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This study deals with the localisation of all nodes in a network, also called as network localisation, based on pairwise distance measurements. The case of a fully connected network is considered, where 'fully connected' refers to that within the whole network every pair of nodes directly connect to each other, thus their pairwise distance can be measured and available. For the localisation of such a network, the multi-dimensional scaling (MDS) algorithm can provide a relative localisation solution, but only a coarse solution when there are measurement errors. To bridge the gap in the localisation performance between the MDS-based and optimum network localisation, the authors propose an efficient subsequent refinement, that is, the iterative least square (LS)/weighted least square (WLS) refinement for the widely existing independent zero-mean Gaussian measurement errors. Analysis and simulation study show that with sufficiently small measurement errors the proposed improved network localisation scheme can achieve, in very limited iterations, the LS/WLS solution, which exhibits the localisation performance the same as the Cramer-Rao lower bound. The authors also extend the proposed refinement to the absolute localisation case with sufficient position-known anchors that are fully and directly connected to all sensors of the network. [ABSTRACT FROM AUTHOR]

The authors have investigated two solar neutrino flux time series data from Sudbury Neutrino Observatory: one is that with D2O and another is with salt. Both these data series being complex, non-linear and non-stationary, the authors have tried to detect the irregularity and multifractality in them using multifractal detrended fluctuation analysis and comparing with the results obtained using wavelet transform modulus maxima algorithm. Singularity spectra of the signals have been obtained to measure the degree of multifractality of the solar neutrino flux signal. [ABSTRACT FROM AUTHOR]

This study proposes a scheme to detect, locate and classify the disturbances of power system signals in the phase space. The scheme is proposed on the belief that the features of a signal can be more clearly revealed if it is embedded in the phase space through a proper embedding strategy. In this study, because of the fact that a disturbance signal comprises two parts - the disturbance and the normal part, it is aptly embedded to the phase space so that the two parts are decoupled and distinguished from each other. The normal part forms a regular shape in the phase space whereas the disturbance forms a shape that deviates from the regular shape, and the two shapes jointly constitute the embedded signal. Consequently, the detection, location and classification are performed based on the information obtained from the embedded signal. The scheme has been tested on six types of disturbances under various conditions, and the simulation results have demonstrated the accuracy and computational efficiency of the scheme. [ABSTRACT FROM AUTHOR]

The pulse compression and Doppler processing (PCDP) method has been extensively used to detect low-speed and uniform-speed targets in linear frequency modulation (LFM) pulse compression radar. However, the PCDP method is affected by range migration (RM) and Doppler frequency migration (DFM) when detecting high-speed and accelerating targets. In this paper, the authors analyse and quantify these effects, and obtain the relationships of the optimal number of pulses and the threshold number of pulses with RM and DFM. It shows that when the number of pulses equals its optimal value, the maximum output signal-to-noise ratio can be obtained; when the number of pulses is greater than its threshold value, the migrations should be compensated. Then the authors propose a method based on the scaling processing and the fractional Fourier transform to remove the two migrations. In the end, the authors give a target detection experiment to show that the proposed method can effectively compensate these migrations. [ABSTRACT FROM AUTHOR]

The authors consider a cooperative communication system where multiple sources transmit data to multiple destinations, being helped by multiple half-duplex relay nodes: relay nodes perform ‘amplify and forward’ as their cooperative technique, with the use of complex weights for forwarding; complex forwarding weights are optimised, based on the obtained channel information, to improve data transmission performance, especially its corresponding diversity order. In particular, the authors investigate the constrained optimisation on the forwarding weights, under the two constraints of interference-free data reception at all destinations and total forwarding power at all relays, to (i) maximise the signal-to-noise ratio (SNR) of any given source–destination pair; (ii) optimise (here maximise the minimum among) the SNRs of all source–destination pairs. Performance (especially diversity order) analysis and Monte-Carlo simulations show the achieved diversity order and the consistence between analytical and numerical results. [ABSTRACT FROM AUTHOR]

The discrete time–frequency matched filter should replicate the continuous time–frequency matched filter, but the methods differ. To avoid aliasing, the discrete method transforms the real-valued signal to the complex-valued analytic signal. The theory for the time–frequency matched filter does not consider the discrete case using the analytic signal. The authors find that the performance of the matched filter degrades when using the analytic, rather than real-valued, signal. This performance degradation is dependent on the signal-to-noise ratio and the signal type. In addition, the authors present a simple algorithm to efficiently compute the time–frequency matched filter. The algorithm with the real-valued signal, comparative to using the analytic signal, requires one-quarter of the computational load. Hence the real-valued signal – and not the analytic signal – enables an accurate and efficient implementation of the time–frequency matched filter. [ABSTRACT FROM AUTHOR]

