Abstract: This paper presents a digital non-linear pulse-width modulation (PWM) controller in a high-voltage (HV) buck-boost DC-DC converter for the piezoelectric transducer of the down-hole acoustic telemetry system. The proposed design controls the generation of output signal with voltage higher than the supply voltage and is targeted to work under high temperature. To minimize the power consumption and silicon area, a simple and efficient design scheme is employed to develop the PWM controller. The proposed PWM controller consists of serial to parallel (S2P) converter, data assign block, a mode and duty cycle controller (MDC), linearly PWM (LPWM) and noise shaper, pulse generator and clock generator. To improve the reliability of circuit operation at higher temperature, this design is fabricated with the 1.0-μm silicon-on-insulator (SOI) CMOS process. The implementation results validated that the proposed design has the advantages of smaller size, lower power consumption and robust thermal stability.
Abstract: This study involves the modeling and monitoring of an ammonia synthesis fixed-bed reactor using partial least squares (PLS) and its variants. The process exhibits complex dynamic behavior due to the presence of heat recycling and feed quench. One limitation of static PLS model in this situation is that it does not take account of the process dynamics and hence dynamic PLS was used. Although it showed, superior performance to static PLS in terms of prediction, the monitoring scheme was inappropriate hence adaptive PLS was considered. A limitation of adaptive PLS is that non-conforming observations also contribute to the model, therefore, a new adaptive approach was developed, robust adaptive dynamic PLS. This approach updates a dynamic PLS model and is robust to non-representative data. The developed methodology showed a clear improvement over existing approaches in terms of the modeling of the reactor and the detection of faults.
Abstract: Information security plays a major role in uplifting the standard of secured communications via global media. In this paper, we have suggested a technique of encryption followed by insertion before transmission. Here, we have implemented two different concepts to carry out the above-specified tasks. We have used a two-point crossover technique of the genetic algorithm to facilitate the encryption process. For each of the uniquely identified rows of pixels, different mathematical methodologies are applied for several conditions checking, in order to figure out all the parent pixels on which we perform the crossover operation. This is done by selecting two crossover points within the pixels thereby producing the newly encrypted child pixels, and hence the encrypted cover image. In the next lap, the first and second order derivative operators are evaluated to increase the security and robustness. The last lap further ensures reapplication of the crossover procedure to form the final stego-image. The complexity of this system as a whole is huge, thereby dissuading the third party interferences. Also, the embedding capacity is very high. Therefore, a larger amount of secret image information can be hidden. The imperceptible vision of the obtained stego-image clearly proves the proficiency of this approach.
Abstract: In this paper, we present a pedestrian detection descriptor called Fused Structure and Texture (FST) features based on the combination of the local phase information with the texture features. Since the phase of the signal conveys more structural information than the magnitude, the phase congruency concept is used to capture the structural features. On the other hand, the Center-Symmetric Local Binary Pattern (CSLBP) approach is used to capture the texture information of the image. The dimension less quantity of the phase congruency and the robustness of the CSLBP operator on the flat images, as well as the blur and illumination changes, lead the proposed descriptor to be more robust and less sensitive to the light variations. The proposed descriptor can be formed by extracting the phase congruency and the CSLBP values of each pixel of the image with respect to its neighborhood. The histogram of the oriented phase and the histogram of the CSLBP values for the local regions in the image are computed and concatenated to construct the FST descriptor. Several experiments were conducted on INRIA and the low resolution DaimlerChrysler datasets to evaluate the detection performance of the pedestrian detection system that is based on the FST descriptor. A linear Support Vector Machine (SVM) is used to train the pedestrian classifier. These experiments showed that the proposed FST descriptor has better detection performance over a set of state of the art feature extraction methodologies.
Abstract: Access to advanced medical services has been one of the medical challenges faced by our present society especially in distant geographical locations which may be inaccessible. Then the need for telemedicine arises through which live videos of a doctor can be streamed to a patient located anywhere in the world at any time. Patients’ medical records contain very sensitive information which should not be made accessible to unauthorized people in order to protect privacy, integrity and confidentiality. This research work focuses on a more robust security measure which is biometric (fingerprint) as a form of access control to data of patients by the medical specialist/practitioner.
Abstract: This paper is devoted to present the advances in the design of a prototype that is able to supervise the complex behavior of water quality parameters such as pH and temperature, via a real-time monitoring system. The current water quality tests that are performed in government water quality institutions in Mexico are carried out in problematic locations and they require taking manual samples. The water samples are then taken to the institution laboratory for examination. In order to automate this process, a water quality monitoring system based on wireless sensor networks is proposed. The system consists of a sensor node which contains one pH sensor, one temperature sensor, a microcontroller, and a ZigBee radio, and a base station composed by a ZigBee radio and a PC. The progress in this investigation shows the development of a water quality monitoring system. Due to recent events that affected water quality in Mexico, the main motivation of this study is to address water quality monitoring systems, so in the near future, a more robust, affordable, and reliable system can be deployed.
