Abstract: All the available algorithms for blind estimation namely constant modulus algorithm (CMA), Decision-Directed Algorithm (DDA/DFE) suffer from the problem of convergence to local minima. Also, if the channel drifts considerably, any DDA looses track of the channel. So, their usage is limited in varying channel conditions. The primary limitation in such cases is the requirement of certain overhead bits in the transmit framework which leads to wasteful use of the bandwidth. Also such arrangements fail to use channel state information (CSI) which is an important aid in improving the quality of reception. In this work, the main objective is to reduce the overhead imposed by the pilot symbols, which in effect reduces the system throughput. Also we formulate an arrangement based on certain dynamic Artificial Neural Network (ANN) topologies which not only contributes towards the lowering of the overhead but also facilitates the use of the CSI. A 2×2 Multiple Input Multiple Output (MIMO) system is simulated and the performance variation with different channel estimation schemes are evaluated. A new semi blind approach based on dynamic ANN is proposed for channel tracking in varying channel conditions and the performance is compared with perfectly known CSI and least square (LS) based estimation.
Abstract: In the normal operation conditions of a pico satellite,
conventional Unscented Kalman Filter (UKF) gives sufficiently good
estimation results. However, if the measurements are not reliable
because of any kind of malfunction in the estimation system, UKF
gives inaccurate results and diverges by time. This study, introduces
Robust Unscented Kalman Filter (RUKF) algorithms with the filter
gain correction for the case of measurement malfunctions. By the use
of defined variables named as measurement noise scale factor, the
faulty measurements are taken into the consideration with a small
weight and the estimations are corrected without affecting the
characteristic of the accurate ones. Two different RUKF algorithms,
one with single scale factor and one with multiple scale factors, are
proposed and applied for the attitude estimation process of a pico
satellite. The results of these algorithms are compared for different
types of measurement faults in different estimation scenarios and
recommendations about their applications are given.
Abstract: In this paper we consider the problem of change
detection and non stationary signals tracking. Using parametric
estimation of signals based on least square lattice adaptive filters we
consider for change detection statistical parametric methods using
likelihood ratio and hypothesis tests. In order to track signals
dynamics, we introduce a compensation procedure in the adaptive
estimation. This will improve the adaptive estimation performances
and fasten it-s convergence after changes detection.
Abstract: Direction of Arrival estimation refers to defining a mathematical function called a pseudospectrum that gives an indication of the angle a signal is impinging on the antenna array. This estimation is an efficient method of improving the quality of service in a communication system by focusing the reception and transmission only in the estimated direction thereby increasing fidelity with a provision to suppress interferers. This improvement is largely dependent on the performance of the algorithm employed in the estimation. Many DOA algorithms exists amongst which are MUSIC, Root-MUSIC and ESPRIT. In this paper, performance of these three algorithms is analyzed in terms of complexity, accuracy as assessed and characterized by the CRLB and memory requirements in various environments and array sizes. It is found that the three algorithms are high resolution and dependent on the operating environment and the array size.
Abstract: Soil erosion is the most serious problem faced at
global and local level. So planning of soil conservation measures has
become prominent agenda in the view of water basin managers. To
plan for the soil conservation measures, the information on soil
erosion is essential. Universal Soil Loss Equation (USLE), Revised
Universal Soil Loss Equation 1 (RUSLE1or RUSLE) and Modified
Universal Soil Loss Equation (MUSLE), RUSLE 1.06, RUSLE1.06c,
RUSLE2 are most widely used conventional erosion estimation
methods. The essential drawbacks of USLE, RUSLE1 equations are
that they are based on average annual values of its parameters and so
their applicability to small temporal scale is questionable. Also these
equations do not estimate runoff generated soil erosion. So
applicability of these equations to estimate runoff generated soil
erosion is questionable. Data used in formation of USLE, RUSLE1
equations was plot data so its applicability at greater spatial scale
needs some scale correction factors to be induced. On the other hand
MUSLE is unsuitable for predicting sediment yield of small and large
events. Although the new revised forms of USLE like RUSLE 1.06,
RUSLE1.06c and RUSLE2 were land use independent and they have
almost cleared all the drawbacks in earlier versions like USLE and
RUSLE1, they are based on the regional data of specific area and
their applicability to other areas having different climate, soil, land
use is questionable. These conventional equations are applicable for
sheet and rill erosion and unable to predict gully erosion and spatial
pattern of rills. So the research was focused on development of nonconventional
(other than conventional) methods of soil erosion
estimation. When these non-conventional methods are combined with
GIS and RS, gives spatial distribution of soil erosion. In the present
paper the review of literature on non- conventional methods of soil
erosion estimation supported by GIS and RS is presented.
