Maximum Frequency Research Articles

A vibration transducer framework for the measurement and reconstruction of vibration signals via low-rate sampling

This study presents a novel, cost-effective, and highly accurate computer vision-based vibration transducer framework, enabling the measurement and reconstruction of vibration signals using video frames captured at a low frame per second (fps) relative to the Nyquist rate of the measured signal. In the proposed framework, an assembly consisting of several spring-mass systems serves as the primary sensor, while a camera module tracks the motion of the seismic masses as the secondary sensor. The setup, along with the modified compressive sensing equation (CS) equation, results in a transducer scheme that can recover the base motion signals from the displacement history of seismic masses sampled at rates much lower than the maximum predominant frequency of the original signal. The performance of the proposed transducer framework has been successfully verified by numerical simulations. In addition, an experimental study has been conducted in order to validate the concept under modelling and measurement error.

Widening the frequency bandgap and reducing thermal conductivity in phononic crystals by tuning structural parameters using a novel computational simulation method

AbstractThis paper introduces and models a phononic structure based on single-crystal silicon, aiming to investigate the width of its frequency bandgap and the impact of key parameters on thermal conductivity. The modeled phononic crystal structure features a periodic arrangement of cylindrical holes in a silicon matrix. This research holds the potential to enhance thermal management performance of thermal metamaterials. Utilizing a 3D finite element method (FEM) model in COMSOL, we have computed phonon dispersion to estimate thermal conductivity. The study systematically has explored the influence of phononic crystal parameters—specifically, porosity, lattice constant, and thickness—along with their interactions on both thermal conductivity and frequency bandgap width.A comprehensive investigation of these parameters has been conducted for their optimization to achieve the maximum frequency bandgap width and minimum thermal conductivity using the response surface method model. Eigenfrequencies and wave vector parameters are extracted from the finite element model using a MATLAB script. Subsequently, thermal conductivity is calculated through the Callaway–Holland model, a simplification of the Boltzmann transport equation (BTE).Our results indicate that the frequency bandgap begins to form at approximately 43% porosity for a lattice constant and thickness of 100 nm each. Furthermore, adjusting the parameters led to a significant reduction in thermal conductivity, decreasing from 43.89 W m−1 K−1 to 0.39 W m−1 K−1. The novelty of our research lies in thermal conductivity control of phononic crystal metamaterials through their parameter variations, or a predictive method of thermal conductivity and its parameter sensitivity. This study advances the state of the art in phononic crystal metamaterial research, contributing to improved thermal management performance by enlarging frequency bandgaps.Overall, our findings deepen the understanding of how porosity, lattice constant, and thickness influence thermal conductivity and frequency bandgap width. They offer valuable insights into optimizing phononic crystal parameters, enhancing thermal management performance, and designing more efficient and effective phononic crystal structures.

Dynamics of tumor in situ fluid circulating tumor DNA in recurrent glioblastomas forecasts treatment efficacy of immune checkpoint blockade coupled with low-dose bevacizumab

PurposeImmune checkpoint blockade (ICB) therapies have shown efficacy in various tumors, but long-term responses in glioblastoma are less than 10%. Quantifying tumor in situ fluid circulating tumor DNA (TISF-ctDNA) and therapeutic dynamics may enable real-time GBM disease burden evaluation. This study explores the potential of tumor in situ fluid circulating tumor DNA (TISF-ctDNA) dynamics in predicting treatment efficacy.MethodsTISF and peripheral blood samples were collected from patients with recurrent glioblastoma (rGBM) undergoing tislelizumab (a programmed death 1 inhibitor) combined with low-dose bevacizumab (an anti-vascular endothelial growth factor antibody) treatment before and during each immunotherapy cycle. Biomarkers evaluated included TISF-ctDNA, measured using Next Generation Sequencing (NGS), and host inflammation markers such as the platelet-to-lymphocyte ratio (PLR).ResultsAll 32 patients received tislelizumab plus low-dose bevacizumab regularly. The median progression-free survival (PFS) was 4.0 months, and overall survival (OS) was 22.3 months. An analysis of 19 patients with continuous evaluable TISF showed baseline TISF-ctDNA abundance did not correlate with OS (p = 0.23) or PFS (p = 0.23). However, a change in TISF-ctDNA maximal Somatic Variant Allelic Frequency (MVAF) after six treatment cycles predicted both PFS (p = 0.02) and OS (p < 0.0001). Lower baseline PLR also correlated with better survival outcomes.ConclusionThe combination of tislelizumab and low-dose bevacizumab therapy appears to be effective in extending both OS and PFS in rGBM patients. Continuous TISF-ctDNA testing shows potential utility in complementing radiological monitoring. The temporal change pattern of TISF MVAF is more predictive of immunotherapy response than imaging. PLR before immunotherapy can screen patients likely to benefit from tislelizumab plus low-dose bevacizumab therapy.Trial registrationThe trial registration number: NCT05502991; Date of registration: 2022-08-14.

