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.

An algorithm to cluster the spectra in a spectrogram

The work described in this technical report is part of an ongoing project at QUT to build practical tools for the manipulation, analysis and visualisation of recordings of the natural environment. This report describes the algorithm we use to cluster the spectra in a spectrogram. The report begins with a brief description of the signal processing that prepares the spectrograms.

Effect of temperature distribution on predicting quality of microwave dehydrated food

During food drying, many other changes occur simultaneously, resulting in an improved overall quality. Among the quality attributes, the structure and its corresponding color influence directly or indirectly other properties of food. In addition, these quality attributes are affected by process conditions, material components and the raw structure of the foodstuff. In this work, the temperature distribution within food materials during microwave drying has been taken into consideration to observe its role in color modification. In order to determine the temperature distribution of microwave-dried food (apple), a thermal imaging camera has been used. The image acquired from the digital camera has been analysed using image J software in order to get the color change of fresh and dried apple. The results show that temperature distribution plays an important role in determining the quality of the food. The thermal imaging camera was deployed to observe the temperature distribution within food materials during drying. It is clearly observed from the higher value of (ERGB =102) and the uneven color change that uneven temperature distribution can influence customer perceptions of the quality of dried food. Simulation of a mathematical model of temperature distribution during microwave drying can make it possible to predict the colour and texture of the microwaved food.

Voltammetric monitoring of gold nanoparticle formation facilitated by glycyl-L-tyrosine : relation to electronic spectra and transmission electron microscopy images

Voltammetric techniques have been introduced to monitor the formation of gold nanoparticles produced via the reaction of the amino acid glycyl-L-tyrosine with Au(III) (bromoaurate) in 0.05 M KOH conditions. The alkaline conditions facilitate amino acid binding to Au(III), inhibit the rate of reduction to Au(0), and provide an excellent supporting electrolyte for voltammetric studies. Data obtained revealed that a range of time-dependent gold solution species are involved in gold nanoparticle formation and that the order in which reagents are mixed is critical to the outcome. Concomitantly with voltammetric measurements, the properties of gold nanoparticles formed are probed by examination of electronic spectra in order to understand how the solution environment present during nanoparticle growth affects the final distribution of the nanoparticles. Images obtained by the ex situ transmission electron microscopy (TEM) technique enable the physical properties of the nanoparticles isolated in the solid state to be assessed. Use of this combination of in situ and ex situ techniques provides a versatile framework for elucidating the details of nanoparticle formation.

Temperature redistribution modelling during intermittent microwave convective heating

Microwave power is used for heating and drying processes because of its faster and volumetric heating capability. Non-uniform temperature distribution during microwave application is a major drawback of these processes. Intermittent application of microwave potentially reduces the impact of non-uniformity and improves energy efficiency by redistributing the temperature. However, temperature re-distribution during intermittent microwave heating has not been investigated adequately. Consequently, in this study, a coupled electromagnetic with heat and mass transfer model was developed using the finite element method embedded in COMSOL-Multyphysics software. Particularly, the temperature redistribution due to intermittent heating was investigated. A series of experiments were performed to validate the simulation. The test specimen was an apple and the temperature distribution was closely monitored by a TIC (Thermal Imaging Camera). The simulated temperature profile matched closely with thermal images obtained from experiments.

Synthesis and spectroscopic characterization of magnesium oxalate nano-crystals

Synthesis of MgC2O4⋅2H2O nano particles was carried out by thermal double decomposition of solutions of oxalic acid dihydrate (C2H2O4⋅2H2O) and Mg(OAc)2⋅4H2O employing CATA-2R microwave reactor. Structural elucidation was carried out by employing X-ray diffraction (XRD), particle size and shape were studied by transmission electron microscopy (TEM) and nature of bonding was investigated by optical absorption and near-infrared (NIR) spectral studies. The powder resulting from this method is pure and possesses distorted rhombic octahedral structure. The synthesized nano rod is 80 nm in diameter and 549 nm in length.

