Abstract: This paper describes a novel optimized JTAG interface circuit between a JTAG controller and target IC. Being able to access JTAG using only one or two pins, this circuit does not change the original boundary scanning test frequency of target IC. Compared with the traditional JTAG interface which based on IEEE std. 1149.1, this reduced pin technology is more applicability in pin limited devices, and it is easier to control the scale of target IC for the designer.
Abstract: In this work, we present a novel active learning approach
for learning a visual object detection system. Our system
is composed of an active learning mechanism as wrapper around
a sub-algorithm which implement an online boosting-based learning
object detector. In the core is a combination of a bootstrap procedure
and a semi automatic learning process based on the online boosting
procedure. The idea is to exploit the availability of classifier during
learning to automatically label training samples and increasingly
improves the classifier. This addresses the issue of reducing labeling
effort meanwhile obtain better performance. In addition, we propose
a verification process for further improvement of the classifier.
The idea is to allow re-update on seen data during learning for
stabilizing the detector. The main contribution of this empirical study
is a demonstration that active learning based on an online boosting
approach trained in this manner can achieve results comparable or
even outperform a framework trained in conventional manner using
much more labeling effort. Empirical experiments on challenging data
set for specific object deteciton problems show the effectiveness of
our approach.
Abstract: In this study, a classification-based video
super-resolution method using artificial neural network (ANN) is
proposed to enhance low-resolution (LR) to high-resolution (HR)
frames. The proposed method consists of four main steps:
classification, motion-trace volume collection, temporal adjustment,
and ANN prediction. A classifier is designed based on the edge
properties of a pixel in the LR frame to identify the spatial information.
To exploit the spatio-temporal information, a motion-trace volume is
collected using motion estimation, which can eliminate unfathomable
object motion in the LR frames. In addition, temporal lateral process is
employed for volume adjustment to reduce unnecessary temporal
features. Finally, ANN is applied to each class to learn the complicated
spatio-temporal relationship between LR and HR frames. Simulation
results show that the proposed method successfully improves both
peak signal-to-noise ratio and perceptual quality.
Abstract: A fault detection and identification (FDI) technique is
presented to create a fault tolerant control system (FTC). The fault
detection is achieved by monitoring the position of the light source
using an array of light sensors. When a decision is made about the
presence of a fault an identification process is initiated to locate the
faulty component and reconfigure the controller signals. The signals
provided by the sensors are predictable; therefore the existence of a
fault is easily identified. Identification of the faulty sensor is based on
the dynamics of the frame. The technique is not restricted to a
particular type of controllers and the results show consistency.
Abstract: Some theoretical and experimental aspects related to
the conceptual analyses concerning the direct correspondence
identification between the shape, area and orientation of plantar
pressure and obtaining adequate corrective insoles by rapid
prototyping are presented in this paper. In the first part of the paper
there is the theoretical-correlative concept, which is the fundament of
correspondence deduction between plantar surface characteristics and
respectively corrective insoles. In the second part of the paper the
experimental equipment used to analyze and perform the
correspondence stages and then the integral ones between the
analyzed foot shapes and the ones with corrective insoles is
presented. In the final parte the results used to adapt the insoles
obtained by rapid prototyping but also some specific aspects and
conclusions of the conceptual analysis of direct and rapid
correspondence are shown.
Abstract: In this research, the researchers have managed to
design a model to investigate the current trend of stock price of the
"IRAN KHODRO corporation" at Tehran Stock Exchange by
utilizing an Adaptive Neuro - Fuzzy Inference system. For the Longterm
Period, a Neuro-Fuzzy with two Triangular membership
functions and four independent Variables including trade volume,
Dividend Per Share (DPS), Price to Earning Ratio (P/E), and also
closing Price and Stock Price fluctuation as an dependent variable are
selected as an optimal model. For the short-term Period, a neureo –
fuzzy model with two triangular membership functions for the first
quarter of a year, two trapezoidal membership functions for the
Second quarter of a year, two Gaussian combination membership
functions for the third quarter of a year and two trapezoidal
membership functions for the fourth quarter of a year were selected
as an optimal model for the stock price forecasting. In addition, three
independent variables including trade volume, price to earning ratio,
closing Stock Price and a dependent variable of stock price
fluctuation were selected as an optimal model. The findings of the
research demonstrate that the trend of stock price could be forecasted
with the lower level of error.
Abstract: Let T and S be a subspace of Cn and Cm, respectively.
Then for A ∈ Cm×n satisfied AT ⊕ S = Cm, the generalized
inverse A(2)
T,S is given by A(2)
T,S = (PS⊥APT )†. In this paper, a
finite formulae is presented to compute generalized inverse A(2)
T,S
under the concept of restricted inner product, which defined as <
A,B >T,S=< PS⊥APT,B > for the A,B ∈ Cm×n. By this
iterative method, when taken the initial matrix X0 = PTA∗PS⊥, the
generalized inverse A(2)
T,S can be obtained within at most mn iteration
steps in absence of roundoff errors. Finally given numerical example
is shown that the iterative formulae is quite efficient.
