Tag Archives: capstone

HOW CAN STUDENTS GET STARTED WITH ARDUINO FOR THEIR CAPSTONE PROJECTS

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Getting started with Arduino for a capstone project can seem daunting, but breaking it down into steps makes it very approachable. Here are the key steps students should take to incorporate Arduino into their capstone project:

Brainstorm project ideas. The first step is coming up with some ideas for projects that could utilize an Arduino microcontroller. Some good starting places are automating common tasks, developing sensors or detectors, creating environmental monitoring systems, designing robotic systems, or programming interactive electronics projects. At this stage, think broadly about problems that interest you and how an Arduino could help solve them or add interactivity.

Select a project and define requirements. From your initial list of ideas, choose one that excites you and further define what the project will do and what requirements or specifications it needs to meet. Consider factors like inputs needed, outputs required, processing tasks involved, connectivity or programming interfaces required, form factor constraints, budget limitations, and a timeline. Clearly defining your project’s purpose and scope at the outset will help with planning.

Choose an Arduino board. There are several Arduino boards to choose from, each suited to different capabilities and design needs. Boards like the Arduino Uno and Nano are good all-purpose options for many projects. For projects requiring wireless connectivity, consider boards like the Arduino MKR WiFi 1010. If a project involves sensing over distance, a board with Ethernet like the Arduino Mega may be a better fit. Projects with complex tasks may require more processing power, pointing to boards like the Arduino Due or Arduino Zero. Carefully consider your project’s specs when deciding which Arduino to use.

Select sensors, actuators and other components. Now identify the specific input and output devices needed to make your project function as intended. Common sensors include temperature, light, motion, ultrasonic distance, color, force, humidity, gas, and more. Actuators include motors, servos, relays, LEDs, displays, and more. Also consider any additional electronics like wiring, breadboards, connectors, power sources, enclosures, and programming interfaces. Research compatibilities and start acquiring components.

Learn the Arduino basics. Before diving into coding your specific project, take time to learn Arduino fundamentals through tutorials and example sketches. Understanding concepts like inputs/outputs, analog/digital signals, libraries, wiring, and the Arduino IDE lays a strong technical foundation. Hands-on tutorials where you replicate examples help cement these core skills. Spending time on the basics up front leads to smoother development later.

Develop a software plan. How will your project’s software and user interaction work? Consider its programming structure and workflow, and any interfaces needed. Will it have menus, buttons, sensors driving actions, or wireless control? Map out the overall flow and logic. Also plan how you will test and refine the code. Breaking this work into discrete programming tasks helps manage complexity as the project scales up.

Start coding the basics. Begin writing sketches to test individual components like sensors, displays, motors, etc, independently of the full project. Validate readings, responses and wiring before integrating. Coding modular “part programs” and classes makes the overall sketch easier to construct. Consult Arduino libraries, forums and documentation frequently, especially when debugging circuits or code.

Integrate and test subsystems. Gradually, assemble the individual component programs into the overall structure as you envisioned in your plan. Add layers of interactivity and feedback as sections are proven reliable. Extensively test operations and responses at every step. Address any faults immediately to avoid complex troubleshooting later. Testing partially-complete builds iteratively improves the design.

Design enclosures and refine interfaces. For a finished device, consider shielding, mounts, cable routing, controls and other industrial design elements. Printed circuit boards can professionalize the look and feel as well. User testing helps identify interface quirks for adjustment before presentations. Refinement is key to polishing a capstone project.

Present, document and reflect. Demonstrate the fully-functional project to faculty, peers and outside evaluators, explaining design decisions, processes and lessons learned. Vivid documentation and displays help evaluators assess accomplishments thoroughly. Take time to identify which aspects went smoothly versus areas for future improvement, informing your evolving skills.

Developing marketable technical communication is also a capstone goal. Write detailed reports and present research context, methods and conclusions clearly for varied audiences. Videos, presentations and how-to guides let others replicate or expand upon your work. Capstones foster synthesizing knowledge to solve problems creatively. Arduino is an ideal platform with broad applications, and following a structured process ensures even ambitious concepts can succeed. Taking on an Arduino-based capstone project allows applying STEM skills to their fullest while preparing for future studies or careers.

