Abstract: The primary objective of the paper is to propose a new method for solving assignment problem under uncertain situation. In the classical assignment problem (AP), zpqdenotes the cost for assigning the qth job to the pth person which is deterministic in nature. Here in some uncertain situation, we have assigned a cost in the form of composite relative degree Fpq instead of and this replaced cost is in the maximization form. In this paper, it has been solved and validated by the two proposed algorithms, a new mathematical formulation of IVIF assignment problem has been presented where the cost has been considered to be an IVIFN and the membership of elements in the set can be explained by positive and negative evidences. To determine the composite relative degree of similarity of IVIFS the concept of similarity measure and the score function is used for validating the solution which is obtained by Composite relative similarity degree method. Further, hypothetical numeric illusion is conducted to clarify the method’s effectiveness and feasibility developed in the study. Finally, conclusion and suggestion for future work are also proposed.
Abstract: For the communication between human and computer
in an interactive computing environment, the gesture recognition is
studied vigorously. Therefore, a lot of studies have proposed efficient
methods about the recognition algorithm using 2D camera captured
images. However, there is a limitation to these methods, such as the
extracted features cannot fully represent the object in real world.
Although many studies used 3D features instead of 2D features for
more accurate gesture recognition, the problem, such as the processing
time to generate 3D objects, is still unsolved in related researches.
Therefore we propose a method to extract the 3D features combined
with the 3D object reconstruction. This method uses the modified
GPU-based visual hull generation algorithm which disables unnecessary
processes, such as the texture calculation to generate three kinds
of 3D projection maps as the 3D feature: a nearest boundary, a farthest
boundary, and a thickness of the object projected on the base-plane. In
the section of experimental results, we present results of proposed
method on eight human postures: T shape, both hands up, right hand
up, left hand up, hands front, stand, sit and bend, and compare the
computational time of the proposed method with that of the previous
methods.
Abstract: A new and novel approach in medicine is the use of
cold plasma for various applications such as sterilization blood
coagulation and cancer cell treatment. In this paper a pin-to-hole
plasma jet suitable for biological applications is investigated and
characterized and the possibility and feasibility of cancer cell
treatment is evaluated. The characterization includes power
consumption via Lissajous method, thermal behavior of plasma using
Infra-red camera as a novel method, Optical Emission Spectroscopy
(OES) to determine the species that are generated. Treatment of
leukemia cancer cells is also implemented and MTT assay is used to
evaluate viability.
Abstract: Atmospheric plasma is emerging as a promising
technology for many industrial sectors, because of its ecological and
economic advantages respect to the traditional production processes.
For textile industry, atmospheric plasma is becoming a valid
alternative to the conventional wet processes, but the plasma
machines realized so far do not allow the treatment of fibrous
mechanically weak material.
Novel atmospheric plasma machine for industrial applications,
developed by VenetoNanotech SCpA in collaboration with Italian
producer of corona equipment ME.RO SpA is presented. The main
feature of this pre-industrial scale machine is the possibility of the inline
plasma treatment of delicate fibrous substrates such as fibre
sleeves, for example wool tops, cotton fibres, polymeric tows,
mineral fibers and so on, avoiding burnings and disruption of the
faint materials.
Abstract: The stability of a software system is one of the most
important quality attributes affecting the maintenance effort. Many
techniques have been proposed to support the analysis of software
stability at the architecture, file, and class level of software systems,
but little effort has been made for that at the feature (i.e., method and
attribute) level. And the assumptions the existing techniques based
on always do not meet the practice to a certain degree. Considering
that, in this paper, we present a novel metric, Stability of Software
(SoS), to measure the stability of object-oriented software systems
by software change propagation analysis using a simulation way
in software dependency networks at feature level. The approach is
evaluated by case studies on eight open source Java programs using
different software structures (one employs design patterns versus one
does not) for the same object-oriented program. The results of the
case studies validate the effectiveness of the proposed metric. The
approach has been fully automated by a tool written in Java.
