Abstract: Groundlessness of application probability-statistic methods are especially shown at an early stage of the aviation GTE technical condition diagnosing, when the volume of the information has property of the fuzzy, limitations, uncertainty and efficiency of application of new technology Soft computing at these diagnosing stages by using the fuzzy logic and neural networks methods. It is made training with high accuracy of multiple linear and nonlinear models (the regression equations) received on the statistical fuzzy data basis. At the information sufficiency it is offered to use recurrent algorithm of aviation GTE technical condition identification on measurements of input and output parameters of the multiple linear and nonlinear generalized models at presence of noise measured (the new recursive least squares method (LSM)). As application of the given technique the estimation of the new operating aviation engine D30KU-154 technical condition at height H=10600 m was made.
Abstract: It has been established that microRNAs (miRNAs) play
an important role in gene expression by post-transcriptional regulation
of messengerRNAs (mRNAs). However, the precise relationships
between microRNAs and their target genes in sense of numbers,
types and biological relevance remain largely unclear. Dissecting the
miRNA-target relationships will render more insights for miRNA
targets identification and validation therefore promote the understanding
of miRNA function. In miRBase, miRanda is the key
algorithm used for target prediction for Zebrafish. This algorithm
is high-throughput but brings lots of false positives (noise). Since
validation of a large scale of targets through laboratory experiments
is very time consuming, several computational methods for miRNA
targets validation should be developed. In this paper, we present an
integrative method to investigate several aspects of the relationships
between miRNAs and their targets with the final purpose of extracting
high confident targets from miRanda predicted targets pool. This is
achieved by using the techniques ranging from statistical tests to
clustering and association rules. Our research focuses on Zebrafish.
It was found that validated targets do not necessarily associate with
the highest sequence matching. Besides, for some miRNA families,
the frequency of their predicted targets is significantly higher in the
genomic region nearby their own physical location. Finally, in a case
study of dre-miR-10 and dre-miR-196, it was found that the predicted
target genes hoxd13a, hoxd11a, hoxd10a and hoxc4a of dre-miR-
10 while hoxa9a, hoxc8a and hoxa13a of dre-miR-196 have similar
characteristics as validated target genes and therefore represent high
confidence target candidates.
Abstract: This paper presents an application of power line
carrier (PLC) for electrical power telemetering. This system has a
special capability of transmitting the measured values to a centralized
computer via power lines. The PLC modem as a passive high-pass
filter is designed for transmitting and receiving information. Its
function is to send the information carrier together with transmitted
data by superimposing it on the 50 Hz power frequency signal. A
microcontroller is employed to function as the main processing of the
modem. It is programmed for PLC control and interfacing with other
devices. Each power meter, connected via a PLC modem, is assigned
with a unique identification number (address) for distinguishing each
device from one another.
Abstract: This paper uses the radial basis function neural
network (RBFNN) for system identification of nonlinear systems.
Five nonlinear systems are used to examine the activity of RBFNN in
system modeling of nonlinear systems; the five nonlinear systems are
dual tank system, single tank system, DC motor system, and two
academic models. The feed forward method is considered in this
work for modelling the non-linear dynamic models, where the KMeans
clustering algorithm used in this paper to select the centers of
radial basis function network, because it is reliable, offers fast
convergence and can handle large data sets. The least mean square
method is used to adjust the weights to the output layer, and
Euclidean distance method used to measure the width of the Gaussian
function.
Abstract: Radio frequency identification (RFID) applications have grown rapidly in many industries, especially in indoor location identification. The advantage of using received signal strength indicator (RSSI) values as an indoor location measurement method is a cost-effective approach without installing extra hardware. Because the accuracy of many positioning schemes using RSSI values is limited by interference factors and the environment, thus it is challenging to use RFID location techniques based on integrating positioning algorithm design. This study proposes the location estimation approach and analyzes a scheme relying on RSSI values to minimize location errors. In addition, this paper examines different factors that affect location accuracy by integrating the backpropagation neural network (BPN) with the LANDMARC algorithm in a training phase and an online phase. First, the training phase computes coordinates obtained from the LANDMARC algorithm, which uses RSSI values and the real coordinates of reference tags as training data for constructing an appropriate BPN architecture and training length. Second, in the online phase, the LANDMARC algorithm calculates the coordinates of tracking tags, which are then used as BPN inputs to obtain location estimates. The results show that the proposed scheme can estimate locations more accurately compared to LANDMARC without extra devices.
Abstract: Mobile banking services present a unique growth
opportunity for mobile operators in emerging markets, and have
already made good progress in bringing financial services to the
previously unbanked populations of many developing countries. The
potential is amazing, but what about the risks? In the complex
process of establishing a mobile banking business model, many kinds
of risks and factors need to be monitored and well-managed. Risk
identification is the first stage of risk management. Correct risk
identification ensures risk management effectiveness. Keeping the
risks low makes it possible to use the full potential of mobile banking
and carry out the planned business strategy. The focus should be on
adoption of consumers which is the main risk factor of mobile
banking services.
