Gonzalo Paez

Objective. To evaluate thermal recovery after controlled cooling using dynamic infrared thermography and to determine whether the recovery process can be described using distinct temporal components associated with different heat transfer dynamics. Approach. A controlled cooling protocol was applied to the plantar region of the foot, followed by thermal recovery monitoring using a radiometric infrared camera. Temperature evolution was analyzed using a two-time-constant exponential model, where τ ₁ and τ ₂ represent the slow and fast recovery components, respectively. A frame-by-frame radiometric calibration procedure was implemented to ensure measurement stability. The distributions of τ ₁ and τ ₂ were evaluated under different cooling durations and recovery windows using non-parametric statistics. Main results. Thermal recovery was consistently described by two-time constants that capture distinct temporal regimes. The fast component ( τ ₂ ) exhibited relatively compact distributions, particularly in the short recovery window, indicating stable and repeatable behavior. In contrast, the slow component ( τ ₁ ) showed larger variability, especially for longer recovery windows. This variability suggests that the slow component ( τ ₁ ) is more sensitive to extended thermal dynamics and inter-subject variability than to measurement noise. The results also showed that longer cooling durations increased the relative contribution of the slow recovery component ( τ ₁ ), revealing multi-scale thermal recovery behavior. Significance. These findings demonstrate that thermal recovery is not a single-scale process but a multi-time dynamic phenomenon in which slow and fast recovery components provide complementary information. By explicitly separating these temporal components, the proposed framework enables a more detailed characterization of thermal dynamics than single-parameter approaches. As a methodological proof-of-concept, this work supports the use of dynamic infrared thermography as a quantitative tool for physiological measurement and establishes a reproducible basis for future clinical studies.

Photoplethysmography (PPG) is a widely used non-invasive optical technique for assessing cardiovascular dynamics and related hemodynamic processes. However, conventional noise-reduction methods can alter signal timing and distort waveform morphology, thereby affecting the identification of physiologically relevant events. Here, we propose a frequency-domain Gaussian filtering framework for selectively extracting harmonics from PPG signals. The method combines a Gaussian band-reject filter centered at 0 Hz to suppress the dominant DC component, reduce the non-pulsatile baseline, and attenuate low-frequency contributions associated with slow modulation processes. Symmetric Gaussian bandpass filters are then applied to isolate harmonic components, with the number of retained bands adapted to the requirements of a given application. As a proof of concept, the framework was applied to both a simulated PPG waveform and an experimental PPG recording. Because of the symmetric zero-phase properties of the filters, the temporal alignment of key PPG events, including the systolic peak, diastolic decay, and dicrotic notch, can be preserved while phase distortion is avoided. Reconstruction from filtered harmonics further suggests that low-order harmonics retain much of the observable waveform structure in the signals analyzed. Overall, this harmonic-based Gaussian filtering provides a promising framework for analysis of PPG signals and motivates further investigation of its potential for extracting physiologically related information from harmonic components, such as bedside monitoring.

Objective. Thoracic electrical bioimpedance (TEB) provides non-invasive, radiation-free monitoring of breathing. The objective of this study was to evaluate a magnitude–phase representation of TEB as a geometric and descriptive framework for respiratory signals, using short-term smoking as a test perturbation rather than a primary physiological endpoint. Approach. Twenty-eight adult smokers (17 women, 11 men) were measured immediately before and after smoking. TEB was acquired at 50 kHz using a four-electrode thoracic configuration, and tidal volume was recorded with a pneumotachometer. Changes in impedance magnitude (|Δ Z |) and phase (Δ ϕ ) were processed using mean-centering, Hanning windowing, Fourier transformation, Gaussian band filtering around the respiratory peak, and inverse reconstruction. Lissajous plots were constructed from Δ| Z |–Δ ϕ signals, and geometric descriptors including semi-axes ( δx, δy ), inclination angle ( θ ), ellipse area ( A ), eccentricity ( e ), and baseline offsets were extracted. Paired statistical tests were applied according to data distribution, and principal component analysis (PCA) was used to organize multiple descriptors and reduce redundancy. Main results. Univariate analyses showed no significant pre–post differences for most variables, except for a higher mean |Δ Z | amplitude in men. In PCA space, ellipse area ( A ) showed consistent differences between pre- and post-smoking distributions across sexes. These differences reflected changes in joint magnitude–phase dispersion rather than statistically significant physiological effects. Inclination, semi-axes, and eccentricity showed substantial overlap between conditions. PCA provided low-dimensional representations that facilitated visualization and comparison of magnitude–phase patterns. Significance. Representing TEB signals as magnitude–phase Lissajous ellipses provides an intuitive and repeatable geometric representation of breathing. Ellipse area is proposed as a composite geometric descriptor of joint magnitude–phase variability, intended for representation and comparison rather than direct physiological inference. This non-invasive and computationally simple framework uses standard hardware and may support future methodological developments in respiratory signal analysis.

