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Articles 1 - 21 of 21
Full-Text Articles in Engineering
Security Of Internet Of Things (Iot) Using Federated Learning And Deep Learning — Recent Advancements, Issues And Prospects, Vinay Gugueoth, Sunitha Safavat, Sachin Shetty
Security Of Internet Of Things (Iot) Using Federated Learning And Deep Learning — Recent Advancements, Issues And Prospects, Vinay Gugueoth, Sunitha Safavat, Sachin Shetty
Electrical & Computer Engineering Faculty Publications
There is a great demand for an efficient security framework which can secure IoT systems from potential adversarial attacks. However, it is challenging to design a suitable security model for IoT considering the dynamic and distributed nature of IoT. This motivates the researchers to focus more on investigating the role of machine learning (ML) in the designing of security models. A brief analysis of different ML algorithms for IoT security is discussed along with the advantages and limitations of ML algorithms. Existing studies state that ML algorithms suffer from the problem of high computational overhead and risk of privacy leakage. …
Technology Adoption Of Computer-Aided Instruction In Healthcare: A Structured Review, Queenie Kate Cabanilla, Frevy Teofilo-Orencia, Rentor Cafino, Armando T. Isla Jr., Jehan Grace Maglaya, Xavier-Lewis Palmer, Lucas Potter, Dave E. Marcial, Lemuel Clark Velasco
Technology Adoption Of Computer-Aided Instruction In Healthcare: A Structured Review, Queenie Kate Cabanilla, Frevy Teofilo-Orencia, Rentor Cafino, Armando T. Isla Jr., Jehan Grace Maglaya, Xavier-Lewis Palmer, Lucas Potter, Dave E. Marcial, Lemuel Clark Velasco
Electrical & Computer Engineering Faculty Publications
Computer-Aided Instruction (CAI) is one of the interactive teaching methods that electronically presents instructional resources and enhances learner performance. In health settings, using CAI is one of the important ways to improve learners' knowledge and usefulness in their healthcare specialization yet there is still a lack of research that offers a comprehensive synthesis of investigating into the adoption of CAI in healthcare. This research aims to provide a comprehensive review of related literatures on the enablers and barriers for technology adoption of CAI in healthcare. 31 journals were analyzed and revealed that several studies were utilizing the Unified Theory of …
Monocular Camera Viewpoint-Invariant Vehicular Traffic Segmentation And Classification Utilizing Small Datasets, Amr Yousef, Jeff Flora, Khan Iftekharuddin
Monocular Camera Viewpoint-Invariant Vehicular Traffic Segmentation And Classification Utilizing Small Datasets, Amr Yousef, Jeff Flora, Khan Iftekharuddin
Electrical & Computer Engineering Faculty Publications
The work presented here develops a computer vision framework that is view angle independent for vehicle segmentation and classification from roadway traffic systems installed by the Virginia Department of Transportation (VDOT). An automated technique for extracting a region of interest is discussed to speed up the processing. The VDOT traffic videos are analyzed for vehicle segmentation using an improved robust low-rank matrix decomposition technique. It presents a new and effective thresholding method that improves segmentation accuracy and simultaneously speeds up the segmentation processing. Size and shape physical descriptors from morphological properties and textural features from the Histogram of Oriented Gradients …
Runtime Energy Savings Based On Machine Learning Models For Multicore Applications, Vaibhav Sundriyal, Masha Sosonkina
Runtime Energy Savings Based On Machine Learning Models For Multicore Applications, Vaibhav Sundriyal, Masha Sosonkina
Electrical & Computer Engineering Faculty Publications
To improve the power consumption of parallel applications at the runtime, modern processors provide frequency scaling and power limiting capabilities. In this work, a runtime strategy is proposed to maximize energy savings under a given performance degradation. Machine learning techniques were utilized to develop performance models which would provide accurate performance prediction with change in operating core-uncore frequency. Experiments, performed on a node (28 cores) of a modern computing platform showed significant energy savings of as much as 26% with performance degradation of as low as 5% under the proposed strategy compared with the execution in the unlimited power case.
