Abstract: A multilayer self organizing neural neural network
(MLSONN) architecture for binary object extraction, guided by a beta
activation function and characterized by backpropagation of errors
estimated from the linear indices of fuzziness of the network output
states, is discussed. Since the MLSONN architecture is designed to
operate in a single point fixed/uniform thresholding scenario, it does
not take into cognizance the heterogeneity of image information in
the extraction process. The performance of the MLSONN architecture
with representative values of the threshold parameters of the beta
activation function employed is also studied. A three layer bidirectional
self organizing neural network (BDSONN) architecture
comprising fully connected neurons, for the extraction of objects from
a noisy background and capable of incorporating the underlying image
context heterogeneity through variable and adaptive thresholding,
is proposed in this article. The input layer of the network architecture
represents the fuzzy membership information of the image scene to
be extracted. The second layer (the intermediate layer) and the final
layer (the output layer) of the network architecture deal with the self
supervised object extraction task by bi-directional propagation of the
network states. Each layer except the output layer is connected to the
next layer following a neighborhood based topology. The output layer
neurons are in turn, connected to the intermediate layer following
similar topology, thus forming a counter-propagating architecture
with the intermediate layer. The novelty of the proposed architecture
is that the assignment/updating of the inter-layer connection weights
are done using the relative fuzzy membership values at the constituent
neurons in the different network layers. Another interesting feature
of the network lies in the fact that the processing capabilities of
the intermediate and the output layer neurons are guided by a beta
activation function, which uses image context sensitive adaptive
thresholding arising out of the fuzzy cardinality estimates of the
different network neighborhood fuzzy subsets, rather than resorting to
fixed and single point thresholding. An application of the proposed
architecture for object extraction is demonstrated using a synthetic
and a real life image. The extraction efficiency of the proposed
network architecture is evaluated by a proposed system transfer index
characteristic of the network.
Abstract: Music segmentation is a key issue in music information
retrieval (MIR) as it provides an insight into the
internal structure of a composition. Structural information about
a composition can improve several tasks related to MIR such
as searching and browsing large music collections, visualizing
musical structure, lyric alignment, and music summarization.
The authors of this paper present the MTSSM framework, a twolayer
framework for the multi-track segmentation of symbolic
music. The strength of this framework lies in the combination of
existing methods for local track segmentation and the application
of global structure information spanning via multiple tracks.
The first layer of the MTSSM uses various string matching
techniques to detect the best candidate segmentations for each
track of a multi-track composition independently. The second
layer combines all single track results and determines the best
segmentation for each track in respect to the global structure of
the composition.
Abstract: We decribe a formal specification and verification of the Rabin public-key scheme in the formal proof system Is-abelle/HOL. The idea is to use the two views of cryptographic verification: the computational approach relying on the vocabulary of probability theory and complexity theory and the formal approach based on ideas and techniques from logic and programming languages. The analysis presented uses a given database to prove formal properties of our implemented functions with computer support. Thema in task in designing a practical formalization of correctness as well as security properties is to cope with the complexity of cryptographic proving. We reduce this complexity by exploring a light-weight formalization that enables both appropriate formal definitions as well as eficient formal proofs. This yields the first computer-proved implementation of the Rabin public-key scheme in Isabelle/HOL. Consequently, we get reliable proofs with a minimal error rate augmenting the used database. This provides a formal basis for more computer proof constructions in this area.
Abstract: The electroencephalograph (EEG) signal is one of the most widely signal used in the bioinformatics field due to its rich information about human tasks. In this work EEG waves classification is achieved using the Discrete Wavelet Transform DWT with Fast Fourier Transform (FFT) by adopting the normalized EEG data. The DWT is used as a classifier of the EEG wave's frequencies, while FFT is implemented to visualize the EEG waves in multi-resolution of DWT. Several real EEG data sets (real EEG data for both normal and abnormal persons) have been tested and the results improve the validity of the proposed technique.
