Abstract: In this study, the locations and areas of commercial
accumulations were detected by using digital yellow page data. An
original buffering method that can accurately create polygons of
commercial accumulations is proposed in this paper.; by using this
method, distribution of commercial accumulations can be easily
created and monitored over a wide area. The locations, areas, and
time-series changes of commercial accumulations in the South Kanto
region can be monitored by integrating polygons of commercial
accumulations with the time-series data of digital yellow page data.
The circumstances of commercial accumulations were shown to vary
according to areas, that is, highly- urbanized regions such as the city
center of Tokyo and prefectural capitals, suburban areas near large
cities, and suburban and rural areas.
Abstract: Application of projective geometry to the theory of two-ports and cascade circuits with a load change is considered. The equations linking the input and output of a two-port are interpreted as projective transformations which have the invariant as a cross-ratio of four points. This invariant has place for all regime parameters in all parts of a cascade circuit. This approach allows justifying the definition of a regime and its change, to calculate a circuit without explicitly finding the aparameters, to transmit accurately an analogue signal through the unstable two-port.
Abstract: This paper introduces an intelligent system, which can be applied in the monitoring of vehicle speed using a single camera. The ability of motion tracking is extremely useful in many automation problems and the solution to this problem will open up many future applications. One of the most common problems in our daily life is the speed detection of vehicles on a highway. In this paper, a novel technique is developed to track multiple moving objects with their speeds being estimated using a sequence of video frames. Field test has been conducted to capture real-life data and the processed results were presented. Multiple object problems and noisy in data are also considered. Implementing this system in real-time is straightforward. The proposal can accurately evaluate the position and the orientation of moving objects in real-time. The transformations and calibration between the 2D image and the actual road are also considered.
Abstract: Field mapping activity for an active volcano mainly in
the Torrid Zone is usually hampered by several problems such as steep
terrain and bad atmosphere conditions. In this paper we present a
simple solution for such problem by a combination Synthetic Aperture
Radar (SAR) and geostatistical methods. By this combination, we
could reduce the speckle effect from the SAR data and then estimate
roughness distribution of the pyroclastic flow deposits. The main
purpose of this study is to detect spatial distribution of new pyroclastic
flow deposits termed as P-zone accurately using the β°data from two
RADARSAT-1 SAR level-0 data. Single scene of Hyperion data and
field observation were used for cross-validation of the SAR results.
Mt. Merapi in central Java, Indonesia, was chosen as a study site and
the eruptions in May-June 2006 were examined. The P-zones were
found in the western and southern flanks. The area size and the longest
flow distance were calculated as 2.3 km2 and 6.8 km, respectively. The
grain size variation of the P-zone was mapped in detail from fine to
coarse deposits regarding the C-band wavelength of 5.6 cm.
Abstract: The segmentation of endovascular tools in fluoroscopy images can be accurately performed automatically or by minimum user intervention, using known modern techniques. It has been proven in literature, but no clinical implementation exists so far because the computational time requirements of such technology have not yet been met. A classical segmentation scheme is composed of edge enhancement filtering, line detection, and segmentation. A new method is presented that consists of a vector that propagates in the image to track an edge as it advances. The filtering is performed progressively in the projected path of the vector, whose orientation allows for oriented edge detection, and a minimal image area is globally filtered. Such an algorithm is rapidly computed and can be implemented in real-time applications. It was tested on medical fluoroscopy images from an endovascular cerebral intervention. Ex- periments showed that the 2D tracking was limited to guidewires without intersection crosspoints, while the 3D implementation was able to cope with such planar difficulties.
Abstract: Diagnosis can be achieved by building a model of a
certain organ under surveillance and comparing it with the real time
physiological measurements taken from the patient. This paper deals
with the presentation of the benefits of using Data Mining techniques
in the computer-aided diagnosis (CAD), focusing on the cancer
detection, in order to help doctors to make optimal decisions quickly
and accurately. In the field of the noninvasive diagnosis techniques,
the endoscopic ultrasound elastography (EUSE) is a recent elasticity
imaging technique, allowing characterizing the difference between
malignant and benign tumors. Digitalizing and summarizing the main
EUSE sample movies features in a vector form concern with the use
of the exploratory data analysis (EDA). Neural networks are then
trained on the corresponding EUSE sample movies vector input in
such a way that these intelligent systems are able to offer a very
precise and objective diagnosis, discriminating between benign and
malignant tumors. A concrete application of these Data Mining
techniques illustrates the suitability and the reliability of this
methodology in CAD.
