Optimization of apodization functions in terahertz transient spectrometry

A quadratic programming optimization procedure for designing asymmetric apodization windows tailored to the shape of time-domain sample waveforms recorded using a terahertz transient spectrometer is proposed. By artificially degrading the waveforms, the performance of the designed window in both the time and the frequency domains is compared with that of conventional rectangular, triangular (Mertz), and Hamming windows. Examples of window optimization assuming Gaussian functions as the building elements of the apodization window are provided. The formulation is sufficiently general to accommodate other basis functions. (C) 2007 Optical Society of America

Tools for regularizing Array Designs

This paper makes a contribution in bridging the theory and practice of the polyhedral model for designing parallel algorithms. Although the theory of polyhedral model is well developed, designers of massively parallel algorithms are unable to benefit from the theory due to the lack of software tools that incorporate the wide range of transformations that are possible in the model. The Uniformization tool that we developed was the first to integrate a number of techniques and to completely automate the transformation step allowing designers to explore a wide range of feasible designs from high-level specifications.

Towards self-ware via swarm-array computing

The work reported in this paper proposes Swarm-Array computing, a novel technique inspired by swarm robotics, and built on the foundations of autonomic and parallel computing. The approach aims to apply autonomic computing constructs to parallel computing systems and in effect achieve the self-ware objectives that describe self-managing systems. The constitution of swarm-array computing comprising four constituents, namely the computing system, the problem/task, the swarm and the landscape is considered. Approaches that bind these constituents together are proposed. Space applications employing FPGAs are identified as a potential area for applying swarm-array computing for building reliable systems. The feasibility of a proposed approach is validated on the SeSAm multi-agent simulator and landscapes are generated using the MATLAB toolkit.

Achieving intelligent agents and its feasibility in swarm-array computing

The work reported in this paper proposes ‘Intelligent Agents’, a Swarm-Array computing approach focused to apply autonomic computing concepts to parallel computing systems and build reliable systems for space applications. Swarm-array computing is a robotics a swarm robotics inspired novel computing approach considered as a path to achieve autonomy in parallel computing systems. In the intelligent agent approach, a task to be executed on parallel computing cores is considered as a swarm of autonomous agents. A task is carried to a computing core by carrier agents and can be seamlessly transferred between cores in the event of a predicted failure, thereby achieving self-* objectives of autonomic computing. The approach is validated on a multi-agent simulator.

Nonlinear channel equalizer design using directional evolutionary multi-objective optimization

In this paper, a new equalizer learning scheme is introduced based on the algorithm of the directional evolutionary multi-objective optimization (EMOO). Whilst nonlinear channel equalizers such as the radial basis function (RBF) equalizers have been widely studied to combat the linear and nonlinear distortions in the modern communication systems, most of them do not take into account the equalizers' generalization capabilities. In this paper, equalizers are designed aiming at improving their generalization capabilities. It is proposed that this objective can be achieved by treating the equalizer design problem as a multi-objective optimization (MOO) problem, with each objective based on one of several training sets, followed by deriving equalizers with good capabilities of recovering the signals for all the training sets. Conventional EMOO which is widely applied in the MOO problems suffers from disadvantages such as slow convergence speed. Directional EMOO improves the computational efficiency of the conventional EMOO by explicitly making use of the directional information. The new equalizer learning scheme based on the directional EMOO is applied to the RBF equalizer design. Computer simulation demonstrates that the new scheme can be used to derive RBF equalizers with good generalization capabilities, i.e., good performance on predicting the unseen samples.

