--- a/project/templates/talk/conf_schedule.html Wed Nov 09 16:40:23 2011 +0530
+++ b/project/templates/talk/conf_schedule.html Sat Nov 12 16:39:00 2011 +0530
@@ -1,6 +1,6 @@
{% extends "base.html" %}
{% block content %}
-<h1 class="title">SciPy.in 2010 Conference Schedule</h1>
+<h1 class="title">SciPy.in 2011 Conference Schedule</h1>
<h2 id="sec-1">Day 1 </h2>
@@ -13,25 +13,22 @@
<tr><th scope="col" class="right">Time</th><th scope="col" class="left">Speaker</th><th scope="col" class="left">Title</th></tr>
</thead>
<tbody>
-<tr><td class="right">09:00-09:30</td><td class="left"></td><td class="left">Inauguration</td></tr>
-<tr><td class="right">09:30-10:30</td><td class="left">Perry Greenfield</td><td class="left"><b>Keynote</b>: <a href="#sec-3_1">How Python Slithered into Astronomy</a></td></tr>
-<tr><td class="right">10:30-10:45</td><td class="left"></td><td class="left">Tea Break</td></tr>
-<tr><td class="right">10:45-11:30</td><td class="left">Fernando Perez</td><td class="left"><b>Special Talk</b>: <a href="#sec-3_2">IPython : Beyond the Simple Shell</a></td></tr>
-<tr><td class="right">11:30-11:50</td><td class="left">Farhat Habib</td><td class="left"><a href="#sec-4_1">Python as a Platform for Scientific Computing Literacy for 10+2 Students: Weighing the Balance</a></td></tr>
-<tr><td class="right">11:50-12:10</td><td class="left">Jayesh Gandhi</td><td class="left"><a href="#sec-4_14">Microcontroller experiment and its simulation using Python</a></td></tr>
-<tr><td class="right">12:10-12:40</td><td class="left">Vaidhy Mayilrangam</td><td class="left"><a href="#sec-4_17">Natural Language Processing Using Python</a></td></tr>
-<tr><td class="right">12:40-13:10</td><td class="left">Georges Khaznadar</td><td class="left"><a href="#sec-4_10">Live media for training in experimental sciences</a></td></tr>
-<tr><td class="right">13:10-14:10</td><td class="left"></td><td class="left">Lunch</td></tr>
-<tr><td class="right">14:10-14:20</td><td class="left">Shubham Chakraborty</td><td class="left"><a href="#sec-4_11">Use of Python and Phoenix-M interface in Robotics</a></td></tr>
-<tr><td class="right">14:20-14:30</td><td class="left">Erroju Rama Krishna</td><td class="left"><a href="#sec-4_8">Simplified and effective Network Simulation using ns-3</a></td></tr>
-<tr><td class="right">14:30-14:40</td><td class="left"></td><td class="left">More Lightning Talks</td></tr>
-<tr><td class="right">14:40-15:10</td><td class="left">Asokan Pichai</td><td class="left"><b>Invited Talk</b>: <a href="#sec-3_3">Teaching Programming with Python</a></td></tr>
-<tr><td class="right">15:10-15:30</td><td class="left">Hemanth Chandran</td><td class="left"><a href="#sec-4_19">Performance Evaluation of HYBRID MAC for 802.11ad: Next Generation Multi-Gbps Wi-Fi using SimPy</a></td></tr>
-<tr><td class="right">15:30-15:50</td><td class="left">Karthikeyan selvaraj</td><td class="left"><a href="#sec-4_9">PyCenter</a></td></tr>
-<tr><td class="right">15:50-16:10</td><td class="left"></td><td class="left">Tea Break</td></tr>
-<tr><td class="right">16:10-16:40</td><td class="left">Satrajit Ghosh</td><td class="left"><b>Invited Talk</b>: <a href="#sec-3_6">Nipype: Opensource platform for unified and replicable interaction with existing neuroimaging tools</a></td></tr>
-<tr><td class="right">16:40-17:00</td><td class="left">Nek Sharan</td><td class="left"><a href="#sec-4_7">Parallel Computation of Axisymmetric Jets</a></td></tr>
-<tr><td class="right">17:00-17:20</td><td class="left">pankaj pandey</td><td class="left"><a href="#sec-4_20">PySPH: Smooth Particle Hydrodynamics with Python</a></td></tr>
+<tr><td class="right">09:00-09:15</td><td class="left"></td><td class="left">Inauguration</td></tr>
+<tr><td class="right">09:15-10:15</td><td class="left">Eric Jones</td><td class="left"><b>Keynote</b></td></tr>
+<tr><td class="right">10:15-10:45</td><td class="left"></td><td class="left">Tea Break</td></tr>
+<tr><td class="right">10:45-11:05</td><td class="left">Versesane (Ankur Gupta)</td><td class="left">Multiprocessing module and Gearman</td></tr>
+<tr><td class="right">11:05-11:25</td><td class="left">ROBSONBENJAMIN</td><td class="left">Automated Measurement of Magnetic properties of Ferro-Magnetic materials using Python</td></tr>
+<tr><td class="right">11:25-12:10</td><td class="left">Mateusz Paprocki</td><td class="left"><b>Invited</b></td></tr>
+<tr><td class="right">12:10-13:10</td><td class="left"></td><td class="left"><b>Lunch</b></td></tr>
+<tr><td class="right">13:10-13:55</td><td class="left">Ajith Kumar</td><td class="left"><b>Invited</b></td></tr>
+<tr><td class="right">13:55-14:15</td><td class="left">Vabasu</td><td class="left">Sentiment Analysis</td></tr>
+<tr><td class="right">14:15-14:45</td><td class="left">Vishalkanaujia</td><td class="left">Exploiting the power of multicore for scientific computing in Python</td></tr>
+<tr><td class="right">14:45-14:55</td><td class="left"></td><td class="left"><b>Lightning Talks</b></td></tr>
+<tr><td class="right">14:55-15:25</td><td class="left"></td><td class="left"><b>Tea</b></td></tr>
+<tr><td class="right">14:25-15:55</td><td class="left">LIFESAVER101</td><td class="left">Building Embedded Systems for Image Processing using Python</td></tr>
+<tr><td class="right">15:55-16:25</td><td class="left">Kunalp</td><td class="left">Smoothed Particle Hydrodynamics with Python</a></td></tr>
+<tr><td class="right">16:25-16:45</td><td class="left">Nivedita88</td><td class="left">Encryptedly yours : Python & Cryptography</td></tr>
+<tr><td class="right">16:45-17:30</td><td class="left">Gael</td><td class="left"><b>Invited</b></td></tr>
</tbody>
</table>
@@ -52,1434 +49,24 @@
<tr><th scope="col" class="right">Time</th><th scope="col" class="left">Speaker</th><th scope="col" class="left">Title</th></tr>
</thead>
<tbody>
-<tr><td class="right">09:00-10:00</td><td class="left">John Hunter</td><td class="left"><b>Special Talk</b>: <a href="#sec-3_4">matplotlib: Beyond the simple plot</a></td></tr>