In this study, moments of a signal as features for classification in active sonar systems are considered. The authors analyse the impact of propagation effects on the moment features, including the effect of random variability in certain channel parameters, such as target distance. Since the propagation model includes frequency-dependent effects that induce non-stationarities in the propagating signal, the authors use a time–frequency based approximation technique to analyse the moments. The results show how particular random channel effects increase the variability in the moment features, and thus provide some insight on the possible degradation in classification performance of the moment features. [ABSTRACT FROM AUTHOR]

IQ signal processing is widely utilised in today's communication systems. However, it usually faces a common problem of front-end distortion such as IQ imbalance and carrier frequency offset (CFO). Effective algorithms exist for estimating and compensating for IQ imbalance as well as CFO, when the two problems are treated separately. With both effects present, most of those algorithms suffer from degraded quality parameter estimates. In this study, the authors proposed a scheme to estimate and compensate for IQ imbalance in the presence of CFO by using a known preamble of a repeated training sequence. In addition, the authors present a modified algorithm to suit the particular case when CFO is small. The performance of our proposed scheme has been examined with computer simulations on IEEE 802.11a signals. It is shown that the proposed method is more robust and renders better performance than the existing method. [ABSTRACT FROM AUTHOR]

This study presents an exact analysis of circuit non-linearity and phase noise effects on orthogonal frequency division multiplexing (OFDM) communication systems. Using an analytical approach, the degradation effects of these impairments on the performance of OFDM systems are studied. A truncated power series is used to represent the third-order non-linear behaviour of a radio frequency circuit. The phase noise is modelled with Lorentzian power density function. Then, by use of an expression for interferences and intermodulation terms, a closed-form signal-to-noise degradation relation is theoretically extracted. The analytical results are compared with simulation outcomes. The comparison shows that bit error rate of the analytical results closely matches with simulation results of the OFDM system. [ABSTRACT FROM AUTHOR]

In this study, a new type of substrate integrated waveguide (SIW) beamforming network (BFN) based on the principle of the modified R-KR lens has been proposed and experimentally verified. Through the detailed analysis of such a structure, a prototype lens with 15 input ports is implemented together with an SIW slot array antenna at 30 GHz, which is able to cover a wide angle of (-59°, 59°) with its 3 dB beam-width. The measured radiation efficiencies of the lens excited at different ports range from 21.6 to 40.8%. This type of BFN takes the advantages of low profile, low cost and high performance, and may be applied to millimetre-wave mobile communications and radars with beam switching technology. [ABSTRACT FROM AUTHOR]

Sensitivity to noise uncertainty is a fundamental limitation of current spectrum sensing strategies in detecting the presence/absence of primary users in cognitive radio (CR). Because of noise uncertainty, the performance of traditional detectors such as matched filter, energy detector and even cyclostationary detectors deteriorates rapidly at low signal-to-noise ratio (SNR). Without accurate estimation of noise power, an absolute ‘SNR wall’ exists in traditional detectors below which robust detection is impossible, no matter how long the observations are. To counteract the effect of noise uncertainty in low SNR, the authors propose a blind frequency-domain entropy-based spectrum sensing scheme. The entropy of the sensed signal is estimated in the frequency domain with probability space partitioned into fixed dimensions. The authors prove that the entropy of noise is a constant and the proposed scheme is thus intrinsically robust against noise uncertainty. Monte Carlo experiments are carried out to verify the robustness and further show that the proposed scheme outperforms classical energy detector and cyclostationary detector in low SNR region with 6 and 4 dB performance improvement, respectively. In addition, the sensing time is reduced to about 75% by the proposed scheme compared to energy detector under the same detection performance. [ABSTRACT FROM AUTHOR]

