Single Sensor Research Articles

Analysis of Long Time Series of Summer Surface Urban Heat Island under the Missing-Filled Satellite Data Scenario.

Surface urban heat islands (SUHIs) are mostly an urban ecological issue. There is a growing demand for the quantification of the SUHI effect, and for its optimization to mitigate the increasing possible hazards caused by SUHI. Satellite-derived land surface temperature (LST) is an important indicator for quantifying SUHIs with frequent coverage. Current LST data with high spatiotemporal resolution is still lacking due to no single satellite sensor that can resolve the trade-off between spatial and temporal resolutions and this greatly limits its applications. To address this issue, we propose a multiscale geographically weighted regression (MGWR) coupling the comprehensive, flexible, spatiotemporal data fusion (CFSDAF) method to generate a high-spatiotemporal-resolution LST dataset. We then analyzed the SUHI intensity (SUHII) in Chengdu City, a typical cloudy and rainy city in China, from 2002 to 2022. Finally, we selected thirteen potential driving factors of SUHIs and analyzed the relation between these thirteen influential drivers and SUHIIs. Results show that: (1) an MGWR outperforms classic methods for downscaling LST, namely geographically weighted regression (GWR) and thermal image sharpening (TsHARP); (2) compared to classic spatiotemporal fusion methods, our method produces more accurate predicted LST images (R2, RMSE, AAD values were in the range of 0.8103 to 0.9476, 1.0601 to 1.4974, 0.8455 to 1.3380); (3) the average summer daytime SUHII increased form 2.08 °C (suburban area as 50% of the urban area) and 2.32 °C (suburban area as 100% of the urban area) in 2002 to 4.93 °C and 5.07 °C, respectively, in 2022 over Chengdu City; and (4) the anthropogenic activity drivers have a higher relative influence on SUHII than other drivers. Therefore, anthropogenic activity driving factors should be considered with CO2 emissions and land use changes for urban planning to mitigate the SUHI effect.

On-line defect recognition of MIG lap welding for stainless steel sheet based on weld image and CMT voltage: Feature fusion and attention weights visualization

At present, sensor recognition technology has become a research hotspot in welding process monitoring. However, most of the existing defect recognition methods are based on single-sensor information, which hinders the further improvement of recognition accuracy and weakens the anti-interference ability under complex conditions. To overcome the shortcomings of single-sensor recognition technology, multi-source information fusion technology can exploit the advantages of different types of sensors to achieve more comprehensive and accurate monitoring of the welding process. However, given the process of fusion of different information, the feature mining mechanism of the deep learning model is still under-explained, and the direct complementarity of the different information remains unclear. In this paper, we propose a hybrid model that uses the fusion of molten pool image and voltage signals to quickly and accurately identify three types of defects in lap MIG welding: burn-through, weld bias, and uneven width. Firstly, a synchronized trigger device is used to acquire molten pool images and weld voltage information, and the voltage data is sliced and matched with the molten pool image data. Secondly, the Short-Time Fourier Transform (STFT) is applied to explore the frequency variation components in the voltage data, and the resulting time-frequency matrix is transformed into a time spectrogram. Finally, the molten pool image and the time spectrogram are simultaneously fed into the hybrid CNN model for online monitoring of welding defect states. The test results show that the average accuracy of the hybrid CNN model in detecting five different welding states reaches 98.87 %, which is significantly higher than three other models based on single sensor information. The visualization of the confusion matrix and Grad-CAM shows that the hybrid CNN model based on multi-source heterogeneous information has a higher accuracy for detecting the transition states that are difficult to detect in welding, and has a more reasonable mechanism for allocating attention, so it can achieve a more accurate and balanced recognition effect in the whole welding manufacturing process. These studies can provide guidance for quality monitoring of welding processes based on multi-source sensor technology, which can be extended to the field of arc additive monitoring.

A task level fusion autonomous switching mechanism.

