Output Signal Research Articles

Synthetic transmembrane DNA receptors enable engineered sensing and actuation

In living organisms, cells synergistically couple cascade reaction pathways to achieve inter- and intracellular signal transduction by transmembrane protein receptors. The construction and assembly of synthetic receptor analogs that can mimic such biological processes is a central goal of synthetic biochemistry and bionanotechnology to endow receptors with user-defined signal transduction effects. However, designing artificial transmembrane receptors with the desired input, output, and performance parameters are challenging. Here we show that the dimerization of synthetic transmembrane DNA receptors executes a systematically engineered sensing and actuation cascade in response to external molecular signals. The synthetic DNA receptors are composed of three parts, including an extracellular signal reception part, a lipophilic transmembrane anchoring part, and an intracellular signal output part. Upon the input of external signals, the DNA receptors can form dimers on the cell surface triggered by configuration changes, leading to a series of downstream cascade events including communication between donor and recipient cells, gene transcription regulation, protein level control, and cell apoptosis. We believe this work establishes a flexible cell surface engineering strategy that is broadly applicable to implement sophisticated biological functions.

CPG-based neural control of peristaltic planar locomotion in an earthworm-like robot: evaluation of nonlinear oscillators.

Earthworm-like robots have excellent locomotion capability in confined environments. Central Pattern Generator (CPG) based controllers utilize the dynamics of coupled nonlinear oscillators to spontaneously generate actuation signals for all segments, which offer significant merits over conventional locomotion control strategies. Thre are a number of oscillators that can be exploited for CPG control, while their performance in controlling peristaltic locomotion has not been systematically evaluated. To advance the state of the art, this study comprehensively evaluates the performance of four widely used nonlinear oscillators-Hopf, Van der Pol, Matsuoka, and Kuramoto-in controlling the planar locomotion of metameric earthworm-like robots. Specifically, the amplitude and phase characteristics of the continuous control signals used by the robot for achieving rectilinear, sidewinding, and arcuate locomotion are first summarized. On this basis, the sufficient parametric conditions for the four oscillator networks to generate the corresponding control signals are derived. Using a six-segment earthworm-like robot prototype as a platform, experiments confirm that the signals output by these oscillator networks can effectively control the robot to achieve the specified planar motion. Furthermore, the effects of the output signal waveforms of different oscillator networks on locomotion trajectories and performance metrics, as well as the effects of transient dynamics on the smoothness of gait transitions when the parameters are varied, are analyzed. The results demonstrate that their applicability varies in terms of locomotion efficiency, trajectory modulation, and smooth gait transitions. The Matsuoka oscillator lacks explicit rules for parameter modulation, the Van der Pol oscillator is advantageous in enhancing the average speed and turning efficiency, and the Hopf and Kuramoto oscillators are advantageous in terms of smooth gait transition. These findings provide valuable insights into the selection of appropriate oscillators in CPG-based controllers and lay the foundation for future CPG-based adaptive control of earthworm-like robots in complex environments.

High-Resistance Grounding Fault Location in High-Voltage Direct Current Transmission Systems Based on Deep Residual Shrinkage Network

Due to the precision limitations of traditional fault location methods in identifying grounding faults in High-Voltage Direct Current (HVDC) transmission systems and considering the high occurrence probability of high-resistance grounding faults in practical engineering scenarios coupled with the sampling accuracy constraints of actual equipment, this article introduces a novel approach for high-resistance grounding fault location in HVDC transmission lines. This method integrates Variational Mode Decomposition (VMD) and Deep Residual Shrinkage Network (DRSN). Initially, VMD is employed to decompose double-ended high-resistance grounding fault signals, extracting the corresponding Intrinsic Mode Functions (IMF). These IMF signals are then preprocessed to construct the input data for the DRSN model. Upon training, the model outputs the precise fault location. To validate the effectiveness of the proposed method, a ±800 kV bipolar HVDC transmission system model is established using PSCAD/EMTDC version 4.6.2 software for simulating high-resistance grounding faults. The sampling accuracy of the model’s output signals is set to 10 kHz, aligning closely with actual engineering equipment specifications. Comprehensive simulation experiments and anti-interference analyses are conducted on the DRSN model. The results demonstrate that the fault location method based on the DRSN exhibits high accuracy in locating high-resistance grounding faults, with a maximum error of less than 1.5 km, even when considering factors such as engineering sampling frequency, fault types, and signal noise.

