Joint diagonalisation (JD) of a set of target matrices is a common approach to solve the blind source separation (BSS) problem. In fact, the separating matrix (the inverse of the mixing matrix) of the sources is the joint diagonaliser of the target matrices. In this study, the authors show that each row of the separating matrix maps the target matrices using a linear mapping into the new vectors which are located on a direct line along the corresponding column of the mixing matrix. Based on this geometrical interpretation, the authors propose a method for solving JD problem, which let us estimate the rows of the separating matrix and the columns of the mixing matrix, (i) independently and in parallel, (ii) consecutively or (iii) simultaneously. Simulation results in different scenarios such as additive noise, ill-posed mixing matrix, and high-dimensional data demonstrate the effectiveness of the proposed method relative to state-of-the-art JD methods. The authors also propose an approach to omit the effect of outlier data, which severely degrades parameters estimation. The proposed approach can be used in JD algorithms to improve their performance in the presence of outlier data.

The minimum variance-based recursive estimator is designed to estimate the unmeasurable states of a non-linear networked system (NLNS). The measurable states are transmitted from sensor to the estimator (S–E) through a communication network. An event-triggered mechanism is proposed to limit data transmission in the S–E network to improve channel utilisation. The control input is transmitted from the controller to actuator (C–A) through another communication network. The networks in the S–E and C–A channels are affected by random packet delay, loss, and uncertain observation. These effects are modelled using five Bernoulli distributed random variables. The NLNS is converted to ‘m’ local linear stochastic augmented models by piecewise linearisation technique. The ‘m’ number of local estimators are designed for every local model using projection theorem. Global estimation is obtained by blending local estimators using T–S fuzzy approach. The proposed algorithm is recursive and has the advantage of scalability and flexibility. Finally, estimator performance is demonstrated using a non-linear tunnel diode and compared with the other popular filters. Monte Carlo simulation is performed and accumulated mean-square error values are calculated. From the simulation results, the proposed filter shows satisfactory performance.

Obtaining channel state information (CSI) at the transmitter side is essential to take advantage of spectral and energy efficiency in massive multiple-input multiple-output systems. In particular, due to a large number of antennas at the base station (BS) side, the required pilots for downlink channel estimation and the following CSI feedback in the uplink path would be prohibitively large when the system employs frequency-division duplex protocol for data transmission. In this study, the authors propose a novel compressed sensing algorithm which exploits the commonly shared sparsity among nearby users to reduce the estimation error and the number of assigned pilots accordingly. Moreover, to gather users’ channel with common sparsity, a clustering procedure is introduced, which groups active users located in the cell according to their mean angle of arrivals received by the uplink signals at the BS antenna array. After clustering users properly, the proposed algorithm can exploit shared support set existing in each cluster efficiently, thereby improving CSI estimation performance. Numerical results demonstrate that the clustering idea along with the proposed algorithm, outperforms other solutions, and is capable of approaching the performance bound when the transmit power is increased.

Optimal integer-order transfer function approximation of linear time-invariant fractional-order systems (FOS) using a straight-forward approach is proposed here. The error between the rational approximant and the FOS (commensurate/non-commensurate/oscillatory types etc.) is minimised directly in the frequency domain, subject to design stability constraints, by using the colliding bodies optimisation (CBO) algorithm. Comparisons with other well-known metaheuristics regarding the computational time and the design effort expended in parameter tuning justify the efficiency of the proposed method. The CBO-optimised models achieve a superior solution quality regarding both the magnitude and phase responses in comparison to the reported literature. Analogue circuit implementations of the models using the current feedback operational amplifier are also presented. Simulations carried out in OrCAD PSPICE environment validate the practical efficacy of the proposed models.

In this study, the monofractal and multifractal properties of inter-beat (R-R) intervals in cardiac signals for normal and pathology classes were studied and applied to a dataset in PhysioNet which consists of 24 h R-R intervals from 54 healthy subjects (hs) and 92 patients with various diagnoses [44 with congestive heart failure (chf), 25 with atrial fibrillation (af) and 23 diagnosed with sudden death syndrome (sd)]. The results in this study indicate that the most suitable method for estimating the monofractal properties of R-R intervals is detrending moving average (DMA). The Hurst exponents ( H ) of the healthy and pathological groups, calculated using the DMA method, are shown to be statistically different by the Kruskal–Wallis test [p-value < 0.01; healthy  = 0.23(0.18–0.27); pathological group  = 0.06(0.03–0.10),  = 0.05(0.03–0.07),  = 0.05(0.03–0.08)]. To study the multifractal properties of R-R intervals, the multifractal detrended fluctuation analysis was used. Formal statistical tests indicate statistically significant differences (p-value < 0.05, ANOVA and pairwise testing) between the width of the spectrum of the chf group  = 1.11 ± 0.12 and the width of the spectra of the af group  = 0.58 ± 0.15, the sd group  = 0.60 ± 0.20, and the hs group  = 0.61 ± 0.08. Using these results, a logistic regression model was developed for differentiating chf from other pathologies (af and sd).

The hybrid cognitive radio (CR) system which combines the two classical interweave and underlay approaches has an improved spectrum efficiency compared to conventional CR systems. In this study, the authors apply the joint spectrum access and power control in a hybrid CR system in the presence of threshold estimation errors. They first formulate the performance metrics of the hybrid CR system under the optimal joint spectrum access and power control strategies by taking into account the threshold estimation errors. Then they show that the threshold estimation error decreases the probability of detecting the primary user (PU) while it increases the false-alarm probability. Next, they show that the threshold estimation error degrades the performance of the hybrid CR system in terms of outage probability, average achievable data rate and the average interference introduced to the PU. To overcome these performance degradations, they propose a modified spectrum access formulation based on the probabilistic spectrum access scheme, which reduces the impact of threshold estimation uncertainty on the performance of the hybrid CR system. Simulation results indicate that the proposed spectrum access can compensate the performance degradation of a CR system up to 65% for a detection probability of 0.7.