Very Low Power Cockcroft-Walton Voltage Multiplier for RF Energy Harvesting Applications

A device was required that could harvest the electromagnetic energy present in ambient radio frequency (RF) signals. A part of this device must convert the AC RF signal received by the antenna into a DC signal that can be used in an embedded application. Since the RF signal amplitude is small, it must first be amplified and rectified to become a usable signal. The Cockcroft-Walton voltage multiplier is a subsystem of the design which ideally converts a 100 mV AC signal coming from the antenna to a 350 mV DC signal. The output of the voltage multiplier is used to power another subsystem. At 10 MHz, the Cockcroft-Walton multiplier was able to output a DC voltage of 350 mV given an AC input signal of 140 mV. The results of the testing show verifiable proof-of-concept that the Cockcroft-Walton voltage multiplier has the potential to be used for low power RF energy harvesting applications

Older Artists and Acknowledging Ageism

Intergenerational (IG) learning has the potential to reinforce ageist ideas, through the culturally produced binary of old and young which often describes IG learning. This research with older artists revealed implicit age bias associated with a modernist tradition in art education which minimized the value of art production viewed as feminine. Language associated with ageism shares the descriptors of the feminine and seep into our perceptions. Cooperative action research with multi-age participants facilitated personal growth and through critical reflection, implicit ageism revealed in the researcher’s prior perspective is revealed

The distribution of amorphous computer outputs

Fitness distributions (landscapes) of programs tend to a limit as they get bigger. Markov minorization gives upper bounds ((15.3 + 2.30m)/ log I) on the length of program run on random or average computing devices. I is the size of the instruction set and m size of output register. Almost all programs are constants. Convergence is exponential with 90% of programs of length 1.6 n2N yielding constants (n = size input register and size of memory = N). This is supported by experiment

Performance of genetic programming optimised Bowtie2 on genome comparison and analytic testing (GCAT) benchmarks.

Genetic studies are increasingly based on short noisy next generation scanners. Typically complete DNA sequences are assembled by matching short NextGen sequences against reference genomes. Despite considerable algorithmic gains since the turn of the millennium, matching both single ended and paired end strings to a reference remains computationally demanding. Further tailoring Bioinformatics tools to each new task or scanner remains highly skilled and labour intensive. With this in mind, we recently demonstrated a genetic programming based automated technique which generated a version of the state-of-the-art alignment tool Bowtie2 which was considerably faster on short sequences produced by a scanner at the Broad Institute and released as part of The Thousand Genome Project

PRNG Random Numbers on GPU

Limited numerical precision of nVidia GeForce 8800 GTX and other GPUs requires careful implementation of PRNGs. The Park-Miller PRNG is programmed using G80’s native Value4f floating point in RapidMind C++. Speed up is more than 40. Code is available via ftp ftp://cs.ucl.ac.uk/genetic/gp-code/random-numbers/gpu park-miller.tar.g

The Energy Conserving Particle-in-Cell Method

A new Particle-in-Cell (PIC) method, that conserves energy exactly, is presented. The particle equations of motion and the Maxwell's equations are differenced implicitly in time by the midpoint rule and solved concurrently by a Jacobian-free Newton Krylov (JFNK) solver. Several tests show that the finite grid instability is eliminated in energy conserving PIC simulations, and the method correctly describes the two-stream and Weibel instabilities, conserving exactly the total energy. The computational time of the energy conserving PIC method increases linearly with the number of particles, and it is rather insensitive to the number of grid points and time step. The kinetic enslavement technique can be effectively used to reduce the problem matrix size and the number of JFNK solver iterations

Repeated sequences in linear genetic programming genomes

Biological chromosomes are replete with repetitive sequences, micro satellites, SSR tracts, ALU, etc. in their DNA base sequences. We started looking for similar phenomena in evolutionary computation. First studies find copious repeated sequences, which can be hierarchically decomposed into shorter sequences, in programs evolved using both homologous and two point crossover but not with headless chicken crossover or other mutations. In bloated programs the small number of effective or expressed instructions appear in both repeated and nonrepeated code. Hinting that building-blocks or code reuse may evolve in unplanned ways. Mackey-Glass chaotic time series prediction and eukaryotic protein localisation (both previously used as artificial intelligence machine learning benchmarks) demonstrate evolution of Shannon information (entropy) and lead to models capable of lossy Kolmogorov compression. Our findings with diverse benchmarks and GP systems suggest this emergent phenomenon may be widespread in genetic systems

Genetic programming in data mining for drug discovery

Genetic programming (GP) is used to extract from rat oral bioavailability (OB) measurements simple, interpretable and predictive QSAR models which both generalise to rats and to marketed drugs in humans. Receiver Operating Characteristics (ROC) curves for the binary classier produced by machine learning show no statistical dierence between rats (albeit without known clearance dierences) and man. Thus evolutionary computing oers the prospect of in silico ADME screening, e.g. for \virtual" chemicals, for pharmaceutical drug discovery

Backstreaming from oil diffusion pumps Quarterly progress report, 1 Jan. - 31 Mar. 1966

Backstreaming from oil diffusion and turbo-molecular pump