Rapid Frequency Estimation

Frequency estimation plays an important role in many digital signal processing applications. Many areas have benefited from the discovery of the Fast Fourier Transform (FFT) decades ago and from the relatively recent advances in modern spectral estimation techniques within the last few decades. As processor and programmable logic technologies advance, unconventional methods for rapid frequency estimation in white Gaussian noise should be considered for real time applications. In this thesis, a practical hardware implementation that combines two known frequency estimation techniques is presented, implemented, and characterized. The combined implementation, using the well known FFT and a less well known modern spectral analysis method known as the Direct State Space (DSS) algorithm, is used to demonstrate and promote application of modern spectral methods in various real time applications, including Electronic Counter Measure (ECM) techniques

Implementation of a Real-Time Beamforming System on Field Programmable Gate Array

Beamforming is an important technique in array signal processing and wireless communication systems. In this project, we investigate the Minimum Variance Distortionless Response (MVDR) beamforming technique and its implementation. The QR-RLS algorithm is chosen because of its advantages of numerical stability and systolic array architecture. The team successfully implemented the real-time beamforming of a linear array with 3 receiving antennas on a Xilinx Virtex-5 FPGA platform. Both the simulation and hardware implementation results are presented in this report

Investigation of peripheral photoplethysmographic morphology changes induced during a hand-elevation study

A hand-elevation study was carried out in the laboratory in order to alter peripheral blood flow with the aim of increasing understanding of factors affecting the morphology of peripheral photoplethysmographic signals. Photoplethysmographic (PPG) signals were recorded from twenty healthy volunteer subjects during a hand-elevation study in which the right hand was raised and lowered relative to heart level, while the left hand remained static. Red and infrared (IR) PPG signals were obtained from the right and left index fingers using a custom-made PPG processing system. PPG features were identified using a feature-detection algorithm based on the first derivative of the PPG signal. The systolic PPG amplitude, the reflection index, crest time, pulse width at half height, and delta T were calculated from 20 s IR PPG signals from three positions of the right hand with respect to heart level (-50, 0, +50 cm) in 19 volunteers. PPG features were found to change with hand elevation. On lowering the hand to 50 cm below heart level, ac systolic PPG amplitudes from the finger decreased by 68.32 %, while raising the arm increased the systolic amplitude by 69.99 %. These changes in amplitude were attributed to changes in hydrostatic pressure and the veno-arterial reflex. Other morphological variables, such as crest time, were found to be statistically significantly different across hand positions, indicating increased vascular resistance on arm elevation than on dependency. It was hypothesized that these morphological PPG changes were influenced by changes in downstream venous resistance, rather than arterial, or arteriolar, resistance. Changes in hand position relative to heart level can significantly affect the morphology of the peripheral ac PPG waveform. These alterations are due to a combination of physical effects and physiological responses to changes in hand position, which alter vascular resistance. Care should be taken when interpreting morphological data derived from PPG signals and methods should be standardized to take these effects into account

Neural Dynamics Underlying Impaired Autonomic and Conditioned Responses Following Amygdala and Orbitofrontal Lesions

A neural model is presented that explains how outcome-specific learning modulates affect, decision-making and Pavlovian conditioned approach responses. The model addresses how brain regions responsible for affective learning and habit learning interact, and answers a central question: What are the relative contributions of the amygdala and orbitofrontal cortex to emotion and behavior? In the model, the amygdala calculates outcome value while the orbitofrontal cortex influences attention and conditioned responding by assigning value information to stimuli. Model simulations replicate autonomic, electrophysiological, and behavioral data associated with three tasks commonly used to assay these phenomena: Food consumption, Pavlovian conditioning, and visual discrimination. Interactions of the basal ganglia and amygdala with sensory and orbitofrontal cortices enable the model to replicate the complex pattern of spared and impaired behavioral and emotional capacities seen following lesions of the amygdala and orbitofrontal cortex.National Science Foundation (SBE-0354378; IIS-97-20333); Office of Naval Research (N00014-01-1-0624); Defense Advanced Research Projects Agency and the Office of Naval Research (N00014-95-1-0409); National Institutes of Health (R29-DC02952

Efficient DSP and Circuit Architectures for Massive MIMO: State-of-the-Art and Future Directions

Massive MIMO is a compelling wireless access concept that relies on the use of an excess number of base-station antennas, relative to the number of active terminals. This technology is a main component of 5G New Radio (NR) and addresses all important requirements of future wireless standards: a great capacity increase, the support of many simultaneous users, and improvement in energy efficiency. Massive MIMO requires the simultaneous processing of signals from many antenna chains, and computational operations on large matrices. The complexity of the digital processing has been viewed as a fundamental obstacle to the feasibility of Massive MIMO in the past. Recent advances on system-algorithm-hardware co-design have led to extremely energy-efficient implementations. These exploit opportunities in deeply-scaled silicon technologies and perform partly distributed processing to cope with the bottlenecks encountered in the interconnection of many signals. For example, prototype ASIC implementations have demonstrated zero-forcing precoding in real time at a 55 mW power consumption (20 MHz bandwidth, 128 antennas, multiplexing of 8 terminals). Coarse and even error-prone digital processing in the antenna paths permits a reduction of consumption with a factor of 2 to 5. This article summarizes the fundamental technical contributions to efficient digital signal processing for Massive MIMO. The opportunities and constraints on operating on low-complexity RF and analog hardware chains are clarified. It illustrates how terminals can benefit from improved energy efficiency. The status of technology and real-life prototypes discussed. Open challenges and directions for future research are suggested.Comment: submitted to IEEE transactions on signal processin

Development of computer technology for medical data analysis to be applied to Apollo and follow-on space missions Final report

Computer technology for medical data analysis of Apollo and follow-on mission

On generalized adaptive neural filter

Linear filters have historically been used in the past as the most useful tools for suppressing noise in signal processing. It has been shown that the optimal filter which minimizes the mean square error (MSE) between the filter output and the desired output is a linear filter provided that the noise is additive white Gaussian noise (AWGN). However, in most signal processing applications, the noise in the channel through which a signal is transmitted is not AWGN; it is not stationary, and it may have unknown characteristics. To overcome the shortcomings of linear filters, nonlinear filters ranging from the median filters to stack filters have been developed. They have been successfully used in a number of applications, such as enhancing the signal-to-noise ratio of the telecommunication receivers, modeling the human vocal tract to synthesize speech in speech processing, and separating out the maternal and fetal electrocardiogram signals to diagnose prenatal ailments. In particular, stack filters have been shown to provide robust noise suppression, and are easily implementable in hardware, but configuring an optimal stack filter remains a challenge. This dissertation takes on this challenge by extending stack filters to a new class of nonlinear adaptive filters called generalized adaptive neural filters (GANFs). The objective of this work is to investigate their performance in terms of the mean absolute error criterion, to evaluate and predict the generalization of various discriminant functions employed for GANFs, and to address issues regarding their applications and implementation. It is shown that GANFs not only extend the class of stack filters, but also have better performance in terms of suppressing non-additive white Gaussian noise. Several results are drawn from the theoretical and experimental work: stack filters can be adaptively configured by neural networks; GANFs encompass a large class of nonlinear sliding-window filters which include stack filters; the mean absolute error (MAE) of the optimal GANF is upper-bounded by that of the optimal stack filter; a suitable class of discriminant functions can be determined before a training scheme is executed; VC dimension (VCdim) theory can be applied to determine the number of training samples; the algorithm presented in configuring GANFs is effective and robust