Evidence of quantum lateral confinement in side-gated resonant tunnelling diodes formed by patterned substrate regrowth

MBE regrowth on patterned np-GaAs wafers has been used to fabricate GaAs/AlGaAs double barrier resonant tunnel diodes with a side-gate in the plane of the quantum well. The physical diameters vary from 1 to 20 μm. For a nominally 1 μm diameter diode the peak current is reduced by more than 95% at a side-gate voltage of -2 V at 1.5 K, which we estimate corresponds to an active tunnel region diameter of 75 nm ± 10 nm. At high gate biases additional structure appears in the conductance data. Differential I-V measurements show a linear dependence of the spacing of subsidiary peaks on gate bias indicating lateral quantum confinement. © 1996 American Institute of Physics.

PARTIALLY COHERENT DYNAMIC PROGRAMMING ALGORITHM FOR CPM DETECTION.

A dynamic programming algorithm for joint data detection and carrier phase estimation of continuous-phase-modulated signal is presented. The intent is to combine the robustness of noncoherent detectors with the superior performance of coherent ones. The algorithm differs from the Viterbi algorithm only in the metric that it maximizes over the possible transmitted data sequences. This metric is influenced both by the correlation with the received signal and the current estimate of the carrier phase. Carrier-phase estimation is based on decision guiding, but there is no external phase-locked loop. Instead, the phase of the best complex correlation with the received signal over the last few signaling intervals is used. The algorithm is slightly more complex than the coherent Viterbi algorithm but does not require narrowband filtering of the recovered carrier, as earlier appproaches did, to achieve the same level of performance.

Energy use of buildings at urban scale: A case study of london school buildings

The diversity of non-domestic buildings at urban scale poses a number of difficulties to develop building stock models. This research proposes an engineering-based bottom-up stock model in a probabilistic manner to address these issues. School buildings are used for illustrating the application of this probabilistic method. Two sampling-based global sensitivity methods are used to identify key factors affecting building energy performance. The sensitivity analysis methods can also create statistical regression models for inverse analysis, which are used to estimate input information for building stock energy models. The effects of different energy saving measures are analysed by changing these building stock input distributions.