Accuracy Of Sensor Research Articles

Multi-Scenario Validation and Assessment of a Particulate Matter Sensor Monitor Optimized by Machine Learning Methods.

The aim was to evaluate and optimize the performance of sensor monitors in measuring PM2.5 and PM10 under typical emission scenarios both indoors and outdoors. Parallel measurements and comparisons of PM2.5 and PM10 were carried out between sensor monitors and standard instruments in typical indoor (2 months) and outdoor environments (1 year) in Shanghai, respectively. The optimized validation model was determined by comparing six machining learning models, adjusting for meteorological and related factors. The intra- and inter-device variation, measurement accuracy, and stability of sensor monitors were calculated and compared before and after validation. Indoor particles were measured in a range of 0.8-370.7 μg/m3 and 1.9-465.2 μg/m3 for PM2.5 and PM10, respectively, while the outdoor ones were in the ranges of 1.0-211.0 μg/m3 and 0.0-493.0 μg/m3, correspondingly. Compared to machine learning models including multivariate linear model (ML), K-nearest neighbor model (KNN), support vector machine model (SVM), decision tree model (DT), and neural network model (MLP), the random forest (RF) model showed the best validation after adjusting for temperature, relative humidity (RH), PM2.5/PM10 ratios, and measurement time lengths (months) for both PM2.5 and PM10, in indoor (R2: 0.97 and 0.91, root-mean-square error (RMSE) of 1.91 μg/m3 and 4.56 μg/m3, respectively) and outdoor environments (R2: 0.90 and 0.80, RMSE of 5.61 μg/m3 and 17.54 μg/m3, respectively), respectively. Sensor monitors could provide reliable measurements of PM2.5 and PM10 with high accuracy and acceptable inter and intra-device consistency under typical indoor and outdoor scenarios after validation by RF model. Adjusting for both climate factors and the ratio of PM2.5/PM10 could improve the validation performance.

Rapid qualitative and quantitative analysis of benzo(b)fluoranthene (BbF) in shrimp using SERS-based sensor coupled with chemometric models

Benzo(b)fluoranthene (BbF), a polycyclic aromatic hydrocarbon (PAH), is a carcinogenic contaminant of concern in seafood. This study developed a simple, rapid, sensitive, and cost-effective surface-enhanced Raman scattering (SERS) sensor (AuNPs) coupled with chemometric models for detecting BbF in shrimp samples. Partial least squares (PLS) regression models were optimized using uninformative variable elimination (UVE), bootstrapping soft shrinkage (BOSS), and competitive adaptive reweighted sampling (CARS). Qualitative analysis was performed using principal component analysis (PCA), linear discriminant analysis (LDA), and k-nearest neighbors (KNN) to differentiate between BbF-contaminated and uncontaminated shrimp samples. The SERS-sensor exhibited excellent sensitivity (LOD = 0.12 ng/mL), repeatability (RSD = 6.21%), and anti-interference performance. CARS-PLS model demonstrated superior predictive ability (R2 = 0.9944), and qualitative analysis discriminated between contaminated and uncontaminated samples. The sensor's accuracy was validated using HPLC, demonstrating the ability of the SERS-sensor coupled with chemometrics to rapidly and reliably detect BbF in shrimp samples.

Off-Design Operation and Cavitation Detection in Centrifugal Pumps Using Vibration and Motor Stator Current Analyses.

Centrifugal pumps are essential in many industrial processes. An accurate operation diagnosis of centrifugal pumps is crucial to ensure their reliable operation and extend their useful life. In real industry applications, many centrifugal pumps lack flowmeters and accurate pressure sensors, and therefore, it is not possible to determine whether the pump is operating near its best efficiency point (BEP). This paper investigates the detection of off-design operation and cavitation for centrifugal pumps with accelerometers and current sensors. To this end, a centrifugal pump was tested under off-design conditions and various levels of cavitation. A three-axis accelerometer and three Hall-effect current sensors were used to collect vibration and stator current signals simultaneously under each state. Both kinds of signals were evaluated for their effectiveness in operation diagnosis. Signal processing methods, including wavelet threshold function, variational mode decomposition (VMD), Park vector modulus transformation, and a marginal spectrum were introduced for feature extraction. Seven families of machine learning-based classification algorithms were evaluated for their performance when used for off-design and cavitation identification. The obtained results, using both types of signals, prove the effectiveness of both approaches and the advantages of combining them in achieving the most reliable operation diagnosis results for centrifugal pumps.

