Abstract: The paper is included within the framework of a
complex research program, which was initiated from the hypothesis
arguing on the existence of a correlation between pineal indolic and
peptide hormones and the somatic development rhythm, including
thus the epithalamium-epiphysis complex involvement. At birds,
pineal gland contains a circadian oscillator, playing a main role in the
temporal organization of the cerebral functions. The secretion of
pineal indolic hormones is characterized by a high endogenous
rhythmic alternation, modulated by the light/darkness (L/D)
succession and by temperature as well. The research has been carried
out using 100 chicken broilers - “Ross" commercial hybrid,
randomly allocated in two experimental batches: Lc batch, reared
under a 12L/12D lighting schedule and Lexp batch, which was photic
pinealectomised through continuous exposition to light (150 lux, 24
hours, 56 days). Chemical and physical features of the meat issued
from breast fillet and thighs muscles have been studied, determining
the dry matter, proteins, fat, collagen, salt content and pH value, as
well. Besides the variations of meat chemical composition in relation
with lighting schedule, other parameters have been studied: live
weight dynamics, feed intake and somatic development degree. The
achieved results became significant since chickens have 7 days of
age, some variations of the studied parameters being registered,
revealing that the pineal gland physiologic activity, in relation with
the lighting schedule, could be interpreted through the monitoring of
the somatic development technological parameters, usually studied
within the chicken broilers rearing aviculture practice.
Abstract: An experiment was conducted to study the effects of different types of probiotic on Sucrase enzyme activity of the small intestine mucosa in male broilers. The experimental design was arranged as randomized completely blocks in 4 × 2 factorial arrangement of treatment. 180 male broilers of Ross 308 commercial hybrid were designated into 4 groups. Three replicates of 15 birds were assigned to each treatment. Control treatments (diet contained no probiotic) were fed according to the NRC as base diet and three treatment groups were fed from the same diet plus three different types of probiotics. Birds were slaughtered after 21 and 42 days and different segments of small intestine (at 1,10,30,50,70 and 90% of total length the small intestine) were taken from each replicates (N=2) Sucrase enzyme activities were measured and recorded. Obtained data were analyzed by Spss (P
Abstract: In automotive systems almost all steps concerning the
calibration of several control systems, e.g., low idle governor or
boost pressure governor, are made with the vehicle because the timeto-
production and cost requirements on the projects do not allow for
the vehicle analysis necessary to build reliable models. Here is
presented a procedure using parametric and NN (neural network)
models that enables the generation of vehicle system models based
on normal ECU engine control unit) vehicle measurements. These
models are locally valid and permit pre and follow-up calibrations so
that, only the final calibrations have to be done with the vehicle.
Abstract: Random Access Memory (RAM) is an important
device in computer system. It can represent the snapshot on how the
computer has been used by the user. With the growth of its
importance, the computer memory has been an issue that has been
discussed in digital forensics. A number of tools have been developed
to retrieve the information from the memory. However, most of the
tools have their limitation in the ability of retrieving the important
information from the computer memory. Hence, this paper is aimed
to discuss the limitation and the setback for two main techniques such
as process signature search and process enumeration. Then, a new
hybrid approach will be presented to minimize the setback in both
individual techniques. This new approach combines both techniques
with the purpose to retrieve the information from the process block
and other objects in the computer memory. Nevertheless, the basic
theory in address translation for x86 platforms will be demonstrated
in this paper.
Abstract: This paper presents an experimental investigation of
transformer dielectric response and solid insulation water content.
The dielectric response was carried out on the base of Hybrid
Frequency Dielectric Spectroscopy and Polarization Current
measurements method (FDS &PC). The calculation of the water
content in paper is based on the water content in oil and the obtained
equilibrium curves. A reference measurements were performed at
equilibrium conditions for water content in oil and paper of
transformer at different stable temperatures (25, 50, 60 and 70°C) to
prepare references to evaluate the insulation behavior at the not
equilibrium conditions. Some measurements performed at the
different simulated normal working modes of transformer operation
at the same temperature where the equilibrium conditions. The
obtained results show that when transformer temperature is mach
more than the its ambient temperature, the transformer temperature
decreases immediately after disconnecting the transformer from the
network and this temperature reduction influences the transformer
insulation condition in the measuring process. In addition to the oil
temperature at the near places to the sensors, the temperature
uniformity in transformer which can be changed by a big change in
the load of transformer before the measuring time will influence the
result. The investigations have shown that the extremely influence of
the time between disconnecting the transformer and beginning the
measurements on the results. And the online monitoring for water
content in paper measurements, on the basis of the oil water content
on line monitoring and the obtained equilibrium curves. The
measurements where performed continuously and for about 50 days
without any disconnection in the prepared the adiabatic room.
