How do video games affect memory? Does it matter what kind of video game or do they all have the same effect? With the development of technology and the growing importance of it in our lives, we have to ask ourselves how it affects us--particularly games. Kids start playing video games at a young age and some continue to play it until they’re adults and even when they’re parents. Video games are fun and all, but how much do they affect our memory? Do different games have different effects on our memory? Other studies have compared reading and games on memory, but I want to see what the different kind of games do...First, we will need to find 20 different participants. To limit the number of variables, I will select the 20 subjects and make sure they are in the same grade, same level of classes, and 50% boy and 50% girl. Each trial will be conducted three times to assure accuracy. They will be tested in grammar, science, and math questions. They will play fifteen minutes of a game and then do the worksheet in the topics. The different games will be Role Playing, First person shooter, and Sports games. We will then see the results of the trials in each of the 3 categories and see how they correlate. Since there will be 180 trials total...

How can an application that measures emitted brain waves give feedback based on a person’s behavior? Brainwaves are vibrations emitted from the brain when neurons communicate with each other. When we are tired, slow, sleeping, or dreaming, we are emitting oscillations with low frequencies. When we are awake or alert, we emit oscillations with higher frequencies. Modern-day scientists use a device called an Electroencephalography (EEG) to measure these brain waves. By creating an application that uses the data from an EEG and creates graphs for the user to see, there are an endless amount of possibilities... There are several types of brain waves, and each type of wave is used in different circumstances. When we are tired, slow, sleeping, or dreaming, we are emitting oscillations with low frequencies. When we are awake or alert, we emit oscillations with higher frequencies. According to Brainworks Neurotherapy, the different types of brain waves can be categorized according to their frequencies into four groups: delta (.5 to 3 Hz), theta (3 to 8 Hz), alpha (8 to 12 Hz), beta (12 to 38 Hz), and gamma waves (38 to 42 Hz)... The device that I will be using to measure brain waves is the Emotiv EPOC. According to Emotive EPOC Product Specifications, the device is a scientific contextual wireless EEG system that has 14 EEG channels and 2 references. Using this device, I will be able to receive brain wave data from the Emotiv which can then be parsed through the server...The first step of the research will be to establish a way to attain access to the raw stream data from the Emotiv EPOC headset. For this task, I will be using “EmoKit” (https://github.com/openyou/emokit), which is an open source driver created by the OpenYou Organization. Next, I will need to create a mechanism that can parse the data received from the Emotiv to the server. For this I will use Node.js, which is a Javascript Runtime for developing server-side web applications. With Node.js I will be able to receive data on any device, whether it is a browser on a computer, or an app on an iPhone... After the Emotiv data is synchronized on another device, the data can then be used to construct a graph for the front-end user to visualize.

What is the effect of a java produced active noise control program on the sound levels of an individual’s surroundings? Sound can be distracting, irritating, and harmful to one’s health. Many manufacturers utilize a method called “active noise control” to partially, if not fully cancel out unwanted sound. This method involves the intake of existing noise, and the simultaneous playback of a synthesized version of the noise called “Anti-noise”, which is a manipulation of the original sine waves of the sound, but inverted. When natural noise and “anti-noise” are played together, the result is either no noise at all, or a reduced volume of the natural noise. Active noise control (ANC) is used by a wide range of manufacturers such as Boeng, Bose, and Chevrolet. Boeng and other aeronautical manufacturers use ANC to reduce noise in the cockpits of their aircraft. Chevrolet and other automobile companies use ANC to reduce noise inside of cars. Bose and other audio companies use ANC for more personal products; headphones. The headphones can play music through them while at the same time cancelling out the noise surrounding the wearer of the headphones. What the problem with those headphones is that their cost is higher than that of regular headphones. Noise cancelling headphones can go for at the least two hundred dollars, while regular headphones can go for as little as five dollars. Active noise control has a large presence in modern day society, but it is not easily accessible by the public. Dennis R. Morgan of IEEE (Institute of Electrical and Electronics Engineers) discusses the many methods used by engineers to implement active noise control on industrial areas with loud fans, machines and vehicles. What is required of an ANC system is a primary sound source, a speaker, and two microphones. One microphone (the reference microphone) receives the primary noise before the second microphone (the error microphone) receives the noise. Both microphones take in the noise and average the two to be processed by an ANC algorithm. Many algorithms exist to process sound and turn it into anti-noise to cancel out primary noise. What would be beneficial to the general public is a program or application that performs Active Noise Control with a computer or smartphone’s internal microphone as an error microphone, a low-cost earphone built-in microphone, and the speakers from those headphones. My research plan will consists of four main steps; the first researching and analyzing Java’s audio development package, the second being a way to input sound, alter it, and play back the altered sound at the same time as natural sound is occurring. The third step, which is the most challenging, will be to research the many algorithms for creating “anti-noise” and to choose one that will be easiest to implement in a java program. The fourth step will be product testing the program, by using the program and measuring the decibel levels before and after the implementation of “Anti-noise”.

How can machine learning techniques be utilized to construct educational storybook scenes from plaintext to allow those learning to read to better conceptualize their literature? Throughout history, technology has made a significant impact both inside and outside the classroom. Since the rise of commercially available computers, the ability to access the Internet and other computing resources has empowered educators and students alike. Educational platforms and applications have enriched the quality and delivery of education, especially for the younger generations of students. Early on in a student’s formal education, the first challenge they must overcome is learning to read. Part of this challenge includes conceptualizing the meaning of written words and attaching a visual association with text. However, breakthroughs in modern computing can help students overcome these challenges. Natural language processing (NLP), or the use of algorithms to glean semantic meaning from human-generated language, has the ability to help young students conceptualize the text they are learning to read...In Peter Norvig and Stuart Russell’s Artificial Intelligence: A Modern Approach they characterize natural language to be ambiguous, which is part of the reason why it is so difficult for both humans and machines to perceive a singular meaning from a piece of text. NLP enables computers to classify and evaluate text through the use of mathematical probabilistic models that determine the significance of words. Once a machine has a basic understanding of the meaning of the text, other techniques such as visual processing can be used to help the computer paint a graphical interpretation of the text...The study will be broken up into several coherent segments. The first step in completing the research will be to design and train a natural language processing model to analyze and deriving semantics from plaintext. The designing and training of this model should be completed no later than February 1, 2016. The construction of the server-side engine that will utilize the natural language processing model to analyze plaintext will be completed alongside researching methods of visual processing algorithms and frameworks that can generate images based on results produced by the model...