The performance of several methods to estimate the number of source signals impinging on a sensor array are compared using a traditional simulator and their performance for synthetic aperture radar tomography is discussed as it is useful in the fields of radar and remote sensing when multichannel arrays are employed. All methods use the sum of the likelihood function with a penalty term. We consider two signal models for model selection and refer to these as suboptimal and optimal. The suboptimal model uses a simplified signal model and the model selection and direction of arrival estimation are done in separate steps. The optimal model uses the actual signal model and the model selection and direction of arrival estimation are done in the same step. In the literature, suboptimal model selection is used because of computational efficiency, but in our radar post processing we are less time constrained and we implement the optimal model for the estimation and compare the performance results. Interestingly we find several methods discussed in the literature do not work using optimal model selection, but can work if the optimal model selection is normalized. We also formulate a new penalty term, numerically tuned so that it gives optimal performance over a particular set of operating conditions, and compare this method as well. The primary contribution of this work is the development of an optimizer that finds a numerically tuned penalty term that outperforms current methods and discussion of the normalization techniques applied to optimal model selection. Simulation results show that the numerically tuned model selection criteria is optimal and that the typical methods do not do well for low snapshots which are common in radar and remote sensing applications. We apply the algorithms to data collected by the CReSIS radar depth sounder and discuss the results.

In addition to model order estimation, array model errors should be estimated to improve direction of arrival estimation. The implementation of a parametric-model is discussed for array calibration that estimates the first and second order array model errors. Simulation results for the gain, phase and location errors are discussed.

The Internet of Things (IoT) is evolving rapidly to every aspect of human life including healthcare, homes, cities, and driverless vehicles that makes humans more dependent on the Internet and related infrastructure. While many researchers have studied the structure of the Internet that is resilient as a whole, new studies are required to investigate the resilience of the edge networks in which people and “things” connect to the Internet. Since the range of service requirements varies at the edge of the network, a wide variety of protocols are needed. In this research proposal, we survey standard protocols and IoT models. Next, we propose an abstract model for smart homes and cities to illustrate the heterogeneity and complexity of network structure. Our initial results show that the heterogeneity of the protocols has a direct effect on the IoT and smart city resilience. As the next step, we make a graph model from the proposed model and do graph theoretic analysis to recognize the fundamental behavior of the network to improve its robustness. We perform the process of improvement through modifying topology, adding extra nodes, and links when necessary. Finally, we will conduct various simulation studies on the model to validate its resilience.

On June 12, 2016, 49 people were killed and another 58 wounded in an attack at Pulse Nightclub in Orlando Florida. This incident was regarded as both hate crime against LGBT people and as a terrorist attack. This project focuses on analyzing tweets a week after the terrorist attack, specifically looking at how different communities within twitter were discussing this event. To understand how the twitter users in different communities are discussing this event, a set of hashtag frequency-based evaluation measures and simulations are proposed. The simulations are used to assess the specific hashtag content of a community. Using community detection algorithms and text analysis tools, significant topics that specific communities are discussing and  the topics that are being avoided by those communities are discovered.

Recently machine learning has created significant advancements in many areas like health, finance, education, sports, etc. which has encouraged the development of many predictive models. In machine learning, we extract hidden, previously unknown, and potentially useful high-level information from low-level data. Cross-validation is a typical strategy for estimating the performance. It simulates the process of fitting to different datasets and seeing how different predictions can be. In this project, we review accuracy estimation methods and compare two resampling methods, such as leaving-one-out and bootstrap. We compared these validation methods using LEM1 rule induction algorithm. Our results indicate that for real-world datasets similar to ours, bootstrap may be optimistic.

Near-infrared spectroscopy has been used as a non-invasive method of determining concentration of chemicals within living tissues of living organisms.  This method employs LEDs of specific frequencies to measure concentration of blood constituents according to the Beer-Lambert Law.  One group of instruments (frequency domain instruments) is based on amplitude modulation of the laser diode or LED light intensity, the measurement of light adsorption and the measurement of modulation phase shift to determine light path length for use in Beer-Lambert Law. This paper describes the design and demonstration of a frequency domain instrument for measuring concentration of oxygenated and de-oxygenated hemoglobin using incoherent optics and an in-phase quadrature (I-Q) receiver design.  The design has been shown to be capable of resolving variations of concentration of test samples and a viable prototype for future, more precise, tools.