Adaptive extended kalman filter for PEMFC membrane water content estimation

Proton Exchange Membrane Fuel Cells are a favorite technology for decarbonizing the transportation sector. However, their large-scale democratization is hampered by their high cost compounded by their unsatisfactory lifespan. To anticipate potential degradation while keeping improving performance, it is essential to maintain an acceptable humidity range inside the cells, especially at the membrane level. However, membrane humidity level is not directly measurable, alternative techniques must be considered to recover this key variable. Here, we develop a real-time software sensor of the membrane water content at the fuel cell’s heart. We build a model describing the membrane water balance, electrochemical behavior, and species mass balance. We then reduce the model and perform an Adaptive Extended Kalman Filter. We perform sensitivity analyses in both steady-state and transient conditions. We validate the filter on a “Worldwide Harmonized Light Vehicles Test Cycles” test procedure. Finally, we obtain a fast and accurate model-based software sensor.

Dual Ion Regulated Eutectogels with High Elasticity and Adhesive Strength for Accurate Strain Sensors

AbstractEutectogels have attracted increasing attention from researchers due to their broad application potential, good environmental stability, biocompatibility, and low cost. Currently reported eutectogels generally show large residue strain and hysteresis, which will cause output signal inconsistency between the loading and unloading processes and is not conducive to accurate strain monitoring. Besides, the low adhesiveness of available eutectogels brings about difficulties in the installation of the sensors and dynamic interfacial stability between the sensors and human skin. Herein, a dual ion regulation strategy is proposed to obtain eutectogels with high resilience, high adhesiveness, ultralow residue strain, wide temperature adaptability (−20–60 °C) and good environmental stability. Strain sensors based on gradient eutectogels exhibit high sensitivity, a wide linear response range, and good cycling stability. The response curve shows negligible electrical hysteresis, providing consistent output signals at the fixed strain regardless of stretch or release. Accurate micrometry with a step as minute as 50 µm is realized. Moreover, the sensors demonstrate higher signal intensity and stability in human physiological signals and motion detection due to the strong interfacial bonding.

Proposal for variability-induced effective radius of elliptical gate-all-around junctionless transistors and its applicability in hydrogen gas sensors

Nano-ribbons with gate-all-around (GAA) architecture have been predicted to serve as next-generation on-chip transistors, among which, the junctionless transistor (JLT) has emerged as a promising candidate, mitigating few limitations of conventional inversion-mode metal–oxide–semiconductor field-effect transistors (MOSFETs). Due to imperfect fabrication process, the GAA architecture is often subject to cross-sectional variabilities. This work presents an analytical model for the effective radius of elliptical cross-section of the channel region having a non-circular oxide surrounding it, resulting in various structural configurations having non-uniform oxide thickness. To demonstrate the impact of cross-sectional variability of GAA-JLTs, few performance parameters, such as, drain current, threshold voltage, and so on, have been evaluated using our proposed formulation for the effective radius and the results were found to agree well with TCAD simulations. As a case study, we have emphasized on the applicability of our proposed model in variability-induced GAA-JLT-based hydrogen sensors using few sensitivity metrics. Our computations show substantial changes in the sensing device as the cross-section deviates from the ideal circular one. Our computations reveal that incorporating the effects of radius variability in the device model along with proper tuning of operating conditions can enhance the performance and accuracy of such sensors.

Controlling stormwater detention ponds under partial observability

Stormwater detention ponds play an important role in urban water management for collecting and conveying rainfall runoff from urban catchment areas to nearby streams. Their purpose is not only to avoid flooding but also to reduce stream erosion and degradation caused by the direct discharge of pollutants to the stream. We model the problem of controlling the discharge rate of water from the ponds as a partially observable hybrid Markov decision process and subsequently use Uppaal Stratego for synthesizing safe and near optimal control strategies. The generated strategies are based on noisy sensor measurements of the water height in the pond, hence the underlying system is only partially observable. We present results analyzing how sensitive the synthesized strategies are with respect to the accuracy of the measurement sensors in both offline and online settings. These types of analyses not only provide insight into the robustness of the generated strategies, but they can also be used for deciding on which measurement sensors to use, thereby balancing sensor cost and accuracy.

