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Topic: Dataset Generation for Deep Learning to Authenticate Wireless Sensor Network (WSN) at Physical Layer for Structural Health Monitoring (SHM) of Transportation Infrastructure Location: Lester W. Cory Conference Room, Science & Engineering Building (SENG), Room 213A Zoom Conference Link: https://umassd.zoom.us/j/93281343753 Meeting ID: 932 8134 3753 Passcode: 518247 Abstract: A wireless sensor network (WSN) for structural health monitoring (SHM) is a network with autonomous, spatially distributed sensor nodes that communicate wirelessly in a cooperative way to monitor physical or environmental conditions. WSN for SHM has garnered interest for protecting transportation infrastructure for the safe operation and maintenance of bridges due to their ability to collect real-time data. Two concerns that arise when designing and deploying these systems are energy consumption and information security. Limited battery capacity on sensor nodes, especially on bridges, can significantly shorten WSN's lifetime. WSNs are left vulnerable to attacks on data integrity, confidentiality and availability from malicious actors masquerading as sensor nodes. This thesis proposes a scheme to protect data transmissions in WSNs for SHM without sacrificing energy consumption. The scheme solves these problems by combining state-of-the-art technologies in deep learning, radio frequency (RF) fingerprinting and RF energy harvesting. RF Fingerprinting leverages process imperfections in transceivers that can be used in a deep neural network to authenticate known sensor nodes. Deep learning is also less computationally intensive than more common forms of data security like encryption and decryption. RF energy harvesting harnesses electromagnetic waves to convert to electrical energy that powers sensor nodes wirelessly. Deep learning requires a dataset to train the model and each device needs its own dataset generation just like collecting fingerprints to establish a directory. This unique feature due to WSN for SHM of transportation infrastructure calls for the need for a framework to systematically generate datasets from individual sensor nodes. This brings out a novel approach of common applications in deep learning. The work shown acts as a proof of concept for this framework of data generation by building a prototype to present its feasibility through experimentation with using RF energy harvesting. This work also provides a framework for generating a dataset of device RF fingerprint to be used in a deep learning network to authenticate each sensor node. Co-Advisor(s): Dr. Hong Liu, Commonwealth Professor, Department of Electrical & Computer Engineering, UMASS Dartmouth Committee Members: Dr. Liudong Xing, Professor, Department of Electrical & Computer Engineering, UMASS Dartmouth; Dr. Ruolin Zhou, Associate Professor, Department of Electrical & Computer Engineering, UMASS Dartmouth NOTE: All ECE Graduate Students are ENCOURAGED to attend. All interested parties are invited to attend. Open to the public. *For further information, please contact Dr. Hong Liu via email at hliu@umassd.edu
Topic: Network-less Wireless Sensing for Structural Health Monitoring (SHM) of Bridges: Unmanned Aerial Vehicle (UAV) Investigations Location: Lester W. Cory Conference Room, Science & Engineering Building (SENG), Room 213A Zoom Conference Link: https://umassd.zoom.us/j/93281343753 Meeting ID: 932 8134 3753 Passcode: 518247 Abstract: The Internet of Things (IoT) has significantly advanced the application of Wireless Sensor Networks (WSNs) in Structural Health Monitoring (SHM), particularly for civil engineering infrastructure such as bridges. Despite the advancements, the widespread application of WSNs in SHM remains hindered by their limited network lifetime, posing a significant hurdle to their adoption. Furthermore, IoT and WSNs open a new attack surface. Designing SHM systems with wireless sensors utilizing no network allows system resiliency to cyber-attacks. Unmanned Aerial Vehicles (UAVs) have been heralded for their potential to overcome these limitations through secure and efficient data collection. This thesis expands on the existing UAV application by proposing a novel UAV-assisted WSN system that employs Bluetooth Low Energy (BLE) as the communication protocol for synchronized data gathering in SHM systems. Our design diverges from traditional multi-hop WSNs by leveraging UAVs as mobile data sinks, reducing the energy burden on individual sensor nodes, and significantly prolonging the sensor's operational life. Through an analytical study, we demonstrate that our UAV-BLE system offers a remarkable improvement in network lifetime in comparison to conventional network routed WSNs. Additionally, the use of BLE facilitates a lightweight authentication scheme, providing secure wireless communication between sensor nodes and the UAV. Thus, this novel approach enhances the overall robustness and longevity of SHM systems. A proof-of-concept implementation utilizing a PASCO bridge kit equipped with wireless load cell sensors, demonstrates the feasibility of our approach. To the best of the authors' knowledge, this is the first exploration of a BLE-centric synchronization scheme in the context of SHM, marking a significant leap toward secure, safe, reliable, and efficient monitoring of civil engineering structures. Co-Advisor(s): Dr. Hong Liu, Commonwealth Professor, Department of Electrical & Computer Engineering, UMASS Dartmouth Committee Members: Dr. Liudong Xing, Professor, Department of Electrical & Computer Engineering, UMASS Dartmouth; Dr. Ruolin Zhou, Associate Professor, Department of Electrical & Computer Engineering, UMASS Dartmouth; Dr. Tzuyang Yu, Professor, Department of Civil and Environmental Engineering, UMASS Lowell NOTE: All ECE Graduate Students are ENCOURAGED to attend. All interested parties are invited to attend. Open to the public. *For further information, please contact Dr. Hong Liu via email at hliu@umassd.edu
Join us as we celebrate our outstanding student leaders and their accomplishments! The Marketplace | UMass Dartmouth 4:30pm Hors D'oeuvres 5:00 pm Presentation of Awards RSVP to claib@umassd.edu by Mon April 22, 2024 Contact: Chris Laib, claib@umassd.edu 508-999-8217 Sponsored by the Division of Student Affairs
Italian Studies invites you to enjoy a year of Spaghetti Westerns. Starting in the 1960s Italian directors began to apply their own artistic approach and their own political and social concerns to the old-fashioned western genre. The result? Some of the most artistically exciting movies of the 1960s and 1970s. All films will be screened in LARTS-111 at 7:00. For questions write msneider@umassd.edu.
