This letter presents the development of curved piezoelectric micromachined ultrasound transducers (pMUTs) using a novel chip-scale glass-blowing method on suspended glass templates. This new approach allows for controllable diaphragm curvature and high fill-factor arrays of varying sizes, making it possible to study the impact of the curvature on the performance of the pMUTs. A finite element analysis (FEA) model was built to guide the design. Thirty percent Scandium-doped Aluminum Nitride (Sc-AlN) was chosen for good piezoelectrical coefficient and biocompatibility. The 100 nm platinum/750 nm Sc-AlN/100 nm gold films are sputtered on the curved glass membrane with diameters ranging from 75 to 750 μ m. pMUT with a diameter of 150 μm with 5.6 μ m depth of curvature had resonance frequencies of 2.2 MHz along with center displacements up to 154 nm/V in air. Impedance measurements at resonance on the pMUTs show k2eff of 1.31%. Optimal curvatures were found experimentally and matched the FEA model. Changes in mode shapes were found when curvatures were deeper than the optimal value.

Many soft sensors have emerged to meet the needs of modern technologies, such as soft robotics, where high conformability and sensor adaptability are required. Little attention has been paid to how these soft sensors perform when used in a curved geometry, such as on the forearm or finger of the robot arm. We explore the effect of indentation by a flat object at radii of curvature as small as 10-mm—corresponding to a wide finger. The three-axis capacitive force sensor is composed of Ecoflex 00-30 pillars forming the dielectric and patterned carbon black/Ecoflex electrodes, mounted on a flexible printed circuit board for bottom connections. As the sensor radius of curvature decreases, the sensor increases in perceived sensitivity to applied force, with normal force sensitivity increasing from 0.27% change in capacitance per Newton (1.4%kPa ⁻¹ ) at 100-mm radii to 0.47%N ⁻¹ (2.4%kPa ⁻¹ ) at 10-mm radii, a 58% increase. Shear sensitivity in the noncurved direction increases by 155%, whereas in the circumferential direction, the sensitivity increases by 366%. This increase in sensitivity is due to the increase in local pressure due to decreasing surface area contact, especially in the circumferential direction when the sensor becomes greater than 25% of the radius of curvature. A similar trend is expected for grasping of rounded objects, where higher curvature will lead to a concentration of stress, with sharp edges being the extreme case. The sensor is applied to a robotic hand to assess normal and shear forces when gripping and lifting objects.

Micro-electro-mechanical systems (MEMS) accelerometers are entering high-end applications, thanks to their improved performance and low costs. Biaxial sensors able to measure two in-plane components of the external acceleration by exploiting a single proof-mass have been recently proposed and optimized both mechanically and electronically in order to minimize cross-axis sensitivity, while preserving a good symmetry between the two axes and high performance. To the author's best knowledge, only a very few commercial high-performance xz - or yz -biaxial MEMS accelerometers are available so far. In this work, we propose an innovative design strategy for xz -biaxial MEMS capacitive accelerometers immune from electrostatic nonlinearities and pull-in instabilities usually related to out-of-plane readout schemes. In particular, thanks to the proposed motion conversion mechanism realizable through the Thelma-double fabrication process of STMicroelectronics, we predict a sensitivity bigger than 30 fF/g on both axes, a cross-axis sensitivity smaller than 0.04% and a nonlinearity lower than 1% at 50 g.

Detecting specific cells and molecules in a sample poses a challenge in remote and resource-limited areas, which has resulted in the rising demand for point-of-care (POC) systems. Here, we demonstrate the design and testing of a smartphone-based tissue identification system, a valuable tool for preliminary screening, or basic tissue identification in resource-constraint settings, remote areas, or POC applications. In this letter, a system is developed to acquire the images of tissue cells such as liver, striated muscle, kidney, nerve, adipose, hyaline cartilage, and brain. The device has been calibrated by comparison with microscopic images of different types of tissue cells. The intensity profile shows relative similarity graphs between the images obtained from the microscope and the designed system. The developed system with upgradation has the potential to solve the problem of the availability of expensive facilities in remote regions.

The sensitivity of an immunosensor mainly depends on its electrode geometry and the proper choice of biorecognition element. The sensitivity of the immunosensor could be improved by varying the metallization ratio of the interdigitated electrodes (IDEs). In this letter, the effect of metallization ratio on the sensitivity of glassy carbon IDEs (GC-IDEs) based immunosensor was presented. The GC-IDEs were fabricated using standard photolithography. The capacitance response was obtained for various bovine serum albumin concentrations (0.125–1 µg/ml) using nonfaradaic electrochemical impedance spectroscopy measurements. The GC-IDEs showed a higher measurement sensitivity of 13.48% for a 0.7 metallization ratio as compared to 4.4% that of 0.5 metallization ratio.

