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Award ID contains: 1637092

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  1. ; ; (, Neurocomputing)
    Full Text Available 
  2. ; ; ; (, Biomedical signal processing and control)
    This paper presents the use of a Restricted Boltzmann Machine to develop an unsupervised machine learning approach to process breathing sounds to predict breathing rates and depth or length of breaths. Breath detection and monitoring has been the subject of several studies involving the health monitoring of patients on respirators. We are proposing to extend the use of non-invasive techniques to provide measures of physical exhaustion or activity. The level of activity or exhaustion could be used to prevent accidents or manage exposure to physically demanding environments such as firefighting or working underwater. 
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    Accepted Manuscript Full Text Available
  3. ; (, Lecture notes in computer science)
    Pedestrian Detection in real world crowded areas is still one of the challenging categories in object detection problems. Various mod- ern detection architectures such as Faster R-CNN, R-FCN and SSD has been analyzed based on speed and accuracy measurements. These mod- els can detect multiple objects with overlaps and localize them using a bounding box framing it. Evaluation of performance parameters pro- vides high speed models which can work on live stream applications in mobile devices or high accurate models which provide state-of-the-art performance for various detection problems.These convolutional neural network models are tested on the Penn-Fudan Dataset as well as Google images with occlusions, which achieves high detection accuracies on each of the detectors. 
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    Accepted Manuscript Full Text Available