Dyadic-based factorizations for regular paraunitary filterbanks and M-band orthogonal wavelets with structural vanishing moments

Paraunitary filterbanks (PUFBs) can be designed and implemented using either degree-one or order-one dyadic-based factorization. This work discusses how regularity of a desired degree is structurally imposed on such factorizations for any number of channels M /spl ges/ 2, without necessarily constraining the phase responses. The regular linear-phase PUFBs become a special case under the proposed framework. We show that the regularity conditions are conveniently expressed in terms of recently reported M-channel lifting structures, which allow for fast, reversible, and possibly multiplierless implementations in addition to improved design efficiency, as suggested by numerical experience. M-band orthonormal wavelets with structural vanishing moments are obtained by iterating the resulting regular PUFBs on the lowpass channel. Design examples are presented and evaluated using a transform-based image coder, and they are found to outperform previously reported designs.     

Lattice structure for regular paraunitary linear-phase filterbanksand M-band orthogonal symmetric wavelets

Parauninary linear-phase (PULP) M-channel uniform filterbanks, which are also known as the generalized lapped orthogonal transforms (GenLOTs), can be designed and implemented using lattice structures. This paper discusses how to impose regularity constraints onto the lattice structure of PULP filterbanks. These conditions are expressed in term of the rotation angles of the lattice components by which the resulting filterbanks are guaranteed to have one or two degrees of regularity, iterating these new regular filterbanks on the lowpass subband generates a large family of symmetric M-band orthonormal wavelets. Design procedures with many design examples are presented. Smooth interpolation using regular PULP filterbanks is illustrated through image coding experiments where the novel M-band wavelets consistently yield smoother reconstructed images and better perceptual quality     

A study of relative phase in complex wavelet domain: Property,statistics and applications in texture image retrieval and segmentation

In this paper, we develop a new approach which exploits the probabilistic properties from the phase information of 2-D complex wavelet coefficients for image modeling. Instead of directly using phases of complex wavelet coefficients, we demonstrate why relative phases should be used. The definition, properties and statistics of relative phases of complex coefficients are studied in detail. We proposed von Mises and wrapped Cauchy for the probability density function (pdf) of relative phases in the complex wavelet domain. The maximum-likelihood method is used to estimate two parameters of von Mises and wrapped Cauchy. We demonstrate that the von Mises and wrapped Cauchy fit well with real data obtained from various real images including texture images as well as standard images. The von Mises and wrapped Cauchy models are compared, and the simulation results show that the wrapped Cauchy fits well with the peaky and heavy-tailed pdf of relative phases and the von Mises fits well with the pdf which is in Gaussian shape. For most of the test images, the wrapped Cauchy model is more accurate than the von Mises model, when images are decomposed by different complex wavelet transforms including dual-tree complex wavelet (DTCWT), pyramidal dual-tree directional filter bank (PDTDFB) and uniform discrete curvelet transform (UDCT). Moreover, the relative phase is applied to obtain new features for texture image retrieval and segmentation applications. Instead of using only real or magnitude coefficients, the new approach uses a feature in which phase information is incorporated, yielding a higher accuracy in texture image retrieval as well as in segmentation. The relative phase information which is complementary to the magnitude is a promising approach in image processing.     

Vonn distribution of relative phase for statistical image modeling in complex wavelet domain

With the assumptions of Gaussian as well as Gaussian scale mixture models for images in wavelet domain, marginal and joint distributions for phases of complex wavelet coefficients are studied in detail. From these hypotheses, we then derive a relative phase probability density function, which is called Vonn distribution, in complex wavelet domain. The maximum-likelihood method is proposed to estimate two Vonn distribution parameters. We demonstrate that the Vonn distribution fits well with behaviors of relative phases from various real images including texture images as well as standard images. The Vonn distribution is compared with other standard circular distributions including von Mises and wrapped Cauchy. The simulation results, in which images are decomposed by various complex wavelet transforms, show that the Vonn distribution is more accurate than other conventional distributions. Moreover, the Vonn model is applied to texture image retrieval application and improves retrieval accuracy.     

