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The paper aims to present a mechatronic device able to micro-position the workpiece and to reject disturbances due to machining operation. A decoupling method is proposed for a parallel kinematic machine (PKM) fixturing platform composed by a 3-DoF flexure-based piezo-actuated mechanism. The parallel platform, with a vertical motion and two rotations, is described and its kinematics and dynamics are studied. The coupling undesirable effect is investigated based on a set of poses. To improve the quasi-static regulator model for a set-point following system, a bump less switching controller and a fine-tuning procedure, to estimate the parameter uncertainty and enable the external disturbance containment in an extended broadband frequency range, are presented. The platform and the piezo-actuator controllers are modelled based on a gain scheduling, standard ISA form method, to guarantee the stability. The accuracy is demonstrated through a set of simulations and experimental comparisons. A sensitivity analysis that evaluates the tracking performance and the disturbance rejection based on the number of signal amplitudes, frequencies, and phases is discussed. A validation phase has shown that the developed architecture presents a steady state error lower than 1.2 µm, a vibration reduction of 96% at 1130 Hz with a maximum resolving time of 6.60 ms.

The Overhauser magnetometer is a scalar quantum magnetometer based on the dynamic nuclear polarization (DNP) effect in the Earth’s magnetic field. Sensitivity is a key technical specification reflecting the ability of instruments to sense small variations of the Earth’s magnetic field and is closely related to the signal-to-noise ratio (SNR) of the free induction decay (FID) signal. In this study, deuterated 15N TEMPONE radical is used in our sensor to obtain high DNP enhancement. The measured SNR of the FID signal is approximately 63/1, and the transverse relaxation time T2 is 2.68 s. The direct measurement method with a single instrument and the synchronous measurement method with two instruments are discussed for sensitivity estimation in time and frequency domains under different electromagnetic interference (EMI) environments and different time periods. For the first time, the correlation coefficient of the magnetic field measured by the two instruments is used to judge the degree of the influence of the environmental noise on the sensitivity estimation. The sensitivity evaluation in the field environment is successfully realized without electrical and magnetic shields. The direct measurement method is susceptible to EMI and cannot work in general electromagnetic environments, except it is sufficiently quiet. The synchronous measurement method has an excellent ability to remove most natural and artificial EMIs and can be used under noisy environments. Direct and synchronous experimental results show that the estimated sensitivity of the JOM-4S magnetometer is approximately 0.01 nT in time domain and approximately 0.01 nT/Hz in frequency domain at a 3 s cycling time. This study provides a low-cost, simple, and effective sensitivity estimation method, which is especially suitable for developers and users to estimate the performance of the instrument.

Tunneling magnetoresistive (TMR) sensors have broad application prospects because of their high sensitivity and small volume. However, the inherent hysteresis characteristics of TMR affect its applications in high accuracy scenarios. It is essential to build a model to describe the attributes of hysteresis of TMR accurately. Preisach model is one of the popular models to describe the behavior of inherent hysteresis for TMR, whereas it presents low accuracy in high-order hysteresis reversal curves. Furthermore, the traditional Preisach model has strict congruence constraints, and the amount of data seriously affects the accuracy. This paper proposes a hysteresis model from a probability perspective. This model has the same computational complexity as the classic Preisach model while presenting higher accuracy, especially in high-order hysteresis reversal curves. When measuring a small amount of data, the error of this method is significantly reduced compared with the classical Preisach model. Besides, the proposed model’s congruence in this paper only needs equal vertical chords.

The SPR phenomenon results in an abrupt change in the optical phase such that one can measure the phase shift of the reflected light as a sensing parameter. Moreover, many studies have demonstrated that the phase changes more acutely than the intensity, leading to a higher sensitivity to the refractive index change. However, currently, the optical phase cannot be measured directly because of its high frequency; therefore, investigators usually have to use complicated techniques for the extraction of phase information. In this study, we propose a simple and effective strategy for measuring the SPR phase shift based on phase-shift interferometry. In this system, the polarization-dependent interference signals are recorded simultaneously by a pixelated polarization camera in a single snapshot. Subsequently, the phase information can be effortlessly acquired by a phase extraction algorithm. Experimentally, the proposed phase-sensitive SPR sensor was successfully applied for the detection of small molecules of glyphosate, which is the most frequently used herbicide worldwide. Additionally, the sensor exhibited a detection limit of 15 ng/mL (0.015 ppm). Regarding its simplicity and effectiveness, we believe that our phase-sensitive SPR system presents a prospective method for acquiring phase signals.

Worldwide, motor vehicle accidents are one of the leading causes of death, with alcohol-related accidents playing a significant role, particularly in child death. Aiming to aid in the prevention of this type of accidents, a novel non-invasive method capable of detecting the presence of alcohol inside a motor vehicle is presented. The proposed methodology uses a series of low-cost alcohol MQ3 sensors located inside the vehicle, whose signals are stored, standardized, time-adjusted, and transformed into 5 s window samples. Statistical features are extracted from each sample and a feature selection strategy is carried out using a genetic algorithm, and a forward selection and backwards elimination methodology. The four features derived from this process were used to construct an SVM classification model that detects presence of alcohol. The experiments yielded 7200 samples, 80% of which were used to train the model. The rest were used to evaluate the performance of the model, which obtained an area under the ROC curve of 0.98 and a sensitivity of 0.979. These results suggest that the proposed methodology can be used to detect the presence of alcohol and enforce prevention actions.

Shape reconstruction is growing as an important real-time monitoring strategy for applications that require rigorous control. Polymer optical fiber sensors (POF) have mechanical properties that allow the measurement of large curvatures, making them appropriate for shape sensing. They are also lightweight, compact and chemically stable, meaning they are easy to install and safer in risky environments. This paper presents a sensor system to detect angles in multiple planes using a POF-intensity-variation-based sensor and a procedure to detect the angular position in different planes. Simulations are performed to demonstrate the correlation between the sensor’s mechanical bending response and their optical response. Cyclic flexion experiments are performed at three test frequencies to obtain the sensitivities and the calibration curves of the sensor at different angular positions of the lateral section. A Fast Fourier Transform (FFT) analysis is tested as a method to estimate angular velocities using POF sensors. The experimental results show that the prototype had high repeatability since its sensitivity was similar using different test frequencies at the same lateral section position. The proposed approach proved itself feasible considering that all linear calibration curves presented a coefficient of determination (R2) higher than 0.9.