Spectrum Sensing With Energy Detection In Multiple Alternating Time Slots, Călin Vlădeanu, Alexandru Marţian, Dimitrie C. Popescu
Spectrum Sensing With Energy Detection In Multiple Alternating Time Slots, Călin Vlădeanu, Alexandru Marţian, Dimitrie C. Popescu
Electrical & Computer Engineering Faculty Publications
Energy detection (ED) represents a low complexity approach used by secondary users (SU) to sense spectrum occupancy by primary users (PU) in cognitive radio (CR) systems. In this paper, we present a new algorithm that senses the spectrum occupancy by performing ED in K consecutive sensing time slots starting from the current slot and continuing by alternating before and after the current slot. We consider a PU traffic model specified in terms of an average duty cycle value, and derive analytical expressions for the false alarm probability (FAP) and correct detection probability (CDP) for any value of K . Our …
Converting Optical Videos To Infrared Videos Using Attention Gan And Its Impact On Target Detection And Classification Performance, Mohammad Shahab Uddin, Reshad Hoque, Kazi Aminul Islam, Chiman Kwan, David Gribben, Jiang Li
Converting Optical Videos To Infrared Videos Using Attention Gan And Its Impact On Target Detection And Classification Performance, Mohammad Shahab Uddin, Reshad Hoque, Kazi Aminul Islam, Chiman Kwan, David Gribben, Jiang Li
Electrical & Computer Engineering Faculty Publications
To apply powerful deep-learning-based algorithms for object detection and classification in infrared videos, it is necessary to have more training data in order to build high-performance models. However, in many surveillance applications, one can have a lot more optical videos than infrared videos. This lack of IR video datasets can be mitigated if optical-to-infrared video conversion is possible. In this paper, we present a new approach for converting optical videos to infrared videos using deep learning. The basic idea is to focus on target areas using attention generative adversarial network (attention GAN), which will preserve the fidelity of target areas. …
Covid-19 And Biocybersecurity's Increasing Role On Defending Forward, Xavier Palmer, Lucas N. Potter, Saltuk Karahan
Covid-19 And Biocybersecurity's Increasing Role On Defending Forward, Xavier Palmer, Lucas N. Potter, Saltuk Karahan
Electrical & Computer Engineering Faculty Publications
The evolving nature of warfare has been changing with cybersecurity and the use of advanced biotechnology in each aspect of the society is expanding and overlapping with the cyberworld. This intersection, which has been described as “biocybersecurity” (BCS), can become a major front of the 21st-century conflicts. There are three lines of BCS which make it a critical component of overall cybersecurity: (1) cyber operations within the area of BCS have life threatening consequences to a greater extent than other cyber operations, (2) the breach in health-related personal data is a significant tool for fatal attacks, and (3) health-related misinformation …
An Accurate Vegetation And Non-Vegetation Differentiation Approach Based On Land Cover Classification, Chiman Kwan, David Gribben, Bulent Ayhan, Jiang Li, Sergio Bernabe, Antonio Plaza
An Accurate Vegetation And Non-Vegetation Differentiation Approach Based On Land Cover Classification, Chiman Kwan, David Gribben, Bulent Ayhan, Jiang Li, Sergio Bernabe, Antonio Plaza
Electrical & Computer Engineering Faculty Publications
Accurate vegetation detection is important for many applications, such as crop yield estimation, landcover land use monitoring, urban growth monitoring, drought monitoring, etc. Popular conventional approaches to vegetation detection incorporate the normalized difference vegetation index (NDVI), which uses the red and near infrared (NIR) bands, and enhanced vegetation index (EVI), which uses red, NIR, and the blue bands. Although NDVI and EVI are efficient, their accuracies still have room for further improvement. In this paper, we propose a new approach to vegetation detection based on land cover classification. That is, we first perform an accurate classification of 15 or more …
Work-In-Progress: Augmented Reality System For Vehicle Health Diagnostics And Maintenance, Yuzhong Shen, Anthony W. Dean, Rafael Landaeta
Work-In-Progress: Augmented Reality System For Vehicle Health Diagnostics And Maintenance, Yuzhong Shen, Anthony W. Dean, Rafael Landaeta
Electrical & Computer Engineering Faculty Publications
