67 

    68 <h2> Coverage</h2>

    69 <h3 id="sec2.2">Ankur Gupta : Multiprocessing module and Gearman</h3>

    70 <h4>Abstract</h4>

    71 <p class="abstract">Large Data Sets and Multi-Core computers are becoming a common place in today's world. 

    72 Code that utilizes all cores at disposal is prerequisite to process large data sets. 

    73 Scaling over multiple machines/cluster allows for horizontal scaling. 

    74 Drawing on experience of working with a Team at HP that created an near real time 

    75 early warning software named OSSA. OSSA processed over 40TB+ compressed data at HP using 32 cores spread over 

    76 a cluster of machine. Multiprocessing and Gearman ( a distributed job queue with Python bindings ) allows 

    77 any simple python script to go distributed with minimal refactoring.</p>

    78 <h4>Slides</h4>

    79 <p>To be uploaded</p>

    80 

    81 <h3 id="sec2.3">Robson Benjamin : Automated Measurement of Magnetic properties of Ferro-Magnetic materials using Python</h3>

    82 <h4>Abstract</h4>

    83 <p>Hysterisis is basically a phenomenon where the behaviour of a system depends on the way the system moves.  

    84 On increasing the magnetizing field H applied to a magnetic material ,  

    85 the corresponding induction B traces a different path when it increases from that when the field  

    86 decreases tracing a loop. It is often referred to as the  B-H loop.</p> 

    87 <p>A ferromagnetic  specimen is placed co-axially in an applied magnetic field. 

    88 The specimen gets magnetised and  the magnetisation undergoes a variation due to the varying field . 

    89 This variation is picked up by a pickup coil which is placed co-axially with the specimen.  

    90 The dB/dt signal thus pickedup  is propotional to dB/dt, which on integration gives the desired  B. 

    91 The H field is sampled as proportional  to the energyzing current.</p>

    92 <p>Data  acquisition of  H and dB/dt  is done using a microcontroller 

    93 based Data acquisition system which is implimented in Python. 

    94 The signal is acquired alternately choosing the H and the dB/dt. 

    95 The acquired data is nose reduced by averaging over various cycles. 

    96 The processed signal dB/dt  is integrated numerically making sure that 

    97 the constant of integration chosen makes B swing equally on both sides of the H axis .  

    98 The electronic circuitry used introduces an extra phase shift. 

    99 This is nulled by running the experiment in air  where B-H curve is only a straight line. 

   100 The retentivity, coercivity and the susceptibility of the specimen are calculated as the modulus 

   101 of the  X and the modulus of the  Y intercepts . 

   102 The result for steel agrees with reported values. 

   103 This method also gives a way of calculating the hysterysis loss in the sample percycle.  

   104 </p>

   105 <h4>Slides</h4>

   106 <p>To be uploaded</p>

   107 

   108 <h3 id="sec2.6">Bala Subrahmanyam Varanasi : Sentiment Analysis</h3>

   109 <h4>Abstract</h4>

   110 <p>This talk will start with a quick overview of my topic - Sentiment analysis, its 

   111 Applications, Opportunities and various Challenges involved in Sentiment Mining. 

   112 Later, we present our machine learning experiments conducted using Natural Language Tool Kit 

   113 (NLTK) with regard to sentiment analysis for the language "Telugu", where this work is less implemented.</p> 

   114 <p>We have developed a Sentiment analyzer for Telugu Language.  

   115 For that we developed movie review corpus from a popular website telugu.oneindia.com as our 

   116 data set which is classified according to subjectivity/objectivity and negative/positive attitude.  

   117 We used different approaches in extracting text features such as bag-of-words model, 

   118 using large movie reviews corpus, restricting to adjectives and adverbs, 

   119 handling negations and bounding word frequencies by a threshold. 

