Abstract: In this paper, low end Digital Signal Processors (DSPs)
are applied to accelerate integer neural networks. The use of DSPs
to accelerate neural networks has been a topic of study for some
time, and has demonstrated significant performance improvements.
Recently, work has been done on integer only neural networks, which
greatly reduces hardware requirements, and thus allows for cheaper
hardware implementation. DSPs with Arithmetic Logic Units (ALUs)
that support floating or fixed point arithmetic are generally more
expensive than their integer only counterparts due to increased circuit
complexity. However if the need for floating or fixed point math
operation can be removed, then simpler, lower cost DSPs can be
used. To achieve this, an integer only neural network is created in
this paper, which is then accelerated by using DSP instructions to
improve performance.
Abstract: Intelligent deep-drawing is an instrumental research field in sheet metal forming. A set of 28 different experimental data have been employed in this paper, investigating the roles of die radius, punch radius, friction coefficients and drawing ratios for axisymmetric workpieces deep drawing. This paper focuses an evolutionary neural network, specifically, error back propagation in collaboration with genetic algorithm. The neural network encompasses a number of different functional nodes defined through the established principles. The input parameters, i.e., punch radii, die radii, friction coefficients and drawing ratios are set to the network; thereafter, the material outputs at two critical points are accurately calculated. The output of the network is used to establish the best parameters leading to the most uniform thickness in the product via the genetic algorithm. This research achieved satisfactory results based on demonstration of neural networks.
Abstract: In this paper, we first consider the quality of service
problems in heterogeneous wireless networks for sending the video
data, which their problem of being real-time is pronounced. At last,
we present a method for ensuring the end-to-end quality of service at
application layer level for adaptable sending of the video data at
heterogeneous wireless networks. To do this, mechanism in different
layers has been used. We have used the stop mechanism, the
adaptation mechanism and the graceful degrade at the application
layer, the multi-level congestion feedback mechanism in the network
layer and connection cutting off decision mechanism in the link
layer. At the end, the presented method and the achieved
improvement is simulated and presented in the NS-2 software.
Abstract: Quality of Service (QoS) Routing aims to find path between source and destination satisfying the QoS requirements which efficiently using the network resources and underlying routing algorithm and to fmd low-cost paths that satisfy given QoS constraints. One of the key issues in providing end-to-end QoS guarantees in packet networks is determining feasible path that satisfies a number of QoS constraints. We present a Optimized Multi- Constrained Routing (OMCR) algorithm for the computation of constrained paths for QoS routing in computer networks. OMCR applies distance vector to construct a shortest path for each destination with reference to a given optimization metric, from which a set of feasible paths are derived at each node. OMCR is able to fmd feasible paths as well as optimize the utilization of network resources. OMCR operates with the hop-by-hop, connectionless routing model in IP Internet and does not create any loops while fmding the feasible paths. Nodes running OMCR not necessarily maintaining global view of network state such as topology, resource information and routing updates are sent only to neighboring nodes whereas its counterpart link-state routing method depend on complete network state for constrained path computation and that incurs excessive communication overhead.
Abstract: This paper outlines the development of a learning retrieval agent. Task of this agent is to extract knowledge of the Active Semantic Network in respect to user-requests. Based on a reinforcement learning approach, the agent learns to interpret the user-s intention. Especially, the learning algorithm focuses on the retrieval of complex long distant relations. Increasing its learnt knowledge with every request-result-evaluation sequence, the agent enhances his capability in finding the intended information.
Abstract: Cellular networks provide voice and data services to the users with mobility. To deliver services to the mobile users, the cellular network is capable of tracking the locations of the users, and allowing user movement during the conversations. These capabilities are achieved by the location management. Location management in mobile communication systems is concerned with those network functions necessary to allow the users to be reached wherever they are in the network coverage area. In a cellular network, a service coverage area is divided into smaller areas of hexagonal shape, referred to as cells. The cellular concept was introduced to reuse the radio frequency. Continued expansion of cellular networks, coupled with an increasingly restricted mobile spectrum, has established the reduction of communication overhead as a highly important issue. Much of this traffic is used in determining the precise location of individual users when relaying calls, with the field of location management aiming to reduce this overhead through prediction of user location. This paper describes and compares various location management schemes in the cellular networks.
Abstract: In this paper, by employing a new Lyapunov functional
and an elementary inequality analysis technique, some sufficient
conditions are derived to ensure the existence and uniqueness of
periodic oscillatory solution for fuzzy bi-directional memory (BAM)
neural networks with time-varying delays, and all other solutions of
the fuzzy BAM neural networks converge the uniqueness periodic
solution. These criteria are presented in terms of system parameters
and have important leading significance in the design and applications
of neural networks. Moreover an example is given to illustrate the
effectiveness and feasible of results obtained.
Abstract: Information of nodes’ locations is an important
criterion for lots of applications in Wireless Sensor Networks. In the
hop-based range-free localization methods, anchors transmit the
localization messages counting a hop count value to the whole
network. Each node receives this message and calculates its own
distance with anchor in hops and then approximates its own position.
