Abstract: Rough set theory is a very effective tool to deal with granularity and vagueness in information systems. Covering-based rough set theory is an extension of classical rough set theory. In this paper, firstly we present the characteristics of the reducible element and the minimal description covering-based rough sets through downsets. Then we establish lattices and topological spaces in coveringbased rough sets through down-sets and up-sets. In this way, one can investigate covering-based rough sets from algebraic and topological points of view.
Abstract: We decribe a formal specification and verification of the Rabin public-key scheme in the formal proof system Is-abelle/HOL. The idea is to use the two views of cryptographic verification: the computational approach relying on the vocabulary of probability theory and complexity theory and the formal approach based on ideas and techniques from logic and programming languages. The analysis presented uses a given database to prove formal properties of our implemented functions with computer support. Thema in task in designing a practical formalization of correctness as well as security properties is to cope with the complexity of cryptographic proving. We reduce this complexity by exploring a light-weight formalization that enables both appropriate formal definitions as well as eficient formal proofs. This yields the first computer-proved implementation of the Rabin public-key scheme in Isabelle/HOL. Consequently, we get reliable proofs with a minimal error rate augmenting the used database. This provides a formal basis for more computer proof constructions in this area.
Abstract: K-Means (KM) is considered one of the major
algorithms widely used in clustering. However, it still has some
problems, and one of them is in its initialization step where it is
normally done randomly. Another problem for KM is that it
converges to local minima. Genetic algorithms are one of the
evolutionary algorithms inspired from nature and utilized in the field
of clustering. In this paper, we propose two algorithms to solve the
initialization problem, Genetic Algorithm Initializes KM (GAIK) and
KM Initializes Genetic Algorithm (KIGA). To show the effectiveness
and efficiency of our algorithms, a comparative study was done
among GAIK, KIGA, Genetic-based Clustering Algorithm (GCA),
and FCM [19].
Abstract: In order to answer the general question: “What does a simple agent with a limited life-time require for constructing a useful representation of the environment?" we propose a robot platform including the simplest probabilistic sensory and motor layers. Then we use the platform as a test-bed for evaluation of the navigational capabilities of the robot with different “brains". We claim that a protocognitive behavior is not a consequence of highly sophisticated sensory–motor organs but instead emerges through an increment of the internal complexity and reutilization of the minimal sensory information. We show that the most fundamental robot element, the short-time memory, is essential in obstacle avoidance. However, in the simplest conditions of no obstacles the straightforward memoryless robot is usually superior. We also demonstrate how a low level action planning, involving essentially nonlinear dynamics, provides a considerable gain to the robot performance dynamically changing the robot strategy. Still, however, for very short life time the brainless robot is superior. Accordingly we suggest that small organisms (or agents) with short life-time does not require complex brains and even can benefit from simple brain-like (reflex) structures. To some extend this may mean that controlling blocks of modern robots are too complicated comparative to their life-time and mechanical abilities.
Abstract: A synchronous network-on-chip using wormhole packet switching
and supporting guaranteed-completion best-effort with low-priority (LP)
and high-priority (HP) wormhole packet delivery service is presented in
this paper. Both our proposed LP and HP message services deliver a good
quality of service in term of lossless packet completion and in-order message
data delivery. However, the LP message service does not guarantee minimal
completion bound. The HP packets will absolutely use 100% bandwidth of
their reserved links if the HP packets are injected from the source node with
maximum injection. Hence, the service are suitable for small size messages
(less than hundred bytes). Otherwise the other HP and LP messages, which
require also the links, will experience relatively high latency depending on the
size of the HP message. The LP packets are routed using a minimal adaptive
routing, while the HP packets are routed using a non-minimal adaptive routing
algorithm. Therefore, an additional 3-bit field, identifying the packet type,
is introduced in their packet headers to classify and to determine the type
of service committed to the packet. Our NoC prototypes have been also
synthesized using a 180-nm CMOS standard-cell technology to evaluate the
cost of implementing the combination of both services.
Abstract: In this paper, we propose an effective relay
communication for layered video transmission as an alternative to
make the most of limited resources in a wireless communication
network where loss often occurs. Relaying brings stable multimedia
services to end clients, compared to multiple description coding
(MDC). Also, retransmission of only parity data about one or more
video layer using channel coder to the end client of the relay device is
paramount to the robustness of the loss situation. Using these
methods in resource-constrained environments, such as real-time user
created content (UCC) with layered video transmission, can provide
high-quality services even in a poor communication environment.
