Search Results (102)

Phase-change memory (PCM) is a promising candidate for in-memory computation and neuromorphic computing due to its high endurance, low cycle-to-cycle variability, and low read noise. However, among other factors, its performance strongly depends on the post-lithography fabrication steps. This study examines the impact of reactive ion etching (RIE) on PCM device performance by evaluating different etching gas mixtures, CHF₃:O₂, H₂:Ar, and Ar, and determining their impact on key device characteristics, particularly initial resistance and cycling stability. The present study demonstrates that a two-step etching approach in which the capping layer is first removed using H₂:Ar and the underlying GST layer is subsequently etched using physical Ar sputtering ensures stable and reliable PCM operation. In contrast, chemically reactive gases negatively impact the initial resistance, cycling stability, and device lifetime, likely due to alterations in the material composition. For the cycling stability evaluation, an advanced measurement algorithm utilizing the aixMATRIX setup by aixACCT Systems is employed. This algorithm enables automated testing, dynamically adjusting biasing parameters based on cell responses, ensuring a stable ON/OFF ratio and high-throughput characterization. Full article

Nowadays, hydrodynamic noise reduction in hydrofoils is of great importance due to their wide applications in marine industries, submarines and water systems. One of the modern methods for reducing this noise is the use of longitudinal grooves on the surface of the hydrofoil. In this study, the effect of longitudinal grooves on the reduction in noise generated around a NACA0015 hydrofoil was investigated. For this purpose, numerical methods based on computational fluid dynamics (CFD) and acoustic analysis using ANSYS Fluent 2024 R1 software were used. The Fuchs–Williams and Hawkings (FW-H) acoustic model was used for acoustic analysis. The results obtained from the hydrofoil without grooves and the hydrofoil equipped with longitudinal grooves were compared. In total, 11 numerical noise reading stations were installed around the hydrofoil to calculate the noise in two modes with and without grooves. The results show that the use of longitudinal grooves reduces the flow turbulence in the area near the hydrofoil surface and, as a result, prevents the formation of large and unstable vortices. This leads to a significant reduction in hydrodynamic noise, especially at low and medium frequencies. This study shows that the appropriate design of longitudinal grooves on the NACA0015 hydrofoil can be used as an effective solution to reduce hydrodynamic noise. The findings of this research can be the basis for the development of quieter hydrofoils in industrial and military applications. The results show that at low frequencies (up to approximately 10 Hz), the noise intensity of the ungrooved hydrofoil is higher than that of the grooved hydrofoil, but in the frequency range of 10 to 20 Hz, the noise intensity of the grooved hydrofoil increases significantly and exceeds that of the ungrooved hydrofoil. Full article

Polarization information is essential for material identification, stress mapping, biological imaging, and robust vision under strong illumination, yet conventional approaches rely on external polarization optics and active biasing, which are bulky, alignment-sensitive, and power-hungry. A more desirable route is to encode polarization at the pixel level and read it out at zero bias, enabling compact, low-noise, and polarization imaging. Low-symmetry layered semiconductors provide persistent in-plane anisotropy as a materials basis for polarization selectivity. Here, we construct an eight-terminal radial ‘star-shaped’ Au/ReS₂ metal-semiconductor junction array pixel that operates in a genuine photovoltaic mode under zero external bias based on the photothermoelectric effect. Based on this, electrical summation of phase-matched multi-junction channels increases the signal amplitude approximately linearly without sacrificing the two-lobed modulation depth, achieving ‘gain by stacking’ without external amplification. The device exhibits millisecond-scale transient response and robust cycling stability and, as a minimal pixel unit, realizes polarization-resolved imaging and pattern recognition. Treating linear combinations of channels as operators in the polarization domain, these results provide a general pixel-level foundation for compact, zero-bias, and scalable polarization cameras and on-pixel computational sensing. Full article

Spectroscopic observations of Earth-like exoplanets and ultra-faint galaxies–top scientific priorities for the coming decades–involve measuring broadband signals at rates of only a few photons per square meter per hour. This imposes exceptional requirements on the detector performance, necessitating dark currents below 1 e⁻/pixel/kilo second, read noise under 1 e⁻/pixel/frame, and the ability to handle large-format arrays–capabilities that are not yet met by most existing infrared detectors. In addition, spaceborne LiDAR systems require photodetectors with exceptional sensitivity, compact size, low power consumption, and multi-channel capability to facilitate long-range range finding, topographic mapping, and active spectroscopy without increasing the instrument burden. MCT Avalanche photodiodes arrays offer high internal gain, pixelation, and photon-counting performance across SW to MW wavelengths needed for multi-beam and multi-wavelength measurements, marking them as a critical enabling technology for next-generation planetary and Earth science LiDAR missions. This work reports the latest progress in developing Hg_1−xCd_xTe linear-mode e-APDs at premier industrial research institutions, including relevant experimental data, simulations and major project planning. Related studies are summarized to demonstrate the practical and iterative approach for device fabrication, which have a transformative impact on the evolution of this discipline. Full article

