Abstract: This paper provides a scheme to improve the read efficiency of anti-collision algorithm in EPCglobal UHF Class-1 Generation-2 RFID standard. In this standard, dynamic frame slotted ALOHA is specified to solve the anti-collision problem. Also, the Q-algorithm with a key parameter C is adopted to dynamically adjust the frame sizes. In the paper, we split the C parameter into two parameters to increase the read speed and derive the optimal values of the two parameters through simulations. The results indicate our method outperforms the original Q-algorithm.
Abstract: In recent years, the relevance feedback technology is regarded in content-based image retrieval. This paper suggests a neural networks feedback algorithm based on the radial basis function, coming to extract the semantic character of image. The results of experiment indicated that the performance of this relevance feedback is better than the feedback algorithm based on Single-RBF.
Abstract: In syntactic pattern recognition a pattern can be
represented by a graph. Given an unknown pattern represented by
a graph g, the problem of recognition is to determine if the graph g
belongs to a language L(G) generated by a graph grammar G. The
so-called IE graphs have been defined in [1] for a description of
patterns. The IE graphs are generated by so-called ETPL(k) graph
grammars defined in [1]. An efficient, parsing algorithm for ETPL(k)
graph grammars for syntactic recognition of patterns represented by
IE graphs has been presented in [1]. In practice, structural
descriptions may contain pattern distortions, so that the assignment
of a graph g, representing an unknown pattern, to
a graph language L(G) generated by an ETPL(k) graph grammar G is
rejected by the ETPL(k) type parsing. Therefore, there is a need for
constructing effective parsing algorithms for recognition of distorted
patterns. The purpose of this paper is to present a new approach to
syntactic recognition of distorted patterns. To take into account all
variations of a distorted pattern under study, a probabilistic
description of the pattern is needed. A random IE graph approach is
proposed here for such a description ([2]).
Abstract: Detection and classification of power quality (PQ)
disturbances is an important consideration to electrical utilities and
many industrial customers so that diagnosis and mitigation of such
disturbance can be implemented quickly. S-transform algorithm and
continuous wavelet transforms (CWT) are time-frequency
algorithms, and both of them are powerful in detection and
classification of PQ disturbances. This paper presents detection and
classification of PQ disturbances using S-transform and CWT
algorithms. The results of detection and classification, provides that
S-transform is more accurate in detection and classification for most
PQ disturbance than CWT algorithm, where as CWT algorithm more
powerful in detection in some disturbances like notching
Abstract: Ant colony optimization is an ant algorithm framework that took inspiration from foraging behavior of ant colonies. Indeed, ACO algorithms use a chemical communication, represented by pheromone trails, to build good solutions. However, ants involve different communication channels to interact. Thus, this paper introduces the acoustic communication between ants while they are foraging. This process allows fine and local exploration of search space and permits optimal solution to be improved.
Abstract: The aim of the current work is to present a comparison among three popular optimization methods in the inverse elastostatics problem (IESP) of flaw detection within a solid. In more details, the performance of a simulated annealing, a Hooke & Jeeves and a sequential quadratic programming algorithm was studied in the test case of one circular flaw in a plate solved by both the boundary element (BEM) and the finite element method (FEM). The proposed optimization methods use a cost function that utilizes the displacements of the static response. The methods were ranked according to the required number of iterations to converge and to their ability to locate the global optimum. Hence, a clear impression regarding the performance of the aforementioned algorithms in flaw identification problems was obtained. Furthermore, the coupling of BEM or FEM with these optimization methods was investigated in order to track differences in their performance.
Abstract: This paper presents a wavelet transform and Support
Vector Machine (SVM) based algorithm for estimating fault location
on transmission lines. The Discrete wavelet transform (DWT) is used
for data pre-processing and this data are used for training and testing
SVM. Five types of mother wavelet are used for signal processing to
identify a suitable wavelet family that is more appropriate for use in
estimating fault location. The results demonstrated the ability of SVM
to generalize the situation from the provided patterns and to
accurately estimate the location of faults with varying fault resistance.
Abstract: Modeling of complex dynamic systems, which are
very complicated to establish mathematical models, requires new and
modern methodologies that will exploit the existing expert
knowledge, human experience and historical data. Fuzzy cognitive
maps are very suitable, simple, and powerful tools for simulation and
analysis of these kinds of dynamic systems. However, human experts
are subjective and can handle only relatively simple fuzzy cognitive
maps; therefore, there is a need of developing new approaches for an
automated generation of fuzzy cognitive maps using historical data.
