Pattern recognition using invariants defined from higher order spectra : 2-D image inputs

A new algorithm for extracting features from images for object recognition is described. The algorithm uses higher order spectra to provide desirable invariance properties, to provide noise immunity, and to incorporate nonlinearity into the feature extraction procedure thereby allowing the use of simple classifiers. An image can be reduced to a set of 1D functions via the Radon transform, or alternatively, the Fourier transform of each 1D projection can be obtained from a radial slice of the 2D Fourier transform of the image according to the Fourier slice theorem. A triple product of Fourier coefficients, referred to as the deterministic bispectrum, is computed for each 1D function and is integrated along radial lines in bifrequency space. Phases of the integrated bispectra are shown to be translation- and scale-invariant. Rotation invariance is achieved by a regrouping of these invariants at a constant radius followed by a second stage of invariant extraction. Rotation invariance is thus converted to translation invariance in the second step. Results using synthetic and actual images show that isolated, compact clusters are formed in feature space. These clusters are linearly separable, indicating that the nonlinearity required in the mapping from the input space to the classification space is incorporated well into the feature extraction stage. The use of higher order spectra results in good noise immunity, as verified with synthetic and real images. Classification of images using the higher order spectra-based algorithm compares favorably to classification using the method of moment invariants

Vibration of L-shaped plates under a deterministic force or moment excitation : a case of statistical energy analysis application

Analytical and closed form solutions are presented in this paper for the vibration response of an L-shaped plate under a point force or a moment excitation. Inter-relationships between wave components of the source and the receiving plates are clearly defined. Explicit expressions are given for the quadratic quantities such as input power, energy flow and kinetic energy distributions of the L-shaped plate. Applications of statistical energy analysis (SEA) formulation in the prediction of the vibration response of finite coupled plate structures under a single deterministic forcing are examined and quantified. It is found that the SEA method can be employed to predict the frequency averaged vibration response and energy flow of coupled plate structures under a deterministic force or moment excitation when the structural system satisfies the following conditions: (1) the coupling loss factors of the coupled subsystems are known; (2) the source location is more than a quarter of the plate bending wavelength away from the source plate edges in the point force excitation case, or is more than a quarter wavelength away from the pair of source plate edges perpendicular to the moment axis in the moment excitation case due to the directional characteristic of moment excitations. SEA overestimates the response of the L-shaped plate when the source location is less than a quarter bending wavelength away from the respective plate edges owing to wave coherence effect at the plate boundary

Deterministic diffraction loss modelling for novel broadband communication in rural environments

This paper presents a deterministic modelling approach to predict diffraction loss for an innovative Multi-User-Single-Antenna (MUSA) MIMO technology, proposed for rural Australian environments. In order to calculate diffraction loss, six receivers have been considered around an access point in a selected rural environment. Generated terrain profiles for six receivers are presented in this paper. Simulation results using classical diffraction models and diffraction theory are also presented by accounting the rural Australian terrain data. Results show that in an area of 900 m by 900 m surrounding the receivers, path loss due to diffraction can range between 5 dB and 35 dB. Diffraction loss maps can contribute to determine the optimal location for receivers of MUSA-MIMO systems in rural areas.

Mean and variance of estimates of the bispectrum of a harmonic random process-an analysis including leakage effects

Analytical expressions are derived for the mean and variance, of estimates of the bispectrum of a real-time series assuming a cosinusoidal model. The effects of spectral leakage, inherent in discrete Fourier transform operation when the modes present in the signal have a nonintegral number of wavelengths in the record, are included in the analysis. A single phase-coupled triad of modes can cause the bispectrum to have a nonzero mean value over the entire region of computation owing to leakage. The variance of bispectral estimates in the presence of leakage has contributions from individual modes and from triads of phase-coupled modes. Time-domain windowing reduces the leakage. The theoretical expressions for the mean and variance of bispectral estimates are derived in terms of a function dependent on an arbitrary symmetric time-domain window applied to the record. the number of data, and the statistics of the phase coupling among triads of modes. The theoretical results are verified by numerical simulations for simple test cases and applied to laboratory data to examine phase coupling in a hypothesis testing framework

