Abstract: In this paper we propose a robust environmental sound classification approach, based on spectrograms features driven from log-Gabor filters. This approach includes two methods. In the first methods, the spectrograms are passed through an appropriate log-Gabor filter banks and the outputs are averaged and underwent an optimal feature selection procedure based on a mutual information criteria. The second method uses the same steps but applied only to three patches extracted from each spectrogram.
To investigate the accuracy of the proposed methods, we conduct experiments using a large database containing 10 environmental sound classes. The classification results based on Multiclass Support Vector Machines show that the second method is the most efficient with an average classification accuracy of 89.62 %.
Abstract: This paper presents a multi-objective order allocation
planning problem with the consideration of various real-world
production features. A novel hybrid intelligent optimization model,
integrating a multi-objective memetic optimization process, a Monte
Carlo simulation technique and a heuristic pruning technique, is
proposed to handle this problem. Experiments based on industrial data
are conducted to validate the proposed model. Results show that (1)
the proposed model can effectively solve the investigated problem by
providing effective production decision-making solutions, which
outperformsan NSGA-II-based optimization process and an industrial
method.
Abstract: Extracting in-play scenes in sport videos is essential for
quantitative analysis and effective video browsing of the sport
activities. Game analysis of badminton as of the other racket sports
requires detecting the start and end of each rally period in an
automated manner. This paper describes an automatic serve scene
detection method employing cubic higher-order local auto-correlation
(CHLAC) and multiple regression analysis (MRA). CHLAC can
extract features of postures and motions of multiple persons without
segmenting and tracking each person by virtue of shift-invariance and
additivity, and necessitate no prior knowledge. Then, the specific
scenes, such as serve, are detected by linear regression (MRA) from
the CHLAC features. To demonstrate the effectiveness of our method,
the experiment was conducted on video sequences of five badminton
matches captured by a single ceiling camera. The averaged precision
and recall rates for the serve scene detection were 95.1% and 96.3%,
respectively.
Abstract: This paper presents the design and implementation of
the WebGD, a CORBA-based document classification and retrieval
system on Internet. The WebGD makes use of such techniques as Web,
CORBA, Java, NLP, fuzzy technique, knowledge-based processing
and database technology. Unified classification and retrieval model,
classifying and retrieving with one reasoning engine and flexible
working mode configuration are some of its main features. The
architecture of WebGD, the unified classification and retrieval model,
the components of the WebGD server and the fuzzy inference engine
are discussed in this paper in detail.
Abstract: Smoothing or filtering of data is first preprocessing step
for noise suppression in many applications involving data analysis.
Moving average is the most popular method of smoothing the data,
generalization of this led to the development of Savitzky-Golay filter.
Many window smoothing methods were developed by convolving
the data with different window functions for different applications;
most widely used window functions are Gaussian or Kaiser. Function
approximation of the data by polynomial regression or Fourier
expansion or wavelet expansion also gives a smoothed data. Wavelets
also smooth the data to great extent by thresholding the wavelet
coefficients. Almost all smoothing methods destroys the peaks and
flatten them when the support of the window is increased. In certain
applications it is desirable to retain peaks while smoothing the data
as much as possible. In this paper we present a methodology called
as peak-wise smoothing that will smooth the data to any desired level
without losing the major peak features.
Abstract: The widely used Total Variation de-noising algorithm can preserve sharp edge, while removing noise. However, since fixed regularization parameter over entire image, small details and textures are often lost in the process. In this paper, we propose a modified Total Variation algorithm to better preserve smaller-scaled features. This is done by allowing an adaptive regularization parameter to control the amount of de-noising in any region of image, according to relative information of local feature scale. Experimental results demonstrate the efficient of the proposed algorithm. Compared with standard Total Variation, our algorithm can better preserve smaller-scaled features and show better performance.
Abstract: The influence of axial magnetic field (B=0.48 T) on
the variation of ionization efficiency coefficient h and secondary
electron emission coefficient g with respect to reduced electric field
E/P is studied at a new range of plane-parallel electrode spacing (0<
d< 20 cm) and different nitrogen working pressure between 0.5-20
Pa. The axial magnetic field is produced from an inductive copper
coil of radius 5.6 cm. The experimental data of breakdown voltage is
adopted to estimate the mean Paschen curves at different working
features. The secondary electron emission coefficient is calculated
from the mean Paschen curve and used to determine the minimum
breakdown voltage. A reduction of discharge voltage of about 25% is
investigated by the applied of axial magnetic field. At high interelectrode
spacing, the effect of axial magnetic field becomes more
significant for the obtained values of h but it was less for the values
of g.
