Abstract: In biomedical implant field, a new formula is given
for the study of Radio Frequency power attenuation by simultaneous
effects of side and angular misalignment of the supply/data transfer
coils. A confrontation with the practical measurements done into a
Faraday cage, allowed a checking of the obtained theoretical results.
The DC supply systems without material connection and the data
transmitters used in the case of biomedical implants, can be well
dimensioned by taking into account the possibility of power
attenuation by misalignment of transfer coils
Abstract: In this paper, we present a comparative study of the
genetic algorithms and Hessian-s methods for optimal research of the
active powers in an electric network of power. The objective function
which is the performance index of production of electrical energy is
minimized by satisfying the constraints of the equality type and
inequality type initially by the Hessian-s methods and in the second
time by the genetic Algorithms. The results found by the application
of AG for the minimization of the electric production costs of power
are very encouraging. The algorithms seem to be an effective
technique to solve a great number of problems and which are in
constant evolution. Nevertheless it should be specified that the
traditional binary representation used for the genetic algorithms
creates problems of optimization of management of the large-sized
networks with high numerical precision.
Abstract: RFID system, in which we give identification number to each item and detect it with radio frequency, supports more variable service than barcode system can do. For example, a refrigerator with RFID reader and internet connection will automatically notify expiration of food validity to us. But, in spite of its convenience, RFID system has some security threats, because anybody can get ID information of item easily. One of most critical threats is privacy invasion. Existing privacy protection schemes or systems have been proposed, and these schemes or systems defend normal users from attempts that any attacker tries to get information using RFID tag value. But, these systems still have weakness that attacker can get information using analogous value instead of original tag value. In this paper, we mention this type of attack more precisely and suggest 'Tag Broker Model', which can defend it. Tag broker in this model translates original tag value to random value, and user can only get random value. Attacker can not use analogous tag value, because he/she is not able to know original one from it.
Abstract: Prediction of sinusoidal signals with time-varying
frequencies has been an important research topic in power electronics
systems. To solve this problem, we propose a new fuzzy
predictive filtering scheme, which is based on a Finite Impulse
Response (FIR) filter bank. Fuzzy logic is introduced here to provide
appropriate interpolation of individual filter outputs. Therefore,
instead of regular 'hard' switching, our method has the advantageous
'soft' switching among different filters. Simulation
comparisons between the fuzzy predictive filtering and conventional
filter bank-based approach are made to demonstrate that the
new scheme can achieve an enhanced prediction performance for
slowly changing sinusoidal input signals.
Abstract: this paper presents an auto-regressive network called the Auto-Regressive Multi-Context Recurrent Neural Network (ARMCRN), which forecasts the daily peak load for two large power plant systems. The auto-regressive network is a combination of both recurrent and non-recurrent networks. Weather component variables are the key elements in forecasting because any change in these variables affects the demand of energy load. So the AR-MCRN is used to learn the relationship between past, previous, and future exogenous and endogenous variables. Experimental results show that using the change in weather components and the change that occurred in past load as inputs to the AR-MCRN, rather than the basic weather parameters and past load itself as inputs to the same network, produce higher accuracy of predicted load. Experimental results also show that using exogenous and endogenous variables as inputs is better than using only the exogenous variables as inputs to the network.
Abstract: In recent years, it has been proposed security
architecture for sensor network.. One of these, TinySec by Chris
Kalof, Naveen Sastry, David Wagner had proposed Link layer security
architecture, considering some problems of sensor network. (i.e :
energy, bandwidth, computation capability,etc). The TinySec employs
CBC_mode of encryption and CBC-MAC for authentication based on
SkipJack Block Cipher. Currently, This TinySec is incorporated in the
TinyOS for sensor network security.
This paper introduces TinyHash based on general hash algorithm.
TinyHash is the module in order to replace parts of authentication and
integrity in the TinySec. it implies that apply hash algorithm on
TinySec architecture. For compatibility about TinySec, Components
in TinyHash is constructed as similar structure of TinySec. And
TinyHash implements the HMAC component for authentication and
the Digest component for integrity of messages. Additionally, we
define the some interfaces for service associated with hash algorithm.
Abstract: Security management has changed from the
management of security equipments and useful interface to manager.
It analyzes the whole security conditions of network and preserves the
network services from attacks. Secure router technology has security
functions, such as intrusion detection, IPsec(IP Security) and access
control, are applied to legacy router for secure networking. It controls
an unauthorized router access and detects an illegal network intrusion.
