Defense Notices

The DC to AC power Inverters (so-called Inverters) are widely used in industrial applications. The multilevel Inverters are becoming increasingly popular in industrial apparatus aimed at medium to high power conversion applications. In comparison to the conventional inverters, they feature superior characteristics such as lower total harmonic distortion (THD), higher efficiency, and lower switching voltage stress{Malinowski, 2010 #9}{Malinowski, 2010 #9}. Nevertheless, the superior characteristics come at the price of a more complex topology with an increased number of power electronic switches. As a general rule in a Inverter topology, as the number of power electronic switches increases, the chances of fault occurrence on of the switches increases, and thus the Inverter’s reliability decreases. Due to the extreme monetary ramifications of the interruption of operation in commercial and industrial applications, high reliability for power Inverters utilized in these sectors is critical. As a result, developing fault-tolerant operation schemes for multilevel Inverters has always been an interesting topic for researchers in related areas. The purpose of this proposal is to develop new control and fault-tolerant strategies for the multilevel power Inverter. In the event of a fault, the line voltages of the faulty Inverters are unbalanced and cannot be applied to the three phase loads. This fault-tolerant strategy generates balanced line voltages without bypassing any healthy and operative Inverter element, makes better use of the Inverter capacity and generates higher output voltage. This strategy exploits the advantages of the Selective Harmonic Elimination (SHE) method in conjunction with a slightly modified Fundamental Phase Shift Compensation technique to generate balanced voltages and manipulate voltage harmonics at the same time. However, due to the distinctive requirement of the strategy to manipulate both amplitude and angle of the harmonics, the conventional SHE technique is not the suitable basis for the proposed strategy. Therefore, in this project a modified Unbalanced SHE technique which can be used as the basis for the fault-tolerant strategy is developed. The proposed strategy is applicable to several classes of multilevel Inverters with three or more voltage levels.

Rule induction is one of the basic and important techniques of data mining. Inducing a rule set for symbolic data is simple and straightforward, but it becomes complex when the attributes are numerical. There are several algorithms available that do the task of rule induction for symbolic data. One such algorithm is PRISM which uses conditional probability for attribute-value selection to induce a rule. 
In the real world scenario, data may comprise of either symbolic or numerical attributes. It becomes difficult to induce a discriminant ruleset on the data with numerical attributes. This project provides an implementation of PRISM to handle numerical data. First, it takes as input, a dataset with numerical attributes and converts them into discrete values using the multiple scanning approach which identifies the cut-points for intervals using minimum conditional entropy. Once discretization completes, PRISM uses these discrete values to induce ruleset for each decision. Thus, this project helps to induce modular rulesets over a numerical dataset. 

The advancement in the field of embedded technology has resulted in its extensive use in almost all the modern electronic devices. Hence, unlike in the past, there is a very crucial need to develop system security tools for these devices. So far most of the research has been concentrated either on security for general computer systems or on static analysis of embedded systems. In this project, we develop tools that explore and monitor the run-time properties of programs/applications as well as the inter-process communication. We also present a case studies in which these tools are be used on a Gumstix (an embedded system) running Poky Linux system to monitor a particular program as well as print out a graph of all inter-process communication on the system.

Having stable dielectric properties extending to frequencies over 110 GHz, Liquid Crystal Polymer (LCP) materials are a new and promising substrate alternative for low-cost production of planar microwave circuits. This project focused on the design of several microwave filter structures using multiple layers for operation in the 2-18 GHz and 10-14 GHz bands. Circuits were simulated and optimized using EDA tools, obtaining good results over the bands of interest. The results show that it is feasible to fabricate these structures on dielectric substrates compatible with off-site manufacturing facilities. It is likewise shown that LCP technology can yield a 3-5x area reduction as compared to cavity-type filters, making them much easier to integrate in a planar circuit.

A cross-layer framework in the Software Defined Networking domain is pro- posed to study the resilience in OpenFlow-based multipath communication. A testbed has been built, using Brocade OpenFlow switches and Dell Poweredge servers. The framework is evaluated against regional challenges. By using differ- ent adjacency matrices, various topologies are built. The behavior of OpenFlow multipath-based communication is studied in case of a single path failure, splitting of traffic and also with multipath TCP enabled traffic. The behavior of different coupled congestion algorithms for MPTCP is also studied. A Web framework is presented to demonstrate the OpenFlow experiment by importing the network topologies and then executing and analyzing user defined regional attacks.

