Assessment of fetal development and wellness throughout pregnancy is essential in the early detection of pregnancy anomalies. Recent advances in wearable technology have led to the development of home pregnancy monitoring systems, which hold potentials for the continuous monitoring of expectant mothers and their unborn children through fe-tal/maternal electrocardiogram (f/mECG) technology. Given the complexity of extracting useful information from f/mECG data, these devices are often equipped with multiple channels and sophisticated signal processing algorithms. Thus, the implementation of f/mECG monitoring in daily life remains unfeasible. In this study, we propose an algorithm for extracting the fetal electrocardiogram (fECG) using a single channel. The algorithm detects the peaks of the maternal ECG (mECG) through a clustering method, followed by peak correction. The mECG signal is then reconstructed with Principal Component Analysis (PCA) before applying the Template Subtraction (TS) method to obtain fECG. Rigorous testing with various online f/mECG databases demonstrates the potential to accurately extract fECG using only a single channel. The evaluation results based on the online dataset achieved an accuracy of 99.60% and an F1 score of 99.30%. This advancement also opens opportunities to optimize home-based f/mECG monitoring, making it more compact and reliable.

In this paper, we present a bandwidth efficient retransmission method employing selective retransmission approach at a modulation layer under orthogonal frequency division multiplexing (OFDM) signaling. The proposed cross-layer design embeds a selective retransmission sub-layer in physical layer (PHY) that targets the retransmission of information symbols transmitted over poor quality OFDM sub-carriers. Most of the times, a few errors in decoded bit stream result in packet failure at medium access control (MAC) layer. The unnecessary retransmission of good quality information symbols of a failed packet has detrimental effect on the overall throughput of transceiver. We propose a cross-layer Chase combining with selective retransmission (CCSR) method by blending Chase combining approach at MAC layer and selective retransmission in PHY. The selective retransmission in PHY targets the poor quality information symbols prior to decoding, which results in lower hybrid automatic repeat reQuest retransmissions at MAC layer. We also present bit-error rate upper bound and throughput lower bound for the CCSR method. In order to maximize the throughput, we formulate optimization problem with respect to the amount of information to be retransmitted in selective retransmission. We also present an impact of selective retransmission on latency. The proposed CCSR method achieves a significant throughput gain as compared with the conventional Chase combining method.

In the literature, there exists a number of blind signal recovery algorithms that are implemented as stochastic gradient descent (SGD)-based adaptive schemes. SGD typically has low complexity at the expense of slower convergence. On the other hand, packet-based data transmission in many practical digital communication systems makes it attractive to develop steepest descent (SD) implementation in order to speed-up convergence. This work aims at developing SD implementation of several well-known blind signal recovery algorithms for multi-channel equalisation and source separation. The authors SD formulation is more amenable to additional parametric and signal subspace constraint for faster convergence and superior performance.

Blind equalization typically achieves parameter optimization through cost minimization using stochastic gradient descent in both batch and adaptive algorithms. In general, stochastic descent algorithms typically require large number of iterations or long data samples to converge. The batch approach is generally based on data reuse (recycling) and re-filtering to recompute the cost gradient after each iterative parameter update, thereby causing long processing delays. In this work, we present a novel steepest descent batch algorithm that does not require data recycling. We consider the popular Constant Modulus Algorithm and the Minimum Entropy Deconvolution for normalized cumulant maximization. Both algorithms utilize 4-th order cumulants. The proposed steepest descent batch implementation of both algorithms converge rapidly in a few iterations and deliver superior performance without the delay due to data recycling and refiltering.

In this work, we propose a new criterion for blind channel shortening based on high order statistical information. This method overcomes the weakness of two existing channel shortening criteria derived from only second order output statistics of the unknown channel. It is applicable to both single-input-single-output and single-input-multiple-output frequency selective channels. The optimization criterion can be achieved through either a stochastic gradient descent algorithm or a batch algorithm. Simulation results demonstrate successful shortening of wireless channels that the existing criteria fail to shorten.

We study the problem of channel shortening in multicarrier modulation systems without training. We reformulate two existing methods, the sum-squared and the sum-absolute autocorrelation minimization algorithms (SAM and SAAM), into semidefinite programming to overcome their shortcoming of local convergence. We present the original SAM and SAAM cost functions into as a batch optimization problem before relaxing the original problem into globally convergent semidefinite programming algorithms. Our batch processor is superior to the original stochastic gradient algorithms in terms of achievable bit rate and signal to interference and noise ratio (SINR).