Abstract: The aim of this article is to explain how features of attacks could be extracted from the packets. It also explains how vectors could be built and then applied to the input of any analysis stage. For analyzing, the work deploys the Feedforward-Back propagation neural network to act as misuse intrusion detection system. It uses ten types if attacks as example for training and testing the neural network. It explains how the packets are analyzed to extract features. The work shows how selecting the right features, building correct vectors and how correct identification of the training methods with nodes- number in hidden layer of any neural network affecting the accuracy of system. In addition, the work shows how to get values of optimal weights and use them to initialize the Artificial Neural Network.
Abstract: In this paper we describe a computer-aided diagnosis (CAD) system for automated detection of pulmonary nodules in computed-tomography (CT) images. After extracting the pulmonary parenchyma using a combination of image processing techniques, a region growing method is applied to detect nodules based on 3D geometric features. We applied the CAD system to CT scans collected in a screening program for lung cancer detection. Each scan consists of a sequence of about 300 slices stored in DICOM (Digital Imaging and Communications in Medicine) format. All malignant nodules were detected and a low false-positive detection rate was achieved.
Abstract: Wrist pulse analysis for identification of health status
is found in Ancient Indian as well as Chinese literature. The preprocessing
of wrist pulse is necessary to remove outlier pulses and
fluctuations prior to the analysis of pulse pressure signal. This paper
discusses the identification of irregular pulses present in the pulse
series and intricacies associated with the extraction of time domain
pulse features. An approach of Dynamic Time Warping (DTW) has
been utilized for the identification of outlier pulses in the wrist pulse
series. The ambiguity present in the identification of pulse features is
resolved with the help of first derivative of Ensemble Average of
wrist pulse series. An algorithm for detecting tidal and dicrotic notch
in individual wrist pulse segment is proposed.
Abstract: This paper addresses integration issues in supply
chain, and tries to investigate how different aspects of integration are
linked with some product features. Integration in this study is
interpreted as "internal", "upstream" (supply), and "downstream"
(demand). Two features of product innovative and quality are
considered. To examine the relationships between supply chain
integrations – as mentioned above, and product features, this research
follows the survey method in automotive industry.The results imply
that supply chain upstream integration has a higher impact on product
quality, comparing to internal and supply chain downstream
integrations. It is also found that the influence of supply chain
downstream integration on product innovation is greater than other
variables. In brief, this study mainly tackles the importance of
specific level of supply chain integrations and its effects on two
product features.
Abstract: Interactions among proteins are the basis of various
life events. So, it is important to recognize and research protein
interaction sites. A control set that contains 149 protein molecules
were used here. Then 10 features were extracted and 4 sample sets
that contained 9 sliding windows were made according to features.
These 4 sample sets were calculated by Radial Basis Functional neutral
networks which were optimized by Particle Swarm Optimization
respectively. Then 4 groups of results were obtained. Finally, these 4
groups of results were integrated by decision fusion (DF) and Genetic
Algorithm based Selected Ensemble (GASEN). A better accuracy was
got by DF and GASEN. So, the integrated methods were proved to
be effective.
Abstract: The problem of ranking (rank regression) has become popular in the machine learning community. This theory relates to problems, in which one has to predict (guess) the order between objects on the basis of vectors describing their observed features. In many ranking algorithms a convex loss function is used instead of the 0-1 loss. It makes these procedures computationally efficient. Hence, convex risk minimizers and their statistical properties are investigated in this paper. Fast rates of convergence are obtained under conditions, that look similarly to the ones from the classification theory. Methods used in this paper come from the theory of U-processes as well as empirical processes.
Abstract: One of the popular methods for recognition of facial
expressions such as happiness, sadness and surprise is based on
deformation of facial features. Motion vectors which show these
deformations can be specified by the optical flow. In this method, for
detecting emotions, the resulted set of motion vectors are compared
with standard deformation template that caused by facial expressions.
In this paper, a new method is introduced to compute the quantity of
likeness in order to make decision based on the importance of
obtained vectors from an optical flow approach. For finding the
vectors, one of the efficient optical flow method developed by
Gautama and VanHulle[17] is used. The suggested method has been
examined over Cohn-Kanade AU-Coded Facial Expression Database,
one of the most comprehensive collections of test images available.