In this paper, a novel and cost-effective photonic platform based on silica–titania material is discussed. The silica–titania thin films were grown utilizing the sol–gel dip-coating method and characterized with the help of the prism-insertion technique. Afterwards, the mode sensitivity analysis of the silica–titania ridge waveguide is investigated via the finite element method. Silica–titania waveguide systems are highly attractive due to their ease of development, low fabrication cost, low propagation losses and operation in both visible and near-infrared wavelength ranges. Finally, a ring resonator (RR) sensor device was modelled for refractive index sensing applications, offering a sensitivity of 230 nm/RIU, a figure of merit (FOM) of 418.2 RIU−1, and Q-factor of 2247.5 at the improved geometric parameters. We believe that the abovementioned integrated photonics platform is highly suitable for high-performance and economically reasonable optical sensing devices.

The use of magnetic nanoparticles (MNPs) in biomedical applications requires the quantitative knowledge of their quantitative distribution within the body. AC Biosusceptometry (ACB) is a biomagnetic technique recently employed to detect MNPs in vivo by measuring the MNPs response when exposed to an alternate magnetic field. The ACB technique presents some interesting characteristics: non-invasiveness, low operational cost, high portability, and no need for magnetic shielding. ACB conventional methods until now provided only qualitative information about the MNPs’ mapping in small animals. We present a theoretical model and experimentally demonstrate the feasibility of ACB reconstructing 2D quantitative images of MNPs’ distributions. We employed an ACB single-channel scanning approach, measuring at 361 sensor positions, to reconstruct MNPs’ spatial distributions. For this, we established a discrete forward problem and solved the ACB system’s inverse problem. Thus, we were able to determine the positions and quantities of MNPs in a field of view of 5×5×1&nbsp, cm3 with good precision and accuracy. The results show the ACB system’s capabilities to reconstruct the quantitative spatial distribution of MNPs with a spatial resolution better than 1&nbsp, cm, and a sensitivity of 1.17&nbsp, mg of MNPs fixed in gypsum. These results show the system’s potential for biomedical application of MNPs in several studies, for example, electrochemical-functionalized MNPs for cancer cell targeting, quantitative sensing, and possibly in vivo imaging.

In the present research work, an electrical resistance tomography (ERT) system is utilized as a means for real-time fault detection and diagnosis (FDD) during a reactive crystallization process. The calcium carbonate crystallization is part of the carbon capture and utilization scheme where process monitoring and malfunction diagnostics strategies are presented. The graphical logic representation of the fault tree analysis methodology is used to develop the system failure states. The measurement consistency due to the use of a single electrode from a set of ERT electrodes for malfunction identification is experimentally and quantitatively investigated based on the sensor sensitivity and standard deviation criteria. Electrical current measurements are employed to develop a LabVIEW-based process automation program by using the process-specific knowledge and historical process data. Averaged electrical current is correlated to the mechanical failure of the stirrer through standard deviation evaluation, and slopes of the measured data are used to monitor the pump and concentrations status. The performance of the implemented methodology for detecting the induced faults and abnormalities is tested at different operating conditions, and a basic signal-based alarming technique is developed.

Stepped-frequency waveform may be used to synthesize a wideband signal with several narrow-band pulses and achieve a high-resolution range profile without increasing the instantaneous bandwidth. Nevertheless, the conventional stepped-frequency waveform is Doppler sensitive, which greatly limits its application to moving targets. For this reason, this paper proposes a waveform design method using a staggered pulse repetition frequency to improve the Doppler tolerance effectively. First, a generalized echo model of the stepped-frequency waveform is constructed in order to analyze the Doppler sensitivity. Then, waveform design is carried out in the stepped-frequency waveform by using a staggered pulse repetition frequency so as to eliminate the high-order phase component that is caused by the target’s velocity. Further, the waveform design method is extended to the sparse stepped-frequency waveform, and we also propose corresponding methods for high-resolution range profile synthesis and motion compensation. Finally, experiments with electromagnetic data verify the high Doppler tolerance of the proposed waveform.

In this paper, a synthetic aperture radar (SAR) change detection approach is proposed based on a structural similarity index measure (SSIM) and multiple-window processing (MWP). The proposed scheme is performed in two steps: (1) generation of a coherence image based on MWP associated with SSIM and (2) gamma correction (GC) filtering. The proposed method is capable of providing a high-quality coherence image because the MWP operation based on SSIM has high sensitivity to the similarity measure for intensity between two SAR images. By finding an optimum value of order of GC, the proposed method can considerably reduce the effect of speckle noise on the coherence image, while retaining nearly all the information related to changed region involved in the change detection map. Several experimental results are presented to demonstrate the effectiveness of the proposed scheme.

Mediumfrequency fiber Bragg grating (FBG) acceleration sensors are used in important applications in mechanical, aerospace and weapon equipment, and have strict requirements in terms of resonance frequency and sensitivity. A novel medium-frequency accelerometer, based on fiber Bragg grating and flexible hinges, is proposed in this paper. The differential structure doubles the sensitivity of the sensor while avoiding temperature effects. The structure model and principle for the sensor are introduced, the sensor’s sensing characteristics are theoretically analyzed, and the structure parameters for the sensor are determined through numerical analysis. The sensing experiments show that the resonance frequency of the sensor is approximately 2800 Hz, the sensitivity is 21.8 pm/g in the flat frequency range of 50–1000 Hz, and the proposed sensor has a good temperature self-compensation function and lateral anti-interference capability.

When grasping objects with uneven or varying shapes, accurate pressure measurement on robot fingers is critical for precise robotic gripping operations. However, measuring the pressure from the sides of the fingertips remains challenging owing to the poor omnidirectionality of the pressure sensor. In this study, we propose an omnidirectional sensitive pressure sensor using a cone-shaped magnet slider and Hall sensor embedded in a flexible elastomer, which guarantees taking pressure measurements from any side of the fingertip. The experimental results indicate that the proposed pressure sensor has a high sensitivity (61.34 mV/kPa) in a wide sensing range (4–90 kPa) without blind spots on the fingertip, which shows promising application prospects in robotics.

