From white elephant to Nobel Prize: Dennis Gabor’s wavefront reconstruction

Dennis Gabor devised a new concept for optical imaging in 1947 that went by a variety of names over the following decade: holoscopy, wavefront reconstruction, interference microscopy, diffraction microscopy and Gaboroscopy. A well-connected and creative research engineer, Gabor worked actively to publicize and exploit his concept, but the scheme failed to capture the interest of many researchers. Gabor’s theory was repeatedly deemed unintuitive and baffling; the technique was appraised by his contemporaries to be of dubious practicality and, at best, constrained to a narrow branch of science. By the late 1950s, Gabor’s subject had been assessed by its handful of practitioners to be a white elephant. Nevertheless, the concept was later rehabilitated by the research of Emmett Leith and Juris Upatnieks at the University of Michigan, and Yury Denisyuk at the Vavilov Institute in Leningrad. What had been judged a failure was recast as a success: evaluations of Gabor’s work were transformed during the 1960s, when it was represented as the foundation on which to construct the new and distinctly different subject of holography, a re-evaluation that gained the Nobel Prize for Physics for Gabor alone in 1971. This paper focuses on the difficulties experienced in constructing a meaningful subject, a practical application and a viable technical community from Gabor’s ideas during the decade 1947-1957

Signal concentration and related concepts in time-frequency and on the unit sphere

Unit sphere signal processing is an increasingly active area of research with applications in computer vision, medical imaging, geophysics, cosmology and wireless communications. However, comparing with signal processing in time-frequency domain, characterization and processing of signals defined on the unit sphere is relatively unfamiliar for most of the engineering researchers. In order to better understand and analysis the current issues using the spherical model, such as analysis of brain neural electronic activities in medical imaging and neuroscience, target detection and tracking in radar systems, earthquake occurrence prediction and seismic origin detection in seismology, it is necessary to set up a systematic theory for unit sphere signal processing. How to efficiently analyze and represent functions defined on the unit sphere are central for the unit sphere signal processing, such as filtering, smoothing, detection and estimation in the presence of noise and interference. Slepian-Landau-Pollak time-frequency energy concentration theory and the essential dimensionality of time-frequency signals by the Fourier transform are the fundamental tools for signal processing in the time-frequency domain. Therefore, our research work starts from the analogies of signals between time-frequency and spatial-spectral. In this thesis, we first formulate the k-th moment time-duration weighting measure for a band-limited signal using a general constrained variational method, where a complete, orthonormal set of optimal band-limited functions with the minimum fourth moment time-duration measure is obtained and the prospective applications are discussed. Further, the formulation to an arbitrary signal with second and fourth moment weighting in both time and frequency domain is also developed and the corresponding optimal functions are obtained, which are helpful for practical waveform designs in communication systems. Next, we develop a k-th spatially global moment azimuthal measure (GMZM) and a k-th spatially local moment zenithal measure (LMZM) for real-valued spectral-limited signals. The corresponding sets of optimal functions are solved and compared with the spherical Slepian functions. In addition, a harmonic multiplication operation is developed on the unit sphere. Using this operation, a spectral moment weighting measure to a spatial-limited signal is formulated and the corresponding optimal functions are solved. However, the performance of these sets of functions and their perspective applications in real world, such as efficiently analysis and representation of spherical signals, is still in exploration. Some spherical quadratic functionals by spherical harmonic multiplication operation are formulated in this thesis. Next, a general quadratic variational framework for signal design on the unit sphere is developed. Using this framework and the quadratic functionals, the general concentration problem to an arbitrary signal defined on the unit sphere to simultaneously achieve maximum energy in the finite spatial region and finite spherical spectrum is solved. Finally, a novel spherical convolution by defining a linear operator is proposed, which not only specializes the isotropic convolution, but also has a well defined spherical harmonic characterization. Furthermore, using the harmonic multiplication operation on the unit sphere, a reconstruction strategy without consideration of noise using analysis-synthesis filters under three different sampling methods is discussed

Effects of partial coherence on holography

The theory of two-beam holography is generalized to include quasi-monochromatic radiation of any degree of spatial coherence. It is shown that a clear, undistorted reconstruction can be obtained provided the reference beam is highly coherent. The effect of the partial coherence of the illumination is only to make the reconstructed image darker. Specifically, the amplitude of the radiation at any point of the image is proportional to the amplitude of the radiation leaving the corresponding point on the object during the exposure, times the magnitude of the coherence between the latter radiation and the reference beam. Holograms made using either a plane or a spherical reference beam are discussed in detail. A technique is developed for measuring the magnitude of the degree of coherence of the radiation at every point on the object surface, with respect to the reference beam, by measuring the irradiance at the real image reconstruction of the object. As an example, the coherence of radiation reaching a plane object from a small, circular, spatially incoherent source is measured. The data are in good agreement with the calculations made using the van Cittert-Zernike Theorem. Motion of the object during the hologram exposure is shown to affect a quantity analogous to coherence. Such a quantity is introduced and it is shown that it affects the reconstruction in a way that is almost identical to that of partial coherence. The technique for measuring coherence is then extended to the measurement of displacements of the object. In particular, the relationship between the object displacement and the appearance of the reconstructed image is analyzed in detail for objects moving with constant or sinusoidal velocity. Several examples are given of measurements of the total displacement of objects moving with constant velocity throughout the exposure time. Displacements of one-third wavelength are easily detected

