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Articles 6571 - 6600 of 36837

Full-Text Articles in Engineering

Optimal Adaptive Output Regulation Of Uncertain Nonlinear Discrete-Time Systems Using Lifelong Concurrent Learning, R. Moghadam, B. Farzanegan, S.(Sarangapani) Jagannathan, P. Natarajan Jan 2022

Optimal Adaptive Output Regulation Of Uncertain Nonlinear Discrete-Time Systems Using Lifelong Concurrent Learning, R. Moghadam, B. Farzanegan, S.(Sarangapani) Jagannathan, P. Natarajan

Electrical and Computer Engineering Faculty Research & Creative Works

This Paper Addresses Neural Network (NN) based Optimal Adaptive Regulation of Uncertain Nonlinear Discrete-Time Systems in Affine Form using Output Feedback Via Lifelong Concurrent Learning. First, an Adaptive NN Observer is Introduced to Estimate Both the State Vector and Control Coefficient Matrix, and its NN Weights Are Adjusted using Both Output Error and Concurrent Learning Term to Relax the Persistency Excitation (PE) Condition. Next, by Utilizing an Actor-Critic Framework for Estimating the Value Functional and Control Policy, the Critic Network Weights Are Tuned Via Both Temporal Different Error and Concurrent Learning Schemes through a Replay Buffer. the Actor NN Weights …

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Identification Of Volatile Organic Liquids By Combining An Array Of Fiber-Optic Sensors And Machine Learning, Wassana Naku, Anand K. Nambisan, Muhammad Roman, Chen Zhu, Rex E. Gerald, Jie Huang Jan 2022

Identification Of Volatile Organic Liquids By Combining An Array Of Fiber-Optic Sensors And Machine Learning, Wassana Naku, Anand K. Nambisan, Muhammad Roman, Chen Zhu, Rex E. Gerald, Jie Huang

Electrical and Computer Engineering Faculty Research & Creative Works

In This Paper, We Report an Array of Fiber-Optic Sensors based on the Fabry-Perot Interference Principle and Machine Learning-Based Analyses for Identifying Volatile Organic Liquids (VOLs). Three Optical Fiber Tip Sensors with Different Surfaces Were Included in the Array of Sensors to Improve the Accuracy for Identifying Liquids: An Intrinsic (Unmodified) Flat Cleaved End face, a Hydrophobic-Coated End face, and a Hydrophilic-Coated End face. the Time-Transient Responses of Evaporating Droplets from the Optical Fiber Tip Sensors Were Monitored and Collected Following the Controlled Immersion Tests of 11 Different Organic Liquids. a Continuous Wavelet Transform Was Used to Convert the Time-Transient …

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Self-Vernier Effect-Assisted Optical Fiber Sensor Based On Microwave Photonics And Its Machine Learning Analysis, Chen Zhu, Jie Huang Jan 2022

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 …

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Review Of Fiber Optic Displacement Sensors, Chen Zhu, Yiyang Zhuang, Bo Liu, Jie Huang Jan 2022

Review Of Fiber Optic Displacement Sensors, Chen Zhu, Yiyang Zhuang, Bo Liu, Jie Huang

Electrical and Computer Engineering Faculty Research & Creative Works

Displacement Measurements Are of Significant Importance in a Variety of Critical Scientific and Engineering Fields, Such as Gravitational Wave Detection, Geophysical Research, and Manufacturing Industries. Due to the Inherent Advantages Such as Compactness, High Sensitivity, and Immunity to Electromagnetic Interference, in Recent Years, Fiber Optic Sensors Have Been Widely Used in an Expansive Range of Sensing Applications, Ranging from Infrastructural Health Monitoring to Chemical and Biological Sensing. of Particular Interest Here, Fiber Optic Displacement Sensors Have Gained Wide Interest and Have Evolved from Basic Intensity Modulation-Based Configurations to More Advanced Structures, Such as Fiber Bragg Grating (FBG)-Based and Interferometric Configurations. …

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Temperature-Insensitive Inclinometer Based On Transmission Line Fabry-Perot Resonators, Jing Guo, Chen Zhu, Yan Tang, Jie Huang Jan 2022

Temperature-Insensitive Inclinometer Based On Transmission Line Fabry-Perot Resonators, Jing Guo, Chen Zhu, Yan Tang, Jie Huang

