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- University of Dayton Electro-optics and Photonics (15)
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Articles 91 - 120 of 625
Full-Text Articles in Engineering
Hydrothermal Growth Of Zinc Oxide (Zno) Nanorods (Nrs) On Screen Printed Ides For Ph Measurement Application, Akshaya Kumar A., Naveen Kumar S.K., Almaw Ayele Aniley, Renny Edwin Fernandez, Shekhar Bhansali
Hydrothermal Growth Of Zinc Oxide (Zno) Nanorods (Nrs) On Screen Printed Ides For Ph Measurement Application, Akshaya Kumar A., Naveen Kumar S.K., Almaw Ayele Aniley, Renny Edwin Fernandez, Shekhar Bhansali
Electrical and Computer Engineering Faculty Publications
There is considerable interest in nanostructured materials with interdigitated electrodes (IDEs) platforms to detect and monitor the level of various ions in numerous applications. Herein, we report the design and fabrication of IDEs based pH sensor by using hydrothermal growth of ZnO nanorods (NRs). A four-step deposition of ZnO seed layer followed by a hydrothermal treatment lead to the heavily ordered ZnO NRs patterns on the screen printed IDEs. The structural, chemical compositional and electrical properties of the NRs were investigated and examined by using field emission scanning electron microscopy (FeSEM), atomic force microscopy (AFM), energy dispersive spectroscopy (EDS), X-ray …
Single And Multiobjective Optimal Reactive Power Dispatch Based On Hybrid Artificial Physics–Particle Swarm Optimization, Tawfiq M. Aljohani, Ahmed F. Ebrahim, Osama A. Mohammed
Single And Multiobjective Optimal Reactive Power Dispatch Based On Hybrid Artificial Physics–Particle Swarm Optimization, Tawfiq M. Aljohani, Ahmed F. Ebrahim, Osama A. Mohammed
Electrical and Computer Engineering Faculty Publications
The optimal reactive power dispatch (ORPD) problem represents a noncontinuous, nonlinear, highly constrained optimization problem that has recently attracted wide research investigation. This paper presents a new hybridization technique for solving the ORPD problem based on the integration of particle swarm optimization (PSO) with artificial physics optimization (APO). This hybridized algorithm is tested and verified on the IEEE 30, IEEE 57, and IEEE 118 bus test systems to solve both single and multiobjective ORPD problems, considering three main aspects. These aspects include active power loss minimization, voltage deviation minimization, and voltage stability improvement. The results prove that the algorithm is …
Automatic Look-Up Table Based Real-Time Phase Unwrapping For Phase Measuring Profilometry And Optimal Reference Frequency Selection, Jianwen Song, Daniel L. Lau, Yo-Sung Ho, Kai Liu
Automatic Look-Up Table Based Real-Time Phase Unwrapping For Phase Measuring Profilometry And Optimal Reference Frequency Selection, Jianwen Song, Daniel L. Lau, Yo-Sung Ho, Kai Liu
Electrical and Computer Engineering Faculty Publications
For temporal phase unwrapping in phase measuring profilometry, it has recently been reported that two phases with co-prime frequencies can be absolutely unwrapped using a look-up table; however, frequency selection and table construction has been performed manually without optimization. In this paper, a universal phase unwrapping method is proposed to unwrap phase flexibly and automatically by using geometric analysis, and thus we can programmatically build a one-dimensional or two-dimensional look-up table for arbitrary two co-prime frequencies to correctly unwrap phases in real time. Moreover, a phase error model related to the defocus effect is derived to figure out an optimal …
Sliding-Mode-Observer-Based Position Estimation For Sensorless Control Of The Planar Switched Reluctance Motor, Jundi Sun, Guang-Zhong Cao, Su-Dan Huang, Yeping Peng, Jiangbiao He, Qing-Quan Qian
Sliding-Mode-Observer-Based Position Estimation For Sensorless Control Of The Planar Switched Reluctance Motor, Jundi Sun, Guang-Zhong Cao, Su-Dan Huang, Yeping Peng, Jiangbiao He, Qing-Quan Qian
Electrical and Computer Engineering Faculty Publications
