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Ensemble Of Recurrent Neural Networks With Long Short-Term Memory Cells For High-Rate Structural Health Monitoring, Vahid Barzegar, Simon Laflamme, Chao Hu, Jacob Dodson 2022 Iowa State University

Ensemble Of Recurrent Neural Networks With Long Short-Term Memory Cells For High-Rate Structural Health Monitoring, Vahid Barzegar, Simon Laflamme, Chao Hu, Jacob Dodson

Civil, Construction and Environmental Engineering Publications

The deployment of systems experiencing high-rate dynamic events, such as hypersonic vehicles, advanced weaponry, and active blast mitigation systems, require high-rate structural health monitoring (HRSHM) capabilities in the sub-millisecond realm to ensure continuous operations and safety. However, the development of high-rate feedback systems is a complex task because these dynamic systems are uniquely characterized by (1) large uncertainties in their external loads, (2) high levels of non-stationarity and heavy disturbance, and (3) unmodeled dynamics from changes in system configuration. In this paper, we present a deep learning algorithm specifically engineered for HRSHM applications. It consists of an ensemble of recurrent ...


Non-Contact Heart Rate Estimation In Low Snr Environments Using Mmwave Radar, Chandler J. Bauder, Aly E. Fathy 2022 University of Tennessee, Knoxville

Non-Contact Heart Rate Estimation In Low Snr Environments Using Mmwave Radar, Chandler J. Bauder, Aly E. Fathy

Faculty Publications and Other Works -- EECS

Extracting accurate heart rate estimates of human subjects from a distance in high-noise scenarios using radar is a common problem. Often, frequency components from sources such as movement and vital signs from other subjects can overpower the weak reflected signal of the heart. In this study, we propose a signal processing scheme using a state-of-the-art Adaptive Multi-Trace Carving algorithm (AMTC) to accurately detect the heart rate signal over time in non-ideal scenarios. In our initial proof-of-concept results, we show a low heart rate estimation mean absolute error (MAE) of 3bpm for a single subject marching in place and less than ...


Machine Learning Based Critical Resource Allocation In Mixed-Traffic Cellular Networks, Mohamed Nomeir 2021 American University in Cairo

Machine Learning Based Critical Resource Allocation In Mixed-Traffic Cellular Networks, Mohamed Nomeir

Theses and Dissertations

The proliferation of cellular networks over the past two decades has encouraged the expansion of their use in many modern applications. These applications involve the use of data traffic of different quality of service (QoS) requirements. Some of these requirements are quite stringent such as in the case of critical Internet of Things (IoT) health care, military and homeland security applications. This situation resulted in imposing a variety of resource allocation requirements on the cellular network operation in a simultaneous manner.

In this thesis, we consider the challenging problem of mixed-traffic resource allocation, or scheduling, in cellular networks. We focus ...


Online Parameter Estimation Under Non-Persistent Excitations For High-Rate Dynamic Systems, Jin Yan, Simon Laflamme, Jonathan Hong, Jacob Dodson 2021 Iowa State University and Palo Alto Research Center

Online Parameter Estimation Under Non-Persistent Excitations For High-Rate Dynamic Systems, Jin Yan, Simon Laflamme, Jonathan Hong, Jacob Dodson

Civil, Construction and Environmental Engineering Publications

High-rate dynamic systems are defined as systems experiencing dynamic events of typical amplitudes higher than 100 gn for a duration of less than 100 ms. They are characterized by 1) large uncertainties on the external loads; 2) high levels of nonstationarity and heavy disturbance; and 3) generation of unmodeled dynamics from changes in mechanical configuration. To fully enable these systems, feedback capabilities must be developed. This includes computationally fast software and low latency hardware. This paper presents a pure time-based online parameter estimation algorithm for high-rate dynamic systems with real-time applicability. The algorithm is based on a model reference ...


