Department of Electrical and Computer Engineering Works

Department of Electrical and Computer Engineering Works

 

Recent Submissions

  • Ayi, Maneesh; El-Sharkawy, Mohamed (IEEE, 2020-01)
    In this paper, we developed a new architecture called Reduced Mobilenet V2 (RMNv2) for CIFAR10 dataset. The baseline architecture of our network is Mobilenet V2. RMNv2 is architecturally modified version of Mobilenet V2. ...
  • Sinha, Debjyoti; El-Sharkawy, Mohamed (IEEE, 2019-10)
    In the field of computer, mobile and embedded vision Convolutional Neural Networks (CNNs) are deep learning models which play a significant role in object detection and recognition. MobileNet is one such efficient, ...
  • Chitanvis, Rajas; Ravi, Niranjan; Zantye, Tanmay; El-Sharkawy, Mohamed (IEEE, 2019-07)
    According to the World Health Organizations (WHO) report nearly 1.25 million people die in road accidents every year. This creates a need for Advanced Driver Assist Systems (ADAS) which can ensure safe travel. To tackle ...
  • Rathi, Neeraj; Kakani, Monika; Rizkalla, Maher; El-Sharkawy, Mohamed (IEEE, 2018-08)
    Fall in recent years have become a potential threat to elder generation. It occurs because of side effects of medication, lack of physical activities, limited vision, and poor mobility. Looking at the problems faced by ...
  • Lotfalizadeh, Hamidreza; Kim, Dongso S. (IEEE, 2020-01)
    With the advent of IoT devices and exponential growth of nodes on the internet, computer networks are facing new challenges, with one of the more important ones being DDoS attacks. In this paper, new features to detect ...
  • Li, Jingjing; Jing, Mengmeng; Lu, Ke; Ding, Zhengming; Zhu, Lei; Huang, Zi (IEEE, 2019)
    Conventional zero-shot learning (ZSL) methods generally learn an embedding, e.g., visual-semantic mapping, to handle the unseen visual samples via an indirect manner. In this paper, we take the advantage of generative ...
  • Katare, Dewant; El-Sharkawy, Mohamed (IEEE, 2019-07)
    Present day autonomous vehicle relies on several sensor technologies for it's autonomous functionality. The sensors based on their type and mounted-location on the vehicle, can be categorized as: line of sight and non-line ...
  • Ravi, Niranjan; El-Sharkawy, Mohamed (IEEE, 2019-10)
    In todays world, the applications of Unmanned Aerial Vehicle (UAV) systems are leaping by extending their scope from military applications on to commercial and medical sectors as well. Owing to this commercialization, the ...
  • Shayesteh, Seemein; Cochran, Zachary; Dhavalikar, Raj; Huelsman, Ian; Madan, Akul; Peters, Taylor; Yago, Ahmed; Wible, Grant; Rizkalla, Maher (IEEE, 2019-10)
    This Full Innovative Practice paper presents a new Peer-Led team Learning (PLTL) recitation model for the sophomore Electronics Analysis and Design course, emphasizing device physics, device models, and analog and digital ...
  • Kollazhi Manghat, Surya; El-Sharkawy, Mohamed (IEEE, 2019-09)
    Safety is the key aspect when comes to driving. Self-driving vehicles are equipped with driver-assistive technologies like Adaptive Cruise Control, Forward Collision Warning system (FCW) and Collsion Mitigation by Breaking ...
  • Duggal, Jayan Kant; El-Sharkawy, Mohamed (IEEE, 2019-07)
    CNNs is the foundation for deep learning and computer vision domain enabling applications such as autonomous driving, face recognition, automatic radiology image reading, etc. But, CNN is a algorithm which is memory and ...
  • Desai, Saurabh Ravindra; Sinha, Debjyoti; El-Sharkawy, Mohamed (IEEE, 2020-01)
    Deep Neural Networks play a very significant role in computer vision applications like image classification, object recognition and detection. They have achieved great success in this field but the main obstacles for ...
  • Duggal, Jayan Kant; El-Sharkawy, Mohamed (IEEE, 2019-09)
    CNN has gained great success in many applications but the major design hurdles for deploying CNN on driver assistance systems or ADAS are limited computation, memory resource, and power budget. Recently, there has been ...
  • Chappa, Ravi Teja N. V. S.; El-Sharkawy, Mohamed (IEEE, 2020-01)
    Convolution neural network is being used in field of autonomous driving vehicles or driver assistance systems (ADAS), and has achieved great success. Before the convolution neural network, traditional machine learning ...
  • Bhamidi, Sree Bala Shruthi; El-Sharkawy, Mohamed (IEEE, 2019-10)
    Convolution Neural Network (CNN) has been the most influential innovations in the filed of Computer Vision. CNN have shown a substantial improvement in the field of Machine Learning. But they do come with their own set of ...
  • Lee, Soonam; Han, Shuo; Salama, Paul; Dunn, Kenneth W.; Delp, Edward J. (IEEE, 2019)
    Due to image blurring image deconvolution is often used for studying biological structures in fluorescence microscopy. Fluorescence microscopy image volumes inherently suffer from intensity inhomogeneity, blur, and are ...
  • Bagwe, Rishikesh Mahesh; Byerly, Andy; dos Santos, Euzeli Cipriano, Jr. (MDPI, 2019)
    This paper proposes an Adaptive Rule-Based Energy Management Strategy (ARBS EMS) for a parallel hybrid electric vehicle (HEV). The aim of the strategy is to facilitate the aftermarket hybridization of medium- and heavy-duty ...
  • Katare, Dewant; El-Sharkawy, Mohamed (IEEE, 2019-09)
    An Autonomous vehicle or present day smart vehicle is equipped with several ADAS safety features such as Blind Spot Detection, Forward Collision Warning, Lane Departure and Parking Assistance, Surround View System, Vehicular ...
  • Ho, David Joon; Han, Shuo; Fu, Chichen; Salama, Paul; Dunn, Kenneth W.; Delp, Edward J. (IEEE, 2019-05)
    Fluorescence microscopy is an essential tool for the analysis of 3D subcellular structures in tissue. An important step in the characterization of tissue involves nuclei segmentation. In this paper, a two-stage method for ...
  • Bhamidi, Sree Bala Shruthi; El-Sharkawy, Mohamed (IEEE, 2020-02)
    The Convolutional Neural Network (CNN) have shown a substantial improvement in the field of Machine Learning. But they do come with their own set of drawbacks. Capsule Networks have addressed the limitations of CNNs and ...

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