Clusters, liquids and crystals of dialkylimidazolium salts. A combined perspective from ab initio computer simulations

We summarize results obtained by a combination of ab initio and classical computer simulations of dialkylimidazolium ionic liquids in different states of aggregation, from crystals to liquids and clusters. Unusual features arising from the competition between electrostatic, dispersion, and hydrogen-bonding interactions are identified at the origin of observed structural patterns. We also discuss the way Brønsted acids interact with ionic liquids leading to the formation of hydrogen-bonded anions.

Special issue on design and programming of signal processors for multimedia communication:Special issue on design and programming of signal processors for multimedia communication

The technical challenges in the design and programming of signal processors for multimedia communication are discussed. The development of terminal equipment to meet such demand presents a significant technical challenge, considering that it is highly desirable that the equipment be cost effective, power efficient, versatile, and extensible for future upgrades. The main challenges in the design and programming of signal processors for multimedia communication are, general-purpose signal processor design, application-specific signal processor design, operating systems and programming support and application programming. The size of FFT is programmable so that it can be used for various OFDM-based communication systems, such as digital audio broadcasting (DAB), digital video broadcasting-terrestrial (DVB-T) and digital video broadcasting-handheld (DVB-H). The clustered architecture design and distributed ping-pong register files in the PAC DSP raise new challenges of code generation.

An adaptable and scalable asymmetric cryptographic processor

In this paper a novel scalable public-key processor architecture is presented that supports modular exponentiation and Elliptic Curve Cryptography over both prime GF(p) and binary GF(2) extension fields. This is achieved by a high performance instruction set that provides a comprehensive range of integer and polynomial basis field arithmetic. The instruction set and associated hardware are generic in nature and do not specifically support any cryptographic algorithms or protocols. Firmware within the device is used to efficiently implement complex and data intensive arithmetic. A firmware library has been developed in order to demonstrate support for numerous exponentiation and ECC approaches, such as different coordinate systems and integer recoding methods. The processor has been developed as a high-performance asymmetric cryptography platform in the form of a scalable Verilog RTL core. Various features of the processor may be scaled, such as the pipeline width and local memory subsystem, in order to suit area, speed and power requirements. The processor is evaluated and compares favourably with previous work in terms of performance while offering an unparalleled degree of flexibility. © 2006 IEEE.

A new Jacobian matrix for optimal learning of single-layer neural networks

This paper investigates the learning of a wide class of single-hidden-layer feedforward neural networks (SLFNs) with two sets of adjustable parameters, i.e., the nonlinear parameters in the hidden nodes and the linear output weights. The main objective is to both speed up the convergence of second-order learning algorithms such as Levenberg-Marquardt (LM), as well as to improve the network performance. This is achieved here by reducing the dimension of the solution space and by introducing a new Jacobian matrix. Unlike conventional supervised learning methods which optimize these two sets of parameters simultaneously, the linear output weights are first converted into dependent parameters, thereby removing the need for their explicit computation. Consequently, the neural network (NN) learning is performed over a solution space of reduced dimension. A new Jacobian matrix is then proposed for use with the popular second-order learning methods in order to achieve a more accurate approximation of the cost function. The efficacy of the proposed method is shown through an analysis of the computational complexity and by presenting simulation results from four different examples.