Here, the authors propose a hybrid classification approach using extreme learning machine (ELM) and sparse representation classifier (SRC) with adaptive threshold, which they called ATELMSRC. ATELMSRC can adaptively adjust the threshold, and make more test images correctly classified by ELM compared with ELMSRC, which not only reduces the classification time greatly but also improves the classification accuracy. In addition, primal augmented Lagrangian method is used in ATELMSRC to speed up the solution of -norm, which also speeds up the classification process. Experimental results on USPS handwritten digits data set and UMIST face data set show that the total classification time of the authors ATELMSRC is very short for large data sets, only 1/310 of SRC, 1/805 of extended SRC (ESRC), and 1/41 of ELMSRC. Meanwhile, the classification accuracy of the authors’ ATELMSRC is 97.80% on USPS handwritten digits data set, and 99.27% on UMIST face data set, which are higher than those of ELM, SRC, ESRC, ELMSRC etc.

A simple and effective adaptive beamforming technique is proposed for uniform linear arrays, which are based on projection processing for covariance matrix construction and desired-signal steering vector estimation. The optimal minimum variance distortion-less response beamformer is closely achieved through approximating the interference-plus-noise covariance matrix by utilising the eigenvalue decomposition of the received signal's covariance matrix. Moreover, the direction-of-arrival (DOA) of the desired signal is estimated by maximising the beamformer output power in a certain angular sector. In particular, the proposed beamformer utilises the aforementioned DOA in order to estimate the desired-signal's steering vector for general steering vector mismatches. Simulation results indicate the better performance of the proposed method in the presence of the different errors compared with some of the recently introduced techniques.

A robust adaptive filter is proposed by using the variational Bayesian (VB) inference to extended target tracking with heavy-tailed noise in clutter. An explicit distribution is used to describe the non-Gaussian heavy-tailed noise based on Student's t-distribution. The need for arbitrary decisions is then eliminated, and the robust operation is provided which is less sensitive to extreme observation. Moreover, an approximate measurement update using the analytical techniques of VB methods is derived to approximate the posterior states at each time step. To obtain a more accurate result, clutter estimation is also integrated considering the uncertainty of target tracking in a cluttered environment. The performance of the proposed algorithm is demonstrated with simulated data.

This study is concerned with the state smoothing problem for a class of non-linear discrete-time stochastic systems with one-step random measurement delay (ORMD). The main contribution is that the forward–backward particle smoothing scheme is successfully extended to the systems with ORMD. First, the particle filter, specially designed to deal with ORMD, is implemented to obtain the filtering distribution and the joint distribution of state history. Then, by marginalising the joint distribution, the one-step fixed-lag smoothing distribution can be obtained. Finally, based on the forward–backward smoothing scheme, the particle approximation of the fixed-interval smoothing distribution can be obtained by re-weighting the particles which have been used in the foregoing one-step fixed-lag smoothing distribution. Simulation results demonstrate the effectiveness of the proposed smoother.

Envelope modified versions of the empirical mode decomposition (EMD) method such as the B-spline interpolation-based EMD (B-EMD) method and cardinal spline interpolation-based EMD (C-EMD) method have been proposed recently for purpose of improving its effectiveness. To shed further light on their performance, the behaviours of these EMD-type methods in the presence of white Gaussian noises are investigated in this study based on extensive numerical experiments. Similarly to the EMD method, it turns out that the envelope modified EMD methods also act as filter banks essentially. However, the spectra among the first several intrinsic mode functions of the B-EMD method have fewer overlaps than those of the EMD and C-EMD methods, which indicate that the B-EMD method has a better ability to alleviate the mode mixing problem for signals with higher frequencies. On the other hand, the C-EMD method is shown to perform better than the EMD and B-EMD methods on separating tones with lower frequencies.

