Work in progress: a quantitative study of effectiveness in group learning

It is generally assumed that group studies are more effective for students than individual studies. The objective of this work in progress is to quantitatively evaluate and analyze the effect of collaborative studies on individual student’s performance. This effort would help the student stimulate interest in group learning and collaboration along with exposing them towards multiple problem solving approaches while working individually or in groups. This way the students are challenged to use their existing knowledge and approach, and augment it further with the knowledge and approach provided by group partners. While there are several efforts that focus on developing new group learning techniques, we intend to study the efficacy of previously proposed techniques under various test settings for EE and CS courses without significantly diverting from the course framework

Analyzing stock market movements using Twitter sentiment analysis

In this paper we investigate the complex relationship between tweet board literature (like bullishness, volume, agreement etc) with the financial market instruments (like volatility, trading volume and stock prices). We have analyzed sentiments for more than 4 million tweets between June 2010 to July 2011 for DJIA, NASDAQ-100 and 13 other big cap technological stocks. Our results show high correlation (up to 0.88 for returns) between stock prices and twitter sentiments. Further, using Granger's Causality Analysis, we have validated that the movement of stock prices and indices are greatly affected in the short term by Twitter discussions. Finally, we have implemented Expert Model Mining System (EMMS) to demonstrate that our forecasted returns give a high value of Rsquare (0.952) with low Maximum Absolute Percentage Error (MaxAPE) of 1.76% for Dow Jones Industrial Average (DJIA)

Tagged repair techniques for defect tolerance in hybrid nano/CMOS architecture

We propose two new repair techniques for hybrid nano/CMOS computing architecture with lookup table based Boolean logic. Our proposed techniques use tagging mechanism to provide high level of defect tolerance and we present theoretical equations to predict the repair capability including an estimate of the repair cost. The repair techniques are efficient in utilization of spare units and capable of targeting upto 20% defect rates, which is higher than recently reported repair techniques

Modeling movements in oil, gold, forex and market indices using search volume index and Twitter sentiments

Study of the forecasting models using large scale microblog discussions and the search behavior data can provide a good insight for better understanding the market movements. In this work we collected a dataset of 2 million tweets and search volume index (SVI from Google) for a period of June 2010 to September 2011. We model a set of comprehensive causative relationships over this dataset for various market securities like equity (Dow Jones Industrial Average-DJIA and NASDAQ-100), commodity markets (oil and gold) and Euro Forex rates. We also investigate the lagged and statistically causative relations of Twitter sentiments developed during active trading days and market inactive days in combination with the search behavior of public before any change in the prices/ indices. Our results show extent of lagged significance with high correlation value upto 0.82 between search volumes and gold price in USD. We find weekly accuracy in direction (up and down prediction) uptil 94.3% for DJIA and 90% for NASDAQ-100 with significant reduction in mean average percentage error for all the forecasting models

Bayesian modeling of quantum-dot-cellular-automata circuits

The goal of this work is to develop a fast, Bayesian Probabilistic Computing model [1],[2] that exploits the induced causality of clocking to arrive at a model with the minimum possible complexity. The probabilities directly model the quantum-mechanical steady-state probabilities (density matrix) or equivalently, the cell polarizations. The attractive feature of this model is that not only does it model the strong dependencies among the cells, but it can be used to compute the steady state cell polarizations, without ..

USE OF INTELLIGENT PUMPING SYSTEM TO DEVELOP RESERVOIR CHARACTERIZATION

With the advancement in drilling and production technologies, deeper and more challenging formations are drilled every day. A pivotal part of sustaining this advancement is to permanently monitor the reservoir. While PDG (Permanent Downhole Gauges) have been in use since 1960s, handling and interpreting tons of rows of data has always been cumbersome. Moreover, the gauges have to be dependable enough to sustain bottom hole conditions for their lifetime (Schlumberger, 2015). Focusing attention to artificial lift applications, downhole P/T data plays a huge role in assessing if the bottom hole conditions are ideal in bringing the fluid to the surface, even if the reservoir has a high deliverability. Interestingly, completion design for submersible pumps nowadays includes downhole sensors for pressure/temperature reading, which opened doors to multiple utilization ideas and innovations. Baker Hughes in 2014 introduced a virtual flow meter concept that recorded pump parameters to optimize the working of an ESP up to 90% accuracy. Standard techniques to monitor flow are not only expensive to operate but also not readily available at all times. The following thesis takes inspiration from their approach to go one step further and gain more knowledge about the reservoir itself using the pump parameters. Through the experimental work, this thesis aims to understand how the reservoir behaves during production and shut in phases to estimate the inflow performance of the well. Estimating accurate reservoir pressures after shut in periods also helps in monitoring the productivity index of the reservoir in study

Roller element bearing acoustic fault detection using smartphone and consumer microphones

Roller element bearings are a common component and crucial to most rotating machinery; their failure makes up around half of the total machine failures, each with the potential to cause extreme damage, injury and downtime. Fault detection through condition monitoring is of significant importance. This paper demonstrates bearing fault detection using widely accessible consumer audio tools. Audio measurements from a smartphone and a standard USB microphone, and vibration measurements from an accelerometer are collected during tests on an electrical induction machine exhibiting a variety of mechanical bearing anomalies. A peak finding method along with use of trained Support Vector Machines (SVMs) classify the faults. It is shown that the classification rate from both the smartphone and the USB microphone was 95 and 100%, respectively, with the direct physically detected vibration results achieving only 75% classification accuracy. This work opens up the opportunity of using readily affordable and accessible acoustic diagnosis and prognosis for early mechanical anomalies on rotating machines

Low Cost FPGA Implementation of a SPI over High Speed Optical SerDes

Serial Peripheral Interface (SPI) is a commonly used communication protocol that allows serial data transfer between a master and a slave device over a short distance. However, if we require just SPI over long distances currently there is no effective low-cost solution. A SerDes provides a solution to this shortcoming by sending parallel data as a serial transmission and converting it back at the receiver end. However, most of the current SerDes implementations are expensive to implement and cater to very high-speed applications, which is not the case in SPI. In this paper, we present a simple to implement and low cost SerDes solution for sending and receiving multiple SPI and GPIO lines. Our proposed solution makes use of a low cost CLPD / FPGA and is applicable for low data rate applications such as SPI. This paper investigates the simplest solution to the problem, whilst maintaining a reliable single wire / optical link. For testing, we have implemented three novel encoding schemes that all provided good results, each measured by performance against resource usage. One of these encoding schemes has shown a drop-out rate as low as 0.001% over a 24-hour period. Our proposed solution when used in conjunction with an optical fibre medium could potentially allow SPI transmission over several kilometres of distance