 

Owning to the ever-increasing data traffic in today’s optical network, how to utilize the optical bandwidth more efficiently has become a critical issue. Optical wavelength division multiplexing (WDM) multiplexes multiple optical carrier signals into a single fiber by using different wavelengths of laser light. Optical cross-connect (OXC) and switches based on optical WDM can greatly improves the performance of optical networks, which results in reduced complexity, signal transparency, and significant electrical energy saving. However, OXC alone cannot fully exploit the availability of optical bandwidth due to its coarse bandwidth granularity imposed by optical filtering. Thus, OXC may not meet the requirements of some applications when the sub-band has a small bandwidth. In order to achieve smaller bandwidth granularities, electrical digital cross-connect (DXC) could be added to the current optical network.

In this work, we proposed a scheme of real-time digital signal processing (DSP) enabled multi-carrier cross-connect which can dynamically assign bandwidth and allocates power to each individual subcarrier channel. This cross-connect is based on digital sub-carrier multiplexing (DSCM), which is a frequency division multiplexing (FDM) technique. Either Nyquist WDM (N-WDM) or orthogonal frequency division multiplexing (OFDM) can be used to implement real-time enabled DSCM. DSCM multiplexes the digital created subcarriers on each optical wavelength. Compared with optical WDM, DSCM has a smaller bandwidth granularity because it multiplexes sub-carriers in electrical domain. DSCM also provides more flexibility since operations such as distortion compensation and signal regeneration could be conducted by using DSP algorithms.

We built a real-time DSP platform based on a Virtex7 FPGA, which allows the test of real-time DSP algorithms for multi-carrier cross-connect in optical systems. We have implemented a real-time DSP enabled multi-carrier cross-connect based on up/down sampling and filtering. This technique can save the DSP resources since local oscillators (LO) are not needed in spectral translation. We got some preliminary results from theoretical analysis, simulation and experiment. The performance and resource cost of this cross-connect has been analyzed. This real-time DSP enabled cross-connect also has the potential to reduce the cost in applications such as the mobile Fronthaul in 5G next-generation wireless networks.

In real world environments, the speech signals received by our ears are usually a combination of different sounds that include not only the target speech, but also acoustic interference like music, background noise, and competing speakers. This interference has negative effect on speech perception and degrades the performance of speech processing applications such as automatic speech recognition (ASR), and hearing aid devices. One way to solve this problem is using source separation algorithms to separate the desired speech from the interfering sounds. Many source separation algorithms have been proposed to improve the performance of ASR systems and hearing aid devices, but it is still challenging for these systems to work efficiently in noisy and reverberant environments. On the other hand, humans have a remarkable ability to separate desired sounds and listen to a specific talker among noise and other talkers. Inspired by the capabilities of human auditory system, a popular method known as auditory scene analysis (ASA) was proposed to separate different sources in a two stage process of segmentation and grouping. The main goal of source separation in ASA is to estimate time frequency masks that optimally match and separate noise signals from a mixture of speech and noise. Three major aims are proposed to improve upon source separation in noisy and reverberant acoustic signals. First, a simple and novel algorithm is proposed to increase the discriminability between two sound sources by magnifying the head-related transfer function of the interfering source. Experimental results show a significant increase in the quality of the recovered target speech. Second, a time frequency masking-based source separation algorithm is proposed that can separate a male speaker from a female speaker in reverberant conditions by using the spatial cues of the sources. Furthermore, the proposed algorithm also has the ability to preserve the location of the sources after separation.

Finally, a supervised speech separation algorithm is proposed based on deep neural networks to estimate the time frequency masks. Initial experiments show promising results for separating sources in noisy and reverberant condition. Continued work is focused on identifying the best network training features and network structure that are robust to different types of noise, speakers, and reverberation. The main goal of the proposed algorithm is to increase the intelligibility and quality of the recovered speech from noisy environments, which has the potential to improve both speech processing applications and signal processing strategies for hearing aid technology.