Investigation of liquid MALDI and optimization for instrument tuning and quantitative measurements

The success of Matrix-assisted laser desorption / ionisation (MALDI) in fields such as proteomics has partially but not exclusively been due to the development of improved data acquisition and sample preparation techniques. This has been required to overcome some of the short comings of the commonly used solid-state MALDI matrices such as - cyano-4-hydroxycinnamic acid (CHCA) and 2,5-dihydroxybenzoic acid (DHB). Solid state matrices form crystalline samples with highly inhomogeneous topography and morphology which results in large fluctuations in analyte signal intensity from spot to spot and positions within the spot. This means that efficient tuning of the mass spectrometer can be impeded and the use of MALDI MS for quantitative measurements is severely impeded. Recently new MALDI liquid matrices have been introduced which promise to be an effective alternative to crystalline matrices. Generally the liquid matrices comprise either ionic liquid matrices (ILMs) or a usually viscous liquid matrix which is doped with a UV lightabsorbing chromophore [1-3]. The advantages are that the droplet surface is smooth and relatively uniform with the analyte homogeneously distributed within. They have the ability to replenish a sampling position between shots negating the need to search for sample hot-spots. Also the liquid nature of the matrix allows for the use of additional additives to change the environment to which the analyte is added.

Uniformization tool for systolic array designs

The paper is concerned with the uniformization of a system of affine recurrence equations. This transformation is used in the design (or compilation) of highly parallel embedded systems (VLSI systolic arrays, signal processing filters, etc.). We present and implement an automatic system to achieve uniformization of systems of affine recurrence equations. We unify the results from many earlier papers, develop some theoretical extensions, and then propose effective uniformization algorithms. Our results can be used in any high level synthesis tool based on polyhedral representation of nested loop computations.

Particle swarm optimization aided orthogonal forward regression for unified data modeling

We propose a unified data modeling approach that is equally applicable to supervised regression and classification applications, as well as to unsupervised probability density function estimation. A particle swarm optimization (PSO) aided orthogonal forward regression (OFR) algorithm based on leave-one-out (LOO) criteria is developed to construct parsimonious radial basis function (RBF) networks with tunable nodes. Each stage of the construction process determines the center vector and diagonal covariance matrix of one RBF node by minimizing the LOO statistics. For regression applications, the LOO criterion is chosen to be the LOO mean square error, while the LOO misclassification rate is adopted in two-class classification applications. By adopting the Parzen window estimate as the desired response, the unsupervised density estimation problem is transformed into a constrained regression problem. This PSO aided OFR algorithm for tunable-node RBF networks is capable of constructing very parsimonious RBF models that generalize well, and our analysis and experimental results demonstrate that the algorithm is computationally even simpler than the efficient regularization assisted orthogonal least square algorithm based on LOO criteria for selecting fixed-node RBF models. Another significant advantage of the proposed learning procedure is that it does not have learning hyperparameters that have to be tuned using costly cross validation. The effectiveness of the proposed PSO aided OFR construction procedure is illustrated using several examples taken from regression and classification, as well as density estimation applications.

Prediction of Parkinson’s Disease tremor onset using a radial basis function neural network based on particle swarm optimization

Deep Brain Stimulation (DBS) has been successfully used throughout the world for the treatment of Parkinson's disease symptoms. To control abnormal spontaneous electrical activity in target brain areas DBS utilizes a continuous stimulation signal. This continuous power draw means that its implanted battery power source needs to be replaced every 18–24 months. To prolong the life span of the battery, a technique to accurately recognize and predict the onset of the Parkinson's disease tremors in human subjects and thus implement an on-demand stimulator is discussed here. The approach is to use a radial basis function neural network (RBFNN) based on particle swarm optimization (PSO) and principal component analysis (PCA) with Local Field Potential (LFP) data recorded via the stimulation electrodes to predict activity related to tremor onset. To test this approach, LFPs from the subthalamic nucleus (STN) obtained through deep brain electrodes implanted in a Parkinson patient are used to train the network. To validate the network's performance, electromyographic (EMG) signals from the patient's forearm are recorded in parallel with the LFPs to accurately determine occurrences of tremor, and these are compared to the performance of the network. It has been found that detection accuracies of up to 89% are possible. Performance comparisons have also been made between a conventional RBFNN and an RBFNN based on PSO which show a marginal decrease in performance but with notable reduction in computational overhead.