Facile fabrication of liquid metal enabled conductive zwitterionic hydrogel for robust electrochemical sensing in complex serum

Any materials present in complex samples that bind the sensing interface non-specifically will greatly decrease the sensitivity and accuracy of the electrochemical sensor. Zwitterionic hydrogel have exhibited extraordinary antifouling ability, but they are limited by the electrical insulativity and weak interface, resulting in a double-edged strategy. Herein, we proposed a novel approach to resolve this challenge thanks to the liquid metal (LM). It was found that with only ultrasonic treatment, a core–shell nanostructure could be easily formed between LM matrix and sodium alginate (SA) polymer, accompanied with the generation of lots of free radical, which initiated the polymerization of zwitterionic monomer and further crosslinking with methylacrylated bovine serum albumin (MBSA). The whole process involved no synthesis of conductive nanomaterials or addition of initiators. Remarkably, the acquired superhydrophilic hydrogel exhibited a surprisingly favourable adhesion to the electrode surface, attributed to the carboxyl groups and related denser gel network resulting from the LM@SA nanoparticles. Finally, the prepared electrochemical immunosensor possessed excellent repeatability, storage stability, selectivity and meanwhile a wide linear range from 10 pg mL−1–100 ng mL−1, and an ultralow detection limit of 8.23 pg mL−1 for the detection of motilin even in 100 % human serum. Moreover, this was a successful attempt of LM-based nanocomposite hydrogel as an electrochemical sensing matrix, and we believe it could present further opportunities toward more satisfying sensing interface for biosensing and bioelectronics with more research in future.

Enhancing vehicular platoon stability in the presence of communication Cyberattacks: A reliable longitudinal cooperative control strategy

This study proposes a reliable longitudinal distributed control strategy for connected and automated vehicles (CAVs) in the presence of communication cyberattacks. The proposed strategy is designed to utilize dependable measurements from onboard sensors to evaluate the reliability of vehicle-to-vehicle (V2V) information and enhance the platoon string stability in an integrated manner. Specifically, this paper proposes a real-time reliability evaluation mechanism for the ego vehicle based on sensing and communication information from multiple predecessors, employing an upper-tailed chi-squared test. The mechanism is then incorporated into a distributed multi-predecessor linear feedback controller to adaptively weigh the preceding vehicles’ information, ensuring robustness against cyberattacks and maintaining the string stability of the vehicular platoon. To better understand the whole framework, sufficient conditions of true string stability and pseudo string stability are theoretically derived, unveiling the disturbance amplification jointly impacted by measurement accuracy of onboard sensors, cyberattack severity in V2V communication, and control parameters. Numerical experiments are conducted to validate the controller across various types of communication cyberattacks. The results suggest that the proposed control strategy can significantly alleviate the impact of cyberattacks and simultaneously dampen traffic oscillations effectively.

Amphibious Multifunctional Hydrogel Flexible Haptic Sensor with Self-Compensation Mechanism.

In recent years, hydrogel-based wearable flexible electronic devices have attracted much attention. However, hydrogel-based sensors are affected by structural fatigue, material aging, and water absorption and swelling, making stability and accuracy a major challenge. In this study, we present a DN-SPEZ dual-network hydrogel prepared using polyvinyl alcohol (PVA), sodium alginate (SA), ethylene glycol (EG), and ZnSO4 and propose a self-calibration compensation strategy. The strategy utilizes a metal salt solution to adjust the carrier concentration of the hydrogel to mitigate the resistance drift phenomenon to improve the stability and accuracy of hydrogel sensors in amphibious scenarios, such as land and water. The ExpGrow model was used to characterize the trend of the ∆R/R0 dynamic response curves of the hydrogels in the stress tests, and the average deviation of the fitted curves ϵ¯ was calculated to quantify the stability differences of different groups. The results showed that the stability of the uncompensated group was much lower than that of the compensated group utilizing LiCl, NaCl, KCl, MgCl2, and AlCl3 solutions (ϵ¯ in the uncompensated group in air was 276.158, 1.888, 2.971, 30.586, and 13.561 times higher than that of the compensated group in LiCl, NaCl, KCl, MgCl2, and AlCl3, respectively; ϵ¯ in the uncompensated group in seawater was 10.287 times, 1.008 times, 1.161 times, 4.986 times, 1.281 times, respectively, higher than that of the compensated group in LiCl, NaCl, KCl, MgCl2 and AlCl3). In addition, for the ranking of the compensation effect of different compensation solutions, the concentration of the compensation solution and the ionic radius and charge of the cation were found to be important factors in determining the compensation effect. Detection of events in amphibious environments such as swallowing, robotic arm grasping, Morse code, and finger-wrist bending was also performed in this study. This work provides a viable method for stability and accuracy enhancement of dual-network hydrogel sensors with strain and pressure sensing capabilities and offers solutions for sensor applications in both airborne and underwater amphibious environments.