Minimal services are also possible. The mathematical analysis shows
that the proposed method reduced the probability of GOP loss rate
compared to MDC and raptor code without relay. The GOP loss rate
is about zero, while MDC and raptor code without relay have a GOP
loss rate of 36% and 70% in case of 10% frame loss rate.
Abstract: Medical applications are among the most impactful
areas of microrobotics. The ultimate goal of medical microrobots is
to reach currently inaccessible areas of the human body and carry out
a host of complex operations such as minimally invasive surgery
(MIS), highly localized drug delivery, and screening for diseases at
their very early stages. Miniature, safe and efficient propulsion
systems hold the key to maturing this technology but they pose
significant challenges. A new type of propulsion developed recently,
uses multi-flagella architecture inspired by the motility mechanism of
prokaryotic microorganisms. There is a lack of efficient methods for
designing this type of propulsion system. The goal of this paper is to
overcome the lack and this way, a numerical strategy is proposed to
design multi-flagella propulsion systems. The strategy is based on the
implementation of the regularized stokeslet and rotlet theory, RFT
theory and new approach of “local corrected velocity". The effects of
shape parameters and angular velocities of each flagellum on overall
flow field and on the robot net forces and moments are considered.
Then a multi-layer perceptron artificial neural network is designed
and employed to adjust the angular velocities of the motors for
propulsion control. The proposed method applied successfully on a
sample configuration and useful demonstrative results is obtained.
Abstract: The network of delivering commodities has been an important design problem in our daily lives and many transportation applications. The delivery performance is evaluated based on the system reliability of delivering commodities from a source node to a sink node in the network. The system reliability is thus maximized to find the optimal routing. However, the design problem is not simple because (1) each path segment has randomly distributed attributes; (2) there are multiple commodities that consume various path capacities; (3) the optimal routing must successfully complete the delivery process within the allowable time constraints. In this paper, we want to focus on the design optimization of the Multi-State Flow Network (MSFN) for multiple commodities. We propose an efficient approach to evaluate the system reliability in the MSFN with respect to randomly distributed path attributes and find the optimal routing subject to the allowable time constraints. The delivery rates, also known as delivery currents, of the path segments are evaluated and the minimal-current arcs are eliminated to reduce the complexity of the MSFN. Accordingly, the correct optimal routing is found and the worst-case reliability is evaluated. It has been shown that the reliability of the optimal routing is at least higher than worst-case measure. Two benchmark examples are utilized to demonstrate the proposed method. The comparisons between the original and the reduced networks show that the proposed method is very efficient.
Abstract: The Neuro-Fuzzy hybridization scheme has become
of research interest in pattern classification over the past decade. The
present paper proposes a novel Modified Adaptive Fuzzy Inference
Engine (MAFIE) for pattern classification. A modified Apriori
algorithm technique is utilized to reduce a minimal set of decision
rules based on input output data sets. A TSK type fuzzy inference
system is constructed by the automatic generation of membership
functions and rules by the fuzzy c-means clustering and Apriori
algorithm technique, respectively. The generated adaptive fuzzy
inference engine is adjusted by the least-squares fit and a conjugate
gradient descent algorithm towards better performance with a
minimal set of rules. The proposed MAFIE is able to reduce the
number of rules which increases exponentially when more input
variables are involved. The performance of the proposed MAFIE is
compared with other existing applications of pattern classification
schemes using Fisher-s Iris and Wisconsin breast cancer data sets and
shown to be very competitive.
Abstract: This paper describes a novel method for automatic
estimation of the contours of weld defect in radiography images.
Generally, the contour detection is the first operation which we apply
in the visual recognition system. Our approach can be described as a
region based maximum likelihood formulation of parametric
deformable contours. This formulation provides robustness against
the poor image quality, and allows simultaneous estimation of the
contour parameters together with other parameters of the model.
Implementation is performed by a deterministic iterative algorithm
with minimal user intervention. Results testify for the very good
performance of the approach especially in synthetic weld defect
images.
