Abstract: This paper considers the benefits gained by using an
efficient quality of service management such as DiffServ technique to
improve the performance of military communications. Low delay and
no blockage must be achieved especially for real time tactical data.
All traffic flows generated by different applications do not need same
bandwidth, same latency, same error ratio and this scalable technique
of packet management based on priority levels is analysed. End to
end architectures supporting various traffic flows and including lowbandwidth
and high-delay HF or SHF military links as well as
unprotected Internet sub domains are studied. A tuning of Diffserv
parameters is proposed in accordance with different loads of various
traffic and different operational situations.
Abstract: The objective of this study is to present the test
results of variable air volume (VAV) air conditioning system
optimized by two objective genetic algorithm (GA). The objective
functions are energy savings and thermal comfort. The optimal set
points for fuzzy logic controller (FLC) are the supply air temperature
(Ts), the supply duct static pressure (Ps), the chilled water
temperature (Tw), and zone temperature (Tz) that is taken as the
problem variables. Supply airflow rate and chilled water flow rate are
considered to be the constraints. The optimal set point values are
obtained from GA process and assigned into fuzzy logic controller
(FLC) in order to conserve energy and maintain thermal comfort in
real time VAV air conditioning system. A VAV air conditioning
system with FLC installed in a software laboratory has been taken for
the purpose of energy analysis. The total energy saving obtained in
VAV GA optimization system with FLC compared with constant air
volume (CAV) system is expected to achieve 31.5%. The optimal
duct static pressure obtained through Genetic fuzzy methodology
attributes to better air distribution by delivering the optimal quantity
of supply air to the conditioned space. This combination enhanced
the advantages of uniform air distribution, thermal comfort and
improved energy savings potential.
Abstract: Wireless Sensor and Actor Networks (WSANs) constitute an emerging and pervasive technology that is attracting increasing interest in the research community for a wide range of applications. WSANs have two important requirements: coordination interactions and real-time communication to perform correct and timely actions. This paper introduces a methodology to facilitate the task of the application programmer focusing on the coordination and real-time requirements of WSANs. The methodology proposed in this model uses a real-time component model, UM-RTCOM, which will help us to achieve the design and implementation of applications in WSAN by using the component oriented paradigm. This will help us to develop software components which offer some very interesting features, such as reusability and adaptability which are very suitable for WSANs as they are very dynamic environments with rapidly changing conditions. In addition, a high-level coordination model based on tuple channels (TC-WSAN) is integrated into the methodology by providing a component-based specification of this model in UM-RTCOM; this will allow us to satisfy both sensor-actor and actor-actor coordination requirements in WSANs. Finally, we present in this paper the design and implementation of an application which will help us to show how the methodology can be easily used in order to achieve the development of WSANs applications.
Abstract: In today-s world, the efficient utilization of wood
resources comes more and more to the mind of forest owners. It is a
very complex challenge to ensure an efficient harvest of the wood
resources. This is one of the scopes the project “Virtual Forest II"
addresses. Its core is a database with data about forests containing
approximately 260 million trees located in North Rhine-Westphalia
(NRW). Based on this data, tree growth simulations and wood
mobilization simulations can be conducted. This paper focuses on the
latter. It describes a discrete-event-simulation with an attached 3-D
real time visualization which simulates timber harvest using trees
from the database with different crop resources. This simulation can
be displayed in 3-D to show the progress of the wood crop. All the
data gathered during the simulation is presented as a detailed
summary afterwards. This summary includes cost-benefit
calculations and can be compared to those of previous runs to
optimize the financial outcome of the timber harvest by exchanging
crop resources or modifying their parameters.
