Abstract: Study investigates the level and extent of voluntary disclosure practice in Croatia. The research was conducted on the sample of 130 medium and large companies. Findings indicate that two thirds of the companies analyzed disclose below-average number of additional information. The explanatory analyses has shown that firm size, listing status and industrial sector significantly and positively affect the level and extent of voluntary disclosure in the annual report of Croatian companies. On the other hand, profitability and ownership structure were found statistically insignificant. Unlike previous studies, this paper deals with level of voluntary disclosure of medium and large companies, as well as companies whose shares are not listed on the organized capital market, which can be found as our contribution. Also, the research makes contribution by providing the insights into voluntary disclosure practices in Croatia, as a case of macro-oriented accounting system economy, i.e. bank oriented economy with an emerging capital market.
Abstract: The objective of this paper is to characterize the spontaneous Electroencephalogram (EEG) signals of four different motor imagery tasks and to show hereby a possible solution for the present binary communication between the brain and a machine ora Brain-Computer Interface (BCI). The processing technique used in this paper was the fractal analysis evaluated by the Critical Exponent Method (CEM). The EEG signal was registered in 5 healthy subjects,sampling 15 measuring channels at 1024 Hz.Each channel was preprocessed by the Laplacian space ltering so as to reduce the space blur and therefore increase the spaceresolution. The EEG of each channel was segmented and its Fractaldimension (FD) calculated. The FD was evaluated in the time interval corresponding to the motor imagery and averaged out for all the subjects (each channel). In order to characterize the FD distribution,the linear regression curves of FD over the electrodes position were applied. The differences FD between the proposed mental tasks are quantied and evaluated for each experimental subject. The obtained results of the proposed method are a substantial fractal dimension in the EEG signal of motor imagery tasks and can be considerably utilized as the multiple-states BCI applications.
Abstract: Waste lubricating oil re-refining adsorption process by
different adsorbent materials was investigated. Adsorbent materials
such as oil adsorbent, egg shale powder, date palm kernel powder,
and acid activated date palm kernel powder were used. The
adsorption process over fixed amount of adsorbent at ambient
conditions was investigated. The adsorption/extraction process was
able to deposit the asphaltenic and metallic contaminants from the
waste oil to lower values. It was found that the date palm kernel
powder with contact time of 4 h was able to give the best conditions
for treating the waste oil. The recovered solvent could be also reused.
It was also found that the activated bentonite gave the best
physical properties followed by the date palm kernel powder.
Abstract: At very high speeds, bubbles form in the underwater vehicles because of sharp trailing edges or of places where the local pressure is lower than the vapor pressure. These bubbles are called cavities and the size of the cavities grows as the velocity increases. A properly designed cavitator can induce the formation of a single big cavity all over the vehicle. Such a vehicle travelling in the vaporous cavity is called a supercavitating vehicle and the present research work mainly focuses on the dynamic modeling of such vehicles. Cavitation of the fins is also accounted and the effect of the same on trajectory is well explained. The entire dynamics has been developed using the state space approach and emphasis is given on the effect of size and angle of attack of the cavitator. Control law has been established for the motion of the vehicle using Non-linear Dynamic Inverse (NDI) with cavitator as the control surface.
Abstract: This paper presents design trade-off and performance impacts of
the amount of pipeline phase of control path signals in a wormhole-switched
network-on-chip (NoC). The numbers of the pipeline phase of the control
path vary between two- and one-cycle pipeline phase. The control paths
consist of the routing request paths for output selection and the arbitration
paths for input selection. Data communications between on-chip routers are
implemented synchronously and for quality of service, the inter-router data
transports are controlled by using a link-level congestion control to avoid
lose of data because of an overflow. The trade-off between the area (logic
cell area) and the performance (bandwidth gain) of two proposed NoC router
microarchitectures are presented in this paper. The performance evaluation is
made by using a traffic scenario with different number of workloads under
2D mesh NoC topology using a static routing algorithm. By using a 130-nm
CMOS standard-cell technology, our NoC routers can be clocked at 1 GHz,
resulting in a high speed network link and high router bandwidth capacity
of about 320 Gbit/s. Based on our experiments, the amount of control path
pipeline stages gives more significant impact on the NoC performance than
the impact on the logic area of the NoC router.
Abstract: The environment pollution with pesticides and heavy
metals is a recognized problem nowadays, with extension to the
global scale the tendency of amplification. Even with all the progress
in the environmental field, both in the emphasize of the effect of the
pollutants upon health, the linked studies environment-health are
insufficient, not only in Romania but all over the world also. We aim
to describe the particular situation in Romania regarding the
uncontrolled use of pesticides, to identify and evaluate the risk zones
for health and the environment in Romania, with the final goal of
designing adequate programs for reduction and control of the risk
sources. An exploratory study was conducted to determine the
magnitude of the pesticide use problem in a population living in
Saliste, a rural setting in Transylvania, Romania. The significant
stakeholders in Saliste region were interviewed and a sample from
the population living in Saliste area was selected to fill in a designed
questionnaire. All the selected participants declared that they used
pesticides in their activities for more than one purpose. They
declared they annually applied pesticides for a period of time
between 11 and 30 years, from 5 to 9 days per year on average,
mainly on crops situated at some distance from the houses but high
risk behavior was identified as the volunteers declared the use of
pesticides in the backyard gardens, near their homes, where children
were playing. The pesticide applicators did not have the necessary
knowledge about safety and exposure. The health data must be
correlated with exposure biomarkers in attempt to identify the
possible health effects of the pesticides exposure. Future plans
include educational campaigns to raise the awareness of the
population on the danger of uncontrolled use of pesticides.
