Abstract: In this paper, the innovative intelligent fuzzy weighted
input estimation method (FWIEM) can be applied to the inverse heat
transfer conduction problem (IHCP) to estimate the unknown
time-varying heat flux efficiently as presented. The feasibility of this
method can be verified by adopting the temperature measurement
experiment. We would like to focus attention on the heat flux
estimation to three kinds of samples (Copper, Iron and Steel/AISI 304)
with the same 3mm thickness. The temperature measurements are then
regarded as the inputs into the FWIEM to estimate the heat flux. The
experiment results show that the proposed algorithm can estimate the
unknown time-varying heat flux on-line.
Abstract: This paper describes a blind algorithm for estimating a time varying and frequency selective fading channel. In order to identify blindly the impulse response of these channels, we have used Higher Order Statistics (HOS) to build our algorithm. In this paper, we have selected two theoretical frequency selective channels as the Proakis-s 'B' channel and the Macchi-s channel, and one practical frequency selective fading channel called Broadband Radio Access Network (BRAN A). The simulation results in noisy environment and for different data input channel, demonstrate that the proposed method could estimate the phase and magnitude of these channels blindly and without any information about the input, except that the input excitation is i.i.d (Identically and Independent Distributed) and non-Gaussian.
Abstract: This paper proposes two novel schemes for pilot-aided
integer frequency offset (IFO) estimation in orthogonal frequency
division multiplexing (OFDM)-based digital video broadcastingterrestrial
(DVB-T) systems. The conventional scheme proposed for
estimating the IFO uses only partial information of combinations
that pilots can provide, which stems from a rigorous assumption
that the channel responses of pilots used for estimating the IFO
change very rapidly. Thus, in this paper, we propose the novel IFO
estimation schemes exploiting all information of combinations that
pilots can provide to improve the performance of IFO estimation.
The simulation results show that the proposed schemes are highly
accurate in terms of the IFO detection probability.
Abstract: Since 2004, we have been developing an in-situ storage image sensor (ISIS) that captures more than 100 consecutive images at a frame rate of 10 Mfps with ultra-high sensitivity as well as the video camera for use with this ISIS. Currently, basic research is continuing in an attempt to increase the frame rate up to 100 Mfps and above. In order to suppress electro-magnetic noise at such high frequency, a digital-noiseless imaging transfer scheme has been developed utilizing solely sinusoidal driving voltages. This paper presents highly efficient-yet-accurate expressions to estimate attenuation as well as phase delay of driving voltages through RC networks of an ultra-high-speed image sensor. Elmore metric for a fundamental RC chain is employed as the first-order approximation. By application of dimensional analysis to SPICE data, we found a simple expression that significantly improves the accuracy of the approximation. Similarly, another simple closed-form model to estimate phase delay through fundamental RC networks is also obtained. Estimation error of both expressions is much less than previous works, only less 2% for most of the cases . The framework of this analysis can be extended to address similar issues of other VLSI structures.
Abstract: Bladder carcinoma is an important worldwide health problem. Both cystoscopy and urine cytology used in detecting bladder cancer suffer from drawbacks where cystoscopy is an invasive method and urine cytology shows low sensitivity in low grade tumors. This study validates easier and less time-consuming techniques to evaluate the value of combined use of angiogenin and clusterin in comparison and combination with voided urine cytology in the detection of bladder cancer patients. This study includes malignant (bladder cancer patients, n= 50), benign (n=20) and healthy (n=20) groups. The studied groups were subjected to cystoscopic examination, detection of bilharzial antibodies, urine cytology, and estimation of urinary angiogenin and clusterin by ELISA. The overall sensitivity and specificity were 66% and 75% for angiogenin, 70% and 82.5% for clusterin and 46% and 80% for voided urine cytology. Combined sensitivity of angiogenin and clusterin with urine cytology increased from 82 to 88%.
Abstract: Formulation of biological profile is one of the modern roles of forensic anthropologist. The present study was conducted to estimate height using foot and shoeprint length of Malaysian population. The present work can be very useful information in the process of identification of individual in forensic cases based on shoeprint evidence. It can help to narrow down suspects and ease the police investigation. Besides, stature is important parameters in determining the partial identify of unidentified and mutilated bodies. Thus, this study can help the problem encountered in cases of mass disaster, massacre, explosions and assault cases. This is because it is very hard to identify parts of bodies in these cases where people are dismembered and become unrecognizable. Samples in this research were collected from 200 Malaysian adults (100 males and 100 females) with age ranging from 20 to 45 years old. In this research, shoeprint length were measured based on the print of the shoes made from the flat shoes. Other information like gender, foot length and height of subject were also recorded. The data was analyzed using IBM® SPSS Statistics 19 software. Results indicated that, foot length has a strong correlation with stature than shoeprint length for both sides of the feet. However, in the unknown, where the gender was undetermined have shown a better correlation in foot length and shoeprint length parameter compared to males and females analyzed separately. In addition, prediction equations are developed to estimate the stature using linear regression analysis of foot length and shoeprint length. However, foot lengths give better prediction than shoeprint length.
