SAR ocean image decomposition using the Gabor expansion

Demonstrates the utility of the Gabor expansion as a new tool in geophysical research. The Gabor expansion provides good time-frequency (or space-wavenumber) localization and is ideally suited to represent nonstationary processes. The properties of this tool are demonstrated by expanding an FM-chirp waveform, and azimuth cuts taken from two different SAR ocean images. The effects of filtering in Gabor phase space are also investigated     

Comparison of Gabor projection and decomposition methods in objectdetection

The popular Gabor projection method is compared with the Gabor decomposition method in object detection tasks. The comparison is made using a novel method based on the cumulative distribution function (CDF) of the responses. We find that Gabor decomposition yields more distinct responses than Gabor projection     

基于Gabor小波和支持向量机的人脸识别方法

基于Gabor小波在图像表征方面的优越性,提出了将Gabor小波和支持向量机(SVM)相结合用于人脸识别的方案。用Gabor小波对人脸图像进行特征提取,再利用SVM策略对特征向量进行分类识别,实验的仿真结果验证了本算法的有效性。     

Skew-circular/circular correlation decomposition of prime-factorDCT

An algorithm to decompose the prime-factor DCT into skew-circular/circular correlation (SCC/CC) by coset decomposition is proposed. The simplest case is when the two factors are odd and relatively prime. In this case, the DCT output components are split into six subgroups. Each subgroup contains three short-length CC or SCC matrix-vector products, and the three products can further be merged into only one short-length SCC or CC matrix-vector product. The six subgroups are independent, thus parallel computing is feasible. By fast computation of the short-length SCC and CC, this algorithm reaches the same or less number of multiplications compared to other efficient prime-factor algorithms     

On the covariance function of stationary binary sequences withgiven mean

Recent work on characterizing the covariance functions of stationary binary sequences has been geometric in flavor. Extreme distributions induced in windows of a given length are obtained from special periodic sequences. The work is extended to sequences with a given mean m, revealing some finer structure     

Unified study on the decomposition for deterministic signals and stationary random signals

The unified decomposition theories and methods for deterministic signals and stationary random signals were deeply studied.According to the stability theory of linear systems,the unified results of signal decomposition under both regular stable and boundary stable conditions were given respectively.The unified results of signal decomposition under both orthogonal projection and self projection conditions were also provided based on the linear space projection theory.The former is clear and definite in its physical meaning,and the latter is clear in its mathematical and geometrical meanings.They are both complement with each other.     

Karhunen-Loeve expansion of the WSSUS channel output and itsapplication to efficient simulation

This paper derives a Karhunen-Loeve (K-L) expansion of the time-varying output of a multipath Rayleigh fading wide-sense-stationary uncorrelated-scattering (WSSUS) channel. It is shown that under the same mean-squared error condition, the number of terms required by the truncated K-L expansion is less than that of the series expansion obtained by using the discrete-path approximation of the channel so that simulation using the K-L expansion is more efficient. This computational advantage becomes more significant as higher simulation accuracy is required. The derived K-L expansion is applied to develop an efficient simulation technique for digital transmission over a multipath Rayleigh fading WSSUS channel using an optimum receiver. We show that the proposed technique requires shorter computation time than two other known simulation techniques     

Synchronization of chaotic injected-laser systems and itsapplication to optical cryptography

We demonstrate that two chaotic systems, each made by two coupled semiconductor lasers, can be synchronized using direct-optical feedback. The robustness of the proposed synchronization scheme against mismatch of source parameters and difference in starting conditions is tested by numerical simulations. Applications to secure data transmission are proposed, namely chaotic masking and chaotic shift keying (CSK)     

Integrated optical digital-to-analogue converter and itsapplication to pulse pattern recognition

An optical digital-to-analogue converter that enables optical pulse pattern recognition, fabricated on the silica-based planar lightwave circuit, is proposed. The converter features an optical transversal filter with a multimode interference combiner. Various kinds of four-bit pulse patterns at 10 Gbit/s have been successfully recognised with the fabricated converter     

Ultralong dispersion-shifted erbium-doped fiber amplifier and itsapplication to soliton transmission

Distributed, dispersion-shifted erbium-doped fiber amplifiers with doping concentrations as low as 0.1-0.5 p.p.m. (0.1-0.5×10^-4 wt.%) were fabricated and their grain characteristics studied for the purpose of soliton amplification. A 9.4-km dual-shape-core-type amplifier with a 0.5-p.p.m. concentration had a gain of more than 20 dB at 1.535 μm and 10 dB at 1.552 μm with a forward pumping configuration, and it could successfully amplify and transmit a 20-ps soliton pulse train at a 2.5-GHz repetition rate. The soliton transmission characteristics of an 18.2 km long fiber amplifier were studied using backward and forward pumping. It was found that A=1.5 soliton pulses with a pulse width of 20 ps could be amplified over 18.2 km at a repetition rate of 5 GHz, where soliton narrowing to 16 ps was observed     

