Signal Acquisition Research Articles

Design and testing of a microwave Doppler-based yield estimation system for machine-picked seed cotton

Abstract Accurately obtaining crop yields is an important part of the precision agriculture technology system; with the increased mechanisation of cotton planting and harvesting, it has become particularly important to accurately obtain seed cotton yield data. In order to achieve accurate estimation of machine-picked seed cotton yield, this paper designs a machine-picked seed cotton yield estimation system based on microwave Doppler principle. Based on LabVIEW software, the signal acquisition circuit is designed; the power spectrum of the echo signal is estimated; the signal echo power is obtained; the relationship model between echo power and seed cotton mass is established by using multiple regression equations; and the reliability of the model is verified by using statistical methods. By adjusting the rotational speed of the fan, the wind speed at the inlet of the cotton pipeline was 10 m s−1, 15 m s−1 and 20 m s−1, and the yield estimation tests were carried out at the three wind speeds, and the data showed that the average absolute percentage errors of the estimation at the three wind speeds were 7.36%, 7.72% and 8.17%, and the mean squared errors were 2.699, 4.938 and 4.026, respectively. The test results show that the accuracy of seed cotton mass estimation under the three wind speeds basically meets the needs of the yield measurement system, and the systematic error of seed cotton yield estimation is the smallest when the wind speed is 10 m s−1.

Hyperspectral acquisition with ScanImage at the single pixel level: application to time domain coherent Raman imaging.

We present a comprehensive strategy and its practical implementation using the commercial ScanImage software platform to perform hyperspectral point scanning microscopy when a fast time-dependent signal varies at each pixel level. In the proposed acquisition scheme, the scan along the X-axis is slowed down while the data acquisition is maintained at a high pace to enable the rapid acquisition of the time-dependent signal at each pixel level. The ScanImage generated raw 2D images have a very asymmetric aspect ratio between X and Y, the X axis encoding both for space and time acquisition. The results are X-axis macro-pixel where the associated time-dependent signal is sampled to provide hyperspectral information. We exemplified the proposed hyperspectral scheme in the context of time-domain coherent Raman imaging, where a pump pulse impulsively excites molecular vibrations that are subsequently probed by a time-delayed probe pulse. In this case, the time-dependent signal is a fast acousto-optics delay line that can scan a delay of 4.5ps in 25μs at each pixel level. With this acquisition scheme, we demonstrate ultra-fast hyperspectral vibrational imaging in the low frequency range [10cm-1, 150 cm-1] over a 500 μm field of view (64 x 64 pixels) in 130ms (∼ 7.5 frames/s). The proposed acquisition scheme can be readily extended to other applications requiring the acquisition of a fast-evolving signal at each pixel level.

Foundation models for cardiovascular disease detection via biosignals from digital stethoscopes

Auscultation of the heart and the electrocardiogram (ECG) are two central components of the cardiac exam. Recent innovations of the stethoscope have enabled the simultaneous acquisition of a high-quality digital acoustic signal and ECG. We present foundation models trained on phonocardiogram (PCG) and ECG data collected from digital stethoscopes during routine clinical practice. We show that these foundation models that are pre-trained on large unlabeled datasets in a self-supervised manner can be fine-tuned for a variety of cardiovascular disease detection tasks. This is the first study that builds foundation models specifically for synchronously captured PCG and ECG data. Our approach is based on the recently developed masked autoencoder framework which we extend to handle multiple signals that are synchronously captured. This paradigm makes it possible to use large capacity models leading to superior performance even though the size of datasets with medical label annotations may be limited.

Comparison of Diffusion-weighted MRI using Singe-Shot Echo-planar Imaging (SS-EPI) and Split Acquisition of Fast Spin Echo Signal (SPLICE) Imaging, a non-EPI technique, in Tumors of the Head and Neck.

