Signal processing

When acoustic measurements of moving vehicles are made by a stationary observer, the Doppler shift has two detrimental effects on the interpretation of the data. The spectra are smeared by the change in Doppler factor during the vehicle pass by, and the motion induced phase shift in the signals causes errors. The measured signals can be corrected back to source time if a moving time delay correction is applied. However, when the signals are sampled digitally this time delay correction requires an estimate to be made of the signal level between samples. This can be achieved by using a digital filter with time varying coefficients which estimates the signal from at least two adjacent samples. Results of both numerical simulations and real applications of this technique will be given.

This thesis focuses on the development of a data acquisition and signal processing system for analysis of electromyogram (EMG) signals. The data acquisition system was based on a personal computer and was set up for simultaneous recording of three analog channels. Two of these channels were used to record the EMG signals from the triceps and biceps muscles respectively, and the third channel was used to record the acceleration signals obtained from an accelerometer placed on the subject's arm. The objective of the signal processing was to find some characteristic parameters for the EMG signals, so that these parameters could be used in a microprocessor based system for Functional Electrical Stimulation (FES). Such a system may be useful in the rehabilitation of patients with partial paralysis of limbs as a result of brain damage.

Signal Integrity is a major bottleneck for DSM designs. Signal integrity refers to wide variety of problems, which leads to misconception. Signal integrity causes delay or noise at the high-level, but this boils down to resistance, capacitance and inductance (RLC) at circuit level. Several analysis and reduction techniques were proposed for reducing these effects on signal integrity. This work solves the misconception by encompassing different problems Chat effect signal integrity and can be good reference for a integrated circuit designer. The objective is to analyze these modeling methods, reduction techniques, tools and make recommendations that aids in developing a methodology for perfect design closure with an emphasis on signal integrity. These recommendations would form a basis for developing a methodology to analyze interference effects at higher levels of abstraction.

A new method for calculating the direction-of-arrival (DOA), and thus the bathymetry of the seafloor, is presented. This method will calculate the DOA directly from the phase difference between the phase centers of the array. In parallel, a bathymetric sidescan sonar system originally built at Woods Hole and now here at Florida Atlantic University's Department of Ocean Engineering, was completed. Once this system was working, the above mentioned signal analysis regime will be implemented on actual data to test its validity.

This thesis provides detailed analysis and design techniques for Wigner-Ville spectrum (WVS) estimators for use with cyclostationary signals. The resulting class of estimators represent a newly defined subset of Cohen's class characterized by a mixed discrete-time/continuous-frequency smoothing kernel. Although both time-variant and shift invariant versions of the estimator are developed, emphasis is placed on the shift-invariant version which is designed to estimate the WVS over an entire period from a single observation. Bias and variance expressions are derived for the new estimator, and these are compared with the general estimator. For this development, we also derive mean and covariance expressions for the general quasi-stationary based estimators, both for the autocorrelation estimator and for the WVS estimator. The concept of quasi-stationarity is extended to cyclostationary models, and we develop a novel measure of kernel smoothing and variance reduction termed the time-bandwidth area. This is a generalization of time-bandwidth product to describe arbitrary kernel functions, even those which are not governed by the uncertainty principle (such as the newly proposed estimators). The properties of the estimator are examined in terms of constraints on the smoothing kernel. In sharp contrast to the conventional estimators based on the quasi-stationary assumption, the low bias and low variance constraints for the new class of estimators do not contradict one another. The relationship between time dependent spectral estimation for nonstationary processes and classical Blackman-Tukey type spectral estimation for stationary processes is developed next. Using examples the utility of the new estimator kernels are shown. It is seen that in random or noisy environments it may be difficult to achieve a reasonable trade-off between variance reduction and bias using conventional estimators. In the examples any assumption of quasi-stationarity sufficient to produce a low variance estimate would destroy many or all of the nonstationary features of the signal. However, since the signals are cyclostationary we can employ the new class of estimators to achieve an excellent balance between bias and variance reduction.

In the current communications age, the capabilities of mobile devices are increasing. The mobiles are capable of communicating at data rates of hundreds of mbps on 4G networks. This enables playback of rich multimedia content comparable to internet and television networks. However, mobile networks need to be spectrum-efficient to be affordable to users. Multimedia Broadcast Multicast Systems (MBMS) is a wireless broadcasting standard that is being drafted to enable multimedia broadcast while focusing on being spectrum-efficient. The hybrid video coding techniques facilitate low bitrate transmission, but result in dependencies across frames. With a mobile environment being error prone, no error correction technique can guarantee error free transmission. Such errors propagate, resulting in quality degradation. With numerous mobiles sharing the broadcast session, any error resilient scheme should account for heterogeneous device capabilities and channel conditions. The current research on wireless video broadcasting focuses on network based techniques such as FEC and retransmissions, which add bandwidth overhead. There is a need to design innovative error resilient techniques that make video codec robust with minimal bandwidth overhead. This Dissertation introduces novel techniques in the area of MBMS systems. First, robust video structures are proposed in Periodic Intra Frame based Prediction (PIFBP) and Periodic Anchor Frame based Prediction (PAFBP) schemes. In these schemes, the Intra frames or anchor frames serve as reference frames for prediction during GOP period. The intermediate frames are independent of others; any errors in such frames are not propagated, thereby resulting in error resilience. In prior art, intra block rate is adapted based on the channel characteristics for error resilience. This scheme has been generalized in multicasting to address a group of users sharing the same session. Average packet loss is used to determine the intra block rate. This improves performance of the overall group and strives for consistent performance. Also, the inherent diversity in the broadcasting session can be used for its advantage. With mobile devices capable of accessing a WLAN during broadcast, they form an adhoc network on a WLAN to recover lost packets. New error recovery schemes are proposed for error recovery and their performance comparison is presented.

Cache memory is used, in most single-core and multi-core processors, to improve performance by bridging the speed gap between the main memory and CPU. Even though cache increases performance, it poses some serious challenges for embedded systems running real-time applications. Cache introduces execution time unpredictability due to its adaptive and dynamic nature and cache consumes vast amount of power to be operated. Energy requirement and execution time predictability are crucial for the success of real-time embedded systems. Various cache optimization schemes have been proposed to address the performance, power consumption, and predictability issues. However, currently available solutions are not adequate for real-time embedded systems as they do not address the performance, power consumption, and execution time predictability issues at the same time. Moreover, existing solutions are not suitable for dealing with multi-core architecture issues. In this dissertation, we develop a methodology through cache optimization for real-time embedded systems that can be used to analyze and improve execution time predictability and performance/power ratio at the same time. This methodology is effective for both single-core and multi-core systems. First, we develop a cache modeling and optimization technique for single-core systems to improve performance. Then, we develop a cache modeling and optimization technique for multi-core systems to improve performance/power ratio. We develop a cache locking scheme to improve execution time predictability for real-time systems. We introduce Miss Table (MT) based cache locking scheme with victim cache (VC) to improve predictability and performance/power ratio. MT holds information about memory blocks, which may cause more misses if not locked, to improve cache locking performance.