Novel wavelength-resonant optoelectronic structure and itsapplication to surface-emitting semiconductor lasers

An optimised design for optoelectronic devices which depends on the interaction between an electromagnetic standing wave and the carrier population is described. The structure consists of quantum well layers spaced at one-half the wavelength of a selected optical transition in quantum wells. This spatial periodicity allows the amplifying or absorbing medium (quantum wells) to coincide with the peaks of the standing wave optical field in the Fabry-Perot cavity. In such a periodic medium, the gain or absorption for the selected wavelength is enhanced by a factor of two compared to a uniform medium. This concept was applied to fabricate a surface-emitting semiconductor laser in the GaAs/AlGaAs system. Lasing was achieved with the shortest gain medium length (320 nm) ever reported     

The continuous spectrum of the inset dielectric guide and itsapplication to waveguide transitions

The analysis of discontinuities in open waveguides, particularly strongly radiating discontinuities, requires a knowledge of the complete modal spectrum of the guide. The continuous spectrum of nonseparable open waveguides, a category which includes many useful high-frequency structures, may be developed from a characteristic Green's function approach. The continuous hybrid spectrum of the inset dielectric guide (IDG), a structure that shows promise for both propagation and antenna applications, is presented as an example. As a verification, this spectrum is used in the analysis of strongly radiating rectangular waveguide to IDG transitions, and a good correlation with experimental data is achieved     

A physical-statistical shadowing correlation model and itsapplication to low-Earth-orbit systems

A shadowing correlation model is proposed in the context of a physical-statistical approach in the view of satellite diversity in built-up areas. The model relies on a model based on the uniform theory of diffraction, applied in a canonical built-up scenario. The correlation model is described and simulation results are presented and analyzed within a physical point of view. The shadowing correlation model is finally applied to several low-Earth-orbit constellations. The impact of geometrical parameters and of the constellation is investigated     

Causal coding of stationary sources and individual sequences with high resolution

In a causal source coding system, the reconstruction of the present source sample is restricted to be a function of the present and past source samples, while the code stream itself may be noncausal and have variable rate. Neuhoff and Gilbert showed that for memoryless sources, optimum performance among all causal source codes is achieved by time-sharing at most two memoryless codes (quantizers) followed by entropy coding. In this work, we extend Neuhoff and Gilbert's result in the limit of small distortion (high resolution) to two new settings. First, we show that at high resolution, an optimal causal code for a stationary source with finite differential entropy rate consists of a uniform quantizer followed by a (sequence) entropy coder. This implies that the price of causality at high resolution is approximately 0.254 bit, i.e., the space-filling loss of the uniform quantizer. Then, we consider individual sequences and introduce a deterministic analogue of differential entropy, which we call "Lempel-Ziv differential entropy." We show that for any bounded individual sequence with finite Lempel-Ziv differential entropy, optimum high-resolution performance among all finite-memory variable-rate causal codes is achieved by dithered scalar uniform quantization followed by Lempel-Ziv coding. As a by-product, we also prove an individual-sequence version of the Shannon lower bound.     

A domain decomposition method for the vector wave equation

A nonoverlapping domain decomposition method (DDM) is presented for the finite-element (FE) solution of electromagnetic scattering problems by inhomogeneous three-dimensional (3-D) bodies. The computational domain is partitioned into concentric subdomains on the interfaces of which conformal vector transmission conditions are prescribed and that can be implemented in the inhomogeneous part. The DDM is numerically implemented when a conformal vector absorbing boundary condition (ABC) is utilized on the outer boundary terminating the FE mesh, while employing the standard edge-based FE formulation. Then, numerical experiments are performed on a sphere and a cone sphere that emphasize the advantages of this technique in terms of memory storage and computing times, especially when the total number of unknowns is very large. Also, these numerical experiments serve as a severe test for the performances of the ABC     

Robust image hashing based on random Gabor filtering and dithered lattice vector quantization

In this paper, we propose a robust-hash function based on random Gabor filtering and dithered lattice vector quantization (LVQ). In order to enhance the robustness against rotation manipulations, the conventional Gabor filter is adapted to be rotation invariant, and the rotation-invariant filter is randomized to facilitate secure feature extraction. Particularly, a novel dithered-LVQ-based quantization scheme is proposed for robust hashing. The dithered-LVQ-based quantization scheme is well suited for robust hashing with several desirable features, including better tradeoff between robustness and discrimination, higher randomness, and secrecy, which are validated by analytical and experimental results. The performance of the proposed hashing algorithm is evaluated over a test image database under various content-preserving manipulations. The proposed hashing algorithm shows superior robustness and discrimination performance compared with other state-of-the-art algorithms, particularly in the robustness against rotations (of large degrees).     

