In this paper, novel knowledge‐aided space‐time adaptive processing (KA‐STAP) algorithms using sparse representation/recovery (SR) techniques by exploiting the spatio‐temporal sparsity are proposed to suppress the clutter for airborne pulsed Doppler radar. The proposed algorithms are not simple combinations of KA and SR techniques. Unlike the existing sparsity‐based STAP algorithms, they reduce the dimension of the sparse signal by using prior knowledge resulting in a lower computational complexity. Different from the KA parametric covariance estimation (KAPE) scheme, they estimate the covariance matrix using SR techniques that avoids complex selections of the Doppler shift and the covariance matrix taper. The details of the selection of potential clutter array manifold vectors according to prior knowledge are discussed and compared with the KAPE scheme. Moreover, the implementation issues and the computational complexity analysis for the proposed algorithms are also considered. Simulation results show that our proposed algorithms obtain a better performance and a lower complexity compared with the sparsity‐based STAP algorithms and outperform the KAPE scheme in presence of errors in prior knowledge.

In this paper, we propose a new consensus clustering algorithm, which is based on an existing clustering paradigm, called enhanced splitting merging awareness tactics (E-SMART). The problem of determining the number of clusters, which affects many state-of-theart consensus clustering algorithms, is addressed by the proposed CoCE-SMART algorithm. The idea behind CoCE-SMART is that SMART is used repeatedly to one dataset, resulting in different clustering results, which might have different numbers of clusters. These SMART clustering results can be combined by clustering the centroids of all clusters as the estimate of real number of clusters can be determined from the SMART clustering results. Three benchmark datasets are utilised to assess the proposed algorithm. The experimental results strongly indicate that the proposed CoCE-SMART algorithm outperforms other state-of-the-art consensus clustering algorithms.

In this paper, we propose a scalable clustering paradigm to address the problems of excessive computational load and limited clustering performance in large-scale data. The proposed method employs the enhanced splitting merging awareness tactics (E-SMART) algorithm. The large-scale dataset is divided into many sub-datasets sampled randomly from original data. These sub-datasets are clustered using E-SMART with the number of clusters K detected automatically and the resulting partitions are combined and re-clustered. We evaluate our method using synthetic fMRI datasets with different noise levels and one real fMRI dataset. Results show that the accuracy and execution time outperforms the traditional clustering algorithms in large-scale datasets.

BACKGROUND: Collective analysis of the increasingly emerging gene expression datasets are required. The recently proposed binarisation of consensus partition matrices (Bi-CoPaM) method can combine clustering results from multiple datasets to identify the subsets of genes which are consistently co-expressed in all of the provided datasets in a tuneable manner. However, results validation and parameter setting are issues that complicate the design of such methods. Moreover, although it is a common practice to test methods by application to synthetic datasets, the mathematical models used to synthesise such datasets are usually based on approximations which may not always be sufficiently representative of real datasets. RESULTS: Here, we propose an unsupervised method for the unification of clustering results from multiple datasets using external specifications (UNCLES). This method has the ability to identify the subsets of genes consistently co-expressed in a subset of datasets while being poorly co-expressed in another subset of datasets, and to identify the subsets of genes consistently co-expressed in all given datasets. We also propose the M-N scatter plots validation technique and adopt it to set the parameters of UNCLES, such as the number of clusters, automatically. Additionally, we propose an approach for the synthesis of gene expression datasets using real data profiles in a way which combines the ground-truth-knowledge of synthetic data and the realistic expression values of real data, and therefore overcomes the problem of faithfulness of synthetic expression data modelling. By application to those datasets, we validate UNCLES while comparing it with other conventional clustering methods, and of particular relevance, biclustering methods. We further validate UNCLES by application to a set of 14 real genome-wide yeast datasets as it produces focused clusters that conform well to known biological facts. Furthermore, in-silico-based hypotheses regarding the function of a few previously unknown genes in those focused clusters are drawn. CONCLUSIONS: The UNCLES method, the M-N scatter plots technique, and the expression data synthesis approach will have wide application for the comprehensive analysis of genomic and other sources of multiple complex biological datasets. Moreover, the derived in-silico-based biological hypotheses represent subjects for future functional studies.