Diffusion-weighted imaging (DWI) using single-shot echo planar imaging (DW-EPI) is susceptible to distortions around air-filled cavities and dental fillings, typical for the head and neck area. Non-EPI, Split acquisition of fast spin echo signals for diffusion imaging (DWSPLICE) could reduce these distortions and enhance image quality, thereby potentially improving recurrence assessment in squamous cell carcinoma (SCC) of the head and neck region. This study evaluated whether DW-SPLICE is a viable alternative to DW-EPI through quantitative and qualitative analyses. The DW-SPLICE sequence was incorporated into the standard 3.0T head and neck MRI protocol with DW-EPI. Retrospective analysis was conducted on two subgroups: firstly benign or malignant lesions, and secondly post-treatment SCC recurrence. In both subgroups Image quality and distortion were scored by two independent radiologists, blinded for DW-technique, and evaluated using mixed-effect linear models. Lesion apparent diffusion coefficient (ADC) values were assessed with inter-class correlation (ICC) and Bland-Altman analyses. DWI's delineation geometric similarity to T1-weighted post-contrast (T1Wc) MRI was evaluated using the Dice Similarity Coefficient (DSC) before and after registration. Recurrence in post-treatment SCC scans was evaluated by the same two radiologists blinded for DW-technique. Recurrence detection rates were then compared between DW-SPLICE and DW-EPI using mixed logistic regression at six months and at one-year post-scan follow-up data. From August 2020 to January 2022, 55 benign or malignant lesion scans (55 patients) and 74 post-treatment SCC scans (66 patients) were analyzed. DW-SPLICE scored better on image quality and showed less overall distortion than DW-EPI (0.04<p<0.001). There was high ADC measurement reliability (ICC=0.93, p<0.001), though a proportional bias was also observed (β=0.11, p=0.03), indicating the bias increases as ADC values rise. DWSPLICE exhibited greater geometric similarity to T1Wc before registration (DSC 0.63 vs 0.47, p<0.001) and outperformed DW-EPI by more accurately identifying recurrences after one year (OR=0.96, p=0.05) but not after six months (OR=0.72, p=0.13). DW-SPLICE surpasses DW-EPI on image distortion and quality and improves diagnostic reliability for detecting recurrent or residual SCC on 3T MRI of the HN. Consistent use of one method for follow-up is advised, as ADC values are not completely interchangeable. Integrating DW-SPLICE can significantly improve tumor assessments in clinical practice. ANTs = Advanced Normalization Tools; DSC = Dice Similarity Coefficient; DW-EPI = Diffusion-weighted single-shot echo planar imaging; DW-MS-EPI = Diffusion-weighted multi-shot echo planar imaging; DW-SPLICE = Diffusion-weighted split acquisition of fast spin echo signals for diffusion imaging; DW-TSE = Diffusion-weighted Turbo Spin Echo; ICC = Intraclass Correlation Coefficient; ROC = Receiver Operating Characteristic; SCC = Squamous cell carcinoma; T1WIc = T1 weighted imaging with gadolinium contrast.

A ZnO-nanorod/PEDOT:PSS nanocomposite functionalized bridge-like membrane type nanomechanical sensing device for ultrasensitive blood lead detection

Lead (Pb) ion detection poses a critical problem, particularly in environmental monitoring, industrial operations, and public health, especially for young children and expecting women. Determining lead levels in blood early on is essential to minimizing the long-term consequences of lead exposure. Several sophisticated detection instruments, such as mass spectrometers which perform with high sensitivity, specificity and accuracy, but require a lab-based setting, multi-step sample preparation, expensive payment and professional operation. It is evident that a highly sensitive, portable, low-cost, quick sample-to-result, blood lead detection device that can be tested at the point-of-care is necessary. Consequently, we developed a unique ZnO/PEDOT:PSS nanocomposite layer integrated with a CMOS MEMS-based bridge-like membrane-type (BM) nanomechanical sensor for detecting lead levels in blood. PEDOT:PSS was combined with ZnO nanorods to increase lead ion binding. The sensor responds seven times better to lead ions using nanorods in the detecting layer. A linear resistance change rate response was found from 0.005 to 10 ppm, with the limit of detection (LOD) of 0.12 ppb. Similarly, our BM nanomechanical sensor can correctly assess Pb2+ in human serum with recovery rates of 86.25–150 %. Measurements of human blood samples from patients with varying lead ion concentrations validated by the standard AAS show a good linear connection with the BM nanomechanical sensors' concentration, with a regression coefficient of 0.92. This describes the first micromachined nanoachanical sensing system for detection of Pb2+ in only 5 μL of human serum sample. The device achieves a time-to-result of less than 10 min. The system is designed to be very sensitive and offers affordable, disposable sensing chips together with a portable signal acquisition platform.

