A computationally efficient selected mapping technique for reducing PAPR of OFDM

In orthogonal frequency division multiplexing (OFDM) system, high value of peak-to-average power ratio (PAPR) is an operational problem that may cause non-linear distortion resulting in high bit error rate. Selected mapping (SLM) is a well known technique that shows good PAPR reduction capability but inflicts added computational overhead. In this paper, using Riemann sequence based SLM method, we applied reverse searching technique to find out low PAPR yielding phase sequences with significant reduction in computational complexity. Additionally, we explored side-information free transmission that achieves higher throughput but sacrifices PAPR reduction. Finally, to overcome this loss in PAPR reduction, we proposed application of Square-rooting companding technique over the output OFDM transmitted signal. Simulation results show that the proposed method is able to compensate the sacrifice in PAPR and achieved PAPR reduction of 8.9 dB with very low computational overhead.     

A computationally efficient superresolution image reconstructionalgorithm

Superresolution reconstruction produces a high-resolution image from a set of low-resolution images. Previous iterative methods for superresolution had not adequately addressed the computational and numerical issues for this ill-conditioned and typically underdetermined large scale problem. We propose efficient block circulant preconditioners for solving the Tikhonov-regularized superresolution problem by the conjugate gradient method. We also extend to underdetermined systems the derivation of the generalized cross-validation method for automatic calculation of regularization parameters. The effectiveness of our preconditioners and regularization techniques is demonstrated with superresolution results for a simulated sequence and a forward looking infrared (FLIR) camera image sequence.     

A computationally efficient discrete Backus-Gilbert footprint-matching algorithm

A computationally efficient discrete Backus-Gilbert (BG) method is derived that is appropriate for resolution-matching applications using oversampled data. The method builds upon existing BG methods and approximation techniques to create a modified set of BG coefficients. The method in its current form is restricted to a resolution-only minimization constraint, but in the future could be extended to use a simultaneous noise minimization constraint using a generalized singular value decomposition (GSVD) approach. A theoretical one-dimensional intercomparison is performed using a hypothetical sensor configuration. A comparison of the discrete BG method with a nondiscrete BG method shows that the new approach can be 250% more efficient while maintaining similar accuracies. In addition, an SVD approximation increases the computational efficiencies an additional 43%-106%, depending upon the scene. Several quadrature methods were also tested. The results suggest that accuracy improvements are possible using customized quadrature in regions containing known physical data discontinuities (such as along coastlines in microwave imagery data). The ability to recompute the modified BG coefficients dynamically at lower computational cost makes this work applicable toward applications in which noise may vary, or where data observations are not available consistently (e.g. in radio frequency interference contaminated environments).     

A computationally efficient approach to the estimation of two- and three-dimensional hidden Markov models.

Statistical modeling methods are becoming indispensable in today's large-scale image analysis. In this paper, we explore a computationally efficient parameter estimation algorithm for two-dimensional (2-D) and three-dimensional (3-D) hidden Markov models (HMMs) and show applications to satellite image segmentation. The proposed parameter estimation algorithm is compared with the first proposed algorithm for 2-D HMMs based on variable state Viterbi. We also propose a 3-D HMM for volume image modeling and apply it to volume image segmentation using a large number of synthetic images with ground truth. Experiments have demonstrated the computational efficiency of the proposed parameter estimation technique for 2-D HMMs and a potential of 3-D HMM as a stochastic modeling tool for volume images.     

A computationally efficient method of timing and phase estimation in TDMA systems using a preamble sequence

Optimum and near optimum methods of burst parameter estimation based on the discrete Fourier transform are presented. The methods are applicable to single sample per symbol demodulators when the burst preamble is chosen to be a discrete frequency component at one quarter of the symbol rate. The proposed methods do not require the squaring of the signal, and hence avoid the loss associated with squaring the noise components. The optimum method is shown to reach the Cramer-Rao bound for symbol timing and carrier phase estimation for the chosen preamble, whereas the more computationally efficient method shows a loss of 1.0dB and yields information regarding the timing offset only. The performances of the two methods in the presence of carrier frequency offset and additive white Gaussian noise are analysed, and implementations are given together with a comparison of the respective computational complexities.     