Abstract: This paper suggests a calibration method to reduce
errors occurring due to mobile robot sliding during location estimation
using the Dead-reckoning. Due to sliding of the mobile robot caused
between its wheels and the road surface while on free run, location
estimation can be erroneous. Sliding especially occurs during
cornering of mobile robot. Therefore, in order to reduce these frequent
sliding errors in cornering, we calibrated the mobile robot-s heading
values using a vision camera and templates of the ceiling.
Abstract: The householder RLS (HRLS) algorithm is an O(N2)
algorithm which recursively updates an arbitrary square-root of the
input data correlation matrix and naturally provides the LS weight
vector. A data dependent householder matrix is applied for such
an update. In this paper a recursive estimate of the eigenvalue
spread and misalignment of the algorithm is presented at a very low
computational cost. Misalignment is found to be highly sensitive to
the eigenvalue spread of input signals, output noise of the system and
exponential window. Simulation results show noticeable degradation
in the misalignment by increase in eigenvalue spread as well as
system-s output noise, while exponential window was kept constant.
Abstract: Ability of accurate and reliable location estimation in
indoor environment is the key issue in developing great number of
context aware applications and Location Based Services (LBS).
Today, the most viable solution for localization is the Received
Signal Strength (RSS) fingerprinting based approach using wireless
local area network (WLAN). This paper presents two RSS
fingerprinting based approaches – first we employ widely used
WLAN based positioning as a reference system and then investigate
the possibility of using GSM signals for positioning. To compare
them, we developed a positioning system in real world environment,
where realistic RSS measurements were collected. Multi-Layer
Perceptron (MLP) neural network was used as the approximation
function that maps RSS fingerprints and locations. Experimental
results indicate advantage of WLAN based approach in the sense of
lower localization error compared to GSM based approach, but GSM
signal coverage by far outreaches WLAN coverage and for some
LBS services requiring less precise accuracy our results indicate that
GSM positioning can also be a viable solution.
Abstract: Zero inflated Strict Arcsine model is a newly developed model which is found to be appropriate in modeling overdispersed count data. In this study, maximum likelihood estimation method is used in estimating the parameters for zero inflated strict arcsine model. Bootstrapping is then employed to compute the confidence intervals for the estimated parameters.
Abstract: In this paper, a novel corner detection method is
presented to stably extract geometrically important corners.
Intensity-based corner detectors such as the Harris corner can detect
corners in noisy environments but has inaccurate corner position and
misses the corners of obtuse angles. Edge-based corner detectors such
as Curvature Scale Space can detect structural corners but show
unstable corner detection due to incomplete edge detection in noisy
environments. The proposed image-based direct curvature estimation
can overcome limitations in both inaccurate structural corner detection
of the Harris corner detector (intensity-based) and the unstable corner
detection of Curvature Scale Space caused by incomplete edge
detection. Various experimental results validate the robustness of the
proposed method.
Abstract: The main objective of this paper is to estimate the cost of road traffic accidents in Egypt. The Human Capital (HC) approach, specifically the Gross-Loss-of-Output methodology, is adopted for estimation. Moreover, cost values obtained by previous national literature are updated using the inflation rates. The results indicate an estimated cost of road traffic accidents in Egypt of approximately 10 billion Egyptian Pounds (about $US 1.8 billion) for the year 2008. In addition, it is expected that this cost will rise in 2009 to 11.8 billion Egyptian Pounds (about $US 2.1 billion).
Abstract: When binary decision diagrams are formed from
uniformly distributed Monte Carlo data for a large number of
variables, the complexity of the decision diagrams exhibits a
predictable relationship to the number of variables and minterms. In
the present work, a neural network model has been used to analyze the
pattern of shortest path length for larger number of Monte Carlo data
points. The neural model shows a strong descriptive power for the
ISCAS benchmark data with an RMS error of 0.102 for the shortest
path length complexity. Therefore, the model can be considered as a
method of predicting path length complexities; this is expected to lead
to minimum time complexity of very large-scale integrated circuitries
and related computer-aided design tools that use binary decision
diagrams.
