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Papers by shankar venkatesan

2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2015

Monitoring health and fitness is emerging as an important benefit that smartphone users could exp... more Monitoring health and fitness is emerging as an important benefit that smartphone users could expect from their mobile devices today. Rule of thumb calorie tracking and recommendation based on selective activity monitoring is widely available today, as both on-device and server based solutions. What is surprisingly not available to the users is a simple application geared towards quantitative fitness tracking. Such an application potentially can be a direct indicator of one's cardio-vascular performance and associated long term health risks. Since wearable devices with various inbuilt sensors like accelerometer, gyroscope, SPO2 and heart rate are increasingly becoming available, it is vital that the enormous data coming from these sensors be used to perform analytics to uncover hidden health and fitness associated facts. A continuous estimation of fitness level employing these wearable devices can potentially help users in setting personalized short and long-term exercise goals leading to positive impact on one's overall health. The present work describes a step in this direction. This work involves an unobtrusive method to track an individual's physical activity seamlessly, estimate calorie consumption during a day by mapping the activity to the calories spent and assess fitness level using heart rate data from wearable sensors. We employ a heart rate based parameter called Endurance to quantitatively estimate cardio-respiratory fitness of a person. This opens up avenues for personalization and adaptiveness by dynamically using individual's personal fitness data towards building robust modeling based on analytical principles.

SIMULATION, 2001

This paper describes the design and development of the DEVS/GDDM environment, a layered simulatio... more This paper describes the design and development of the DEVS/GDDM environment, a layered simulation environment that supports data dis tribution management and allows us to study space-based quantization schemes. These schemes aim to achieve effective reduction of data commu nication in distributed simulation. After a brief review of the space-based quantization scheme and an HLA-Interface environment, we discuss the design issues of the DEVS/GDDM environ ment. We analyze system performance and scalability of the space-based quantization scheme, especially with predictive and multiplex ing extensions, and empirical results for a ballis tic missiles simulation executing on the DEVS/ GDDM environment on NT networking plat forms. The results indicate the DEVS/GDDM environment is very effective and scalable due to reduced local computation demands and ex tremely favorable communication data reduction.

Computer-Aided Design, 1989

ABSTRACT

2013 3rd IEEE International Advance Computing Conference (IACC), 2013

ABSTRACT

Video Stabilization, which is important for better analysis and user experience, is typically don... more Video Stabilization, which is important for better analysis and user experience, is typically done through Global Motion Estimation (GME) and Compensation. GME can be done in image domain using many techniques or in Transform domain using the well-known Phase Correlation methods which relate motion to phase shift in the spectrum. While image domain methods are generally slower (due to dense vector field computations), they can do global as well as local motion estimation. Transform domain methods cannot normally do local motion, but are faster and more accurate on homogeneous images, and are resilient to even rapid illumination changes and large motion. However both these approaches can become very time consuming if one needs more accuracy and smoothness because of the nature of the tradeoff. We show here that wavelet transforms can be used in a novel way to achieve a very smooth stabilization along with a significant speedup in this Fourier domain computation without sacrificing ac...

The University of Arizona definition and implementation of the DEVS framework is well known in th... more The University of Arizona definition and implementation of the DEVS framework is well known in the community of researchers that work on DEVS [1,2,6]. Not only does it provide an Object-Oriented implementation in C++ (and Java), but it also has a tight HLA connectivity (which was replaced by the HLA Interface developed at Lockheed Martin [4,5]). We present a faster and more efficient implementation of the University of Arizona DEVS here, which improves and clarifies many features of their implementation.

We propose a Low-Dimensional Deep Feature based Face Alignment (LDFFA) method to address the prob... more We propose a Low-Dimensional Deep Feature based Face Alignment (LDFFA) method to address the problem of face alignment “in-the-wild”. Recently, Deep Bottleneck Features (DBF) has been proposed as an effective channel to represent input with compact, low-dimensional descriptors. The locations of fiducial landmarks of human faces could be effectively represented using low dimensional features due to the large correlation between them. In this paper, we propose a novel deep CNN with a bottleneck layer which learns to extract a low-dimensional representation (DBF) of the fiducial landmarks from images of human faces. We pre-train the CNN with a large dataset of synthetically annotated data so that the extracted DBFs are robust across variations in pose, occlusions, and illumination. Our experiments show that the proposed approach demonstrates near real-time performance and higher accuracy when compared with state-of-the-art results on numerous benchmarks.

Multi-task learning (MTL) involves the simultaneous training of two or more related tasks over sh... more Multi-task learning (MTL) involves the simultaneous training of two or more related tasks over shared representations. In this work, we apply MTL to audio-visual automatic speech recognition(AV-ASR). Our primary task is to learn a mapping between audio-visual fused features and frame labels obtained from acoustic GMM/HMM model. This is combined with an auxiliary task which maps visual features to frame labels obtained from a separate visual GMM/HMM model. The MTL model is tested at various levels of babble noise and the results are compared with a base-line hybrid DNN-HMM AV-ASR model. Our results indicate that MTL is especially useful at higher level of noise. Compared to base-line, upto 7\% relative improvement in WER is reported at -3 SNR dB

