A Wavelet Approach to the Selective Computation of Eigenvalues for Small Signal Stability Analysis

This paper proposes a method to assess the small signal stability of a power system network by selective determination of the modal eigenvalues. This uses an accelerating polynomial transform, designed using approximate eigenvalues
obtained from a wavelet approximation. Application to the IEEE 14 bus network model produced computational savings of 20%,over the QR algorithm.

Online CORDIC algorithm and VLSI architecture for implementing QR-array processors

A novel most significant digit first CORDIC architecture is presented that is suitable for the VLSI design of systolic array processor cells for performing QR decomposition. This is based on an on-line CORDIC algorithm with a constant scale factor and a latency independent of the wordlength. This has been derived through the extension of previously published CORDIC algorithms. It is shown that simplifying the calculation of convergence bounds also greatly simplifies the derivation of suitable VLSI architectures. Design studies, based on a 0.35-µ CMOS standard cell process, indicate that 20 such QR processor cells operating at rates suitable for radar beamfoming can be readily accommodated on a single chip.

Fast Moving Window Algorithm for QR and Cholesky Decompositions

This paper proposes a fast moving window algorithm for QR and Cholesky decompositions by simultaneously applying data updating and downdating. The developed procedure is based on inner products and entails a similar downdating to that of the Chambers’ approach. For adding and deleting one row of data from the original matrix, a detailed analysis shows that the proposed algorithm outperforms existing ones in terms or computational efficiency, if the number of columns exceeds 7. For a large number of columns, the proposed algorithm is numerically superior compared to the traditional sequential technique.

QR decomposition-based matrix inversion for high performance embedded MIMO receivers

Real-time matrix inversion is a key enabling technology in multiple-input multiple-output (MIMO) communications systems, such as 802.11n. To date, however, no matrix inversion implementation has been devised which supports real-time operation for these standards. In this paper, we overcome this barrier by presenting a novel matrix inversion algorithm which is ideally suited to high performance floating-point implementation. We show how the resulting architecture offers fundamentally higher performance than currently published matrix inversion approaches and we use it to create the first reported architecture capable of supporting real-time 802.11n operation. Specifically, we present a matrix inversion approach based on modified squared Givens rotations (MSGR). This is a new QR decomposition algorithm which overcomes critical limitations in other QR algorithms that prohibits their application to MIMO systems. In addition, we present a novel modification that further reduces the complexity of MSGR by almost 20%. This enables real-time implementation with negligible reduction in the accuracy of the inversion operation, or the BER of a MIMO receiver based on this.

An algorithm to predict pregnancy in assisted conception

BACKGROUND: Male fertility potential cannot be measured by conventional parameters for assisted reproduction by intracytoplasmic sperm injection. This study determines the relationship between testicular and ejaculated sperm mitochondrial (mt) DNA deletions, nuclear (n) DNA fragmentation and fertilisation and pregnancy rates in ICSI. METHODS: Ejaculated sperm were obtained from 77 men and testicular sperm from 28 men with obstructive azoospermia undergoing ICSI. Testicular sperm were retrieved using a Trucut needle. MtDNA analysed using a long polymerase chain reaction. The alkaline Comet assay determined nDNA fragmentation. RESULTS: Of subjects who achieved a pregnancy (50%) using testicular sperm, only 26% had partners�??�?�¢?? sperm with wild type (WT) mtDNA. Of pregnant subjects (38%) using ejaculated sperm, only 8% had partner sperm with WT mtDNA.. In each, the successful group had less mtDNA deletions and less nDNA fragmentation. There were inverse relationships between pregnancy and mtDNA deletion numbers, size and nDNA fragmentation for both testicular and ejaculated sperm. No relationships were observed with fertilisation rates. An algorithm for the prediction of pregnancy is presented based on the quality of sperm nDNA and mtDNA. CONCLUSION: In both testicular and ejaculated sperm, mtDNA deletions and nDNA fragmentation are closely associated with pregnancy in ICSI.