This paper proposes a position estimation method for a planar switched reluctance motor (PSRM). In the method, a second-order sliding mode observer (SMO) is used to achieve sensorless control of a PSRM for the first time. A sensorless closed-loop control strategy based on the SMO without a position sensor for the PSRM is constructed. The SMO mainly consists of a flux linkage estimation, an adaptive current estimation, an observing error calculation, and a position estimation section. An adaptive current observer is applied in the current estimation section to minimize the error between the measured and estimated currents and to increase …
Details On Csa-Sbl: An Algorithm For Sparse Bayesian Learning Boosted By Partial Erroneous Support Knowledge, Mohammad Shekaramiz, Todd K. Moon, Jacob H. Gunther
Details On Csa-Sbl: An Algorithm For Sparse Bayesian Learning Boosted By Partial Erroneous Support Knowledge, Mohammad Shekaramiz, Todd K. Moon, Jacob H. Gunther
Electrical and Computer Engineering Faculty Publications
This report provides details on CSA-SBL(VB) algorithm for the recovery of sparse signals with unknown clustering pattern. More specifically, we deal with the recovery of sparse signals with unknown clustering pattern in the case of having partial erroneous prior knowledge on the supports of the signal. In [1], we provided a modified sparse Bayesian learning model to incorporate prior knowledge and simultaneously learn the unknown clustering pattern. For this purpose, we added one more layer to support-aided sparse Bayesian learning algorithm (SA-SBL) that was proposed in [2]. This layer adds a prior on the shape parameters of Gamma distributions, those …
Bayesian Compressive Sensing Of Sparse Signals With Unknown Clustering Patterns, Mohammad Shekaramiz, Todd K. Moon, Jacob H. Gunther
Bayesian Compressive Sensing Of Sparse Signals With Unknown Clustering Patterns, Mohammad Shekaramiz, Todd K. Moon, Jacob H. Gunther
Electrical and Computer Engineering Faculty Publications
We consider the sparse recovery problem of signals with an unknown clustering pattern in the context of multiple measurement vectors (MMVs) using the compressive sensing (CS) technique. For many MMVs in practice, the solution matrix exhibits some sort of clustered sparsity pattern, or clumpy behavior, along each column, as well as joint sparsity across the columns. In this paper, we propose a new sparse Bayesian learning (SBL) method that incorporates a total variation-like prior as a measure of the overall clustering pattern in the solution. We further incorporate a parameter in this prior to account for the emphasis on the …
A State-Of-The-Art Survey On Deep Learning Theory And Architectures, Md Zahangir Alom, Tarek M. Taha, Christopher Yakopcic, Stefan Westberg, Paheding Sidike, Mst Shamima Nasrin, Mahmudul Hasan, Brian C. Van Essen, Abdul A. S. Awwal, Vijayan K. Asari
A State-Of-The-Art Survey On Deep Learning Theory And Architectures, Md Zahangir Alom, Tarek M. Taha, Christopher Yakopcic, Stefan Westberg, Paheding Sidike, Mst Shamima Nasrin, Mahmudul Hasan, Brian C. Van Essen, Abdul A. S. Awwal, Vijayan K. Asari
Electrical and Computer Engineering Faculty Publications
In recent years, deep learning has garnered tremendous success in a variety of application domains. This new field of machine learning has been growing rapidly and has been applied to most traditional application domains, as well as some new areas that present more opportunities. Different methods have been proposed based on different categories of learning, including supervised, semi-supervised, and un-supervised learning. Experimental results show state-of-the-art performance using deep learning when compared to traditional machine learning approaches in the fields of image processing, computer vision, speech recognition, machine translation, art, medical imaging, medical information processing, robotics and control, bioinformatics, natural language …
Design And Analysis Of Long-Stroke Planar Switched Reluctance Motor For Positioning Applications, Su-Dan Huang, Guang-Zhong Cao, Yeping Peng, Chao Wu, Deliang Liang, Jiangbiao He
Design And Analysis Of Long-Stroke Planar Switched Reluctance Motor For Positioning Applications, Su-Dan Huang, Guang-Zhong Cao, Yeping Peng, Chao Wu, Deliang Liang, Jiangbiao He
