An iterative equalization method for nonirreducible MIMO FIR channels

This paper deals with the equalization of a nonirreducible multiple-input multiple-output (MIMO) finite-impulse-response (FIR) channel provided that the estimate of the channel matrix is available. An iterative method is developed to perform the channel equalization. The effectiveness of the proposed equalization method is demonstrated by simulation examples.

Parallelization of the discrete gradient method of non-smooth optimization and its applications

We investigate parallelization and performance of the discrete gradient method of nonsmooth optimization. This derivative free method is shown to be an effective optimization tool, able to skip many shallow local minima of nonconvex nondifferentiable objective functions. Although this is a sequential iterative method, we were able to parallelize critical steps of the algorithm, and this lead to a significant improvement in performance on multiprocessor computer clusters. We applied this method to a difficult polyatomic clusters problem in computational chemistry, and found this method to outperform other algorithms.

Iteratively predict protein functions from protein-protein interactions

Current similarity-based approaches of predicting protein functions from protein-protein interaction (PPI) data usually make use of available information in the PPI network to predict functions of un-annotated proteins, and the prediction is a one-off procedure. However the interactions between proteins are more likely to be mutual rather than static and mono-directed. In other words, the un-annotated proteins, once their functions are predicted, will in turn affect the similarities between proteins. In this paper, we propose an innovative iteration algorithm that incorporates this dynamic feature of protein interaction into the protein function prediction, aiming to achieve higher prediction accuracies and get more reasonable results. With our algorithm, instead of one-off function predictions, functions are assigned to an unannotated protein iteratively until the functional similarities between proteins achieve a stable state. The experimental results show that our iterative method can provide better prediction results than one-off prediction methods with higher prediction accuracies, and is stable for large protein datasets.

Correlated differential privacy: hiding information in non-IID data set

Privacy preserving on data mining and data release has attracted an increasing research interest over a number of decades. Differential privacy is one influential privacy notion that offers a rigorous and provable privacy guarantee for data mining and data release. Existing studies on differential privacy assume that in a data set, records are sampled independently. However, in real-world applications, records in a data set are rarely independent. The relationships among records are referred to as correlated information and the data set is defined as correlated data set. A differential privacy technique performed on a correlated data set will disclose more information than expected, and this is a serious privacy violation. Although recent research was concerned with this new privacy violation, it still calls for a solid solution for the correlated data set. Moreover, how to decrease the large amount of noise incurred via differential privacy in correlated data set is yet to be explored. To fill the gap, this paper proposes an effective correlated differential privacy solution by defining the correlated sensitivity and designing a correlated data releasing mechanism. With consideration of the correlated levels between records, the proposed correlated sensitivity can significantly decrease the noise compared with traditional global sensitivity. The correlated data releasing mechanism correlated iteration mechanism is designed based on an iterative method to answer a large number of queries. Compared with the traditional method, the proposed correlated differential privacy solution enhances the privacy guarantee for a correlated data set with less accuracy cost. Experimental results show that the proposed solution outperforms traditional differential privacy in terms of mean square error on large group of queries. This also suggests the correlated differential privacy can successfully retain the utility while preserving the privacy.

Review of approximate analyses of sheet forming processes

Approximate models are often used for the following purposes: in on-line control systems of metal forming processes where calculation speed is critical; to obtain quick, quantitative information on the magnitude of the main variables in the early stages of process design; to illustrate the role of the major variables in the process; as an initial check on numerical modelling; and as a basis for quick calculations on processes in teaching and training packages. The models often share many similarities; for example, an arbitrary geometric assumption of deformation giving a simplified strain distribution, simple material property descriptions - such as an elastic, perfectly plastic law - and mathematical short cuts such as a linear approximation of a polynomial expression. In many cases, the output differs significantly from experiment and performance or efficiency factors are developed by experience to tune the models. In recent years, analytical models have been widely used at Deakin University in the design of experiments and equipment and as a pre-cursor to more detailed numerical analyses. Examples that are reviewed in this paper include deformation of sandwich material having a weak, elastic core, load prediction in deep drawing, bending of strip (particularly of ageing steel where kinking may occur), process analysis of low-pressure hydroforming of tubing, analysis of the rejection rates in stamping, and the determination of constitutive models by an inverse method applied to bending tests.

