Raju Gottumukkala

Raju Gottumukkala
University of Louisiana at Lafayette | ULL

About

54
Publications
14,800
Reads
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433
Citations
Citations since 2017
40 Research Items
338 Citations
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2017201820192020202120222023020406080100
2017201820192020202120222023020406080100

Publications

Publications (54)
Article
Full-text available
Video frame prediction is needed for various computer-vision-based systems such as self-driving vehicles and video streaming. This paper proposes a novel Inception-based convolutional recurrent neural network (RNN) as an enhancement to a basic gated convolutional RNN. A basic gated convolutional RNN has fixed-size kernels that are hyperparameters o...
Preprint
Full-text available
Affective computing has garnered researchers' attention and interest in recent years as there is a need for AI systems to better understand and react to human emotions. However, analyzing human emotions, such as mood or stress, is quite complex. While various stress studies use facial expressions and wearables, most existing datasets rely on proces...
Article
Affective computing has garnered researchers’ attention and interest in recent years as there is a need for AI systems to better understand and react to human emotions. However, analyzing human emotions, such as mood or stress, is quite complex. While various stress studies use facial expressions and wearables, most existing datasets rely on proces...
Article
Omnidirectional mobile robots are widely used in studies and services as they are effective and efficient in moving in any direction regardless of their current orientation. These significant properties are very useful in energy-efficient navigation and obstacle avoidance in dynamic environments. The literature on modeling and control of omni-wheel...
Article
Full-text available
Travel patterns and mobility affect the spread of infectious diseases like COVID-19. However, we do not know to what extent local vs. visitor mobility affects the growth in the number of cases. This study evaluates the impact of state-level local vs. visitor mobility in understanding the growth with respect to the number of cases for COVID spread i...
Article
Full-text available
In this work, performance analysis and comparison of three photovoltaic technologies are carried out in the Louisiana climate. During the calendar year of 2018, the University of Louisiana at Lafayette constructed and commissioned a 1.1 MW solar photovoltaic power plant for researching solar power in southern Louisiana and for partial energy demand...
Article
Full-text available
Stochastic Computing (SC) is an alternative computing paradigm that promises high robustness to noise and outstanding area- and power-efficiency compared to traditional binary. It also enables the design of fully parallel and scalable computations. Despite its advantage, SC suffers from long latency and high energy consumption compared to conventio...
Article
In this paper, a cyber-resilient consensus based distributed control scheme is proposed for islanded AC microgrids. Meanwhile, the detrimental effects of common types of cyber-attacks on the conventional consensus scheme are analytically discussed, the sliding mode control concept is employed to enhance the overall resilience of the distributed con...
Article
Full-text available
Advances in wearable technologies provide the opportunity to continuously monitor many physiological variables. Stress detection has gained increased attention in recent years, especially because early stress detection can help individuals better manage health to minimize the negative impacts of long-term stress exposure. This paper provides a uniq...
Article
Full-text available
Containing the COVID-19 pandemic while balancing the economy has proven to be quite a challenge for the world. We still have limited understanding of which combination of policies have been most effective in flattening the curve; given the challenges of the dynamic and evolving nature of the pandemic, lack of quality data etc. This paper introduces...
Preprint
Full-text available
Background: Travel patterns of humans play a major part in the spread of infectious diseases. This was evident in the geographical spread of COVID-19 in the United States. However, the impact of this mobility and the transmission of the virus due to local travel, compared to the population traveling across state boundaries, is unknown. This study e...
Article
Full-text available
Human mobility plays an important role in the dynamics of infectious disease spread. Evidence from the initial nationwide lockdowns for COVID− 19 indicates that restricting human mobility is an effective strategy to contain the spread. While a direct correlation was observed early on, it is not known how mobility impacted COVID− 19 infection growth...
Article
Full-text available
Various time series forecasting methods have been successfully applied for the water-stage forecasting problem. Graphical time series models are a class of multivariate time series to model the spatio-temporal dependencies between the sensors. Constructing graph-based models involve data pre-processing and correlation analysis to capture the dynami...
Preprint
Full-text available
Containing the COVID-19 pandemic while balancing the economy has proven to be quite a challenge for the world. We still have limited understanding of which combination of policies have been most effective in flattening the curve; given the challenges of the dynamic and evolving nature of the pandemic, lack of quality data etc. This paper introduces...
Preprint
Full-text available
Advances in wearable technologies provide the opportunity to continuously monitor many physiological variables. Stress detection has gained increased attention in recent years, especially because early stress detection can help individuals better manage health to minimize the negative impacts of long-term stress exposure. This paper provides a uniq...
Article