Track before detect (TBD) refers to simultaneous detection and tracking using unthresholded sensor responses over time. The motivation for TBD is its capacity to deal with low signal-to-noise ratio (SNR) targets. Previously, the achievable error for TBD has been established using Cramér–Rao analysis. Although computationally simple the Cramér–Rao bound is not useful at low SNR as it does not predict the threshold effect. A more accurate notion of the achievable performance at low SNRs is provided by the computationally more complicated Barankin bound. The computational complexity of the Barankin bound arises from the need to optimise over a number of test points, with the tightness of bound increasing with the number of test points. An approximation to the Barankin bound is proposed which permits the use of multiple test points with reasonable computational expense. The improvements in threshold SNR prediction offered by the proposed bound are demonstrated in numerical examples. [ABSTRACT FROM AUTHOR]

The use of transmit antenna selection algorithms in multiple-antenna systems enables significant reduction in implementation cost and complexity while maintaining acceptable performance. An attractive and quite flexible selection algorithm is to allow the receiver to pick any of the transmit antennas that can satisfy a predetermined performance target. Such an algorithm is referred to as the arbitrarily ordered transmit antenna selection algorithm. However, the effectiveness of transmit antenna selection is decreased by several propagation impairments over the feedback channel from the receiver to the transmitter. Of these impairments, the feedback channel time delay may impose a significant impact on the achieved performance. This paper aims to investigate the impact of this time delay on the performance of receive maximal-ratio combining (MRC) diversity employing the arbitrarily ordered transmit antenna selection algorithm. In order to obtain quantitative measures for this impact, new expressions for various performance criteria are obtained by using the new derived formulas for the probability density function (pdf) and the moment generating function (MGF) of the combined signal-to-noise ratio (SNR). Numerical and simulation results are presented to illustrate the effect of delayed (i.e. outdated) feedback information on the system performance for various transmit antenna selection scenarios. [ABSTRACT FROM AUTHOR]

In this paper, a novel two-dimensional direction of arrival (2-D DOA) estimation method is proposed based on a new array configuration when uncorrelated and coherent signals coexist. The DOAs of uncorrelated signals are estimated using the non-zero eigenvalues and corresponding eigenvectors of the DOA matrix (DOAM) combined with our proposed criterion. Meanwhile, we can form a new matrix without the information of uncorrelated signals. Then the coherent signals are resolved with the redefined DOAM that is constructed by the smoothed matrices of the new matrix. Simulation results demonstrate the effectiveness and efficiency of the proposed method. Other arrays that contain multiple identical central-symmetric subarrays (e.g. uniform rectangular arrays) can also be applied with our method. [ABSTRACT FROM AUTHOR]

Blind coherent detection of convolutional turbo codes is a hard problem in the presence of strong phase noise. Since the operating signal-to-noise ratio is usually very low, phase synchronisation algorithms suffer from phase ambiguities and cycle slips. A possible remedy is to perform joint phase estimation and decoding on a combined state-space model for the time-varying phase and the component convolutional codes. We demonstrate that joint phase estimation and decoding is in fact mandatory only for one component code, while ordinary BCJR decoding can be used for the second component code. Monte Carlo simulations for the turbo code used in the DVB-RCS standard show that the performances of the proposed scheme are close to decoding with perfect knowledge of the phase. [ABSTRACT FROM AUTHOR]

For 10GBASE-T systems, variation in a multiple-input-multiple-output (MIMO) channel degrades the decision-point signal-to-noise ratio (DP-SNR) owing to imperfect pre-equalisation in the Tomlinson–Harashima precoding (THP) at the transmitter sides and catastrophic error propagation in far-end crosstalk (FEXT) cancellation at the receiver sides. Moreover, by using fixed THP coefficients during data transmission, as specified in the 10GBASE-T standard, and the non-linearity of THP pose challenges in the design of adaptive receivers. The authors propose an adaptive two-stage equalisation and FEXT cancellation (TS-EFC) architecture without updating the THP coefficients to combat channel variation at both the transmitter and receiver sides. In the first stage, we propose a new non-decision-directed FEXT canceller at the transmitter side using a joint training architecture to avoid error propagation. In the second stage, we devise an adaptive MIMO equaliser together with a novel pre-processing unit at the receiver side to combat channel variation. The pre-processing unit can eliminate the non-linearity issue by estimating both effective data sequences and precoded channel inputs. In addition, we develop a block least mean square algorithm that exploits the properties of two-dimensional modulated symbols for updating coefficients of the adaptive MIMO equaliser. Simulation results show that our TS-EFC architecture is robust against channel variation and significantly improves the DP-SNR. It eliminates the error propagation and also achieves faster convergence rates during the adaptation process. [ABSTRACT FROM AUTHOR]

