Mathematical optimization is applicable to nearly every scientific discipline. This thesis specifically focuses on optimization applications to digital communications and signal processing. Within the digital communications framework, the channel encoder attempts to encode a message from a source (the sender) in such a way that the channel decoder can utilize the encoding to correct errors in the message caused by the transmission over the channel. Low-density parity-check (LDPC) codes are an especially popular code for this purpose. Following the channel encoder in the digital communications framework, the modulator converts the encoded message bits to a physical waveform, which is sent over the channel and converted back to bits at the demodulator. The modulator and demodulator present special challenges for what is known as the two-antenna problem. The main results of this work are two algorithms related to the development of optimization methods for LDPC codes and the two-antenna problem. Current methods for optimization of LDPC codes analyze the degree distribution pair asymptotically as block length approaches infinity. This effectively ignores the discrete nature of the space of valid degree distribution pairs for LDPC codes of finite block length. While large codes are likely to conform reasonably well to the infinite block length analysis, shorter codes have no such guarantee. Chapter 2 more thoroughly introduces LDPC codes, and Chapter 3 presents and analyzes an algorithm for completely enumerating the space of all valid degree distribution pairs for a given block length, code rate, maximum variable node degree, and maximum check node degree. This algorithm is then demonstrated on an example LDPC code of finite block length. Finally, we discuss how the result of this algorithm can be utilized by discrete optimization routines to form novel methods for the optimization of small block length LDPC codes. In order to solve the two-antenna problem, which is introduced in greater detail in Chapter 2, it is necessary to obtain reliable estimates of the timing offset and channel gains caused by the transmission of the signal through the channel. The timing offset estimator can be formulated as an optimization problem, and an optimization method used to solve it was previously developed. However, this optimization method does not utilize gradient information, and as a result is inefficient. Chapter 4 presents and analyzes an improved gradient-based optimization method that solves the two-antenna problem much more efficiently.

Members of society today embrace multiple communication media for various purposes and intents. Text messaging has been identified as the medium of choice for continual relationship maintenance and text messaging from mobile devices overshadows all other media forms for the support of social connections. Text messaging is changing everything from how operators market their plans to how advertisers and service providers reach consumers. But just as technology usage of social media and internet access are different across generational boundaries, text messaging usage and habits may also be different for various generational groups. The majority of peer-reviewed research regarding text messaging usage habits has focused on adolescent and young adult users with less attention on text messaging usage habits by older adults; there is a scarcity of peer-reviewed research examining cross-generation text messaging habits and texting usage patterns. The primary goal of this study was to assess the similarities and differences in text messaging usage habits, purposes, and support of social connections differentiated by five of the commonly designated generational groups in America; the Post-War Silent Generation, Baby Boomers, Generation X, Millennials, and Generation Z. A mixed methods study provided data on the text messaging usage habits of members of the generational groups using a pool of adult college students, members of the researcher’s LinkedIn network, and data from a survey service to determine to what extent differences and similarities exist between users’ text messaging usage habits within each generational group. Results indicated generational group membership has a significant effect on a participant’s messaging volume (UV), text messaging partner choices (TMPC), and text messaging social habits (SH), regardless of gender, education level, or employment status. The older the generational group, the more likely they are to prefer talking over texting and to have issues with the device interface. The Post-War Silent generation texts their spouses the least of any group, while Generation X texts their spouses the most, and all generational groups with the exception of Generation Z would limit texting while driving. Generational characteristics seem to have some influence over texting behaviors. Contributions to the existing body of knowledge in the human computer interaction field include an investigation of factors that contribute to each generational group’s willingness to embrace or reject the text messaging medium, and an investigation into the into how each generation views and exploits the texting medium.

It is known that a matched filter is optimal for a signal corrupted by Gaussian noise. In a wireless environment, the received signal may be corrupted by Gaussian noise and a variety of other channel disturbances: cochannel interference, multiple access interference, large and small-scale fading, etc. Adaptive filtering is the usual approach to mitigating this channel distortion. Existing adaptive filtering techniques usually attempt to minimize the mean square error (MSE) of some aspect of the received signal, with respect to the desired aspect of that signal. Adaptive minimization of MSE does not always guarantee minimization of bit error rate (BER). The main focus of this research involves estimation of the probability density function (PDF) of the received signal; this PDF estimate is used to adaptively determine a solution that minimizes BER. To this end, a new adaptive procedure called the Minimum BER Estimation (MBE) algorithm has been developed. MBE shows improvement over the Least Mean Squares (LMS) algorithm for most simulations involving interference and in some multipath situations. Furthermore, the new algorithm is more robust than LMS to changes in algorithm parameters such as stepsize and window width. / Master of Science

Most publications discussing missile interception systems assume a zero computer response time. This thesis studies the impact of computer response time on single-missile single-target and multiple- missile multiple-target systems. Simulation results for the final miss distance as the computer response time increases are presented. A simple online cooperative adjustment model for multiple-missile multiple-target system is created for the purpose of studying the computer delay effect.

Capabilities-based networks present a fundamental shift in the security design of network architectures. Instead of permitting the transmission of packets from any source to any destination, routers deny forwarding by default. For a successful transmission, packets need to positively identify themselves and their permissions to the router. A major challenge for a high performance implementation of such a network is an efficient design of the credentials that are carried in the packet and the verification procedure on the router. A network protocol that implements data path credentials based on Bloom filters is presented in this thesis. Our prototype implementation shows that there is some connection setup cost associated with this type of secure communication. However, once a connection is established, the throughput performance of a capabilities-based connection is similar to that of conventional TCP.