In vitro clonal micropropagation of selected Malus niedzwetzkyana Dieck forms demonstrating high resilience when introduced into urban environments

Background. Currently, the use of an interdisciplinary approach based on a combination of traditional introduction methods and clonal micropropagation techniques makes it possible to solve one of the key problems of introduction – the establishment of bioresource collections consisting of selected plant accessions with valuable agronomic traits and resistance to unfavorable urban environments. Materials and methods. A plant introduction study resulted in identifying seven specimens of Malus niedzwetzkyana Dieck with resilience to urban environments, high rate of crown development, and longevity. They served as source material for the development of a clonal propagation protocol and in vitro preservation of selected genotypes of this species. Results. It was shown for M. niedzwetzkyana that the most favorable time for taking its plant material for introduction into in vitro culture is the beginning of the active growth of vegetative shoots after flowering. The most optimal sterilization technique for such plant material was a stepwise regime using alcohol, sodium hypochlorite, and silver nitrate: it provided from 50 to 70% of sterile explants and the maximum percentage of meristem proliferation. Combining 0.8 mg/L of benzylaminopurine (BAP) with 0.14 mg/L of indole-3-butyric acid (IBA) at the stage of microclonal propagation ensured a significant increase of the reproduction coefficient (on average up to 5.3 ± 0.7) and an improvement in morphometric parameters of microshoots; the maximum frequency of shoot proliferation was 100%. The yield of shoots adapted to ex vitro conditions was 90%. Conclusion. The developed clonal micropropagation protocol made it possible to introduce selected M. niedzwetzkyana forms into in vitro culture and reproduce them in order to set up a resource base for further fundamental and applied research into the system of the genus Malus Mill.

The effect of the molting cycle on the acoustic characteristics of clicks emitted by Litopenaeus vannamei

In penaeid shrimp, the mandibles are responsible for emitting click sounds when they collide during food intake, which has been used to assess feeding activity by passive acoustic monitoring. However, the acoustic parameters of the clicks could be affected by variations in the thickness of mandible cuticle during the molting cycle. Thus, the aim of this study was to evaluate the effects of the molting cycle on the acoustic characteristics of clicks emitted during the feeding activity of Litopenaeus vannamei fed commercial pelleted diet. Shrimp with an average weight of 8.75 ± 1.05 g were individually maintained in 24 tanks in a recirculating water system. A total of 12 animals had their feeding activity individually recorded soon after ecdysis (postmolt group), while another 12 were recorded in the middle of their molting cycle (intermolt group). Recordings took place in anechoic chambers with a hydrophone connected to a digital recorder (sampling rate of 192 kHz), and the click acoustic parameters were characterized in Raven® 1.5 Pro software. After the recordings, the shrimp had their mandibles removed for length measurement and histological analysis of the mandibular cuticle. Body (weight and length) and mandibular measurements did not differ between shrimp groups, but those in the postmolt stage showed significantly lower cuticle thickness. The acoustic characteristics of the clicks were affected by shrimp molting stages, with significantly lower maximum energy (dB) and maximum frequency (kHz) in postmolt shrimp, probably related to their lower mandibular cuticle thickness. These results may contribute to the assessment of shrimp feeding behavior associated with the molting cycle, as well as to the optimization of algorithms controlling acoustic automatic feeders in farming systems.

Design of Low Power Control Unit for RISC-V Processor Core

This research work focuses on the development of a low-power decode logic for a RISC-V processor core with specifications. The goal is to create a controller that performs all six groups of instruction formats outlined in the RV32I Base Integer Instruction Set. The control unit is designed to decode a total of 13 instruction sets, allowing for a comprehensive range of operations. A single instruction pipeline approach is implemented in the design to optimize performance. The synthesis of the design is carried out using the 32 nm standard library, resulting in a maximum operating frequency of 666.67 MHz. To further enhance power efficiency, clock gating techniques are employed, leading to a reduction in power consumption by 18.72 % from 112.15 µW to 91.45 µW. Additionally, the layout of the design is optimized, resulting in an area of 354.74 mm2. The successful development of this low-power decode logic demonstrates its potential for integration into larger RISC-V processor cores. Future enhancements can include expanding the instruction decoding capability to encompass the full range of 47 instructions in the RV32I Base Integer Instruction Set, as well as exploring additional pipeline stages to further improve performance. The results achieved in this project contribute to the ongoing pursuit of power-efficient and high-performance processor designs based on the RISC-V architecture.