Positioning technology is an important component of environmental perception. It is also the basis for autonomous decision-making and motion control of firefighting robots. However, some issues such as positioning in indoor scenarios still remain inherent challenges. The positioning accuracy of the fire emergency reaction dispatching (FERD) system is far from adequate to support some applications for firefighting and rescue in indoor scenarios with multiple obstacles. To solve this problem, this paper proposes a fusion module based on the Blackboard architecture. This module aims to improve the positioning accuracy of a single sensor of the unmanned vehicles within the FERD system. To reduce the risk of autonomous decision-making of the unmanned vehicles, this module uses a comprehensive manner of multiple channels to complement or correct the positioning of the firefighting robots. Specifically, this module has been developed to fusion a variety of relevant processes for precise positioning. This process mainly includes six strategies. These strategies are the denoising, spatial alignment, confidence degree update, observation filtering, data fusion, and fusion decision. These strategies merge with the current scenarios-related parameter data, empirical data on sensor errors, and information to form a series of norms. This paper then proceeds to gain experience data with the confidence degree, error of different sensors, and timeliness of this module by training in an indoor scenario with multiple obstacles. This process is from data of multiple sensors (bottom-level) to control decisions knowledge-based (up-level). This process can obtain globally optimal positioning results. Finally, this paper evaluates the performance of this fusion module for the FERD system. The experimental results show that this fusion module can effectively improve positioning accuracy in an indoor scenario with multiple obstacles. Code is available at https://github.com/lvbingyu-zeze/gopath/tree/master.

A highly sensitive pressure sensor based on Ag-graphene-PDMS film and its applications

The fast progress in artificial intelligence has increased the need for wearable electronics. Herein, a piezoresistive pressure sensor consisting of three layers has been proposed. The soft sensing film has been fabricated by filling PDMS (polydimethylsiloxane) with Ag-graphene by a simple mixing method, which possesses low cost and excellent sensing properties, including high sensitivity, high conductivity, and quick response to different pressures. Furthermore, the characterizations were performed by using a scanning electron microscope to study the microstructure and surface morphologies of the Ag-graphene-PDMS film to explore its internal working mechanism. Moreover, the single sensor and sensor array were tested for many applications. The results showed that the fabricated pressure sensor effectively identified various human movements and distinguished pressures with different values and positions. As a result, this sensor has a wide range of applications, including smart wearable technologies and soft robotics.

Learning Time-multiplexed phase-coded apertures for snapshot spectral-depth imaging.

Depth and spectral imaging are essential technologies for a myriad of applications but have been conventionally studied as individual problems. Recent efforts have been made to optically encode spectral-depth (SD) information jointly in a single image sensor measurement, subsequently decoded by a computational algorithm. The performance of single snapshot SD imaging systems mainly depends on the optical modulation function, referred to as codification, and the computational methods used to recover the SD information from the coded measurement. The optical modulation has been conventionally realized using coded apertures (CAs), phase masks, prisms or gratings, active illumination, and many others. In this work, we propose an optical modulation (codification) strategy that employs a color-coded aperture (CCA) in conjunction with a time-varying phase-coded aperture and a spatially-varying pixel shutter, thus yielding an effective time-multiplexed coded aperture (TMCA). We show that the proposed TMCA entails a spatially-variant point spread function (PSF) for a constant depth in a scene, which, in turn, facilitates the distinguishability, and therefore, better recovery of the depth information. Further, the selective filtering of specific spectral bands by the CCA encodes relevant spectral information that is disentangled using a reconstruction algorithm. We leverage the advances of deep learning techniques to jointly learn the optical modulation and the computational decoding algorithm in an end-to-end (E2E) framework. We demonstrate via simulations and with a real testbed prototype that the proposed TMCA strategy outperforms state-of-the-art snapshot SD imaging alternatives in both spectral and depth reconstruction quality.

Simplified top-down fabrication of sub-micron silicon nanowires

Abstract Silicon nanowires are among the most promising nanotechnology building blocks in innovative devices with numerous applications as nanoelectromechanical systems. Downscaling the physical size of these devices and optimization of material functionalities by engineering their structure are two promising strategies for further enhancement of their performance for integrated circuits and future-generation sensors and actuators. Integration of silicon nanowires as transduction elements for inertial sensor applications is one prominent example for an intelligent combination of such building blocks for multiple functionalities within a single sensor. Currently, the efforts in this field are marred by the lack of batch fabrication techniques compatible with semiconductor manufacturing. Development of new fabrication techniques for such one-dimensional structures will eliminate the drawbacks associated with assembly issues. The current study aims to explore the limits of batch fabrication for a single nanowire within a thick Si layer. The objective of the current work goes beyond the state of the art with significant improvements to the recent viable approach on the monolithic fabrication of nanowires, which was based on a conformal side-wall coating for the protection of the nanoscale silicon line followed by deep etch of the substrate transforming the protected layer into a silicon nanowire. The newly developed fabrication approach eliminates side wall protection and thereby reduces both process complexity and process temperature. The technique yields promising results with possible improvements for future micro and nanofabrication processes.