JQR-Net: joint quantitative reconstruction network for dual-modal photoacoustic and optical coherence tomography imaging.

Photoacoustic (PA) and optical coherence tomography (OCT) imaging are complementary imaging modalities with distinct contrast mechanisms, penetration depths, and spatial resolutions. Integrating these two modalities into a dual-modal PA-OCT imaging system enables the simultaneous acquisition of multimodal signals within a single scan. This integration supports quantitative reconstruction of tissue characteristics, offering a more precise and comprehensive analysis than single-modal imaging. In this paper, we propose a deep learning approach for joint quantitative reconstruction in dual-modal PA-OCT imaging, potentially advancing imaging capabilities for detailed tissue examination and disease analysis. We develop a deep neural network that performs end-to-end mapping from photoacoustically induced pressure signals and backscattered OCT signals to parametric images representing the spatial distribution of optical absorption and attenuation coefficients. This network provides both morphological and functional insights. To the best of our knowledge, this is the first deep learning model designed to simultaneously reconstruct multiple tissue characteristic parameters from dual-modal imaging signals, facilitating in-depth tissue characterization.

Analytical model of an active noise control system for performance analysis of adaptive algorithms in HVAC system

In real-world acoustic scenarios related to the problem with the low-frequency noise such as HVAC systems, the signal is a stochastic, non-stationary and time-varying process where the performance of the active noise control technique mainly depends on the characteristics of the adaptive filtering techniques. In this paper, a method for analytical modeling of active noise control of signals in MATLAB/Simulink is presented, in which the acquisition of audio signals is provided by using a National Instruments control and acquisition module. For the realization of the active noise control, an adaptive filtering system model for signal analysis has been developed, using three adaptive algorithms (LMS,RLS and NLMS). The analysis of the characteristics of each of the three investigated adaptive algorithms is performed according to four parameters: Mean Square Error (MSE), convergence speed, stability and robustness. The conclusions are presented through a comparative analysis of the results obtained from the implemented methodology. The individual characteristics and parameters that are observed for correct adaptation and optimal results of each of the adaptive filters are explained in detail.

How artificial intelligence applied to sound recognition can help gas leak detection in acoustically complex environments?

From production to storage and transport, detecting leaks of flammable gases is a major challenge, particularly in terms of occupational safety and health, and environmental considerations. The emergence of hydrogen as an energy source makes acoustic-based technologies a key element in this value chain. For these reasons, Metravib Engineering, in collaboration with the R&amp;D Safety Project of TotalEnergies One Tech, has been developing since 2019 the AGLED system, for the early detection, localization and classification of these leaks. The system performs acoustic detection in the audible frequency range, using antennas consisting of 4 microphones and physics-based artificial intelligence. Over 5,600 signals were recorded on a site fairly representative of gas production and storage facilities in order to build a robust learning database. Several algorithms were implemented to limit the number of false detections delivered by the system. A relearning strategy was developed to handle unexpected disturbances relative to specific noise environment. This paper describes the overall architecture of the system, from signal acquisition to gas leak detection and 3D localization. It delves into the relearning algorithm and its performance during two full-scale industrial pilots of TotalEnergies - La Mède (France) and Tempa Rossa (Italy) - that included nitrogen controlled leaks tests campaigns.

