College of Engineering at UMass Dartmouth

Join us in celebrating Earth! We will have various activities with prizes, live music, and tables from campus departments and partners!

Campus Quad

DEPARTMENT OF ELECTRICAL AND COMPUTER ENGINEERING (ECE) UMASSD CYBER SECURITY EDUCATION CLUB (CSEC) jointly with* COE National Cybersecurity Center & ECE Seminar Annual Guest Lecture for ECE548/488 Cyber Threats and Security Management Topic: Digital Forensics in Homicide & Missing Persons Investigations -Current technology trends and challenges Speaker: Brock Morrissette, Sergeant, Massachusetts State Police, Bristol County District Attorney's Office, State Police Detective Unit Location: Science & Engineering Building (SENG), Room 222 Abstract: Sgt. Morrissette will briefly cover the current digital forensic responses in critical incident cases along with the current trends and best practices for successful investigations and prosecutions. Sgt. Morrissette will detail how critical and intermingled digital forensics has become a part of almost every homicide investigation. He will discuss current limitations and problems facing investigators and future challenges facing the forensics / cyber industry. Biography: Sergeant Brock Morrissette is a member of the Bristol County District Attorney's Office State Police Detective Unit where he has served since October 2015 as a Homicide investigator. Specializing in digital forensics, Sgt. Morrissette is a certified mobile device forensic examiner and a subject matter expert in geolocation and cellular records analysis. He has testified as a recognized expert in multiple successful Homicide prosecutions. He is a member of the State Police Critical Incident Technology Investigations (CITI) team. Sgt. Morrissette is a graduate of the State Police Academy in 2014 from the 81st RTT. He graduated from UMass Dartmouth with BS/MS degrees in Computer Engineering and previously worked in electronics manufacturing for Lockheed Martin for eight years. The Seminars is open to the public free of charge. *For further information, please contact Dr. Hong Liu via email at hliu@umassd.edu

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Spring 2024 Course Withdrawal period (grade of a W) ends for the Third 5-week session MLT-MLS Program classes.

Online

Celebrate Denim Day, a sexual violence prevention and education campaign. Support survivors by wearing jeans on April 24th, take a picture, and tag @UMASSD_CWGS on Instagram. There is no excuse and never an invitation to rape.

Center for Women, Gender & Sexuality

Mechanical Engineering MS Project Presentation by Mr. Joshua James Banez DATE: April 24, 2024 TIME: 3:00 p.m. - 5:00 p.m. LOCATION: Science & Engineering (SENG), Room 110 (Materials Science Lab) Zoom link: https://us04web.zoom.us/j/6854837191?pwd=YmZJY3liZjRiM1NXT1VJNnlpMi9uUT09 (Contact scunha@umassd.edu or mraessi@umassd.edu for the Meeting ID# and Passcode) TOPIC: Advancing the AST Probe Calibration Process through Computational and Experimental Analyses and Novel Fixture Design ABSTRACT: Ametek Brookfield's Advanced Sensing Technology (AST) probe calibration process currently takes over 109 hours to fully complete, which is very long and costly. By shortening the calibration process, lead-times on AST instruments could be decreased, more probes could be calibrated, and the cost to manufacture would be reduced. AST probes are high-tech and precise measurement tools used in a variety of applications to report temperature and viscosity of the desired fluid. The calibration process for such an instrument requires time in an environmental chamber for three different cycles: the burn-in cycle, the air cycle, and the oil cycle. The burn-in cycle is a 27-hour process that cycles the temperature to set a base for the AST probes. The probes are then put through a 16-hour calibration where the probes are suspended in air and then a 66-hour calibration where the probes are submersed in a calibrated oil standard. The solution chosen to speed up calibration required re-design of the current tray fixture for holding the oil in the calibration cycle. The current fixture takes up a lot of mass in the system during both the air and oil cycles of the calibration process. This mass leads to a greater heat capacity in the system which, in turn, adds to the time it takes for the system to come to steady state at each calibration point. The project involved both designing a new fixture and analyzing the time that could be saved in the calibration cycle. Experimental testing with prototypes was then used alongside the simulation to provide more accurate results. Using a combined knowledge of heat transfer, thermal systems, design, and manufacturing engineering this project was able to create a novel fixture that reduced the heat capacity in the system by 31.65% during the oil calibration. This led to an estimated time savings of 33 hours, or 50%, for the just oil cycle from the simulation analysis. The prototypes were machined and experimentally tested to show an actual time savings for the oil cycle of 29.8 hours resulting in an oil cycle that takes only 54.85% of the current time. The new design delivers similar levels of accuracy in the calibration process, while significantly shortening the process. ADVISOR: - Dr. Mehdi Raessi, Professor, Department of Mechanical Engineering, UMass Dartmouth COMMITTEE MEMBERS: - Dr. Wenzhen Huang, Professor, Department of Mechanical Engineering, UMass Dartmouth - Dr. Sankha Bhowmick, Professor / Chairperson, Department of Mechanical Engineering, UMass Dartmouth Open to the public. All MNE students are encouraged to attend. For more information, please contact Dr. Mehdi Raessi (mraessi@umassd.edu).

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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

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