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Full-Text Articles in Physical Sciences and Mathematics
Artificial Intelligence-Enabled Exploratory Cyber-Physical Safety Analyzer Framework For Civilian Urban Air Mobility, Md. Shirajum Munir, Sumit Howlader Dipro, Kamrul Hasan, Tariqul Islam, Sachin Shetty
Artificial Intelligence-Enabled Exploratory Cyber-Physical Safety Analyzer Framework For Civilian Urban Air Mobility, Md. Shirajum Munir, Sumit Howlader Dipro, Kamrul Hasan, Tariqul Islam, Sachin Shetty
VMASC Publications
Urban air mobility (UAM) has become a potential candidate for civilization for serving smart citizens, such as through delivery, surveillance, and air taxis. However, safety concerns have grown since commercial UAM uses a publicly available communication infrastructure that enhances the risk of jamming and spoofing attacks to steal or crash crafts in UAM. To protect commercial UAM from cyberattacks and theft, this work proposes an artificial intelligence (AI)-enabled exploratory cyber-physical safety analyzer framework. The proposed framework devises supervised learning-based AI schemes such as decision tree, random forests, logistic regression, K-nearest neighbors (KNN), and long short-term memory (LSTM) for predicting and …
Ultrasensitive Tapered Optical Fiber Refractive Index, Erem Ujah, Meimei Lai, Gymama Slaughter
Ultrasensitive Tapered Optical Fiber Refractive Index, Erem Ujah, Meimei Lai, Gymama Slaughter
Electrical & Computer Engineering Faculty Publications
Refractive index (RI) sensors are of great interest for label-free optical biosensing. A tapered optical fiber (TOF) RI sensor with micron-sized waist diameters can dramatically enhance sensor sensitivity by reducing the mode volume over a long distance. Here, a simple and fast method is used to fabricate highly sensitive refractive index sensors based on localized surface plasmon resonance (LSPR). Two TOFs (l = 5 mm) with waist diameters of 5 µm and 12 µm demonstrated sensitivity enhancement at λ = 1559 nm for glucose sensing (5-45 wt%) at room temperature. The optical power transmission decreased with increasing glucose concentration due …
Toward Real-Time, Robust Wearable Sensor Fall Detection Using Deep Learning Methods: A Feasibility Study, Haben Yhdego, Christopher Paolini, Michel Audette
Toward Real-Time, Robust Wearable Sensor Fall Detection Using Deep Learning Methods: A Feasibility Study, Haben Yhdego, Christopher Paolini, Michel Audette
Electrical & Computer Engineering Faculty Publications
Real-time fall detection using a wearable sensor remains a challenging problem due to high gait variability. Furthermore, finding the type of sensor to use and the optimal location of the sensors are also essential factors for real-time fall-detection systems. This work presents real-time fall-detection methods using deep learning models. Early detection of falls, followed by pneumatic protection, is one of the most effective means of ensuring the safety of the elderly. First, we developed and compared different data-segmentation techniques for sliding windows. Next, we implemented various techniques to balance the datasets because collecting fall datasets in the real-time setting has …
Transfer Learning Using Infrared And Optical Full Motion Video Data For Gender Classification, Alexander M. Glandon, Joe Zalameda, Khan M. Iftekharuddin, Gabor F. Fulop (Ed.), David Z. Ting (Ed.), Lucy L. Zheng (Ed.)
Transfer Learning Using Infrared And Optical Full Motion Video Data For Gender Classification, Alexander M. Glandon, Joe Zalameda, Khan M. Iftekharuddin, Gabor F. Fulop (Ed.), David Z. Ting (Ed.), Lucy L. Zheng (Ed.)
Electrical & Computer Engineering Faculty Publications
This work is a review and extension of our ongoing research in human recognition analysis using multimodality motion sensor data. We review our work on hand crafted feature engineering for motion capture skeleton (MoCap) data, from the Air Force Research Lab for human gender followed by depth scan based skeleton extraction using LIDAR data from the Army Night Vision Lab for person identification. We then build on these works to demonstrate a transfer learning sensor fusion approach for using the larger MoCap and smaller LIDAR data for gender classification.