Abstract: Membrane distillation (MD) is a rising technology for
seawater or brine desalination process. In this work, an air gap
membrane distillation (AGMD) performance was investigated for
aqueous NaCl solution along with natural ground water and seawater.
In order to enhance the performance of the AGMD process in
desalination, that is, to get more flux, it is necessary to study the
effect of operating parameters on the yield of distillate water. The
influence of operational parameters such as feed flow rate, feed
temperature, feed salt concentration, coolant temperature and air gap
thickness on the membrane distillation (MD) permeation flux have
been investigated for low and high salt solution. the natural
application of ground water and seawater over 90 h continuous
operation, scale deposits observed on the membrane surface and
reduction in flux represents 23% for ground water and 60% for
seawater, in 90 h. This reduction was eliminated (less than 14 %) by
acidification of feed water. Hence, promote the research attention in
apply of AGMD for the ground water as well as seawater
desalination over today-s conventional RO operation.
Abstract: Environmental investments, including ecological
projects, relating to the protection of atmosphere are today a need.
However, investing in the environment should be based on rational
management rules. This comes across a problem of selecting a
method to assess substances reduced during projects. Therefore, a
method allowing for the assessment of decision rationality has to be
found.
The purpose of this article is to present and systematise pollution
reduction assessment methods and illustrate theoretical analyses with
empirical data.
Empirical results confirm theoretical considerations, which proved
that the only method for judging pollution reduction, free of apparent
disadvantages, is the Eco 99-ratio method. To make decisions on
environmental projects, financing institutions should take into
account a rationality rule. Therefore the Eco 99-ratio method could
be applied to make decisions relating to environmental investments in
the area of air protection.
Abstract: As the performance of the filtering system depends
upon the accuracy of the noise detection scheme, in this paper, we
present a new scheme for impulse noise detection based on two
levels of decision. In this scheme in the first stage we coarsely
identify the corrupted pixels and in the second stage we finally
decide whether the pixel under consideration is really corrupt or not.
The efficacy of the proposed filter has been confirmed by extensive
simulations.
Abstract: The aim of this study was to develop a storm water quality improvement strategy plan (WQISP) which assists managers and decision makers of local city councils in enhancing their activities to improve regional water quality. City of Gosnells in Western Australia has been considered as a case study. The procedure on developing the WQISP consists of reviewing existing water quality data, identifying water quality issues in the study areas and developing a decision making tool for the officers, managers and decision makers. It was found that land use type is the main factor affecting the water quality. Therefore, activities, sources and pollutants related to different land use types including residential, industrial, agricultural and commercial are given high importance during the study. Semi-structured interviews were carried out with coordinators of different management sections of the regional councils in order to understand the associated management framework and issues. The issues identified from these interviews were used in preparing the decision making tool. Variables associated with the defined “value versus threat" decision making tool are obtained from the intensive literature review. The main recommendations provided for improvement of water quality in local city councils, include non-structural, structural and management controls and potential impacts of climate change.
Abstract: Location-aware computing is a type of pervasive
computing that utilizes user-s location as a dominant factor for
providing urban services and application-related usages. One of the
important urban services is navigation instruction for wayfinders in a
city especially when the user is a tourist. The services which are
presented to the tourists should provide adapted location aware
instructions. In order to achieve this goal, the main challenge is to
find spatial relevant objects and location-dependent information. The
aim of this paper is the development of a reusable location-aware
model to handle spatial relevancy parameters in urban location-aware
systems. In this way we utilized ontology as an approach which could
manage spatial relevancy by defining a generic model. Our
contribution is the introduction of an ontological model based on the
directed interval algebra principles. Indeed, it is assumed that the
basic elements of our ontology are the spatial intervals for the user
and his/her related contexts. The relationships between them would
model the spatial relevancy parameters. The implementation language
for the model is OWLs, a web ontology language. The achieved
results show that our proposed location-aware model and the
application adaptation strategies provide appropriate services for the
user.
Abstract: A new and cost effective robotic device was designed
for remote tele surgery using dual tone multi frequency technology
(DTMF). Tele system with Dual Tone Multiple Frequency has a large
capability in sending and receiving of data in hardware and software.
The robot consists of DC motors for arm movements and it is
controlled manually through a mobile phone through DTMF
Technology. The system enables the surgeon from base station to
send commands through mobile phone to the patient’s robotic system
which includes two robotic arms that translate the input into actual
instrument manipulation. A mobile phone attached to the
microcontroller 8051 which can activate robot through relays. The
Remote robot-assisted tele surgery eliminates geographic constraints
for getting surgical expertise where it is needed and allows an expert
surgeon to teach or proctor the performance of surgical technique by
real-time intervention.