-<tr><td class="right">10:00-10:45</td><td class="left">Prabhu Ramachandran</td><td class="left"><b>Invited Talk</b>: <a href="#sec-3_5">Mayavi : Bringing Data to Life</a></td></tr>
-<tr><td class="right">10:45-11:00</td><td class="left"></td><td class="left">Tea</td></tr>
-<tr><td class="right">11:00-11:45</td><td class="left">Stéfan van der Walt</td><td class="left"><b>Invited Talk</b>: <a href="#sec-3_7">In Pursuit of a Pythonic PhD</a></td></tr>
-<tr><td class="right">11:45-12:15</td><td class="left">Dharhas Pothina</td><td class="left"><a href="#sec-4_6">HyPy & HydroPic: Using python to analyze hydrographic survey data</a></td></tr>
-<tr><td class="right">12:15-12:35</td><td class="left">Prashant Agrawal</td><td class="left"><a href="#sec-4_18">A Parallel 3D Flow Solver in Python Based on Vortex Methods</a></td></tr>
-<tr><td class="right">12:35-13:05</td><td class="left">Ajith Kumar</td><td class="left"><a href="#sec-4_12">Python in Science Experiments using Phoenix</a></td></tr>
-<tr><td class="right">13:05-14:05</td><td class="left"></td><td class="left">Lunch</td></tr>
-<tr><td class="right">14:05-14:15</td><td class="left">Harikrishna</td><td class="left"><a href="#sec-4_23">Python based Galaxy workflow integration on GARUDA Grid</a></td></tr>
-<tr><td class="right">14:15-14:25</td><td class="left">Arun C. H.</td><td class="left"><a href="#sec-4_3">Automation of an Optical Spectrometer</a></td></tr>
-<tr><td class="right">14:25-14:35</td><td class="left"></td><td class="left"><a href="#More==Lightning==Talks">More Lightning Talks</a></td></tr>
-<tr><td class="right">14:35-14:55</td><td class="left">Krishnakant Mane</td><td class="left"><a href="#sec-4_22">Convincing Universities to include Python</a></td></tr>
-<tr><td class="right">14:55-15:15</td><td class="left">Shantanu Choudhary</td><td class="left"><a href="#sec-4_4">"Python" Swiss army knife for Prototyping, Research and Fun.</a></td></tr>
-<tr><td class="right">15:15-15:35</td><td class="left">Puneeth Chaganti</td><td class="left"><a href="#sec-4_21">Pictures, Songs and Python</a></td></tr>
-<tr><td class="right">15:35-15:55</td><td class="left">Hrishikesh Deshpande</td><td class="left"><a href="#sec-4_5">Wavelet based denoising of ECG using Python</a></td></tr>
-<tr><td class="right">15:55-16:10</td><td class="left"></td><td class="left">Tea-Break</td></tr>
-<tr><td class="right">16:10-16:40</td><td class="left">Jarrod Millman</td><td class="left"><b>Invited Talk</b><a href="#sec-3_8">Building an open development community for neuroimaging analysis</a></td></tr>
-<tr><td class="right">16:40-17:00</td><td class="left">Ramakrishna Reddy Yekulla</td><td class="left"><a href="#sec-4_13">Building and Packaging your Scientific Python Application For Linux Distributions</a></td></tr>
-<tr><td class="right">17:00-17:20</td><td class="left">Yogesh Karpate</td><td class="left"><a href="#sec-4_16">Automatic Proteomic Finger Printing using Scipy</a></td></tr>
-<tr><td class="right">17:20-17:40</td><td class="left">Manjusha Joshi</td><td class="left"><a href="#sec-4_15">SAGE for Scientific computing and Education enhancement</a></td></tr>
+<tr><td class="right">09:00-09:45</td><td class="left">Prabhu Ramachandran</td><td class="left"><b>Invited</b></td></tr>
+<tr><td class="right">09:45-10:05</td><td class="left">Mahendrapnaik</td><td class="left">Large amounts of data downloading and processing in python with facebook data as reference</td></tr>
+<tr><td class="right">10:05-10:15</td><td class="left"></td><td class="left"><b>Lightning Talks</b></td></tr>
+<tr><td class="right">10:15-10:45</td><td class="left"></td><td class="left"><b>Tea</b></td></tr>
+<tr><td class="right">10:45-11:05</td><td class="left">Deshpandehn</td><td class="left">Higher Order Statistics in Python</td></tr>
+<tr><td class="right">11:05-11:25</td><td class="left">shubham_23</td><td class="left">Combination of Python and Phoenix-M as a low cost substitute for PLC</td></tr>
+<tr><td class="right">11:25-12:10</td><td class="left">Emmanuelle</td><td class="left"><b>Invited</b></td></tr>
+<tr><td class="right">12:10-13:10</td><td class="left"></td><td class="left"><b>Lunch</b></td></tr>
+<tr><td class="right">13:10-13:55</td><td class="left">Asokan</td><td class="left"><b>Invited</b></td></tr>
+<tr><td class="right">13:55-14:15</td><td class="left">Jaidev</td><td class="left">A Python Toolbox for the Hilbert-Huang Transform</td></tr>
+<tr><td class="right">14:15-14:45</td><td class="left">Cjgiridhar</td><td class="left">Diving in to Byte-code optimization in Python</td></tr>
+<tr><td class="right">14:45-14:55</td><td class="left"></td><td class="left"><b>Lightning Talks</b></td></tr>
+<tr><td class="right">14:55-15:25</td><td class="left"></td><td class="left"><b>Tea</b></td></tr>
+<tr><td class="right">15:25-16:05</td><td class="left">Ole Nielsen</td><td class="left"><b>Invited</b></td></tr>
+<tr><td class="right">16:05-16:35</td><td class="left">Kunalp</td><td class="left">GPU Accelerated Computational Fluid Dynamics with Python</td></tr>
+<tr><td class="right">16:35-16:45</td><td class="left">Cons0ul</td><td class="left">Reverse Engineering and python</td></tr>
+<tr><td class="right">16:10-16:40</td><td class="left">Jarrod Millman</td><td class="left"><b>Invited</b></td></tr>
</tbody>
</table>
-
-
-
-
-
-
-
-<h2 id="sec-3">Invited Talks </h2>
-
-
-
-
-
-
-<h3 id="sec-3_1">How Python Slithered into Astronomy </h3>
-
-
-<p>Perry Greenfield
-</p>
-
-
-
-<h4 id="sec-3_1_1">Talk/Paper Abstract </h4>
-
-
-<p>I will talk about how Python was used to solve our problems for
-the Hubble Space Telescope. From humble beginnings as a glue
-element for our legacy software, it has become a cornerstone of
-our scientific software for HST and the next large space
-telescope, the James Webb Space Telescope, as well as many other
-astronomy projects. The talk will also cover some of the history
-of essential elements for scientific Python and where future
-work is needed, and why Python is so well suited for scientific
-software.