The recently proposed UNCLES method has the ability to unify clustering results from multiple datasets under different types of external specifications. It can also tunably tighten the results such that many objects are unassigned from all of the clusters to obtain few tight clusters. Despite the success of this method, setting its parameters, such as the number of clusters (K) and the tuning parameters δ and (δ+, δ-), has never been automated. As its clusters vary in size, they cannot be validated by the existing validation indices. In this study we present a technique of validation based on our proposed M-N scatter plots. This technique has the ability to provide better fitness values for the clusters which include more objects while preserving their tightness. This well suits the nature of the results of UNCLES. We have applied this technique to a set of bacterial microarray datasets as well as a set of English vowels datasets. Our results demonstrate the success of the M-N plots in selecting the best few clusters out of a pool of clusters generated under varying K, δ, and (δ+, δ-) values. Our results also show that the best few clusters can be originated from different partitions, which shows the power of our technique in evaluating individual clusters rather than whole partitions. Finally, despite proposing this technique within the context of the UNCLES framework, it is readily applicable to other clustering results, especially when the parameters are not confidently predefined.

To meet the demand of clustering high dimensional data efficiently, in this paper, we propose a component-wise expectation conditional maximisation (CW-ECM) algorithm and integrate it within the recent proposed splitting-while-merging framework, which is called splitting-merging awareness tactics (SMART), for the mixture of factor analysers (MFA) model. The new algorithm has two advantages: it has ability to converge to actual or close actual number of clusters by a splitting-while-merging strategy, and it avoids the local maxima effectively and efficiently. Furthermore, we improve the splitting strategy in the original SMART framework and save more computational effort. We test out algorithm in two benchmark datasets and compare it with the state-of-the-art algorithms using many validation metrics. The results show that the proposed algorithm outperforms the compared algorithms in clustering performance with significantly less computational complexity.

BACKGROUND: The scale and complexity of genomic data lend themselves to analysis using sophisticated mathematical techniques to yield information that can generate new hypotheses and so guide further experimental investigations. An ensemble clustering method has the ability to perform consensus clustering over the same set of genes from different microarray datasets by combining results from different clustering methods into a single consensus result. RESULTS: In this paper we have performed comprehensive analysis of forty yeast microarray datasets. One recently described Bi-CoPaM method can analyse expressions of the same set of genes from various microarray datasets while using different clustering methods, and then combine these results into a single consensus result whose clusters' tightness is tunable from tight, specific clusters to wide, overlapping clusters. This has been adopted in a novel way over genome-wide data from forty yeast microarray datasets to discover two clusters of genes that are consistently co-expressed over all of these datasets from different biological contexts and various experimental conditions. Most strikingly, average expression profiles of those clusters are consistently negatively correlated in all of the forty datasets while neither profile leads or lags the other. CONCLUSIONS: The first cluster is enriched with ribosomal biogenesis genes. The biological processes of most of the genes in the second cluster are either unknown or apparently unrelated although they show high connectivity in protein-protein and genetic interaction networks. Therefore, it is possible that this mostly uncharacterised cluster and the ribosomal biogenesis cluster are transcriptionally oppositely regulated by some common machinery. Moreover, we anticipate that the genes included in this previously unknown cluster participate in generic, in contrast to specific, stress response processes. These novel findings illuminate coordinated gene expression in yeast and suggest several hypotheses for future experimental functional work. Additionally, we have demonstrated the usefulness of the Bi-CoPaM-based approach, which may be helpful for the analysis of other groups of (microarray) datasets from other species and systems for the exploration of global genetic co-expression.