Abstract: This paper presents an application of the improved
QFD method for determining the specifications of kitchen utensils
rack. By using the improved method, the subjective nature in original
QFD was reduced; particularly in defining the relationship between
customer requirement and engineering characteristics. The regression
analysis that was used for obtaining the relationship functions
between customer requirement and engineering characteristics also
accommodated the inaccurateness of the competitive assessment
results. The improved method which is represented in the form of a
mathematical model had become a formal guidance to allocate the
resource for improving the specifications of kitchen utensils rack.
The specifications obtained had led to the achievement of the highest
feasible customer satisfaction.
Abstract: It is hard to percept the interaction process with machines when visual information is not available. In this paper, we have addressed this issue to provide interaction through visual techniques. Posture recognition is done for American Sign Language to recognize static alphabets and numbers. 3D information is exploited to obtain segmentation of hands and face using normal Gaussian distribution and depth information. Features for posture recognition are computed using statistical and geometrical properties which are translation, rotation and scale invariant. Hu-Moment as statistical features and; circularity and rectangularity as geometrical features are incorporated to build the feature vectors. These feature vectors are used to train SVM for classification that recognizes static alphabets and numbers. For the alphabets, curvature analysis is carried out to reduce the misclassifications. The experimental results show that proposed system recognizes posture symbols by achieving recognition rate of 98.65% and 98.6% for ASL alphabets and numbers respectively.
Abstract: This paper presents features that characterize power
quality disturbances from recorded voltage waveforms using wavelet
transform. The discrete wavelet transform has been used to detect
and analyze power quality disturbances. The disturbances of interest
include sag, swell, outage and transient. A power system network has
been simulated by Electromagnetic Transients Program. Voltage
waveforms at strategic points have been obtained for analysis, which
includes different power quality disturbances. Then wavelet has been
chosen to perform feature extraction. The outputs of the feature
extraction are the wavelet coefficients representing the power quality
disturbance signal. Wavelet coefficients at different levels reveal the
time localizing information about the variation of the signal.
Abstract: Environment today is featured with aging population,
increasing prevalence of chronic disease and complex of medical
treatment. Safe use of pharmaceutics relied very much on the efforts
made by both the health- related organizations and as well as the
government agencies. As far as the specialization concern in providing
health services to the patients, the government actively issued and
implemented the divisions of medical treatment and pharmaceutical to
improve the quality of care and to reduce medication errors and ensure
public health. Pharmaceutical sub-sector policy has been implemented
for 13 years. This study attempts to explore the factors that affect the
patients- behavior intention of refilling a prescription from a NHIB
pharmacy. Samples were those patients refilling their prescriptions
with the case NHIB pharmacies. A self-administered questionnaire
was used to collect respondents- information while the patients or
family members visit the pharmacy for the refilling. 1,200
questionnaires were dispatched in 37 pharmacies that randomly
selected from Pingtung City, Dongkang, Chaozhou, Hengchun areas.
732 responses were gained with 604 valid samples for further analyses.
Results of data analyses indicated that respondents- attitude,
subjective norm, perceived behavior control and behavior intentions
toward refilling behavior varied from some demographic variables to
another. This research also suggested adding actual behavior, either by
a self-report or observed, into the research.
Abstract: The protein domain structure has been widely used as the most informative sequence feature to computationally predict protein-protein interactions. However, in a recent study, a research group has reported a very high accuracy of 94% using hydrophobicity feature. Therefore, in this study we compare and verify the usefulness of protein domain structure and hydrophobicity properties as the sequence features. Using the Support Vector Machines (SVM) as the learning system, our results indicate that both features achieved accuracy of nearly 80%. Furthermore, domains structure had receiver operating characteristic (ROC) score of 0.8480 with running time of 34 seconds, while hydrophobicity had ROC score of 0.8159 with running time of 20,571 seconds (5.7 hours). These results indicate that protein-protein interaction can be predicted from domain structure with reliable accuracy and acceptable running time.