To enhance features of different electromagnetic interference (EMI) signals, which are significant for further feature extraction and pattern recognition, the authors propose an EMI signal feature enhancement method based on extreme energy difference and a deep auto-encoder. Experimental results show that this method can effectively enhance features of EMI signals and improve recognition accuracy.

A novel algorithm for the realisation of an orthogonal digital system performing three-dimensional filtering for a separable transfer function is presented in this study. The algorithm is based on a state-space approach and consists of synthesis and implementation algorithms. A structure composed of Givens rotators and delay elements is obtained. A coordinate rotation digital computer algorithm has been used to implement Givens rotators in a pipelined structure. The obtained structure has been realised on a field-programmable gate array (FPGA) chip and its performance has been evaluated. It achieved good finite precision, good sensitivity of filter amplitude to filter coefficients, less noise, better impulse response, and less FPGA chip occupation. To verify the obtained results, they have been compared to the results obtained using a direct-form structure consisting of adders, multipliers, and delay elements.

In actual condition, array elements deficiency or transmission errors lead to incomplete data, which is called sparse aperture (SA) data. In inverse synthetic aperture radar (ISAR) imaging, this large-gaped data produces poor-quality ISAR images when using traditional range–Doppler algorithm. Recently, imaging algorithms based on compressed sensing (CS) theory alleviate this problem effectively because CS theory indicates that sparse signal can be reconstructed from incomplete measurements. However, the basis mismatch problem in CS-based algorithms may degrade the ISAR image. In this study, a reweighted atomic-norm minimisation (ANM) (RAM)-based imaging method is proposed. RAM is a gridless sparse method, which can enhance sparsity and resolution. RAM formulates an optimisation problem and iteratively carries out ANM with a sound reweighting strategy. By reformulating the RAM as a semi-definite programme, the echoes with full aperture (FA) are reconstructed from SA data. After that, ISAR imaging with the reconstructed FA data is achieved via the conventional azimuth compression method. Simulated and real data results demonstrate the effectiveness and superiority of the proposed method.

Recent advances in the speaker recognition (SR) field showed remarkably accurate and outperforming algorithms. However, their performances drastically degrade when the sparse amount of data is available. Nowadays, recognising a speaker identity when only a small amount of speech data is involved for testing and training remains a key consideration since many real world applications often have access to only speech data having a limited duration. In this study, the authors present a new improved approach, based on new information detected from the speech signal, to improve the task of automatic speaker identification. In doing so, they highlight how the detection of the speaker dialect can be explored to address the research problem related to short utterance SR. Results obtained with the new regional system are presented which provide a comparison between this system and the state-of-the-art systems for speaker identification task.

Here, the authors propose a scalable scrambling algorithm operating in the discrete cosine transform (DCT) domain within the JPEG codec. The goal is to ensure that people are no more identifiable while keeping their actions still understandable regardless of the image size. For each 8 × 8 block, the authors encrypt the DCT coefficients to protect data information, and shift them towards the high frequencies to make the DC position available. Whereas encrypted coefficients appear as noise in the protected image, the DC position is dedicated to restitute some of the original information (e.g. the average colour associated with one or a group of blocks). The proposed approach automatically sets the value of each DC according to the region of interest size in order to keep the level of privacy protection strong enough. Comparing to existing methods, the proposed privacy protection framework provides flexibility concerning the appearance of the protected version which makes it stronger for protecting the privacy even during potential attacks. Moreover, the method does not cause excessive perturbation for the recognition of the actions and slightly decreases the efficiency of the JPEG standard.

In this study, a new hybrid anti-jamming system is proposed for kinematic global positioning system receivers. The proposed system employs a short-time Fourier transform (STFT)-based pre-correlation block to guarantee that the receiver can acquire at least four satellites in jamming environments. It also employs a discrete wavelet transform-based denoising block in the navigation unit of the receiver to increase positioning accuracy, which was degraded due to the jamming and also due to the movement of the receiver. Simulation results demonstrate that the proposed system has a better anti-jamming performance compared with previous methods. They show that the average positioning accuracies of the proposed system are 47, 45, and 43% better than the standard STFT-based mitigation method, wavelet-packets transform (WPT)-assisted filter, and WPT-based hybrid system, respectively.