Deep feature interactive network for machinery fault diagnosis using multi-source heterogeneous data

Infrared thermal images have been applied for monitoring health condition of machines due to the noncontact and nonintrusive manner. While fault diagnosis performance of those deep neural networks (DNNs) that use infrared thermal images is restricted by the information learned from single sensor. In this study, multi-source heterogeneous data (i.e., infrared thermal images and vibration signals) are used for machinery fault diagnosis. A new DNN, i.e., deep feature interactive network (DFINet) is proposed for machinery fault diagnosis, where a novel interactive feature extraction module is developed for adaptive feature fusion on multi-source heterogeneous data. Firstly, the private and public features of multi-source heterogeneous data are extracted separately by measuring the distribution discrepancy between heterogeneous features in the feature interactive module. The feature splicing is implemented to interactively fuse common fault features of heterogeneous data and to preserve private unique features. A global feature fusion module is further proposed for adaptive fusion of superficial local features and deep abstract features learned by different feature interactive modules. The experimental results on a rotor test-bed and gearbox test-bed indicate that DFINet is promising for fusion and feature extraction on multi-source heterogeneous data in machinery fault diagnosis.

A 3.0 µm Pixels and 1.5 µm Pixels Combined Complementary Metal-Oxide Semiconductor Image Sensor for High Dynamic Range Vision beyond 106 dB.

We propose a new concept image sensor suitable for viewing and sensing applications. This is a report of a CMOS image sensor with a pixel architecture consisting of a 1.5 μm pixel with four-floating-diffusions-shared pixel structures and a 3.0 μm pixel with an in-pixel capacitor. These pixels are four small quadrate pixels and one big square pixel, also called quadrate-square pixels. They are arranged in a staggered pitch array. The 1.5 μm pixel pitch allows for a resolution high enough to recognize distant road signs. The 3 μm pixel with intra-pixel capacitance provides two types of signal outputs: a low-noise signal with high conversion efficiency and a highly saturated signal output, resulting in a high dynamic range (HDR). Two types of signals with long exposure times are read out from the vertical pixel, and four types of signals are read out from the horizontal pixel. In addition, two signals with short exposure times are read out again from the square pixel. A total of eight different signals are read out. This allows two rows to be read out simultaneously while reducing motion blur. This architecture achieves both an HDR of 106 dB and LED flicker mitigation (LFM), as well as being motion-artifact-free and motion-blur-less. As a result, moving subjects can be accurately recognized and detected with good color reproducibility in any lighting environment. This allows a single sensor to deliver the performance required for viewing and sensing applications.

Flexible Optoelectronic Multimodal Proximity/Pressure/Temperature Sensors with Low Signal Interference.

Multimodal tactile sensors are a crucial part of intelligent human-machine interaction and collaboration. Simultaneous detection of proximity, pressure, and temperature on a single sensor can greatly promote the safety, interactivity and compactness of interaction systems. However, severe signal interference and complex decoupling algorithm hinder the actual applications. Here, we report a flexible optoelectronic multimodal sensor capable of detecting and decoupling proximity/pressure/temperature by integrating a light waveguide and an interdigital electrode into a compact fibrous sensor. Negligible signal interference is realized by combining heterogeneous sensing mechanisms of optics and electronics, which encodes proximity into capacitance, pressure into light intensity and temperature into resistance. The sensor exhibited a large sensing distance of 225 mm with fast responses for proximity detection, a pressure sensitivity of 0.42 N-1 , and a temperature sensitivity of 7%/°C. As a proof of concept, the doll equipped with the sensor can accurately discriminate and detect various stimuli, thus achieving safe and immersive interactions with the user. This work opens up promising paths for self-decoupled multimodal sensors and related human/machine/environment interaction applications. This article is protected by copyright. All rights reserved.

Temperature-modulated sensing characteristics of ultrafine Au nanoparticle-loaded porous ZnO nanobelts for identification and determination of BTEX

The identification and determination of benzene, toluene, ethylbenzene, and xylene (BTEX) has always been a formidable challenge for chemiresistive metal oxide sensors owing to their structural similarity and low reactivity, as well as the intrinsic cross sensitivity of metal oxides. In this paper, a temperature-modulated sensing strategy is proposed for the identification and determination of BTEX using a high-performance chemiresistive sensor. Ultrafine Au nanoparticle-loaded porous ZnO nanobelts as sensing materials were synthesized through an exchange reaction followed by thermal oxidation, which exhibited high response toward BTEX. Under dynamic modulation of working temperature, the distinguishable characteristic curves were demonstrated for each BTEX compound. By employing the linear discrimination and convolutional neural network analyses, highly effective BTEX identification was achieved among all investigated volatile organic compounds, which is difficult to realize for single chemiresistive sensors at constant working temperatures. Furthermore, quantitative analysis of BTEX concentrations was accomplished by establishing the relationship between concentration and response at specific points on their response curves. This developed strategy is expected to pave a new way for constructing highly sensitive gas sensors for the identification and analysis of hazardous gases, thereby enhancing their applicability in environmental monitoring.