An antifouling electrochemical aptasensor based on a Y-shaped peptide for tetracycline detection in milk

Nonspecific adsorption of non-target components (especially proteins) in food matrix on sensing surfaces deteriorates accuracy and sensitivity of electrochemical sensors, which is a key challenge for food safety sensing. Therefore, electrochemical sensors with antifouling capability of high-protein food matrices are highly desired. In this paper, an efficient and simple antifouling electrochemical aptasensor for tetracycline (TC) analysis was constructed based on a self-designed Y-shaped peptide [CPPPPEK-(RSERSERSE)2] consisting of cysteine as the anchoring segment, polyproline as the supporting segment, and -EK-(RSERSERSE)2 as antifouling branches. Hydrophilic and electrically neutral amino acid sequence and Y-shaped space structure of the peptide endow the sensing surface with good antifouling performance in protein solutions and diluted milk. Under optimized experiment conditions, the proposed aptasensor can detect TC with a wide linear range (0.01–100 ng mL−1) and a limit of detection of 5.3 pg mL−1 (S/N = 3). The spiked recovery experiments confirmed high accuracy of the aptasensor in diluted milk without other pretreatments (recoveries: 90.96%–95.86%). This strategy offers a valid way to reduce matrix interference, showing a promise to be extended to other sensing systems for different targets.

Hidden Markov model–based asynchronous fault-tolerant control for mode-dependent time-delayed Markovian jump systems with uncertain conditional probability

In control engineering, time-delayed systems are quite common. Modern industrial systems often involve delays due to complicated network environments, the accuracy of sensors and actuators, or signal processing. Speaking of Markovian jump systems, such systems undergo discrete mode changes caused by a Markov process. Hence, it is reasonable to consider the mode-dependent time delays. In this paper, the asynchronous fault-tolerant controller is developed for mode-dependent time-delayed Markovian jump systems. First, the estimations of states and actuator fault are provided by designing an intermediate observer. Then, based on these estimations, the asynchronous fault-tolerant controller is designed by virtue of the hidden Markov process with uncertain conditional probability. Finally, the existence conditions of both the designed observer and fault-tolerant controller are given in terms of linear matrix inequalities. In the meantime, a numerical example is illustrated to validate the effectiveness and potential of the proposed method.

Development and calibration of large deformation-compliant six-axis force sensor

Improving the measurement accuracy and minimising the coupling between directions are the keys to researching the compliant six-axis force sensors. The use of a six-axis force sensor to accurately monitor the ground reaction force and centre of pressure during human motion is of great significance in the fields of biomechanics and pathological gait diagnosis. Although complete force information can be obtained using a commercial six-axis force sensor, its high stiffness affects the natural gait and easily leads to human fatigue. A compliant six-axis force sensor based on a flexible optical waveguide is proposed, in which the force and torque of six dimensions are detected by reasonably arranging six modular sensing units, and the mechanical decoupling of some dimensions is realised in theory. For the interdimensional coupling and error caused by machining process factors, as well as the nonlinear relationship between the input and output of the proposed compliant six-axis force sensor, a DE-RBF decoupling algorithm is proposed to decouple the calibration data. Compared with the least squares method (LSM) and the radial basis function (RBF) neural network decoupling algorithm, the obtained type-I errors were reduced by 87.7629%, 43.6265%, respectively, and type-II errors by 35.3312%, 56.9162%, respectively. The decoupling result’s maximum type-I and type-II errors were reduced from 7.7125% and 2.7382% in LSM and 3.1029% and 2.8917% in RBF to 0.5916% and 0.9558%, respectively. The measurement accuracy of the compliant six-axis force sensor was significantly higher; however, the time effectiveness of the proposed DE-RBF decoupling algorithm was slightly lower than that of the RBF neural network by 2.47%. In conclusion, the decoupling accuracy and timeliness of the proposed DE-RBF decoupling algorithm can satisfy the requirements of compliant six-axis force sensors to monitor low-frequency biomechanical signals, such as human motion.

