Abstract: Freeways are originally designed to provide high
mobility to road users. However, the increase in population and
vehicle numbers has led to increasing congestions around the world.
Daily recurrent congestion substantially reduces the freeway capacity
when it is most needed. Building new highways and expanding the
existing ones is an expensive solution and impractical in many
situations. Intelligent and vision-based techniques can, however, be
efficient tools in monitoring highways and increasing the capacity of
the existing infrastructures. The crucial step for highway monitoring
is vehicle detection. In this paper, we propose one of such
techniques. The approach is based on artificial neural networks
(ANN) for vehicles detection and counting. The detection process
uses the freeway video images and starts by automatically extracting
the image background from the successive video frames. Once the
background is identified, subsequent frames are used to detect
moving objects through image subtraction. The result is segmented
using Sobel operator for edge detection. The ANN is, then, used in
the detection and counting phase. Applying this technique to the
busiest freeway in Riyadh (King Fahd Road) achieved higher than
98% detection accuracy despite the light intensity changes, the
occlusion situations, and shadows.
Abstract: More and more home videos are being generated with the ever growing popularity of digital cameras and camcorders. For many home videos, a photo rendering, whether capturing a moment or a scene within the video, provides a complementary representation to the video. In this paper, a video motion mining framework for creative rendering is presented. The user-s capture intent is derived by analyzing video motions, and respective metadata is generated for each capture type. The metadata can be used in a number of applications, such as creating video thumbnail, generating panorama posters, and producing slideshows of video.
Abstract: This article outlines conceptualization and
implementation of an intelligent system capable of extracting
knowledge from databases. Use of hybridized features of both the
Rough and Fuzzy Set theory render the developed system flexibility
in dealing with discreet as well as continuous datasets. A raw data set
provided to the system, is initially transformed in a computer legible
format followed by pruning of the data set. The refined data set is
then processed through various Rough Set operators which enable
discovery of parameter relationships and interdependencies. The
discovered knowledge is automatically transformed into a rule base
expressed in Fuzzy terms. Two exemplary cancer repository datasets
(for Breast and Lung Cancer) have been used to test and implement
the proposed framework.
Abstract: Control chart pattern recognition is one of the most important tools to identify the process state in statistical process control. The abnormal process state could be classified by the recognition of unnatural patterns that arise from assignable causes. In this study, a wavelet based neural network approach is proposed for the recognition of control chart patterns that have various characteristics. The procedure of proposed control chart pattern recognizer comprises three stages. First, multi-resolution wavelet analysis is used to generate time-shape and time-frequency coefficients that have detail information about the patterns. Second, distance based features are extracted by a bi-directional Kohonen network to make reduced and robust information. Third, a back-propagation network classifier is trained by these features. The accuracy of the proposed method is shown by the performance evaluation with numerical results.
Abstract: The purpose of this study was to evaluate and
compare new indices based on the discrete wavelet transform
with another spectral parameters proposed in the literature as
mean average voltage, median frequency and ratios between
spectral moments applied to estimate acute exercise-induced
changes in power output, i.e., to assess peripheral muscle
fatigue during a dynamic fatiguing protocol. 15 trained
subjects performed 5 sets consisting of 10 leg press, with 2
minutes rest between sets. Surface electromyography was
recorded from vastus medialis (VM) muscle. Several surface
electromyographic parameters were compared to detect
peripheral muscle fatigue. These were: mean average voltage
(MAV), median spectral frequency (Fmed), Dimitrov spectral
index of muscle fatigue (FInsm5), as well as other five
parameters obtained from the discrete wavelet transform
(DWT) as ratios between different scales. The new wavelet
indices achieved the best results in Pearson correlation
coefficients with power output changes during acute dynamic
contractions. Their regressions were significantly different
from MAV and Fmed. On the other hand, they showed the
highest robustness in presence of additive white gaussian
noise for different signal to noise ratios (SNRs). Therefore,
peripheral impairments assessed by sEMG wavelet indices
may be a relevant factor involved in the loss of power output
after dynamic high-loading fatiguing task.
Abstract: This paper proposes a technique to block adult images displayed in websites. The filter is designed so as to perform even in exceptional cases such as, where face detection is not possible or improper face visibility. This is achieved by using an alternative phase to extract the MFC (Most Frequent Color) from the Human Body regions estimated using a biometric of anthropometric distances between fixed rigidly connected body locations. The logical results generated can be protected from overriding by a firewall or intrusion, by encrypting the result in a SSH data packet.
Abstract: Speech corpus is one of the major components in a
Speech Processing System where one of the primary requirements
is to recognize an input sample. The quality and details captured
in speech corpus directly affects the precision of recognition. The
current work proposes a platform for speech corpus generation using
an adaptive LMS filter and LPC cepstrum, as a part of an ANN
based Speech Recognition System which is exclusively designed to
recognize isolated numerals of Assamese language- a major language
in the North Eastern part of India. The work focuses on designing an
optimal feature extraction block and a few ANN based cooperative
architectures so that the performance of the Speech Recognition
System can be improved.