With the widespread adoption of mobile reading across diverse scenarios, understanding environmental impacts on attention has become crucial for reading performance optimization. Building upon this premise, the study examined the impacts of different reading environments on attention during mobile reading, utilizing a mixed-methods approach that combined eye-tracking experiments with semi-structured interviews. Thirty-two college students participated in the study. Quantitative attention metrics, including total fixation duration and fixation count, were collected through eye-tracking, while qualitative data regarding perceived environmental influences were obtained through interviews. The results indicated that the impact of different environments on mobile reading attention varies significantly, as this variation is primarily attributable to environmental complexity and individual interest. Environments characterized by multisensory inputs or dynamic disturbances, such as fluctuating noise and visual motion, were found to induce greater attentional dispersion compared to monotonous, low-variation environments. Notably, more complex potential task-like disturbances (e.g., answering calls, conversations) were found to cause the greatest distraction. Moreover, stimuli aligned with an individual’s interests were more likely to divert attention compared to those that did not. These findings contribute methodological insights for optimizing mobile reading experiences across diverse environmental contexts. Full article

Background/Objectives: Iterative reconstruction (IR) techniques were developed to address the shortcomings of filtered back projection (FBP), yet research comparing different types of IR is still missing. This work investigates how reducing radiation dose influences both image quality and noise profiles when using two iterative reconstruction techniques—Sinogram-Affirmed Iterative Reconstruction (SAFIRE) and Advanced Modeled Iterative Reconstruction (ADMIRE)—in comparison to filtered back projection (FBP) in non-enhanced head CT (NECT). Methods: In this retrospective single-center study, 21 consecutive patients underwent standard NECT on a 128-slice CT scanner. Raw data simulated dose reductions to 90% and 70% of the original dose via ReconCT software. For each dose level, images were reconstructed with FBP, SAFIRE 3, and ADMIRE 3. Image noise power spectra quantified objective image noise. Two blinded neuroradiologists scored overall image quality, image noise, image contrast, detail, and artifacts on a 10-point Likert scale in a consensus reading. Quantitative Hounsfield unit (HU) measurements were obtained in white and gray matter regions. Statistical analyses included the Wilcoxon signed-rank test, mixed-effects modeling, ANOVA, and post hoc pairwise comparisons with Bonferroni correction. Results: Both iterative reconstructions significantly reduced image noise compared to FBP across all dose levels (p < 0.001). ADMIRE exhibited superior image noise suppression at low (<0.51 1/mm) and high (>1.31 1/mm) spatial frequencies, whereas SAFIRE performed better in the mid-frequency range (0.51–1.31 1/mm). Subjective scores for overall quality, image noise, image contrast, and detail were higher for ADMIRE and SAFIRE versus FBP at the original dose and simulated doses of 90% and 70% (all p < 0.001). ADMIRE outperformed SAFIRE in artifact reduction (p < 0.001), while SAFIRE achieved slightly higher image contrast scores (p < 0.001). Objective HU values remained stable across reconstruction methods, although SAFIRE yielded marginally higher gray and white matter (WM) attenuations (p < 0.01). Conclusions: Both IR techniques—ADMIRE and SAFIRE—achieved substantial noise reduction and improved image quality relative to FBP in non-enhanced head CT at standard and reduced dose levels on the specific CT system and reconstruction strength tested. ADMIRE showed enhanced suppression of low- and high-frequency image noise and fewer artifacts, while SAFIRE preserved image contrast and reduced mid-frequency noise. These findings support the potential of iterative reconstruction to optimize radiation dose in NECT protocols in line with the ALARA principle, although broader validation in multi-vendor, multi-center settings is warranted. Full article

Microplastics are ubiquitous in the environment and represent a serious risk to ecosystems and human health. This study describes a prototype sensor designed to detect microplastics by analyzing samples with multiple wavelengths of light. The system uses samples of commercially available microplastics and includes passive low-pass filters to reduce noise for accurate readings. The prototype measures light attenuation and produces color spectra that helps identify microplastics. Its design focuses on precision, electronic control, and optical sensing, making it an affordable option for environmental monitoring. Early experiments demonstrate that the prototype is sensitive and reliable under a variety of conditions. Future improvements may add machine learning and fuzzy logic to increase accuracy and expand its use. Full article

