Abstract: Numerous divergence measures (spectral distance, cepstral
distance, difference of the cepstral coefficients, Kullback-Leibler
divergence, distance given by the General Likelihood Ratio, distance
defined by the Recursive Bayesian Changepoint Detector and the
Mahalanobis measure) are compared in this study. The measures are
used for detection of abrupt spectral changes in synthetic AR signals
via the sliding window algorithm. Two experiments are performed;
the first is focused on detection of single boundary while the second
concentrates on detection of a couple of boundaries. Accuracy of
detection is judged for each method; the measures are compared
according to results of both experiments.
Abstract: The lubricating properties of commercially available
mucins originating from different animal organs, namely bovine
submaxillary mucin (BSM) and porcine gastric mucin (PGM), have
been characterized at polymeric surfaces for biomedical applications.
Atomic force microscopy (AFM) and pin-on-disk tribometry have
been employed for tribological studies at nanoscale and macroscale
contacts, respectively. Polystyrene (PS) was employed to represent
‘rigid’ contacts, whereas poly(dimethylsiloxane) (PDMS) was
employed to represent ‘soft contacts’. To understand the lubricating
properties of mucins in correlation with the coverage on surfaces,
adsorption properties of mucins onto the polymeric substrates have
been characterized by means of optical waveguide light-mode
spectroscopy (OWLS). Both mucins showed facile adsorption onto
both polymeric substrates, but the lubricity was highly dependent
upon the pH change between 2 and 7.
Abstract: This paper discusses the effectiveness of the EEG signal
for human identification using four or less of channels of two different
types of EEG recordings. Studies have shown that the EEG signal
has biometric potential because signal varies from person to person
and impossible to replicate and steal. Data were collected from 10
male subjects while resting with eyes open and eyes closed in 5
separate sessions conducted over a course of two weeks. Features
were extracted using the wavelet packet decomposition and analyzed
to obtain the feature vectors. Subsequently, the neural networks
algorithm was used to classify the feature vectors. Results show that,
whether or not the subjects- eyes were open are insignificant for a 4–
channel biometrics system with a classification rate of 81%. However,
for a 2–channel system, the P4 channel should not be included if data
is acquired with the subjects- eyes open. It was observed that for 2–
channel system using only the C3 and C4 channels, a classification
rate of 71% was achieved.
Abstract: Functional gastrointestinal disorders (FGID) affect millions of people spread all age regardless of race and sex. Emotional stress and obesity have been associated with increased reporting of gastrointestinal (GI) symptoms, but the relationship between FGID and risk factors (emotional stress or obesity) is unclear. Our aim was to assess the changes of the mechanical characteristics on the gastrointestinal tracts of the mentally fatigued obese and normal rat models. Finally, using the physical characteristics with micro-indentation test, we made a close investigation into the relation between FGID and risk factors quantitatively.
Abstract: Highly pathogenic avian influenza (HPAI) H5N1 viruses have created demand for a cost-effective vaccine to prevent a pandemic of the disease. Here, we report that Trichoplusia ni (T. ni) larvae can act as a cost-effective bioreactor to produce recombinant HA5 (rH5HA) proteins as an potential effective vaccine for chickens. To facilitate the recombinant virus identification, virus titer determination and access the infected larvae, we employed the internal ribosome entry site (IRES) derived from Perina nuda virus (PnV, belongs to insect picorna like Iflavirus genus) to construct a bi-cistronic baculovirus expression vector that can express the rH5HA protein and enhanced green fluorescent protein (EGFP) simultaneously. Western blot analysis revealed that the 70 kDa rH5HA protein and partially cleaved products (40 kDa H5HA1) were generated in T. ni larvae infected with recombinant baculovirus carrying the H5HA gene. These data suggest that the baculovirus-larvae recombinant protein expression system could be a cost-effective platform for H5N1 vaccine production.
Abstract: The cardiovascular system has become the most
important subject of clinical research, particularly measurement of
arterial blood flow. Therefore correct determination of arterial
diameter is crucial. We propose a novel, semi-automatic method for
artery lumen detection. The method is based on Gaussian probability
function. Usability of our proposed method was assessed by
analyzing ultrasound B-mode CFA video sequences acquired from
eleven healthy volunteers. The correlation coefficient between the
manual and semi-automatic measurement of arterial diameter was
0.996. Our proposed method for detecting artery boundary is novel
and accurate enough for the measurement of artery diameter.
Abstract: Imaging is defined as the process of obtaining
geometric images either two dimensional or three dimensional by scanning or digitizing the existing objects or products. In this research, it applied to retrieve 3D information of the human skin
surface in medical application. This research focuses on analyzing
and determining volume of leg ulcers using imaging devices. Volume
determination is one of the important criteria in clinical assessment of leg ulcer. The volume and size of the leg ulcer wound will give the
indication on responding to treatment whether healing or worsening.
