Abstract: Sustainability in rural production system can only be achieved if it can suitably satisfy the local requirement as well as the outside demand with the changing time. With the increased pressure from the food sector in a globalised world, the agrarian economy
needs to re-organise its cultivable land system to be compatible with new management practices as well as the multiple needs of various stakeholders and the changing resource scenario. An attempt has been made to transform this problem into a multi-objective decisionmaking problem considering various objectives, resource constraints and conditional constraints. An interactive fuzzy multi-objective
programming approach has been used for such a purpose taking a
case study in Indian context to demonstrate the validity of the method.
Abstract: Fatigue tests of specimen-s with numerous holes are
presented. The tests were made up till fatigue cracks have been
created on both sides of the hole. Their extension was stopping with
pressed plastic deformation at the mouth of the detected crack. It is
shown that the moments of occurrence of cracks on holes are
stochastically dependent. This dependence has positive and negative
correlation relations. Shown that the positive correlation is formed
across of the applied force, while negative one – along it. The
negative relationship extends over a greater distance. The
mathematical model of dependence area formation is represented as
well as the estimating of model parameters. The positive correlation
of fatigue cracks origination can be considered as an extension of one
main crack. With negative correlation the first crack locates the place
of its origin, leading to the appearance of multiple cracks; do not
merge with each other.
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: A fuzzy classifier using multiple ellipsoids approximating decision regions for classification is to be designed in this paper. An algorithm called Gustafson-Kessel algorithm (GKA) with an adaptive distance norm based on covariance matrices of prototype data points is adopted to learn the ellipsoids. GKA is able toadapt the distance norm to the underlying distribution of the prototypedata points except that the sizes of ellipsoids need to be determined a priori. To overcome GKA's inability to determine appropriate size ofellipsoid, the genetic algorithm (GA) is applied to learn the size ofellipsoid. With GA combined with GKA, it will be shown in this paper that the proposed method outperforms the benchmark algorithms as well as algorithms in the field.
Abstract: In a state-of-the-art industrial production line of
photovoltaic products the handling and automation processes are of
particular importance and implication. While processing a fully
functional crystalline solar cell an as-cut photovoltaic wafer is subject
to numerous repeated handling steps. With respect to stronger
requirements in productivity and decreasing rejections due to defects
the mechanical stress on the thin wafers has to be reduced to a
minimum as the fragility increases by decreasing wafer thicknesses.
In relation to the increasing wafer fragility, researches at the
Fraunhofer Institutes IPA and CSP showed a negative correlation
between multiple handling processes and the wafer integrity. Recent
work therefore focused on the analysis and optimization of the dry
wafer stack separation process with compressed air. The achievement
of a wafer sensitive process capability and a high production
throughput rate is the basic motivation in this research.
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: Fuzzy fingerprint vault is a recently developed cryptographic construct based on the polynomial reconstruction problem to secure critical data with the fingerprint data. However, the previous researches are not applicable to the fingerprint having a few minutiae since they use a fixed degree of the polynomial without considering the number of fingerprint minutiae. To solve this problem, we use an adaptive degree of the polynomial considering the number of minutiae extracted from each user. Also, we apply multiple polynomials to avoid the possible degradation of the security of a simple solution(i.e., using a low-degree polynomial). Based on the experimental results, our method can make the possible attack difficult 2192 times more than using a low-degree polynomial as well as verify the users having a few minutiae.
Abstract: In this paper a novel method for multiple one dimensional real valued sinusoidal signal frequency estimation in the presence of additive Gaussian noise is postulated. A computationally simple frequency estimation method with efficient statistical performance is attractive in many array signal processing applications. The prime focus of this paper is to combine the subspace-based technique and a simple peak search approach. This paper presents a variant of the Propagator Method (PM), where a collaborative approach of SUMWE and Propagator method is applied in order to estimate the multiple real valued sine wave frequencies. A new data model is proposed, which gives the dimension of the signal subspace is equal to the number of frequencies present in the observation. But, the signal subspace dimension is twice the number of frequencies in the conventional MUSIC method for estimating frequencies of real-valued sinusoidal signal. The statistical analysis of the proposed method is studied, and the explicit expression of asymptotic (large-sample) mean-squared-error (MSE) or variance of the estimation error is derived. The performance of the method is demonstrated, and the theoretical analysis is substantiated through numerical examples. The proposed method can achieve sustainable high estimation accuracy and frequency resolution at a lower SNR, which is verified by simulation by comparing with conventional MUSIC, ESPRIT and Propagator Method.