One of the main applications of rough set theory is rule induction. If the input data set contains inconsistencies, using rough set theory leads to inducing certain and possible rule sets. 
In this project, the concept of a maximal consistent block is applied to formulate a new approximation to a concept in the incomplete data set with a higher level of accuracy. This method does not require change in the size of the original incomplete data set. Two interpretations of missing attribute values are discussed: lost values and “do not care” conditions. The main objective is to compare maximal consistent blocks and characteristics sets in terms of cardinality of lower and upper approximations. Four incomplete data sets are used for experiments with varying levels of missing information. The next objective is to compare the decision rules induced and cases covered by both techniques. The experiments show that the both techniques provide the same lower approximations for all the datasets with “do not care” conditions. The best results are achieved by maximal consistent blocks for upper approximations for three datasets and there is a tie for the other data set. 

In data mining, rules are the most popular symbolic representation of knowledge. Classification of data and extracting of classification rules from the data is a difficult process, and there are different approaches to this process. One such approach is inductive learning. Inductive learning involves the process of learning from examples - where a system tries to induce a set of rules from a set of observed examples. Inductive learning methods produce distinct concept descriptions when given identical training data and there are questions about the quality of the different rule sets produced. This project work is aimed at comparing and analyzing the rule sets induced by different inductive learning systems. In this project, three different algorithms AQ, CART and C4.5 are used to induce rule sets from different data sets. An analysis is carried out in terms of the total number of rules and the total number of conditions present in the rules. These space complexity measures such as rule count and condition count show that AQ tends to produce more complex rule sets than C4.5 and CART. AQ algorithm has been implemented as a part of project and is used to induce the rule sets.

License plate Recognition method is a technique to detect license plate numbers from the vehicle images. This method has become an important part of our life with an increase in traffic and crime every now and then. It uses computer vision and pattern recognition technologies. Various techniques have been proposed so far and they work best within boundaries.This detection technique helps in finding the accurate location of license plates and extracting characters of the plates. The license plate detection is a three-stage process that includes license plate detection, character segmentation and character recognition. The first stage is the extraction of the number plate as it occupies a small portion of the whole image. After tracking down the license plate, localizing of the characters is done. The character recognition is the last stage of the detection and template matching is the most common method used for it. The results achieved by the above experiment were quite accurate which showed the robustness of the investigated algorithm.

The objective of this project is to leverage the open source Angular JS, Node JS, and Ionic Framework along with Cordova to develop “A Hybrid Mobile Application” for students and “A Responsive Web Application” for professor to conduct classroom centered “Dynamic Tests”. Dynamic Tests are the test taking environments where questions can be posted to students in the form of quizzes during a classroom setup. Guided by the specifications set by the professor, students answer and submit the quiz from their mobile devices. The results are generated instantaneously after the completion of the test session and can be viewed by the professor. The web application performs statistical analysis of the responses by considering the factors that the professor had set to measure the students’ performance. This advanced methodology of test taking is highly beneficial as it gives a clear picture to the professor the level of understanding of all the students in any chosen topic immediately after the test. It helps to improvise the teaching methods. This is also very advantageous to students since it helps them to come out of their hesitation to clarify their doubts as their marks become the measure of their understanding which is directly uncovered before the professor. This application overall improves the classroom experience to help students gain higher standards.

Most of the present classification algorithms require the input data with discretized attributes. If the input data contains numerical attributes, we need to convert such attributes into discrete values (intervals) before performing classification. Discretization algorithms for real value attributes are very important for applications such as artificial intelligence and machine learning. In this project we discuss an implementation of the ChiMerge algorithm for discretization of numerical attributes, a robust algorithm, which uses X2 statistic to determine interval similarity as it constructs intervals in a bottom-up merging process. ChiMerge provides a reliable summarization of numerical attributes and determines the number of intervals.