Video stabilization is essential for long-range electro-optical systems, especially in situations when the field of view is narrow, since the system shake may produce highly deteriorating effects. It is important that the stabilization works for different camera types, i.e., different parts of the electromagnetic spectrum independently of the weather conditions and any form of image distortion. In this paper, we propose a method for real-time video stabilization that uses only gyroscope measurements, analyze its performance, and implement and validate it on a real-world professional electro-optical system developed at Vlatacom Institute. Camera movements are modeled with 3D rotations obtained by integration of MEMS gyroscope measurements. The 3D orientation estimation quality depends on the gyroscope characteristics, we provide a detailed discussion on the criteria for gyroscope selection in terms of the sensitivity, measurement noise, and drift stability. Furthermore, we propose a method for improving the unwanted motion estimation quality using interpolation in the quaternion domain. We also propose practical solutions for eliminating disturbances originating from gyro bias instability and noise. In order to evaluate the quality of our solution, we compared the performance of our implementation with two feature-based digital stabilization methods. The general advantage of the proposed methods is its drastically lower computational complexity, hence, it can be implemented for a low price independent of the used electro-optical sensor system.

In rehabilitation, the Fugl–Meyer assessment (FMA) is a typical clinical instrument to assess upper-extremity motor function of stroke patients, but it cannot measure fine changes of motor function (both in recovery and deterioration) due to its limited sensitivity. This paper introduces a sensor-based automated FMA system that addresses this limitation with a continuous rating algorithm. The system consists of a depth sensor (Kinect V2) and an algorithm to rate the continuous FM scale based on fuzzy inference. Using a binary logic based classification method developed from a linguistic scoring guideline of FMA, we designed fuzzy input/output variables, fuzzy rules, membership functions, and a defuzzification method for several representative FMA tests. A pilot trial with nine stroke patients was performed to test the feasibility of the proposed approach. The continuous FM scale from the proposed algorithm exhibited a high correlation with the clinician rated scores and the results showed the possibility of more sensitive upper-extremity motor function assessment.

A tactile sensor array is a crucial component for applying physical sensors to a humanoid robot. This work focused on developing a palm-size tactile sensor array (56.0 mm × 56.0 mm) to apply object recognition for the humanoid robot hand. This sensor was based on a PCB technology operating with the piezoresistive principle. A conductive polymer composites sheet was used as a sensing element and the matrix array of this sensor was 16 × 16 pixels. The sensitivity of this sensor was evaluated and the sensor was installed on the robot hand. The tactile images, with resolution enhancement using bicubic interpolation obtained from 20 classes, were used to train and test 19 different DCNNs. InceptionResNetV2 provided superior performance with 91.82% accuracy. However, using the multimodal learning method that included InceptionResNetV2 and XceptionNet, the highest recognition rate of 92.73% was achieved. Moreover, this recognition rate improved when the object exploration was applied to demonstrate.

The on-chip integration of multiple biochemical sensors based on field-effect electrolyte-insulator-semiconductor capacitors (EISCAP) is challenging due to technological difficulties in realization of electrically isolated EISCAPs on the same Si chip. In this work, we present a new simple design for an array of on-chip integrated, individually electrically addressable EISCAPs with an additional control gate (CG-EISCAP). The existence of the CG enables an addressable activation or deactivation of on-chip integrated individual CG-EISCAPs by simple electrical switching the CG of each sensor in various setups, and makes the new design capable for multianalyte detection without cross-talk effects between the sensors in the array. The new designed CG-EISCAP chip was modelled in so-called floating/short-circuited and floating/capacitively-coupled setups, and the corresponding electrical equivalent circuits were developed. In addition, the capacitance-voltage curves of the CG-EISCAP chip in different setups were simulated and compared with that of a single EISCAP sensor. Moreover, the sensitivity of the CG-EISCAP chip to surface potential changes induced by biochemical reactions was simulated and an impact of different parameters, such as gate voltage, insulator thickness and doping concentration in Si, on the sensitivity has been discussed.

(1) Background: Semi-active prosthetic feet can provide adaptation in different circumstances, enabling greater function with less weight and complexity than fully powered prostheses. However, determining how to control semi-active devices is still a challenge. The dynamic mean ankle moment arm (DMAMA) provides a suitable biomechanical metric, as its simplicity matches that of a semi-active device. However, it is unknown how stiffness and locomotion modes affect DMAMA, which is necessary to create closed-loop controllers for semi-active devices. In this work, we develop a method to use only a prosthesis-embedded load sensor to measure DMAMA and classify locomotion modes, with the goal of achieving mode-dependent, closed-loop control of DMAMA using a variable-stiffness prosthesis. We study how stiffness and ground incline affect the DMAMA, and we establish the feasibility of classifying locomotion modes based exclusively on the load sensor. (2) Methods: Human subjects walked on level ground, ramps, and stairs while wearing a variable-stiffness prosthesis in low-, medium-, and high-stiffness settings. We computed DMAMA from sagittal load sensor data and prosthesis geometric measurements. We used linear mixed-effects models to determine subject-independent and subject-dependent sensitivity of DMAMA to incline and stiffness. We also used a machine learning model to classify locomotion modes using only the load sensor. (3) Results: We found a positive linear sensitivity of DMAMA to stiffness on ramps and level ground. Additionally, we found a positive linear sensitivity of DMAMA to ground slope in the low- and medium-stiffness conditions and a negative interaction effect between slope and stiffness. Considerable variability suggests that applications of DMAMA as a control input should look at the running average over several strides. To examine the efficacy of real-time DMAMA-based control systems, we used a machine learning model to classify locomotion modes using only the load sensor. The classifier achieved over 95% accuracy. (4) Conclusions: Based on these findings, DMAMA has potential for use as a closed-loop control input to adapt semi-active prostheses to different locomotion modes.

In order to calibrate the magnetic measurement system used in guided munition on site, a two-stage calibration (TSC) scheme without reference is proposed in this paper. Analyzing the interfering magnetic field in the projectile and misalignment angles between the projectile coordinate system and measurement coordinate system establishes a proper mathematical equivalent model and derives a calibration method. The first stage is ellipsoid fitting to obtain the equivalent zero-offset, equivalent sensitivity and equivalent non-orthogonal angles of the sensor, the second stage is to calibrate the misalignment angles between the projectile coordinate system and the measurement coordinate system with the three-position calibration (TPC) method. Complete calibration is convenient to operate and does not need an additional reference, which has wide applicability. The simulation results show that the deviation in the measured value after compensation is within 100 nT. The experiment proves that the error of compensated magnetic value is about 150 nT, which meets the accuracy of requirements in guided munitions.