Simulation And Fabrication Of All Oxide-Based Ito/Tio2/Cuo/Au Heterostructure For Solar Cell Applications, Sajal Islam 2021 Missouri State University

Simulation And Fabrication Of All Oxide-Based Ito/Tio2/Cuo/Au Heterostructure For Solar Cell Applications, Sajal Islam

MSU Graduate Theses

Oxide heterostructures have drawn great attention lately, due to their environment-friendly properties and potential applications in optoelectronic devices. In this work, a simulation study of a heterojunction solar cell was performed with SCAPS (a solar cell simulator) using TiO2 as an n-type and CuO as a p-type layer. The thickness and the dopant-dependent simulations have shown that the solar cell operates at a maximum efficiency of 19.2% when the thickness of the TiO2/CuO layers is chosen 1.4µm/1.2µm compared to the 11.5% efficiency when FTO is replaced with ITO. An indium-doped tin oxide (ITO) vs ...


Roadway-Embedded Transmitters And Multi-Pad Receivers For High Power Dynamic Wireless Power Transfer, Benny J. Varghese 2021 Utah State University

Roadway-Embedded Transmitters And Multi-Pad Receivers For High Power Dynamic Wireless Power Transfer, Benny J. Varghese

All Graduate Theses and Dissertations

Electric vehicles (EVs) offer considerable economic and environmental benefits to society. Despite the decreasing vehicle costs and increasing range of newer EVs, the problem of range anxiety still exists. Range anxiety, at its core, is an issue of charging speeds rather than a concern about the driving range. Dynamic wireless charging of EVs is seen as a potential solution to this issue of range anxiety. Further, wireless charging technology also helps the push towards level 5 autonomy and opens new opportunities for how an EV can be utilized.

Dynamic wireless power transfer (DWPT) systems typically require a high initial investment ...


Network Management, Optimization And Security With Machine Learning Applications In Wireless Networks, Mariam Nabil 2021 American University in Cairo

Network Management, Optimization And Security With Machine Learning Applications In Wireless Networks, Mariam Nabil

Theses and Dissertations

Wireless communication networks are emerging fast with a lot of challenges and ambitions. Requirements that are expected to be delivered by modern wireless networks are complex, multi-dimensional, and sometimes contradicting. In this thesis, we investigate several types of emerging wireless networks and tackle some challenges of these various networks. We focus on three main challenges. Those are Resource Optimization, Network Management, and Cyber Security. We present multiple views of these three aspects and propose solutions to probable scenarios. The first challenge (Resource Optimization) is studied in Wireless Powered Communication Networks (WPCNs). WPCNs are considered a very promising approach towards sustainable ...


Improving Noise Immunity And Efficiency Using High-Precision Iterative Codes, Sherzod Shukhratovich Atadjanov, Aziza Ahmadjanovna Tursunova 2021 Tashkent University of Information Technologies named after Muhammad al-Khwarizmi

Improving Noise Immunity And Efficiency Using High-Precision Iterative Codes, Sherzod Shukhratovich Atadjanov, Aziza Ahmadjanovna Tursunova

Bulletin of TUIT: Management and Communication Technologies

The article discusses the issues of ensuring noise immunity in digital broadcasting systems, shows the importance of the transition to the optimal code and the need to use it in the field of noiseless coding in various areas of telecommunication transmission and reception of digital signals. The previous algorithms and error-correcting coding methods based on the Gray code, which are used in multi-level digital broadcast modulation schemes to minimize the intensity of bit errors, are highlighted. A model of error-correcting coding by the Gray method and methods for estimating the probability of error for the Gray code are presented. Based ...


Array-Based Guided Wave Source Location Using Dispersion Compensation, Andrew Downs, Ronald A. Roberts, Jiming Song 2021 Iowa State University

Array-Based Guided Wave Source Location Using Dispersion Compensation, Andrew Downs, Ronald A. Roberts, Jiming Song

Electrical and Computer Engineering Publications

An important advantage of guided waves is their ability to propagate large distances and yield more information about flaws than bulk waves. Unfortunately, the multi-modal, dispersive nature of guided waves makes them difficult to use for locating flaws. In this work, we present a method and experimental data for removing the deleterious effects of multi-mode dispersion allowing for source localization at frequencies comparable to those of bulk waves. Time domain signals are obtained using a novel 64-element phased array and processed to extract wave number and frequency spectra. By an application of Auld’s electro-mechanical reciprocity relation, mode contributions are ...


