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A system for exciting Rubidium atoms to Rydberg states has been constructed to study the interactions between them and metal surfaces. This thesis describes a method to reach the f Rydberg series using diode lasers. Since the diode lasers need to be frequency stabilized for this excitation, a transfer cavity stabilization method was implemented using the Pound-Drever-Hall technique. To obtain the necessary frequency modulation the diode laser was current modulated at ∼ 6 MHz. A noise cancellation circuit was used to suppress detection of the accompanying residual intensity modulation.

This thesis is composed of three parts. The first part is devoted to introducing the various noise sources in transistors and their equivalent noise models. Based on the equivalent noise models, the theory of noise cancellation in a low-noise amplifier is derived in detail. The second part is to perform an experiment to validate the theory of low-noise amplifier using common-gate noise cancellation technique. By adjusting the transconductance of individual transistor, the simulated and measured noise figures are compared under different noise cancellation conditions. The third part is to design a low-noise amplifier RFIC using common-source noise cancellation technique for DVB-H applications. This RFIC was implemented in a TSMC 0.18£gm process and measured to show successful noise cancellation capability in a wide frequency range.

A system for exciting Rubidium atoms to Rydberg states has been constructed to study the interactions between them and metal surfaces. This thesis describes a method to reach the f Rydberg series using diode lasers. Since the diode lasers need to be frequency stabilized for this excitation, a transfer cavity stabilization method was implemented using the Pound-Drever-Hall technique. To obtain the necessary frequency modulation the diode laser was current modulated at ∼ 6 MHz. A noise cancellation circuit was used to suppress detection of the accompanying residual intensity modulation.

No description available.

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.

For the fractional-N frequency synthesizers using delta-sigma modulation (DSM) techniques, higher PLL bandwidth is highly desirable in order to achieve faster settling time. As the PLL bandwidth is increased, more quantization noises pass through the PLL so that the output phase noise performance is degraded. There is a tradeoff between phase-noise performance and PLL bandwidth. To improve the problem, the thesis studies the quantization noise cancellation technique. With this technique, the PLL bandwidth can be increased without the cost of degrading phase-noise performance. With the help of Agilent EEsof¡¦s ADS, the phase-noise performance of the studied fractional-N frequency synthesizers can be predicted. For demonstration, this research implements a 2.6 GHz fractional-N frequency synthesizer hybrid module, and compares the measured phase noises with and without the technique under considering various combinations of MASH DSM orders and PLL bandwidth. Another demonstration of this thesis is to design a PLL IC using TSMC 0.18 £gm CMOS process, and make a discussion on the testing performance of the PLL IC.