrast to prior work on class-based learning, where the fundamental goal is classification and any implicit or explicit metric is binary, we focus on perceptual metrics that express the degree of (dis)similarity between objects. We find that standard active learning approaches degrade when annotations are requested for batches of triplets at a time: our studies suggest that correlation among triplets is responsible. In this work, we propose a novel method to decorrelate batches of triplets, that jointly balances informativeness and diversity while decoupling the choice of heuristic for each criterion. Experiments indicate our method is general, adaptable, and outperforms the state-of-the-art.

scholarly journals OnTask

2020 ◽  
Vol 2 (1) ◽  
pp. 42
Author(s):  
Steve Leichtweis
Keyword(s):  
Learning Analytics ◽  
Teaching And Learning ◽  
Teaching Practice ◽  
Course Design ◽  
Learning Experience ◽  
Technology Enhanced Learning ◽  
Learning Approaches ◽  
Laptop Computer ◽  
Data Set ◽  
Hands On

Universities are increasingly being expected to ensure student success while at the same time delivering larger courses.  Within this environment, the provision of effective and timely feedback to students and creating opportunities for genuine engagement between teachers and students is increasingly difficult if not impossible for many instructors, despite the known value and importance of feedback (Timperley & Hattie, 2007) and instructor presence (Garrison, Anderson & Archer, 2010).  Similar to other tertiary institutions, the University of Auckland has adopted various technology-enhanced learning approaches and technologies, including learning analytics in an attempt to support teaching and learning at scale.  The increased use of educational technology to support learning provides a variety of data sources for teachers to provide personalised feedback and improve the overall learning experience for students.  This workshop is targeted to teachers interested in the use of learning data to provide personalized support to learners.  Participants will have a hands-on opportunity to use the open-source tool OnTask (Pardo, et al. 2018) within some common teaching scenarios with a synthetically generated data set.  The facilitators will also share and discuss how OnTask is currently being used in universities to support student experience, teaching practice and course design.  As this is a hands-on workshop, participants must bring a laptop computer to work with the online tool and the prepared scenarios.  References   Garrison, D. R., Anderson, T., & Archer, W. (2010). The first decade of the community of inquiry framework: A retrospective. The internet and higher education, 13(1-2), 5-9. Hattie, J., & Timperley, H. (2007). The power of feedback. Review of educational research, 77(1), 81-112. Pardo, A., Bartimote-Aufflick, K., Shum, S. B., Dawson, S., Gao, J., Gaševic, D., Leichtweis, S., Liu, D., Martínez-Maldonado, R., Mirriahi, N. and Moskal, A. C. M. (2018). OnTask: Delivering Data-Informed, Personalized Learning Support Actions. Journal of Learning Analytics, 5(3), 235-249.

scholarly journals Comparison of Machine Learning Approaches to Improve Diagnosis of Optic Neuropathy Using Photopic Negative Response Measured Using a Handheld Device

2021 ◽  
Vol 8 ◽  
Author(s):  
Tina Diao ◽  
Fareshta Kushzad ◽  
Megh D. Patel ◽  
Megha P. Bindiganavale ◽  
Munam Wasi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Optic Neuropathy ◽  
Negative Response ◽  
Photopic Negative Response ◽  
Learning Approaches ◽  
Data Set ◽  
Full Field ◽  
Handheld Device ◽  
Technical Requirements

The photopic negative response of the full-field electroretinogram (ERG) is reduced in optic neuropathies. However, technical requirements for measurement and poor classification performance have limited widespread clinical application. Recent advances in hardware facilitate efficient clinic-based recording of the full-field ERG. Time series classification, a machine learning approach, may improve classification by using the entire ERG waveform as the input. In this study, full-field ERGs were recorded in 217 eyes (109 optic neuropathy and 108 controls) of 155 subjects. User-defined ERG features including photopic negative response were reduced in optic neuropathy eyes (p < 0.0005, generalized estimating equation models accounting for age). However, classification of optic neuropathy based on user-defined features was only fair with receiver operating characteristic area under the curve ranging between 0.62 and 0.68 and F1 score at the optimal cutoff ranging between 0.30 and 0.33. In comparison, machine learning classifiers using a variety of time series analysis approaches had F1 scores of 0.58–0.76 on a test data set. Time series classifications are promising for improving optic neuropathy diagnosis using ERG waveforms. Larger sample sizes will be important to refine the models.

scholarly journals Time Windows Voting Classifier for COVID-19 Mortality Prediction

2021 ◽  
Author(s):  
TIONG GOH ◽  
MengJun Liu
Keyword(s):  
Time Window ◽  
Majority Vote ◽  
Time Windows ◽  
Mortality Prediction ◽  
Data Set ◽  
Risk Of Mortality ◽  
Machine Learning Model ◽  
Partitioned Data ◽  
Considerable Impact ◽  
Extended Duration

The ability to predict COVID-19 patients' level of severity (death or survival) enables clinicians to prioritise treatment. Recently, using three blood biomarkers, an interpretable machine learning model was developed to predict the mortality of COVID-19 patients. The method was reported to be suffering from performance stability because the identified biomarkers are not consistent predictors over an extended duration. To sustain performance, the proposed method partitioned data into three different time windows. For each window, an end-classifier, a mid-classifier and a front-classifier were designed respectively using the XGboost single tree approach. These time window classifiers were integrated into a majority vote classifier and tested with an isolated test data set. The voting classifier strengthens the overall performance of 90% cumulative accuracy from a 14 days window to a 21 days prediction window. An additional 7 days of prediction window can have a considerable impact on a patient's chance of survival. This study validated the feasibility of the time window voting classifier and further support the selection of biomarkers features set for the early prognosis of patients with a higher risk of mortality.

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