Single sensor three point on-line measurement method of precision rolling bearings

With the continuous development of machining methods, it is necessary to explore grinding and measuring methods to ensure the fatigue life and improved accuracy of bearings. In this study, a single-sensor three-point (SSTP) on-line outer-diameter measuring instrument was designed to ensure the consistency of the machining accuracy of precision bearing rings. Moreover, the mathematical model of the measuring instrument was established. The roundness and cylindricity error measurements of the ring outer diameter of precision bearings A and B form different manufacturers of countries were compared and analyzed. In addition, the accuracy measurement of the experimental grinder was investigated through multidimensional experiments. The results demonstrated a measuring and repetitive accuracy of ±1.25 and 1 μm, respectively. The measuring results conformed to the 3 б principle. The measured outer diameter was within the actual value of the workpiece outer diameter ±3 б, and the accuracy was over 99.73%. The instrument did only measure the outer diameter, but also measured the waviness error. The outer diameter and rotating speed did not clearly affect the measuring accuracy. The use of a lubricating fluid reduced the contact resistance of the on-line measuring instrument and significantly reduced the absolute error of outer diameter measurement, which is beneficial in improving the outer diameter measurement accuracy of the instrument.

High-aspect-ratio dielectric pillar with nanocavity backed by metal substrate in the infrared range.

We investigated absorption and field enhancements of shallow nanocavities on top of high-aspect-ratio dielectric pillars in the infrared range. The structure includes a high-aspect-ratio nanopillar array of high refractive index, with nano-cavities on top of the pillars, and a metal plane at the bottom. The enhancement factor of electric field intensity reaches 3180 in the nanocavities and peak absorption reaches 99%. We also investigated the finite-size effect of the presented structure to simulate real experiments. Due to its narrow absorption bandwidth 3.5 nm, it can work as a refractive index sensor with sensitivity 297.5 nm/RIU and figure of merit 85. This paves the way to directly control light field at the nanoscales in the infrared light range. The investigated nanostructure will find applications in multifunctional photonics devices such as chips for culturing cells, refractive index sensors, biosensors of single molecule detection and nonlinear sensors.

Multimodal Asynchronous Kalman Filter for monitoring unstable rock slopes

Unstable rock slopes pose a hazard to inhabitants and infrastructure in their vicinity, necessitating advanced monitoring methodologies for timely risk assessment and mitigation. Recent geotechnical monitoring techniques often rely on sensor data fusion to enhance forecasting for imminent failures. Our investigation extends beyond a single sensor type to data fusion for heterogeneous sensor networks using a Multimodal Asynchronous Kalman Filter. We illustrate the application of the proposed method on a case study data set consisting of data from an on-site sensor network enriched by remote sensing data. Employing a Multimodal Asynchronous Kalman Filter, we capitalise on the distinct resolutions inherent in each sensor input. The outcome was a combined dataset with a high spatiotemporal resolution. Our approach facilitates the estimation of essential physical attributes for monitored objects, encompassing translation, rotation, velocities and accelerations. The case study site was an unstable rock section of ca. 50.000 m 3 in Aurland, Norway, which collapsed as a multi-stage failure in July 2023. Our method can be transposed to various sites with distinct sensor networks, enhancing state estimations for objects on unstable rock slopes. These estimations can significantly improve applications such as risk assessment and robust early-warning systems, enhancing predictions of critical failure points.

