Search Results (73)

Acute myocardial infarction (AMI) is a rapidly progressing cardiovascular condition associated with high mortality. Myoglobin is an early biomarker of AMI; however, its detection using conventional methods is limited by complex workflows and low resistance to interference. In this study, we developed an integrated myoglobin detection platform that combined magneto-immunoassay with microfluidic technology. A giant magnetoresistance (GMR) sensor was fabricated using micro-electro-mechanical systems (MEMS). The designed microfluidic chip integrated sample pretreatment, immunoreaction, and magnetic signal capture functionalities. Its built-in GMR sensor, labeled with magnetic nanoparticles, directly converted the “antigen–antibody” biochemical signal into detectable magnetoresistance changes, thereby enabling the indirect detection of myoglobin. A magneto-immunoassay analysis system consisted of a fluidic drive, magnetic field control, and data acquisition functions. Various key parameters were optimized, including EDC/NHS concentration, antibody concentration, and magnetic bead size. Under the optimal conditions and using 1 μm magnetic beads as labels and an external detection magnetic field of 60 Oe, the platform successfully detected myoglobin at 75 ng/mL with ∆MR ≥ 0.202%. Specificity tests demonstrated that the platform had high specificity for myoglobin and could effectively distinguish myoglobin from bovine serum albumin (BSA) and troponin I. This study presents a rapid, accurate myoglobin detection platform that can be applied for the early diagnosis of AMI. Full article

The intrinsic noise of giant magnetoresistive (GMR) sensors is large at low frequencies, and their resolution is inevitably significantly limited. Investigation of GMR noise requires the use of measurement systems that have lower noise than the sample. In this context, the main purpose of this study is to evaluate the effectiveness of two electronic noise measurement configurations of a single GMR sensing element. The first method connects the sample in a voltage divider configuration and the second method connects in a Wheatstone bridge configuration. Three amplification set-ups were investigated: a low-noise amplifier, an ultra-low-noise amplifier and an instrumentation amplifier. Using cross-correlation, the noise of the measurement system introduced by the amplifiers was reduced. Noise spectra were recorded at room temperature in the frequency range of 0.5 Hz to 10 kHz, under different sample bias voltages. The measurements were performed in zero applied magnetic field and in a field corresponding to the maximum sensitivity of the sensor. From the noise spectra, the detectivity of the sensor was determined to be in the 200–300 nT/√Hz range. Good agreement was observed between the results obtained using all three set-ups, suggesting the effectiveness of the noise measurement systems applied to the magnetoresistive sensor. Full article

Learning from demonstration with multiple executions must contend with time warping, sensor noise, and alternating quasi-stationary and transition phases. We propose a label-free pipeline that couples unsupervised segmentation, duration-explicit alignment, and probabilistic encoding. A dimensionless multi-feature saliency (velocity, acceleration, curvature, direction-change rate) yields scale-robust keyframes via persistent peak–valley pairs and non-maximum suppression. A hidden semi-Markov model (HSMM) with explicit duration distributions is jointly trained across demonstrations to align trajectories on a shared semantic time base. Segment-level probabilistic motion models (GMM/GMR or ProMP, optionally combined with DMP) produce mean trajectories with calibrated covariances, directly interfacing with constrained planners. Feature weights are tuned without labels by minimizing cross-demonstration structural dispersion on the simplex via CMA-ES. Across UAV flight, autonomous driving, and robotic manipulation, the method reduces phase-boundary dispersion by 31% on UAV-Sim and by 30–36% under monotone time warps, noise, and missing data (vs. HMM); improves the sparsity–fidelity trade-off (higher time compression at comparable reconstruction error) with lower jerk; and attains nominal 2σ coverage (94–96%), indicating well-calibrated uncertainty. Ablations attribute the gains to persistence plus NMS, weight self-calibration, and duration-explicit alignment. The framework is scale-aware and computationally practical, and its uncertainty outputs feed directly into MPC/OMPL for risk-aware execution. Full article

This paper presents a real-time framework for bilateral gait reconstruction and adaptive joint control using sparse inertial sensing. The system estimates full lower-limb motion from a single-side inertial measurement unit (IMU) by applying a pipeline that includes signal smoothing, temporal alignment via Dynamic Time Warping (DTW), and motion modeling using Gaussian Mixture Models with Regression (GMM-GMR). Contralateral leg trajectories are inferred using both ideal and adaptive symmetry-based models to capture inter-limb variations. The reconstructed motion serves as reference input for joint-level control. A classical Proportional–Integral–Derivative (PID) controller is first evaluated, demonstrating satisfactory results under simplified dynamics but notable performance loss when virtual stiffness and gravity compensation are introduced. To address this, an adaptive fuzzy PID controller is implemented, which dynamically adjusts control gains based on real-time tracking error through a fuzzy inference system. This approach enhances control stability and motion fidelity under varying conditions. The combined estimation and control framework enables accurate bilateral gait tracking and smooth joint control using minimal sensing, offering a practical solution for wearable robotic systems such as exoskeletons or smart prosthetics. Full article