Study and circuit design of stochastic resonance system based on memristor chaos induction

Abstract Memristor chaotic research has become a hotspot in the academic world, however, there are few explorations combining memristor and stochastic resonance, and the correlation research between chaos and stochastic resonance is still in the preliminary stage. In this paper, we focus on the stochastic resonance induced by memristor chaos, which enhances the dynamics of chaotic systems through the introduction of memristor and induces the memristor stochastic resonance under certain conditions. Firstly, the memristor chaos model is constructed, and the memristor stochastic resonance model is constructed by adjusting the parameters of the memristor chaos model. Secondly, the combination of dynamics analysis and experimental verification is used to analyze the memristor stochastic resonance and to investigate the trend of the output signal of the system under different amplitudes of the input signal. Finally, the practicality and reliability of the constructed model are further verified through the design and testing of the analogue circuit, which provides a strong support for the practical application of the memristor chaos induced stochastic resonance model.

Dual-Network MXene/Polyurethane Composite Foams for Both Stretchable and Compressible Electromagnetic Interference Shielding and Strain Sensors.

Miniaturized and wearable electronic products require electromagnetic interference shielding (EMI) materials with sensing functions to cope with complex application situations. Herein, an effective dual-network structure is designed to fabricate two-dimensional transition metal carbides and nitrides/bacterial cellulose-thermoplastic polyurethane (MXene/BC-TPU) foams with intriguing EMI shielding property and piezoelectric sensing ability under both compressive and tensile strains. Anisotropic MXene/BC aerogels, as conductive networks, with impressive conductivity (1912 S m-1), ultrahigh EMI shielding effectiveness (SE) of 86 dB, and absolute SE up to 63,608 dB cm2 g-1 can be built by directional freezing. Interpenetrated neuro-like TPU network is embedded into the MXene/BC aerogels by controllable coagulation to provide elasticity. Inheriting the elastic TPU network, the light weight MXene/BC-TPU foams show superb elasticity and remarkable fatigue resistance suffering both compressive and tensile strains with a wide strain range (-80-80%). Meanwhile, benefiting from the separated conductive MXene/BC network and the elastic TPU network, the MXene/BC-TPU foams display an outstanding EMI SE (76 dB) with an EMI SE retention of 86.8% after 5000 compression-release cycles and an EMI SE retention of 69.7% after 100 stretch-release cycles, respectively. Furthermore, the MXene/BC-TPU foams reveal stable resistance signal output as piezoresistive sensors in a wide strain range of -80-80%. The highly conductive, stretchable, and compressible MXene/BC-TPU foams are suitable as EMI shielding and motion monitoring materials for wearable electronics.

A Neural Device Inspired by Neuronal Oscillatory Activity with Intrinsic Perception and Decision-Making.

Bionic neural devices often feature complex structures with multiple interfaces, requiring extensive post-processing. In this paper, a neural device with intrinsic perception and decision-making (NDIPD), inspired by neuronal oscillatory activity is introduced. The device utilizes alternating signals generated by coupling the human body with the power-frequency electromagnetic field as both a signal source and energy source, mimicking neuronal oscillatory activity. The peaks and valleys of the alternating signal are differentially modulated to replicate the baseline shift process in neuronal oscillatory activity. By comparing the amplitude of the peaks and valleys in the NDIPD's electrical output signal, the device achieves intrinsic perception and decision-making regarding the location of mechanical stimulation. This is accomplished using a single interface, which reduces data transmission, simplifies functionality, and eliminates the need for an external power supply. The NDIPD demonstrates a low-pressure detection limit (<0.02 N), fast response time (<20ms), and exceptional stability (>200000 cycles). It shows great potential for applications such as game control, UAV navigation, and virtual vehicle driving. The innovative energy supply method and sensing mechanism are expected to open new avenues in the development of bionic neural devices.

