An adaptive filter-based method for robust, automatic detection and frequency estimation of whistles

This paper proposes an adaptive filter-based method for detection and frequency estimation of whistle calls, such as the calls of birds and marine mammals, which are typically analyzed in the time-frequency domain using a spectrogram. The approach taken here is based on adaptive notch filtering, which is an established technique for frequency tracking. For application to automatic whistle processing, methods for detection and improved frequency tracking through frequency crossings as well as interfering transients are developed and coupled to the frequency tracker. Background noise estimation and compensation is accomplished using order statistics and pre-whitening. Using simulated signals as well as recorded calls of marine mammals and a human whistled speech utterance, it is shown that the proposed method can detect more simultaneous whistles than two competing spectrogram-based methods while not reporting any false alarms on the example datasets. In one example, it extracts complete 1.4 and 1.8 s bottlenose dolphin whistles successfully through frequency crossings. The method performs detection and estimates frequency tracks even at high sweep rates. The algorithm is also shown to be effective on human whistled utterances.     

An automatic frequency domain modal parameter estimation algorithm

In this article, a frequency-domain modal parameter estimation method is proposed. The algorithm automatically separates physical poles from mathematical ones. An important issue in the automatization of the algorithm is the inclusion of noise information to estimate the standard deviations of the poles. These standard deviations are used (together with other features) as the inputs of a fuzzy clustering algorithm. The clustering algorithm then classifies the poles into the mathematical and physical ones. The method requires no user interaction, and a parameter is available quantifying the success of the classification.     

An improved least square channel estimation algorithm for coherent optical OFDM system

This paper presents an improved processing added to conventional least square (LS) channel estimation to modify its performance for coherent optical orthogonal frequency division multiplexing (CO-OFDM) system. By testing selected limitation factors of the existing algorithms, the influence of our improved algorithm to the performance of CO-OFDM system were studied and compared with other published algorithms. The simulation results of the study demonstrated that the proposed approaches achieved better channel estimation performance and are considered as a more appropriate alternative for CO-OFDM system with the tradeoff between complexity and performance.     

Color adjustment algorithm adapted to the spectral reflectance estimation method

We are developing a daily health monitoring system that uses mobile phones with cameras and analyzes physiological conditions from R, G, and B intensity levels. However, since it is affected by various imaging conditions of the image input, color correction is required for accurate health monitoring. Therefore, we developed and validated a colorcorrection algorithm to derive reliable color information by correcting the spectral reflectance using the Wiener estimation and a color chart.     

An NLMS algorithm with optimized preparatory step-size parameters for SQUID-based MCG data processing

We present a new least-mean-square algorithm of adaptive filtering to improve the signal to noise ratio for magnetocardiography data collected with high-temperature SQUID-based magnetometers. By frequently adjusting the adaptive parameter $\alpha $ to systematic optimum values in the course of the programmed procedure, the convergence is accelerated with a highest speed and the minimum steady-state error is obtained simultaneously. This algorithm may be applied to eliminate other non-steady relevant noises as well.     

An improved algorithm for in situ adaptive tabulation

In situ adaptive tabulation (ISAT) is a proven storage/retrieval method which efficiently provides accurate approximations to high-dimensional functions which are computationally expensive to evaluate. Previous applications of ISAT to computations of turbulent combustion have resulted in speed-ups of up to a thousand. In this paper, improvements to the original ISAT algorithm are described and demonstrated using two test problems. The principal improvements are in the table-searching strategies and the addition of an error checking and correction algorithm. Compared to an earlier version of ISAT, reductions in CPU time and storage requirements by factors of 2 and 5, respectively, are observed for the most challenging, 54-dimensional test problem.     

A comparative study between LMS and PSO algorithms on the optical channel estimation for radio over fiber systems

In this paper the particle swarm optimization (PSO) and least mean square (LMS) algorithms are comparatively studied to estimate the optical communication channel parameters for radio over fiber systems. It is observed that especially in low noise one tap optical channels, the convergence of LMS algorithm is approximately same with PSO algorithm. On the other hand, as a communication medium, selecting high noisy fiber optical channels or free space optical channels; PSO reaches better mean square error values. The computational complexity which is one of the most important features for optimization algorithms has also been taken into account.     

An adaptive system for active cancellation of the acoustic field with an auxiliary identification channel

An adaptive system for active cancellation of a narrow-band random acoustic field with an auxiliary identification channel for modeling a time-dependent acoustic path is investigated. The stability criterion of the system is found. The active cancellation system proposed has been tested experimentally.Lobachevskii State University, Nizhny Novgorod. Translated from Izvestiya Vysshikh Uchebnykh Zavedenii, Radiofizika, Vol. 38, Nos. 3–4, pp. 196–201, March–April, 1995.     

An adaptive integrated rule-based algorithm for license plate localization

This paper addresses a license plate localization (LPL) algorithm for a complex background. Most of LPL algorithm works on restricted conditions, as well as on a principle of sequential elimination of blocks from image level to final LP candidate region. In most of algorithms, blocks are filtered out for not satisfying required LP features in a top-down approach and this may result in a poor efficiency in a complex scenario. The major steps of the proposed approach are adaptive edge mapping, saliency measure of edge based rules with confidence level estimation using fuzzy rules and final step for reassessment of decision by colour attributes filtering. The proposed algorithm is adaptive to across the country variations in LP standards, as well as it is tested on two data sets each one consisting of more than 700 images, set-1 being for good images while set-2 including only constrained images. The algorithm is tested for a low contrast due to overexposure or poor lighting, existence of multiple plates, variation in aspect ratio and compatible background conditions. It has been observed, that the performance degradation imposing complex condition is nominal.     