As(V) removal using a magnetic layered double hydroxide composite

The Mg–Fe–Zr layered double hydroxide/Fe₃O₄ composite was synthesized by co-precipitation of layered double hydroxide (LDH) precursors in the presence of Fe₃O₄ particles and its arsenic adsorption behavior was investigated. The material characterization by XRD, TEM, surface area analysis, SEM-EDX, and VSM revealed that the composite was comprised of Fe₃O₄ particles covered by an LDH. The As(V) adsorption capacity of the composite (188 mg/g) was achieved at pH 3. The kinetics studies and adsorption isotherms suggested a two-stepped adsorption mechanism of the monolayer adsorption inside the interlayers of LDH.     

A Class of Multiresolution Directional Filter Banks

In this paper, we introduced a class of directional filter banks (DFBs) having the previously proposed uniform DFB (uDFB) as a special case. Except for the uDFB, each DFB in this class can be used to decompose an image yielding up to 12 directions while maintaining perfect reconstruction and maximal decimation. A multiresolution representation can be obtained by repeating the same decomposition at the lowpass band. The permissible property of the filter banks in cases of being implemented by a tree structure and by direct implementation is discussed. The result shows that only one DFB in the class, called the uniform quincunx DFB (uqDFB), satisfies the permissible property when being implemented directly without using the tree structure. The nonuniform quincunx DFB (nuqDFB) is then constructed from the uqDFB by merging its two lowpass subbands. An alternative structure for constructing the nuqDFB is presented. The new structure, while yielding the same frequency partitioning, allows the DFB to be realized with complexity comparable to that of the separable wavelet filter bank. The connection between the discrete filter bank and the continuous directional wavelet is also established. Numerical experiments on directional feature extractions, image denoising and nonlinear approximation are presented at the end of the paper to demonstrate the potential of the nuqDFB     

Hybrid transistor manipulation controlled by light within a PANDA microring resonator

In this paper, the novel type of transistor known as a hybrid transistor is proposed, in which all types of transistors can be formed by using a microring resonator called a PANDA microring resonator. In principle, such a transistor can be used to form for various transistor types by using the atom/molecule trapping tools, which is named by an optical tweezer, where in application all type of transistors, especially, molecule and photon transistors can be performed by using the trapping tools, which will be described in details.     

Removal of the eye-blink artifacts from EEGs via STF-TS modeling and robust minimum variance beamforming

A novel scheme for the removal of eye-blink (EB) artifacts from electroencephalogram (EEG) signals based on a novel space-time-frequency (STF) model of EEGs and robust minimum variance beamformer (RMVB) is proposed. In this method, in order to remove the artifact, the RMVB is provided with a priori information, namely, an estimation of the steering vector corresponding to the point source EB artifact. The artifact-removed EEGs are subsequently reconstructed by deflation. The a priori knowledge, the vector corresponding to the spatial distribution of the EB factor, is identified using the STF model of EEGs, provided by the parallel factor analysis (PARAFAC) method. In order to reduce the computational complexity present in the estimation of the STF model using the three-way PARAFAC, the time domain is subdivided into a number of segments, and a four-way array is then set to estimate the STF-time/segment (TS) model of the data using the four-way PARAFAC. The correct number of the factors of the STF model is effectively estimated by using a novel core consistency diagnostic- (CORCONDIA-) based measure. Subsequently, the STF-TS model is shown to closely approximate the classic STF model, with significantly lower computational cost. The results confirm that the proposed algorithm effectively identifies and removes the EB artifact from raw EEG measurements.     

Integrated high-speed intelligent utility tie unit for disbursed/renewable generation facilities

Following the steps of the gas industry, the traditional paradigm of the vertically integrated electrical utility structure has begun to change. In the United States, the Federal Energy Regulatory Commission has issued several rules and Notices of Proposed Rulemaking to set the road map for the utility deregulation. The crisis in California has drawn great attention and sparked intense discussion within the utility industry. One general conclusion is to rejuvenate the idea of integrated resource planning and promote the distributed generation via traditional or renewable generation facilities for the deregulated utility systems. Fuel cell and photovoltaic are the most promising renewable generation technologies for the residential and small commercial users. It is desirable for these facilities to be interconnected with the utility grid to perform peak shaving, demand reduction, and to serve as emergency and standby power supply. However, the mismatch between the utility tie protection and the equipment protection makes it impossible for the fuel cell and/or photovoltaic to serve as emergency and standby power supply when the utility supply is lost due to nearby external faults. To overcome this issue, this paper discusses the development of an integrated high-speed intelligent utility tie monitoring, control, and protection system to replace the traditional tie breakers for those residential and small commercial facilities with disbursed/renewable generation facilities.