Analysis of trends in the development of silicon photonics shows the high efficiency regarding the creation of optical sensors. The concept of bimodal sensors, which suggests moving away from the usual paradigm based only on single-mode waveguides and using the inter-mode interaction of guided optical waves in a two-mode optical waveguide, is developed in the present paper. In this case, the interaction occurs in the presence of an asymmetric periodic perturbation of the refractive index above the waveguide surface. Such a system has unique dispersion properties that lead to the implementation of collinear Bragg diffraction with the mode number transformation, in which there is an extremely high dependence of the Bragg wavelength on the change in the refractive index of the environment. This is called the “effect of dispersion-enhanced sensitivity”. In this paper, it is shown by numerical calculation methods that the effect can be used to create optical sensors with the homogeneous sensitivity higher than 3000 nm/RIU, which is many times better than that of sensors in single-mode waveguide structures.

Aim: To determine whether an AI model and single sensor measuring acceleration and ECG could model cognitive and physical fatigue for a self-paced trail run. Methods: A field-based protocol of continuous fatigue repeated hourly induced physical (~45 minutes) and cognitive (~10 minutes) fatigue on one healthy participant. Physical load was a 3.8 km, 200 m vertical gain, trail run with acceleration and electrocardiogram (ECG) data collected using a single sensor. Cognitive load was a Multi Attribute Test Battery (MATB) and separate assessment battery including the Finger Tap Test (FTT), Stroop, Trail Making A and B, Spatial Memory, Paced Visual Serial Addition Test (PVSAT), and a vertical jump. A fatigue prediction model was implemented using a Convolutional Neural Network (CNN). Results: When the fatigue test battery results were compared for sensitivity to the protocol load, FTT right hand (R2 0.71) and Jump Height (R2 0.78) were the most sensitive while the other tests were less sensitive (R2 values Stroop 0.49, Trail Making A 0.29, Trail Making B 0.05, PVSAT 0.03, spatial memory 0.003). Best prediction results were achieved with a rolling average of 200 predictions (102.4 s), during set activity types, mean absolute error for ‘walk up’ (MAE200 12.5%) and range of absolute error for ‘run down‘ (RAE200 16.7%). Conclusion: We were able to measure cognitive and physical fatigue using a single wearable sensor during a practical field protocol including contextual factors in conjunction with a neural network model. This research has practical application to fatigue research in the field.

For the tracking of high-dynamic satellite navigation signals, the conventional scalar tracking loop (STL) is vulnerable. Frequent signal-tracking interruption affects the continuity of navigation. The vector tracking loop (VTL) can overcome this disadvantage. However, there are some difficulties in implementing existing vector tracking methods on a real-time hardware receiver, such as the synchronization problem and computation load. This paper proposes an implementation framework of VTL based on a partial open-loop numerically controlled oscillator (NCO) control mode that can be implemented with minor modifications on an existing receiver platform. The structure of VTL, the design of the navigation filter, and the key points of hardware implementation are introduced in detail. Lastly, the VTL performance was verified by a GPS simulator test. The results show that the proposed VTL can run in real-time and be significantly improved in the tracking continuity of high-dynamic signals, tracking sensitivity, positioning accuracy, and recovery time for interrupted signals compared with those of STL.

In this paper, we describe a low-cost microwave microfluidic system of ultrahigh sensitivity for detecting small changes in the concentration of polar solutions (liquid dielectrics) in the 2.4 GHz ISM band. Its principle of operation is based on microwave interferometry, which is implemented using planar microstrip lines and integrated microwave components. The key features of this system include small solution intake (&lt, 200 µL per measurement), short time of measurement (ca. 20 ms), ultrahigh sensitivity of concentration changes (up to 55 dB/%), and low error of measurement (below 0.1%). The ultrahigh sensitivity was proven experimentally by measurements of the fat content of milk. In addition, it is a user-friendly system due to an effortless and fast calibration procedure. Moreover, it can be made relatively compact (&lt, 20 cm2) and features low power consumption (200 mW). Thus, the proposed system is perfect for industrial applications, especially for highly integrated lab-on-chip devices.

[EN] The optimization performed in this study is based on the finite dimensions model of the concentric ring electrode as opposed to the negligible dimensions model used in the past. This makes the optimization problem comprehensive, as all of the electrode parameters including, for the first time, the radius of the central disc and individual widths of concentric rings, are optimized simultaneously. The optimization criterion used is maximizing the accuracy of the surface Laplacian estimation, as the ability to estimate the Laplacian at each electrode constitutes primary biomedical significance of concentric ring electrodes. For tripolar concentric ring electrodes, the optimal configuration was compared to previously proposed linearly increasing inter-ring distances and constant inter-ring distances configurations of the same size and based on the same finite dimensions model. The obtained analytic results suggest that previously proposed configurations correspond to almost two-fold and more than three-fold increases in the Laplacian estimation error compared with the optimal configuration proposed in this study, respectively. These analytic results are confirmed using finite element method modeling, which was adapted to the finite dimensions model of the concentric ring electrode for the first time. Moreover, the finite element method modeling results suggest that optimal electrode configuration may also offer improved sensitivity and spatial resolution., This research was funded by the National Science Foundation (NSF) Division of Human Resource Development (HRD) Tribal Colleges and Universities Program (TCUP), grant number 1914787

This work introduces an array prototype based on a Frequency Modulation (FM) encoding architecture to transfer multiple sensor signals on a single wire. The use case presented adopts Hall-effect sensors as an example to represent a much larger range of sensor types (e.g., proximity and temperature). This work aims to contribute to large area artificial skin systems which are a key element to enhance robotic platforms. Artificial skin will allow robotic platforms to have spatial awareness which will make interaction with objects and users safe. The FM-based architecture has been developed to address limitations in large-scale artificial skin scalability. Scalability issues include power requirements, number of wires needed, as well as frequency, density, and sensitivity bottlenecks. In this work, eight sensor signals are simultaneously acquired, transferred on a single wire and decoded in real-time. The overall taxel array current consumption is 36 mA. The work experimentally validates and demonstrates that different input signals can be effectively transferred using this approach minimizing wiring and power consumption of the taxel array. Four different tests using single as well as multiple stimuli are presented. Observations on performances, noise, and taxel array behaviour are reported. The results show that the taxel array is reliable and effective in detecting the applied stimuli.