A computationally efficient engine for flexible intrusion detection

Pattern matching for network security and intrusion detection demands exceptionally high performance. This paper describes a novel systolic array-based string matching architecture using a buffered, two-comparator variation of the Knuth-Morris-Pratt (KMP) algorithm. The architecture compares favorably with the state-of-the-art hardwired designs while providing on-the-fly reconfiguration, efficient hardware utilization, and high clock rates. KMP is a well-known computationally efficient string-matching technique that uses a single comparator and a precomputed transition table. Through the use of the transition table, the number of redundant comparisons performed is reduced. Through various algorithmic changes, we enable KMP to be used in hardware, providing the computational efficiency of the serial algorithm and the high throughput of a parallel hardware architecture. The efficiency of the system allows for a faster and denser implementation than any other RAM-based exact match system. We add a second comparator and an input buffer and then prove that the modified algorithm can function efficiently implemented as an element of a systolic array. The system can accept at least one character in each cycle while guaranteeing that the stream will never stall. In this paper, we prove the bound on the buffer size and running time of the systolic array, discuss the architectural considerations involved in the FPGA implementation, and provide performance comparisons against other approaches.     

A computationally efficient two-step implementation of the GLRT

In this paper, the performance of a new two-step adaptive detection algorithm is analyzed. The two-step GLRT consists of an initial adaptive matched filter (AMF) test followed by a generalized likelihood ratio test (GLRT). Analytical expressions are provided for the probability of false alarm (P_FA) and the probability of detection (P_D) in unknown complex Gaussian interference. The analysis shows that the two-step GLRT significantly reduces the computational load over the GLRT while maintaining detection and sidelobe rejection performance commensurate with the GLRT. The two-step GLRT detection algorithm is also compared with another two-step detection algorithm: the adaptive sidelobe blanker (ASB). Both the two-step GLRT and the ASB are characterized in terms of the mainbeam detection performance and the rejection of sidelobe targets. We demonstrate that for a given P_FA, the two-step GLRT has a broad range of threshold pairs (one threshold for the AMF test and one for the GLRT) that provide performance identical to the GLRT. This is in contrast with the ASB, where the threshold pairs that maximize the P_D are a function of the target's signal-to-interference-plus-noise ratio (SINR). Hence, for a fixed pair of thresholds, the two-step GLRT can provide slightly better mainbeam detection performance than the ASB in the transition region from low to high detection probabilities     

A computationally efficient FIR MMSE-DFE for CCI-impaireddispersive channels

Fast algorithms for the computation of the FIR MMSE-DFE in the presence of ISI, CCI, and colored noise are presented. Substantial reductions in computational complexity are achieved by using the powerful analytical tools of Cholesky factorization and displacement structure to fully exploit the structure of the problem. Both symbol-spaced and fractionally spaced feedforward filters are considered. Finally, we give a detailed complexity evaluation of the proposed algorithm for scenarios typical of the US TDMA digital cellular standard IS-54     

Nonparametric multivariate density estimation: a comparative study

The paper algorithmically and empirically studies two major types of nonparametric multivariate density estimation techniques, where no assumption is made about the data being drawn from any of known parametric families of distribution. The first type is the popular kernel method (and several of its variants) which uses locally tuned radial basis (e.g., Gaussian) functions to interpolate the multidimensional density; the second type is based on an exploratory projection pursuit technique which interprets the multidimensional density through the construction of several 1D densities along highly “interesting” projections of multidimensional data. Performance evaluations using training data from mixture Gaussian and mixture Cauchy densities are presented. The results show that the curse of dimensionality and the sensitivity of control parameters have a much more adverse impact on the kernel density estimators than on the projection pursuit density estimators     

A new approach to designing computationally efficient interpolatedFIR filters

More efficient use of multipliers in FIR filters can be achieved at the expense of a slight increase in delay by designing sparse filter structures. We have developed a new, relatively simple approach to designing sparse cascaded filters, also described in the literature as interpolated FIR filters. Our method is heuristic in nature, but gives surprisingly good results without requiring iterative design or investigation of a large number of alternative parameterizations. The design uses the efficient and widely available Remez exchange algorithm along with some routines that we have written for Matlab. Although the resulting designs are not optimal in a minimax-error sense, they have reduced RMS error, which may be attractive for some applications. We give design examples, and study the effects of coefficient quantization     