Dashanna Hanlon will share her perspective as a Black Doula on Reproductive Rights and Advocacy in Action. Come find out about career pathways in birthing justice, what does a birth Doula do? She will discuss her role in fighting the black maternal mortality health epidemic in the United States. Light Refreshments will be served. Bring your friends! Sponsored by Women's and Gender Studies and Health & Society. Room CCB 340
Observatory Open House For updates on weather conditions please refer to www.assne.org
Mechanical Engineering (MNE) Senior Design (Capstone) Presentations April 30, 2024 9:00 a.m. - 4:30 p.m. (Poster and prototype preview begins at 8:00 a.m.) Woodland Commons The Mechanical Engineering Department is proud to share this highly anticipated event with students, faculty, staff, family, friends, and any other interested guests! This is a culmination of the Class of 2024's Senior year team project with industry, or UMD research faculty. Attend all day, or come and go as your schedule allows. For more information please contact Dr. Hamed Samandari/Instructor (hsamandari@umassd.edu) or Sue Cunha/Administrative Assistant (scunha@umassd.edu).
Topic: Multi-phase Algorithm Design for Accurate and Efficient Model Fitting Location: Claire T. Carney Library (LIB), Room 314 Zoom Conference Link: https://umassd.zoom.us/j/98963429286 Meeting ID: 989 6342 9286 Passcode: 283650 Abstract: Recent research applies soft computing techniques to fit software reliability growth models. However, runtime performance and the distribution of the distance from an optimal solution over multiple runs must be explicitly considered to justify the practical utility of these approaches, promote comparison, and support reproducible research. This paper presents a meta-optimization framework to design multi-phase algorithms for this purpose. The approach combines initial parameter estimation techniques from statistical algorithms, the global search properties of soft computing, and the rapid convergence of numerical methods. Designs that exhibit the best balance between runtime performance and accuracy are identified. The approach is illustrated through nonhomogeneous Poisson process and covariate software reliability growth models, including a cross-validation step on data sets not used to identify designs. The results indicate the nonhomogeneous Poisson process model considered is too simple to benefit from soft computing because it incurs additional runtime with no increase in accuracy attained. However, a multi-phase design for the covariate software reliability growth model consisting of the bat algorithm followed by a numerical method achieves better performance and converges consistently, compared to a numerical method only. The implementation of a framework-designed algorithm into a software reliability tool is demonstrated. The proposed approach also supports higher-dimensional covariate software reliability growth model fitting suitable for implementation in further tools. Co-Advisor(s): Dr. Lance Fiondella, Associate Professor, Department of Electrical & Computer Engineering, UMASS Dartmouth Committee Members: Dr. Hong Liu, Commonwealth Professor, Department of Electrical & Computer Engineering, UMASS Dartmouth; Dr. Ruolin Zhou, Associate Professor, Department of Electrical & Computer Engineering, UMASS Dartmouth NOTE: All ECE Graduate Students are ENCOURAGED to attend. All interested parties are invited to attend. Open to the public. *For further information, please contact Dr. Lance Fiondella via email at lfiondella@umassd.edu
Celebrate students accomplishment with other LGBTQ+ Graduates! The Marketplace | UMass Dartmouth 4:00 to 7:00pm Contact Juli Parker, juli.parker@umassd.edu, 508-910-4582 Sponsored by the Center for Women, Gender & Sexuality
Mechanical Engineering MS Project Presentation by Mr. Noah Whitney DATE: May 3, 2024 TIME: 3:00 P.M. - 5:00 P.M. LOCATION: Zoom link: https://umassd.zoom.us/j/93989772725?pwd=Ulk5ME9KYnpvMGhBU2toZ1dKeHE3dz09 (Contact scunha@umassd.edu for Meeting ID# and Passcode) TOPIC: Ion Beam Figuring Prototype for Guiding the Polishing Process of Optical Substrates ABSTRACT: For high efficiency optical components, substrate surface smoothness is critical. This project aims to create a polishing method for substrates that can be performed at Plymouth Grating Laboratory, for the purpose of creating ultra-high efficiency diffraction gratings. Traditional polishing methods, such as pitch polishing and lap polishing, use an abrasive slurry to mechanically smooth substrate surfaces. This method can be quite expensive and often fails for large scale optics. Therefore, a non-mechanical polishing technique called Ion Beam Figuring is proposed as an alternative method for substrate polishing. This method can be achieved on site at Plymouth Grating Laboratory while simultaneously reducing cost and increasing the likelihood of a successful polish. To achieve this method of polishing, a framework is developed for experimentally profiling a radio frequency ion source, via broadband spectroscopy. Next, a prototype 1-dimensional Ion Beam Figuring method