   120 We conclude our study with explanation of observed trends in accuracy rates and providing directions for future work.</p>

   121 <h4>Slides</h4>

   122 <p>To be uploaded</p>

   123 <h3 id="sec2.7">Vishal Kanaujia : Exploiting the power of multicore for scientific computing in Python</h3>

   124 <h4>Abstract</h4>

   125 <p>Multicore systems offer abundant potential for parallel computing, 

   126 and Python developers are flocking to tap this power. 

   127 Python is gaining popularity in high performance computing with rich set of libraries and frameworks.</p>

   128 <p>Typically, scientific applications viz. modeling weather patterns, 

   129 seismographic data, astronomical analysis etc, deal with huge data-set. 

   130 Processing of this raw data for further analysis is a highly CPU-intensive task. 

   131 Hence it is critical that design and development of these applications should 

   132 look towards utilizing multiple CPU cores in an efficient manner for high performance.</p>

   133 

   134 <p>This talk discusses different methods to achieve parallelism in 

   135 Python applications and analyze these methods for effectiveness and suitability.</p> 

   136 

   137 <h4>Agenda</h4>

   138 <ul>

   139 	<li>Problem context: Big data problem</li>

   140 	<li>Designing Python programs for multicores</li>

   141 	<li>Achieving parallelism

   142 		<ul>

   143 			<li>Multithreading and the infamous GIL</li>

   144 			<li>Exploring multiprocessing</li>

   145 			<li>Jython concurrency</li>

   146 		</ul>

   147 	</li>

   148 </ul>

   149 <h4>Slides</h4>

   150 <p>To be uploaded</p>

   151 

   152 <h3 id="sec2.8">Jayneil Dalal : Building Embedded Systems for Image Processing using Python</h3>

   153 <h4>Abstract</h4>

   154 <p>I plan to teach everyone how to import the very popular and powerful 

   155 OpenCV library to Python and use it for image processing. 

   156 I will also cover the installation of the same as it is a very 

   157 cumbersome and a bit difficult task. Then we will do basic image processing programs . 

   158 Then I will teach how to interact with an embedded system(Arduino) using Pyserial 

   159 module and carry out different actions(Turn on LED etc.) 

   160 So finally we will develop a full fledged embedded system. 

   161 For e.g.: We will do image processing to detect a certain object in a given 

   162 image and based on the output of that, the embedded system will do a certain task. 

   163 If in a given image using facial recognition, a face is detected then an LED will be turned ON! All using python.

   164 </p>

   165 <h4>Slides</h4>

   166 <p>To be uploaded</p>

   167 

   168 

   169 <h3 id="sec2.9">Kunal Puri : Smoothed Particle Hydrodynamics with Python</h3>

   170 <h4>Abstract</h4>

   171 <p>We present PySPH as a framework for smoothed particle hydrodynamics simulations in Python. 

   172 PySPH can be used for a wide class of problems including fluid dynamics, solid mechanics and 

   173 compressible gas dynamics. We demonstrate how to run simulations and view the results with PySPH from the end-user's perspective.

   174 </p> 

   175 

   176 <p>Note: This is intended to be a magazine-style article as the PySPH architecture is discussed elsewhere.</p>

   177 <h4>Slides</h4>

   178 <p>To be uploaded</p>

   179 

   180 <h3 id="sec2.10">Nivedita Datta : Encryptedly yours : Python & Cryptography</h3>

   181 <h4>Abstract</h4>

   182 <p>In today's world, the hard truth about protecting electronic messages and 

   183 transactions is that no matter how advanced the technology being used, 

   184 there is no guarantee of absolute security. As quickly as researchers develop 

   185 ever-more-rigorous methods for keeping private information private, 

   186 others figure out how to skirt those safeguards. That's particularly worrisome as our 

   187 society becomes more and more dependent on e-commerce. Scientists say that even measures 

   188 now considered virtually 'unbreakable' might someday be broken, by either mathematicians or 

   189 computers that develop new algorithms to crack the protective code.