Detection, Classification, and Location of Open-Circuit and Short-Circuit Faults in Solar Photovoltaic Array: An Approach Using Single Sensor

Detection, Classification, and Location of Open-Circuit and Short-Circuit Faults in Solar Photovoltaic Array: An Approach Using Single Sensor

Рефрактометрический детектор белков плазмы крови на основе наклонной волоконной брэгговской решетки с функциональным покрытием из антител

The article demonstrates the results of the development of a selective fiber optic biosensor based on tilted fiber Bragg grating. The underlying principle of the sensor involves shifts in the transmission spectrum of tilted grating, which transforms in accordance with the changes in the composition of the external environment. Optical signal propagating along the fiber couples with a tilted grating resulting in a number of cladding modes which form an evanescent field of the sensor directly interacting with the external environment. Potentially high sensitivity to the refractive index of the environment opens up prospects of using such structures as high-precision sensors of solution concentrations; however, for applications in the field of biomedicine, selective sensitivity is critically important, providing unambiguous detection of individual target components in a multicomponent composition. To ensure the selective sensitivity of the sensors presented in this work, functional coatings of antibodies against two types of human blood protein molecules were used in their design. The paper describes a technique for creating experimental samples of biosensors, involving inscription of fiber Bragg gratings and deposition of functional antibody-based coatings onto the surface of the fiber. Experimental results are presented highlighting problems occurring during detection with a single sensor. As a solution, the work proposes a differential measurement scheme involving two sensors modified against two different proteins. According to the results of studies during which fibrinogen and D-dimer were used as target proteins, the selective detection of the studied proteins in concentrations of up to 0.1 mg/ml is clearly demonstrated, which was achieved through the use of a differential measurement scheme.

A compact online proton spectrometer for diagnosis of picosecond intense-laser accelerated protons

A compact online proton spectrometer based on a CMOS plate detector was proposed for the diagnosis of picosecond intense-laser accelerated protons. Inside the CMOS plate detector, a composite structure that composed of a scintillator screen, a fiber optic plate (FOP), and a CMOS sensor was designed to detect the protons safely and efficiently. The scintillator screen converted the incident protons to visible light, which, in turn, was collected by using the FOP and detected by using the CMOS sensor. An additional aluminum housing was designed to decrease the strong electromagnetic pulse (EMP) impact on the electronic readout system from picosecond laser–plasma interaction. This online spectrometer is compact and cost-effective than the system based on the micro-channel plate or scintillator screen in combination with an electron multiplying CCD camera and has better resistance to radiation damage than the system based on a single CMOS sensor.

Machine-learning-assisted wearable PVA/Acrylic fluorescent layer-based triboelectric sensor for motion, gait and individual recognition

Flexible sensors with precise perception ability play an important role in environmental sensing. The organic fusion between the sensitive structure and sensitive material presents some challenges for the overall design and integration of the sensor, including designing multiple sensitive branch structures, simplifying system design, and sensing multiple stress modes. Here, based on a same-plane interdigital electrode design, we developed a triboelectric sensor and its array (TSA) that mimics the synaptic structure of a neural cell. The interdigital electrode structure can effectively simulate the synaptic structure of neural cells and achieve effective perception of multi-directional stress. The enhancement of the triboelectric effect is achieved through layer-by-layer self-assembly technology between PVA film and acrylic fluorescent layer. The interface effect between PVA film and acrylic fluorescent layer can effectively enhance the electrostatic effect. Further analysis with deep learning CNN can extract higher-level motion features. A single sensor can be used to identify which part of the body is producing the movement (98.89%). A TSA with different numbers of sensors can achieve high-precision recognition of gesture numbers (99.25%), foot shape, gait (99.23%), and individuals (99.75%). The sensor can be flexibly manufactured and integrated according to specific application scenarios. The long-term goal is to effectively help limb-injured patients with rehabilitation therapy and build intelligent home environment.

Supporting the Management of Gestational Diabetes Mellitus With Comprehensive Self-Tracking: Mixed Methods Study of Wearable Sensors.