Ionospheric clutter mitigation is an important issue for high-frequency surface wave radar (HFSWR) signal processing. Clutter results from a sky wave propagation mode, which is backscattered by ionospheric ionisation irregularities. The random behaviour and the high strength of clutter signals can strongly limit the HFSWR detection capabilities. Here, an ionospheric clutter mitigation processing using wavelets is described. This new approach is founded on the ability of wavelets to separate signals having different variation scales or different directions of variations. The results obtained on real clutter signals with simulated targets are reported. Using the proposed method, one can expect, at mid-latitude, an improvement of the target-to-clutter ratio of 20–30 dB. [ABSTRACT FROM AUTHOR]

New generation of high-intensity aluminium gallium arsenide (AlGaAs) and aluminium indium gallium phosphide (AlInGaP) light-emitting diodes (LEDs) which have permitted the replacement of incandescent-based traffic lights with LED-based traffic lights, unfolds the potential of implementing visible-light communication (VLC) system on outdoor environment. The feasibility of outdoor VLC system is undoubtedly questionable because of the significant ambient-light noise caused by daylight. Existing performance studies related to this system have not taken into account the effect of ambient-light noise which varies largely from day time to night time. The authors propose an analytical daylight noise model based on a modified Blackbody radiation model to capture the effect of ambient-light noise and conduct an in-depth study on the impact of daylight on the system performance. The proposed daylight noise model allows us to perform analytical analysis which produces relatively accurate results with less complexity, as compared to the existing time-consuming simulation. The authors also introduce a new receiver structure employing the selective combining technique to significantly reduce the effect of background noise. From numerical analysis, the authors show that the new receiver structure is able to achieve a signal-to-noise ratio (SNR) improvement of approximately 5 dB and establish a stable communication link at any time of the day. [ABSTRACT FROM AUTHOR]

The authors have previously demonstrated how a transmitter (Tx), a reciprocal transmitter/receiver (Tx/Rx) signal path and two unidirectional receiver (Rx) paths can be used together with short, open and load standards for the absolute vector error correction (AVEC) of a Tx/Rx module. Once calibrated, this Tx/Rx module can then provide accurate vector measurements of the signals that are flowing into and/or out of the test port. In order to simplify the analysis, the AVEC technique was applied to a simplified baseband circuit that did not include frequency conversion mixers in a previous paper. Now, in this paper the authors first show how the AVEC technique can be extended to the vector calibration of high-frequency receivers that involve frequency conversion mixers. The authors then show how to calibrate a system that allows for wideband absolute phase relationship measurements of periodic modulated signals, provided that the same local oscillator is employed for the two down-conversion receivers, and different radio frequencies and intermediate frequencies are employed in these receivers. This novel AVEC technique is one of the key concepts in the design of a wideband absolute vector signal measurement system, which overcomes the limitations of traditional measurement instruments by combining the features of vector signal analysers, spectrum analysers and vector network analysers. [ABSTRACT FROM AUTHOR]

Two new approaches for detecting a manoeuvring air target in strong sea clutter are proposed. Whereas one is based on the adaptive chirplet decomposition, the other is inspired by spectral subtraction. Unlike the other time–frequency transform-based methods, the approaches presented can both be used to compute a signal's time waveform with suppressed sea clutter. Experiments with real-world data samples show that both methods can effectively enhance the Doppler radar signals associated with a manoeuvring air target by more than 15 dB. [ABSTRACT FROM AUTHOR]

The limitations in performance and robustness imposed by explicitly considering a communication channel in a control loop have received increased attention in recent years. Previous results in the literature have stated these limitations in terms of a minimal transmission data rate necessary for stabilisation. In this paper a signal-to-noise ratio (SNR) approach is used to study two specific cases: (i) performance in terms of model matching and (ii) robustness against a multiplicative uncertainty in the plant model. The analysis performed leads to closed-form expressions that allow the quantification of the extra SNR required in both cases. [ABSTRACT FROM AUTHOR]

