Fast Response Research Articles

Federated In-Network Machine Learning for Privacy-Preserving IoT Traffic Analysis

The expanding use of Internet-of-Things (IoT) has driven machine learning (ML)-based traffic analysis. 5G networks’ standards, requiring low-latency communications for time-critical services, pose new challenges to traffic analysis. They necessitate fast analysis and response, preventing service disruption or security impact on network infrastructure. Distributed intelligence on IoT edge has been studied to analyze traffic, but introduces delays and raises privacy concerns. Federated learning can address privacy concerns, but does not meet latency requirements. In this article, we propose FLIP4: an efficient federated learning-based framework for in-network traffic analysis. Our solution introduces a lightweight federated tree-based model, offloaded and running within network devices. FLIP4 consumes less resources than previous solutions and reduces communication overheads, making it well-suited for IoT edge traffic analysis. It ensures prompt mitigation and minimal impact on services in the presence of false alerts using two approaches (metering and dropping), thereby balancing learning accuracy and privacy requirements.

Underwater Vibration Sensor to Enable Automated and Contactless Voice Recognition

AbstractIndividuals suffering from voice disabilities have limited access to currently available automation technologies that operate through voice commands. To address this issue, an alternative voice recognition approach is essential without directly monitoring the audio signals generated from the vocal cord. In this work, the design of a chemically reactive and conductive sponge is reported to create an underwater vibration sensor with a fast response time and high sensitivity, through orthogonal modulation of conductivity (40–2150 kΩ), water repellence (0°–154°) and mechanical properties (0.32–2.63 MPa). This class of porous sponge sensors enables the identification of subtle water waves generated at the air–water interface and extends its utility to detecting a variety of locomotion (squatting, jumping, walking, etc.), as well as automated voice recognition using a deep learning model without direct contact with the human body. Overall, this underwater vibration sensor provides a novel basis for remote interaction with automated technologies, which finds use in medical diagnostics, human‐machine interfaces, and underwater communication systems.

Deep learning pipeline for accelerating virtual screening in drug discovery.

In the race to combat ever-evolving diseases, the drug discovery process often faces the hurdles of high-cost and time-consuming procedures. To tackle these challenges and enhance the efficiency of identifying new therapeutic agents, we introduce VirtuDockDL, which is a streamlined Python-based web platform utilizing deep learning for drug discovery. This pipeline employs a Graph Neural Network to analyze and predict the effectiveness of various compounds as potential drug candidates. During the validation phase, VirtuDockDL was instrumental in identifying non-covalent inhibitors against the VP35 protein of the Marburg virus, a critical target given the virus's high fatality rate and limited treatment options. Further, in benchmarking, VirtuDockDL achieved 99% accuracy, an F1 score of 0.992, and an AUC of 0.99 on the HER2 dataset, surpassing DeepChem (89% accuracy) and AutoDock Vina (82% accuracy). Compared to RosettaVS, MzDOCK, and PyRMD, VirtuDockDL outperformed them by combining both ligand- and structure-based screening with deep learning. While RosettaVS excels in accurate docking but lacks high-throughput screening, and PyRMD focuses on ligand-based methods without AI integration, VirtuDockDL offers superior predictive accuracy and full automation for large-scale datasets, making it ideal for comprehensive drug discovery workflows. These results underscore the tool's capability to identify high-affinity inhibitors accurately across various targets, including the HER2 protein for cancer therapy, TEM-1 beta-lactamase for bacterial infections, and the CYP51 enzyme for fungal infections like Candidiasis. To sum up, VirtuDockDL combines user-friendly interface design with powerful computational capabilities to facilitate rapid, cost-effective drug discovery and development. The integration of AI in drug discovery could potentially transform the landscape of pharmaceutical research, providing faster responses to global health challenges. The VirtuDockDL is available at https://github.com/FatimaNoor74/VirtuDockDL .

