Abstract: Traffic density, an indicator of traffic
conditions, is one of the most critical characteristics to
Intelligent Transport Systems (ITS). This paper investigates
recursive traffic density estimation using the information
provided from inductive loop detectors. On the basis of the
phenomenological relationship between speed and density, the
existing studies incorporate a state space model and update the
density estimate using vehicular speed observations via the
extended Kalman filter, where an approximation is made
because of the linearization of the nonlinear observation
equation. In practice, this may lead to substantial estimation
errors. This paper incorporates a suitable transformation to
deal with the nonlinear observation equation so that the
approximation is avoided when using Kalman filter to
estimate the traffic density. A numerical study is conducted. It
is shown that the developed method outperforms the existing
methods for traffic density estimation.
Abstract: This paper proposes a three-phase four-wire currentcontrolled
Voltage Source Inverter (CC-VSI) for both power quality
improvement and PV energy extraction. For power quality
improvement, the CC-VSI works as a grid current-controlling shunt
active power filter to compensate for harmonic and reactive power of
loads. Then, the PV array is coupled to the DC bus of the CC-VSI
and supplies active power to the grid. The MPPT controller employs
the particle swarm optimization technique. The output of the MPPT
controller is a DC voltage that determines the DC-bus voltage
according to PV maximum power. The PSO method is simple and
effective especially for a partially shaded PV array. From computer
simulation results, it proves that grid currents are sinusoidal and inphase
with grid voltages, while the PV maximum active power is
delivered to loads.
Abstract: Lung cancer accounts for the most cancer related deaths for men as well as for women. The identification of cancer associated genes and the related pathways are essential to provide an important possibility in the prevention of many types of cancer. In this work two filter approaches, namely the information gain and the biomarker identifier (BMI) are used for the identification of different types of small-cell and non-small-cell lung cancer. A new method to determine the BMI thresholds is proposed to prioritize genes (i.e., primary, secondary and tertiary) using a k-means clustering approach. Sets of key genes were identified that can be found in several pathways. It turned out that the modified BMI is well suited for microarray data and therefore BMI is proposed as a powerful tool for the search for new and so far undiscovered genes related to cancer.
Abstract: The paper deals with the comparison study of
harmonic detection methods for a shunt active power filter. The
%THD and the power factor value at the PCC point after
compensation are considered for the comparison. There are three
harmonic detection methods used in the paper that are synchronous
reference frame method, synchronous detection method, and DQ axis
with Fourier method. In addition, the ideal current source is used to
represent the active power filter by assuming an infinitely fast
controller action of the active power filter. The simulation results
show that the DQ axis with Fourier method provides the minimum
%THD after compensation compared with other methods. However,
the power factor value at the PCC point after compensation is slightly
lower than that of synchronous detection method.
Abstract: In this paper, we propose a novel adaptive
spatiotemporal filter that utilizes image sequences in order to remove
noise. The consecutive frames include: current, previous and next
noisy frames. The filter proposed in this paper is based upon the
weighted averaging pixels intensity and noise variance in image
sequences. It utilizes the Appropriate Number of Consecutive Frames
(ANCF) based on the noisy pixels intensity among the frames. The
number of consecutive frames is adaptively calculated for each
region in image and its value may change from one region to another
region depending on the pixels intensity within the region. The
weights are determined by a well-defined mathematical criterion,
which is adaptive to the feature of spatiotemporal pixels of the
consecutive frames. It is experimentally shown that the proposed
filter can preserve image structures and edges under motion while
suppressing noise, and thus can be effectively used in image
sequences filtering. In addition, the AWA filter using ANCF is
particularly well suited for filtering sequences that contain segments
with abruptly changing scene content due to, for example, rapid
zooming and changes in the view of the camera.
Abstract: Iris localization is a very important approach in
biometric identification systems. Identification process usually is
implemented in three levels: iris localization, feature extraction, and
pattern matching finally. Accuracy of iris localization as the first step
affects all other levels and this shows the importance of iris
localization in an iris based biometric system. In this paper, we
consider Daugman iris localization method as a standard method,
propose a new method in this field and then analyze and compare the
results of them on a standard set of iris images. The proposed method
is based on the detection of circular edge of iris, and improved by
fuzzy circles and surface energy difference contexts. Implementation
of this method is so easy and compared to the other methods, have a
rather high accuracy and speed. Test results show that the accuracy of
our proposed method is about Daugman method and computation
speed of it is 10 times faster.
