Abstract: The aim of this study was to design and simulate a
particular type of Asynchronous State Machine (ASM), namely a
‘traffic light controller’ (TLC), operated at a frequency of 0.5 Hz.
The design task involved two main stages: firstly, designing a 4-bit
binary counter using J-K flip flops as the timing signal and,
subsequently, attaining the digital logic by deploying ASM design
process. The TLC was designed such that it showed a sequence of
three different colours, i.e. red, yellow and green, corresponding to
set thresholds by deploying the least number of AND, OR and NOT
gates possible. The software Multisim was deployed to design such
circuit and simulate it for circuit troubleshooting in order for it to
display the output sequence of the three different colours on the
traffic light in the correct order. A clock signal, an asynchronous 4-
bit binary counter that was designed through the use of J-K flip flops
along with an ASM were used to complete this sequence, which was
programmed to be repeated indefinitely. Eventually, the circuit was
debugged and optimized, thus displaying the correct waveforms of
the three outputs through the logic analyser. However, hazards
occurred when the frequency was increased to 10 MHz. This was
attributed to delays in the feedback being too high.
Abstract: A method which allows a diabetic quadriplegic patient
that has had four limb amputations (above the knee and elbow) to
self-administer injections of insulin has been designed. The aim of
this research project is to improve a quadriplegic patient’s selfmanagement,
affected by diabetes, by designing a suitable device for
self-administering insulin.
The quadriplegic patient affected by diabetes has to be able to selfadminister
insulin safely and independently to guarantee stable
healthy conditions. The device also should be designed to adapt to a
number of different varying personal characteristics such as height
and body weight.
Abstract: Kinematic data wisely correlate vector quantities in
space to scalar parameters in time to assess the degree of symmetry
between the intact limb and the amputated limb with respect to a
normal model derived from the gait of control group participants.
Furthermore, these particular data allow a doctor to preliminarily
evaluate the usefulness of a certain rehabilitation therapy.
Kinetic curves allow the analysis of ground reaction forces (GRFs)
to assess the appropriateness of human motion.
Electromyography (EMG) allows the analysis of the fundamental
lower limb force contributions to quantify the level of gait
asymmetry. However, the use of this technological tool is expensive
and requires patient’s hospitalization. This research work suggests
overcoming the above limitations by applying artificial neural
networks.
Abstract: In this research work, neural networks were applied to
classify two types of hip joint implants based on the relative hip joint
implant side speed and three components of each ground reaction
force. The condition of walking gait at normal velocity was used and
carried out with each of the two hip joint implants assessed. Ground
reaction forces’ kinetic temporal changes were considered in the first
approach followed but discarded in the second one. Ground reaction
force components were obtained from eighteen patients under such
gait condition, half of which had a hip implant type I-II, whilst the
other half had the hip implant, defined as type III by Orthoload®.
After pre-processing raw gait kinetic data and selecting the time
frames needed for the analysis, the ground reaction force components
were used to train a MLP neural network, which learnt to distinguish
the two hip joint implants in the abovementioned condition. Further
to training, unknown hip implant side and ground reaction force
components were presented to the neural networks, which assigned
those features into the right class with a reasonably high accuracy for
the hip implant type I-II and the type III. The results suggest that
neural networks could be successfully applied in the performance
assessment of hip joint implants.
Abstract: A blood pressure monitor or sphygmomanometer can
be either manual or automatic, employing respectively either the
auscultatory method or the oscillometric method.
The manual version of the sphygmomanometer involves an
inflatable cuff with a stethoscope adopted to detect the sounds
generated by the arterial walls to measure blood pressure in an artery.
An automatic sphygmomanometer can be effectively used to
monitor blood pressure through a pressure sensor, which detects
vibrations provoked by oscillations of the arterial walls.
The pressure sensor implemented in this device improves the
accuracy of the measurements taken.