This book is Part I of a two-part set that introduces the Java programminglanguage. The text assumes the student will be using the BlueJ developmentenvironment and provides some introductory BlueJ material. Our experiencehas been that BlueJ is easy to learn and provides a good programmingenvironment for the beginner programmer.

This book is Part II of a two-part set that introduces the Java programming language. The text assumes the student will be using the BlueJ development environment and provides some introductory BlueJ material. Our experience has been that BlueJ is easy to learn and provides a good programming environment for the beginner programmer.

This is an open-source and open access book on how to do Data Science using Julia. The book describes the basics of the Julia programming language DataFrames.jl for data manipulation and Makie.jl for data visualization.

You will learn to:

- Read CSV and Excel data into Julia
- Process data in Julia, that is, learn how to answer data questions
- Filter and subset data
- Handle missing data
- Join multiple data sources together
- Group and summarize data
- Export data out of Julia to CSV and Excel files
- Plot data with different Makie.jl backends
- Save visualizations in several formats such as PNG or PDF
- Use different plotting functions to make diverse data visualizations
- Customize visualizations with attributes
- Use and create new plotting themes
- Add LaTeX elements to plots
- Manipulate color and palettes
- Create complex figure layouts

Hands-on experience designing, conducting, analyzing, and presenting experiments on the structure and processing of human language. Focuses on constructing, conducting, analyzing, and presenting an original and independent experimental project of publishable quality. Develops skills in reading and writing scientific research reports in cognitive science, including evaluating the methods section of a published paper, reading and understanding graphical displays and statistical claims about data, and evaluating theoretical claims based on experimental data. Instruction and practice in oral and written communication provided.

The "Music of the Future" is a lesson activity that combines STEAM/Maker Education, programming with Scratch, and MakeyMakey. This lesson activity aims to develop 21st-century skills, which are the basics of product design, block-based programming, electronic programming, and collaboration skills. Here, we present the overview of this lesson activity, the comprehensive lesson plan for the teachers, and instruction for the students. 

Student groups are challenged to program robots with light sensors to follow a black line. Learning both the logic and skills behind programming robots for this challenge helps students improve their understanding of how robots "think" and widens their appreciation for the complexity involved in programming LEGO® MINDSTORMS® NXT robots to do what appears to be a simple task. They test their ideas for approaches to solve the problem and ultimately learn a (provided) working programming solution. They think of real-world applications for line-follower robots that use sensor input. A PowerPoint® presentation and pre/post quizzes are provided.

Students are challenged to design and program Arduino-controlled robots that behave like simple versions of the automated guided vehicles engineers design for real-world applications. Using Arduino microcontroller boards, infrared (IR) sensors, servomotors, attachable wheels and plastic containers (for the robot frame), they make "Lunch-Bots." Teams program the robots to meet the project constraints—to follow a line of reflective tape, make turns and stop at a designated spot to deliver a package, such as a sandwich or pizza slice. They read and interpret analog voltages from IR sensors, compare how infrared reflects differently off different materials, and write Arduino programs that use IR sensor inputs to control the servomotors. Through the process, students experience the entire engineering design process. Pre/post-quizzes and coding help documents are provided.

This is an introductory course to MATLAB, the high-performance interactive software. Topics include MATLAB Basics, Plotting, Scripts & Functions and Programming. Additional resources are also provided.

This course covers basic topics in autonomous marine vehicles, focusing mainly on software and algorithms for autonomous decision making (autonomy) by underwater vehicles operating in the ocean environments, autonomously adapting to the environment for improved sensing performance. It will introduce students to underwater acoustic communication environment, as well as the various options for undersea navigation, both crucial to the operation of collaborative undersea networks for environmental sensing. Sensors for acoustic, biological and chemical sensing by underwater vehicles and their integration with the autonomy system for environmentally adaptive undersea mapping and observation will be covered. The subject will have a significant lab component, involving the use of the MOOS-IvP autonomy software infrastructure for developing integrated sensing, modeling and control solutions for a variety of ocean observation problems, using simulation environments and a field testbed with small autonomous surface craft and underwater vehicles operated on the Charles River.

Based on their experience exploring the Mars rover Curiosity and learning about what engineers must go through to develop a vehicle like Curiosity, students create Android apps that can control LEGO MINDSTORMS(TM) NXT robots, simulating the difficulties the Curiosity rover could encounter. The activity goal is to teach students programming design and programming skills using MIT's App Inventor software as the vehicle for the learning. The (free to download) App Inventor program enables Android apps to be created using building blocks without having to actually know a programming language. At activity end, students are ready to apply what they learn to write other applications for Android devices.