Blind source separation by fully nonnegative constrained iterative volume maximization

Blind source separation (BSS) has been widely discussed in many real applications. Recently, under the assumption that both of the sources and the mixing matrix are nonnegative, Wang develop an amazing BSS method by using volume maximization. However, the algorithm that they have proposed can guarantee the nonnegativities of the sources only, but cannot obtain a nonnegative mixing matrix necessarily. In this letter, by introducing additional constraints, a method for fully nonnegative constrained iterative volume maximization (FNCIVM) is proposed. The result is with more interpretation, while the algorithm is based on solving a single linear programming problem. Numerical experiments with synthetic signals and real-world images are performed, which show the effectiveness of the proposed method.

On blind separability based on the temporal predictability method

This letter discusses blind separability based on temporal predictability (Stone, 2001; Xie, He, & Fu, 2005). Our results show that the sources are separable using the temporal predictability method if and only if they have different temporal structures (i.e., autocorrelations). Consequently, the applicability and limitations of the temporal predictability method are clarified. In addition, instead of using generalized eigendecomposition, we suggest using joint approximate diagonalization algorithms to improve the robustness of the method. A new criterion is presented to evaluate the separation results. Numerical simulations are performed to demonstrate the validity of the theoretical results.

Ultra-wideline 14N solid-state NMR as a method for differentiating polymorphs: glycine as a case study

Nitrogen-14 solid-state NMR (SSNMR) is utilized to differentiate three polymorphic forms and a hydrochloride (HCl) salt of the amino acid glycine. Frequency-swept Wideband, Uniform Rate, Smooth Truncated (WURST) pulses were used in conjunction with Carr-Purcell Meiboom-Gill refocusing, in the form of the WURST-CPMG pulse sequence, for all spectral acquisitions. The ¹⁴N quadrupolar interaction is shown to be very sensitive to variations in the local electric field gradients (EFGs) about the ¹⁴N nucleus; hence, differentiation of the samples is accomplished through determination of the quadrupolar parameters CQ and ηQ, which are obtained from analytical simulations of the ¹⁴N SSNMR powder patterns of stationary samples (i.e., static NMR spectra). Additionally, differentiation of the polymorphs is also possible via the measurement of ¹⁴N effective transverse relaxation time constants, T^eff2(¹⁴N). Plane-wave density functional theory (DFT) calculations, which exploit the periodicity of crystal lattices, are utilized to confirm the experimentally determined quadrupolar parameters as well as to determine the orientation of the ¹⁴N EFG tensors in the molecular frames. Several signal-enhancement techniques are also discussed to help improve the sensitivity of the ¹⁴N SSNMR acquisition method, including the use of selective deuteration, the application of the BRoadband Adiabatic INversion Cross-Polarization (BRAIN-CP) technique, and the use of variable-temperature (VT) experiments. Finally, we examine several cases where ¹⁴N VT experiments employing Carr-Purcell-Meiboom-Gill (CPMG) refocusing are used to approximate the rotational energy barriers for RNH3⁺ groups.

Switch point finding using polynomial regression for fuzzy type reduction algorithms

Karnik-Mendel (KM) algorithm is the most widely used type reduction (TR) method in literature for the design of interval type-2 fuzzy logic systems (IT2FLS). Its iterative nature for finding left and right switch points is its Achilles heel. Despite a decade of research, none of the alternative TR methods offer uncertainty measures equivalent to KM algorithm. This paper takes a data-driven approach to tackle the computational burden of this algorithm while keeping its key features. We propose a regression method to approximate left and right switch points found by KM algorithm. Approximator only uses the firing intervals, rnles centroids, and FLS strnctural features as inputs. Once training is done, it can precisely approximate the left and right switch points through basic vector multiplications. Comprehensive simulation results demonstrate that the approximation accuracy for a wide variety of FLSs is 100%. Flexibility, ease of implementation, and speed are other features of the proposed method.