Abstract: Packing problems arise in a wide variety of application
areas. The basic problem is that of determining an efficient arrangement
of different objects in a region without any overlap and
with minimal wasted gap between shapes. This paper presents a
novel population based approach for optimizing arrangement of irregular
shapes. In this approach, each shape is coded as an agent and
the agents' reproductions and grouping policies results in arrangements
of the objects in positions with least wasted area between
them. The approach is implemented in an application for cutting
sheets and test results on several problems from literature are presented.
Abstract: A new dynamic clustering approach (DCPSO), based
on Particle Swarm Optimization, is proposed. This approach is
applied to unsupervised image classification. The proposed approach
automatically determines the "optimum" number of clusters and
simultaneously clusters the data set with minimal user interference.
The algorithm starts by partitioning the data set into a relatively large
number of clusters to reduce the effects of initial conditions. Using
binary particle swarm optimization the "best" number of clusters is
selected. The centers of the chosen clusters is then refined via the Kmeans
clustering algorithm. The experiments conducted show that
the proposed approach generally found the "optimum" number of
clusters on the tested images.
Abstract: We study different types of aggregation operators and
the decision making process with minimization of regret. We analyze
the original work developed by Savage and the recent work
developed by Yager that generalizes the MMR method creating a
parameterized family of minimal regret methods by using the ordered
weighted averaging (OWA) operator. We suggest a new method that
uses different types of geometric operators such as the weighted
geometric mean or the ordered weighted geometric operator (OWG)
to generalize the MMR method obtaining a new parameterized family
of minimal regret methods. The main result obtained in this method
is that it allows to aggregate negative numbers in the OWG operator.
Finally, we give an illustrative example.
Abstract: Low-density parity-check (LDPC) codes have been shown to deliver capacity approaching performance; however, problematic graphical structures (e.g. trapping sets) in the Tanner graph of some LDPC codes can cause high error floors in bit-error-ratio (BER) performance under conventional sum-product algorithm (SPA). This paper presents a serial concatenation scheme to avoid the trapping sets and to lower the error floors of LDPC code. The outer code in the proposed concatenation is the LDPC, and the inner code is a high rate array code. This approach applies an interactive hybrid process between the BCJR decoding for the array code and the SPA for the LDPC code together with bit-pinning and bit-flipping techniques. Margulis code of size (2640, 1320) has been used for the simulation and it has been shown that the proposed concatenation and decoding scheme can considerably improve the error floor performance with minimal rate loss.
Abstract: The recognition of human faces, especially those with
different orientations is a challenging and important problem in image
analysis and classification. This paper proposes an effective scheme
for rotation invariant face recognition using Log-Polar Transform and
Discrete Cosine Transform combined features. The rotation invariant
feature extraction for a given face image involves applying the logpolar
transform to eliminate the rotation effect and to produce a row
shifted log-polar image. The discrete cosine transform is then applied
to eliminate the row shift effect and to generate the low-dimensional
feature vector. A PSO-based feature selection algorithm is utilized to
search the feature vector space for the optimal feature subset.
Evolution is driven by a fitness function defined in terms of
maximizing the between-class separation (scatter index).
Experimental results, based on the ORL face database using testing
data sets for images with different orientations; show that the
proposed system outperforms other face recognition methods. The
overall recognition rate for the rotated test images being 97%,
demonstrating that the extracted feature vector is an effective rotation
invariant feature set with minimal set of selected features.
Abstract: Structural representation and technology mapping of
a Boolean function is an important problem in the design of nonregenerative
digital logic circuits (also called combinational logic
circuits). Library aware function manipulation offers a solution to
this problem. Compact multi-level representation of binary networks,
based on simple circuit structures, such as AND-Inverter Graphs
(AIG) [1] [5], NAND Graphs, OR-Inverter Graphs (OIG), AND-OR
Graphs (AOG), AND-OR-Inverter Graphs (AOIG), AND-XORInverter
Graphs, Reduced Boolean Circuits [8] does exist in
literature. In this work, we discuss a novel and efficient graph
realization for combinational logic circuits, represented using a
NAND-NOR-Inverter Graph (NNIG), which is composed of only
two-input NAND (NAND2), NOR (NOR2) and inverter (INV) cells.
