In 2001, the Federal Communications Commission made available a large block of spectrum known as the 60 GHz band. The 60 GHz band is attractive because it provides the opportunity of multi-Gbps data rates with unlicensed commercial use. One of the main challenges facing the use of this band is poor propagation characteristics including high path loss and strong attenuation due to oxygen absorption. Antenna arrays have been proposed as a means of combating these effects. This thesis provides an analysis of array processing for communication systems operating in the 60 GHz band. Based on measurement campaigns at 60 GHz, deterministic modeling of the channel through ray tracing is proposed. We conduct a site-specific study using ray tracing to model an outdoor and an indoor environment on the Virginia Tech campus. Because arrays are required for antenna gain and adaptability, we explore the use of arrays as a form of equalization in the presence of channel-induced intersymbol interference. The first contribution of this thesis is to establish the expected performance achieved by arrays in the outdoor environment. The second contribution is to analyze the performance of adaptive algorithms applied to array processing in mobile indoor and outdoor environments. / Master of Science

The use of ultra-wideband (UWB) signals holds great promise for remote monitoring of vital-signs which has applications in the medical, for first responder and in security. Previous research has shown the feasibility of a UWB-based radar system for respiratory and heartbeat rate estimation. Some simulation and real experimental results are presented to demonstrate the capability of the respiration rate detection. However, past analysis are mostly based upon the assumption of an ideal experiment environment. The accuracy of the estimation and interference factors of this technology has not been investigated. This thesis establishes an analytical framework for the FFT-based signal processing algorithms to detect periodic bio-signals from a single target. Based on both simulation and experimental data, three basic challenges are identified: (1) Small body movement during the measurement interval results in slow variations in the consecutive received waveforms which mask the signals of interest. (2) The relatively strong respiratory signal with its harmonics greatly impact the detection of heartbeat rate. (3) The non-stationary nature of bio-signals creates challenges for spectral analysis. Having identified these problems, adaptive signal processing techniques have been developed which effectively mitigate these problems. Specifically, an ellipse-fitting algorithm is adopted to track and compensate the aperiodic large-scale body motion, and a wavelet-based filter is applied for attenuating the interference caused by respiratory harmonics to accurately estimate the heartbeat frequency. Additionally, the spectrum estimation of non-stationary signals is examined using a different transform method. Results from simulation and experiments show that substantial improvement is obtained by the use of these techniques. Further, this thesis examines the possibility of multi-target detection based on the same measurement setup. Array processing techniques with subspace-based algorithms are applied to estimate multiple respiration rates from different targets. The combination of array processing and single- target detection techniques are developed to extract the heartbeat rates. The performance is examined via simulation and experimental results and the limitation of the current measurement setup is discussed. / Master of Science

Time delay estimation (TDE)-based algorithms for estimation of direction of arrival (DOA) have been most popular for use with speech signals. This is due to their simplicity and low computational requirements. Though other algorithms, like the steered response power with phase transform (SRP-PHAT), are available that perform better than TDE based algorithms, the huge computational load required for this algorithm makes it unsuitable for applications that require fast refresh rates using short frames. In addition, the estimation errors that do occur with SRP-PHAT tend to be large. This kind of performance is unsuitable for an application such as video camera steering, which is much less tolerant to large errors than it is to small errors. We propose an improved TDE-based DOA estimation algorithm called time delay selection (TIDES) based on either minimizing the weighted least squares error (MWLSE) or minimizing the time delay separation (MWTDS). In the TIDES algorithm, we consider not only the maximum likelihood (ML) TDEs for each pair of microphones, but also other secondary delays corresponding to smaller peaks in the generalized cross-correlation (GCC). From these multiple candidate delays for each microphone pair, we form all possible combinations of time delay sets. From among these we pick one set based on one of the two criteria mentioned above and perform least squares DOA estimation using the selected set of time delays. The MWLSE criterion selects that set of time delays that minimizes the least squares error. The MWTDS criterion selects that set of time delays that has minimum distance from a statistically averaged set of time delays from previously selected time delays. Both TIDES algorithms are shown to out-perform the ML-TDE algorithm in moderate signal to reverberation ratios. In fact, TIDES-MWTDS gives fewer large errors than even the SRP-PHAT algorithm, which makes it very suitable for video camera steering applications. Under small signal to reverberation ratio environments, TIDES-MWTDS breaks down, but TIDES-MWLSE is still shown to out-perform the algorithm based on ML-TDE. / Master of Science

