Design of Broadband Non-Foster Circuits Based on Resonant Tunneling Diodes

A non-Foster circuit (NFC) based on the resonant tunneling diode (RTD) is proposed for application to broadband impedance matching of electrically small antennas (ESAs). NFCs have traditionally been implemented with transistor pairs to achieve negative impedance, but these have limitations with respect to performance and operational bandwidth at high frequencies. At certain biasing voltages, double barrier RTDs behave as negative differential resistance (NDR) devices, which may be transformed to exhibit negative impedance. In contrast to the transistor-based NFC, these structures serve to gyrate or invert the load impedance, such that an inductive load will lead to a negative capacitance, and vice versa. This device is termed a negative impedance inverter (NII). We demonstrate negative impedance behavior for prototypes with measurements of negative resistance at up to 3 GHz, and device gain of around 5 dB from DC to 4 GHz. Design for stability of the RTD is performed using the Nyquist stability criterion. Stabilized negative capacitance NFCs show optimum performance from DC to the GHz range depending upon the load value. These NFCs are used to impedance match an antenna at low frequencies. An antenna with only one resonance at 3.5 GHz has been transformed with two different matching circuits: to an antenna encompassing the 1 to 2 GHz range; as well as the VHF/UHF bands from 300 MHz to 1 GHz. Additionally, RTDs have been demonstrated for operation at up to THz frequencies, so this topology can be extended to higher frequencies subject to fabrication constraints

Atrial Fibrillation Stratification via Fibrillatory Wave Characterization Using the Filter Diagonalization Method

We use the Filter Diagonalization Method (FDM), a harmonic inversion technique, to extract f-wave features in electrocardiographic (ECG) traces for atrial fibrillation (AF) stratification. The FDM detects f-wave frequencies and amplitudes at frame sizes of 0.15 seconds. We demonstrate our method on a dataset comprising of ECG recordings from 23 patients (61.65 ± 11.63 years, 78.26% male) before cryoablation; 2 paroxysmal AF, 16 early persistent AF (12 months duration). Moreover, some of these patients received adenosine to enhance their RR intervals before ablation. Our method extracts features from FDM outputs to train statistical machine learning classifiers. Tenfold cross-validation demonstrates that the Random Forest and Decision Tree models performed best for the pre-ablation without and with adenosine datasets, with accuracy 60.89 ± 0.31% and 59.58% ± 0.04%, respectively. While the results are modest, they demonstrate that f-wave features can be used for AF stratification. The accuracies are similar for the two tests, slightly better for the case without adenosine, showing that the FDM can successfully model short f-waves without the need to concatenate f-wave sequences or adenosine to elongate RR intervals

The filter diagonalisation method for music signal analysis: frame-wise vibrato detection and estimation

This research is supported in part by the China Scholarship Counci

A Gaussian Process model for UAV localization using millimetre wave radar

The detection and positioning of unmanned aerial vehicles has become essential for both automation and surveillance tasks, in recent years. The design of accurate drone localization systems is challenging, especially in cluttered environments, where the target may be partially or even completely obscured. This paper proposes a precise detection and 3D localization system for drones, by means of a millimetre wave radar. Drone locations are estimated from spatial heatmaps of the received radar signals, which are obtained by applying the super-resolution MUSIC algorithm. These estimates are improved by analysis of the micro-Doppler effect, generated by the rotating propellers, which aids detection in poor visibility conditions. A novel Gaussian Process Regression model is developed, in order to compensate for systematic biases in the radar data. The complete system produces accurate estimates of the target range and direction, and is shown to outperform direct spectral analysis methods

Graphene-based tunable non-foster circuit for VHF applications

received: 2016-04-04 accepted: 2016-05-24 published: 2016-06-01The authors would like to acknowledge financial supports from the Engineering and Physical Sciences Research Council (EPSRC) on Grant “Graphene Flexible Electronics and Optoelectronics” (EP/K01711X/1) and the EU Graphene Flagship (FP7-ICT-604391)