-</p>
-
-
-
-
-
-
-<h3 id="sec-3_2">IPython : Beyond the Simple Shell </h3>
-
-
-<p>Fernando Perez
-</p>
-
-
-
-<h4 id="sec-3_2_1">Talk/Paper Abstract </h4>
-
-
-<p>IPython is a widely used system for interactive computing in
-Python that extends the capabilities of the Python shell with
-operating system access, powerful object introspection,
-customizable "magic" commands and many more features. It also
-contains a set of tools to control parallel computations via
-high-level interfaces that can be used either interactively or
-in long-running batch mode. In this talk I will outline some of
-the main features of IPython as it has been widely adopted by
-the scientific Python user base, and will then focus on recent
-developments. Using the high performance ZeroMQ networking
-library, we have recently restructured IPython to decouple the
-kernel executing user code from the control interface. This
-allows us to expose multiple clients with different
-capabilities, including a terminal-based one, a rich Qt client
-and a web-based one with full matplotlib support. In conjunction
-with the new HTML5 matplotlib backend, this architecture opens
-the door for a rich web-based environment for interactive,
-collaborative and parallel computing. There is much interesting
-development to be done on this front, and I hope to encourage
-participants at the sprints during the conference to join this
-effort.
-</p>
-
-
-
-
-
-
-<h3 id="sec-3_3">Teaching Programming with Python </h3>
-
-
-<p>Asokan Pichai
-</p>
-
-
-
-<h4 id="sec-3_3_1">Talk/Paper Abstract </h4>
-
-
-<p>As a trainer I have been engaged a lot for teaching fresh
-Software Engineers and software job aspirants. Before starting
-on the language, platform specific areas I teach a part I refer
-to as Problem Solving and Programming Logic. I have used Python
-for this portion of training in the last 12+years. In this talk
-I wish to share my experiences and approaches. This talk is
-intended at Teachers, Trainers, Python Evangelists, and HR
-Managers [if they lose their way and miraculously find
-themselves in SciPy :-)]
-</p>
-
-
-
-
-
-
-<h3 id="sec-3_4">matplotlib: Beyond the simple plot </h3>
-
-
-<p>John Hunter
-</p>
-
-
-
-<h4 id="sec-3_4_1">Talk/Paper Abstract </h4>
-
-
-<p>matplotlib, a python package for making sophisticated
-publication quality 2D graphics, and some 3D, has long supported
-a wide variety of basic plotting types such line graphs, bar
-charts, images, spectral plots, and more. In this talk, we will
-look at some of the new features and performance enhancements in
-matplotlib as well as some of the comparatively undiscovered
-features such as interacting with your data and graphics, and
-animating plot elements with the new animations API. We will
-explore the performance with large datasets utilizing the new
-path simplification algorithm, and discuss areas where
-performance improvements are still needed. Finally, we will
-demonstrate the new HTML5 backend, which in combination with the
-new HTML5 IPython front-end under development, will enable an
-interactive Python shell with interactive graphics in a web
-browser.
-</p>
-
-
-
-
-
-<h3 id="sec-3_5">Mayavi : Bringing Data to Life </h3>
-
-
-<p>Prabhu Ramachandran
-</p>
-
-
-
-<h4 id="sec-3_5_1">Talk/Paper Abstract </h4>
-
-
-<p>Mayavi is a powerful 3D plotting package implemented in
-Python. It includes both a standalone user interface along with
-a powerful yet simple scripting interface. The key feature of
-Mayavi though is that it allows a Python user to rapidly
-visualize data in the form of NumPy arrays. Apart from these
-basic features, Mayavi has some advanced features. These
-include, automatic script recording, embedding into a custom
-user dialog and application. Mayavi can also be run in an
-offscreen mode and be embedded in a sage notebook
-(<a href="http://www.sagemath.org">http://www.sagemath.org</a>). We will first rapidly demonstrate
-these key features of Mayavi. We will then discuss some of the
-underlying technologies like enthought.traits, traitsUI and TVTK
-that form the basis of Mayavi. The objective of this is to
-demonstrate the wide range of capabilities that both Mayavi and
-its underlying technologies provide the Python programmer.
-</p>
-
-
-
-
-
-<h3 id="sec-3_6">Nipype: Opensource platform for unified and replicable interaction with existing neuroimaging tools </h3>
-
-
-<p>Satrajit Ghosh
-</p>
-
-
-
-<h4 id="sec-3_6_1">Talk/Paper Abstract </h4>
-
-
-<p>Current neuroimaging software offer users an incredible
-opportunity to analyze their data in different ways, with
-different underlying assumptions. However, this has resulted in
-a heterogeneous collection of specialized applications without
-transparent interoperability or a uniform operating
-interface. Nipype, an open-source, community-developed
-initiative under the umbrella of Nipy, is a Python project that
-solves these issues by providing a uniform interface to existing
-neuroimaging software and by facilitating interaction between
-these packages within a single workflow. Nipype provides an
-environment that encourages interactive exploration of
-neuroimaging algorithms from different packages, eases the
-design of workflows within and between packages, and reduces the
-learning curve necessary to use different packages. Nipype is
-creating a collaborative platform for neuroimaging software
-development in a high-level language and addressing limitations
-of existing pipeline systems.
-</p>
-
-
-
-
-
-
-
-
-
-<h3 id="sec-3_7">In Pursuit of a Pythonic PhD </h3>
-
-
-<p>Stéfan van der Walt
-</p>
-
-
-
-<h4 id="sec-3_7_1">Talk/Paper Abstract </h4>
-
-
-<p>In May of 2005, I started a pilgrimage to transform myself into
-a doctor of engineering. Little did I know, then, that my
-journey would bring me in touch with some of the most creative,
-vibrant and inspiring minds in the open source world, and that
-an opportunity would arise to help realise their (and now my)
-dream: a completely free and open environment for performing
-cutting edge science. In this talk, I take you on my journey,
-and along the way introduce the NumPy and SciPy projects, our
-community, the early days of packaging, our documentation
-project, the publication of conference proceedings as well as
-work-shops and sprints around the world. I may even tell you a
-bit about my PhD on super-resolution imaging!
-</p>
-
-
-
-
-
-
-<h3 id="sec-3_8">Building an open development community for neuroimaging analysis</h3>
-
-
-<p>Jarrod Millman
-</p>
-
-
-
-<h4 id="sec-3_8_1">Talk/Paper Abstract </h4>
-
-
-<p>Programming is becoming increasingly important to scientific activity. As its
-importance grows, the need for better software tools becomes more and more
-central to scientific practice. However, many fields of science rely on
-badly written, poorly documented, and insufficiently tested codebases.
-Moreover, scientific software packages often implement only the approaches
-and algorithms needed or promoted by the specific lab where the software
-was written.</p>
-
-<p>In this talk, I will illustrate this situation by discussing some of the
-weaknesses of the software ecosystem for neuroimaging analysis circa 2004.
-I will then describe how several of my colleagues and I are attempting
-to rectify this situation with a project called Neuroimaging in Python
-(http://nipy.org). Specifically, I will discuss the approach we've taken
-(e.g., using Python) and the lessons we've learned.
-</p>
-
-
-
-
-
-
-
-
-<h2 id="sec-4">Submitted Talks </h2>
-
-
-
-
-
-
-<h3 id="sec-4_1">Python as a Platform for Scientific Computing Literacy for 10+2 Students: Weighing the Balance </h3>
-
-
-<p>Farhat Habib
-</p>
-
-
-
-<h4 id="sec-4_1_1">Talk/Paper Abstract </h4>
-
-
-<p>The use of Python as a language for introducing computing is
-becoming increasingly widespread. Here we report out findings
-from two years of running an introduction to computing course
-with Python as the programming language, and building upon it,
-using SciPy as a scientific computing language in a course on
-scientific computing.
