Browsing by Author "Russomanno, David J."

Now showing 1 - 6 of 6
  • Thumbnail Image
    Item
    Advancing profiling sensors with a wireless approach
    (2013-11-20) Galvis, Alejandro; Russomanno, David J.; Li, Feng; Rizkalla, Maher E.; King, Brian
    In general, profiling sensors are low-cost crude imagers that typically utilize a sparse detector array, whereas traditional cameras employ a dense focal-plane array. Profiling sensors are of particular interest in applications that require classification of a sensed object into broad categories, such as human, animal, or vehicle. However, profiling sensors have many other applications in which reliable classification of a crude silhouette or profile produced by the sensor is of value. The notion of a profiling sensor was first realized by a Near-Infrared (N-IR), retro-reflective prototype consisting of a vertical column of sparse detectors. Alternative arrangements of detectors have been implemented in which a subset of the detectors have been offset from the vertical column and placed at arbitrary locations along the anticipated path of the objects of interest. All prior work with the N-IR, retro-reflective profiling sensors has consisted of wired detectors. This thesis surveys prior work and advances this work with a wireless profiling sensor prototype in which each detector is a wireless sensor node and the aggregation of these nodes comprises a profiling sensor’s field of view. In this novel approach, a base station pre-processes the data collected from the sensor nodes, including data realignment, prior to its classification through a back-propagation neural network. Such a wireless detector configuration advances deployment options for N-IR, retro-reflective profiling sensors.
  • Thumbnail Image
    Item
    Advancing Profiling Sensors with a Wireless Approach
    (MDPI, 2012-11-22) Galvis, Alex; Russomanno, David J.; Electrical and Computer Engineering, Purdue School of Engineering and Technology
    The notion of a profiling sensor was first realized by a Near-Infrared (N-IR) retro-reflective prototype consisting of a vertical column of wired sparse detectors. This paper extends that prior work and presents a wireless version of a profiling sensor as a collection of sensor nodes. The sensor incorporates wireless sensing elements, a distributed data collection and aggregation scheme, and an enhanced classification technique. In this novel approach, a base station pre-processes the data collected from the sensor nodes and performs data re-alignment. A back-propagation neural network was also developed for the wireless version of the N-IR profiling sensor that classifies objects into the broad categories of human, animal or vehicle with an accuracy of approximately 94%. These enhancements improve deployment options as compared with the first generation of wired profiling sensors, possibly increasing the application scenarios for such sensors, including intelligent fence applications.
  • Thumbnail Image
    Item
    An Initial Exploration of Engineering Student Perceptions of COVID’s Impact on Connectedness, Learning, and STEM Identity
    (American Society of Engineering Education, 2021-07-26) Stewart, Craig O.; Darbeheshti, Maryam; Ivey, Stephanie S.; Russomanno, David J.; Cummings, Miriam Howland; Simon, Gregory Edward; Schupbach, William Taylor; Jacobson, Mike S.; Altman, Tom; Alfrey, Karen D.; Goodman, Katherine; Electrical and Computer Engineering, School of Engineering and Technology
    This paper studied the development of STEM identity for freshman students in Engineering. An Urban Research University received a 5-year S-STEM award in fall 2018. So far, two cohorts of scholars have received the scholarship as well as academic support, mentoring support, and customized advising from faculty and upper level peers. The objective of this project is to help underrepresented and talented students in engineering to pursue an undergraduate degree. A Multi-Layered Mentoring(MLM) Program was established, and several interviews were conducted with scholarship recipients. The qualitative and qualitative analysis of the student success shows an improvement in GPA of students in the program as compared to the rest of the school. The students not only received financial help through the program based on their unmet needs, they are were placed in an engineering learning community (ELC). The participants in ELC and MLM programs agreed to participate in research studies to assess their success. This NSF funded program also helped freshman students be involved in a hands-on Design Innovations class where they learned design process and human centered design. The students were surveyed on a regular basis to identify their needs and were approached by faculty advisor as well as their mentors to trouble shoot their concerns and help them with both social and academic aspects of their concerns. The first cohort joined the program in AY 2019-2020, as freshmen. This cohort had experienced a full semester of in-person engagement before the COVID-19 hit in the middle of the second semester of their freshman year. We have researched the impact of the pandemic on their academic progress, sense of belonging, and STEM identity. The second cohort joined the program in AY 2020-2021. They have not had the chance to experience the campus life and their perspective of college life is very different than the first cohort. The STEM identity was one of the success indicators for freshman students who entered the university in one of the most difficult and un-usual circumstances under the COVID-19 pandemic.