Development and evaluation of a novel force myography (FMG) based human-machine interface for transradial prosthetics: A comparative study with electromyography (EMG) techniques

Human-Machine Interfaces (HMIs) must bridge human skills with machine control, notably in active prosthesis. Electromyography (EMG) is the most popular prosthesis actuation method. However, researchers need new HMIs for active prosthesis development. This research introduces Forcemyography (FMG) based HMI using force- sensitive resistors (FSRs) to capture myoelectric data. A particularly constructed case with two integral parts—an internal and an exterior component—is FMG. This FMG enclosure adapts effortlessly to the user's limb and shape for best performance and comfort. FMG and sEMG were compared in the experimental evaluation. Stability, forecast accuracy, hair and sweat resistance, wearability, and cost-effectiveness were examined. FMG signals outperformed sEMG signals in this comprehensive examination. FMG outperformed sEMG in stability and prediction accuracy. FMG produced outstanding results right from capture without post-processing, a unique advantage. The specialised enclosure protected FSRs from outside interference and delivered precise signals. The Forcemyography (FMG)-based HMI is a breakthrough in active prosthesis technology. Its versatility, consistent signal acquisition, and user-centric design promise improved human-machine interactions. FMG leads the way to smooth, intuitive, and efficient humanmachine interface.

A selective frequency damping and Janus adhesive hydrogel as bioelectronic interfaces for clinical trials

Maintaining stillness is essential for accurate bioelectrical signal acquisition, but dynamic noise from breathing remains unavoidable. Isotropic adhesives are often used as bioelectronic interfaces to ensure signal fidelity, but they can leave irreversible residues, compromising device accuracy. We propose a hydrogel with selective frequency damping and asymmetric adhesion as a bioelectronic interface. This hydrogel mitigates dynamic noise from breathing, with a damping effect in the breathing frequency range 60 times greater than at other frequencies. It also exhibits an asymmetric adhesion difference of up to 537 times, preventing residues. By homogenizing ion distribution, extending Debye length, and densifying the electric field, the hydrogel ensures stable signal transmission over 10,000 cycles. Additionally, it can non-invasively diagnose otitis media with higher sensitivity than invasive probes and has been effective in clinical polysomnography monitoring, aiding in the diagnosis of obstructive sleep apnea.

Implementation of Wearable Wireless Chest Patch Monitoring Terminal

A wearable wireless chest patch monitoring terminal is designed to realize the acquisition, processing, and wireless transmission of ECG, respiration, and body temperature signals. The analog front-end ADS1292R, which integrates respiratory impedance and ECG front-end, is utilized to collect human ECG and respiratory signals. The body temperature is collected using a low-power, high-precision digital temperature sensor MAX30208. A filter algorithm for signal processing and wireless transmission is designed through a low-power nRF52840 Bluetooth SoC with an Arm Cortex-M4F kernel. The experimental results show that the designed monitoring terminal can monitor the ECG, respiration, and body temperature parameters of the human body in real-time and send the monitoring results via Bluetooth, with a continuous working time of more than 13 hours. The wearable wireless chest patch monitoring terminal features good portability, long standby time, and high measurement accuracy, and it has promising application prospects in the fields of family health monitoring, mobile medical treatment, and smart healthcare.

X-ray ghost imaging with a specially developed beam splitter.

