In order to achieve Full Duplex (FD) communication, currently studied solutionsfor the commercial sector rely on advanced Self-Interferece Cancellation (SIC)techniques to remove the transmitted signal from the received one. This thesis expandsthe research of these techniques by evaluating their potential usage in militarycommunication scenarios where requirements and conditions are distinct,firstly, by identifying, categorizing and describing a set of previously proposedsic techniques and secondly, by performing a comprehensive simulation andanalysis of two suggested sic techniques as part of a FD transceiver. Though themajority of the considered SIC techniques was determined to be potentially implementablein military FD transceivers, some frequency-dependent techniquesand techniques prohibiting omnidirectional communication could not be used.The simulation and analysis of the two suggested SIC techniques show that undercertain conditions, such as limited transmission power and/or reflective environment,close to complete suppression could be realized even with high nonlineardistortion in the transmit chain.

This research investigates the application of multiuser interference suppression to direct-sequence code-division multiple-access (DS-CDMA) for peer-to-peer packet radio networks. The emphasis of this work is to develop and validate efficient interference suppression techniques through selective cancellation of interference; next, the combination of interference suppression with error correction coding is studied. A decoder-assisted frame synchronization technique is proposed for future packet radio system. The performance of DS-CDMA in packet radio networks suffers from the near-far problem. This near-far problem can be alleviated by using either a multiuser receiver or a single-user adaptive receiver along with centralized or distributed power control. The first part of this dissertation compares the use of these receivers in a peer-to-peer environment. Next, we investigate how interference cancellation can be combined with forward error correction coding for throughput enhancement of the system. Although receivers using interference suppression are simple in structure, the performance degrades due to the lack of exact knowledge of the interfering signal in cancellation and also due to biased decision statistics for the parallel cancellation case. We consider a system that employs both partial parallel interference cancellation and convolutional coding. Information is shared between the operations of interference cancellation and decoding in an iterative manner, using log-likelihood ratios of the estimated coded symbols. We investigate the performance of this system for both synchronous and asynchronous CDMA systems, and for both equal and unequal signal powers. Finally, a new code-assisted frame synchronization scheme, which uses the soft-information of the decoder, is proposed and evaluated. The sync bits are placed in the mid-amble, and encoded as a part of the data sequence using the error correction encoder to resolve time ambiguities. This technique is applied for turbo decoder-assisted frame synchronization. The performance improvement of these proposed techniques over conventional synchronization techniques is explored via simulation. / Ph. D.

In Code Division Multiple Access (COMA) systems, the multiple access interference limits the capacity of current systems which use a matched filter or correlation receiver. It has been shown by previous research that multiuser detection receivers employing interference cancellation techniques can significantly improve the capacity of COMA systems. Error correction coding is also an important technique for overcoming severe channel degradation. In this project, we investigate the performance of multiuser receivers which use the combination of interference cancellation techniques and error correction codes. Specifically, we look at the combination of multistage interference cancellation and convolutional coding. Two different combination schemes are proposed and the performance of these two schemes is studied. The first scheme is a partitioned approach where the multistage interference cancellation is in front of the decoder and is performed on the coded data. The second scheme is an integrated approach where the decoded data is used to reconstruct the transmitted signals in order to do interference cancellation. Both of these two schemes result in significant performance improvement over a receiver using either multistage interference cancellation or convolutional coding techniques only, for a reasonable range of operating points. The first scheme is recommended due to its soft-decision estimation of transmitted signal and implementation considerations. The analytical results for the first scheme are presented. Simulation results for both of these two schemes are obtained and compared. / Master of Science

The communications industry is experiencing an explosion in the demand for personal communications services (PCS). Several digital technologies have been proposed to replace overburdened analog systems. One system that has gained increasing popularity in North America is a 1.25 MHz Code Division Multiple Access (CDMA) system (IS-95). In CDMA systems, multiple access interference limits the capacity of any system using conventional single user correlation or matched filter receivers. Previous research has shown that multiuser detection receivers that employ interference cancellation techniques can significantly improve the capacity of a CDMA system. This thesis studies two such structures: the successive interference cancellation scheme and the parallel interference cancellation scheme. These multiuser receivers are integrated into an IS-95 compatible receiver model which is simulated in software. This thesis develops simulation software that simulates IS-95 with conventional and multiuser receivers in multipath channels and when near-far conditions exist. Simulation results present the robustness of multiuser receivers to near-far in a practical system. In addition to multiuser implemenations, quantization effects from finite bit analog to digital converters (ADC) in CDMA systems will also be simulated. / Master of Science

The objective of the research is to achieve a systematic combination of acoustic echo reduction components that together achieve a robust performance of the MCAEC system as a whole. Conventional approaches to the acoustic echo reduction system typically assume that individual components would perform ideally. For example, the adaptive algorithm for AEC is often developed in the absence of strong near-end signal, the algorithm for RES is often an added module that is developed as a separate noise reduction component, and the decorrelation procedure for MCAEC is yet another add-on module that simply introduces some form of distortion to the reference signal. The main challenge is in designing a consistent criterion across all modules that can be jointly optimized to form a more consistent framework for acoustic echo reduction. The decorrelation procedure can potentially benefit from the system approach as well if it is designed by taking the near-end listener into account. The MCAEC system should be optimized not only for the echo cancellation and suppression performance, but also for the reference signal quality after the added distortion from the decorrelation procedure. Finally, a tuning strategy is presented to jointly optimize the parameters across all modules using object criteria.

