Person:
Ferri, Bonnie H.

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https://hdl.handle.net/1853/71256
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    Three Minute Thesis (3MT) 2021
    (Georgia Institute of Technology, 2021-04-09) Black, James ; Ferri, Bonnie H. ; Garbers, Jeff ; Glassman, Clara ; Javaid, Saad ; Lee, Hohyun ; Li, Hongmo ; McSweeney, Megan ; Eslampanah Sendi, Mohammad Sadegh ; Sharp, Leslie N. ; Shi, Yifeng ; Tricker, Andrew ; Vanderwoude, Jelly ; Young, Hee Yoon ; Zia, Muhammad Saad
    For the first time, the final round of Georgia Tech’s annual Three Minute Thesis (3MT) Competition will be held virtually. Ten Ph.D. students and one master’s student (who was awarded first place in the master’s category and will be competing for the People’s Choice Award) made the cut to participate in the finals.
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    A cohesive program of experimental modules distributed throughout the ECE curriculum
    (Georgia Institute of Technology, 2011-12-01) Ferri, Bonnie H. ; Williams, Douglas B. ; Michaels, Jennifer E.
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    Distributed Fault-Tolerance for Event Detection Using Heterogeneous Wireless Sensor Networks
    (Georgia Institute of Technology, 2006) Ould-Ahmed-Vall, ElMoustapha ; Riley, George F. ; Ferri, Bonnie H.
    Distributed event detection using wireless sensor networks has received growing interest in recent years. In such applications, a large number of inexpensive and unreliable sensor nodes are distributed in a geographical region to make firm and accurate local decisions about the presence or absence of specific events based on their sensor readings. However, sensor readings can be unreliable, due to either noise in the sensor readings or hardware failures in the devices, and may cause nodes to make erroneous local decisions. We present a general fault-tolerant event detection scheme that allows nodes to detect erroneous local decisions based on the local decisions reported by their neighbors. This detection scheme does not assume homogeneity of sensor nodes and can handle cases where nodes have different accuracy levels. We prove analytically that the derived fault-tolerant estimator is optimal under the maximum a posteriori (MAP) criterion. An equivalent weighted voting scheme is also derived. Further, we describe two new error models that take into account the neighbor distance and the geographical distributions of the two decision quorums. These models are particularly suitable for detection applications where the event under consideration is highly localized. Our fault-tolerant estimator is simulated using a network of 1024 nodes deployed randomly in a square region and assigned random probability of failures.
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    Distributed Global Identification for Sensor Networks
    (Georgia Institute of Technology, 2005) Ould-Ahmed-Vall, ElMoustapha ; Blough, Douglas M. ; Ferri, Bonnie H. ; Riley, George F.
    A sensor network consists of a set of battery-powered nodes, which collaborate to perform sensing tasks in a given environment. It may contain one or more base stations to collect sensed data and possibly relay it to a central processing and storage system. These networks are characterized by scarcity of resources, in particular the available energy. We present a distributed algorithm to solve the unique ID assignment problem. The proposed solution starts by assigning long unique IDs and organizing nodes in a tree structure. This tree structure is used to compute the size of the network. Then, unique IDs are assigned using the minimum number of bytes. Globally unique IDs are useful in providing many network functions, e.g. configuration, monitoring of individual nodes, and various security mechanisms. Theoretical and simulation analysis of the proposed solution have been preformed. The results demonstrate that a high percentage of nodes (more than 99%) are assigned globally unique IDs at the termination of the algorithm when the algorithm parameters are set properly. Furthermore, the algorithm terminates in a relatively short time that scales well with the network size. For example, the algorithm terminates in about 5 minutes for a network of 1,000 nodes.
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    A Real-Time Curve Evolution-Based Image Fusion Algorithm for Multisensory Image Segmentation
    (Georgia Institute of Technology, 2003-04) Ding, Yuhua ; Vuchtsevunos, George J. ; Yezzi, Anthony ; Daley, Wayne ; Ferri, Bonnie H.
    A partial differential equation (PDE)-based feature-level image fusion approach is proposed for multisensory image segmentation. The energy functional of the proposed fusion model is a weighted sum of several functionals, each constructed based on the characteristics of the sensor image. The weight selection decides the way that the model handles redundant, conflicting, or complementary information involved in the multisensory data. The method is implemented using level sets and is fast enough for real-time segmentation tasks. Finally the algorithm is applied to the segmentation of x-ray and visual images, and the results show that the fusion algorithm is efficient, accurate, and robust.
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    A Real-Time Curve Evolution-Based Image Fusion Algorithm for Multisensory Image Segmentation
    (Georgia Institute of Technology, 2003-04) Ding, Yuhua ; Vuchtsevunos, George J. ; Yezzi, Anthony ; Daley, Wayne ; Ferri, Bonnie H.
    A partial differential equation (PDE)-based feature-level image fusion approach is proposed for multisensory image segmentation. The energy functional of the proposed fusion model is a weighted sum of several functionals, each constructed based on the characteristics of the sensor image. The weight selection decides the way that the model handles redundant, conflicting, or complementary information involved in the multisensory data. The method is implemented using level sets and is fast enough for real-time segmentation tasks. Finally the algorithm is applied to the segmentation of x-ray and visual images, and the results show that the fusion algorithm is efficient, accurate, and robust.
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    Fusion of Visible and X-Ray Sensing Modalities for the Enhancement of Bone Detection in Poultry Products
    (Georgia Institute of Technology, 2000) Vachtsevanos, George J. ; Daley, Wayne D. ; Ferri, Bonnie H. ; Yezzi, Anthony ; Ding, Yuhua
    The U.S. demand for deboned chicken has risen greatly in the past 5 years, with the expectations that this demand will oniy continue at an accelerated level. The standard inspection process for bones in meat is for workers to manually feel for bones. It is clear that this time-consuming manual inspection method is insufficient to meet the increasing demand for deboned meat products. Georgia Tech Electrical Engineering faculty and Research Scientists in conjunction with a leading x-ray equipment manufacturer are working together on the development of a system to fuse information from visible images and x-ray images to enhance the accuracy of detection. Currently there are some bones that x-ray systems have difficulty detecting. These are usually relatively thin and are located near the surface of the meat. A primary example is a fanbone (so called because of its shape). We will describe and present results from work geared towards the development of an integrated system that would fuse visible and x-ray information. Significant benefits to the poultry industry are anticipated in terms of reduced processing costs, improved inspection performance and increased throughput through the use of the integrated system to be described. Additionally, generic aspects of the proposed technologies may be applicable to other food processing industries.
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    On singular perturbation theory for piecewise-linear systems
    (Georgia Institute of Technology, 1988-08) Ferri, Bonnie H.