Abstract: Nowadays, OCR systems have got several
applications and are increasingly employed in daily life. Much
research has been done regarding the identification of Latin,
Japanese, and Chinese characters. However, very little investigation
has been performed regarding Farsi/Arabic characters recognition.
Probably the reason is difficulty and complexity of those characters
identification compared to the others and limitation of IT activities in
Farsi and Arabic speaking countries. In this paper, a technique has
been employed to identify isolated Farsi/Arabic characters. A chain
code based algorithm along with other significant peculiarities such
as number and location of dots and auxiliary parts, and the number of
holes existing in the isolated character has been used in this study to
identify Farsi/Arabic characters. Experimental results show the
relatively high accuracy of the method developed when it is tested on
several standard Farsi fonts.
Abstract: In this paper, an efficient structural approach for
recognizing on-line handwritten digits is proposed. After reading
the digit from the user, the slope is estimated and normalized for
adjacent nodes. Based on the changing of signs of the slope values,
the primitives are identified and extracted. The names of these
primitives are represented by strings, and then a finite state
machine, which contains the grammars of the digits, is traced to
identify the digit. Finally, if there is any ambiguity, it will be
resolved. Experiments showed that this technique is flexible and
can achieve high recognition accuracy for the shapes of the digits
represented in this work.
Abstract: The paper proposes a way of parallel processing of
SURF and Optical Flow for moving object recognition and tracking.
The object recognition and tracking is one of the most important task
in computer vision, however disadvantage are many operations cause
processing speed slower so that it can-t do real-time object recognition
and tracking. The proposed method uses a typical way of feature
extraction SURF and moving object Optical Flow for reduce
disadvantage and real-time moving object recognition and tracking,
and parallel processing techniques for speed improvement. First
analyse that an image from DB and acquired through the camera using
SURF for compared to the same object recognition then set ROI
(Region of Interest) for tracking movement of feature points using
Optical Flow. Secondly, using Multi-Thread is for improved
processing speed and recognition by parallel processing. Finally,
performance is evaluated and verified efficiency of algorithm
throughout the experiment.
Abstract: This work evaluated the effect of temperature on headspace solid-phase microextraction of volatile and furanic compounds in coated fish sticks. The major goal was the analysis of the samples as consumed, to reproduce volatile compounds people feel when consuming those products. Extraction at 37 ºC (the human body temperature) throughout the HS-SPME analysis of volatile and furanic compounds in coated fish was compared with higher extraction temperatures, which are frequently used for this kind of determinations. The profile of volatile compounds found in deepfried (F) and non-fried (NF) coated fish at 37 and 50 ºC was different from that obtained at 80 ºC. Concerning furan and its derivatives, an extra formation of these compounds was observed at higher extraction temperatures. The analysis of volatile and furanic compounds in fish coated sticks simulating the cooking and eating conditions can be reliably carried out setting the headspace absorption temperature at 37 ºC.
Abstract: Vehicle detection is the critical step for highway monitoring. In this paper we propose background subtraction and edge detection technique for vehicle detection. This technique uses the advantages of both approaches. The practical applications approved the effectiveness of this method. This method consists of two procedures: First, automatic background extraction procedure, in which the background is extracted automatically from the successive frames; Second vehicles detection procedure, which depend on edge detection and background subtraction. Experimental results show the effective application of this algorithm. Vehicles detection rate was higher than 91%.
Abstract: Fourty one strains of ESBL producing P.aeruginosa
which were previously isolated from burn patients in Kerman
University general hospital, Iran were subjected to PCR, RFLP and
sequencing in order to determine the type of extended spectrum β-
lactamases (ESBL), the restriction digestion pattern and possibility of
mutation among detected genes. DNA extraction was carried out by
phenol chloroform method. PCR for detection of bla genes was
performed using specific primer for each gene. Restriction Fragment
Length Polymorphism (RFLP) for ESBL genes was carried out using
EcoRI, NheI, PVUII, EcoRV, DdeI, and PstI restriction enzymes. The
PCR products were subjected to direct sequencing of both the strands
for identification of the ESBL genes.The blaCTX-M, blaVEB-1, blaPER-1,
blaGES-1, blaOXA-1, blaOXA-4 and blaOXA-10 genes were detected in the
(n=1) 2.43%, (n=41)100%, (n=28) 68.3%, (n=10) 24.4%, (n=29)
70.7%, (n=7)17.1% and (n=38) 92.7% of the ESBL producing isolates
respectively. The RFLP analysis showed that each ESBL gene has
identical pattern of digestion among the isolated strains. Sequencing
of the ESBL genes confirmed the genuinety of PCR products and
revealed no mutation in the restriction sites of the above genes. From
results of the present investigation it can be concluded that blaVEB-1
and blaCTX-M were the most and the least frequently isolated ESBL
genes among the P.aeruginosa strains isolated from burn patients. The
RFLP and sequencing analysis revealed that same clone of the bla
genes were indeed existed among the antibiotic resistant strains.
