A5060586529- Emotion and Mood Recognition
- Anomaly Detection Techniques and Applications
- Data-Driven Disease Surveillance
- COVID-19 epidemiological studies
- Chaos-based Image/Signal Encryption
- Network Security and Intrusion Detection
- Solar Radiation and Photovoltaics
- Photovoltaic System Optimization Techniques
- Robotic Locomotion and Control
- Cryptography and Data Security
- COVID-19 Pandemic Impacts
- Control and Dynamics of Mobile Robots
- Spam and Phishing Detection
- Privacy-Preserving Technologies in Data
- Robotic Path Planning Algorithms
- Vehicular Ad Hoc Networks (VANETs)
- Energy Load and Power Forecasting
- Influenza Virus Research Studies
- Complex Network Analysis Techniques
- Image and Video Quality Assessment
- Mechanical Engineering and Vibrations Research
- Business Process Modeling and Analysis
- Data Stream Mining Techniques
- Dynamics and Control of Mechanical Systems
- Human Mobility and Location-Based Analysis
University of Louisiana at Lafayette
2016-2025
Tampere University
2023
Los Alamitos Medical Center
2017
Louisiana Tech University
2005
Influenza vaccines could be improved by platforms inducing cross-reactive immunity. Immunodominance of the influenza hemagglutinin (HA) head in currently licensed impedes induction neutralizing stem-directed antibodies. A vaccine without variable HA domain has potential to focus immune response on conserved stem. This first-in-human dose-escalation open-label phase 1 clinical trial (NCT03814720) tested an stabilized stem ferritin nanoparticle (H1ssF) based H1 A/New Caledonia/20/1999....
We propose a novel data-driven machine learning method using long short-term memory (LSTM)-based multi-stage forecasting for influenza forecasting. The aspects of the include following: 1) introduction LSTM to capture temporal dynamics seasonal flu and 2) technique influence external variables that includes geographical proximity climatic such as humidity, temperature, precipitation, sun exposure. proposed model is compared against two state-of-the-art techniques publicly available datasets....
Variation in solar irradiance causes power generation fluctuations plants. Power grid operators need accurate forecasts to manage this variability. Many factors affect irradiance, including the time of year, weather and day. Cloud cover is one most important variables that affects generation, but also characterized by a high degree variability uncertainty. Deep learning methods have ability learn long-term dependencies within sequential data. We investigate application Gated Recurrent Units...
Abstract Advances in wearable technologies provide the opportunity to monitor many physiological variables continuously. Stress detection has gained increased attention recent years, mainly because early stress can help individuals better manage health minimize negative impacts of long-term exposure. This paper provides a unique dataset created natural working environment hospital. is collection biometric data nurses during COVID-19 outbreak. Studying work complex due social, cultural, and...
In this work, performance analysis and comparison of three photovoltaic technologies are carried out in the Louisiana climate. During calendar year 2018, University at Lafayette constructed commissioned a 1.1 MW solar power plant for researching southern partial energy demand university. It was one largest plants when receives an annual insolation 4.88 kWh/m2/d latitude minus five degrees (25°) tilt. The has total 4142 modules incorporates module technologies. Preliminary data from system...
Electric Vehicle Supply Equipment (EVSE), also known as charging stations, are available for electric vehicles. EVSE contain computers that connected to the Internet. These systems serve important control functions such authorization, vehicles, and connecting local power grid. Charging stations authorize users vehicles using RFID cards, Bluetooth, or Wi-Fi. Moreover, there many sensing, communication computational components in EVSEs potentially vulnerable cyber-security attacks. We have...
The lack of security in today's in-vehicle network make connected vehicles vulnerable to many types cyber-attacks. Replay-based injection attacks are one the easiest type denial-of-service where attacker floods with malicious traffic intent alter vehicle's normal behavior. may exploit this vulnerability launch targeted low-rate which difficult detect because during looks like regular traffic. In paper, we propose a sequence mining approach Control Area Network (CAN). We discuss four...
Power grid operators rely on solar irradiance forecasts to manage uncertainty and variability associated with power. Meteorological factors such as cloud cover, wind direction, speed affect are a high degree of uncertainty. Statistical models fail accurately capture the dependence between these irradiance. In this paper, we introduce idea applying multivariate Gated Recurrent Units (GRU) forecast Direct Normal Irradiance (DNI) hourly. The proposed GRU-based forecasting method is evaluated...
In this article, a cyber-resilient consensus-based distributed control scheme is proposed for islanded ac microgrids. Considering the impacts of false data injection (FDI) attacks on conventional consensus algorithms, hybrid solution presented based combination multiobjective sliding-mode and communication link quality observer to provide reliable performance against different types FDIs. Unlike commonly developed observer-based approach aims form complete localized without requirements...
