Abstract: Sequential pattern mining is a challenging task in data mining area with large applications. One among those applications is mining patterns from weblog. Recent times, weblog is highly dynamic and some of them may become absolute over time. In addition, users may frequently change the threshold value during the data mining process until acquiring required output or mining interesting rules. Some of the recently proposed algorithms for mining weblog, build the tree with two scans and always consume large time and space. In this paper, we build Revised PLWAP with Non-frequent Items (RePLNI-tree) with single scan for all items. While mining sequential patterns, the links related to the nonfrequent items are not considered. Hence, it is not required to delete or maintain the information of nodes while revising the tree for mining updated transactions. The algorithm supports both incremental and interactive mining. It is not required to re-compute the patterns each time, while weblog is updated or minimum support changed. The performance of the proposed tree is better, even the size of incremental database is more than 50% of existing one. For evaluation purpose, we have used the benchmark weblog dataset and found that the performance of proposed tree is encouraging compared to some of the recently proposed approaches.
Abstract: Liver segmentation is the first significant process for
liver diagnosis of the Computed Tomography. It segments the liver
structure from other abdominal organs. Sophisticated filtering techniques
are indispensable for a proper segmentation. In this paper, we
employ a 3D anisotropic diffusion as a preprocessing step. While
removing image noise, this technique preserve the significant parts
of the image, typically edges, lines or other details that are important
for the interpretation of the image. The segmentation task is done
by using thresholding with automatic threshold values selection and
finally the false liver region is eliminated using 3D connected component.
The result shows that by employing the 3D anisotropic filtering,
better liver segmentation results could be achieved eventhough simple
segmentation technique is used.
Abstract: Power consumption of nodes in ad hoc networks is a
critical issue as they predominantly operate on batteries. In order to
improve the lifetime of an ad hoc network, all the nodes must be
utilized evenly and the power required for connections must be
minimized. In this project a link layer algorithm known as Power
Aware medium Access Control (PAMAC) protocol is proposed
which enables the network layer to select a route with minimum total
power requirement among the possible routes between a source and a
destination provided all nodes in the routes have battery capacity
above a threshold. When the battery capacity goes below a
predefined threshold, routes going through these nodes will be
avoided and these nodes will act only as source and destination.
Further, the first few nodes whose battery power drained to the set
threshold value are pushed to the exterior part of the network and the
nodes in the exterior are brought to the interior. Since less total
power is required to forward packets for each connection. The
network layer protocol AOMDV is basically an extension to the
AODV routing protocol. AOMDV is designed to form multiple
routes to the destination and it also avoid the loop formation so that it
reduces the unnecessary congestion to the channel. In this project, the
performance of AOMDV is evaluated using PAMAC as a MAC layer
protocol and the average power consumption, throughput and
average end to end delay of the network are calculated and the results
are compared with that of the other network layer protocol AODV.
Abstract: Dorsal hand vein pattern is an emerging biometric which is attracting the attention of researchers, of late. Research is being carried out on existing techniques in the hope of improving them or finding more efficient ones. In this work, Principle Component Analysis (PCA) , which is a successful method, originally applied on face biometric is being modified using Cholesky decomposition and Lanczos algorithm to extract the dorsal hand vein features. This modified technique decreases the number of computation and hence decreases the processing time. The eigenveins were successfully computed and projected onto the vein space. The system was tested on a database of 200 images and using a threshold value of 0.9 to obtain the False Acceptance Rate (FAR) and False Rejection Rate (FRR). This modified algorithm is desirable when developing biometric security system since it significantly decreases the matching time.
Abstract: In the paper, the relative performances on spectral
classification of short exon and intron sequences of the human and
eleven model organisms is studied. In the simulations, all
combinations of sixteen one-sequence numerical representations, four
threshold values, and four window lengths are considered. Sequences
of 150-base length are chosen and for each organism, a total of
16,000 sequences are used for training and testing. Results indicate
that an appropriate combination of one-sequence numerical
representation, threshold value, and window length is essential for
arriving at top spectral classification results. For fixed-length
sequences, the precisions on exon and intron classification obtained
for different organisms are not the same because of their genomic
differences. In general, precision increases as sequence length
increases.