This paper reports on the high performance of a thick-waveguide guided mode resonance (GMR) sensor. Theoretical calculations revealed that when light incidents on the grating and excites the negative first-order diffraction order, by increasing the waveguide thickness, both a high sensitivity and high figure of merit (FOM) can be obtained. Experimentally, we achieved a sensitivity of 1255.78 nm/RIU, a resonance linewidth of 0.59 nm at the resonance wavelength of 535 nm, an FOM as high as 2128 RIU⁻¹, and a detection limit as low as 1.74 × 10⁻⁷ RIU. To our knowledge, this performance represents the highest comprehensive level for current GMR sensors. Additionally, the use of a microfluidic hemisphere and polymer materials effectively reduces the liquid consumption under oblique incidence and the fabrication cost in practical application. Overall, the proposed GMR sensor exhibits great potential in label-free biosensing. Full article

Guided mode resonance (GMR) sensors are known for their ultrasensitive and label-free detection, achieved by assessing refractive index (RI) variations on grating surfaces. However, conventional systems often require manual adjustments, which limits their practical applicability. Therefore, this study enhances the practicality of GMR sensors by introducing an optimized detection system based on the Jones matrix method. In addition, finite element method simulations were performed to optimize the GMR sensor structure parameter. The GMR sensor chip consists of three main components: a cyclic olefin copolymer (COC) substrate with a one-dimensional grating structure of a period of ~295 nm, a height of ~100 nm, and a ~130 nm thick TiO₂ waveguide layer that enhances the light confinement; an integrated COC microfluidic module featuring a microchannel; and flexible tubes for efficient sample handling. A GMR sensor in conjunction with a specially designed system was used to perform RI measurements across varying concentrations of sucrose. The results demonstrate its exceptional performance, with a normalized sensitivity (S_n) and RI resolution (R_s) of 0.4 RIU⁻¹ and 8.15 × 10⁻⁵ RIU, respectively. The proposed detection system not only offers improved user-friendliness and cost efficiency but also delivers an enhanced performance, making it ideal for scientific and industrial applications, including biosensing and optical metrology, where precise polarization control is crucial. Full article

Magnetic biosensors have wide applications in biological target detection due to their advantages such as low background noise, convenient detection, and low requirements for sample pretreatment. However, existing magnetic biosensors still have the drawback of low sensitivity compared to optical and electrochemical biosensors. This paper presents the novel design of a high-sensitivity magnetic biosensor by utilizing the magnetic field line convergence effect, which was applied to bacterial detection. The results indicate that it can achieve a detection limitation of 10 CFU/mL, demonstrating that it can be implemented in high-sensitivity biological target detection. Full article

Guided mode resonance (GMR)-based sensors have emerged as a promising technology for the early screening of cancer, offering advantages such as sensitivity, specificity, low cost, non-invasiveness, and portability. This review article provides a comprehensive overview of the latest advancements in GMR technology and its applications in biosensing, with a specific focus on cancer. The current state of cancer diagnosis and the critical need for point-of-care (POC) devices to address these challenges are discussed in detail. Furthermore, the review systematically explores various strategies employed in GMR-based cancer detection including design principles and the integration of advanced technologies. Additionally, it aims to provide researchers valuable insights for developing GMR sensors capable of detecting cancer biomarkers outside the laboratory environment. Full article

In this study, we developed an optofluidic chip consisting of a guided-mode resonance (GMR) sensor incorporated into a microfluidic chip to achieve simultaneous blood plasma separation and label-free albumin detection. A sedimentation chamber is integrated into the microfluidic chip to achieve plasma separation through differences in density. After a blood sample is loaded into the optofluidic chip in two stages with controlled flow rates, the blood cells are kept in the sedimentation chamber, enabling only the plasma to reach the GMR sensor for albumin detection. This GMR sensor, fabricated using plastic replica molding, achieved a bulk sensitivity of 175.66 nm/RIU. With surface-bound antibodies, the GMR sensor exhibited a limit of detection of 0.16 μg/mL for recombinant albumin in buffer solution. Overall, our findings demonstrate the potential of our integrated chip for use in clinical samples for biomarker detection in point-of-care applications. Full article

Current sensors play a vital role in power systems, industrial production, smart devices and other fields, which can provide critical current information in the systems for the safety and efficiency managements. The development of magneto-resistive effect technology in recent years expedites the research process of the current sensors in industrial-level applications. In the review, starting with the development status of the current sensors, the physical mechanisms of the relevant magneto-resistive effects and their early applications as the current sensors are introduced. Several design methods of the magnetic sensors, as well as their merits and shortcomings, are summarized. The performance parameters of the magnetic sensors based on AMR, GMR, TMR and Hall effects are reviewed, including the front-end amplification circuits and conditioning circuits. The industrial applications of the current sensors in the fields of automobiles and photovoltaic inverters are enumerated. The criterions for the current sensors to be used in different scenarios are discussed. In the future, it is imperative to continue the research and development of novel current sensors in order to satisfy the increasingly stringent demands of the industrial developments, in terms of the performance, cost and reliability of the current sensors. Full article