-</p>
-
-<p>
-The course is designed as a general computing course for
-introducing computing to first year undergraduate students of
-science. We find that a large majority of our incoming students
-have no prior exposure to programming and none of the students
-had any exposure to Python. Thus, the design of the course is
-such that it allows everybody to be brought up to speed with
-general programming concepts. Later, the students will later
-specialize in varied topics from Biology to pure Mathematics,
-thus, the course emphasizes general computing concepts over
-specialized techniques. At a second course in Scien- tific
-Computing numerical methods are introduced with the aid of
-Scipy. The introduction to computing course has been taught
-twice in Fall 2009 and 2010 to batches of around 100 students
-each. In this paper we report our experience with teaching
-Python and student and faculty feedback related to the course.
-</p>
-
-
-
-
-
-
-<h3 id="sec-4_2">Usb Connectivity Using Python </h3>
-
-
-<p>Arun C. H.
-</p>
-
-
-
-<h4 id="sec-4_2_1">Talk/Paper Abstract </h4>
-
-
-<p>Host software using Python interpreter language to communicate
-with the USB Mass Storage class device is developed and
-tested. The <sub>usic18F4550</sub>.pyd module encapsulating all the
-functions needed to configure USB is developed. The Python
-extension .pyd using C/C++ functions compatible for Windows make
-use of SWIG, distutils and MinGW. SWIG gives the flexibility to
-access lower level C/C++ code through more convenient and higher
-level languages such as Python, Java, etc. Simplified Wrapper and
-Interface Generator (SWIG) is a middle interface between Python
-and C/C++. The purpose of the Python interface is to allow the
-user to initialize and configure USB through a convenient
-scripting layer. The module is built around libusb which can
-control an USB device with just a few lines. Libusb-win32 is a
-port of the USB library to the Windows operating system. The
-library allows user space applications to access any USB device on
-Windows in a generic way without writing any line of kernel driver
-code. A simple data acquisition system for measuring analog
-voltage, setting and reading the status of a particular pin of the
-micro controller is fabricated. It is interfaced to PC using USB
-port that confirms to library USB win32 device. The USB DAQ
-hardware consists of a PIC18F4550 micro-controller and the
-essential components needed for USB configuration.
-</p>
-
-
-
-
-
-
-
-
-<h3 id="sec-4_3">Automation of an Optical Spectrometer </h3>
-
-
-<p>Arun C. H.
-</p>
-
-
-
-<h4 id="sec-4_3_1">Talk/Paper Abstract </h4>
-
-
-<p>This paper describes the automation performed for an Optical
-Spectrometer in order to precisely monitor angles, change
-dispersing angle and hence measure wave length of light using a
-data logger, necessary hardware and Python. Automating instruments
-through programs provides great deal of power, flexibility and
-precision. Optical Spectrometers are devices which analyze the
-wave length of light, and are typically used to identify
-materials, and study their optical properties. A broad spectrum of
-light is dispersed using a grating and the dispersed light is
-measured using a photo transistor. The signal is processed and
-acquired using a data logger. Transfer of data, changing angle of
-diffraction are all done using the Python. The angle of
-diffraction is varied by rotating the detector to pick up lines
-using a stepper motor. The Stepper motor has 180 steps or 2
-degrees per step. A resolution of 0.1 degree is achieved in the
-spectrometer by using the proper gear ratio. The data logger is
-interfaced to the computer through a serial port. The stepper
-motor is also interfaced to the computer through another serial
-port. Python is chosen here for its succinct notation and is
-implemented in a Linux environment.
-</p>
-
-
-
-
-
-
-
-
-<h3 id="sec-4_4">"Python" Swiss army knife for Prototyping, Research and Fun. </h3>
-
-
-<p>Shantanu Choudhary
-</p>
-
-
-
-<h4 id="sec-4_4_1">Talk/Paper Abstract </h4>
-
-
-<p>This talk would be covering usage of Python in different scenarios which helped me through my work:
-</p><ul>
-<li>
-Small mlab(Mayavi) scripts which helped in better understanding
-of problem statement.
-</li>
-<li>
-Python3.0 and its blender API's for writing plugins which are
-used for Open Source Animation movie project
-Tube(tube.freefac.org)
-</li>
-<li>
-PyOpenCL Python's interfacing for OpenCL which helped in
-prototyping and speed up of application.
-</li>
-</ul>
-
-
-
-
-
-
-
-
-<h3 id="sec-4_5">Wavelet based denoising of ECG using Python </h3>
-
-
-<p>Hrishikesh Deshpande
-</p>
-
-
-
-<h4 id="sec-4_5_1">Talk/Paper Abstract </h4>
-
-
-<p>The python module "RemNoise" is presented. It allows user to
-automatically denoise one-dimensional signal using wavelet
-transform. It also removes baseline wandering and motion
-artifacts. While RemNoise is developed primarily for biological
-signals like ECG, its design is generic enough that it should be
-useful to applications involving one-dimensional signals. The
-basic idea behind this work is to use multi-resolution property of
-wavelet transform that allows to study non-stationary signals in
-greater depth. Any signal can be decomposed into detail and
-approximation coefficients, which can further be decomposed into
-higher levels and this approach can be used to analyze the signal
-in time-frequency domain. The very first step in any
-data-processing application is to pre-process the data to make it
-noise-free. Removing noise using wavelet transform involves
-transforming the dataset into wavelet domain, zero out all
-transform coefficients using suitable thresholding method and
-reconstruct the data by taking its inverse wavelet transform. This
-module makes use of PyWavelets, Numpy and Matplotlib libraries in
-Python, and involves thresholding wavelet coefficients of the data
-using one of the several thresholding methods. It also allows
-multiplicative threshold rescaling to take into consideration
-detail coefficients in each level of wavelet decomposition. The
-user can select wavelet family and level of decompositions as
-required. To evaluate the module, we experimented with several
-complex one-dimensional signals and compared the results with
-equivalent procedures in MATLAB. The results showed that RemNoise
-is excellent module to preprocess data for noise-removal.
-</p>
-
-
-
-
-
-
-
-
-<h3 id="sec-4_6">HyPy & HydroPic: Using python to analyze hydrographic survey data </h3>
-
-
-<p>Dharhas Pothina
-</p>
-
-
-
-<h4 id="sec-4_6_1">Talk/Paper Abstract </h4>
-
-
-
-<p>
-The Texas Water Development Board(TWDB) collects hydrographic
-survey data in lakes, rivers and estuaries. The data collected
-includes single, dual and tri-frequency echo sounder data
-collected in conjunction with survey grade GPS systems. This raw
-data is processed to develop accurate representations of
-bathymetry and sedimentation in the water bodies surveyed.
-</p>
-<p>
-This talk provides an overview of how the Texas Water Development
-Board (TWDB) is using python to streamline and automate the
-process of converting raw hydrographic survey data to finished
-products that can then be used in other engineering applications
-such as hydrodynamic models, determining lake
-elevation-area-capacity relationships and sediment contour maps,
-etc.
-</p>
-<p>
-The first part of this talk will present HyPy, a python module
-(i.e. function library) for hydrographic survey data
-analysis. This module contains functions to read in data from
-several brands of depth sounders, conduct anisotropic
-interpolations along river channels, apply tidal and elevation
-corrections, apply corrections to boat path due to loss of GPS
-signals as well as a variety of convenience functions for dealing
-with spatial data.
