Pinned Repositories
Android-mobile-App-development
Recent Mobile Devices have created a revolution in the use of computers in a vast array of new areas including psychology, medicine, global health, music, banking, cooking, exploring, travel, shopping, and games. We believe that we have just scratched the surface of what is possible, and so the purpose of this course is to encourage creativity in the creation of new applications of mobile devices.This is a project-based course in which the goal is to produce a working app by the end of course. Graduate students from all disciplines are encouraged to take this course for credit. Projects will be done in groups of 2 or 3. Students with programming skills will be matched with those from non-programming backgrounds to do projects in the latter students’ disciplines. There will be four kinds of lectures: On the capabilities of modern mobile devices at both a technical and lay level for non-specialists. Case studies of innovative applications, linking to methods of innovating. Mobile device programming basics Project Proposals and Presentations Graduate students with programming-oriented backgrounds will be graded based on the technical quality of the project, and on their interaction with non-programming project partner(s). Students working on the non-programming portion of the project will be graded, in part, from a faculty member from their own department, and on their ability to engage with their programming partner(s). Grading will be on a few basic programming assignments, project proposals, and final project report and presentation. Non-technical students will do some basic programming. The course will support the use of Google Android-based platforms, but those who have access to other platforms (such as Apple iPhone or RIM Blackberry or Nokia Maemo) are welcome to use those.
api
BIXI
GooglePeopleAPI
GPU_Course-assignment
In total, there are 3 assignments for this course. The first assignment is writing a CUDA program containing a kernel “arradd”, that adds a number X to all elements of a one-dimensional array A. The elements of A and X should be single precision floating-point numbers (float). The elements of A should be initialized so that A[i] = i / 3.0f; Have your program vary the number of elements in A from 1 Million to MIN(maximum number that can be supported by single invocation of a GPU kernel, 256 Million) in power of two steps, i.e., 1M, 2M, 4M, 8M, 16M, etc. For every different array size, have your program print three time measurements: the time required to copy A from the CPU to the GPU, the time taken by the kernel, and the time required to copy the data from the GPU to the CPU. Use the event timer calls to measure these intervals. The second assignment is writing a CUDA program that finds the maximum or the minimum among the elements of an array of N integers, and try to optimize the program for speed as much as possible. The third assignment is writing a filter for a BMP image. The image will be provided as a two dimensional array of bytes. Each pixel is represented by three consecutive bytes for three color channels (Red, Green, Blue), each with a value from 0 to 255. To get the blur effect the value of each output pixel will be determined by the corresponding input pixel value plus all neighboring pixel values. The neighboring pixels are the ones that locate within a fixed RADIUS of the target pixel. For example, if we want to calculate the output value for a pixel when RADIUS is 2, we need a 5x5 block of pixels of the image with the target pixel in center. Following shows a sample 5x5 block of pixels that are required to compute the final value for the center pixel (pixel with values [46,47,48]). Note that representing a pixel by "[x,y,z]" means that the pixel has a value of x, y, and z for its red, green, and blue channels, respectively.
GPU_Final-project
Human-iris-detection
Using ASMs to detect human iris. Active shape models (ASMs) are statistical models of the shape of objects which iteratively deform to fit to an example of the object in a new image, developed by Tim Cootes and Chris Taylor in 1995.The shapes are constrained by the PDM (point distribution model) Statistical Shape Model to vary only in ways seen in a training set of labelled examples. The shape of an object is represented by a set of points (controlled by the shape model). The ASM algorithm aims to match the model to a new image.
Mobile-Perimeter
Programming-Massively-Parallel-Multiprocessors-and-Heterogeneous-Systems
Simple-GPU-Project
zsy2053's Repositories
zsy2053/Human-iris-detection
Using ASMs to detect human iris. Active shape models (ASMs) are statistical models of the shape of objects which iteratively deform to fit to an example of the object in a new image, developed by Tim Cootes and Chris Taylor in 1995.The shapes are constrained by the PDM (point distribution model) Statistical Shape Model to vary only in ways seen in a training set of labelled examples. The shape of an object is represented by a set of points (controlled by the shape model). The ASM algorithm aims to match the model to a new image.
zsy2053/Mobile-Perimeter
zsy2053/Programming-Massively-Parallel-Multiprocessors-and-Heterogeneous-Systems
zsy2053/Simple-GPU-Project
zsy2053/BIXI
zsy2053/cvst-TTCAnalytics-java
The TTC report created with java
zsy2053/leetcode
zsy2053/make-it-so
Automatically exported from code.google.com/p/make-it-so
zsy2053/Part-time-excel-job
This is some works I did for an indian consultant company.