Time and again, fiber optic medium has proved to be the best means for transporting global data traffic which is following an exponential growth trajectory. High bandwidth applications based on cloud, virtual reality and big data, necessitates maximum effective utilization of available fiber bandwidth. To this end, multicarrier superchannel transmission systems, aided by robust digital signal processing both at transmitter and receiver, have proved to enhance spectral efficiency and achieve multi tera-bit per second data rates.

With respect to transmission sources, laser technology too has made significant strides, especially in the domain of multiwavelength sources such as quantum dot passive mode-locked laser (QD-PMLL) based optical frequency combs. In the present research work, we characterize the phase dynamics of comb lines from a QD-PMLL based on a novel multiheterodyne coherent detection technique. The inherently broad linewidth of comb lines which is in the order of tens of MHz, make it difficult for conventional digital phase noise compensation algorithms to track the large phase noise especially for low baud rate subcarriers using higher cardinality modulation formats. In the context of multi-subcarrier Nyquist pulse shaped superchannel transmission system with coherent detection, we demonstrate through measurements, an efficient phase noise compensation technique called “Digital Mixing” which exploits the mutual phase coherence among the comb lines. For QPSK and 16 QAM modulation formats, digital mixing provided significant improvement in bit error rate (BER) performance.  For short reach data center and passive optical network-based applications, which adopt direct detection, a single optical amplifier is generally used meet the power budget requirements to achieve the desired BER.  Semiconductor Optical Amplifier (SOA) with its small form factor, is a low-cost power booster that can be designed to operate in any desired wavelength and most importantly can be integrated with the transmitter. However, saturated SOAs introduce nonlinear distortions on the amplified signal. Alongside SOA, the photodiode also introduces nonlinear mixing in the form of Signal-Signal Beat Interference (SSBI). In this research, we study the impact of SOA nonlinearity on the effectiveness of SSBI compensation in a direct detection OFDM based transmission system. We experimentally demonstrate a digital compensation technique to undo the SOA nonlinearity effect by digitally back-propagating the received signal through a virtual SOA, thereby effectively eliminating the SSBI. ​

Many online social networks are increasingly being used as information sharing platforms. With a massive increase in the number of users participating in information sharing, an enormous amount of information becomes available on such sites. It is vital to preserve user’s privacy, without preventing them from socialization. Unfortunately, many existing models overlooked a very important fact, that is, a user may want different information boundary preference for different information. To address this short coming, in this paper, I will introduce a ‘social circle’ model, which follows the concepts of ‘private information boundaries’ and ‘restricted access and limited control’. While facilitating socialization, the social circle model also provides some privacy protection capabilities. I then utilize this model to analyze the most popular social networks (such as Facebook, Google+, VKontakte, Flickr, and Instagram) and demonstrate the potential privacy vulnerabilities in some of these networking sites. Lastly, I discuss the implication of the analysis and possible future directions. 

The non-ending growth of data traffic resulting from the continuing emergence of high-data-rate-demanding applications sets huge capacity requirements on optical interconnects and transport networks. This requires optical communication schemes in these networks to make the best possible use of the available optical spectrum per a single optical channel to enable transmission of multiple tens of tera-bits per second per a single fiber core in high capacity transport networks. Therefore, advanced modulation formats are required to be used in conjunction with energy-efficient and robust transceiver schemes. Important challenges facing these goals are the stringent requirements on the characteristics of optical components comprising these systems. Especially the laser sources. Laser phase noise is one of the most important performance-limiting factors in systems with high spectral efficiency. In this research work, we study the effects of different laser phase noise characteristics on the performance of different optical communication schemes. A novel, simple and accurate phase noise characterization technique is proposed. Experimental results show that the proposed technique is very accurate in estimating the performance of lasers in coherent systems employing digital phase recovery techniques. A novel multi-heterodyne scheme for characterizing the phase noise of laser frequency comb sources is also proposed and validated by experimental results. This proposed scheme is the first one of its type capable of measuring the differential phase noise between multiple spectral lines instantaneously by a single measurement. Moreover, extended relations between system performance and detailed characteristics of laser phase noise are also analyzed and modeled. The results of this study show that the commonly-used metric to estimate the performance of lasers with a specific phase recovery scheme, linewidth-symbol-period product, is not necessarily accurate for all types of lasers, and description of FM-noise power spectral profile is required for accurate performance estimation. We also propose an energy- and cost-efficient transmission scheme suitable for metro and long-reach data-center-interconnect links based on direct detection of field-modulated optical signals with advanced modulation formats, allowing for higher spectral efficiency. The proposed system combines the Kramers-Kronig coherent receiver technique, with the use of quantum-dot multi-mode laser sources, to generate and transmit multi-channel optical signals using a single diode laser source. Experimental results of the proposed system show that high modulation formats can be employed, with high robustness against laser phase noise and frequency drifting.

