Jacqueline Walker

Over the last twenty years, many technologies and services have come to rely on the GPS for precise timing. Concern is increasing about the wisdom of being reliant on a single timing solution provided by a single country and because of the susceptibility of the GPS signal to unintentional interference, jamming and spoofing. In this paper, we report on further development of our system for timing signal transfer from a precision reference clock using commercial satellite links. The system will have master stations tracking the satellite position and using TWSTFT measurements to synchronize their clocks, transmitting data with the reference timing signal to allow slave stations to adjust the PPS timing signal, compensating for the satellite motion and other uncertainties in the path delay. We will report on a simulation of the full system, including models for the master station clocks and TWTT measurements, using a Kalman filter to track the satellite position.

Time transfer over satellite links has been explored since the satellite era began. Currently, Two Way Satellite Time and Frequency Transfer (TWSTFT) is routinely used between national timing laboratories to align national timing standards, and the Global Positioning System (GPS) provides accurate timing signals in addition to its more familiar navigation solution. This paper reports on a method for transferring time from a reference clock over commercial geostationary satellite links with a specified low level of uncertainty at the receiving stations, using only the ephemeris information provided by the satellite operator. An initial experiment, reported here, showed that with one master station, measuring aggregate extraneous delays and transmitting positioning and delay data plus a correction factor to the slave stations, allowed transfer of a 1 pps (pulse per second) timing signal with a standard deviation of 72 to 98 ns and peak-to-peak variations of 500 to 600 ns, when measured against a GPS reference. Subsequent analysis of the experiment uncovered some issues with the implementation, suggesting that these results could be substantially improved upon. Furthermore, a simulation of the system that modeled the extraneous delays produced results similar to those obtained in the experiment.

Modern telecommunications networks maintain synchronization and distribute accurate timing information using approaches with strict requirements on clock quality and jitter and wander at interfaces. In the future, packet networks may be used for timing distribution and synchronization e.g. using IEEE 1588v2 specifying the Precision Time Protocol (PTP). Without amelioration, there may be effects on data buffers within telecommunications networks which have been designed with particular assumptions of clock quality. Applications such as digital audio and video also have synchronization requirements and limits on jitter and wander tolerance. Attention is needed on the impact of clock quality due to clock synchronization on the synchronization and jitter and wander limits for data services over networks. A well-understood approach to the transfer of timing for a data service over a packet network is the Synchronous Residual Time Stamp (SRTS) method. We previously presented a novel approach to modelling and visualising jitter spectra in SRTS timing transfer due to jitter in the reference clocks. In this paper, we present experimental results, which validate the appropriateness of our approximations. Experimental results also show the existence of some spectral components not predicted from the modelling. In the paper we discuss the origins of these spectral components.

Signals from extracellular electrodes in neural systems record voltages resulting from activity in many neurons. Detecting action potentials (spikes) in a small number of specific (target) neurons is difficult because many neurons, both near and more distant, contribute to the signal at the electrode. We consider some nearby neurons as target neurons (providing a signal) and all the other contributions to the signal as noise. A new algorithm for spike detection has been developed: this applies a cepstrum of bispectrum (CoB) estimated inverse filter to provide blind equalization. This technique is based on higher order statistics, and seeks to find a sequence of event times or delta sequence. We show that the CoB-based technique can achieve a 98% hit rate on an extracellular signal containing three spike trains at up to 0 dB SNR. Threshold setting for this technique is discussed, and we show the application of the technique to some real signals. We compare performance with four established techniques and report that the CoB-based algorithm performs best.

We introduce a reduced parameter synthesis model for the spectral synthesis of musical sounds, which preserves the timbre and the naturalness of the musical sound. It also provides large flexibility for the user and reduces the number of synthesis parameters compared to traditional analysis/re-synthesis methods. The proposed model is almost completely independent from a previous spectral analysis. We present a frequency estimation method using a random walk to keep the naturalness of the sound without using a separate noise model. Three different approaches have been tested to estimate the amplitude values for the synthesis, namely, local optimization, the use of a lowpass filter and polynomial fitting. All of these approaches give good results, especially for the sustain part of the signal.

This study aims to create an automatic musical instrument classifier by extracting audio features from real sample sounds. These features are reduced using Principal Component Analysis and the resultant data is used to train a Multi-Layered Perceptron. We found that the RMS temporal envelope and the evolution of the centroid gave the most interesting results of the features studied. These results were found to be competitive whether the scope of the data was across one octave or across the range of each instrument.

Particle swarm has proven to be competitive to other evolutionary algorithms in the field of optimization, and in many cases enables a faster convergence to the ideal solution. However, like any optimization algorithm it seems to have difficulties handling optimization problems of high dimension. Here we first show that dimensionality is really a problem for the classical particle swarm algorithms. We then show that increasing the swarm size can be necessary to handle problem of high dimensions but is not enough. We also show that the issue of scalability occurs more quickly on some functions.