X-ray ghost imaging with a crystal beam splitter has advantages in highly efficient imaging due to the simultaneous acquisition of signals from both the object beam and reference beam. However, beam splitting with a large field of view, uniform distribution and high correlation has been a great challenge up to now. Therefore, a dedicated beam splitter has been developed by optimizing the optical layout of a synchrotron radiation beamline and the fabrication process of a Laue crystal. A large field of view, consistent size, uniform intensity distribution and high correlation were obtained simultaneously for the two split beams. Modulated by a piece of copper foam upstream of the splitter, a correlation of 92% between the speckle fields of the object and reference beam and a Glauber function of 1.25 were achieved. Taking advantage of synthetic aperture X-ray ghost imaging (SAXGI), a circuit board of size 880 × 330 pixels was successfully imaged with high fidelity. In addition, even though 16 measurements corresponding to a sampling rate of 1% in SAXGI were used for image reconstruction, the skeleton structure of the circuit board can still be determined. In conclusion, the specially developed beam splitter is applicable for the efficient implementation of X-ray ghost imaging.

Research on Spatial Localization Method of Magnetic Nanoparticle Samples Based on Second Harmonic Waves

Existing magnetic tracer detection systems primarily rely on fundamental wave signal acquisition using non-differential sensor configurations. These sensors are highly susceptible to external interference and lack tomographic localization capabilities, hindering their clinical application. To address these limitations, this paper presents a novel method for achieving the deep spatial localization of tracers. The method exploits second harmonic signal detection at non-zero field points. By considering the combined nonlinear characteristics of the coil’s axial spatial magnetic field distribution and the Langevin function, a correlation model linking the signal peak and bias field is established. This model enables the determination of the tracer’s precise spatial location. Building on this framework, a handheld device for localizing magnetic nanoparticle tracers was developed. The device harnesses the second harmonic response generated by coupling an AC excitation field with a DC bias field. Our findings demonstrate that under conditions of reduced coil turns and weak excitation fields, the DC bias field exhibits exclusive dependence on the axial distance of the detection point, independent of particle concentration. This implies that the saturated DC bias field corresponding to the second harmonic signal can be used to determine the magnetic nanoparticle sample detection depth. The experimental results validated the method’s high accuracy, with axial detection distance and concentration reduction errors of only 4.8% and 4.1%, respectively. This research paves the way for handheld probes capable of tomographic tracer detection, offering a novel approach for advancing magnetically sensitive biomedical detection technologies.

Nucleobase‐Driven Wearable Ionogel Electronics for Long‐Term Human Motion Detection and Electrophysiological Signal Monitoring

AbstractIonogels are considered as ideal candidates for constructing flexible electronics due to their superior electrical conductivity, flexibility, high thermal and electrochemical stability. However, it remains a great challenge to simultaneously achieve high sensitivity, repeated adhesion, good self‐healing, and biocompatibility through a straightforward strategy. Herein, inspired by nucleobase‐tackified strategy, a multifunctional adhesive ionogel is developed through one‐step radical polymerization of acrylated adenine/uracil (Aa/Ua) and acrylic acid (AA) monomers in sodium caseinate (SC) stabilized liquid metal dispersions. As a soft conductive filler, the incorporating of liquid metal not only improves the electrical conductivity, but also enhances the mechanical strength, satisfying the stretchable sensing application. The large amount of noncovalent interactions (hydrogen bonding, metal coordination, and ion‐dipole interactions) within the networks enable the ionogels to possess excellent stretchability, skin‐like softness, good self‐healing, and strong adhesion. Based on these desirable characteristics, the ionogel is suitable for wearable strain sensors to precisely detect diverse human movements under extreme environments. Moreover, the seamless adhesion with human skin allows the ionogel to function as bioelectrode patch for long‐term and high‐quality electrophysiological signal acquisition. This research provides a promising strategy for designing ionogels with tailored functionalities for wearable electronics that satisfy diverse application requirements.

Self-Healing and Shear-Stiffening Electrodes for Wearable Biopotential Sensing and Gesture Recognition.