Robust image hash functions using higher order spectra

Robust hashing is an emerging field that can be used to hash certain data types in applications unsuitable for traditional cryptographic hashing methods. Traditional hashing functions have been used extensively for data/message integrity, data/message authentication, efficient file identification and password verification. These applications are possible because the hashing process is compressive, allowing for efficient comparisons in the hash domain but non-invertible meaning hashes can be used without revealing the original data. These techniques were developed with deterministic (non-changing) inputs such as files and passwords. For such data types a 1-bit or one character change can be significant, as a result the hashing process is sensitive to any change in the input. Unfortunately, there are certain applications where input data are not perfectly deterministic and minor changes cannot be avoided. Digital images and biometric features are two types of data where such changes exist but do not alter the meaning or appearance of the input. For such data types cryptographic hash functions cannot be usefully applied. In light of this, robust hashing has been developed as an alternative to cryptographic hashing and is designed to be robust to minor changes in the input. Although similar in name, robust hashing is fundamentally different from cryptographic hashing. Current robust hashing techniques are not based on cryptographic methods, but instead on pattern recognition techniques. Modern robust hashing algorithms consist of feature extraction followed by a randomization stage that introduces non-invertibility and compression, followed by quantization and binary encoding to produce a binary hash output. In order to preserve robustness of the extracted features, most randomization methods are linear and this is detrimental to the security aspects required of hash functions. Furthermore, the quantization and encoding stages used to binarize real-valued features requires the learning of appropriate quantization thresholds. How these thresholds are learnt has an important effect on hashing accuracy and the mere presence of such thresholds are a source of information leakage that can reduce hashing security. This dissertation outlines a systematic investigation of the quantization and encoding stages of robust hash functions. While existing literature has focused on the importance of quantization scheme, this research is the first to emphasise the importance of the quantizer training on both hashing accuracy and hashing security. The quantizer training process is presented in a statistical framework which allows a theoretical analysis of the effects of quantizer training on hashing performance. This is experimentally verified using a number of baseline robust image hashing algorithms over a large database of real world images. This dissertation also proposes a new randomization method for robust image hashing based on Higher Order Spectra (HOS) and Radon projections. The method is non-linear and this is an essential requirement for non-invertibility. The method is also designed to produce features more suited for quantization and encoding. The system can operate without the need for quantizer training, is more easily encoded and displays improved hashing performance when compared to existing robust image hashing algorithms. The dissertation also shows how the HOS method can be adapted to work with biometric features obtained from 2D and 3D face images.

Deterministic control of structural and optical properties of plasma-grown vertical graphene nanosheet networks via nitrogen gas variation

The effect of nitrogen on the growth of vertically oriented graphene nanosheets on catalyst-free silicon and glass substrates in a plasma-assisted process is studied. Different concentrations of nitrogen were found to act as versatile control knobs that could be used to tailor the length, number density and structural properties of the nanosheets. Nanosheets with different structural characteristics exhibit markedly different optical properties. The nanosheet samples were treated with a bovine serum albumin protein solution to investigate the effects of this variation on the optical properties for biosensing through confocal micro-Raman spectroscopy and UV-Vis spectrophotometry. © 2012 Optical Society of America.

Made-to-order nanocarbons through deterministic plasma nanotechnology

Through a combinatorial approach involving experimental measurement and plasma modelling, it is shown that a high degree of control over diamond-like nanocarbon film sp3/sp2 ratio (and hence film properties) may be exercised, starting at the level of electrons (through modification of the plasma electron energy distribution function). Hydrogenated amorphous carbon nanoparticle films with high percentages of diamond-like bonds are grown using a middle-frequency (2 MHz) inductively coupled Ar + CH4 plasma. The sp3 fractions measured by X-ray photoelectron spectroscopy (XPS) and Raman spectroscopy in the thin films are explained qualitatively using sp3/sp2 ratios 1) derived from calculated sp3 and sp2 hybridized precursor species densities in a global plasma discharge model and 2) measured experimentally. It is shown that at high discharge power and lower CH4 concentrations, the sp3/sp2 fraction is higher. Our results suggest that a combination of predictive modeling and experimental studies is instrumental to achieve deterministically grown made-to-order diamond-like nanocarbons suitable for a variety of applications spanning from nano-magnetic resonance imaging to spin-flip quantum information devices. This deterministic approach can be extended to graphene, carbon nanotips, nanodiamond and other nanocarbon materials for a variety of applications

Deterministic plasma-aided synthesis of high-quality nanoislanded nc-SiC films

Despite major advances in the fabrication and characterization of SiC and related materials, there has been no convincing evidence of the synthesis of nanodevice-quality nanoislanded SiC films at low, ultralarge scale integration technology-compatible process temperatures. The authors report on a low-temperature (400 °C) plasma-assisted rf magnetron sputtering deposition of high-quality nanocrystalline SiC films made of uniform-size nanoislands that almost completely cover the Si(100) surface. These nanoislands are chemically pure, highly stoichiometric, have a typical size of 20-35 nm, and contain small (∼5 nm) nanocrystalline inclusions. The properties of nanocrystalline SiC films can be effectively controlled by the plasma parameters.

Deterministic nanoassembly : Neutral or plasma route?

It is shown that, owing to selective delivery of ionic and neutral building blocks directly from the ionized gas phase and via surface migration, plasma environments offer a better deal of deterministic synthesis of ordered nanoassemblies compared to thermal chemical vapor deposition. The results of hybrid Monte Carlo (gas phase) and adatom self-organization (surface) simulation suggest that higher aspect ratios and better size and pattern uniformity of carbon nanotip microemitters can be achieved via the plasma route. © 2006 American Institute of Physics.