ABSTRACT Previous studies have shown that soiling losses on photovoltaic (PV) modules can lead to reduced power output of up 80% in PV systems. Therefore, accurate determination loss plays a crucial role predicting and ensuring optimized cleaning schedules. The study focused measuring at 1.1 MW outdoor testing facility Louisiana, United States, using DustIQ device, commercially available sensor. maximum recorded for Sensor 1 was 7.5% August 27, 2023, during the dry season. measured data...
Stochastic Computing (SC) is an alternative computing paradigm that promises high robustness to noise and outstanding area- power-efficiency compared traditional binary. It also enables the design of fully parallel scalable computations. Despite its advantage, SC suffers from long latency energy consumption conventional binary computing, especially with current CMOS technology. The cost conversion between stochastic representation takes a significant circuits. In-Memory Computation (IMC)...
Abstract Human mobility plays an important role in the dynamics of infectious disease spread. Evidence from initial nationwide lockdowns for COVID− 19 indicates that restricting human is effective strategy to contain While a direct correlation was observed early on, it not known how impacted infection growth rates once are lifted, primarily due modulation by other factors such as face masks, social distancing, and non-linear patterns both growth. This paper introduces piece-wise approach...
The problem of video frame prediction has received much interest due to its relevance many computer vision applications such as autonomous vehicles or robotics. Supervised methods for rely on labeled data, which may not always be available. In this paper, we provide a novel unsupervised deep-learning method called Inception-based LSTM prediction. general idea inception networks is implement wider instead deeper networks. This network design was shown improve the performance image...
We study the problem of learning to rank from multiple information sources. Though multi-view and have been studied extensively leading a wide range applications, as synergy both topics has received little attention. The aim paper is propose composite ranking method while keeping close correlation with individual rankings simultaneously. present generic framework for subspace (MvSL2R), two novel solutions are introduced under framework. first solution captures feature mappings within each...
Travel patterns and mobility affect the spread of infectious diseases like COVID-19. However, we do not know to what extent local vs. visitor affects growth in number cases. This study evaluates impact state-level understanding with respect cases for COVID United States between March 1, 2020, December 31, 2020. Two metrics, namely transmission risk, were extracted from data capture potential COVID-19 through mobility. A combination three factors: current cases, are used model future using...
Solar irradiance is the measurement of amount power from Sun per unit area. has a very high degree variability, due to many environmental factors, including cloud cover, relative humidity, and air temperature. Predicting solar useful for measuring future energy production scheduling. Real-time can be forecasted using either maching learning or physics-based models, both having their own respective trade-offs. To overcome limitations machine simulations, we propose novel framework in...
Public safety officials want to have maximum situational-awareness though real-time information, such as video content, for natural disaster management. The content can be generated by surveillance cameras or crowd-sourced (e.g., using smart-phones) and live-streamed the Incident Commander. Such contents need processed adapt characteristics of specialized multi-view display devices. When a occurs, there is surge in number videos streamed Commander that oversubscribes processing servers...
Security and confidentiality of big data stored in the cloud are important concerns for many organizations to adopt services. One common approach address is client-side encryption where encrypted on client machine before being cloud. Having cloud, however, limits ability clustering, which a crucial part analytics applications, such as search systems. To overcome limitation, this paper, we present an named ClustCrypt efficient topic-based clustering unstructured dynamically estimates optimal...
Mass traffic evacuations during Hurricanes Rita and Katrina demonstrated limitations of static planning-based evacuation models based on data from historical events. Evacuation dynamics are complex due to the number people vehicles, road networks, uncertainty perception event, public safety advisories, human decisions regarding routes behaviors. We describe a system under development for real-time information driven decision support planning response. This consists an prediction model...
Abstract Containing the COVID-19 pandemic while balancing economy has proven to be quite a challenge for world. We still have limited understanding of which combination policies been most effective in flattening curve; given challenges dynamic and evolving nature pandemic, lack quality data etc. This paper introduces novel mining-based approach understand effects different non-pharmaceutical interventions containing infection rate. used association rule mining perform descriptive on publicly...
Abstract Omnidirectional locomotion provides wheeled mobile robots (WMR) with better maneuverability and flexibility, which enhances their energy efficiency dexterity. Universal omni-wheels are one of the best categories wheels that can be used to develop a WMR (Amarasiri et al., 2022, “Robust Dynamic Modeling Trajectory Tracking Controller Omni-Wheeled Mobile Robot,” ASME Letters Dyn. Sys. Control., 2(4), p. 040902. 10.1115/1.4055690). We study dynamic modeling controllers for train in...
Affective computing has garnered researchers' attention and interest in recent years as there is a need for AI systems to better understand react human emotions. However, analyzing emotions, such mood or stress, quite complex. While various stress studies use facial expressions wearables, most existing datasets rely on processing data from single modality. This paper presents EmpathicSchool, novel dataset that captures the associated physiological signals, heart rate, electrodermal activity,...