A broadband tunable THz sensor based on graphene metasurface

In this paper, we design a polarization-independent and angle-insensitive wideband THz graphene metamaterial sensor. The proposed metasurface sensor consists of six layers, which are silicon dioxide substrate, metal reflector, air layer, graphene metasurface with microstructure, ion-gel layer and silicon dioxide dielectric layer from bottom to top. The sensor was simulated by using CST Microwave Studio software, and the absorption and sensing performance were analyzed. The results show that the absorption rate is more than 95 % in the range of 1.05–4.07 THz. In addition, when the Fermi energy level of graphene is changed, the absorption rate can be adjusted from 20 % to more than 95 %. At the same time, the absorptivity of the device can be maintained above 80 % before 60° when the TE and TM waves are incident, and it is insensitive to the polarization angle due to the central symmetry of the graphene surface microstructure. By changing the thickness of the air layer, the device can achieve maximum absorption at h2=30μm, where the maximum frequency shift sensitivity is 2.179 THz/RIU. The influence of structure parameters and incident angle on the absorption spectrum shows that the proposed structure has good stability. The proposed metasurface sensor has potential application prospects in biological detection and medical monitoring, broadening the application range of THz functional devices.

Novel 4H–SiC MESFET with high ability in gate capacitances control for high frequency applications

The ability to control the gate capacitances is crucial for high-frequency applications, as it affects the device's frequency characteristics, gain and power handling capabilities. We present a 4H–SiC metal-semiconductor field-effect transistor (MESFET) with high gate capacitance control ability for high-frequency applications. The proposed structure (GCC-MESFET) consists of a step gate and a SiC well for adjusting the channel depletion layer and modifying the channel charges. Therefore, the gate capacitances will be controlled (GCC-MESFET). The proposed structure significantly improves the cut-off frequency (fT) and maximum oscillation frequency (fmax). The fT has increased from 23.5 GHz to 33 GHz and the fmax from 50.1 GHz to 54.4 GHz in the proposed structure compared to a conventional structure (C-MESFET). The results show that the DC maximum output power density (Pmax), DC transconductance (gm), cut-off frequency (fT) and maximum oscillation frequency (fmax) of GCC-MESFET improve in comparison with a conventional structure (C-MESFET). It is necessary to mention that the drain current and the breakdown voltage of the proposed structure increase by 48 % and 20 % respectively, compared with the C-MESFET structure due to modifying the channel charges and adjusting the electric field. So, the proposed structure can be used for high current, high voltage, high-power and high frequency applications.

A novel frequency constrained unit commitment considering VSC-HVDC's frequency support in asynchronous interconnected system under renewable energy Source's uncertainty

The increasing share of renewable energy source (RES) poses a challenge to the frequency security of the power system. Frequency constrained unit commitment (FCUC) serves as an effective measure to address this challenge at the operational level. This paper introduces a novel FCUC model applicable to the asynchronous interconnected system connected by voltage source converter based HVDC (VSCHVDC), fully taking into account the frequency support capability of VSCHVDC in order to reduce the demand for synchronous generators' inertia and reserve while ensuring frequency security, thereby lowering operating costs. Additionally, the uncertainty of RES is also considered. The constraint expressions for three frequency indicators are derived based on a system frequency response model that includes frequency support from VSCHVDC. The proposed model optimizes unit commitment, generation and reserve dispatch and VSCHVDC transmission power and frequency response parameters, while addressing RES uncertainty using the distributionally robust chance constrained approach. In response to the highly nonlinear characteristics of the maximum frequency derivation (MFD) constraints, we propose an intelligent sampling-based support vector machine to convexify the MFD constraints and introduce a two-stage decomposition algorithm for solving the model. The effectiveness of the proposed model is demonstrated based on a modified IEEE RTS-79 system.