Electrical and Computer Engineering Faculty Research & Creative Works

This Article Reports a Novel Self-Compensated Inclinometer with a Wide Dynamic Range and High Measurement Resolution based on Two Hollow Coaxial Cable Fabry-Perot Resonators (HCC-FPRs). the HCC-FPR is Formed between a Metal Post that Shorts the Inner Conductor and the Outer Conductor of the HCC and the Water Level Inside the HCC. Changes in Tilt Angles Result in Variations of the Water Level in the HCC, Leading to Changes in the Cavity Length of the HCC-FPR. by Tracking the Shift of the Resonance Wavelength of the HCC-FPR, a Single HCC-FPR Can Be Used as a Sensor Device for Tilt Measurements. …

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Leveraging Machine Learning For Detecting Iot-Based Interference In Operational Wifi Networks, Josh Pulse Jan 2022

Leveraging Machine Learning For Detecting Iot-Based Interference In Operational Wifi Networks, Josh Pulse

Honors Program Theses

IoT (Internet of Things) devices have become increasingly popular in recent years while WiFi continues to serve as primary network provider indoors. With the advancements in technology, the networks of IoT devices continue to weave closely with indoor WiFi network deployments. Both kinds of these networks primarily operate in 2.4 GHz ISM Band (though latest WiFi standards can operate in 5 GHz and 60 GHz bands, too). With the multitude of tiny IoT sensors being deployed indoors alongside operational WiFi networks, severe interference scenarios cannot be ruled out. As a result of this interference, performance of WiFi networks is bound …

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Techno-Economic Feasibility Of Electrifying Food Markets In Nigeria With Biogas Hybrid Mini-Grids, Demi Temitope Ogunwo Jan 2022

Techno-Economic Feasibility Of Electrifying Food Markets In Nigeria With Biogas Hybrid Mini-Grids, Demi Temitope Ogunwo

Cal Poly Humboldt theses and projects

This thesis explored the feasibility of electrifying a food market in an urban city in Nigeria with a hybrid biogas-powered mini-grid. Under the Energizing Economies Initiative of the Rural Electrification Agency of Nigeria, nine markets in the country currently receive constant access to electricity via hybrid mini-grid systems. As a majority of these systems are diesel-solar-battery systems, this thesis explored the use of biogas generators as a substitute for diesel generators in hybrid mini-grids for food markets. A fruit and vegetable market in Ketu, Lagos was used as a case study for the research. The research for this thesis was …

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Segmenting Technical Drawing Figures In Us Patents, Md Reshad Ul Hoque, Xin Wei, Muntabir Hasan Choudhury, Kehinde Ajayi, Martin Gryder, Jian Wu, Diane Oyen Jan 2022

Segmenting Technical Drawing Figures In Us Patents, Md Reshad Ul Hoque, Xin Wei, Muntabir Hasan Choudhury, Kehinde Ajayi, Martin Gryder, Jian Wu, Diane Oyen

Computer Science Faculty Publications

Image segmentation is the core computer vision problem for identifying objects within a scene. Segmentation is a challenging task because the prediction for each pixel label requires contextual information. Most recent research deals with the segmentation of natural images rather than drawings. However, there is very little research on sketched image segmentation. In this study, we introduce heuristic (point-shooting) and deep learning-based methods (U-Net, HR-Net, MedT, DETR) to segment technical drawings in US patent documents. Our proposed methods on the US Patent dataset achieved over 90% accuracy where transformer performs well with 97% segmentation accuracy, which is promising and computationally …

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Detecting The Presence Of Electronic Devices In Smart Homes Using Harmonic Radar, Beatrice Perez, Gregory Mazzaro, Timothy J. Pierson, David Kotz Jan 2022

Detecting The Presence Of Electronic Devices In Smart Homes Using Harmonic Radar, Beatrice Perez, Gregory Mazzaro, Timothy J. Pierson, David Kotz

Dartmouth Scholarship

Data about users is collected constantly by phones, cameras, Internet websites, and others. The advent of so-called ‘Smart Things' now enable ever-more sensitive data to be collected inside that most private of spaces: the home. The first step in helping users regain control of their information (inside their home) is to alert them to the presence of potentially unwanted electronics. In this paper, we present a system that could help homeowners (or home dwellers) find electronic devices in their living space. Specifically, we demonstrate the use of harmonic radars (sometimes called nonlinear junction detectors), which have also been used in …

