Danchi Jiang

A recurrent neural network for solving Sylvester equation with time-varying coefficients

Presents a recurrent neural network for solving the Sylvester equation with time-varying coefficient matrices. The recurrent neural network with implicit dynamics is deliberately developed in the way that its trajectory is guaranteed to converge exponentially to the time-varying solution of a given Sylvester equation. Theoretical results of convergence and sensitivity analysis are presented to show the desirable properties of the recurrent neural network. Simulation results of time-varying matrix inversion and online nonlinear output regulation via pole assignment for the ball and beam system and the inverted pendulum on a cart system are also included to demonstrate the effectiveness and performance of the proposed neural network.

A Lagrangian network for kinematic control of redundant robot manipulators

A recurrent neural network, called the Lagrangian network, is presented for the kinematic control of redundant robot manipulators. The optimal redundancy resolution is determined by the Lagrangian network through real-time solution to the inverse kinematics problem formulated as a quadratic optimization problem. While the signal for a desired velocity of the end-effector is fed into the inputs of the Lagrangian network, it generates the joint velocity vector of the manipulator in its outputs along with the associated Lagrange multipliers. The proposed Lagrangian network is shown to be capable of asymptotic tracking for the motion control of kinematically redundant manipulators.

Optimal Bit Loading Algorithm for Power-Line Communication Systems subject to Individual Channel Power Constraints

Multichannel modulation technique has been successfully applied to power-line communication networks because of its elegant water-filling algorithm for optimal bit-loading. This algorithm maximizes the total channel capacity of the communication system by appropriately matching the signal power to the channel noise level. This paper extends such an algorithm to the case where transmission power at different channels are constrained individually. Such constraints arise naturally in power-line communication systems. An algorithm is carefully developed for such a unfriendly communication environment. This algorithm is an iterative water-filling algorithm in nature, so that the simple and intuitive feature of the well- known water-filling algorithm can be preserved. The individual channel constraints are met using an iterative procedure, which guarantees the optimal solution can be reached within finite iterations. An illustrative system is simulated to demonstrate the efficiency of the proposed algorithm. Furthermore, the proposed algorithm is also well justified using theoretical results.

Analog computing for real-time solution of time-varying linear equations

An implicit recurrent neural network model (IRNN) is proposed for solving on-line time-varying linear equations. Such a neural network can be implemented as analog circuits or VLSI. Excellent convergent properties have been revealed by careful theoretical analysis. In the specific case where the linear equation is obtained from a time-varying Sylvester equation, the proposed IRNN model coincides with some existing recurrent neural networks reported in recent literature, where simulation examples have been reported to demonstrate the effectiveness and efficiency.

A recurrent neural network for real-time semidefinite programming

Semidefinite programming problem is an important optimization problem that has been extensively investigated. A real-time solution method for solving such a problem, however, is still not yet available. This paper proposes a novel recurrent neural network for this purpose. First, an auxiliary cost function is introduced to minimize the duality gap between the admissible points of the primal problem and the corresponding dual problem. Then a dynamical system is constructed to drive the duality gap to zero exponentially along any trajectory by modifying the gradient of the auxiliary cost function. Furthermore, a subsystem is developed to circumvent in the computation of matrix inverse, so that the resulting overall dynamical system can be realized using a recurrent neural network. The architecture of the resulting neural network is discussed. The operating characteristics and performance of the proposed approach are demonstrated by means of simulation results.

A two-layer recurrent neural network for kinematic control of redundant manipulators

A recurrent neural network is presented for the kinematic control of kinematically redundant robot manipulators. The proposed recurrent neural network is composed of two bidirectionally connected layers of neuron arrays. While the signals of desired velocity of the end-effector are fed into the input layer, the output layer generates the joint velocity vector of the manipulator. The proposed recurrent neural network is shown to be capable of asymptotic tracking for the motion control of kinematically redundant manipulators.

Image transmission over underwater acoustic environment using OFDM technique with HQAM mapper

Performance of underwater acoustic communication system is influenced on channel characteristic. Especially, an important feature of the underwater acoustic communication is multipath propagation and Doppler spread cause by the surface and bottom reflections. In this paper, we study the application of high speed image transmission using multi-carrier modulation, where this capability can enable the next generation of undersea expeditions. We use Hierarchical Quadrature Amplitude Modulation (HQAM) as Zero-padded (ZP) orthogonal frequency division multiplexing (OFDM) mapper to be unequal error protection for the transmitted bit stream. We proved capability of ZP-OFDM multi-carrier modulation based on HQAM mapper with Reed Solomon channel coder in sensitive bit error reduction without receiver equalizer. Proposed scheme evaluated using different types of images for different values of the modulation parameter and underwater channel physical parameters.

SPIHT coded image transmission over underwater acoustic channel with unequal error protection using HQAM

Set Partitioning in Hierarchical Trees (SPIHT) is an efficient wavelet-based progressive image-compression technique, designed to minimize the mean-squared error (MSE) between the original and decoded imagery. Since underwater acoustic channel suffer from highly significant bit error rates, some mechanism to protect the encoded image required. In this paper we present Reed Solomon channel coder combined with Hierarchical Quadrature Amplitude Modulation (HQAM) as unequal error protection technique to protect the transmitted sensitive coded bits. This paper proved that Reed Solomon channel coding combined with HQAM method can reduced sensitive bit error for SPHIT coded image to transmit over underwater acoustic channel. Proposed scheme is evaluated using different types of images for different values of the modulation parameter and different underwater channel physical parameters.

A novel partitioning strategy for distribution networks featuring many small scale generators

The modern power distribution network is constantly changing with the introduction of small scale distributed generators (DG). DG offer great opportunities such as voltage support and reduced customer costs. However, high DG penetration can give rise to network constraint breaches such as voltage and frequency limits, fault ride through capability, system security, reliability and stability. To avoid these breaches regulation of DG is essential. With the introduction of vast numbers of DG, common regulation approaches can lead to under utilisation of DG resources, requiring unnecessary high voltage (HV) grid imports and increasing line losses. In this study we aim to design a network partitioning strategy that enables the efficient management of DG, and maximises DG output and reduces costs. We introduce a subnet partitioning strategy that maximises the utilisation of DG power by balancing it with local demand, where locality is quantified in a modified electrical distance. We also present a case study that compares three partitioning strategies under a variety of network demands. Results show that the proposed power balanced partitioning technique can provide a structure to the DG regulation that offers better efficiency of DG operation than other partitioning strategies. In addition, the adaptive nature of the partitioning can make it capable of reducing HV grid power imports, reducing line losses, and improving voltage profiles. Furthermore, such a partitioning approach can add robustness over a range of network conditions.

Signal dependent Rician noise denoising using nonlinear filter

MR images are increasingly used for diagnostic and surgical procedures, as they offer better soft tissue contrast and advanced imaging capabilities. Similar to other imaging modalities, MR images are also subjected to various forms of noises and artifacts. The noise affecting MRI images is known as Rician noise and displays a nonlinear and signal dependent behavior. In this paper we propose a nonlinear filtering method for Rician noise denoising. Nonlinear filters are more capable in addressing signal dependent behavior of noise and offer good denoising with better edge preserving capabilities. A nonlinear filter based on homomorphic filter characteristics has been designed to address Rician noise in MR images. The proposed filter has been implemented on synthetic images and MR images of the articular cartilage. The efficiency of the proposed filtering method is verified by computing the PSNR and SSIM index of the image. The proposed nonlinear filter performs good denoising with improvement in the image quality as observed from the PSNR values of the image. It also offers edge preservation and can be used for both structural MRI and soft tissue study effectively.