Abstract: Enzymatic hydrolysis of starch from natural sources
finds potential application in commercial production of alcoholic
beverage and bioethanol. In this study the effect of starch
concentration, temperature, time and enzyme concentration were
studied and optimized for hydrolysis of cassava (Manihot esculenta)
starch powder (of mesh 80/120) into glucose syrup by immobilized
(using Polyacrylamide gel) a-amylase using central composite
design. The experimental result on enzymatic hydrolysis of cassava
starch was subjected to multiple linear regression analysis using
MINITAB 14 software. Positive linear effect of starch concentration,
enzyme concentration and time was observed on hydrolysis of
cassava starch by a-amylase. The statistical significance of the model
was validated by F-test for analysis of variance (p < 0.01). The
optimum value of starch concentration temperature, time and enzyme
concentration were found to be 4.5% (w/v), 45oC, 150 min, and 1%
(w/v) enzyme. The maximum glucose yield at optimum condition
was 5.17 mg/mL.
Abstract: A universal current-mode biquad is described which
represents an economical variant of well-known KHN (Kerwin,
Huelsman, Newcomb) voltage-mode filter. The circuit consists of
two multiple-output OTAs and of two grounded capacitors. Utilizing
simple splitter of the input current and a pair of jumpers, all the basic
2nd-order transfer functions can be implemented. The principle is
verified by Spice simulation on the level of a CMOS structure of
OTAs.
Abstract: Optical burst switching (OBS) has been proposed to
realize the next generation Internet based on the wavelength division
multiplexing (WDM) network technologies. In the OBS, the burst
contention is one of the major problems. The deflection routing has
been designed for resolving the problem. However, the deflection
routing becomes difficult to prevent from the burst contentions as the
network load becomes high. In this paper, we introduce a flow rate
control methods to reduce burst contentions. We propose new flow
rate control methods based on the leaky bucket algorithm and
deflection routing, i.e. separate leaky bucket deflection method, and
dynamic leaky bucket deflection method. In proposed methods, edge
nodes which generate data bursts carry out the flow rate control
protocols. In order to verify the effectiveness of the flow rate control in
OBS networks, we show that the proposed methods improve the
network utilization and reduce the burst loss probability through
computer simulations.
Abstract: Multiple sequence alignment is a fundamental part in
many bioinformatics applications such as phylogenetic analysis.
Many alignment methods have been proposed. Each method gives a
different result for the same data set, and consequently generates a
different phylogenetic tree. Hence, the chosen alignment method
affects the resulting tree. However in the literature, there is no
evaluation of multiple alignment methods based on the comparison of
their phylogenetic trees. This work evaluates the following eight
aligners: ClustalX, T-Coffee, SAGA, MUSCLE, MAFFT, DIALIGN,
ProbCons and Align-m, based on their phylogenetic trees (test trees)
produced on a given data set. The Neighbor-Joining method is used
to estimate trees. Three criteria, namely, the dNNI, the dRF and the
Id_Tree are established to test the ability of different alignment
methods to produce closer test tree compared to the reference one
(true tree). Results show that the method which produces the most
accurate alignment gives the nearest test tree to the reference tree.
MUSCLE outperforms all aligners with respect to the three criteria
and for all datasets, performing particularly better when sequence
identities are within 10-20%. It is followed by T-Coffee at lower
sequence identity (30%), trees scores of all methods
become similar.
Abstract: Stochastic resonance (SR) is a phenomenon whereby
the signal transmission or signal processing through certain nonlinear
systems can be improved by adding noise. This paper discusses SR in
nonlinear signal detection by a simple test statistic, which can be
computed from multiple noisy data in a binary decision problem based
on a maximum a posteriori probability criterion. The performance of
detection is assessed by the probability of detection error Per . When
the input signal is subthreshold signal, we establish that benefit from
noise can be gained for different noises and confirm further that the
subthreshold SR exists in nonlinear signal detection. The efficacy of
SR is significantly improved and the minimum of Per can
dramatically approach to zero as the sample number increases. These
results show the robustness of SR in signal detection and extend the
applicability of SR in signal processing.