Abstract: Texture is an important characteristic in real and
synthetic scenes. Texture analysis plays a critical role in inspecting
surfaces and provides important techniques in a variety of
applications. Although several descriptors have been presented to
extract texture features, the development of object recognition is still a
difficult task due to the complex aspects of texture. Recently, many
robust and scaling-invariant image features such as SIFT, SURF and
ORB have been successfully used in image retrieval and object
recognition. In this paper, we have tried to compare the performance
for texture classification using these feature descriptors with k-means
clustering. Different classifiers including K-NN, Naive Bayes, Back
Propagation Neural Network , Decision Tree and Kstar were applied in
three texture image sets － UIUCTex, KTH-TIPS and Brodatz,
respectively. Experimental results reveal SIFTS as the best average
accuracy rate holder in UIUCTex, KTH-TIPS and SURF is
advantaged in Brodatz texture set. BP neuro network works best in the
test set classification among all used classifiers.
Abstract: We present a new subband adaptive filter (R-SAF)
which is robust against impulsive noise in system identification. To
address the vulnerability of adaptive filters based on the L2-norm
optimization criterion against impulsive noise, the R-SAF comes from
the L1-norm optimization criterion with a constraint on the energy
of the weight update. Minimizing L1-norm of the a posteriori error
in each subband with a constraint on minimum disturbance gives
rise to the robustness against the impulsive noise and the capable
convergence performance. Experimental results clearly demonstrate
that the proposed R-SAF outperforms the classical adaptive filtering
algorithms when impulsive noise as well as background noise exist.
Abstract: In this work, we use the Fault detection and isolation and the Fault tolerant control based on sliding mode observer in order to introduce the well diagnosis of a nonlinear system. The robustness of the proposed observer for the two techniques is tested through a physical example. The results in this paper show the interaction between the Fault tolerant control and the Diagnosis procedure.
Abstract: Hydrologic models are increasingly used as tools to
predict stormwater quantity and quality from urban catchments.
However, due to a range of practical issues, most models produce
gross errors in simulating complex hydraulic and hydrologic systems.
Difficulty in finding a robust approach for model calibration is one of
the main issues. Though automatic calibration techniques are
available, they are rarely used in common commercial hydraulic and
hydrologic modelling software e.g. MIKE URBAN. This is partly
due to the need for a large number of parameters and large datasets in
the calibration process. To overcome this practical issue, a
framework for automatic calibration of a hydrologic model was
developed in R platform and presented in this paper. The model was
developed based on the time-area conceptualization. Four calibration
parameters, including initial loss, reduction factor, time of
concentration and time-lag were considered as the primary set of
parameters. Using these parameters, automatic calibration was
performed using Approximate Bayesian Computation (ABC). ABC is
a simulation-based technique for performing Bayesian inference
when the likelihood is intractable or computationally expensive to
compute. To test the performance and usefulness, the technique was
used to simulate three small catchments in Gold Coast. For
comparison, simulation outcomes from the same three catchments
using commercial modelling software, MIKE URBAN were used.
The graphical comparison shows strong agreement of MIKE URBAN
result within the upper and lower 95% credible intervals of posterior
predictions as obtained via ABC. Statistical validation for posterior
predictions of runoff result using coefficient of determination (CD),
root mean square error (RMSE) and maximum error (ME) was found
reasonable for three study catchments. The main benefit of using
ABC over MIKE URBAN is that ABC provides a posterior
distribution for runoff flow prediction, and therefore associated
uncertainty in predictions can be obtained. In contrast, MIKE
URBAN just provides a point estimate. Based on the results of the
analysis, it appears as though ABC the developed framework
performs well for automatic calibration.
Abstract: Nowadays, food safety is a great public concern;
therefore, robust and effective techniques are required for detecting
the safety situation of goods. Hyperspectral Imaging (HSI) is an
attractive material for researchers to inspect food quality and safety
estimation such as meat quality assessment, automated poultry
carcass inspection, quality evaluation of fish, bruise detection of
apples, quality analysis and grading of citrus fruits, bruise detection
of strawberry, visualization of sugar distribution of melons,
measuring ripening of tomatoes, defect detection of pickling
cucumber, and classification of wheat kernels. HSI can be used to
concurrently collect large amounts of spatial and spectral data on the
objects being observed. This technique yields with exceptional
detection skills, which otherwise cannot be achieved with either
imaging or spectroscopy alone. This paper presents a nonlinear
technique based on kernel Fukunaga-Koontz transform (KFKT) for
detection of fat content in ground meat using HSI. The KFKT which
is the nonlinear version of FKT is one of the most effective
techniques for solving problems involving two-pattern nature. The
conventional FKT method has been improved with kernel machines
for increasing the nonlinear discrimination ability and capturing
higher order of statistics of data. The proposed approach in this paper
aims to segment the fat content of the ground meat by regarding the
fat as target class which is tried to be separated from the remaining
classes (as clutter). We have applied the KFKT on visible and nearinfrared
(VNIR) hyperspectral images of ground meat to determine
fat percentage. The experimental studies indicate that the proposed
technique produces high detection performance for fat ratio in ground
meat.
