Signal Variations Research Articles

Using Arduino Uno and EMG Electrodes for Electromyographic Signal Measurement and Servo Control

This paper explores the integration of Arduino Uno with surface electromyography (sEMG) electrodes for measuring muscle electrical activity and controlling servo motors based on these signals. By analyzing electromyographic signals from various hand postures, such as opening and closing, signal variations were thoroughly evaluated. The study implements a servo control mechanism that adjusts the servo motors position based on computed root mean square (RMS) voltage values derived from EMG signals. The experimental results demonstrate that the servo control system effectively mirrors hand movements, providing a feasible solution for biomedical applications and human-machine interfaces. This research highlights the potential for using affordable hardware for sophisticated control systems and suggests future enhancements to improve system accuracy and applicability.

The effects of age and other individual factors on radiation induced ESR signals from fingernails

Biodosimetry is crucial for assessing ionizing radiation exposure to guide medical responses. Electron spin resonance (ESR) spectroscopy using fingernails can be effectively used for both occupational and public dose assessments in radiological accidents because of their accessibility and ability to retain stable radiation-induced free radicals. However, despite two decades of research, challenges remain in achieving accurate fingernail dosimetry, mainly owing to the variation in ESR signals among individuals. The purpose of this study was to explore inter-individual differences in ESR signals in fingernails to improve the accuracy and reliability of extremity dosimetry. Fingernail samples were collected from 15 participants (age: 11–64 years), irradiated with X-rays (160 kV, 6.3 mA) at 0, 5, 10, and 20 Gy, and measured using ESR spectroscopy. The effects of individual factors, such as age, sex, health condition, and lifestyle, on radiation-induced ESR signals (RIS) were investigated. Younger participants exhibited stronger RIS intensities and a more linear dose–response relationship. The RIS intensity in female samples tended to be higher than that in male samples. Interestingly, the fingernals of middle-aged donors who regularly took vitamin supplements showed significantly higher ESR signal intensities than those of similar-age donors who did not take supplements. Notable reductions in RIS intensity during storage in a freezer were observed only in older donor samples irradiated at higher doses. These findings underscores the importance of considering age and other individual factors in the calibration for fingernail dosimetry.

Decoupling Carrier Dynamics and Energy Transport in Ultrafast Near-Field Nanoscopy.

Ultrafast near-field optical nanoscopy has emerged as a powerful platform to characterize low-dimensional materials. While analytical and numerical models have been established to account for photoexcited carrier dynamics, quantitative evaluation of the associated pulsed laser heating remains elusive. Here, we decouple the photocarrier density and temperature increase in near-field nanoscopy by integrating the two-temperature model (TTM) with finite-difference time-domain (FDTD) simulations. These results reveal that the electron-phonon coupling in a silicon film after femtosecond laser excitation is most pronounced within approximately 3 ps─substantially shorter than the photocarrier decay time scale at tens of picoseconds. Moreover, the coupled TTM-FDTD method indicates that ultrafast laser heating can cause up to a 14% variation in the near-field signal at a 220 μJ/cm2 pump pulse fluence. Our numerical results are further validated by transient experiments, highlighting the potential of this method for investigations of carrier and thermal phenomena in emerging nanomaterials and nanodevices.

Dynamic domain adaptive EEG emotion recognition based on multi-source selection.

Emotion recognition based on electroencephalogram (EEG) has always been a research hotspot. However, due to significant individual variations in EEG signals, cross-subject emotion recognition based on EEG remains a challenging issue to address. In this article, we propose a dynamic domain-adaptive EEG emotion recognition method based on multi-source selection. The method considers each subject as a separate domain, filters suitable source domains from multiple subjects by assessing their resemblance, then further extracts the common and domain-specific features of the source and target domains, and then employs dynamic domain adaptation to mitigate inter-domain discrepancies. Global domain differences and local subdomain differences are also considered, and a dynamic factor is added so that the model training process first focuses on global distribution differences and gradually switches to local subdomain distributions. We conducted cross-subject and cross-session experiments on the SEED and SEED-IV datasets, respectively, and the cross-subject accuracies were 89.76% and 65.28%; the cross-session experiments were 91.63% and 67.83%. The experimental outcomes affirm the efficacy of the EEG emotion recognition approach put forward in this paper.