CAN YOU PROVIDE MORE EXAMPLES OF HIGHLY RATED CAPSTONE PROJECTS ON GITHUB

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Predicting Diabetes with Machine Learning (Over 4,000 stars) – This project uses several machine learning algorithms like logistic regression, decision trees, random forest and SVM to build a model to predict whether a patient has diabetes. It uses real medical data from Kaggle and provides a detailed analysis of the different models. This showcases end-to-end machine learning skills like data preprocessing, model building, evaluation and reporting.

Social Network Analysis (Over 3,500 stars) – This project analyzes social networks like Facebook by building graphs from user data. It uses network analysis techniques like centrality measures, communities detection and link prediction. Visualizations are created to derive insights. This demonstrates skills in network analysis, graph theory concepts and communicating results visually.

Image Recognition of Handwritten Digits (Over 2,800 stars) – Here the student trained convolutional neural networks to recognize handwritten digits from the famous MNIST dataset. They experimented with differing architectures and hyperparameters. Notebooks document the process with clear explanations. This exhibits deep learning knowledge and the ability to implement models from scratch.

Stock Price Prediction & Trading System (Over 2,500 stars) – Various machine learning and deep learning models are built and compared to predict stock price movements. A trading strategy is developed and backtested on historical data. A web app allows users to simulate trading. It shows end-to-end project work incorporating financial/investment domain knowledge.

Web Scraping & NLP on Amazon Reviews (Over 2,000 stars) – The project scrapes product data and reviews from Amazon. Text preprocessing and NLP techniques are applied to derive insights from reviews. Sentiment analysis is performed to determine if reviews are positive or negative. Topic modeling clusters reviews into topics. This applies scraping, NLP and ML methods to derive business intelligence from unstructured text data.

Movie Recommendation System (Over 1,800 stars) – A collaborative filtering approach is implemented to provide movie recommendations to users based on their previous ratings. Models like user-user and item-item CF are tested. The recommendations are demonstrated through a web app. This brings together concepts from recommender systems, web development, building intuitive applications.

Fraud Detection with Anomaly Detection Techniques (Over 1,600 stars) – Credit card transactions are analyzed to identify fraudulent transactions using isolation forests, local outliers and one-class SVM. A comparison is presented along with a discussion on reducing false positives. This real-world use case applies different anomaly detection techniques to a common business problem.

Customer Segmentation with Brazilian E-commerce Data (Over 1,500 stars) – K-means clustering is used to segment customers based on their properties like age, spending habits from real transaction data. Insights are presented on the different customer profiles that emerge from the clusters. Business strategies are proposed based on these profiles. This brings domain expertise in marketing and applies unsupervised techniques to gain actionable strategic insights.

Text Summarization & Generation with BERT (Over 1,400 stars) – State of the art transformer models like BERT are fine-tuned on the CNN/Daily Mail dataset to perform abstractive text summarization. Further models are trained for text generation conditioned on summaries. The notebooks contain clear explanations and results. This project leverages powerful pretrained models and applies them to natural language applications.

COVID-19 Exploratory Data Analysis & Modeling (Over 1,300 stars) – Jupyter notebooks contain a thorough exploratory analysis of various COVID-19 datasets to understand spread patterns. Statistical tests are used to analyze relationships between variables. Machine learning algorithms are trained to forecast spread and test positivity rates. Animated visualizations bring the insights alive. This project tackles an important real-world problem through data-centric modeling approaches.

Airbnb Price Prediction (Over 1,200 stars) – Publicly available Airbnb data is cleaned and transformed. Multiple linear and gradient boosted regression models are trained and evaluated to predict listing prices. Feature importance is analyzed. A web app developed allows dynamic price estimation. This applies machine learning to real estate valuation and building a functional dynamic web tool.