Electrical and Computer Engineering Faculty Publications
This paper presents the design, control, and experimental performance evaluation of a long-stroke planar switched reluctance motor (PSRM) for positioning applications. Based on comprehensive consideration of the electromagnetic and mechanical characteristics of the PSRM, a motor design is first developed to reduce the force ripple and deformation. A control scheme with LuGre friction compensation is then proposed to improve the positioning accuracy of the PSRM. Furthermore, this control scheme is proven to ensure the stable motion of the PSRM system. Additionally, the response speed and steady-state error of the PSRM system with this control scheme are theoretically analyzed. Finally, the …
Details On O-Sbl(Mcmc): A Compressive Sensing Algorithm For Sparse Signal Recovery For The Smv/Mmv Problem Using Sparse Bayesian Learning And Markov Chain Monte Carlo Inference, Mohammad Shekaramiz, Todd K. Moon, Jacob H. Gunther
Details On O-Sbl(Mcmc): A Compressive Sensing Algorithm For Sparse Signal Recovery For The Smv/Mmv Problem Using Sparse Bayesian Learning And Markov Chain Monte Carlo Inference, Mohammad Shekaramiz, Todd K. Moon, Jacob H. Gunther
Electrical and Computer Engineering Faculty Publications
This report provides details on O-SBL(MCMC) algorithm for the recovery of jointly-sparse signals for the multiple measurement vector (MMV) problem. For the MMVs with this structure, the solution matrix, which is a collection of sparse vectors, is expected to exhibit joint sparsity across the columns. The notion of joint sparsity here means that the columns of the solution matrix share common supports. This algorithm employs a sparse Bayesian learning (SBL) model to encourage the joint sparsity structure across the columns of the solution. While the proposed algorithm is constructed for the MMV problems, it can also be applied to the …
A Note On Bayesian Linear Regression, Mohammad Shekaramiz, Todd K. Moon
A Note On Bayesian Linear Regression, Mohammad Shekaramiz, Todd K. Moon
Electrical and Computer Engineering Faculty Publications
In this report, we briefly discuss Bayesian linear regression as well as the proof for the inference to perform prediction based on the training data using this technique.
Details On Amp-B-Sbl: An Algorithm For Recovery Of Clustered Sparse Signals Using Approximate Message Passing [1-3], Mohammad Shekaramiz, Todd K. Moon, Jacob H. Gunther
Details On Amp-B-Sbl: An Algorithm For Recovery Of Clustered Sparse Signals Using Approximate Message Passing [1-3], Mohammad Shekaramiz, Todd K. Moon, Jacob H. Gunther
Electrical and Computer Engineering Faculty Publications
Solving the inverse problem of compressive sensing in the context of single measurement vector (SMV) problem with an unknown block-sparsity structure is considered. For this purpose, we propose a sparse Bayesian learning (SBL) algorithm simplified via the approximate message passing (AMP) framework. In order to encourage the block-sparsity structure, we incorporate the concept of total variation, called Sigma-Delta, as a measure of block-sparsity on the support set of the solution. The AMP framework reduces the computational load of the proposed SBL algorithm and as a result makes it faster compared to the message passing framework. Furthermore, in terms of the …
A Note On Kriging And Gaussian Processes, Mohammad Shekaramiz, Todd K. Moon, Jacob H. Gunther
A Note On Kriging And Gaussian Processes, Mohammad Shekaramiz, Todd K. Moon, Jacob H. Gunther
Electrical and Computer Engineering Faculty Publications
An introduction to gaussian processes and kriging.
Details On Gaussian Process Regression (Gpr) And Semi-Gpr Modeling, Mohammad Shekaramiz, Todd K. Moon, Jacob H. Gunther
Details On Gaussian Process Regression (Gpr) And Semi-Gpr Modeling, Mohammad Shekaramiz, Todd K. Moon, Jacob H. Gunther
Electrical and Computer Engineering Faculty Publications
This report tends to provide details on how to perform predictions using Gaussian process regression (GPR) modeling. In this case, we represent proofs for prediction using non-parametric GPR modeling for noise-free predictions as well as prediction using semi-parametric GPR for noisy observations.