Abstract: In this paper, we propose a novel spatiotemporal fuzzy
based algorithm for noise filtering of image sequences. Our proposed algorithm uses adaptive weights based on a triangular membership
functions. In this algorithm median filter is used to suppress noise.
Experimental results show when the images are corrupted by highdensity
Salt and Pepper noise, our fuzzy based algorithm for noise filtering of image sequences, are much more effective in suppressing
noise and preserving edges than the previously reported algorithms such as [1-7]. Indeed, assigned weights to noisy pixels are very
adaptive so that they well make use of correlation of pixels. On the other hand, the motion estimation methods are erroneous and in highdensity noise they may degrade the filter performance. Therefore, our
proposed fuzzy algorithm doesn-t need any estimation of motion trajectory. The proposed algorithm admissibly removes noise without having any knowledge of Salt and Pepper noise density.
Abstract: Psoriasis is a widespread skin disease affecting up to 2% population with plaque psoriasis accounting to about 80%. It can be identified as a red lesion and for the higher severity the lesion is usually covered with rough scale. Psoriasis Area Severity Index (PASI) scoring is the gold standard method for measuring psoriasis severity. Scaliness is one of PASI parameter that needs to be quantified in PASI scoring. Surface roughness of lesion can be used as a scaliness feature, since existing scale on lesion surface makes the lesion rougher. The dermatologist usually assesses the severity through their tactile sense, therefore direct contact between doctor and patient is required. The problem is the doctor may not assess the lesion objectively. In this paper, a digital image analysis technique is developed to objectively determine the scaliness of the psoriasis lesion and provide the PASI scaliness score. Psoriasis lesion is modelled by a rough surface. The rough surface is created by superimposing a smooth average (curve) surface with a triangular waveform. For roughness determination, a polynomial surface fitting is used to estimate average surface followed by a subtraction between rough and average surface to give elevation surface (surface deviations). Roughness index is calculated by using average roughness equation to the height map matrix. The roughness algorithm has been tested to 444 lesion models. From roughness validation result, only 6 models can not be accepted (percentage error is greater than 10%). These errors occur due the scanned image quality. Roughness algorithm is validated for roughness measurement on abrasive papers at flat surface. The Pearson-s correlation coefficient of grade value (G) of abrasive paper and Ra is -0.9488, its shows there is a strong relation between G and Ra. The algorithm needs to be improved by surface filtering, especially to overcome a problem with noisy data.
Abstract: The proper selection of the AC-side passive filter
interconnecting the voltage source converter to the power supply is
essential to obtain satisfactory performances of an active power filter
system. The use of the LCL-type filter has the advantage of
eliminating the high frequency switching harmonics in the current
injected into the power supply. This paper is mainly focused on
analyzing the influence of the interface filter parameters on the active
filtering performances. Some design aspects are pointed out. Thus,
the design of the AC interface filter starts from transfer functions by
imposing the filter performance which refers to the significant current
attenuation of the switching harmonics without affecting the
harmonics to be compensated. A Matlab/Simulink model of the entire
active filtering system including a concrete nonlinear load has been
developed to examine the system performances. It is shown that a
gamma LC filter could accomplish the attenuation requirement of the
current provided by converter. Moreover, the existence of an optimal
value of the grid-side inductance which minimizes the total harmonic
distortion factor of the power supply current is pointed out.
Nevertheless, a small converter-side inductance and a damping
resistance in series with the filter capacitance are absolutely needed
in order to keep the ripple and oscillations of the current at the
converter side within acceptable limits. The effect of change in the
LCL-filter parameters is evaluated. It is concluded that good active
filtering performances can be achieved with small values of the
capacitance and converter-side inductance.
Abstract: Many studies have been conducted for derivation of
attenuation relationships worldwide, however few relationships have
been developed to use for the seismic region of Iranian plateau and
only few of these studies have been conducted for derivation of
attenuation relationships for parameters such as uniform duration.
Uniform duration is the total time during which the acceleration is
larger than a given threshold value (default is 5% of PGA). In this
study, the database was same as that used previously by Ghodrati
Amiri et al. (2007) with same correction methods for earthquake
records in Iran. However in this study, records from earthquakes with
MS< 4.0 were excluded from this database, each record has
individually filtered afterward, and therefore the dataset has been
expanded. These new set of attenuation relationships for Iran are
derived based on tectonic conditions with soil classification into rock
and soil. Earthquake parameters were chosen to be
hypocentral distance and magnitude in order to make it easier to use
the relationships for seismic hazard analysis. Tehran is the capital
city of Iran wit ha large number of important structures. In this study,
a probabilistic approach has been utilized for seismic hazard
assessment of this city. The resulting uniform duration against return
period diagrams are suggested to be used in any projects in the area.
