We present the design, fabrication, and testing of a microscale positioning system. The system was designed in the SUMMiT V process to produce in-plane, bidirectional, micrometer-scale linear motion of a shuttle using a ratcheting mechanism and multilayered chevron actuators. A single latching system with oppositely faced ratchet teeth on either side of the shuttle is used for achieving the bidirectional actuation. The device has a smaller footprint and fewer electrical connections compared to similar devices, without compromising performance. Moreover, this device is capable of generating more force compared to previous devices. A LabVIEW-based optical characterization setup was developed for automated testing of the device. The device produced a maximum displacement of ~180 μm with a step size of 9 μm.

We report on the design, fabrication, and testing of a bidirectional two-axis MEMS positioning system that is used to move a stage. Sandia National Laboratories' MEMS foundry process was utilized for the fabrication of the device; this process incorporates five layers of polysilicon and four sacrificial layers of silicon dioxide. The positioning assembly consists of a pin and slotted-arm arrangement, wherein the pin is constrained between the slotted arms and the positioning of the pin is achieved by identical electrostatic drives on both axes. The electrostatic drives consist of a comb drive interfaced with a compliant-mechanism-based distance multiplier. The stage is connected to a cantilever arm extending from the positioning pin. The crosstalk between the X- and Y-axes was less than 0.3 ¿m through displacements of ~30 ¿m in both X- and Y-directions. The displacement shows a quadratic relationship with the applied voltage.

User authentication (UA) is the process by which biometric techniques are used by a person to gain access to a physical or virtual site. UA has been implemented in various applications such as financial transactions, data privacy, and access control. Various techniques, such as facial and fingerprint recognition, have been proposed for healthcare monitoring to address biometric recognition problems. Photoplethysmography (PPG) technology is an optical sensing technique which collects volumetric blood change data from the subject’s skin near the fingertips, earlobes, or forehead. PPG signals can be readily acquired from devices such as smartphones, smartwatches, or web cameras. Classical machine learning techniques, such as decision trees, support vector machine (SVM), and k-nearest neighbor (kNN), have been proposed for PPG identification. We developed a UA classification method for smart devices using long short-term memory (LSTM). Specifically, our UA classifier algorithm uses raw signals so as not to lose the specific characteristics of the PPG signal coming from each user’s specific behavior. In the UA context, false positive and false negative rates are crucial. We recruited thirty healthy subjects and used a smartphone to take PPG data. Experimental results show that our Bi-LSTM-based UA algorithm based on the feature-based machine learning and raw data-based deep learning approaches provides 95.0% and 96.7% accuracy, respectively.

This paper describes the design, assembly, and implementation of a hand-held, magnetic-field-based sensor system that can be adapted for a variety of sensing applications. The miniaturized system is based on the Chemical Identification by Magneto-Elastic Sensing (ChIMES) technology, which uses three concentric solenoid coils to wirelessly interrogate a sensor body comprised of a response material coupled to a magnetoelastic wire. The response material expands when it encounters a target, imposing mechanical stress on the wire and altering its magnetic permeability. The sensor bodies are passive, requiring no external power source, and they are small, measuring about 15 mm in length and 0.3 mm in diameter. Up to four sensor bodies can be configured as an evenly spaced linear array. The sensor system operates by applying a low-frequency, current-stabilized, filtered triangle wave to a uniform-density excitation coil to switch the magnetic domains within the wire. The responses from the sensors are picked up by a detection coil as stress-induced changes in the Faraday voltage, and the strong magnetic field induced by the excitation coil in the detection coil is nullified by a cancellation coil reverse-wound in series with the detection coil. The responses of the sensors in an array are separated in time by a linear gradient DC biasing coil. The sensors can be interrogated through metallic and nonmetallic barriers. The signals from the detection coil and the excitation coil are digitized by a pair of bipolar analog-to-digital converters. A Raspberry Pi single-board computer and associated software performs data acquisition and controls all aspects of the sensor system hardware. The program allows the user to select the number of sensors in the array, the type of signal that is being collected, and the number of samples to take. The program also allows for signal processing of the sensor data, such as baseline correction. The program can differentiate sensor peaks from each other and calculate the magnitude of each sensor response with less than 1% error. The data is then displayed along with a graph of the signal.