Engineering Commons^™

Review—Machine Learning Techniques In Wireless Sensor Network Based Precision Agriculture, Yemeserach Mekonnen, Srikanth Namuduri, Lamar Burton, Arif I. Sarwat, Shekhar Bhansali

Electrical and Computer Engineering Faculty Publications

The use of sensors and the Internet of Things (IoT) is key to moving the world's agriculture to a more productive and sustainable path. Recent advancements in IoT, Wireless Sensor Networks (WSN), and Information and Communication Technology (ICT) have the potential to address some of the environmental, economic, and technical challenges as well as opportunities in this sector. As the number of interconnected devices continues to grow, this generates more big data with multiple modalities and spatial and temporal variations. Intelligent processing and analysis of this big data are necessary to developing a higher level of knowledge base and insights …

Introducing Phonetic Information To Speaker Embedding For Speaker Verification, Yi Liu, Liang He, Michael T. Johnson

Electrical and Computer Engineering Faculty Publications

Phonetic information is one of the most essential components of a speech signal, playing an important role for many speech processing tasks. However, it is difficult to integrate phonetic information into speaker verification systems since it occurs primarily at the frame level while speaker characteristics typically reside at the segment level. In deep neural network-based speaker verification, existing methods only apply phonetic information to the frame-wise trained speaker embeddings. To improve this weakness, this paper proposes phonetic adaptation and hybrid multi-task learning and further combines these into c-vector and simplified c-vector architectures. Experiments on National Institute of Standards and Technology …

A Novel Audiovisual P300-Speller Paradigm Based On Cross-Modal Spatial And Semantic Congruence, Zhaohua Lu, Changchun University Of Science And Technology, Ning Gao, Jingjing Yang, Ou Bai

Electrical and Computer Engineering Faculty Publications

Objective: Although many studies have attempted to improve the performance of the visual-based P300-speller system, its performance is still not satisfactory. The current system has limitations for patients with neurodegenerative diseases, in which muscular control of the eyes may be impaired or deteriorate over time. Some studies have shown that the audiovisual stimuli with spatial and semantic congruence elicited larger event-related potential (ERP) amplitudes than do unimodal visual stimuli. Therefore, this study proposed a novel multisensory P300-speller based on audiovisual spatial and semantic congruence.

Methods: We designed a novel audiovisual P300-speller paradigm (AV spelling paradigm) in which the pronunciation and …

Enhanced Crystallinity Of Triple-Cation Perovskite Film Via Doping Nh4Scn, Ziji Liu, Detao Liu, Hao Chen, Long Ji, Hualin Zheng, Yiding Gu, Feng Wang, Zhi Chen, Shibin Li

Electrical and Computer Engineering Faculty Publications

The trap-state density in perovskite films largely determines the photovoltaic performance of perovskite solar cells (PSCs). Increasing the crystal grain size in perovskite films is an effective method to reduce the trap-state density. Here, we have added NH₄SCN into perovskite precursor solution to obtain perovskite films with an increased crystal grain size. The perovskite with increased crystal grain size shows a much lower trap-state density compared with reference perovskite films, resulting in an improved photovoltaic performance in PSCs. The champion photovoltaic device has achieved a power conversion efficiency of 19.36%. The proposed method may also impact other optoelectronic …

Fusion Of Interpolated Frames Superresolution In The Presence Of Atmospheric Optical Turbulence, Russell C. Hardie, Michael A. Rucci, Barry K. Karch, Alexander J. Dapore, Douglas R. Droege, Joseph C. French

Electrical and Computer Engineering Faculty Publications

An extension of the fusion of interpolated frames superresolution (FIF SR) method to perform SR in the presence of atmospheric optical turbulence is presented. The goal of such processing is to improve the performance of imaging systems impacted by turbulence. We provide an optical transfer function analysis that illustrates regimes where significant degradation from both aliasing and turbulence may be present in imaging systems. This analysis demonstrates the potential need for simultaneous SR and turbulence mitigation (TM). While the FIF SR method was not originally proposed to address this joint restoration problem, we believe it is well suited for this …

Stamina: Stochastic Approximate Model-Checker For Infinite-State Analysis, Thackur Neupane, Chris J. Myers, Curtis Madsen, Hao Zheng, Zhen Zhang

Electrical and Computer Engineering Faculty Publications

Stochastic model checking is a technique for analyzing systems that possess probabilistic characteristics. However, its scalability is limited as probabilistic models of real-world applications typically have very large or infinite state space. This paper presents a new infinite state CTMC model checker, STAMINA, with improved scalability. It uses a novel state space approximation method to reduce large and possibly infinite state CTMC models to finite state representations that are amenable to existing stochastic model checkers. It is integrated with a new property-guided state expansion approach that improves the analysis accuracy. Demonstration of the tool on several benchmark examples shows promising …

Latent Class Model With Application To Speaker Diarization, Liang He, Xianhong Chen, Can Xu, Yi Liu, Jia Liu, Michael T. Johnson

Electrical and Computer Engineering Faculty Publications

In this paper, we apply a latent class model (LCM) to the task of speaker diarization. LCM is similar to Patrick Kenny’s variational Bayes (VB) method in that it uses soft information and avoids premature hard decisions in its iterations. In contrast to the VB method, which is based on a generative model, LCM provides a framework allowing both generative and discriminative models. The discriminative property is realized through the use of i-vector (Ivec), probabilistic linear discriminative analysis (PLDA), and a support vector machine (SVM) in this work. Systems denoted as LCM-Ivec-PLDA, LCM-Ivec-SVM, and LCM-Ivec-Hybrid are introduced. In addition, three …

A Computationally Efficient U-Net Architecture For Lung Segmentation In Chest Radiographs, Barath Narayanan, Russell C. Hardie