Controlled-bandgap silicon nitride nanomaterials : deterministic nitrogenation in high-density plasmas

To overcome major problems associated with insufficient incorporation of nitrogen in hydrogenated amorphous silicon nitride (a-SiNx:H) nanomaterials, which in turn impedes the development of controlled-bandgap nanodevices, here we demonstrate the possibility to achieve effective bandgap control in a broad range by using high-density inductively coupled plasmas. This achievement is related to the outstanding dissociation ability of such plasmas. It is shown that the compositional, structural, optical, and morphological properties of the synthesized a-SiNx:H nanomaterials can be effectively tailored through the manipulation of the flow rate ratio of the silane to nitrogen gases X. In particular, a wide bandgap of 5.21 eV can be uniquely achieved at a low flow rate ratio of the nitrogen to silane gas of 1.0, whereas typically used values often exceed 20.0. These results are highly-relevant to the development of the next-generation nanodevices that rely on the effective control of the functional nano-layer bandgap energies.

Plasma Nanoscience : from nature's mastery to deterministic plasma-aided nanofabrication

This paper introduces the plasma-nanoscience research area and shows the way from Nature's mastery in assembling nanosized dust grains in the Universe to deterministic plasma-aided nanofabrication. The concept of deterministic nanoassembly is explained, and the multidisciplinary approach to bridge the spatial gap of nine orders of magnitude between the sizes of plasma reactors and atomic building units is discussed. Ongoing numerical simulation and experimental efforts on highly controlled synthesis of carbon nanotip and semiconducting quantum-dot structures show potential benefits of using ionized-gas environments in nanofabrication. © 2007 IEEE.

Plasma Nanoscience : Basic Concepts and Applications of Deterministic Nanofabrication

Filling the need for a single work specifically addressing how to use plasma for the fabrication of nanoscale structures, this book is the first to cover plasma deposition in sufficient depth. The author has worked with numerous R&D institutions around the world, and here he begins with an introductory overview of plasma processing at micro- and nanoscales, as well as the current problems and challenges, before going on to address surface preparation, generation and diagnostics, transport and the manipulation of nano units.

Deterministic control of plasma-assembled self-organized Ge/Si quantum dot arrays

Self-assembly of size-uniform and spatially ordered quantum dot (QD) arrays is one of the major challenges in the development of the new generation of semiconducting nanoelectronic and photonic devices. Assembly of Ge QD (in the ∼5-20 nm size range) arrays from randomly generated position and size-nonuniform nanodot patterns on plasma-exposed Si (100) surfaces is studied using hybrid multiscale numerical simulations. It is shown, by properly manipulating the incoming ion/neutral flux from the plasma and the surface temperature, the uniformity of the nanodot size within the array can be improved by 34%-53%, with the best improvement achieved at low surface temperatures and high external incoming fluxes, which are intrinsic to plasma-aided processes. Using a plasma-based process also leads to an improvement (∼22% at 700 K surface temperature and 0.1 MLs incoming flux from the plasma) of the spatial order of a randomly sampled nanodot ensemble, which self-organizes to position the dots equidistantly to their neighbors within the array. Remarkable improvements in QD ordering and size uniformity can be achieved at high growth rates (a few nms) and a surface temperature as low as 600 K, which broadens the range of suitable substrates to temperature-sensitive ultrathin nanofilms and polymers. The results of this study are generic, can also be applied to nonplasma-based techniques, and as such contributes to the development of deterministic strategies of nanoassembly of self-ordered arrays of size-uniform QDs, in the size range where nanodot ordering cannot be achieved by presently available pattern delineation techniques.

Deterministic shape control in plasma-aided nanotip assembly

The possibility of deterministic plasma-assisted reshaping of capped cylindrical seed nanotips by manipulating the plasma parameter-dependent sheath width is shown. Multiscale hybrid gas phase/solid surface numerical experiments reveal that under the wide-sheath conditions the nanotips widen at the base and when the sheath is narrow, they sharpen up. By combining the wide- and narrow-sheath stages in a single process, it turns out possible to synthesize wide-base nanotips with long- and narrow-apex spikes, ideal for electron microemitter applications. This plasma-based approach is generic and can be applied to a larger number of multipurpose nanoassemblies. © 2005 American Institute of Physics.

Deterministic surface growth of single-crystalline iron oxide nanostructures in nonequilibrium plasma

An innovative approach to precise tailoring of surface density, shapes, and sizes of single-crystalline α-Fe 2O 3 nanowires and nanobelts by controlling interactions of reactive oxygen plasma-generated species with the Fe surface is proposed. This strongly nonequilibrium, rapid, almost incubation-free, high-rate growth directly from the solid-solid interface can also be applied to other oxide materials and is based on deterministic control of the density of oxygen species and the surface conditions, which determine the nanostructure nucleation and growth.