The networks are constructed on the basis of irredundant disjunctive
and conjunctive normal forms, after factoring, comprising terms with
minimum support. Construction of a NNIG for a non-regenerative
function in normal form would be straightforward, whereas for the
complementary phase, it would be developed by considering a virtual
instance of the function. However, the choice of best NNIG for a
given function would be based upon literal count, cell count and
DAG node count of the implementation at the technology
independent stage. In case of a tie, the final decision would be made
after extracting the physical design parameters.
We have considered AIG representation for reduced disjunctive
normal form and the best of OIG/AOG/AOIG for the minimized
conjunctive normal forms. This is necessitated due to the nature of
certain functions, such as Achilles- heel functions. NNIGs are found
to exhibit 3.97% lesser node count compared to AIGs and
OIG/AOG/AOIGs; consume 23.74% and 10.79% lesser library cells
than AIGs and OIG/AOG/AOIGs for the various samples considered.
We compare the power efficiency and delay improvement achieved
by optimal NNIGs over minimal AIGs and OIG/AOG/AOIGs for
various case studies. In comparison with functionally equivalent,
irredundant and compact AIGs, NNIGs report mean savings in power
and delay of 43.71% and 25.85% respectively, after technology
mapping with a 0.35 micron TSMC CMOS process. For a
comparison with OIG/AOG/AOIGs, NNIGs demonstrate average
savings in power and delay by 47.51% and 24.83%. With respect to
device count needed for implementation with static CMOS logic
style, NNIGs utilize 37.85% and 33.95% lesser transistors than their
AIG and OIG/AOG/AOIG counterparts.
Abstract: Optimization of rational geometrical and mechanical
parameters of panel with curved plywood ribs is considered in this
paper. The panel consists of cylindrical plywood ribs manufactured
from Finish plywood, upper and bottom plywood flange, stiffness
diaphragms. Panel is filled with foam. Minimal ratio of structure self
weight and load that could be applied to structure is considered as
rationality criteria. Optimization is done, by using classical beam
theory without nonlinearities. Optimization of discreet design
variables is done by Genetic algorithm.
Abstract: If there exists a nonempty, proper subset S of the set of all (n + 1)(n + 2)/2 inertias such that S Ôèå i(A) is sufficient for any n × n zero-nonzero pattern A to be inertially arbitrary, then S is called a critical set of inertias for zero-nonzero patterns of order n. If no proper subset of S is a critical set, then S is called a minimal critical set of inertias. In [3], Kim, Olesky and Driessche identified all minimal critical sets of inertias for 2 × 2 zero-nonzero patterns. Identifying all minimal critical sets of inertias for n × n zero-nonzero patterns with n ≥ 3 is posed as an open question in [3]. In this paper, all minimal critical sets of inertias for 3 × 3 zero-nonzero patterns are identified. It is shown that the sets {(0, 0, 3), (3, 0, 0)}, {(0, 0, 3), (0, 3, 0)}, {(0, 0, 3), (0, 1, 2)}, {(0, 0, 3), (1, 0, 2)}, {(0, 0, 3), (2, 0, 1)} and {(0, 0, 3), (0, 2, 1)} are the only minimal critical sets of inertias for 3 × 3 irreducible zerononzero patterns.
Abstract: Having considered tactile sensing and palpation of a
surgeon in order to detect kidney stone during open surgery; we
present the 2D model of nephrolithiasis (two dimensional model of
kidney containing a simulated stone). The effects of stone existence
that appear on the surface of kidney (because of exerting mechanical
load) are determined. Using Finite element method, it is illustrated
that the created stress patterns on the surface of kidney and stress
graphs not only show existence of stone inside kidney, but also show
its exact location.
Abstract: In this paper an algorithm for fast wavelength calibration of Optical Spectrum Analyzers (OSAs) using low power reference gas spectra is proposed. In existing OSAs a reference spectrum with low noise for precise detection of the reference extreme values is needed. To generate this spectrum costly hardware with high optical power is necessary. With this new wavelength calibration algorithm it is possible to use a noisy reference spectrum and therefore hardware costs can be cut. With this algorithm the reference spectrum is filtered and the key information is extracted by segmenting and finding the local minima and maxima. Afterwards slope and offset of a linear correction function for best matching the measured and theoretical spectra are found by correlating the measured with the stored minima. With this algorithm a reliable wavelength referencing of an OSA can be implemented on a microcontroller with a calculation time of less than one second.