For this project, the design, implementation, characterization, calibration and possible applications of a multiple transducer stethoscope apparatus were investigated. The multi-transducer sensor array design consists of five standard stethoscope diaphragms mounted to a rigid frame for a-priori knowledge of their relative spatial locations in the x-y plane, with compliant z-direction positioning to ensure good contact and pressure against the subject’s skin for reliable acoustic coupling. When this apparatus is properly placed on the body, it can digitally capture the same important body sounds investigated with standard acoustic stethoscopes; especially heart sounds. Acoustic signal inputs from each diaphragm are converted to electrical signals through microphone pickups installed in the stethoscope connective tubing; and are subsequently sampled and digitized for analysis. With this system, we are able to simultaneously interrogate internal body sounds at a sampling rate of 2 KHz, as most heart sounds of interest occur below 200 Hz. This system was characterized and calibrated by chirp and impulse signal tests. After calibrating the system, a variety of methods for combining the individual sensor channel data to improve the detectability of different signals of interest were explored using variable-delay beam forming. S1 and S2 heart sound recognition with optimized beam forming delays and inter-symbol noise elimination were investigated for improved discernment of the S1 or S2 heart sounds by a user. Also, stereophonic presentation of heart sounds was also produced to allow future investigation of its potential clinical diagnostic efficacy.

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Driven by the surging need for seamless connectivity, research in the wireless communication area has dramatically evolved over the years to meet the increasing demand for data rate and seamless coverage. Such evolvement concurs with a notable increase in data traffic and the widespread of data-hungry devices, thereby inflicting stringent requirements on terrestrial networks. Despite the tremendous advances achieved through the past generations of wireless systems, almost half of the world's population remains unconnected, leading to an accentuated digital divide problem. Therefore, this work invigorates a new connectivity solution that integrates aerial and terrestrial communications with a high-altitude platform station (HAPS) to promote a sustainable connectivity landscape. The connectivity solution adopted in this thesis specifically integrates terrestrial base stations with hot-air balloons under the framework of a cloud-enabled HAPS via a data-sharing fronthauling strategy. The aerial (hot-air balloons) and terrestrial base stations, grouped into disjoint clusters, coordinate their mutual transmission to serve aerial (i.e., drones) and terrestrial users. This work studies the downlink communication from the cloud-enabled HAPS to the aerial and terrestrial users under practical system considerations, namely the limited transmit power and the limited-capacity fronthaul link, per-base station. To this end, the first part of the thesis devises a specific optimization problem that maximizes the network sum-rate while accounting for system design constraints to determine the user association strategy, i.e., user to terrestrial clusters or user to air clusters, and the associated beamforming vectors. The second part of the thesis, then, designs a different resource allocations optimization problem that accounts for the fairness among the users, thus adopting a proportionally fair scheduling scheme to assign users on frequency tones to maximize the log of the long-term average rate. On this account, the work solves a handful of non-convex intricate optimization problems using techniques from optimization theory, namely, fractional programming and $\ell_0$-norm approximation. The work consequently outlines the gains realized by providing on-demand coverage in crowded and unserved areas. Moreover, the thesis illustrates the benefits of coordinating the operations of aerial and terrestrial base stations for interference management, load-balancing, and fairness measures.

<p>Future wireless applications will require networks to provide high rates, reduced power consumption, reliable communications, and low latencies in a wide range of deployment scenarios. To support the never-ending growth in wireless data traffic, a solution is to operate wireless networks on the wide bandwidth available at higher frequencies, e.g., millimeter wave (mmWave) and sub-terahertz (sub-THz) bands. However, new challenges arise as networks operating at higher frequencies experience harsher propagation characteristics. To compensate for such severe signal attenuation, the directional beamforming via massive multipleinput multiple-output (MIMO) is adopted to provide array gains, but it necessitates accurate MIMO channel state information incurring unacceptably large training overhead. Wireless system engineers will require to develop fast and efficient channel training algorithms for massive MIMO systems. Another new challenge arises in scenarios without a direct link between the source and destination due to serious pathloss, which requires cooperative relay beamforming to enhance the communication coverage. The beamforming weights of the distributed relays and the receive combiner can be jointly optimized to enhance Quality-of-Service in multi-user relay beamforming networks. Our contributions cover three specific topics as follows: First, we develop a learning-based beam alignment approach, which enables the position-aided beam recommendation to support users at new positions, to reduce the training overhead in MIMO systems. Second, we propose a compressed training framework to estimate the time-varying sub-THz MIMO-OFDM channels with dual-wideband effect. Lastly, we propose a joint relay beamforming and receive combiner design, considering an optimization problem formulation that maximizes the minimum of the receiving signal-to-interference-plus-noise ratios among multiple users. In each specific topic, we provide the algorithms and show the numerical results to demonstrate the improved performance over the state-of-the-art techniques.</p>

No / Study is presented into the performance of Fast Fourier Transform (FFT) and Discrete Wavelet Transform (DWT) and MIMO-DWT with transmit beamforming. Feedback loop has been used between the equalizer at the transmitter to the receiver which provided the channel state information which was then used to construct a steering matrix for the transmission sequence such that the received signals at the transmitter can be combined constructively in order to provide a reliable and improved system for next generation wireless systems. As convolution in time domain equals multiplication in frequency domain no such counterpart exist for the symbols in space, means linear convolution and Intersymbol Interference (ISI) generation so both zero forcing (ZF) and minimum mean squared error (MMSE) equalizations have been employed. The results show superior performance improvement and in addition allow keeping the processing, power and implementation cost at the transmitter which has less constraints and the results also show that both equalization algorithms perform alike in wavelets and the ISI is spread equally between different wavelet domains.