This article presents a 14-bit chipless RFID label which, in addition to classical identification feature, can be used as decimal numeric keypad, allowing the deployment of secure access control applications. A low-cost single layer label comprising 10 RF loop scatterers is used to code information in the frequency domain. In addition, each resonator is associated to a digit in the decimal number system, and the difference in the spectrum caused by the touch event is exploited for the detection of each key pressing. The shape of the resonators has been carefully selected to be both highly resonant and to show high sensitivity to the presence or absence of the human finger. The concept is validated by measurements in an office environment using an FCC compliant low-cost chipless reader and microstrip vivaldi antennas. Simple detection algorithms are proposed for both identification and touch sensing in real environment.

Highly sensitive and flexible composite sensors with pressure and temperature sensing abilities are of great importance in human motion monitoring, robotic skins, and automobile seats when checking the boarding status. Several studies have been conducted to improve the temperature-pressure sensitivity; however, they require a complex fabrication process for micro-nanostructures, which are material-dependent. Therefore, there is a need to develop the structural designs to improve the sensing abilities. Herein, we demonstrate a flexible composite with an enhanced pressure and temperature sensing performance. Its structural design consists of a multilayered composite construction with an elastic modulus gradient. Controlled stress concentration and distribution induced by a micropatterned structure between the layers improves its pressure and temperature sensing performance. The proposed composite sensor can monitor a wide range of pressure and temperature stimuli and also has potential applications as an automotive seat sensor for simultaneous human temperature detection and occupant weight sensing.

In this paper, a displacement sensor with an electrically extremely small size and high sensitivity is proposed based on an elaborately designed metamaterial element, i.e., coupled split-ring resonators (SRRs). The sensor consists of a feeding structure with a rectangular opening loop and a sensing structure with double-layer coupled SRRs. The movable double-layer structures can be used to measure the relative displacement. The size of microwave displacement sensors can be significantly reduced due to the compact feeding and sensing structures. By adjusting the position of the split gap within the resonator, the detection directions of the displacement sensing can be further expanded accordingly (along with the x- or y-axis) without increasing its physical size. Compared with previous works, the extremely compact size of 0.05λ0 × 0.05λ0 (λ0 denotes the free-space wavelength), a high sensitivity, and a high quality factor (Q-factor) can be achieved by the proposed sensor. From the perspective of the advantages above, the proposed sensor holds promise for being applied in many high-precision industrial measurement scenarios.

Comparative analysis of the sensitivity of two surface plasmon resonance (SPR) biosensors was conducted on a single-metallic Au sensor and bimetallic Ag–Au sensor, using a cathepsin S sensor as an example. Numerically modeled resonance curves of Au and Ag–Au layers, with parameters verified by the results of experimental reflectance measurement of real-life systems, were used for the analysis of these sensors. Mutual relationships were determined between ∂Y/∂n components of sensitivity of the Y signal in the SPR measurement to change the refractive index n of the near-surface sensing layer and ∂n/∂c sensitivity of refractive index n to change the analyte’s concentration, c, for both types of sensors. Obtained results were related to experimentally determined calibration curves of both sensors. A characteristic feature arising from the comparison of calibration curves is the similar level of Au and Ag–Au biosensors’ sensitivity in the linear range, where the signal of the AgAu sensor is at a level several times greater. It was shown that the influence of sensing surface morphology on the ∂n/∂c sensitivity component had to be incorporated to explain the features of calibration curves of sensors. The shape of the sensory surface relief was proposed to increase the sensor sensitivity at low analyte concentrations.

An electrothermal piezoresistive cantilever (EPC) sensor is a low-cost MEMS resonance sensor that provides self-actuating and self-sensing capabilities. In the platform, which is of MEMS-cantilever shape, the EPC sensor offers several advantages in terms of physical, chemical, and biological sensing, e.g., high sensitivity, low cost, simple procedure, and quick response. However, a crosstalk effect is generated by the coupling of parasitic elements from the actuation part to the sensing part. This study presents a parasitic feedthrough subtraction (PFS) method to mitigate a crosstalk effect in an electrothermal piezoresistive cantilever (EPC) resonance sensor. The PFS method is employed to identify a resonance phase that is, furthermore, deployed to a phase-locked loop (PLL)-based system to track and lock the resonance frequency of the EPC sensor under cigarette smoke exposure. The performance of the EPC sensor is further evaluated and compared to an AFM-microcantilever sensor and a commercial particle counter (DC1100-PRO). The particle mass–concentration measurement result generated from cigarette-smoke puffs shows a good agreement between these three detectors.

Early detection of thyroid nodules can greatly contribute to the prediction of cancer burdening and the steering of personalized management. We propose a novel multimodal MRI-based computer-aided diagnosis (CAD) system that differentiates malignant from benign thyroid nodules. The proposed CAD is based on a novel convolutional neural network (CNN)-based texture learning architecture. The main contribution of our system is three-fold. Firstly, our system is the first of its kind to combine T2-weighted MRI and apparent diffusion coefficient (ADC) maps using a CNN to model thyroid cancer. Secondly, it learns independent texture features for each input, giving it more advanced capabilities to simultaneously extract complex texture patterns from both modalities. Finally, the proposed system uses multiple channels for each input to combine multiple scans collected into the deep learning process using different values of the configurable diffusion gradient coefficient. Accordingly, the proposed system would enable the learning of more advanced radiomics with an additional advantage of visualizing the texture patterns after learning. We evaluated the proposed system using data collected from a cohort of 49 patients with pathologically proven thyroid nodules. The accuracy of the proposed system has also been compared against recent CNN models as well as multiple machine learning (ML) frameworks that use hand-crafted features. Our system achieved the highest performance among all compared methods with a diagnostic accuracy of 0.87, specificity of 0.97, and sensitivity of 0.69. The results suggest that texture features extracted using deep learning can contribute to the protocols of cancer diagnosis and treatment and can lead to the advancement of precision medicine.