Sulfur dioxide gas sensor based on vanadium oxide doped TiO2 nanopaper

Abstract The gas sensor based on TiO2 nanomaterials is promising for both industry and daily life because of its simple fabrication, low cost, high sensitivity, and easy application to micro-devices. However, its selectivity is relatively low and strongly influenced by the environment, thus limiting its practical application. In this work, we prepared TiO2 nanopaper by methods of hydrothermal synthesis and conventional paper preparation. To improve the selectivity, we added V2O5 by immersing the as-prepared nanopaper in an ammonium metavanadate (NH4VO3) solution evenly dispersed in ethanol. Nanopaper is a random array of long nanowires and nanofibers, which retains its shape and structure at high temperature, unlike pure nanowires, due to its high porosity and mechanical stability. V2O5 leads to a selective sensing performance with high catalytic activity for SO2 gas. The surface structure of the sensing material and its porosity were characterized by SEM, XRD, XPS. The improved sensing material exhibited a high response of about 22.6 for 100 ppm SO2 and a fast response and recovery of 9 s and 14 s, respectively. It also exhibited good reproducibility and selectivity in other interfering gases. Furthermore, the long-term sensing performance in the atmospheric environment was maintained for about 50 days.

Ion‐Selective Mobility Differential Amplifier: Enhancing Pressure‐Induced Voltage Response in Hydrogels

Piezoionics is an emerging mechanical‐electrical energy conversion paradigm that enables self‐powered sensing systems for next‐generation intelligent wearable electronics. However, there are currently no rational design approaches to enhance the stimulus response of piezoionic devices. Here, we present a strategy using crown ether as ion‐selective mobility differential amplifiers for enhancing the pressured‐induced voltage response in ionic polyvinyl alcohol (PVA) hydrogels. The crown ether grafted PVA (PVA‐CE) hydrogel prototype achieves a 30‐fold amplified piezoionic coefficient of 1490 nV Pa−1 within 0 – 1 kPa, compared to 49 nV Pa−1 of the unmodified PVA. The PVA‐CE exhibits an ultra‐low pressure detection limit of 0.2 Pa with a fast response time of 18.1 ms. Leveraging these properties, we further demonstrate a human somatosensory network analogous PVA‐CE piezoionic skin using arrayed pressure sensing. These capabilities hold great promise for emerging healthcare applications such as synthetic biology, soft robotics, and beyond.

Ion-Selective Mobility Differential Amplifier: Enhancing Pressure-Induced Voltage Response in Hydrogels.

Piezoionics is an emerging mechanical-electrical energy conversion paradigm that enables self-powered sensing systems for next-generation intelligent wearable electronics. However, there are currently no rational design approaches to enhance the stimulus response of piezoionic devices. Here, we present a strategy using crown ether as ion-selective mobility differential amplifiers for enhancing the pressured-induced voltage response in ionic polyvinyl alcohol (PVA) hydrogels. The crown ether grafted PVA (PVA-CE) hydrogel prototype achieves a 30-fold amplified piezoionic coefficient of 1490 nV Pa-1 within 0 - 1 kPa, compared to 49 nV Pa-1 of the unmodified PVA. The PVA-CE exhibits an ultra-low pressure detection limit of 0.2 Pa with a fast response time of 18.1 ms. Leveraging these properties, we further demonstrate a human somatosensory network analogous PVA-CE piezoionic skin using arrayed pressure sensing. These capabilities hold great promise for emerging healthcare applications such as synthetic biology, soft robotics, and beyond.

Dissecting the causal role of early inferior frontal activation in reading.

Cognitive models of reading assume that speech production occurs after visual and phonological processing of written words. This traditional view is at odds with more recent magnetoencephalography studies showing that the left posterior inferior frontal cortex (pIFC) classically associated with spoken production responds to print at 100-150 ms after word-onset, almost simultaneously with posterior brain regions for visual and phonological processing. Yet the theoretical significance of this fast neural response remains open to date. We used transcranial magnetic stimulation (TMS) to investigate how the left pIFC contributes to the early stage of reading. In Experiment 1, 23 adult participants (14 females) performed three different tasks about written words (oral reading, semantic judgment and perceptual judgment) while single-pulse TMS was delivered to the left pIFC, fusiform gyrus or supramarginal gyrus at different time points (50 to 200 ms after word-onset). A robust double dissociation was found between tasks and stimulation sites - oral reading, but not other control tasks, was disrupted only when TMS was delivered to pIFC at 100 ms. This task-specific impact of pIFC stimulation was further corroborated in Experiment 2, which revealed another double dissociation between oral reading and picture naming. These results demonstrate that the left pIFC specifically and causally mediates rapid computation of speech motor codes at the earliest stage of reading and suggest that this fast sublexical neural pathway for pronunciation, although seemingly dormant, is fully functioning in literate adults. Our results further suggest that these left-hemisphere systems for reading overall act faster than known previously.Significance Statement Recent neuroimaging data suggest that left posterior inferior frontal cortex, classically associated with spoken production, responds to print simultaneously with left fusiform and supramarginal gyri, each responsible for visual and phonological processing, contrary to traditional serial cascade models of reading. While the region is now known to mediate different aspects of cognitive processing, the functional significance of this fast neural response remains unclear. Using transcranial magnetic stimulation, we show that early inferior frontal activation plays a specific and causal role in speeded oral reading at 100 ms after word-onset. This fast sublexical neural pathway for pronunciation, although seemingly dormant, is fully functioning in literate adults. We also propose that the left-hemisphere reading systems act differently and faster than known previously.