We present the architecture, implementation, and on-sky validation of the fully automated acquisition and guiding system (AG) developed for the WEAVE instrument on the William Herschel Telescope. The AG operates in two distinct modes, corresponding to the observing modes of WEAVE. For the large integral field unit, an off-axis imaging guider is used, for which we have devised an automatic acquisition method based on pattern recognition of stellar asterisms matched against Gaia predictions. For the multi-object spectrograph and the mini-integral field units, a multifibre guider uses up to eight coherent image guide fibre bundles to derive and apply continuous corrections in azimuth, altitude, and rotation. The system performs complete astrometric calculations, including atmospheric differential refraction and instrument flexure, for each guide frame, enabling accurate target placement and stable closed-loop guiding in all configurations. To support development, commissioning, and operational validation, we have also built a high-fidelity simulation mode that reproduces the behaviour of the telescope control system and of the AG cameras, and we release the standalone camera simulator as open-source software. Using two years of routine WEAVE operations spanning commissioning and early survey phases, we present a statistically robust characterization of AG performance, demonstrating that both modes meet design requirements and are ready for sustained survey operations.

We describe a bispectral, 1000-frames per second imaging instrument working simultaneously in two spectral bands. These bands may be selected for a specific application; however, we implement a pair centered at 4.3 μm and 4.66 μm. Synchronization is accomplished by employing a single focal plane array. To demonstrate the performance of the bispectral imager, we apply it to the methane flame of a Bunsen burner in a near conjugate configuration with flame image length subtending at about 200 pixels. The instrument detects bispectral puffing at 2 Hz, pulsations, and bispectral radiation oscillations, first reported here in two spectral intervals. The period of oscillatory spectral components in two bands is the same, about 3 Hz for this flame, with delay of a quarter period between them, first reported here. With 1-ms integration time, we detect significant formation of turbulence and vortices, especially pronounced in the region where the flame transitions into a plume. We display bispectral ratioed images of flames in near-real time with either the laboratory or the field device.

Near-infrared spectroscopy provides useful biological information after the radiation has penetrated through the tissue, within the therapeutic window. One of the significant shortcomings of the current applications of spectroscopic techniques to a live subject is that the subject may be uncooperative and the sample undergoes significant temporal variations, due to his health status that, from radiometric point of view, introduce measurement noise. We describe a novel wavelength selection method for monitoring, based on a standard deviation map, that allows low-noise sensitivity. It may be used with spectral transillumination, transmission, or reflection signals, including those corrupted by noise and unavoidable temporal effects. We apply it to the selection of two wavelengths for the case of pulse oximetry. Using spectroscopic data, we generate a map of standard deviation that we propose as a figure-of-merit in the presence of the noise introduced by the living subject. Even in the presence of diverse sources of noise, we identify four wavelength domains with standard deviation, minimally sensitive to temporal noise, and two wavelengths domains with low sensitivity to temporal noise.

We are developing a system to convert infrared into visible radiation, employing β-diketonate chelate europium (III) thenoyltrifluoroacetonate (EuTTA) as the active medium of conversion. The operational principle of the proposed device is based on the variation of EuTTA fluorescence spectral power and lifetime with temperature. Previously, we characterized the fluorescence properties of the EuTTA in order to achieve IR-to-visible conversion and presented the work performed in order to calibrate our experimental setup. The results showed that a relation between the temperature variations of EuTTA film and those of the impinging IR radiation could be established. However, experimental data exhibited sufficient noise to cause significant errors between the measured and the actual temperature. In this work, we apply a filtering analysis using Fourier theory in order reduce the error in the data, increasing the resolution of our system. After the filtering, the standard deviation in the calculation of fluorescence parameters was reduced and therefore the thermal resolution of our system improved to 0.07 K.