Abstract: Multicarrier code-division multiple-access is one of the
effective techniques to gain its multiple access capability, robustness
against fading, and to mitigate the ISI. In this paper, we propose an
improved mulcarrier CDMA system with adaptive subchannel
allocation. We analyzed the performance of our proposed system in
frequency selective fading environment with narrowband interference
existing and compared it with that of parallel transmission over many
subchannels (namely, conventional MC-CDMA scheme) and
DS-CDMA system. Simulation results show that adaptive subchannel
allocation scheme, when used in conventional multicarrier CDMA
system, the performance will be greatly improved.
Abstract: In the planning point of view, it is essential to have
mode choice, due to the massive amount of incurred in transportation
systems. The intercity travellers in Libya have distinct features, as
against travellers from other countries, which includes cultural and
socioeconomic factors. Consequently, the goal of this study is to
recognize the behavior of intercity travel using disaggregate models,
for projecting the demand of nation-level intercity travel in Libya.
Multinomial Logit Model for all the intercity trips has been
formulated to examine the national-level intercity transportation in
Libya. The Multinomial logit model was calibrated using nationwide
revealed preferences (RP) and stated preferences (SP) survey. The
model was developed for deference purpose of intercity trips (work,
social and recreational). The variables of the model have been
predicted based on maximum likelihood method. The data needed for
model development were obtained from all major intercity corridors
in Libya. The final sample size consisted of 1300 interviews. About
two-thirds of these data were used for model calibration, and the
remaining parts were used for model validation. This study, which is
the first of its kind in Libya, investigates the intercity traveler’s
mode-choice behavior. The intercity travel mode-choice model was
successfully calibrated and validated. The outcomes indicate that, the
overall model is effective and yields higher precision of estimation.
The proposed model is beneficial, due to the fact that, it is receptive
to a lot of variables, and can be employed to determine the impact of
modifications in the numerous characteristics on the need for various
travel modes. Estimations of the model might also be of valuable to
planners, who can estimate possibilities for various modes and
determine the impact of unique policy modifications on the need for
intercity travel.
Abstract: We present on the method of inverse coherence matrix for the estimation of network connectivity from multivariate time series of a complex system. In a model system of coupled chaotic oscillators, it is shown that the inverse coherence matrix defined as the inverse of cross coherence matrix is proportional to the network connectivity. Therefore the inverse coherence matrix could be used for the distinction between the directly connected links from indirectly connected links in a complex network. We compare the result of network estimation using the method of the inverse coherence matrix with the results obtained from the coherence matrix and the partial coherence matrix.
Abstract: This paper proposes the stochastic tabu search (STS)
for improving the measurement scheme for power system state
estimation. If the original measured scheme is not observable, the
additional measurements with minimum number of measurements are
added into the system by STS so that there is no critical measurement
pair. The random bit flipping and bit exchanging perturbations are
used for generating the neighborhood solutions in STS. The Pδ
observable concept is used to determine the network observability.
Test results of 10 bus, IEEE 14 and 30 bus systems are shown that
STS can improve the original measured scheme to be observable
without critical measurement pair. Moreover, the results of STS are
superior to deterministic tabu search (DTS) in terms of the best
solution hit.
Abstract: Array signal processing involves signal enumeration and source localization. Array signal processing is centered on the ability to fuse temporal and spatial information captured via sampling signals emitted from a number of sources at the sensors of an array in order to carry out a specific estimation task: source characteristics (mainly localization of the sources) and/or array characteristics (mainly array geometry) estimation. Array signal processing is a part of signal processing that uses sensors organized in patterns or arrays, to detect signals and to determine information about them. Beamforming is a general signal processing technique used to control the directionality of the reception or transmission of a signal. Using Beamforming we can direct the majority of signal energy we receive from a group of array. Multiple signal classification (MUSIC) is a highly popular eigenstructure-based estimation method of direction of arrival (DOA) with high resolution. This Paper enumerates the effect of missing sensors in DOA estimation. The accuracy of the MUSIC-based DOA estimation is degraded significantly both by the effects of the missing sensors among the receiving array elements and the unequal channel gain and phase errors of the receiver.
Abstract: The Carrier Frequency Offset (CFO) due to timevarying
fading channel is the main cause of the loss of orthogonality
among OFDM subcarriers which is linked to inter-carrier interference
(ICI). Hence, it is necessary to precisely estimate and compensate the
CFO. Especially for mobile broadband communications, CFO and
channel gain also have to be estimated and tracked to maintain the
system performance. Thus, synchronization pilots are embedded in
every OFDM symbol to track the variations. In this paper, we present
the pilot scheme for both channel and CFO estimation where channel
estimation process can be carried out with only one OFDM symbol.
Additional, the proposed pilot scheme also provides better
performance in CFO estimation comparing with the conventional
orthogonal pilot scheme due to the increasing of signal-tointerference
ratio.
Abstract: Air conditioning is mainly use as human comfort
cooling medium. It use more in high temperatures are country such as
Malaysia. Proper estimation of cooling load will archive ideal
temperature. Without proper estimation can lead to over estimation or
under estimation. The ideal temperature should be comfort enough.
