Stochastic transition in synchronized spiking nanooscillators.

This work reports that synchronization of Mott material-based nanoscale coupled spiking oscillators can be drastically different from that in conventional harmonic oscillators. We investigated the synchronization of spiking nanooscillators mediated by thermal interactions due to the close physical proximity of the devices. Controlling the driving voltage enables in-phase 1:1 and 2:1 integer synchronization modes between neighboring oscillators. Transition between these two integer modes occurs through an unusual stochastic synchronization regime instead of the loss of spiking coherence. In the stochastic synchronization regime, random length spiking sequences belonging to the 1:1 and 2:1 integer modes are intermixed. The occurrence of this stochasticity is an important factor that must be taken into account in the design of large-scale spiking networks for hardware-level implementation of novel computational paradigms such as neuromorphic and stochastic computing.

A comprehensive overview of diffuse correlation spectroscopy: Theoretical framework, recent advances in hardware, analysis, and applications.

Diffuse correlation spectroscopy (DCS) is a powerful tool for assessing microvascular hemodynamic in deep tissues. Recent advances in sensors, lasers, and deep learning have further boosted the development of new DCS methods. However, newcomers might feel overwhelmed, not only by the already-complex DCS theoretical framework but also by the broad range of component options and system architectures. To facilitate new entry to this exciting field, we present a comprehensive review of DCS hardware architectures (continuous-wave, frequency-domain, and time-domain) and summarize corresponding theoretical models. Further, we discuss new applications of highly integrated silicon single-photon avalanche diode (SPAD) sensors in DCS, compare SPADs with existing sensors, and review other components (lasers, sensors, and correlators), as well as data analysis tools, including deep learning. Potential applications in medical diagnosis are discussed and an outlook for the future directions is provided, to offer effective guidance to embark on DCS research.

Achieving Cross Time-Domain Multiplexed Signal Cascade and Cancer Exosomes Identification by Bridging Long Lifetime Phosphor to NIR-II Lanthanide Energy Transfer.

Designing lanthanide luminescence lifetime sensors in the second near-infrared (NIR-II) window holds great potentials for physiological studies. However, the single lifetime signal is confined to one or two orders of magnitude of signal variation, which limits the sensitivity of lifetime probes. In this study, a lifetime cascade system, i.e., ZGO:Mn, Eu-DNA-1/TCPP-PEI70K@Yb-AptEpCAM, with a variety of signals (τm, τn, τµ, τm/τn and τm/τµ) is constructed for exosome identification using time-domain multiplexing. The sensitized ligand TCPP acts as both target-modulated switch and a bridge for connecting long lifetime ZGO:Mn, Eu-DNA-1 emitter to lanthanide Yb3+. This drives successive dual-path energy transfer and forms two D(donor)-A(acceptor) pairs. The lifetime variation is dominantly modulated by arranging TCPP as energy intermediate relay to covert milliseconds to nanoseconds to microseconds. It enables a broad lifetime range of six orders of magnitude. The presence of exosome specifically recognizes aptamers on TCPP-PEI70K@Yb-AptEpCAM to impede D-A pairs and reverse multiplexed response signals of the lifetime cascade system. The ratio lifetime signals τm/τn and τm/τµ achieve prominent exosome quantification and exosome type differentiation attributed to signal amplification. The cascade system relying on lifetime criteria can realize precise quantization and provide an effective strategy for subsequent physiological study.

Correlating Young's Modulus with High Thermal Conductivity in Organic Conjugated Small Molecules.