The experimental results show that our method could correctly
recognize the facial expressions in 94% of case studies. The results
also show that only a few number of image frames (three frames) are
sufficient to detect facial expressions with rate of success of about
83.3%. This is a significant improvement over the available methods.
Abstract: The pipe inspection operation is the difficult detective
performance. Almost applications are mainly relies on a manual
recognition of defective areas that have carried out detection by an
engineer. Therefore, an automation process task becomes a necessary
in order to avoid the cost incurred in such a manual process. An
automated monitoring method to obtain a complete picture of the
sewer condition is proposed in this work. The focus of the research is
the automated identification and classification of discontinuities in
the internal surface of the pipe. The methodology consists of several
processing stages including image segmentation into the potential
defect regions and geometrical characteristic features. Automatic
recognition and classification of pipe defects are carried out by means
of using an artificial neural network technique (ANN) based on
Radial Basic Function (RBF). Experiments in a realistic environment
have been conducted and results are presented.
Abstract: This paper attempts to discuss the evolution of the
retrieval techniques focusing on development, challenges and trends
of the image retrieval. It highlights both the already addressed and
outstanding issues. The explosive growth of image data leads to the
need of research and development of Image Retrieval. However,
Image retrieval researches are moving from keyword, to low level
features and to semantic features. Drive towards semantic features is
due to the problem of the keywords which can be very subjective and
time consuming while low level features cannot always describe high
level concepts in the users- mind.
Abstract: In this paper, we present an improved fast and robust
search algorithm for copy detection using histogram-based features for
short MPEG video clips from large video database. There are two
types of histogram features used to generate more robust features. The
first one is based on the adjacent pixel intensity difference quantization
(APIDQ) algorithm, which had been reliably applied to human face
recognition previously. An APIDQ histogram is utilized as the feature
vector of the frame image. Another one is ordinal histogram feature
which is robust to color distortion. Furthermore, by Combining with a
temporal division method, the spatial and temporal features of the
video sequence are integrated to realize fast and robust video search
for copy detection. Experimental results show the proposed algorithm
can detect the similar video clip more accurately and robust than
conventional fast video search algorithm.
Abstract: In this paper, we propose an improved fast search
algorithm using combined histogram features and temporal division
method for short MPEG video clips from large video database. There
are two types of histogram features used to generate more robust
features. The first one is based on the adjacent pixel intensity
difference quantization (APIDQ) algorithm, which had been reliably
applied to human face recognition previously. An APIDQ histogram is
utilized as the feature vector of the frame image. Another one is
ordinal feature which is robust to color distortion. Combined with
active search [4], a temporal pruning algorithm, fast and robust video
search can be realized. The proposed search algorithm has been
evaluated by 6 hours of video to search for given 200 MPEG video
clips which each length is 30 seconds. Experimental results show the
proposed algorithm can detect the similar video clip in merely 120ms,
and Equal Error Rate (ERR) of 1% is achieved, which is more
accurately and robust than conventional fast video search algorithm.
Abstract: This work discusses an innovative methodology for
deployment of service quality characteristics. Four groups of organizational features that may influence the quality of services are identified: human resource, technology, planning, and organizational
relationships. A House of Service Quality (HOSQ) matrix is built to
extract the desired improvement in the service quality characteristics
and to translate them into a hierarchy of important organizational
features. The Mean Square Error (MSE) criterion enables the
pinpointing of the few essential service quality characteristics to be
improved as well as selection of the vital organizational features. The
method was implemented in an engineering supply enterprise and
provides useful information on its vital service dimensions.
Abstract: In this paper we present a new approach to detecting a
flaw in T.O.F.D (Time Of Flight Diffraction) type ultrasonic image
based on texture features. Texture is one of the most important
features used in recognizing patterns in an image. The paper
describes texture features based on 2D Gabor functions, i.e.,
Gaussian shaped band-pass filters, with dyadic treatment of the radial
spatial frequency range and multiple orientations, which represent an
appropriate choice for tasks requiring simultaneous measurement in
both space and frequency domains. The most relevant features are
used as input data on a Fuzzy c-mean clustering classifier. The
classes that exist are only two: 'defects' or 'no defects'. The proposed
approach is tested on the T.O.F.D image achieved at the laboratory
and on the industrial field.