The achievement of flexible skin electrodes for dynamic monitoring of biopotential is one of the challenging issues in flexible electronics due to the interference of large acceleration and heavy sweat that influence the stability of skin-electrode interfaces. This work presents materials and techniques to achieve self-healing and shear-stiffening electrodes and an associated flexible system that can be used for multichannel biopotential measurement on the skin. The electrode that is based on a composite of silver (Ag) flakes, Ag nanowires, and polyborosiloxane offers an electrical conductivity of 9.71 × 104 S/m and a rheological characteristic that ensures stable and fully conformal contact with skin and easy removal under different shear rates. The electrode can maintain its conductivity even after being stretched by more than 60% and becomes self-healed after mechanical damage. The combination of the electrodes with a screen-printed multichannel flexible sensor allows stable monitoring of both static and dynamic electromyography signals, leading to the acquisition of high-quality multilead biopotential signals that can be readily extracted to yield gesture recognition results with over 97.42% accuracy. The conductive self-healing materials and flexible sensors may be utilized in various daily biopotential sensing applications, allowing highly stable dynamic measurement to facilitate artificial intelligence-enabled health condition diagnosis and human-computer interface.

An efficient approach for EMG controlled pattern recognition system based on MUAP identification and segregation

An Electromyography (EMG) based pattern recognition system constitutes various steps of signal processing and control engineering from signal acquisition to real-time control. Efficient control of external devices largely depends on the signal processing steps executed before the final output. This work presents a new approach to signal processing using Motor Unit Action Potential (MUAP) based signal decomposition and segmentation. An MUAP is a neurological response during muscle contraction. Due to the higher contact area of surface electrodes, MUAPs from multiple muscles are captured. An MUAP generated from a single muscle usually has identical waveshapes and similar discharging rates and usually lasts for 8–15 ms. These are known as primary MUAPs. The proposed algorithm identifies and uses the primary observed MUAPs for feature extraction and classification. Firstly, noise signals are eliminated by a determined noise margin, which also separates the active muscle movement signals. Next, a novel MUAP identification algorithm is implemented to detect the MUAP trains. Then, identified primary MUAPs are used to make segments with variable widths to extract feature vectors. Based on the correlation score of all the primary MUAPs, the segmentation is performed, which results in segmentation width varying from 110–200 ms. The achieved segmentation width is lesser than the conventional overlapping and non-overlapping methods — the proposed approach results in a 20 to 50% reduction in the segmentation width. Four different classifiers are tested during the machine learning stage to investigate the performance of the proposed approach. The obtained feature sets are then used to train the Linear Discriminant Analysis (LDA), K-Nearest Neighbor (kNN), Decision Tree (DT), and Random Forest (RF) classifiers. The classifiers are tested with precision, recall, F1 score, and accuracy. The kNN and DT classifiers performed better than the LDA and RF classifiers. The maximum precision and recall are 100% while the maximum achieved accuracy is 98.56%. The comparative results show higher accuracy even at lower segmentation widths than the conventional constant window scheme. The kNN and DT classifiers provide a 5% to 15% increment in accuracy compared to the constant window segmentation-based approach.

Design and Development of Miniature Measuring Instrument for Parachute Cords Dynamic Load for Stepless Parachute Opening.

Spacecraft recovery technology is crucial in the field of aerospace, in which the parachute plays a key role in slowing down the descent speed of the spacecraft and realizing a smooth landing. In order to construct a dynamically adjustable parachute deployment strategy, it is necessary to measure the parachute dynamic load accurately in real-time. However, the existing sensor measurement scheme makes it difficult to meet the measurement requirements due to its large structure and complex wiring. In order to meet the current demand for real-time measurement of parachute cords dynamic load, a miniature measuring instrument is designed. According to the function and technical requirements of the miniature measuring instrument, the hardware modules of the acquisition system are selected and designed, and the integration debugging and performance optimization of the microcontroller module, A/D sampling module, signal acquisition circuit, and power supply module are carried out. The software of the parachute cords tension acquisition system based on the miniature measuring instrument is developed. The Load Cell is modeled by using SolidWorks 2022 and statically analyzed by using Ansys 2022 R1 Workbench finite element analysis software. Then the final structure of the Load Cell and the pasting position of the strain gauge are determined through the results analysis as well as experimental verification. The hardware module of the signal acquisition system for the miniature measuring instrument is then encapsulated. The force value of the miniature measuring instrument is calibrated and tested many times by using the microcomputer-controlled electronic universal testing machine. The experimental results show that the designed miniature measuring instrument has accurate data, strong stability, and good real-time performance, which meets the demand for real-time accurate measurement of miniature measuring instruments, and can provide reliable data for parachute cords parameter validation and stepless unfolding design.