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A Graph-Based Approach To Boundary Estimation With Mobile Sensors, Sean Onufer Stalley, Dingyu Wang, Gautam Dasarathy, John Lipor Jan 2022

A Graph-Based Approach To Boundary Estimation With Mobile Sensors, Sean Onufer Stalley, Dingyu Wang, Gautam Dasarathy, John Lipor

Electrical and Computer Engineering Faculty Publications and Presentations

We consider the problem of adaptive sampling for boundary estimation, where the goal is to identify the two dimensional spatial extent of a phenomenon of interest. Motivated by applications in estimating the spread of wildfires with a mobile sensor, we present a novel graph-based algorithm that is efficient in both the number of samples taken and the distance traveled. The key idea behind our approach is that by sampling locations close to known cut edges (edges whose vertices lie on opposite sides of the boundary), we can reliably find additional cut edges. Our approach repeats this process of using the …

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Incentivizing Distributed Energy Resource Participation In Grid Services, Tylor Slay, John M. Acken, Robert B. Bass Jan 2022

Incentivizing Distributed Energy Resource Participation In Grid Services, Tylor Slay, John M. Acken, Robert B. Bass

Electrical and Computer Engineering Faculty Publications and Presentations

The bulk power system is experiencing a dramatic shift as renewable generation growth continues to accelerate. Large-scale renewables adoption will help societies transition to a low-carbon, low-cost, and environmental-friendly electrical power system. However, the transition from a paradigm of generation following load to one where load follows generation will require large-scale interconnection and coordinated operation of Distributed Energy Resources (DERs), supported by open communication protocols. In this future grid scenario, DER aggregations will provide critical grid services that enable high penetration levels of renewable generation. This position paper presents an Energy Service Interface (ESI) that defines scope for ensuring secure, …

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A Field Test Of Direct Load Control Of Water Heaters And Its Implications For Consumers, Midrar Adham, Manasseh Obi, Robert B. Bass Jan 2022

A Field Test Of Direct Load Control Of Water Heaters And Its Implications For Consumers, Midrar Adham, Manasseh Obi, Robert B. Bass

Electrical and Computer Engineering Faculty Publications and Presentations

Utilities and customers are now operating more closely than ever. The prevailing numbers of grid-interactive Distributed Energy Resources are being integrated to provide grid reliability and stability. Different methods of control have been implemented to utilize these Distributed Energy Resources, such as Service-Oriented Load Control and Direct Load Control. This paper investigates the issues associated with the latter. A Direct Load Control method is applied to two Distributed Energy Resources, Electric Water Heater and Heat Pump Water Heater. A load shifting scenario is created where each water heater turns off during water draw events that coincide with peak demand periods. …

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Dominant Frequency Component Tracking Of Noisy Time-Varying Signals Using The Linear Predictive Coding Pole Processing Method, Jin Xu, Mark Davis, Ruairí De Fréin Jan 2022

Dominant Frequency Component Tracking Of Noisy Time-Varying Signals Using The Linear Predictive Coding Pole Processing Method, Jin Xu, Mark Davis, Ruairí De Fréin

Articles

The linear predictive coding pole processing (LPCPP) method proposed in our previous work overcomes the shortcomings of the LPC method, especially its sensitivity to noise and the filter order. The LPCPP method is a parameterised method that involves processing the LPC poles to produce a series of reduced-order filter transfer functions to estimate the dominant frequency components of a signal. This paper analyses the ability of the LPCPP method to track the frequency changes of noisy, time-varying signals in real-time. Linear chirped frequency modulation signals are used in a series of experiments to simulate signals with different rates of frequency …

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Generalized Regression Neural Network And Fitness Dependent Optimization: Application To Energy Harvesting Of Centralized Teg Systems, Adeel Feroz Mirza, Syed Kamran Haider, Abbas Ahmed, Ateeq Ur Rehman, Muhammad Shafiq, Mohit Bajaj, Hossam Zawbaa, Pawel Szczepankowski, Salah Kamel Jan 2022