Syntheses of NCN and NC3N from ionic precursors in the gas phase and an unusual rearrangement of neutral NC3N : A joint experimental and theoretical study

Neutral NCN is made in a mass spectrometer by charge stripping of NCN-., while neutral dicyanocarbene NCCCN can be formed by neutralization of either the corresponding anionic and cationic species, NCCCN-. and NCCCN+.. Theoretical calculations at the RCCSD(T)/aug-cc-pVTZ//B3LYP/6-31+G(d) level of theory indicate that the (3)Sigma (-)(g) State of NCCCN is 18 kcal mol(-1) more stable than the (1)A(1) state. While the majority of neutrals formed from either NCCCN-. or NCCCN+. correspond to NCCCN, a proportion of the neutral NCCCN molecules have sufficient excess energy to effect rearrangement, as evidenced by a loss of atomic carbon in the neutralization reionization (NR) spectra of either NCCCN+. and NCCCN-.. C-13 labeling studies indicate that loss of carbon occurs statistically following or accompanied by scrambling of all three carbon atoms. A theoretical study at the B3LYP/6-31+G(d)//B3LYP/6-31+G(d) level of theory indicates that C loss is a consequence of the rearrangement sequence NCCCN --> CNCCN --> CNCNC and that C scrambling occurs within singlet CNCCN via the intermediacy of a four-membered C-2v-symmetrical transition structure.

Analysis of photoluminescence background of Raman spectra of carbon nanotips grown by plasma-enhanced chemical vapor deposition

Carbon nanotips with different structures were synthesized by plasma-enhanced hot filament chemical vapor deposition and plasma-enhanced chemical vapor deposition using different deposition conditions, and they were investigated by scanning electron microscopy and Raman spectroscopy. The results indicate that the photoluminescence background of the Raman spectra is different for different carbon nanotips. Additionally, the Raman spectra of the carbon nanotips synthesized using nitrogen-containing gas precursors show a peak located at about 2120 cm-1 besides the common D and G peaks. The observed difference in the photoluminescence background is related to the growth mechanisms, structural properties, and surface morphology of a-C:H and a-C:H:N nanotips, in particular, the sizes of the emissive tips.

On natures of sub-gap photoelectric and optical spectra in nitrogen-contaminated nanocrystalline diamond

Experimentally observed optical and photoelectrical spectra of nitrogen-contaminated (unintentionally doped) nano-crystalline CVD diamond films are simulated using semi-empirical adiabatic General Skettrup Model (GSM), which presumes dominant contributions of defect states from sp 3-coordinated intra-granular carbon atoms to intra-band single electron spectrum N(E) of the material. This picture disagrees with a common viewpoint that the N(E) spectrum of the gap states in diamond powders and polycrystalline CVD films mainly originates from π and π* bonds of sp2-coordinated carbon atoms, which are distributed nearly uniformly over outer surfaces and/or interfaces of the diamond grains. The GSM predicts as well strong effect of granular morphology on the density of intra-band defect states in polycrystalline diamonds.

Microwave plasma discharge produced and sustained by the surface wave propagating along a metal wire

A theoretical model of the plasma discharge in a metal cylinder pumped by an operating gas of an arbitrary nature is presented. The ionization is carried out by the surface wave (SW) propagating along a coaxial metal wire. The model includes the local dispersion relation, the energy balance equation, and the relation between the absorbed power per unit length and the local plasma density. Two typical regimes of the discharge are analyzed. In both regimes the axial and radial profiles of the plasma density of the electromagnetic field components and of the SW intensity are obtained. The possible use of the obtained results in plasma technology are discussed.

Microwave gas discharge produced and sustained by a surface wave propagating along a cylindrical metal antenna with a dielectric coating

The structure of a microwave gas discharge produced and sustained by a surface wave (SW) propagating along a cylindrical metal antenna with a dielectric coating is studied. The SW that produces and sustains the microwave gas discharge propagates along an external magnetic field and has an eigenfrequency in the range between the electron cyclotron and electron plasma frequencies. The presence of a dielectric (vacuum) sheath region separating the antenna from the plasma is assumed. The spatial distributions of the produced plasma density, electromagnetic fields, energy flow density, phase velocity and reverse skin depth of the SW are obtained analytically and numerically.