-</p>
-<p>
-In the second part of the talk we present HydroPic, a simple
-Traits based application built of top of HyPy. HydroPic is
-designed to semi-automate the determination of sediment volume in
-a lake. Current techniques require the visual inspection of images
-of echo sounder returns along each individual profile. We show
-that this current methodology is slow and subject to high human
-variability. We present a new technique that uses computer vision
-edge detection algorithms available in python to semi-automate
-this process. HydroPic wraps these algorithms into a easy to use
-interface that allows efficient processing of data for an entire
-lake.
-</p>
-
-
-
-
-
-
-<h3 id="sec-4_7">Parallel Computation of Axisymmetric Jets </h3>
-
-
-<p>Nek Sharan
-</p>
-
-
-
-<h4 id="sec-4_7_1">Talk/Paper Abstract </h4>
-
-
-<p>Flow field for imperfectly expanded jet has been simulated using
-Python for prediction of jet screech frequency. This plays an
-important role in the design of advanced aircraft engine nozzle,
-since screech could cause sonic fatigue failure. For computation,
-unsteady axisymmetric Navier-Stokes equation is solved using fifth
-order Weighted Essentially Non-Oscillatory (WENO) scheme with a
-subgrid scale Large-Eddy Simulation (LES) model. Smagorinsky’s
-eddy viscosity model is used for subgrid scale modeling with
-second order (Total Variation Diminishing) TVD Runge Kutta time
-stepping. The performance of Python code is enhanced by using
-different Cython constructs like declaration of variables and
-numpy arrays, switching off bound check and wrap around etc. Speed
-up obtained from these methods have been individually clocked and
-compared with the Python code as well as an existing in-house C
-code. Profiling was used to highlight and eliminate the expensive
-sections of the code.
-</p>
-<p>
-Further, both shared and distributed memory architectures have
-been employed for parallelization. Shared memory parallel
-processing is implemented through a thread based model by manual
-release of Global Interpreter Lock (GIL). GIL ensures safe and
-exclusive access of Python interpreter internals to running
-thread. Hence while one thread is running with GIL the other
-threads are put on hold until the running thread ends or is forced
-to wait. Therefore to run two threads simultaneously, GIL was
-manually released using "with nogil" statement. The relative
-independence of radial and axial spatial derivative computation
-provides an option of putting them in parallel threads. On the
-other hand, distributed memory parallel processing is through MPI
-based domain decomposition, where the domain is split radially
-with an interface of three grid points. Each sub-domain is
-delegated to a different processor and communication, in the form
-of message transmission, ensures update of interface grid
-points. Performance analyses with increase in number of processors
-indicate a trade-off between computation and communication. A
-combined thread and MPI based model is attempted to harness the
-benefits from both forms of architectures.
-</p>
-
-
-
-
-
-
-
-
-<h3 id="sec-4_8">Simplified and effective Network Simulation using ns-3 </h3>
-
-
-<p>Erroju Rama Krishna
-</p>
-
-
-
-<h4 id="sec-4_8_1">Talk/Paper Abstract </h4>
-
-
-
-<p>
-Network simulation has great significance in the research areas of
-modern networks. The ns-2 is the popular simulation tool which
-proved this, in the successive path of ns-2 by maintaining the
-efficiency of the existing mechanism it has been explored with a
-new face and enhanced power of python scripting in ns-3. Python
-scripting can be added to legacy projects just as well as new
-ones, so developers don't have to abandon their old C/C++ code
-libraries, but in the ns-2 it is not possible to run a simulation
-purely from C++ (i.e., as a main() program without any OTcl), ns-3
-does have new capabilities (such as handling multiple interfaces
-on nodes correctly, use of IP addressing and more alignment with
-Internet protocols and designs, more detailed 802.11 models, etc.)
-</p>
-<p>
-In ns-3, the simulator is written entirely in C++, with optional
-Python bindings. Simulation scripts can therefore be written in
-C++ or in Python. The results of some simulations can be
-visualized by nam, but new animators are under development. Since
-ns-3 generates pcap packet trace files, other utilities can be
-used to analyze traces as well.
-</p>
-<p>
-In this paper the efficiency and effectiveness of IP addressing
-simulation model of ns-3 is compared with the ns-2 simulation
-model,ns-3 model consisting of the scripts written in Python which
-makes the modeling simpler and effective
-</p>
-
-
-
-
-
-
-
-<h3 id="sec-4_9">PyCenter </h3>
-
-
-<p>Karthikeyan selvaraj
-</p>
-
-
-
-<h4 id="sec-4_9_1">Talk/Paper Abstract </h4>
-
-
-<p>The primary objective is defining a centralized testing
-environment and a model of testing framework which integrates all
-projects in testing in a single unit.
-</p>
-<p>
-The implementation of concurrent processing systems and adopting
-client server architecture and with partitioned server zones for
-environment manipulation, allows the server to run test requests
-from different projects with different environment and testing
-requests. The implementation provides features of auto-test
-generation, scheduled job run from server, thin and thick clients.
-</p>
-
-<p>
-The core engine facilitates the management of tests from all the
-clients with priority and remote scheduling. It has an extended
-configuration utility to manipulate test parameters and watch
-dynamic changes. It not only acts as a request pre-preprocessor
-but also a sophisticated test bed by its implementation. It is
-provided with storage and manipulation segment for every
-registered project in the server zone. The system schedules and
-records events and user activities thereby the results can be
-drilled and examined to core code level with activates and system
-states at the test event point.
-</p>
-<p>
-The system generates test cases both in human readable as well as
-executable system formats. The generated tests are based on a
-pre-defined logic in the system which can be extended to adopt new
-cases based on user requests. These are facilitated by a template
-system which has a predefined set of cases for various test types
-like compatibility, load, performance, code coverage, dependency
-and compliance testing. It is also extended with capabilities like
-centralized directory systems for user management with roles and
-privileges for authentication and authorization, global mailer
-utilities, Result consolidator and Visualizer.
-</p>
-<p>
-With the effective implementation of the system with its minimal
-requirements, the entire testing procedure can be automated with
-the testers being effectively used for configuring, ideating and
-managing the test system and scenarios. The overhead of managing
-the test procedures like environment pre-processing, test
-execution, results collection and presentation are completely
-evaded from the testing life cycle.
-</p>
-
-
-
-
-
-
-
-
-<h3 id="sec-4_10">Live media for training in experimental sciences </h3>
-
-
-<p>Georges Khaznadar
-</p>
-
-
-
-<h4 id="sec-4_10_1">Talk/Paper Abstract </h4>
-
-
-<p>A system for distance learning in the field of Physics and
-Electricity has been used for three years with some success for 15
-years old students. The students are given a little case
-containing a PHOENIX box (see
-<a href="http://www.iuac.res.in/~elab/phoenix/">http://www.iuac.res.in/~elab/phoenix/</a>) featuring electric analog
-and digital I/O interfaces, some unexpensive discrete components
-and a live (bootable) USB stick.
-</p>
-<p>
-The PHOENIX project was started by Inter University Accelerator
-Centre in New Delhi, with the objective of improving the
-laboratory facilities at Indian Universities, and growing with the
-support of the user community. PHOENIX depends heavily on Python
-language. The data acquisition, analysis and writing simulation
-programs to teach science and computation.