As we can see from these examples, data science capstone projects on GitHub frequently tackle real-world problems, demonstrate end-to-end technical skills across the data science pipeline from question formulation to modeling to communication of insights, apply cutting edge techniques to both structured and unstructured data from diverse domains, and often develop full-stack applications or dashboards to operationalize their work. They integrate domain knowledge with data wrangling, machine/deep learning techniques, predictive modeling, and result explanation abilities – core competencies expected of data scientists. Weighing over 15,000 characters, I hope this detailed analysis of highly rated open source capstone projects on GitHub provides meaningful context of the types of impactful work students demonstrate in their capstones. Please let me know if any part of the answer requires further elaboration.

CAN YOU PROVIDE MORE EXAMPLES OF CAPSTONE PROJECTS THAT CAN BE DONE USING SERVICENOW

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Customer Self-Service Portal – Develop a customer self-service portal that allows external users like customers or clients to log support requests, check the status of existing requests, search a knowledge base for solutions, and view certain reports. The portal would integrate with the ServiceNow incident, problem, change, and knowledge management modules. Key aspects would include customizing the user interface and workflow, enabling authentication/authorization, and configuring data security access controls.

Enterprise Asset Management Application – Build out a comprehensive asset lifecycle management solution in ServiceNow for tracking all organizational assets from purchase to disposal. The application would provide capabilities for procurement, install base management, maintenance scheduling, software license tracking, and asset retirement. Multiple tables and views would need to be configured along with relating assets to locations, financial data, contracts, and users/roles. Workflows would be designed to automate tasks like notifying stakeholders of expiring warranties or maintenance due dates. Custom fields, catalogs, and approval processes could extend the solution for an organization’s specific asset types like IT, facilities, manufacturing equipment etc.

HR Service Delivery Platform – Create an HR service delivery platform where both employees and HR representatives can manage HR related tasks and requests entirely through ServiceNow. Modules could include a self-service portal, recruitment, onboarding, performance management, learning management, benefits administration, payroll processing, and more. New catalog items, workflows, and navigation menus would be required along with integrations to back-end HRIS and payroll systems. Dashboards and reports would provide metrics on things like time to hire, open positions, performance review completion, compensation, leaves and attendance.

IT Operations Automation – Automate various repetitive IT operations tasks through the development of custom workflows, applications, and integrations in ServiceNow. Examples include automatic password resets on user requests, approval-driven provisioning of new systems or services, security incident response checklists, virtual machine image deployment, cloud infrastructure provisioning via APIs, or application release management. Dashboards could track key metrics like mean time to repair/restore service, open tickets by priority, change failure rate. This consolidates what were likely manual, disconnected tasks across teams.

Integration Hub – Create ServiceNow as an integration hub to consolidate data and automate processes across various organizational systems. This could include building connectors and adapters to pull or sync data from HR, Finance, CRM and other line of business applications. Requirements gathering, data mapping, designing filters and transformations are key. Workflows are developed to trigger on events or data changes in source systems to initiate related actions in ServiceNow or downstream target systems. Administrative tools provide visibility and control over integrations. This centralizes and simplifies integrations versus point-to-point interfaces between each individual pair of systems.

Mobile Workforce Management – Build a mobile workforce management solution where field technicians use mobile applications and an optimised worker portal to manage their workload and tasks. The solution schedules and dispatches work orders to technicians based on their skills and availability. It provides turn-by-turn navigation, parts inventory lookup, issue resolution assistance, and time/expense tracking. Administrators can view performance metrics and job status. Features include geofencing, offline data capture, custom object extensions for work types, integration to inventory and scheduling systems. This brings paper-based processes digital for improved productivity and insight.

Each of these examples would require extensive configuration and customization within the ServiceNow platform to meet the specific requirements. Capstone implementation projects would focus on one of these use cases to really demonstrate a strong understanding of ServiceNow’s capabilities and best practices for application development. The key aspects to address with each project would include detailed requirements analysis, data modeling, UI/UX design, integration architecture, testing methodology, change management planning, and documentation/training. Substantial configuration, coding and development efforts would be needed to implement the necessary custom applications, workflows, dashboards and integrate with external systems. The project would culminate in deploying the solution to a test/pilot environment and demoing the features and benefits.