A Cumulant-Based Method for Acquiring GNSS Signals.

Global Navigation Satellite Systems (GNSS) provide positioning, velocity, and time services for civilian applications. A critical step in the positioning process is the acquisition of visible satellites in the sky. Modern GNSS systems, such as Galileo-developed and maintained by the European Union-utilize a new modulation technique known as Binary Offset Carrier (BOC). However, BOC signals introduce multiple side-peaks in their autocorrelation function, which can lead to significant errors during the acquisition process. In this paper, we propose a novel acquisition method based on higher-order cumulants that effectively eliminates these side-peaks. This method is capable of simultaneously acquiring both conventional ranging signals, such as GPS C/A code, and BOC-modulated signals. The effectiveness of the proposed method is demonstrated through the acquisition of simulated signals, with a comparison to traditional methods. Additionally, we apply the proposed method to real satellite signals to further validate its performance. Our results show that the proposed method successfully suppresses side-peaks, improves acquisition accuracy in weak signal environments, and demonstrates potential for indoor GNSS applications. The study concludes that while the method may increase computational load, its performance in challenging conditions makes it a promising approach for future GNSS receiver designs.

Graphene-Doped Thermoplastic Polyurethane Nanocomposite Film-Based Triboelectric Nanogenerator for Self-Powered Sport Sensor.

Triboelectric nanogenerators (TENGs), as novel electronic devices for converting mechanical energy into electrical energy, are better suited as signal-testing sensors or as components within larger wearable Internet of Things (IoT) or Artificial Intelligence (AI) systems, where they handle small-device power supply and signal acquisition. Consequently, TENGs hold promising applications in self-powered sensor technology. As global energy supplies become increasingly tight, research into self-powered sensors has become critical. This study presents a self-powered sport sensor system utilizing a triboelectric nanogenerator (TENG), which incorporates a thermoplastic polyurethane (TPU) film doped with graphene and polytetrafluoroethylene (PTFE) as friction materials. The graphene-doped TPU nanocomposite film-based TENG (GT-TENG) demonstrates excellent working durability. Furthermore, the GT-TENG not only consistently powers an LED but also supplies energy to a sports timer and an electronic watch. It serves additionally as a self-powered sensor for monitoring human movement. The design of this self-powered motion sensor system effectively harnesses human kinetic energy, integrating it seamlessly with sport sensing capabilities.

Physical extraction of antigen and information

To respond and adapt, cells use surface receptors to sense environmental cues. While biochemical signal processing inside the cell is studied in depth, less is known about how physical processes during cell-cell contact impact signal acquisition. New experiments found that fast-evolving immune B cells in germinal centers (GCs) apply force to acquire antigen clusters prior to internalization, suggesting adaptive benefits of physical information extraction. We present a theory of stochastic antigen transfer and show that maximizing information gain via physical extraction can explain the dramatic phenotypic transition from naive to GC B cells-attenuated receptor signaling, enhanced force usage, and decentralized contact architecture. Our model suggests that binding-lifetime measurement and physical extraction serve as complementary modes of antigen recognition, greatly extending the dynamic range of affinity discrimination when combined. This physical-information framework further predicts that the optimal size of receptor clusters decreases as affinity improves, rationalizing the use of a multifocal synaptic pattern seen in GC B cells. By linking extraction dynamics to selection fidelity via discriminatory performance, we propose that cells may physically enhance information acquisition to sustain adaptive evolution.