Abstract: In order to accelerate the similarity search in highdimensional database, we propose a new hierarchical indexing method. It is composed of offline and online phases. Our contribution concerns both phases. In the offline phase, after gathering the whole of the data in clusters and constructing a hierarchical index, the main originality of our contribution consists to develop a method to construct bounding forms of clusters to avoid overlapping. For the online phase, our idea improves considerably performances of similarity search. However, for this second phase, we have also developed an adapted search algorithm. Our method baptized NOHIS (Non-Overlapping Hierarchical Index Structure) use the Principal Direction Divisive Partitioning (PDDP) as algorithm of clustering. The principle of the PDDP is to divide data recursively into two sub-clusters; division is done by using the hyper-plane orthogonal to the principal direction derived from the covariance matrix and passing through the centroid of the cluster to divide. Data of each two sub-clusters obtained are including by a minimum bounding rectangle (MBR). The two MBRs are directed according to the principal direction. Consequently, the nonoverlapping between the two forms is assured. Experiments use databases containing image descriptors. Results show that the proposed method outperforms sequential scan and SRtree in processing k-nearest neighbors.
Abstract: The thermal expansion behaviour of silicon carbide
(SCS-2) fibre reinforced 6061 aluminium matrix composite subjected
to the influenced thermal mechanical cycling (TMC) process were
investigated. The thermal stress has important effect on the
longitudinal thermal expansion coefficient of the composites. The
present paper used experimental data of the thermal expansion
behaviour of a SiC/Al composite for temperatures up to 370°C, in
which their data was used for carrying out modelling of theoretical
predictions.
Abstract: The purpose of this research is to study the concepts
of multiple Cartesian product, variety of multiple algebras and to
present some examples. In the theory of multiple algebras, like other
theories, deriving new things and concepts from the things and
concepts available in the context is important. For example, the first
were obtained from the quotient of a group modulo the equivalence
relation defined by a subgroup of it. Gratzer showed that every
multiple algebra can be obtained from the quotient of a universal
algebra modulo a given equivalence relation.
The purpose of this study is examination of multiple algebras and
basic relations defined on them as well as introduction to some
algebraic structures derived from multiple algebras. Among the
structures obtained from multiple algebras, this article studies submultiple
algebras, quotients of multiple algebras and the Cartesian
product of multiple algebras.
Abstract: The identification and classification of weeds are of
major technical and economical importance in the agricultural
industry. To automate these activities, like in shape, color and
texture, weed control system is feasible. The goal of this paper is to
build a real-time, machine vision weed control system that can detect
weed locations. In order to accomplish this objective, a real-time
robotic system is developed to identify and locate outdoor plants
using machine vision technology and pattern recognition. The
algorithm is developed to classify images into broad and narrow class
for real-time selective herbicide application. The developed
algorithm has been tested on weeds at various locations, which have
shown that the algorithm to be very effectiveness in weed
identification. Further the results show a very reliable performance
on weeds under varying field conditions. The analysis of the results
shows over 90 percent classification accuracy over 140 sample
images (broad and narrow) with 70 samples from each category of
weeds.
Abstract: This paper presents a microstrip meandered open
circuited stub with bandstop characteristic. The proposed structure is
designed on a high frequency laminate with dielectric constant of 4.0
and board thickness of 0.508 millimeters. The scattering parameters
and electromagnetic field distributions at various frequencies are
investigated by modeling the structure with three dimensional
electromagnetic simulation tool. In order to describe the resonant
and bandstop characteristic of the meandered open circuited stub, a
Smith chart as well as electric field at various frequencies and phases
is illustrated accordingly. The structure can be an alternative method
in suppressing the harmonic response of a bandpass filter.
Abstract: In this paper, a new time discontinuous expanded mixed finite element method is proposed and analyzed for two-order convection-dominated diffusion problem. The proofs of the stability of the proposed scheme and the uniqueness of the discrete solution are given. Moreover, the error estimates of the scalar unknown, its gradient and its flux in the L1( ¯ J,L2( )-norm are obtained.
Abstract: Distributed denial-of-service (DDoS) attacks pose a
serious threat to network security. There have been a lot of
methodologies and tools devised to detect DDoS attacks and reduce
the damage they cause. Still, most of the methods cannot
simultaneously achieve (1) efficient detection with a small number of
false alarms and (2) real-time transfer of packets. Here, we introduce
a method for proactive detection of DDoS attacks, by classifying the
network status, to be utilized in the detection stage of the proposed
anti-DDoS framework. Initially, we analyse the DDoS architecture
and obtain details of its phases. Then, we investigate the procedures
of DDoS attacks and select variables based on these features. Finally,
we apply the k-nearest neighbour (k-NN) method to classify the
network status into each phase of DDoS attack. The simulation result
showed that each phase of the attack scenario is classified well and
we could detect DDoS attack in the early stage.