UNT Theses and Dissertations - 107 Matching Results

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An Accelerometer-based Gesture Recognition System for a Tactical Communications Application

Description: In modern society, computers are primarily interacted with via keyboards, touch screens, voice recognition, video analysis, and many others. For certain applications, these methods may be the most efficient interface. However, there are applications that we can conceive where a more natural interface could be convenient and connect humans and computers in a more intuitive and natural way. These applications are gesture recognition systems and range from the interpretation of sign language by a computer to virtual reality control. This Thesis proposes a gesture recognition system that primarily uses accelerometers to capture gestures from a tactical communications application. A segmentation algorithm is developed based on the accelerometer energy to segment these gestures from an input sequence. Using signal processing and machine learning techniques, the segments are reduced to mathematical features and classified with support vector machines. Experimental results show that the system achieves an overall gesture recognition accuracy of 98.9%. Additional methods, such as non-gesture recognition/suppression, are also proposed and tested.
Date: December 2015
Creator: Tidwell, Robert S., Jr.
Partner: UNT Libraries

Algorithm Optimizations in Genomic Analysis Using Entropic Dissection

Description: In recent years, the collection of genomic data has skyrocketed and databases of genomic data are growing at a faster rate than ever before. Although many computational methods have been developed to interpret these data, they tend to struggle to process the ever increasing file sizes that are being produced and fail to take advantage of the advances in multi-core processors by using parallel processing. In some instances, loss of accuracy has been a necessary trade off to allow faster computation of the data. This thesis discusses one such algorithm that has been developed and how changes were made to allow larger input file sizes and reduce the time required to achieve a result without sacrificing accuracy. An information entropy based algorithm was used as a basis to demonstrate these techniques. The algorithm dissects the distinctive patterns underlying genomic data efficiently requiring no a priori knowledge, and thus is applicable in a variety of biological research applications. This research describes how parallel processing and object-oriented programming techniques were used to process larger files in less time and achieve a more accurate result from the algorithm. Through object oriented techniques, the maximum allowable input file size was significantly increased from 200 mb to 2000 mb. Using parallel processing techniques allowed the program to finish processing data in less than half the time of the sequential version. The accuracy of the algorithm was improved by reducing data loss throughout the algorithm. Finally, adding user-friendly options enabled the program to use requests more effectively and further customize the logic used within the algorithm.
Date: August 2015
Creator: Danks, Jacob R.
Partner: UNT Libraries

Anchor Nodes Placement for Effective Passive Localization

Description: Wireless sensor networks are composed of sensor nodes, which can monitor an environment and observe events of interest. These networks are applied in various fields including but not limited to environmental, industrial and habitat monitoring. In many applications, the exact location of the sensor nodes is unknown after deployment. Localization is a process used to find sensor node's positional coordinates, which is vital information. The localization is generally assisted by anchor nodes that are also sensor nodes but with known locations. Anchor nodes generally are expensive and need to be optimally placed for effective localization. Passive localization is one of the localization techniques where the sensor nodes silently listen to the global events like thunder sounds, seismic waves, lighting, etc. According to previous studies, the ideal location to place anchor nodes was on the perimeter of the sensor network. This may not be the case in passive localization, since the function of anchor nodes here is different than the anchor nodes used in other localization systems. I do extensive studies on positioning anchor nodes for effective localization. Several simulations are run in dense and sparse networks for proper positioning of anchor nodes. I show that, for effective passive localization, the optimal placement of the anchor nodes is at the center of the network in such a way that no three anchor nodes share linearity. The more the non-linearity, the better the localization. The localization for our network design proves better when I place anchor nodes at right angles.
Date: August 2010
Creator: Pasupathy, Karthikeyan
Partner: UNT Libraries

An Approach Towards Self-Supervised Classification Using Cyc

Description: Due to the long duration required to perform manual knowledge entry by human knowledge engineers it is desirable to find methods to automatically acquire knowledge about the world by accessing online information. In this work I examine using the Cyc ontology to guide the creation of Naïve Bayes classifiers to provide knowledge about items described in Wikipedia articles. Given an initial set of Wikipedia articles the system uses the ontology to create positive and negative training sets for the classifiers in each category. The order in which classifiers are generated and used to test articles is also guided by the ontology. The research conducted shows that a system can be created that utilizes statistical text classification methods to extract information from an ad-hoc generated information source like Wikipedia for use in a formal semantic ontology like Cyc. Benefits and limitations of the system are discussed along with future work.
Date: December 2006
Creator: Coursey, Kino High
Partner: UNT Libraries

