Abstract: The development of wireless communication technologies has changed our living style in global level. After the international success of mobile telephony standards, the location and time independent voice connection has become a default method in daily telecommunications. As for today, highly advanced multimedia messaging plays a key role in value added service handling. Along with evolving data services, the need for more complex applications can be seen, including the mobile usage of broadcast technologies. Here performance of a system design for terrestrial multimedia content is examined with emphasis on mobile reception. This review paper has accommodated the understanding of physical layer role and the flavour of terrestrial channel effects on the terrestrial multimedia transmission using OFDM keeping DVB-H as benchmark standard.
Abstract: Reducing energy consumption of embedded systems requires careful memory management. It has been shown that Scratch- Pad Memories (SPMs) are low size, low cost, efficient (i.e. energy saving) data structures directly managed at the software level. In this paper, the focus is on heuristic methods for SPMs management. A method is efficient if the number of accesses to SPM is as large as possible and if all available space (i.e. bits) is used. A Tabu Search (TS) approach for memory management is proposed which is, to the best of our knowledge, a new original alternative to the best known existing heuristic (BEH). In fact, experimentations performed on benchmarks show that the Tabu Search method is as efficient as BEH (in terms of energy consumption) but BEH requires a sorting which can be computationally expensive for a large amount of data. TS is easy to implement and since no sorting is necessary, unlike BEH, the corresponding sorting time is saved. In addition to that, in a dynamic perspective where the maximum capacity of the SPM is not known in advance, the TS heuristic will perform better than BEH.
Abstract: The drastic increase in the usage of SMS technology
has led service providers to seek for a solution that enable users of
mobile devices to access services through SMSs. This has resulted in
the proposal of solutions towards SMS-based service invocation in
service oriented environments. However, the dynamic nature of
service-oriented environments coupled with sudden load peaks
generated by service request, poses performance challenges to
infrastructures for supporting SMS-based service invocation. To
address this problem we adopt load balancing techniques. A load
balancing model with adaptive load balancing and load monitoring
mechanisms as its key constructs is proposed. The load balancing
model then led to realization of Least Loaded Load Balancing
Framework (LLLBF). Evaluation of LLLBF benchmarked with round
robin (RR) scheme on the queuing approach showed LLLBF
outperformed RR in terms of response time and throughput.
However, LLLBF achieved better result in the cost of high
processing power.
Abstract: The importance of supply chain and logistics
management has been widely recognised. Effective management of
the supply chain can reduce costs and lead times and improve
responsiveness to changing customer demands. This paper proposes a
multi-matrix real-coded Generic Algorithm (MRGA) based
optimisation tool that minimises total costs associated within supply
chain logistics. According to finite capacity constraints of all parties
within the chain, Genetic Algorithm (GA) often produces infeasible
chromosomes during initialisation and evolution processes. In the
proposed algorithm, chromosome initialisation procedure, crossover
and mutation operations that always guarantee feasible solutions
were embedded. The proposed algorithm was tested using three sizes
of benchmarking dataset of logistic chain network, which are typical
of those faced by most global manufacturing companies. A half
fractional factorial design was carried out to investigate the influence
of alternative crossover and mutation operators by varying GA
parameters. The analysis of experimental results suggested that the
quality of solutions obtained is sensitive to the ways in which the
genetic parameters and operators are set.
Abstract: Virtualization and high performance computing have been discussed from a performance perspective in recent publications. We present and discuss a flexible and efficient approach to the management of virtual clusters. A virtual machine management tool is extended to function as a fabric for cluster deployment and management. We show how features such as saving the state of a running cluster can be used to avoid disruption. We also compare our approach to the traditional methods of cluster deployment and present benchmarks which illustrate the efficiency of our approach.
