Phase change materials (PCMs) are highly attractive for nonvolatile electrical and all-optical memory applications because of unique features such as ultrafast and reversible phase transitions, long-term endurance, and high scalability to nanoscale dimensions. Understanding their transient characteristics upon phase transition in both the electrical and the optical domains is essential for using PCMs in future multifunctional optoelectronic circuits. Here, we use a PCM nanowire embedded into a nanophotonic circuit to study switching dynamics in mixed-mode operation. Evanescent coupling between light traveling along waveguides and a phase-change nanowire enables reversible phase transition between amorphous and crystalline states. We perform time-resolved measurements of the transient change in both the optical transmission and resistance of the nanowire and show reversible switching operations in both the optical and the electrical domains. Our results pave the way toward on-chip multifunctional optoelectronic integrated devices, waveguide integrated memories, and hybrid processing applications.

Heart rate variability (HRV) analysis represents an important tool for the characterization of complex cardiovascular control. HRV indexes are usually calculated from electrocardiographic (ECG) recordings after measuring the time duration between consecutive R peaks, and this is considered the gold standard. An alternative method consists of assessing the Pulse Rate Variability (PRV) from signals acquired through photoplethysmography, a technique also employed for the continuous noninvasive monitoring of blood pressure. In this work, we carry out a thorough analysis and comparison of short term variability indexes computed from HRV time series obtained from the ECG and from PRV time series obtained from continuous blood pressure (CBP) signals, in order to evaluate the reliability of using CBP-based recordings in place of standard ECG tracks. The analysis has been carried out on short time series (300 beats) of HRV and PRV in 76 subjects studied in different conditions: resting in the supine position, postural stress during 45° head-up tilt, and mental stress during computation of arithmetic test. Nine different indexes have been taken into account, computed in the time domain (mean, variance, root mean square of the successive differences), frequency domain (low-to-high frequency power ratio LF/HF, HF spectral power and central frequency) and information domain (entropy, conditional entropy, self entropy). Thorough validation has been performed using comparison of the HRV and PRV distributions, robust linear regression, and Bland-Altman plots. Results demonstrate the feasibility of extracting HRV indexes from CBP-based data, showing an overall relatively good agreement of time, frequency, and information domain measures. The agreement decreased during postural and mental arithmetic stress, especially with regard to band-power ratio, conditional and self entropy. This finding suggests to use caution in adopting PRV as a surrogate of HRV during stress conditions.

Heart rate variability (HRV; variability of the RR interval of the electrocardiogram) results from the activity of several coexisting control mechanisms, which involve the influence of respiration (RESP) and systolic blood pressure (SBP) oscillations operating across multiple temporal scales and changing in different physiological states. In this study, multiscale information decomposition is used to dissect the physiological mechanisms related to the genesis of HRV in 78 young volunteers monitored at rest and during postural and mental stress evoked by head-up tilt (HUT) and mental arithmetics (MA). After representing RR, RESP and SBP at different time scales through a recently proposed method based on multivariate state space models, the joint information transfer T RESP , SBP → RR is decomposed into unique, redundant and synergistic components, describing the strength of baroreflex modulation independent of respiration ( U SBP → RR ), nonbaroreflex ( U RESP → RR ) and baroreflex-mediated ( R RESP , SBP → RR ) respiratory influences, and simultaneous presence of baroreflex and nonbaroreflex respiratory influences ( S RESP , SBP → RR ), respectively. We find that fast (short time scale) HRV oscillations—respiratory sinus arrhythmia—originate from the coexistence of baroreflex and nonbaroreflex (central) mechanisms at rest, with a stronger baroreflex involvement during HUT. Focusing on slower HRV oscillations, the baroreflex origin is dominant and MA leads to its higher involvement. Respiration influences independent on baroreflex are present at long time scales, and are enhanced during HUT.

Two versions of a PPG/ECG combined system have been realized and tested. In a first version a multisite system has been equipped by integrating 3 PPG optodes and 3 ECG leads, whereas in another setup a portable version has been carried out. Both versions have been realized by equipping the optical probes with SiPM detectors. SiPM technology is expected to bring relevant advantages in PPG systems and overcome the limitations of physiological information extracted by state of the art PPG, such as poor sensitivity of detectors used for backscattered light detection and motion artifacts seriously affecting the measurements repeatability and pulse waveform stability. This contribution presents the intermediate results of development in the frame of the European H2020-ECSEL Project ASTONISH (n.692470), including SiPM based PPG optodes, and the acquisition electronic components used for simultaneous recording of both PPG/ECG signals. The accurate monitoring of dynamic changes of physiological data through a non-invasive integrated system, including hemodynamic parameters (e.g. heart rate, tissue perfusion etc.) and heart electrical activity can play an important role in a wide variety of applications (e.g. healthcare, fitness and cardiovascular disease). In this work we describe also a method to process PPG waveform according to a PPG process pipeline for pattern recognition. Some examples of PPG waveform signal analysis and the preliminary results of acquisitions obtained through the intermediate demonstrator systems have been reported.