Automated Classification of Emotions Using Song Lyrics

Description: This thesis explores the classification of emotions in song lyrics, using automatic approaches applied to a novel corpus of 100 popular songs. I use crowd sourcing via Amazon Mechanical Turk to collect line-level emotions annotations for this collection of song lyrics. I then build classifiers that rely on textual features to automatically identify the presence of one or more of the following six Ekman emotions: anger, disgust, fear, joy, sadness and surprise. I compare different classification systems and evaluate the performance of the automatic systems against the manual annotations. I also introduce a system that uses data collected from the social network Twitter. I use the Twitter API to collect a large corpus of tweets manually labeled by their authors for one of the six emotions of interest. I then compare the classification of emotions obtained when training on data automatically collected from Twitter versus data obtained through crowd sourced annotations.
Date: December 2012
Creator: Schellenberg, Rajitha
Partner: UNT Libraries

Automated Defense Against Worm Propagation.

Description: Worms have caused significant destruction over the last few years. Network security elements such as firewalls, IDS, etc have been ineffective against worms. Some worms are so fast that a manual intervention is not possible. This brings in the need for a stronger security architecture which can automatically react to stop worm propagation. The method has to be signature independent so that it can stop new worms. In this thesis, an automated defense system (ADS) is developed to automate defense against worms and contain the worm to a level where manual intervention is possible. This is accomplished with a two level architecture with feedback at each level. The inner loop is based on control system theory and uses the properties of PID (proportional, integral and differential controller). The outer loop works at the network level and stops the worm to reach its spread saturation point. In our lab setup, we verified that with only inner loop active the worm was delayed, and with both loops active we were able to restrict the propagation to 10% of the targeted hosts. One concern for deployment of a worm containment mechanism was degradation of throughput for legitimate traffic. We found that with proper intelligent algorithm we can minimize the degradation to an acceptable level.
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Date: December 2005
Creator: Patwardhan, Sudeep
Partner: UNT Libraries

Automated GUI Tests Generation for Android Apps Using Q-learning

Description: Mobile applications are growing in popularity and pose new problems in the area of software testing. In particular, mobile applications heavily depend upon user interactions and a dynamically changing environment of system events. In this thesis, we focus on user-driven events and use Q-learning, a reinforcement machine learning algorithm, to generate tests for Android applications under test (AUT). We implement a framework that automates the generation of GUI test cases by using our Q-learning approach and compare it to a uniform random (UR) implementation. A novel feature of our approach is that we generate user-driven event sequences through the GUI, without the source code or the model of the AUT. Hence, considerable amount of cost and time are saved by avoiding the need for model generation for generating the tests. Our results show that the systematic path exploration used by Q-learning results in higher average code coverage in comparison to the uniform random approach.
Date: May 2017
Creator: Koppula, Sreedevi
Partner: UNT Libraries

Automated Syndromic Surveillance using Intelligent Mobile Agents

Description: Current syndromic surveillance systems utilize centralized databases that are neither scalable in storage space nor in computing power. Such systems are limited in the amount of syndromic data that may be collected and analyzed for the early detection of infectious disease outbreaks. However, with the increased prevalence of international travel, public health monitoring must extend beyond the borders of municipalities or states which will require the ability to store vasts amount of data and significant computing power for analyzing the data. Intelligent mobile agents may be used to create a distributed surveillance system that will utilize the hard drives and computer processing unit (CPU) power of the hosts on the agent network where the syndromic information is located. This thesis proposes the design of a mobile agent-based syndromic surveillance system and an agent decision model for outbreak detection. Simulation results indicate that mobile agents are capable of detecting an outbreak that occurs at all hosts the agent is monitoring. Further study of agent decision models is required to account for localized epidemics and variable agent movement rates.
Date: December 2007
Creator: Miller, Paul
Partner: UNT Libraries