   190 </p>

   191 

   192 <h4>Agenda</h4>

   193 <ul>

   194 	<li>What is cryptography</li>

   195 	<li>Why cryptography</li>

   196 	<li>Basic terminologies</li>

   197 	<li>

   198 		Classification of cryptographic algorithms

   199 		<ul>

   200 			<li>Stream cipher and block ciphers</li>

   201 			<li>Public key and private key algorithms</li>

   202 		</ul>

   203 	</li>

   204 	<li>Introduction to hashing</li>

   205 	<li>Introduction to pycrypto module</li>

   206 	<li>pycrypto installation steps</li>

   207 	<li>Code for few cryptographic and hashing algorithms</li>

   208 </ul>

   209 

   210 <h4>Slides</h4>

   211 <p>To be uploaded</p>

   212 

   213 <h3 id="sec2.13">Mahendra Naik : Large amounts of data downloading and processing in python with facebook data as reference</h3>

   214 <p>Python is an easy to learn language which helps for rapid development of applications. 

   215 The only visbile hindrance to python is the speed of processing ,primarily because it is a scripting language. 

   216 Scientific computing involves processing large amounts of data in a very short period of time. 

   217 This paper talks about an efficient algorithm to process massive(GB's) textual data in time interval of less than a second. 

   218 There will not be any changes to core python. 

   219 The existing python libraries will be used to process this data. 

   220 The main aspect of the project is that we will not be dealing with the old data stored in the filesystem . 

   221 We will be downloading data from the internet and the processing will happen in real-time. 

   222 So, an effective caching , if any used should be implemented. 

   223 A database like MYSQL will be used to store the data.</p> 

   224 <p>Pythreads will be used for parallel downloading and processing of data. 

   225 So a constant stream of huge data will be downloaded and later processed for the required data. 

   226 This algorithm can find use in scientific applications where a large data needs to processes in real-time. 

   227 And this will be achieved without making any changes to core python. 

   228 The data we will be processing on would be retrieved from facebook. 

   229 Facebook was choosen because of its massive userbase and the massive data stored for almost every user. 

   230 Another reason for choosing facebook was the availability of api's to access data. 

   231 Facebook exposes its data to developers through facebook platform. 

   232 We will be using facebook's graph api to download data from facebook. 

   233 Graph api stores each and every element from facebook as an id. 

   234 The data from all the id's from 1 to a very huge number (eg:10 billion) 

   235 will be extracted from facebook and will be processed to retrieve the required data. 

   236 The main intention of the project is to implement an algorithm to process massive amounts of data in real time using python . 

   237 As explained above we will take facebook as the reference data.</p>

   238 <h4>Slides</h4>

   239 <p>To be uploaded</p>

   240 

   241 <h3 id="sec2.14">Hrishikesh Deshpande : Higher Order Statistics in Python</h3>

   242 <h4>Abstract</h4>

   243 <p>In many signal and image processing applications, correlation and power spectrum have been used as primary tools; the information contained in the power spectrum is provided by auto-correlation and is sufficient for complete statistical descriptions of Gaussian signals of known means. However, there exist some practical situations where one needs to look beyond auto-correlation operation to extract information pertaining to deviation from Gaussianity and the presence of phase relations. Higher Order Statistics (HOS) are the extensions of second order measures to higher orders and have proven to be useful in problems where non-gaussianity, non-minimal phase or non-linearity has some role to play. In recent years, the field of HOS has continued its expansion, and applications have been found in fields as diverse as economics, speech, medical, seismic data processing, plasma physics and optics. In this paper, we present a module named PyHOS, which provides elementary higher order statistics functions in Python and further discuss an application of HOS for biomedical signals. This module makes use of SciPy, Numpy and Matplotlib libraries in Python. To evaluate the module, we experimented with several complex signals and compared the results with equivalent procedures in MATLAB. The results showed that PyHOS is excellent module to analyze or study signals using their higher order statistics features.</p>