Decarbonizing Rural Residential Buildings In Cold Climates: A Techno-Economic Analysis Of Heating Electrification, Filippo Padovani, Nelson Sommerfeldt, Francesca Longobardi, Joshua M. Pearce 2021 The Royal Institute of Technology (KTH)

Decarbonizing Rural Residential Buildings In Cold Climates: A Techno-Economic Analysis Of Heating Electrification, Filippo Padovani, Nelson Sommerfeldt, Francesca Longobardi, Joshua M. Pearce

Michigan Tech Publications

Given the need for decarbonization of the heating sector and the acute need of a propane replacement in the U.S. Upper Midwest, this study quantifies the techno-economic characteristics of sustainable heating electrification in isolated rural, residential buildings in cold climates without natural gas supply. Archetypal buildings are modeled under four levels of electrification. At each electrification level, a parametric solar photovoltaic (PV) sizing analysis is performed and the total life cycle cost, renewable fraction and greenhouse gas (GHG) emissions are calculated based on the primary energy supply for each building type. Cost optimal solutions are stress-tested with multi-dimensional sensitivity ...


Benchmarking Small-Dataset Structure-Activity-Relationship Models For Prediction Of Wnt Signaling Inhibition, Mahtab Kokabi 2021 University of Massachusetts Amherst

Benchmarking Small-Dataset Structure-Activity-Relationship Models For Prediction Of Wnt Signaling Inhibition, Mahtab Kokabi

Masters Theses

Quantitative structure-activity relationship (QSAR) models based on machine learning algorithms are powerful tools to expedite drug discovery processes and therapeutics development. Given the cost in acquiring large-sized training datasets, it is useful to examine if QSAR analysis can reasonably predict drug activity with only a small-sized dataset (size < 100) and benchmark these small-dataset QSAR models in application-specific studies. To this end, here we present a systematic benchmarking study on small-dataset QSAR models built for prediction of effective Wnt signaling inhibitors, which are essential to therapeutics development in prevalent human diseases (e.g., cancer). Specifically, we examined a total of 72 two-dimensional (2D) QSAR models based on 4 best-performing algorithms, 6 commonly used molecular fingerprints, and 3 typical fingerprint lengths. We trained these models using a training dataset (56 compounds), benchmarked their performance on 4 figures-of-merit (FOMs), and examined their prediction accuracy using an external validation dataset (14 compounds). Our data show that the model performance is maximized when: 1) molecular fingerprints are selected to provide sufficient, unique, and not overly detailed representations of the chemical structures of drug compounds; 2) algorithms are selected to reduce the number of false predictions due to class imbalance in the dataset; and 3) models are selected to reach balanced performance on all 4 FOMs. These results may provide general guidelines in developing high-performance small-dataset QSAR models for drug activity prediction.


On Improving Robustness Of Hardware Security Primitives And Resistance To Reverse Engineering Attacks, Vinay C. Patil 2021 University of Massachusetts Amherst

On Improving Robustness Of Hardware Security Primitives And Resistance To Reverse Engineering Attacks, Vinay C. Patil

Doctoral Dissertations

The continued growth of information technology (IT) industry and proliferation of interconnected devices has aggravated the problem of ensuring security and necessitated the need for novel, robust solutions. Physically unclonable functions (PUFs) have emerged as promising secure hardware primitives that can utilize the disorder introduced during manufacturing process to generate unique keys. They can be utilized as \textit{lightweight} roots-of-trust for use in authentication and key generation systems. Unlike insecure non-volatile memory (NVM) based key storage systems, PUFs provide an advantage -- no party, including the manufacturer, should be able to replicate the physical disorder and thus, effectively clone the PUF ...