Abstract: The major problem that wireless communication
systems undergo is multipath fading caused by scattering of the
transmitted signal. However, we can treat multipath propagation as
multiple channels between the transmitter and receiver to improve
the signal-to-scattering-noise ratio. While using Single Input
Multiple Output (SIMO) systems, the diversity receivers extract
multiple signal branches or copies of the same signal received from
different channels and apply gain combining schemes such as Root
Mean Square Gain Combining (RMSGC). RMSGC asymptotically
yields an identical performance to that of the theoretically optimal
Maximum Ratio Combining (MRC) for values of mean Signal-to-
Noise-Ratio (SNR) above a certain threshold value without the need
for SNR estimation. This paper introduces an improvement of
RMSGC using two different issues. We found that post-detection and
de-noising the received signals improve the performance of RMSGC
and lower the threshold SNR.
Abstract: This paper introduces a new signal denoising based on the Empirical mode decomposition (EMD) framework. The method is a fully data driven approach. Noisy signal is decomposed adaptively into oscillatory components called Intrinsic mode functions (IMFs) by means of a process called sifting. The EMD denoising involves filtering or thresholding each IMF and reconstructs the estimated signal using the processed IMFs. The EMD can be combined with a filtering approach or with nonlinear transformation. In this work the Savitzky-Golay filter and shoftthresholding are investigated. For thresholding, IMF samples are shrinked or scaled below a threshold value. The standard deviation of the noise is estimated for every IMF. The threshold is derived for the Gaussian white noise. The method is tested on simulated and real data and compared with averaging, median and wavelet approaches.
Abstract: In many sensor network applications, sensor nodes are deployed in open environments, and hence are vulnerable to physical attacks, potentially compromising the node's cryptographic keys. False sensing report can be injected through compromised nodes, which can lead to not only false alarms but also the depletion of limited energy resource in battery powered networks. Ye et al. proposed a statistical en-route filtering scheme (SEF) to detect such false reports during the forwarding process. In this scheme, the choice of a security threshold value is important since it trades off detection power and overhead. In this paper, we propose a fuzzy logic for determining a security threshold value in the SEF based sensor networks. The fuzzy logic determines a security threshold by considering the number of partitions in a global key pool, the number of compromised partitions, and the energy level of nodes. The fuzzy based threshold value can conserve energy, while it provides sufficient detection power.
Abstract: High speed networks provide realtime variable bit rate
service with diversified traffic flow characteristics and quality
requirements. The variable bit rate traffic has stringent delay and
packet loss requirements. The burstiness of the correlated traffic
makes dynamic buffer management highly desirable to satisfy the
Quality of Service (QoS) requirements. This paper presents an
algorithm for optimization of adaptive buffer allocation scheme for
traffic based on loss of consecutive packets in data-stream and buffer
occupancy level. Buffer is designed to allow the input traffic to be
partitioned into different priority classes and based on the input
traffic behavior it controls the threshold dynamically. This algorithm
allows input packets to enter into buffer if its occupancy level is less
than the threshold value for priority of that packet. The threshold is
dynamically varied in runtime based on packet loss behavior. The
simulation is run for two priority classes of the input traffic –
realtime and non-realtime classes. The simulation results show that
Adaptive Partial Buffer Sharing (ADPBS) has better performance
than Static Partial Buffer Sharing (SPBS) and First In First Out
(FIFO) queue under the same traffic conditions.
Abstract: The quest of providing more secure identification
system has led to a rise in developing biometric systems. Dorsal
hand vein pattern is an emerging biometric which has attracted the
attention of many researchers, of late. Different approaches have
been used to extract the vein pattern and match them. In this work,
Principle Component Analysis (PCA) which is a method that has
been successfully applied on human faces and hand geometry is
applied on the dorsal hand vein pattern. PCA has been used to obtain
eigenveins which is a low dimensional representation of vein pattern
features. Low cost CCD cameras were used to obtain the vein
images. The extraction of the vein pattern was obtained by applying
morphology. We have applied noise reduction filters to enhance the
vein patterns. The system has been successfully tested on a database
of 200 images using a threshold value of 0.9. The results obtained are
encouraging.
Abstract: This paper reports on the theoretical performance
analysis of the 1.3 μm In0.42Ga0.58As /In0.26Ga0.74As multiple quantum
well (MQW) vertical cavity surface emitting laser (VCSEL) on the
ternary In0.31Ga0.69As substrate. The output power of 2.2 mW has
been obtained at room temperature for 7.5 mA injection current. The
material gain has been estimated to be ~3156 cm-1 at room
temperature with the injection carrier concentration of 2×1017 cm-3.
The modulation bandwidth of this laser is measured to be 9.34 GHz
at room temperature for the biasing current of 2 mA above the
threshold value. The outcomes reveal that the proposed InGaAsbased
MQW laser is the promising one for optical communication
system.