Generalized Regression Neural Network And Fitness Dependent Optimization: Application To Energy Harvesting Of Centralized Teg Systems, Adeel Feroz Mirza, Syed Kamran Haider, Abbas Ahmed, Ateeq Ur Rehman, Muhammad Shafiq, Mohit Bajaj, Hossam Zawbaa, Pawel Szczepankowski, Salah Kamel

Articles

The thermoelectric generator (TEG) system has attracted extensive attention because of its applications in centralized solar heat utilization and recoverable heat energy. The operating efficiency of the TEG system is highly affected by operating conditions. In a series-parallel structure, due to diverse temperature differences, the TEG modules show non-linear performance. Due to the non-uniform temperature distribution (NUTD) condition, several maximum power points (MPPs) appear on the P/V curve. In multiple MPPs, the true global maximum power points (GMPP) are very important for optimum action. The existing conventional technologies have slow tracking speed, low productivity, and unwanted fluctuations in voltage curves. …

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Investigation Of The Combustion Of Exhaust Gas Recirculation In Diesel Engines With A Particulate Filter And Selective Catalytic Reactor Technologies For Environmental Gas Reduction, Megavath Vijay Kumar, Alur Veeresh Babu, Ch. Rami Reddy, A. Pandian, Mohit Bajaj, Hossam Zawbaa, Salah Kamel Jan 2022

Investigation Of The Combustion Of Exhaust Gas Recirculation In Diesel Engines With A Particulate Filter And Selective Catalytic Reactor Technologies For Environmental Gas Reduction, Megavath Vijay Kumar, Alur Veeresh Babu, Ch. Rami Reddy, A. Pandian, Mohit Bajaj, Hossam Zawbaa, Salah Kamel

Articles

The Diesel Engine, invented by the German engineer Rudolf Diesel, was a marvelous creation that changed the way the automobile industry worked. It is an internal combustion engine that compresses air to elevate its pressure and temperature so high that the atomized diesel fuel undergoes combustion almost instantaneously when it is sprayed into the combustion chamber. The Major advantage of a Compression Ignition (CI) engine compared to a Spark Ignition (SI) engine is the higher compression ratio achieved in the former, making it more efficient. This makes diesel engines more suitable for heavy-duty vehicles, which require more torque to overcome …

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Human Mental Workload: A Survey And A Novel Inclusive Definition, Luca Longo, Christopher D. Wickens, Gabriella Hancock, P.A. Hancock Jan 2022

Human Mental Workload: A Survey And A Novel Inclusive Definition, Luca Longo, Christopher D. Wickens, Gabriella Hancock, P.A. Hancock

Articles

Human mental workload is arguably the most invoked multidimensional construct in Human Factors and Ergonomics, getting momentum also in Neuroscience and Neuroergonomics. Uncertainties exist in its characterization, motivating the design and development of computational models, thus recently and actively receiving support from the discipline of Computer Science. However, its role in human performance prediction is assured. This work is aimed at providing a synthesis of the current state of the art in human mental workload assessment through considerations, definitions, measurement techniques as well as applications, Findings suggest that, despite an increasing number of associated research works, a single, reliable and …

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Information Theoretical Analysis Of The Uniqueness Of Iris Biometrics, Katelyn M. Hampel Jan 2022

Information Theoretical Analysis Of The Uniqueness Of Iris Biometrics, Katelyn M. Hampel

Graduate Theses, Dissertations, and Problem Reports (ETD)

With the rapid globalization of technology in the world, the need for a more reliable and secure online method of authentication is required. This can be achieved by using each individual’s distinctive biometric identifiers, such as the face, iris, fingerprint, palmprint, etc.; however, there is a bound to the uniqueness of each identifier and consequently, a limit to the capacity that a biometric recognition system can sustain before false matches occur. Therefore, knowing the limitations on the maximum population that a biometric modality can uniquely represent is essential now more than ever. In an effort to address the general problem, …

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Performance Of Sensor Fusion For Vehicular Applications, Nikola Janevski Jan 2022

Performance Of Sensor Fusion For Vehicular Applications, Nikola Janevski

Graduate Theses, Dissertations, and Problem Reports (ETD)