In space gravitational wave detection, the phase information of interfering signals is read out by a phasemeter, typically output sampling at a MHz frequency. To transmit the phase information between space and ground, it must be downsampled; however, spectral aliasing during downsampling will affect the performance of the phasemeter.This paper presents a four-module cascaded downsampling filter (FCDF) with detailed module parameter design. On-board experiments conducted in a phasemeter environment demonstrate that the FCDF achieves a passband attenuation of less than $8.68\times {10}^{-6}$ dB and a stopband attenuation exceeding 160 dB, enabling downsampling from 80 MHz to 3.4 Hz. Additionally, the FCDF offers improved low-frequency noise suppression, which can enhance phasemeter performance. Full article

We review recent articles that focus on the main issues identified in high-frequency financial data analysis. The issues to be addressed include nonstationarity, low signal-to-noise ratios, asynchronous data, imbalanced data, and intraday seasonality. We focus on the research articles and survey papers published since 2020 on recent developments and new ideas that address the issues, while commonly used approaches in the literature are also reviewed. The methods for addressing the issues are mainly classified into two groups: data preprocessing methods and quantitative methods. The latter include various statistical, econometric, and machine learning methods. We also provide easy-to-read charts and tables to summarize all the surveyed methods and articles. Full article

Several stochastic $H_{\infty }$ filters for estimating the attitude of a rigid body from line-of-sight measurements and rate gyro readings are developed. The measurements are corrupted by white noise with unknown variances. Our approach consists of estimating the quaternion while attenuating the transmission gain from the unknown variances and initial errors to the current estimation error. The time-varying $H_{\infty }$ gain is computed by solving algebraic and differential linear matrix inequalities for a given transmission threshold, which is iteratively lowered until feasibility fails. Thanks to the bilinear structure of the quaternion state-space model, the algorithm parameters are independent of the state. The case of a gyro drift is addressed, too. Extensive Monte-Carlo simulations show that the proposed stochastic $H_{\infty }$ quaternion filters perform well for a wide range of noise variances. The actual attenuation, which improves with the noise variance and is worst in the noise-free case, is better than the guaranteed attenuation by one order of magnitude. The proposed stochastic $H_{\infty }$ filter produces smaller biases than nonlinear Kalman or unscented filters and similar standard deviations at large noise levels. An essential advantage of this $H_{\infty }$ filter is that the gains are independent of the quaternion, which makes it insensitive to modeling errors. This desired feature is illustrated by comparing its performances against those of unmatched nonlinear optimal filters. When provided with too high or too low noise variances, the multiplicative Kalman filter and the unscented quaternion filter are outperformed by the $H_{\infty }$ filter, which essentially delivers identical error magnitudes. Full article

In the context of smart agriculture, accurate soil moisture monitoring is crucial to optimise irrigation, improve water usage efficiency and increase crop yields. Although low-cost capacitive sensors are used to make monitoring affordable, these sensors face accuracy challenges that often result in inefficient irrigation practices. This paper presents a method for calibrating capacitive soil moisture sensors through data assimilation. The method was validated using data collected from a farm in Dos Hermanas, Seville, Spain, which utilises a drip irrigation system. The proposed solution integrates the Hydrus 1D model with particle filter (PF) and the Iterative Ensemble Smoother (IES) to continuously update and refine the model and sensor calibration parameters. The methodology includes the implementation of physical constraints, ensuring that the updated parameters remain within physically plausible ranges. Soil moisture was measured using low-cost SoilWatch 10 capacitive sensors and ThetaProbe ML3 high-precision sensors as a reference. Furthermore, a comparison was carried out between the PF and IES methods. The results demonstrate that the data assimilation approach markedly enhances the precision of sensor readings, aligning them closely with reference measurements and model simulations. The PF method demonstrated superior performance, achieving an 84.8% improvement in accuracy compared to the raw sensor readings. This substantial improvement was measured against high-precision reference sensors, confirming the effectiveness of the PF method in calibrating low-cost capacitive sensors. In contrast, the IES method showed a 68% improvement in accuracy, which, while still considerable, was outperformed by the PF. By effectively mitigating observation noise and sensor biases, this approach proves robust and practical for large-scale implementations in precision agriculture. Full article