Generic SoC QR Array Processor for Adaptive Beamforming

A generic architecture for implementing a QR array processor in silicon is presented. This improves on previous research by considerably simplifying the derivation of timing schedules for a QR system implemented as a folded linear array, where account has to be taken of processor cell latency and timing at the detailed circuit level. The architecture and scheduling derived have been used to create a generator for the rapid design of System-on-a-Chip (SoC) cores for QR decomposition. This is demonstrated through the design of a single-chip architecture for implementing an adaptive beamformer for radar applications. Published as IEEE Trans Circuits and Systems Part II, Analog and Digital Signal Processing, April 2003 NOT Express Briefs. Parts 1 and II of Journal reorganised since then into Regular Papers and Express briefs

Hardware Performance Analysis of the SHACAL-2 Encryption Algorithm

A hardware performance analysis of the SHACAL-2 encryption algorithm is presented in this paper. SHACAL-2 was one of four symmetric key algorithms chosen in the New European Schemes for Signatures, Integrity and Encryption (NESSIE) initiative in 2003. The paper describes a fully pipelined encryption SHACAL-2 architecture implemented on a Xilinx Field Programmable Gate Array (FPGA) device that achieves a throughput of over 25 Gbps. This is the fastest private key encryption algorithm architecture currently available. The SHACAL-2 decryption algorithm is also defined in the paper as it was not provided in the NESSIE submission.

A two-stage algorithm for identification of nonlinear dynamic systems

This paper investigates the two-stage stepwise identification for a class of nonlinear dynamic systems that can be described by linear-in-the-parameters models, and the model has to be built from a very large pool of basis functions or model terms. The main objective is to improve the compactness of the model that is obtained by the forward stepwise methods, while retaining the computational efficiency. The proposed algorithm first generates an initial model using a forward stepwise procedure. The significance of each selected term is then reviewed at the second stage and all insignificant ones are replaced, resulting in an optimised compact model with significantly improved performance. The main contribution of this paper is that these two stages are performed within a well-defined regression context, leading to significantly reduced computational complexity. The efficiency of the algorithm is confirmed by the computational complexity analysis, and its effectiveness is demonstrated by the simulation results.

A Hybrid Forward Algorithm for RBF Neural Network Construction

This paper proposes a novel hybrid forward algorithm (HFA) for the construction of radial basis function (RBF) neural networks with tunable nodes. The main objective is to efficiently and effectively produce a parsimonious RBF neural network that generalizes well. In this study, it is achieved through simultaneous network structure determination and parameter optimization on the continuous parameter space. This is a mixed integer hard problem and the proposed HFA tackles this problem using an integrated analytic framework, leading to significantly improved network performance and reduced memory usage for the network construction. The computational complexity analysis confirms the efficiency of the proposed algorithm, and the simulation results demonstrate its effectiveness

A fast hybrid algorithm for exoplanetary transit searches

We present a fast and efficient hybrid algorithm for selecting exoplanetary candidates from wide-field transit surveys. Our method is based on the widely used SysRem and Box Least-Squares (BLS) algorithms. Patterns of systematic error that are common to all stars on the frame are mapped and eliminated using the SysRem algorithm. The remaining systematic errors caused by spatially localized flat-fielding and other errors are quantified using a boxcar-smoothing method. We show that the dimensions of the search-parameter space can be reduced greatly by carrying out an initial BLS search on a coarse grid of reduced dimensions, followed by Newton-Raphson refinement of the transit parameters in the vicinity of the most significant solutions. We illustrate the method's operation by applying it to data from one field of the SuperWASP survey, comprising 2300 observations of 7840 stars brighter than V = 13.0. We identify 11 likely transit candidates. We reject stars that exhibit significant ellipsoidal variations caused indicative of a stellar-mass companion. We use colours and proper motions from the Two Micron All Sky Survey and USNO-B1.0 surveys to estimate the stellar parameters and the companion radius. We find that two stars showing unambiguous transit signals pass all these tests, and so qualify for detailed high-resolution spectroscopic follow-up.