Gustavo Vejarano

Associate Professor

  • Los Angeles CA UNITED STATES
  • Pereira 143
  • Electrical and Computer Engineering

Seaver College of Science and Engineering

Contact

Media

Biography

Contact:
Phone: 310.338.5761
Email: Gustavo.Vejarano@lmu.edu
Office: Pereira North 143
URL: http://gvejaran.intemnets.lmu.build

Gustavo Vejarano is an Associate Professor and Graduate Program Director in the Department of Electrical and Computer Engineering at Loyola Marymount University. He earned his B.S. in electrical engineering at Universidad del Valle, Cali, Colombia in 2005 and his M.S. and Ph.D. in electrical and computer engineering at the University of Florida, Gainesville, FL in 2009 and 2011 respectively. In 2006, he worked for ZTE Corporation as engineer for the deployment of a city-wide cellular network. During his Ph.D., he conducted research on wireless communication networks at the Wireless and Mobile Systems Laboratory under Dr. Janise McNair's direction. Dr. Vejarano's research group at Loyola Marymount University is the Intelligent and Embedded Networks and Systems Laboratory (Intemnets Lab) where students conduct research under his direction on intelligent and embedded networked systems. Dr. Vejarano is the faculty advisor of the IEEE, CubeSat, and SHPE Student Organizations at LMU, Councilor of the Engineering Division of the Council on Undergraduate Research, and Chair of the IEEE Coastal Los Angeles Section.

Education

University of Florida

Ph.D.

Electrical and Computer Engineering

2011

University of Florida

M.S.

Electrical and Computer Engineering

2009

Universidad del Valle

B.S.

Electrical Engineering

2005

Affiliations

  • Institute of Electrical and Electronics Engineerings (IEEE) (2007-present)
  • IEEE Communications Society (2009-present)
  • IEEE Computer Society (2010-present)
  • Association for Computing Machinery (ACM) (2009-present)
  • Society of Hispanic Professional Engineers (SHPE) (2006-present)
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Languages

  • English
  • Spanish

Media Appearances

A Conversation With Gustavo Vejarano

LMU Magazine  online

2024-01-09

Gustavo Vejarano is a professor of electrical and computer engineering in the LMU Frank R. Seaver College of Science and Engineering. He has received a two-year, $200,000 National Science Foundation grant as principal investigator to study the use of groups of drones to fight wildfires. We asked him about his research goals, challenges of operating drones in fire environments, and the role of artificial intelligence in the project. Vejarano was interviewed by Editor Joseph Wakelee-Lynch.

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NSF Grant Enables Research on Drone Networks that Could Revolutionize Wildfire Monitoring

LMU Public and Media Relations  online

2023-12-01

Thanks to a substantial grant of nearly $200,000 over two years from the National Science Foundation, Vejarano’s project, titled “ERI: Fault-Tolerant Monitoring of Moving Clusters of Targets using Collaborative Unmanned Aerial Vehicles,” aims to harness the power of collaborative drones to monitor ground activities, particularly wildfires, with unprecedented efficiency.

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LMU Hosts Mars Expo

LMU Public and Media Relations  online

2023-11-17

“Promoting science, technology, engineering, and mathematics among K-12 students is important,” said Gustavo Vejarano, associate professor of electrical and computer engineering, who brought the event to LMU’s campus. “Space exploration is a great way to inspire kids, our next generation of STEM professionals.”

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Research Grants

ERI: Fault-Tolerant Monitoring of Moving Clusters of Targets using Collaborative Unmanned Aerial Vehicles

Foundational Research in Robotics, National Science Foundation

2023-09-01

Unmanned aerial vehicles, or drones, have successfully been used to monitor ground activity. However, using small drones for extended periods of time is not yet possible, thus limiting their use. For instance, small quadcopters that can be easily transported and deployed do not exceed forty minutes of flying time in most cases and are susceptible to unexpected failure such as damage from natural hazards. On the other hand, robust quadcopters of longer flying times have large dimensions and weight that greatly delay deployment. As an alternative to a single robust drone, this award supports fundamental research to endow a network of small drones to monitor ground activity with the goal of uninterrupted operation and fault tolerance. Therefore, the solution to this challenge enables drones to join and leave the network as failures and/or battery depletions arise and in the absence of central command to avoid a single point of failure (e.g., loss of communication with central command). This award aims at autonomous drones that collaborate by sharing information with one another including knowledge of targets detected. The sharing of information increases the reliability of detection and localization of target clusters and enables coordination of flight formations and search paths. A demonstration of this concept is applied to the case of wildfire monitoring. The performance of the drone network is evaluated at the San Dimas Experimental Forest in coordination with the US Forest Service via flights over prescribed fires. Furthermore, this award sustains research at a predominantly undergraduate institution and enables a meaningful collaboration with the US Forest Service.

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Configuration of the CubeSat I2C Bus

Summer Opportunities for Advanced Research Award, Seaver College of Science and Engineering, Loyola Marymount University

2022-05-16

This summer project is to support the progress made on the LMU Cubesat Project. Funds are requested for one student who will continue the work he started in Spring 2022. His assignment has two objectives: (1) to interface the satellite‐communication system (Endurosat Transciever) with the microcontroller of the Cubesat platform (dsPIC33 microcontroller) via I2C (communication protocol for embedded systems), and (2) to write code for the real‐time operating system of the platform (Salvo OS) that configures the communication system in beacon mode. Depending on the progress, a third objective will be considered: (3) to configure the communication system in data mode to transmit test bytes from the cubesat to the ground station.