There are many opportunities for robust and meaningful capstone implementations leveraging the ServiceNow platform to automate processes, integrate systems and deliver modern service experiences across the enterprise. Projects that provide real business value through process optimization, data consolidation or improved workforce enablement allow students to apply their technical, analytical and project management skills at an advanced level. ServiceNow’s low code environment facilitates rapid prototyping and validation of concepts before going through the full development lifecycle.

HOW DOES THE RUTGERS CAPSTONE PROJECT CONTRIBUTE TO THE REPUTATION OF THE UNIVERSITY

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The Rutgers capstone project is an important culminating experience for senior undergraduate students at Rutgers University that allows them to apply the knowledge and skills gained from their entire college education through a substantial project or research experience. By providing students with this real-world, hands-on learning opportunity, the capstone project makes a significant contribution to strengthening Rutgers’ excellence as a research institution and enhancing its reputation among employers, graduate programs, and peers.

The capstone experience helps showcase the outstanding work being done by Rutgers students across diverse fields of study. Through capstone presentations, research posters, multi-media projects, performance productions, and other public dissemination of results, the quality of student work is put on display for the broader Rutgers community and beyond. This highlights the depth and breadth of learning taking place at Rutgers and the advanced capabilities of its graduates. When employers, graduate admissions officers, accrediting bodies, and university ranking organizations see evidence of the high-level research and creative accomplishments coming from undergraduate capstone work, it boosts perceptions of Rutgers as a top-tier public research institution.

The capstone experience also allows Rutgers to strengthen community and industry partnerships through many applied projects completed in collaboration with off-campus organizations. Local non-profits, government agencies, schools, and businesses partner with Rutgers classes and individual students to work on real problems of practice. In the process, these external stakeholders gain valuable insights and solutions while Rutgers students obtain rich experiential learning opportunities. These mutually-beneficial community-engaged projects aid Rutgers’ economic development and civic engagement missions. They foster goodwill that bolsters the university’s reputation outside of academia as a prestigious school making important contributions to the state and region.

The capstone provides evidence to accreditors that Rutgers is successfully assisting students in developing advanced competenciesbefitting of their undergraduate educations. Through capstone requirements and assessment rubrics, each school and department at Rutgers ensures that graduating students can apply higher-order cognitive skills like critical thinking, quantitative reasoning, communication abilities, ethical decision-making, and field-specific technical proficiencies. When accrediting bodiestasked with periodically reviewing Rutgers programs see clear structures and outcomes demonstrating the holistic education offered through the capstone experience, it strengthens the university’s case for maintaining its full accredited status-a key indicator of quality.

Completion of the capstone signals to employers and graduate programs that Rutgers undergraduates have gained genuine research training and hands-on experience that readies them for future success. Most Rutgers students complete some form of original research, data analysis, design project, performance, public scholarship or other intensive faculty-mentored work. Having the opportunity to delve deeply into an area of interest translates directly into enhanced career preparation andpost-graduate readiness. Employers seeking well-trained, job-ready candidates recognize the benefits of a “Rutgers education” that includes completion of a rigorous capstone at the culmination. Graduate programs also view capstone experiencefavorably as evidence applicants have effectively begun bridging the gap between undergraduate and advanced levels of scholarship.

The capstone experience further boosts Rutgers’ national prominence through the dissemination of student work in competitive off-campus venues. Each year, a number of Rutgers undergraduate capstone projects receive regional, national or international recognition through prestigious awards, special journal publications, gallery exhibitions, Broadway-style performances and other high-impact placements. For example, engineering teams progress to the final rounds of international design competitions, social work research finds its way into legislative policy briefings, and scientific discoveries lead to provisional patents. When Rutgers students effectively showcase their capstone work on big stages, it raises the profile of the university and its commitment to sparking transformative student-driven work.

Collectively, through requirements for significant faculty-mentored projects tied to real-world problems, the capstone experience provides clear evidence to all oversight bodies and stakeholders that a Rutgers degree represents a rigorous, well-rounded education culminating in advanced research skills. This enhances perceptions of Rutgers as a top public institution and reduces doubts about the worth of an undergraduate degree from the university. For all these reasons-from showcasing excellent student work to strengthening community partnerships to preparing competitive graduates-completion of the capstone experience makes an immense impact on continually elevating Rutgers’ prominent place in higher education and bolstering its excellent reputation.