DEVELOPMENT OF A WEBSITE-BASED FETAL MOVEMENT MONITORING TOOL

Background: The development of technology by utilizing sensors to record body bioelectricity is a challenge in the transformation of health in the pillar of biotechnology that can be used to detect fetal movement with a non-transmission and inexpensive system. Advances in sensor technology can be used for long-term fetal movement recording. This study discusses the development of a system that can be used by pregnant women to monitor fetal movement using an accelerometer that has a fairly high accuracy in detecting fetal movement. Mobile phone applications to monitor fetal movement using an accelerometer in areas with limited resources have a fairly high level of accuracy and can be used as a tool in monitoring fetal movement. Research on fetal movement recording devices can provide valuable information about the condition of the fetus and its health, and has developed into a reliable technology that can be used in clinical settings. Objective: to develop a website-based fetal recording device. Method: The research method used is the research and development (R&D) method, this method is used to develop certain products and test the effectiveness of the product. Results: The results displayed in the software are in the form of excel which will be processed into a logarithm of fetal movement recorded by the electrode and then an analysis of fetal movement signals will be carried out compared to the movement results from the mother's perception. Conclusion: The prototype demonstrates superior noise reduction capabilities but may require optimized preprocessing to preserve event-related signal variations.

Links Between Variation in Movement-Based Visual Signals and Social Communication Complexity in an Asian Agamid Lizard Phrynocephalus vlangalii.

The social complexity hypothesis suggests that complex social interactions drive the evolution of sophisticated communicative signals. While the relationship between social communication and the complexity of sound and color signals has been extensively studied, the correlation between social communication and movement-based visual signal complexity remains underexplored. In this study, we selected the Asian agamid lizard, Phrynocephalus vlangalii, as our model system. Through a combination of controlled experiments, behavioral observations, and signal quantification, we examined the relationship between social communications and variation in movement-based visual signals and tested our social complexity hypothesis. Our experiments revealed that males significantly decreased the tail coil duration during the mating season to deal with high social interaction. Conversely, females significantly increased the tail display duration and variation in mean tail coil amplitude in line with the intensity of parental care, and tail display duration showed a significant and positive correlation with female reproductive investment. These findings suggest that social communication plays an important role in shaping the changes in movement-based visual signals, providing new evidence for the social complexity hypothesis.

Epilepsy Prediction and Detection Using Attention-CssCDBN with Dual-Task Learning

Epilepsy is a group of neurological disorders characterized by epileptic seizures, and it affects tens of millions of people worldwide. Currently, the most effective diagnostic method employs the monitoring of brain activity through electroencephalogram (EEG). However, it is critical to predict epileptic seizures in patients prior to their onset, allowing for the administration of preventive medications before the seizure occurs. As a pivotal application of artificial intelligence in medical treatment, learning the features of EEGs for epilepsy prediction and detection remains a challenging problem, primarily due to the presence of intra-class and inter-class variations in EEG signals. In this study, we propose the spatio-temporal EEGNet, which integrates contractive slab and spike convolutional deep belief network (CssCDBN) with a self-attention architecture, augmented by dual-task learning to address this issue. Initially, our model was designed to extract high-order and deep representations from EEG spectrum images, enabling the simultaneous capture of spatial and temporal information. Furthermore, EEG-based verification aids in reducing intra-class variation by considering the time correlation of the EEG during the fine-tuning stage, resulting in easier inference and training. The results demonstrate the notable efficacy of our proposed method. Our method achieved a sensitivity of 98.5%, a false-positive rate (FPR) of 0.041, a prediction time of 50.92 min during the epilepsy prediction task, and an accuracy of 94.1% during the epilepsy detection task, demonstrating significant improvements over current state-of-the-art methods.

Detecting Early Degradation of Wood Ultrastructure with Nonlinear Optical Imaging and Fluorescence Lifetime Analysis.