Network virtualization has become a primary enabler to solve the internet ossi- fication problem. It allows to run multiple architectures or protocols on a shared physical infrastructure. One of the important aspects of network virtualization is to have a virtual network (VN) mapping technique which uses the substrate resources efficiently. Currently, there exists very few VN mapping techniques and there is no common evaluation strategy which can test these algorithms effectively. In this thesis, we advocate the need for such a tool and develop it by considering a wide spectrum of parameters and simulation scenarios. We also provide various performance metrics and do a comparison study of the existing algorithms. Based on the comparative study, we point out the positives and negatives of the existing mapping algorithms and propose a new LP formulation based on Hub location approach that efficiently allocates substrate resources to the virtual network requests. Our results show that our algorithm does better in terms of number of successful network mappings and average time to map while balancing load on the network.

Emerging Ultra Wideband (UWB) Orthogonal Frequency Division Multiplexing (OFDM) systems hold the promise of delivering wireless data at high speeds, exceeding hundreds of megabits per second over typical distances of 10 meters or less. The purpose of this Thesis is to estimate the timing accuracies required with such systems in order to achieve Bit Error Rates (BER) of the order of magnitude of 10-12 and thereby avoid overloading the correction of irreducible errors due to misaligned timing errors to a small absolute number of bits in error in real-time relative to a data rate of hundreds of megabits per second. Our research approach involves managing bit error rates through identifying maximum timing synchronization errors. Thus, it became our research goal to determine the timing accuracies required to avoid operation of communication systems within the asymptotic region of BER flaring at low BERs in the resultant BER curves. We propose pushing physical layer bit error rates to below 10-12 before using forward error correction (FEC) codes. This way, the maximum reserve is maintained for the FEC hardware to correct for burst as well as recurring bit errors due to corrupt bits caused by other than timing synchronization errors.

As wireless protocols become easier to implement, more products come with wireless connectivity. This latest push for wireless connectivity has left a gap in the development of the security and the reliability of some protocols. These wireless protocols can be used in the growing field of IoT where wireless sensors are used to share information throughout a network. IoT is being implemented in homes, agriculture, manufactory, and in the medical field. Disrupting a wireless device from proper communication could potentially result in production loss, security issues, and bodily harm. The 802.15.4/ZigBee protocol is used in low power, low data rate, and low cost wireless applications such as medical devices and home automation devices. This protocol uses CSMA-CA (Carrier Sense Multiple Access w/ Collision Avoidance) which allows for multiple ZigBee devices to transmit simultaneousness and allows for wireless coexistence with the existing protocols at the same frequency band. The CSMA-CA MAC layer seems to introduce an unintentional gap in the reliability of the protocol. By creating a 16-tone signal with center frequencies located in the center of the multiple access channels, all channels will appear to be in use and the ZigBee device will be unable to transmit data. The jamming device will be created using the following hardware implementation. An FPGA connected to a high-speed Digital to Analog Converter will be used to create a digital signal synthesizer device that will create the 16-tone signal. The 16-tone signal will then be mixed up to the 2.4 GHz band, amplified, and radiated using a 2.4 GHz up-converter device. The transmitted jamming signal will cause the ZigBee MAC layer to wait indefinitely for the channel to clear. Since the channel will not clear, the MAC layer will not allow any transmission and the ZigBee devices will not communicate.

With recent advances in technology, Vehicular Ad-hoc Networks (VANETs) have grown in application. One of these areas of application is Vehicle Safety Communication (VSC) technology. VSC technology allows for vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications that enhance vehicle safety and driving experience. However, these newly developing technologies bring with them a concern for the vehicular privacy of drivers. Vehicles already employ the use of pseudonyms, unique identifiers used with signal messages for a limited period of time, to prevent long term tracking. But can attackers still attack vehicular privacy even when vehicles employ a pseudonym change strategy? The major contribution of this paper is a new attack model that uses long-distance pseudonym changing and short-distance non-changing protocols to associate vehicles with their respective pseudonyms.

Modern companies are increasingly relying on groups of individuals to reach organizational goals and objectives, however many organizations struggle to cultivate optimal teams that can maximize performance. Fortunately, existing research has established that group personality composition (GPC), across five dimensions of personality, is a promising indicator of team effectiveness. Additionally, recent advances in technology have enabled groups of humans to form real-time, closed-loop systems that are modeled after natural swarms, like flocks of birds and colonies of bees. These Artificial Swarm Intelligences (ASI) have been shown to amplify performance in a wide range of tasks, from forecasting financial markets to prioritizing conflicting objectives. The present research examines the effects of group personality composition on team performance and investigates the impact of measuring GPC through ASI systems. 541 participants, across 111 groups, were administered a set of well-accepted and vetted psychometric assessments to capture the personality configurations and social sensitivities of teams. While group-level personality averages explained 10% of the variance in team performance, when group personality composition was measured through human swarms, it was able to explain 29% of the variance, representing a 19% amplification in predictive capacity. Finally, a series of machine learning models were applied and trained to predict group effectiveness. Multivariate Linear Regression and Logistic Regression achieved the highest performance exhibiting 0.19 mean squared error and 81.8% classification accuracy.