Abstract: Australian government agencies have a natural desire
to provide migrants a wide range of opportunities. Consequently,
government online services should be equally available to migrants
with a non-English speaking background (NESB). Despite the
commendable efforts of governments and local agencies in Australia
to provide such services, in reality, many NESB communities are not
taking advantage of these services. This article–based on an
extensive case study regarding the use of online government services
by the Arabic NESB community in Australia–reports on the
possible reasons for this issue, as well as suggestions for
improvement. The conclusion is that Australia should implement
ICT-based or e-government policies, programmes, and services that
more accurately reflect migrant cultures and languages so that
migrant integration can be more fully accomplished. Specifically, this
article presents an NESB Model that adopts the value of usercentricity
or a more individual-focused approach to government
online services in Australia.
Abstract: This paper presents a new method of fault detection and isolation (FDI) for polymer electrolyte membrane (PEM) fuel cell (FC) dynamic systems under an open-loop scheme. This method uses a radial basis function (RBF) neural network to perform fault identification, classification and isolation. The novelty is that the RBF model of independent mode is used to predict the future outputs of the FC stack. One actuator fault, one component fault and three sensor faults have been introduced to the PEMFC systems experience faults between -7% to +10% of fault size in real-time operation. To validate the results, a benchmark model developed by Michigan University is used in the simulation to investigate the effect of these five faults. The developed independent RBF model is tested on MATLAB R2009a/Simulink environment. The simulation results confirm the effectiveness of the proposed method for FDI under an open-loop condition. By using this method, the RBF networks able to detect and isolate all five faults accordingly and accurately.
Abstract: In this paper, the modelling and design of artificial neural network architecture for load forecasting purposes is investigated. The primary pre-requisite for power system planning is to arrive at realistic estimates of future demand of power, which is known as Load Forecasting. Short Term Load Forecasting (STLF) helps in determining the economic, reliable and secure operating strategies for power system. The dependence of load on several factors makes the load forecasting a very challenging job. An over estimation of the load may cause premature investment and unnecessary blocking of the capital where as under estimation of load may result in shortage of equipment and circuits. It is always better to plan the system for the load slightly higher than expected one so that no exigency may arise. In this paper, a load-forecasting model is proposed using a multilayer neural network with an appropriately modified back propagation learning algorithm. Once the neural network model is designed and trained, it can forecast the load of the power system 24 hours ahead on daily basis and can also forecast the cumulative load on daily basis. The real load data that is used for the Artificial Neural Network training was taken from LDC, Gujarat Electricity Board, Jambuva, Gujarat, India. The results show that the load forecasting of the ANN model follows the actual load pattern more accurately throughout the forecasted period.
Abstract: There are many researches to detect collision between real object and virtual object in 3D space. In general, these techniques are need to huge computing power. So, many research and study are constructed by using cloud computing, network computing, and distribute computing. As a reason of these, this paper proposed a novel fast 3D collision detection algorithm between real and virtual object using 2D intersection area. Proposed algorithm uses 4 multiple cameras and coarse-and-fine method to improve accuracy and speed performance of collision detection. In the coarse step, this system examines the intersection area between real and virtual object silhouettes from all camera views. The result of this step is the index of virtual sensors which has a possibility of collision in 3D space. To decide collision accurately, at the fine step, this system examines the collision detection in 3D space by using the visual hull algorithm. Performance of the algorithm is verified by comparing with existing algorithm. We believe proposed algorithm help many other research, study and application fields such as HCI, augmented reality, intelligent space, and so on.