Validity indices have been investigated for decades. However, since there is no study of noise-resistance performance of these indices in the literature, there is no guideline for determining the best clustering in noisy data sets, especially microarray data sets. In this paper, we propose a generalized parametric validity (GPV) index which employs two tunable parameters α and β to control the proportions of objects being considered to calculate the dissimilarities. The greatest advantage of the proposed GPV index is its noise-resistance ability, which results from the flexibility of tuning the parameters. Several rules are set to guide the selection of parameter values. To illustrate the noise-resistance performance of the proposed index, we evaluate the GPV index for assessing five clustering algorithms in two gene expression data simulation models with different noise levels and compare the ability of determining the number of clusters with eight existing indices. We also test the GPV in three groups of real gene expression data sets. The experimental results suggest that the proposed GPV index has superior noise-resistance ability and provides fairly accurate judgements.

Many methods have been proposed to identify informative subsets of genes in microarray studies in order to focus the research. For instance, the recently proposed binarization of consensus partition matrices (Bi-CoPaM) method has, amongst its various features, the ability to generate tight clusters of genes while leaving many genes unassigned from all clusters. We propose exploiting this particular feature by applying the Bi-CoPaM over genome-wide microarray data from multiple datasets to generate more clusters than required. Then, these clusters are tightened so that most of their genes are left unassigned from all clusters, and most of the clusters are left totally empty. The tightened clusters, which are still not empty, include those genes that are consistently co-expressed in multiple datasets when examined by various clustering methods. An example of this is demonstrated in this paper for cyclic and acyclic genes as well as for genes that are highly expressed and that are not. Thus, the results of our proposed approach cannot be reproduced by other methods of genes' periodicity identification or by other methods of clustering.

In this paper, we propose a splitting-while-merging algorithm with finite mixture models (FMM) built on an improved splitting merging awareness tactics (SMART). The main property of SMART is that it does not require any dataset-dependent parameters or a priori knowledge about the datasets. The improved SMART framework integrates clustering selection criterion, which plays a vital role in the new algorithm. In the SMART-FMM implementation, the modified component-wise EM of mixtures is employed as a learning and merging technique and a model order selection algorithm is used as a clustering selection criterion. One demonstration example and one real microarray gene expression dataset are studied using our approach. The numerical results show that SMART-FMM is superior and more effective than others.

In uplink orthogonal frequency division multiple access (OFDMA) systems with carrier frequency offsets (CFOs), there always be a dilemma that high performance and low complexity cannot be obtained simultaneously. In this study, in order to achieve better trade‐off between performance and complexity, the authors propose a grouped minimum mean squared error (G‐MMSE)‐based multi‐stage interference cancellation (MIC) scheme. The first stage of the proposed scheme is a G‐MMSE detector, where the signal is detected group by group using banks of partial MMSE filters. The signal group can be either user based or subcarrier based. Multiple novel interference cancellation (IC) units are serially concatenated with the G‐MMSE detector. Reusing the filters in the G‐MMSE detector significantly reduces the computational complexity in the subsequent IC units as shown by the complexity analysis. The performance of the proposed G‐MMSE‐MIC schemes are evaluated by theoretical analysis and simulation. The results show that the proposed schemes outperform other existing schemes with considerably low complexity.

This study proposes a novel approach for the analysis of brain responses in the modality of ongoing EEG elicited by the naturalistic and continuous music stimulus. The 512-second long EEG data (recorded with 64 electrodes) are first decomposed into 64 components by independent component analysis (ICA) for each participant. Then, the spatial maps showing dipolar brain activity are selected in terms of the residual dipole variance through a single dipole model in brain imaging, and clustered into a pre-defined number (estimated by the minimum description length) of clusters. Subsequently, the temporal courses of the EEG theta and alpha oscillations of each component for each cluster are produced and correlated with the temporal courses of tonal and rhythmic features of the music. Using this approach, we found that the extracted temporal courses of the theta and alpha oscillations along central and occipital area of scalp in two of the selected clusters significantly correlated with the musical features representing progressions in the rhythmic content of the stimulus. We suggest that this demonstrates that with the proposed approach, we have managed to discover what kinds of brain responses were elicited when a participant was listening continuously to the long piece of naturalistic music.

