Abstract: In this paper we propose a method which improves the efficiency of video coding. Our method combines an adaptive GOP (group of pictures) structure and the shot cut detection. We have analyzed different approaches for shot cut detection with aim to choose the most appropriate one. The next step is to situate N frames to the positions of detected cuts during the process of video encoding. Finally the efficiency of the proposed method is confirmed by simulations and the obtained results are compared with fixed GOP structures of sizes 4, 8, 12, 16, 32, 64, 128 and GOP structure with length of entire video. Proposed method achieved the gain in bit rate from 0.37% to 50.59%, while providing PSNR (Peak Signal-to-Noise Ratio) gain from 1.33% to 0.26% in comparison to simulated fixed GOP structures.
Abstract: we propose a new normalized LMS (NLMS) algorithm, which gives satisfactory performance in certain applications in comaprison with con-ventional NLMS recursion. This new algorithm can be treated as a block based simplification of NLMS algorithm with significantly reduced number of multi¬ply and accumulate as well as division operations. It is also shown that such a recursion can be easily implemented in block floating point (BFP) arithmetic, treating the implementational issues much efficiently. In particular, the core challenges of a BFP realization to such adaptive filters are mainly considered in this regard. A global upper bound on the step size control parameter of the new algorithm due to BFP implementation is also proposed to prevent overflow in filtering as well as weight updating operations jointly.
Abstract: Wireless Sensor Networks can be used to monitor the
physical phenomenon in such areas where human approach is nearly
impossible. Hence the limited power supply is the major constraint of
the WSNs due to the use of non-rechargeable batteries in sensor
nodes. A lot of researches are going on to reduce the energy
consumption of sensor nodes. Energy map can be used with
clustering, data dissemination and routing techniques to reduce the
power consumption of WSNs. Energy map can also be used to know
which part of the network is going to fail in near future. In this paper,
Energy map is constructed using the prediction based approach.
Adaptive alpha GM(1,1) model is used as the prediction model.
GM(1,1) is being used worldwide in many applications for predicting
future values of time series using some past values due to its high
computational efficiency and accuracy.
Abstract: Digital watermarking is one of the techniques for
copyright protection. In this paper, a normalization-based robust
image watermarking scheme which encompasses singular value
decomposition (SVD) and discrete cosine transform (DCT)
techniques is proposed. For the proposed scheme, the host image is
first normalized to a standard form and divided into non-overlapping
image blocks. SVD is applied to each block. By concatenating the
first singular values (SV) of adjacent blocks of the normalized image,
a SV block is obtained. DCT is then carried out on the SV blocks to
produce SVD-DCT blocks. A watermark bit is embedded in the highfrequency
band of a SVD-DCT block by imposing a particular
relationship between two pseudo-randomly selected DCT
coefficients. An adaptive frequency mask is used to adjust local
watermark embedding strength. Watermark extraction involves
mainly the inverse process. The watermark extracting method is blind
and efficient. Experimental results show that the quality degradation
of watermarked image caused by the embedded watermark is visually
transparent. Results also show that the proposed scheme is robust
against various image processing operations and geometric attacks.
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.
Abstract: Volume rendering is widely used in medical CT image
visualization. Applying 3D image visualization to diagnosis
application can require accurate volume rendering with high
resolution. Interpolation is important in medical image processing
applications such as image compression or volume resampling.
However, it can distort the original image data because of edge
blurring or blocking effects when image enhancement procedures
were applied. In this paper, we proposed adaptive tension control
method exploiting gradient information to achieve high resolution
medical image enhancement in volume visualization, where restored
images are similar to original images as much as possible. The
experimental results show that the proposed method can improve
image quality associated with the adaptive tension control efficacy.
Abstract: In this paper, Optimum adaptive loading algorithms
are applied to multicarrier system with Space-Time Block Coding
(STBC) scheme associated with space-time processing based on
singular-value decomposition (SVD) of the channel matrix over
Rayleigh fading channels. SVD method has been employed in
MIMO-OFDM system in order to overcome subchannel interference.
Chaw-s and Compello-s algorithms have been implemented to obtain
a bit and power allocation for each subcarrier assuming instantaneous
channel knowledge. The adaptive loaded SVD-STBC scheme is
capable of providing both full-rate and full-diversity for any number
of transmit antennas. The effectiveness of these techniques has
demonstrated through the simulation of an Adaptive loaded SVDSTBC
system, and the comparison shown that the proposed
algorithms ensure better performance in the case of MIMO.