Electrical and Computer Engineering Faculty Publications

Lung segmentation plays a crucial role in computer-aided diagnosis using Chest Radiographs (CRs). We implement a U-Net architecture for lung segmentation in CRs across multiple publicly available datasets. We utilize a private dataset with 160 CRs provided by the Riverain Medical Group for training purposes. A publicly available dataset provided by the Japanese Radiological Scientific Technology (JRST) is used for testing. The active shape model-based results would serve as the ground truth for both these datasets. In addition, we also study the performance of our algorithm on a publicly available Shenzhen dataset which contains 566 CRs with manually segmented lungs …

Co-Optimization Approach To Post-Storm Recovery For Interdependent Power And Transportation Systems, Yinyin Ge, Lili Du, Hongzing Ye

Electrical and Computer Engineering Faculty Publications

The power and transportation systems are urban interdependent critical infrastructures (CIs). During the post-disaster restoration process, transportation mobility and power restoration process are interdependent, and their functionalities significantly affect other well-beings of other urban CIs. Therefore, to enhance the resilience of urban CIs, successful recovery strategies should promote CI function cooperatively and synergistically to distribute goods and services efficiently. This paper develops an integrative framework that addresses the challenges of enhancing the recovery efficiency of urban power and transportation systems in short-term recovery period. Specifically, the post-storm recovery process is considered as a scheduling problem under the constraints representing crew …

Approximation Techniques For Stochastic Analysis Of Biological Systems, Thakur Neupane, Zhen Zhang, Curtis Madsen, Hao Zheng, Chris J. Myers

Electrical and Computer Engineering Faculty Publications

There has been an increasing demand for formal methods in the design process of safety-critical synthetic genetic circuits. Probabilistic model checking techniques have demonstrated significant potential in analyzing the intrinsic probabilistic behaviors of complex genetic circuit designs. However, its inability to scale limits its applicability in practice. This chapter addresses the scalability problem by presenting a state-space approximation method to remove unlikely states resulting in a reduced, finite state representation of the infinite-state continuous-time Markov chain that is amenable to probabilistic model checking. The proposed method is evaluated on a design of a genetic toggle switch. Comparisons with another state-of-the-art …

Nanocomposite Bienzymatic Sensor For Monitoring Xanthine In Wound Diagnostics, Sohini Roychoudhury, Yogeswaran Umasankar, Pulak Bhushan, Penelope A. Hirt, Flor E. Macquhae, Luis J. Borda, Hadar A. Lev-Tov, Robert Kirsner, Shekhar Bhansali

Electrical and Computer Engineering Faculty Publications

This work reports a biosensor for monitoring xanthine for potential wound healing assessment. Active substrate of the biosensor has xanthine oxidase (XO) and horseradish peroxidase (HRP) physisorbed on a nanocomposite of multiwalled carbon nanotubes (MWCNT) decorated with gold nanoparticles (AuNP). The presence of HRP provided a two-fold increase in response to xanthine, and a three-fold increase in response to the nanocomposite. With a sensitivity of 155.71 nA μM−1 cm−2 the biosensor offers a detection limit of 1.3 μM, with linear response between 22 μM and 0.4 mM. Clinical sample analyses showed the feasibility of xanthine detection from biofluids in a …

Exploration Vs. Data Refinement Via Multiple Mobile Sensors, Mohammad Shekaramiz, Todd K. Moon, Jacob H. Gunther

Electrical and Computer Engineering Faculty Publications

We examine the deployment of multiple mobile sensors to explore an unknown region to map regions containing concentration of a physical quantity such as heat, electron density, and so on. The exploration trades off between two desiderata: to continue taking data in a region known to contain the quantity of interest with the intent of refining the measurements vs. taking data in unobserved areas to attempt to discover new regions where the quantity may exist. Making reasonable and practical decisions to simultaneously fulfill both goals of exploration and data refinement seem to be hard and contradictory. For this purpose, we …

Case Study On The Effects Of Partial Solar Eclipse On Distributed Pv Systems And Management Areas, Aditya Sundararajan, Temitayo O. Olowu, Longfei Wei, Shahinur Rahman, Arif I. Sarwat

Electrical and Computer Engineering Faculty Publications

Photovoltaic (PV) systems are weather-dependent. A solar eclipse causes significant changes in these parameters, thereby impacting PV generation profile, performance, and power quality of larger grid, where they connect to. This study presents a case study to evaluate the impacts of the solar eclipse of 21 August 2017, on two real-world grid-tied PV systems (1.4 MW and 355 kW) in Miami and Daytona, Florida, the feeders they are connected to, and the management areas they belong to. Four types of analyses are conducted to obtain a comprehensive picture of the impacts using 1 min PV generation data, hourly weather data, …

Energy Efficient Network-On-Chip Architectures For Many-Core Near-Threshold Computing System, Chidhambaranathan Rajamanikkam, Jayashankara S. Rajesh, Koushik Chakraborty, Meher Samineni

Electrical and Computer Engineering Faculty Publications

Near threshold computing has unraveled a promising design space for energy efficient computing. However, it is still plagued by sub-optimal system performance. Application characteristics and hardware non-idealities of conventional architectures (those optimized for nominal voltage) prevent us from fully leveraging the potential of NTC systems. Increasing the computational core count still forms the bedrock of a multitude of contemporary works that address the problem of performance degradation in NTC systems. However, these works do not categorically address the shortcomings of the conventional on-chip interconnect fabric in a many core environment. In this work, we quantitatively demonstrate the performance bottleneck created …

Hydrothermal Growth Of Zinc Oxide (Zno) Nanorods (Nrs) On Screen Printed Ides For Ph Measurement Application, Aksaya 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 …

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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 …