Interspeech 2018, Sep 2, 2018

Fibonacci Quarterly

ABSTRACT

Ars Combinatoria -Waterloo then Winnipeg-

ABSTRACT

2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2015

Monitoring health and fitness is emerging as an important benefit that smartphone users could exp... more Monitoring health and fitness is emerging as an important benefit that smartphone users could expect from their mobile devices today. Rule of thumb calorie tracking and recommendation based on selective activity monitoring is widely available today, as both on-device and server based solutions. What is surprisingly not available to the users is a simple application geared towards quantitative fitness tracking. Such an application potentially can be a direct indicator of one's cardio-vascular performance and associated long term health risks. Since wearable devices with various inbuilt sensors like accelerometer, gyroscope, SPO2 and heart rate are increasingly becoming available, it is vital that the enormous data coming from these sensors be used to perform analytics to uncover hidden health and fitness associated facts. A continuous estimation of fitness level employing these wearable devices can potentially help users in setting personalized short and long-term exercise goals leading to positive impact on one's overall health. The present work describes a step in this direction. This work involves an unobtrusive method to track an individual's physical activity seamlessly, estimate calorie consumption during a day by mapping the activity to the calories spent and assess fitness level using heart rate data from wearable sensors. We employ a heart rate based parameter called Endurance to quantitatively estimate cardio-respiratory fitness of a person. This opens up avenues for personalization and adaptiveness by dynamically using individual's personal fitness data towards building robust modeling based on analytical principles.

SIMULATION, 2001

This paper describes the design and development of the DEVS/GDDM environment, a layered simulatio... more This paper describes the design and development of the DEVS/GDDM environment, a layered simulation environment that supports data dis tribution management and allows us to study space-based quantization schemes. These schemes aim to achieve effective reduction of data commu nication in distributed simulation. After a brief review of the space-based quantization scheme and an HLA-Interface environment, we discuss the design issues of the DEVS/GDDM environ ment. We analyze system performance and scalability of the space-based quantization scheme, especially with predictive and multiplex ing extensions, and empirical results for a ballis tic missiles simulation executing on the DEVS/ GDDM environment on NT networking plat forms. The results indicate the DEVS/GDDM environment is very effective and scalable due to reduced local computation demands and ex tremely favorable communication data reduction.

Computer-Aided Design, 1989

ABSTRACT

2013 3rd IEEE International Advance Computing Conference (IACC), 2013

ABSTRACT

Video Stabilization, which is important for better analysis and user experience, is typically don... more Video Stabilization, which is important for better analysis and user experience, is typically done through Global Motion Estimation (GME) and Compensation. GME can be done in image domain using many techniques or in Transform domain using the well-known Phase Correlation methods which relate motion to phase shift in the spectrum. While image domain methods are generally slower (due to dense vector field computations), they can do global as well as local motion estimation. Transform domain methods cannot normally do local motion, but are faster and more accurate on homogeneous images, and are resilient to even rapid illumination changes and large motion. However both these approaches can become very time consuming if one needs more accuracy and smoothness because of the nature of the tradeoff. We show here that wavelet transforms can be used in a novel way to achieve a very smooth stabilization along with a significant speedup in this Fourier domain computation without sacrificing ac...

The University of Arizona definition and implementation of the DEVS framework is well known in th... more The University of Arizona definition and implementation of the DEVS framework is well known in the community of researchers that work on DEVS [1,2,6]. Not only does it provide an Object-Oriented implementation in C++ (and Java), but it also has a tight HLA connectivity (which was replaced by the HLA Interface developed at Lockheed Martin [4,5]). We present a faster and more efficient implementation of the University of Arizona DEVS here, which improves and clarifies many features of their implementation.

We propose a Low-Dimensional Deep Feature based Face Alignment (LDFFA) method to address the prob... more We propose a Low-Dimensional Deep Feature based Face Alignment (LDFFA) method to address the problem of face alignment “in-the-wild”. Recently, Deep Bottleneck Features (DBF) has been proposed as an effective channel to represent input with compact, low-dimensional descriptors. The locations of fiducial landmarks of human faces could be effectively represented using low dimensional features due to the large correlation between them. In this paper, we propose a novel deep CNN with a bottleneck layer which learns to extract a low-dimensional representation (DBF) of the fiducial landmarks from images of human faces. We pre-train the CNN with a large dataset of synthetically annotated data so that the extracted DBFs are robust across variations in pose, occlusions, and illumination. Our experiments show that the proposed approach demonstrates near real-time performance and higher accuracy when compared with state-of-the-art results on numerous benchmarks.

Multi-task learning (MTL) involves the simultaneous training of two or more related tasks over sh... more Multi-task learning (MTL) involves the simultaneous training of two or more related tasks over shared representations. In this work, we apply MTL to audio-visual automatic speech recognition(AV-ASR). Our primary task is to learn a mapping between audio-visual fused features and frame labels obtained from acoustic GMM/HMM model. This is combined with an auxiliary task which maps visual features to frame labels obtained from a separate visual GMM/HMM model. The MTL model is tested at various levels of babble noise and the results are compared with a base-line hybrid DNN-HMM AV-ASR model. Our results indicate that MTL is especially useful at higher level of noise. Compared to base-line, upto 7\% relative improvement in WER is reported at -3 SNR dB

Interspeech 2018, Sep 2, 2018

Fibonacci Quarterly

ABSTRACT

Ars Combinatoria -Waterloo then Winnipeg-

ABSTRACT