Wireless Networking for Collaborative Drones

Continuing Faculty Grant, Seaver College of Science and Engineering, Loyola Marymount University

2021-05-17

Drones are currently being used for a wide variety of applications in the filming, entertainment, defense, and transportation industries. In many of the applications, drones are individual systems that operate independently from other drones. For example, an operator controls one single drone to film an event or to participate in a drone-race competition. However, as application scenarios become more complex, several drones may be considered that work collaboratively to perform one specific task. The technical problem addressed in this proposal considers this collaborative scenario. Specifically, the problem consists of localization of multiple targets on ground in minimum time. When more than one drone is considered, the target-search time can be reduced because different search regions can be covered simultaneously by different drones. Therefore, in order to minimize the search time, the distance between drones increases. From a wireless networking perspective, the increase in distance is a challenge because the transmission power may have to be increased at levels that may not be feasible when the network is to be maintained fully connected (i.e., each drone can communicate with any other drone directly). Infeasibility may be due to different reasons such as required transmission power that exceeds specifications or that depletes drones’ batteries too fast. This project aims at developing transmission-power, information-routing, and drone formation algorithms that enable minimization of target-search time to determine target locations.

Courses

EECE-3200 Junior Lab II

This course is a continuation of ELEC-301 Junior Lab I with emphasis on design of both analog and digital systems. It also introduces basic programming on assembler and hardware description languages for FPGA-based system design.

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EECE-4110 Analog and Digital Communication Systems

This course is divided into three main parts. The first one is an introduction to the analysis of digital communication systems. The second part is an introduction to the theory of probability, random processes, and spectral analysis. The third part builds on this theory in order to perform the analysis of analog and digital communication systems.

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EECE-5140 Computer Architecture with VHDL

Students will design computing systems that meet desired requirements including organization, functionality, and operation of hardware and instruction sets. Students will also be introduced to VHDL to implement the designed computer architectures.

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Articles

Targeted Broadcast in Vehicular Ad-Hoc Networks

2023 IEEE Latin-American Conference on Communications

Roberto Ventura, William Bjorndahl, Gustavo Vejarano

2023-11-15

Vehicular ad-hoc networks (VANETs) experience large variations in network connectivity due to changes on traffic levels. At low traffic, vehicles move fast and are distant from one another, so they establish intermittent links reducing connectivity. At high traffic, vehicles move slowly and are in close proximity to one another, increasing connectivity. This paper proposes a novel multi-hop broadcast algorithm that enables vehicles to estimate the traffic level within their two-hop neighborhood and use routing metrics selectively depending on the measured traffic level. Another novel concept is that the broadcast message is targeted to a section of the road, broadcast area, which has an expiration time. The algorithm is designed with the goal of increasing the speed that the broadcast message propagates along the road to deliver it to all vehicles ahead and behind the source vehicle that reach the broadcast area before it expires. Simulation results confirm that the broadcast message is delivered reliably and propagates at a speed that increases with the traffic level and plateaus at high traffic.

Point Set Registration for Target Localization using Unmanned Aerial Vehicles

ACM Transactions on Spatial Algorithms and Systems

Dhruvil Darji, Gustavo Vejarano

2023-09-01

The problem of point set registration (PSR) on images obtained using a group of unmanned aerial vehicles (UAV) is addressed in this paper. UAVs are given a flight plan each, which they execute autonomously. A flight plan consists of a series of GPS coordinates and altitude that indicate where the UAV stops and hovers momentarily to capture an image of stationary targets on ground. A PSR algorithm is proposed that given any two images and corresponding GPS coordinates and altitude, estimates the overlap between the images, identifies targets in the overlapping area, and matches these targets according to the geometric patterns they form. The algorithm estimates the overlap considering the error in UAVs’ locations due to wind, and it differentiates similar geometrical patterns by their GPS location. The algorithm is evaluated using the percent of targets in the overlapping area that are matched correctly and the percent of overlapping images matched correctly. The target-matching rate achieved using only the GPS locations of targets varied from 44% to 55% for target densities that varied from 6.4 down to 3.2 targets/m2. The proposed algorithm achieved target-matching rates of 48% to 87%. Well-known algorithms for PSR achieved lower rates on average.

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Pothole Detection from Dash Camera Images using YOLOv5

26th International Conference on Image Processing, Computer Vision, & Pattern Recognition (IPCV'22)

Ashit Patel, Lei Huang, Gustavo Vejarano

2022-07-25

In this paper, we propose a new solution to automatically detect potholes on the road surface from dash camera images using a state-of-the-art deep learning based object detection algorithm, namely, You Only Look Once version 5 (YOLOv5). The dash camera image data were preprocessed and augmented as inputs to Convolutional Neural Network (CNN) models, which are trained to output the detected potholes with location bounding boxes. Through transferred learning, different sizes of CNN models with different layer architectures are evaluated in terms of mean Average Precision (mAP) and the number of frames per second (fps). Compared with previous work, experimental results show that our proposed solution using YOLOv5 achieved higher detection accuracy at faster detection speeds, while enabling tradeoffs between accuracy and speed with three different model size options.

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