Abstract: This paper describes a simple way to control the speed
of PMBLDC motor using Fuzzy logic control method. In the
conventional PI controller the performance of the motor system is
simulated and the speed is regulated by using PI controller. These
methods used to improve the performance of PMSM drives, but in
some cases at different operating conditions when the dynamics of
the system also vary over time and it can change the reference speed,
parameter variations and the load disturbance. The simulation is
powered with the MATLAB program to get a reliable and flexible
simulation. In order to highlight the effectiveness of the speed control
method the FLC method is used. The proposed method targeted in
achieving the improved dynamic performance and avoids the
variations of the motor drive. This drive has high accuracy, robust
operation from near zero to high speed. The effectiveness and
flexibility of the individual techniques of the speed control method
will be thoroughly discussed for merits and demerits and finally
verified through simulation and experimental results for comparative
analysis.
Abstract: This paper outlines the development of an
experimental technique in quantifying supersonic jet flows, in an
attempt to avoid seeding particle problems frequently associated with
particle-image velocimetry (PIV) techniques at high Mach numbers.
Based on optical flow algorithms, the idea behind the technique
involves using high speed cameras to capture Schlieren images of the
supersonic jet shear layers, before they are subjected to an adapted
optical flow algorithm based on the Horn-Schnuck method to
determine the associated flow fields. The proposed method is capable
of offering full-field unsteady flow information with potentially
higher accuracy and resolution than existing point-measurements or
PIV techniques. Preliminary study via numerical simulations of a
circular de Laval jet nozzle successfully reveals flow and shock
structures typically associated with supersonic jet flows, which serve
as useful data for subsequent validation of the optical flow based
experimental results. For experimental technique, a Z-type Schlieren
setup is proposed with supersonic jet operated in cold mode,
stagnation pressure of 4 bar and exit Mach of 1.5. High-speed singleframe
or double-frame cameras are used to capture successive
Schlieren images. As implementation of optical flow technique to
supersonic flows remains rare, the current focus revolves around
methodology validation through synthetic images. The results of
validation test offers valuable insight into how the optical flow
algorithm can be further improved to improve robustness and
accuracy. Despite these challenges however, this supersonic flow
measurement technique may potentially offer a simpler way to
identify and quantify the fine spatial structures within the shock shear
layer.
Abstract: Orthogonal Frequency Division Multiplexing
(OFDM) has been used in many advanced wireless communication
systems due to its high spectral efficiency and robustness to
frequency selective fading channels. However, the major concern
with OFDM system is the high peak-to-average power ratio (PAPR)
of the transmitted signal. Some of the popular techniques used for
PAPR reduction in OFDM system are conventional partial transmit
sequences (CPTS) and clipping. In this paper, a parallel
combination/hybrid scheme of PAPR reduction using clipping and
CPTS algorithms is proposed. The proposed method intelligently
applies both the algorithms in order to reduce both PAPR as well as
computational complexity. The proposed scheme slightly degrades
bit error rate (BER) performance due to clipping operation and it can
be reduced by selecting an appropriate value of the clipping ratio
(CR). The simulation results show that the proposed algorithm
achieves significant PAPR reduction with much reduced
computational complexity.
Abstract: Segmentation of left ventricle (LV) from cardiac
ultrasound images provides a quantitative functional analysis of the
heart to diagnose disease. Active Shape Model (ASM) is widely used
for LV segmentation, but it suffers from the drawback that
initialization of the shape model is not sufficiently close to the target,
especially when dealing with abnormal shapes in disease. In this work,
a two-step framework is improved to achieve a fast and efficient LV
segmentation. First, a robust and efficient detection based on Hough
forest localizes cardiac feature points. Such feature points are used to
predict the initial fitting of the LV shape model. Second, ASM is
applied to further fit the LV shape model to the cardiac ultrasound
image. With the robust initialization, ASM is able to achieve more
accurate segmentation. The performance of the proposed method is
evaluated on a dataset of 810 cardiac ultrasound images that are mostly
abnormal shapes. This proposed method is compared with several
combinations of ASM and existing initialization methods. Our
experiment results demonstrate that accuracy of the proposed method
for feature point detection for initialization was 40% higher than the
existing methods. Moreover, the proposed method significantly
reduces the number of necessary ASM fitting loops and thus speeds up
the whole segmentation process. Therefore, the proposed method is
able to achieve more accurate and efficient segmentation results and is
applicable to unusual shapes of heart with cardiac diseases, such as left
atrial enlargement.
