In this article, an accurate and low-cost clock delay generation system integrated in an automated test equipment (ATE) environment is presented. The input to this system is entirely digital and is driven by a single clock, which can be programmed from the ATE High Speed Digital (HSD) unit. Moreover, the digital input patterns can easily be generated in software off-line; hence, making this system ideal for automated test routines. The system is first discussed and characterized in Matlab under static and dynamic operating conditions. For the static behavior, the impact of the various design tradeoffs on the time resolution is investigated. With regards to the dynamic behavior, the linearity is assessed spectrally with a sinusoidal input and statistically using a Gaussian noise signal. A discrete prototype board is built to validate the correct operation of the system mounted on an A TE to function as a whole. With proper compensation and calibration, a delay resolution of 15 ps was achieved over an 8.4 ns range using a low-speed reference clock running at 16.67 MHz. It is shown through clock scaling that this resolution can improve in direct proportion to increases in the clock frequency.

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Temporal seizures due to hippocampal origins are very common among epileptic patients. Presented is a novel seizure prediction approach employing correlation and chaos theories. The early identification of seizure signature allows for various preventive measures to be undertaken. Electro-encephalography signals are spectrally broken down into the following sub-bands: delta; theta; alpha; beta; and gamma. The proposed approach consists of observing a high correlation level between any pair of electrodes for the lower frequencies and a decrease in the Lyapunov index (chaos or entropy) for the higher frequencies. Power spectral density and statistical analysis tools were used to determine threshold levels for the lower frequencies. After studying all five sub-bands, the analysis has revealed that the seizure signature can be extracted from the delta band and the high frequencies. High frequencies are defined as both the gamma band and the ripples occurring within the 60-120 Hz sub-band. To validate the proposed approach, six patients from both sexes and various age groups with temporal epilepsies originating from the hippocampal area were studied using the Freiburg database. An average seizure prediction of 30 min, an anticipation accuracy of 72%, and a false-positive rate of 0% were accomplished throughout 200 h of recording time.