Attaining elevated thermal conductivity in organic materials stands as a coveted objective, particularly within electronic packaging, thermal interface materials, and organic matrix heat exchangers. These applications have reignited interest in researching thermally conductive organic materials. The understanding of thermal transport mechanisms in these organic materials is currently constrained. This study concentrates on N, N'-dioctyl-3,4,9,10-perylenedicarboximide (PTCDI-C8), an organic conjugated crystal. A correlation between elevated thermal conductivity and augmented Young's modulus is substantiated through meticulous experimentation. Achievement via employing the physical vapor transport method, capitalizing on the robust C═C covalent linkages running through the organic matrix chain, bolstered by π-π stacking and noncovalent affiliations that intertwine the chains. The coexistence of these dynamic interactions, alongside the perpendicular alignment of PTCDI-C8 molecules, is confirmed through structural analysis. PTCDI-C8 thin film exhibits an out-of-plane thermal conductivity of 3.1 ± 0.1 W m-1 K-1, as determined by time-domain thermoreflectance. This outpaces conventional organic materials by an order of magnitude. Nanoindentation tests and molecular dynamics simulations elucidate how molecular orientation and intermolecular forces within PTCDI-C8 molecules drive the film's high Young's modulus, contributing to its elevated thermal conductivity. This study's progress offers theoretical guidance for designing high thermal conductivity organic materials, expanding their applications and performance potential.

Corrections for Rabi oscillations in cardiac chemical exchange saturation transfer MRI under the influence of very short preparation pulses.

Very short chemical exchange saturation transfer (CEST) pulses are beneficial in cardiac continuous wave (cw) CEST MRI, especially in small animals because of their rapid heartbeat; however, they result in signal modulations caused by Rabi oscillations. Therefore, we implemented two different filter techniques, DOwnsampling by SEparation of CEST spectrum into two parts (DOSE) and time domain (TD)-based filtering, to correct for these signal corruptions, allowing a reliable quantification of glucose-weighted CEST (glucoCEST) MRI contrast. In our study, cw CEST measurements were performed on a 9.4-T small animal BioSpec system using CEST pulses in the range of 10 to 200 ms. Experimental dependencies of Rabi oscillations on key MRI parameters were validated by Bloch-McConnell (BM) simulations. Filter efficiency was explored in a glucose concentration series as well as in the myocardium of healthy mice (n = 8), and glucoCEST contrast was subsequently quantified. The experimental results showed that the impact of Rabi oscillations on CEST spectra increased with decreasing CEST pulse length, optimized B0 homogeneity, and shorter T2 relaxation time, in accordance with results from BM simulations. Both investigated filter techniques reduced these signal modulations significantly, with DOSE filtering preserving the amplitude and TD filtering the spectral information of CEST data more accurately. Upon filter application, a significant decrease in glucoCEST contrast in the myocardium of healthy mice was observed after glucose infusion (pTD = 0.0079, pDOSE = 0.0044). To conclude, this study offers comprehensive experimental insights into Rabi oscillations within CEST MRI data along with methodological considerations that could be further advanced into a robust and precise cardiac cw CEST protocol by integrating DOSE and TD filtering into the standard CEST analysis pipeline.

Optimized absorption of light in perovskite nanowire solar cells.

Metal halide perovskite nanowires (PrvNWs) have recently emerged as an interesting path for nanostructured solar cells. Here, we model the absorption of light in PrvNW arrays for varying diameter and length of the PrvNWs and period for the array by solving the Maxwell equations. For long enough bare PrvNW arrays, we find that the optimum diameter is fixed to that which places the absorption peak from the HE11waveguide mode in the PrvNWs to the vicinity of the bandgap wavelength. In contrast, when we include a transparent conductive oxide (TCO) top contact layer, the optimum diameter shifts to a larger value by 100 nm. The origin of this shift is traced to a reduced reflection at the interface between the TCO layer and the PrvNW array when the PrvNW's diameter is larger. Overall, we find that 1500 nm long PrvNWs can reach 90% of the broadband absorption potential, making this system of high interest for photovoltaics.

Unlocking Solutions: Innovative Approaches to Identifying and Mitigating the Environmental Impacts of Undocumented Orphan Wells in the United States.

In the United States, hundreds of thousands of undocumented orphan wells have been abandoned, leaving the burden of managing environmental hazards to governmental agencies or the public. These wells, a result of over a century of fossil fuel extraction without adequate regulation, lack basic information like location and depth, emit greenhouse gases, and leak toxic substances into groundwater. For most of these wells, basic information such as well location and depth is unknown or unverified. Addressing this issue necessitates innovative and interdisciplinary approaches for locating, characterizing, and mitigating their environmental impacts. Our survey of the United States revealed the need for tools to identify well locations and assess conditions, prompting the development of technologies including machine learning to automatically extract information from old records (95%+ accuracy), remote sensing technologies like aero-magnetometers to find buried wells, and cost-effective methods for estimating methane emissions. Notably, fixed-wing drones equipped with magnetometers have emerged as cost-effective and efficient for discovering unknown wells, offering advantages over helicopters and quadcopters. Efforts also involved leveraging local knowledge through outreach to state and tribal governments as well as citizen science initiatives. These initiatives aim to significantly contribute to environmental sustainability by reducing greenhouse gases and improving air and water quality.