Sensor fusion is a key system in Advanced Driver Assistance Systems, ADAS. The perfor-
mance of the sensor fusion depends on many factors such as the sensors used, the kinematic
model used in the Extended Kalman Filter, EKF, the motion of the vehicles, the type of
road, the density of vehicles, and the gating methods. The interactions between parameters
and the extent to which individual parameters contribute to the overall accuracy of a sensor
fusion system can be difficult to assess.
In this study, a full-factorial experimental evaluation of a sensor fusion system based
on a real vehicle was performed. …

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Generation Of High Performing Morph Datasets, Kelsey Lynn O'Haire Jan 2022

Generation Of High Performing Morph Datasets, Kelsey Lynn O'Haire

Graduate Theses, Dissertations, and Problem Reports (ETD)

Facial recognition systems play a vital role in our everyday lives. We rely on this technology from menial tasks to issues as vital as national security. While strides have been made over the past ten years to improve facial recognition systems, morphed face images are a viable threat to the reliability of these systems. Morphed images are generated by combining the face images of two subjects. The resulting morphed face shares the likeness of the contributing subjects, confusing both humans and face verification algorithms. This vulnerability has grave consequences for facial recognition systems used on international borders or for law …

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Magneto-Exothermic Catalytic Chemical Reaction Along A Curved Surface, Muhammad Ashraf, Uzma Ahmad, Saqib Zia, Rama S. R. Gorla, Amnah S. Al-Johani, Ilyas Khan, Mulugeta Andualem Jan 2022

Magneto-Exothermic Catalytic Chemical Reaction Along A Curved Surface, Muhammad Ashraf, Uzma Ahmad, Saqib Zia, Rama S. R. Gorla, Amnah S. Al-Johani, Ilyas Khan, Mulugeta Andualem

Faculty Publications

In the current study, the physical behavior of the boundary layer flows along a curved surface owing exothermic catalytic chemical reaction, and the magnetic field is investigated. The mathematical model comprised of a part of momentum, energy, and mass equations, which are solved using a finite difference method along with primitive variable formulation. Numerical solutions, using the method of quantitative differentiation, are made with the appropriate choice of dimensionless parameters. Analysis of the results obtained shows that the field temperature and flow of fluids are strongly influenced by the combined effects of catalytic chemical reactions and the magnetic field. The …

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Particle Identification And Tracking In Real Time Using Machine Learning On Fpga, F. Barbosa, L. Belfore, C. Dickover, C. Fanelli, S. Furletov, Y. Furletova, L. Jokhovets, D. Lawrence, D. Romanov Jan 2022

Particle Identification And Tracking In Real Time Using Machine Learning On Fpga, F. Barbosa, L. Belfore, C. Dickover, C. Fanelli, S. Furletov, Y. Furletova, L. Jokhovets, D. Lawrence, D. Romanov

Electrical & Computer Engineering Faculty Publications

This project is a multi-disciplinary endeavour between Physics, Electrical Engineering, and Computer Engineering. The purpose is to develop and implement an FPGA(*) based Machine Learning algorithm for real-time particle identification, filtering, and data reduction. This is important research that can be applied to streaming readout systems being developed now at JLab and other facilities. Real-time data processing is a frontier field in experimental physics, especially in HEP. The application of FPGAs at the trigger level is used by many current and planned experiments (CMS, LHCb, Belle2, PANDA). Usually they use conventional processing algorithms. LHCb has implemented ML elements for real-time …

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Srf Cavity Fault Classification And Prediction At Jefferson Lab, Chris Tennant, Adam Carpenter, Lasitha Vidyaratne, Md. Monibor Rahman, Khan Iftekharuddin Jan 2022

Srf Cavity Fault Classification And Prediction At Jefferson Lab, Chris Tennant, Adam Carpenter, Lasitha Vidyaratne, Md. Monibor Rahman, Khan Iftekharuddin

Electrical & Computer Engineering Faculty Publications

Over the last few years several machine learning projects at Jefferson Lab have had a common focus to optimize operation of superconducting RF (SRF) cavities in the Continuous Electron Beam Accelerator Facility (CEBAF). In this talk we highlight work to identify and classify types of faults from C100-type cavities and then to extend those capabilities to provide real-time fault prediction. Early prediction may enable mitigation strategies to prevent some types of faults. In our approach we apply a two-step fault prediction pipeline. In the first step, a model distinguishes between faulty and normal signals. In the second step, signals flagged …

