Bayesian inference and learning in Gaussian process state-space models with Particle MCMC

State-space models are successfully used in many areas of science, engineering and economics to model time series and dynamical systems. We present a fully Bayesian approach to inference and learning (i.e. state estimation and system identification) in nonlinear nonparametric state-space models. We place a Gaussian process prior over the state transition dynamics, resulting in a flexible model able to capture complex dynamical phenomena. To enable efficient inference, we marginalize over the transition dynamics function and, instead, infer directly the joint smoothing distribution using specially tailored Particle Markov Chain Monte Carlo samplers. Once a sample from the smoothing distribution is computed, the state transition predictive distribution can be formulated analytically. Our approach preserves the full nonparametric expressivity of the model and can make use of sparse Gaussian processes to greatly reduce computational complexity.

Non-compressive wideband spectrum sensing with sub-Nyquist sampling rates

In this paper, we propose a low complexity and reliable wideband spectrum sensing technique that operates at sub-Nyquist sampling rates. Unlike the majority of other sub-Nyquist spectrum sensing algorithms that rely on the Compressive Sensing (CS) methodology, the introduced method does not entail solving an optimisation problem. It is characterised by simplicity and low computational complexity without compromising the system performance and yet delivers substantial reductions on the operational sampling rates. The reliability guidelines of the devised non-compressive sensing approach are provided and simulations are presented to illustrate its superior performance. © 2013 IEEE.

A 10-bit 50-MS/s sample-and-hold circuit with low distortion sampling switches

A fully-differential switched-capacitor sample-and-hold (S/H) circuit used in a 10-bit 50-MS/s pipeline analog-to-digital converter (ADC) was designed and fabricated using a 0.35-μm CMOS process. Capacitor fliparound architecture was used in the S/H circuit to lower the power consumption. In addition, a gain-boosted operational transconductance amplifier (OTA) was designed with a DC gain of 94 dB and a unit gain bandwidth of 460 MHz at a phase margin of 63 degree, which matches the S/H circuit. A novel double-side bootstrapped switch was used, improving the precision of the whole circuit. The measured results have shown that the S/H circuit reaches a spurious free dynamic range (SFDR) of 67 dB and a signal-to-noise ratio (SNR) of 62.1 dB for a 2.5 MHz input signal with 50 MS/s sampling rate. The 0.12 mm~2 S/H circuit operates from a 3.3 V supply and consumes 13.6 mW.

A New Method to Investigate InGaAsP Single-Photon Avalanche Diodes Using a Digital Sampling Oscilloscope

A near-infrared single-photon detection system is established by using pigtailed InGaAs/InP avalanche photodiodes. With a 50GHz digital sampling oscilloscope, the function and process of gated-mode (Geiger-mode) single-photon detection are intuitionally demonstrated for the first time. The performance of the detector as a gated-mode single-photon counter at wavelengths of 1310 and 1550nm is investigated. At the operation temperature of 203K,a quantum efficiency of 52% with a dark count probability per gate of 2. 4 * 10~(-3), and a gate pulse repetition rate of 50kHz are obtained at 1550nm. The corresponding parameters are 43% , 8. 5 * 10~(-3), and 200kHz at 238K.

Characteristics of the sampling paddle modules of neutron wall from the cosmic ray test

In the construction of a large area neutron detector (neutron wall) that is used to detect neutrons at GeV energies, the performances of all the sampling paddle modules prepared for the neutron wall are investigated with a specially designed test bench. Tested by cosmic rays, an average intrinsic time resolution of 222.5 ps is achieved at the center of the modules. The light attenuation length and the effective speed of the light in the module are also investigated.

A spatial sampling optimization package using MSN theory

The density and distribution of spatial samples heavily affect the precision and reliability of estimated population attributes. An optimization method based on Mean of Surface with Nonhomogeneity (MSN) theory has been developed into a computer package with the purpose of improving accuracy in the global estimation of some spatial properties, given a spatial sample distributed over a heterogeneous surface; and in return, for a given variance of estimation, the program can export both the optimal number of sample units needed and their appropriate distribution within a specified research area. (C) 2010 Elsevier Ltd. All rights reserved.