Abstract: Decision feedback equalizers are commonly employed to reduce the error caused by intersymbol interference. Here, an adaptive decision feedback equalizer is presented with a new adaptation algorithm. The algorithm follows a block-based approach of normalized least mean square (NLMS) algorithm with set-membership filtering and achieves a significantly less computational complexity over its conventional NLMS counterpart with set-membership filtering. It is shown in the results that the proposed algorithm yields similar type of bit error rate performance over a reasonable signal to noise ratio in comparison with the latter one.
Abstract: Efficient storage, transmission and use of video information are key requirements in many multimedia applications currently being addressed by MPEG-4. To fulfill these requirements, a new approach for representing video information which relies on an object-based representation, has been adopted. Therefore, objectbased watermarking schemes are needed for copyright protection. This paper proposes a novel blind object watermarking scheme for images and video using the in place lifting shape adaptive-discrete wavelet transform (SA-DWT). In order to make the watermark robust and transparent, the watermark is embedded in the average of wavelet blocks using the visual model based on the human visual system. Wavelet coefficients n least significant bits (LSBs) are adjusted in concert with the average. Simulation results shows that the proposed watermarking scheme is perceptually invisible and robust against many attacks such as lossy image/video compression (e.g. JPEG, JPEG2000 and MPEG-4), scaling, adding noise, filtering, etc.
Abstract: This paper presents a novel approach for the design of
microwave circuits using Adaptive Network Fuzzy Inference
Optimizer (ANFIO). The method takes advantage of direct synthesis
of subsections of the amplifier using very fast and accurate ANFIO
models based on exact simulations using ADS. A mapping from
course space to fine space known as space mapping is also used. The
proposed synthesis approach takes into account the noise and
scattering parameters due to parasitic elements to achieve optimal
results. The overall ANFIO system is capable of designing different
LNAs at different noise and scattering criteria. This approach offers
significantly reduced time in the design of microwave amplifiers
within the validity range of the ANFIO system. The method has been
proven to work efficiently for a 2.4GHz LNA example. The S21 of
10.1 dB and noise figure (NF) of 2.7 dB achieved for ANFIO while
S21 of 9.05 dB and NF of 2.6 dB achieved for ANN.
Abstract: We developed a multi-camera control system that a (one) cameraman can operate several cameras at a compact studio. we analyzed a workflow of a cameraman of some program shootings with two cameras and clarified their heavy tasks. The system based on a dynamic workflow which adapts a program progressing and recommends of cameraman. we perform the automation of multicamera controls by modeling of studio environment and perform automatic camera adjustment for suitable angle of view with face detection. Our experiment at a real program shooting showed that one cameraman can carry out the task of shooting sufficiently.
Abstract: An adaptive fuzzy PID controller with gain scheduling is proposed in this paper. The structure of the proposed gain scheduled fuzzy PID (GS_FPID) controller consists of both fuzzy PI-like controller and fuzzy PD-like controller. Both of fuzzy PI-like and PD-like controllers are weighted through adaptive gain scheduling, which are also determined by fuzzy logic inference. A modified genetic algorithm called accumulated genetic algorithm is designed to learn the parameters of fuzzy inference system. In order to learn the number of fuzzy rules required for the TSK model, the fuzzy rules are learned in an accumulated way. In other words, the parameters learned in the previous rules are accumulated and updated along with the parameters in the current rule. It will be shown that the proposed GS_FPID controllers learned by the accumulated GA perform well for not only the regular linear systems but also the higher order and time-delayed systems.
Abstract: Currently, most of distance learning courses can only
deliver standard material to students. Students receive course content
passively which leads to the neglect of the goal of education – “to suit
the teaching to the ability of students". Providing appropriate course
content according to students- ability is the main goal of this paper.
Except offering a series of conventional learning services, abundant
information available, and instant message delivery, a complete online
learning environment should be able to distinguish between students-
ability and provide learning courses that best suit their ability.
However, if a distance learning site contains well-designed course
content and design but fails to provide adaptive courses, students will
gradually loss their interests and confidence in learning and result in
ineffective learning or discontinued learning. In this paper, an
intelligent tutoring system is proposed and it consists of several
modules working cooperatively in order to build an adaptive learning
environment for distance education. The operation of the system is
based on the result of Self-Organizing Map (SOM) to divide students
into different groups according to their learning ability and learning
interests and then provide them with suitable course content.
Accordingly, the problem of information overload and internet traffic
problem can be solved because the amount of traffic accessing the
same content is reduced.
Abstract: A new Feed-Forward/Feedback Generalized
Minimum Variance Pole-placement Controller to incorporate the
robustness of classical pole-placement into the flexibility of
generalized minimum variance self-tuning controller for Single-Input
Single-Output (SISO) has been proposed in this paper. The design,
which provides the user with an adaptive mechanism, which ensures
that the closed loop poles are, located at their pre-specified positions.
