We demonstrate a methodology for non-contact classification of five different plastic types using an inexpensive direct time-of-flight (ToF) sensor, the AMS TMF8801, designed for consumer electronics. The direct ToF sensor measures the time for a brief pulse of light to return from the material with the intensity change and spatial and temporal spread of the returned light conveying information on the optical properties of the material. We use measured ToF histogram data of all five plastics, captured at a range of sensor to material distances, to train a classifier that achieves 96% accuracy on a test dataset. To extend the generality and provide insight into the classification process, we fit the ToF histogram data to a physics-based model that differentiates between surface scattering and subsurface scattering. Three optical parameters of the ratio of direct to subsurface intensity, the object distance, and the time constant of the subsurface exponential decay are used as features for a classifier that achieves 88% accuracy. Additional measurements at a fixed distance of 22.5 cm showed perfect classification and revealed that Poisson noise is not the most significant source of variation when measurements are taken over a range of object distances. In total, this work proposes optical parameters for material classification that are robust over object distance and measurable by miniature direct time-of-flight sensors designed for installation in smartphones.

We describe a custom and open source field-programmable gate array (FPGA)-based data acquisition (DAQ) system developed for electrophysiology and generally useful for closed-loop feedback experiments. FPGA acquisition and processing are combined with high-speed analog and digital converters to enable real-time feedback. The digital approach eases experimental setup and repeatability by allowing for system identification and in situ tuning of filter bandwidths. The FPGA system includes I2C and serial peripheral interface controllers, 1 GiB dynamic RAM for data buffering, and a USB3 interface to Python software. The DAQ system uses common HDMI connectors to support daughtercards that can be customized for a given experiment to make the system modular and expandable. The FPGA-based digital signal processing (DSP) is used to generate fourth-order digital infinite impulse response filters and feedback with microsecond latency. The FPGA-based DSP and an analog inner-loop are demonstrated via an experiment that rapidly steps the voltage of a capacitor isolated from the system by a considerable resistance using a feedback approach that adjusts the driving voltage based on the digitized capacitor current.

We are developing a data acquisition system (DAQ) for real-time feedback that uses FPGA based control of and acquisition from various electronic chips, or peripherals. Because these peripherals communicate over multiple protocols (SPI, I2C, LVDS) through an FPGA, we designed pyripherals to organize and abstract registers, the communication protocol, and the host computer interface to each communication controller. The software and firmware are designed for Opal Kelly FPGA modules, yet the Python developments are generally useful to organize communication with peripheral chips.

Direct time-of-flight (dToF) sensors that measure depth by pulsing a laser and timing the photon return are used in many applications, including consumer electronics for proximity sensing and depth map generation. A histogram of photon return times is measured and then processed to estimate object depth. By collecting many photons that span multiple bins of the histogram the final depth estimate interpolates between time-to-digital converter (TDC) bins to produce a result that is more precise than the converter resolution. The precision of this interpolation depends on the temporal spread of the measurement, the resolution of the TDC, and the number of signal and background photons measured. There is a need for dToF depth precision models to guide design and predict and tune performance during use. In this article, we present models that estimate sensor depth precision versus dToF design parameters and photons measured. We use Monte Carlo simulations and experimental measurements to prove the accuracy of the models. With proven models in hand, we investigate a dToF sensor design by first presenting the dependence of precision upon the TDC resolution and the signal-to-noise ratio. Second, we experimentally measure the depth precision versus the intensity of background illumination. The models closely match the measurements of background susceptibility and locate a transition point of background intensity below which precision is constant and above which the precision continuously degrades. Finally, experimental measurements demonstrate how the modeling enables dynamic tuning: from a single histogram we estimate precision, thus enabling sensor exposure time tuning for a target precision or prediction of the precision given a change in object distance or background illumination. This work presents straightforward models verified by simulation and measurement. These models guide dToF design and enable dynamic adjustments that benefit power-constrained usage scenarios.