Abstract: This paper presents the development and application of an adaptive neuro fuzzy inference system (ANFIS) based intelligent hybrid neuro fuzzy controller for automatic generation control (AGC) of two-area interconnected thermal power system with reheat non linearity. The dynamic response of the system has been studied for 1% step load perturbation in area-1. The performance of the proposed neuro fuzzy controller is compared against conventional proportional-integral (PI) controller, state feedback linear quadratic regulator (LQR) controller and fuzzy gain scheduled proportionalintegral (FGSPI) controller. Comparative analysis demonstrates that the proposed intelligent neuro fuzzy controller is the most effective of all in improving the transients of frequency and tie-line power deviations against small step load disturbances. Simulations have been performed using Matlab®.
Abstract: This paper addresses an efficient technique to embed and detect digital fingerprint code. Orthogonal modulation method is a straightforward and widely used approach for digital fingerprinting but shows several limitations in computational cost and signal efficiency. Coded modulation method can solve these limitations in theory. However it is difficult to perform well in practice if host signals are not available during tracing colluders, other kinds of attacks are applied, and the size of fingerprint code becomes large. In this paper, we propose a hybrid modulation method, in which the merits of or-thogonal modulation and coded modulation method are combined so that we can achieve low computational cost and high signal efficiency. To analyze the performance, we design a new fingerprint code based on GD-PBIBD theory and modulate this code into images by our method using spread-spectrum watermarking on frequency domain. The results show that the proposed method can efficiently handle large fingerprint code and trace colluders against averaging attacks.
Abstract: Conventional approaches in the implementation of logic programming applications on embedded systems are solely of software nature. As a consequence, a compiler is needed that transforms the initial declarative logic program to its equivalent procedural one, to be programmed to the microprocessor. This approach increases the complexity of the final implementation and reduces the overall system's performance. On the contrary, presenting hardware implementations which are only capable of supporting logic programs prevents their use in applications where logic programs need to be intertwined with traditional procedural ones, for a specific application. We exploit HW/SW codesign methods to present a microprocessor, capable of supporting hybrid applications using both programming approaches. We take advantage of the close relationship between attribute grammar (AG) evaluation and knowledge engineering methods to present a programmable hardware parser that performs logic derivations and combine it with an extension of a conventional RISC microprocessor that performs the unification process to report the success or failure of those derivations. The extended RISC microprocessor is still capable of executing conventional procedural programs, thus hybrid applications can be implemented. The presented implementation is programmable, supports the execution of hybrid applications, increases the performance of logic derivations (experimental analysis yields an approximate 1000% increase in performance) and reduces the complexity of the final implemented code. The proposed hardware design is supported by a proposed extended C-language called C-AG.
Abstract: Electroencephalogram (EEG) recordings are often
contaminated with ocular and muscle artifacts. In this paper, the
canonical correlation analysis (CCA) is used as blind source
separation (BSS) technique (BSS-CCA) to decompose the artifact
contaminated EEG into component signals. We combine the BSSCCA
technique with wavelet filtering approach for minimizing both
ocular and muscle artifacts simultaneously, and refer the proposed
method as wavelet enhanced BSS-CCA. In this approach, after
careful visual inspection, the muscle artifact components are
discarded and ocular artifact components are subjected to wavelet
filtering to retain high frequency cerebral information, and then clean
EEG is reconstructed. The performance of the proposed wavelet
enhanced BSS-CCA method is tested on real EEG recordings
contaminated with ocular and muscle artifacts, for which power
spectral density is used as a quantitative measure. Our results suggest
that the proposed hybrid approach minimizes ocular and muscle
artifacts effectively, minimally affecting underlying cerebral activity
in EEG recordings.
Abstract: Multiplication algorithms have considerable effect on
processors performance. A new high-speed, low-power
multiplication algorithm has been presented using modified Dadda
tree structure. Three important modifications have been implemented
in inner product generation step, inner product reduction step and
final addition step. Optimized algorithms have to be used into basic
computation components, such as multiplication algorithms. In this
paper, we proposed a new algorithm to reduce power, delay, and
transistor count of a multiplication algorithm implemented using low
power modified counter. This work presents a novel design for
Dadda multiplication algorithms. The proposed multiplication
algorithm includes structured parts, which have important effect on
inner product reduction tree. In this paper, a 1.3V, 64-bit carry hybrid
adder is presented for fast, low voltage applications. The new 64-bit
adder uses a new circuit to implement the proposed carry hybrid
adder. The new adder using 80 nm CMOS technology has been
implemented on 700 MHz clock frequency. The proposed
multiplication algorithm has achieved 14 percent improvement in
transistor count, 13 percent reduction in delay and 12 percent
modification in power consumption in compared with conventional
designs.