Abstract: In geometrical camera calibration, the objective is to
determine a set of camera parameters that describe the mapping
between 3D references coordinates and 2D image coordinates. In this
paper, a technique of calibration and tracking based on both a least
squares method is presented and a correlation technique developed as
part of an augmented reality system. This approach is fast and it can
be used for a real time system
Abstract: The future of business intelligence (BI) is to integrate
intelligence into operational systems that works in real-time
analyzing small chunks of data based on requirements on continuous
basis. This is moving away from traditional approach of doing
analysis on ad-hoc basis or sporadically in passive and off-line mode
analyzing huge amount data. Various AI techniques such as expert
systems, case-based reasoning, neural-networks play important role
in building business intelligent systems. Since BI involves various
tasks and models various types of problems, hybrid intelligent
techniques can be better choice. Intelligent systems accessible
through web services make it easier to integrate them into existing
operational systems to add intelligence in every business processes.
These can be built to be invoked in modular and distributed way to
work in real time. Functionality of such systems can be extended to
get external inputs compatible with formats like RSS. In this paper,
we describe a framework that use effective combinations of these
techniques, accessible through web services and work in real-time.
We have successfully developed various prototype systems and done
few commercial deployments in the area of personalization and
recommendation on mobile and websites.
Abstract: During the past several years, face recognition in video
has received significant attention. Not only the wide range of
commercial and law enforcement applications, but also the availability
of feasible technologies after several decades of research contributes
to the trend. Although current face recognition systems have reached a
certain level of maturity, their development is still limited by the
conditions brought about by many real applications. For example,
recognition images of video sequence acquired in an open
environment with changes in illumination and/or pose and/or facial
occlusion and/or low resolution of acquired image remains a largely
unsolved problem. In other words, current algorithms are yet to be
developed. This paper provides an up-to-date survey of video-based
face recognition research. To present a comprehensive survey, we
categorize existing video based recognition approaches and present
detailed descriptions of representative methods within each category.
In addition, relevant topics such as real time detection, real time
tracking for video, issues such as illumination, pose, 3D and low
resolution are covered.
Abstract: This paper addresses the problem of determining the current 3D location of a moving object and robustly tracking it from a sequence of camera images. The approach presented here uses a particle filter and does not perform any explicit triangulation. Only the color of the object to be tracked is required, but not any precisemotion model. The observation model we have developed avoids the color filtering of the entire image. That and the Monte Carlotechniques inside the particle filter provide real time performance.Experiments with two real cameras are presented and lessons learned are commented. The approach scales easily to more than two cameras and new sensor cues.
Abstract: Cytogenetic analysis still remains the gold standard method for prenatal diagnosis of trisomy 21 (Down syndrome, DS). Nevertheless, the conventional cytogenetic analysis needs live cultured cells and is too time-consuming for clinical application. In contrast, molecular methods such as FISH, QF-PCR, MLPA and quantitative Real-time PCR are rapid assays with results available in 24h. In the present study, we have successfully used a novel MGB TaqMan probe-based real time PCR assay for rapid diagnosis of trisomy 21 status in Down syndrome samples. We have also compared the results of this molecular method with corresponding results obtained by the cytogenetic analysis. Blood samples obtained from DS patients (n=25) and normal controls (n=20) were tested by quantitative Real-time PCR in parallel to standard G-banding analysis. Genomic DNA was extracted from peripheral blood lymphocytes. A high precision TaqMan probe quantitative Real-time PCR assay was developed to determine the gene dosage of DSCAM (target gene on 21q22.2) relative to PMP22 (reference gene on 17p11.2). The DSCAM/PMP22 ratio was calculated according to the formula; ratio=2 -ΔΔCT. The quantitative Real-time PCR was able to distinguish between trisomy 21 samples and normal controls with the gene ratios of 1.49±0.13 and 1.03±0.04 respectively (p value
Abstract: This paper presents an adaptive motion estimator
that can be dynamically reconfigured by the best algorithm
depending on the variation of the video nature during the lifetime
of an application under running. The 4 Step Search (4SS) and the
Gradient Search (GS) algorithms are integrated in the estimator in
order to be used in the case of rapid and slow video sequences
respectively. The Full Search Block Matching (FSBM) algorithm
has been also integrated in order to be used in the case of the
video sequences which are not real time oriented.