In this study, a new learning algorithm, which is called Big Bang-Big
Crunch, is proposed for the first time in literature for an automated
generation of fuzzy cognitive maps from data. Two real-world
examples; namely a process control system and radiation therapy
process, and one synthetic model are used to emphasize the
effectiveness and usefulness of the proposed methodology.
Abstract: Vertex configuration for a vertex in an orthogonal
pseudo-polyhedron is an identity of a vertex that is determined by the
number of edges, dihedral angles, and non-manifold properties
meeting at the vertex. There are up to sixteen vertex configurations
for any orthogonal pseudo-polyhedron (OPP). Understanding the
relationship between these vertex configurations will give us insight
into the structure of an OPP and help us design better algorithms for
many 3-dimensional geometric problems. In this paper, 16 vertex
configurations for OPP are described first. This is followed by a
number of formulas giving insight into the relationship between
different vertex configurations in an OPP. These formulas
will be useful as an extension of orthogonal polyhedra usefulness on
pattern analysis in 3D-digital images.
Abstract: Efficient modulo 2n+1 adders are important for
several applications including residue number system, digital signal
processors and cryptography algorithms. In this paper we present a
novel modulo 2n+1 addition algorithm for a recently represented
number system. The proposed approach is introduced for the
reduction of the power dissipated. In a conventional modulo 2n+1
adder, all operands have (n+1)-bit length. To avoid using (n+1)-bit
circuits, the diminished-1 and carry save diminished-1 number
systems can be effectively used in applications. In the paper, we also
derive two new architectures for designing modulo 2n+1 adder, based
on n-bit ripple-carry adder. The first architecture is a faster design
whereas the second one uses less hardware. In the proposed method,
the special treatment required for zero operands in Diminished-1
number system is removed. In the fastest modulo 2n+1 adders in
normal binary system, there are 3-operand adders. This problem is
also resolved in this paper. The proposed architectures are compared
with some efficient adders based on ripple-carry adder and highspeed
adder. It is shown that the hardware overhead and power
consumption will be reduced. As well as power reduction, in some
cases, power-delay product will be also reduced.
Abstract: Wireless Sensor Network is Multi hop Self-configuring
Wireless Network consisting of sensor nodes. The deployment of
wireless sensor networks in many application areas, e.g., aggregation
services, requires self-organization of the network nodes into clusters.
Efficient way to enhance the lifetime of the system is to partition the
network into distinct clusters with a high energy node as cluster head.
The different methods of node clustering techniques have appeared in
the literature, and roughly fall into two families; those based on the
construction of a dominating set and those which are based solely on
energy considerations. Energy optimized cluster formation for a set
of randomly scattered wireless sensors is presented. Sensors within a
cluster are expected to be communicating with cluster head only. The
energy constraint and limited computing resources of the sensor nodes
present the major challenges in gathering the data. In this paper we
propose a framework to study how partially correlated data affect the
performance of clustering algorithms. The total energy consumption
and network lifetime can be analyzed by combining random geometry
techniques and rate distortion theory. We also present the relation
between compression distortion and data correlation.
Abstract: A low-complexity, high-accurate frequency offset
estimation for multi-band orthogonal frequency division multiplexing (MB-OFDM) based ultra-wide band systems is presented regarding different carrier frequency offsets, different channel frequency
responses, different preamble patterns in different bands. Utilizing a
half-cycle Constant Amplitude Zero Auto Correlation (CAZAC) sequence as the preamble sequence, the estimator with a semi-cross
contrast scheme between two successive OFDM symbols is proposed. The CRLB and complexity of the proposed algorithm are derived.
Compared to the reference estimators, the proposed method achieves
significantly less complexity (about 50%) for all preamble patterns of the MB-OFDM systems. The CRLBs turn out to be of well performance.
Abstract: For improving the efficiency of human 3D tracking, we
present an algorithm to track 3D Arm Motion. First, the Hierarchy
Limb Model (HLM) is proposed based on the human 3D skeleton
model. Second, via graph decomposition, the arm motion state space,
modeled by HLM, can be discomposed into two low dimension
subspaces: root nodes and leaf nodes. Finally, Rao-Blackwellised
Particle Filter is used to estimate the 3D arm motion. The result of
experiment shows that our algorithm can advance the computation
efficiency.