Abstract: As the material used for fuselage structure must
possess low density, high strength to weight ratio, the selection of
appropriate materials for fuselage structure is one of the most
important tasks. Aluminum metal itself is soft and low in strength. It
can be made stronger by giving proper combination of suitable alloy
addition, mechanical treatment and thermal treatment. The usual
thermal treatment given to aluminum alloys is called age-hardening
or precipitation hardening. In this paper, the studies are carried out on
7075 aluminum alloy which is how to improve strength level for
fuselage structure. The marked effect of the strength on the ternary
alloy is clearly demonstrated at several ageing times and
temperatures. It is concluded that aluminum-zinc-magnesium alloy
can get the highest strength level in natural ageing.
Abstract: The Implementation of the Union law faces major
challenges today. If for a long period of time, the Community and the
Union have persevered in their legislative vocation, now one can
notice that this large legislative quantity has complicated the task of
knowledge and of application the European standards. Under these
circumstances, it became necessary, in order to give effectiveness to
the European legislation, the development of some operational
application criteria and the generation of some new implementation
tools. The correct application of the European Union legislation by
the national public administrations was considered by the European
Commission as being crucial for further integration and proper
functioning of the internal market. Among the initiatives launched in
the past years to promote the exchange of good administrative
practices in the correct application of European Union legislation,
SOLVIT net has proved to be one of the most effective.
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: Nowadays the construction industry is growing specially among developing counties. Iran also has a critical role in these industries in terms of workers disorders. Work-related musculoskeletal disorders (WMSDs) assign 7% of the whole diseases in the society, which make some limitations. One of the main factors, which are ended to WMSDs, is awkward posture. Steel bar bending is considered as one of the prominent performance among construction workers. In this case study we conducted to find the major tasks of bar benders and the most important related risk factors. This study was carried out among twenty workers (18-45 years) as our volunteer samples in some construction sites with less than 6 floors in two regions of Tehran municipality. The data was gathered through in depth observation, interview and questionnaire. Also postural analysis was done by OWAS. In another part of study we used NMQ for gathering some data about psychosocial effects of work related disorders. Our findings show that 64% of workers were not aware of work risks, also about 59% of workers had troubles in their wrists, hands, and especially among workers who worked in steel bar bending. In 46% cases low back pain were prevalence. Considering with gathered data and results, awkward postures and long term tasks and its duration are known as the main risk factors in WMSDs among construction workers, so work-rest schedule and also tools design should be considered to make an ergonomic condition for the mentioned workers.
Abstract: Avionics software is safe-critical embedded software
and its architecture is evolving from traditional federated architectures
to Integrated Modular Avionics (IMA) to improve resource usability.
ARINC 653 (Avionics Application Standard Software Interface) is a
software specification for space and time partitioning in Safety-critical
avionics Real-time operating systems. Arinc653 uses two-level
scheduling strategies, but current modeling tools only apply to simple
problems of Arinc653 two-level scheduling, which only contain time
property. In avionics industry, we are always manually allocating
tasks and calculating the timing table of a real-time system to ensure
it-s running as we design. In this paper we represent an automatically
generating strategy which applies to the two scheduling problems with
dependent constraints in Arinc653 partition run-time environment. It
provides the functionality of automatic generation from the task and partition models to scheduling policy through allocating the tasks to the partitions while following the constraints, and then we design a simulating mechanism to check whether our policy is schedulable or
not
Abstract: The automatic construction of large, high-resolution
image vistas (mosaics) is an active area of research in the fields of
photogrammetry [1,2], computer vision [1,4], medical image
processing [4], computer graphics [3] and biometrics [8]. Image
stitching is one of the possible options to get image mosaics. Vista
Creation in image processing is used to construct an image with a
large field of view than that could be obtained with a single
photograph. It refers to transforming and stitching multiple images
into a new aggregate image without any visible seam or distortion in
the overlapping areas. Vista creation process aligns two partial
images over each other and blends them together. Image mosaics
allow one to compensate for differences in viewing geometry. Thus
they can be used to simplify tasks by simulating the condition in
which the scene is viewed from a fixed position with single camera.