In this paper, a generalized phase spatial shift keying (GPSSK) modulation is introduced as an improvement of the generalized space shift keying (GSSK) modulation which lays the concept of using the antenna indices to convey information. The idea behind the proposed scheme is to map the bit vector corresponding to one symbol in two information bit blocks: the first block selects the antennas used during the transmission and the second block chooses the transmitted symbols, which are distinguished by their phases. The receiver estimates the indices of the active antennas and the transmitted signal which are used to de-map the block of information bits. To validate the results, an upper bound on the bit error rate (BER) of GPSSK is derived. Both the analytical and simulation results show that the proposed scheme provides performance gain and improved spectrum efficiency over GSSK with low detection complexity.

In this paper, a new successive interference cancellation (SIC) strategy for multiple-input multiple output (MIMO) spatial multiplexing systems is developed to combat the error propagation (EP) in decision feedback systems. The proposed scheme employs a parallel multi-branch (MB) structure. Each branch employs a SIC with multi-feedback (MF) strategy to detects the signals according to their respective ordering pattern. The MF-SIC scheme considers the feedback diversity by using a number of selected constellation points as the feedback set if a previous decision is considered unreliable. The shadow area constraint (SAC) is proposed to reduce the computational complexity by avoiding redundant MF processing with reliable decisions. The MB-MF-SIC achieves a higher detection diversity by selecting the branch which yields the signal estimates with the best performance according to the maximum likelihood rule. The simulation results show that the MB-MF-SIC scheme successfully mitigates the EP and approaches the ML performance while requiring lower complexity than sphere decoders.

In this paper, we consider interference cancellation (IC) schemes for uplink orthogonal frequency division multiple-access (OFDMA) systems with carrier frequency offsets (CFOs). To improve the detection performance without increasing the computational complexity of the detector, we propose a low-complexity multi-stage interference cancellation (MIC) scheme based on a grouped minimum mean squared error (GMMSE) algorithm. In the proposed scheme, a novel IC strategy is employed. The simulation results show that the proposed IC scheme outperforms other existing schemes with considerable low complexity in both subband carrier assignment scheme (CAS) and generalized CAS scenarios.

In this study, the authors propose a novel successive interference cancellation (SIC) strategy for multiple-input multiple-output spatial multiplexing systems based on a structure with multiple interference cancellation branches. The proposed multi-branch SIC (MB-SIC) structure employs multiple SIC schemes in parallel and each branch detects the signal according to its respective ordering pattern. By selecting the branch which yields the estimates with the best performance according to the selection rule, the MB-SIC detector, therefore, achieves higher detection diversity. The authors consider three selection rules for the proposed detector, namely, the maximum likelihood (ML), the minimum mean square error and the constant modulus criteria. An efficient adaptive receiver is developed to update the filter weight vectors and estimate the channel using the recursive least squares algorithm. Furthermore a bit error probability performance analysis is carried out. The simulation results reveal that the authors' scheme successfully mitigates the error propagation and approaches the performance of the optimal ML detector, while requiring a significantly lower complexity than the ML and sphere decoder detectors.

In this paper we propose a novel switched-interleaving algorithm based on limited feedback for both uplink and downlink DS-CDMA systems. The proposed switched chip-interleaving DS-CDMA scheme requires the cooperation between the transmitter and the receiver, and a feedback channel sending the index of the interleaver to be used. The transmit chip-interleaver is chosen by the receiver from a codebook of interleaving matrices known to both the receiver and the transmitter and the codebook index is sent back using a limited number of bits. In order to design the codebook, we consider a number of different chip patterns by using random interleavers, block interleavers and a proposed frequently selected patterns method (FSP). The best interleaving patterns are chosen by the selection functions of the received signal to interference plus noise ratio (SINR) for both downlink and uplink systems. Since the selection function needs to determine the best interleaver based on the channel state information, it is necessary to predict reliably the channel state information for typical delay values. We present symbol-based and block-based linear minimum mean squared error (MMSE) receivers for interference suppression. Simulation results show that our proposed algorithm achieves significantly better performance than the conventional DS-CDMA (C-CDMA) systems and the existing chip-interleaving, linear precoding and adaptive spreading techniques.