is created using a custom MATLAB program. This program yields a dwell time map, which guides the figuring process by determining the position and time for which the ion beam etches the substrate. This prototype Ion Beam Figuring program will be based on a Fourier transform deconvolution method. Once this program was created, initial validations were performed using test case surface profiles to ensure the program properly computes parameters used in the IBF process. This project provides a foundation to produce large polished substrates with higher reliability and a significantly decreased cost. This is a key step in creating high efficiency meter class diffraction gratings to be used in some of the highest power laser systems in the world. These laser systems can be used in fusion, biomedical, defense, and semiconductor industries. ADVISOR: - Dr. Jun Li, Assistant Professor, Department of Mechanical Engineering, UMass Dartmouth COMMITTEE MEMBERS: - Dr. Wenzhen Huang, Professor, Department of Mechanical Engineering, UMass Dartmouth - Dr. Alfa Heryudono, Associate Professor, Department of Mathematics, UMass Dartmouth Open to the public. All MNE students are encouraged to attend. For more information, please contact Dr. Jun Li (jun.li@umassd.edu).
Mechanical Engineering MS Thesis Defense by Ms. Stephanie DeCarvalho DATE: May 10, 2024 TIME: 1:00 P.M. - 3:00 P.M. LOCATION: ZOOM link: https://umassd.zoom.us/j/91399406281?pwd=aUNpWS8ybFQ5eFNIWlVmRGNjbmlaZz09 (Contact scunha@umassd.edu for Meeting ID and PassCode) TOPIC: Computational Modeling of Materials and Structures for Biomedical Applications: from 3D Printed Implants to Tissue Growth ABSTRACT: Computational modeling has been increasingly used to aid and improve engineering design, fabrication, and manufacturing. In the biomedical field, scientists and clinicians could use computational models to better understand biological phenomena and develop more precise treatment strategies. This thesis employs computational modeling of materials and structures to study two examples in biomedical applications: the development of a patient-specific additively manufactured knee implant and the prediction of an embryonic chick in its first stages of growth. Additive manufacturing (AM) has emerged as an innovative way of manufacturing products of complicated and customized geometries. Bioengineering has a special interest in AM as the possibility of creating patient specific implants that can help increase satisfaction and comfort with procedures. This study explores the use of patient data in combination with finite element modeling and analysis to evaluate the performance of an AM knee implant. The approach is demonstrated on a distal femur replacement for a 50-year-old male patient from the open-access Natural Knee Data. The performance of the implant is influenced by the printing process parameters that are used to print the part. The results show that build orientations have a significant impact on both shape distortions and residual stresses. Understanding the developmental growth from a single cell into a more complex multicellular structure contributes to topics such as tissue engineering and growth defects as well as developing individual treatments. In combination with experimental results, computational analysis can increase the understanding of the behavior of organisms. Morphomechanics are used to create a computational model to simulate the tissue growth in the embryonic chick during the early stages of its development. As the chick embryo develops, the behavior and positioning of the embryo is affected by the membrane in which it develops. The effects of the growth and its surroundings result in a series of coupled bending and twisting of the embryonic body. Using computational modeling in personalized medical implants and developmental biology, this study contributes to the goals of advancing precision health initiatives. Patient-specific implants that are created to be a perfect fit would increase the probability of more patients recovering with diminished pain, increased mobility, and an improvement in their quality of life. The deformation and behavior of biological development supports the research of quantifying health conditions that may result from environmental, developmental, or genetic influences. Understanding these factors supports the advancement of preventative medical research to preserve the health of patients. ADVISOR: - Dr. Jun Li, Assistant Professor of Mechanical Engineering, College of Engineering, UMass Dartmouth COMMITTEE MEMBERS: - Dr. Wenzhen Huang, Professor of Mechanical Engineering, College of Engineering, UMass Dartmouth - Dr. Alfa Heryudono, Associate Professor of Department of Mathematics, UMass Dartmouth Open to the public. All MNE students are encouraged to attend. For more information, please contact Dr. Jun Li (jun.li@umassd.edu).
UMass Dartmouth celebrates African American, Latino, Asian and Native American graduates of color. 10:00 A.M. to 1:00 P.M. Marketplace Contact: Donna Moore, dmoore@umassd.edu or 508-999-9222 Sponsored by the Frederick Douglass Unity House & BH4S