Extreme learning machine‐based super‐twisting integral terminal sliding mode speed control of permanent magnet synchronous motors

AbstractThis article proposes an extreme learning machine (ELM)‐based super‐twisting integral terminal sliding mode control (STITSMC) for speed regulation of a permanent magnet synchronous motor (PMSM). First, the PMSM is modeled in a non‐cascade control structure for fast system response and uncertainty compensation in the speed and torque loops. Second, the STITSMC is designed with integral actions in both the sliding surface and the reaching law to reduce chattering. Third, the ELM is constructed to compensate for the system lumped disturbance, and relax the disturbance upper bound required by the controller which further reduces the chattering. Fourth, the stability of the whole control system is proved based on the Lyapunov method and the finite time convergence regions are derived for both the reaching and the sliding phases. Finally, the comparative simulations and experiments are conducted to show the superiority of the proposed control.

Germanium-incorporated Si-Ge-Si heterojunction phototransistors for a high-limit of detection and wide linear dynamic range near-infrared light detection

This paper investigates overcoming the limitations of conventional silicon (Si) bipolar junction transistors (BJTs) for near-infrared (NIR) light detection. Silicon BJTs have inherent limitations in the NIR region due to silicon’s bandgap. To address this, the paper proposes high-performance silicon-germanium-silicon (Si-Ge-Si) heterojunction bipolar phototransistors (HPTs). The key innovation is introducing a thin germanium (Ge) layer at the base-collector junction and engineering its doping concentration. This Ge layer extends the BJT phototransistor’s optical response into the NIR region by enhancing optical absorption. The paper analyses the performance of the HPTs, demonstrating linear photoresponsivity of 432 mA/W over a wide optical power range, leading to an exceptional linear dynamic range of 159 dB and a very low dark current, which produces a limit of detection of -99.64 dBm. Additionally, the device exhibits a large photo-to-dark current ratio of 2.16 ×107 (at optical power of 5 µW) and a fast response time of 11.35 ps to modulated NIR optical signals within the linear dynamic range. This research paves the way for high-performance CMOS-compatible NIR phototransistors using Si-Ge-Si heterostructures.

Ultrasensitive, Fast-Response, and Stretchable Temperature Microsensor Based on a Stable Encapsulated Organohydrogel Film for Wearable Applications.

Ionic conductive hydrogel-based temperature sensors have emerged as promising candidates due to their good stretchability and biocompatibility. However, the unsatisfactory sensitivity, sluggish response/recovery speed, and poor environmental stability limit their applications for accurate long-term health monitoring and robot perception, especially in extreme environments. To address these concerns, here, the stretchable temperature sensors based on a double-side elastomer-encapsulated thin-film organohydrogel (DETO) architecture are proposed with impressive performance. It is found that the water-polyol binary solvent, organohydrogel film, and sandwiched device structure play important roles in the temperature sensing performance. By modifying the composition of binary solvent and thicknesses of organohydrogel and elastomer films, the DETO microsensors realize a thickness of only 380 μm, unprecedented temperature sensitivity (37.96%/°C), fast response time (6.01 s) and recovery time (10.53 s), wide detection range (25-95.7 °C), and good stretchability (40% strain), which are superior to those of conventional hydrogel-based sensors. Furthermore, the device displays good environmental stability with negligible dehydration and prolonged operation duration. With these attributes, the wearable sensor is exploited for the real-time monitoring of various physiological signals such as human skin temperature and respiration patterns as well as temperature perception for robots.