-</p>
-<p>
-The hardware design of PHOENIX box is freely available.
-</p>
-<p>
-The live bootable stick provides a free/libre operating system,
-and a few dozens educational applications, including applications
-developed with Scipy to drive the PHOENIX box and manage the
-acquired measurements. The user interface has been made as
-intuitive as possible: the main window shows a photo of the front
-face of the PHOENIX acquisition device, its connections behaving
-like widgets to express their states, and a subwindow displays in
-real time the signals connected to it. A booklet gives
-general-purpose hints for the usage of the acquisition device. The
-educational interaction is done with a free learning management
-system.
-</p>
-<p>
-The talk will show how such live media can be used as powerful
-training systems, allowing students to access at home exactly the
-same environment they can find in the school, and providing them a
-lot of structured examples.
-</p>
-<p>
-This talk addresses people who are involved in education and
-training in scientific fields. It describes one method which
-allows distance learning (however requiring a few initial lessons
-to be given non-remotely), and enables students to become fluent
-with Python and its scientific extensions, while learning physics
-and electricity. This method uses Internet connections to allow
-remote interactions, but does not rely on a wide bandwidth, as the
-complete learning environment is provided by the live medium,
-which is shared by teacher and students after their beginning
-lessons.
-</p>
-
-
-
-
-
-
-
-<h3 id="sec-4_11">Use of Python and Phoenix-M interface in Robotics </h3>
-
-
-<p>Shubham Chakraborty
-</p>
-
-
-
-<h4 id="sec-4_11_1">Talk/Paper Abstract </h4>
-
-
-<p>In this paper I will show how to use Python programming with a
-computer interface such as Phoenix-M to drive simple robots. In my
-quest towards Artificial Intelligence (AI) I am experimenting with
-a lot of different possibilities in Robotics. This one is trying
-to mimic the working of a simple insect's autonomous nervous
-system using hard wiring and some minimal software usage. This is
-the precursor to my advanced robotics and AI integration where I
-plan to use an new paradigm of AI based on Machine Learning and
-Self Consciousness via Knowledge Feedback and Update process.
-</p>
-
-
-
-
-
-
-
-
-<h3 id="sec-4_12">Python in Science Experiments using Phoenix </h3>
-
-
-<p>Ajith Kumar
-</p>
-
-
-
-<h4 id="sec-4_12_1">Talk/Paper Abstract </h4>
-
-
-<p>Phoenix is a hardware plus software framework for developing
-computer interfaced science experiments. Sensor and control
-elements connected to Phoenix can be accessed using Python. Text
-based and GUI programs are available for several
-experiments. Python programming language is used as a tool for
-data acquisition, analysis and visualization.
-</p>
-<p>
-Objective of the project is to improve the laboratory facilities
-at the Universities and also to utilize computers in a better
-manner to teach science. The hardware design is freely
-available. The project is based on Free Software tools and the
-code is distributed under GNU General Public License.
-</p>
-
-
-
-
-
-
-<h3 id="sec-4_13">Building and Packaging your Scientific Python Application For Linux Distributions </h3>
-
-
-<p>Ramakrishna Reddy Yekulla
-</p>
-
-
-
-<h4 id="sec-4_13_1">Talk/Paper Abstract </h4>
-
-
-<p>If you are an Independent Researcher, Academic Project or an
-Enterprise software Company building large scale scientific python
-applications, there is a huge community of packagers who look at
-upstream python projects to get those packages into upstream
-distributions. This talk focuses on practices, making your
-applications easy to package so that they can be bundled with
-Linux distributions. Additionally this talk would be more hands
-on, more like a workshop. The audience are encouraged to bring as
-many python applications possible, using the techniques showed in
-the talk and help them package it for fedora.
-</p>
-
-
-
-
-
-
-
-
-<h3 id="sec-4_14">Microcontroller experiment and its simulation using Python </h3>
-
-
-<p>Jayesh Gandhi
-</p>
-
-
-
-<h4 id="sec-4_14_1">Talk/Paper Abstract </h4>
-
-
-<p>Electronics in industrial has been passing through revolution due
-to extensive use of Microcontroller. These electronic devices are
-having a high capability to handle multiple events. Their
-capability to communicate with the computers has made the
-revolution possible. Therefore it is very important to have
-trained Personnel in Microcontroller. In the present work
-experiments for study of Microcontrollers and its peripherals with
-Simulation using Python is carried out. This facilitates the
-teachers to demonstrate the experiments in the classroom sessions
-using simulations. Then the same experiments can be carried out in
-the labs (using the same simulation setup) and the microcontroller
-hardware to visualize and understand the experiments. Python is
-selected due to its versatility and also to promote the use of
-open source software in the education.
-</p>
-<p>
-Here we demonstrate the experiment of driving seven segment
-displays by microcontroller. Four seven segment displays are
-interfaced with the microcontroller through a single BCD to seven
-segments Display Decoder/Driver (74LS47) and switching
-transistors. The microcontroller switches on the first transistor
-connected to the first display and puts the number to be displayed
-on 74LS47. Then it pause a while, switches off the first display
-and puts the number to be displayed on the second display and
-switches it on. A similar action is carried out for all the
-display and the cycle is repeated again and again. Now we can
-control the microcontroller action using the serial port of the
-computer through python. Simulating the seven segment display
-using VPYTHON module and communicating the same action to the
-microcontroller, we can demonstrate the switching action of the
-display at a very slow rate. It is possible to actually see each
-display glowing individually one after another. Now we can
-gradually increase the rate of switching the display. You see each
-display glowing for a few milliseconds. Finally the refresh rate
-is taken very high to around more than 25 times a second we see
-that all the display glowing simultaneously.
-</p>
-<p>
-Hence it is possible to simulate and demonstrate experiments and
-understand the capabilities of the microcontroller with a lot of
-ease and at a very low cost.
-</p>
-
-
-
-
-
-
-
-<h3 id="sec-4_15">SAGE for Scientific computing and Education enhancement </h3>
-
-
-<p>Manjusha Joshi
-</p>
-
-
-
-<h4 id="sec-4_15_1">Talk/Paper Abstract </h4>
-
-
-
-<p>
-Sage is Free open source software for Mathematics.
-</p>
-<p>
-Sage can handle long integer computations, symbolic computing,
-Matrices etc. Sage is used for Cryptography, Number Theory, Graph
-Theory in education field. Note book feature in Sage, allow user
-to record all work on worksheet for future use. These worksheets
-can be publish for information sharing, students and trainer can
-exchange knowledge, share, experiment through worksheets.
-</p>
-<p>
-Sage is an advanced computing tool which can enhance education in
-India.
