Abstract: In this paper we use the property of co-occurrence
matrix in finding parallel lines in binary pictures for fingerprint
identification. In our proposed algorithm, we reduce the noise by
filtering the fingerprint images and then transfer the fingerprint
images to binary images using a proper threshold. Next, we divide
the binary images into some regions having parallel lines in the same
direction. The lines in each region have a specific angle that can be
used for comparison. This method is simple, performs the
comparison step quickly and has a good resistance in the presence of
the noise.
Abstract: Intrusion detection is a mechanism used to protect a
system and analyse and predict the behaviours of system users. An
ideal intrusion detection system is hard to achieve due to
nonlinearity, and irrelevant or redundant features. This study
introduces a new anomaly-based intrusion detection model. The
suggested model is based on particle swarm optimisation and
nonlinear, multi-class and multi-kernel support vector machines.
Particle swarm optimisation is used for feature selection by applying
a new formula to update the position and the velocity of a particle;
the support vector machine is used as a classifier. The proposed
model is tested and compared with the other methods using the KDD
CUP 1999 dataset. The results indicate that this new method achieves
better accuracy rates than previous methods.
Abstract: Nowaday-s, many organizations use systems that
support business process as a whole or partially. However, in some
application domains, like software development and health care
processes, a normative Process Aware System (PAS) is not suitable,
because a flexible support is needed to respond rapidly to new
process models. On the other hand, a flexible Process Aware System
may be vulnerable to undesirable and fraudulent executions, which
imposes a tradeoff between flexibility and security. In order to make
this tradeoff available, a genetic-based anomaly detection model for
logs of Process Aware Systems is presented in this paper. The
detection of an anomalous trace is based on discovering an
appropriate process model by using genetic process mining and
detecting traces that do not fit the appropriate model as anomalous
trace; therefore, when used in PAS, this model is an automated
solution that can support coexistence of flexibility and security.
Abstract: In the past few years, the use of wireless sensor networks (WSNs) potentially increased in applications such as intrusion detection, forest fire detection, disaster management and battle field. Sensor nodes are generally battery operated low cost devices. The key challenge in the design and operation of WSNs is to prolong the network life time by reducing the energy consumption among sensor nodes. Node clustering is one of the most promising techniques for energy conservation. This paper presents a novel clustering algorithm which maximizes the network lifetime by reducing the number of communication among sensor nodes. This approach also includes new distributed cluster formation technique that enables self-organization of large number of nodes, algorithm for maintaining constant number of clusters by prior selection of cluster head and rotating the role of cluster head to evenly distribute the energy load among all sensor nodes.
Abstract: In these days, multimedia data is transmitted and
processed in compressed format. Due to the decoding procedure and
filtering for edge detection, the feature extraction process of MPEG-7
Edge Histogram Descriptor is time-consuming as well as
computationally expensive. To improve efficiency of compressed
image retrieval, we propose a new edge histogram generation
algorithm in DCT domain in this paper. Using the edge information
provided by only two AC coefficients of DCT coefficients, we can get
edge directions and strengths directly in DCT domain. The
experimental results demonstrate that our system has good
performance in terms of retrieval efficiency and effectiveness.
Abstract: We present a new algorithm for nonlinear dimensionality reduction that consistently uses global information, and that enables understanding the intrinsic geometry of non-convex manifolds. Compared to methods that consider only local information, our method appears to be more robust to noise. Unlike most methods that incorporate global information, the proposed approach automatically handles non-convexity of the data manifold. We demonstrate the performance of our algorithm and compare it to state-of-the-art methods on synthetic as well as real data.
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: Diagnostic and detection of the arterial stiffness is
very important; which gives indication of the associated increased risk of cardiovascular diseases. To make a cheap and easy method for general screening technique to avoid the future cardiovascular
complexes , due to the rising of the arterial stiffness ; a proposed algorithm depending on photoplethysmogram to be used. The
photoplethysmograph signals would be processed in MATLAB. The
signal will be filtered, baseline wandering removed, peaks and
valleys detected and normalization of the signals should be achieved
.The area under the catacrotic phase of the photoplethysmogram
pulse curve is calculated using trapezoidal algorithm ; then will used
in cooperation with other parameters such as age, height, blood
pressure in neural network for arterial stiffness detection. The Neural
network were implemented with sensitivity of 80%, accuracy 85%
and specificity of 90% were got from the patients data. It is
concluded that neural network can detect the arterial STIFFNESS
depending on risk factor parameters.
Abstract: Nowadays, driving support systems, such as car
navigation systems, are getting common, and they support drivers in
several aspects. It is important for driving support systems to detect
status of driver's consciousness. Particularly, detecting driver's
drowsiness could prevent drivers from collisions caused by drowsy
driving. In this paper, we discuss the various artificial detection
methods for detecting driver's drowsiness processing technique. This
system is based on facial images analysis for warning the driver of
drowsiness or in attention to prevent traffic accidents.