A personalized clinical assessment: multi-sensor approach for understanding musculoskeletal health in the frail population.

BACKGROUND: Sarcopenia is a muscle disorder causing a progressive reduction of muscle mass and strength, but the mechanism of its manifestation is still partially unknown. The three main parameters to assess are: muscle strength, muscle volume or quality and low physical performance. There is not a definitive approach to assess the musculoskeletal condition of frail population and often the available tests to be performed in those clinical bedridden patients is reduced because of physical impairments. In this paper, we propose a novel instrumental multi-domain and non-invasive approach during a well-defined protocol of measurements for overcoming these limitations. A group of 28 bedridden elder people, subjected to surgery after hip fracture, was asked to perform voluntary isometric contractions at the 80% of their maximum voluntary contraction with the non-injured leg. The sensor employed before and/or during the exercise were: ultrasound to determine the muscle architecture (vastus lateralis); force acquisition with a load cell placed on the chair, giving an indication of the muscle strength; surface electromyography (EMG) for monitoring muscular electrical activity; time-domain (TD) near-infrared spectroscopy (NIRS) for evaluating muscle oxidative metabolism. RESULTS: A personalized "report card" for each subject was created. It includes: the force diagram (both instantaneous and cumulative, expected and measured); the EMG-force diagram for a comparison between EMG derived median frequency and measured force; two graphs related to the hemodynamic parameters for muscle oxidative metabolism evaluation, i.e., oxy-, deoxy-, total-hemoglobin and tissue oxygen saturation for the whole exercise period. A table with the absolute values of the previous hemodynamic parameters during the rest and the ultrasound related parameters are also included. CONCLUSIONS: In this work, we present the union of protocols, multi-domain sensors and parameters for the evaluation of the musculoskeletal condition. The novelties are the use of sensors of different nature, i.e., force, electrical and optical, together with a new way to visualize and combine the results, by means of a concise, exhaustive and personalized medical report card for each patient. This assessment, totally non-invasive, is focused on a bedridden population, but can be extended to the monitoring of rehabilitation progresses or of the training of athletes.

Human hand gesture recognition using fast Fourier transform with coot optimization based on deep neural network.

Hand motion detection is particularly important for managing the movement of individuals who have limbs amputated. The existing algorithm is complex, time-consuming and difficult to achieve better accuracy. A DNN is suggested to recognize human hand movements in order to get over these problems.Initially, the raw input EMG signal is captured then the signal is pre-processed using high-pass Butterworth filter and low-pass filter which is utilized to eliminate the noise present in the signal. After that pre-processed EMG signal is segmented using sliding window which is used for solving the issue of overlapping. Then the features are extracted from the segmented signal using Fast Fourier Transform. Then selected the appropriate and optimal number of features from the feature subset using coot optimization algorithm. After that selected features are given as input for deep neural network classifier for recognizing the hand movements of human. The simulation analysis shows that the proposed method obtain 95% accuracy, 0.05% error, precision is 94%, and specificity is 92%.The simulation analysis shows that the developed approach attain better performance compared to other existing approaches. This prediction model helps in controlling the movement of amputee patients suffering from disable hand motion and improve their living standard.

Comprehensive linear and nonlinear heart rate variability normative data in children.