Abstract: This paper presents an adaptive technique for generation
of data required for construction of artificial neural network-based
performance model of nano-scale CMOS inverter circuit. The training
data are generated from the samples through SPICE simulation. The
proposed algorithm has been compared to standard progressive sampling
algorithms like arithmetic sampling and geometric sampling.
The advantages of the present approach over the others have been
demonstrated. The ANN predicted results have been compared with
actual SPICE results. A very good accuracy has been obtained.
Abstract: In this paper, we propose a supervised method for
color image classification based on a multilevel sigmoidal neural
network (MSNN) model. In this method, images are classified into
five categories, i.e., “Car", “Building", “Mountain", “Farm" and
“Coast". This classification is performed without any segmentation
processes. To verify the learning capabilities of the proposed method,
we compare our MSNN model with the traditional Sigmoidal Neural
Network (SNN) model. Results of comparison have shown that the
MSNN model performs better than the traditional SNN model in the
context of training run time and classification rate. Both color
moments and multi-level wavelets decomposition technique are used
to extract features from images. The proposed method has been
tested on a variety of real and synthetic images.
Abstract: In this paper a new embedded Singly Diagonally
Implicit Runge-Kutta Nystrom fourth order in fifth order method for
solving special second order initial value problems is derived. A
standard set of test problems are tested upon and comparisons on the
numerical results are made when the same set of test problems are
reduced to first order systems and solved using the existing
embedded diagonally implicit Runge-Kutta method. The results
suggests the superiority of the new method.
Abstract: This paper presents the results of enhancing images from a left and right stereo pair in order to increase the resolution of a 3D representation of a scene generated from that same pair. A new neural network structure known as a Self Delaying Dynamic Network (SDN) has been used to perform the enhancement. The advantage of SDNs over existing techniques such as bicubic interpolation is their ability to cope with motion and noise effects. SDNs are used to generate two high resolution images, one based on frames taken from the left view of the subject, and one based on the frames from the right. This new high resolution stereo pair is then processed by a disparity map generator. The disparity map generated is compared to two other disparity maps generated from the same scene. The first is a map generated from an original high resolution stereo pair and the second is a map generated using a stereo pair which has been enhanced using bicubic interpolation. The maps generated using the SDN enhanced pairs match more closely the target maps. The addition of extra noise into the input images is less problematic for the SDN system which is still able to out perform bicubic interpolation.
Abstract: This paper develops an unscented grid-based filter
and a smoother for accurate nonlinear modeling and analysis
of time series. The filter uses unscented deterministic sampling
during both the time and measurement updating phases, to approximate
directly the distributions of the latent state variable. A
complementary grid smoother is also made to enable computing
of the likelihood. This helps us to formulate an expectation
maximisation algorithm for maximum likelihood estimation of
the state noise and the observation noise. Empirical investigations
show that the proposed unscented grid filter/smoother compares
favourably to other similar filters on nonlinear estimation tasks.
Abstract: Scale Time Offset Robust Modulation (STORM) [1]–
[3] is a high bandwidth waveform design that adds time-scale
to embedded reference modulations using only time-delay [4]. In
an environment where each user has a specific delay and scale,
identification of the user with the highest signal power and that
user-s phase is facilitated by the STORM processor. Both of these
parameters are required in an efficient multiuser detection algorithm.
In this paper, the STORM modulation approach is evaluated with
a direct sequence spread quadrature phase shift keying (DS-QPSK)
system. A misconception of the STORM time scale modulation is that
a fine temporal resolution is required at the receiver. STORM will
be applied to a QPSK code division multiaccess (CDMA) system
by modifying the spreading codes. Specifically, the in-phase code
will use a typical spreading code, and the quadrature code will
use a time-delayed and time-scaled version of the in-phase code.
Subsequently, the same temporal resolution in the receiver is required
before and after the application of STORM. In this paper, the bit error
performance of STORM in a synchronous CDMA system is evaluated
and compared to theory, and the bit error performance of STORM
incorporated in a single user WCDMA downlink is presented to
demonstrate the applicability of STORM in a modern communication
system.
Abstract: The localized corrosion behavior of laser surface
melted 304L austenitic stainless steel was studied by
potentiodynamic polarization test. The extent of improvement in
corrosion resistance was governed by the preferred orientation and
the percentage of delta ferrite present on the surface of the laser
melted sample. It was established by orientation imaging microscopy
that the highest pitting potential value was obtained when grains were
oriented in the most close- packed [101] direction compared to the
random distribution of the base metal and other laser surface melted
samples oriented in [001] direction. The sample with lower
percentage of ferrite had good pitting resistance.