While obtaining partial images the geometric anomalies like rotation,
scaling are bound to happen. To nullify effect of rotation of partial
images on process of vista creation, we are proposing rotation
invariant vista creation algorithm in this paper. Rotation of partial
image parts in the proposed method of vista creation may introduce
some missing region in the vista. To correct this error, that is to fill
the missing region further we have used image inpainting method on
the created vista. This missing view regeneration method also
overcomes the problem of missing view [31] in vista due to cropping,
irregular boundaries of partial image parts and errors in digitization
[35]. The method of missing view regeneration generates the missing
view of vista using the information present in vista itself.
Abstract: The demand for autonomous resource
management for distributed systems has increased in recent
years. Distributed systems require an efficient and powerful
communication mechanism between applications running on
different hosts and networks. The use of mobile agent
technology to distribute and delegate management tasks
promises to overcome the scalability and flexibility limitations
of the currently used centralized management approach. This
work proposes a multiagent system that adopts mobile agents
as a technology for tasks distribution, results collection, and
management of resources in large-scale distributed systems. A
new mobile agent-based approach for collecting results from
distributed system elements is presented. The technique of
artificial intelligence based on intelligent agents giving the
system a proactive behavior. The presented results are based
on a design example of an application operating in a mobile
environment.
Abstract: Evidence-based medicine is a new direction in modern healthcare. Its task is to prevent, diagnose and medicate diseases using medical evidence. Medical data about a large patient population is analyzed to perform healthcare management and medical research. In order to obtain the best evidence for a given disease, external clinical expertise as well as internal clinical experience must be available to the healthcare practitioners at right time and in the right manner. External evidence-based knowledge can not be applied directly to the patient without adjusting it to the patient-s health condition. We propose a data warehouse based approach as a suitable solution for the integration of external evidence-based data sources into the existing clinical information system and data mining techniques for finding appropriate therapy for a given patient and a given disease. Through integration of data warehousing, OLAP and data mining techniques in the healthcare area, an easy to use decision support platform, which supports decision making process of care givers and clinical managers, is built. We present three case studies, which show, that a clinical data warehouse that facilitates evidence-based medicine is a reliable, powerful and user-friendly platform for strategic decision making, which has a great relevance for the practice and acceptance of evidence-based medicine.
Abstract: For evaluating the severity of Chronic Obstructive Pulmonary Disease (COPD), one is interested in inspecting the airway wall thickening due to inflammation. Although airway segmentations have being well developed to reconstruct in high order, airway wall segmentation remains a challenge task. While tackling such problem as a multi-surface segmentation, the interrelation within surfaces needs to be considered. We propose a new method for three-dimensional airway wall segmentation using spring structural active contour model. The method incorporates the gravitational field of the image and repelling force field of the inner lumen as the soft constraint and the geometric spring structure of active contour as the hard constraint to approximate a three-dimensional coupled surface readily for thickness measurements. The results show the preservation of topology constraints of coupled surfaces. In conclusion, our springy, soft-tissue-like structure ensures the globally optimal solution and waives the shortness following by the inevitable improper inner surface constraint.
Abstract: In this work the characteristics of spatial signal detec¬tion from an antenna array in various sample cases are investigated. Cases for a various number of available prior information about the received signal and the background noise are considered. The spatial difference between a signal and noise is only used. The performance characteristics and detecting curves are presented. All test-statistics are obtained on the basis of the generalized likelihood ratio (GLR). The received results are correct for a short and long sample.
Abstract: Data mining, which is the exploration of
knowledge from the large set of data, generated as a result of
the various data processing activities. Frequent Pattern Mining
is a very important task in data mining. The previous
approaches applied to generate frequent set generally adopt
candidate generation and pruning techniques for the
satisfaction of the desired objective. This paper shows how
the different approaches achieve the objective of frequent
mining along with the complexities required to perform the
job. This paper will also look for hardware approach of cache
coherence to improve efficiency of the above process. The
process of data mining is helpful in generation of support
systems that can help in Management, Bioinformatics,
Biotechnology, Medical Science, Statistics, Mathematics,
Banking, Networking and other Computer related
applications. This paper proposes the use of both upward and
downward closure property for the extraction of frequent item
sets which reduces the total number of scans required for the
generation of Candidate Sets.
