# Sitemap

A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.

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## Future Blog Post

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## Blog Post number 4

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## Blog Post number 3

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## Blog Post number 2

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## Blog Post number 1

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## Determining drivable free-space for autonomous vehicles

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In various examples, sensor data may be received that represents a field of view of a sensor of a vehicle located in a physical environment. The sensor data may be applied to a machine learning model that computes both a set of boundary points that correspond to a boundary dividing drivable free-space from non-drivable space in the physical environment and class labels for boundary points of the set of boundary points that correspond to the boundary. Locations within the physical environment may be determined from the set of boundary points represented by the sensor data, and the vehicle may be controlled through the physical environment within the drivable free-space using the locations and the class labels.

## Portfolio item number 1

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## Portfolio item number 2

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## Voice Activity Detection using Temporal Characteristics of Autocorrelation Lag and Maximum Spectral Amplitude in Sub-bands

Published in Proceedings of the 11th International Conference on Natural Language Processing, 2014

A new method for ECG artifacts detection from noncardiovascular physiological signals namely Electroencephalogram (EEG), Electrooculogram (EOG) and Electromyogram (EMG), without the need of any additional synchronous ECG channel, is being proposed. This ECG artifacts (R peaks) detection method uses Slope Sum Function and Teager Kaiser Energy operator with an adaptive threshold. The performance of algorithm has been evaluated on PhysioNet database of challenge 2014 and MIT BIH polysomnographic database. The algorithm has shown improved ECG artifacts detection results as compared to that of direct application of Teager Kaiser energy operator on noncardiovascular signals. The detection rates of ECG artifacts with the new method is 96.12 percent with FN rate of 3.88 percent and FP rate of 3.16 percent for PhysioNet database challenge 2014. For MIT BIH database the artifacts detection rate is 95.57 percent with FN rate of 4.44 percent and FP rate of 3.57 percent, which shows an excellent performance in ECG artifacts detection.

## ECG artifacts detection in noncardiovascular signals using Slope Sum Function and Teager Kaiser Energy

Published in 2015 International Conference on BioSignal Analysis, Processing and Systems (ICBAPS), 2015

A new method for ECG artifacts detection from noncardiovascular physiological signals namely Electroencephalogram (EEG), Electrooculogram (EOG) and Electromyogram (EMG), without the need of any additional synchronous ECG channel, is being proposed. This ECG artifacts (R peaks) detection method uses Slope Sum Function and Teager Kaiser Energy operator with an adaptive threshold. The performance of algorithm has been evaluated on PhysioNet database of challenge 2014 and MIT BIH polysomnographic database. The algorithm has shown improved ECG artifacts detection results as compared to that of direct application of Teager Kaiser energy operator on noncardiovascular signals. The detection rates of ECG artifacts with the new method is 96.12 percent with FN rate of 3.88 percent and FP rate of 3.16 percent for PhysioNet database challenge 2014. For MIT BIH database the artifacts detection rate is 95.57 percent with FN rate of 4.44 percent and FP rate of 3.57 percent, which shows an excellent performance in ECG artifacts detection.

## Heart rate estimation from non-cardiovascular signals using slope sum function and Teager energy

Published in 2015 International Conference on Industrial Instrumentation and Control (ICIC), 2015

Cardiovascular physiological signals such as electrocardiogram (ECG) and arterial blood pressure (ABP) provide direct measure of heart rate. But ECG & ABP recorded in the intensive care unit (ICU) are often seriously corrupted by noise and missing data, which lead to errors in Heart Rate (HR) estimation and incidences of false alarm from ICU monitors. Cardiac activity, because of its relatively high electrical energy, introduces ECG artifacts in non-cardiovascular physiological signals namely Electroencephalogram (EEG), Electrooculogram (EOG) and Electromyogram (EMG) recordings. This paper presents a HR estimation method from non-cardiovascular signals by detection of R-peaks of ECG artifacts in these signals using slope sum function, energy based function, and a novel Signal Quality Index (SQI) assessment technique. SQIs of non-cardiovascular physiological signals are obtained by correlation of Teager-Kaiser energy (TKE) of these signals with TKE of either ECG or ABP signal. This method was evaluated on PhysioNet database of challenge 2014. The average rMSE of HR estimate for EEG, EOG & EMG signal is 6.59 bpm, 4.20 bpm & 7.37 bpm respectively. The proposed method gives an accurate estimation of HR from non-cardiovascular signals and is quite useful when ECG or ABP signal are either corrupt or missing.