Abstract: This paper proposes new hybrid approaches for face
recognition. Gabor wavelets representation of face images is an
effective approach for both facial action recognition and face
identification. Perform dimensionality reduction and linear
discriminate analysis on the down sampled Gabor wavelet faces can
increase the discriminate ability. Nearest feature space is extended to
various similarity measures. In our experiments, proposed Gabor
wavelet faces combined with extended neural net feature space
classifier shows very good performance, which can achieve 93 %
maximum correct recognition rate on ORL data set without any preprocessing
step.
Abstract: The main purpose of this paper is to research on the
methodologies of BYD to implement the opportune innovation. BYD
is a Chinese company which has the IT component manufacture, the
rechargeable battery and the automobile businesses. The paper deals
with the innovation methodology as the same as the IPR management
BYD implements in order to obtain the rapid growth of technology
development with the reasonable cost of money and time.
Abstract: In this paper the neural network-based controller is
designed for motion control of a mobile robot. This paper treats the
problems of trajectory following and posture stabilization of the
mobile robot with nonholonomic constraints. For this purpose the
recurrent neural network with one hidden layer is used. It learns
relationship between linear velocities and error positions of the
mobile robot. This neural network is trained on-line using the
backpropagation optimization algorithm with an adaptive learning
rate. The optimization algorithm is performed at each sample time to
compute the optimal control inputs. The performance of the proposed
system is investigated using a kinematic model of the mobile robot.
Abstract: As the Computed Tomography(CT) requires normally
hundreds of projections to reconstruct the image, patients are exposed
to more X-ray energy, which may cause side effects such as cancer.
Even when the variability of the particles in the object is very less,
Computed Tomography requires many projections for good quality
reconstruction. In this paper, less variability of the particles in an
object has been exploited to obtain good quality reconstruction.
Though the reconstructed image and the original image have same
projections, in general, they need not be the same. In addition
to projections, if a priori information about the image is known,
it is possible to obtain good quality reconstructed image. In this
paper, it has been shown by experimental results why conventional
algorithms fail to reconstruct from a few projections, and an efficient
polynomial time algorithm has been given to reconstruct a bi-level
image from its projections along row and column, and a known sub
image of unknown image with smoothness constraints by reducing the
reconstruction problem to integral max flow problem. This paper also
discusses the necessary and sufficient conditions for uniqueness and
extension of 2D-bi-level image reconstruction to 3D-bi-level image
reconstruction.
Abstract: Tomato powder has good potential as substitute of tomato paste and other tomato products. In order to protect physicochemical properties and nutritional quality of tomato during dehydration process, investigation was carried out using different drying methods and pretreatments. Solar drier and continuous conveyor (tunnel) drier were used for dehydration where as calcium chloride (CaCl2), potassium metabisulphite (KMS), calcium chloride and potassium metabisulphite (CaCl2 +KMS), and sodium chloride (NaCl) selected for treatment.. lycopene content, dehydration ratio, rehydration ratio and non-enzymatic browning in addition to moisture, sugar and titrable acidity were studied. Results show that pre-treatment with CaCl2 and NaCl increased water removal and moisture mobility in tomato slices during drying of tomatoes. Where CaCl2 used along with KMS the NEB was recorded the least compared to other treatments and the best results were obtained while using the two chemicals in combination form. Storage studies in LDPE polymeric and metalized polyesters films showed less changes in the products packed in metallized polyester pouches and even after 6 months lycopene content did not decrease more than 20% as compared to the control sample and provide extension of shelf life in acceptable condition for 6 months. In most of the quality characteristics tunnel drier samples presented better values in comparison to solar drier.
Abstract: Nowadays there are more than thirty maturity models
in different knowledge areas. Maturity model is an area of interest
that contributes organizations to find out where they are in a specific
knowledge area and how to improve it. As Information Resource
Management (IRM) is the concept that information is a major
corporate resource and must be managed using the same basic
principles used to manage other assets, assessment of the current
IRM status and reveal the improvement points can play a critical role
in developing an appropriate information structure in organizations.
In this paper we proposed a framework for information resource
management maturity model (IRM3) that includes ten best practices
for the maturity assessment of the organizations' IRM.
Abstract: Computed tomography and laminography are heavily investigated in a compressive sensing based image reconstruction framework to reduce the dose to the patients as well as to the radiosensitive devices such as multilayer microelectronic circuit boards. Nowadays researchers are actively working on optimizing the compressive sensing based iterative image reconstruction algorithm to obtain better quality images. However, the effects of the sampled data’s properties on reconstructed the image’s quality, particularly in an insufficient sampled data conditions have not been explored in computed laminography. In this paper, we investigated the effects of two data properties i.e. sampling density and data incoherence on the reconstructed image obtained by conventional computed laminography and a recently proposed method called spherical sinusoidal scanning scheme. We have found that in a compressive sensing based image reconstruction framework, the image quality mainly depends upon the data incoherence when the data is uniformly sampled.