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Deeppose: Detecting Gps Spoofing Attack Via Deep Recurrent Neural Network, Peng Jiang, Hongyi Wu, Chunsheng Xin Jan 2022

Deeppose: Detecting Gps Spoofing Attack Via Deep Recurrent Neural Network, Peng Jiang, Hongyi Wu, Chunsheng Xin

Electrical & Computer Engineering Faculty Publications

The Global Positioning System (GPS) has become a foundation for most location-based services and navigation systems, such as autonomous vehicles, drones, ships, and wearable devices. However, it is a challenge to verify if the reported geographic locations are valid due to various GPS spoofing tools. Pervasive tools, such as Fake GPS, Lockito, and software-defined radio, enable ordinary users to hijack and report fake GPS coordinates and cheat the monitoring server without being detected. Furthermore, it is also a challenge to get accurate sensor readings on mobile devices because of the high noise level introduced by commercial motion sensors. To this …

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Hybridization From Guest-Host Interactions Reduces The Thermal Conductivity Of Metal-Organic Frameworks, Mallory E. Decoster, Hasan Babaei, Sangeun S. Jung, Zeinab M. Hassan, John T. Gaskins, Ashutosh Giri, Emma M. Tiernan, John A. Tomko, Helmut Baumgart, Pamela M. Norris, Alan J.H. Mcgaughey, Christopher E. Wilmer, Engelbert Redel, Gaurav Giri, Patrick E. Hopkins Jan 2022

Hybridization From Guest-Host Interactions Reduces The Thermal Conductivity Of Metal-Organic Frameworks, Mallory E. Decoster, Hasan Babaei, Sangeun S. Jung, Zeinab M. Hassan, John T. Gaskins, Ashutosh Giri, Emma M. Tiernan, John A. Tomko, Helmut Baumgart, Pamela M. Norris, Alan J.H. Mcgaughey, Christopher E. Wilmer, Engelbert Redel, Gaurav Giri, Patrick E. Hopkins

Electrical & Computer Engineering Faculty Publications

We experimentally and theoretically investigate the thermal conductivity and mechanical properties of polycrystalline HKUST-1 metal–organic frameworks (MOFs) infiltrated with three guest molecules: tetracyanoquinodimethane (TCNQ), 2,3,5,6-tetrafluoro-7,7,8,8-tetracyanoquinodimethane (F4-TCNQ), and (cyclohexane-1,4-diylidene)dimalononitrile (H4-TCNQ). This allows for modification of the interaction strength between the guest and host, presenting an opportunity to study the fundamental atomic scale mechanisms of how guest molecules impact the thermal conductivity of large unit cell porous crystals. The thermal conductivities of the guest@MOF systems decrease significantly, by on average a factor of 4, for all infiltrated samples as compared to the uninfiltrated, pristine HKUST-1. This reduction in thermal conductivity goes in …

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8-Plate Multi-Resonant Coupling Using A Class-E2 Power Converter For Misalignments In Capacitive Wireless Power Transfer, Yashwanth Bezawada, Shirshak K. Dhali Jan 2022

8-Plate Multi-Resonant Coupling Using A Class-E2 Power Converter For Misalignments In Capacitive Wireless Power Transfer, Yashwanth Bezawada, Shirshak K. Dhali

Electrical & Computer Engineering Faculty Publications

Misalignment is a common issue in wireless power transfer systems. It shifts the resonant frequency away from the operating frequency that affects the power flow and efficiency from the charging station to the load. This work proposes a novel capacitive wireless power transfer (CPT) using an 8-plate multi-resonant capacitive coupling to minimize the effect of misalignments. A single-active switch class-E2 power converter is utilized to achieve multi-resonance through the selection of different resonant inductors. Simulations show a widening of the resonant frequency band which offers better performance than a regular 4-plate capacitive coupling for misalignments. The hardware results of …

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Spectrum Sensing With Energy Detection In Multiple Alternating Time Slots, Călin Vlădeanu, Alexandru Marţian, Dimitrie C. Popescu Jan 2022

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 …

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Using Skeleton Correction To Improve Flash Lidar-Based Gait Recognition, Nasrin Sadeghzadehyazdi, Tamal Batabyal, Alexander Glandon, Nibir Dhar, Babajide Familoni, Khan Iftekharuddin, Scott T. Acton Jan 2022