Abstract: System-level design based on high-level abstractions
is becoming increasingly important in hardware and embedded
system design. This paper analyzes meta-design techniques oriented
at developing meta-programs and meta-models for well-understood
domains. Meta-design techniques include meta-programming and
meta-modeling. At the programming level of design process, metadesign
means developing generic components that are usable in a
wider context of application than original domain components. At the
modeling level, meta-design means developing design patterns that
describe general solutions to the common recurring design problems,
and meta-models that describe the relationship between different
types of design models and abstractions. The paper describes and
evaluates the implementation of meta-design in hardware design
domain using object-oriented and meta-programming techniques.
The presented ideas are illustrated with a case study.
Abstract: In modern distributed software systems, the issue of communication among composing parts represents a critical point, but the idea of extending conventional programming languages with general purpose communication constructs seems difficult to realize. As a consequence, there is a (growing) gap between the abstraction level required by distributed applications and the concepts provided by platforms that enable communication. This work intends to discuss how the Model Driven Software Development approach can be considered as a mature technology to generate in automatic way the schematic part of applications related to communication, by providing at the same time high level specialized languages useful in all the phases of software production. To achieve the goal, a stack of languages (meta-meta¬models) has been introduced in order to describe – at different levels of abstraction – the collaborative behavior of generic entities in terms of communication actions related to a taxonomy of messages. Finally, the generation of platforms for communication is viewed as a form of specification of language semantics, that provides executable models of applications together with model-checking supports and effective runtime environments.
Abstract: This paper describes a system-level SoC energy
consumption estimation method based on a dynamic behavior of
embedded software in the early stages of the SoC development. A
major problem of SOC development is development rework caused by
unreliable energy consumption estimation at the early stages. The
energy consumption of an SoC used in embedded systems is strongly
affected by the dynamic behavior of the software. At the early stages
of SoC development, modeling with a high level of abstraction is
required for both the dynamic behavior of the software, and the
behavior of the SoC. We estimate the energy consumption by a UML
model-based simulation. The proposed method is applied for an actual
embedded system in an MFP. The energy consumption estimation of
the SoC is more accurate than conventional methods and this proposed
method is promising to reduce the chance of development rework in
the SoC development. ∈
Abstract: The effects of enzyme action and heat pretreatment on oil extraction yield from sunflower kernels were analysed using hexane extraction with Soxhlet, and aqueous extraction with incubator shaker. Ground kernels of raw and heat treated kernels, each with and without Viscozyme treatment were used. Microscopic images of the kernels were taken to analyse the visible effects of each treatment on the cotyledon cell structure of the kernels. Heat pretreated kernels before both extraction processes produced enhanced oil extraction yields than the control, with steam explosion the most efficient. In hexane extraction, applying a combination of steam explosion and Viscozyme treatments to the kernels before the extraction gave the maximum oil extractable in 1 hour; while for aqueous extraction, raw kernels treated with Viscozyme gave the highest oil extraction yield. Remarkable cotyledon cell disruption was evident in kernels treated with Viscozyme; whereas steam explosion and conventional heat treated kernels had similar effects.
Abstract: This paper presents the source extraction system which can extract only target signals with constraints on source localization in on-line systems. The proposed system is a kind of methods for enhancing a target signal and suppressing other interference signals. But, the performance of proposed system is superior to any other methods and the extraction of target source is comparatively complete. The method has a beamforming concept and uses an improved time-frequency (TF) mask-based BSS algorithm to separate a target signal from multiple noise sources. The target sources are assumed to be in front and test data was recorded in a reverberant room. The experimental results of the proposed method was evaluated by the PESQ score of real-recording sentences and showed a noticeable speech enhancement.
Abstract: In this paper we present an efficient system for
independent speaker speech recognition based on neural network
approach. The proposed architecture comprises two phases: a
preprocessing phase which consists in segmental normalization and
features extraction and a classification phase which uses neural
networks based on nonparametric density estimation namely the
general regression neural network (GRNN). The relative
performances of the proposed model are compared to the similar
recognition systems based on the Multilayer Perceptron (MLP), the
Recurrent Neural Network (RNN) and the well known Discrete
Hidden Markov Model (HMM-VQ) that we have achieved also.
Experimental results obtained with Arabic digits have shown that the
use of nonparametric density estimation with an appropriate
smoothing factor (spread) improves the generalization power of the
neural network. The word error rate (WER) is reduced significantly
over the baseline HMM method. GRNN computation is a successful
alternative to the other neural network and DHMM.
Abstract: Knowing the geometrical object pose of products in manufacturing line before robot manipulation is required and less time consuming for overall shape measurement. In order to perform it, the information of shape representation and matching of objects is become required. Objects are compared with its descriptor that conceptually subtracted from each other to form scalar metric. When the metric value is smaller, the object is considered closed to each other. Rotating the object from static pose in some direction introduce the change of value in scalar metric value of boundary information after feature extraction of related object. In this paper, a proposal method for indexing technique for retrieval of 3D geometrical models based on similarity between boundaries shapes in order to measure 3D CAD object pose using object shape feature matching for Computer Aided Testing (CAT) system in production line is proposed. In experimental results shows the effectiveness of proposed method.