This thesis examines the use of ripple correlation control as a maximum power point tracking algorithm with an interleaved SEPIC converter for use with a solar array. The suitability of existing topologies for use with photovoltaic applications and the tradeoffs involved are discussed. The advantages of interleaving in converters are examined and the benefits it provides to photovoltaic applications are discussed. An interleaved SEPIC converter operated in interleaved mode with a photovoltaic array is studied. The operation of ripple correlation control as a maximum power point tracking technique applied to the interleaved SEPIC converter is examined and simulations with results are presented. / text

We define a new graph operation called the inner power of a graph. The construction is similar to the direct power of graphs, except that factors are intertwined in such a way that certain structural properties of graphs are more clearly reflected in their inner powers. We investigate various properties of inner powers, such as connectivity, bipartiteness, and their interaction with the direct product. We explore possible connections between inner powers and the problem of cancellation over the direct product of graphs.

Ever since the birth of the telephony system, the problem with echoes, arising from impedance mismatch in 2/4-wire hybrids, or acoustic echoes where a loudspeaker signal is picked up by a closely located microphone, has been ever present. The removal of these echoes is crucial in order to achieve an acceptable audio quality for conversation. Today, the perhaps most common way for echo removal is through cancellation, where an adaptive filter is used to produce an estimated replica of the echo which is then subtracted from the echo-infested signal. Echo cancellation in practice requires extensive control of the filter adaptation process in order to obtain as rapid convergence as possible while also achieving robustness towards disturbances. Moreover, despite the rapid advancement in the computational capabilities of modern digital signal processors there is a constant demand for low-complexity solutions that can be implemented using low power and low cost hardware. This thesis presents low-complexity solutions for echo cancellation related to both the actual filter adaptation process itself as well as for controlling the adaptation process in order to obtain a robust system. Extensive simulations and evaluations using real world recorded signals are used to demonstrate the performance of the proposed solutions.

Over the past decade, there has been an increasing interest in the real-time monitoring of ambulatory vital signs such as heart rate (HR) and arterial blood oxygen saturation (SpO2) using wearable medical sensors during field operations. These measurements can convey valuable information regarding the state of health and allow first responders and front-line medics to better monitor and prioritize medical intervention of military combatants, firefighters, miners and mountaineers in case of medical emergencies. However, the primary challenge encountered when using these sensors in a non-clinical environment has been the presence of persistent motion artifacts (MA) embedded in the acquired physiological signal. These artifacts are caused by the random displacement of the sensor from the skin and lead to erroneous output readings. Several signal processing techniques, such as time and frequency domain segmentation, signal reconstruction techniques and adaptive noise cancellation (ANC), have been previously developed in an offline environment to address MA in photoplethysmography (PPG) with varying degrees of success. However, the performance of these algorithms in a spasmodic noise environment usually associated with basic day to day ambulatory activities has still not been fully investigated. Therefore, the focus of this research has been to develop novel MA algorithms to combat the effects of these artifacts. The specific aim of this thesis was to design two novel motion artifact (MA) algorithms using a combination of higher order statistical tools namely Kurtosis (K) for classifying 10 s PPG data segments, as either ‘clean’ or ‘corrupt’ and then extracting the aforementioned vital parameters. To overcome the effects of MA, the first algorithm (termed ‘MNA’) processes these ‘corrupt’ PPG data segments by identifying abnormal amplitudes changes. The second algorithm (termed ‘MNAC’), filters these ‘corrupt’ data segments using a 16th order normalized least mean square (NLMS) ANC filter and then extracts HR and SpO2.

Over the past decade, there has been an increasing interest in the real-time monitoring of ambulatory vital signs such as heart rate (HR) and arterial blood oxygen saturation (SpO2) using wearable medical sensors during field operations. These measurements can convey valuable information regarding the state of health and allow first responders and front-line medics to better monitor and prioritize medical intervention of military combatants, firefighters, miners and mountaineers in case of medical emergencies. However, the primary challenge encountered when using these sensors in a non-clinical environment has been the presence of persistent motion artifacts (MA) embedded in the acquired physiological signal. These artifacts are caused by the random displacement of the sensor from the skin and lead to erroneous output readings. Several signal processing techniques, such as time and frequency domain segmentation, signal reconstruction techniques and adaptive noise cancellation (ANC), have been previously developed in an offline environment to address MA in photoplethysmography (PPG) with varying degrees of success. However, the performance of these algorithms in a spasmodic noise environment usually associated with basic day to day ambulatory activities has still not been fully investigated. Therefore, the focus of this research has been to develop novel MA algorithms to combat the effects of these artifacts. The specific aim of this thesis was to design two novel motion artifact (MA) algorithms using a combination of higher order statistical tools namely Kurtosis (K) for classifying 10 s PPG data segments, as either ‘clean’ or ‘corrupt’ and then extracting the aforementioned vital parameters. To overcome the effects of MA, the first algorithm (termed ‘MNA’) processes these ‘corrupt’ PPG data segments by identifying abnormal amplitudes changes. The second algorithm (termed ‘MNAC’), filters these ‘corrupt’ data segments using a 16th order normalized least mean square (NLMS) ANC filter and then extracts HR and SpO2.