Abstract: This paper presents an algorithm to estimate the parameters of two closely spaced sinusoids, providing a frequency resolution that is more than 800 times greater than that obtained by using the Discrete Fourier Transform (DFT). The strategy uses a highly optimized grid search approach to accurately estimate frequency, amplitude and phase of both sinusoids, keeping at the same time the computational effort at reasonable levels. The proposed method has three main characteristics: 1) a high frequency resolution; 2) frequency, amplitude and phase are all estimated at once using one single package; 3) it does not rely on any statistical assumption or constraint. Potential applications to this strategy include the difficult task of resolving coincident partials of instruments in musical signals.
Abstract: In order to enhance the usability of the human computer interface (HCI) on the touchscreen, this study explored the optimal tactile depth and effect of visual cues on the user-s tendency to touch the touchscreen icons. The experimental program was designed on the touchscreen in this study. Results indicated that the ratio of the icon size to the tactile depth was 1:0.106. There were significant effects of experienced users and novices on the tactile feedback depth (p < 0.01). In addition, the results proved that the visual cues provided a feedback that helped to guide the user-s touch icons accurately and increased the capture efficiency for a tactile recognition field. This tactile recognition field was 18.6 mm in length. There was consistency between the experienced users and novices under the visual cue effects. Finally, the study developed an applied design with touch feedback for touchscreen icons.
Abstract: This paper presents the development of an MODAPTS based cost estimating system to help designers in estimating the manufacturing cost of a assembly products which is belonged from the workers in working fields. Competitiveness of manufacturing cost is getting harder because of the development of Information and telecommunication, but also globalization. Therefore, the accuracy of the assembly cost estimation is getting important. DFA and MODAPTS is useful method for measuring the working hour. But these two methods are used just as a timetable. Therefore, in this paper, we suggest the process of measuring the working hours by MODAPTS which includes the working field-s accurate information. In addition, we adduce the estimation method of accuracy assembly cost with the real information. This research could be useful for designers that can estimate the assembly cost more accurately, and also effective for the companies that which are concerned to reduce the product cost.
Abstract: In this manuscript, a wavelet-based blind
watermarking scheme has been proposed as a means to provide
security to authenticity of a fingerprint. The information used for
identification or verification of a fingerprint mainly lies in its
minutiae. By robust watermarking of the minutiae in the fingerprint
image itself, the useful information can be extracted accurately even
if the fingerprint is severely degraded. The minutiae are converted in
a binary watermark and embedding these watermarks in the detail
regions increases the robustness of watermarking, at little to no
additional impact on image quality. It has been experimentally shown
that when the minutiae is embedded into wavelet detail coefficients
of a fingerprint image in spread spectrum fashion using a
pseudorandom sequence, the robustness is observed to have a
proportional response while perceptual invisibility has an inversely
proportional response to amplification factor “K". The DWT-based
technique has been found to be very robust against noises,
geometrical distortions filtering and JPEG compression attacks and is
also found to give remarkably better performance than DCT-based
technique in terms of correlation coefficient and number of erroneous
minutiae.
Abstract: One of the difficulties of the vibration-based damage identification methods is the nonuniqueness of the results of damage identification. The different damage locations and severity may cause the identical response signal, which is even more severe for detection of the multiple damage. This paper proposes a new strategy for damage detection to avoid this nonuniqueness. This strategy firstly determines the approximates damage area based on the statistical pattern recognition method using the dynamic strain signal measured by the distributed fiber Bragg grating, and then accurately evaluates the damage information based on the Bayesian model updating method using the experimental modal data. The stochastic simulation method is then used to compute the high-dimensional integral in the Bayesian problem. Finally, an experiment of the plate structure, simulating one part of mechanical structure, is used to verify the effectiveness of this approach.
Abstract: In this paper, the differential quadrature method is applied to simulate natural convection in an inclined cubic cavity using velocity-vorticity formulation. The numerical capability of the present algorithm is demonstrated by application to natural convection in an inclined cubic cavity. The velocity Poisson equations, the vorticity transport equations and the energy equation are all solved as a coupled system of equations for the seven field variables consisting of three velocities, three vorticities and temperature. The coupled equations are simultaneously solved by imposing the vorticity definition at boundary without requiring the explicit specification of the vorticity boundary conditions. Test results obtained for an inclined cubic cavity with different angle of inclinations for Rayleigh number equal to 103, 104, 105 and 106 indicate that the present coupled solution algorithm could predict the benchmark results for temperature and flow fields. Thus, it is convinced that the present formulation is capable of solving coupled Navier-Stokes equations effectively and accurately.
