A. Bruce Carlson

Bio: A. Bruce Carlson is an academic researcher. The author has contributed to research in topics: Analog signal & Noise floor. The author has an hindex of 1, co-authored 1 publications receiving 451 citations.

Communication systems: an introduction to signals and noise in electrical communication

Abstract: 1 Introduction 2 Signals and Spectra 3 Signal Transmission and Filtering 4 Linear CW Modulation 5 Exponential CW Modulation 6 Sampling and Pulse Modulation 7 Analog Communication Systems 8 Probability and Random Variables 9 Random Signals and Noise 10 Noise in Analog Modulation Systems 11 Baseband Digital Transmission 12 Digitization Techniques for Analog Messages and Computer Networks 13 Channel Coding and Encryption 14 Bandpass Digital Transmission 15 Spread Spectrum Systems 16 Information and Detection Theory Appendix: Circuit and System Noise

Fundamental Limitations in Filtering and Control

Abstract: The issue of fundamental limitations in filtering and control lies at the very heart of any feedback system design, since it reveals what is and is not achievable on the basis of that system's structural and dynamic characteristics Alongside new succinct treatments of Bode's original results from the 1940s, this book presents a comprehensive analysis of modern results, featuring contemporary developments in multivariable systems, sampled-data, periodic and nonlinear problems The text gives particular prominence to sensitivity functions which measure the fundamental qualities of the system, including performance and robustness With extensive appendices covering the necessary background on complex variable theory, this book is an ideal self-contained resource for researchers and practitioners in this field

Models for Bearing Damage Detection in Induction Motors Using Stator Current Monitoring

Abstract: This paper describes a new analytical model for the influence of rolling-element bearing faults on induction motor stator current. Bearing problems are one major cause for drive failures. Their detection is possible by vibration monitoring of characteristic bearing frequencies. As it is possible to detect other machine faults by monitoring the stator current, a great interest exists in applying the same method for bearing fault detection. After a presentation of the existing fault model, a new detailed approach is proposed. It is based on the following two effects of a bearing fault: 1. the introduction of a particular radial rotor movement and 2. load torque variations caused by the bearing fault. The theoretical study results in new expressions for the stator current frequency content. Experimental tests with artificial and realistic bearing damage were conducted by measuring vibration, torque, and stator current. The obtained results by spectral analysis of the measured quantities validate the proposed theoretical approach.

Power spectral density of unevenly sampled data by least-square analysis: performance and application to heart rate signals

Abstract: This work studies the frequency behavior of a least-square method to estimate the power spectral density of unevenly sampled signals. When the uneven sampling can be modeled as uniform sampling plus a stationary random deviation, this spectrum results in a periodic repetition of the original continuous time spectrum at the mean Nyquist frequency, with a low-pass effect affecting upper frequency bands that depends on the sampling dispersion. If the dispersion is small compared with the mean sampling period, the estimation at the base band is unbiased with practically no dispersion. When uneven sampling is modeled by a deterministic sinusoidal variation respect to the uniform sampling the obtained results are in agreement with those obtained for small random deviation. This approximation is usually well satisfied in signals like heart rate (HR) series. The theoretically predicted performance has been tested and corroborated with simulated and real HR signals. The Lomb method has been compared with the classical power spectral density (PSD) estimators that include resampling to get uniform sampling. The authors have found that the Lomb method avoids the major problem of classical methods: the low-pass effect of the resampling. Also only frequencies up to the mean Nyquist frequency should be considered (lower than 0.5 Hz if the HR is lower than 60 bpm). It is concluded that for PSD estimation of unevenly sampled signals the Lomb method is more suitable than fast Fourier transform or autoregressive estimate with linear or cubic interpolation. In extreme situations (low-HR or high-frequency components) the Lomb estimate still introduces high-frequency contamination that suggest further studies of superior performance interpolators. In the case of HR signals the authors have also marked the convenience of selecting a stationary heart rate period to carry out a heart rate variability analysis.

Signal modeling for ion cyclotron resonance

Abstract: A new signal model, called the rotating electric monopole signal model, is developed for ion cyclotron resonance (ICR) experiments. Unlike prior signal models, the model is applicable to all forms of ICR spectroscopy which use radio‐frequency detection methods. The signal model consists of a rotating electric monopole which is shown to be equivalent for signal generation purposes to a rotating electric dipole and for a macroscopic number of ions to a rotating electric polarization. The rotating polarization induces an alternating charge in the plates of the ICR cell and an alternating current between and an alternating voltage across the plates of the ICR cell. A noise analysis is performed, and if amplifier noise is neglected, the ICR sensitivity according to the model is found to be inversely proportional to the ion mass. This is the same sensitivity dependence as is found with conventional ICR power absorption experiments. The ICR voltage signal strength is inversely proportional to ion mass if the monitoring circuit is predominantly resistive and is independent of ion mass if the circuit is predominantly capacitive. The model clearly illustrates the parallelism between signal generation in ICR spectroscopy and signal generation in other coherent radiation spectroscopies such as NMR and microwave spectroscopy.