Real-time sound synthesis on a multi-processor platform

Real-time sound synthesis means that the calculation and output of each sound sample for a channel of audio information must be completed within a sample period. At a broadcasting standard, a sampling rate of 32,000 Hz, the maximum period available is 31.25 μsec. Such requirements demand a large amount of data processing power. An effective solution for this problem is a multi-processor platform; a parallel and distributed processing system. The suitability of the MIDI [Music Instrument Digital Interface] standard, published in 1983, as a controller for real-time applications is examined. Many musicians have expressed doubts on the decade old standard's ability for real-time performance. These have been investigated by measuring timing in various musical gestures, and by comparing these with the subjective characteristics of human perception. An implementation and its optimisation of real-time additive synthesis programs on a multi-transputer network are described. A prototype 81-polyphonic-note- organ configuration was implemented. By devising and deploying monitoring processes, the network's performance was measured and enhanced, leading to an efficient usage; the 88-note configuration. Since 88 simultaneous notes are rarely necessary in most performances, a scheduling program for dynamic note allocation was then introduced to achieve further efficiency gains. Considering calculation redundancies still further, a multi-sampling rate approach was applied as a further step to achieve an optimal performance. The theories underlining sound granulation, as a means of constructing complex sounds from grains, and the real-time implementation of this technique are outlined. The idea of sound granulation is quite similar to the quantum-wave theory, "acoustic quanta". Despite the conceptual simplicity, the signal processing requirements set tough demands, providing a challenge for this audio synthesis engine. Three issues arising from the results of the implementations above are discussed; the efficiency of the applications implemented, provisions for new processors and an optimal network architecture for sound synthesis

Some applications of holographic interferometry and Speckle correlation techniques to the study of plant growth and physiology

Imperial Users onl

Expectation suppression across sensory modalitites: a MEG investigation

140 p.In the last few decades, a lot of research focus has been to understand how the human brain generates expectation about the incoming sensory responses and how it deals with surprise or unpredictable input. It is evident in predictive processing literature that the human brain suppresses the neural responses to predictable/expected stimuli (termed as expectation suppression effect). This thesis provide evidence to how expectation suppression is affected by content-based expectations (what) and temporal uncertainty (when) across sensory modalities (visual and auditory) using state-of-art Magnetoencephalography (MEG) imaging. The result shows that visual domain is more sensitive to content-based expectations (what) more than the timing (when), also visual domain shows sensitivity to timing (when) only if what was predictable. However, Auditory domain is equally sensitive to what and when features, showing enhanced suppression to expectation compared to visual domain. This thesis concludes conclude that the sensory modalities deal differently with the contextual expectations and temporal predictability. This suggests that while investigating predictive processing in the human brain, the modality specific differences should be considered, since the predictive mechanism at work in one domain should not necessarily be generalized to other domains as well

A cybernetic approach to prediction with an outline of an adaptive optical computer

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Since the pioneering work of Kolmogoroff and Wiener the use of computing devices to solve problems concerned with - the prediction of future states of a time series has stimulated a large amount of research. Despite all this, however the results have been disappointing. If significant progress were to be made in this field it would lead not only to the possibility of forecasting economic events, the weather, earthquakes, epidemics, and so on, but also -to the possibility of simulating these system. Approaches involving the programming of a computer to carry out this task run into the difficulty of defining the variables involved in a precise enough manner, whereas using the computer -to investigate all the past events of that time series requires a large processing time and an enormous memory store. This thesis examines an approach to this problem which involves the use of a device for processing information in a parallel manner. The system envisaged consists of a holographic recognition device controlled by a digital computer -a combination of analogue and digital techniques. The principle of this device is that developed by Gabor and others, and allows the system to learn to predict the future of a time series. The system learns to predict the future of a time series by using a past length of time series as a training set. Using this training set it attempts to predict the next values of the time series, which can then be compared with the actual time series. The system, then, attempts to optimize its prediction by minimizing the error between the predicted and actual values

A study of digital holographic filter generation

Problems associated with digital computer generation of holograms are discussed along with a criteria for producing optimum digital holograms. This criteria revolves around amplitude resolution and spatial frequency limitations induced by the computer and plotter process