Transcathether aortic heart valve replacement (TAVR) is a widespread approach to treating patients with severe aortic stenosis. A TAVR implant is ideally positioned to access numerous clinically relevant signals including arterial blood pressure, pulse wave velocity, electrocardiogram (ECG), patient motion, heart rate, respiration, and blood oxygenation. Unlike medical devices such as pacemakers, TAVR implants are purely mechanical structures with no sensing capabilities. In this work, we address this unmet clinical need by incorporating an Inter-Integrated Circuit (I2C) sensor network within a TAVR stent frame and designing sensor modules that can physically connect to the network at various landing zones. To illustrate this approach, we designed and built a sensor circuit board populated with a commercial inertial measurement unit (IMU) that can detect clinically useful metrics including pulse wave velocity at the aortic root. We use two spatially separated accelerometers to measure pulse wave propagation time with a standard deviation of 140 μs, which translates to an uncertainty of the pulse wave velocity of +/-0.2 m/s. The sensor modules connect to a customized stent frame containing the necessary I2C conductors. Our data suggest that a fully instrumented TAVR paradigm is feasible using this frame design and modular sensor approach.

Ultrasonic sensors have dominated miniaturized depth measurement applications such as robot collision avoidance and walking cane hazard detection yet have limited spatial resolution. Optical time-of-flight (ToF) depth sensors offer the potential for improved spatial resolution, however, ToF depth-sensing cameras may be too large and power-hungry for hand-held applications. We address this gap by experimentally evaluating an infrared ToF sensor (the ST VL53L1X) that uses a single-photon avalanche photodiode array to provide coarse spatial resolution while remaining miniaturized and low-power, thus allowing the generation of hazard maps in hand-held applications. We develop methods and present characterization results for distance measurement accuracy, noise, error, and tolerable ambient illumination. The IR ToF sensor sustains accuracy better than 2% up to a distance of 3000mm for a 73% reflective target in the presence of zero interfering ambient light. We characterize the spatial resolution enabled by this region-of-interest and find off-axis pointing of up to 15.7° in steps of 2.5°. Many hazard detection systems may be moving, which dynamically changes the position and pointing of the depth sensor. We demonstrate the use of a 9-degree-of-freedom (3-axis accelerometer, gyroscope, and magnetometer) inertial measurement unit (IMU) to track sensor pointing. The ToF sensor combined with an IMU forms the basis for a miniaturized depth mapping solution that consumes 97.5mW when operating at 30Hz, and requires simple serial interfaces to a microcontroller.

Polymers filled with conductive carbon black allow for the 3D printing of electrically conductive samples. The resistivity of these 3D printed samples depends on both the microscopic parameters of the carbon black filler and also on the macroscopic arrangement of the extrudites that build up the 3D printed sample. To investigate this dependence, we characterize the resistivity of five different printing infill patterns and find that a cross-ply pattern, which has extrudites oriented both in the direction of current flow and perpendicular to the direction of current flow has a lower resistivity of 0.229 Ohm m than the resistivity of 0.458 Ohm m found for a uni-ply pattern with all extrudites oriented in the direction of current flow. A Monte Carlo simulation of a large network of variable resistors illustrates the feasibility that the lower resistivity of the cross-ply pattern is caused by cross-flow which diverts current around areas of high local resistance. The same type of 3D printed conductive samples are tested as electromagnetic shields at frequencies up to 3.0 GHz using a custom-designed flanged coaxial sample holder. The shielding effectiveness of three sample infill patterns and four sample infill densities is compared. Cross-ply and angle-ply samples show the most efficient shielding effectiveness (normalized to sample density) of 17.5dB/g cm^3 at an infill density of 50% and would be the infill pattern of choice in an application constrained by weight or material.

Tools that standardize and automate experimental data collection are needed for greater confidence in research results. The National Synchrotron Light Source-II (NSLS-II) has generated an open-source Python data acquisition, management, and analysis software suite that automates x-ray experiments and collects an experimental record that facilitates complete reproducibility. Here we show that the NSLS-II tools are not only useful for x-ray science at large-scale facilities by presenting an add-on package that adapts these tools for use in a small laboratory with common physics and electrical engineering instruments. The composite software suite eases and automates the execution of experiments, records extensive metadata, stores data in portable containers, and speeds analysis through tools for comprehensive searches. In total, this software suite increases the reproducibility of laboratory experiments. We demonstrate the software via the evaluation of two lock-in amplifiers — the miniature ADA2200 and the ubiquitous SRS SR810. The frequency resolution, signal-to-noise ratio, and dynamic reserve of the lock-in amplifiers are measured and presented. The usage of the software suite is described throughout these measurements so that the reader can implement the tools in their lab.