Abstract: This paper presents a simple and original method for
the generation of short monocycle pulses based on the transient
response of a passive band-pass filter. The recorded sub-nanosecond
pulses show a good symmetry and a small ringing (13 % of the peak
amplitude). Their spectral density covers the range 3.1 GHz to
10.6 GHz. The possibility to adapt the pulse spectral density to the
indoor FCC frequency mask is demonstrated with a prototype
working at a reduced frequency (FCC/1000). A detection technique is
proposed.
Abstract: The need of high frame-rate imaging has been triggered by the new applications of ultrasound imaging to transient elastography and real-time 3D ultrasound. Using plane wave excitation (PWE) is one of the methods to achieve very high frame-rate imaging since an image can be formed with a single insonification. However, due to the lack of transmit focusing, the image quality with PWE is lower compared with those using conventional focused transmission. To solve this problem, we propose a filter-retrieved transmit focusing (FRF) technique combined with cross-correlation weighting (FRF+CC weighting) for high frame-rate imaging with PWE. A restrospective focusing filter is designed to simultaneously minimize the predefined sidelobe energy associated with single PWE and the filter energy related to the signal-to-noise-ratio (SNR). This filter attempts to maintain the mainlobe signals and to reduce the sidelobe ones, which gives similar mainlobe signals and different sidelobes between the original PWE and the FRF baseband data. Normalized cross-correlation coefficient at zero lag is calculated to quantify the degree of similarity at each imaging point and used as a weighting matrix to the FRF baseband data to further suppress sidelobes, thus improving the filter-retrieved focusing quality.
Abstract: On-line handwritten scripts are usually dealt with pen tip traces from pen-down to pen-up positions. Time evaluation of the pen coordinates is also considered along with trajectory information. However, the data obtained needs a lot of preprocessing including filtering, smoothing, slant removing and size normalization before recognition process. Instead of doing such lengthy preprocessing, this paper presents a simple approach to extract the useful character information. This work evaluates the use of the counter- propagation neural network (CPN) and presents feature extraction mechanism in full detail to work with on-line handwriting recognition. The obtained recognition rates were 60% to 94% using the CPN for different sets of character samples. This paper also describes a performance study in which a recognition mechanism with multiple thresholds is evaluated for counter-propagation architecture. The results indicate that the application of multiple thresholds has significant effect on recognition mechanism. The method is applicable for off-line character recognition as well. The technique is tested for upper-case English alphabets for a number of different styles from different peoples.
Abstract: Raw wood vinegar was purified by both standing and
filtering methods. Toxicity tests were conducted under laboratory
conditions by the topical application method (contact poison) and
feeding method (stomach poison). Larvicidal activities of wood
vinegar at four different concentrations (10, 15, 20, 25 and 30 %)
were studied against second instar larvae of housefly (Musca
domestica L.). Four replicates were maintained for all treatments and
controls. Larval mortality was recorded up to 96 hours and compared
with the larval survivability by two methods of larvicidal bioassay.
Percent pupation and percent adult emergence were observed in
treated M. domestica. The study revealed that the feeding method
gave higher efficiency compared with the topical application method.
Larval mortality increased with increasing concentration of wood
vinegar and the duration of exposure. No mortality was found in
treated M. domestica larvae at minimum 10% concentration of wood
vinegar through the experiments. The treated larvae were maintained
up to pupa and adult emergence. At 30% maximum concentration
larval duration was extended to 11 days in M. domestica for topical
application method and 9 days for feeding method. Similarly the
pupal durations were also increased with increased concentrations
(16 and 24 days for topical application method and feeding method
respectively at 30% concentration) of the treatments.
Abstract: This study proposes novel hybrid social network analysis and collaborative filtering approach to enhance the performance of recommender systems. The proposed model selects subgroups of users in Internet community through social network analysis (SNA), and then performs clustering analysis using the information about subgroups. Finally, it makes recommendations using cluster-indexing CF based on the clustering results. This study tries to use the cores in subgroups as an initial seed for a conventional clustering algorithm. This model chooses five cores which have the highest value of degree centrality from SNA, and then performs clustering analysis by using the cores as initial centroids (cluster centers). Then, the model amplifies the impact of friends in social network in the process of cluster-indexing CF.