Understanding the deterioration processes in wooden artefacts is essential for accurately assessing their conservation status and developing effective preservation strategies. Advanced imaging techniques are currently being explored to study the impact of chemical changes on the structural and mechanical properties of wood. Nonlinear optical modalities, including second harmonic generation (SHG) and two-photon excited fluorescence (TPEF), combined with fluorescence lifetime imaging microscopy (FLIM), offer a promising non-destructive diagnostic method for evaluating lignocellulose-based materials. In this study, we employed a nonlinear multimodal approach to examine the effects of artificially induced delignification on samples of Norway spruce (Picea abies) and European beech (Fagus sylvatica) subjected to increasing treatment durations. The integration of SHG/TPEF imaging and multi-component fluorescence lifetime analysis enabled the detection of localized variations in nonlinear signals and τ-phase of key biopolymers within wood cell walls. This methodology provides a powerful tool for early detection of wood deterioration, facilitating proactive conservation efforts of wooden artefacts.

Characterization of an Inorganic Powder-Based Scintillation Detector Under a UHDR Electron Beam.

(1) Background: Ultra-high dose rate (UHDR) radiation therapy needs a reliable dosimetry solution and scintillation detectors are promising candidates. In this study, we characterized an inorganic powder-based scintillation detector under a 9 MeV UHDR electron beam. (2) Methods: A mixture of ZnS:Ag powder and optic glue was coupled to an 8 m Eska GH-4001-P polymethyl methacrylate (PMMA) optical fiber. We evaluated the dependence of the detector on dose per pulse (DPP), pulse repetition frequency (PRF), and pulse width (PW). Additionally, we determined the stability and the reproducibility of the detector. (3) Results: The signal ratio between the PMMA clear optical fiber and the ZnS:Ag scintillator was around 210. ZnS:Ag produced a signal yield 54 times greater than that of a BCF-12 plastic scintillator. Signal variation with PRF changes was under 0.5%. The signal was linear to the integrated dose up to the maximum deliverable dose, 180 Gy. The variation in signal was linear to the change in both PW and DPP. Regarding stability, the standard deviation of 10 consecutive irradiations was 0.83%. For the reproducibility, all daily measurements varied within ±1.5%. (4) Conclusions: These findings show that the ZnS:Ag detector can be used for accurate dosimetry with UHDR beams.

Electron-Beam-Induced Negative Differential Transconductance Homojunction Device Based on van der Waals Materials for Functionally Complete Ternary Computing.

Negative differential transconductance (NDT) devices have emerged as promising candidates for multivalued logic computing, and particularly for ternary logic systems. To enable computation of any ternary operation, it is essential to have a functionally complete set of ternary logic gates, which remains unrealized with current NDT technologies, posing a critical limitation for higher-level circuit design. Additionally, NDT devices typically rely on heterojunctions, complicating fabrication and impacting reliability due to the introduction of additional materials and interfaces. Here, we utilize an electron beam to develop tungsten diselenide (WSe2) homojunction NDT devices with W-shaped current-voltage (I-V) characteristics. We demonstrate that electron beam enables the manipulation of Se atoms in WSe2, facilitating controllable and spatially precise tailoring of the WSe2 work function. The electron-beam treatment applied to a part of the WSe2 channel induces a lateral homojunction and ultimately results in the W-shaped I-V curves, which enable both one-input and two-input ternary logic gates. We propose and implement a balanced circuit design for two-input ternary NAND, AND, NOR, and OR gates, featuring a low device count, full-swing operation, and minimized output signal variations. Together with three types of ternary inverters also designed in this work, they form a functionally complete set of ternary logic gates─a prerequisite for practical ternary computing. This work addresses a critical gap in the development of NDT-based ternary computing by ensuring functional completeness and highlights the versatility of electron-beam treatment as an engineering tool for tailoring the properties of two-dimensional van der Waals materials.