Abstract: Model Predictive Control (MPC) is an established control
technique in a wide range of process industries. The reason for
this success is its ability to handle multivariable systems and systems
having input, output or state constraints. Neverthless comparing to
PID controller, the implementation of the MPC in miniaturized
devices like Field Programmable Gate Arrays (FPGA) and microcontrollers
has historically been very small scale due to its complexity in
implementation and its computation time requirement. At the same
time, such embedded technologies have become an enabler for future
manufacturing enterprisers as well as a transformer of organizations
and markets. In this work, we take advantage of these recent advances
in this area in the deployment of one of the most studied and applied
control technique in the industrial engineering. In this paper, we
propose an efficient firmware for the implementation of constrained
MPC in the performed STM32 microcontroller using interior point
method. Indeed, performances study shows good execution speed
and low computational burden. These results encourage to develop
predictive control algorithms to be programmed in industrial standard
processes. The PID anti windup controller was also implemented in
the STM32 in order to make a performance comparison with the
MPC. The main features of the proposed constrained MPC framework
are illustrated through two examples.
Abstract: This paper presents the use of a newly created network
structure known as a Self-Delaying Dynamic Network (SDN) to
create a high resolution image from a set of time stepped input
frames. These SDNs are non-recurrent temporal neural networks
which can process time sampled data. SDNs can store input data
for a lifecycle and feature dynamic logic based connections between
layers. Several low resolution images and one high resolution image
of a scene were presented to the SDN during training by a Genetic
Algorithm. The SDN was trained to process the input frames in order
to recreate the high resolution image. The trained SDN was then used
to enhance a number of unseen noisy image sets. The quality of high
resolution images produced by the SDN is compared to that of high
resolution images generated using Bi-Cubic interpolation. The SDN
produced images are superior in several ways to the images produced
using Bi-Cubic interpolation.
Abstract: A series of microarray experiments produces observations
of differential expression for thousands of genes across multiple
conditions.
Principal component analysis(PCA) has been widely used in
multivariate data analysis to reduce the dimensionality of the data in
order to simplify subsequent analysis and allow for summarization of
the data in a parsimonious manner. PCA, which can be implemented
via a singular value decomposition(SVD), is useful for analysis of
microarray data.
For application of PCA using SVD we use the DNA microarray
data for the small round blue cell tumors(SRBCT) of childhood
by Khan et al.(2001). To decide the number of components which
account for sufficient amount of information we draw scree plot.
Biplot, a graphic display associated with PCA, reveals important
features that exhibit relationship between variables and also the
relationship of variables with observations.
Abstract: In modern human computer interaction systems
(HCI), emotion recognition is becoming an imperative characteristic.
The quest for effective and reliable emotion recognition in HCI has
resulted in a need for better face detection, feature extraction and
classification. In this paper we present results of feature space analysis
after briefly explaining our fully automatic vision based emotion
recognition method. We demonstrate the compactness of the feature
space and show how the 2d/3d based method achieves superior features
for the purpose of emotion classification. Also it is exposed that
through feature normalization a widely person independent feature
space is created. As a consequence, the classifier architecture has
only a minor influence on the classification result. This is particularly
elucidated with the help of confusion matrices. For this purpose
advanced classification algorithms, such as Support Vector Machines
and Artificial Neural Networks are employed, as well as the simple k-
Nearest Neighbor classifier.
Abstract: With the development of Internet and databases application techniques, the demand that lots of databases in the Internet are permitted to remote query and access for authorized users becomes common, and the problem that how to protect the copyright of relational databases arises. This paper simply introduces the knowledge of cloud model firstly, includes cloud generators and similar cloud. And then combined with the property of the cloud, a method of protecting relational databases copyright with cloud watermark is proposed according to the idea of digital watermark and the property of relational databases. Meanwhile, the corresponding watermark algorithms such as cloud watermark embedding algorithm and detection algorithm are proposed. Then, some experiments are run and the results are analyzed to validate the correctness and feasibility of the watermark scheme. In the end, the foreground of watermarking relational database and its research direction are prospected.