Gestational diabetes mellitus (GDM) is an increasing health risk for pregnant women as well as their children. Telehealth interventions targeted at the management of GDM have been shown to be effective, but they still require health care professionals for providing guidance and feedback. Feedback from wearable sensors has been suggested to support the self-management of GDM, but it is unknown how self-tracking should be designed in clinical care. This study aimed to investigate how to support the self-management of GDM with self-tracking of continuous blood glucose and lifestyle factors without help from health care personnel. We examined comprehensive self-tracking from self-discovery (ie, learning associations between glucose levels and lifestyle) and user experience perspectives. We conducted a mixed methods study where women with GDM (N=10) used a continuous glucose monitor (CGM; Medtronic Guardian) and 3 physical activity sensors: activity bracelet (Garmin Vivosmart 3), hip-worn sensor (UKK Exsed), and electrocardiography sensor (Firstbeat 2) for a week. We collected data from the sensors, and after use, participants took part in semistructured interviews about the wearable sensors. Acceptability of the wearable sensors was evaluated with the Unified Theory of Acceptance and Use of Technology (UTAUT) questionnaire. Moreover, maternal nutrition data were collected with a 3-day food diary, and self-reported physical activity data were collected with a logbook. We found that the CGM was the most useful sensor for the self-discovery process, especially when learning associations between glucose and nutrition intake. We identified new challenges for using data from the CGM and physical activity sensors in supporting self-discovery in GDM. These challenges included (1) dispersion of glucose and physical activity data in separate applications, (2) absence of important trackable features like amount of light physical activity and physical activities other than walking, (3) discrepancy in the data between different wearable physical activity sensors and between CGMs and capillary glucose meters, and (4) discrepancy in perceived and measured quantification of physical activity. We found the body placement of sensors to be a key factor in measurement quality and preference, and ultimately a challenge for collecting data. For example, a wrist-worn sensor was used for longer compared with a hip-worn sensor. In general, there was a high acceptance for wearable sensors. A mobile app that combines glucose, nutrition, and physical activity data in a single view is needed to support self-discovery. The design should support tracking features that are important for women with GDM (such as light physical activity), and data for each feature should originate from a single sensor to avoid discrepancy and redundancy. Future work with a larger sample should involve evaluation of the effects of such a mobile app on clinical outcomes. Clinicaltrials.gov NCT03941652; https://clinicaltrials.gov/study/NCT03941652.

GNSS/UWB/INS indoor and outdoor seamless positioning algorithm based on federal filtering

An integrated navigation system plays an important role in the application of indoor and outdoor seamless positioning. Essentially, it is a flexible fusion positioning of multiple sensors with different combinations. The Global Navigation Satellite System (GNSS) can provide high-precision positioning and navigation in an outdoor environment with good observation conditions. Ultra-wide Band (UWB) has the characteristics of great bandwidth and anti-multipath, which can be applied to provide high-precision positioning and navigation indoors. Inertial Navigation System (INS) has independent navigation capabilities that can be tightly combined with GNSS and UWB to enhance positioning robustness. In response to the problem of a single sensor struggling to provide reliable location information for indoor and outdoor seamless positioning, this study applies federal filtering to integrate and construct the GNSS/UWB/INS integrated positioning algorithm based on Horizontal Dilation of Precision (HDOP) of UWB and carrier-to-noise ratio of GNSS weighting. The gross errors are first detected during the data preprocess, and an adaptive weighting approach to reduce the influence of the anomalous observations is constructed. The tight combination of GNSS/INS and UWB/INS is implemented in the subfilter of the federal filtering. In the main filter, the weights of the two subfilters are adjusted based on HDOP and carrier-to-noise ratio weighting, and the information is allocated and fused to reduce the impact of potential poor UWB geometric configuration on the positioning solutions. It is proven that the error in the horizontal direction in the indoor and outdoor seam area is less than 10 cm. Compared with conventional federal filtering and Huber-based Kalman filtering, the algorithm in this study improves the accuracy of the reference point in indoor and outdoor seam area by 46.1% and 15.4%, respectively, which is beneficial for applications in indoor and outdoor seamless activities.

An “off-on-off” type electrochemical biosensor for detecting multiple biomarkers with DNAzyme-mediated entropy-driven catalytic and DSN enzyme-assisted recycling amplification

In this work, an intelligent and versatile electrochemical biosensor was constructed to detect two types of biomarkers by utilizing “off-on-off” switching. Firstly, human apurinic/apyrimidinic endonuclease1(APE1) mediated specific cleavage of the AP site, initiating activation DNAzyme and entropy-driven catalytic (EDC) reaction. Subsequently, large amounts of ferrocene labeled single-stranded DNA was released and captured with a remarkable electrochemical signal, achieving “off-on” state. In the presence of microRNA 21(miRNA-21), the DNA/RNA heteroduplexes were formed and cleaved by duplex-specific nuclease (DSN) with recovery the target miRNA-21, causing the current suppression in an “on-off” state. This sensor achieved highly sensitive detection of APE1 and miRNA-21 with a detection limit of 2.5 mU·mL−1 and 1.33 × 10−20 M, respectively, and also exhibited good selectivity, reproducibility and stability. Moreover, this proposed biosensor made it possible to realize analysis of multiple types of biomarkers on a single sensor, which improved utilization and analysis efficiency compared to traditional sensors. This study might open a new avenue to design multifunctional sensing platform for biological research and early disease diagnosis.