Abstract: In this paper we propose a novel Run Time Interface
(RTI) technique to provide an efficient environment for MPI jobs on
the heterogeneous architecture of PARAM Padma. It suggests an
innovative, unified framework for the job management interface
system in parallel and distributed computing. This approach employs
proxy scheme. The implementation shows that the proposed RTI is
highly scalable and stable. Moreover RTI provides the storage access
for the MPI jobs in various operating system platforms and improve
the data access performance through high performance C-DAC
Parallel File System (C-PFS). The performance of the RTI is
evaluated by using the standard HPC benchmark suites and the
simulation results show that the proposed RTI gives good
performance on large scale supercomputing system.
Abstract: Protein-protein interactions (PPI) play a crucial role in many biological processes such as cell signalling, transcription, translation, replication, signal transduction, and drug targeting, etc. Structural information about protein-protein interaction is essential for understanding the molecular mechanisms of these processes. Structures of protein-protein complexes are still difficult to obtain by biophysical methods such as NMR and X-ray crystallography, and therefore protein-protein docking computation is considered an important approach for understanding protein-protein interactions. However, reliable prediction of the protein-protein complexes is still under way. In the past decades, several grid-based docking algorithms based on the Katchalski-Katzir scoring scheme were developed, e.g., FTDock, ZDOCK, HADDOCK, RosettaDock, HEX, etc. However, the success rate of protein-protein docking prediction is still far from ideal. In this work, we first propose a more practical measure for evaluating the success of protein-protein docking predictions,the rate of first success (RFS), which is similar to the concept of mean first passage time (MFPT). Accordingly, we have assessed the ZDOCK bound and unbound benchmarks 2.0 and 3.0. We also createda new benchmark set for protein-protein docking predictions, in which the complexes have experimentally determined binding affinity data. We performed free energy calculation based on the solution of non-linear Poisson-Boltzmann equation (nlPBE) to improve the binding mode prediction. We used the well-studied thebarnase-barstarsystem to validate the parameters for free energy calculations. Besides,thenlPBE-based free energy calculations were conducted for the badly predicted cases by ZDOCK and ZRANK. We found that direct molecular mechanics energetics cannot be used to discriminate the native binding pose from the decoys.Our results indicate that nlPBE-based calculations appeared to be one of the promising approaches for improving the success rate of binding pose predictions.
Abstract: The back-propagation algorithm calculates the weight
changes of an artificial neural network, and a two-term algorithm
with a dynamically optimal learning rate and a momentum factor
is commonly used. Recently the addition of an extra term, called a
proportional factor (PF), to the two-term BP algorithm was proposed.
The third term increases the speed of the BP algorithm. However,
the PF term also reduces the convergence of the BP algorithm, and
optimization approaches for evaluating the learning parameters are
required to facilitate the application of the three terms BP algorithm.
This paper considers the optimization of the new back-propagation
algorithm by using derivative information. A family of approaches
exploiting the derivatives with respect to the learning rate, momentum
factor and proportional factor is presented. These autonomously
compute the derivatives in the weight space, by using information
gathered from the forward and backward procedures. The three-term
BP algorithm and the optimization approaches are evaluated using
the benchmark XOR problem.
Abstract: A registration framework for image-guided robotic
surgery is proposed for three emergency neurosurgical procedures,
namely Intracranial Pressure (ICP) Monitoring, External Ventricular
Drainage (EVD) and evacuation of a Chronic Subdural Haematoma
(CSDH). The registration paradigm uses CT and white light as
modalities. This paper presents two simulation studies for a
preliminary evaluation of the registration protocol: (1) The loci of the
Target Registration Error (TRE) in the patient-s axial, coronal and
sagittal views were simulated based on a Fiducial Localisation Error
(FLE) of 5 mm and (2) Simulation of the actual framework using
projected views from a surface rendered CT model to represent white
light images of the patient. Craniofacial features were employed as
the registration basis to map the CT space onto the simulated
intraoperative space. Photogrammetry experiments on an artificial
skull were also performed to benchmark the results obtained from the
second simulation. The results of both simulations show that the
proposed protocol can provide a 5mm accuracy for these
neurosurgical procedures.