In addition, the controller design which has a feed-forward/feedback
structure overcomes the certain limitations existing in similar poleplacement
control designs whilst retaining the simplicity of
adaptation mechanisms used in other designs. It tracks set-point
changes with the desired speed of response, penalizes excessive
control action, and can be applied to non-minimum phase systems.
Besides, at steady state, the controller has the ability to regulate the
constant load disturbance to zero. Example simulation results using
both simulated and real plant models demonstrate the effectiveness of
the proposed controller.
Abstract: In this paper, we demonstrate the adaptive
least-mean-square (LMS) filter modeling using SystemC. SystemC is
a modeling language that allows designer to model both hardware and
software component and makes it possible to design from high level
system of abstraction to low level system of abstraction. We produced
five adaptive least-mean-square filter models that are classed as five
abstraction levels using SystemC proceeding from the abstract model
to the more concrete model.
Abstract: The dynamics of the Autonomous Underwater
Vehicles (AUVs) are highly nonlinear and time varying and the hydrodynamic coefficients of vehicles are difficult to estimate
accurately because of the variations of these coefficients with
different navigation conditions and external disturbances. This study presents the on-line system identification of AUV dynamics to obtain
the coupled nonlinear dynamic model of AUV as a black box. This black box has an input-output relationship based upon on-line
adaptive fuzzy model and adaptive neural fuzzy network (ANFN)
model techniques to overcome the uncertain external disturbance and
the difficulties of modelling the hydrodynamic forces of the AUVs instead of using the mathematical model with hydrodynamic parameters estimation. The models- parameters are adapted according
to the back propagation algorithm based upon the error between the
identified model and the actual output of the plant. The proposed
ANFN model adopts a functional link neural network (FLNN) as the
consequent part of the fuzzy rules. Thus, the consequent part of the
ANFN model is a nonlinear combination of input variables. Fuzzy
control system is applied to guide and control the AUV using both
adaptive models and mathematical model. Simulation results show
the superiority of the proposed adaptive neural fuzzy network
(ANFN) model in tracking of the behavior of the AUV accurately
even in the presence of noise and disturbance.
Abstract: This paper presents an adaptive nonlinear position
controller with velocity constraint, capable of combining the
input-output linearization technique and Lyapunov stability theory.
Based on the Lyapunov stability theory, the adaptation law of the
proposed controller is derived along with the verification of the overall
system-s stability. Computer simulation results demonstrate that the
proposed controller is robust and it can ensure transient stability of
BLDCM, under the occurrence of a large sudden fault.
Abstract: In H.264/AVC video encoding, rate-distortion
optimization for mode selection plays a significant role to achieve
outstanding performance in compression efficiency and video quality.
However, this mode selection process also makes the encoding
process extremely complex, especially in the computation of the ratedistortion
cost function, which includes the computations of the sum
of squared difference (SSD) between the original and reconstructed
image blocks and context-based entropy coding of the block. In this
paper, a transform-domain rate-distortion optimization accelerator
based on fast SSD (FSSD) and VLC-based rate estimation algorithm
is proposed. This algorithm could significantly simplify the hardware
architecture for the rate-distortion cost computation with only
ignorable performance degradation. An efficient hardware structure
for implementing the proposed transform-domain rate-distortion
optimization accelerator is also proposed. Simulation results
demonstrated that the proposed algorithm reduces about 47% of total
encoding time with negligible degradation of coding performance.
The proposed method can be easily applied to many mobile video
application areas such as a digital camera and a DMB (Digital
Multimedia Broadcasting) phone.
Abstract: In recent years in Kazakhstan, as well as in all countries, we have been talking not only about the professional stress, but also professional Burnout Syndrome of employees. Burnout is essentially a response to chronic emotional stress – manifests itself in the form of chronic fatigue, despondency, unmotivated aggression, anger, and others. This condition is due to mental fatigue among teachers as a sort of payment for overstrain when professional commitments include the impact of “heat your soul", emotional investment. The emergence of professional Burnout among teachers is due to the system of interrelated and mutually reinforcing factors relating to the various levels of the personality: individually-psychological level is psychodynamic special subject characteristics of valuemotivational sphere and formation of skills and habits of selfregulation; the socio-psychological level includes especially the Organization and interpersonal interaction of a teacher. Signs of the Burnout were observed in 15 testees, and virtually a symptom could be observed in every teacher. As a result of the diagnosis 48% of teachers had the signs of stress (phase syndrome), resulting in a sense of anxiety, mood, heightened emotional susceptibility. The following results have also been got:-the fall of General energy potential – 14 pers. -Psychosomatic and psycho vegetative syndrome – 26 pers. -emotional deficit-34 pers. -emotional Burnout Syndrome-6 pers. The problem of professional Burnout of teachers in the current conditions should become not only meaningful, but particularly relevant. The quality of education of the younger generation depends on professional development; teachers- training level, and how “healthy" teachers are. That is why the systematic maintenance of pedagogic-professional development for teachers (including disclosure of professional Burnout Syndrome factors) takes on a special meaning.