Abstract: this paper gives a novel approach towards real-time speed estimation of multiple traffic vehicles using fuzzy logic and image processing techniques with proper arrangement of camera parameters. The described algorithm consists of several important steps. First, the background is estimated by computing median over time window of specific frames. Second, the foreground is extracted using fuzzy similarity approach (FSA) between estimated background pixels and the current frame pixels containing foreground and background. Third, the traffic lanes are divided into two parts for both direction vehicles for parallel processing. Finally, the speeds of vehicles are estimated by Maximum a Posterior Probability (MAP) estimator. True ground speed is determined by utilizing infrared sensors for three different vehicles and the results are compared to the proposed algorithm with an accuracy of ± 0.74 kmph.
Abstract: The general global behavior of particle S a non-linear (Q - xy)2 potential cannot be revealed a Poincare surface of section method (PSS) because inost trajectories take practically infinitely long time to integrate numerically before they come back to the surface. In this study as an alternative to PSS, a multiple scale perturbation is applied to analyze global adiabatic, non-adiabatic and chaotic behavior of particles in this potential. It was found that the results can be summarized as a form of a Fermi-like map. Additionally, this method gives a variation of global stochasticity criteria with Q.
Abstract: This paper reports on the theoretical performance
analysis of the 1.3 μm In0.42Ga0.58As /In0.26Ga0.74As multiple quantum
well (MQW) vertical cavity surface emitting laser (VCSEL) on the
ternary In0.31Ga0.69As substrate. The output power of 2.2 mW has
been obtained at room temperature for 7.5 mA injection current. The
material gain has been estimated to be ~3156 cm-1 at room
temperature with the injection carrier concentration of 2×1017 cm-3.
The modulation bandwidth of this laser is measured to be 9.34 GHz
at room temperature for the biasing current of 2 mA above the
threshold value. The outcomes reveal that the proposed InGaAsbased
MQW laser is the promising one for optical communication
system.
Abstract: This paper discusses the use of explorative data
mining tools that allow the educator to explore new relationships
between reported learning experiences and actual activities,
even if there are multiple dimensions with a large number
of measured items. The underlying technology is based on
the so-called Compendium Platform for Reproducible Computing
(http://www.freestatistics.org) which was built on top the computational
R Framework (http://www.wessa.net).
Abstract: A series of microarray experiments produces observations
of differential expression for thousands of genes across multiple
conditions.
Principal component analysis(PCA) has been widely used in
multivariate data analysis to reduce the dimensionality of the data in
order to simplify subsequent analysis and allow for summarization of
the data in a parsimonious manner. PCA, which can be implemented
via a singular value decomposition(SVD), is useful for analysis of
microarray data.
For application of PCA using SVD we use the DNA microarray
data for the small round blue cell tumors(SRBCT) of childhood
by Khan et al.(2001). To decide the number of components which
account for sufficient amount of information we draw scree plot.
Biplot, a graphic display associated with PCA, reveals important
features that exhibit relationship between variables and also the
relationship of variables with observations.
Abstract: The National Agricultural Biotechnology Information
Center (NABIC) plays a leading role in the biotechnology information
database for agricultural plants in Korea. Since 2002, we have
concentrated on functional genomics of major crops, building an
integrated biotechnology database for agro-biotech information that
focuses on bioinformatics of major agricultural resources such as rice,
Chinese cabbage, and microorganisms. In the NABIC,
integration-based biotechnology database provides useful information
through a user-friendly web interface that allows analysis of genome
infrastructure, multiple plants, microbial resources, and living
modified organisms.
Abstract: Falling has been one of the major concerns and threats
to the independence of the elderly in their daily lives. With the
worldwide significant growth of the aging population, it is essential
to have a promising solution of fall detection which is able to operate
at high accuracy in real-time and supports large scale implementation
using multiple cameras. Field Programmable Gate Array (FPGA) is a
highly promising tool to be used as a hardware accelerator in many
emerging embedded vision based system. Thus, it is the main
objective of this paper to present an FPGA-based solution of visual
based fall detection to meet stringent real-time requirements with
high accuracy. The hardware architecture of visual based fall
detection which utilizes the pixel locality to reduce memory accesses
is proposed. By exploiting the parallel and pipeline architecture of
FPGA, our hardware implementation of visual based fall detection
using FGPA is able to achieve a performance of 60fps for a series of
video analytical functions at VGA resolutions (640x480). The results
of this work show that FPGA has great potentials and impacts in
enabling large scale vision system in the future healthcare industry
due to its flexibility and scalability.