   244 <h4>Slides</h4>

   245 <p>To be uploaded</p>

   246 

   247 <h3 id="sec2.15">Shubham Chakraborty : Combination of Python and Phoenix-M as a low cost substitute for PLC</h3>

   248 <h4>Abstract</h4>

   249 <p>In this paper I will show how the combination of Python programming language and Phoenix-M interface (created by IUAC, New Delhi) can be used as a low cost substitute for PLC (Programmable Logic Controllers). In Home Automation this combination can be used for a variety of purposes. </p>

   250 <h4>Slides</h4>

   251 <p>To be uploaded</p>

   252 

   253 <h3 id="sec2.18">Jaidev Deshpande : A Python Toolbox for the Hilbert-Huang Transform</h3>

   254 <h4>Abstract</h4>

   255 <p>This paper introduces the PyHHT project. The aim of the project is to develop a Python toolbox for the Hilbert-Huang Transform (HHT) for nonlinear and nonstationary data analysis. The HHT is an algorithmic tool particularly useful for the time-frequency analysis of nonlinear and nonstationary data. It uses an iterative algorithm called Empirical Mode Decomposition (EMD) to break a signal down into so-called Intrinsic Mode Functions (IMFs). These IMFs are characterized by being piecewise narrowband and amplitude/frequency modulated, thus making them suitable for Hilbert spectral analysis.</p>

   256 

   257 <p>HHT is primarily an algorithmic tool and is relatively simple to implement. Therefore, even a crude implementation of the HHT is quite powerful for a given class of signals. Thus, one of the motivations for building a toolbox is to sustain the power of HHT across a variety of applications. This can be achieved by bringing together different heuristics associated with HHT on one programming platform (since HHT is largely algorithmic, there are a great many heuristics). It is thus the purpose of the toolbox to provide those implementations of the HHT that are popular in the literature. Along with making the application of HHT more dexterous and flexible, the toolbox will also be a good research tool as it provides a platform for comparison of different HHT implementations. It also supports comparison with conventional data analysis tools like Fourier and Wavelets.</p>

   258 

   259 <p>Most of the existing implementations of the HHT have functions that are drawn from different numerical computing packages, and hence are generalized, not optimized particularly for HHT. PyHHT includes functions that are optimized specifically for analysis with HHT. They are designed to operate at the least possible computational complexity, thus greatly increasing the performance of the analysis. The paper includes examples of such components of EMD which have been optimized to operate at the least possible expense – in comparison with conventional implementations. This optimization can be done in a number of ways. One example of optimizing conventional algorithms for PyHHT discussed in the paper is that of cubic spline interpolation. It is a major bottleneck in the EMD method (needs to be performed twice over the entire range of the signal in a single iteration). Most implementations for cubic splines involve the use of Gaussian elimination, whereas for PyHHT the much simpler tridiagonal system of equations will suffice. Furthermore, it can be improved using many different methods like using NumPy vectorization, the weave and blitz functions in SciPy, or by using the Python-C/C++ API. Thus, the portability of Python comes in handy when optimizing the algorithm on so many different levels. The paper also discusses the possibility of further improving such functions that are the biggest bottlenecks in the EMD algorithm.</p>

   260 

   261 <p>Other heuristics of the HHT include imposing different stopping conditions for the iterative EMD process. Once the IMFs of the original signal are obtained, their time-frequency-energy distributions can be obtained. PyHHT uses Matplotlib to visualize the distributions. The IMFs can further be used in computer vision and machine learning applications. PyHHT uses a number of statistical and information theoretic screening tools to detect the useful IMFs from among the decomposed data.</p>

   262 

   263 <p>Finally we perform HHT on a few test signals and compare it with the corresponding Fourier and Wavelet analyses. We comment on the advantages and limitations of the HHT method and discuss future improvements in the PyHHT project.</p>

   264 <h4>Slides</h4>

   265 <p>To be uploaded</p>

   266 

   267 <h3 id="sec2.19">Chetan Giridhar : Diving in to Byte-code optimization in Python</h3>

