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Browsing by Author "Liang, Yao"
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Item Accelerating complex modeling workflows in CyberWater using on-demand HPC/Cloud resources(IEEE, 2021-09) Li, Feng; Chen, Ranran; Fu, Yuankun; Song, Fengguang; Liang, Yao; Ranawaka, Isuru; Pamidighantam, Sudhakar; Luna, Daniel; Liang, Xu; Computer Information and Graphics Technology, School of Engineering and TechnologyWorkflow management systems (WMSs) are commonly used to organize/automate sequences of tasks as workflows to accelerate scientific discoveries. During complex workflow modeling, a local interactive workflow environment is desirable, as users usually rely on their rich, local environments for fast prototyping and refinements before they consider using more powerful computing resources. However, existing WMSs do not simultaneously support local interactive workflow environments and HPC resources. In this paper, we present an on-demand access mechanism to remote HPC resources from desktop/laptop-based workflow management software to compose, monitor and analyze scientific workflows in the CyberWater project. Cyber-Water is an open-data and open-modeling software framework for environmental and water communities. In this work, we extend the open-model, open-data design of CyberWater with on-demand HPC accessing capacity. In particular, we design and implement the LaunchAgent library, which can be integrated into the local desktop environment to allow on-demand usage of remote resources for hydrology-related workflows. LaunchAgent manages authentication to remote resources, prepares the computationally-intensive or data-intensive tasks as batch jobs, submits jobs to remote resources, and monitors the quality of services for the users. LaunchAgent interacts seamlessly with other existing components in CyberWater, which is now able to provide advantages of both feature-rich desktop software experience and increased computation power through on-demand HPC/Cloud usage. In our evaluations, we demonstrate how a hydrology workflow that consists of both local and remote tasks can be constructed and show that the added on-demand HPC/Cloud usage helps speeding up hydrology workflows while allowing intuitive workflow configurations and execution using a desktop graphical user interface.Item Analysis of Pseudo-Symmetry in Protein Homo-Oligomers(2018-12) Rajendran, Catherine Jenifer Rajam; Fang, Shiaofen; Liu, Jing-Yuan; Liang, YaoSymmetry plays a significant role in protein structural assembly and function. This is especially true for large homo-oligomeric protein complexes due to stability and finite control of function. But, symmetry in proteins are not perfect due to unknown reasons and leads to pseudosymmetry. This study focuses on symmetry analysis of homo-oligomers, specifically homo-dimers, homo-trimers and homo-tetramers. We defined Off Symmetry (OS) to measure the overall symmetry of the protein and Structural Index (SI) to quantify the structural difference and Assembly Index (AI) to quantify the assembly difference between the subunits. In most of the symmetrical homo-trimer and homo-tetramer proteins, Assembly Index contributes more to Off Symmetry and in the case of homo-dimer, Structural index contributes more than the Assembly Index. The main chain atom Carbon-Alpha (CA) is more symmetrical than the first side chain atom Carbon-Beta (CB), suggesting protein mobility may contribute to the pseudosymmetry. In addition, Pearson coefficient correlation between their Off-Symmetry and their respective atoms B-Factor (temperature factor) are calculated. We found that the individual residues of a protein in all the subunits are correlated to their average B-Factor of these residues. The correlation with BFactor is stronger in Structure Index than Assembly Index. All these results suggest that protein dynamics play an important role and therefore a larger off-symmetry may indicate a more mobile and flexible protein complex.Item A Compressed Data Collection System For Use In Wireless Sensor Networks(2013-03-06) Erratt, Newlyn S.; Liang, Yao; Raje, Rajeev; Tuceryan, MihranOne of the most common goals of a wireless sensor network is to collect sensor data. The goal of this thesis is to provide an easy to use and energy-e fficient system for deploying data collection sensor networks. There are numerous challenges associated with deploying a wireless sensor network for collection of sensor data; among these challenges are reducing energy consumption and the fact that users interested in collecting data may not be familiar with software design. This thesis presents a complete system, comprised of the Compression Data-stream Protocol and a general gateway for data collection in wireless sensor networks, which attempts to provide an easy to use, energy efficient and complete system for data collection in sensor networks. The Compressed Data-stream Protocol is a transport layer compression protocol with a primary goal, in this work, to reduce energy consumption. Energy consumption of the radio in wireless sensor network nodes is expensive and the Com-pressed Data-stream Protocol has been shown in simulations to reduce energy used on transmission and reception by around 26%. The general gateway has been designed in such a way as to make customization simple without requiring vast knowledge of sensor networks and software development. This, along with the modular nature of the Compressed Data-stream Protocol, enables the creation of an easy to deploy and easy to configure sensor network for data collection. Findings show that individual components work well and that the system as a whole performs without errors. This system, the components of which will eventually be released as open source, provides a platform for researchers purely interested in the data gathered to deploy a sensor network without being restricted to specific vendors of hardware.Item Compressed Sensing in Multi-Hop Large-Scale Wireless