Full-text available
Power grid operators rely on solar irradiance forecasts to manage uncertainty and variability associated with solar power. Meteorological factors such as cloud cover, wind direction, and wind speed affect irradiance and are associated with a high degree of variability and uncertainty. Statistical models fail to accurately capture the dependence bet...
Preprint
Full-text available
Cloud-based enterprise search services (e.g., Amazon Kendra) are enchanting to big data owners by providing them with convenient search solutions over their enterprise big datasets. However, individuals and businesses that deal with confidential big data (eg, credential documents) are reluctant to fully embrace such services, due to valid concerns...
Article
Full-text available
In this paper, we proposed a novel deep-learning method called Inception LSTM for video frame prediction. A standard convolutional LSTM uses a single size kernel for each of its gates. Having multiple kernel sizes within a single gate would provide a richer features that would otherwise not be possible with a single kernel. Our key idea is to intro...
Article
Full-text available
The lack of security in today’s in-vehicle network make connected vehicles vulnerable to many types of cyber-attacks. Replay-based injection attacks are one of the easiest type of denial-of-service attacks where the attacker floods the in-vehicle network with malicious traffic with intent to alter the vehicle’s normal behavior. The attacker may exp...
Conference Paper
This paper describes a numerical analysis supported by small scale experiments for demonstrating a monitoring and leak detection methodology. This study can be used to build a full-scale water pipeline monitoring and response system. The monitoring system is able to monitor the pipeline health and respond to hazard conditions through the use of mul...
Article
Full-text available
Variation in solar irradiance causes power generation fluctuations in solar power plants. Power grid operators need accurate irradiance forecasts to manage this variability. Many factors affect irradiance, including the time of year, weather and time of day. Cloud cover is one of the most important variables that affects solar power generation, but...
Article
In this paper, we proposed a novel deep-learning method called Inception LSTM for video frame prediction. A standard convolutional LSTM uses a single size kernel for each of its gates. Having multiple kernel sizes within a single gate would provide a richer features that would otherwise not be possible with a single kernel. Our key idea is to intro...
Preprint
Full-text available
The problem of video frame prediction has received much interest due to its relevance to many computer vision applications such as autonomous vehicles or robotics. Supervised methods for video frame prediction rely on labeled data, which may not always be available. In this paper, we provide a novel unsupervised deep-learning method called Inceptio...
Preprint
Security and confidentiality of big data stored in the cloud are important concerns for many organizations to adopt cloud services. One common approach to address the concerns is client-side encryption where data is encrypted on the client machine before being stored in the cloud. Having encrypted data in the cloud, however, limits the ability of d...
Preprint
Full-text available
Cloud service providers offer a low-cost and convenient solution to host unstructured data. However, cloud services act as third-party solutions and do not provide control of the data to users. This has raised security and privacy concerns for many organizations (users) with sensitive data to utilize cloud-based solutions. User-side encryption can...
Conference Paper
Full-text available
Processing high-volume, high-velocity data streams is an important big data problem in many sciences, engineering, and technology domains. There are many open-source distributed stream processing and cloud platforms that offer low-latency stream processing at scale, but the visualization and user-interaction components of these systems are limited...
Conference Paper
we present an approach named ClustCrypt for efficient topic-based clustering of encrypted unstructured big data in the cloud. ClustCrypt dynamically estimates the optimal number of clusters based on the statistical characteristics of encrypted data. It also provides clustering approach for encrypted data. We deploy ClustCrypt within the context of...
Article
Full-text available
We provide data-driven machine learning methods that are capable of making real-time influenza forecasts that integrate the impacts of climatic factors and geographical proximity to achieve better forecasting performance. The key contributions of our approach are both applying deep learning methods and incorporation of environmental and spatio-temp...
Conference Paper
Full-text available
Video traffic accounts for more than 70% of Internet traffic and is likely to reach 90% in 2020. Live video streaming is used in many applications such as live events, video conferencing, surveillance and public safety. With live streaming, latency, video quality (resolution, bit rate), and jitter become important. Video streaming protocols such as...
Article
We study the problem of learning to rank from multiple sources. Though multi-view learning and learning to rank have been studied extensively leading to a wide range of applications, multi-view learning to rank as a synergy of both topics has received little attention. The aim of the paper is to propose a composite ranking method while keeping a cl...
Conference Paper
For the last decade, the automatic generation of hypothesis from the literature has been widely studied. One common approach is to model biomedical literature as a concept network; then a prediction model is applied to predict the future relationships (links) between pairs of concept. Typically, this link prediction task can be cast into in one of...
Chapter
