Abstract: Studies in neuroscience suggest that both global and
local feature information are crucial for perception and recognition of
faces. It is widely believed that local feature is less sensitive to
variations caused by illumination, expression and illumination. In
this paper, we target at designing and learning local features for face
recognition. We designed three types of local features. They are
semi-global feature, local patch feature and tangent shape feature.
The designing of semi-global feature aims at taking advantage of
global-like feature and meanwhile avoiding suppressing AdaBoost
algorithm in boosting weak classifies established from small local
patches. The designing of local patch feature targets at automatically
selecting discriminative features, and is thus different with traditional
ways, in which local patches are usually selected manually to cover
the salient facial components. Also, shape feature is considered in
this paper for frontal view face recognition. These features are
selected and combined under the framework of boosting algorithm
and cascade structure. The experimental results demonstrate that the
proposed approach outperforms the standard eigenface method and
Bayesian method. Moreover, the selected local features and
observations in the experiments are enlightening to researches in
local feature design in face recognition.
Abstract: The mosaicing technique has been employed in more and more application fields, from entertainment to scientific ones. In the latter case, often the final evaluation is still left to human beings, that assess visually the quality of the mosaic. Many times, a lack of objective measurements in microscopic mosaicing may prevent the mosaic from being used as a starting image for further analysis. In this work we analyze three different metrics and indexes, in the domain of signal analysis, image analysis and visual quality, to measure the quality of different aspects of the mosaicing procedure, such as registration errors and visual quality. As the case study we consider the mosaicing algorithm we developed. The experiments have been carried out by considering mosaics with very different features: histological samples, that are made of detailed and contrasted images, and live stem cells, that show a very low contrast and low detail levels.
Abstract: Contour filter strips planted with perennial vegetation
can be used to improve surface and ground water quality by reducing
pollutant, such as NO3-N, and sediment outflow from cropland to a
river or lake. Meanwhile, the filter strips of perennial grass with biofuel
potentials also have economic benefits of producing ethanol. In
this study, The Soil and Water Assessment Tool (SWAT) model was
applied to the Walnut Creek Watershed to examine the effectiveness
of contour strips in reducing NO3-N outflows from crop fields to the
river or lake. Required input data include watershed topography,
slope, soil type, land-use, management practices in the watershed and
climate parameters (precipitation, maximum/minimum air
temperature, solar radiation, wind speed and relative humidity).
Numerical experiments were conducted to identify potential
subbasins in the watershed that have high water quality impact, and
to examine the effects of strip size and location on NO3-N reduction
in the subbasins under various meteorological conditions (dry,
average and wet). Variable sizes of contour strips (10%, 20%, 30%
and 50%, respectively, of a subbasin area) planted with perennial
switchgrass were selected for simulating the effects of strip size and
location on stream water quality. Simulation results showed that a
filter strip having 10%-50% of the subbasin area could lead to 55%-
90% NO3-N reduction in the subbasin during an average rainfall
year. Strips occupying 10-20% of the subbasin area were found to be
more efficient in reducing NO3-N when placed along the contour
than that when placed along the river. The results of this study can
assist in cost-benefit analysis and decision-making in best water
resources management practices for environmental protection.
Abstract: Sensorized instruments that accurately measure the interaction forces (between biological tissue and instrument endeffector) during surgical procedures offer surgeons a greater sense of immersion during minimally invasive robotic surgery. Although there is ongoing research into force measurement involving surgical graspers little corresponding effort has been carried out on the measurement of forces between scissor blades and tissue. This paper presents the design and development of a force measurement test apparatus, which will serve as a sensor characterization and evaluation platform. The primary aim of the experiments is to ascertain whether the system can differentiate between tissue samples with differing mechanical properties in a reliable, repeatable manner. Force-angular displacement curves highlight trends in the cutting process as well the forces generated along the blade during a cutting procedure. Future applications of the test equipment will involve the assessment of new direct force sensing technologies for telerobotic surgery.
Abstract: In this paper, an efficient structural approach for
recognizing on-line handwritten digits is proposed. After reading
the digit from the user, the slope is estimated and normalized for
adjacent nodes. Based on the changing of signs of the slope values,
the primitives are identified and extracted. The names of these
primitives are represented by strings, and then a finite state
machine, which contains the grammars of the digits, is traced to
identify the digit. Finally, if there is any ambiguity, it will be
resolved. Experiments showed that this technique is flexible and
can achieve high recognition accuracy for the shapes of the digits
represented in this work.
Abstract: This paper proposes the numerical simulation of the
investment casting of gold jewelry. It aims to study the behavior of
fluid flow during mould filling and solidification and to optimize the
process parameters, which lead to predict and control casting defects
such as gas porosity and shrinkage porosity. A finite difference
method, computer simulation software FLOW-3D was used to
simulate the jewelry casting process. The simplified model was
designed for both numerical simulation and real casting production.
A set of sensor acquisitions were allocated on the different positions
of the wax tree of the model to detect filling times, while a set of
thermocouples were allocated to detect the temperature during
casting and cooling. Those detected data were applied to validate the
results of the numerical simulation to the results of the real casting.