A new computationally efficient discrete bit-loading algorithm for DMT applications

This letter presents a new bit-loading algorithm for discrete multitone systems that converges faster to the same bit allocation as the optimal discrete bit-filling and bit-removal methods. The algorithm exploits the differences between the subchannel gain-to-noise ratios in order to determine an initial bit allocation and then performs a multiple-bits loading procedure for achieving the requested target rate. Numerical results using asymmetric digital subscriber test loops demonstrate the computational efficiency of the proposed algorithm.     

A computationally efficient nonuniform FIR digital filter bank for hearing aids

A computationally efficient nonuniform digital FIR filter bank is proposed for hearing aid applications. The eight nonuniform spaced subbands are formed with the help of frequency-response masking technique. Two half-band finite-impulse response (FIR) filters are employed as prototypes resulting in significant improvements in the computational efficiency. We show, by means of example, that an eight-band nonuniform FIR filter bank with stopband attenuation of 80 dB can be implemented with 15 multipliers. The performance of the filter bank is enhanced by optimizing the gains for each subband. The tests on various hearing loss cases suggest that the proposed filter achieves reasonable good matching between audiograms and magnitude responses of the filter bank at very low computational cost.     

A computationally efficient iterative solution of themultidestination optimal dynamic routing problem

The dynamic routing problem for multiple destination networks is considered. The minimum time rather than total delay cost functional is employed. The problem is solved through an iterative link-by-link optimization. Each link capacity is optimally partitioned by examining the upper bounds for the evacuation time imposed through different capacity allocations for each origin/destination pair traffic. The computational complexity per iteration is polynomial in the number of network nodes. This is due to the examination of origin/destination pairs rather then destinations alone as in previous work where a similar approach led to exponential complexity. Sufficient conditions for the convergence of the iterative algorithm to the optimum are given. If these are not satisfied supplementary steps are described which conduct the algorithm to the desired solution. These involve exponential computational complexity     

A computationally efficient frequency-domain LMS algorithm withconstraints on the adaptive filter

The frequency domain implementation of the LMS algorithm is attractive due to both the reduced computational complexity and the potential of faster convergence compared with the time domain implementation. Another advantage is the potential of using frequency-domain constraints on the adaptive filter, such as limiting its magnitude response or limiting the power of its output signal. This paper presents a computationally efficient algorithm that allows the incorporation of various frequency domain constraints into the LMS algorithm. A penalty function formulation is used with a steepest descent search to adapt the filter so that it converges to the new constrained minimum. The formulation of the algorithm is derived first, after which the use of some practical constraints with this algorithm and a simulation example for adaptive blind equalization are described     

An accurate and computationally efficient semi-empirical model for arsenic implants into single-crystal (100) silicon

In this paper is reported an accurate and computationally efficient semiempirical model based on an extensive set of experimental data for arsenic implants into (100) single-crystal silicon. Experimental and model development details are given, and issues of the measurements are discussed. The newly developed model has explicit dependence on tilt angle, rotation angle, and dose, in addition to energy. Comparisons between the model predictions and experimental data are made in order to demonstrate the accuracy of this model.     

A computationally efficient model of single electron transistor for analog IC simulation

A compact analytical single electron transistor (SET) model is proposed. This model is based on the “orthodox theory” of single electron tunneling, valid for unlimited range of drain to source voltage, valid for single or multi-gate, symmetric or asymmetric devices and takes the background charge effect into account. This model is computationally efficient in comparison with existing models. SET characteristics produced by the proposed model have been verified against Monte Carlo simulator SIMON and show good agreement. This model has been implemented in HSPICE simulator through its Verilog-A interface to enable simulation with conventional MOS devices and single electron inverter has been simulated and verified with SIMON results. At high operating temperature, the thermionic current is taken into account.