Abstract: Feed is one of the factors which play an important role in determining a successful development of an aquaculture industry. It is always critical to produce the best aquaculture diet at a minimum cost in order to trim down the operational cost and gain more profit. However, the feed mix problem becomes increasingly difficult since many issues need to be considered simultaneously. Thus, the purpose of this paper is to review the current techniques used by nutritionist and researchers to tackle the issues. Additionally, this paper introduce an enhance algorithm which is deemed suitable to deal with all the issues arise. The proposed technique refers to Hybrid Genetic Algorithm which is expected to obtain the minimum cost diet for farmed animal, while satisfying nutritional requirements. Hybrid GA technique with artificial bee algorithm is expected to reduce the penalty function and provide a better solution for the feed mix problem.
Abstract: Due to heightened concerns over environmental and economic issues the growing important of air pollution, and the importance of conserving fossil fuel resources in the world, the automotive industry is now forced to produce more fuel efficient, low emission vehicles and new drive system technologies. One of the most promising technologies to receive attention is the hybrid electric vehicle (HEV), which consists of two or more energy sources that supply energy to electric traction motors that in turn drive the wheels. This paper presents the various structures of HEV systems, the basic theoretical knowledge for describing their operation and the general behaviour of the HEV in acceleration, cruise and deceleration phases. The conventional design and sizing of a series HEV is studied. A conventional bus and its series configuration are defined and evaluated using the ADVISOR. In this section the simulation of a standard driving cycle and prediction of its fuel consumption and emissions of the HEV are discussed. Finally the bus performance is investigated to establish whether it can satisfy the performance, fuel consumption and emissions requested. The validity of the simulation has been established by the close conformity between the fuel consumption of the conventional bus reported by the manufacturer to what has achieved from the simulation.
Abstract: Although automotive industry has brought different beneficiaries to human life, it is being pointed out as one of the major cause of global air pollution which resulted in climate change, smog, green house gases (GHGs), and human diseases by many reasons. Since auto industry is one of the largest consumers of fossil fuels, the realization of green innovations is becoming a crucial choice to meet the challenges towards sustainable development. Recently, many auto manufacturers have embarked on green technology initiatives to gain a competitive advantage in the global market; however, innovative manufacturing systems and technologies can enhance operational performance only if the human resource management is in place to elicit the motivation of the employees and develop their organizational expertise. No organization can perform at peak levels unless each employee is committed to the company goals and works as an effective team member. Strategic human resource practices are the primary means by which firms can shape the skills, attitudes, and behavior of individuals to align with the business strategic objectives. This study investigates on the comprehensive approach of multiple advanced technology innovations and human resource management at Toyota Motor Corporation as the market leader of full hybrid technology in the automotive industry. Then, HRM framework of the company is described and three sets of human resource practices that support the innovation-oriented HR system, presented. Finally, a conceptual framework for innovativeness in green technology in automotive industry by applying a deliberate strategic HR management system and knowledge management with the intervening factors of organizational culture, knowledge application and knowledge sharing is proposed.
Abstract: In this paper, image compression using hybrid vector
quantization scheme such as Multistage Vector Quantization
(MSVQ) and Pyramid Vector Quantization (PVQ) are introduced. A
combined MSVQ and PVQ are utilized to take advantages provided
by both of them. In the wavelet decomposition of the image, most of
the information often resides in the lowest frequency subband.
MSVQ is applied to significant low frequency coefficients. PVQ is
utilized to quantize the coefficients of other high frequency
subbands. The wavelet coefficients are derived using lifting scheme.
The main aim of the proposed scheme is to achieve high compression
ratio without much compromise in the image quality. The results are
compared with the existing image compression scheme using MSVQ.
Abstract: Many natural language expressions are ambiguous, and
need to draw on other sources of information to be interpreted.