Parametric investigations on dynamic responses of porous functionally graded al/al2o3 plates: effects of homogenization models and material distributions

This study presents a numerical analysis of the dynamic responses exhibited by functionally graded Al/Al2O3 plates that incorporate porosity. It explores the influence of critical parameters such as the thickness-to-span ratio and the porosity coefficient on the non-dimensional fundamental frequencies. Various micromechanical homogenization models, including Voigt, Mori-Tanaka, LRVE, Tamura, and Reuss, are utilized in conjunction with different material volume fraction distribution profiles: Power-law, Viola-Tornabene four-parameter, and Trigonometric. The research considers four distinct porosity variation patterns: uniform, non-uniform, logarithmic non-uniform, and mass-density. The governing equations are addressed using the Navier solution technique. The findings indicate that the Viola-Tornabene model results in the highest fundamental frequencies, succeeded by the Power-law and Trigonometric models. Among the porosity patterns, mass-density porosity yields the maximum frequencies, whereas uniform porosity leads to the lowest. Furthermore, an increase in the porosity coefficient typically raises the frequencies, with the exception of uniform porosity, while an increase in the thickness-to-span ratio results in a reduction of frequencies across all models. These insights are essential for the optimization of designs for functionally graded porous plates.

Full-Waveform Inversion Imaging of Cortical Bone Using Phased Array Tomography.

Classic ultrasound bone imaging modalities usually demand either a prior knowledge or an advanced estimation on speed of sound (SoS), which not only renders to a burdensome imaging process but also supplies a limited resolution. To overcome these drawbacks, this article proposed a frequency-domain full-waveform inversion (FDFWI) modality using phased array tomography for high-accuracy cortical bone imaging. A transmission scenario of ultrasound wave in 2-D space was presented in the frequency domain to simulate the forward wavefield propagation. Iterations in the inversion process were performed by matching the simulation wavefield to the experimental one from low to high discrete frequency points. Moreover, the association between the maximum initial frequency and the initial SoS model was explored to prevent the occurrence of cycle-skipping phenomenon, which could lead to the outcomes being trapped in local minima. The feasibility and effectiveness of the proposed imaging scheme were testified by simulation, phantom, and ex-vivo studies, with mean relative errors of cortical part being 3.18%, 8.71%, and 9.36%, respectively. It is verified that the proposed FDFWI method is an effective way for parametric imaging of cortical bone without any prior knowledge of sound speed.

RSD-based high-performance radix-4 Montgomery Modular Multiplication for Elliptic Curve Cryptography

This paper proposes a high-performance radix-4 Montgomery Modular Multiplication (MMM) algorithm and its corresponding hardware architecture for Elliptic Curve Cryptography (ECC), in which the quotient and the partial product accumulation are computed in parallel in each iteration. Additionally, in this MMM, the Redundant Signed Digit (RSD) representation and the Signed Digit Adder (SDA) are used to eliminate the long carry chain and achieve parallel computation, as well as remove pre-computation and integrate modular reduction operations. Our MMM algorithm is implemented in 256-bit and 1024-bit versions on Xilinx Virtex-6 and Virtex-7 FPGAs, respectively. It consumes only 1.55k/10.18k Look-Up Tables (LUTs), takes 133/517 clock cycles, and runs at maximum frequencies of 558.8/641.7 MHz. According to the comparison in terms of Area Time Product (ATP), our design can achieve the ATP of 0.369 over the 256-bit NIST prime domain, which is approximately half of that of the state-of-the-art works. The Scalar Point Multiplication (SPM) scheme using this MMM algorithm consumes 14.19k LUTs and completes a single Scalar Point Multiplication (SPM) operation in 0.217 ms, and it also has a lower ATP than most other SPM algorithms currently in existence.

Study on the Influence of Thermoplastic Microcapsules on the Sulfate Resistance and Self-Healing Performance of Limestone Calcined Clay Cement Concrete.

Limestone calcined clay cement (LC3), enhanced through reactions with volcanic ash and the interaction between limestone and clay, significantly improves the performance of cementitious materials. It has the potential to cut CO2 emissions by up to 30% and energy consumption in cement manufacture by 15% to 20%, providing a promising prospect for the large-scale production of low-carbon cement with a lower environmental effect. To effectively manufacture LC3 concrete, this study utilized limestone (15%), calcined clay (30%), and gypsum (5%) as supplementary cementitious materials (SCMs), replacing 50% of ordinary Portland cement (OPC). However, in regions abundant in sulfate, sulfate attack can cause interior cracking of concrete, reducing the longevity of the building. To address this issue, microcapsules containing microcrystalline wax, ceresine wax, and nano-CaCO3 encapsulated in epoxy resin were prepared and successfully incorporated into LC3 concrete. Sulfate resistance tests were conducted through sulfate dry-wet cycles, comparing samples with and without microcapsules. The findings revealed that the initial mechanical and permeability properties of LC3 concrete did not significantly differ from OPC concrete. LC3 concrete with added microcapsules (SP4) exhibited enhanced resistance to sulfate attack, reducing mass loss and compressive strength degradation. SEM images displayed a mesh-like structure of repair products in SP4. After 14 days of self-repair, SP4 exhibited a 44.2% harmful pore ratio, 98.1% compressive strength retention, 88.7% chloride ion diffusion coefficient retention, 91.12 mV maximum amplitude, and 9.14 mV maximum frequency amplitude. The experimental results indicate that the presence of microcapsules enhances the sulfate attack self-healing performance of LC3 concrete.