Automatic Removal of Complex Shadows From Indoor Videos

Description: Shadows in indoor scenarios are usually characterized with multiple light sources that produce complex shadow patterns of a single object. Without removing shadow, the foreground object tends to be erroneously segmented. The inconsistent hue and intensity of shadows make automatic removal a challenging task. In this thesis, a dynamic thresholding and transfer learning-based method for removing shadows is proposed. The method suppresses light shadows with a dynamically computed threshold and removes dark shadows using an online learning strategy that is built upon a base classifier trained with manually annotated examples and refined with the automatically identified examples in the new videos. Experimental results demonstrate that despite variation of lighting conditions in videos our proposed method is able to adapt to the videos and remove shadows effectively. The sensitivity of shadow detection changes slightly with different confidence levels used in example selection for classifier retraining and high confidence level usually yields better performance with less retraining iterations.
Date: August 2015
Creator: Mohapatra, Deepankar
Partner: UNT Libraries

Automatic Tagging of Communication Data

Description: Globally distributed software teams are widespread throughout industry. But finding reliable methods that can properly assess a team's activities is a real challenge. Methods such as surveys and manual coding of activities are too time consuming and are often unreliable. Recent advances in information retrieval and linguistics, however, suggest that automated and/or semi-automated text classification algorithms could be an effective way of finding differences in the communication patterns among individuals and groups. Communication among group members is frequent and generates a significant amount of data. Thus having a web-based tool that can automatically analyze the communication patterns among global software teams could lead to a better understanding of group performance. The goal of this thesis, therefore, is to compare automatic and semi-automatic measures of communication and evaluate their effectiveness in classifying different types of group activities that occur within a global software development project. In order to achieve this goal, we developed a web-based component that can be used to help clean and classify communication activities. The component was then used to compare different automated text classification techniques on various group activities to determine their effectiveness in correctly classifying data from a global software development team project.
Date: August 2012
Creator: Hoyt, Matthew Ray
Partner: UNT Libraries

Brain Computer Interface (BCI) Applications: Privacy Threats and Countermeasures

Description: In recent years, brain computer interfaces (BCIs) have gained popularity in non-medical domains such as the gaming, entertainment, personal health, and marketing industries. A growing number of companies offer various inexpensive consumer grade BCIs and some of these companies have recently introduced the concept of BCI "App stores" in order to facilitate the expansion of BCI applications and provide software development kits (SDKs) for other developers to create new applications for their devices. The BCI applications access to users' unique brainwave signals, which consequently allows them to make inferences about users' thoughts and mental processes. Since there are no specific standards that govern the development of BCI applications, its users are at the risk of privacy breaches. In this work, we perform first comprehensive analysis of BCI App stores including software development kits (SDKs), application programming interfaces (APIs), and BCI applications w.r.t privacy issues. The goal is to understand the way brainwave signals are handled by BCI applications and what threats to the privacy of users exist. Our findings show that most applications have unrestricted access to users' brainwave signals and can easily extract private information about their users without them even noticing. We discuss potential privacy threats posed by current practices used in BCI App stores and then describe some countermeasures that could be used to mitigate the privacy threats. Also, develop a prototype which gives the BCI app users a choice to restrict their brain signal dynamically.
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Date: May 2017
Creator: Bhalotiya, Anuj Arun
Partner: UNT Libraries

A CAM-Based, High-Performance Classifier-Scheduler for a Video Network Processor.

Description: Classification and scheduling are key functionalities of a network processor. Network processors are equipped with application specific integrated circuits (ASIC), so that as IP (Internet Protocol) packets arrive, they can be processed directly without using the central processing unit. A new network processor is proposed called the video network processor (VNP) for real time broadcasting of video streams for IP television (IPTV). This thesis explores the challenge in designing a combined classification and scheduling module for a VNP. I propose and design the classifier-scheduler module which will classify and schedule data for VNP. The proposed module discriminates between IP packets and video packets. The video packets are further processed for digital rights management (DRM). IP packets which carry regular traffic will traverse without any modification. Basic architecture of VNP and architecture of classifier-scheduler module based on content addressable memory (CAM) and random access memory (RAM) has been proposed. The module has been designed and simulated in Xilinx 9.1i; is built in ISE simulator with a throughput of 1.79 Mbps and a maximum working frequency of 111.89 MHz at a power dissipation of 33.6mW. The code has been translated and mapped for Spartan and Virtex family of devices.
Date: May 2008
Creator: Tarigopula, Srivamsi
Partner: UNT Libraries