Interpretation of the e word تعاون to be considered as a noun or a verb
depends on the presence of contextual cues. To interpret words we
need to be able to discriminate between different usages. This paper
proposes a hybrid of based- rules and a machine learning method for
tagging Arabic words. The particularity of Arabic word that may be
composed of stem, plus affixes and clitics, a small number of rules
dominate the performance (affixes include inflexional markers for
tense, gender and number/ clitics include some prepositions,
conjunctions and others). Tagging is closely related to the notion of
word class used in syntax. This method is based firstly on rules (that
considered the post-position, ending of a word, and patterns), and
then the anomaly are corrected by adopting a memory-based learning
method (MBL). The memory_based learning is an efficient method to
integrate various sources of information, and handling exceptional
data in natural language processing tasks. Secondly checking the
exceptional cases of rules and more information is made available to
the learner for treating those exceptional cases. To evaluate the
proposed method a number of experiments has been run, and in
order, to improve the importance of the various information in
learning.
Abstract: One of the best ways for achievement of conventional
vehicle changing to hybrid case is trustworthy simulation result and
using of driving realities. For this object, in this paper, at first sevendegree-
of-freedom dynamical model of vehicle will be shown. Then
by using of statically model of engine, gear box, clutch, differential,
electrical machine and battery, the hybrid automobile modeling will
be down and forward simulation of vehicle for pedals to wheels
power transformation will be obtained. Then by design of a fuzzy
controller and using the proper rule base, fuel economy and
regenerative braking will be marked. Finally a series of
MATLAB/SIMULINK simulation results will be proved the
effectiveness of proposed structure.
Abstract: Scalability poses a severe threat to the existing
DRAM technology. The capacitors that are used for storing and
sensing charge in DRAM are generally not scaled beyond 42nm.
This is because; the capacitors must be sufficiently large for reliable
sensing and charge storage mechanism. This leaves DRAM memory
scaling in jeopardy, as charge sensing and storage mechanisms
become extremely difficult. In this paper we provide an overview of
the potential and the possibilities of using Phase Change Memory
(PCM) as an alternative for the existing DRAM technology. The
main challenges that we encounter in using PCM are, the limited
endurance, high access latencies, and higher dynamic energy
consumption than that of the conventional DRAM. We then provide
an overview of various methods, which can be employed to
overcome these drawbacks. Hybrid memories involving both PCM
and DRAM can be used, to achieve good tradeoffs in access latency
and storage density. We conclude by presenting, the results of these
methods that makes PCM a potential replacement for the current
DRAM technology.
Abstract: This paper presents an indirect adaptive stabilization
scheme for first-order continuous-time systems under saturated input
which is described by a sigmoidal function. The singularities are
avoided through a modification scheme for the estimated plant
parameter vector so that its associated Sylvester matrix is guaranteed
to be non-singular and then the estimated plant model is controllable.
The modification mechanism involves the use of a hysteresis
switching function. An alternative hybrid scheme, whose estimated
parameters are updated at sampling instants is also given to solve a
similar adaptive stabilization problem. Such a scheme also uses
hysteresis switching for modification of the parameter estimates so as
to ensure the controllability of the estimated plant model.
Abstract: Exploring an autistic child in Elementary school is a
difficult task that must be fully thought out and the teachers should
be aware of the many challenges they face raising their child
especially the behavioral problems of autistic children. Hence there
arises a need for developing Artificial intelligence (AI)
Contemporary Techniques to help diagnosis to discover autistic
people.
In this research, we suggest designing architecture of expert
system that combine Cognitive Maps (CM) with Case Based
Reasoning technique (CBR) in order to reduce time and costs of
traditional diagnosis process for the early detection to discover
autistic children. The teacher is supposed to enter child's information
for analyzing by CM module. Then, the reasoning processor would
translate the output into a case to be solved a current problem by
CBR module. We will implement a prototype for the model as a
proof of concept using java and MYSQL.
This will be provided a new hybrid approach that will achieve new
synergies and improve problem solving capabilities in AI. And we
will predict that will reduce time, costs, the number of human errors
and make expertise available to more people who want who want to
serve autistic children and their families.
Abstract: In this work, we propose a hybrid heuristic in order to
solve the Team Orienteering Problem (TOP). Given a set of points (or
customers), each with associated score (profit or benefit), and a team
that has a fixed number of members, the problem to solve is to visit a
subset of points in order to maximize the total collected score. Each
member performs a tour starting at the start point, visiting distinct
customers and the tour terminates at the arrival point. In addition,
each point is visited at most once, and the total time in each tour
cannot be greater than a given value. The proposed heuristic combines
beam search and a local optimization strategy. The algorithm was
tested on several sets of instances and encouraging results were
obtained.