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Electrical and Computer Engineering
Electrical and Computer Engineering Faculty Research & Creative Works
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Full-Text Articles in Engineering
Machine Learning Assisted High-Sensitivity And Large-Dynamic-Range Curvature Sensor Based On No-Core Fiber And Hollow-Core Fiber, Chen Zhu, Yiyang Zhuang, Jie Huang
Machine Learning Assisted High-Sensitivity And Large-Dynamic-Range Curvature Sensor Based On No-Core Fiber And Hollow-Core Fiber, Chen Zhu, Yiyang Zhuang, Jie Huang
Electrical and Computer Engineering Faculty Research & Creative Works
Simultaneously Increasing the Sensitivity and Dynamic Range of an Optical Fiber Sensor is Desired and Yet Challenging. in This Article, We Demonstrate an Optical Fiber Curvature Sensor based on a No-Core Fiber (NCF) Cascaded with a Hollow-Core Fiber (HCF), Realizing Simultaneously High Sensitivity and a Large Dynamic Range with the Assistance of Machine Learning Analysis. the Sensor is Fabricated by Simply Fusion Splicing a Section of NCF and HCF to Two Single-Mode Fibers (SMFs), Forming the SMF-NCF-HCF-SMF Hybrid Structure. It is Shown that the Multimode Interference in the NCF Can Increase the Sensitivity of the Device for Curvature Measurements, Compared …
Snow Parameters Inversion From Passive Microwave Remote Sensing Measurements By Deep Convolutional Neural Networks, Heming Yao, Yanming Zhang, Lijun Jiang, Hong Tat Ewe, Michael Ng
Snow Parameters Inversion From Passive Microwave Remote Sensing Measurements By Deep Convolutional Neural Networks, Heming Yao, Yanming Zhang, Lijun Jiang, Hong Tat Ewe, Michael Ng
Electrical and Computer Engineering Faculty Research & Creative Works
This paper proposes a novel inverse method based on the deep convolutional neural network (ConvNet) to extract snow's layer thickness and temperature via passive microwave remote sensing (PMRS). The proposed ConvNet is trained using simulated data obtained through conventional computational electromagnetic methods. Compared with the traditional inverse method, the trained ConvNet can predict the result with higher accuracy. Besides, the proposed method has a strong tolerance for noise. The proposed ConvNet composes three pairs of convolutional and activation layers with one additional fully connected layer to realize regression, i.e., the inversion of snow parameters. The feasibility of the proposed method …
Predicting Compressive Strength Of Alkali-Activated Systems Based On The Network Topology And Phase Assemblages Using Tree-Structure Computing Algorithms, Rohan Bhat, Taihao Han, Sai Akshay Ponduru, Arianit Reka, Jie Huang, Gaurav Sant, Aditya Kumar
Predicting Compressive Strength Of Alkali-Activated Systems Based On The Network Topology And Phase Assemblages Using Tree-Structure Computing Algorithms, Rohan Bhat, Taihao Han, Sai Akshay Ponduru, Arianit Reka, Jie Huang, Gaurav Sant, Aditya Kumar
Electrical and Computer Engineering Faculty Research & Creative Works
Alkali-activated system is an environment-friendly, sustainable construction material utilized to replace ordinary Portland cement (OPC) that contributes to 9% of the global carbon footprint. Moreover, the alkali-activated system has exhibited superior strength at early ages and better corrosion resistance compared to OPC. The current state of analytical and machine learning models cannot produce highly reliable predictions of the compressive strength of alkali-activated systems made from different types of aluminosilicate-rich precursors owing to substantive variation in the chemical compositions and reactivity of these precursors. In this study, a random forest model with two constraints (i.e., topological network and thermodynamic constraints) is …
An Intelligent Distributed Ledger Construction Algorithm For Iot, Charles Rawlins, Jagannathan Sarangapani
An Intelligent Distributed Ledger Construction Algorithm For Iot, Charles Rawlins, Jagannathan Sarangapani
Electrical and Computer Engineering Faculty Research & Creative Works
Blockchain is the next generation of secure data management that creates near-immutable decentralized storage. Secure cryptography created a niche for blockchain to provide alternatives to well-known security compromises. However, design bottlenecks with traditional blockchain data structures scale poorly with increased network usage and are extremely computation-intensive. This made the technology difficult to combine with limited devices, like those in Internet of Things networks. In protocols like IOTA, replacement of blockchain's linked-list queue processing with a lightweight dynamic ledger showed remarkable throughput performance increase. However, current stochastic algorithms for ledger construction suffer distinct trade-offs between efficiency and security. This work proposed …
A Dnn-Ensemble Method For Error Reduction And Training Data Selection In Dnn Based Modeling, Ling Zhang, Da Li, Jiayi He, Bhyrav Mutnury, Bo Pu, Xiao Ding Cai, Chulsoon Hwang, Jun Fan, James L. Drewniak, Er Ping Li
A Dnn-Ensemble Method For Error Reduction And Training Data Selection In Dnn Based Modeling, Ling Zhang, Da Li, Jiayi He, Bhyrav Mutnury, Bo Pu, Xiao Ding Cai, Chulsoon Hwang, Jun Fan, James L. Drewniak, Er Ping Li
Electrical and Computer Engineering Faculty Research & Creative Works
Deep neural networks (DNNs) have been widely adopted in modeling electromagnetic compatibility (EMC) problems, but the training data acquisition is usually time-consuming through various simulators. This paper presents a powerful approach using an ensemble of DNN s to effectively reduce the training data size in DNN-based modeling problems. A batch of training data with the largest uncertainties is selected using active learning through the variance among the ensemble of DNNs. Subsequently, a greedy sampling algorithm is applied to select a data subset using diversity. Thus, the proposed method can achieve both uncertainty and diversity in data selection. By averaging the …
Self-Vernier Effect-Assisted Optical Fiber Sensor Based On Microwave Photonics And Its Machine Learning Analysis, Chen Zhu, Jie Huang
Self-Vernier Effect-Assisted Optical Fiber Sensor Based On Microwave Photonics And Its Machine Learning Analysis, Chen Zhu, Jie Huang
Electrical and Computer Engineering Faculty Research & Creative Works
Optical Vernier Effect Has Been Recently Demonstrated as a Tool to Enhance the Sensitivity of Optical Fiber Interferometric Sensors and Has Become a Hot Topic in the Last Few Years. the Generation of the Vernier Effect Relies on the Superposition of Interferograms of Two Interferometers (A Sensing One and a Reference One) with Marginally Different Optical Path Differences (OPDs), Where an Amplitude Modulation-Like Signal is Sustained in the Output Spectrum. the Vernier Modulation Envelope Exhibits Significantly Magnified Sensitivity in Response to External Perturbations, compared to the Individual Sensing Interferometer, Providing a New Route to New Generations of Ultra-Sensitive Optical Fiber …