Defense Notices

Identifying missing bed in radar data is very important in sea level changes. Increase in sea level is a problem of global importance because of its impact on infrastructure. Ice sheets in the Greenland and Antarctic are melting and increasing their contribution to sea level change over the last decade. Measuring ice sheets thickness is required to estimate sea level rise. We need to use several algorithms, pre-defined functions to extract the weak bed echoes, but we don’t have a tool in Matlab which contains some important algorithms like ImageJ. We can’t process all the data in ImageJ as Matlab produces better results compared to ImageJ as some of the functions like window and symmetric selection around center in FFT domain are not implemented in ImageJ. 
In this project, we will investigate the application of some image processing techniques using a GUI developed for analyzing ice sounding radargrams. One key advantage of the tool is that the image processing techniques are applied in a single GUI instead of doing separately. We apply these techniques on the data which came after applying extensive signal processing techniques. After performing these techniques, we compare the processed data with the original data. 

Mobility models generate the mobility patterns of the nodes in a given system. Mobility models help us to analyze and study the characteristic of new and existing systems. Various mobility models implemented in network simulation tools like ns-3 does not model the patterns of human mobility. The main idea of this project is to implement the truncated Lévy walk mobility model in ns-3. The model has two variations, in the first variation, the flight length and pause time of the nodes are determined from the truncated Pareto distribution and in the second variation, Lévy distribution models the flight length and pause time distributions and the values are obtained by Lévy α-stable random number generator. The mobility patterns of the nodes are generated and analyzed for the model by changing various model attributes. Further studies can be done to understand the behavior of these models for different ad hoc networking protocols. 

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. 

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.