The measurement of subtle morphologic beat-to-beat variability in the electrocardiogram (ECG)/vectorcardiogram (VCG) is complicated by the presence of noise which is caused by, e.g., respiration and muscular activity. A method was recently presented which reduces the influence of such noise by performing spatial and temporal alignment of VCG loops. The alignment is performed in terms of scaling, rotation and time synchronization of the loops. Using an ECG simulation model based on propagation of action potentials in cardiac tissue, the ability of the method to separate morphologic variability of physiological origin from respiratory activity was studied. Morphologic variability was created by introducing a random variation in action potential propagation between different compartments. The results indicate that the separation of these two activities can be done accurately at low to moderate noise levels (less than 10 microV). At high noise levels, the estimation of the rotation angles was found to break down in an abrupt manner. It was also shown that the breakdown noise level is strongly dependent on loop morphology; a planar loop corresponds to a lower breakdown noise level than does a nonplanar loop.

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Internet-of-vehicle (IoV) is a general concept referring to, e.g., autonomous drive based vehicle-to-everything (V2X) communications or moving relays. Here, high rate and reliability demands call for advanced multi-antenna techniques and millimeter-wave (mmw) based communications. However, the sensitivity of the mmw signals to blockage may limit the system performance, especially in highways/rural areas with limited building reflectors/base station deployments and highspeed devices. To avoid the blockage, various techniques have been proposed among which reconfigurable intelligent surface (RIS) is a candidate. RIS, however, has been mainly of interest in stationary/low mobility scenarios, due to the associated channel state information acquisition and beam management overhead as well as imperfect reflection. In this article, we study the potentials and challenges of RIS-assisted dynamic blockage avoidance in IoV networks. Particularly, by designing region-based RIS pre-selection as well as blockage prediction schemes, we show that RIS-assisted communication has the potential to boost the performance of IoV networks. However, there are still issues to be solved before RIS can be practically deployed in IoV networks.

A method for detecting body position changes that uses the surface vectorcardiogram (VCG) is presented. Such changes are often manifested as sudden shifts in the electrical axis of the heart and can erroneously be interpreted as acute ischaemic events. Axis shifts were detected by analysing the rotation angles obtained from the alignment of successive VCG loops to a reference loop. Following the rejection of angles originating from noise events, the detection of body position changes was performed on the angle series using a Bayesian approach. On a database of ECG recordings from normal subjects performing a predefined sequence of body position changes, a detection rate of 92% and a false alarm rate of 7% was achieved.

This paper presents a novel event detector for implantable devices. The algorithm is based on a signal model which describes an event as a linear combination of basis functions. The linear combination involves two fundamental electrogram waveforms represented at different time scales. An efficient, low-complexity detector is developed using the dyadic wavelet transform with integer filter coefficients, and a generalized likelihood ratio test. The results show that reliable detection is obtained at an intermediate signal-to-noise ratio (SNR = 25 dB) for various common noise sources. In terms of probabilities of missed events and false alarms, an over-all performance of 0.7% and 0.1%, respectively, was achieved on electrograms corrupted by the different noise types at an intermediate SNR.