An improved novel joint channel estimation algorithm for the 112 Gbit/s PDM CO-OFDM system

In this paper, an improved novel joint channel estimation algorithm is proposed for the 112 Gbit/s PDM CO-OFDM system. This method combines the advantages of the intra-symbol frequency domain averaging (ISFA) method and the time domain averaging (TA) method, which can realize more accuracy channel estimation. Simulation results show that this proposal could promise a relatively stable performance even under a rapidly time varying environment. It significantly outperforms the ISFA method by 2.2 dB when the DGD equals 1000 ps, and has the advantages of 0.5 dB over ISFA in considering the laser linewidth and frequency offset.     

Fast intersections on nested tetrahedrons (FINT): An algorithm for adaptive finite element based distributed parameter estimation

A variety of biomedical imaging techniques such as optical and fluorescence tomography, electrical impedance tomography, and ultrasound imaging can be cast as inverse problems, wherein image reconstruction involves the estimation of spatially distributed parameter(s) of the PDE system describing the physics of the imaging process. Finite element discretization of imaged domain with tetrahedral elements is a popular way of solving the forward and inverse imaging problems on complicated geometries. A dual-adaptive mesh-based approach wherein, one mesh is used for solving the forward imaging problem and the other mesh used for iteratively estimating the unknown distributed parameter, can result in high resolution image reconstruction at minimum computation effort, if both the meshes are allowed to adapt independently. Till date, no efficient method has been reported to identify and resolve intersection between tetrahedrons in independently refined or coarsened dual meshes. Herein, we report a fast and robust algorithm to identify and resolve intersection of tetrahedrons within nested dual meshes generated by 8-similar subtetrahedron subdivision scheme. The algorithm exploits finite element weight functions and gives rise to a set of weight functions on each vertex of disjoint tetrahedron pieces that completely cover up the intersection region of two tetrahedrons. The procedure enables fully adaptive tetrahedral finite elements by supporting independent refinement and coarsening of each individual mesh while preserving fast identification and resolution of intersection. The computational efficiency of the algorithm is demonstrated by diffuse photon density wave solutions obtained from a single- and a dual-mesh, and by reconstructing a fluorescent inclusion in simulated phantom from boundary frequency domain fluorescence measurements.     

A guide to using shadowing filters for forecasting and state estimation

Most traditional, and many modern, methods of state estimation have a number a failings when applied to high-dimensional and nonlinear systems. A promising new alternative that avoids many of these failings is a shadowing filter. An additional advantage of shadowing filters is that when combined with indistinguishable states they provide a consistent generalisation to the imperfect model scenario. This paper is a practical guide to implementing shadowing filters in nonlinear systems. It provides a detailed account of various tests and procedures to ensure successful implementation of a shadowing filter using gradient descent of indeterminism.     

A DSP based channel selection algorithm for adaptive transmission in indoor power line

Unpredictable noise and transfer function in power line channel make it a hostile environment for communication. The paper presents an algorithm which is implemented on a TMS320C6713 Digital Signal Processor (DSP) to select the best channel suitable for transmission. A design of a suitable hardware where the system can be used is also presented.     

An algorithm for the automatic detection of anomalous temperature variations at ground level: its application to the METEOSAT-2 satellite

Summary The algorithm, starting from techniques typical of image processing and information theory, is an instrument for the detection of anomalous variations in any physical parameter measured by remote sensing. Within a software system capable of checking the validity of such a measurement, comparing it with extant historical data and reporting it, the algorithm acts as a filter able to amplify the anomaly. The aim of this work is to see if it possible to detect anomalous temperature variations in some way connected with fire at ground level by means of the sensors in the infrared heat band of the METEOSAT-2 satellite. The choice of this geostationary satellite was made because, despite low spatial resolution (at the latitude of Sardinia pixels are about 7 km square), it has good time resolution (one slot every 30 minutes).     

An adaptive two-dimensional wavelet-vaguelette algorithm for the computation of flame balls

This paper is concerned with the numerical simulation of two-dimensional flame balls. We describe a Galerkin-type discretization in an adaptive basis of orthogonal wavelets. The solution is represented by means of a reduced basis being adapted in each time step. This algorithm is applied to compute the evolution of circular and elliptic thermodiffusive flames. In particular, we study the influence of the Lewis number, the strength of radiative losses and of the initial radius. The results show different scenarios. We find repeated splitting of the flame ball which is studied in some detail, extinction after excessive growth and also obtain quasi-steady flame balls.     

主动声纳的自适应显示数据缩减算法

本文讨论了主动声纳系统中主动检测结果的数据显示问题.首先介绍传统显示数据缩减的几种算法,在这些基础上,我们提出一种新颖的自适应显示数据缩减算法.此算法利用宽带信号特征和主动检测统计特征,通过分段加权平均并取用子段平均最大值的方法,自适应地缩减待显示数据.仿真结果表明,此算法可以提高数据输出的信息量,减小输出信噪比损失,其性能优于传统的几种显示数据缩减算法,有助于提高人机交互的可靠性,并且算法简单,适于实时实现.     

An adaptive multilevel local defect correction technique with application to combustion

The standard local defect correction (LDC) method has been extended to include multilevel adaptive gridding, domain decomposition and regridding. The domain decomposition algorithm provides a natural route for parallelization by employing many small tensor-product grids, rather than a single large unstructured grid; this algorithm can greatly reduce memory usage. The above properties are illustrated by successfully applying the new algorithm to a simple heat transfer problem with an analytical solution, and by subsequently solving the more complex problem of an axisymmetric laminar Bunsen flame with one-step chemistry. The simulation data show excellent agreement with results previously published in the literature.