A sensitive and efficient method for microRNAs (miRNAs) detection is strongly desired by clinicians and, in recent years, the search for such a method has drawn much attention. There has been significant interest in using miRNA as biomarkers for multiple diseases and conditions in clinical diagnostics. Presently, most miRNA detection methods suffer from drawbacks, e.g., low sensitivity, long assay time, expensive equipment, trained personnel, or unsuitability for point-of-care. New methodologies are needed to overcome these limitations to allow rapid, sensitive, low-cost, easy-to-use, and portable methods for miRNA detection at the point of care. In this work, to overcome these shortcomings, we integrated capacitive sensing and alternating current electrokinetic effects to detect specific miRNA-16b molecules, as a model, with the limit of detection reaching 1.0 femto molar (fM) levels. The specificity of the sensor was verified by testing miRNA-25, which has the same length as miRNA-16b. The sensor we developed demonstrated significant improvements in sensitivity, response time and cost over other miRNA detection methods, and has application potential at point-of-care.

Digging two vertical shafts with the multiple blasts technique gave the opportunity to measure the induced angular motions in a horizontal plane with well-defined positions of sources. Three kinds of rotation rate sensors, sharing an underground location, were used. Two of them—a Fiber-Optic System for Rotational Events &amp, phenomena Monitoring (FOSREM) and a prototypical seismometer housing the liquid-filled torus—sensed the rotation, while a microarray of two double-pendulum seismometers sensed both the rotation and symmetric strain. The FOSREM was sampled at 656.168 Hz, while all the others were only sampled at 100 Hz. There were considerable differences within the results gathered from the mining blasts, which should be attributed to two causes. The first one is the difference in principles of the operation and sampling rates of the devices used, while the other is the complex and spatially variable character of the studied wave fields. Additionally, we established that the liquid-filled sensor, due to its relatively low sensitivity, proved to be viable only during a registration of strong ground motions. Overall, a comparative study of three different rotational seismometers was performed during mining-induced strong ground motions with well-localized sources.

This paper proposes a highly sensitive surface plasmon resonance (SPR) refractive index sensor based on the photonic crystal fiber (PCF). The optical properties of the PCF are investigated by modulating the refractive index of a liquid analyte. The finite element method (FEM) is used to calculate and analyze the PCF structure. After optimization, the fiber can achieve high linearity of 0.9931 and an average refractive index sensitivity of up to 14,771.4 nm/RIU over a refractive index range from 1.47 to 1.52, with the maximum wavelength sensitivity of 18,000.5 nm/RIU. The proposed structure can be used in various sensing applications, including biological monitoring, environmental monitoring, and chemical production with the modification and analysis of the proposed structure.

The constant development and miniaturization of MEMS sensors invariably provides new possibilities for their use in health-related and medical applications. The application of MEMS devices in posturographic systems allows faster diagnosis and significantly facilitates the work of medical staff. MEMS accelerometers constitute a vital part of such systems, particularly those intended for monitoring patients with imbalance disorders. The correct design of such sensors is crucial for gathering data about patient movement and ensuring the good overall performance of the entire system. This paper presents the design and measurements of a three-axis accelerometer dedicated for use in a device which tracks patient movement. Its main focus is the characterization of the sensor, comparing different designs and evaluating the impact of the packaging and readout circuit integration on sensor operation. Extensive testing and measurements confirm that the designed accelerometer works correctly and allows identifying the best design in terms of sensitivity/stability. Moreover, the response of the proposed sensor as a function of the applied acceleration demonstrates very good linearity only if the readout circuit is integrated in the same package as the MEMS sensor.

As key components of low-cost sensor systems in air quality monitoring, electrochemical gas sensors have recently received a lot of interest but suffer from unit-to-unit variability and different drift components such as aging and concept drift, depending on the calibration approach. Magnitudes of drift can vary across sensors of the same type, and uniform recalibration intervals might lead to insufficient performance for some sensors. This publication evaluates the opportunity to perform predictive maintenance solely by the use of calibration data, thereby detecting the optimal moment for recalibration and improving recalibration intervals and measurement results. Specifically, the idea is to define confidence regions around the calibration data and to monitor the relative position of incoming sensor signals during operation. The emphasis lies on four algorithms from unsupervised anomaly detection—namely, robust covariance, local outlier factor, one-class support vector machine, and isolation forest. Moreover, the behavior of unit-to-unit variability and various drift components on the performance of the algorithms is discussed by analyzing published field experiments and by performing Monte Carlo simulations based on sensing and aging models. Although unsupervised anomaly detection on calibration data can disclose the reliability of measurement results, simulation results suggest that this does not translate to every sensor system due to unfavorable arrangements of baseline drifts paired with sensitivity drift.

Background and Objective: The use of computer-aided detection (CAD) systems can help radiologists make objective decisions and reduce the dependence on invasive techniques. In this study, a CAD system that detects and identifies prostate cancer from diffusion-weighted imaging (DWI) is developed. Methods: The proposed system first uses non-negative matrix factorization (NMF) to integrate three different types of features for the accurate segmentation of prostate regions. Then, discriminatory features in the form of apparent diffusion coefficient (ADC) volumes are estimated from the segmented regions. The ADC maps that constitute these volumes are labeled by a radiologist to identify the ADC maps with malignant or benign tumors. Finally, transfer learning is used to fine-tune two different previously-trained convolutional neural network (CNN) models (AlexNet and VGGNet) for detecting and identifying prostate cancer. Results: Multiple experiments were conducted to evaluate the accuracy of different CNN models using DWI datasets acquired at nine distinct b-values that included both high and low b-values. The average accuracy of AlexNet at the nine b-values was 89.2±1.5% with average sensitivity and specificity of 87.5±2.3% and 90.9±1.9%. These results improved with the use of the deeper CNN model (VGGNet). The average accuracy of VGGNet was 91.2±1.3% with sensitivity and specificity of 91.7±1.7% and 90.1±2.8%. Conclusions: The results of the conducted experiments emphasize the feasibility and accuracy of the developed system and the improvement of this accuracy using the deeper CNN.

