In this paper, Finite Rate of Innovation (FRI) is applied to time domain reflectometry and it is aimed at significantly reducing the data acquisition requirements. The sensitivity of FRI to quantisation noise is addressed given the stringent practical constraints on the resolution of the deployed analogue to digital converters in miniature reflectometry sensors. Dithering with averaging is prop...

Micromachined inertial sensors are often incorporated in closed loop force feedback structures; a particularly advantageous approach is based upon the inclusion of the sensing element in a sigma-delta modulator (Σ∆M) type control structure. The order of the Σ∆M, and hence the noise shaping properties, is usually limited by the dynamics of the mechanical sensing element and may be insufficient f...

Abstract This paper aims to develop an effective sensor fusion model for turning processes the detection of tool wear. Fusion sensors’ data combined with novelty algorithm and learning vector quantisation (LVQ) neural networks is used detect wear present diagnostic prognostic information. To reduce number sensors required in monitoring system support fusion, ASPS approach (Automated Sensor Sign...

In quantum physics, the operators associated with the position and the momentum of a particle are unbounded operators and C∗-algebraic quantisation does therefore not deal with such operators. In the present article, I propose a quantisation of the Lie-Poisson structure of the dual of a Lie algebroid which deals with a big enough class of functions to include the above mentioned example. As an ...

Colour quantisation algorithms are essential for displaying true colour images using a limited palette of distinct colours. The choice of a good colour palette is crucial as it directly determines the quality of the resulting image. Colour quantisation can also be seen as a clustering problem where the task is to identify those clusters that best represent the colours in an image. In this paper...

This paper compares the latency, accuracy, training time and hardware costs of neural networks compressed with our new multi-objective evolutionary algorithm called NEMOKD, quantisation. We evaluate NEMOKD on Intel’s Movidius Myriad X VPU processor, quantisation Xilinx’s programmable Z7020 FPGA hardware. Evolving models increases inference accuracy by up to 82% at cost 38% increased throughput ...

Colour-based retrieval is one of the major content-based retrieval techniques. The main idea used for colour based retrieval techniques is to store the image in terms of a colour map which has a nite set of colour bins. An eecient retrieval scheme should preserve all the distinct colours of an image with a small number of colour bins. Uniform Quantisation is one of the major quantisation techni...

The discrete cosine transform (DCT) is widely applied in modern codecs to remove spatial redundancies, with the resulting DCT coefficients being quantised to achieve compression as well as bit-rate control. In distributed video coding (DVC) architectures like DISCOVER, DCT coefficient quantisation is traditionally performed using predetermined quantisation matrices (QM), which means the compres...

An efficient and exact dynamic programming algorithm is introduced to quantise a continuous random variable into a discrete random variable that maximises the likelihood of the quantised probability distribution for the original continuous random variable. Quantisation is often useful before statistical analysis and modelling of large discrete network models from observations of multiple contin...

In this paper we consider the generation of discrete white noise. Despite this seems to be a simple problem, common noise generator implementations do not deliver comparable results at different sampling rates. First we define what we mean with “comparable results”. From this we conclude, that the variance of the random variables shall grow proportionally to the sampling rate. Eventually we con...