A new method to determine the work-function difference and itsapplication to calibrate the boron-segregation coefficient

The authors present a technique to determine the work-function difference from a plot of the threshold voltage (V_T) versus oxide thickness (T_ox) curve. The extraction errors caused by the electrical characteristics of the oxide and the SiO ₂/Si interface can be minimized by the V_T-T_ox technique. The boron segregation coefficient can be calibrated from the slope of the V_T -T_ox curve. Comparisons between the experimental data and simulation results are made, and good agreement is obtained     

An exact solution of the generalized exponential integral and itsapplication to moment method formulations

The generalized exponential integral is one of the most fundamental integrals in antenna theory and for many years exact solutions to this integral have been sought. This paper considers an exact solution to the generalized exponential integral which is completely general and independent of the usual restrictions involving the wavelength, field point distance and dipole length is considered. The exact series representation presented converges rapidly in the induction and near-field regions of the antenna, and therefore provides an alternative to numerical integration. Two method of moments formulations are considered. They use the exact expression for the generalized exponential integral in the computation of the impedance matrix elements. It is demonstrated that, for very thin straight-wire antennas, an asymptotic expansion can be used to obtain a numerically convenient form of the generalized exponential integral     

Conditional distribution learning with neural networks and itsapplication to channel equalization

We present a conditional distribution learning formulation for real-time signal processing with neural networks based on an extension of maximum likelihood theory-partial likelihood (PL) estimation-which allows for (i) dependent observations and (ii) sequential processing. For a general neural network conditional distribution model, we establish a fundamental information-theoretic connection, the equivalence of maximum PL estimation, and accumulated relative entropy (ARE) minimization, and obtain large sample properties of PL for the general case of dependent observations. As an example, the binary case with the sigmoidal perceptron as the probability model is presented. It is shown that the single and multilayer perceptron (MLP) models satisfy conditions for the equivalence of the two cost functions: ARE and negative log partial likelihood. The practical issue of their gradient descent minimization is then studied within the well-formed cost functions framework. It is shown that these are well-formed cost functions for networks without hidden units; hence, their gradient descent minimization is guaranteed to converge to a solution if one exists on such networks. The formulation is applied to adaptive channel equalization, and simulation results are presented to show the ability of the least relative entropy equalizer to realize complex decision boundaries and to recover during training from convergence at the wrong extreme in cases where the mean square error-based MLP equalizer cannot     

Classification of normal and abnormal brain MRI slices using Gabor texture and support vector machines

In computational and clinical environments, autoclassification of brain magnetic resonance image (MRI) slices as normal and abnormal is challenging. The purpose of this study is to investigate the computer vision and machine learning methods for classification of brain magnetic resonance (MR) slices. In routine health-care units, MR scanners are being used to generate a massive number of brain slices, underlying the anatomical details. Pathological assessment from this medical data is being carried out manually by the radiologists or neuro-oncologists. It is almost impossible to analyze each slice manually due to the large amount of data produced by MRI devices at each moment. Irrefutably, if an automated protocol performing this task is executed, not only the radiologist will be assisted, but a better pathological assessment process can also be expected. Numerous schemes have been reported to address the issue of autoclassification of brain MRI slices as normal and abnormal, but accuracy, robustness and optimization are still an open issue. The proposed method, using Gabor filter and support vector machines, classifies brain MRI slices as normal or abnormal. Accuracy, sensitivity, specificity and ROC-curve have been used as standard quantitative measures to evaluate the proposed algorithm. To the best of our knowledge, this is the first study in which experiments have been performed on Whole Brain Atlas-Harvard Medical School (HMS) dataset, achieving an accuracy of 97.5%, sensitivity of 99%, specificity of 92% and ROC-curve as 0.99. To test the robustness against medical traits based on ethnicity and to achieve optimization, a locally developed dataset has also been used for experiments and remarkable results with accuracy (96.5%), sensitivity (98%), specificity (92%) and ROC-curve (0.97) were achieved. Comparison with state-of-the-art methods proved the overall efficacy of the proposed method.