Nas últimas décadas, a poluição sonora tornou-se um grande problema para a sociedade. É por esta razão que a indústria tem aumentado seus esforços para reduzir a emissão de ruído. Para fazer isso, é importante localizar quais partes das fontes sonoras são as que emitem maior energia acústica. Conhecer os pontos de emissão é necessário para ter o controle das mesmas e assim poder reduzir o impacto acústico-ambiental. Técnicas como \"beamforming\" e \"Near-Field Acoustic Holography\" (NAH) permitem a obtenção de imagens acústicas. Essas imagens são obtidas usando um arranjo de microfones localizado a uma distância relativa de uma fonte emissora de ruído. Uma vez adquiridos os dados experimentais pode-se obter a localização e magnitude dos principais pontos de emissão de ruído. Do mesmo modo, ajudam a localizar fontes aeroacústicas e vibro acústicas porque são ferramentas de propósito geral. Usualmente, estes tipos de fontes trabalham em diferentes faixas de frequência de emissão. Recentemente, foi desenvolvida a transformada de Kronecker para arranjos de microfones, a qual fornece uma redução significativa do custo computacional quando aplicada a diversos métodos de reconstrução de imagens, desde que os microfones estejam distribuídos em um arranjo separável. Este trabalho de mestrado propõe realizar medições com sinais reais, usando diversos algoritmos desenvolvidos anteriormente em uma tese de doutorado, quanto à qualidade do resultado obtido e à complexidade computacional, e o desenvolvimento de alternativas para tratamento de dados quando alguns microfones do arranjo apresentarem defeito. Para reduzir o impacto de falhas em microfones e manter a condição de que o arranjo seja separável, foi desenvolvida uma alternativa para utilizar os algoritmos rápidos, eliminando-se apenas os microfones com defeito, de maneira que os resultados finais serão obtidos levando-se em conta todos os microfones do arranjo. / In recent decades, noise pollution has become a major problem for society. It is for this reason that the industry has increased its efforts to reduce the emission of noise. To do this, it is important to find out which parts of the sound sources are emitting greater acoustic energy. Knowing the emission points is required to keep track of them and thus be able to reduce acoustic and environmental impact. Techniques such as \"beamforming\" and \"Near-Field Acoustic Holography\" (NAH) allow obtaining acoustic images. These images are obtained using an array of microphones located at a relative distance from a noise source. Once acquired experimental data, one can obtain the location and the magnitude of the main points of noise emission. Similarly, we can find aeroacoustic and vibroacoustic sources, because they are general-purpose tools. Usually, these types of sources work in different frequency bands of the emission. Recently, a Kronecker Array Transform (KAT) was developed to microphone arrays, which provides a significant reduction in computational cost when applied to various image reconstruction methods, provided that the microphones are distributed in a separable array. This thesis proposes perform measurements with real signals using different algorithms previously developed in a dissertation about the quality of the obtained results and computational complexity, and the development of alternatives for data processing when some microphones of the array are defective. To reduce the impact of failures in microphones and maintain the condition that the array is separable, one alternative has been developed to use the fast algorithms by removing only the defective microphones, so that the final results will be obtained taking into account every microphone of the array.

Nas últimas décadas, a poluição sonora tornou-se um grande problema para a sociedade. É por esta razão que a indústria tem aumentado seus esforços para reduzir a emissão de ruído. Para fazer isso, é importante localizar quais partes das fontes sonoras são as que emitem maior energia acústica. Conhecer os pontos de emissão é necessário para ter o controle das mesmas e assim poder reduzir o impacto acústico-ambiental. Técnicas como \"beamforming\" e \"Near-Field Acoustic Holography\" (NAH) permitem a obtenção de imagens acústicas. Essas imagens são obtidas usando um arranjo de microfones localizado a uma distância relativa de uma fonte emissora de ruído. Uma vez adquiridos os dados experimentais pode-se obter a localização e magnitude dos principais pontos de emissão de ruído. Do mesmo modo, ajudam a localizar fontes aeroacústicas e vibro acústicas porque são ferramentas de propósito geral. Usualmente, estes tipos de fontes trabalham em diferentes faixas de frequência de emissão. Recentemente, foi desenvolvida a transformada de Kronecker para arranjos de microfones, a qual fornece uma redução significativa do custo computacional quando aplicada a diversos métodos de reconstrução de imagens, desde que os microfones estejam distribuídos em um arranjo separável. Este trabalho de mestrado propõe realizar medições com sinais reais, usando diversos algoritmos desenvolvidos anteriormente em uma tese de doutorado, quanto à qualidade do resultado obtido e à complexidade computacional, e o desenvolvimento de alternativas para tratamento de dados quando alguns microfones do arranjo apresentarem defeito. Para reduzir o impacto de falhas em microfones e manter a condição de que o arranjo seja separável, foi desenvolvida uma alternativa para utilizar os algoritmos rápidos, eliminando-se apenas os microfones com defeito, de maneira que os resultados finais serão obtidos levando-se em conta todos os microfones do arranjo. / In recent decades, noise pollution has become a major problem for society. It is for this reason that the industry has increased its efforts to reduce the emission of noise. To do this, it is important to find out which parts of the sound sources are emitting greater acoustic energy. Knowing the emission points is required to keep track of them and thus be able to reduce acoustic and environmental impact. Techniques such as \"beamforming\" and \"Near-Field Acoustic Holography\" (NAH) allow obtaining acoustic images. These images are obtained using an array of microphones located at a relative distance from a noise source. Once acquired experimental data, one can obtain the location and the magnitude of the main points of noise emission. Similarly, we can find aeroacoustic and vibroacoustic sources, because they are general-purpose tools. Usually, these types of sources work in different frequency bands of the emission. Recently, a Kronecker Array Transform (KAT) was developed to microphone arrays, which provides a significant reduction in computational cost when applied to various image reconstruction methods, provided that the microphones are distributed in a separable array. This thesis proposes perform measurements with real signals using different algorithms previously developed in a dissertation about the quality of the obtained results and computational complexity, and the development of alternatives for data processing when some microphones of the array are defective. To reduce the impact of failures in microphones and maintain the condition that the array is separable, one alternative has been developed to use the fast algorithms by removing only the defective microphones, so that the final results will be obtained taking into account every microphone of the array.