Abstract: A simple and easy algorithm is presented for a fast calculation of kernel functions which required in fluid simulations using the Smoothed Particle Hydrodynamic (SPH) method. Present proposed algorithm improves the Linked-list algorithm and adopts the Pair-Wise Interaction technique, which are widely used for evaluating kernel functions in fluid simulations using the SPH method. The algorithm is easy to be implemented without any complexities in programming. Some benchmark examples are used to show the simulation time saved by using the proposed algorithm. Parametric studies on the number of divisions for sub-domains, smoothing length and total amount of particles are conducted to show the effectiveness of the present technique. A compact formulation is proposed for practical usage.
Abstract: In field of Computer Science and Mathematics,
sorting algorithm is an algorithm that puts elements of a list in a
certain order i.e. ascending or descending. Sorting is perhaps the
most widely studied problem in computer science and is frequently
used as a benchmark of a system-s performance. This paper
presented the comparative performance study of four sorting
algorithms on different platform. For each machine, it is found that
the algorithm depends upon the number of elements to be sorted. In
addition, as expected, results show that the relative performance of
the algorithms differed on the various machines. So, algorithm
performance is dependent on data size and there exists impact of
hardware also.
Abstract: Shot boundary detection is a fundamental step for the organization of large video data. In this paper, we propose a new method for video gradual shots detection and classification, using advantages of fractal analysis and AIS-based classifier. Proposed features are “vertical intercept" and “fractal dimension" of each frame of videos which are computed using Fourier transform coefficients. We also used a classifier based on Clonal Selection Algorithm. We have carried out our solution and assessed it according to the TRECVID2006 benchmark dataset.
Abstract: The job shop scheduling problem (JSSP) is a
notoriously difficult problem in combinatorial optimization. This
paper presents a hybrid artificial immune system for the JSSP with the
objective of minimizing makespan. The proposed approach combines
the artificial immune system, which has a powerful global exploration
capability, with the local search method, which can exploit the optimal
antibody. The antibody coding scheme is based on the operation based
representation. The decoding procedure limits the search space to the
set of full active schedules. In each generation, a local search heuristic
based on the neighborhood structure proposed by Nowicki and
Smutnicki is applied to improve the solutions. The approach is tested
on 43 benchmark problems taken from the literature and compared
with other approaches. The computation results validate the
effectiveness of the proposed algorithm.
Abstract: In the effort to reduce water consumption for resorts,
more water conservation practices need to be implemented. Hence
water audits need to be performed to obtain a baseline of water
consumption, before planning water conservation practices. In this
study, a water audit framework specifically for resorts was created,
and the audit was performed on two resorts: Resort A in Langkawi,
Malaysia; and Resort B in Miri, Malaysia. From the audit, the total
daily water consumption for Resorts A and B were estimated to be
180m3 and 330 m3 respectively, while the actual water consumption
(based on water meter readings) were 175 m3 and 325 m3. This
suggests that the audit framework is reasonably accurate and may be
used to account for most of the water consumption sources in a
resort. The daily water consumption per guest is about 500 litres. The
water consumption of both resorts is poorly rated compared with
established benchmarks. Water conservation measures were
suggested for both resorts.
Abstract: Flow through micro and mini channels requires relatively
high driving pressure due to the large fluid pressure drop
through these channels. Consequently the forces acting on the walls of
the channel due to the fluid pressure are also large. Due to these forces
there are displacement fields set up in the solid substrate containing
the channels. If the movement of the substrate is constrained at some
points, then stress fields are established in the substrate. On the other
hand, if the deformation of the channel shape is sufficiently large
then its effect on the fluid flow is important to be calculated. Such
coupled fluid-solid systems form a class of problems known as fluidstructure
interactions. In the present work a co-located finite volume
discretization procedure on unstructured meshes is described for
solving fluid-structure interaction type of problems. A linear elastic
solid is assumed for which the effect of the channel deformation
on the flow is neglected. Thus the governing equations for the
fluid and the solid are decoupled and are solved separately. The
procedure is validated by solving two benchmark problems, one from
fluid mechanics and another from solid mechanics. A fluid-structure
interaction problem of flow through a U-shaped channel embedded
in a plate is solved.