Gaussian Kernel Approximations Require Only Bit-Shifts

An approach to approximate the 2D Gaussian filter for all possible kernel sizes based on the binary optimization technique is introduced. The approximate filter coefficients are designed as negative powers of two, allowing hardware implementation with remarkable savings in the chip area. The proposed approximate filters were evaluated and compared with competing methods using both similarity analysis and edge detection applications. The proposed method and the competing works for masks of size 3×3, 5×5, and 7×7 were implemented in a Xilinx Artix-7 FPGA. The proposed method showed up to a 60.0% reduction in DSP usage and a 75.0% increase in the maximum operating frequency when compared with state-of-art methods for the 7×7 kernel size case and a 48.8% reduction in the dynamic power normalized by the maximum operating frequency.

Chitosan pyrolysis in the presence of a ZnCl2/NaCl salts for carbons with electrocatalytic activity in oxygen reduction reaction in alkaline solutions.

The one-step carbonization of low cost and abundant chitosan biopolymer in the presence of salt eutectics ZnCl2/NaCl results in nitrogen-doped carbon nanostructures(8.5 wt.% total nitrogen content). NaCl yields the spacious 3D structure, which allows external oxygen to easily reach the active sites for the oxygen reductionreaction (ORR) distinguished by their high onset potential and the maximum turnover frequency of 0.132 e site⁻1s⁻1. Data show that the presence of NaCl during the synthesis exhibits the formation of pores having large specific volumes and surface(specific surface area of 1217 m2g-1), and holds advantage by their pores characteristics such as their micro-size part, which provides a platform for mass transport distribution in three-dimensional N-doped catalysts for ORR. It holds benefit over sample pre-treated with LiCl in terms of the micropores specific volume and area, seen as their percentage rate, measured in the BET. Therefore, the average concentration of the active site on the surface is larger.

Evaluation of blood MSI burden dynamics to trace immune checkpoint inhibitor therapy efficacy through the course of treatment.

Analysis of serial liquid biopsy (LB) samples has been found to be a promising approach for the monitoring of tumor dynamics in the course of therapy for patients with colorectal cancer (CRC). Currently, somatic mutations are used for tracing the dynamics of the tumor via LB. However, the analysis of the dynamic changes in the molecular signatures such as microsatellite instability (MSI) is not currently used. We hypothesized that changes in blood MSI burden (bMSI) could be registered using serial LB sampling in the course of immune checkpoint inhibitors (ICI), and that its changes could potentially correlate with treatment outcomes. We report the preliminary findings of the observational trial launched to study (NCT06414304) the dynamics of bMSI in 9 MSI-positive CRC patients receiving ICI. NGS-based MSI testing was performed on both pre-treatment FFPE and serial LB samples. For patients who had detectable bMSI burden in any of the LB samples (n = 8, 89%), median bMSI was 1.4% (range, 0.01-40%). Among patients with detectable MSI in available FFPE samples, median MSI burden was 29.3% (range, 10-40%). bMSI detected in baseline LB and FFPE samples were positively correlated (Pearson's R 0.47). Maximal variant allele frequencies of driver mutations observed in LB were also positively correlated with bMSI burden (Pearson's R 0.7). Patients who had clinical benefit had undetectable bMSI burden at follow-up. Our results provide the rationale for further validation of bMSI as a predictive biomarker of ICI in MSI-positive patients.

Comparative analysis of microwave radiation generated by the interaction between electron beam and plasma under different methods

This article presents a physical model that describes the interaction between surface electron beams and plasma. The dispersion relations for beam plasma interactions were derived using perturbation method and field matching methods. The study investigates how different parameters affect radiation frequency and bandwidth. The results indicate that as electron beam velocity increases, the associated kinetic energy also rises, leading to an increase in both the maximum radiation frequency and bandwidth at high frequencies. Conversely, the radiation bandwidth at low frequencies decreases. Similarly, a higher plasma density results in a greater maximum radiation frequency, but the high-frequency bandwidth decreases, while the low-frequency bandwidth increases. Additionally, when the electron density and electron velocity of the electron beam remain constant, increasing the plasma density can increase the microwave radiation frequency However, there exists a plasma density threshold, beyond which high-frequency electromagnetic waves are no longer radiated.

