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Nasser Kehtarnavaz
University of Texas at Dallas
368Publications
31H-index
3,498Citations
Publications 368
Newest
Published on Jun 27, 2019
This paper presents a semi-supervised faster region-based convolutional neural network (SF-RCNN) approach to detect persons and to classify the load carried by them in video data captured from distances several miles away via high-power lens video cameras. For detection, a set of computationally efficient image processing steps are considered to identify moving areas that may contain a person. These areas are then passed onto a faster RCNN classifier whose convolutional layers consist of ResNet5...
Published on May 1, 2019 in ICASSP (International Conference on Acoustics, Speech, and Signal Processing)
Nasim Alamdari3
Estimated H-index: 3
(UTD: University of Texas at Dallas),
Edward Lobarinas20
Estimated H-index: 20
(UTD: University of Texas at Dallas)
+ 0 AuthorsNasser Kehtarnavaz31
Estimated H-index: 31
(UTD: University of Texas at Dallas)
This paper presents an educational tool to learn about how hearing aid compression fitting is prescribed from a signal processing perspective. An interactive web-based program has been developed based on the widely used DSL-v5 fitting rationale. This program can be accessed and used from any internet browser to generate the parameters of compression curves that correspond to the nine frequency bands used in DSL-v5. These parameters are then transferred to a smartphone in the form of a datafile t...
Published in arXiv: Learning
Abhishek Sehgal2
Estimated H-index: 2
,
Nasser Kehtarnavaz31
Estimated H-index: 31
(UTD: University of Texas at Dallas)
Deep learning solutions are being increasingly used in mobile applications. Although there are many open-source software tools for the development of deep learning solutions, there are no guidelines in one place in a unified manner for using these tools towards real-time deployment of these solutions on smartphones. From the variety of available deep learning tools, the most suited ones are used in this paper to enable real-time deployment of deep learning inference networks on smartphones. A un...
Published on Apr 26, 2019in arXiv: Audio and Speech Processing
Nasim Alamdari3
Estimated H-index: 3
,
Arian Azarang , Nasser Kehtarnavaz31
Estimated H-index: 31
This paper presents a self-supervised deep neural network solution to speech denoising by easing the requirement that clean speech signals need to be available for network training. This self-supervised approach is based on training a Fully Convolutional Neutral Network to map a noisy speech signal to another noisy version of the speech signal. To show the effectiveness of the developed approach, four commonly used objective performance measures are used to compare the self-supervised approach t...
Published in Sensors 3.03
Haoran Wei , Roozbeh Jafari29
Estimated H-index: 29
,
Nasser Kehtarnavaz31
Estimated H-index: 31
This paper presents the simultaneous utilization of video images and inertial signals that are captured at the same time via a video camera and a wearable inertial sensor within a fusion framework in order to achieve a more robust human action recognition compared to the situations when each sensing modality is used individually. The data captured by these sensors are turned into 3D video images and 2D inertial images that are then fed as inputs into a 3D convolutional neural network and a 2D co...
Published on Jan 1, 2019in IEEE Access 4.10
Arian Azarang (UTD: University of Texas at Dallas), Hafez Eslami Manoochehri (UTD: University of Texas at Dallas), Nasser Kehtarnavaz31
Estimated H-index: 31
(UTD: University of Texas at Dallas)
This paper presents a deep learning-based pansharpening method for fusion of panchromatic and multispectral images in remote sensing applications. This method can be categorized as a component substitution method in which a convolutional autoencoder network is trained to generate original panchromatic images from their spatially degraded versions. Low resolution multispectral images are then fed into the trained convolutional autoencoder network to generate estimated high resolution multispectra...
Published on Jan 1, 2019
Nasim Alamdari3
Estimated H-index: 3
(UTD: University of Texas at Dallas),
Nasser Kehtarnavaz31
Estimated H-index: 31
(UTD: University of Texas at Dallas)
This paper presents a real-time unsupervised noise classifier smartphone app which is designed to operate in realistic audio environments. This app addresses the two limitations of a previously developed smartphone app for unsupervised noise classification. A voice activity detection is added to separate the presence of speech frames from noise frames and thus to lower misclassifications when operating in realistic audio environments. In addition, buffers are added to allow a stable operation of...
Published on Jan 1, 2019
Neha Dawar (UTD: University of Texas at Dallas), Sarah Ostadabbas10
Estimated H-index: 10
(NU: Northeastern University),
Nasser Kehtarnavaz31
Estimated H-index: 31
(UTD: University of Texas at Dallas)
This article covers a deep learning-based decision fusion approach for action or gesture recognition via simultaneous utilization of a depth camera and a wearable inertial sensor. The deep learning approach involves using a convolutional neural network (CNN) for depth images captured by a depth camera and a combination of CNN and long short–term memory network for inertial signals captured by a wearable inertial sensor, followed by a decision-level fusion. Due to the limited size of the training...
Published on Dec 17, 2018in Synthesis Lectures on Signal Processing
Nasser Kehtarnavaz31
Estimated H-index: 31
(UTD: University of Texas at Dallas),
Abhishek Sehgal2
Estimated H-index: 2
(UTD: University of Texas at Dallas),
Shane Parris2
Estimated H-index: 2
(UTD: University of Texas at Dallas)
Real-time or applied digital signal processing courses are offered as follow-ups to conventional or theory-oriented digital signal processing courses in many engineering programs for the purpose of teaching students the technical know-how for putting signal processing algorithms or theory into practical use. These courses normally involve access to a teaching laboratory that is equipped with hardware boards, in particular DSP boards, together with their supporting software. A number of textbooks...
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