Elastic Nanoparticle‐Reinforced, Conductive Structural Color Hydrogel With Super Stretchability, Self‐Adhesion, Self‐Healing as Electrical/Optical Dual‐Responsive Visual Electronic Skins

ABSTRACTDeveloping smart hydrogel with excellent physicochemical properties and multiple signal output capability for interactively electronic skin still remains challenging. Here, conductive structural color hydrogels with desirable physicochemical properties (including high stretchability and robustness, self‐adhesion and self‐healing) were developed to provide synchronous electronic and visual color signals for e‐skins. Highly charged elastic nanoparticles were elaborately used as building units for structural color and the hydrogel were prepared by the self‐assembly of the nanoparticle to form a non‐close‐packed array in a mixture comprised of acrylamide, silkworm silk fiber proteins (SF), reduced graphene oxide (rGO) and then photopolymerization. Benefiting from the improved interfacial compatibility between flexible hydrogel network and elastic nanoparticle, covalent cross‐linking network structure and synergistic multiple non‐covalent bonding interactions, the hydrogel exhibits extraordinary mechanical properties, excellent self‐adhesion to diverse substrates and self‐healing at room temperature. In addition, the hydrogel exhibited sensitive resistance changes and synchronous structural color changes under strain. As a proof‐to‐concept, the hydrogel displayed superior capability for the color‐response and the electrical signal response of various human motions, the spatial distribution of external mechanical stimuli as well as identification of different external stimuli, indicating promising applications in the fields of interactive visual electronic skin, wearable devices, and human–machine interfaces.

Advancements in genetic circuits as part of intelligent biotherapy for the treatment of bladder cancer: A review

Background: Bladder cancer poses a significant threat to human health. In recent years, genetic circuit therapy has emerged as a novel alternative for precision tumor treatment, demonstrating promising potential for clinical application. Compared to traditional drugs, genetic circuits &amp;ndash; typically carried by plasmids &amp;ndash; offer advantages such as modularity, druggability, and shorter drug development cycles. These circuits can identify multiple molecular signals from tumors and integrate them through logic gates to specifically target tumor cells. Furthermore, by assembling effector modules, they can induce specific forms of cell death in tumor cells or alter malignant phenotypes, thereby reshaping the immune microenvironment to produce efficient, durable, and controllable antitumor effects. The urinary system serves as an ideal model for this new therapy due to its accessibility from the outside. Several genetic circuits have already been validated for the treatment of bladder cancer. This review outlined the effectiveness and potential value of genetic circuits in bladder cancer therapy. Objective: This review focused on the design principles of genetic circuits, their ability to recognize and convert signals, their therapeutic signal output, and the associated delivery vehicles. We also discussed the challenges and future prospects of genetic circuits as a novel form of &amp;ldquo;intelligent biotherapy.&amp;rdquo; Conclusion: The gene circuit can identify multiple signals, processing complex information, and generating multiple effects, thus providing a new approach for personalized treatment of tumors.

Combined Detection of Serum Brain-Derived Neurotrophic Factor and Interleukin-6 for Evaluating Therapeutic Efficacy in Major Depressive Disorder.

The assessment of major depressive disorder (MDD) treatment efficacy typically relies on clinician-rated scales of depressive symptoms with a notable absence of predictive biomarkers. In this study, a concentration ratio of serum brain-derived neurotrophic factor (BDNF) to interleukin-6 (IL-6) was identified as a promising predictive biomarker. To overcome the significant disparity in blood levels between BDNF (ng·mL-1 range) and IL-6 (pg·mL-1 range), a dual-pathway signal amplification method involving small carbon dots (Fe-CDs and Pb-CDs) was employed. Fe-CDs exhibited a nanozyme-like behavior, effectively enhancing the signal through solution reactions, rendering it ideal for IL-6 detection at low concentrations. Meanwhile, Pb-CDs, laden with numerous signal molecules, amplified signals via the surface pathway and were suitable for BDNF detection. This dual signal output approach met the distinct sensitivity needs for quantifying IL-6 and BDNF, achieving detection limits of 0.1 pg·mL-1 and 0.02 ng·mL-1, respectively. Analysis of clinical samples showed that the concentration ratio of BDNF to IL-6 not only effectively differentiates MDD patients from healthy controls but also strongly correlates with individual treatment responses, affirming its value as a biomarker for MDD diagnosis and treatment efficacy evaluation.