Abstract: This paper proposes a new technique to detect code
clones from the lexical and syntactic point of view, which is based
on PALEX source code representation. The PALEX code contains
the recorded parsing actions and also lexical formatting information
including white spaces and comments. We can record a list of parsing
actions (shift, reduce, and reading a token) during a compiling process
after a compiler finishes analyzing the source code. The proposed
technique has advantages for syntax sensitive approach and language
independency.
Abstract: In this work we will present a new approach for shot transition auto-detection. Our approach is based on the analysis of Spatio-Temporal Video Slice (STVS) edges extracted from videos. The proposed approach is capable to efficiently detect both abrupt shot transitions 'cuts' and gradual ones such as fade-in, fade-out and dissolve. Compared to other techniques, our method is distinguished by its high level of precision and speed. Those performances are obtained due to minimizing the problem of the boundary shot detection to a simple 2D image partitioning problem.
Abstract: In this paper, novel statistical sampling based equalization techniques and CNN based detection are proposed to increase the spectral efficiency of multiuser communication systems over fading channels. Multiuser communication combined with selective fading can result in interferences which severely deteriorate the quality of service in wireless data transmission (e.g. CDMA in mobile communication). The paper introduces new equalization methods to combat interferences by minimizing the Bit Error Rate (BER) as a function of the equalizer coefficients. This provides higher performance than the traditional Minimum Mean Square Error equalization. Since the calculation of BER as a function of the equalizer coefficients is of exponential complexity, statistical sampling methods are proposed to approximate the gradient which yields fast equalization and superior performance to the traditional algorithms. Efficient estimation of the gradient is achieved by using stratified sampling and the Li-Silvester bounds. A simple mechanism is derived to identify the dominant samples in real-time, for the sake of efficient estimation. The equalizer weights are adapted recursively by minimizing the estimated BER. The near-optimal performance of the new algorithms is also demonstrated by extensive simulations. The paper has also developed a (Cellular Neural Network) CNN based approach to detection. In this case fast quadratic optimization has been carried out by t, whereas the task of equalizer is to ensure the required template structure (sparseness) for the CNN. The performance of the method has also been analyzed by simulations.
Abstract: Surveillance system is widely used in the traffic
monitoring. The deployment of cameras is moving toward a
ubiquitous camera (UbiCam) environment. In our previous study, a
novel service, called GPS-VT, was firstly proposed by incorporating
global positioning system (GPS) and visual tracking techniques for
the UbiCam environment. The first prototype is called GODTA
(GPS-based Moving Object Detection and Tracking Approach). For a
moving person carried GPS-enabled mobile device, he can be
tracking when he enters the field-of-view (FOV) of a camera
according to his real-time GPS coordinate. In this paper, GPS-VT
service is applied to the tracking of vehicles. The moving speed of a
vehicle is much faster than a person. It means that the time passing
through the FOV is much shorter than that of a person. Besides, the
update interval of GPS coordinate is once per second, it is
asynchronous with the frame rate of the real-time image. The above
asynchronous is worsen by the network transmission delay. These
factors are the main challenging to fulfill GPS-VT service on a
vehicle.In order to overcome the influence of the above factors, a
back-propagation neural network (BPNN) is used to predict the
possible lane before the vehicle enters the FOV of a camera. Then, a
template matching technique is used for the visual tracking of a target
vehicle. The experimental result shows that the target vehicle can be
located and tracking successfully. The success location rate of the
implemented prototype is higher than that of the previous GODTA.
Abstract: In this paper, the vessel inscribed trigonometry (VITM) for the vessel progression orientation (VPO) is proposed in the two-dimensional fundus image. The VPO is a major factor in the optic disc (OD) detection which is a basic process in the retina analysis. To measure the VPO, skeletons of vessel are used. First, the vessels are classified into three classes as vessel end, vessel branch and vessel stem. And the chain code maps of VS are generated. Next, two farthest neighborhoods of each point on VS are searched by the proposed angle restriction. Lastly, a gradient of the straight line between two farthest neighborhoods is estimated to measure the VPO. VITM is validated by comparing with manual results and 2D Gaussian templates. It is confirmed that VPO of the proposed mensuration is correct enough to detect OD from the results of experiment which applied VITM to detect OD in fundus images.