Abstract: In this paper, a robust fault detection and isolation
(FDI) scheme is developed to monitor a multivariable nonlinear
chemical process called the Chylla-Haase polymerization reactor,
when it is under the cascade PI control. The scheme employs a radial
basis function neural network (RBFNN) in an independent mode to
model the process dynamics, and using the weighted sum-squared
prediction error as the residual. The Recursive Orthogonal Least
Squares algorithm (ROLS) is employed to train the model to
overcome the training difficulty of the independent mode of the
network. Then, another RBFNN is used as a fault classifier to isolate
faults from different features involved in the residual vector. Several
actuator and sensor faults are simulated in a nonlinear simulation of
the reactor in Simulink. The scheme is used to detect and isolate the
faults on-line. The simulation results show the effectiveness of the
scheme even the process is subjected to disturbances and
uncertainties including significant changes in the monomer feed rate,
fouling factor, impurity factor, ambient temperature, and
measurement noise. The simulation results are presented to illustrate
the effectiveness and robustness of the proposed method.
Abstract: Concurrent planning of project scheduling and
material ordering has been increasingly addressed within last decades
as an approach to improve the project execution costs. Therefore, we
have taken the problem into consideration in this paper, aiming to
maximize schedules quality robustness, in addition to minimize the
relevant costs. In this regard, a bi-objective mathematical model is
developed to formulate the problem. Moreover, it is possible to
utilize the all-unit discount for materials purchasing. The problem is
then solved by the E-constraint method, and the Pareto front is
obtained for a variety of robustness values. The applicability and
efficiency of the proposed model is tested by different numerical
instances, finally.
Abstract: Global economy today is full of sophistication. All
over the world, business and marketing practices are undergoing
unprecedented transformation. In realization of this fact, the federal
government of Nigeria has put in place a robust transformation
agenda in order to put Nigeria in a better position to be a competitive
player and in the process transform all sectors of its economy. New
technologies, especially the Internet, are the driving force behind this
transformation. However, technology has inadvertently affected the
way businesses are done thus necessitating the acquisition of new
skills. In developing countries like Nigeria, citizens are still battling
with effective application of those technologies. Obviously, students
of business education need to acquire relevant business knowledge to
be able to transit into the world of work on graduation from school
and compete favorably in the labor market. Therefore, effective
utilization of social media by both teachers and students can help
extensively in empowering students with the needed skills. Social
media which is a group of Internet-based applications built on the
ideological foundations of Web 2.0, that allow the creation and
exchange of user generated content, and if incorporated into the
classroom experience may be the needed answer to unemployment
and poverty in Nigeria as beneficiaries can easily connect with
existing and potential enterprises and customers, engage with them
and reinforce mutual business benefits. Challenges and benefits of
social media use in education in Nigeria universities were revealed in
this study.
Abstract: Liver segmentation from medical images poses more
challenges than analogous segmentations of other organs. This
contribution introduces a liver segmentation method from a series of
computer tomography images. Overall, we present a novel method for
segmenting liver by coupling density matching with shape priors.
Density matching signifies a tracking method which operates via
maximizing the Bhattacharyya similarity measure between the
photometric distribution from an estimated image region and a model
photometric distribution. Density matching controls the direction of
the evolution process and slows down the evolving contour in regions
with weak edges. The shape prior improves the robustness of density
matching and discourages the evolving contour from exceeding liver’s
boundaries at regions with weak boundaries. The model is
implemented using a modified distance regularized level set (DRLS)
model. The experimental results show that the method achieves a
satisfactory result. By comparing with the original DRLS model, it is
evident that the proposed model herein is more effective in addressing
the over segmentation problem. Finally, we gauge our performance of
our model against matrices comprising of accuracy, sensitivity, and
specificity.
Abstract: Direct Torque Control (DTC) is an AC drive control
method especially designed to provide fast and robust responses. In
this paper a progressive algorithm for direct torque control of threephase
induction drive system supplied by photovoltaic arrays using
voltage source inverter to control motor torque and flux with
maximum power point tracking at different level of insolation is
presented. Experimental results of the new DTC method obtained by
an experimental rapid prototype system for drives are presented.
Simulation and experimental results confirm that the proposed system
gives quick, robust torque and speed responses at constant switching
frequencies.