In this paper, a low-complexity multiple feedback successive interference cancellation (MF-SIC) strategy is proposed for the uplink of multiuser multiple-input multiple-output (MU-MIMO) systems. In the proposed MF-SIC algorithm with shadow area constraints (SAC), an enhanced interference cancellation is achieved by introducing {constellation points as the candidates} to combat the error propagation in decision feedback loops. We also combine the MF-SIC with multi-branch (MB) processing, which achieves a higher detection diversity order. For coded systems, a low-complexity soft-input soft-output (SISO) iterative (turbo) detector is proposed based on the MF and the MB-MF interference suppression techniques. The computational complexity of the MF-SIC is comparable to the conventional SIC algorithm since very little additional complexity is required. Simulation results show that the algorithms significantly outperform the conventional SIC scheme and approach the optimal detector.

In this paper, a reduced-rank knowledge-aided technique for MIMO radar space-time adaptive processing (STAP) design is proposed. We focus on the advantage of MIMO radars in achieving better spatial resolution by employing the colocated antennas. The scheme is based on knowledge-aided constrained joint iterative optimization of adaptive filters (KAC-JIOAF) and takes advantage of the a priori covariance matrix by employing additional linear constraints in the design. A recursive least squares (RLS) implementation is derived to reduce the computational complexity. We evaluate the algorithm in terms of signal-to-interference-plus-noise ratio (SINR) and probability of detection PD performance and compare it with the state-of-the-art reduced-rank algorithms. Simulations show that the proposed algorithm outperforms existing reduced-rank algorithms.

Knowledge-aided space-time adaptive processing (KA-STAP) algorithms, which incorporate a priori knowledge into radar signal processing methods, have the potential to substantially enhance detection performance while combating heterogeneous clutter effects. In this paper, we develop a KA-STAP algorithm to estimate the inverse interference covariance matrix rather than the covariance matrix itself, by combining the inverse of the covariance known a priori, R0-1, and the inverse sample covariance matrix estimate R̂-1. The computational load is greatly reduced due to the avoidance of the matrix inversion operation. We also develop a cost-effective algorithm based on the minimum variance (MV) criterion for computing the mixing parameter that performs a convex combination of R0-1and R̂-1. Simulations show the potential of our proposed algorithm, which obtain substantial performance improvements over prior art.

In this paper we propose a novel switched interleaving algorithm based on limited feedback for downlink DS-CDMA systems. The proposed switched chip-interleaving DS-CDMA scheme requires the cooperation among the transmitter, the receiver and a feedback channel sending the index of the interleaver to be used. The transmit chip-inter leaver is chosen by the receiver from a codebook of interleaving matrices known to both the receiver and the transmitter and we send back the codebook index using a limited number of bits. In order to design the codebook, we consider patterns such as the block interleavers, and a selection function is designed to maximize the received signal to interference plus noise ratio (SINR). We present block-based and symbol-based linear minimum mean squared error (MMSE) receivers for interference suppression. Simulation results show that our proposed algorithm achieves significantly better performance than the conventional CDMA systems and the existing chip-interleaving schemes.

In this paper we propose a novel transmission scheme based on switched interleaving and limited feedback for downlink DS-CDMA systems. The proposed switched interleaving DS-CDMA scheme (SI-CDMA) requires the cooperation among the transmitter, the receiver and a feedback channel sending the index of the interleaver to be used. The transmit chip-interleaver is chosen by the receiver from a codebook of interleaving matrices known to both the receiver and the transmitter and the codebook index is sent back from the feedback channel using a limited number of bits. In order to design the codebook, we consider patterns such as the block interleavers, and a selection function is designed to maximize the received signal to interference plus noise ratio (SINR). We present a block-based linear minimum mean squared error (MMSE) receiver for interference suppression. Simulation results show that our proposed algorithm achieves significantly better performance than the conventional CDMA systems and the existing chip-interleaving schemes.