   268 <h4>Abstract</h4>

   269 <p>The rapid development cycle and performance makes Python as a preferred choice for HPC applications. Python is an interpreted language , running on Python Virtual Machine. Python VM then translates and executes byte-code on native platform. A Python application follows classical phases of compilation and byte-code generation is very similar to intermediate code. The byte-codes are platform neutral and enables Python applications with the power of portability. Performance of a Python application could factored on:

   270 <ul>

   271 	<li>Quality of generated byte-code</li> 

   272 	<li>Efficiency of Python VM</li>

   273 </ul>

   274 </p>

   275 <p>This talk discusses the internals of Python byte code, generation and potential optimization to improve run time performance of applications.</p>

   276 

   277 <h4>Agenda</h4>

   278 <ul>Python Virtual Machine: internals

   279 <li>Reverse engineering: Python byte code ("pyc" files)

   280     <ul><li>Exploring Python dis-assembler for pyc</li></ul></li>

   281 <li>Optimizing python byte code for time-efficiency

   282    <ul><li>Peephole optimization</li>

   283    <li>Tweaking the Python VM</li></ul></li>

   284 <li>Does PyPy helps?</li>

   285 </ul>

   286 <h4>Slides</h4>

   287 <p>To be uploaded</p>

   288 

   289 <h3 id="sec2.21">Kunal puri : GPU Accelerated Computational Fluid Dynamics with Python</h3>

   290 <h4>Abstract</h4>

   291 <p>Computational fluid dynamics (CFD) is a field dominated by code that

   292 is written in either Fortran or C/C++. An example is the well known

   293 open source CFD tool, OpenFOAM, that adopts C++ as the language of

   294 implementation.\newline A language like Python would be the ideal

   295 choice but for the performance penalty incurred. Indeed, equivalent

   296 Python code is at least an order of magnitude slower than C/C++ or

   297 Fortran.</p>

   298 

   299 <p>A common approach is to combine the best of both worlds wherein the

   300 computationally expensive routines that form a small core is written

   301 in a high performance language and the rest of the software framework

   302 is built around this core using Python. We adopt such a model to

   303 develop a code for the incompressible Navier Stokes equations using

   304 OpenCL as the underlying language and target graphics processing units

   305 (GPUs) as the execution device.

   306 </p>

   307 <p>

   308 The data-parallel nature of most CFD algorithms renders them ideal for

   309 execution on the highly parallel GPU architectures, which are designed

   310 to run tens of thousands of light-weight threads simultaneously. The

   311 result is that well designed GPU code can outperform it's CPU

   312 counterpart by an order of magnitude in terms of speed.

   313 </p>

   314 

   315 <p>

   316 We use the Python binding for OpenCL, PyOpenCL to run the code on the

   317 GPU. The result is an almost pure Python CFD code that is faster than

   318 it's CPU counterpart and is relatively easy to extend to more

   319 complicated problems.  We consider only two dimensional domains with

   320 structured Cartesian meshes for simplicity. We focus on GPU specific

   321 optimizations of the code like memory coalescing, cache utilization

   322 and memory transfer bandwidth which are essential for good

   323 performance. Our target platform is a system with four Tesla c2050

   324 Nvidia GPUs, each having 3 Gigabytes of global memory.\newline The

   325 code is validated against solutions from an equivalent CPU version and

   326 we present results for the transient incompressible flow past an

   327 impulsively started cylinder.

   328 </p>

   329 

   330 <h4>Slides</h4>

   331 <p>To be uploaded</p>

   332 

   333 <h3 id="sec2.22">Sachin Shinde : Reverse Engineering and python</h3>

   334 <h4>Abstract</h4>

   335 <p>The paper is about how we can use python for writing tools for reverse engineering and assembly code analysis it will talk about basic and modules those are available for doing reverse engineering. </p>

   336 <h4>Slides</h4>

   337 <p>To be uploaded</p>