Abstract: In this study, we examined gender differences in: (1) a
flexible remembering task, that asked for episodic memory decisions
at an item-specific versus category-based level, and (2) the retrieval
specificity of autobiographical memory during free recall.
Differences favouring women were found on both measures.
Furthermore, a significant association was observed, across gender
groups, between level of specificity in the autobiographical memory
interview and sensitivity to gist on the flexible remembering task.
These results suggest that similar cognitive processes may partially
contribute to both the ability for specific autobiographical recall and
the capacity for inhibition of gist-information on the flexible
remembering task.
Abstract: A novel adaptive fuzzy trajectory tracking algorithm of Stewart platform based motion platform is proposed to compensate path deviation and degradation of controller-s performance due to actuator torque limit. The algorithm can be divided into two parts: the real-time trajectory shaping part and the joint space adaptive fuzzy controller part. For a reference trajectory in task space whenever any of the actuators is saturated, the desired acceleration of the reference trajectory is modified on-line by using dynamic model of motion platform. Meanwhile an additional action with respect to the difference between the nominal and modified trajectories is utilized in the non-saturated region of actuators to reduce the path error. Using modified trajectory as input, the joint space controller incorporates compute torque controller, leg velocity observer and fuzzy disturbance observer with saturation compensation. It can ensure stability and tracking performance of controller in present of external disturbance and position only measurement. Simulation results verify the effectiveness of proposed control scheme.
Abstract: Horizontal wells are proven to be better producers
because they can be extended for a long distance in the pay zone.
Engineers have the technical means to forecast the well productivity
for a given horizontal length. However, experiences have shown that
the actual production rate is often significantly less than that of
forecasted. It is a difficult task, if not impossible to identify the real
reason why a horizontal well is not producing what was forecasted.
Often the source of problem lies in the drilling of horizontal section
such as permeability reduction in the pay zone due to mud invasion
or snaky well patterns created during drilling. Although drillers aim
to drill a constant inclination hole in the pay zone, the more frequent
outcome is a sinusoidal wellbore trajectory. The two factors, which
play an important role in wellbore tortuosity, are the inclination and
side force at bit. A constant inclination horizontal well can only be
drilled if the bit face is maintained perpendicular to longitudinal axis
of bottom hole assembly (BHA) while keeping the side force nil at
the bit. This approach assumes that there exists no formation force at
bit. Hence, an appropriate BHA can be designed if bit side force and
bit tilt are determined accurately. The Artificial Neural Network
(ANN) is superior to existing analytical techniques. In this study, the
neural networks have been employed as a general approximation tool
for estimation of the bit side forces. A number of samples are
analyzed with ANN for parameters of bit side force and the results
are compared with exact analysis. Back Propagation Neural network
(BPN) is used to approximation of bit side forces. Resultant low
relative error value of the test indicates the usability of the BPN in
this area.
Abstract: This paper proposes a novel approach that combines statistical models and support vector machines. A hybrid scheme which appropriately incorporates the advantages of both the generative and discriminant model paradigms is described and evaluated. Support vector machines (SVMs) are trained to divide the whole speakers' space into small subsets of speakers within a hierarchical tree structure. During testing a speech token is assigned to its corresponding group and evaluation using gaussian mixture models (GMMs) is then processed. Experimental results show that the proposed method can significantly improve the performance of text independent speaker identification task. We report improvements of up to 50% reduction in identification error rate compared to the baseline statistical model.
Abstract: There are various solutions for improving existing overhead line systems with the general purpose of increasing their limited capacity. The capacity reserve of the existing overhead lines is an important problem that must be considered from different aspects. The paper contains a comparative analysis of the mechanical and thermal limitations of an existing overhead line based on certain calculation conditions characterizing the examined variants. The methodology of the proposed estimation of the permissible conductor temperature and maximum load current is described in detail. The transmission line model consists of specific information of an existing overhead line of the Latvian power network. The main purpose of the simulation tasks is to find an additional capacity reserve by using accurate mathematical models. The results of the obtained data are presented.