People working and studying indoors for a long time can easily experience mental fatigue and stress. Virtual natural elements introduced into indoor environments can stimulate the human visual and auditory senses, thus relieving psychological stress. In this study, stress induction was achieved through noise playback, and the recovery effects on psychological stress of three set indoor environments, visual, auditory, and audio-visual, were investigated through changes in subjects’ facial expressions, electromyographic (EMG) signals, and subjective questionnaires. The experiment found that after stress induction through noise, the participants’ stress levels changed significantly. At this time, the subject scored low on the questionnaire, with electromyography readings higher than usual, and micro-expression recognition indicated negative emotions. After the restoration effects under the three working conditions of visual, auditory, and audio-visual combination, the average EMG values during the recovery period decreased from the baseline period (10 min after the subject acclimated to the environment), respectively. The results indicate that all three restoration conditions have the effect of relieving psychological stress, with the stress recovery effects of auditory and audio-visual conditions being superior to visual conditions. This study is of great significance for creating comfortable indoor environments and minimizing psychological pressure on indoor office workers. Full article

Small open reading frames (sORFs; <300 nucleotides or <100 amino acids) are widespread across all genomes, and an increasing variety of them appear to be translating from non-genic regions. Over the past few decades, peptides produced from sORFs have been identified as functional in various organisms, from bacteria to humans. Despite recent advances in next-generation sequencing and proteomics, accurate annotation and classification of sORFs remain a rate-limiting step toward reliable and high-throughput detection of small proteins from non-genic regions. Additionally, the cost of computational methods utilizing machine learning is lower than that of biological experiments, and they can be employed to detect sORFs, laying the groundwork for biological experiments. We present D-sORF, a machine-learning framework that integrates the statistical nucleotide context and motif information around the start codon to predict coding sORFs. D-sORF scores directly for coding identity and requires only the underlying genomic sequence, without incorporating parameters such as the conservation, which, in the case of sORFs, may increase the dispersion of scores within the significantly less conserved non-genic regions. D-sORF achieves 94.74% precision and 92.37% accuracy for small ORFs (using the 99 nt medium length window). When D-sORF is applied to sORFs associated with ribosomes, the identification of transcripts producing peptides (annotated by the Ensembl IDs) is similar to or superior to experimental methodologies based on ribosome-sequencing (Ribo-Seq) profiling. In parallel, the recognition of putative negative data, such as the intron-containing transcripts that associate with ribosomes, remains remarkably low, indicating that D-sORF could be efficiently applied to filter out false-positive sORFs from Ribo-Seq data because of the non-productive ribosomal binding or noise inherent in these protocols. Full article

A typical solid-state quantum sensor can be developed based on negatively charged nitrogen-vacancy (NV⁻) centers in diamond. The electron spin state of NV⁻ can be controlled and read at room temperature. Through optical detection magnetic resonance (ODMR) technology, temperature measurement can be achieved at the nanoscale. The key to ODMR technology is to apply microwave resonance to manipulate the electron spin state of the NV⁻. Therefore, the microwave field characteristics formed near the NV⁻ have a crucial impact on the sensitivity of ODMR measurement. This article mainly focuses on the temperature situation in cellular applications and simulates the influence of structural parameters of double open loop resonant (DOLR) microwave antennas and broadband large-area (BLA) microwave antennas on the microwave field’s resonance frequency, quality factor Q, magnetic field strength, uniformity, etc. The parameters are optimized to have sufficient bandwidth, high signal-to-noise ratio, low power loss, and high magnetic field strength in the temperature range of 36 °C to 42.5 °C. Finally, the ODMR spectra are used for effect comparison, and the signal-to-noise ratio and Q values of the ODMR spectra are compared when using different antennas. We have provided an optimization method for the design of microwave antennas and it is concluded that the DOLR microwave antenna is more suitable for living cell temperature measurement in the future. Full article

The quartz tuning fork used as an acoustic sensor in quartz-enhanced photo-acoustic spectroscopy gas detection systems is usually read out by means of a transimpedance preamplifier based on a low-noise operational amplifier closed in a feedback loop. The gain–bandwidth product of the operational amplifier used in the circuit is a key parameter which must be properly chosen to guarantee that the circuit works as expected. Here, we demonstrate that if the value of this parameter is not sufficiently large, the response of the preamplifier exhibits a peak at a frequency which does not coincide with the series resonant frequency of the quartz tuning fork. If this peak frequency is selected for modulating the laser bias current and is also used as the reference frequency of the lock-in amplifier, a penalty results in terms of signal-to-noise ratio at the output of the QEPAS sensor. This worsens the performance of the gas sensing system in terms of ultimate detection limits. We show that this happens when the front-end preamplifier of the quartz tuning fork is based on some amplifier models that are typically used for such application, both when the integration time of the lock-in amplifier filter is long, to boost noise rejection, and when it is short, in order to comply with a relevant measurement rate. Full article