The voice coil motor (VCM) is a simple electromechanical linear motor used in space constrained applications such as pumps, precision positioning, and mobile camera lens actuation. The motion of a VCM is determined by the applied current as well as the mechanical parameters of mass, spring constant, and damping coefficient. VCM motion and mechanical parameters can be determined by a position sensor, but such a sensor may be too bulky for a miniaturized solution. To overcome this limitation, measurements of the VCM electrical impedance versus frequency can be combined with an electromechanical model to identify mechanical parameters. Here we detail an analytical model that relates the electrical impedance to mechanical parameters and demonstrate a miniaturized electrical impedance analyzer for VCMs designed around the AD5933 integrated circuit. The electrical instrument measures impedances of a typical VCM coil of ∼10 Ω with a signal-to-noise ratio of 84.7 dB. We display the effectiveness of the analytical model and impedance analyzer by identifying mechanical parameters of mass, spring constant, and damping coefficient using electrical impedance measurements alone. We experimentally modified the system mass and detected the changes using electrical impedance with a mean error of 5.6% and a Pearson’s correlation coefficient of ρ = 0.95. Repeated measurements of a single VCM configuration demonstrated that the natural frequency, knowledge of which is critical for optimal efficiency, was detected with a variation of 0.2%. A departure from harmonic motion was observed at low velocities. We explain this departure by adding static friction to the model of VCM motion. The AD5933-based miniaturized VCM driver and impedance analyzer coupled with a model that relates mechanical motion to electrical impedance is a viable instrument for in-situ diagnostics and tuning of VCMs.

We present a light detection system (for point-of-care diagnostics) consisting of a multiple-sampling readout that forgoes the resolution limit set by a low-cost microcontroller ADC. Experimental measurements demonstrate a >5*10^6 input range and a noise floor of <210 fA.

Dynamic X-ray studies can reach temporal resolutions limited by only the X-ray pulse duration if the detector is fast enough to segregate synchrotron pulses. An analog integrating pixel array detector with in-pixel storage and temporal resolution of around 150 ns, sufficient to isolate pulses, is presented. Analog integration minimizes count-rate limitations and in-pixel storage captures successive pulses. Fundamental tests of noise and linearity as well as high-speed laser measurements are shown. The detector resolved individual bunch trains at the Cornell High Energy Synchrotron Source at levels of up to 3.7×10$^3$ X-rays per pixel per train. When applied to turn-by-turn X-ray beam characterization, single-shot intensity measurements were made with a repeatability of 0.4% and horizontal oscillations of the positron cloud were detected.

Test results are presented of a pixel array detector (PAD) developed for x-ray imaging at the Stanford Linear Coherent Light Source (LCLS). The basic module of the PAD consists of two bump-bonded chips: a reverse-biased silicon diode chip of 185 × 194 pixels, each of which is coupled by bump-bonds to a charge integrating CMOS ASIC with digitization in each pixel. The LCLS experiment requires a high signal-to-noise ratio for detection of single 8keV x-rays, a pixel full-well exceeding 1,000 8keV x-rays, a frame-rate of 120Hz, and the ability to handle the arrival of thousands of x-rays per pixel in tens of femtoseconds. Measurements have verified a pixel full-well value of 2,700 8keV x-rays. Single 8keV photon detection has been shown with a signal-to-noise ratio of > 6. Line-spread response measurements confirmed charge spreading to be limited to nearest neighbor pixels. Modules still functioned after dosages up to 75Mrad(Si) at the detector face. Work is proceeding to incorporate an array of modules into a large-area detector.

We have used self-propagating exothermic reactions in Al/Ni multilayers as a means to explore the effect of rapid heating on phase transformations. Using time-resolved synchrotron x-ray microdiffraction with an extremely fast detector, we were able to examine the reaction sequence in detail at heating rates of $10^{6}$ K/s. We observed that the intermediate phases formed during the self-propagating reactions are different from those formed at lower heating rates, even though the final phases are the same. In situ characterization is essential, as other means of studying self-propagating reactions such as quenching the reaction followed by ex situ analysis provide different-and potentially misleading—results.