Capacity and Throughput Optimization in Multi-cell 3G WCDMA Networks

Description: User modeling enables in the computation of the traffic density in a cellular network, which can be used to optimize the placement of base stations and radio network controllers as well as to analyze the performance of resource management algorithms towards meeting the final goal: the calculation and maximization of network capacity and throughput for different data rate services. An analytical model is presented for approximating the user distributions in multi-cell third generation wideband code division multiple access (WCDMA) networks using 2-dimensional Gaussian distributions by determining the means and the standard deviations of the distributions for every cell. This model allows for the calculation of the inter-cell interference and the reverse-link capacity of the network. An analytical model for optimizing capacity in multi-cell WCDMA networks is presented. Capacity is optimized for different spreading factors and for perfect and imperfect power control. Numerical results show that the SIR threshold for the received signals is decreased by 0.5 to 1.5 dB due to the imperfect power control. The results also show that the determined parameters of the 2-dimensional Gaussian model match well with traditional methods for modeling user distribution. A call admission control algorithm is designed that maximizes the throughput in multi-cell WCDMA networks. Numerical results are presented for different spreading factors and for several mobility scenarios. Our methods of optimizing capacity and throughput are computationally efficient, accurate, and can be implemented in large WCDMA networks.
Date: December 2005
Creator: Nguyen, Son
Partner: UNT Libraries

Classifying Pairwise Object Interactions: A Trajectory Analytics Approach

Description: We have a huge amount of video data from extensively available surveillance cameras and increasingly growing technology to record the motion of a moving object in the form of trajectory data. With proliferation of location-enabled devices and ongoing growth in smartphone penetration as well as advancements in exploiting image processing techniques, tracking moving objects is more flawlessly achievable. In this work, we explore some domain-independent qualitative and quantitative features in raw trajectory (spatio-temporal) data in videos captured by a fixed single wide-angle view camera sensor in outdoor areas. We study the efficacy of those features in classifying four basic high level actions by employing two supervised learning algorithms and show how each of the features affect the learning algorithms’ overall accuracy as a single factor or confounded with others.
Date: May 2015
Creator: Janmohammadi, Siamak
Partner: UNT Libraries

CLUE: A Cluster Evaluation Tool

Description: Modern high performance computing is dependent on parallel processing systems. Most current benchmarks reveal only the high level computational throughput metrics, which may be sufficient for single processor systems, but can lead to a misrepresentation of true system capability for parallel systems. A new benchmark is therefore proposed. CLUE (Cluster Evaluator) uses a cellular automata algorithm to evaluate the scalability of parallel processing machines. The benchmark also uses algorithmic variations to evaluate individual system components' impact on the overall serial fraction and efficiency. CLUE is not a replacement for other performance-centric benchmarks, but rather shows the scalability of a system and provides metrics to reveal where one can improve overall performance. CLUE is a new benchmark which demonstrates a better comparison among different parallel systems than existing benchmarks and can diagnose where a particular parallel system can be optimized.
Date: December 2006
Creator: Parker, Brandon S.
Partner: UNT Libraries

CMOS Active Pixel Sensors for Digital Cameras: Current State-of-the-Art

Description: Image sensors play a vital role in many image sensing and capture applications. Among the various types of image sensors, complementary metal oxide semiconductor (CMOS) based active pixel sensors (APS), which are characterized by reduced pixel size, give fast readouts and reduced noise. APS are used in many applications such as mobile cameras, digital cameras, Webcams, and many consumer, commercial and scientific applications. With these developments and applications, CMOS APS designs are challenging the old and mature technology of charged couple device (CCD) sensors. With the continuous improvements of APS architecture, pixel designs, along with the development of nanometer CMOS fabrications technologies, APS are optimized for optical sensing. In addition, APS offers very low-power and low-voltage operations and is suitable for monolithic integration, thus allowing manufacturers to integrate more functionality on the array and building low-cost camera-on-a-chip. In this thesis, I explore the current state-of-the-art of CMOS APS by examining various types of APS. I show design and simulation results of one of the most commonly used APS in consumer applications, i.e. photodiode based APS. We also present an approach for technology scaling of the devices in photodiode APS to present CMOS technologies. Finally, I present the most modern CMOS APS technologies by reviewing different design models. The design of the photodiode APS is implemented using commercial CAD tools.
Date: May 2007
Creator: Palakodety, Atmaram
Partner: UNT Libraries