Collaborative Beamforming (CB) has been introduced in Wireless Sensor Networks (WSNs) context as a long-distance and power-efficient communication scheme. One challenge for CB is the randomness of sensor node locations where different network realizations result in different CB beampatterns. First, we study the effect of sensor node spatial distribution on the CB beampattern. The characteristics of the CB beampattern are derived for circular Gaussian distributed sensor nodes and compared with the case of uniform distributed sensor nodes. It is shown that the mainlobe behavior of the CB beampattern is essentially deterministic. This suggests that the average beampattern characteristics are suitable for describing the mainlobe of a sample beampattern. However, the CB beampattern sidelobes are random and highly depends on the particular sensor node locations. Second, we introduce the multi-link CB and address the problem of random sidelobes where high level sidelobes can cause unacceptable interference to unintended Base Stations or Access Points (BSs/APs). Centralized sidelobe control techniques are impractical for distributed sensor nodes because of the associated communication overhead for each sensor node. Therefore, we propose a node selection scheme as an alternative to the centralized sidelobe control which aims at minimizing the interference at unintended BSs/APs. Our algorithm is based on the use of the inherent randomness of the channels and a low feedback that approves/rejects tested random node combinations. The performance of the proposed algorithm is analyzed in terms of the average number of trials and the achievable interference suppression and transmission rate. Finally, we study CB with power control aiming at prolonging the lifetime of a cluster of sensor nodes in the WSN. The energy available at different sensor nodes may not be the same since different sensor nodes may perform different tasks and not equally frequently. CB with power control can be used to balance the individual sensor nodes' lifetimes. Thus, we propose a distributed algorithm for CB with power control that is based on the Residual Energy Information (REI) at each sensor node while achieving the required average SNR at the BS/AP. The effectiveness of the proposed CB with power control is illustrated by simulations. / Communications

In this thesis we develop algorithms for estimating broadband source signals from a mixture using only two sensors. This is motivated by what is known in the literature as cocktail party effect, the ability of human beings to listen to the desired source from a mixture of sources with at most two ears. Such a study lets us, achieve a better understanding of the auditory pathway in the brain and confirmation of the results from physiology and psychoacoustics, have a clue to search for an equivalent structure in the brain which corresponds to the modification which improves the algorithm, come up with a benchmark system to automate the evaluation of the systems like 'surround sound', perform speech recognition in noisy environments. Moreover, it is possible that, what we learn about the replication of the functional units in the brain may help us in replacing those using signal processing units for patients suffering due to the defects in these units. There are two parts to the thesis. In the first part we assume the source signals to be broadband and having strong spectral overlap. Channel is assumed to have a few strong multipaths. We propose an algorithm to estimate all the strong multi-paths from each source to the sensors for more than two sources with measurement from two sensors. Because the channel matrix is not invertible when the number of sources is more than the number of sensors, we make use of the estimates of the multi-path delays for each source to improve the SIR of the sources. In the second part we look at a specific scenario of colored signals and channel being one with a prominent direct path. Speech signals as the sources in a weakly reverberant room and a pair of microphones as the sensors satisfy these conditions. We consider the case with and without a head like structure between the microphones. The head like structure we used was a cubical block of wood. We propose an algorithm for separating sources under such a scenario. We identify the features of speech and the channel which makes it possible for the human auditory system to solve the cocktail party problem. These properties are the same as that satisfied by our model. The algorithm works well in a partly acoustically treated room, (with three persons speaking and two microphones and data acquired using standard PC setup) and not so well in a heavily reverberant scenario. We see that there are similarities in the processing steps involved in the algorithm and what we know of the way our auditory system works, especially so in the regions before the auditory cortex in the auditory pathway. Based on the above experiments we give reasons to support the hypothesis about why all the known organisms need to have only two ears and not more but may have more than two eyes to their advantage. Our results also indicate that part of pitch estimation for individual sources might be occurring in the brain after separating the individual source components. This might solve the dilemma of having to do multi-pitch estimation. Recent works suggest that there are parallel pathways in the brain up to the primary auditory cortex which deal with temporal cue based processing and spatial cue based processing. Our model seem to mimic the pathway which makes use of the spatial cues.

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