The performance of six existing model selection criteria is compared, which are commonly used in time series and regression analysis, when they are applied to the problem of the number of signals in the multiple signal classification (MUSIC) method. The five criteria are Akaike Information Criterion (AIC), Hannan and Quinn Criterion, Bayesian Information Criterion (BIC), Corrected AIC (AICc) and the recently introduced Vector Corrected Kullback Information Criterion (KICvc) and Weighted-Average Information Criterion (WIC). The general form of the above information criteria consists of a log likelihood function expressed in terms of the eigenvalues of the sample covariance matrix and a unique penalty term. In our estimation procedure, the number of signals is obtained by minimising each of the above criteria. Several simulated data sets, including a linear antenna array data set, are adopted for the comparison purpose. The authors show that, in simple MUSIC additive white noise model, for small sample size n, WIC performs nearly as well as AICc and outperforms other criteria, and for moderately large to large n, WIC performs nearly as well as BIC and outperforms other criteria. Therefore when the authors are not certain of the relative sample size, WIC may be a practical alternative to any criterion. The main purpose is to draw the attention and interests of signal processing researchers to adopt more recent statistical model selection criteria, such as WIC, in general signal processing problems. [ABSTRACT FROM AUTHOR]

The complexities and latencies of iterative belief propagation (BP) and threshold decoding techniques for a new class of convolutional self-orthogonal codes are analysed using forward-only pipeline architectures. Computer simulation results show that over the additive white Gaussian noise channel at moderate signal-to-noise ratios, BP decoding yields one half the decoding delay when compared with threshold decoding at essentially the same error performances. However, the reduction in the decoding delay using BP is obtained at the cost of an increase of the implementation complexity. [ABSTRACT FROM AUTHOR]

Single carrier (SC) code division multiple access (CDMA) with block transmission has been shown to be more effective while utilising a low-complexity equaliser to combat frequency-selective fading channels, when compared with conventional direct sequence CDMA technology. It also has lower peak-to-average power ratio and lower frequency sensitivity compared with multicarrier CDMA. The authors propose two blind channel estimation methods for uplink multiple input multiple output SC-CDMA systems with block transmisssion-one is the subspace-based method and the other is the so-called autocorrelation contribution method (ACM). Both the methods provide close performance to the case with perfect channel knowledge at high signal-to-noise ratio (SNR) without any training data required. It is shown that ACM yields a better performance than the subspace method at a lower SNR, and a similar performance at a high SNR, with the advantages of avoiding rank determination and noise power estimation as in the subspace method. In addition, the authors integrate layered space frequency equalisation with blind channel estimation, which provides improved performance over the conventional linear equalisation, by employing successive interference cancellation. [ABSTRACT FROM AUTHOR]

An intelligent approach for high impedance fault (HIF) detection in power distribution feeders using advanced signal-processing techniques such as time–time and time–frequency transforms combined with neural network is presented. As the detection of HIFs is generally difficult by the conventional over-current relays, both time and frequency information are required to be extracted to detect and classify HIF from no fault (NF). In the proposed approach, S- and TT-transforms are used to extract time–frequency and time–time distributions of the HIF and NF signals, respectively. The features extracted using S- and TT-transforms are used to train and test the probabilistic neural network (PNN) for an accurate classification of HIF from NF. A qualitative comparison is made between the HIF classification results obtained from feed forward neural network and PNN with same features as inputs. As the combined signal-processing techniques and PNN take one cycle for HIF identification from the fault inception, the proposed approach was found to be the most suitable for HIF classification in power distribution networks with wide variations in operating conditions. [ABSTRACT FROM AUTHOR]

An adaptive pseudo-noise code acquisition scheme based on automatic multipath signal cancellation for mobile communication systems in frequency-selective Rayleigh fading channel is proposed. The proposed system combines a serial strategy and an automatic censored cell-averaging constant false alarm rate (CFAR) detector based on ordered data variability. This system does not require any a priori information about the number of interferences caused by the presence of multipath signals in the reference channel. The mean acquisition time and the detection performances of the proposed processor are evaluated and compared with those of the conventional adaptive acquisition scheme based on fixed-censoring point detector. It is shown that the considered scheme outperforms significantly the conventional one. The effects of various channel parameters on the acquisition performance, namely the number of resolvable paths, the partial correlation length and the signal-to-noise ratio are also investigated. [ABSTRACT FROM AUTHOR]

The effective signal-to-noise and distortion ratio (SNDR) at the output of a nonlinear amplifier is defined through the decomposition of the nonlinear output into correlated output and uncorrelated distortion. The analysis is based on the orthogonalisation of the nonlinear behavioual model that allows the accurate estimation of the effective in-band (or co-channel) distortion and hence determination of SNDR. Fundamental issues regarding the evaluation of the effective in-band distortion and its effect on digitally modulated signals are discussed. Simulations of in-band distortion and SNDR of WCDMA signals are verified experimentally using feed-forward cancelation. [ABSTRACT FROM AUTHOR]