This paper relates to a security engine management of router based on
a security policy, which is the definition of security function against a
network intrusion. This paper explains the security policy and designs
the structure of security engine management framework.
Abstract: This paper proposes a new optimal feedback controller
for voltage source converters VSC's, for current regulated voltage
source converters, which allows compensate the harmonics of current
produced by nonlinear loads and load reactive power. The aim of the
present paper is to describe a novel switching signal generation
technique called optimal controller which guarantees that the injected
currents follow the reference currents determined by the
compensation strategy, with the smallest possible tracking error and
fixed switching frequency. It is compared with well-known
hysteresis current controller HCC. The validity of presented method
and its comparison with HCC is studied through simulation results.
Abstract: In this paper, a novel approach for robust trajectory tracking of induction motor drive is presented. By combining variable structure systems theory with fuzzy logic concept and neural network techniques, a new algorithm is developed. Fuzzy logic was used for the adaptation of the learning algorithm to improve the robustness of learning and operating of the neural network. The developed control algorithm is robust to parameter variations and external influences. It also assures precise trajectory tracking with the prescribed dynamics. The algorithm was verified by simulation and the results obtained demonstrate the effectiveness of the designed controller of induction motor drives which considered as highly non linear dynamic complex systems and variable characteristics over the operating conditions.
Abstract: Recently, the RFID (Radio Frequency
Identification) technology attracts the world market attention as
essential technology for ubiquitous environment. The RFID
market has focused on transponders and reader development.
But that concern has shifted to RFID software like as
high-valued e-business applications, RFID middleware and
related development tools. However, due to the high sensitivity
of data and service transaction within the RFID network,
security consideration must be addressed. In order to guarantee
trusted e-business based on RFID technology, we propose a
security enhanced RFID middleware system. Our proposal is
compliant with EPCglobal ALE (Application Level Events),
which is standard interface for middleware and its clients. We
show how to provide strengthened security and trust by
protecting transported data between middleware and its client,
and stored data in middleware. Moreover, we achieve the
identification and service access control against illegal service
abuse. Our system enables secure RFID middleware service
and trusted e-business service.
Abstract: The paper deals with an analysis of visibility records collected from 210 European airports to obtain a realistic estimation of the availability of Free Space Optical (FSO) data links. Commercially available optical links usually operate in the 850nm waveband. Thus the influence of the atmosphere on the optical beam and on the visible light is similar. Long-term visibility records represent an invaluable source of data for the estimation of the quality of service of FSO links. The model used characterizes both the statistical properties of fade depths and the statistical properties of individual fade durations. Results are presented for Italy, France, and Germany.
Abstract: In this paper a method for designing of nonlinear controller for a fuzzy model of Double Inverted Pendulum is proposed. This system can be considered as a fuzzy large-scale system that includes offset terms and disturbance in each subsystem. Offset terms are deterministic and disturbances are satisfied a matching condition that is mentioned in the paper. Based on Lyapunov theorem, a nonlinear controller is designed for this fuzzy system (as a model reference base) which is simple in computation and guarantees stability. This idea can be used for other fuzzy large- scale systems that include more subsystems Finally, the results are shown.
Abstract: This paper reports the feasibility of the ARMA model
to describe a bursty video source transmitting over a AAL5 ATM link
(VBR traffic). The traffic represents the activity of the action movie
"Lethal Weapon 3" transmitted over the ATM network using the Fore
System AVA-200 ATM video codec with a peak rate of 100 Mbps
and a frame rate of 25. The model parameters were estimated for a
single video source and independently multiplexed video sources. It
was found that the model ARMA (2, 4) is well-suited for the real data
in terms of average rate traffic profile, probability density function,
autocorrelation function, burstiness measure, and the pole-zero
distribution of the filter model.
Abstract: Using state space technique and GF(2) theory, a
simulation model for external exclusive NOR type LFSR structures is
developed. Through this tool a systematic procedure is devised for
computing pseudo-random binary sequences from such structures.
Abstract: Markov games are a generalization of Markov
decision process to a multi-agent setting. Two-player zero-sum
Markov game framework offers an effective platform for designing
robust controllers. This paper presents two novel controller design
algorithms that use ideas from game-theory literature to produce
reliable controllers that are able to maintain performance in presence
of noise and parameter variations. A more widely used approach for
controller design is the H∞ optimal control, which suffers from high
computational demand and at times, may be infeasible. Our approach
generates an optimal control policy for the agent (controller) via a
simple Linear Program enabling the controller to learn about the
unknown environment. The controller is facing an unknown
environment, and in our formulation this environment corresponds to
the behavior rules of the noise modeled as the opponent. Proposed
controller architectures attempt to improve controller reliability by a
gradual mixing of algorithmic approaches drawn from the game
theory literature and the Minimax-Q Markov game solution
approach, in a reinforcement-learning framework. We test the
proposed algorithms on a simulated Inverted Pendulum Swing-up
task and compare its performance against standard Q learning.