Abstract: Sense-antisense gene pair (SAGP) is a pair of two oppositely transcribed genes sharing a common region on a chromosome. In the mammalian genomes, SAGPs can be organized in more complex sense-antisense gene architectures (CSAGA) in which at least one gene could share loci with two or more antisense partners. Many dozens of CSAGAs can be found in the human genome. However, CSAGAs have not been systematically identified and characterized in context of their role in human diseases including cancers. In this work we characterize the structural-functional properties of a cluster of 5 genes –TMEM97, IFT20, TNFAIP1, POLDIP2 and TMEM199, termed TNFAIP1 / POLDIP2 module. This cluster is organized as CSAGA in cytoband 17q11.2. Affymetrix U133A&B expression data of two large cohorts (410 atients, in total) of breast cancer patients and patient survival data were used. For the both studied cohorts, we demonstrate (i) strong and reproducible transcriptional co-regulatory patterns of genes of TNFAIP1/POLDIP2 module in breast cancer cell subtypes and (ii) significant associations of TNFAIP1/POLDIP2 CSAGA with amplification of the CSAGA region in breast cancer, (ii) cancer aggressiveness (e.g. genetic grades) and (iv) disease free patient-s survival. Moreover, gene pairs of this module demonstrate strong synergetic effect in the prognosis of time of breast cancer relapse. We suggest that TNFAIP1/ POLDIP2 cluster can be considered as a novel type of structural-functional gene modules in the human genome.
Abstract: Quality control charts are very effective in detecting
out of control signals but when a control chart signals an out of
control condition of the process mean, searching for a special cause
in the vicinity of the signal time would not always lead to prompt
identification of the source(s) of the out of control condition as the
change point in the process parameter(s) is usually different from the
signal time. It is very important to manufacturer to determine at what
point and which parameters in the past caused the signal. Early
warning of process change would expedite the search for the special
causes and enhance quality at lower cost. In this paper the quality
variables under investigation are assumed to follow a multivariate
normal distribution with known means and variance-covariance
matrix and the process means after one step change remain at the new
level until the special cause is being identified and removed, also it is
supposed that only one variable could be changed at the same time.
This research applies artificial neural network (ANN) to identify the
time the change occurred and the parameter which caused the change
or shift. The performance of the approach was assessed through a
computer simulation experiment. The results show that neural
network performs effectively and equally well for the whole shift
magnitude which has been considered.
Abstract: This paper deals with the localization of the wideband sources. We develop a new approach for estimating the wide band sources parameters. This method is based on the high order statistics of the recorded data in order to eliminate the Gaussian components from the signals received on the various hydrophones.In fact the noise of sea bottom is regarded as being Gaussian. Thanks to the coherent signal subspace algorithm based on the cumulant matrix of the received data instead of the cross-spectral matrix the wideband correlated sources are perfectly located in the very noisy environment. We demonstrate the performance of the proposed algorithm on the real data recorded during an underwater acoustics experiments.
Abstract: The paper is concerned with relationships between
SSME and ICTs and focuses on the role of Web 2.0 tools in
the service development process. The research presented aims at
exploring how collaborative technologies can support and improve
service processes, highlighting customer centrality and value coproduction.
The core idea of the paper is the centrality of user
participation and the collaborative technologies as enabling factors;
Wikipedia is analyzed as an example. The result of such analysis is
the identification and description of a pattern characterising specific
services in which users collaborate by means of web tools with value
co-producers during the service process. The pattern of collaborative
co-production concerning several categories of services including
knowledge based services is then discussed.
Abstract: Single biometric modality recognition is not able to meet the high performance supplies in most cases with its application become more and more broadly. Multimodal biometrics identification represents an emerging trend recently. This paper investigates a novel algorithm based on fusion of both fingerprint and fingervein biometrics. For both biometric recognition, we employ the Monogenic Local Binary Pattern (MonoLBP). This operator integrate the orginal LBP (Local Binary Pattern ) with both other rotation invariant measures: local phase and local surface type. Experimental results confirm that a weighted sum based proposed fusion achieves excellent identification performances opposite unimodal biometric systems. The AUC of proposed approach based on combining the two modalities has very close to unity (0.93).
Abstract: MicroRNAs (miRNAs) are small, non-coding and
regulatory RNAs about 20 to 24 nucleotides long. Their conserved
nature among the various organisms makes them a good source of
new miRNAs discovery by comparative genomics approach. The
study resulted in 21 miRNAs of 20 pre-miRNAs belonging to 16
families (miR156, 157, 158, 164, 165, 168, 169, 172, 319, 390, 393,
394, 395, 400, 472 and 861) in evergreen spruce tree (Picea). The
miRNA families; miR 157, 158, 164, 165, 168, 169, 319, 390, 393,
394, 400, 472 and 861 are reported for the first time in the Picea. All
20 miRNA precursors form stable minimum free energy stem-loop
structure as their orthologues form in Arabidopsis and the mature
miRNA reside in the stem portion of the stem loop structure. Sixteen
(16) miRNAs are from Picea glauca and five (5) belong to Picea
sitchensis. Their targets consist of transcription factors, growth
related, stressed related and hypothetical proteins.