Abstract: Network security attacks are the violation of
information security policy that received much attention to the
computational intelligence society in the last decades. Data mining
has become a very useful technique for detecting network intrusions
by extracting useful knowledge from large number of network data
or logs. Naïve Bayesian classifier is one of the most popular data
mining algorithm for classification, which provides an optimal way
to predict the class of an unknown example. It has been tested that
one set of probability derived from data is not good enough to have
good classification rate. In this paper, we proposed a new learning
algorithm for mining network logs to detect network intrusions
through naïve Bayesian classifier, which first clusters the network
logs into several groups based on similarity of logs, and then
calculates the prior and conditional probabilities for each group of
logs. For classifying a new log, the algorithm checks in which cluster
the log belongs and then use that cluster-s probability set to classify
the new log. We tested the performance of our proposed algorithm by
employing KDD99 benchmark network intrusion detection dataset,
and the experimental results proved that it improves detection rates
as well as reduces false positives for different types of network
intrusions.
Abstract: An adaptive Fuzzy Inference Perceptual model has
been proposed for watermarking of digital images. The model
depends on the human visual characteristics of image sub-regions in
the frequency multi-resolution wavelet domain. In the proposed
model, a multi-variable fuzzy based architecture has been designed to
produce a perceptual membership degree for both candidate
embedding sub-regions and strength watermark embedding factor.
Different sizes of benchmark images with different sizes of
watermarks have been applied on the model. Several experimental
attacks have been applied such as JPEG compression, noises and
rotation, to ensure the robustness of the scheme. In addition, the
model has been compared with different watermarking schemes. The
proposed model showed its robustness to attacks and at the same time
achieved a high level of imperceptibility.
Abstract: This paper presents a new heuristic algorithm for the classical symmetric traveling salesman problem (TSP). The idea of the algorithm is to cut a TSP tour into overlapped blocks and then each block is improved separately. It is conjectured that the chance of improving a good solution by moving a node to a position far away from its original one is small. By doing intensive search in each block, it is possible to further improve a TSP tour that cannot be improved by other local search methods. To test the performance of the proposed algorithm, computational experiments are carried out based on benchmark problem instances. The computational results show that algorithm proposed in this paper is efficient for solving the TSPs.
Abstract: The volume of XML data exchange is explosively
increasing, and the need for efficient mechanisms of XML data
management is vital. Many XML storage models have been proposed
for storing XML DTD-independent documents in relational database
systems. Benchmarking is the best way to highlight pros and cons of
different approaches. In this study, we use a common benchmarking
scheme, known as XMark to compare the most cited and newly
proposed DTD-independent methods in terms of logical reads,
physical I/O, CPU time and duration. We show the effect of Label
Path, extracting values and storing in another table and type of join
needed for each method-s query answering.
Abstract: Benchmarking cleaner production performance is an
effective way of pollution control and emission reduction in coal-fired
power industry. A benchmarking method using two-stage
super-efficiency data envelopment analysis for coal-fired power plants
is proposed – firstly, to improve the cleaner production performance of
DEA-inefficient or weakly DEA-efficient plants, then to select the
benchmark from performance-improved power plants. An empirical
study is carried out with the survey data of 24 coal-fired power plants.
The result shows that in the first stage the performance of 16 plants is
DEA-efficient and that of 8 plants is relatively inefficient. The target
values for improving DEA-inefficient plants are acquired by
projection analysis. The efficient performance of 24 power plants and
the benchmarking plant is achieved in the second stage. The two-stage
benchmarking method is practical to select the optimal benchmark in
the cleaner production of coal-fired power industry and will
continuously improve plants- cleaner production performance.