Point Cloud Framework for Rendering 3D Models Using Google Tango

This project seeks to demonstrate the feasibility of point cloud meshing for capturing and modeling three dimensional objects on consumer smart phones and tablets. Traditional methods of capturing objects require hundreds of images, are very slow and consume a large amount of cellular data for the average consumer. Software developers need a starting point for capturing and meshing point clouds to create 3D models as hardware manufacturers provide the tools to capture point cloud data. The project uses Googles Tango computer vision library for Android to capture point clouds on devices with depth-sensing hardware. The point clouds are combined and meshed as models for use in 3D rendering projects. We expect our results to be embraced by the Android market because capturing point clouds is fast and does not carry a large data footprint

Optimizing Human Performance in Mobile Text Entry

Although text entry on mobile phones is abundant, research strives to achieve desktop typing performance "on the go". But how can researchers evaluate new and existing mobile text entry techniques? How can they ensure that evaluations are conducted in a consistent manner that facilitates comparison? What forms of input are possible on a mobile device? Do the audio and haptic feedback options with most touchscreen keyboards affect performance? What influences users' preference for one feedback or another? Can rearranging the characters and keys of a keyboard improve performance? This dissertation answers these questions and more. The developed TEMA software allows researchers to evaluate mobile text entry methods in an easy, detailed, and consistent manner. Many in academia and industry have adopted it. TEMA was used to evaluate a typical QWERTY keyboard with multiple options for audio and haptic feedback. Though feedback did not have a significant effect on performance, a survey revealed that users' choice of feedback is influenced by social and technical factors. Another study using TEMA showed that novice users entered text faster using a tapping technique than with a gesture or handwriting technique. This motivated rearranging the keys and characters to create a new keyboard, MIME, that would provide better performance for expert users. Data on character frequency and key selection times were gathered and used to design MIME. A longitudinal user study using TEMA revealed an entry speed of 17 wpm and a total error rate of 1.7% for MIME, compared to 23 wpm and 5.2% for QWERTY. Although MIME's entry speed did not surpass QWERTY's during the study, it is projected to do so after twelve hours of practice. MIME's error rate was consistently low and significantly lower than QWERTY's. In addition, participants found MIME more comfortable to use, with some reporting hand soreness after using QWERTY for extended periods

Business Analytics Using Predictive Algorithms

In today's data-driven business landscape, organizations strive to extract actionable insights and make informed decisions using their vast data. Business analytics, combining data analysis, statistical modeling, and predictive algorithms, is crucial for transforming raw data into meaningful information. However, there are gaps in the field, such as limited industry focus, algorithm comparison, and data quality challenges. This work aims to address these gaps by demonstrating how predictive algorithms can be applied across business domains for pattern identification, trend forecasting, and accurate predictions. The report focuses on sales forecasting and topic modeling, comparing the performance of various algorithms including Linear Regression, Random Forest Regression, XGBoost, LSTMs, and ARIMA. It emphasizes the importance of data preprocessing, feature selection, and model evaluation for reliable sales forecasts, while utilizing S-BERT, UMAP, and HDBScan unsupervised algorithms for extracting valuable insights from unstructured textual data

The Wall: A mobile app to identify and store social events from a digital image using computer vision

Social events, promoted in print media using posters, flyers and banners often fail to attract an audience because we frequently forget the details of the event when we pass-by the promotion on the street. Smaller venues or artists often rely on low-cost, street-level marketing campaigns in areas of high foot traffic areas to develop interest in an event. These venues or artist are often without a budget for online marketing or have a target demographic outside the typical Social Media consumer which makes attracting an audience difficult. This project aimed to solve the problem of storing and reminding the user of upcoming events, advertised in print media, by developing a mobile app to automatically identify and event information from an image taken by the user. The project is an N-tier system comprising: a front-end using AngularJS, Ionic and Cordova; a cloud Firebase database to store the user\u27s registration and logon credentials; Google Vision API to automatically segment and identify event information and the Google Calendar API to store and remind the user of upcoming events. The project was managed using the Agile Development methodology Scrum. The challenge of this project was in developing a solution to automatically and reliably identify event information from print media which often contains a wide variety of layouts, orientations, font types, colours and contrast variations between the information and any graphics present. In addition, the solution needed to understand the semantics of the text relating to the event name and location. The development frameworks and APIs chosen were unfamiliar to the team but were used because of their technical suitability and their ongoing and increasing popularity in the industry. Functional testing was based on a set of over 50 test images. Testing concluded that the solution retrieves date and time information consistently, however, more work is required to successfully segment and recognise event location and title. User Experience (UX) was measured in a cross-sectional survey of 75 participants. The results were positive and are discussed here