A new family of dual-tree based dyadic complex (approximate analytic) wavelet tight frames that is an extension of higher density discrete wavelet transform (DWT) introduced by Selesnick (in A Higher-density DWT, IEEE Transaction on Signal Processing) is proposed. Because the proposed wavelet frames are good combinations of the higher-density DWT and the dual tree complex wavelet transform, the corresponding new wavelet transform can be potentially applied to the applications when properties of both transforms are required simultaneously. The design problem to obtain finite impulse response filters satisfying the numerous constraints imposed by this new wavelet transform is addressed. The proposed wavelet frames are compactly supported, nearly shift-invariant, having vanishing moments and intermediate scales. Several explicit examples are discussed to illustrate the construction and properties of the wavelet frames. [ABSTRACT FROM AUTHOR]

The problem of radar target classification is examined for the case when more than one perspective or viewing angle of the target is available to the sensor. Using full-scale target signature measurements as the source data, it is shown how, for the first time, multiple perspectives enhance the classification performance. Indeed this is the case even if only one additional perspective is available for exploitation. Further, we explore the classification performance both as a function of the number of perspectives and of the signal to noise ratio. Three approaches to high range resolution profile multi-perspective classification have been implemented. This removes any possible bias that could be introduced by a single individual classifier. The results show, for all three, a consistent improvement in the classification performance, as the number of perspectives is increased. The techniques employed also provide considerable insight into the classification process highlighting the degree of complexity of this extremely challenging problem. [ABSTRACT FROM AUTHOR]

In general, existing blind source separation (BSS) algorithms have been designed based on assumptions that there are at least an equal number of sources and sensors and/or there is no noise present. These ideal situations rarely exist and hence the algorithms do not produce optimum results where either one or both of the assumptions have been violated. Underdetermined mixtures where the number of source signals is greater than the number of observed signals are encountered in neural signal detection from a small number of sensors, image modelling, EEG data and biomedical applications. A new algorithm to estimate unknown source signals from fewer observed signals and simultaneously reduce noise is proposed. The proposed algorithm is based upon a model that addresses the issue of noise and offers greater accuracy with standard assumptions relaxed. It offers an attractive solution with no pre-requisites on the nature of the signals and noise for the algorithm to be applicable. Most BSS algorithms adopt batch processing and are only applicable to orthogonal mixtures. The proposed algorithm does not require the mixing matrix to be orthogonal and can be implemented in an adaptive format to track time-varying mixtures. Results supporting the algorithm's superiority and comparisons with results from other algorithms are also presented to prove the tracking capability of our adaptive algorithm. Synthetic and real-time simulations demonstrate the improvement in convergence and accuracy of estimated signals with fewer restrictions in terms of its applicability. [ABSTRACT FROM AUTHOR]

This authors develop a novel technique for blind source separation (BSS) of nonlinearly mixed signals. A new type of nonlinear mixture is derived where a linear mixing matrix is slotted between two layers of multiple mutually inverse nonlinearities. The paper discusses the separability of this new mixing model within the BSS context. This model further culminates to a framework where the separation solution integrates the theory of series reversion with the Weierstrass neural network and the hidden neurons are spanned by a set of mutually inversed activation functions. Simulations have been undertaken to support the theory of the developed scheme and the results indicate promising performance. The proposed method outperforms other tested algorithms in recovering both synthetic and the real-life recorded signals. The method of selecting the optimum order of the Weierstrass series has also been derived and implemented to balance the computational complexity and the accuracy of signal separation. [ABSTRACT FROM AUTHOR]

We study the multiuser diversity in both multiple-access channel (MAC) and broadcast channel (BC) in multiple-input multiple-output (MIMO) systems, where more than one users are communicating with the base station simultaneously. We consider multi-user scheduling with linear receivers for uplink MIMO MAC and linear precoders for downlink MIMO BC. In each time slot, a group of users is selected for either transmission or reception. A selection criterion is presented to optimise the detection signal-to-noise ratio. We show that the linear processings achieve asymptotically the same detection signal-to-noise ratio for downlink and uplink. This makes it convenient to use a single scheduling algorithm in both downlink and uplink. Furthermore, we present a performance analysis of the proposed scheduling algorithm. Both the analysis and simulations have shown that the scheduling algorithm can provide a significant gain in capacity and error performance for both MIMO BC and MIMO MAC with linear processing. [ABSTRACT FROM AUTHOR]