Dual-parameter measurements of refractive index and methane concentration based on electromagnetic Fano resonance are proposed. Two independent Fano resonances can be produced through electric dipole and toroidal dipole resonance in an all-dielectric metasurface separately. The linear relationship between the spectral peak-shifts and the parameters to be measured will be obtained directly. The refractive index (RI) sensitivity and gas sensitivity are 1305.6 nm/refractive index unit (RIU), −0.295 nm/% for one resonance peak (dip1), and 456.6 nm/RIU, −0.61 nm/% for another resonance peak (dip2). Such a metasurface has simpler structure and higher sensitivity, which is beneficial for environmental gas monitoring or multi-parameter measurements.

Microwave planar sensors employ conventional passive complementary split ring resonators (CSRR) as their sensitive region. In this work, a novel planar reflective sensor is introduced that deploys CSRRs as the front-end sensing element at fres=6 GHz with an extra loss-compensating negative resistance that restores the dissipated power in the sensor that is used in dielectric material characterization. It is shown that the S11 notch of −15 dB can be improved down to −40 dB without loss of sensitivity. An application of this design is shown in discriminating different states of vanadium redox solutions with highly lossy conditions of fully charged V5+ and fully discharged V4+ electrolytes.

Electrowetting-on-dielectric (EWOD) is a microfluidic technology used for manipulating liquid droplets at microliter to nanoliter scale. EWOD has the ability to facilitate the accurate manipulation of liquid droplets, i.e., transporting, dispensing, splitting, and mixing. In this work, EWOD fabrication with suitable and affordable materials is proposed for creating EWOD lab-on-a-chip platforms. The EWOD platforms are applied for the diagnosis of early mortality syndrome (EMS) in shrimp by utilizing the colorimetric loop-mediated isothermal amplification method with pH-sensitive xylenol orange (LAMP–XO) diagnosis technique. The qualitative sensitivity is observed by comparing the limit of detection (LOD) while performing the LAMP–XO diagnosis test on the proposed lab-on-a-chip EWOD platform, alongside standard LAMP laboratory tests. The comparison results confirm the reliability of EMS diagnosis on the EWOD platform with qualitative sensitivity for detecting the EMS DNA plasmid concentration at 102 copies in a similar manner to the common LAMP diagnosis tests.

The paper explores the possibility of using high-resolution fiber Bragg grating (FBG) sensing technology for on-specimen strain measurement in the laboratory. The approach provides a means to assess the surface deformation of the specimen, both the axial and radial, through a chain of FBG sensor (C-FBG), in a basic setup of a uniaxial compression test. The method is cost-effective, straightforward and can be commercialized. Two C-FBG, one was applied directly to the sample (FBGBare), and the other was packaged (FBGPack) for ease of application. The approach measures the local strain with high-resolution and accuracy levels that match up to the existing local strain measuring sensors. The approach enables the evaluation of small-strain properties of the specimen intelligently. The finite element model analysis deployed has proven the adaptability of the technique for measuring material deformation. The adhesive thickness and packaging technique have been shown to influence the sensitivity of the FBG sensors. Owing to the relative ease and low-cost of instrumentation, the suggested method has a great potential to be routinely applied for elemental testing in the laboratory.

Deep reinforcement learning (DRL) has been utilized in numerous computer vision tasks, such as object detection, autonomous driving, etc. However, relatively few DRL methods have been proposed in the area of image segmentation, particularly in left ventricle segmentation. Reinforcement learning-based methods in earlier works often rely on learning proper thresholds to perform segmentation, and the segmentation results are inaccurate due to the sensitivity of the threshold. To tackle this problem, a novel DRL agent is designed to imitate the human process to perform LV segmentation. For this purpose, we formulate the segmentation problem as a Markov decision process and innovatively optimize it through DRL. The proposed DRL agent consists of two neural networks, i.e., First-P-Net and Next-P-Net. The First-P-Net locates the initial edge point, and the Next-P-Net locates the remaining edge points successively and ultimately obtains a closed segmentation result. The experimental results show that the proposed model has outperformed the previous reinforcement learning methods and achieved comparable performances compared with deep learning baselines on two widely used LV endocardium segmentation datasets, namely Automated Cardiac Diagnosis Challenge (ACDC) 2017 dataset, and Sunnybrook 2009 dataset. Moreover, the proposed model achieves higher F-measure accuracy compared with deep learning methods when training with a very limited number of samples.

Fiber Bragg gratings (FBGs) are widely applied in optical sensing systems due to their advantages including being simple to use, high sensitivity, and having great potential for integration into optical communication systems. A common method used for FBG sensing systems is wavelength interrogation. The performance of interrogation based sensing systems is significantly determined by the accuracy of the wavelength peak detection processing. Direct maximum value readout (DMVR) is the simplest peak detection method. However, the detection accuracy of DMVR is sensitive to noise and the sampling resolution. Many modified peak detection methods, such as filtering and curve fitting schemes, have been studied in recent decades. Though these methods are less sensitive to noise and have better sensing accuracy at lower sampling resolutions, they also confer increased processing complexity. As massive sensors may be deployed for applications such as the Internet of things (IoT) and artificial intelligence (AI), lower levels of processing complexity are required. In this paper, an efficient scheme applying a three-point peak detection estimator is proposed and studied, which shows a performance that is close to the curve fitting methods along with reduced complexity. A proof-of-concept experiment for temperature sensing is performed. 34% accuracy improvement compared to the DMVR is demonstrated.

This study presents a developed ultrasonic water level detection (UWLD) system with an energy-efficient design and dual-target monitoring. The water level monitoring system with a non-contact sensor is one of the suitable methods since it is not directly exposed to water. In addition, a web-based monitoring system using a cloud computing platform is a well-known technique to provide real-time water level monitoring. However, the long-term stable operation of remotely communicating units is an issue for real-time water level monitoring. Therefore, this paper proposes a UWLD unit using a low-power consumption design for renewable energy harvesting (e.g., solar) by controlling the unit with dual microcontrollers (MCUs) to improve the energy efficiency of the system. In addition, dual targeting to the pavement and streamside is uniquely designed to monitor both the urban inundation and stream overflow. The real-time water level monitoring data obtained from the proposed UWLD system is analyzed with water level changing rate (WLCR) and water level index. The quantified WLCR and water level index with various sampling rates present a different sensitivity to heavy rain.