In order to efficiently reduce the computational cost while
achieving better visual quality with low cost power, the proposed
motion estimator is based on a Variable Block Size (VBS) scheme
that uses only the 16x16, 16x8, 8x16 and 8x8 modes.
Experimental results show that the adaptive motion estimator
allows better results in term of Peak Signal to Noise Ratio
(PSNR), computational cost, FPGA occupied area, and dissipated
power relatively to the most popular variable block size schemes
presented in the literature.
Abstract: Bloom filter is a probabilistic and memory efficient
data structure designed to answer rapidly whether an element is
present in a set. It tells that the element is definitely not in the set but
its presence is with certain probability. The trade-off to use Bloom
filter is a certain configurable risk of false positives. The odds of a
false positive can be made very low if the number of hash function is
sufficiently large. For spam detection, weight is attached to each set
of elements. The spam weight for a word is a measure used to rate the
e-mail. Each word is assigned to a Bloom filter based on its weight.
The proposed work introduces an enhanced concept in Bloom filter
called Bin Bloom Filter (BBF). The performance of BBF over
conventional Bloom filter is evaluated under various optimization
techniques. Real time data set and synthetic data sets are used for
experimental analysis and the results are demonstrated for bin sizes 4,
5, 6 and 7. Finally analyzing the results, it is found that the BBF
which uses heuristic techniques performs better than the traditional
Bloom filter in spam detection.
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: Motion detection is a basic operation in the selection of significant segments of the video signals. For an effective Human Computer Intelligent Interaction, the computer needs to recognize the motion and track the moving object. Here an efficient neural network system is proposed for motion detection from the static background. This method mainly consists of four parts like Frame Separation, Rough Motion Detection, Network Formation and Training, Object Tracking. This paper can be used to verify real time detections in such a way that it can be used in defense applications, bio-medical applications and robotics. This can also be used for obtaining detection information related to the size, location and direction of motion of moving objects for assessment purposes. The time taken for video tracking by this Neural Network is only few seconds.
Abstract: Packet switched data network like Internet, which has
traditionally supported throughput sensitive applications such as email
and file transfer, is increasingly supporting delay-sensitive
multimedia applications such as interactive video. These delaysensitive
applications would often rather sacrifice some throughput
for better delay. Unfortunately, the current packet switched network
does not offer choices, but instead provides monolithic best-effort
service to all applications. This paper evaluates Class Based Queuing
(CBQ), Coordinated Earliest Deadline First (CEDF), Weighted
Switch Deficit Round Robin (WSDRR) and RED-Boston scheduling
schemes that is sensitive to delay bound expectations for variety of
real time applications and an enhancement of WSDRR is proposed.
Abstract: One of the essential components of much of DSP
application is noise cancellation. Changes in real time signals are
quite rapid and swift. In noise cancellation, a reference signal which
is an approximation of noise signal (that corrupts the original
information signal) is obtained and then subtracted from the noise
bearing signal to obtain a noise free signal. This approximation of
noise signal is obtained through adaptive filters which are self
adjusting. As the changes in real time signals are abrupt, this needs
adaptive algorithm that converges fast and is stable. Least mean
square (LMS) and normalized LMS (NLMS) are two widely used
algorithms because of their plainness in calculations and
implementation. But their convergence rates are small. Adaptive
averaging filters (AFA) are also used because they have high
convergence, but they are less stable. This paper provides the
comparative study of LMS and Normalized NLMS, AFA and new
enhanced average adaptive (Average NLMS-ANLMS) filters for noise
cancelling application using speech signals.
Abstract: Mobile adhoc network (MANET) is a collection of
mobile devices which form a communication network with no preexisting
wiring or infrastructure. Multiple routing protocols have
been developed for MANETs. As MANETs gain popularity, their
need to support real time applications is growing as well. Such
applications have stringent quality of service (QoS) requirements
such as throughput, end-to-end delay, and energy. Due to dynamic
topology and bandwidth constraint supporting QoS is a challenging
task. QoS aware routing is an important building block for QoS
support. The primary goal of the QoS aware protocol is to determine
the path from source to destination that satisfies the QoS
requirements. This paper proposes a new energy and delay aware
protocol called energy and delay aware TORA (EDTORA) based on
extension of Temporally Ordered Routing Protocol (TORA).Energy
and delay verifications of query packet have been done in each node.