Locating restaurants in an unfamiliar place, reporting potholes to the local DOT, obtaining real-time traffic conditions... All of these are examples of geospatial web apps that are revolutionizing how people obtain and share information about the world. In GEOG 863, you will learn how to build apps like these. You'll start with a quick look at the fundamentals of web programming (HTML and CSS) before diving in to using JavaScript and a mapping application programming interface (API) developed by Esri. Using this API, you'll create both 2D and 3D visualizations of your own data and learn how to develop a user interface to enable users to interact with your map.

As part of a design challenge, students learn how to use a rotation sensor (located inside the casing of a LEGO® MINDSTORMS ® NXT motor) to measure how far a robot moves with each rotation. Through experimentation and measurement with the sensor, student pairs determine the relationship between the number of rotations of the robot's wheels and the distance traveled by the robot. Then they use this ratio to program LEGO robots to move precise distances in a contest of accuracy. The robot that gets closest to the goal without touching the toy figures at the finish line is the winning programming design. Students learn how rotational sensors measure distance, how mathematics can be used for real-world purposes, and about potential sources of error due to gearing when using rotation sensor readings for distance calculations. They also become familiar with the engineering design process as they engage in its steps, from understanding the problem to multiple test/improve iterations to successful design.

We will explore the mathematical strategies behind popular games, toys, and puzzles. Topics covered will combine basic fundamentals of game theory, probability, group theory, and elementary programming concepts. Each week will consist of a lecture and discussion followed by game play to implement the concepts learned in class.

Students work as engineers to learn about the properties of molecules and how they move in 3D space through the use of LEGO MINDSTORMS(TM) NXT robotics. They design and build molecular models and use different robotic sensors to control the movement of the molecular simulations. Students learn about the size of atoms, Newman projections, and the relationship of energy and strain on atoms. This unique modular modeling activity is especially helpful in providing students with a spatial and tactile understanding of how molecules behave.

Students' understanding of how robotic touch sensors work is reinforced through a hands-on design challenge involving LEGO MINDSTORMS(TM) NXT intelligent bricks, motors and touch sensors. They learn programming skills and logic design in parallel as they program robot computers to play sounds and rotate a wheel when a touch sensor is pressed, and then produce different responses if a different touch sensor is activated. Students see first-hand how robots can take input from sensors and use it to make decisions to move as programmed, including simultaneously moving a motor and playing music. A PowerPoint® presentation and pre/post quizzes are provided.

Using new knowledge acquired in the associated lesson, students program LEGO MINDSTORMS(TM) NXT robots to go through a maze using movement blocks. The maze is created on the classroom floor with cardboard boxes as its walls. Student pairs follow the steps of the engineering design process to brainstorm, design and test programs to success. Through this activity, students understand how to create and test a basic program. A PowerPoint® presentation, pre/post quizzes and worksheet are provided.

This resource is a video abstract of a research paper created by Research Square on behalf of its authors. It provides a synopsis that's easy to understand, and can be used to introduce the topics it covers to students, researchers, and the general public. The video's transcript is also provided in full, with a portion provided below for preview:

"Researchers from China have created an algorithm that could make today’s automated systems even more powerful. The algorithm is based on a technique called model predictive control. This type of control is what makes many smart guidance systems, well, smart. Consider, for example, a self-guided robot. To be useful, the robot must be able to manage its fuel resources wisely under a slew of conditions that are subject to change, including terrain, wind speed, and distance traveled. Model predictive control tracks all these factors on the fly to ensure the robot travels along the most efficient route. The key to mapping that route is determining what set of actions the robot can carry out to meet all the environmental constraints it faces simultaneously. Mathematically, this small cluster of solutions is what’s known as the feasible set of the system—the discovery of which can be treated as a geometry problem. The objective: find the shape that contains the full set of possible solutions..."

The rest of the transcript, along with a link to the research itself, is available on the resource itself.

The Non-Programmers' Tutorial For Python is a tutorial designed to be an introduction to the Python programming language. This guide is for someone with no programming experience.

The aim of the students from the Numeric Photography class at the MIT Media Laboratory was to present an exhibition of digital artworks which blend photography and computation, in the context of scene capture, image play, and interaction. Equipped with low end digital cameras, students created weekly software projects to explore aesthetic issues in signal processing and interaction design. The results are more than a hundred Java® applets, many of which are interactive, that suggest new avenues for image play on the computer. These weekly exercises led to the final product, an exhibition of the student work.