The presented study concerns development of a facial detection algorithm operating robustly in the thermal infrared spectrum. The paper presents a brief review of existing face detection algorithms, describes the experiment methodology and selected algorithms. For the comparative study of facial detection three methods presenting three different approaches were chosen, namely the Viola–Jones, YOLOv2 and Faster-RCNN. All these algorithms were investigated along with various configurations and parameters and evaluated using three publicly available thermal face datasets. The comparison of the original results of various experiments for the selected algorithms is presented.

Development of facial recognition or expression recognition algorithms requires input data to thoroughly test the performance of algorithms in various conditions. Researchers are developing various methods to face challenges like illumination, pose and expression changes, as well as facial disguises. In this paper, we propose and establish a dataset of thermal facial images, which contains a set of neutral images in various poses as well as a set of facial images with different posed expressions collected with a thermal infrared camera. Since the properties of face in the thermal domain strongly depend on time, in order to show the impact of aging, collection of the dataset has been repeated and a corresponding set of data is provided. The paper describes the measurement methodology and database structure. We present baseline results of processing using state-of-the-art facial descriptors combined with distance metrics for thermal face reidentification. Three selected local descriptors, a histogram of oriented gradients, local binary patterns and local derivative patterns are used for elementary assessment of the database. The dataset offers a wide range of capabilities – from thermal face recognition to thermal expression recognition.

This paper presents a study on the influence of psychophysical stimuli on facial thermal emissions. Two independent groups of stimuli are proposed to investigate facial changes resulting from human stress and physical exhaustion. One pertains to physical effort while the other is linked to stress invoked by solving a simple written test. Subjects’ face reactions were measured through collecting and analysing long-wavelength infrared images. A methodology for numerical processing of images is proposed. Results of numerical analysis with respect to different facial regions of interest are provided. An automatic deep learning based algorithm to classify specific thermal face patterns is proposed. The algorithm consists of detection of regions of interests as well as numerical analysis of thermal energy emissions of facial parts. The results of presented experiments allowed the authors to associate emission changes in specific facial regions with psychophysical stimulations of the person being examined. This work proves high usability of thermal imaging to capture changes of heat distribution of face as reactions for external stimuli.

The ultrasonic flowmeter which is described in this paper, measures the transit of time of an ultrasonic pulse. This device consists of two ultrasonic transducers and a high resolution time interval measurement module. An ultrasonic transducer emits a characteristic wave packet (transmit mode). When the transducer is in receive mode, a characteristic wave packet is formed and it is connected to the time interval measurement module inputs. The time interval measurement module allows registration of transit time differences of a few pulses in the packet. In practice, during a single measuring cycle a few time-stamps are registered. Moreover, the measurement process is also synchronous and, by applying the statistics, the time interval measurement uncertainty improves even in a single measurement. In this article, besides a detailed discussion on the principle of operation of the ultrasonic flowmeter implemented in the FPGA structure, also the test results are presented and discussed

Most systems used in quantum physics experiments require the efficient and simultaneous recording different multi-photon coincidence detection events. In such experiments, the single-photon gated counting systems can be applicable. The main sources of errors in these systems are both instability of the clock source and their imperfect synchronization with the excitation source. Below, we propose a solution for improvement of the metrological parameters of such measuring systems. Thus, we designed a novel integrated circuit dedicated to registration of signals from a photon number resolving detectors including a phase synchronizer module. This paper presents the architecture of a high-resolution (~60 ps) digital phase synchronizer module cooperating with a multi-channel coincidence counter. The main characteristic feature of the presented system is its ability to fast synchronization (requiring only one clock period) with the measuring process. Therefore, it is designed to work with various excitation sources of a very wide frequency range. Implementation of the phase synchronizer module in an FPGA device enabled to reduce the synchronization error value from 2.857 ns to 214.8 ps.

The designing process of high resolution time interval measurement systems creates many problems that need to be eliminated. The problems are: the latch error, the nonlinearity conversion, the different duty cycle coefficient of the clock signal, and the clock signal jitter. Factors listed above affect the result of measurement. The FPGA (Field Programmable Gate Array) structure also imposes some restrictions, especially when a tapped delay line is constructed. The article describes the high resolution time-to-digital converter, implemented in a FPGA structure, and the types of errors that appear there. The method of minimization and processing of data to reduce the influence of errors on the measurement is also described.

Background: The right phrenic nerve is vulnerable to injury (PNI) during cryoballoon ablation (CBA) isolation of the right pulmonary veins. The complication can be transient or persistent. The reported incidence of PNI fluctuates from 4.73% to 24.7% depending on changes over time, CBA generation, and selected protective methods.
Methods: Through September 2019, a database search was performed on MEDLINE, EMBASE, and Cochrane Database. In the selected articles, the references were also extensively searched. The study provides a comprehensive meta-analysis of the overall prevalence of PNI, assesses the transient to persis-tent PNI ratio, the outcome of using compound motor action potentials (CMAP), and estimated average time to nerve recovery.
Results: From 2008 to 2019, 10,341 records from 48 trials were included. Out of 783 PNI retrieved from the studies, 589 (5.7%) and 194 (1.9%) were persistent. CMAP caused a significant reduction in the risk of persistent PNI from 2.3% to 1.1% (p = 0.05; odds ratio [OR] 2.13) in all CBA groups. The mean time to PNI recovery extended beyond the hospital discharge was significantly shorter in CMAP group at three months on average versus non CMAP at six months (p = 0.012). CMAP (in contrast to non-CMAP procedures) detects PNI earlier from 4 to 16 sec (p <0.05; I2 = 74.53%) and 3 to 9º (p <0.05; I2 = 97.24%) earlier.
Conclusions: Right PNI extending beyond hospitalization is a relatively rare complication. CMAP use causes a significant decrease in the risk of prolonged injury and shortens the time to recovery.