The resulting comparisons signify that the numerical simulation can
be used as an effective tool in investment-casting-process
optimization and casting-defect prediction.
Abstract: It is well known that Logistic Regression is the gold
standard method for predicting clinical outcome, especially
predicting risk of mortality. In this paper, the Decision Tree method
has been proposed to solve specific problems that commonly use
Logistic Regression as a solution. The Biochemistry and
Haematology Outcome Model (BHOM) dataset obtained from
Portsmouth NHS Hospital from 1 January to 31 December 2001 was
divided into four subsets. One subset of training data was used to
generate a model, and the model obtained was then applied to three
testing datasets. The performance of each model from both methods
was then compared using calibration (the χ2 test or chi-test) and
discrimination (area under ROC curve or c-index). The experiment
presented that both methods have reasonable results in the case of the
c-index. However, in some cases the calibration value (χ2) obtained
quite a high result. After conducting experiments and investigating
the advantages and disadvantages of each method, we can conclude
that Decision Trees can be seen as a worthy alternative to Logistic
Regression in the area of Data Mining.
Abstract: The article deals with technical support of intracranial single unit activity measurement. The parameters of the whole measuring set were tested in order to assure the optimal conditions of extracellular single-unit recording. Metal microelectrodes for measuring the single-unit were tested during animal experiments. From signals recorded during these experiments, requirements for the measuring set parameters were defined. The impedance parameters of the metal microelectrodes were measured. The frequency-gain and autonomous noise properties of preamplifier and amplifier were verified. The measurement and the description of the extracellular single unit activity could help in prognoses of brain tissue damage recovery.
Abstract: Availability of high dimensional biological datasets such as from gene expression, proteomic, and metabolic experiments can be leveraged for the diagnosis and prognosis of diseases. Many classification methods in this area have been studied to predict disease states and separate between predefined classes such as patients with a special disease versus healthy controls. However, most of the existing research only focuses on a specific dataset. There is a lack of generic comparison between classifiers, which might provide a guideline for biologists or bioinformaticians to select the proper algorithm for new datasets. In this study, we compare the performance of popular classifiers, which are Support Vector Machine (SVM), Logistic Regression, k-Nearest Neighbor (k-NN), Naive Bayes, Decision Tree, and Random Forest based on mock datasets. We mimic common biological scenarios simulating various proportions of real discriminating biomarkers and different effect sizes thereof. The result shows that SVM performs quite stable and reaches a higher AUC compared to other methods. This may be explained due to the ability of SVM to minimize the probability of error. Moreover, Decision Tree with its good applicability for diagnosis and prognosis shows good performance in our experimental setup. Logistic Regression and Random Forest, however, strongly depend on the ratio of discriminators and perform better when having a higher number of discriminators.
Abstract: Prostate cancer is one of the most frequent cancers in men and is a major cause of mortality in the most of countries. In many diagnostic and treatment procedures for prostate disease accurate detection of prostate boundaries in transrectal ultrasound (TRUS) images is required. This is a challenging and difficult task due to weak prostate boundaries, speckle noise and the short range of gray levels. In this paper a novel method for automatic prostate segmentation in TRUS images is presented. This method involves preprocessing (edge preserving noise reduction and smoothing) and prostate segmentation. The speckle reduction has been achieved by using stick filter and top-hat transform has been implemented for smoothing. A feed forward neural network and local binary pattern together have been use to find a point inside prostate object. Finally the boundary of prostate is extracted by the inside point and an active contour algorithm. A numbers of experiments are conducted to validate this method and results showed that this new algorithm extracted the prostate boundary with MSE less than 4.6% relative to boundary provided manually by physicians.
Abstract: A novel path planning approach is presented to solve
optimal path in stochastic, time-varying networks under priori traffic
information. Most existing studies make use of dynamic programming
to find optimal path. However, those methods are proved to
be unable to obtain global optimal value, moreover, how to design
efficient algorithms is also another challenge.
This paper employs a decision theoretic framework for defining
optimal path: for a given source S and destination D in urban transit
network, we seek an S - D path of lowest expected travel time
where its link travel times are discrete random variables. To solve
deficiency caused by the methods of dynamic programming, such as
curse of dimensionality and violation of optimal principle, an integer
programming model is built to realize assignment of discrete travel
time variables to arcs. Simultaneously, pruning techniques are also
applied to reduce computation complexity in the algorithm. The final
experiments show the feasibility of the novel approach.
Abstract: The most important parameter in transformers life
expectancy is the hot-spot temperature level which accelerates the
rate of aging of the insulation. The aim of this paper is to present
thermal models for transformers loaded at prefabricated MV/LV
transformer substations and outdoor situations. The hot-spot
temperature of transformers is studied using their top-oil temperature
rise models. The thermal models proposed for hot-spot and top-oil
temperatures of different operating situations are compared. Since the
thermal transfer is different for indoor and outdoor transformers
considering their operating conditions, their hot-spot thermal models
differ from each other. The proposed thermal models are verified by
the results obtained from the experiments carried out on a typical
1600 kVA, 30 /0.4 kV, ONAN transformer for both indoor and
outdoor situations.