  • Thumbnail Image
    Item
    Ontological Problem-Solving Framework for Assigning Sensor Systems and Algorithms to High-Level Missions
    (MDPI, 2011-08-29) Qualls, Joseph; Russomanno, David J.; Electrical and Computer Engineering, School of Engineering and Technology
    The lack of knowledge models to represent sensor systems, algorithms, and missions makes opportunistically discovering a synthesis of systems and algorithms that can satisfy high-level mission specifications impractical. A novel ontological problem-solving framework has been designed that leverages knowledge models describing sensors, algorithms, and high-level missions to facilitate automated inference of assigning systems to subtasks that may satisfy a given mission specification. To demonstrate the efficacy of the ontological problem-solving architecture, a family of persistence surveillance sensor systems and algorithms has been instantiated in a prototype environment to demonstrate the assignment of systems to subtasks of high-level missions.
  • Thumbnail Image
    Item
    Ontological Problem-Solving Framework for Dynamically Configuring Sensor Systems and Algorithms
    (MDPI, 2011-03-15) Qualls, Joseph; Russomanno, David J.; Electrical and Computer Engineering, School of Engineering and Technology
    The deployment of ubiquitous sensor systems and algorithms has led to many challenges, such as matching sensor systems to compatible algorithms which are capable of satisfying a task. Compounding the challenges is the lack of the requisite knowledge models needed to discover sensors and algorithms and to subsequently integrate their capabilities to satisfy a specific task. A novel ontological problem-solving framework has been designed to match sensors to compatible algorithms to form synthesized systems, which are capable of satisfying a task and then assigning the synthesized systems to high-level missions. The approach designed for the ontological problem-solving framework has been instantiated in the context of a persistence surveillance prototype environment, which includes profiling sensor systems and algorithms to demonstrate proof-of-concept principles. Even though the problem-solving approach was instantiated with profiling sensor systems and algorithms, the ontological framework may be useful with other heterogeneous sensing-system environments.
  • Thumbnail Image
    Item
    The Role of International Administration [IA] in the Globally Engaged University
    (IEEE, 2017) Diemer, Timothy Todd; Russomanno, David J.; Nalim, M. Razi; Piekarzewska, Agnieszka; Dato, Shaari B. Md Nor; Engineering Technology, School of Engineering and Technology
    This paper describes best practice and effective techniques in international administration (IA) within the Globally Engaged University. The Globally Engaged University is one that continually promotes, communicates, initiates, controls, monitors, and evaluates international activity in at least one of its major academic units [5]. Emphasis is IA within engineering and technology higher education. Nonetheless, the description of best practice in IA is also applicable to various other academic disciplines at the Globally Engaged University. In describing best practice in IA, the paper adds the perspective of the authors' applied experience. The combined IA experience among the coauthors includes successful international programs in South and Southeast Asia, the European Union, and the USA. Three of the co-authors are senior administrators within one and the same university in the USA. Another co-author is a senior administrator at a university in Malaysia. Broadening the perspective yet further, another team member is an IA specialist at a Globally Engaged University in Poland. The authors compare and contrast techniques and organizational structure common to IA at three differing locations: USA, Malaysia, and Poland as a central member state of the European Union.