Abstract: The objective of this paper is to propose an adaptive multi threshold for image segmentation precisely in object detection. Due to the different types of license plates being used, the requirement of an automatic LPR is rather different for each country. The proposed technique is applied on Malaysian LPR application. It is based on Multi Layer Perceptron trained by back propagation. The proposed adaptive threshold is introduced to find the optimum threshold values. The technique relies on the peak value from the graph of the number object versus specific range of threshold values. The proposed approach has improved the overall performance compared to current optimal threshold techniques. Further improvement on this method is in progress to accommodate real time system specification.
Abstract: It is well recognized that the green house gases such
as Chlorofluoro Carbon (CFC), CH4, CO2 etc. are responsible
directly or indirectly for the increase in the average global temperature
of the Earth. The presence of CFC is responsible for
the depletion of ozone concentration in the atmosphere due to
which the heat accompanied with the sun rays are less absorbed
causing increase in the atmospheric temperature of the Earth. The
gases like CH4 and CO2 are also responsible for the increase in
the atmospheric temperature. The increase in the temperature level
directly or indirectly affects the dynamics of interacting species
systems. Therefore, in this paper a mathematical model is proposed
and analysed using stability theory to asses the effects of increasing
temperature due to greenhouse gases on the survival or extinction of
populations in a prey-predator system. A threshold value in terms
of a stress parameter is obtained which determines the extinction or
existence of populations in the underlying system.
Abstract: In this paper, we were introduces a skin detection
method using a histogram approximation based on the mean shift
algorithm. The proposed method applies the mean shift procedure to a
histogram of a skin map of the input image, generated by comparison
with standard skin colors in the CbCr color space, and divides the
background from the skin region by selecting the maximum value
according to brightness level. The proposed method detects the skin
region using the mean shift procedure to determine a maximum value
that becomes the dividing point, rather than using a manually selected
threshold value, as in existing techniques. Even when skin color is
contaminated by illumination, the procedure can accurately segment
the skin region and the background region. The proposed method may
be useful in detecting facial regions as a pretreatment for face
recognition in various types of illumination.
Abstract: The fuzzy technique is an operator introduced in order
to simulate at a mathematical level the compensatory behavior in
process of decision making or subjective evaluation. The following
paper introduces such operators on hand of computer vision
application.
In this paper a novel method based on fuzzy logic reasoning
strategy is proposed for edge detection in digital images without
determining the threshold value. The proposed approach begins by
segmenting the images into regions using floating 3x3 binary matrix.
The edge pixels are mapped to a range of values distinct from each
other. The robustness of the proposed method results for different
captured images are compared to those obtained with the linear Sobel
operator. It is gave a permanent effect in the lines smoothness and
straightness for the straight lines and good roundness for the curved
lines. In the same time the corners get sharper and can be defined
easily.
Abstract: Many studies have been conducted for derivation of
attenuation relationships worldwide, however few relationships have
been developed to use for the seismic region of Iranian plateau and
only few of these studies have been conducted for derivation of
attenuation relationships for parameters such as uniform duration.
Uniform duration is the total time during which the acceleration is
larger than a given threshold value (default is 5% of PGA). In this
study, the database was same as that used previously by Ghodrati
Amiri et al. (2007) with same correction methods for earthquake
records in Iran. However in this study, records from earthquakes with
MS< 4.0 were excluded from this database, each record has
individually filtered afterward, and therefore the dataset has been
expanded. These new set of attenuation relationships for Iran are
derived based on tectonic conditions with soil classification into rock
and soil. Earthquake parameters were chosen to be
hypocentral distance and magnitude in order to make it easier to use
the relationships for seismic hazard analysis. Tehran is the capital
city of Iran wit ha large number of important structures. In this study,
a probabilistic approach has been utilized for seismic hazard
assessment of this city. The resulting uniform duration against return
period diagrams are suggested to be used in any projects in the area.
Abstract: An appropriate method for fault identification and classification on extra high voltage transmission line using discrete wavelet transform is proposed in this paper. The sharp variations of the generated short circuit transient signals which are recorded at the sending end of the transmission line are adopted to identify the fault. The threshold values involve fault classification and these are done on the basis of the multiresolution analysis. A comparative study of the performance is also presented for Discrete Fourier Transform (DFT) based Artificial Neural Network (ANN) and Discrete Wavelet Transform (DWT). The results prove that the proposed method is an effective and efficient one in obtaining the accurate result within short duration of time by using Daubechies 4 and 9. Simulation of the power system is done using MATLAB.