Different imaging techniques are expected to give different result (and accuracies) in generating data and images. Midpoint projection
algorithm was used to reconstruct the cavity to solid model and compute the volume. Misinterpretation of the results can affect the
treatment efficacy. The objectives of this paper is to compare the
accuracy between two 3D data acquisition method, which is laser
triangulation and structured light methods, It was shown that using models with known volume, that structured-light-based 3D technique
produces better accuracy compared with laser triangulation data
acquisition method for leg ulcer volume determination.
Abstract: Many recent electrophysiological studies have
revealed the importance of investigating meditation state in order to
achieve an increased understanding of autonomous control of
cardiovascular functions. In this paper, we characterize heart rate
variability (HRV) time series acquired during meditation using
nonlinear dynamical parameters. We have computed minimum
embedding dimension (MED), correlation dimension (CD), largest
Lyapunov exponent (LLE), and nonlinearity scores (NLS) from HRV
time series of eight Chi and four Kundalini meditation practitioners.
The pre-meditation state has been used as a baseline (control) state to
compare the estimated parameters. The chaotic nature of HRV during
both pre-meditation and meditation is confirmed by MED. The
meditation state showed a significant decrease in the value of CD and
increase in the value of LLE of HRV, in comparison with premeditation
state, indicating a less complex and less predictable nature
of HRV. In addition, it was shown that the HRV of meditation state
is having highest NLS than pre-meditation state. The study indicated
highly nonlinear dynamic nature of cardiac states as revealed by
HRV during meditation state, rather considering it as a quiescent
state.
Abstract: Diabetes is one of the high prevalence diseases
worldwide with increased number of complications, with retinopathy
as one of the most common one. This paper describes how data
mining and case-based reasoning were integrated to predict
retinopathy prevalence among diabetes patients in Malaysia. The
knowledge base required was built after literature reviews and
interviews with medical experts. A total of 140 diabetes patients- data
were used to train the prediction system. A voting mechanism selects
the best prediction results from the two techniques used. It has been
successfully proven that both data mining and case-based reasoning
can be used for retinopathy prediction with an improved accuracy of
85%.
Abstract: It is necessary to incorporate technological advances
achieved in the field of engineering into dentistry in order to enhance
the process of diagnosis, treatment planning and enable the doctors to
render better treatment to their patients. To achieve this ultimate goal
long distance collaborations are often necessary. This paper discusses
the various collaborative tools and their applications to solve a few
burning problems confronted by the dentists. Customization is often
the solution to most of the problems. But rapid designing,
development and cost effective manufacturing is a difficult task to
achieve. This problem can be solved using the technique of digital
manufacturing. Cases from 6 major branches of dentistry have been
discussed and possible solutions with the help of state of art
technology using rapid digital manufacturing have been proposed in
the present paper. The paper also entails the usage of existing tools in
collaborative and digital manufacturing area.
Abstract: The analysis to detect arrhythmias and life-threatening
conditions are highly essential in today world and this analysis
can be accomplished by advanced non-linear processing methods
for accurate analysis of the complex signals of heartbeat dynamics.
In this perspective, recent developments in the field of multiscale
information content have lead to the Microcanonical Multiscale
Formalism (MMF). We show that such framework provides several
signal analysis techniques that are especially adapted to the
study of heartbeat dynamics. In this paper, we just show first hand
results of whether the considered heartbeat dynamics signals have
the multiscale properties by computing local preticability exponents
(LPEs) and the Unpredictable Points Manifold (UPM), and thereby
computing the singularity spectrum.
Abstract: The use of machine vision to inspect the outcome of
surgical tasks is investigated, with the aim of incorporating this
approach in robotic surgery systems. Machine vision is a non-contact
form of inspection i.e. no part of the vision system is in direct contact
with the patient, and is therefore well suited for surgery where
sterility is an important consideration,. As a proof-of-concept, three
primary surgical tasks for a common neurosurgical procedure were
inspected using machine vision. Experiments were performed on
cadaveric pig heads to simulate the two possible outcomes i.e.
satisfactory or unsatisfactory, for tasks involved in making a burr
hole, namely incision, retraction, and drilling. We identify low level
image features to distinguish the two outcomes, as well as report on
results that validate our proposed approach. The potential of using
machine vision in a surgical environment, and the challenges that
must be addressed, are identified and discussed.
Abstract: SEMG (Surface Electromyogram) is one of the
bio-signals and is generated from the muscle. And there are many
research results that use forearm EMG to detect hand motions. In this
paper, we will talk about our developed the robot hand system that can
control grasping power by SEMG. In our system, we suppose that
muscle power is proportional to the amplitude of SEMG. The power is
estimated and the grip power of a robot hand is able to be controlled
using estimated muscle power in our system. In addition, to perform a
more precise control can be considered to build a closed loop feedback
system as an object to a subject to pressure from the edge of hand. Our
objectives of this study are the development of a method that makes
perfect detection of the hand grip force possible using SEMG patterns,
and applying this method to the man-machine interface.