Abstract: Subsurface erosion in river banks and its details, in
spite of its occurrence in various parts of the world has rarely been
paid attention by researchers. In this paper, quantitative concept of
the subsurface bank erosion has been investigated for vertical banks.
Vertical banks were simulated experimentally by considering a sandy
erodible layer overlaid by clayey one under uniformly distributed
constant overhead pressure. Results of the experiments are indicated
that rate of sandy layer erosion is decreased by an increase in
overburden; likewise, substituting 20% of coarse (3.5 mm) sand layer
bed material by fine material (1.4 mm) may lead to a decrease in
erosion rate by one-third. This signifies the importance of the bed
material composition effect on sandy layers erosion due to subsurface
erosion in river banks.
Abstract: In this paper, we propose a new approach to query-by-humming, focusing on MP3 songs database. Since MP3 songs are much more difficult in melody representation than symbolic performance data, we adopt to extract feature descriptors from the vocal sounds part of the songs. Our approach is based on signal filtering, sub-band spectral processing, MDCT coefficients analysis and peak energy detection by ignorance of the background music as much as possible. Finally, we apply dual dynamic programming algorithm for feature similarity matching. Experiments will show us its online performance in precision and efficiency.
Abstract: Color constancy algorithms are generally based on the
simplified assumption about the spectral distribution or the reflection
attributes of the scene surface. However, in reality, these assumptions
are too restrictive. The methodology is proposed to extend existing
algorithm to applying color constancy locally to image patches rather
than globally to the entire images.
In this paper, a method based on low-level image features using
superpixels is proposed. Superpixel segmentation partition an image
into regions that are approximately uniform in size and shape. Instead
of using entire pixel set for estimating the illuminant, only superpixels
with the most valuable information are used. Based on large scale
experiments on real-world scenes, it can be derived that the estimation
is more accurate using superpixels than when using the entire image.
Abstract: In this paper, we propose a novel improvement for the generalized Lloyd Algorithm (GLA). Our algorithm makes use of an M-tree index built on the codebook which makes it possible to reduce the number of distance computations when the nearest code words are searched. Our method does not impose the use of any specific distance function, but works with any metric distance, making it more general than many other fast GLA variants. Finally, we present the positive results of our performance experiments.
Abstract: The aim of this work was to investigate the potential of soil microorganisms and the burhead plant, as well as the combination of soil microorganisms and plants to remediate monoethylene glycol (MEG), diethylene glycol (DEG), and triethylene glycol (TEG) in synthetic wastewater. The result showed that a system containing both burhead plant and soil microorganisms had the highest efficiency in EGs removal. Around 100% of MEG and DEG and 85% of TEG were removed within 15 days of the experiments. However, the burhead plant had higher removal efficiency than soil microorganisms for MEG and DEG but the same for TEG in the study systems. The removal rate of EGs in the study system related to the molecular weight of the compounds and MEG, the smallest glycol, was removed faster than DEG and TEG by both the burhead plant and soil microorganisms in the study system.
Abstract: In this study, a software has been developed to predict
the optimum conditions for drying of cotton based yarn bobbins in a
hot air dryer. For this purpose, firstly, a suitable drying model has
been specified using experimental drying behavior for different
values of drying parameters. Drying parameters in the experiments
were drying temperature, drying pressure, and volumetric flow rate of
drying air. After obtaining a suitable drying model, additional curve
fittings have been performed to obtain equations for drying time and
energy consumption taking into account the effects of drying
parameters. Then, a software has been developed using Visual Basic
programming language to predict the optimum drying conditions for
drying time and energy consumption.
Abstract: In the paper, the results of sensitivity analysis of the influence of initial imperfections on the web stress state of a thinwalled girder are presented. The results of the study corroborate a very good and effective agreement of experiments with theory. Most input random quantities were found experimentally. The change of sensitivity coefficients in dependence on working load value is analysed. The stress was analysed by means of a geometrically and materially non-linear solution by applying the program ANSYS. This research study offers important background for theoretical studies of stability problems, post-critical effects and limit states of thin-walled steel structures.
Abstract: Although lots of experiments have been done in enhanced oil recovery, the number of experiments which consider the effects of local and global heterogeneity on efficiency of enhanced oil recovery based on the polymer-surfactant flooding is low and rarely done. In this research, we have done numerous experiments of water flooding and polymer-surfactant flooding on a five spot glass micromodel in different conditions such as different positions of layers. In these experiments, five different micromodels with three different pore structures are designed. Three models with different layer orientation, one homogenous model and one heterogeneous model are designed. In order to import the effect of heterogeneity of porous media, three types of pore structures are distributed accidentally and with equal ratio throughout heterogeneous micromodel network according to random normal distribution. The results show that maximum EOR recovery factor will happen in a situation where the layers are orthogonal to the path of mainstream and the minimum EOR recovery factor will happen in a situation where the model is heterogeneous. This experiments show that in polymer-surfactant flooding, with increase of angles of layers the EOR recovery factor will increase and this recovery factor is strongly affected by local heterogeneity around the injection zone.