Abstract: In this paper, a clustering algorithm named KHarmonic
means (KHM) was employed in the training of Radial
Basis Function Networks (RBFNs). KHM organized the data in
clusters and determined the centres of the basis function. The popular
clustering algorithms, namely K-means (KM) and Fuzzy c-means
(FCM), are highly dependent on the initial identification of elements
that represent the cluster well. In KHM, the problem can be avoided.
This leads to improvement in the classification performance when
compared to other clustering algorithms. A comparison of the
classification accuracy was performed between KM, FCM and KHM.
The classification performance is based on the benchmark data sets:
Iris Plant, Diabetes and Breast Cancer. RBFN training with the KHM
algorithm shows better accuracy in classification problem.
Abstract: This paper presents an idea to improve the efficiency
of security checks in airports through the active tracking and
monitoring of passengers and staff using OFDM modulation
technique and Finger print authentication. The details of the
passenger are multiplexed using OFDM .To authenticate the
passenger, the fingerprint along with important identification
information is collected. The details of the passenger can be
transmitted after necessary modulation, and received using various
transceivers placed within the premises of the airport, and checked at
the appropriate check points, thereby increasing the efficiency of
checking. OFDM has been employed for spectral efficiency.
Abstract: Radio Frequency Identification (RFID) initially introduced
during WW-II, has revolutionized the world with its numerous
benefits and plethora of implementations in diverse areas ranging
from manufacturing to agriculture to healthcare to hotel management.
This work reviews the current research in this area with emphasis
on applications for supply chain management and to develop a
taxonomic framework to classify literature which will enable swift
and easy content analysis and also help identify areas for future
research.
Abstract: Biometrics methods include recognition techniques
such as fingerprint, iris, hand geometry, voice, face, ears and gait. The gait recognition approach has some advantages, for example it
does not need the prior concern of the observed subject and it can
record many biometric features in order to make deeper analysis, but
most of the research proposals use high computational cost. This
paper shows a gait recognition system with feature subtraction on a
bundle rectangle drawn over the observed person. Statistical results
within a database of 500 videos are shown.
Abstract: Continuous-time delta-sigma analog digital converter (ADC) for radio frequency identification (RFID) complementary metal oxide semiconductor (CMOS) biosensor has been reported. This delta-sigma ADC is suitable for digital conversion of biosensor signal because of small process variation, and variable input range. As the input range of continuous-time switched current delta-sigma ADC (Dynamic range : 50 dB) can be limited by using current reference, amplification of biosensor signal is unnecessary. The input range is switched to wide input range mode or narrow input range mode by command of current reference. When the narrow input range mode, the input range becomes ± 0.8 V. The measured power consumption is 5 mW and chip area is 0.31 mm^2 using 1.2 um standard CMOS process. Additionally, automatic input range detecting system is proposed because of RFID biosensor applications.
Abstract: The paper provides an in-depth tutorial of mathematical
construction of maximal length sequences (m-sequences) via primitive
polynomials and how to map the same when implemented in
shift registers. It is equally important to check whether a polynomial
is primitive or not so as to get proper m-sequences. A fast method to
identify primitive polynomials over binary fields is proposed where
the complexity is considerably less in comparison with the standard
procedures for the same purpose.
Abstract: Human identification at a distance has recently gained
growing interest from computer vision researchers. Gait recognition
aims essentially to address this problem by identifying people based
on the way they walk [1]. Gait recognition has 3 steps. The first step
is preprocessing, the second step is feature extraction and the third
one is classification. This paper focuses on the classification step that
is essential to increase the CCR (Correct Classification Rate).
Multilayer Perceptron (MLP) is used in this work. Neural Networks
imitate the human brain to perform intelligent tasks [3].They can
represent complicated relationships between input and output and
acquire knowledge about these relationships directly from the data
[2]. In this paper we apply MLP NN for 11 views in our database and
compare the CCR values for these views. Experiments are performed
with the NLPR databases, and the effectiveness of the proposed
method for gait recognition is demonstrated.
Abstract: The paper deals with the estimation of amplitude and phase of an analogue multi-harmonic band-limited signal from irregularly spaced sampling values. To this end, assuming the signal fundamental frequency is known in advance (i.e., estimated at an independent stage), a complexity-reduced algorithm for signal reconstruction in time domain is proposed. The reduction in complexity is achieved owing to completely new analytical and summarized expressions that enable a quick estimation at a low numerical error. The proposed algorithm for the calculation of the unknown parameters requires O((2M+1)2) flops, while the straightforward solution of the obtained equations takes O((2M+1)3) flops (M is the number of the harmonic components). It is applied in signal reconstruction, spectral estimation, system identification, as well as in other important signal processing problems. The proposed method of processing can be used for precise RMS measurements (for power and energy) of a periodic signal based on the presented signal reconstruction. The paper investigates the errors related to the signal parameter estimation, and there is a computer simulation that demonstrates the accuracy of these algorithms.