On The Stability Analysis Of Perturbed Continuous T-S Fuzzy Models, Mohammad Shekaramiz, Farid Sheikholeslam
On The Stability Analysis Of Perturbed Continuous T-S Fuzzy Models, Mohammad Shekaramiz, Farid Sheikholeslam
Electrical and Computer Engineering Faculty Publications
This paper deals with the stability problem of continuous-time Takagi-Sugeno (T-S) fuzzy models. Based on the Tanaka and Sugeno theorem, a new systematic method is introduced to investigate the asymptotic stability of T-S models in case of having second-order and symmetric state matrices. This stability criterion has the merit that selection of the common positive-definite matrix P is independent of the sub-diagonal entries of the state matrices. It means for a set of fuzzy models having the same main diagonal state matrices, it suffices to apply the method once. Furthermore, the method can be applied to T-S models having certain …
On The Stability Analysis Of Linear Continuous-Time Distributed Systems, Mohammad Shekaramiz
On The Stability Analysis Of Linear Continuous-Time Distributed Systems, Mohammad Shekaramiz
Electrical and Computer Engineering Faculty Publications
This paper discusses the stability problem of linear continuous-time distributed systems. When dealing with large-scale systems, usually there is not thorough knowledge of the interconnection models between different parts of the entire system. In this case, a useful stability analysis method should be able to deal with high dimensional systems accompanied with bounded uncertainties for its interconnections. In this paper, in order to formulate the stability criterion for large-scale systems, stability analysis of LTI systems is first considered. Based on the existing methods for estimating the spectra of square matrices, sufficient criteria are proposed to guarantee the asymptotic stability of …
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 …
Performance Analysis Of Feature Selection Techniques For Support Vector Machine And Its Application For Lung Nodule Detection, Barath Narayanan, Russell C. Hardie, Temesguen Messay Kebede
Performance Analysis Of Feature Selection Techniques For Support Vector Machine And Its Application For Lung Nodule Detection, Barath Narayanan, Russell C. Hardie, Temesguen Messay Kebede
Electrical and Computer Engineering Faculty Publications
Lung cancer typically exhibits its presence with the formation of pulmonary nodules. Computer Aided Detection (CAD) of such nodules in CT scans would be of valuable help in lung cancer screening. Typical CAD system is comprised of a candidate detector and a feature-based classifier. In this research, we study and explore the performance of Support Vector Machine (SVM) based on a large set of features. We study the performance of SVM as a function of the number of features. Our results indicate that SVM is more robust and computationally faster with a large set of features and less prone to …
Intelligent Sensing And Decision Making In Smart Technologies, Wenbing Zhao, Jinsong Wu, Peng Shi, Hongqiao Wang
Intelligent Sensing And Decision Making In Smart Technologies, Wenbing Zhao, Jinsong Wu, Peng Shi, Hongqiao Wang
Electrical and Computer Engineering Faculty Publications
No abstract provided.