-</p>
-
-
-
-
-
-
-
-
-
-<h3 id="sec-4_16">Automatic Proteomic Finger Printing using Scipy </h3>
-
-
-<p>Yogesh Karpate
-</p>
-
-
-
-<h4 id="sec-4_16_1">Talk/Paper Abstract </h4>
-
-
-<p>The idea is to demonstrate the PyProt (Python Proteomics), an
-approach to classify mass spectrometry data and efficient use of
-statistical methods to look for the potential prevalent disease
-markers and proteomic pattern diagnostics. Serum proteomic pattern
-diagnostics can be used to differentiate samples from the patients
-with and without disease. Profile patterns are generated using
-surface-enhanced laser desorption and ionization (SELDI) protein
-mass spectrometry. This technology has the potential to improve
-clinical diagnostic tests for cancer pathologies. There are two
-datasets used in this study which are taken from the FDA-NCI
-Clinical Proteomics Program Databank. First data is of ovarian
-cancer and second is of Premalignant Pancreatic Cancer .The Pyprot
-uses the high-resolution ovarian cancer data set that was
-generated using the WCX2 protein array. The ovarian cancer dataset
-includes 95 controls and 121 ovarian cancer sets, where as
-pancreatic cancer dataset has 101 controls and 80 pancreatic
-cancer sets. There are two modules designed and implemented in
-python using Numpy , Scipy and Matplotlib. There are two different
-kinds of classifications implemented here, first to classify the
-ovarian cancer data set. Second type focuses on randomly
-commingled study set of murine sera. it explores the ability of
-the low molecular weight information archive to classify and
-discriminate premalignant pancreatic cancer compared to the
-control animals.
-</p>
-<p>
-A crucial issue for classification is feature selection which
-selects the relevant features in order to focus the learning
-search. A relaxed setting for feature selection is known as
-feature ranking, which ranks the features with respect to their
-relevance. Pyprot comprises of two modules; First includes
-implementation of feature ranking in Python using fisher ratio and
-t square statistical test to avoid large feature space. In second
-module, Multilayer perceptron (MLP) feed forward neural network
-model with static back propagation algorithm is used to classify
-.The results are excellent and matched with databank results and
-concludes that PyProt is useful tool for proteomic finger
-printing.
-</p>
-
-
-
-
-
-
-
-
-
-
-<h3 id="sec-4_17">Natural Language Processing Using Python </h3>
-
-
-<p>Vaidhy Mayilrangam
-</p>
-
-
-
-<h4 id="sec-4_17_1">Talk/Paper Abstract </h4>
-
-
-<p>The purpose of this talk is to give a high-level overview of
-various text mining techniques, the statistical approaches and the
-interesting problems.
-</p>
-<p>
-The talk will start with a short summary of two key areas – namely
-information retrieval (IR) and information extraction (IE). We
-will then discuss how to use the knowledge gained for
-summarization and translation. We will talk about how to measure
-the correctness of results. As part of measuring the correctness,
-we will discuss about different kinds of statistical approaches
-for classifying and clustering data.
-</p>
-<p>
-We will do a short dive into NLP specific problems - identifying
-sentence boundaries, parts of speech, noun and verb phrases and
-named entities. We will also have a sample session on how to use
-Python’s NLTK to accomplish these tasks.
-</p>
-
-
-
-
-
-
-
-<h3 id="sec-4_18">A Parallel 3D Flow Solver in Python Based on Vortex Methods </h3>
-
-
-<p>Prashant Agrawal
-</p>
-
-
-
-<h4 id="sec-4_18_1">Talk/Paper Abstract </h4>
-
-
-<p>A 3D flow solver for incompressible flow around arbitrary 3D
-bodies is developed. The solver is based on vortex methods whose
-grid-free nature makes it very general. It uses vortex particles
-to represent the flow-field. Vortex particles (or blobs) are
-released from the boundary, and these advect, stretch and diffuse
-according to the Navier-Stokes equations.
-</p>
-<p>
-The solver is based on a generic and extensible design. This has
-been made possible mainly by following a universal theme of using
-blobs in every component of the solver. Advection of the
-particles is implemented using a parallel fast multipole
-method. Diffusion is simulated using the Vorticity Redistribution
-Technique (VRT). To control the number of blobs, merging of nearby
-blobs is also performed.
-</p>
-<p>
-Each component of the solver is parallelized. The boundary,
-advection and stretching algorithms are based on the same parallel
-velocity algorithm. Domain decomposition for parallel velocity
-calculator is performed using Space Filling Curves. Diffusion,
-which requires knowledge of each particle's neighbours, uses a
-parallelized fast neighbour finder which is based on a bin data
-structure. The same neighbour finder is used in merging also.
-</p>
-<p>
-The code is written completely in Python. It is well-documented
-and well-tested. The code base is around 4500 lines long. The
-design follows an object oriented approach which makes it
-extensible enough to add new features and alternate algorithms to
-perform specific tasks.
-</p>
-<p>
-The solver is also designed to run in a parallel environment
-involving multiple processors. This parallel implementation is
-written using mpi4py, an MPI implementation in Python.
-</p>
-<p>
-Rigorous testing is performed using Python's unittest module. Some
-standard example cases are also solved using the present solver.
-</p>
-<p>
-In this talk we will outline the overall design of the solver and
-the algorithms used. We discuss the benefits of Python and also
-some of the current limitations with respect to parallel testing.
-</p>
-
-
-
-
-
-
-
-<h3 id="sec-4_19">Performance Evaluation of HYBRID MAC for 802.11ad: Next Generation Multi-Gbps Wi-Fi using SimPy </h3>
-
-
-<p>Hemanth Chandran
-</p>
-
-
-
-<h4 id="sec-4_19_1">Talk/Paper Abstract </h4>
-
-
-<p>Next generation Wireless Local Area Networks (WLAN) is targeting
-at multi giga bits per second throughput by utilizing the
-unlicensed spectrum available at 60 GHz, millimeter wavelength
-(mmwave).Towards achieving the above goal a new standard namely
-the 802.11ad is under consideration. Due to the limited range and
-other typical characteristics like high path loss etc., of these
-mmwave radios the requirement of the Medium Access Control (MAC)
-are totally different.
-</p>
-<p>
-The conventional MAC protocols tend to achieve different
-objectives under different conditions. For example, the (Carrier
-Sense Multiple Access / Collision Avoidance) CSMA/CA technique is
-robust and simple and works well in overlapping network
-scenarios. It is also suitable for bursty type of traffic. On the
-other hand CSMA/CA is not suitable for power management since it
-needs the stations to be awake always. Moreover it requires an
-omni directional antenna pattern for the receiver which is
-practically not feasible in 60 GHz band.
-</p>
-<p>
-A Time Division Multiple Access (TDMA) based MAC is efficient for
-Quality of Service (QoS) sensitive traffic. It is also useful for
-power saving since the station knows their schedule and can
-therefore power down in non scheduled periods.
-</p>
-<p>
-For 60 GHz usages especially applications like wireless display,
-sync and go, and large file transfer, TDMA appears to be a
-suitable choice. Whereas for applications that require low latency
-channel access (e.g. Internet access etc.)TDMA appears to be
-inefficient due to the latency involved in bandwidth reservation.
-</p>
-<p>
-Another choice is the polling MAC which is highly efficient for
-the directional communication in the 60 GHz band. This provides an
-improved data rates with directional communication as well as acts
-as an interference mitigation scheme. On the contrary polling may
-not be efficient for power saving and also not efficient to take
-advantage of statistical traffic multiplexing. This technique also
-leads to wastage of power due to polling the stations without
-traffic to transmit.
-</p>
-<p>
-Having the above facts in mind and considering the variety of
-applications involved in the next generation WLAN systems
-operating at 60 GHz, it can be concluded that no individual MAC
-scheme can support the traffic requirements.