This letter investigates the utilization of pulse heating and machine learning techniques to overcome the limitations associated with traditional testing methods for metal oxide semiconductor (MOS) gas sensors. These limitations include long-term drift, high power consumption, and challenges in multitasking. Pulsed heating is used to improve long-term stability and significantly reduce power consumption. Three machine learning approaches on top of two models are specially tailored to simultaneously handle gas identification and concentration detection tasks. The experimental results corroborate the robust classification aptitude of all three models and their satisfactory regression accuracy. Moreover, each model offers distinct advantages and can be utilized to meet particular requirements. This letter highlights the potential of pulse heating combined with machine learning to enhance the capabilities of MOS gas sensors.

In the autonomous driving environment, an obstructed view of the road can cause a delayed response time, making it challenging to avoid potential accidents. Thus, the method for detecting the blind spot is required, and the demand for sharing the detection results between vehicles through communication systems is increasing. In this letter, we propose a novel method of sharing the detection results between vehicles in automotive radar systems. We propose to use frequency-division multiplexing to reallocate the frequency band designated for automotive radar systems into two separate bands, one for the target detection and the other for the vehicle-to-vehicle (V2V) communications. In addition, the detection results are encoded by using amplitude modulation in the signals for V2V communications. The proposed method of sharing the detection results between vehicles is verified through several simulation scenarios. Through the simulations, the detection result of the preceding vehicle can be successfully shared to the following vehicle. Moreover, the average error between the actual and shared detection results is lower than 1%.

The Coulombic efficiency (CE) measurement is a promising method to quickly determine the potential lifetime of a battery cell. It will be demonstrated that high-precision measurements are required to determine the CE of modern cells. For this purpose, a comparison of our in-house developed precision hardware and two commercially available hardwares is performed. Furthermore, it is shown by means of a model that the CE can be misleading when it comes to the determination of cell aging. As a better alternative, the (conventional) differential capacity analysis ( ΔC ) is presented and the difference compared to the CE is demonstrated by a precision measurement.

Epilepsy is a neurological disorder that affects the brain, as well as the human body's nerves and spinal cord, adversely causing unusual and uncontrollable behavior. This letter presents the prediction of epilepsy based on machine learning modeling on the Children's Hospital Boston and Massachusetts Institute of Technology EEG Scalp database. The novel features are proposed, known as root mean square (RMS) of RMS, mean absolute value, and waveform length (RRWM) and generalized method of moments (GMM). This letter also presents finding the best machine learning model by comparing accuracies for a given dataset by varying window size and strides with various classification algorithms. The best model is observed using K-nearest neighbour (KNN) algorithm, Window size 300, and processed through hyper-parameter tuning deployed with the proposed features. Dataset preprocessing and conditioning are done using numpy, pandas, Keras, and SciKit-Learn libraries. The proposed feature RRWM performs the highest accuracy with 99.0256% and time complexity as 13 ms, whereas GMM being the fastest, has a single computation time of 8.99 ms for epilepsy detection.

Device-free localization is a promising technique for indoor positioning in infrastructures, as it eliminates the reliance on custom and costly hardware. In this study, we present the development of a compact and cost-effective bistatic radar sensor that utilizes DW3000 ultrawide-band (UWB) transceivers. The effectiveness of the sensor was evaluated through comprehensive assessments of various channels, with particular emphasis on channel 9, employing channel impulse response analysis. To enhance the accuracy of time delay estimation for multipath components scattered by moving targets, we developed a weighting function. The experimental results demonstrate the potential of device-free localization sensors as a viable solution for indoor positioning systems, offering enhanced accessibility and cost reduction compared to traditional approaches.

Passive 3D Time-of-Flight (ToF) imaging faces a significant challenge in accurate depth recovery, where the source position is unavailable. In this letter, we present a probabilistic approach based on the Kalman filter that keeps track of source location, thus avoiding the need for an initial guess to jointly determine the 3D source position and correct depth information. The proposed approach is able to reach a source location error of 0.8 cm by exploiting pseudo-measurements of the plane fitting constraint acquired by a passive ToF camera, which exploits a bistatic algorithm with a gradient descent method. Computer experiments are carried out to demonstrate the robustness of the proposed method in realistic scenarios where no initial guess is available. The results show the feasibility of blind source localization with mm accuracy. This contributes to widening the applicability of passive 3D imaging in a number of application fields, such as autonomous driving and robot navigation.

Inertial measurement units (IMUs) are used for inertial motion tracking (IMT) in a growing number of applications as sensor fusion methods are being advanced in three directions: magnetometer-free IMT methods that eliminate the effect of magnetic disturbances; sparse IMT approaches that lead to reduced setup complexity; and automatic self-calibration of sensor-to-segment positions or orientations. In this letter, we propose an approach that combines all three achievements and, for the first time, enables plug-and-play, magnetometer-free, and sparse IMT. This is accomplished by training a recurrent neural-network-based observer (RNNo) on just-in-time generated simulated motion data of kinematic chains. We demonstrate that domain-specific training data augmentations lead to a trained RNNo which zero shot generalizes to previously unseen experimental data and, thus, overcomes the sim-to-real gap. The trained RNNo achieves a tracking error of <4 degrees when estimating the relative pose of a three-segment kinematic chain with two hinge joints. The proposed method offers a novel simulation-data-driven approach for solving complex sparse sensing problems while assuring robust and plug-and-play generalizability to experimental data.