Resource Allocation In Distributed Service Networks, Nitish Kumar Panigrahy 2021 University of Massachusetts Amherst

Resource Allocation In Distributed Service Networks, Nitish Kumar Panigrahy

Doctoral Dissertations

The past few years have witnessed significant growth in the use of distributed network analytics involving agile code, data and computational resources. In many such networked systems, for example, Internet of Things (IoT), a large number of smart devices, sensors, processing and storage resources are widely distributed in a geographic region. These devices and resources distributed over a physical space are collectively called a distributed service network. Efficient resource allocation in such high performance service networks remains one of the most critical problems. In this thesis, we model and optimize the allocation of resources in a distributed service network. This ...


Dataflow Management In The Internet Of Things: Sensing, Control, And Security, Dawei Wei, Huansheng Ning, Feifei Shi, Yueliang Wan, Jiabo Xu, Shunkun Yang, Li Zhu 2021 School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China, Beijing Engineering Research Center for Cyberspace Data Analysis and Applications, Beijing 100083, China

Dataflow Management In The Internet Of Things: Sensing, Control, And Security, Dawei Wei, Huansheng Ning, Feifei Shi, Yueliang Wan, Jiabo Xu, Shunkun Yang, Li Zhu

Tsinghua Science and Technology

The pervasiveness of the smart Internet of Things (IoTs) enables many electric sensors and devices to be connected and generates a large amount of dataflow. Compared with traditional big data, the streaming dataflow is faced with representative challenges, such as high speed, strong variability, rough continuity, and demanding timeliness, which pose severe tests of its efficient management. In this paper, we provide an overall review of IoT dataflow management. We first analyze the key challenges faced with IoT dataflow and initially overview the related techniques in dataflow management, spanning dataflow sensing, mining, control, security, privacy protection, etc. Then, we illustrate ...


Access Control And Authorization In Smart Homes: A Survey, Ziarmal Nazar Mohammad, Fadi Farha, Adnan O.M Abuassba, Shunkun Yang, Fang Zhou 2021 School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China

Access Control And Authorization In Smart Homes: A Survey, Ziarmal Nazar Mohammad, Fadi Farha, Adnan O.M Abuassba, Shunkun Yang, Fang Zhou

Tsinghua Science and Technology

With the rapid development of cyberspace and smart home technology, human life is changing to a new virtual dimension with several promises for improving its quality. Moreover, the heterogeneous, dynamic, and internet-connected nature of smart homes brings many privacy and security difficulties. Unauthorized access to the smart home system is one of the most harmful actions and can cause several trust problems and relationship conflicts between family members and invoke home privacy issues. Access control is one of the best solutions for handling this threat, and it has been used to protect smart homes and other Internet of Things domains ...


News Keyword Extraction Algorithm Based On Semantic Clustering And Word Graph Model, Ao Xiong, Derong Liu, Hongkang Tian, Zhengyuan Liu, Peng Yu, Michel Kadoch 2021 State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China

News Keyword Extraction Algorithm Based On Semantic Clustering And Word Graph Model, Ao Xiong, Derong Liu, Hongkang Tian, Zhengyuan Liu, Peng Yu, Michel Kadoch

Tsinghua Science and Technology

The internet is an abundant source of news every day. Thus, efficient algorithms to extract keywords from the text are important to obtain information quickly. However, the precision and recall of mature keyword extraction algorithms need improvement. TextRank, which is derived from the PageRank algorithm, uses word graphs to spread the weight of words. The keyword weight propagation in TextRank focuses only on word frequency. To improve the performance of the algorithm, we propose Semantic Clustering TextRank (SCTR), a semantic clustering news keyword extraction algorithm based on TextRank. Firstly, the word vectors generated by the Bidirectional Encoder Representation from Transformers ...