This paper discusses undergraduate research to develop an augmented reality (AR) system for diagnostics and maintenance of the Joint Light Tactical Vehicle (JLTV) employed by U.S. Army and U.S. Marine Corps. The JLTV’s diagnostic information will be accessed by attaching a Bluetooth adaptor (Ford Reference Vehicle Interface) to JLTV’s On-board diagnostics (OBD) system. The proposed AR system will be developed for mobile devices (Android and iOS tablets and phones) and it communicates with the JLTV’s OBD via Bluetooth. The AR application will contain a simplistic user interface that reads diagnostic data from the JLTV, shows vehicle sensors, and allows users …
Vegetation Detection Using Deep Learning And Conventional Methods, Bulent Ayhan, Chiman Kwan, Bence Budavari, Liyun Kwan, Yan Lu, Daniel Perez, Jiang Li, Dimitrios Skarlatos, Marinos Vlachos
Vegetation Detection Using Deep Learning And Conventional Methods, Bulent Ayhan, Chiman Kwan, Bence Budavari, Liyun Kwan, Yan Lu, Daniel Perez, Jiang Li, Dimitrios Skarlatos, Marinos Vlachos
Electrical & Computer Engineering Faculty Publications
Land cover classification with the focus on chlorophyll-rich vegetation detection plays an important role in urban growth monitoring and planning, autonomous navigation, drone mapping, biodiversity conservation, etc. Conventional approaches usually apply the normalized difference vegetation index (NDVI) for vegetation detection. In this paper, we investigate the performance of deep learning and conventional methods for vegetation detection. Two deep learning methods, DeepLabV3+ and our customized convolutional neural network (CNN) were evaluated with respect to their detection performance when training and testing datasets originated from different geographical sites with different image resolutions. A novel object-based vegetation detection approach, which utilizes NDVI, computer …
Deepmag+ : Sniffing Mobile Apps In Magnetic Field Through Deep Learning, Rui Ning, Cong Wang, Chunsheng Xin, Jiang Li, Hongyi Wu
Deepmag+ : Sniffing Mobile Apps In Magnetic Field Through Deep Learning, Rui Ning, Cong Wang, Chunsheng Xin, Jiang Li, Hongyi Wu
Electrical & Computer Engineering Faculty Publications
This paper reports a new side-channel attack to smartphones using the unrestricted magnetic sensor data. We demonstrate that attackers can effectively infer the Apps being used on a smartphone with an accuracy of over 80%, through training a deep Convolutional Neural Networks (CNN). Various signal processing strategies have been studied for feature extractions, including a tempogram based scheme. Moreover, by further exploiting the unrestricted motion sensor to cluster magnetometer data, the sniffing accuracy can increase to as high as 98%. To mitigate such attacks, we propose a noise injection scheme that can effectively reduce the App sniffing accuracy to only …
Computational Modeling Of Trust Factors Using Reinforcement Learning, C. M. Kuzio, A. Dinh, C. Stone, L. Vidyaratne, K. M. Iftekharuddin
Computational Modeling Of Trust Factors Using Reinforcement Learning, C. M. Kuzio, A. Dinh, C. Stone, L. Vidyaratne, K. M. Iftekharuddin
Electrical & Computer Engineering Faculty Publications
As machine-learning algorithms continue to expand their scope and approach more ambiguous goals, they may be required to make decisions based on data that is often incomplete, imprecise, and uncertain. The capabilities of these models must, in turn, evolve to meet the increasingly complex challenges associated with the deployment and integration of intelligent systems into modern society. Historical variability in the performance of traditional machine-learning models in dynamic environments leads to ambiguity of trust in decisions made by such algorithms. Consequently, the objective of this work is to develop a novel computational model that effectively quantifies the reliability of autonomous …
Wireless Sensor Networks For Smart Communications, Mu Zhou, Qilian Liang, Hongyi Wu, Weixiao Meng, Kunjie Xu
Wireless Sensor Networks For Smart Communications, Mu Zhou, Qilian Liang, Hongyi Wu, Weixiao Meng, Kunjie Xu
Electrical & Computer Engineering Faculty Publications
(First paragraph) In the first edition of the special issue titled “Wireless Sensor Networks for Smart Communications”, a total of 22 manuscripts were received and 6 of these were accepted. This issue demonstrated that network congestion, user mobility, and adjacent spectrum interference are the main reasons for the degradation ofcommunication quality inWireless Sensor Networks (WSNs).
