Your relationships with your colleagues is important! Provide some work delight by getting a round of coffee once in a while (or get some colleague to do it for you).
This application will help you to remember and transport the hot beverage of choice for up-to five colleagues (plus one additional beverage for your self). Make great use of the perfectly flat surface of your own (or the companies) iPad.
The current version provides a width range of hot beverages to choose from:
– Coffee black
– Coffee sugar
– Coffee creamer
– Coffee sugar & cream
– Bean coffee black
– Bean coffee sugar
– Cappuccino sugar
– Espresso sugar
– Chocolate sugar
– Tea sugar
– (Hot) Water
Start using the full office environment potential of your iPad today, and download this application!
Apples reason to not approve the application for the App Store: “We found your app encourages behavior that could result in damage to the user’s device, which is not in compliance with the App Store Review Guidelines.
Specifically, your App encourages the user to transport hot liquids on the device.”
A fun little test setup that uses three (cheap) webcams and a (static) line projector, to measure the outline and curvatures of a human fingernail. Resulting in an Bézier curves based 3D model, that approximates the subjects fingernail.
Graduation project at the Visualization Group of Eindhoven University of Technology, with the goal add genome size data support to the existing DNAVis2 sequence browser. The basic DNAVis2 sequence browser is an OpenGL accelerated visualization tool to visualize and explore a small number of annotated DNA sequences. The application is written in Java using the NetBeans framework and the JOGL OpenGL bindings.
The extended version is designed and implemented during this master project and adds the data structures and visualizations needed to visualize and explore the data of a complete genome, consisting of 10.000-100.000 annotated DNA sequences. Two approaches are used to provide more inside into the dataset as a whole.
The first approach makes use of the higher level DNA structures to visualize the data distribution and to provide a high level interface to lower level annotations and sequencing data. This provides the biologist with the tools to browse and create sub-selection using the DNA’s structural properties.
The second approach provides a versatile interface to cross-reference various data properties across all abstraction levels. This tool provides the biologist with an inside into the annotation data of the dataset as a whole (or a predefined sub-selection of the data), this results cross-reference visualizations than can contain more then 10.000 row and column items.
The face and pupil recognition application is the result of a computer graphics class, this application makes use of C99/C++ and the OpenCV (Open Source Computer Vision) library to find the face of a user and track his pupils using an inexpensive webcam. The idea it to calculate the viewing position on the computer screen using the webcam images. The resulting application was able to calculate a raw approximation of the screen location after small calibration procedure that uses 5 points on the screen (center, and all four corners). The application is also able to locate the user’s nostrils and mouth.
The flow visualisation application is the result of a visualization master class, this application uses a number of visualization techniques that are implemented using (OpenGL and C99/C++) to simulate smoke flow in infinite space. This application can visualize the smoke density, particle flows, and pressure areas resulting for a dynamically created smoke source.
Graduation project at Philips TASS (now TASS Software Professionals), with the goal to design and implement a FPGA based video encoder and decoder application using the Celoxica RC200 (Xilinx Virtex II FPGA based) development board and the Handel-C hardware description language.
The FPGA video encoder and decoder are based on the RmaxCmax compression algorithm developed by Philips Research Laboratories. This compression algorithm uses discrete cosine transforms (DCT), like MPEG and JPEG. The algorithm transforms pixel blocks (of 8×4 or 8×8) into DCT coefficient blocks. The bit-planes of these DCT coefficient blocks (starting with the most significant bit-plane) are then compressed using zonal coding (meaning that only a small area of the bit-plane will be transmitted/stored). This data is then serialized in a way that the most significant data is transmitted/stored first. The resulting bit-string can be cut off at any length (to increase the compression), the remaining bit-string will produce the “best possible” image quality for the remaining data size.
The graphical user interface (GUI) is programed within the FPGA itself, and provides the user with a fast and simple interface to view the encoding/decoding process and tweak the settings. The user can select a part of the video input, set the quality (read bit-rate), and can view the resulting decoded video output.