This paper proposes an action recognition algorithm based on the capsule network and Kalman filter called “Reading Pictures Instead of Looking” (RPIL). This method resolves the convolutional neural network’s over sensitivity to rotation and scaling and increases the interpretability of the model as per the spatial coordinates in graphics. The capsule network is first used to obtain the components of the target human body. The detected parts and their attribute parameters (e.g., spatial coordinates, color) are then analyzed by Bert. A Kalman filter analyzes the predicted capsules and filters out any misinformation to prevent the action recognition results from being affected by incorrectly predicted capsules. The parameters between neuron layers are evaluated, then the structure is pruned into a dendritic network to enhance the computational efficiency of the algorithm. This minimizes the dependence of in-depth learning on the random features extracted by the CNN without sacrificing the model’s accuracy. The association between hidden layers of the neural network is also explained. With a 90% observation rate, the OAD dataset test precision is 83.3%, the ChaLearn Gesture dataset test precision is 72.2%, and the G3D dataset test precision is 86.5%. The RPILNet also satisfies real-time operation requirements (&gt, 30 fps).

As the safety of a human body is the main priority while interacting with robots, the field of tactile sensors has expanded for acquiring tactile information and ensuring safe human–robot interaction (HRI). Existing lightweight and thin tactile sensors exhibit high performance in detecting their surroundings. However, unexpected collisions caused by malfunctions or sudden external collisions can still cause injuries to rigid robots with thin tactile sensors. In this study, we present a sensitive balloon sensor for contact sensing and alleviating physical collisions over a large area of rigid robots. The balloon sensor is a pressure sensor composed of an inflatable body of low-density polyethylene (LDPE), and a highly sensitive and flexible strain sensor laminated onto it. The mechanical crack-based strain sensor with high sensitivity enables the detection of extremely small changes in the strain of the balloon. Adjusting the geometric parameters of the balloon allows for a large and easily customizable sensing area. The weight of the balloon sensor was approximately 2 g. The sensor is employed with a servo motor and detects a finger or a sheet of rolled paper gently touching it, without being damaged.

The Micro-Opto-Electro-Mechanical Systems (MOEMS) accelerometer is a new type of accelerometer that combines the merits of optical measurement and Micro-Electro-Mechanical Systems (MEMS) to enable high precision, small volume, and anti-electromagnetism disturbance measurement of acceleration, which makes it a promising candidate for inertial navigation and seismic monitoring. This paper proposes a modified micro-grating-based accelerometer and introduces a new design method to characterize the grating interferometer. A MEMS sensor chip with high sensitivity was designed and fabricated, and the processing circuit was modified. The micro-grating interference measurement system was modeled, and the response sensitivity was analyzed. The accelerometer was then built and benchmarked with a commercial seismometer in detail. Compared to the previous prototype in the experiment, the results indicate that the noise floor has an ultra-low self-noise of 15 ng/Hz1/2.

Magnetoelectric resonators have been studied for the detection of small amplitude and low frequency magnetic fields via the delta-E effect, mainly in fundamental bending or bulk resonance modes. Here, we present an experimental and theoretical investigation of magnetoelectric thin-film cantilevers that can be operated in bending modes (BMs) and torsion modes (TMs) as a magnetic field sensor. A magnetoelastic macrospin model is combined with an electromechanical finite element model and a general description of the delta-E effect of all stiffness tensor components Cij is derived. Simulations confirm quantitatively that the delta-E effect of the C66 component has the promising potential of significantly increasing the magnetic sensitivity and the maximum normalized frequency change ∆fr. However, the electrical excitation of TMs remains challenging and is found to significantly diminish the gain in sensitivity. Experiments reveal the dependency of the sensitivity and ∆fr of TMs on the mode number, which differs fundamentally from BMs and is well explained by our model. Because the contribution of C11 to the TMs increases with the mode number, the first-order TM yields the highest magnetic sensitivity. Overall, general insights are gained for the design of high-sensitivity delta-E effect sensors, as well as for frequency tunable devices based on the delta-E effect.

Long-period optical fiber gratings (LPGs) are one of the widely used concepts for the sensing of refractive index (RI) changes. Negative curvature hollow-core fibers (NCHCFs), with their relatively large internal diameters that are easy to fill with liquids, appear as a very interesting medium to combine with the idea of LPGs and use for RI sensing. However, to date, there has been no investigation of the RI sensing capabilities of the NCHCF-based LPGs. The results presented in the paper do not only address this matter, but also compare the RI sensitivities of the NCHCFs alone and the gratings. By modeling two revolver-type fibers, with their internal diameters reflecting the results of the possible LPG-inscription process, the authors show that the fibers’ transmission windows shift in response to the RI change, resulting in changes in RI sensitivities as high as −4411 nm/RIU. On the contrary, the shift in the transmission dip of the NCHCF-based LPGs corresponds to a sensitivity of −658 nm/RIU. A general confirmation of these results was ensured by comparing the analytical formulas describing the sensitivities of the NCHCFs and the NCHCF-based LPGs.

Surgical gestures detection can provide targeted, automated surgical skill assessment and feedback during surgical training for robot-assisted surgery (RAS). Several sources including surgical videos, robot tool kinematics, and an electromyogram (EMG) have been proposed to reach this goal. We aimed to extract features from electroencephalogram (EEG) data and use them in machine learning algorithms to classify robot-assisted surgical gestures. EEG was collected from five RAS surgeons with varying experience while performing 34 robot-assisted radical prostatectomies over the course of three years. Eight dominant hand and six non-dominant hand gesture types were extracted and synchronized with associated EEG data. Network neuroscience algorithms were utilized to extract functional brain network and power spectral density features. Sixty extracted features were used as input to machine learning algorithms to classify gesture types. The analysis of variance (ANOVA) F-value statistical method was used for feature selection and 10-fold cross-validation was used to validate the proposed method. The proposed feature set used in the extra trees (ET) algorithm classified eight gesture types performed by the dominant hand of five RAS surgeons with an accuracy of 90%, precision: 90%, sensitivity: 88%, and also classified six gesture types performed by the non-dominant hand with an accuracy of 93%, precision: 94%, sensitivity: 94%.