Classical light vs. nonclassical light: Characterizations and interesting applications

We briefly review the ideas that have shaped modern optics and have led to various applications of light ranging from spectroscopy to astrophysics, and street lights to quantum communication. The review is primarily focused on the modern applications of classical light and nonclassical light. Specific attention has been given to the applications of squeezed, antibunched, and entangled states of radiation field. Applications of Fock states (especially single photon states) in the field of quantum communication are also discussed.Comment: 32 pages, 3 figures, a review on applications of ligh

A review of RFI mitigation techniques in microwave radiometry

Radio frequency interference (RFI) is a well-known problem in microwave radiometry (MWR). Any undesired signal overlapping the MWR protected frequency bands introduces a bias in the measurements, which can corrupt the retrieved geophysical parameters. This paper presents a literature review of RFI detection and mitigation techniques for microwave radiometry from space. The reviewed techniques are divided between real aperture and aperture synthesis. A discussion and assessment of the application of RFI mitigation techniques is presented for each type of radiometer.Peer ReviewedPostprint (published version

Testing quantum mechanics: a statistical approach

As experiments continue to push the quantum-classical boundary using increasingly complex dynamical systems, the interpretation of experimental data becomes more and more challenging: when the observations are noisy, indirect, and limited, how can we be sure that we are observing quantum behavior? This tutorial highlights some of the difficulties in such experimental tests of quantum mechanics, using optomechanics as the central example, and discusses how the issues can be resolved using techniques from statistics and insights from quantum information theory.Comment: v1: 2 pages; v2: invited tutorial for Quantum Measurements and Quantum Metrology, substantial expansion of v1, 19 pages; v3: accepted; v4: corrected some errors, publishe

Space/time/frequency methods in adaptive radar

Radar systems may be processed with various space, time and frequency techniques. Advanced radar systems are required to detect targets in the presence of jamming and clutter. This work studies the application of two types of radar systems. It is well known that targets moving along-track within a Synthetic Aperture Radar field of view are imaged as defocused objects. The SAR stripmap mode is tuned to stationary ground targets and the mismatch between the SAR processing parameters and the target motion parameters causes the energy to spill over to adjacent image pixels, thus hindering target feature extraction and reducing the probability of detection. The problem can be remedied by generating the image using a filter matched to the actual target motion parameters, effectively focusing the SAR image on the target. For a fixed rate of motion the target velocity can be estimated from the slope of the Doppler frequency characteristic. The problem is similar to the classical problem of estimating the instantaneous frequency of a linear FM signal (chirp). The Wigner-Ville distribution, the Gabor expansion, the Short-Time Fourier transform and the Continuous Wavelet Transform are compared with respect to their performance in noisy SAR data to estimate the instantaneous Doppler frequency of range compressed SAR data. It is shown that these methods exhibit sharp signal-to-noise threshold effects. The space-time radar problem is well suited to the application of techniques that take advantage of the low-rank property of the space-time covariance matrix. It is shown that reduced-rank methods outperform full-rank space-time adaptive processing when the space-time covariance matrix is estimated from a dataset with limited support. The utility of reduced-rank methods is demonstrated by theoretical analysis, simulations and analysis of real data. It is shown that reduced-rank processing has two effects on the performance: increased statistical stability which tends to improve performance, and introduction of a bias which lowers the signal-to-noise ratio. A method for evaluating the theoretical conditioned SNR for fixed reduced-rank transforms is also presented

Generating Pictures from Waves: Aspects of Image Formation

Thesis Supervisor: Gregory W. Wornell Title: Professor of Electrical Engineering and Computer ScienceThe research communities, technologies, and tools for image formation are diverse. On the one hand, computer vision and graphics researchers analyze incoherent light using coarse geometric approximations from optics. On the other hand, array signal processing and acoustics researchers analyze coherent sound waves using stochastic estimation theory and diffraction formulas from physics. The ability to inexpensively fabricate analog circuitry and digital logic for millimeter-wave radar and ultrasound creates opportunities in comparing diverse perspectives on image formation, and presents challenges in implementing imaging systems that scale in size. We present algorithms, architectures, and abstractions for image formation that relate the different communities, technologies, and tools. We address practical technical challenges in operating millimeter-wave radar and ultrasound systems in the presence of phase noise and scattering. We model a broad class of physical phenomena with isotropic point sources. We show that the optimal source location estimator for coherent waves reduces to processing an image produced by a conventional camera, provided the sources are wellseparated relative to the system resolution, and in the limit of small wavelength and globally incoherent light. We introduce quasi light fields to generalize the incoherent image formation process to coherent waves, offering resolution tradeoffs that surpass the traditional Fourier uncertainty principle by leveraging time-frequency distributions. We show that the number of sensors in a coherent imaging array defines a stable operating point relative to the phase noise. We introduce a digital phase tightening algorithm to reduce phase noise. We present a system identification framework for multiple-input multiple-output (MIMO) ultrasound imaging that generalizes existing approaches with time-varying filters. Our theoretical results enable the application of traditional techniques in incoherent imaging to coherent imaging, and vice versa. Our practical results suggest a methodology for designing millimeter-wave imaging systems. Our conclusions reinforce architectural principles governing transmitter and receiver design, the role of analog and digital circuity, and the tradeoff between data rate and data precision.Microsoft Research, MIT Lincoln Laboratory, and the C2S2 Focus Center, one of six research centers funded under the Focus Center Research Program (FCRP), a Semiconductor Research Corporation entity

Bilinear time-frequency representations of heart rate variability and respiration during stress

Recently, joint time-frequency signal representation has received considerable attention as a powerful tool for analyzing a variety of signals and systems. In particular, if the frequency content is time varying as in signals of biological origin which often do not comply with the stationarity assumptions, then this approach is quite attractive. In this dissertation, we explore the possibility of better representation of two particular biological signals, namely heart rate variability (HRV) and respiration. We propose the use of time-frequency analysis as a new and innovative approach to examine the physical and mental exertion attributed to exercise. Two studies are used for the main investigation, the preliminary and anticipation protocols. In the first phase of this work, the application of five different bilinear representations on modeled HRV test signals and experimental HRV and respiration signals of the preliminary protocol is evaluated. Each distribution: the short time Fourier transform (STFT), the pseudo Wigner-Ville (WVD), the smoothed pseudo Wigner-Ville (SPWVD), The Choi-Williams (CWD), and the Born-Jordan-Cohen (RID) has unique characteristics which is shown to affect the amount of smoothing and the generation of cross-terms differently . The CWD and the SPWVD are chosen for further application because of overcoming the drawbacks of the other distributions by providing higher resolution in time arid frequency while suppressing interferences between the signal components. In the second phase of this research, the SPWVD and CWD are used to investigate the presence of an anticipatory component due to the stressful exercise condition as reflected in the HRV signal from a change in behavior in the autonomic nervous system. By expanding the concept of spectral analysis of heart rate variability (HRV) into time-frequency analysis, we are able to quantitatively assess the parasympathetic (HF) and sympatho-vagal balance (LF:HF) changes as a function of time. As a result, the assessment of the autonomic nervous system during rapid changes is made. A new methodology is also proposed that adaptively uncovers the region of parasympathetic activity. It is well known that parasympathetic activity is highly correlated with the respiration frequency. This technique traces the respiration frequency and extracts the corresponding parasympathetic activity from the heart rate variability signal by adaptive filtering