A more accurate z-domain loop transfer function of a continuous-time bandpass ΣΔ modulator, that takes into consideration a finite resonator quality factor and deviated centre frequency, is derived. This loop transfer function is used to find the noise transfer function. The effect of the resonator Q in the depth of the notch on the noise transfer function is analysed. A derivation of a mathematical expression for SNR of a continuous-time bandpass ΣΔ modulator with a resonator, that has a finite Q and deviated centre frequency, is provided. Moreover, the dependence of the modulator's SNR on the resonator Q and centre frequency accuracy is presented. A comparison is made between the modulator SNR, calculated using the derived mathematical expression, and previously published experimental results that validates the accuracy of the derived mathematical formula. This mathematical formula for SNR helps the designer to predict practically the maximum SNR of modulators before implementation. [ABSTRACT FROM AUTHOR]

A method of applying lifting-based wavelet domain Wiener filter (LBWDMF) in image enhancement is proposed. Lifting schemes have emerged as a powerful method for implementing biorthogonal wavelet filters. They exploit the similarity of the filter coefficients between the low-pass and high-pass filters to provide a higher speed of execution, compared to classical wavelet transforms. LBWDMF not only helps in reducing the number of computations but also achieves lossy to lossless performance with finite precision. The proposed method utilises the multi-scale characteristics of the wavelet transform and the local statistics of each subband. The proposed method transforms an image into the wavelet domain using lifting-based wavelet filters and then applies a Wiener filter in the wavelet domain and finally transforms the result into the spatial domain. When the peak signal-to-noise ratio (PSNR) is low, transforming an image to the lifting-based wavelet domain and applying the Wiener filter in the wavelet domain produces better results than directly applying Wiener filter in spatial domain. In other words each subband is processed independently in the wavelet domain by a Wiener filter. Moreover, in order to validate the effectiveness of the proposed method the result obtained using the proposed method is compared to those using the spatial domain Wiener filter (SDWF) and classical wavelet domain Wiener filter (CWDWF). Experimental results show that the proposed method has better performance over SDWF and CWDWF both visually and in terms of PSNR. [ABSTRACT FROM AUTHOR]

A new approach to error concealment is presented which exploits both spatial and temporal information from the current and previous frames. The technique consists of two stages, motion vector estimation and enhancement of the estimated motion vector. In the first stage the proposed method estimates a replacement for the corrupted motion vector by applying dynamic weights to related motion vectors from the top, bottom, left and right sub-macroblocks. The estimated motion vectors are then enhanced using a new approach based on edge detection in the second stage. The experimental results for several test video sequences are compared with conventional error concealment methods and higher performance is achieved in both objective peak signal-to-noise ratio measurements and subjective visual quality. [ABSTRACT FROM AUTHOR]

Blind equalisation of an FIR multi-input multi-output channel system is an important task for numerous applications such as speech separation, dec-reverberation, communication, signal processing and control, etc. In this paper, a cost function with the knowledge of correlation is reconstructed and a new online algorithm derived with a natural gradient search method for blind source separation of convolutional mixtures. Its implementation is simple and practical. Furthermore, the equivariance property is possessed by the algorithm. Simulations indicate the ability of the algorithm to perform blind equalisation under the weaker condition (the FIR system is equalisable) and also to make speech separation and de-reverberation simultaneous. [ABSTRACT FROM AUTHOR]

Diagonal loading is one of the most widely used and effective methods to improve robustness of adaptive beamformers. The authors consider its application to the case of steering vector errors, i.e. when there exists a mismatch between the actual steering vector of interest and the presumed one. More precisely, the problem addressed is that of optimally selecting the loading level with a view to maximising the signal-to-interference-plus-noise ratio in the presence of steering vector errors. First, an expression is derived for the optimal loading for a given steering vector error and it is shown that this loading is negative. Next, random steering errors are considered and the optimal loading is averaged with respect to the probability density function of the steering vector errors, yielding a very simple expression for the average optimal loading. Numerical simulations attest to the validity of the analysis and show that diagonal loading with the derived optimal loading factor provides a performance close to optimum. [ABSTRACT FROM AUTHOR]

Based on the linear prediction property of sinusoidal signals, a new adaptive method is proposed for frequency estimation of a real tone in white noise. Using the least mean square approach, the estimator is computationally efficient and it provides unbiased and direct frequency measurements on a sample-by-sample basis. Convergence behaviour of the estimated frequency is analysed and its variance in white Gaussian noise is derived. Computer simulations are included to corroborate the theoretical analysis and to show its comparative performance with two adaptive frequency estimators in non-stationary environments. [ABSTRACT FROM AUTHOR]

Studies the blind restoration of nonlinearly mixed signals using multilayer polynomial neural network. Mathematical model of the nonlinear mixing system; Obtaining the optimum framework for the nonlinear demixing system from the derived mixing model; Effective approach in updating the parameters of the polynomial neural network.