Trident: Comprehensive Choke Error Mitigation In Ntc Systems, Aatreyi Bal, Sanghamitra Roy, Koushik Chakraborty
Trident: Comprehensive Choke Error Mitigation In Ntc Systems, Aatreyi Bal, Sanghamitra Roy, Koushik Chakraborty
Electrical and Computer Engineering Faculty Publications
Near threshold computing (NTC) systems have been inherently plagued with heightened process variation (PV) sensitivity. Choke points are an intriguing manifestation of this PV sensitivity. In this paper, we explore the probability of minimum timing violations, caused by choke points, in an NTC system, and their nontrivial impacts on the system reliability. We show that conventional timing error mitigation techniques are inefficient in tackling choke point-induced minimum timing violations. Consequently, we propose a comprehensive error mitigation technique, Trident, to tackle choke points at NTC. Trident offers a 1.37 × performance improvement and a 1.11 × energy-efficiency gain over Razor at …
Short-Term Wind Speed Forecasting Via Stacked Extreme Learning Machine With Generalized Correntropy, Xiong Luo, Jiankun Sun, Long Wang, Weiping Wang, Wenbing Zhao, Jinsong Wu, Jenq-Haur Wang, Zijun Zhang
Short-Term Wind Speed Forecasting Via Stacked Extreme Learning Machine With Generalized Correntropy, Xiong Luo, Jiankun Sun, Long Wang, Weiping Wang, Wenbing Zhao, Jinsong Wu, Jenq-Haur Wang, Zijun Zhang
Electrical and Computer Engineering Faculty Publications
Recently, wind speed forecasting as an effective computing technique plays an important role in advancing industry informatics, while dealing with these issues of control and operation for renewable power systems. However, it is facing some increasing difficulties to handle the large-scale dataset generated in these forecasting applications, with the purpose of ensuring stable computing performance. In response to such limitation, this paper proposes a more practical approach through the combination of extreme-learning machine (ELM) method and deep-learning model. ELM is a novel computing paradigm that enables the neural network (NN) based learning to be achieved with fast training speed and …
Active Disturbance Rejection Control Of Lcl-Filtered Grid-Connected Inverter Using Pade Approximation, Abdeldjabar Benrabah, Dianguo Xu, Zhiqiang Gao
Active Disturbance Rejection Control Of Lcl-Filtered Grid-Connected Inverter Using Pade Approximation, Abdeldjabar Benrabah, Dianguo Xu, Zhiqiang Gao
Electrical and Computer Engineering Faculty Publications
In this paper, a simplified robust control is proposed to improve the performance of a three-phase current controlled voltage source inverter connected to the grid through an inductive-capacitive-inductive ( LCL) filter. The presence of the LCL-filter resonance complicates the dynamics of the control system and limits its overall performance, particularly when disturbances and parametric uncertainty are considered. To solve this problem, a robust active damping method based on the linear active disturbance rejection control (LADRC) is proposed. The simplification is made possible by order reduction in the plant transfer function using Padé approximation. Simulation results show that the proposed LADRC-based …
Resource Allocation In The Cognitive Radio Network-Aided Internet Of Things For The Cyber-Physical-Social System: An Efficient Jaya Algorithm, Xiong Luo, Zhijie He, Zhigang Zhao, Long Wang, Weiping Wang, Huansheng Ning, Jenq-Haur Wang, Wenbing Zhao, Jun Zhang
Resource Allocation In The Cognitive Radio Network-Aided Internet Of Things For The Cyber-Physical-Social System: An Efficient Jaya Algorithm, Xiong Luo, Zhijie He, Zhigang Zhao, Long Wang, Weiping Wang, Huansheng Ning, Jenq-Haur Wang, Wenbing Zhao, Jun Zhang
Electrical and Computer Engineering Faculty Publications
Currently, there is a growing demand for the use of communication network bandwidth for the Internet of Things (IoT) within the cyber-physical-social system (CPSS), while needing progressively more powerful technologies for using scarce spectrum resources. Then, cognitive radio networks (CRNs) as one of those important solutions mentioned above, are used to achieve IoT effectively. Generally, dynamic resource allocation plays a crucial role in the design of CRN-aided IoT systems. Aiming at this issue, orthogonal frequency division multiplexing (OFDM) has been identified as one of the successful technologies, which works with a multi-carrier parallel radio transmission strategy. In this article, through …
Design Of An Automatic Defect Identification Method Based Ecpt For Pneumatic Pressure Equipment, Bo Zhang, Yuhua Cheng, Chun Yin, Xuegang Huang, Sara Dadras, Hadi Malek
Design Of An Automatic Defect Identification Method Based Ecpt For Pneumatic Pressure Equipment, Bo Zhang, Yuhua Cheng, Chun Yin, Xuegang Huang, Sara Dadras, Hadi Malek
Electrical and Computer Engineering Faculty Publications
In this paper, in order to achieve automatic defect identification for pneumatic pressure equipment, an improved feature extraction algorithm eddy current pulsed thermography (ECPT) is presented. The presented feature extraction algorithm contains four elements: data block selection; variable step search; relation value classification; and between-class distance decision function. The data block selection and variable step search are integrated to decrease the redundant computations in the automatic defect identification. The goal of the classification and between-class distance calculation is to select the typical features of thermographic sequence. The main image information can be extracted by the method precisely and efficiently. Experimental …