Sensor Networks Based on Routing Topology Tomography(IEEE, 2018) Li, Yimei; Liang, Yao; Computer and Information Science, School of ScienceData acquisition from multi-hop large-scale outdoor wireless sensor network (WSN) deployments for environmental monitoring is full of challenges. This is because of the severe resource constraints on tiny battery-operated motes (e.g., bandwidth, memory, power, and computing capacity), the data acquisition volume from large-scale WSNs, and the highly dynamic wireless link conditions in outdoor harsh communication environments. We present a novel compressed sensing approach, which can recover the sensing data at the sink with high fidelity when a very few data packets need to be collected, leading to a significant reduction of the network transmissions and thus an extension of the WSN lifetime. Interplaying with the dynamic WSN routing topology, the proposed approach is both efficient and simple to implement on the resource-constrained motes without motes' storing of any part of the random projection matrix, as opposed to other existing compressed sensing-based schemes. We further propose a systematic method via machine learning to find a suitable representation basis, for any given WSN deployment and data field, which is both sparse and incoherent with the random projection matrix in compressed sensing for data collection. We validate our approach and evaluate its performance using a real-world outdoor multihop WSN testbed deployment in situ. The results demonstrate that our approach significantly outperforms existing compressed sensing approaches by reducing data recovery errors by an order of magnitude for the entire WSN observation field while drastically reducing wireless communication costs at the same time.Item CyberWater: An Open Framework for Data and Model Integration(2024-05) Chen, Ranran; Liang, Yao; Song, Fengguang; Xia, Yuni; Zheng, JiangyuWorkflow management systems (WMSs) are commonly used to organize/automate sequences of tasks as workflows to accelerate scientific discoveries. During complex workflow modeling, a local interactive workflow environment is desirable, as users usually rely on their rich, local environments for fast prototyping and refinements before they consider using more powerful computing resources. This dissertation delves into the innovative development of the CyberWater framework based on Workflow Management Systems (WMSs). Against the backdrop of data-intensive and complex models, CyberWater exemplifies the transition of intricate data into insightful and actionable knowledge and introduces the nuanced architecture of CyberWater, particularly focusing on its adaptation and enhancement from the VisTrails system. It highlights the significance of control and data flow mechanisms and the introduction of new data formats for effective data processing within the CyberWater framework. This study presents an in-depth analysis of the design and implementation of Generic Model Agent Toolkits. The discussion centers on template-based component mechanisms and the integration with popular platforms, while emphasizing the toolkits ability to facilitate on-demand access to High-Performance Computing resources for large-scale data handling. Besides, the development of an asynchronously controlled workflow within CyberWater is also explored. This innovative approach enhances computational performance by optimizing pipeline-level parallelism and allows for on-demand submissions of HPC jobs, significantly improving the efficiency of data processing. A comprehensive methodology for model-driven development and Python code integration within the CyberWater framework and innovative applications of GPT models for automated data retrieval are introduced in this research as well. It examines the implementation of Git Actions for system automation in data retrieval processes and discusses the transformation of raw data into a compatible format, enhancing the adaptability and reliability of the data retrieval component in the adaptive generic model agent toolkit component. For the development and maintenance of software within the CyberWater framework, the use of tools like GitHub for version control and outlining automated processes has been applied for software updates and error reporting. Except that, the user data collection also emphasizes the role of the CyberWater Server in these processes. In conclusion, this dissertation presents our comprehensive work on the CyberWater framework’s advancements, setting new standards in scientific workflow management and demonstrating how technological innovation can significantly elevate the process of scientific discovery.Item CyberWater: An Open Framework for Data and Model Integration in Water Science and Engineering(ACM, 2022-10-17) Chen, Ranran; Li, Feng; Bieger, Drew; Song, Fengguang; Liang, Yao; Luna, Daniel; Young, Ryan; Liang, Xu; Pamidighantam, Sudhakar; Computer and Information Science, School of ScienceThe CyberWater project is to build an open-data open-model framework for easy and incremental integration of heterogeneous data sources and diverse scientific models across disciplines in the broad water domain. The CyberWater framework extends the open-data open-model framework called Meta-Scientific-Modeling (MSM) that provides a system-wide data and model integration platform. On top of MSM, the CyberWater framework provides a set of toolkits, and external system integration engines, to further facilitate users' scientific modeling and collaboration across disciplines. For example, the developed generic model agent toolkit enables users to integrate their computational models into CyberWater via graphical user interface configuration without coding, which further simplifies the data and model integration and model coupling. CyberWater adopts a graphical scientific workflow system, VisTrails, ensuring data provenance and reproducible computing. CyberWater supports novel access to high-performance computing resources on demand for