Defense Notices

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.

Wireless Power Transfer is commonly known as that electrical energy transfer from source to load in some certain distance without any wire connecting in between. It has been almost two hundred when people first noticed the electromagnetic induction phenomenon. After that, Nikola Tesla tried to use this concept to build the first wireless power transfer device. Today, the most common technic is used for transfer power wirelessly is known as inductive coupling. It has revolutionized the transmission of power in various application.  Wireless power transfer is one of the simplest and inexpensive way to transfer energy, and it will change the behavior of how people are going to use their devices.

With the development of science and technology. A new method of wireless power transfer through the coupled magnetic resonances could be the next technology that bring the future nearer. It significantly increases the transmission distance and efficiency. This project shows that this is very simple way to charge the low power devices wirelessly by using coupled magnetic resonances. It also presents how easy to set up the system compare to the conventional copper cables and current carrying wire.

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.

Enormous amount of data is being generated due to advancement in technology. The basic question of discovering knowledge from the data generated is still pertinent. Data mining guides us in discovering patterns or rules. Rules are frequently identified by a technique known as rule induction, which is regarded as the benchmark technique in data mining primarily developed to handle symbolic data. Real life data often consists of numerical attributes and hence, in order to completely utilize the power of rule induction, a form of preprocessing step is involved which converts numeric data into symbolic data known as discretization.

We present two entropy-based discretization techniques known as dominant attribute and multiple scanning approach, respectively. These approaches were implemented as two explicit algorithms in C# programming language and applied on nine well known numerical data sets. For every dataset in multiple scanning approach, experiment was repeated with incremental scans until interval counts were stable. Preliminary results suggest that multiple scanning approach performs better than dominant attribute approach in terms of producing comparatively smaller and simpler error rate.

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.

Incomplete and numerical data are common for many application domains. There have been many approaches to handle missing data in statistical analysis and data mining. To deal with numerical data, discretization is crucial for many machine learning algorithms. However, few work has been done for discretization on incomplete data.

This research mainly focuses on the question whether conducting discretization as preprocessing gives better results than using a data mining method alone. Multiple Scanning is an entropy based discretization method. Previous research shown that it outperforms commonly used discretization methods: Equal Width or Equal Frequency discretization. In this work, Multiple Scanning is tested on C4.5 and MLEM2 on in- complete numerical data sets. Results show for some data sets, the setup utilizing Multiple Scanning as preprocessing performs better, for the other data sets, C4.5 or MLEM2 should be used by themselves. Our secondary objective is to test which of the three known interpretations of missing attribute value is better when using MLEM2. Results show that running MLEM2 on data sets with attribute-concept values performs worse than the other two types of missing values. Last, we compared error rate be- tween C4.5 combined with Multiple Scanning (MS-C4.5) and MLEM2 combined with Multiple Scanning (MS-MLEM2) on data sets with different percentage of missing at- tribute values. Possible rules induced by MS-MLEM2 give a better result on data sets with "do-not-care" conditions. MS-C4.5 is preferred on data sets with lost values and attribute-concept values.

Our conclusion is that there are no universal optimal solutions for all data sets. Setup should be custom-made based on the data sets.

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. ​

The Chinese Postman Problem also known as Route Inspection Problem is a famous arc routing problem in Graph theory. In this problem, a postman has to deliver mail to the streets such that all the streets are visited at least once and return to his starting point. The problem is to find out a path called the optimal postman tour such that the distance travelled by the postman by following this path is always the minimum distance that has to be travelled to visit all the streets at least once. In graph theory, we represent the street system as a weighted graph whose edges represent the streets and the street intersections are represented by the vertices. A graph can be directed, undirected or a mixed graph. Directed and undirected edges represent the one way and the two way streets respectively. A mixed graph has both the directed and undirected edges.

The Chinese postman problem can be divided into several sub problems of which finding the minimum cost perfect matching is the critical part. For a directed graph, the minimum cost perfect matching of a bipartite graph has to be computed. For an undirected graph, the minimum cost perfect matching of a general graph has to be computed. There are different matching algorithms to compute the minimum cost perfect matching efficiently. In this project, I have understood and implemented four different matching algorithms used in computing an optimal postman tour, the Edmond’s Blossom Algorithm and a Branch and Bound Algorithm for the directed graph and the Hungarian Algorithm and a Branch and Bound Algorithm for the undirected graph. The objective of this project is to compare the performance of these matching algorithms on graphs of different sizes and densities."

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. 

Moral inclinations expressed in user-generated content such as online reviews or tweets can provide useful insights to understand users’ behavior and activities in social networks, for example, to predict users’ rating behavior, perform customer feedback mining, and study users' tendency to spread abusive content on these social platforms.  In this work, we want to answer two important research questions. First, if the moral attributes of social network data can provide additional useful information about users' behavior and how to utilize this information to enhance our understanding. To answer this question, we used the Moral FoundationsTheory and Doc2Vec, a Natural Language Processing technique, to compute the quantified moral loadings of user-generated textual contents in social networks. We used conditional relative frequency and the correlations between the moral foundations as two measures to study the moral break down of the social network data, utilizing a dataset of Yelp reviews and a dataset of tweets on abusive user-generated content. Our findings indicated that these moral features are tightly bound with users' behavior in social networks. The second question we want to answer is if we can use the quantified moral loadings as new boosting features to improve the differentiation, classification, and prediction of social network activities. To test our hypothesis, we adopted our new moral features in a multi-class classification approach to distinguish hateful and offensive tweets in a labeled dataset, and compared with the baseline approach that only uses conventional text mining features such as tf-idf features, Part of Speech (PoS) tags, etc. Our findings demonstrated that the moral features improved the performance of the baseline approach in terms of precision, recall, and F-measure.​