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ARC Study Guides

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ARC Study Guides

The Academic Resource Center offers student-directed services whenever possible. The following documents were developed by peer tutors to help other students improve their writing skills and style in the University setting. In addition, these documents are fully downloadable and in print-ready form.

Documenting Sources

2010 APA Guide
CSE Citation Handout
MLA Citation Handout
Chicago Manual of Style Handout

Grammar and Mechanics

Articles and Determiners
Colon and Semicolon Exercises
Commonly Confused Words
Contraction Exercises
Direct and Indirect Objects
Homophones
Homophone Exercises
Misused Verb Forms
Parallelism
Parts of Irregular Verbs
Commas

Rhetoric and Composition

Sentence Composition
Signal Words
Using Quotations
Using Specific Details
Writing Effective Sentences
Writing with Consistency: Point of View

Writing Beyond the Classroom

Graduate School Personal Statements
Résumé and Cover Letter Handout
Public Speaking Tips

Writing in the Disciplines

Biology

Writing Lab Reports

Business

Writing Formal Analytical Reports

History

Reading, Writing, and Researching for History: A Guide for College Students (Created by Bowdoin College)

Literature

Analyzing Literature
Understanding Poetry
Writing About Literature

Philosophy

Writing and Reading About Philosophy

Social Science

Writing in the Social Sciences

Resources

Writing Center Referral Form

How to schedule an appointment

Making a tutoring appointment

Schedule Appointment

Making a tutoring appointment

Schedule Appointment

Events

Apr

1:00PM

CPE Master of Science Thesis Defense by Christopher Dentremont - ECE Department

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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Apr

2:30PM

CPE Master of Science Thesis Defense by Bryce Afonso - ECE Department

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

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Apr

9:00AM

Mechanical Engineering Senior Design (Capstone) Presentations, Class of 2024

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).

Woodland Commons, UMass Dartmouth Campus

Apr

10:00AM

ELEC Research Component of PhD Qualifier Exam by Joshua Steakelum - ECE Department

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

Claire T. Carney Library, Room 314

May

3:00PM

Mechanical Engineering MS Project Presentation by Mr. Noah Whitney

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).

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