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Articles 31 - 38 of 38
Full-Text Articles in Engineering
Simple Stability Criteria For Uncertain Continuous-Time Linear Systems, Mohammad Shekaramiz
Simple Stability Criteria For Uncertain Continuous-Time Linear Systems, Mohammad Shekaramiz
Electrical and Computer Engineering Faculty Publications
This paper mainly deals with the stability problem of continuous-time linear systems having uncertainties. Instead of using the tradition types of Lyapunov functions, this paper provides a very different method to investigate the stability of such systems. Hence, it reduces the conservativeness of having structured uncertainties belonging to convex sets. Based on a famous theorem that specifies regions containing all the eigenvalues of a complex square matrix, sufficient criteria are proposed to guarantee the asymptotic stability of linear systems. The main merit of this method is in analyzing linear systems having uncertainties. Moreover, the proposed criteria can also be used …
Performance Analysis Of Machine Learning And Deep Learning Architectures For Malaria Detection On Cell Images, Barath Narayanan, Redha Ali, Russell C. Hardie
Performance Analysis Of Machine Learning And Deep Learning Architectures For Malaria Detection On Cell Images, Barath Narayanan, Redha Ali, Russell C. Hardie
Electrical and Computer Engineering Faculty Publications
Plasmodium malaria is a parasitic protozoan that causes malaria in humans. Computer aided detection of Plasmodium is a research area attracting great interest. In this paper, we study the performance of various machine learning and deep learning approaches for the detection of Plasmodium on cell images from digital microscopy. We make use of a publicly available dataset composed of 27,558 cell images with equal instances of parasitized (contains Plasmodium) and uninfected (no Plasmodium) cells. We randomly split the dataset into groups of 80% and 20% for training and testing purposes, respectively. We apply color constancy and spatially resample all images …
State Estimation For An Agonistic‐Antagonistic Muscle System, Thang Tien Nguyen, Holly Warner, Hung La, Hanieh Mohammadi, Daniel J. Simon, Hanz Richter
State Estimation For An Agonistic‐Antagonistic Muscle System, Thang Tien Nguyen, Holly Warner, Hung La, Hanieh Mohammadi, Daniel J. Simon, Hanz Richter
Electrical and Computer Engineering Faculty Publications
Research on assistive technology, rehabilitation, and prosthetics requires the understanding of human machine interaction, in which human muscular properties play a pivotal role. This paper studies a nonlinear agonistic‐antagonistic muscle system based on the Hill muscle model. To investigate the characteristics of the muscle model, the problem of estimating the state variables and activation signals of the dual muscle system is considered. In this work, parameter uncertainty and unknown inputs are taken into account for the estimation problem. Three observers are presented: a high gain observer, a sliding mode observer, and an adaptive sliding mode observer. Theoretical analysis shows the …
Active Recall Networks For Multiperspectivity Learning Through Shared Latent Space Optimization, Theus Aspiras, Ruixu Liu, Vijayan K. Asari
Active Recall Networks For Multiperspectivity Learning Through Shared Latent Space Optimization, Theus Aspiras, Ruixu Liu, Vijayan K. Asari
Electrical and Computer Engineering Faculty Publications
Given that there are numerous amounts of unlabeled data available for usage in training neural networks, it is desirable to implement a neural network architecture and training paradigm to maximize the ability of the latent space representation. Through multiple perspectives of the latent space using adversarial learning and autoencoding, data requirements can be reduced, which improves learning ability across domains. The entire goal of the proposed work is not to train exhaustively, but to train with multiperspectivity. We propose a new neural network architecture called Active Recall Network (ARN) for learning with less labels by optimizing the latent space. This …
Recurrent Residual U-Net For Medical Image Segmentation, Md Zahangir Alom, Christopher Yakopcic, Mahmudul Hasan, Tarek M. Taha, Vijayan K. Asari
Recurrent Residual U-Net For Medical Image Segmentation, Md Zahangir Alom, Christopher Yakopcic, Mahmudul Hasan, Tarek M. Taha, Vijayan K. Asari
Electrical and Computer Engineering Faculty Publications
Deep learning (DL)-based semantic segmentation methods have been providing state-of-the-art performance in the past few years. More specifically, these techniques have been successfully applied in medical image classification, segmentation, and detection tasks. One DL technique, U-Net, has become one of the most popular for these applications. We propose a recurrent U-Net model and a recurrent residual U-Net model, which are named RU-Net and R2U-Net, respectively. The proposed models utilize the power of U-Net, residual networks, and recurrent convolutional neural networks. There are several advantages to using these proposed architectures for segmentation tasks. First, a residual unit helps when training deep …
Deep Temporal Convolutional Networks For Short-Term Traffic Flow Forecasting, Wentian Zhao, Yanyun Gao, Tingxiang Ji, Xili Wan, Feng Ye, Guangwei Bai
Deep Temporal Convolutional Networks For Short-Term Traffic Flow Forecasting, Wentian Zhao, Yanyun Gao, Tingxiang Ji, Xili Wan, Feng Ye, Guangwei Bai
Electrical and Computer Engineering Faculty Publications
To reduce the increasingly congestion in cities, it is essential for intelligent transportation system (ITS) to accurately forecast the short-term traffic flow to identify the potential congestion sites. In recent years, the emerging deep learning method has been introduced to design traffic flow predictors, such as recurrent neural network (RNN) and long short-term memory (LSTM), which has demonstrated its promising results. In this paper, different from existing work, we study the temporal convolutional network (TCN) and propose a deep learning framework based on TCN model for short-term city-wide traffic forecast to accurately capture the temporal and spatial evolution of traffic …
A Survey Of Techniques For Mobile Service Encrypted Traffic Classification Using Deep Learning, Pan Wang, Xuejiao Chen, Feng Ye, Zhixin Sun
A Survey Of Techniques For Mobile Service Encrypted Traffic Classification Using Deep Learning, Pan Wang, Xuejiao Chen, Feng Ye, Zhixin Sun
Electrical and Computer Engineering Faculty Publications
The rapid adoption of mobile devices has dramatically changed the access to various net- working services and led to the explosion of mobile service traffic. Mobile service traffic classification has been a crucial task that attracts strong interest in mobile network management and security as well as machine learning communities for past decades. However, with more and more adoptions of encryption over mobile services, it brings a lot of challenges about mobile traffic classification. Although classical machine learning approaches can solve many issues that port and payload-based methods cannot solve, it still has some limitations, such as time-consuming, costly handcrafted …
High Order Volumetric Directional Pattern For Video-Based Face Recognition, Almabrok Essa, Vijayan Asari
High Order Volumetric Directional Pattern For Video-Based Face Recognition, Almabrok Essa, Vijayan Asari
Electrical and Computer Engineering Faculty Publications
Describing the dynamic textures has attracted growing attention in the field of computer vision and pattern recognition. In this paper, a novel approach for recognizing dynamic textures, namely, high order volumetric directional pattern (HOVDP), is proposed. It is an extension of the volumetric directional pattern (VDP) which extracts and fuses the temporal information (dynamic features) from three consecutive frames. HOVDP combines the movement and appearance features together considering the nth order volumetric directional variation patterns of all neighboring pixels from three consecutive frames. In experiments with two challenging video face databases, YouTube Celebrities and Honda/UCSD, HOVDP clearly outperformed a set …