Тестирование эффективности гибридной методики шумоподавления в речевом сигнале для системы видеоконференций

The issue of audio signal quality during video conferences is considered. The effect of noise on the quality and intelligibility of the speech signal is described. The analysis of the noise reduction process in the audio signal in real time is carried out. The main approaches to solving the problem of noise reduction in real time are highlighted, namely the approach with recognition and elimination of noise and the approach with voice recognition and elimination of sounds different from the speech signal. An algorithm for intelligent noise reduction using artificial intelligence methods has been developed, characterized by the presence of mechanisms for adjusting the “intensity” of the noise reduction effect on the speech signal, as well as adjusting the “sensitivity” to noise, allowing to influence the efficiency of the algorithm for specific operating conditions without the need to retrain the neural network model of the algorithm. Software has been developed that implements a hybrid noise reduction technique in the form of a module that was implemented in a previously developed software package for organizing and conducting video conferences. Three typical scenarios have been identified, for each of which a reference (noiseless) speech signal has been selected, as well as a set of noises for these scenarios for conducting two types of tests in order to test the effectiveness of the noise reduction module in various conditions of its application. Experiments were carried out with a test of the effectiveness of noise reduction of noise segments during speech pauses using the methods of RMS signal volume and visual spectrogram analysis with calculation of the percentage of the number of samples of rhythms in the output signal. Experiments were carried out with a test of the effectiveness of noise reduction of a noisy speech signal using objective methods for evaluating the quality of the speech signal ViSQOL and NISQA. The test results are presented in the form of tables.

An Envelope-to-Cycle Difference Compensation Method for eLoran Signals in Seawater Based on a Variable Step Size Least Mean Square Algorithm

The dispersion effect of seawater can cause the envelop distortion of underwater eLoran signals, which causes the envelope-to-cycle difference (ECD) to exceed the standard range. Furthermore, it results in incorrect cycle identification and significant positioning errors. However, few studies have focused on the distortion caused by the dispersion effect. In this study, we propose an accurate underwater eLoran ECD compensation method based on a variable step size least mean square (VSS-LMS) algorithm. First, a systematic modeling approach was employed to investigate the impact of dispersion effects on Loran signals. Second, the VSS-LMS algorithm was introduced to update the filter weight vector in response to discrepancies in the input signal. Finally, the input signal was subjected to an adaptive transversal filtering process, resulting in an output signal that adhered to the specifications of the ECD standard. The efficacy and superiority of the proposed algorithm were demonstrated by experimentation and simulation. When the depth of seawater exceeds 2 m, the ECD value of the original eLoran signal exceeds the standard range, precluding the possibility of cycle identification. However, when the depth of seawater reaches 4 m, the ECD of the signal compensated by the proposed algorithm adaptively compensates for the normal range, thereby enabling the accurate recognition of cycles.

Fundamental limits to the generation of highly displaced bright squeezed light using linear optics and parametric amplifiers

High-quality squeezed light is an important resource for a variety of applications. Multiple methods for generating squeezed light are known, having been demonstrated theoretically and experimentally. However, the effectiveness of these methods—in particular, the inherent limitations to the signals that can be produced—has received little consideration. Here we present a comparative theoretical analysis for generating a highly displaced squeezed light from a linear optical method—a beamsplitter mixing a squeezed vacuum and a strong coherent state—and well-studied parametric amplification methods including an optical parametric oscillator, an optical parametric amplifier, and a dissipative optomechanical squeezer seeded with coherent states. We show that the quality of highly displaced squeezed states that can be generated using these methods is limited on a fundamental level by the physical mechanism utilized; across all methods there are significant trade-offs between displacement, squeezing, and overall uncertainty. We explore the nature and extent of these trade-offs specific to each mechanism and identify the optimal operation modes for each. Finally, we identify the conditions for minimum-uncertainty squeezing in arbitrary parametric amplifying systems and show that displacing the output signal will in general violate these conditions, adding noise and degrading squeezing. Published by the American Physical Society 2025

Split crRNA Precisely Assisted Cas12a Expansion Strategy for Simultaneous, Discriminative, and Low-Threshold Determination of Two miRNAs Associated with Multiple Sclerosis.