Design and Development of Self Balancing Two Wheeler

The Self-balancing robots represent a fascinating intersection of technology and practical application. This project aims to design and develop a self-balancing two- wheeler robot capable of maintaining stability and balance autonomously. Drawing inspiration from principles observed in human balance and locomotion, the robot employs a combination of sensors, actuators, and control algorithms to achieve its objective. The project begins with an in-depth literature survey, exploring existing research and methodologies in the field of self-balancing robotics. Leveraging insights from key publications, the project outlines fundamental principles, sensor fusion techniques, advanced control methodologies, and challenges inherent in self-balancing robot design. Key components including an Arduino Uno board, accelerometers, gyroscopes, DC motors, batteries, and optional reaction wheels are carefully selected and integrated into the robot's hardware architecture. A systematic approach to hardware selection, assembly, sensor integration, microcontroller programming, and motor control ensures a coherent and functional design. Throughout the development process, emphasis is placed on rigorous testing, calibration, and iterative improvement. The robot is subjected to various environmental conditions and scenarios to validate its stability and performance. Calibration procedures refine sensor accuracy and control algorithms, enhancing the robot's ability to maintain balance under dynamic conditions. The project culminates in the creation of a comprehensive documentation package, detailing schematics, code explanations, assembly instructions, and testing methodologies. Additionally, a presentation showcases the robot's functionality, design rationale, and potential applications. By meticulously following the outlined steps and leveraging insights from existing research, this project aims to contribute to the advancement of self-balancing robotics and serve as a foundation for future exploration and innovation in this exciting field

Smartphone‐Based Molecularly Imprinted Photonic Crystal Hydrogel Sensor for the Label‐Free Detection of Bisphenol A

AbstractHere, the implementation of a smartphone‐based portable molecularly imprinted photonic crystal hydrogel (MIPCH) colorimetric sensor for the visual and label‐free detection of bisphenol A (BPA) in water samples is reported. The sensor is prepared by photopolymerizing BPA‐added hydrogel precursor solution within the voids of a photonic crystal opal film, followed by the extraction of BPA molecules. Rebinding of the BPA analyte to the MIPCH causes the hydrogel to swell, leading to a significant redshift in the diffracting spectrum. The MIPCH sensor exhibits a low limit of detection of 0.96 × 10−15 m. The sensor also shows linear response in the femtomolar range, rapid responsiveness (≤6 min), selective detection of BPA in complex matrices, long‐term stability, and reproducibility. Images of the sensor responses are used to train a deep‐learning‐based regression model on a smartphone to predict the BPA concentration quantitatively. Integration of the regression model with the developed sensor provides an accurate and portable smart sensor platform well‐suited for real‐time and on‐field monitoring of BPA.

Survey on Optimal Crop Prediction using Soil and Weather Analysis

This survey paper provides a comprehensive overview of methodologies, technologies, and approaches for analyzing soil environments to optimize crop production. It covers traditional soil sampling, advanced techniques like remote sensing, and real-time monitoring using sensor networks. The integration of sensor data with IoT and machine learning for decision support is explored. Soil health assessment and site-specific management strategies enabled by precision agriculture are emphasized. Challenges such as data integration and sensor accuracy are addressed, along with future research directions including low-cost sensing solutions and improved data analytics. Overall, the paper serves as a valuable resource for optimizing crop production sustainably, contributing to efficient agricultural practices in a changing climate

Calibration Methods for Low-Cost Particulate Matter Sensors Considering Seasonal Variability.

Many countries use low-cost sensors for high-resolution monitoring of particulate matter (PM2.5 and PM10) to manage public health. To enhance the accuracy of low-cost sensors, studies have been conducted to calibrate them considering environmental variables. Previous studies have considered various variables to calibrate seasonal variations in the PM concentration but have limitations in properly accounting for seasonal variability. This study considered the meridian altitude to account for seasonal variations in the PM concentration. In the PM10 calibration, we considered the calibrated PM2.5 as a subset of PM10. To validate the proposed methodology, we used the feedforward neural network, support vector machine, generalized additive model, and stepwise linear regression algorithms to analyze the results for different combinations of input variables. The inclusion of the meridian altitude enhanced the accuracy and explanatory power of the calibration model. For PM2.5, the combination of relative humidity, temperature, and meridian altitude yielded the best performance, with an average R2 of 0.93 and root mean square error of 5.6 µg/m3. For PM10, the average mean absolute percentage error decreased from 27.41% to 18.55% when considering the meridian altitude and further decreased to 15.35% when calibrated PM2.5 was added.