Exploring carrier agents in swarm-array computing

How can a bridge be built between autonomic computing approaches and parallel computing systems? The work reported in this paper is motivated towards bridging this gap by proposing a swarm-array computing approach based on ‘Intelligent Agents’ to achieve autonomy for distributed parallel computing systems. In the proposed approach, a task to be executed on parallel computing cores is carried onto a computing core by carrier agents that can seamlessly transfer between processing cores in the event of a predicted failure. The cognitive capabilities of the carrier agents on a parallel processing core serves in achieving the self-ware objectives of autonomic computing, hence applying autonomic computing concepts for the benefit of parallel computing systems. The feasibility of the proposed approach is validated by simulation studies using a multi-agent simulator on an FPGA (Field-Programmable Gate Array) and experimental studies using MPI (Message Passing Interface) on a computer cluster. Preliminary results confirm that applying autonomic computing principles to parallel computing systems is beneficial.

A cluster-based implementation of a fault-tolerant parallel reduction algorithm using swarm-array computing

Recent research in multi-agent systems incorporate fault tolerance concepts. However, the research does not explore the extension and implementation of such ideas for large scale parallel computing systems. The work reported in this paper investigates a swarm array computing approach, namely ‘Intelligent Agents’. In the approach considered a task to be executed on a parallel computing system is decomposed to sub-tasks and mapped onto agents that traverse an abstracted hardware layer. The agents intercommunicate across processors to share information during the event of a predicted core/processor failure and for successfully completing the task. The agents hence contribute towards fault tolerance and towards building reliable systems. The feasibility of the approach is validated by simulations on an FPGA using a multi-agent simulator and implementation of a parallel reduction algorithm on a computer cluster using the Message Passing Interface.

Infrared filters for space-flight focal plane array applications

Infrared filters and coatings have been employed on many sensing radiometer instruments to measure the thermal emission profiles and concentrations of certian chemical constituents found in planetary atmospheres. The High Resolution Dynamics Limb Sounder ( HIRDLS) is an example of the most recent developments in limb-viewing radiometry by employing a cooled focal plane detector array to provide simultaneous multi-channel monitoring of emission from gas and aerosols over an altitude range between 8 - 70 km. The use of spectrally selective cooled detectors in focal plane arrays has simplified the optical layout of radiometers, greatly reducing the number of components in the optical train. this has inevitably led to increased demands for the enviromnetal durability of the focal plane filters because of the need to cut sub-millimeter sizes, whilst maintaining an optimal spectral performance. Additionally the remaining refractive optical elements require antireflection coatings which must cover the entire spectral range of the focal plane array channels, in this case 6 to 18µm, with a minimum of reflection and absorption. This paper describes the optical layout and spectral design requirements for filteriong in the HIRDLS instrument, and reports progress on the manufacturing and testing of the sub-millimetre sized cooled filters. We also report on the spectral and environmental performance of prototype wideband antireflection coatings which satisfy the requirements above.

High-performance infrared filters for the HIRDLS 21-channel focal plane detector array

The High Resolution Dynamics Limb Sounder is described, with particular reference to the atmospheric measurements to be made and the rationale behind the measurement strategy. The demands this strategy places on the filters to be used in the instrument and the designs to which this leads to are described. A second set of filters at an intermediate image plane to reduce "Ghost Imaging" is discussed together with their required spectral properties. A method of combining the spectral characteristics of the primary and secondary filters in each channel are combined together with the spectral response of the detectors and other optical elements to obtain the system spectral response weighted appropriately for the Planck function and atmospheric limb absorption. This method is used to demonstrate whether the out-of-band spectral blocking requirement for a channel is being met and an example calculation is demonstrated showing how the blocking is built up for a representative channel. Finally, the techniques used to produce filters of the necessary sub-millimetre sizes together with the testing methods and procedures used to assess the environmental durability and establish space flight quality are discussed.

Detecting the number of signals using antenna array: a single threshold solution

A new approach is presented to identify the number of incoming signals in antenna array processing. The new method exploits the inherent properties existing in the noise eigenvalues of the covariance matrix of the array output. A single threshold has been established concerning information about the signal and noise strength, data length, and array size. When the subspace-based algorithms are adopted the computation cost of the signal number detector can almost be neglected. The performance of the threshold is robust against low SNR and short data length.