Abstract: One of the essential components of much of DSP
application is noise cancellation. Changes in real time signals are
quite rapid and swift. In noise cancellation, a reference signal which
is an approximation of noise signal (that corrupts the original
information signal) is obtained and then subtracted from the noise
bearing signal to obtain a noise free signal. This approximation of
noise signal is obtained through adaptive filters which are self
adjusting. As the changes in real time signals are abrupt, this needs
adaptive algorithm that converges fast and is stable. Least mean
square (LMS) and normalized LMS (NLMS) are two widely used
algorithms because of their plainness in calculations and
implementation. But their convergence rates are small. Adaptive
averaging filters (AFA) are also used because they have high
convergence, but they are less stable. This paper provides the
comparative study of LMS and Normalized NLMS, AFA and new
enhanced average adaptive (Average NLMS-ANLMS) filters for noise
cancelling application using speech signals.
Abstract: The protection issues in distribution systems with open and closed-loop are studied, and a generalized protection setting scheme based on the traditional over current protection theories is proposed to meet the new requirements. The setting method is expected to be easier realized using computer program, so that the on-line adaptive setting for coordination in distribution system can be implemented. An automatic setting program is created and several cases are taken into practice. The setting results are verified by the coordination curves of the protective devices which are plotted using MATLAB.
Abstract: Heart disease (HD) is a major cause of morbidity and mortality in the modern society. Medical diagnosis is an important but complicated task that should be performed accurately and efficiently and its automation would be very useful. All doctors are unfortunately not equally skilled in every sub specialty and they are in many places a scarce resource. A system for automated medical diagnosis would enhance medical care and reduce costs. In this paper, a new approach based on coactive neuro-fuzzy inference system (CANFIS) was presented for prediction of heart disease. The proposed CANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach which is then integrated with genetic algorithm to diagnose the presence of the disease. The performances of the CANFIS model were evaluated in terms of training performances and classification accuracies and the results showed that the proposed CANFIS model has great potential in predicting the heart disease.
Abstract: Image restoration involves elimination of noise. Filtering techniques were adopted so far to restore images since last five decades. In this paper, we consider the problem of image restoration degraded by a blur function and corrupted by random noise. A method for reducing additive noise in images by explicit analysis of local image statistics is introduced and compared to other noise reduction methods. The proposed method, which makes use of an a priori noise model, has been evaluated on various types of images. Bayesian based algorithms and technique of image processing have been described and substantiated with experimentation using MATLAB.
Abstract: Mostly the systems are dealing with time varying
signals. The Power efficiency can be achieved by adapting the system
activity according to the input signal variations. In this context
an adaptive rate filtering technique, based on the level crossing sampling
is devised. It adapts the sampling frequency and the filter order
by following the input signal local variations. Thus, it correlates the
processing activity with the signal variations. Interpolation is required
in the proposed technique. A drastic reduction in the interpolation
error is achieved by employing the symmetry during the interpolation
process. Processing error of the proposed technique is
calculated. The computational complexity of the proposed filtering
technique is deduced and compared to the classical one. Results
promise a significant gain of the computational efficiency and hence
of the power consumption.
Abstract: This paper proposes a Web service and serviceoriented
architecture (SOA) for a computer-adaptive testing (CAT)
process on e-learning systems. The proposed architecture is
developed to solve an interoperability problem of the CAT process by
using Web service. The proposed SOA and Web service define all
services needed for the interactions between systems in order to
deliver items and essential data from Web service to the CAT Webbased
application. These services are implemented in a XML-based
architecture, platform independence and interoperability between the
Web service and CAT Web-based applications.
Abstract: This paper addresses the design of predictive
networked controller with adaptation of a communication delay. The
networked control system contains random delays from sensor to
controller and from controller to actuator. The proposed predictive
controller includes an adaptation loop which decreases the influence
of communication delay on the control performance. Also, the
predictive controller contains a filter which improves the robustness
of the control system. The performance of the proposed adaptive
predictive controller is demonstrated by simulation results in
comparison with PI controller and predictive controller with constant
delay.
Abstract: Facial expression analysis plays a significant role for
human computer interaction. Automatic analysis of human facial
expression is still a challenging problem with many applications. In
this paper, we propose neuro-fuzzy based automatic facial expression
recognition system to recognize the human facial expressions like
happy, fear, sad, angry, disgust and surprise. Initially facial image is
segmented into three regions from which the uniform Local Binary
Pattern (LBP) texture features distributions are extracted and
represented as a histogram descriptor. The facial expressions are
recognized using Multiple Adaptive Neuro Fuzzy Inference System
(MANFIS). The proposed system designed and tested with JAFFE
face database. The proposed model reports 94.29% of classification
accuracy.