Reports on results of an analysis of a three-layer nonlinear auto-association network with linear output neurons and sigmoidal hidden neurons. Operation of hidden layer neurons in linear region; Statistical relations governing the operation of auto-association network; Proposed pruning algorithm to find out the minimum number of hidden neurons needed to reconstruct the input data within a certain error threshold.

Studies the natural gradient type algorithm (NGA) for the separation of cyclostationary sources. Interpretation of blind source separation; Case of complex valued source signals; Properties of the algorithm.

Discusses criteria for dictionary redundancy elimination. Disregarding linearly dependent atoms; Selection of linearly independent atoms; Implementation by the modified Gram-Schmidt orthogonalisation with pivoting technique; Obtaining reciprocal waveforms within the workings of the selection process.

Discusses a method to improve sinusoidal modelling based on energy-adaptive matching pursuits for audio coding. Use of an over-complete dictionary composed of complex exponentials to reduce the complexity of the algorithm; Analysis-synthesis windows scheme that avoids overlapping; Performance of the proposed method.

Studies the problems of blind identification and equalisation (BIE) of finite impulse response (FIR) channels in multiuser digital communications. Non-Gaussian nature and statistical independence of the users' data streams; Blind signal separation based on higher-order statistics; Joint-space time equalisation through the direct application of a higher-order statistics-based blind signal separation method.

The iterative hard-threshold algorithm has recently been proposed to handle sparse regularisation problems arising in compressed sensing and other sparse signal processing problems. The algorithm is attractive owing to its simplicity; however, it converges slowly. An accelerated algorithm of this iterative method is proposed based on momentum and adaptive learning rate. Experiments with the sparse signal reconstruction show the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

A novel and robust method for heart sound activity detection (HSAD) is presented. In this method, a new discriminative feature is developed based on the lag-1 autocorrelation coefficient and feature smoothing is introduced to identify the endpoints of low-level heart murmurs. Several experiments on a large scale phonocardiogram database show that the method significantly outperforms the other HSAD methods under varying levels of heart sounds and different types of noise. For a signal-to-noise ratio of 5 dB, the method achieves a sensitivity of 99.56% and a positive predictivity of 99.41% and has a smallest average time error value of 4.26 ms. [ABSTRACT FROM AUTHOR]

Futurologist Elina Hiltunen explains the importance of so-called 'weak signals' for predicting new trends in technological and social development. [ABSTRACT FROM AUTHOR]

An adaptive acoustic echo cancellation method based on independent component analysis, which can remove nonlinear echo components, is presented. Since this method takes into account the independence between a far-end signal and the echo-cancelled output to cancel acoustic echoes which are independent of near-end signals, it can continue to update AEC parameters even in double-talk situations, thereby improving the cancellation performance. Experiments show that the proposed method provides better performance than the conventional least-mean-square methods for both linear and nonlinear AEC systems without requiring separate double-talk detection (DTD). [ABSTRACT FROM AUTHOR]

A magnetic hand motion tracking system for a variety of applications requiring human–machine interaction is presented. Small permanent magnets are fixed on fingernails as signal sources. A series of magnetic sensors on a wristband measure magnetic signals related to the movements of the fingertips. The measured data are transmitted to the application wirelessly. A hand geometric model and a least-square source localisation algorithm are utilised to calculate the hand postures. This system does not require wire connections and can be used conveniently and unobstructively. [ABSTRACT FROM AUTHOR]

A new blind equalisation approach for binary phase-shift keying (BPSK) signals is proposed, based on a certain phase encoding process. The embedded phase property will enable blind signal equalisation without the need for any training data. The performance of the new receiver is equivalent to that of a non-blind trained receiver with 1/3 of its data reserved for training. The new receiver is therefore very suitable for dynamic channels, where it will save significant throughput without sacrificing performance. [ABSTRACT FROM AUTHOR]