## Detecting Fast Radio Bursts Using Convolutional Neural Networks

Published in 2018 2nd URSI Atlantic Radio Science Meeting (AT-RASC), 2015

Fast Radio Burst (FRB) are high intensity bursts of radio energy lasting only a few milliseconds with large dispersion measures, suggesting extragalactic origin [1]. FRBs have gained intense interest in the astronomical community because their physical origin is not well understood, and because they may be useful as a probe of the intergalactic medium. With its large field of view, the Allen Telescope Array (ATA) is an instrument that is well-designed to observe FRBs over a wide frequency range (simultaneously from 1-10 GHz). Like many other cm-wave observatories (Parkes, ASKAP, MeerKAT, GMRT) the ATA is developing high speed detection systems to identify FRBs that fortuitously appear in our field of view during commensal observations. FRB detection is somewhat challenging since a computationally intensive de-dispersion search must be performed at GB/s data rates in a time-frequency analysis. Here we explore an alternative detection method that may offer greater reliability and/or less computational effort for the discovery of FRBs. We use deep neural networks to distinguish between Fast Radio Bursts and other astronomical or interfering signals. To generate training data for deep neural networks, we simulate FRB-like signals based on the characteristics and shape of previously observed FRBs [2]. Convolutional neural networks (CNN) have previously shown good results in classifying radio signals in SETI institute’s code challenge, ML4SETI 2017. We train CNNs on spectrograms generated from simulated FRB signals, and will evaluate CNN performance in terms of accuracy and computational requirements as compared with a more conventional de-dispersion search [3,4].

## Deep Convolutional Neural Networks for the Generation of High Fidelity Images from Radio Interferometer Visibility Data

Published in 2018 2nd URSI Atlantic Radio Science Meeting (AT-RASC), 2018

Last year, the SETI Institute in collaboration with IBM, lead an effort to apply convolutional neural networks to the problem of identifying the class of unknown signals in simulated single-dish radio telescope data, with much success. In this paper we look at a different problem in radio astronomy, the construction of high fidelity images from stored correlator visibility files. This work is motivated by the amazing recent successes of CNN autoencoders developed for photographic image processing in the machine learning community. The second half of a photographic autoencoder has a structure very similar to what would be required for image generation from raw radio interferometer data (visibilities). In this paper, we argue that a deep convolutional neural network (CNN) can be a highly (computationally) effective approach to radio interferometer image generation starting from raw visibilities and producing high fidelity, cleaned (deconvolved) images as an output. We consider the linear and nonlinear operations performed in image generation and how they have analogs in a standard CNN. We also discuss the potential computational cost savings that might be had by replacing our complicated image processing pipelines with neural networks. A toy model CNN is developed and initial results will be presented.

## Narrow-band signal classification using Deep Convolutional Neural Networks

Published in 2018 2nd URSI Atlantic Radio Science Meeting (AT-RASC), 2018

With vast amounts of data collected every night by telescopes like Allen Telescope Array, there is a need for a real time system that can accurately distinguish between signals of interest from radio frequency interference. Seti Institute and IBM jointly organized a code challenge and hackathon in June 2017 to encourage efforts towards this goal using machine learning techniques. In this paper, we present our approach based on Convolutional neural networks for classification of narrow band signals. By converting the radio signals into 2D spectrogram image, the problem of signal classification can be transformed to as image classification problem. Deep convolutional networks are currently the state-of-art techniques in image classification. We demonstrate the effectiveness of CNN technique by using a simple Alexnet network with 5 convolutional layers. Our approach achieved a score of 0.21 based on the logloss metric in the hackathon. We also extend our work to detect signals with multiple labels simultaneously and initial results for the same will be presented in this paper.

## A priori guarantees of finite-time convergence for Deep Neural Networks

Published in arXiv preprint arXiv:2009.0750, 2020

In this paper, we perform Lyapunov based analysis of the loss function to derive an a priori upper bound on the settling time of deep neural networks. While previous studies have attempted to understand deep learning using control theory framework, there is limited work on a priori finite time convergence analysis. Drawing from the advances in analysis of finite-time control of non-linear systems, we provide a priori guarantees of finite-time convergence in a deterministic control theoretic setting. We formulate the supervised learning framework as a control problem where weights of the network are control inputs and learning translates into a tracking problem. An analytical formula for finite-time upper bound on settling time is computed a priori under the assumptions of boundedness of input. Finally, we prove the robustness and sensitivity of the loss function against input perturbations.

## Talk 1 on Relevant Topic in Your Field

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## Conference Proceeding talk 3 on Relevant Topic in Your Field

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## Teaching experience 1

Undergraduate course, University 1, Department, 2014

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## Teaching experience 2

Workshop, University 1, Department, 2015

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