users' computational expensive model tasks. We demonstrate merits of CyberWater by a use case of hydrologic modeling workflow.Item The design and implementation of mobile deluge on Android platform for wireless sensor network reprogramming(2017-11-28) Faruk, MD Omor; Liang, Yao; Tuceryan, Mihran; Mukhopadhyay, SnehasisWireless Sensor Networks (WSN) is being used in various applications including environmental monitoring, site inspection and military. WSN is a distributed network of sensor devices that can be used to monitor temperature, humidity, light and other important metrics. The software that runs on the sensor devices define how the device should operate. In real world WSN deployment, device software update is required to maintain optimal operation. In this thesis, we propose a novel idea of updating the software of the sensor nodes using a mobile device running on Android Operating System. Our implementation builds upon Mobile Deluge with few enhancement which is a method of re-programming WSN with laptop computer. We have evaluated our application performance by lab experiments and in real world deployments of WSN and found the application stable and battery efficient.Item Design and Implementation of Web-based Data and Network Management System for Heterogeneous Wireless Sensor Networks(2011-03-09) Yu, Qun; Liang, Yao; Zou, Xukai; Xia, YuniToday, Wireless Sensor Networks (WSNs) are forming an exciting new area to have dramatic impacts on science and engineering innovations. New WSN-based technologies, such as body sensor networks in medical and health care and environmental monitoring sensor networks, are emerging. Sensor networks are quickly becoming a flexible, inexpensive, and reliable platform to provide solutions for a wide variety of applications in real-world settings. The increase in the proliferation of sensor networks has paralleled the use of more heterogeneous systems in deployment. In this thesis, our work attempts to develop a new network management and data collection framework for heterogeneous wireless sensor networks called as Heterogeneous Wireless Sensor Networks Management System (H-WSNMS), which enables to manage and operate various sensor network systems with unified control and management services and interface. The H-WSNMS framework aims to provide a scheme to manage, query, and interact with sensor network systems. By introducing the concept of Virtual Command Set (VCS), a series of unified application interfaces and Metadata (XML files) across multiple WSNs are designed and implement the scalability and flexibility of the management functions for heterogeneous wireless sensor networks, which is demonstrated though through a series of web-based WSN management Applications such as Monitoring, Configuration, Reprogram, Data Collection and so on. The tests and application trials confirm the feasibility of our approach but also still reveal a number of challenges to be taken into account when deploying wireless sensor and actuator networks at industrial sites, which will be considered by our future research work.Item Designing and experimenting with e-DTS 3.0(2014-08-29) Phadke, Aboli Manas; Raje, Rajeev; Tuceryan, Mihran; Liang, YaoWith the advances in embedded technology and the omnipresence of smartphones, tracking systems do not need to be confined to a specific tracking environment. By introducing mobile devices into a tracking system, we can leverage their mobility and the availability of multiple sensors such as camera, Wi-Fi, Bluetooth and Inertial sensors. This thesis proposes to improve the existing tracking systems, enhanced Distributed Tracking System (e-DTS 2.0) [19] and enhanced Distributed Object Tracking System (eDOTS)[26], in the form of e-DTS 3.0 and provides an empirical analysis of these improvements. The enhancements proposed are to introduce Android-based mobile devices into the tracking system, to use multiple sensors on the mobile devices such as the camera, the Wi-Fi and Bluetooth sensors and inertial sensors and to utilize possible resources that may be available in the environment to make the tracking opportunistic. This thesis empirically validates the proposed enhancements through the experiments carried out on a prototype of e-DTS 3.0.Item Detection and Localization of Root Damages in Underground Sewer Systems using Deep Neural Networks and Computer Vision Techniques(2022-12) Zheng, Muzi; Fang, Shiaofen; Tuceryan, Mihran; Liang, YaoThe maintenance of a healthy sewer infrastructure is a major challenge due to the root damages from nearby plants that grow through pipe cracks or loose joints, which may lead to serious pipe blockages and collapse. Traditional inspections based on video surveillance to identify and localize root damages within such complex sewer networks are inefficient, laborious, and error-prone. Therefore, this study aims to develop a robust and efficient approach to automatically detect root damages and localize their circumferential and longitudinal positions in CCTV inspection videos by applying deep neural networks and computer vision techniques. With twenty inspection videos collected from various resources, keyframes were extracted from each video according to the difference in a LUV color space with certain selections of local maxima. To recognize distance information from video subtitles, OCR models such as Tesseract and CRNN-CTC were implemented and led to a 90% of recognition accuracy. In addition, a pre-trained segmentation model was applied to detect root damages, but it also found many false positive predictions. By applying a well-tuned YoloV3 model on the detection of pipe joints leveraging the Convex Hull Overlap (CHO) feature, we were able to achieve a 20% improvement on the reliability and accuracy of damage identifications. Moreover, an end-to-end deep learning pipeline that involved Triangle Similarity Theorem (TST) was successfully designed to predict the longitudinal position of each identified root damage. The prediction error was less than 1.0 feet.