Abstract: Detecting object in video sequence is a challenging
mission for identifying, tracking moving objects. Background
removal considered as a basic step in detected moving objects tasks.
Dual static cameras placed in front and rear moving platform
gathered information which is used to detect objects. Background
change regarding with speed and direction moving platform, so
moving objects distinguished become complicated. In this paper, we
propose framework allows detection moving object with variety of
speed and direction dynamically. Object detection technique built on
two levels the first level apply background removal and edge
detection to generate moving areas. The second level apply Moving
Areas Filter (MAF) then calculate Correlation Score (CS) for
adjusted moving area. Merging moving areas with closer CS and
marked as moving object. Experiment result is prepared on real scene
acquired by dual static cameras without overlap in sense. Results
showing accuracy in detecting objects compared with optical flow
and Mixture Module Gaussian (MMG), Accurate ratio produced to
measure accurate detection moving object.
Abstract: In this paper, we present a method for edge
segmentation of satellite images based on 2-D Phase Congruency
(PC) model. The proposed approach is composed by two steps: The
contextual non linear smoothing algorithm (CNLS) is used to smooth
the input images. Then, the 2D stretched Gabor filter (S-G filter)
based on proposed angular variation is developed in order to avoid
the multiple responses in the previous work. An assessment of our
proposed method performance is provided in terms of accuracy of
satellite image edge segmentation. The proposed method is compared
with others known approaches.
Abstract: The effect of varying holding temperature on hatching success, occurrence of deformities and mortality rates were investigated for goldlined seabream eggs. Wild broodstock (600 g) were stocked at a 2:1 male-female ratio in a 2 m3 fiberglass tank supplied with filtered seawater (37 g L-1 salinity, temp. range 24±0.5 oC [day] and 22±1 oC [night], DO2 in excess of 5.0mg L-1). Females were injected with 200 IU kg-1 HCG between 08.00 and 10.00 h and returned to tanks to spawn following which eggs were collected by hand using a 100μm net. Fertilized eggs at the gastrulation stage (120 L-1) were randomly placed into one of 12 experimental 6 L aerated (DO2 5 mg L-1) plastic containers with water temperatures maintained at 24±0.5 oC (ambient), 26±0.5 oC, 28± 0.5 oC and 30±0.5 oC using thermostats. Each treatment was undertaken in triplicate using a 12:12 photophase:scotophase photoperiod. No differences were recorded between eggs reared at 24 and 26 oC with respect to viability, deformity, mortality or unhatched egg rates. Increasing temperature reduced the number of viable eggs with those at 30 oC returning poorest performance (P < 0.05). Mortality levels were lowest for eggs incubated at 24 and 26 oC. The greatest level of deformities recorded was that for eggs reared at 28 oC.
Abstract: This paper proposes a method for speckle reduction in
medical ultrasound imaging while preserving the edges with the
added advantages of adaptive noise filtering and speed. A nonlinear
image diffusion method that incorporates local image parameter,
namely, scatterer density in addition to gradient, to weight the
nonlinear diffusion process, is proposed. The method was tested for
the isotropic case with a contrast detail phantom and varieties of
clinical ultrasound images, and then compared to linear and some
other diffusion enhancement methods. Different diffusion parameters
were tested and tuned to best reduce speckle noise and preserve
edges. The method showed superior performance measured both
quantitatively and qualitatively when incorporating scatterer density
into the diffusivity function. The proposed filter can be used as a
preprocessing step for ultrasound image enhancement before
applying automatic segmentation, automatic volumetric calculations,
or 3D ultrasound volume rendering.
Abstract: Image target detection and tracking methods based on
target information such as intensity, shape model, histogram and
target dynamics have been proven to be robust to target model
variations and background clutters as shown by recent researches.
However, no definitive answer has been given to occluded target by
counter measure or limited field of view(FOV). In this paper, we
will present a novel tracking method using filtering and computational
geometry. This paper has two central goals: 1) to deal with vulnerable
target measurements; and 2) to maintain target tracking out of FOV
using non-target-originated information. The experimental results,
obtained with airborne images, show a robust tracking ability with
respect to the existing approaches. In exploring the questions of target
tracking, this paper will be limited to consideration of airborne image.