In this paper we propose a novel successive interference cancellation (SIC) strategy for multiple-input multiple-output (MIMO) spatial multiplexing systems based on multiple interference cancellation branches. The proposed detection structure employs SICs on several parallel branches which are equipped with different ordering patterns so that each branch produces a symbol estimate vector by exploiting a certain ordering pattern. The novel detector, therefore, achieves higher detection diversity by selecting the branch which yields the estimates with the best performance according to the selection rule. We consider three selection rules for the proposed detector, namely, maximum likelihood (ML), minimum mean square error (MMSE), constant modulus (CM) criteria. The simulation results reveal that our scheme successfully mitigates the error propagation and approaches the performance of the optimal ML detector, while requiring a significantly lower complexity than the ML detector.

In this paper, we consider the effect of impulsive noise on the performance of multicarrier code division multiple access (MC-CDMA) systems and analyze the performance by examining the MC-CDMA system model in time domain. We have discovered that conventional Walsh-code based MC-CDMA is less robust to the presence of impulsive noise than the corresponding direct sequence (DS) CDMA and multicarrier modulation based (MCM) systems. Furthermore, it is demonstrated that the performance of MC-CDMA depends strongly on the selection of the utilized spreading codes. A modified MC-CDMA structure, called MC-SI-CDMA, is proposed and investigated in impulsive noise that employs subcarrier interleaving (SI) to reduce the cross- correlation between the time-domain MC-CDMA waveforms and the impulsive noise. Computer simulation results conducted to support our analysis indicate that the proposed MC-SI- CDMA system in impulsive noise can provide a performance improvement of 2.5 dB at a bit error rate (BER) level of 10-3compared with the DS-CDMA system.

The performance of a downlink synchronous MCCDMA system with joint frequency-time domain spreading (MC-2D-CDMA) is investigated in this paper. We propose a two dimensional adaptive minimum mean square error (MMSE) receiver, which works in decision-directed mode after an initial training period. A subcarrier phase tracker, which comprises a bank of phase locked-loops (PLLs), is employed in the receiver to track the fading phase variability. Furthermore, a simplified phase tracker structure is proposed to reduce the system complexity. The performance of the data detector and the behavior of the phase tracker are analyzed theorectically in this paper and are shown to match the simulation results. Both analysis and simulation indicate that the proposed system outperforms the conventional MC-DS-CDMA systems by exploiting frequency diversity and facilitating subcarrier synchronization.

In this paper, the performance of a downlink synchronous MC-CDMA system with joint frequency-time domain spreading over time-variant frequency-selective Rayleigh fading channels is investigated. We propose an adaptive two dimensional MMSE (minimum mean square error) receiver, which works in decision-directed model after the initial training period. Due to the time variability, a subcarrier phase tracker, which comprises a bank of phase locked-loops (PLLs), is employed in the proposed receiver to track the fading phase variability. Furthermore, a simplified phase tracker structure is proposed to reduce the complexity of system. The theoretical bit error rate (BER) is derived and simulation results indicate that the proposed receiver can achieve good frequency diversity and subcarrier synchronisation, compared to the conventional MCCDMA structures.

In this paper, iterative detection is applied in the receiver for downlink of both direct sequence (DS) and multi-carrier (MC) code division multiple access (CDMA) systems in the flat Rayleigh fading channels. A joint iterative detection and phase recovery scheme is proposed. Unlike the conventional bit-interleaved coded modulation with iterative decoding (BICM-ID) structure, the proposed receiver feeds the extrinsic information from soft-input soft-output (SISO) decoder back to update both the adaptive minimum mean squared error (MMSE) correlator and phase locked-loop (PLL) instead of de-mapper. We also investigate how the second-order PLL benefits from the iterative process over the time-varying flat Rayleigh fading channel. Simulation results illustrate that the iterative process significantly improves the performance of the adaptive detector by suppressing multiple access interference (MAI). Additionally, the tracking capability of PLL is enhanced by reducing the equivalent noise power.