Abstract: In this paper, algorithm estimating the blood pressure
was proposed using the pulse transit time (PTT) as a more convenient
method of measuring the blood pressure. After measuring ECG and
pressure pulse, and photoplethysmography, the PTT was calculated
from the acquired signals. Thereafter, the system to indirectly measure
the systolic pressure and the diastolic pressure was composed using
the statistic method. In comparison between the blood pressure
indirectly measured by proposed algorithm estimating the blood
pressure and real blood pressure measured by conventional
sphygmomanometer, the systolic pressure indicates the mean error of
±3.24mmHg and the standard deviation of 2.53mmHg, while the
diastolic pressure indicates the satisfactory result, that is, the mean
error of ±1.80mmHg and the standard deviation of 1.39mmHg. These
results are satisfied with the regulation of ANSI/AAMI for
certification of sphygmomanometer that real measurement error value
should be within the mean error of ±5mmHg and the standard
deviation of 8mmHg. These results are suggest the possibility of
applying to portable and long time blood pressure monitoring system
hereafter.
Abstract: Various models have been derived by studying large number of completed software projects from various organizations and applications to explore how project sizes mapped into project effort. But, still there is a need to prediction accuracy of the models. As Neuro-fuzzy based system is able to approximate the non-linear function with more precision. So, Neuro-Fuzzy system is used as a soft computing approach to generate model by formulating the relationship based on its training. In this paper, Neuro-Fuzzy technique is used for software estimation modeling of on NASA software project data and performance of the developed models are compared with the Halstead, Walston-Felix, Bailey-Basili and Doty Models mentioned in the literature.
Abstract: Optical flow is a research topic of interest for many
years. It has, until recently, been largely inapplicable to real-time
applications due to its computationally expensive nature. This paper
presents a new reliable flow technique which is combined with a
motion detection algorithm, from stationary camera image streams,
to allow flow-based analyses of moving entities, such as rigidity, in
real-time. The combination of the optical flow analysis with motion
detection technique greatly reduces the expensive computation of
flow vectors as compared with standard approaches, rendering the
method to be applicable in real-time implementation. This paper
describes also the hardware implementation of a proposed pipelined
system to estimate the flow vectors from image sequences in real
time. This design can process 768 x 576 images at a very high frame
rate that reaches to 156 fps in a single low cost FPGA chip, which is
adequate for most real-time vision applications.
Abstract: This paper describes a segmentation algorithm based
on the cooperation of an optical flow estimation method with edge
detection and region growing procedures.
The proposed method has been developed as a pre-processing
stage to be used in methodologies and tools for video/image indexing
and retrieval by content. The addressed problem consists in
extracting whole objects from background for producing images of
single complete objects from videos or photos. The extracted images
are used for calculating the object visual features necessary for both
indexing and retrieval processes.
The first task of the algorithm exploits the cues from motion
analysis for moving area detection. Objects and background are then
refined using respectively edge detection and region growing
procedures. These tasks are iteratively performed until objects and
background are completely resolved.
The developed method has been applied to a variety of indoor and
outdoor scenes where objects of different type and shape are
represented on variously textured background.
Abstract: The ability to recognize humans and their activities by computer vision is a very important task, with many potential application. Study of human motion analysis is related to several research areas of computer vision such as the motion capture, detection, tracking and segmentation of people. In this paper, we describe a segmentation method for extracting human body contour in modified HLS color space. To estimate a background, the modified HLS color space is proposed, and the background features are estimated by using the HLS color components. Here, the large amount of human dataset, which was collected from DV cameras, is pre-processed. The human body and its contour is successfully extracted from the image sequences.
Abstract: Linear stochastic estimation and quadratic stochastic
estimation techniques were applied to estimate the entire velocity
flow-field of an open cavity with a length to depth ratio of 2. The
estimations were done through the use of instantaneous velocity
magnitude as estimators. These measurements were obtained by
Particle Image Velocimetry. The predicted flow was compared
against the original flow-field in terms of the Reynolds stresses and
turbulent kinetic energy. Quadratic stochastic estimation proved to be
more superior than linear stochastic estimation in resolving the shear
layer flow. When the velocity fluctuations were scaled up in the
quadratic estimate, both the time-averaged quantities and the
instantaneous cavity flow can be predicted to a rather accurate extent.
Abstract: The background estimation approach using a small
window median filter is presented on the bases of analyzing IR point
target, noise and clutter model. After simplifying the two-dimensional
filter, a simple method of adopting one-dimensional median filter is
illustrated to make estimations of background according to the
characteristics of IR scanning system. The adaptive threshold is used
to segment canceled image in the background. Experimental results
show that the algorithm achieved good performance and satisfy the
requirement of big size image-s real-time processing.