Listen to Your Face: A Face Authentication Scheme Based on Acoustic Signals

Face authentication (FA) schemes are widely adopted in smart homes nowadays. However, existing FA systems for smart appliances are commonly camera-based and hence experience performance degradation in poor illumination conditions. Mainstream FA systems based on radio frequency require dedicated hardware that is inaccessible to many appliances. In this paper, we propose an acoustic signals-based FA scheme that extracts acoustic signal features associated with facial 3D geometries to achieve FA named SoundFace . This scheme can be widely deployed on most appliances in home environments. We propose a novel two-stage locating approach based on acoustic sensing to capture the signal variation of the user’s face and separate the face region echoes from multipath interferences in the distance dimension. To obtain distinguishable facial features, we design a Convolutional Neural Network (CNN)-based feature extractor. In addition, the acoustic signal is highly susceptible to different changes in practical authentication. To overcome it, we utilize a transfer learning technique with little training overhead to enable SoundFace resilient to various authentication changes. Extensive evaluations demonstrate that SoundFace achieves an average true authentication rate of over 96.2% and an equal error rate of 4.2%, and it is robust to various real-world settings.

Wearable Temperature Sensor Enhanced Volatilomics Technique for Swift and Convenient Detection of Latrogenic Botulism.

Accurately assessing potential side effects following botulinum neurotoxin (BoNT) injection remains a formidable challenge. To address this issue, an innovative approach is developed that combines a wearable temperature sensor with a sophisticated volatilomics technique, aimed at facilitating the rapid and convenient prediction of potential physical discomfort related to latrogenic botulism. The investigation identifies five volatile organic compounds (VOCs)-acetone, styrene, ethanol, 2-pentanone, and n-butano-as promising markers indicative of BoNT poisoning. Specifically, a handheld breath analyzer, featuring a yttrium stabilized zirconia (YSZ)-based gas sensor array, alongside a wearable temperature sensor integrated with a bio-compatible methacrylated gelatin (GelMA) sensing film, are developed to simultaneously monitor breath signal variations and body temperature fluctuations. Preliminary animal testing validates the effectiveness of the integrated approach, achieving an accuracy exceeding 91.2% in early detection of physical discomfort associated with BoNT poisoning. These promising findings represent a significant advancement towards the early identification of BoNT-related issues, enabling timely intervention and improved management strategies.

Experimental investigation of low-frequency and pulsed eddy current testing in thickness measurement

ABSTRACT This paper experimentally compares low-frequency and pulsed eddy current testing techniques in measuring plate thickness. The experimental setups for both methods were built using general commercial devices. A single polarity rectangular wave with a 10 Hz pulse repetition frequency and 50% duty was utilised as the excitation wave in pulsed eddy current testing. Ferromagnetic steel and nonferromagnetic aluminium alloy plates of 1, 3, 5, and 10 mm thicknesses were tested at various lift-off distances. Using peak amplitude, valley amplitude, time to attenuate, logarithmic slope, and reciprocal square root of the logarithmic slope as features, pulsed eddy current testing could effectively evaluate the steel plates thicker than 3 mm and all the aluminium alloy plates. In contrast, low-frequency eddy current testing using a 10 Hz excitation frequency could evaluate both steel and aluminium alloy plates using signal phase. This indicates that redundant frequency components may impair the effectiveness of pulsed eddy current testing. Moreover, low signal-to-noise ratio and simple signal processing methods struggle to detect minor signal variations. Additionally, the linearity of phase versus thickness for steel is better than for aluminium alloy, potentially indicating that ferromagnetic materials enhance the coupling between the coil and the plate.

Quantitative perturbation-phenotype maps reveal nonlinear responses underlying robustness of PAR-dependent asymmetric cell division.

A key challenge in the development of an organism is to maintain robust phenotypic outcomes in the face of perturbation. Yet, it is often unclear how such robust outcomes are encoded by developmental networks. Here, we use the Caenorhabditis elegans zygote as a model to understand sources of developmental robustness during PAR polarity-dependent asymmetric cell division. By quantitatively linking alterations in protein dosage to phenotype in individual embryos, we show that spatial information in the zygote is read out in a highly nonlinear fashion and, as a result, phenotypes are highly canalized against substantial variation in input signals. Our data point towards robustness of the conserved PAR polarity network that renders polarity axis specification resistant to variations in both the strength of upstream symmetry-breaking cues and PAR protein dosage. Analogously, downstream pathways involved in cell size and fate asymmetry are robust to dosage-dependent changes in the local concentrations of PAR proteins, implying nontrivial complexity in translating PAR concentration profiles into pathway outputs. We propose that these nonlinear signal-response dynamics between symmetry-breaking, PAR polarity, and asymmetric division modules effectively insulate each individual module from variation arising in others. This decoupling helps maintain the embryo along the correct developmental trajectory, thereby ensuring that asymmetric division is robust to perturbation. Such modular organization of developmental networks is likely to be a general mechanism to achieve robust developmental outcomes.