Abstract: F-actin fibrils are the cytoskeleton of osteocytes. They react in a dynamic manner to mechanical loading, and strength and
reposition their efforts to reinforce the cells structure. We hypothesize that f-actin is temporarly disrupted after loading and repolymerizes
in a new orientation to oppose the applied load. In vitro studies are conducted to determine f-actin disruption after varying mechanical stimulus parameters that are known to affect bone
formation. Results indicate that the f-actin cytoskeleton is disrupted in vitro as a function of applied mechanical stimulus parameters and
that the f-actin bundles reassemble after loading induced disruption
within 3 minutes after cessation of loading. The disruption of the factin
cytoskeleton depends on the magnitude of stretch, the numbers
of loading cycles, frequency, the insertion of rest between loading
cycles and extracellular calcium. In vivo studies also demonstrate
disruption of the f-actin cytoskeleton in cells embedded in the bone
matrix immediately after mechanical loading. These studies suggest
that adaptation of the f-actin fiber bundles of the cytoskeleton in
response to applied loads occurs by disruption and subsequent repolymerization.
Abstract: Sleep spindles are the most interesting hallmark of
stage 2 sleep EEG. Their accurate identification in a
polysomnographic signal is essential for sleep professionals to help
them mark Stage 2 sleep. Sleep Spindles are also promising objective
indicators for neurodegenerative disorders. Visual spindle scoring
however is a tedious workload. In this paper three different
approaches are used for the automatic detection of sleep spindles:
Short Time Fourier Transform, Wavelet Transform and Wave
Morphology for Spindle Detection. In order to improve the results, a
combination of the three detectors is presented and comparison with
human expert scorers is performed. The best performance is obtained
with a combination of the three algorithms which resulted in a
sensitivity and specificity of 94% when compared to human expert
scorers.
Abstract: A two dimensional three segments coupled pendulum system that mathematically models human arm configuration was developed along with constructing and solving the equations of motions for this model using the energy (work) based approach of Lagrange. The equations of motion of the model were solved iteratively both as an initial value problem and as a two point boundary value problem. In the initial value problem solutions, both the initial system configuration (segment angles) and initial system velocity (segment angular velocities) were used as inputs, whereas, in the two point boundary value problem solutions initial and final configurations and time were used as inputs to solve for the trajectory of motion. The results suggest that the model solutions are sensitive to small changes in the dynamic forces applied to the system as well as to the initial and boundary conditions used. To overcome the system sensitivity a new approach is suggested.
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: The application of Neural Network for disease
diagnosis has made great progress and is widely used by physicians.
An Electrocardiogram carries vital information about heart activity and physicians use this signal for cardiac disease diagnosis which
was the great motivation towards our study. In our work, tachycardia
features obtained are used for the training and testing of a Neural
Network. In this study we are using Fuzzy Probabilistic Neural
Networks as an automatic technique for ECG signal analysis. As
every real signal recorded by the equipment can have different
artifacts, we needed to do some preprocessing steps before feeding it
to our system. Wavelet transform is used for extracting the
morphological parameters of the ECG signal. The outcome of the
approach for the variety of arrhythmias shows the represented
approach is superior than prior presented algorithms with an average
accuracy of about %95 for more than 7 tachy arrhythmias.
Abstract: Image processing for capsule endoscopy requires large
memory and it takes hours for diagnosis since operation time is
normally more than 8 hours. A real-time analysis algorithm of capsule
images can be clinically very useful. It can differentiate abnormal
tissue from health structure and provide with correlation information
among the images. Bleeding is our interest in this regard and we
propose a method of detecting frames with potential bleeding in
real-time. Our detection algorithm is based on statistical analysis and
the shapes of bleeding spots. We tested our algorithm with 30 cases of
capsule endoscopy in the digestive track. Results were excellent where
a sensitivity of 99% and a specificity of 97% were achieved in
detecting the image frames with bleeding spots.
Abstract: Sensory nerves in the foot play an important part in the diagnosis of various neuropathydisorders, especially in diabetes mellitus.However, a detailed description of the anatomical distribution of the nerves is currently lacking. A computationalmodel of the afferent nerves inthe foot may bea useful tool for the study of diabetic neuropathy. In this study, we present the development of an anatomically-based model of various major sensory nerves of the sole and dorsal sidesof the foot. In addition, we presentan algorithm for generating synthetic somatosensory nerve networks in the big-toe region of a right foot model. The algorithm was based on a modified version of the Monte Carlo algorithm, with the capability of being able to vary the intra-epidermal nerve fiber density in differentregionsof the foot model. Preliminary results from the combinedmodel show the realistic anatomical structure of the major nerves as well as the smaller somatosensory nerves of the foot. The model may now be developed to investigate the functional outcomes of structural neuropathyindiabetic patients.