A Multimode Optical Sensor for Selective and Sensitive Detection of Harmful Heavy Metal Cr(VI) in Fresh Water and Sea Water.

Water pollution originating from heavy metals has shown great impacts on the ecological environment and human health due to their extremely low biodegradability. Hexavalent chromium Cr(VI), as one harmful heavy metal with strong oxidation, high biological permeability, and high carcinogenicity, is becoming an increasingly serious threat to human health. Therefore, conveniently but accurately, monitoring the Cr(VI) level in water to maintain its normal level and ensuring the stability of the ecosystem and human health become very valuable. However, most of these heavy metal sensors reported are turn-off type single-emission sensors. In this work, a ratiometric fluorescence/colorimetry/smartphone triple-mode turn-on optical sensor for Cr(VI) was developed based on a multifunctional metal-organic framework platform. The detection limits for these three mutual verification modes were only 1.28, 4.89, and 68.4 nM, respectively. Additionally, the color changes of the detection system under sunlight can also be observed directly by the naked eye. The accuracy and practicability of this multimode sensor were further proved by the detection of Cr(VI) in actual water and seawater samples, and the recovery rate ranged from 97.308 to 104.041%.

Sensorless model-based displacement estimator for piezoelectric actuator through a practical three-stage mechanism

Piezoelectric elements (PEMs) are used in a variety of applications. In this paper, we developed a new simple sensorless method for a Piezoelectric Actuator (PEA), which includes piezostack elements and a three-stage amplification mechanism.This research focuses on a piezoelectric actuator that incorporates a three-stage amplification system, where the outcome of one stage serves as the input for the subsequent one. The actuator receives two types of inputs: the voltage applied to the piezoelectric elements and the mechanical load it carries. Its output is defined by the rotation angle observed at the end of the third amplification stage. To indirectly measure the actuator's displacement, a basic external circuit is utilized.The precise movement of these actuators is essential. To circumvent the high costs and limitations associated with highly accurate displacement sensors, there has been a growing interest in sensorless control methods. Certain electrical signals, when measured, can provide an estimation of displacement. However, induced voltage measurements are not effective for piezoelectric stacks. Two more promising measures are the voltage and current of the piezoelectric material.Given that the electrical charge on these actuators closely reflects their displacement with minimal hysteresis across a broad frequency spectrum, it's proposed that displacement can be effectively gauged through current measurements that assess charge.The core contribution of this paper is the introduction and validation, both theoretically and experimentally, of a hybrid algorithm that leverages these two electrical signals to enhance the accuracy of displacement estimates. This was confirmed using a laboratory setup.The primary benefit of this research is the presentation of a straightforward sensorless control algorithm, poised for further exploration within the realm of piezoelectric actuators. The simplicity of both the theoretical model and the sensorless technique facilitates their application across a diverse range of piezoelectric actuators and amplification systems, thereby streamlining the design, modeling, and control strategy development for various actuators.The innovation of this study stems from the application of an uncomplicated sensorless estimation algorithm, coupled with a system-level perspective on piezoelectric actuators. This approach utilizes a simple, adaptable model suitable for a wide array of applications and operational techniques.

Real-time uncertainty reduction in laser triangulation via dynamic speckle correlation

The speckle-induced measurement uncertainty is one of the major bottlenecks to achieve high measurement accuracy in laser triangulation sensors (LTS). Conventional methods for improving uncertainty focus on suppressing speckles and refining centroid extraction algorithms, overlooking the potential information about object motion that speckles inherently contain. To address this deficiency, a dynamic speckle correlation (DSC) method is proposed to reduce speckle-induced measurement uncertainty in laser triangulation systems. This method uses the correlation between speckle patterns and the movement of the object being measured, thereby enabling real-time error compensation without any speckle reduction mechanisms. To verify the effectiveness of the DSC method, we have designed a compact LTS based on oblique incident laser. Extensive experiments demonstrate that, for displacement measurement of objects with surface roughness ranging from Ra0.4 to Ra3.2, the DSC method can effectively reduce the root mean square (RMS) measurement error to 3∼5μm. Compared with using the traditional grayscale centroid method alone, the measurement accuracy after DSC compensation is improved by at least 20%.