Abstract: Heat source addition to the axisymmetric supersonic
inlet may improve the performance parameters, which will increase
the inlet efficiency. In this investigation the heat has been added to
the flow field at some distance ahead of an axisymmetric inlet by
adding an imaginary thermal source upstream of cowl lip. The effect
of heat addition on the drag coefficient, mass flow rate and the
overall efficiency of the inlet have been investigated. The results
show that heat addition causes flow separation, hence to prevent this
phenomena, roughness has been added on the spike surface.
However, heat addition reduces the drag coefficient and the inlet
mass flow rate considerably. Furthermore, the effects of position,
size, and shape on the inlet performance were studied. It is found that
the thermal source deflects the flow streamlines. By improper
location of the thermal source, the optimum condition has been
obtained. For the optimum condition, the drag coefficient is
considerably reduced and the inlet mass flow rate and its efficiency
have been increased slightly. The optimum shape of the heat source
is obtained too.
Abstract: Mammals are known to use Interaural Intensity Difference (IID) to determine azimuthal position of high frequency sounds. In the Lateral Superior Olive (LSO) neurons have firing behaviours which vary systematicaly with IID. Those neurons receive excitatory inputs from the ipsilateral ear and inhibitory inputs from the contralateral one. The IID sensitivity of a LSO neuron is thought to be due to delay differences between both ears, delays due to different synaptic delays and to intensity-dependent delays. In this paper we model the auditory pathway until the LSO. Inputs to LSO neurons are at first numerous and differ in their relative delays. Spike Timing-Dependent Plasticity is then used to prune those connections. We compare the pruned neuron responses with physiological data and analyse the relationship between IID-s of teacher stimuli and IID sensitivities of trained LSO neurons.
Abstract: This study introduces a new method for detecting,
sorting, and localizing spikes from multiunit EEG recordings. The
method combines the wavelet transform, which localizes distinctive
spike features, with Super-Paramagnetic Clustering (SPC) algorithm,
which allows automatic classification of the data without assumptions
such as low variance or Gaussian distributions. Moreover, the method
is capable of setting amplitude thresholds for spike detection. The
method makes use of several real EEG data sets, and accordingly the
spikes are detected, clustered and their times were detected.
Abstract: Emerging Bio-engineering fields such as Brain
Computer Interfaces, neuroprothesis devices and modeling and
simulation of neural networks have led to increased research activity
in algorithms for the detection, isolation and classification of Action
Potentials (AP) from noisy data trains. Current techniques in the field
of 'unsupervised no-prior knowledge' biosignal processing include
energy operators, wavelet detection and adaptive thresholding. These
tend to bias towards larger AP waveforms, AP may be missed due to
deviations in spike shape and frequency and correlated noise
spectrums can cause false detection. Also, such algorithms tend to
suffer from large computational expense.
A new signal detection technique based upon the ideas of phasespace
diagrams and trajectories is proposed based upon the use of a
delayed copy of the AP to highlight discontinuities relative to
background noise. This idea has been used to create algorithms that
are computationally inexpensive and address the above problems.
Distinct AP have been picked out and manually classified from
real physiological data recorded from a cockroach. To facilitate
testing of the new technique, an Auto Regressive Moving Average
(ARMA) noise model has been constructed bases upon background
noise of the recordings. Along with the AP classification means this
model enables generation of realistic neuronal data sets at arbitrary
signal to noise ratio (SNR).
Abstract: Many experimental results suggest that more precise spike timing is significant in neural information processing. We construct a self-organization model using the spatiotemporal pat-terns, where Spike-Timing Dependent Plasticity (STDP) tunes the conduction delays between neurons. We show that, for highly syn-chronized inputs, the fluctuation of conduction delays causes globally continuous and locally distributed firing patterns through the self-organization.
Abstract: Silicon is a beneficial element for plant growth. It
helps plants to overcome multiple stresses, alleviates metal toxicity
and improves nutrient imbalance. Field experiment was conducted as
split-split plot arranged in a randomized complete block design with
four replications. Irrigation system include continues flooding and
deficit as main plots and nitrogen rates N0, N46, N92, and N138 kg/ha
as sub plots and silicon rates Si0 & Si500 kg/ha as sub-subplots.
Results indicate that grain yield had not significant difference
between irrigation systems. Flooding irrigation had higher biological
yield than deficit irrigation whereas, no significant difference in grain
and straw yield. Nitrogen application increased grain, biological and
straw yield. Silicon application increased grain, biological and straw
yield but, decreased harvest index. Flooding irrigation had higher
number of total tillers / hill than deficit irrigation, but deficit
irrigation had higher number of fertile tillers / hill than flooding
irrigation. Silicon increased number of filled spikelet and decreased
blank spikelet. With high nitrogen application decreased 1000-grain
weight. It can be concluded that if the nitrogen application was high
and water supplied was available we could have silicon application
until increase grain yield.
Abstract: A recent neurospiking coding scheme for feature extraction from biosonar echoes of various plants is examined with avariety of stochastic classifiers. Feature vectors derived are employedin well-known stochastic classifiers, including nearest-neighborhood,single Gaussian and a Gaussian mixture with EM optimization.Classifiers' performances are evaluated by using cross-validation and bootstrapping techniques. It is shown that the various classifers perform equivalently and that the modified preprocessing configuration yields considerably improved results.
Abstract: The primary cause of Total Hip Replacement (THR)
failure for younger patients is aseptic loosening. This complication is
twice more likely to happen in acetabular cup than in femoral stem.
Excessive micromotion between bone and implant will cause
loosening and it depends in patient activities, age and bone. In this
project, the effects of different metal back design of press fit on
osseointegration of the acetabular cup are carried out. Commercial
acetabular cup designs, namely Spiked, Superfix and Quadrafix are
modelled and analyzed using commercial finite element software.
The diameter of acetabular cup is based on the diameter of acetabular
rim to make sure the component fit to the acetabular cavity. A new
design of acetabular cup are proposed and analyzed to get better
osseointegration between the bones and implant interface. Results
shows that the proposed acetabular cup designs are more stable
compared to other designs with respect to stress and displacement
aspects.