This study is to develop a program to calculate an ideal cooling load
demand, which is match with heat gain. Through this study, it is easy
to calculate cooling load estimation. Objective of this study are to
develop user-friendly and easy excess cooling load program. This is
to insure the cooling load can be estimate by any of the individual
rather than them using rule-of-thumb. Developed software is carryout
by using Matlab-GUI. These developments are only valid for
common building in Malaysia only. An office building was select as
case study to verify the applicable and accuracy of develop software.
In conclusion, the main objective has successfully where developed
software is user friendly and easily to estimate cooling load demand.
Abstract: Traffic density, an indicator of traffic
conditions, is one of the most critical characteristics to
Intelligent Transport Systems (ITS). This paper investigates
recursive traffic density estimation using the information
provided from inductive loop detectors. On the basis of the
phenomenological relationship between speed and density, the
existing studies incorporate a state space model and update the
density estimate using vehicular speed observations via the
extended Kalman filter, where an approximation is made
because of the linearization of the nonlinear observation
equation. In practice, this may lead to substantial estimation
errors. This paper incorporates a suitable transformation to
deal with the nonlinear observation equation so that the
approximation is avoided when using Kalman filter to
estimate the traffic density. A numerical study is conducted. It
is shown that the developed method outperforms the existing
methods for traffic density estimation.
Abstract: This paper introduces the application of seismic wave method in earthquake prediction and early estimation. The advantages of the seismic wave method over the traditional earthquake prediction method are demonstrated. An example is presented in this study to show the accuracy and efficiency of using the seismic wave method in predicting a medium-sized earthquake swarm occurred in Wencheng, Zhejiang, China. By applying this method, correct predictions were made on the day after this earthquake swarm started and the day the maximum earthquake occurred, which provided scientific bases for governmental decision-making.
Abstract: PPG is a potential tool in clinical applications. Among such, the relationship between respiration and PPG signal has attracted attention in past decades. In this research, a bivariate AR spectral estimation method was utilized for the coherence analysis between these two signals. Ten healthy subjects participated in this research with signals measured at different respiratory rates. The results demonstrate that high coherence exists between respiration and PPG signal, whereas the coherence disappears in breath-holding experiments. These results imply that PPG signal reveals the respiratory information. The utilized method may provide an attractive alternative approach for the related researches.
Abstract: In this paper, a new technique of signal detection has been proposed for detecting the orthogonal frequency-division multiplexing (OFDM) signal in the presence of nonlinear distortion.There are several advantages of OFDM communications system.However, one of the existing problems is remain considered as the nonlinear distortion generated by high-power-amplifier at the transmitter end due to the large dynamic range of an OFDM signal. The proposed method is the maximum likelihood detection with the symbol estimation. When the training data are available, the neural network has been used to learn the characteristic of received signal and to estimate the new positions of the transmitted symbol which are provided to the maximum likelihood detector. Resulting in the system performance, the nonlinear distortions of a traveling wave tube amplifier with OFDM signal are considered in this paper.Simulation results of the bit-error-rate performance are obtained with 16-QAM OFDM systems.
Abstract: This paper suggests an improved integer frequency
offset (IFO) estimation scheme using P1 symbol for orthogonal
frequency division multiplexing (OFDM) based the second generation
terrestrial digital video broadcasting (DVB-T2) system. Proposed
IFO estimator is designed by a low-complexity blind IFO estimation
scheme, which is implemented with complex additions. Also, we
propose active carriers (ACs) selection scheme in order to prevent
performance degradation in blind IFO estimation. The simulation
results show that under the AWGN and TU6 channels, the proposed
method has low complexity than conventional method and almost
similar performance in comparison with the conventional method.
Abstract: Quantitative measurements of tumor in general and tumor volume in particular, become more realistic with the use of Magnetic Resonance imaging, especially when the tumor morphological changes become irregular and difficult to assess by clinical examination. However, tumor volume estimation strongly depends on the image segmentation, which is fuzzy by nature. In this paper a fuzzy approach is presented for tumor volume segmentation based on the fuzzy connectedness algorithm. The fuzzy affinity matrix resulting from segmentation is then used to estimate a fuzzy volume based on a certainty parameter, an Alpha Cut, defined by the user. The proposed method was shown to highly affect treatment decisions. A statistical analysis was performed in this study to validate the results based on a manual method for volume estimation and the importance of using the Alpha Cut is further explained.
Abstract: In gas lifted oil fields, the lift gas should be distributed optimally among the wells which share gas from a common source to maximize total oil production. One of the objectives of the paper is to show that a linear MPC consisting of a control objective and an economic objective can be used both as an optimizer and a controller for gas lifted systems. The MPC is based on linearized model of the oil field developed from first principles modeling. Simulation results show that the total oil production is increased by 3.4%. Difficulties in accurately measuring the bottom hole pressure using sensors in harsh operating conditions can be resolved by using an Unscented Kalman Filter (UKF) for estimation. In oil fields where input disturbance (total supply of gas) is not measured, UKF can also be used for disturbance estimation. Increased total oil production due to optimization leads to increased profit.