Comparative Study of RSS-Based Collaborative Localization Methods in Wireless Sensor Networks

Description: In this thesis two collaborative localization techniques are studied: multidimensional scaling (MDS) and maximum likelihood estimator (MLE). A synthesis of a new location estimation method through a serial integration of these two techniques, such that an estimate is first obtained using MDS and then MLE is employed to fine-tune the MDS solution, was the subject of this research using various simulation and experimental studies. In the simulations, important issues including the effects of sensor node density, reference node density and different deployment strategies of reference nodes were addressed. In the experimental study, the path loss model of indoor environments is developed by determining the environment-specific parameters from the experimental measurement data. Then, the empirical path loss model is employed in the analysis and simulation study of the performance of collaborative localization techniques.
Date: December 2006
Creator: Koneru, Avanthi
Partner: UNT Libraries

Comparison and Evaluation of Existing Analog Circuit Simulator using Sigma-Delta Modulator

Description: In the world of VLSI (very large scale integration) technology, there are many different types of circuit simulators that are used to design and predict the circuit behavior before actual fabrication of the circuit. In this thesis, I compared and evaluated existing circuit simulators by considering standard benchmark circuits. The circuit simulators which I evaluated and explored are Ngspice, Tclspice, Winspice (open source) and Spectre® (commercial). I also tested standard benchmarks using these circuit simulators and compared their outputs. The simulators are evaluated using design metrics in order to quantify their performance and identify efficient circuit simulators. In addition, I designed a sigma-delta modulator and its individual components using the analog behavioral language Verilog-A. Initially, I performed simulations of individual components of the sigma-delta modulator and later of the whole system. Finally, CMOS (complementary metal-oxide semiconductor) transistor-level circuits were designed for the differential amplifier, operational amplifier and comparator of the modulator.
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Date: December 2006
Creator: Ale, Anil Kumar
Partner: UNT Libraries

Computational Epidemiology - Analyzing Exposure Risk: A Deterministic, Agent-Based Approach

Description: Many infectious diseases are spread through interactions between susceptible and infectious individuals. Keeping track of where each exposure to the disease took place, when it took place, and which individuals were involved in the exposure can give public health officials important information that they may use to formulate their interventions. Further, knowing which individuals in the population are at the highest risk of becoming infected with the disease may prove to be a useful tool for public health officials trying to curtail the spread of the disease. Epidemiological models are needed to allow epidemiologists to study the population dynamics of transmission of infectious agents and the potential impact of infectious disease control programs. While many agent-based computational epidemiological models exist in the literature, they focus on the spread of disease rather than exposure risk. These models are designed to simulate very large populations, representing individuals as agents, and using random experiments and probabilities in an attempt to more realistically guide the course of the modeled disease outbreak. The work presented in this thesis focuses on tracking exposure risk to chickenpox in an elementary school setting. This setting is chosen due to the high level of detailed information realistically available to school administrators regarding individuals' schedules and movements. Using an agent-based approach, contacts between individuals are tracked and analyzed with respect to both individuals and locations. The results are then analyzed using a combination of tools from computer science and geographic information science.
Date: August 2009
Creator: O'Neill II, Martin Joseph
Partner: UNT Libraries

Cross Language Information Retrieval for Languages with Scarce Resources

Description: Our generation has experienced one of the most dramatic changes in how society communicates. Today, we have online information on almost any imaginable topic. However, most of this information is available in only a few dozen languages. In this thesis, I explore the use of parallel texts to enable cross-language information retrieval (CLIR) for languages with scarce resources. To build the parallel text I use the Bible. I evaluate different variables and their impact on the resulting CLIR system, specifically: (1) the CLIR results when using different amounts of parallel text; (2) the role of paraphrasing on the quality of the CLIR output; (3) the impact on accuracy when translating the query versus translating the collection of documents; and finally (4) how the results are affected by the use of different dialects. The results show that all these variables have a direct impact on the quality of the CLIR system.
Date: May 2009
Creator: Loza, Christian
Partner: UNT Libraries