A Survey Of Using Machine Learning In Iot Security And The Challenges Faced By Researchers, Khawlah M. Harahsheh, Chung-Hao Chen
A Survey Of Using Machine Learning In Iot Security And The Challenges Faced By Researchers, Khawlah M. Harahsheh, Chung-Hao Chen
Electrical & Computer Engineering Faculty Publications
The Internet of Things (IoT) has become more popular in the last 15 years as it has significantly improved and gained control in multiple fields. We are nowadays surrounded by billions of IoT devices that directly integrate with our lives, some of them are at the center of our homes, and others control sensitive data such as military fields, healthcare, and datacenters, among others. This popularity makes factories and companies compete to produce and develop many types of those devices without caring about how secure they are. On the other hand, IoT is considered a good insecure environment for cyber …
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 …
Magnetic Field Sensors For Detection Of Trapped Flux In Superconducting Radio Frequency Cavities, Ishwari Prasad Parajuli, Gianluigi Ciovati, Jean R. Delayen
Magnetic Field Sensors For Detection Of Trapped Flux In Superconducting Radio Frequency Cavities, Ishwari Prasad Parajuli, Gianluigi Ciovati, Jean R. Delayen
Physics Faculty Publications
Superconducting radio frequency (SRF) cavities are fundamental building blocks of modern particle accelerators. They operate at liquid helium temperatures (2–4 K) to achieve very high quality factors (1010–1011). Trapping of magnetic flux within the superconductor is a significant contribution to the residual RF losses, which limit the achievable quality factor. Suitable diagnostic tools are in high demand to understand the mechanisms of flux trapping in technical superconductors, and the fundamental components of such diagnostic tools are magnetic field sensors. We have studied the performance of commercially available Hall probes, anisotropic magnetoresistive sensors, and flux-gate magnetometers with …
Disaster Damage Categorization Applying Satellite Images And Machine Learning Algorithm, Farinaz Sabz Ali Pour, Adrian Gheorghe
Disaster Damage Categorization Applying Satellite Images And Machine Learning Algorithm, Farinaz Sabz Ali Pour, Adrian Gheorghe
Engineering Management & Systems Engineering Faculty Publications
Special information has a significant role in disaster management. Land cover mapping can detect short- and long-term changes and monitor the vulnerable habitats. It is an effective evaluation to be included in the disaster management system to protect the conservation areas. The critical visual and statistical information presented to the decision-makers can help in mitigation or adaption before crossing a threshold. This paper aims to contribute in the academic and the practice aspects by offering a potential solution to enhance the disaster data source effectiveness. The key research question that the authors try to answer in this paper is how …
Coupled Photonic Crystal Micro-Cavities With Ultra-Low Threshold Power For Stiumulated Raman Scattering, Qiang Liu, Zhengbiao Ouyang, Sacharia Albin
Coupled Photonic Crystal Micro-Cavities With Ultra-Low Threshold Power For Stiumulated Raman Scattering, Qiang Liu, Zhengbiao Ouyang, Sacharia Albin
Electrical & Computer Engineering Faculty Publications
We propose coupled cavities to realize a strong enhancement of the Raman scattering. Five sub cavities are embedded in the photonic crystals. Simulations through finite-difference time-domain (FDTD) method demonstrate that one cavity, which is used to propagate the pump beam at the optical-communication wavelength, has a Q factor as high as 1.254 × 108 and modal volume as small as 0.03μm3 (0.3192(λ/n)3). These parameters result in ultra-small threshold lasing power ~17.7nW and 2.58nW for Stokes and anti-Stokes respectively. The cavities are designed to support the required Stokes and anti-Stokes modal spacing in silicon. The proposed structure …
Emergent Behavior In Massively-Deployed Sensor Networks, Ekaterina Shurkova, Ruzana Ishak, Stephan Olariu, Shaharuddin Salleh
Emergent Behavior In Massively-Deployed Sensor Networks, Ekaterina Shurkova, Ruzana Ishak, Stephan Olariu, Shaharuddin Salleh
Computer Science Faculty Publications
The phenomenal advances in MEMS and nanotechnology make it feasible to build small devices, referred to as sensors that are able to sense, compute and communicate over small distances. The massive deployment of these small devices raises the fascinating question of whether or not the sensors, as a collectivity, will display emergent behavior, just as living organisms do. In this work we report on a recent effort intended to observe emerging behavior of large groups of sensor nodes, like living cells demonstrate. Imagine a massive deployment of sensors that can be in two states "red" and "blue". At deployment time …