Articles — Project / Lab Report

This is a project done as a part of academic curriculum

We explore ways of allowing for the offloading of computationally rigorous tasks from devices with slow logical processors onto a network of anonymous peer-processors. Recent advances in secret sharing schemes, decentralized consensus mechanisms, and multiparty computation (MPC) protocols are combined to create a P2P MPC market. Unlike other computational "clouds", ours is able to generically compute any arithmetic circuit, providing a viable platform for processing on the semantic web. Finally, we show that such a system works in a hostile environment, that it scales well, and that it adapts very easily to any future advances in the complexity theoretic cryptography used. Specifically, we show that the feasibility of our system can only improve, and is historically guaranteed to do so.

With rapid electrification of transportation , it is becoming increasingly important to have a comprehensive understanding of criteria used in motor selection.For that design and comparative evaluation of interior permanent magnet synchronous motor ,induction motor and switched reluctance motor are needed.A fast finite element analysis (FEA) modeling approach is addressed for induction motor design.Optimal turn off and turn on angles with current chopping control and angular position control are found for Switched Reluctance Motors (SRM).Noise Vibration and Harshness (NVH) analysis are done using workbench ANSYS analysis.Simulation and analytical results show that each motor topology demonstrates its own unique characteristics for Electric Vehicle / Hybrid Electric Vehicle.Each motor's highest efficiency is located at different torque-speed regions for the criteria defined.Stator geometry ,pole/slot combination and control strategy differentiate Noise Vibration and Harshness performance.

Benford law states that the occurrence of digits from 0-9 in a large set of data is not uniformly distributed but instead in a decreasing logarithmic distribution with 1 occurring at most. Almost all set of data follows this trend however this law is widely used as a base for various fraud detection and forensic accounting. Benford’s law is an observation that leading digits in data derived from measurements doesn’t follow uniform distribution. Different financial statements such as cash flows, income statement and balance sheet of the 20 tech companies of the Fortune 500 are analyzed in this project. Cash flow is the net amount of cash and cash-equivalents moving into and out of a business. Income statement is a financial statement that measures a company's financial performance over a specific accounting period. Balance sheet is a financial statement that summarizes a company's assets, liabilities and shareholders’ equity at a specific point in time. All of these data of financial statements are extracted from Morning Star database and are analyzed by Python program written by me.I also wrote the Python program to calculate Benford's second digit and third digit probability using the formula. I would like to thank Prof. Erin Wagner and Dr. Courtney Taylor for helping in this research project.

The Hudson School, Hoboken, New Jersey. Chemistry, May 6th, 2015

The purpose of this lab is to determine the spring constant of a given spring. This spring constant is given by the relation between the force exerted on the spring and the distance the spring is either stretched or compressed. This relationship is given through Hooke’s law which we are going to get a better understanding of throughout this lab.

Gene regulatory networks have an important role to study the behaviour of genes. By analysing these Gene Regulatory Networks we can get the detailed information i.e. the occurrence of diseases by changing behaviour of GRNs. Many different approaches are used (i.e. qualitative modelling and hybrid modelling) and various tools (i.e. GenoTech, GINsim) have been developed to model and simulate gene regulatory networks. GenoTech allows the user to specify a GRN on Graphical User Interface (GUI) according to the asynchronous multivalued logical functions of René Thomas, and to simulate and/or analyse its qualitative dynamical behaviour. René Thomas discrete modelling of gene regulatory network (GRN) is a well known approach to study the dynamics of genes. It deals with some parameters which reflect the possible targets of trajectories. Those parameters are priory unknown. These unknown parameters are fetched using another model checking tool SMBioNet. SMBioNet produces all the possible parameters satisfying the given Computational Logic Tree (CTL) formula as input. This approach involving logical parameters and conditions also known as qualitative modelling of GRN. However, this approach neglects the time delays for a gene to pass from one level of expression to another one i.e. inhibition to activation and vice versa. To find out these time delays, another modelling tool HyTech is used to perform hybrid modelling of GRN. We have developed a Java based tool called GenNet http://asanian.com/gennet to facilitate the model checking user by providing a unique GUI layout for both qualitative and quantitative modelling of GRNs. As we discussed, three separate modelling tools are used for complete modelling and analysis of a GRN. This process is much lengthy and takes too much time. GenNet assists the modelling users by providing some extra features i.e. CTL editor, parameters filtering and input/output files management. GenNet takes a GRN network as input and does all the rest of computations i.e. CTL verification, K-parameters generation, parameter implication to GRN, state graph, hybrid modelling and parameter filtration automatically. GenNet serves the user by computing the results within seconds that were taking hours and days of manual computation

目的： 熟悉分析實驗數據的方法，測量並分析誤差。 學習有效數的處理。 了解螺旋測微器(Micrometer)的操作方式並用以測定微小物體的厚度或直徑等。 了解游標尺(Vernier caliper)的操作方式並用以測量塑膠管的深度及內、外徑。

This report gives an overview of the various machine learning algorithms implemented to detect certain comments that may appear insulting to another participant on a social networking platform. Feature selection was performed using n-grams, and the WEKA machine learning toolkit was used to build supervised learning clasifiers, that provided an accuracy of 82% on the test dataset. The dataset was obtained from the popular data science competition portal, Kaggle.