Scalable And Fully Distributed Localization In Large-Scale Sensor Networks, Miao Jin, Su Xia, Hongyi Wu, Xianfeng David Gu
Scalable And Fully Distributed Localization In Large-Scale Sensor Networks, Miao Jin, Su Xia, Hongyi Wu, Xianfeng David Gu
Electrical & Computer Engineering Faculty Publications
This work proposes a novel connectivity-based localization algorithm, well suitable for large-scale sensor networks with complex shapes and a non-uniform nodal distribution. In contrast to current state-of-the-art connectivity-based localization methods, the proposed algorithm is highly scalable with linear computation and communication costs with respect to the size of the network; and fully distributed where each node only needs the information of its neighbors without cumbersome partitioning and merging process. The algorithm is theoretically guaranteed and numerically stable. Moreover, the algorithm can be readily extended to the localization of networks with a one-hop transmission range distance measurement, and the propagation of …
Impact Of Trucking Network Flow On Preferred Biorefinery Locations In The Southern United States, Timothy M. Young, Lee D. Han, James H. Perdue, Stephanie R. Hargrove, Frank M. Guess, Xia Huang, Chung-Hao Chen
Impact Of Trucking Network Flow On Preferred Biorefinery Locations In The Southern United States, Timothy M. Young, Lee D. Han, James H. Perdue, Stephanie R. Hargrove, Frank M. Guess, Xia Huang, Chung-Hao Chen
Electrical & Computer Engineering Faculty Publications
The impact of the trucking transportation network flow was modeled for the southern United States. The study addresses a gap in existing research by applying a Bayesian logistic regression and Geographic Information System (GIS) geospatial analysis to predict biorefinery site locations. A one-way trucking cost assuming a 128.8 km (80-mile) haul distance was estimated by the Biomass Site Assessment model. The "median family income," "timberland annual growth-to-removal ratio," and "transportation delays" were significant in determining mill location. Transportation delays that directly impacted the costs of trucking are presented. A logistic model with Bayesian inference was used to identify preferred site …
Deep Models For Engagement Assessment With Scarce Label Information, Feng Li, Guangfan Zhang, Wei Wang, Roger Xu, Tom Schnell, Jonathan Wen, Frederic Mckenzie, Jiang Li
Deep Models For Engagement Assessment With Scarce Label Information, Feng Li, Guangfan Zhang, Wei Wang, Roger Xu, Tom Schnell, Jonathan Wen, Frederic Mckenzie, Jiang Li
Electrical & Computer Engineering Faculty Publications
Task engagement is defined as loadings on energetic arousal (affect), task motivation, and concentration (cognition) [1]. It is usually challenging and expensive to label cognitive state data, and traditional computational models trained with limited label information for engagement assessment do not perform well because of overfitting. In this paper, we proposed two deep models (i.e., a deep classifier and a deep autoencoder) for engagement assessment with scarce label information. We recruited 15 pilots to conduct a 4-h flight simulation from Seattle to Chicago and recorded their electroencephalograph (EEG) signals during the simulation. Experts carefully examined the EEG signals and labeled …
Impact Of Data Resolution On Peak Hour Factor Estimation For Transportation Decisions, Jan-Mou Li, Lee D. Han, Chung-Hao Chen
Impact Of Data Resolution On Peak Hour Factor Estimation For Transportation Decisions, Jan-Mou Li, Lee D. Han, Chung-Hao Chen
Electrical & Computer Engineering Faculty Publications
Inductance loop detection systems serve as a primary data source to contemporary traffic information systems. Measures like 20-second or 30-second average velocity, flow, and lane occupancy can be aggregated from individual loop detector actuation sampled at 60 Hz typically. Practically, these measures would sometimes be further aggregated into a much lower, e.g. 15-minute, resolution and then the raw data were lost. Valuable traffic information like flow variation may be distorted when the lower resolution aggregation is practiced. A biased conclusion could be drawn from a data integration system consisted of this kind of distortions. Three approaches estimating a peak hour …