BACKGROUND: The autonomic nervous system (ANS) is critical in regulating involuntary bodily functions, including heart rate. Heart rate variability (HRV) reflects the complex interplay between the ANS and humoral factors, making it a valuable noninvasive tool for assessing autonomic function. While HRV has been extensively studied in adults, normative data for HRV in children, primarily based on long-term rhythm recordings, are limited. OBJECTIVE: This study aimed to establish comprehensive normative data for HRV in children. METHODS: In this retrospective study, we examined 24-h Holter monitors of children aged 1 day to 18 years, divided into six age groups, at Nemours Children's Health in Orlando, Florida, spanning the years 2013-2023. HRV analysis encompassed time-domain, frequency-domain, and nonlinear indices. RESULTS: Holter data for a total of 247 patients in six age groups were included. An age-related uptrend was observed in all time- and frequency-domain variables except the normalized unit of low-frequency power. Entropy analysis revealed contradictory results among different entropy techniques. Sample and approximate entropy analyses were consistent and showed less complexity and more predictability of HRV with decreasing heart rate, while Shannon entropy analysis showed the opposite. Fractal detrended fluctuation analysis exhibited significant decreases across the age groups, suggestive of diminishing self-similarity of HRV patterns. CONCLUSION: Control of heart rate and HRV is a highly complex process and requires further study for a better understanding. It seems that no single parameter can fully elucidate the entire process. A combination of time-domain, frequency-domain, and nonlinear indices may be necessary to explain HRV behavior in the growing body.

Continuous Monitoring of Pseudopolymorphic Transition in Ezetimibe Using T1 Relaxation with Time-Domain NMR.

The purpose of this study was to continuously monitor the pseudopolymorphic transition from anhydrate to monohydrate by measuring the NMR relaxation using time-domain NMR (TD-NMR). Taking advantage of the simplicity of the low-field NMR instrument configuration, which is an advantage of TD-NMR, the NMR instrument was connected to a humidity controller to monitor the pseudopolymorphic transition. First, ezetimibe (EZT) monohydrate was prepared from its anhydrate using a saturated salt solution method, and T1 relaxation of EZT monohydrate and anhydrate was measured without a humidity controller. The T1 relaxation results confirmed that EZT anhydrate and monohydrate could be distinguished using T1 relaxation measurement. Next, continuous monitoring was conducted by TD-NMR and connected to a humidity controller. Anhydrous EZT was placed in an NMR glass tube and the T1 relaxation measurement was repeated while maintaining the humidity on the side entering the NMR tube at 80% relative humidity. The T1 relaxation became gradually faster from the initial to middle monitoring phases. The final T1 relaxation was then recovered fully and these T1 relaxation times were the same as the T1 relaxation of EZT monohydrate. This study successfully monitored the pseudopolymorphic transition from EZT anhydrate to monohydrate via NMR relaxation.

Fluorophore Interactions with the Surface Modes and Internal Modes of a Photonic Crystal.

The metal-ligand complex tris(2,2'-bipyridine)ruthenium(II) chloride (Ru probe) displays a broad emission spectrum ranging from 540 to 730 nm. The emission spectra of Ru probe were measured when placed on top of a one-dimensional photonic crystal (1DPC), which supports both Bloch surface wave (BSW) and internal modes for wavelengths below 640 nm and only internal modes above 640 nm. The S-polarized emission spectra, with the electric vector parallel to the 1DPC surface, were found to be strongly dependent on the observation angle through the coupling prism. Also, the usual single broad-emission spectrum of Ru probe on glass was converted into two or more narrow-band-spectrum on the 1DPC, with emission band maxima dependent on the observation angle. The two S-polarized emission band peaks for Ru probe were found to be consistent with coupling to the BSW and first internal mode (IM1) of the 1DPC. The same spectral shifts and changes in emission maxima were observed by using Kretschmann and reverse Kretschmann illuminations. As the coupling requires the emitter to be in proximity with the photonic structure, we calculated near- and far-field distributions of a dipole directly located on the 1DPC surface. Finite-Difference Time-Domain (FDTD) simulations were performed to confirm fluorophore coupling to the BSW and internal modes (IMs). Both the measured and simulated results showed that IM coupled emission is significant. Coupling to the IM mode occurred at longer wavelengths where the 1DPC did not support a BSW. These results demonstrate that a simple Bragg grating, without a BSW mode, can be used for detection of surface-bound fluorophores.

Feedback Beamforming in the Time Domain.