A review of research on optical true time delay technology

Light controlled phased array has the advantages of fast response speed, compact system, diverse functions, and flexible control, and has been widely applied in many scientific and technological fields. Optical true delay technology (OTTD) is the most direct technical means to achieve phase delay of optical carrier signals, and it is also the most basic technical means to implement optical controlled beamforming systems. In order to fully understand the optical true delay technology, this article first elaborates on the principle of phased array antennas and the reasons for beam strabismus, and analyzes the impact of true delay on the performance of phased array radar. Then, the basic principle, technological progress, and related applications of optical true delay are introduced. Taking four common structures of optical true delay lines as examples,which are micro-ring resonant cavity array, grating ture time delay line, multi-path switchable optical ture time delay line（OTTDL）, and wavelength selective optical delay line, their performance in delay accuracy, adjustable delay range, and frequency bandwidth are compared. Finally, the current problems and future development trends of optical controlled beamforming technology were summarized.

A Strong and Tough Ion‐gel Enabled by Hierarchical Meshing and Ion Hybridizations Collaboration

AbstractIon‐gels, with inherent flexibility, tunable conductivity, and multi‐stimulus response, have attracted significant attention in flexible/wearable electronics. However, the design of ion‐gels that exhibit both strength and toughness is challenging. In this study, a novel ion‐gel design is proposed that mimics the hierarchical meshing structure of leaves in combination with ion hybridization. Polyacrylamide (PAM) is incorporated in TEMPO oxidized cellulose nanofibers (TOCNFs) clusters by in situ polymerization, generating a hydrogel with micro/nanoscale entangled networks. Replacement of water by a metal halide ionic liquid ([BMIm]ZnxCly) in the hydrogel resulted in the formation of an ion hybrid network with supramolecular interactions. The integration of the PAM/TOCNF polymer network with [BMIm]ZnxCly resulted in ion‐gels with high strength (5.9 MPa), toughness (22 MJ m−3), and enhanced elastic modulus (30 MPa) combined with non‐flammability, heat and cold resistance. While having fast responsiveness (36 ms) of sensing signal and stability of power supply even at 4000 cycle collisions. Stable signal output even at high (200 °C) /sub‐zero temperatures. The proposed strategy offers a new approach to the material design of flexible/wearable electronics.

Societal Impact of Test Automation: Reducing Human Error in Critical Systems

This article explores the profound societal impact of test automation across critical sectors such as healthcare, finance, transportation, and energy. It examines how automated testing processes significantly reduce human error, enhance system reliability, and improve service quality. The article presents compelling evidence from various studies and reports, demonstrating substantial improvements in medical diagnostics, financial fraud detection, transportation safety, and energy distribution efficiency. Beyond error reduction, the article discusses broader societal benefits, including enhanced accuracy in data processing, faster emergency response times, improved service quality, and strategic resource allocation. The article underscores the crucial role of test automation in addressing the challenges posed by increasingly complex technological systems and its far-reaching implications for public safety, economic stability, and overall quality of life.

GlutaR: AHigh-Performance Fluorescent Protein-Based Sensor for Spatiotemporal Monitoring of Glutamine Dynamics In Vivo.

Glutamine is the most abundant amino acid in human blood and muscle, and is integral to a wide variety of functions in cancer cells. However, the inability to monitor the subcellular distribution of glutamine in real-time has obscured understanding of glutamine metabolism under physiological and pathological conditions. Here, we report the development of a genetically encoded fluorescent sensor and demonstrate how this GlnBP-cpYFP fusion "GlutaR sensor" undergoes glutamine-induced conformational changes reflected in detectable fluorescence responses. Obtained after iterative screening of approximately 1,600 variants, GlutaR exhibits a ratiometric readout, fast response kinetics, and high responsivity, and we demonstrate its selectivity for monitoring glutamine fluctuations in multiple cell types. Additionally, using digitonin permeabilization of GlutaR HeLa cells, we generated a calibration curve and performedin situtitration to quantify free glutamine concentrations in subcellular compartments (cytosol, nucleus, mitochondria). Subsequently, we applied GlutaR to investigate how chemical and genetic inhibition of GS and GLS differentially alter glutamine levels in subcellular compartments. Finally, we demonstrate GlutaR's ability to monitor dynamic glutamine levels in muscle and liver tissues of diabetic micein vivo.These findings collectively demonstrate GlutaR as a versatile tool for the spatiotemporal characterization of glutamine metabolism in living cells and tissues.

