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Yes / The present work investigates the operation performance of 2-element configuration multiple input Multiple Output (MIMO) antennas system using Cylindrical Dielectric Resonator (CDR). The MIMO antenna arrays achieve 22.2% impedance bandwidth at S11 ≤ -10 covering the bandwidth from 10GHz to 12.5GHz that meets the essential requirements of wide band communications applications. The first array gives a maximum isolation of 27dB at an element spacing of 22mm, whereas the second array presents a maximum isolation of 42.55dB at element spacing of 12.25mm.

International Telemetering Conference Proceedings / October 25-28, 1999 / Riviera Hotel and Convention Center, Las Vegas, Nevada / An agile RF routing system has been developed which utilizes phasing techniques to direct signal power to any one of four orthogonally mounted antennae, or either set of two antennae mounted 180° apart on a launch vehicle. The system has been integrated into a telemetry transmitter and has shown superior performance to traditional methods of antennae switching. The unit is self-correcting to maintain maximum RF power at the desired antenna port(s) across a dynamic mission environment. Due to its low loss and high reliability, this method of antennae switching provides a robust RF link.

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A central dilemma faced by biological systems that process fluctuating signals is the need for balancing sensitivity and robustness to input variations. Termed “Change Detection” and “Invariance”, the balancing act between the two features of input processing is observed in the olfactory system (odorant onset detection vs. odorant identification) and in gene regulatory networks (fold-change detection). Focusing on the Drosophila olfactory pathway, we hypothesized that a simple, yet universal model could describe not only this ubiquitous phenomenon, but also provide a characterization of the functional logic organizing olfactory processing. In this thesis, we introduced a class of operators, called Spatio-Temporal Divisive Normalization Processors (DNP), which we characterized at both the implementational level (differential DNP described by dynamical systems) and the algorithmic level (convolutional DNP described by nonlinear systems theory). We showed that the DNP is an invertible operator that naturally balances change detection and input invariance, and applied the DNP to circuit-level models of the Antennal Lobe and the Mushroom Body to describe the functional logic of mono-molecular and mixture odorant processing in the Drosophila early olfactory pathway. First, we studied the Drosophila Antennal Lobe, which receives as input the output responses of the Olfactory Sensory Neurons (OSNs) in the Drosophila Antenna, where the odorant object identity is multiplicatively coupled with the odorant concentration waveform. We hypothesized that the Antennal Lobe decouples semantic information in the odorant object identity from the syntactic information in the odorant concentration waveform. We found that single-channel physiological recording of Projection Neurons can be linearly decomposed into a concentration-invariant piecewise-constant component, and two transient components boosting the positive/negative concentration contrast that indicates odorant onset/offset. We hypothesized that the piecewise-constant component, in the multi-channel context, is the recovered odorant identity vector. We developed models of the Local Neuron (LN) pathways in the Antennal Lobe termed the differential Divisive Normalization Processors (DNPs) which robustly extract odorant identity (semantic information) and ON/OFF odorant event-timing (syntactic information). For real-time processing with spiking PNs, we showed that the phase-space of the PN Biophysical Spike Generator models offers an intuit perspective for the representations of recovered odorant semantics and computed odorant syntactics. Finally, we provided theoretical and computational evidence for the robustness of the functional logic of the AL as an ON-OFF odorant object identity recovery processor across odorant identities, concentration amplitudes, and waveform profiles. Next, we studied the interface between Drosophila Antennal Lobe and the Mushroom Body. In particular, we developed a model for the expansion/normalization circuit comprised of the Projection Neurons (PNs), the Kenyon Cells (KCs), and the Anterior Paired Lateral Neuron (APL). By modeling the APL to KC inhibition using differential DNP, we show that the PN-KC-APL circuit can achieve a high-dimensional sparse spatio-temporal representation of odorant inputs across odorant identities, concentrations, and mixture compositions. By formulating the problem of identifying odorant components in a mixture as a blind source separation problem, we showed that the KC spatio-temporal representations of odorant mixture inputs lead to better odorant demixing performance than that of the PNs. Furthermore, by comparing PN-KC-APL circuits with different configurations (e.g., the number of KCs, strengths of APL to KC inhibition, and the number of PNs connected to the same KC), we demonstrated computationally that both the expansion and normalization components of the PN-KC-APL circuits are necessary to achieve the improved odorant demixing performance. Additionally, we provided a theoretical characterization of the expansion/normalization circuit that explains the differences between mixture representations at the level of the PNs vs KCs. We, therefore, concluded that the Mushroom Body encoding of the odorant mixture is uniquely suited for balancing the sensitivity and specificity of odorant mixture representations. Closely related to the differential DNP is its algorithmic analog termed the convolutional DNP, which has been previously shown to outperform other state-of-the-art algorithmic level models (e.g. Channel Identification Machines, Linear-Nonlear Cascade) in capturing the input/output relationships of differential DNP models. We showed that convolutional DNPs underlie many models of neural circuits arising in auditory and visual systems. However, due to the highly nonlinear nature of convolutional DNP in applications such as contrast gain control, there has been a lack of quantitative characterization of the information content of the signals at the output of these circuits. We studied the information content of convolutional DNP output by formulating the problem as input signal recovery given output samples. We show both theoretically and computationally that the convolutional DNP is an invertible transform given sufficient (potentially asynchronous) output samples. Finally, we put forward the FlyBrainLab, an interactive computing platform that integrates 3D exploration/visualization of diverse datasets with interactive exploration of the functional logic of modeled executable brain circuits. FlyBrainLab’s User Interface, Utilities Libraries, Circuit Libraries as well as models of molecular transduction were instrumental in accelerating the computational explorations of the olfactory circuit models presented herein. Additionally, FlyBrainLab was designed with comparative studies in mind, which we demonstrated by juxtaposing, amongst others, models of odorant representation in the larval and adult Drosophila olfactory pathways.