However the estimative distances can provoke large error, and affect
the localization precision. To solve the problem, this paper proposes
an algorithm, which makes the unknown nodes fix the nearest anchor
as a reference and select two other anchors which are the most
accurate to achieve the estimated location. Compared to the DV-Hop
algorithm, experiment results illustrate that proposed algorithm has
less average localization error and is more effective.
Abstract: This paper discusses the Urdu script characteristics,
Urdu Nastaleeq and a simple but a novel and robust technique to
recognize the printed Urdu script without a lexicon. Urdu being a
family of Arabic script is cursive and complex script in its nature, the
main complexity of Urdu compound/connected text is not its
connections but the forms/shapes the characters change when it is
placed at initial, middle or at the end of a word. The characters
recognition technique presented here is using the inherited
complexity of Urdu script to solve the problem. A word is scanned
and analyzed for the level of its complexity, the point where the level
of complexity changes is marked for a character, segmented and
feeded to Neural Networks. A prototype of the system has been
tested on Urdu text and currently achieves 93.4% accuracy on the
average.
Abstract: The importance of our country-s communication
system is noticeable when a disaster occurs. The communication
system in our country includes wired and wireless telephone
networks, radio, satellite system and more increasingly internet. Even
though our communication system is most extensive and dependable,
extreme conditions can put a strain on them. Interoperability between
heterogeneous wireless networks can be used to provide efficient
communication for emergency first response. IEEE 802.21 specifies
Media Independent Handover (MIH) services to enhance the mobile
user experience by optimizing handovers between heterogeneous
access networks. This paper presents an algorithm to improve
congestion control in MIH framework. It is analytically shown that
by including time factor in network selection we can optimize
congestion in the network.
Abstract: Distributed Power generation has gained a lot of
attention in recent times due to constraints associated with
conventional power generation and new advancements in DG
technologies .The need to operate the power system economically
and with optimum levels of reliability has further led to an increase
in interest in Distributed Generation. However it is important to place
Distributed Generator on an optimum location so that the purpose of
loss minimization and voltage regulation is dully served on the
feeder. This paper investigates the impact of DG units installation on
electric losses, reliability and voltage profile of distribution networks.
In this paper, our aim would be to find optimal distributed
generation allocation for loss reduction subjected to constraint of
voltage regulation in distribution network. The system is further
analyzed for increased levels of Reliability. Distributed Generator
offers the additional advantage of increase in reliability levels as
suggested by the improvements in various reliability indices such as
SAIDI, CAIDI and AENS. Comparative studies are performed and
related results are addressed. An analytical technique is used in order
to find the optimal location of Distributed Generator. The suggested
technique is programmed under MATLAB software. The results
clearly indicate that DG can reduce the electrical line loss while
simultaneously improving the reliability of the system.
Abstract: Video sensor networks operate on stringent requirements
of latency. Packets have a deadline within which they have
to be delivered. Violation of the deadline causes a packet to be
treated as lost and the loss of packets ultimately affects the quality
of the application. Network latency is typically a function of many
interacting components. In this paper, we propose ways of reducing
the forwarding latency of a packet at intermediate nodes. The
forwarding latency is caused by a combination of processing delay
and queueing delay. The former is incurred in order to determine the
next hop in dynamic routing. We show that unless link failures in a
very specific and unlikely pattern, a vast majority of these lookups
are redundant. To counter this we propose source routing as the
routing strategy. However, source routing suffers from issues related
to scalability and being impervious to network dynamics. We propose
solutions to counter these and show that source routing is definitely
a viable option in practical sized video networks. We also propose a
fast and fair packet scheduling algorithm that reduces queueing delay
at the nodes. We support our claims through extensive simulation on
realistic topologies with practical traffic loads and failure patterns.
Abstract: This method decrease usage power (expenditure) in networks on chips (NOC). This method data coding for data transferring in order to reduces expenditure. This method uses data compression reduces the size. Expenditure calculation in NOC occurs inside of NOC based on grown models and transitive activities in entry ports. The goal of simulating is to weigh expenditure for encoding, decoding and compressing in Baseline networks and reduction of switches in this type of networks. KeywordsNetworks on chip, Compression, Encoding, Baseline networks, Banyan networks.
Abstract: The main goal of this work is to propose a way for
combined use of two nontraditional algorithms by solving topological
problems on telecommunications concentrator networks. The
algorithms suggested are the Simulated Annealing algorithm and the
Genetic Algorithm. The Algorithm of Simulated Annealing unifies
the well known local search algorithms. In addition - Simulated
Annealing allows acceptation of moves in the search space witch lead
to decisions with higher cost in order to attempt to overcome any
local minima obtained. The Genetic Algorithm is a heuristic approach
witch is being used in wide areas of optimization works. In the last
years this approach is also widely implemented in
Telecommunications Networks Planning. In order to solve less or
more complex planning problem it is important to find the most
appropriate parameters for initializing the function of the algorithm.