Abstract: Stochastic modeling of network traffic is an area of
significant research activity for current and future broadband
communication networks. Multimedia traffic is statistically
characterized by a bursty variable bit rate (VBR) profile. In this
paper, we develop an improved model for uniform activity level
video sources in ATM using a doubly stochastic autoregressive
model driven by an underlying spatial point process. We then
examine a number of burstiness metrics such as the peak-to-average
ratio (PAR), the temporal autocovariance function (ACF) and the
traffic measurements histogram. We found that the former measure is
most suitable for capturing the burstiness of single scene video
traffic. In the last phase of this work, we analyse statistical
multiplexing of several constant scene video sources. This proved,
expectedly, to be advantageous with respect to reducing the
burstiness of the traffic, as long as the sources are statistically
independent. We observed that the burstiness was rapidly
diminishing, with the largest gain occuring when only around 5
sources are multiplexed. The novel model used in this paper for
characterizing uniform activity video was thus found to be an
Abstract: In this paper, the application of multiple Elman neural networks to time series data regression problems is studied. An ensemble of Elman networks is formed by boosting to enhance the performance of the individual networks. A modified version of the AdaBoost algorithm is employed to integrate the predictions from multiple networks. Two benchmark time series data sets, i.e., the Sunspot and Box-Jenkins gas furnace problems, are used to assess the effectiveness of the proposed system. The simulation results reveal that an ensemble of boosted Elman networks can achieve a higher degree of generalization as well as performance than that of the individual networks. The results are compared with those from other learning systems, and implications of the performance are discussed.
Abstract: This paper is about hiding RFID tag identifier (ID)
using handheld device like a cellular phone. By modifying the tag ID
of objects periodically or manually using cellular phone built-in a
RFID reader chip or with a external RFID reader device, we can
prevent other people from gathering the information related with
objects querying information server (like an EPC IS) with a tag ID or
deriving the information from tag ID-s code structure or tracking the
location of the objects and the owner of the objects. In this paper, we
use a cryptographic algorithm for modification and restoring of RFID
tag ID, and for one original tag ID, there are several different
temporary tag ID, periodically.
Abstract: This paper proposes a VPN Accelerator Board
(VPN-AB), a virtual private network (VPN) protocol designed for
trust channel security system (TCSS). TCSS supports safety
communication channel between security nodes in internet. It
furnishes authentication, confidentiality, integrity, and access control
to security node to transmit data packets with IPsec protocol. TCSS
consists of internet key exchange block, security association block,
and IPsec engine block. The internet key exchange block negotiates
crypto algorithm and key used in IPsec engine block. Security
Association blocks setting-up and manages security association
information. IPsec engine block treats IPsec packets and consists of
networking functions for communication. The IPsec engine block
should be embodied by H/W and in-line mode transaction for high
speed IPsec processing. Our VPN-AB is implemented with high speed
security processor that supports many cryptographic algorithms and
in-line mode. We evaluate a small TCSS communication environment,
and measure a performance of VPN-AB in the environment. The
experiment results show that VPN-AB gets a performance throughput
of maximum 15.645Gbps when we set the IPsec protocol with
3DES-HMAC-MD5 tunnel mode.
Abstract: In this paper, we consider the problem of tracking
multiple maneuvering targets using switching multiple target motion
models. With this paper, we aim to contribute in solving the problem
of model-based body motion estimation by using data coming from
visual sensors. The Interacting Multiple Model (IMM) algorithm is
specially designed to track accurately targets whose state and/or
measurement (assumed to be linear) models changes during motion
transition. However, when these models are nonlinear, the IMM
algorithm must be modified in order to guarantee an accurate track.
In this paper we propose to avoid the Extended Kalman filter because
of its limitations and substitute it with the Unscented Kalman filter
which seems to be more efficient especially according to the
simulation results obtained with the nonlinear IMM algorithm (IMMUKF).
To resolve the problem of data association, the JPDA
approach is combined with the IMM-UKF algorithm, the derived
algorithm is noted JPDA-IMM-UKF.