Switched and adaptive parasitic monopole array radiating structures are investigated. Antenna design is orientated toward increasing practicability for implementation in terrestrial wireless communication systems. A number of antennas are designed with the aid of optimization and commercial simulation software. Simulation procedure was verified with the experimental manufacture and measurement of the arrays. The antennas presented in this thesis comprise an active monopole surrounded by a ring of parasitic monopoles. Parasitic radiators are constructed with static loading to enable simple experimental realization. Beam positions of an electrically steered equivalent antenna are thus simulated. Antenna symmetry ensures the beam can be reproduced throughout the azimuth. Complex antenna geometries require antenna design through optimization. A genetic algorithm is employed with HFSS and NEC for electromagnetic analysis. The robust optimization method couples with simulation software flexibility to provide an effective design tool for arbitrary structures. The genetic algorithm is employed strictly for design and not complete structural optimization. Dual band, five and six element switched parasitic antennas are presented. Lumped elemental loading along the radiators provide resonance and directed radiation at two GSM frequencies. Load value, radiator dimension and spacing are incorporated as design parameters. Experimentally built, 10dB return loss bandwidths of 17.2% and 9.6% and front to back ratios of 12.6dB and 8.4dB at 900MHz and 1900MHz respectively are measured. To reduce the ground requirements of monopole arrays, a skirted ground structure for switched parasitic antennas is analyzed. A six element switched parasitic monopole array with conductive ground skirt exhibits a front to back ratio of 10.7dB and main lobe gain of 6.4dBi at 1.575GHz. Radiation is not elevated despite lateral ground terminating at the parasitic elements. Skirt height is observed to linearly control radiation elevation, depressing the principal lobe through 40 degrees from 23 degrees above the horizontal. The Electronically Steerable Passive Array Radiator or ESPAR antenna is an adaptive parasitic monopole array. An ESPAR radiating structure incorporating a conductive ground skirt is designed for operation at 2.4GHz. Utility is confirmed with a frequency sensitivity analysis showing consistent electrical characteristics over an 8.1% bandwidth. The antenna design is improved with optimization to reduce average principal lobe elevation from 25 degrees to 9.7 degrees.

The use of directional antennae in ad-hoc networks has received growing attention in recent years because of the benefits including, high spatial reuse, higher antenna gains, etc. At the same time, using directional antennae introduces new challenges. For example, the problem of deafness where receiver nodes may not hear handshake messages because their antennae beams are not pointing in the direction of the sender. To address these issues, new directional MAC protocols are required. In the literature, the existing directional MAC protocols assumed that nodes can operate in both directional and omni-directional modes. However, using both directional and omni-directional modes of operation leads to the asymmetry-in-gain problem and defeats the purpose of using directional antennae. In this thesis, we propose a directional-to-directional (DtD) MAC protocol where both the sender and the receiver operate in directional mode only. The first part of our design studies the issues related to directional MAC protocols and we use this knowledge to carefully design the DtD MAC protocol. The DtD MAC protocol is fully distributed, does not require synchronization, eliminates the asymmetry-in-gain problem and alleviates the problems due to deafness. To evaluate the performance of the DtD MAC protocol, we build an analytical model that measures the saturation throughput of the DtD MAC protocol in terms of the number of nodes contending for the channel, the packet payload size and the antennae beamwidth. The analytical results were verified through extensive simulations. We show that the DtD MAC protocol can provide significant throughput improvement in ad-hoc networks if the number of antennae sectors is chosen appropriately. Furthermore, we study the fairness of DtD MAC using Jain's Fairness Index. Finally, the performance of the DtD MAC protocol is evaluated for the high data rate Millimeter Wave (mmWave) technology. The results obtained are promising and show that DtD MAC can improve the performance of networks using such high data rate technologies.