Higher order spectra based support vector machine for arrhythmia classification

Heart rate variability (HRV) refers to the regulation of the sinoatrial node, the natural pacemaker of the heart by the sympathetic and parasympathetic branches of the autonomic nervous system. HRV analysis is an important tool to observe the heart’s ability to respond to normal regulatory impulses that affect its rhythm. Like many bio-signals, HRV signals are non-linear in nature. Higher order spectral analysis (HOS) is known to be a good tool for the analysis of non-linear systems and provides good noise immunity. A computer-based arrhythmia detection system of cardiac states is very useful in diagnostics and disease management. In this work, we studied the identification of the HRV signals using features derived from HOS. These features were fed to the support vector machine (SVM) for classification. Our proposed system can classify the normal and other four classes of arrhythmia with an average accuracy of more than 85%.

Structural characterization of hydrogen peroxide‐oxidized anthracites by X-ray diffraction, Fourier transform infrared spectroscopy, and Raman spectra

The structural characteristics of raw coal and hydrogen peroxide (H2O2)-oxidized coals were investigated using scanning electron microscopy, X-ray diffraction (XRD), Raman spectra, and Fourier transform infrared (FT-IR) spectroscopy. The results indicate that the derivative coals oxidized by H2O2 are improved noticeably in aromaticity and show an increase first and then a decrease up to the highest aromaticity at 24 h. The stacking layer number of crystalline carbon decreases and the aspect ratio (width versus stacking height) increases with an increase in oxidation time. The content of crystalline carbon shows the same change tendency as the aromaticity measured by XRD. The hydroxyl bands of oxidized coals become much stronger due to an increase in soluble fatty acids and alcohols as a result of the oxidation of the aromatic and aliphatic C‐H bonds. In addition, the derivative coals display a decrease first and then an increase in the intensity of aliphatic C‐H bond and present a diametrically opposite tendency in the aromatic C‐H bonds with an increase in oxidation time. There is good agreement with the changes of aromaticity and crystalline carbon content as measured by XRD and Raman spectra. The particle size of oxidized coals (<200 nm in width) shows a significant decrease compared with that of raw coal (1 μm). This study reveals that the optimal oxidation time is ∼24 h for improving the aromaticity and crystalline carbon content of H2O2-oxidized coals. This process can help us obtain superfine crystalline carbon materials similar to graphite in structure.

Application of higher-order spectra for automated grading of diabetic maculopathy

Diabetic macular edema (DME) is one of the most common causes of visual loss among diabetes mellitus patients. Early detection and successive treatment may improve the visual acuity. DME is mainly graded into non-clinically significant macular edema (NCSME) and clinically significant macular edema according to the location of hard exudates in the macula region. DME can be identified by manual examination of fundus images. It is laborious and resource intensive. Hence, in this work, automated grading of DME is proposed using higher-order spectra (HOS) of Radon transform projections of the fundus images. We have used third-order cumulants and bispectrum magnitude, in this work, as features, and compared their performance. They can capture subtle changes in the fundus image. Spectral regression discriminant analysis (SRDA) reduces feature dimension, and minimum redundancy maximum relevance method is used to rank the significant SRDA components. Ranked features are fed to various supervised classifiers, viz. Naive Bayes, AdaBoost and support vector machine, to discriminate No DME, NCSME and clinically significant macular edema classes. The performance of our system is evaluated using the publicly available MESSIDOR dataset (300 images) and also verified with a local dataset (300 images). Our results show that HOS cumulants and bispectrum magnitude obtained an average accuracy of 95.56 and 94.39 % for MESSIDOR dataset and 95.93 and 93.33 % for local dataset, respectively.