Energy harvesting from solar sources in an attempt to increase efficiency has sparked interest in many communities to develop more energy harvesting applications for renewable energy topics. Advanced technical methods are required to ensure the maximum available power is harnessed from the photovoltaic (PV) system. This dissertation proposed a new discrete-in-time extremum-seeking (ES) based technique for tracking the maximum power point of a photovoltaic array. The proposed method is a true maximum power point tracker that can be implemented with reasonable processing effort on an expensive digital controller. The dissertation presents a stability analysis of the proposed method to guarantee the convergence of the algorithm.

Two types of PV systems were designed and comprehensive frame work of control design was considered for a stand-alone and a three-phase grid connected system.

Grid-tied systems commonly have a two-stage power electronics interface which is necessitated due to the inherent limitation of the DC-AC (Inverter) power converging stage. However, a one stage converter topology, denoted as Quasi-Z-source inverter (q-ZSI) was selected that interface the PV panel which overcomes the inverter limitations to harvest the maximum available power.

A powerful control scheme called Model Predictive Control with Finite Set (MPC-FS) was designed to control the grid connected system. The predictive control was selected to achieve a robust controller with superior dynamic response in conjunction with the extremum-seeking algorithm to enhance the system behavior.

The proposed method exhibited better performance in comparison to conventional Maximum Power Point Tracking (MPPT) methods and require less computational effort than the complex mathematical methods.​

Many important machine learning and data mining algorithms rely on a measure to provide a notion of distance or dissimilarity. Naive metrics such as the Euclidean distance are incapable of leveraging task-specific information, and consider all features as equal. A learned distance metric can become much more effective by honing in on structure specific to a task. Additionally, it is often extremely desirable for a metric to be sparse, as this vastly increases the ability to interpret the distance metric. In this dissertation, we explore several current problems in distance metric learning and put forth solutions which make use of structured sparsity.

The first contribution of this dissertation begins with a classic approach in distance metric learning and address a scenario where distance metric learning is typically inapplicable, i.e., the case of learning on heterogeneous data in a high-dimensional input space. We construct a projection-free distance metric learning algorithm which utilizes structured sparse updates and successfully demonstrate its application to learn a metric with over a billion parameters.

The second contribution of this dissertation focuses on an intriguing regression-based approach to distance metric learning. Under this regression approach there are two sets of parameters to learn; those which parameterize the metric, and those defining the so-called ``virtual points''. We begin with an exploration of the metric parameterization and develop a structured sparse approach to robustify the metric to noisy, corrupted, or irrelevant data. We then focus on the virtual points and develop a new method for learning the metric and constraints together in a simultaneous manner. It is demonstrate through empirical means that our approach results in a distance metric which is more effective than the current state of-the-art.

Machine learning algorithms have recently become ingrained in an incredibly diverse amount of technology. The focus of this dissertation is to develop more effective techniques to learn a distance metric. We believe that this work has the potential for broad-reaching impacts, as learning a more effective metric typically results in more accurate metric-based machine learning algorithms.

Dynamic Binary Translators(DBTs) have a variety of uses, like instrumentation, profiling, security, portability, etc. In order for the desired application to run with these enhanced additional features(not originally part of its design), it is to be run under the control of Dynamic Binary Translator. The application can be thought of as the guest application, to be run with in a controlled environment of the translator, which would be the host application. That way, the intended application execution flow can be enforced by the translator, thereby inducing the desired behavior in the application on the host platform(combination of Operating System and Hardware). Depending on the implementation of the translator(host application), the guest application can either have code compiled for the host platform, or a different platform. It would be the responsibility of the translator to make appropriate code/binary translation of the guest application code, to be run on the host platform.