Abstract: This paper introduces a new method called ARPDC (Advanced Robust Parallel Distributed Compensation) for automatic control of nonlinear systems. This method improves a quality of robust control by interpolating of robust and optimal controller. The weight of each controller is determined by an original criteria function for model validity and disturbance appreciation. ARPDC method is based on nonlinear Takagi-Sugeno (T-S) fuzzy systems and Parallel Distributed Compensation (PDC) control scheme. The relaxed stability conditions of ARPDC control of nominal system have been derived. The advantages of presented method are demonstrated on the inverse pendulum benchmark problem. From comparison between three different controllers (robust, optimal and ARPDC) follows, that ARPDC control is almost optimal with the robustness close to the robust controller. The results indicate that ARPDC algorithm can be a good alternative not only for a robust control, but in some cases also to an adaptive control of nonlinear systems.
Abstract: Nowadays, the pace of business change is such that,
increasingly, new functionality has to be realized and reliably
installed in a matter of days, or even hours. Consequently, more and
more business processes are prone to a continuous change. The
objective of the research in progress is to use the MAP model, in a
conceptual modeling method for flexible and adaptive business
process. This method can be used to capture the flexibility
dimensions of a business process; it takes inspiration from
modularity concept in the object oriented paradigm to establish a
hierarchical construction of the BP modeling. Its intent is to provide
a flexible modeling that allows companies to quickly adapt their
business processes.
Abstract: In this paper a PID control strategy using neural
network adaptive RASP1 wavelet for WECS-s control is proposed.
It is based on single layer feedforward neural networks with hidden
nodes of adaptive RASP1 wavelet functions controller and an infinite
impulse response (IIR) recurrent structure. The IIR is combined by
cascading to the network to provide double local structure resulting
in improving speed of learning. This particular neuro PID controller
assumes a certain model structure to approximately identify the
system dynamics of the unknown plant (WECS-s) and generate the
control signal. The results are applied to a typical turbine/generator
pair, showing the feasibility of the proposed solution.
Abstract: In this paper, we proposed a novel receiver algorithm
for coherent underwater acoustic communications. The proposed
receiver is composed of three parts: (1) Doppler tracking and
correction, (2) Time reversal channel estimation and combining, and
(3) Joint iterative equalization and decoding (JIED). To reduce
computational complexity and optimize the equalization algorithm,
Time reversal (TR) channel estimation and combining is adopted to
simplify multi-channel adaptive decision feedback equalizer (ADFE)
into single channel ADFE without reducing the system performance.
Simultaneously, the turbo theory is adopted to form joint iterative
ADFE and convolutional decoder (JIED). In JIED scheme, the ADFE
and decoder exchange soft information in an iterative manner, which
can enhance the equalizer performance using decoding gain. The
simulation results show that the proposed algorithm can reduce
computational complexity and improve the performance of equalizer.
Therefore, the performance of coherent underwater acoustic
communications can be improved greatly.
Abstract: Adaptive observers used in sensorless control of induction motors suffer from instability especally in regenerating mode. In this paper, an optimal feed back gain design is proposed, it can reduce the instability region in the torque speed plane .
Abstract: Biologically human brain processes information in both unimodal and multimodal approaches. In fact, information is progressively abstracted and seamlessly fused. Subsequently, the fusion of multimodal inputs allows a holistic understanding of a problem. The proliferation of technology has exponentially produced various sources of data, which could be likened to being the state of multimodality in human brain. Therefore, this is an inspiration to develop a methodology for exploring multimodal data and further identifying multi-view patterns. Specifically, we propose a brain inspired conceptual model that allows exploration and identification of patterns at different levels of granularity, different types of hierarchies and different types of modalities. A structurally adaptive neural network is deployed to implement the proposed model. Furthermore, the acquisition of multi-view patterns with the proposed model is demonstrated and discussed with some experimental results.
Abstract: Magneto-rheological (MR) fluid damper is a semiactive
control device that has recently received more attention by the
vibration control community. But inherent hysteretic and highly
nonlinear dynamics of MR fluid damper is one of the challenging
aspects to employ its unique characteristics. The combination of
artificial neural network (ANN) and fuzzy logic system (FLS) have
been used to imitate more precisely the behavior of this device.
However, the derivative-based nature of adaptive networks causes
some deficiencies. Therefore, in this paper, a novel approach that
employ genetic algorithm, as a free-derivative algorithm, to enhance
the capability of fuzzy systems, is proposed. The proposed method
used to model MR damper. The results will be compared with
adaptive neuro-fuzzy inference system (ANFIS) model, which is one
of the well-known approaches in soft computing framework, and two
best parametric models of MR damper. Data are generated based on
benchmark program by applying a number of famous earthquake
records.