Special Issue On Information Dissemination And New Services In P2p Systems, Min Song, Sachin Shetty, Wenbin Jiang, E. K. Park
Special Issue On Information Dissemination And New Services In P2p Systems, Min Song, Sachin Shetty, Wenbin Jiang, E. K. Park
Electrical & Computer Engineering Faculty Publications
Information dissemination is an important P2P application that has received considerable research attention in recent years. P2P information dissemination systems range from simple file sharing applications to more complex systems that allows users to securely and efficiently publish, organize, index, search, update and retrieve data in a distributed storage medium. For complex P2P information dissemination systems, there is a need for features which include security, anonymity, fairness, scalability, resource management, and organization capabilities. For effective information dissemination, following features of P2P systems and infrastructure need to be updated: distributed object location and routing mechanisms, novel approaches to content replication, caching …
Security In Ad Hoc Networks And Pervasive Computing, Isaac Z. Wu, X.-Y. Li, M. Song, C.-M. Liu
Security In Ad Hoc Networks And Pervasive Computing, Isaac Z. Wu, X.-Y. Li, M. Song, C.-M. Liu
Electrical & Computer Engineering Faculty Publications
Pervasive computing is an exciting and blooming research field, in which innovative techniques and applications are continuously emerging and aim to provide ambient and personalized services to users with high quality. Ad hoc networks are wireless, self-organizing systems formed by cooperating nodes within communication range of each other that form temporary networks. Their topology is dynamic, decentralized, ever changing and the nodes may move around arbitrarily. The last few years have witnessed a wealth of research ideas on ad hoc networking that are moving rapidly into implemented standards. Technology under development for ad hoc networks and pervasive computing is making …
The Formal Laplace-Borel Transform Of Fliess Operators And The Composition Product, Yaqin Li, W. Steven Gray
The Formal Laplace-Borel Transform Of Fliess Operators And The Composition Product, Yaqin Li, W. Steven Gray
Electrical & Computer Engineering Faculty Publications
The formal Laplace-Borel transform of an analytic integral operator, known as a Fliess operator, is defined and developed. Then, in conjunction with the composition product over formal power series, the formal Laplace-Borel transform is shown to provide an isomorphism between the semigroup of all Fliess operators under operator composition and the semigroup of all locally convergent formal power series under the composition product. Finally, the formal Laplace-Borel transform is applied in a systems theory setting to explicitly derive the relationship between the formal Laplace transform of the input and output functions of a Fliess operator. This gives a compact interpretation …
Design And Implementation Of Fuzzy Logic Controllers. Thesis Final Report, 27 July 1992 - 1 January 1993, Osama A. Abihana, Oscar R. Gonzalez
Design And Implementation Of Fuzzy Logic Controllers. Thesis Final Report, 27 July 1992 - 1 January 1993, Osama A. Abihana, Oscar R. Gonzalez
Electrical & Computer Engineering Faculty Publications
The main objectives of our research are to present a self-contained overview of fuzzy sets and fuzzy logic, develop a methodology for control system design using fuzzy logic controllers, and to design and implement a fuzzy logic controller for a real system. We first present the fundamental concepts of fuzzy sets and fuzzy logic. Fuzzy sets and basic fuzzy operations are defined. In addition, for control systems, it is important to understand the concepts of linguistic values, term sets, fuzzy rule base, inference methods, and defuzzification methods. Second, we introduce a four-step fuzzy logic control system design procedure. The design …