Abstract: One of the biggest problems of SMEs is their tendencies to financial distress because of insufficient finance background. In this study, an Early Warning System (EWS) model based on data mining for financial risk detection is presented. CHAID algorithm has been used for development of the EWS. Developed EWS can be served like a tailor made financial advisor in decision making process of the firms with its automated nature to the ones who have inadequate financial background. Besides, an application of the model implemented which covered 7,853 SMEs based on Turkish Central Bank (TCB) 2007 data. By using EWS model, 31 risk profiles, 15 risk indicators, 2 early warning signals, and 4 financial road maps has been determined for financial risk mitigation.
Abstract: Mobile agents are a powerful approach to develop distributed systems since they migrate to hosts on which they have the resources to execute individual tasks. In a dynamic environment like a peer-to-peer network, Agents have to be generated frequently and dispatched to the network. Thus they will certainly consume a certain amount of bandwidth of each link in the network if there are too many agents migration through one or several links at the same time, they will introduce too much transferring overhead to the links eventually, these links will be busy and indirectly block the network traffic, therefore, there is a need of developing routing algorithms that consider about traffic load. In this paper we seek to create cooperation between a probabilistic manner according to the quality measure of the network traffic situation and the agent's migration decision making to the next hop based on decision tree learning algorithms.
Abstract: This paper presents an efficient VLSI architecture
design to achieve real time video processing using Full-Search Block
Matching (FSBM) algorithm. The design employs parallel bank
architecture with minimum latency, maximum throughput, and full
hardware utilization. We use nine parallel processors in our
architecture and each controlled by a state machine. State machine
control implementation makes the design very simple and cost
effective. The design is implemented using VHDL and the
programming techniques we incorporated makes the design
completely programmable in the sense that the search ranges and the
block sizes can be varied to suit any given requirements. The design
can operate at frequencies up to 36 MHz and it can function in QCIF
and CIF video resolution at 1.46 MHz and 5.86 MHz, respectively.
Abstract: Clustering categorical data is more complicated than
the numerical clustering because of its special properties. Scalability
and memory constraint is the challenging problem in clustering large
data set. This paper presents an incremental algorithm to cluster the
categorical data. Frequencies of attribute values contribute much in
clustering similar categorical objects. In this paper we propose new
similarity measures based on the frequencies of attribute values and
its cardinalities. The proposed measures and the algorithm are
experimented with the data sets from UCI data repository. Results
prove that the proposed method generates better clusters than the
existing one.
Abstract: In this paper we present the PC cluster built at R.V.
College of Engineering (with great help from the Department of
Computer Science and Electrical Engineering). The structure of the
cluster is described and the performance is evaluated by rendering of
complex 3D Persistence of Vision (POV) images by the Ray-Tracing
algorithm. Here, we propose an unexampled method to render such
images, distributedly on a low cost scalable.
Abstract: An important structuring mechanism for knowledge bases is building clusters based on the content of their knowledge objects. The objects are clustered based on the principle of maximizing the intraclass similarity and minimizing the interclass similarity. Clustering can also facilitate taxonomy formation, that is, the organization of observations into a hierarchy of classes that group similar events together. Hierarchical representation allows us to easily manage the complexity of knowledge, to view the knowledge at different levels of details, and to focus our attention on the interesting aspects only. One of such efficient and easy to understand systems is Hierarchical Production rule (HPRs) system. A HPR, a standard production rule augmented with generality and specificity information, is of the following form Decision If < condition> Generality Specificity . HPRs systems are capable of handling taxonomical structures inherent in the knowledge about the real world. In this paper, a set of related HPRs is called a cluster and is represented by a HPR-tree. This paper discusses an algorithm based on cumulative learning scenario for dynamic structuring of clusters. The proposed scheme incrementally incorporates new knowledge into the set of clusters from the previous episodes and also maintains summary of clusters as Synopsis to be used in the future episodes. Examples are given to demonstrate the behaviour of the proposed scheme. The suggested incremental structuring of clusters would be useful in mining data streams.
Abstract: Statistical analysis of electrophysiological recordings
obtained under, e.g. tactile, stimulation frequently suggests participation
in the network dynamics of experimentally unobserved “hidden"
neurons. Such interneurons making synapses to experimentally
recorded neurons may strongly alter their dynamical responses to
the stimuli. We propose a mathematical method that formalizes this
possibility and provides an algorithm for inferring on the presence
and dynamics of hidden neurons based on fitting of the experimental
data to spike trains generated by the network model. The model
makes use of Integrate and Fire neurons “chemically" coupled
through exponentially decaying synaptic currents. We test the method
on simulated data and also provide an example of its application to
the experimental recording from the Dorsal Column Nuclei neurons
of the rat under tactile stimulation of a hind limb.