-</p>
-<p>
-In this paper we use SimPy to do a Discrete Event Simulation
-modeling of a proposed hybrid MAC protocol which dynamically
-adjusts the channel times between contention and reservation based
-MAC schemes, based on the traffic demand in the network.
-</p>
-<p>
-We plan to model the problem of admission control and scheduling
-using DES using SimPy. SimPy v2.1.0 is being used for the
-simulation purposes of the proposed Hybrid MAC. We are new to
-using Python for scientific purposes and have just begun using
-this powerful tool to get meaningful and useful results. We plan
-to share our learning experience and how SimPy is increasingly
-becoming a useful tool (apart from regular modeling tools like
-Opnet / NS2).
-</p>
-
-
-
-
-
-
-
-
-
-<h3 id="sec-4_20">PySPH: Smooth Particle Hydrodynamics with Python </h3>
-
-
-<p>pankaj pandey
-</p>
-
-
-
-<h4 id="sec-4_20_1">Talk/Paper Abstract </h4>
-
-
-
-<p>
-We present a python/cython implementation of an SPH framework
-called PySPH. SPH (Smooth Particle Hydrodynamics) is a numerical
-technique for the solution of the continuum equations of fluid and
-solid mechanics.
-</p>
-<p>
-PySPH was written to be a tool which requires only a basic working
-knowledge of python. Although PySPH may be run on distributed
-memory machines, no working knowledge of parallelism is required
-of the user as the same code may be run either in serial or in
-parallel only by proper invocation of the mpirun command.
-</p>
-<p>
-In PySPH, we follow the message passing paradigm, using the mpi4py
-python binding. The performance critical aspects of the SPH
-algorithm are optimized with cython which provides the look and
-feel of python but the performance near to that of a C/C++
-implementation.
-</p>
-<p>
-PySPH is divided into three main modules. The base module provides
-the data structures for the particles, and algorithms for nearest
-neighbor retrieval. The sph module builds on this to describe the
-interactions between particles and defines classes to manage this
-interaction. These two modules provide the basic functionality as
-dictated by the SPH algorithm and of these, a developer would most
-likely be working with the sph module to enhance the functionality
-of PySPH. The solver module typically manages the simulation being
-run. Most of the functions and classes in this module are written
-in pure python which makes is relatively easy to write new solvers
-based on the provided functionality.
-</p>
-<p>
-We use PySPH to solve the shock tube problem in gas dynamics and
-the classical dam break problem for incompressible fluids. We also
-demonstrate how to extend PySPH to solve a problem in solid
-mechanics which requires additions to the sph module.
-</p>
-
-
-
-
-
-
-<h3 id="sec-4_21">Pictures, Songs and Python </h3>
-
-
-<p>Puneeth Chaganti
-</p>
-
-
-
-<h4 id="sec-4_21_1">Talk/Paper Abstract </h4>
-
-
-<p>The aim of this talk is to get students, specially undergrads
-excited about Python. Most of what will be shown, is out there on
-the Open web. We just wish to draw attention of the students and
-get them excited about Python and possibly image processing and
-may be even cognition. We hope that this talk will help retain
-more participants for the tutorials and sprint sessions.
-</p>
-<p>
-The talk will have two parts. The talk will not consist of any
-deep research or amazing code. It's a mash-up of some weekend
-hacks, if they could be called so. We reiterate that the idea is
-not to show the algorithms or the code and ideas. It is, to show
-the power that Python gives.
-</p>
-<p>
-The first part of the talk will deal with the colour Blue. We'll
-show some code to illustrate how our eyes suck at blue (1), if
-they really do. But, ironically, a statistical analysis that we
-did on "Rolling Stones Magazine's Top 500 Songs of All time" (2),
-revealed that the occurrences of blue are more than twice the
-number of occurrences of red and green! We'll show the code used
-to fetch the lyrics and count the occurrences.
-</p>
-<p>
-The second part of the talk will show some simple hacks with
-images. First, a simple script that converts images into ASCII
-art. We hacked up a very rudimentary algo to convert images to
-ASCII and it works well for "machine generated images." Next, a
-sample program that uses OpenCV (3) that can detect faces. We wish
-to show OpenCV since it has some really powerful stuff for image
-processing.
-</p>
-<p>
-(1) <a href="http://nfggames.com/games/ntsc/visual.shtm">http://nfggames.com/games/ntsc/visual.shtm</a>
-(2) <a href="http://web.archive.org/web/20080622145429/www.rollingstone.com/news/coverstory/500songs">http://web.archive.org/web/20080622145429/www.rollingstone.com/news/coverstory/500songs</a>
-(3) <a href="http://en.wikipedia.org/wiki/OpenCV">http://en.wikipedia.org/wiki/OpenCV</a>
-</p>
-
-
-
-
-
-
-
-
-
-<h3 id="sec-4_22">Convincing Universities to include Python </h3>
-
-
-<p>Krishnakant Mane
-</p>
-
-
-
-<h4 id="sec-4_22_1">Talk/Paper Abstract </h4>
-
-
-<p>Python has been around for a long enough time now that it needs
-serious attention from the educational institutes which teach
-computer science. Today Python is known for its simple syntax
-yet powerful performance (if not the fastest performance which
-is any ways not needed all the time ). From Scientific computing
-till graphical user interfaces and from system administration
-till web application development, it is used in many
-domains. However due to Industrial propaganda leading to
-promotion of other interpreted languages (free or proprietary)?
-Python has not got the justice in educational sector which it
-deserves. This paper will talk on methodologies which can be
-adopted to convince the universities for including Python in
-their curriculum. The speaker will provide an insight into his
-experience on success in getting Python included in some
-Universities. A case of SNDT University will be discussed where
-the curriculum designers have decided to have Python in their
-courses from the next year. The speaker will share his ideas
-which led to this inclusion. these will include,
-</p>
-<ul>
-<li>
-Begin by doing series of Python workshops
-</li>
-<li>
-Provide information and opportunities for python based projects
-</li>
-<li>
-make the faculties aware of teaching ease
-</li>
-<li>
-clear the FUD regarding jobs
-</li>
-</ul>
-
-
-
-
-
-
-
-
-<h3 id="sec-4_23">Python based Galaxy workflow integration on GARUDA Grid </h3>
-
-
-<p>Harikrishna
-</p>
-
-
-
-<h4 id="sec-4_23_1">Talk/Paper Abstract </h4>
-
-
-<p>Bioinformatics applications being complex problem involving
-multiple comparisons, alignment, mapping and analysis can be
-managed better using workflow solutions. Galaxy is an open web
-based platform developed in Python for genomic research. Python
-is a light weight dynamic language making Galaxy to be modular
-and expandable. Bioinformatics applications being compute and
-data intensive scale well in grid computing environments. In
-this paper we describe bringing the Galaxy workflow to the
-Garuda Grid computing infrastructure for enabling bioinformatics
-applications. GAURDA grid is an aggregation of heterogeneous
-resources and advanced capabilities for scientific
-applications. Here we present the integration of galaxy workflow
-tool with GARUDA grid middleware to enable computational
-biologists to perform complex problems on the grid environment
-through a web browser.
-</p>
-
+<br/><br/>
{% endblock content %}