MEMS-tunable topological bilayer metasurfaces for reconfigurable dual-state phase control

Tunable optical metasurfaces (MSs) have demonstrated exceptional capabilities in actively manipulating light fields. However, most existing tunable MSs are limited to controlling only one functionality. Here, by combining a MEMS mirror with a plasmonic bilayer MS (BMS), we develop an electrically driven MEMS-BMS platform enabling complete reflection phase transformation and switching between two encoded functionalities by actuating the MEMS mirror. This capability stems from different optical responses of each MS layer at distinct MEMS-BMS separations, due to evolving topological singularities in a defined parameter space. With this tunable topological MEMS-BMS platform, we demonstrate polarization-independent MEMS-BMS for reconfigurable diffraction gratings, achieving ∼25% efficiency, ∼0.75 contrast at 850-nm wavelength, and fast response (∼5µs). The MEMS-BMS arrangement for generating vortex beams with switchable topological charges of ±1 is also demonstrated, evidenced by distinct near- and far-field interferograms. Our work expands the scope of tunable MSs by exploiting dynamic topological phases in the MEMS-BMS arrangement, paving the way for multifunctional tunable meta-optics.

Design and Optimization of Electromagnetically Driven End-Effector

The excellent flexibility and plasticity of a flexible robotic hand enable it to grip objects of various shapes and sizes. Currently, most of the flexible robotic hand fingers on the market use pneumatic drives. However, the unstable power of the pneumatic drive can cause the object gripped by the finger to slip. Additionally, the pneumatic pump can create noise pollution. To address these issues, a flexible end-effector is designed using an electromagnetic coil as the power source. The electromagnetic-driven end-effector has high precision and fast response, without the need for external devices. It only requires controlling the current to control the magnetic force of the electromagnet, thereby controlling the degree of end-effector bending. The finite element simulation is used to analyze the impact of the structural parameters of the end-effector on bending degree. And the optimal basic parameters of the end-effector are determined. Under the same intensity of the current, when the bottom layer thickness is 5 mm, end-effector knuckle spacing is 4 mm, the maximum angle of end-effector bending is reached.

Integral Sliding Mode‐Composite Nonlinear Feedback Control Strategy for Microgrid Inverter Systems

To enhance the dynamic performance and robustness of the voltage control system of islanded microgrid inverters, a new control strategy combining integral sliding mode (ISM) control and composite nonlinear feedback (CNF) control is proposed. In ISM control, firstly, a new reaching law is designed to improve movement quality in the reaching phase by improving the power term and introducing the inverse tangent function. Then, to improve disturbance observation accuracy, a variable gain extended state observer is designed by improving the regulation mechanism of the state variables according to deviation control. Using the idea of variable damping, linear and nonlinear feedback are combined into CNF control. Specifically, linear feedback provides a small damping ratio for faster system response, while nonlinear feedback increases the damping ratio to improve steady‐state performance. Simulation results show that the integral sliding mode‐composite nonlinear feedback (ISM‐CNF) control strategy cam balances convergence speed and chattering better and achieves higher steady‐state accuracy than conventional strategies. Moreover, ISM‐CNF control has better robustness to reference voltage variations and disturbances caused by sudden load changes. Therefore, the ISM‐CNF control strategy can accomplish voltage control of islanded microgrid inverters quickly and steadily, effectively suppressing system disturbances and enhancing stability and power quality. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Optimized evaluation of the quality of sensor video internet of things (VIOT) by the integration of big data and artificial intelligence