Real-time source localization is crucial for high-end automation and artificial intelligence (AI) products. However, a low signal-to-noise ratio (SNR) and limited processing time can reduce localization accuracy. This work proposes a new architecture for a time-domain feedback-based beamformer that meets real-time processing demands. The main objective of this design is to locate reflective sources by estimating their direction of arrival (DOA) and signal range. Incorporating a feedback mechanism in this architecture refines localization precision, a unique aspect of this approach. We conducted an in-depth analysis to compare the effectiveness of time-domain feedback beamforming against conventional time-domain methods, highlighting their benefits and limitations. Our evaluation of the proposed architecture, based on critical performance indicators such as peak-to-sidelobe ratio, mainlobe width, and directivity factor, demonstrates its ability to improve beamformer effectiveness significantly.

A Hardware Encoder-Based Synchronization Method for a Fast Terahertz TDS Imaging System Based on the ECOPS Scheme.

In this paper, we report our use of a hardware encoder-based synchronization method for a fast terahertz time-domain spectroscopy raster scanner built with the commercially available TeraFlash Smart platform. We describe the principles of our method, including our incorporation of synchronization signals from various devices included in the scanner. We also describe its implementation in a microcontroller with a dedicated counter. By such means, a fast scanning mode was obtained, which was 35 times faster than a traditional step-by-step approach. To validate the proposed synchronization method, we carried out measurements using the USAF 1951 resolution test and a fiberglass plate with a set of intentionally introduced defects. Our results confirmed that the TDS scanner with the developed synchronization method was able to capture high-quality images with resolutions as high as those obtained using traditional step-by-step scanning, but with significantly reduced scanning times.

Fast Three-Dimensional Profilometry with Large Depth of Field.

By applying a high projection rate, the binary defocusing technique can dramatically increase 3D imaging speed. However, existing methods are sensitive to the varied defocusing degree, and have limited depth of field (DoF). To this end, a time-domain Gaussian fitting method is proposed in this paper. The concept of a time-domain Gaussian curve is firstly put forward, and the procedure of determining projector coordinates with a time-domain Gaussian curve is illustrated in detail. The neural network technique is applied to rapidly compute peak positions of time-domain Gaussian curves. Relying on the computing power of the neural network, the proposed method can reduce the computing time greatly. The binary defocusing technique can be combined with the neural network, and fast 3D profilometry with a large depth of field is achieved. Moreover, because the time-domain Gaussian curve is extracted from individual image pixel, it will not deform according to a complex surface, so the proposed method is also suitable for measuring a complex surface. It is demonstrated by the experiment results that our proposed method can extends the system DoF by five times, and both the data acquisition time and computing time can be reduced to less than 35 ms.

Thickness Characterization of Steel Plate Coating Materials with Terahertz Time-Domain Reflection Spectroscopy Based on BP Neural Network.

Accurate monitoring of steel plate coating thickness is crucial in construction quality control and durability assessments. To address this challenge, this study introduces a terahertz time-domain reflection spectroscopy based on a BP neural network model to achieve a quantitative visualization characterization of coating thickness. The BP neural network eliminates the inherent dependence of terahertz reflection spectroscopy on the refractive index value in thickness calculation. This trained BP neural network model effectively establishes a functional relationship between signal feature parameters and the corresponding thickness values. Additionally, the proposed model can innovatively measure different coating materials' refractive indexes, revealing the corresponding values for the black paint, white paint, epoxy resin, and rubber as 2.212, 1.967, 1.924, and 2.185, respectively. The experimental results demonstrate the trained BP neural network model possesses remarkable accuracy in predicting coating thickness within the scanning area, achieving a precision level exceeding 96%. This method enables the visualization of coating thickness and the extraction of thickness characterization values. Furthermore, using the thickness imaging results as a reference, the method can accurately identify the thickness abnormalities across the scanning area, locating the position and size of potential defects such as internal scratches and foreign object defects. This innovative approach offers a superior means of monitoring and assessing the thickness distribution quality of the steel plate coating layer materials.

Accurate Nonstandard Path Integral Models for Arbitrary Dielectric Boundaries in 2-D NS-FDTD Domains.