Multiple sclerosis (MS) can proceed into secondary progressive MS accompanied by persistent neurological deterioration; therefore, accurate diagnosis of MS is of vital significance. Irregularities of microRNAs (miRNAs) expression have been observed in MS, so miRNAs have been evaluated as novel biomarkers and therapeutic targets. Herein, a new strategy named split crRNA precisely assisted Cas12a expansion (SPACE) was developed for simultaneous, discriminative, and low-threshold determination of two MS-related miRNAs: miRNA-155 and miRNA-326. On the one hand, owing to the property that split crRNA could activate Cas12a, miRNAs were designed as the spacers of crRNA to combine with scaffold. These integrated crRNAs then recognized the activators, activating Cas12a and enabling RNA target identification. On the other hand, the SPACE strategy dexterously integrated the activator with reporter probe, and utilized Cas12a's cis-cleavage to achieve simultaneous detection and differential signal output for miRNA-155 and miRNA-326. Moreover, trans-cleavage with ultra-high efficiency was assembled in the SPACE strategy to achieve sensitive quantification of total miRNAs in blood samples at low thresholds. Overall, the diversified and integrated design of the SPACE strategy enabled simultaneous, discriminative, and low-threshold detection of dual MS-related miRNAs in one pot and one step, providing a reliable and accurate Cas12a detection tool for clinical low-threshold diagnosis.

Complex dynamics in chain HNN with parameter-relied equilibria and memristive electromagnetic induction.

Investigating the dynamics of neural networks under electromagnetic induction contributes to understanding the complex electrical activity in the brain. This paper proposes a memristive chain Hopfield neural network (MCHNN) containing unidirectional synaptic connections, where a flux-controlled memristor mimics the electromagnetic induction between neurons. Under different parameters, the equilibria of MCHNN have different numbers and properties, thus producing diverse dynamics. Numerical analysis shows that there are diverse coexisting attractors, such as point attractors and periodic and chaotic attractors, which are yielded from different initial conditions. Moreover, the memristor's internal parameter can be considered as a special signal controller. It acts on the oscillation amplitude of the neuron's output signal, along with amplitude control and offset-boosting about the flux. By building a feasible hardware platform, the numerical analysis outcomes are supported, and the existence of the proposed MCHNN is verified. In addition, the NIST test outcomes indicate that MCHNN has good pseudo-randomness and is suitable for engineering applications.

Simulation for regulatable assembly of large-scale photonic crystal: Application in flexible pressure sensor with visual sensing

Photonic crystal (PC) sensors have attracted increasing attention for their visual signal output. However, their applications are hindered by the small-scale, defective arrays, and the time-consuming self-assembly. Herein, an efficient self-skinning method for regulating assembly of polystyrene (PS) microspheres and a large-scale PC sensor have been developed. The flowability and rapid reconfigurability of pre-crystallized PS dispersions were investigated through numerical simulation, allowing for control of the assembly through the colloid skin. The simulation and SEM characterization verify that the regulation of colloid skin suppresses the uneven evaporation flux and thus the large-scale and crack-free PC arrays (100 × 400 mm2) are fabricated within 150 s. The PC sensor shows reversible visual signal output with mechanochromic sensitivity of 4.95–12.1 nm∙kPa−1. The scalable PC sensor achieves sensitive-monitoring of human plantar pressure through visual signal output, suggesting a promising potential in gait recognition, health analysis, and so on.

Neuromorphic system using capacitor synapses

Artificial intelligences are indispensable social infrastructures, neural networks are embodiment methodologies, and neuromorphic systems are promising solutions for compact size and low energy. Memristors were first prepared for the synapse devices but incur energy consumption, and memcapacitors were next prepared but have small dynamic ranges of capacitance. In this research, we have developed a neuromorphic system using capacitor synapses. Here, multiple capacitors have binary-weighted capacitances and are controlled to be connected to intermediate signals. They are discharged through transistors, and when they fall below the threshold voltage, the output signals are inverted. After all, electric charges in the multiple capacitances are summed and measured by the inverting intervals, which is the same as multiply–accumulate operation. A large-scale integration chip is actually fabricated. The working is confirmed by MNIST, and the circuit-aware rounding improves the accuracy to 96%, indicating a sufficient possibility for practical applications, and the energy efficiency is 163 GOPS/W even by the 180 nm technology, indicating a great potential for low energy consumption.

Dual-OR Logic-Gated Lateral Flow Strip Assay Based on Colorimetric-Fluorescence Dual Indication for Screening of HPV16/18 in Multiple Scenarios.