Abstract: For cognitive radio networks, there is a major
spectrum sensing problem, i.e. dynamic spectrum management. It is
an important issue to sense and identify the spectrum holes in
cognitive radio networks. The first-order derivative scheme is usually
used to detect the edge of the spectrum. In this paper, a novel
spectrum sensing technique for cognitive radio is presented. The
proposed algorithm offers efficient edge detection. Then, simulation
results show the performance of the first-order derivative scheme and
the proposed scheme and depict that the proposed scheme obtains
better performance than does the first-order derivative scheme.
Abstract: Congestion control is one of the fundamental issues in computer networks. Without proper congestion control mechanisms there is the possibility of inefficient utilization of resources, ultimately leading to network collapse. Hence congestion control is an effort to adapt the performance of a network to changes in the traffic load without adversely affecting users perceived utilities. AIMD (Additive Increase Multiplicative Decrease) is the best algorithm among the set of liner algorithms because it reflects good efficiency as well as good fairness. Our control model is based on the assumption of the original AIMD algorithm; we show that both efficiency and fairness of AIMD can be improved. We call our approach is New AIMD. We present experimental results with TCP that match the expectation of our theoretical analysis.
Abstract: Computer networks are essential part in computerbased
information systems. The performance of these networks has a
great influence on the whole information system. Measuring the
usability criteria and customers satisfaction on small computer
network is very important. In this article, an effective approach for
measuring the usability of business network in an information system
is introduced. The usability process for networking provides us with a
flexible and a cost-effective way to assess the usability of a network
and its products. In addition, the proposed approach can be used to
certify network product usability late in the development cycle.
Furthermore, it can be used to help in developing usable interfaces
very early in the cycle and to give a way to measure, track, and
improve usability. Moreover, a new approach for fast information
processing over computer networks is presented. The entire data are
collected together in a long vector and then tested as a one input
pattern. Proposed fast time delay neural networks (FTDNNs) use
cross correlation in the frequency domain between the tested data and
the input weights of neural networks. It is proved mathematically and
practically that the number of computation steps required for the
presented time delay neural networks is less than that needed by
conventional time delay neural networks (CTDNNs). Simulation
results using MATLAB confirm the theoretical computations.
Abstract: This paper attempts to establish the fact that Multi
State Network Classification is essential for performance
enhancement of Transport protocols over Satellite based Networks. A
model to classify Multi State network condition taking into
consideration both congestion and channel error is evolved. In order
to arrive at such a model an analysis of the impact of congestion and
channel error on RTT values has been carried out using ns2. The
analysis results are also reported in the paper. The inference drawn
from this analysis is used to develop a novel statistical RTT based
model for multi state network classification.
An Adaptive Multi State Proactive Transport Protocol consisting
of Proactive Slow Start, State based Error Recovery, Timeout Action
and Proactive Reduction is proposed which uses the multi state
network state classification model. This paper also confirms through
detail simulation and analysis that a prior knowledge about the
overall characteristics of the network helps in enhancing the
performance of the protocol over satellite channel which is
significantly affected due to channel noise and congestion.
The necessary augmentation of ns2 simulator is done for
simulating the multi state network classification logic. This
simulation has been used in detail evaluation of the protocol under
varied levels of congestion and channel noise. The performance
enhancement of this protocol with reference to established protocols
namely TCP SACK and Vegas has been discussed. The results as
discussed in this paper clearly reveal that the proposed protocol
always outperforms its peers and show a significant improvement in
very high error conditions as envisaged in the design of the protocol.
Abstract: A number of routing algorithms based on learning
automata technique have been proposed for communication
networks. How ever, there has been little work on the effects of
variation of graph scarcity on the performance of these algorithms. In
this paper, a comprehensive study is launched to investigate the
performance of LASPA, the first learning automata based solution to
the dynamic shortest path routing, across different graph structures
with varying scarcities. The sensitivity of three main performance
parameters of the algorithm, being average number of processed
nodes, scanned edges and average time per update, to variation in
graph scarcity is reported. Simulation results indicate that the LASPA
algorithm can adapt well to the scarcity variation in graph structure
and gives much better outputs than the existing dynamic and fixed
algorithms in terms of performance criteria.
Abstract: A wireless sensor network with a large number of tiny sensor nodes can be used as an effective tool for gathering data in various situations. One of the major issues in wireless sensor networks is developing an energy-efficient routing protocol which has a significant impact on the overall lifetime of the sensor network. In this paper, we propose a novel hierarchical with static clustering routing protocol called Energy-Efficient Protocol with Static Clustering (EEPSC). EEPSC, partitions the network into static clusters, eliminates the overhead of dynamic clustering and utilizes temporary-cluster-heads to distribute the energy load among high-power sensor nodes; thus extends network lifetime. We have conducted simulation-based evaluations to compare the performance of EEPSC against Low-Energy Adaptive Clustering Hierarchy (LEACH). Our experiment results show that EEPSC outperforms LEACH in terms of network lifetime and power consumption minimization.