Multivariate distance dispersion entropy: a complexity analysis method capturing intra- and inter-channel signal variations for multichannel data

Multivariate distance dispersion entropy: a complexity analysis method capturing intra- and inter-channel signal variations for multichannel data

Differences in the force exerted during wrist movements are explained by a general mathematical model

The goal of previous efforts has been to create hand prostheses with natural movements. Researchers have used tools to identify patterns in muscle signals associated with different hand movements. While many studies have successfully classified types of hand movements, it's important to analyze speed and strength to ensure that the resulting movements are natural. In this study, 16 healthy subjects were evaluated for (two) different forces and (six) hand movements using surface electromyographic (sEMG) records. A mixed effects model was used to examine the relationship between force and forearm sEMG signals. The results showed high R2 values (median 0.9) and significant random effects, indicating that sEMG signals can explain variations in force signals during different hand movements by introducing the type of movement as part of the random effects of the model.

Light touch alters vestibular-evoked balance responses: insights into dynamics of sensorimotor reweighting.

Integrated multisensory feedback plays a crucial role in balance control. Minimal fingertip contact with a surface (light touch), reduces the center of pressure (CoP) by adding sensory information about postural orientation and balance state. Electrical vestibular stimulation (EVS) can increase sway by adding erroneous vestibular cues. This juxtaposition of conflicting sensory cues can be exploited to explore the dynamics of sensorimotor adaptations. We used continuous stochastic EVS (0-25 Hz; ±4 mA; 200-300 s) to evoke balance responses in CoP (experiment 1, experiment 2). Systems analyses (coherence, gain) quantified coupling and size of balance responses to EVS. We had participants either touch (TOUCH; <2 N) or not touch (NO-TOUCH) a load cell during EVS (experiment 1, experiment 2), or we intermittently removed the touch surface (experiment 2) to measure the effects of light touch on vestibular-evoked balance responses. We hypothesized that coherence and gain between EVS and CoP would decrease, consistent with the central nervous system (CNS) down-weighting vestibular cues that conflict with light touch. Light touch reduced CoP displacement but increased variation in the CoP signal explained by EVS input. Significant coherence between EVS and CoP was observed up to ∼30 Hz in both conditions but was significantly greater in the TOUCH condition from 12 to 28.5 Hz. Conversely, EVS-CoP gain was 63% lower in TOUCH compared with NO-TOUCH. Our findings show that light touch can reduce the size of vestibular-evoked responses but also increase high-frequency vestibular contributions for sway. This suggests that the CNS can use discrete changes in sensory inputs to alter balance behavior but cannot fully suppress responses to a potent cue.NEW & NOTEWORTHY This study reveals that minimal fingertip contact (light touch) during balance tasks not only diminishes the impact of electrical vestibular stimulation (EVS) on sway but also exposes a high-frequency center of pressure element, correlated to vestibular inputs, not typically seen in free standing. Specifically, light touch decreases the magnitude of EVS-induced sway while increasing coherence with EVS at higher frequencies. This illustrates the central nervous system's capacity to adaptively reweight sensorimotor processes for balance control.

Research on detection performance of millimeter wave fuze under rainfall condition