Abstract: This research work proposed a study of fruit bruise detection by means of a biospeckle method, selecting the papaya fruit (Carica papaya) as testing body. Papaya is recognized as a fruit of outstanding nutritional qualities, showing high vitamin A content, calcium, carbohydrates, exhibiting high popularity all over the world, considering consumption and acceptability. The commercialization of papaya faces special problems which are associated to bruise generation during harvesting, packing and transportation. Papaya is classified as climacteric fruit, permitting to be harvested before the maturation is completed. However, by one side bruise generation is partially controlled once the fruit flesh exhibits high mechanical firmness. By the other side, mechanical loads can set a future bruise at that maturation stage, when it can not be detected yet by conventional methods. Mechanical damages of fruit skin leave an entrance door to microorganisms and pathogens, which will cause severe losses of quality attributes. Traditional techniques of fruit quality inspection include total soluble solids determination, mechanical firmness tests, visual inspections, which would hardly meet required conditions for a fully automated process. However, the pertinent literature reveals a new method named biospeckle which is based on the laser reflectance and interference phenomenon. The laser biospeckle or dynamic speckle is quantified by means of the Moment of Inertia, named after its mechanical counterpart due to similarity between the defining formulae. Biospeckle techniques are able to quantify biological activities of living tissues, which has been applied to seed viability analysis, vegetable senescence and similar topics. Since the biospeckle techniques can monitor tissue physiology, it could also detect changes in the fruit caused by mechanical damages. The proposed technique holds non invasive character, being able to generate numerical results consistent with an adequate automation. The experimental tests associated to this research work included the selection of papaya fruit at different maturation stages which were submitted to artificial mechanical bruising tests. Damages were visually compared with the frequency maps yielded by the biospeckle technique. Results were considered in close agreement.
Abstract: Edge detection is usually the first step in medical
image processing. However, the difficulty increases when a
conventional kernel-based edge detector is applied to ultrasonic
images with a textural pattern and speckle noise. We designed an
adaptive diffusion filter to remove speckle noise while preserving the
initial edges detected by using a Sobel edge detector. We also propose
a genetic algorithm for edge selection to form complete boundaries of
the detected entities. We designed two fitness functions to evaluate
whether a criterion with a complex edge configuration can render a
better result than a simple criterion such as the strength of gradient.
The edges obtained by using a complex fitness function are thicker and
more fragmented than those obtained by using a simple fitness
function, suggesting that a complex edge selecting scheme is not
necessary for good edge detection in medical ultrasonic images;
instead, a proper noise-smoothing filter is the key.
Abstract: This paper investigates the problem of automated defect
detection for textile fabrics and proposes a new optimal filter design
method to solve this problem. Gabor Wavelet Network (GWN) is
chosen as the major technique to extract the texture features from
textile fabrics. Based on the features extracted, an optimal Gabor filter
can be designed. In view of this optimal filter, a new semi-supervised
defect detection scheme is proposed, which consists of one real-valued
Gabor filter and one smoothing filter. The performance of the scheme
is evaluated by using an offline test database with 78 homogeneous
textile images. The test results exhibit accurate defect detection with
low false alarm, thus showing the effectiveness and robustness of the
proposed scheme. To evaluate the detection scheme comprehensively,
a prototyped detection system is developed to conduct a real time test.
The experiment results obtained confirm the efficiency and
effectiveness of the proposed detection scheme.
Abstract: The study of soil for agriculture purposes has
remained the main focus of research since the beginning of civilization as humans- food related requirements remained closely linked with the soil. The study of soil has generated an interest
among the researchers for very similar other reasons including transmission, reflection and refraction of signals for deploying
wireless underground sensor networks or for the monitoring of objects on (or in ) soil in the form of better understanding of soil
electromagnetic characteristics properties. The moisture content has
been very instrumental in such studies as it decides on the resistance of the soil, and hence the attenuation on signals traveling through soil
or the attenuation the signals may suffer upon their impact on soil. This work is related testing and characterizing a measurement circuit
meant for the detection of moisture level content in soil.
Abstract: Unintentional falls are rife throughout the ages and
have been the common factor of serious or critical injuries especially
for the elderly society. Fortunately, owing to the recent rapid
advancement in technology, fall detection system is made possible,
enabling detection of falling events for the elderly, monitoring the
patient and consequently provides emergency support in the event of
falling. This paper presents a review of 3 main categories of fall
detection techniques, ranging from year 2005 to year 2010. This
paper will be focusing on discussing the techniques alongside with
summary and conclusion for them.
Abstract: Eight Etawah cross bred does were divided into two
groups based on body condition score (BCS). Group I (BSC 2, body
weight 25-30 kg; n = 4), and Group II (BSC 3, body weight, 35-40 kg, n=4). All does received intravaginal controlled internal drug
release devices (CIDR) for 10 days, and a prostaglandin F2α at 48 h before CIDR removal. Estrus detection was carried out using
vasectomized buck. Vaginal epithelium was taken to determine estrus cycle. Blood samples were taken every 3-6 hours, started from
moment of CIDR removal until the end of estrus. The results showed vaginal histological indicated estrus occurred at the hours of 25 to 60
and 30 to 70 post CIDR removal in BCS 2 and 3, respectively. Progesterone peak of BCS 2 and BCS 3 were 0.18±0.31 and
0.48±0.31 ng/mL on the hour 0 post CIDR removal. Estradiol -17ß
peak of each group was 53.25±35.08 and 89.91±92.84 pg/mL at 48
post CIDR removal. LH surge only occurred on BCS 3 groups, the LH concentrations were 9.9± 9.1; 4.5± 4.0; and 18.2± 9.1 ng/mL at
45, 48 and 51 hours post CIDR removal, respectively. It was concluded that the BCS had effects on vaginal histological changes
and LH surge.