Tera-sample-per-second single-shot device analyzer.

With the ever-increasing need for bandwidth in data centers and 5G mobile communications, technologies for rapid characterization of wide-band devices are in high demand. We report an instrument for extremely fast characterization of the electronic and optoelectronic devices with 27 ns frequency-response acquisition time at the effective sampling rate of 2.5 Tera-sample/s and an ultra-low effective timing jitter of 5.4 fs. This instrument features automated digital signal processing algorithms including time-series segmentation and frame alignment, impulse localization and Tikhonov regularized deconvolution for single-shot impulse and frequency response measurements. The system is based on the photonic time-stretch and features phase diversity to eliminate frequency fading and extend the bandwidth of the instrument.

Single-shot network analyzer for extremely fast measurements.

A new instrument for fast measurement of frequency response of high-bandwidth optical and electronic devices is reported. Single-shot frequency spectrum measurements are enabled by time-stretch technology. An extremely fast measurement time of 27 ns is reported for the instrument. The reported instrument enables single-shot impulse response measurements with a 40 GHz bandwidth, which could be extended to beyond 100 GHz by using a faster electro-optic modulator. An ultra-low jitter of 20.5 fs is reported for the proposed instrument. The impulse responses measured using this technique are shown to correspond consistently with the manufacturer's specifications for the device under test. The reported instrument makes possible high-speed network parameter measurements, thereby enabling high-speed production-level testing of high-bandwidth opto-electronic devices/circuits/subsystems/systems and complex permittivity measurement of dielectric materials at a much reduced test time, lowering the test costs in a production environment.

Coherent time-stretch transformation for real-time capture of wideband signals.

Time stretch transformation of wideband waveforms boosts the performance of analog-to-digital converters and digital signal processors by slowing down analog electrical signals before digitization. The transform is based on dispersive Fourier transformation implemented in the optical domain. A coherent receiver would be ideal for capturing the time-stretched optical signal. Coherent receivers offer improved sensitivity, allow for digital cancellation of dispersion-induced impairments and optical nonlinearities, and enable decoding of phase-modulated optical data formats. Because time-stretch uses a chirped broadband (>1 THz) optical carrier, a new coherent detection technique is required. In this paper, we introduce and demonstrate coherent time stretch transformation; a technique that combines dispersive Fourier transform with optically broadband coherent detection.

Intelligent sensing in dynamic environments using markov decision process.

In a network of low-powered wireless sensors, it is essential to capture as many environmental events as possible while still preserving the battery life of the sensor node. This paper focuses on a real-time learning algorithm to extend the lifetime of a sensor node to sense and transmit environmental events. A common method that is generally adopted in ad-hoc sensor networks is to periodically put the sensor nodes to sleep. The purpose of the learning algorithm is to couple the sensor's sleeping behavior to the natural statistics of the environment hence that it can be in optimal harmony with changes in the environment, the sensors can sleep when steady environment and stay awake when turbulent environment. This paper presents theoretical and experimental validation of a reward based learning algorithm that can be implemented on an embedded sensor. The key contribution of the proposed approach is the design and implementation of a reward function that satisfies a trade-off between the above two mutually contradicting objectives, and a linear critic function to approximate the discounted sum of future rewards in order to perform policy learning.

An autonomous wearable system for diurnal sweat biomarker data acquisition.

To track dynamically varying and physiologically relevant biomarker profiles in sweat, autonomous wearable platforms are required to periodically sample and analyze sweat with minimal or no user intervention. Previously reported sweat sensors are functionally limited to capturing biomarker information at one time-point/period, thereby necessitating repeated user intervention to increase the temporal granularity of biomarker data. Accordingly, we present a compact multi-compartment wearable system, where each compartment can be activated to autonomously induce/modulate sweat secretion (via iontophoretic actuation) and analyze sweat at set time points. This system was developed following a hybrid-flex design and a vertical integration scheme-integrating the required functional modules: miniaturized iontophoresis interfaces, adhesive thin film microfluidic-sensing module, and control/readout electronics. The system was deployed in a human subject study to track the diurnal variation of sweat glucose levels in relation to the daily food intake. The demonstrated autonomous operation for diurnal sweat biomarker data acquisition illustrates the system's suitability for large-scale and longitudinal personal health monitoring applications.