Abstract Aiming at the characteristics that the performance of millimeter wave fuze is easily affected by the natural environment, this paper studies the performance of millimeter wave fuze under rainfall conditions. By analyzing the theory of rain attenuation effect and backscattering effect, it is concluded that the rain attenuation effect reduces the received power of the signal, and the backscattering effect reduces the signal to interference plus noise ratio (SINR) of the receiving end. Based on this, the signal propagation path model under rainfall conditions is established. Finally, the variation of the maximum reliable detection range with signal wavelength and rainfall rate is simulated under different SINR requirements, and the variation of signal to noise ratio (SNR) and SINR with rainfall rate is compared under different distances. The simulation results show that both the rain attenuation effect and the backscattering effect lead to the decrease of the maximum reliable detection distance of millimeter wave fuze. With the increase of rainfall rate, the detection performance of millimeter wave fuze decreases, and the detection distance decreases by about 23.2 % under extreme conditions. The rain backscattering effect on millimeter wave fuze is higher than its attenuation effect, and the influence of rain backscattering effect on millimeter wave fuze increases with the increase of detection distance. When the detection distance increases from 15 m to 30 m, the difference between SNR and SINR at the receiving end of millimeter wave fuze increases by about 3 times. This study has important reference significance for the application of millimeter wave fuze in precision guided weapons.

A Hardware-Efficient Novelty-Aware Spike Sorting Approach for Brain-Implantable Microsystems.

Unsupervised spike sorting, a vital processing step in real-time brain-implantable microsystems, is faced with the prominent challenge of managing nonstationarity in neural signals. In long-term recordings, spike waveforms gradually change and new source neurons are likely to become activated. Adaptive spike sorters combined with on-implant training units effectively process the nonstationary signals at the cost of high hardware resource utilization. On the other hand, static approaches, while being hardware-friendly, are subjected to decreased processing performance in such recordings where the neural signal characteristics gradually change. To strike a balance between the hardware cost and processing performance, this study proposes a hardware-efficient novelty-aware spike sorting approach that is capable of dealing with both variated spike waveforms and spike waveforms generated from new source neurons. Its improved hardware efficiency compared to adaptive ones and capability of dealing with nonstationary signals make it attractive for implantable applications. The proposed novelty-aware spike sorting especially would be a good fit for brain-computer interfaces where long-term, real-time interaction with the brain is required, and the available on-implant hardware resources are limited. Our unsupervised spike sorting benefits from a novelty detection process to deal with neural signal variations. It tracks the spike features so that in case of detecting an unexpected change (novelty detection) both on and off-implant parameters are updated to preserve the performance in new state. To make the proposed approach agile enough to be suitable for brain implants, the on-implant computations are reduced while the computational burden is realized off-implant. The performance of our proposed approach is evaluated using both synthetic and real datasets. The results demonstrate that, in the mean, it is capable of detecting 94.31% of novel spikes (wave-drifted or emerged spikes) with a classification accuracy (CA) of 96.31%. Moreover, an FPGA prototype of the on-implant circuit is implemented and tested. It is shown that in comparison to the OSORT algorithm, a pivotal spike sorting method, our spike sorting provides a higher CA at significantly lower hardware resources. The proposed circuit is also implemented in a 180-nm standard CMOS process, achieving a power consumption of 1.78[Formula: see text][Formula: see text] per channel and a chip area of 0.07[Formula: see text]mm2 per channel.

A Study on the Timing Sensitivity of the Transient Dose Rate Effect on Complementary Metal-Oxide-Semiconductor Image Sensor Readout Circuits.

Complementary Metal-Oxide-Semiconductor (CMOS) image sensors (CISs), known for their high integration, low cost, and superior performance, have found widespread applications in satellite and space exploration. However, the readout circuits of pixel arrays are vulnerable to functional failures in complex or intense radiation environments, particularly due to transient γ radiation. Using Technology Computer-Aided Design (TCAD) device simulations and Simulation Program with Integrated Circuit Emphasis (SPICE) circuit simulations, combined with a double-exponential current source fault injection method, this study investigates the transient dose rate effect (TDRE) on a typical readout circuit of CISs. It presents the variations in the photoelectric signal under different dose rates and at different occurrence moments of the TDRE. The results show that, under low dose rates, the CIS readout circuit can still perform data acquisition and digital processing, with the photoelectric signal exhibiting some sensitivity to the occurrence moment. At high dose rates, however, the photoelectric signal not only remains sensitive to the occurrence moment but also shows significant discreteness. Further analysis of the CIS readout circuit sequence suggests that the occurrence moment is a critical factor affecting the circuit's performance and should not be overlooked. These findings provide valuable insights and references for further research on the TDRE in circuits.