AbstractThe application of sensor video internet of things technology to large-scale integrated work can significantly improve the working quality of employees. However, the degree of improvement in working quality is still difficult to measure in a systematic, intelligent, stable, and accurate manner. local optimization and adjustment after evaluation are still relatively challenging, To address these issues, the study proposes a method of optimizing the evaluation of sensor video quality through the integration of big data and AI techniques. A large-scale integrated distance education system in the field of education and training with a certain application basis is adopted as a case. Including big data and AI techniques such as integrated intelligent agent modules, recommendation algorithms, and transaction optimization algorithms, a new agent-oriented system design with fast response speed, strong scalability, convenient local optimization, and greater stability is achieved. According to the network topology structure of the distance education system in colleges and universities, this paper uses queuing theory to analyze the system performance of the system. The focus of this paper is the quantitative relationship between system communication intensity ρ, user arrival rate λ, system channel capacity n and system waiting delay, blocking probability, average queue length, system throughput and other important performance indicators. In teaching evaluation, the key factor that affects the quality of classroom teaching, that is, Developing a comprehensive system for evaluating classroom instruction is crucial. By incorporating student feedback, leveraging data mining techniques, and harnessing computer technology, a holistic framework for gathering, analyzing, and generating actionable insights on teaching performance is established. This approach makes the evaluation process more systematic and evidence-based, identifying 12 key elements that influence classroom education standards. In the experimental section, the student assessment data sets I1 and I2 exhibit experimental values (statistics) that significantly exceed the thresholds, with a minimum support of 0.32 and a confidence level of 0.61. Moreover, the Boolean matrix is divided into 90 points. The rule U1Ua ≥ U2 is identified as a subset of {U1U2Ua} within the large item set, signifying a strong association rule. These findings confirm the robustness of the artificial intelligence model proposed in this paper for video quality prediction. The optimized sensor video quality evaluation method not only meets a satisfactory confidence level and matching value but also demonstrates good reliability and relevance in the evaluation criteria.

Photoelectric Interaction and Photochromic Mechanism of K0.5Na0.5NbO3 Ceramics.

Rare-earth materials are widely used in the optical field due to their rich energy-level structures, and if endowed with photoelectric response characteristics, they are expected to be applied to photoelectric multifunctional devices. In this paper, Ho3+- and Yb3+-codoped K0.5Na0.5NbO3 (KNN) ferroelectric ceramics are synthesized, and the up-conversion and down-conversion luminescence intensity as well as the decay degree after photochromism can be controlled by the content of Yb3+. Strong green luminescence visible to the naked eye is realized, and the maximum luminescence decay is more than 50%. 2 s of color change fully indicates that the ceramic has the characteristics of fast response photochromic, under the most efficient wavelength of 405 nm by changing the irradiation wavelength. By incorporating data from thermal and electrical stimulation experiments, a comprehensive photochromic mechanism diagram is constructed, where the photochromic properties are predominantly governed by defects. It is believed that this work will provide a theoretical basis for the design of high-performance photochromic ceramics.

Wearable System Integrating Dual Piezoresistive and Photoplethysmography Sensors for Simultaneous Pulse Wave Monitoring.

Wearable, flexible piezoresistive pressure sensors have garnered substantial interest due to their diverse applications in fields such as electronic skin, robotic limbs, and cardiovascular monitoring. Among these applications, arterial full pulse waveform monitoring stands out as a critical area of research. The emergence of piezoresistive pressure sensors as a prominent tool for capturing pulse waveforms has led to extensive investigations. However, the lack of a linear response while achieving high sensitivity, limited compactness of signal collection systems, and scaling issues are a few potential causes of the gap between research advances and technology market readiness. Here, we present a scalable dual piezoresistive sensor that uses two complementary resistance-changing mechanisms to balance the trade-off between linear response and high sensitivity. This synergic design enables excellent sensitivity of 8.4 kPa-1 and near linear pressure response. It boasts a fast response time of 95/145 ms for loading and unloading at a pressure of 10.1 kPa. The durability tests, encompassing nearly 5000 two-stage compression cycles, validate the reliable performance of the sensor, even after prolonged use. Leveraging these performance metrics, we developed a high-resolution and compact signal collection device capable of accurately detecting the three distinct peaks associated with the full pulse waveform, including the systolic and reflected diastolic peaks. Moreover, the signal acquisition system incorporates a photoplethysmography sensor, which, when paired with the dual piezoresistive sensor, offers new insights into localized vascular health monitoring. The unique feature of our approach is its ability to perform localized vascular monitoring using multiple sensors placed on different veins. The simultaneous detection of forward- and backward-going pulse waves at the body extremities allows for a comprehensive evaluation of localized vascular health, which is not achievable with a single sensor. Finally, the comparison of wrist pulse waveforms between the compact signal collection device and a standard sourcemeter (such as the Keithley 2460) confirmed that the wearable sensing system is on par with conventional sourcemeters in terms of capturing the typical details of the full pulse waveform (i.e., systolic peak, dicrotic notch, and diastolic peak). This validation underscores the reliability and effectiveness of the developed wearable sensing system for practical and continuous pulse waveform monitoring.