Abstract: This paper presents a boarding on biometric
authentication through the Keystrokes Dynamics that it intends to
identify a person from its habitual rhythm to type in conventional
keyboard. Seven done experiments: verifying amount of prototypes,
threshold, features and the variation of the choice of the times of the
features vector. The results show that the use of the Keystroke
Dynamics is simple and efficient for personal authentication, getting
optimum resulted using 90% of the features with 4.44% FRR and 0%
FAR.
Abstract: Content-based music retrieval generally involves analyzing, searching and retrieving music based on low or high level features of a song which normally used to represent artists, songs or music genre. Identifying them would normally involve feature extraction and classification tasks. Theoretically the greater features analyzed, the better the classification accuracy can be achieved but with longer execution time. Technique to select significant features is important as it will reduce dimensions of feature used in classification and contributes to the accuracy. Artificial Immune System (AIS) approach will be investigated and applied in the classification task. Bio-inspired audio content-based retrieval framework (B-ACRF) is proposed at the end of this paper where it embraces issues that need further consideration in music retrieval performances.
Abstract: Partitioning is a critical area of VLSI CAD. In order to build complex digital logic circuits its often essential to sub-divide multi -million transistor design into manageable Pieces. This paper looks at the various partitioning techniques aspects of VLSI CAD, targeted at various applications. We proposed an evolutionary time-series model and a statistical glitch prediction system using a neural network with selection of global feature by making use of clustering method model, for partitioning a circuit. For evolutionary time-series model, we made use of genetic, memetic & neuro-memetic techniques. Our work focused in use of clustering methods - K-means & EM methodology. A comparative study is provided for all techniques to solve the problem of circuit partitioning pertaining to VLSI design. The performance of all approaches is compared using benchmark data provided by MCNC standard cell placement benchmark net lists. Analysis of the investigational results proved that the Neuro-memetic model achieves greater performance then other model in recognizing sub-circuits with minimum amount of interconnections between them.
Abstract: Although Face detection is not a recent activity in the
field of image processing, it is still an open area for research. The
greatest step in this field is the work reported by Viola and its recent
analogous is Huang et al. Both of them use similar features and also
similar training process. The former is just for detecting upright
faces, but the latter can detect multi-view faces in still grayscale
images using new features called 'sparse feature'. Finding these
features is very time consuming and inefficient by proposed methods.
Here, we propose a new approach for finding sparse features using a
genetic algorithm system. This method requires less computational
cost and gets more effective features in learning process for face
detection that causes more accuracy.
Abstract: This paper presents an approach for an unequal error
protection of facial features of personal ID images coding. We
consider unequal error protection (UEP) strategies for the efficient
progressive transmission of embedded image codes over noisy
channels. This new method is based on the progressive image
compression embedded zerotree wavelet (EZW) algorithm and UEP
technique with defined region of interest (ROI). In this case is ROI
equal facial features within personal ID image. ROI technique is
important in applications with different parts of importance. In ROI
coding, a chosen ROI is encoded with higher quality than the
background (BG). Unequal error protection of image is provided by
different coding techniques and encoding LL band separately. In our
proposed method, image is divided into two parts (ROI, BG) that
consist of more important bytes (MIB) and less important bytes
(LIB). The proposed unequal error protection of image transmission
has shown to be more appropriate to low bit rate applications,
producing better quality output for ROI of the compresses image.
The experimental results verify effectiveness of the design. The
results of our method demonstrate the comparison of the UEP of
image transmission with defined ROI with facial features and the
equal error protection (EEP) over additive white gaussian noise
(AWGN) channel.