Security Issues And Defensive Approaches In Deep Learning Frameworks, Hongsong Chen, Yongpeng Zhang, Jing Xie 2021 Department of Computer Science, University of Science and Technology Beijing (USTB), Beijing 100083, China, Beijing Key Laboratory of Knowledge Engineering for Materials Science, Beijing 100083, China

Security Issues And Defensive Approaches In Deep Learning Frameworks, Hongsong Chen, Yongpeng Zhang, Jing Xie

Tsinghua Science and Technology

Deep learning frameworks promote the development of artificial intelligence and demonstrate considerable potential in numerous applications. However, the security issues of deep learning frameworks are among the main risks preventing the wide application of it. Attacks on deep learning frameworks by malicious internal or external attackers would exert substantial effects on society and life. We start with a description of the framework of deep learning algorithms and a detailed analysis of attacks and vulnerabilities in them. We propose a highly comprehensive classification approach for security issues and defensive approaches in deep learning frameworks and connect different attacks to corresponding defensive ...


Parallel-Data-Based Social Evolution Modeling, Weishan Zhang, Zhaoxiang Hou, Xiao Wang, Zhidong Xu, Xin Liu, Fei-Yue Wang 2021 China University of Petroleum, Qingdao 266580, China, Qingdao Academy of Intelligent Industry, Qingdao 266111, China

Parallel-Data-Based Social Evolution Modeling, Weishan Zhang, Zhaoxiang Hou, Xiao Wang, Zhidong Xu, Xin Liu, Fei-Yue Wang

Tsinghua Science and Technology

Abnormal or drastic changes in the natural environment may lead to unexpected events, such as tsunamis and earthquakes, which are becoming a major threat to national economy. Currently, no effective assessment approach can deduce a situation and determine the optimal response strategy when a natural disaster occurs. In this study, we propose a social evolution modeling approach and construct a deduction model for self-playing, self-learning, and self-upgrading on the basis of the idea of parallel data and reinforcement learning. The proposed approach can evaluate the impact of an event, deduce the situation, and provide optimal strategies for decision-making. Taking the ...


Quality-Aware User Recruitment Based On Federated Learning In Mobile Crowd Sensing, Wei Zhang, Zhuo Li, Xin Chen 2021 Beijing Key Laboratory of Internet Culture and Digital Dissemination Research, and School of Computer Science, Beijing Information Science and Technology University, Beijing 100101, China

Quality-Aware User Recruitment Based On Federated Learning In Mobile Crowd Sensing, Wei Zhang, Zhuo Li, Xin Chen

Tsinghua Science and Technology

With the rapid development of mobile devices, the use of Mobile Crowd Sensing (MCS) mode has become popular to complete more intelligent and complex sensing tasks. However, large-scale data collection may reduce the quality of sensed data. Thus, quality control is a key problem in MCS. With the emergence of the federated learning framework, the number of complex intelligent calculations that can be completed on mobile devices has increased. In this study, we formulate a quality-aware user recruitment problem as an optimization problem. We predict the quality of sensed data from different users by analyzing the correlation between data and ...


Evchain: An Anonymous Blockchain-Based System For Charging-Connected Electric Vehicles, Shiyuan Xu, Xue Chen, Yunhua He 2021 North China University of Technology, Beijing 100144, China

Evchain: An Anonymous Blockchain-Based System For Charging-Connected Electric Vehicles, Shiyuan Xu, Xue Chen, Yunhua He

Tsinghua Science and Technology

Purchases of electric vehicles have been increasing in recent years. These vehicles differ from traditional fossil-fuel-based vehicles especially in the time consumed to keep them running. Electric-Vehicle-charging Service Providers (EVSPs) must arrange reasonable charging times for users in advance. Most EVSP services are based on third-party platforms, but reliance on third-party platforms creates a lack of security, leaving users vulnerable to attacks and user-privacy leakages. In this paper, we propose an anonymous blockchain-based system for charging-connected electric vehicles that eliminates third-party platforms through blockchain technology and the establishment of a multi-party security system between electric vehicles and EVSPs. In our ...


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