However, there will be a run-time/execution-time overhead in the translator, when performing the additional tasks to run the guest application in a controlled fashion. This run-time overhead has been limiting the usage of DBT's on a large scale, where response times can be critical. There is often a trade-off between the benefits of using a DBT against the overall application response time. So, there is a need to research/explore ways of faster application execution through DBT's(given their large code-base).

With the evolution of the multi-core and GPU hardware architectures, paralleization of software can be employed through multiple threads, which can concurrently run parts of code and potentially doing more work at the same time. The proper design of parallel applications or parallelizing parts of existing code, can lead to faster application run-time's, by taking advantage of the hardware architecture support to parallel programs.

We explore the possibility of improving the performance of a DBT named DynamoRIO. The basic idea is to improve its performance by speeding-up the process of guest code translation, through multiple threads translating multiple pieces of code concurrently. In an ideal case, all the required code blocks for application execution would be available ahead of time(eager translation), without any wait/overhead at run-time, and also giving it the enhanced features through the DBT. For efficient run-time eager translation there is also a need for heuristics, to better predict the next likely code block to be executed. That could potentially bring down the less productive code translations at run-time. The goal is to get application speed-up through eager translation, coupled with block prediction heuristics, leading to an execution time close to that of native run.

In Data mining, Decision Tree is a type of classification model which uses a tree-like data structure to organize the data to obtain meaningful information. We may use Decision Tree for important predictive analysis in data mining. 

In this project, we compare two decision tree generating algorithms CART and the modified ID3 algorithm using different datasets with discrete and continuous numerical values. A new approach to handle the continuous numerical values is implemented in this project since the basic ID3 algorithm is inefficient in handling the continuous numerical values. In the modified ID3 algorithm, we discretize the continuous numerical values by creating cut-points. The decision trees generated by the modified algorithm contain fewer nodes and branches compared to basic ID3. 

The results from the experiments indicate that there is statistically insignificant difference between CART and modified ID3 in terms of accuracy on test data. On the other hand, the size of the decision tree generated by CART is smaller than the decision tree generated by modified ID3. 

In the current era of big data, huge amount of data can be easily collected, but the unprocessed data is not useful on its own. It can be useful only when we are able to find interesting patterns or hidden knowledge. The algorithm to find interesting patterns is known as Rule Induction Algorithm. Rule induction is a special area of data mining and machine learning in which formal rules are extracted from a dataset. The extracted rules may represent some general or local (isolated) patterns related to the data.
In this report, we will focus on the IRIM (Interesting Rule Inducing Module) which induces strong interesting rules that covers most of the concept. Usually, the rules induced by IRIM provides interesting and surprising insight to the expert in the domain area.
The IRIM algorithm was implemented using Python and pySpark library, which is specially customize for data mining. Further, the IRIM algorithm was extended to handle the different types of missing data. Then at the end the performance of the IRIM algorithm with and without missing data feature was analyzed. As an example, interesting rules induced from IRIS dataset are shown.

The DC to AC power Converters (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.  Nevertheless, the superior characteristics come at the price of a more complex topology with an increased number of power electronic switches. The increased number of power electronics switches results in more complicated control strategies for the inverter. Moreover, as the number of power electronic switches increases, the chances of fault occurrence 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 simple control strategies for normal and fault-tolerant operation of multilevel inverters has always been an interesting topic for researchers in related areas.  The purpose of this dissertation is to develop new control and fault-tolerant strategies for the multilevel power inverter.  For the normal operation of the inverter, a new high switching frequency technique is developed.  The proposed method extends the utilization of the dc link voltage while minimizing the dv/dt of the switches. In the event of a fault, the line voltages of the faulty inverters are unbalanced and cannot be applied to the three phase loads. For the faulty condition of the inverter, three novel fault-tolerant techniques are developed. The proposed fault-tolerant strategies generate balanced line voltages without bypassing any healthy and operative inverter element, makes better use of the inverter capacity and generates higher output voltage. These strategies exploit the advantages of the Selective Harmonic Elimination (SHE) and Space Vector Modulation (SVM) methods in conjunction with a slightly modified Fundamental Phase Shift Compensation (FPSC) technique to generate balanced voltages and manipulate voltage harmonics at the same time.  The proposed strategies are applicable to several classes of multilevel inverters with three or more voltage levels.