We present an analytical model describing the full electromagnetic propagation in a THz time-domain spectroscopy (THz-TDS) system, from the THz pulses via Optical Rectification to the detection via Electro Optic-Sampling. While several investigations deal singularly with the many elements that constitute a THz-TDS, in our work we pay particular attention to the modelling of the time-frequency behaviour of all the stages which compose the experimental set-up. Therefore, our model considers the following main aspects: (i) pump beam focusing into the generation crystal; (ii) phase-matching inside both the generation and detection crystals; (iii) chromatic dispersion and absorption inside the crystals; (iv) Fabry-Perot effect; (v) diffraction outside, i.e. along the propagation, (vi) focalization and overlapping between THz and probe beams, (vii) electro-optic sampling. In order to validate our model, we report on the comparison between the simulations and the experimental data obtained from the same set-up, showing their good agreement.G eneration and detection of THz electromagnetic waves (0.1-10 3 10 12 Hz) is not a novel topic 1-3 , but it has been arousing an ever-increasing interest only in the last decade. Recent studies demonstrated that many substances and particularly bio-polymers like proteins, amino and DNA possess specific global and sub-global modes in the THz band. Hence, particular consideration has been recently devoted to THz spectroscopy since it reveals information on the conformational stage of molecules and potentially enables their discrimination in various compounds. Indeed, THz pulses have been broadly used, ranging from spectroscopy and time-resolved pump-probe experiments to imaging applications 4,5 . Moreover, the negligible ionization power of the THz radiations compared to optical and X-rays technologies make them perfectly suitable for biological applications 6 . For the above-mentioned reasons, THz technology are penetrating in lots of areas, beyond fundamental Physics investigation, spanning from medical care and homeland security to cultural heritage conservation 7-13 . The most challenging technology development remains in the area of THz sources and detectors, since, in general, both wide band and high power are required at the same time. Thanks to the improvement of the pulsed laser technology, different types of stable THz sources have been developed so far, with peak power values ranging from kW to MW 14,15 . In particular, Optical Rectification (OR) 16 combined with Electro-Optic Sampling (EOS) detection is still the configuration of choice in the few standard high energy pulsed THz system. For this reason, in this paper we present an analytical model able to simulate the full electromagnetic propagation which takes place in the THz-TDS set-up 17 , sketched in Fig. 1 (refer to Methods), based on OR and EOS and employing ,110. ZnTe crystals. We will explain and justify the approach and the approximations which underlie the modelling of each stage and that allowed us to build a ...

͑͒We describe a technique for surface domain engineering in congruent lithium niobate single crystals. The method is based on conventional electric-field poling, but involves an intentional overpoling step that inverts all the material apart from a thin surface region directly below the patterned photoresist. The surface poled structures show good domain uniformity, and the technique has so far been applied to produce domain periods as small as ϳ1 m. The technique is fully compatible with nonlinear optical integrated devices based on waveguide structures. ͓͔

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Information storage, reflecting the capability of a dynamical system to keep predictable information during its evolution over time, is a key element of intrinsic distributed computation, useful for the description of the dynamical complexity of several physical and biological processes. Here we introduce a parametric framework which allows to compute information storage across multiple time scales in stochastic processes displaying both short-term dynamics and long-range correlations (LRC). The framework exploits the theory of state space models to provide the multiscale representation of linear fractionally integrated autoregressive (ARFI) processes, from which information storage is computed at any given time scale relating the process variance to the prediction error variance. This enables the theoretical assessment and a computationally reliable quantification of a complexity measure which incorporates the effects of LRC together with that of short-term dynamics. The proposed measure is first assessed in simulated ARFI processes reproducing different types of autoregressive (AR) dynamics and different degrees of LRC, studying both the theoretical values and the finite sample performance. We find that LRC alter substantially the complexity of ARFI processes even at short time scales, and that reliable estimation of complexity can be achieved at longer time scales only when LRC are properly modeled. Then, we assess multiscale information storage in physiological time series measured in humans during resting state and postural stress, revealing unprecedented responses to stress of the complexity of heart period and systolic arterial pressure variability, which are related to the different role played by LRC in the two conditions.