Simulation results show that the proposed protocol has a higher
performance than TORA in terms of network lifetime, packet
delivery ratio and end-to-end delay.
Abstract: Starting from the basic pillars of the supportability
analysis this paper queries its characteristics in LCI (Life Cycle
Integration) environment. The research methodology contents a
review of modern logistics engineering literature with the objective to
collect and synthesize the knowledge relating to standards of
supportability design in e-logistics environment. The results show
that LCI framework has properties which are in fully compatibility
with the requirement of simultaneous logistics support and productservice
bundle design. The proposed approach is a contribution to the
more comprehensive and efficient supportability design process.
Also, contributions are reflected through a greater consistency of
collected data, automated creation of reports suitable for different
analysis, as well as the possibility of their customization according
with customer needs. In addition to this, convenience of this approach
is its practical use in real time. In a broader sense, LCI allows
integration of enterprises on a worldwide basis facilitating electronic
business.
Abstract: There has been a growing interest in implementing humanoid avatars in networked virtual environment. However, most existing avatar communication systems do not take avatars- social backgrounds into consideration. This paper proposes a novel humanoid avatar animation system to represent personalities and facial emotions of avatars based on culture, profession, mood, age, taste, and so forth. We extract semantic keywords from the input text through natural language processing, and then the animations of personalized avatars are retrieved and displayed according to the order of the keywords. Our primary work is focused on giving avatars runtime instruction from multiple natural languages. Experiments with Chinese, Japanese and English input based on the prototype show that interactive avatar animations can be displayed in real time and be made available online. This system provides a more natural and interesting means of human communication, and therefore is expected to be used for cross-cultural communication, multiuser online games, and other entertainment applications.
Abstract: Designing and implementing intelligent systems has become a crucial factor for the innovation and development of better products of space technologies. A neural network is a parallel system, capable of resolving paradigms that linear computing cannot. Field programmable gate array (FPGA) is a digital device that owns reprogrammable properties and robust flexibility. For the neural network based instrument prototype in real time application, conventional specific VLSI neural chip design suffers the limitation in time and cost. With low precision artificial neural network design, FPGAs have higher speed and smaller size for real time application than the VLSI and DSP chips. So, many researchers have made great efforts on the realization of neural network (NN) using FPGA technique. In this paper, an introduction of ANN and FPGA technique are briefly shown. Also, Hardware Description Language (VHDL) code has been proposed to implement ANNs as well as to present simulation results with floating point arithmetic. Synthesis results for ANN controller are developed using Precision RTL. Proposed VHDL implementation creates a flexible, fast method and high degree of parallelism for implementing ANN. The implementation of multi-layer NN using lookup table LUT reduces the resource utilization for implementation and time for execution.
Abstract: The approach based on the wavelet transform has
been widely used for image denoising due to its multi-resolution
nature, its ability to produce high levels of noise reduction and the
low level of distortion introduced. However, by removing noise, high
frequency components belonging to edges are also removed, which
leads to blurring the signal features. This paper proposes a new
method of image noise reduction based on local variance and edge
analysis. The analysis is performed by dividing an image into 32 x 32
pixel blocks, and transforming the data into wavelet domain. Fast
lifting wavelet spatial-frequency decomposition and reconstruction is
developed with the advantages of being computationally efficient and
boundary effects minimized. The adaptive thresholding by local
variance estimation and edge strength measurement can effectively
reduce image noise while preserve the features of the original image
corresponding to the boundaries of the objects. Experimental results
demonstrate that the method performs well for images contaminated
by natural and artificial noise, and is suitable to be adapted for
different class of images and type of noises. The proposed algorithm
provides a potential solution with parallel computation for real time
or embedded system application.