Cuff-less Blood Pressure Measurement Using a Smart Phone

Description: Blood pressure is vital sign information that physicians often need as preliminary data for immediate intervention during emergency situations or for regular monitoring of people with cardiovascular diseases. Despite the availability of portable blood pressure meters in the market, they are not regularly carried by people, creating a need for an ultra-portable measurement platform or device that can be easily carried and used at all times. One such device is the smartphone which, according to comScore survey is used by 26.2% of the US adult population. the mass production of these phones with built-in sensors and high computation power has created numerous possibilities for application development in different domains including biomedical. Motivated by this capability and their extensive usage, this thesis focuses on developing a blood pressure measurement platform on smartphones. Specifically, I developed a blood pressure measurement system on a smart phone using the built-in camera and a customized external microphone. the system consists of first obtaining heart beats using the microphone and finger pulse with the camera, and finally calculating the blood pressure using the recorded data. I developed techniques for finding the best location for obtaining the data, making the system usable by all categories of people. the proposed system resulted in accuracies between 90-100%, when compared to traditional blood pressure meters. the second part of this thesis presents a new system for remote heart beat monitoring using the smart phone. with the proposed system, heart beats can be transferred live by patients and monitored by physicians remotely for diagnosis. the proposed blood pressure measurement and remote monitoring systems will be able to facilitate information acquisition and decision making by the 9-1-1 operators.
Date: May 2012
Creator: Jonnada, Srikanth
Partner: UNT Libraries

Ddos Defense Against Botnets in the Mobile Cloud

Description: Mobile phone advancements and ubiquitous internet connectivity are resulting in ever expanding possibilities in the application of smart phones. Users of mobile phones are now capable of hosting server applications from their personal devices. Whether providing services individually or in an ad hoc network setting the devices are currently not configured for defending against distributed denial of service (DDoS) attacks. These attacks, often launched from a botnet, have existed in the space of personal computing for decades but recently have begun showing up on mobile devices. Research is done first into the required steps to develop a potential botnet on the Android platform. This includes testing for the amount of malicious traffic an Android phone would be capable of generating for a DDoS attack. On the other end of the spectrum is the need of mobile devices running networked applications to develop security against DDoS attacks. For this mobile, phones are setup, with web servers running Apache to simulate users running internet connected applications for either local ad hoc networks or serving to the internet. Testing is done for the viability of using commonly available modules developed for Apache and intended for servers as well as finding baseline capabilities of mobiles to handle higher traffic volumes. Given the unique challenge of the limited resources a mobile phone can dedicate to Apache when compared to a dedicated hosting server a new method was needed. A proposed defense algorithm is developed for mitigating DDoS attacks against the mobile server that takes into account the limited resources available on the mobile device. The algorithm is tested against TCP socket flooding for effectiveness and shown to perform better than the common Apache module installations on a mobile device.
Date: May 2014
Creator: Jensen, David
Partner: UNT Libraries

Design and Analysis of Novel Verifiable Voting Schemes

Description: Free and fair elections are the basis for democracy, but conducting elections is not an easy task. Different groups of people are trying to influence the outcome of the election in their favor using the range of methods, from campaigning for a particular candidate to well-financed lobbying. Often the stakes are too high, and the methods are illegal. Two main properties of any voting scheme are the privacy of a voter’s choice and the integrity of the tally. Unfortunately, they are mutually exclusive. Integrity requires making elections transparent and auditable, but at the same time, we must preserve a voter’s privacy. It is always a trade-off between these two requirements. Current voting schemes favor privacy over auditability, and thus, they are vulnerable to voting fraud. I propose two novel voting systems that can achieve both privacy and verifiability. The first protocol is based on cryptographical primitives to ensure the integrity of the final tally and privacy of the voter. The second protocol is a simple paper-based voting scheme that achieves almost the same level of security without usage of cryptography.
Date: December 2013
Creator: Yestekov, Yernat
Partner: UNT Libraries

Design and Optimization of Components in a 45nm CMOS Phase Locked Loop

Description: A novel scheme of optimizing the individual components of a phase locked loop (PLL) which is used for stable clock generation and synchronization of signals is considered in this work. Verilog-A is used for the high level system design of the main components of the PLL, followed by the individual component wise optimization. The design of experiments (DOE) approach to optimize the analog, 45nm voltage controlled oscillator (VCO) is presented. Also a mixed signal analysis using the analog and digital Verilog behavior of components is studied. Overall a high level system design of a PLL, a systematic optimization of each of its components, and an analog and mixed signal behavioral design approach have been implemented using cadence custom IC design tools.
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Date: December 2006
Creator: Sarivisetti, Gayathri
Partner: UNT Libraries