Abstract: In high powered dense wavelength division
multiplexed (WDM) systems with low chromatic dispersion,
four-wave mixing (FWM) can prove to be a major source of noise.
The MultiCanonical Monte Carlo Method (MCMC) and the Split
Step Fourier Method (SSFM) are combined to accurately evaluate the
probability density function of the decision variable of a receiver,
limited by FWM. The combination of the two methods leads to more
accurate results, and offers the possibility of adding other optical
noises such as the Amplified Spontaneous Emission (ASE) noise.
Abstract: The aged are faced with increasing risk for falls. The
aged have the easily fragile bones than others. When falls have
occurred, it is important to detect this emergency state because such
events often lead to more serious illness or even death. A
implementation of PDA system, for detection of emergency situation,
was developed using 3-axis accelerometer in this paper as follows.
The signals were acquired from the 3-axis accelerometer, and then
transmitted to the PDA through Bluetooth module. This system can
classify the human activity, and also detect the emergency state like
falls. When the fall occurs, the system generates the alarm on the
PDA. If a subject does not respond to the alarm, the system determines
whether the current situation is an emergency state or not, and then
sends some information to the emergency center in the case of urgent
situation. Three different studies were conducted on 12 experimental
subjects, with results indicating a good accuracy. The first study was
performed to detect the posture change of human daily activity. The
second study was performed to detect the correct direction of fall. The
third study was conducted to check the classification of the daily
physical activity. Each test was lasted at least 1 min. in third study.
The output of acceleration signal was compared and evaluated by
changing a various posture after attaching a 3-axis accelerometer
module on the chest. The newly developed system has some important
features such as portability, convenience and low cost. One of the
main advantages of this system is that it is available at home
healthcare environment. Another important feature lies in low cost to
manufacture device. The implemented system can detect the fall
accurately, so will be widely used in emergency situation.
Abstract: The uses of road map in daily activities are numerous
but it is a hassle to construct and update a road map whenever there
are changes. In Universiti Malaysia Sarawak, research on Automatic
Road Extraction (ARE) was explored to solve the difficulties in
updating road map. The research started with using Satellite Image
(SI), or in short, the ARE-SI project. A Hybrid Simple Colour Space
Segmentation & Edge Detection (Hybrid SCSS-EDGE) algorithm
was developed to extract roads automatically from satellite-taken
images. In order to extract the road network accurately, the satellite
image must be analyzed prior to the extraction process. The
characteristics of these elements are analyzed and consequently the
relationships among them are determined. In this study, the road
regions are extracted based on colour space elements and edge details
of roads. Besides, edge detection method is applied to further filter
out the non-road regions. The extracted road regions are validated by
using a segmentation method. These results are valuable for building
road map and detecting the changes of the existing road database.
The proposed Hybrid Simple Colour Space Segmentation and Edge
Detection (Hybrid SCSS-EDGE) algorithm can perform the tasks
fully automatic, where the user only needs to input a high-resolution
satellite image and wait for the result. Moreover, this system can
work on complex road network and generate the extraction result in
seconds.
Abstract: This work presents a novel means of extracting fixedlength parameters from voice signals, such that words can be recognized
in linear time. The power and the zero crossing rate are first
calculated segment by segment from a voice signal; by doing so, two
feature sequences are generated. We then construct an FIR system
across these two sequences. The parameters of this FIR system, used
as the input of a multilayer proceptron recognizer, can be derived by
recursive LSE (least-square estimation), implying that the complexity of overall process is linear to the signal size. In the second part of
this work, we introduce a weighting factor λ to emphasize recent
input; therefore, we can further recognize continuous speech signals.
Experiments employ the voice signals of numbers, from zero to nine, spoken in Mandarin Chinese. The proposed method is verified to
recognize voice signals efficiently and accurately.