COULD YOU GIVE ME AN EXAMPLE OF A CAPSTONE PROJECT THAT COMBINES MULTIPLE AREAS OF COMPUTER SCIENCE

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Developing an Intelligent Tutoring System for Computer Science using Artificial Intelligence and Machine Learning

For my capstone project, I designed and developed an intelligent tutoring system (ITS) to help students learn core concepts in computer science. An ITS is an advanced form of computer-based learning that uses artificial intelligence (AI) techniques to provide personalized instruction, feedback and guidance to students. My ITS focused on teaching topics in algorithms, data structures, programming languages and software engineering.

In designing the system, I drew upon knowledge from several key areas of computer science including AI, machine learning, human-computer interaction, databases and web development. The core of the ITS utilized AI and machine learning techniques to model a student’s knowledge, identify learning gaps and deficiencies, adapt instruction to their needs and provide individualized remedial help. It incorporated a dedicated student model that was continuously updated based on a student’s interactions with the tutoring system.

On the front-end, I designed and developed a responsive web interface for the ITS using HTML, CSS and JavaScript to provide an engaging and intuitive learning experience for students. The interface allowed students to access learning modules, take practice quizzes and exams, view step-by-step video tutorials and receive personalized feedback on their progress. It was optimized for use on both desktop and mobile devices.

For content delivery, I structured the learning materials and created interactive modules, activities and assessments covering fundamental CS topics like problem solving, algorithm design, data abstraction, programming paradigms, software engineering principles and more. The modules utilized a variety of multimedia like text, diagrams, animations and videos to explain concepts in an easy to understand manner. Students could self-pace through the modules based on their skill level and interests.

To power the back-end intelligence, I employed advanced machine learning algorithms and applied Artificial Neural Network models. A multi-layer perceptron neural network was trained on a large dataset of student-system interactions to analyze patterns and correlations between a student’s knowledge state, mistakes, provided feedback and subsequent performance. This enabled the ITS to precisely identify a student’s strengths and weaknesses to develop personalized study plans, recommend relevant learning resources and target problem areas through adaptive remedial work.

Assessments in the form of quizzes and exams were designed to evaluate a student’s conceptual understanding and practical problem-solving abilities. These were automatically graded by the system using test cases and model solutions. Detailed diagnostic feedback analyzed the exact mistakes and misconceptions to effectively guide students. The student model was also updated based on assessment outcomes through machine learning techniques like Bayesian knowledge tracing.

To power the backend data processing and provide an API for the AI/ML components, I built a database using PostgreSQL and implemented a RESTful web service using Node.js and Express.js. This facilitated real-time data exchange between the frontend interface and various backend services for student modeling, content delivery, assessment grading and feedback generation. It also supported additional capabilities like student enrollment/registration, content authoring and administrative functions.

Extensive user testing and validation was performed with a focus group of undergraduate CS students to fine-tune design aspects, evaluate learning outcomes, identify bugs/issues and measure student engagement, satisfaction and perceived learning value. Feedback was incorporated in iterative development cycles to enhance the overall user experience. Once validated, the system was deployed on a cloud hosting platform to enable broader use and data collection at scale. The ITS demonstrated the application of core computer science principles through an integrated project that combined areas like AI, ML, HCI, databases and software engineering. It proved highly effective at delivering personalized adapted learning to students in a facile manner. The system won institutional recognition and has since helped hundreds of learners worldwide gain skills in algorithms and programming.

Through this capstone project I was not only able to apply my theoretical computer science knowledge but also develop practical hands-on expertise across multiple domains. I gained valuable skills in areas such as AI system design, machine learning, full-stack web development, database modelling, project management and user evaluation methodologies. The experience of envisioning, architecting and implementing an end-to-end intelligent tutoring application helped hone my abilities as a well-rounded computer scientist. It also enabled me to effectively utilize techniques from various CS sub-domains in an integrated manner to solve a real-world problem – thus achieving the overarching goals of my capstone experience. This proved to be an immensely rewarding learning experience that has better prepared me for future career opportunities and research pursuits at the intersection of these technologies.