Using Skeleton Correction To Improve Flash Lidar-Based Gait Recognition, Nasrin Sadeghzadehyazdi, Tamal Batabyal, Alexander Glandon, Nibir Dhar, Babajide Familoni, Khan Iftekharuddin, Scott T. Acton

Electrical & Computer Engineering Faculty Publications

This paper presents GlidarPoly, an efficacious pipeline of 3D gait recognition for flash lidar data based on pose estimation and robust correction of erroneous and missing joint measurements. A flash lidar can provide new opportunities for gait recognition through a fast acquisition of depth and intensity data over an extended range of distance. However, the flash lidar data are plagued by artifacts, outliers, noise, and sometimes missing measurements, which negatively affects the performance of existing analytics solutions. We present a filtering mechanism that corrects noisy and missing skeleton joint measurements to improve gait recognition. Furthermore, robust statistics are integrated with …

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A Channel State Information Based Virtual Mac Spoofing Detector, Peng Jiang, Hongyi Wu, Chunsheng Xin Jan 2022

A Channel State Information Based Virtual Mac Spoofing Detector, Peng Jiang, Hongyi Wu, Chunsheng Xin

Electrical & Computer Engineering Faculty Publications

Physical layer security has attracted lots of attention with the expansion of wireless devices to the edge networks in recent years. Due to limited authentication mechanisms, MAC spoofing attack, also known as the identity attack, threatens wireless systems. In this paper, we study a new type of MAC spoofing attack, the virtual MAC spoofing attack, in a tight environment with strong spatial similarities, which can create multiple counterfeits entities powered by the virtualization technologies to interrupt regular services. We develop a system to effectively detect such virtual MAC spoofing attacks via the deep learning method as a countermeasure. …

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Radiomic Texture Feature Descriptor To Distinguish Recurrent Brain Tumor From Radiation Necrosis Using Multimodal Mri, M. S. Sadique, A. Temtam, E. Lappinen, K. M. Iftekharuddin Jan 2022

Radiomic Texture Feature Descriptor To Distinguish Recurrent Brain Tumor From Radiation Necrosis Using Multimodal Mri, M. S. Sadique, A. Temtam, E. Lappinen, K. M. Iftekharuddin

Electrical & Computer Engineering Faculty Publications

Despite multimodal aggressive treatment with chemo-radiation-therapy, and surgical resection, Glioblastoma Multiforme (GBM) may recur which is known as recurrent brain tumor (rBT), There are several instances where benign and malignant pathologies might appear very similar on radiographic imaging. One such illustration is radiation necrosis (RN) (a moderately benign impact of radiation treatment) which are visually almost indistinguishable from rBT on structural magnetic resonance imaging (MRI). There is hence a need for identification of reliable non-invasive quantitative measurements on routinely acquired brain MRI scans: pre-contrast T1-weighted (T1), post-contrast T1-weighted (T1Gd), T2-weighted (T2), and T2 Fluid Attenuated Inversion Recovery (FLAIR) that can …

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Uncertainty Estimation In Classification Of Mgnt Using Radiogenomics For Glioblastoma Patients, W. Farzana, Z. A. Shboul, A. Temtam, K. M. Iftekharuddin Jan 2022

Uncertainty Estimation In Classification Of Mgnt Using Radiogenomics For Glioblastoma Patients, W. Farzana, Z. A. Shboul, A. Temtam, K. M. Iftekharuddin

Electrical & Computer Engineering Faculty Publications

Glioblastoma Multiforme (GBM) is one of the most malignant brain tumors among all high-grade brain cancers. Temozolomide (TMZ) is the first-line chemotherapeutic regimen for glioblastoma patients. The methylation status of the O6-methylguanine-DNA-methyltransferase (MGMT) gene is a prognostic biomarker for tumor sensitivity to TMZ chemotherapy. However, the standardized procedure for assessing the methylation status of MGMT is an invasive surgical biopsy, and accuracy is susceptible to resection sample and heterogeneity of the tumor. Recently, radio-genomics which associates radiological image phenotype with genetic or molecular mutations has shown promise in the non-invasive assessment of radiotherapeutic treatment. This study proposes a machine-learning framework …

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