The incidence of cervical cancer continues to rise in underdeveloped regions due to low human papillomavirus (HPV) vaccination rates and inadequate screening systems. To achieve convenient, rapid, and accurate detection of HPV, we developed a three-wire lateral flow strip assay system based on dual-OR logic gates for rapid and simultaneous detection of HPV subtypes 16 and 18 in a single test. The system combines three-branch-catalytic hairpin assembly (TCHA)-mediated signal amplification with simple OR logic gate-based signal output to improve detection rates while enabling HPV 16/18 subtype identification. The detection limit of the method was calculated to be 10 aM for the selected target sequences. Meanwhile, the method showed excellent specificity with no false-positive output in real-world detection. The sensitivity of the colorimetric test strips exceeded 90%, while the sensitivity of the fluorescence-based test strips surpassed 95% in detecting clinical samples, demonstrating a high degree of concordance with the results obtained from the real-time quantitative polymerase chain reaction (qPCR). This method provides a simple and easy method for the rapid screening of HPV.

An inserting pilot frequency-doubling millimeter wave radio over fiber system without bit walk-off effect by polarization modulator

Abstract An inserting pilot frequency-doubling millimeter-wave (MMW) radio over fiber (ROF) system is proposed to overcome fiber dispersion by polarization modulator (PoIM). At the central station, the optical carrier is first split into two beams, one beam is used as the pilot, and the other is modulated by a PolM with a composite radio frequency drive signal which is formed by combining the phase modulated data signal and the amplified data signal. By adjusting key parameters of the system, the output main components of the PolM are ±1st order sidebands, the downlink data signal is modulated only on the +1st order sideband. At the base station (BS), the pilot is reflected out by a fiber Bragg grating (FBG) and used as the optical carrier of uplink for carrier reuse. By feeding the output signal of the FBG into the photodetector, the frequency-doubling MMW signal with downlink is produced. In the case of BER is 10−9, and the length of fiber is 40 km and 80 km, the power penalty is 0.51 dB and 2.54 dB for the downlink, 0.49 dB and 4.72 dB for the uplink, respectively. Compared to the downlink of a conventional ROF system, in the 40 km and 80 km fiber transmission distance, our scheme raises the sensitivity of the receiver to 3.42 dB and 2.85 dB, respectively. Our designed ROF system can overcome the bit walk-off effect and have no problems with extinction ratio limitation and the DC drift. Carrier reuse realized by inserting a pilot can overcome the problems of downlink performance degradation and tunability restriction caused by conventional carrier reuse methods

Optical neural networks based on perovskite solar cells

Optical neural networks (ONNs) are a class of emerging computing platforms that leverage the properties of light to perform ultra-fast computations with ultra-low energy consumption. ONNs often use CCD cameras as the output layer. In this work, we propose the use of perovskite solar cells as a promising alternative to imaging cameras in ONN designs. Solar cells are ubiquitous, versatile, highly customizable, and can be fabricated quickly in laboratories. Their large acquisition area and outstanding efficiency enable them to generate output signals with a large dynamic range without the need for amplification. Here we have experimentally demonstrated the feasibility of using perovskite solar cells for capturing ONN output states, as well as the capability of single-layer random ONNs to achieve excellent performance even with a very limited number of pixels. Our results show that the solar-cell-based ONN setup consistently outperforms the same setup with CCD cameras of the same resolution. These findings highlight the potential of solar-cell-based ONNs as an ideal choice for automated and battery-free edge-computing applications.

A Biomimetic Passive Mechanotransduction Mechanism Based on Interfacial Regulation of Ionic p-n Junctions.

Natural skin receptors use ions as signal carriers, while most of the developed artificial tactile sensors utilize electrons as information carriers. To imitate the biological ionic sensing behavior, here, we present a kind of biomimetic, ionic, and fully passive mechanotransduction mechanism leveraging mechanical modulation of interfacial ionic p-n junction (IPNJ) through microchannels. Sensors based on this mechanism do not rely on an external power supply and can encode external tactile stimuli into highly analogous signal outputs to those of natural skin receptors, in terms of both signal type (i.e., ionic potential difference) and signal intensity (≈120 mV). More importantly, the instant interfacial IPNJ regulation characteristic endows the sensors with superior performance when compared to the state-of-the-art piezoionic sensors, including a low detection limit of 0.01 N, fast response/recovery speeds (16 ms/16 ms), ultralow power consumption (pW level), excellent reproducibility (over 100,000 cycles), and good capabilities to resolve both static and dynamic mechanical stimulations. As demonstrations, machine-learning-assisted high accuracy (over 99%) surface texture recognition and object classification are successfully demonstrated with the sensors integrated on robotic hands. This work enriches the family of mechanical sensing mechanisms and provides a path to mimicking natural tactile sensory systems for smart skins, artificial prostheses, and intelligent robots.