FPGA based time-to-digital converters

Time-to-digital converters are a key component in many photonics systems, ranging from LiDAR, quantum key distribution, quantum optics experiments and time correlated single photon counting applications. A novel efficient timeto- digital converter non-linearity calibration technique has been developed and demonstrated on a Spartan 6 LX150 field programmable gate array (FPGA). Most FPGA based time-to-digital converters either use post processing or have calibration techniques which do not focus on minimizing resource utilization. With the move towards imaging with arrays of single photon detectors, scalable timing instrumentation is required. The calibration system demonstrated minimizes block memory utilization, using the same memory for probability density function measurement and cumulative distribution function generation, creating a look up table which can be used to calibrate the sub-clock timing module of the time-to-digital converter. The system developed contains 16 time-to-digital converters and demonstrates an average accuracy of 21ps RMS (14.85ps single channel) with a resolution of 1.86ps

Generation of heuristics by transforming the problem representation

This paper formally defines the idea of transforming one problem representation into another. The power of changing the problem representation is demonstrated in the context of heuristic generation. We prove that each problem transformation induces an admissible and monotonic heuristic on the original problem. Furthermore we show that every admissible and monotonic heuristic is induced by some problem transformation. This result generalizes and unifies several approaches for heuristic formation reported on in the literature. We give four techniques for generating problem transformations and we apply these techniques to generate several heuristics found in the literature. We also show that changing the problem representation can prove (automatically) that some problems are unsolvable

Generation of heuristics by problem transformation

We define problem transformations and show that each problem transformation induces an admissible and monotonic heuristic on the original problem. Furthermore we show that every admissible and monotonic heuristic is induced by some problem transformation. This result generalizes and unifies several approaches for heuristic formation reported on in the literature. We give four techniques for generating problem transformations and we apply these techniques to generate several heuristics found in the literature. We also introduce a variant of the relational representation framework which has some advantages

Detecting short periods of elevated workload. A compari­son of nine workload assessment techniques

The present experiment tested the merits of 9 common workload assessment techniques with relatively short periods of workload in a car-driving task. Twelve participants drove an instrumented car and performed a visually loading task and a mentally loading task for 10, 30, and 60 s. The results show that 10-s periods of visual and mental workload can be measured successfully with subjective ratings and secondary task performance. With respect to longer loading periods (30 and 60 s), steering frequency was found to be sensitive to visual workload, and skin conductance response (SCR) was sensitive to mental workload. The results lead to preliminary guidelines that will help applied researchers to determine which techniques are best suited for assessing visual and mental workload

Correlation function apparatus Patent

Circuitry for developing autocorrelation function continuously within signal receiving perio

Improving Photoelectron Counting and Particle Identification in Scintillation Detectors with Bayesian Techniques

Many current and future dark matter and neutrino detectors are designed to measure scintillation light with a large array of photomultiplier tubes (PMTs). The energy resolution and particle identification capabilities of these detectors depend in part on the ability to accurately identify individual photoelectrons in PMT waveforms despite large variability in pulse amplitudes and pulse pileup. We describe a Bayesian technique that can identify the times of individual photoelectrons in a sampled PMT waveform without deconvolution, even when pileup is present. To demonstrate the technique, we apply it to the general problem of particle identification in single-phase liquid argon dark matter detectors. Using the output of the Bayesian photoelectron counting algorithm described in this paper, we construct several test statistics for rejection of backgrounds for dark matter searches in argon. Compared to simpler methods based on either observed charge or peak finding, the photoelectron counting technique improves both energy resolution and particle identification of low energy events in calibration data from the DEAP-1 detector and simulation of the larger MiniCLEAN dark matter detector.Comment: 16 pages, 16 figure