This research work proposes an image analysis-based algorithm to enhance the time–frequency (T–F) mask obtained in the initial segmentation of CASA-based monaural speech separation system to improve speech quality and intelligibility. It consists of labelling the initial segmentation mask, boundary extraction, active pixel detection and eliminating the non-active pixels related to noise. In labelling, the T–F mask obtained is labelled as periodicity pixel ( P ) matrix and non-periodicity pixel ( NP ) matrix. Next speech boundaries are created by connecting all the possible nearby P and NP matrix. Some speech boundary may include noisy T–F units as holes; these holes are treated using the proposed algorithm. The proposed algorithm is evaluated with the quality and intelligibility measures such as signal to noise ratio (SNR), perceptual evaluation of speech quality, , , coherence speech intelligibility index (CSII), normalised covariance metric (NCM), and short-time objective intelligibility (STOI). The experimental results show that the proposed algorithm improves the speech quality by increasing the SNR with an average value of 9.91 dB and reduces the by an average value of 25.6% and also improves the speech intelligibility in terms of CSII, NCM, and STOI when compared with the input noisy speech mixture

In this study, the authors propose a novel method that provides weapon sound (WS) interference reduction of military instruction sound or instructions sound (IS). This method comprises of the following steps. In the first step, the mixed signal is split into its basic constituents using principal component analysis. The second step provides the intrinsic mode functions using the empirical mode decomposition method. In the third step, the fundamental frequency component is extracted by cepstrum analysis. The localisation of WS is done using the grid search window-dominant signal subspace-based method in the fourth step. Multiscale prediction and filtering using Daubechies wavelet-based prediction are applied in the final step. The proposed method provides better results than existing baseline methods, for various signal-to-noise ratios. The simulations have also been performed on recorded WS corrupted IS signals from three different distances for the various input signal-to-weapon noise ratios, to verify the accuracy of the proposed method.

Source localisation withtime-difference-of-arrival (TDOA) and frequency-difference-of-arrival (FDOA)measurements is of great interest since it can provide the location information with highaccuracy.Although the maximumlikelihood (ML) estimator exhibits excellent asymptotic properties, the non-linearity and non-convexity of ML estimator requiremuch computation resources.In this study, source localisation with TDOA and FDOA measurementsis developed viaMonteCarlo importance sampling (IS).In particular, the optimalperformance can be guaranteed by constructing an optimalimportance function whosecovariance is equivalent to the inverse of Fisher information matrix.The derived variance of the proposed estimator showsgood consistency with the theoretical lowerbound. The improved performance of the proposed method is due to the optimal selection ofimportance function and it canconverge to the global optimum with a large number of samples. Although an initial estimate of source localisation information isrequired, the proposedmethod is robust to this a priori knowledge via IS. Moreover, the scenario ofconsidering sensor location uncertainties is analysed and the corresponding IS based solution is derived. Simulation results show that the proposed methods can achieve the Cramér–Rao lower bound at moderate level noises and is superior to several existing methods.

The authors investigate a multi-frequency signal which is decomposed failure by the traditional empirical mode decomposition (EMD) method. Moreover, the multi-frequency signal submerged in the coloured noise increases the difficulty in signal decomposition. As a result, this noisy signal is decomposed unsuccessfully by the cooperation of the adaptive stochastic resonance (SR) in the classic bistable system and EMD. Then, a method combined adaptive SR in the periodic potential system and EMD is put forward to realise the decomposition. Meanwhile, the random particle swarm optimisation algorithm is applied to reach the optimal situation when signal-to-noise ratio attains the maximum value. Different simulation results verify the effectiveness of the proposed method. The proposed method might be useful in dealing with signal processing problems.