The main objective of the work is to investigate the capacity of a single magnetoresistance (MR) element to measure AC electrical currents. An instrumentation system is presented to characterize individually the four active elements of an MR bridge current sensor preserving their internal connections. The system suggests the possibility to sense electrical currents using only one element of the bridge opening the way to design new MR sensors based on this concept. Sensitivity, offset and non-linearity deviation were obtained using bridges of tunnel (TMR)- and giant (GMR)-based MR technologies. The single element embedded in a Wheatstone bridge configuration is used for practical current measurements in a 50 Hz line. An electronic circuitry is proposed to measure alternating (AC) currents with a single MR element, including a lock-in amplifier and an interface to properly convert the signal to its root mean square (rms) value with a resolution of 250 mA peak in the 125 A range. Full article

Guided-mode resonance (GMR) gratings have emerged as a promising sensing technology, with a growing number of applications in diverse fields. This study aimed to identify the optimal design parameters of a simple-to-fabricate and high-performance one-dimensional GMR grating. The structural parameters of the GMR grating were optimized, and a high-refractive-index thin film was simulated on the grating surface, resulting in efficient confinement of the electric field energy within the waveguide. Numerical simulations demonstrated that the optimized GMR grating exhibited remarkable sensitivity (252 nm/RIU) and an extremely narrow full width at half maximum (2 × 10⁻⁴ nm), resulting in an ultra-high figure of merit (839,666) at an incident angle of 50°. This performance is several orders of magnitude higher than that of conventional GMR sensors. To broaden the scope of the study and to make it more relevant to practical applications, simulations were also conducted at incident angles of 60° and 70°. This holistic approach sought to develop a comprehensive understanding of the performance of the GMR-based sensor under diverse operational conditions. Full article

Handheld biosensors have attracted substantial attention for numerous applications, including disease diagnosis, drug dosage monitoring, and environmental sensing. This study presents a novel handheld biosensor based on a gradient grating period guided-mode resonance (GGP-GMR) sensor. Unlike conventional GMR sensors, the proposed sensor’s grating period varies along the device length; hence, the resonant wavelength varies linearly along the device length. If a GGP-GMR sensor is illuminated with a narrow band of light at normal incidence, the light resonates and reflects at a specific period but transmits at other periods; this can be observed as a dark band by using a complementary metal oxide semiconductor (CMOS) underneath the sensor. The concentration of a target analyte can be determined by monitoring the shift of this dark band. We designed and fabricated a handheld device incorporating a light-emitting diode (LED) light source, the necessary optics, an optofluidic chip with an embedded GGP-GMR sensor, and a CMOS. LEDs with different beam angles and bandpass filters with different full width at half maximum values were investigated to optimize the dark band quality and improve the accuracy of the subsequent image analysis. Substrate materials with different refractive indices and waveguide thicknesses were also investigated to maximize the GGP-GMR sensor’s figure of merit. Experiments were performed to validate the proposed handheld biosensor, which achieved a limit of detection (LOD) of 1.09 × 10⁻³ RIU for bulk solution measurement. The sensor’s performance in the multiplexed detection of albumin and creatinine solutions at concentrations of 0–500 μg/mL and 0–10 mg/mL, respectively, was investigated; the corresponding LODs were 0.66 and 0.61 μg/mL. Full article

Integrating biosensors with smartphones is becoming an increasingly popular method for detecting various biomolecules and could replace expensive laboratory-based instruments. In this work, we demonstrate a novel smartphone-based biosensor system with a gradient grating period guided-mode resonance (GGP-GMR) sensor. The sensor comprises numerous gratings which each correspond to and block the light of a specific resonant wavelength. This results in a dark band, which is observed using a CCD underneath the GGP-GMR sensor. By monitoring the shift in the dark band, the concentration of a molecule in a sample can be determined. The sensor is illuminated by a light-emitting diode, and the light transmitted through the GGP-GMR sensor is directly captured by a smartphone, which then displays the results. Experiments were performed to validate the proposed smartphone biosensor and a limit of detection (LOD) of 1.50 × 10⁻³ RIU was achieved for sucrose solutions. Additionally, multiplexed detection was demonstrated for albumin and creatinine solutions at concentrations of 0–500 and 0–1 mg/mL, respectively; the corresponding LODs were 1.18 and 20.56 μg/mL. Full article

In light of the nonlinearity, high dimensionality, and time-varying nature of the operational conditions of the pulverizer in power plants, as well as the challenge of the real-time monitoring of quality variables in the process, a data-driven KPCA–Bagging–GMR framework for soft sensors using reduced dimensions and ensemble learning is proposed. Firstly, the methodology employs a Kernel Principal Component Analysis to effectively reduce the dimensionality of the collected process data in a nonlinear manner. Secondly, the reduced principal components are then utilized to reconstruct a refined set of input samples, followed by the application of the Bagging algorithm to obtain multiple subsets of the samples and develop corresponding Gaussian Mixture Regression models. Ultimately, the fusion output is achieved by calculating the weights of each local model based on Bayesian posterior probabilities. By conducting simulation experiments on the coal mill, the proposed approach has been validated as demonstrating superior predictive accuracy and excellent generalization capabilities. Full article