Systolic algorithms and applications

The computer performance has been improved tremendously since the development of the first allpurpose, all electronic digital computer in 1946. However, engineers, scientists and researchers keep making more efforts to further improve the computer performance to meet the demanding requirements for many applications. There are basically two ways to improve the computer performance in terms of computational speed. One way is to use faster devices (VLSI chips). Although faster and faster VLSI components have contributed a great deal on the improvement of computation speed, the breakthroughs in increasing switching speed and circuit densities of VLSI devices will be diflicult and costly in future. The other way is to use parallel processing architectures which employ multiple processors to perform a computation task. When multiple processors working together, an appropriate architecture is very important to achieve the maximum performance in a cost-effective manner. Systolic arrays are ideally qualified for computationally intensive applications with inherent massive parallelism because they capitalize on regular, modular, rhythmic, synchronous, concurrent processes that require intensive, repetitive computation. This thesis can be divided into three parts. The first part is an introductory part containing Chap. I and Chap. 2. The second part, composed of Chap. 3 and Chap. 4 concerns with the systolic design methodology. The third part deals with the several systolic array design for different applications....

Cold rolling mill thickness control using the cascade-correlation neural network

The improvements in thickness accuracy of a steel strip produced by a tandem cold-roIling mill are of substantial interest to the steel industry. In this paper, we designed a direct model-reference adaptive control (MRAC)&nbsp; scheme that exploits the natural level of excitation existing in the closed-loop with a dynamically constructed cascade-correlation neural network (CCNN) as a controller for cold roIling mill thickness control. Simulation results show that the combination of a such a direct MRAC scheme and the dynamically constructed CCNN significantly improves the thickness accuracy in the presence of disturbances and noise in comparison with to the conventional PID controllers.<br /

Solving combinatorial optimization problems using augmented lagrange chaotic simulated annealing

Chaotic simulated annealing (CSA) proposed by Chen and Aihara has been successfully used to solve a variety of combinatorial optimization problems. CSA uses a penalty term to enforce solution validity as in the original Hopfield–Tank approach. There exists a conflict between solution quality and solution validity in the penalty approach. It is often difficult to adjust the relative magnitude of the penalty term, so as to achieve a high quality solution which is at the same time valid. To overcome this, we incorporate augmented Lagrange multipliers into CSA, obtaining a method that we call augmented Lagrange chaotic simulated annealing (AL-CSA). Simulation results on two constrained optimization benchmarks derived from the Hopfield–Tank formulation of the traveling salesman problem show that AL-CSA can maintain CSA’s good solution quality while avoiding the potential difficulties associated with penalty terms. Furthermore, AL-CSA’s convergence time is shorter and choice of system parameters is easier compared to CSA.Accepted versio

Enhanced ISAR imaging by exploiting the continuity of the target scene

This paper presents a novel inverse synthetic aperture radar (ISAR) imaging method by exploiting the inherent continuity of the scatterers on the target scene to obtain enhanced target images within a Bayesian framework. A simplified radar system is utilized by transmitting the sparse probing frequency signal, where the ISAR imaging problem can be converted to deal with underdetermined linear inverse scattering. Following the Bayesian compressive sensing (BCS) theory, a hierarchical Bayesian prior is employed to model the scatterers in the range-Doppler plane. In contrast to the independent prior on each scatterer in the conventional BCS, a correlated prior is proposed to statistically encourage the continuity structure of the scatterers in the target region. To overcome the intractability of the posterior distribution, the Gibbs sampling strategy is used for Bayesian inference. The parameters of the signal model are inferred efficiently from samples obtained by the Gibbs sampler. Because the proposed method is a data-driven learning process, the tedious parameter tuning process required by the convex optimization-based approaches can be avoided. Both the synthetic and the experimental results demonstrate that the proposed algorithm can achieve substantial improvements in the scenarios of limited measurements and low signal-to-noise ratio compared with other reported algorithms for ISAR imaging problems.Accepted versio

Biodegradable, Hydrogen Peroxide, and Glutathione Dual Responsive Nanoparticles for Potential Programmable Paclitaxel Release

Reactive oxygen species (ROS) and glutathione (GSH) dual responsive nanoparticulate drug delivery systems (nano-DDSs) hold great promise to improve the therapeutic efficacy and alleviate the side effects of chemo drugs in cancer theranosis. Herein, hydrogen peroxide (H₂O₂) and GSH dual responsive thioketal nanoparticle (TKN) was rationally designed for paclitaxel (PTX) delivery. Compared to other stimuli-sensitive nano-DDSs, this dual responsive DDS is not only sensitive to biologically relevant H₂O₂ and GSH for on-demand drug release but also biodegradable into biocompatible byproducts after fulfilling its delivering task. Considering the heterogeneous redox potential gradient, the PTX loaded TKNs (PTX-TKNs) might first respond to the extracellular ROS and then to the intracellular GSH, achieving a programmable release of PTX at the tumor site. The selective toxicity of PTX-TKNs to tumor cells with high levels of ROS and GSH was verified both <i>in vitro</i> and in <i>vivo</i>