Abstract: Clusters of microcalcifications in mammograms are an
important sign of breast cancer. This paper presents a complete
Computer Aided Detection (CAD) scheme for automatic detection of
clustered microcalcifications in digital mammograms. The proposed
system, MammoScan μCaD, consists of three main steps. Firstly
all potential microcalcifications are detected using a a method for
feature extraction, VarMet, and adaptive thresholding. This will also
give a number of false detections. The goal of the second step,
Classifier level 1, is to remove everything but microcalcifications.
The last step, Classifier level 2, uses learned dictionaries and sparse
representations as a texture classification technique to distinguish
single, benign microcalcifications from clustered microcalcifications,
in addition to remove some remaining false detections. The system
is trained and tested on true digital data from Stavanger University
Hospital, and the results are evaluated by radiologists. The overall
results are promising, with a sensitivity > 90 % and a low false
detection rate (approx 1 unwanted pr. image, or 0.3 false pr. image).
Abstract: In this paper, we are going to determine the threshold levels of adaptive modulation in a burst by burst CDMA system by a suboptimum method so that the above method attempts to increase the average bit per symbol (BPS) rate of transceiver system by switching between the different modulation modes in variable channel condition. In this method, we choose the minimum values of average bit error rate (BER) and maximum values of average BPS on different values of average channel signal to noise ratio (SNR) and then calculate the relative threshold levels of them, so that when the instantaneous SNR increases, a higher order modulation be employed for increasing throughput and vise-versa when the instantaneous SNR decreases, a lower order modulation be employed for improvement of BER. In transmission step, by this adaptive modulation method, in according to comparison between obtained estimation of pilot symbols and a set of above suboptimum threshold levels, above system chooses one of states no transmission, BPSK, 4QAM and square 16QAM for modulation of data. The expected channel in this paper is a slow Rayleigh fading.
Abstract: Our adaptive multimodal system aims at correctly
presenting a mathematical expression to visually impaired users.
Given an interaction context (i.e. combination of user, environment
and system resources) as well as the complexity of the expression
itself and the user-s preferences, the suitability scores of different
presentation format are calculated. Unlike the current state-of-the art
solutions, our approach takes into account the user-s situation and not
imposes a solution that is not suitable to his context and capacity. In
this wok, we present our methodology for calculating the
mathematical expression complexity and the results of our
experiment. Finally, this paper discusses the concepts and principles
applied on our system as well as their validation through cases
studies. This work is our original contribution to an ongoing research
to make informatics more accessible to handicapped users.
Abstract: In Data mining, Fuzzy clustering algorithms have
demonstrated advantage over crisp clustering algorithms in dealing
with the challenges posed by large collections of vague and uncertain
natural data. This paper reviews concept of fuzzy logic and fuzzy
clustering. The classical fuzzy c-means algorithm is presented and its
limitations are highlighted. Based on the study of the fuzzy c-means
algorithm and its extensions, we propose a modification to the cmeans
algorithm to overcome the limitations of it in calculating the
new cluster centers and in finding the membership values with
natural data. The efficiency of the new modified method is
demonstrated on real data collected for Bhutan-s Gross National
Happiness (GNH) program.
Abstract: Segmentation of Magnetic Resonance Imaging (MRI) images is the most challenging problems in medical imaging. This paper compares the performances of Seed-Based Region Growing (SBRG), Adaptive Network-Based Fuzzy Inference System (ANFIS) and Fuzzy c-Means (FCM) in brain abnormalities segmentation. Controlled experimental data is used, which designed in such a way that prior knowledge of the size of the abnormalities are known. This is done by cutting various sizes of abnormalities and pasting it onto normal brain tissues. The normal tissues or the background are divided into three different categories. The segmentation is done with fifty seven data of each category. The knowledge of the size of the abnormalities by the number of pixels are then compared with segmentation results of three techniques proposed. It was proven that the ANFIS returns the best segmentation performances in light abnormalities, whereas the SBRG on the other hand performed well in dark abnormalities segmentation.
Abstract: In order to provide accurate heart rate variability
indices of sympathetic and parasympathetic activity, the low
frequency and high frequency components of an RR heart rate signal
must be adequately separated. This is not always possible by just
applying spectral analysis, as power from the high and low frequency
components often leak into their adjacent bands. Furthermore,
without the respiratory spectra it is not obvious that the low
frequency component is not another respiratory component, which
can appear in the lower band. This paper describes an adaptive filter,
which aids the separation of the low frequency sympathetic and high
frequency parasympathetic components from an ECG R-R interval
signal, enabling the attainment of more accurate heart rate variability
measures. The algorithm is applied to simulated signals and heart rate
and respiratory signals acquired from an ambulatory monitor
incorporating single lead ECG and inductive plethysmography
sensors embedded in a garment. The results show an improvement
over standard heart rate variability spectral measurements.