An efficient path integral (PI) model for the accurate analysis of curved dielectric structures on coarse grids via the two-dimensional nonstandard finite-difference time-domain (NS-FDTD) technique is introduced in this paper. In contrast to previous PI implementations of the perfectly electric conductor case, which accommodates orthogonal cells in the vicinity of curved surfaces, the novel PI model employs the occupation ratio of dielectrics in the necessary cells, providing thus a straightforward and instructive means to treat an assortment of practical applications. For its verification, the reflection from a flat plate and the scattering from a cylinder using the PI model are investigated. Results indicate that the featured methodology can enable the reliable and precise modeling of arbitrarily shaped dielectrics in the NS-FDTD algorithm on coarse grids.

FPGA-Microprocessor Based Sensor for Faults Detection in Induction Motors Using Time-Frequency and Machine Learning Methods.

Induction motors (IM) play a fundamental role in the industrial sector because they are robust, efficient, and low-cost machines. Changes in the environment, installation errors, or modifications to working conditions can generate faults in induction motors. The trend on IM fault detection is focused on the design techniques and sensors capable of evaluating multiple faults with various signals using non-invasive analysis. The methodology is based on processing electric current signals by applying the short-time Fourier transform (STFT). Additionally, the computation of the mean and standard deviation of infrared thermograms is proposed as main indicators. The proposed system combines both parameters by means of Support Vector Machine and k-nearest-neighbor classifiers. The development of the diagnostic system was done with digital hardware implementations using a Xilinx PYNQ Z2 card that integrates an FPGA with a microprocessor, thus taking advantage of the acquisition and processing of digital signals and images in hardware. The proposed method has proved to be effective for the classification of healthy (HLT), misalignment (MAMT), unbalance (UNB), damaged bearing (BDF), and broken rotor bar (BRB) faults with an accuracy close to 99%.

Stimulus-Response Plots as a Novel Bowel-Sound-Based Method for Evaluating Motor Response to Drinking in Healthy People.

Constipation is a common gastrointestinal disorder that impairs quality of life. Evaluating bowel motility via traditional methods, such as MRI and radiography, is expensive and inconvenient. Bowel sound (BS) analysis has been proposed as an alternative, with BS-time-domain acoustic features (BSTDAFs) being effective for evaluating bowel motility via several food and drink consumption tests. However, the effect of BSTDAFs before drink consumption on those after drink consumption is yet to be investigated. This study used BS-based stimulus-response plots (BSSRPs) to investigate this effect on 20 participants who underwent drinking tests. A strong negative correlation was observed between the number of BSs per minute before carbonated water consumption and the ratio of that before and after carbonated water consumption. However, a similar trend was not observed when the participants drank cold water. These findings suggest that when carbonated water is drunk, bowel motility before ingestion affects motor response to ingestion. This study provides a non-invasive BS-based approach for evaluating motor response to food and drink, offering a new research window for investigators in this field.

SNR Enhancement for Comparator-Based Ultra-Low-Sampling Φ-OTDR System Using Compressed Sensing.

The large amount of sampled data in coherent phase-sensitive optical time-domain reflectometry (Φ-OTDR) brings heavy data transmission, processing, and storage burdens. By using the comparator combined with undersampling, we achieve simultaneous reduction of sampling rate and sampling resolution in hardware, thus greatly decreasing the sampled data volume. But this way will inevitably cause the deterioration of detection signal-to-noise ratio (SNR) due to the quantization noise's dramatic increase. To address this problem, denoising the demodulated phase signals using compressed sensing, which exploits the sparsity of spectrally sparse vibration, is proposed, thereby effectively enhancing the detection SNR. In experiments, the comparator with a sampling parameter of 62.5 MS/s and 1 bit successfully captures the 80 MHz beat signal, where the sampled data volume per second is only 7.45 MB. Then, when the piezoelectric transducer's driving voltage is 1 Vpp, 300 mVpp, and 100 mVpp respectively, the SNRs of the reconstructed 200 Hz sinusoidal signals are respectively enhanced by 23.7 dB, 26.1 dB, and 28.7 dB by using compressed sensing. Moreover, multi-frequency vibrations can also be accurately reconstructed with a high SNR. Therefore, the proposed technique can effectively enhance the system's performance while greatly reducing its hardware burden.