Adaptive feature mode decomposition method for bearing fault diagnosis under strong noise

In practical mechanical equipment operation, bearing vibration signals are challenging to analyze due to their non-stationarity and low signal-to-noise ratio for traditional detection methods. As a new signal decomposition method, the feature mode decomposition (FMD) method has been successfully applied to bearing fault diagnostics. However, FMD’s decomposition efficiency is easily influenced by the input parameter settings, and the efficiency of the decomposed signals is related to the number of initialized filter banks. For this reason, this paper proposes an adaptive parameterized feature mode decomposition method. Firstly, based bearing fault diagnosis method using a cuckoo search algorithm with logarithmic decline of nonlinear inertial weights and random adjustment discovery probability (DWCS), which is used to optimize the three parameters of the FMD. Then, a new approach to finding out the maximum feature fault frequency and its multiplicative feature frequency is proposed, called the feature frequency ratio (FFR), obtained from the envelope spectrum of bearing fault signal. Finally, this paper uses the DWCS based on the maximum FFR to adaptively select the best FMD parameter combination, which is verified by simulation, the results of the proposed AFMD can effectively extract bearing fault features under strong background noise with a signal-to-noise ratio of −15 dB, and actual experiments further verify the superiority of the method, which not only improves the accuracy of fault diagnosis under strong background noise, but also contributes to the overall maintenance and operational efficiency of the mechanical system.

Detection of CO2, H2S, NH3, and gas mixtures using a quartz crystal microbalance sensor array with five sensitive materials

The large-scale development of chicken farms has inevitably led to an increase in the emission of harmful gases, which has prompted rapid research development on the detection of harmful gases, such as quartz crystal microbalance (QCM) sensors. In this research, a QCM sensor array modified with ethyl cellulose (EC), polyethyleneimine (PEI), polypyrrole (PPy), tetramethylammonium fluoride tetrahydrate (TMAF), and Nitrogen-doped tungsten carbide supported on carbon nanosheets (WC@NC) was established to detect gas mixtures. By analyzing the relationship between the fundamental frequency shift and the number of sensitive material layers for the QCM sensor, the optimal number of layers was determined. The QCM sensor array consisted of a blank QCM sensor, an 8-layer EC sensor, a 4-layer PEI sensor, a 3-layer PPy sensor, an 8-layer TMAF sensor, and an 8-layer WC@NC sensor. The results showed that the PEI sensor exhibited maximum frequency shifts when detecting gases, while TMAF and WC@NC films had the highest contribution to NH3 detection. TMAF, PEI, and WC@NC played significant roles in the binary classification of exceeding the standard detection of NH3. Additionally, WC@NC and EC sensors played an important role in detecting H2S and CO2, respectively. The QCM sensor array could accurately classify the excessive state of CO2. Overall, the QCM sensor array demonstrated a correct recognition rate of 87.5 %, indicating its potential for classifying the exceedance of NH3, CO2, and H2S gases in gas mixtures. This work provides basic information for research on QCM sensor arrays in gas detection.

Respiratory Rate Estimation from Thermal Video Data Using Spatio-Temporal Deep Learning.

Thermal videos provide a privacy-preserving yet information-rich data source for remote health monitoring, especially for respiration rate (RR) estimation. This paper introduces an end-to-end deep learning approach to RR measurement using thermal video data. A detection transformer (DeTr) first finds the subject's facial region of interest in each thermal frame. A respiratory signal is estimated from a dynamically cropped thermal video using 3D convolutional neural networks and bi-directional long short-term memory stages. To account for the expected phase shift between the respiration measured using a respiratory effort belt vs. a facial video, a novel loss function based on negative maximum cross-correlation and absolute frequency peak difference was introduced. Thermal recordings from 22 subjects, with simultaneous gold standard respiratory effort measurements, were studied while sitting or standing, both with and without a face mask. The RR estimation results showed that our proposed method outperformed existing models, achieving an error of only 1.6 breaths per minute across the four conditions. The proposed method sets a new State-of-the-Art for RR estimation accuracy, while still permitting real-time RR estimation.