Decision makers in multiple domains are increasingly looking for ways to improve the understanding of real-world phenomena through data collected from Internet devices, including low-cost sensors, smart phones, and online activity. Examples include detecting environmental changes, understanding the impacts of adverse manmade and natural disasters,...
Book
Full-text available
Several real-world observations from streaming data sources, such as sensors, click streams, and social media, can be modeled as time-evolving graphs. There is a lot of interest in domains such as cybersecurity, epidemiology networks, social community networks, and recommendation networks to both study and build systems to track the evolutionary pr...
Conference Paper
This paper presents a flu monitoring system that utilizes prescriptions-based data. It provides evidence-base information that may be "useful" to many users, e.g., Medical professionals, public health administrators, patients, prescription drugs manufacturers, elementary/middle/high schools. The system consists of a real-time flu surveillance engin...
Patent
A system and method of modeling and evaluating workflows that provides workflow auto generation and Hierarchical Dependence Graphs for workflows. Modeling and evaluation of workflows is accomplished by accessing a knowledge database containing service descriptions, generating valid workflows models, simulating workflow and obtaining customer requir...
Article
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 of people and vehicles, road networks, the uncertainty and perception of the event, public safety advisories, and human decisions rega...
Patent
A method and system for translating a JDF workflow into a colored Petri net representation. Once the workflow has been converted, the colored Petri net is validated and analyzed. This provides the ability to identify potential deadlock conditions within a JDF workflow. In addition, the model network can be used to simulate throughput and turnaround...
Article
Reliability estimation of High Performance Computing (HPC) systems enables resource allocation, and fault tolerance frameworks to minimize the performance loss due to unexpected failures. Recent studies have shown that compute nodes in HPC systems follow a time varying failure rate distribution such as Weibull, instead of the exponential distributi...
Article
Full-text available
Louisiana researchers and universities are leading a concentrated, collaborative effort to advance statewide e-Research through a new cyberinfrastructure: computing systems, data storage systems, advanced instruments and data repositories, visualization environments and people, all linked together by software programs and high-performance networks....
Article
Full-text available
The growing demand for more computational power to solve complex scientific problems is driving the physical scale of the system to hundreds and thousands of nodes. Ideally, scaling up the number of nodes should minimize the completion time, but algorithmic and system environment factors limit the scalability of parallel applications. Reliability i...
Conference Paper
Failures and downtimes have severe impact on the performance of parallel programs in a large scale High Performance Computing (HPC) environment. There were several research efforts to understand the failure behavior of computing systems. However, the presence of multitude of hardware and software components required for uninterrupted operation of p...
Conference Paper
Cluster computing has been attracting more and more attention from both the industry and the academia for its enormous computing power, cost effectiveness, and scalability. Availability is a key system attribute that needs to be considered both at system design stage and must reflect the actuality. System monitoring and logging enables identifying...
Article
Full-text available
MOLAR is a multi-institutional research effort that concentrates on adaptive, reliable, and efficient operating and runtime system (OS/R) solutions for ultra-scale high-end scientific computing on the next generation of supercomputers. This research addresses the challenges outlined in FAST-OS (forum to address scalable technology for runtime and o...
Conference Paper
Production printing workflow is a high-volume and high-speed printing process normally consisting of a set of complex and inter-related tasks namely pre-press, press and post-press procedures. Today many production printing vendors are increasingly offering heterogeneous devices and related software products that autonomously interoperate as a prod...
Article
Full-text available
The growing demand for more computational power to solve complex scientific problems is driving the physical scale of the system to hundreds and thousands of nodes. Ideally, scaling up the number of nodes should minimize the completion time, but algorithmic and system environment factors limit the scalability of parallel applications. Reliability i...

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Projects

Projects (4)
Project
Design a Multimodal Indoor Positioning System that anonymously distinguishes and tracks users in an indoor environment using BLE and Computer Vision
Project
The problem of video frame prediction has received much interest due to its relevance to many computer vision applications such as autonomous vehicles or robotics. Supervised methods for video frame prediction 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 for video frame prediction. The general idea of inception networks is to implement wider networks instead of deeper networks. The proposed method is evaluated on both Inception-v1 and Inception-v2 structures. The proposed Inception LSTM methods are compared with convolutional LSTM when applied using PredNet predictive coding framework for both the KITTI and KTH data sets. We observed that the Inception based LSTM outperforms the convolutional LSTM. Also, Inception LSTM has better prediction performance compared to Inception v2 LSTM. However, Inception v2 LSTM has a lower computational cost compared to Inception LSTM.