|Commenced in January 1999 || Frequency: Monthly || Edition: International|| Paper Count: 13 |
Computer, Electrical, Automation, Control and Information Engineering
Enhanced Planar Pattern Tracking for an Outdoor Augmented Reality System
In this paper, a scalable augmented reality framework for handheld devices is presented. The presented framework is enabled by using a server-client data communication structure, in which the search for tracking targets among a database of images is performed on the server-side while pixel-wise 3D tracking is performed on the client-side, which, in this case, is a handheld mobile device. Image search on the server-side adopts a residual-enhanced image descriptors representation that gives the framework a scalability property. The tracking algorithm on the client-side is based on a gravity-aligned feature descriptor which takes the advantage of a sensor-equipped mobile device and an optimized intensity-based image alignment approach that ensures the accuracy of 3D tracking. Automatic content streaming is achieved by using a key-frame selection algorithm, client working phase monitoring and standardized rules for content communication between the server and client. The recognition accuracy test performed on a standard dataset shows that the method adopted in the presented framework outperforms the Bag-of-Words (BoW) method that has been used in some of the previous systems. Experimental test conducted on a set of video sequences indicated the real-time performance of the tracking system with a frame rate at 15-30 frames per second. The presented framework is exposed to be functional in practical situations with a demonstration application on a campus walk-around.
Signal Processing Approach to Study Multifractality and Singularity of Solar Wind Speed Time Series
This paper investigates the nature of the fluctuation of the daily average Solar wind speed time series collected over a period of 2492 days, from 1st January, 1997 to 28th October, 2003. The degree of self-similarity and scalability of the Solar Wind Speed signal has been explored to characterise the signal fluctuation. Multi-fractal Detrended Fluctuation Analysis (MFDFA) method has been implemented on the signal which is under investigation to perform this task. Furthermore, the singularity spectra of the signals have been also obtained to gauge the extent of the multifractality of the time series signal.
Standard Languages for Creating a Database to Display Financial Statements on a Web Application
XHTML and XBRL are the standard languages for creating a database for the purpose of displaying financial statements on web applications. Today, XBRL is one of the most popular languages for business reporting. A large number of countries in the world recognize the role of XBRL language for financial reporting and the benefits that the reporting format provides in the collection, analysis, preparation, publication and the exchange of data (information) which is the positive side of this language. Here we present all advantages and opportunities that a company may have by using the XBRL format for business reporting. Also, this paper presents XBRL and other languages that are used for creating the database, such XML, XHTML, etc. The role of the AJAX complex model and technology will be explained in detail, and during the exchange of financial data between the web client and web server. Here will be mentioned basic layers of the network for data exchange via the web.
Analytics Model in a Telehealth Center Based on Cloud Computing and Local Storage
Some of the main goals about telecare such as monitoring, treatment, telediagnostic are deployed with the integration of applications with specific appliances. In order to achieve a coherent model to integrate software, hardware, and healthcare systems, different telehealth models with Internet of Things (IoT), cloud computing, artificial intelligence, etc. have been implemented, and their advantages are still under analysis. In this paper, we propose an integrated model based on IoT architecture and cloud computing telehealth center. Analytics module is presented as a solution to control an ideal diagnostic about some diseases. Specific features are then compared with the recently deployed conventional models in telemedicine. The main advantage of this model is the availability of controlling the security and privacy about patient information and the optimization on processing and acquiring clinical parameters according to technical characteristics.
Stochastic Model Predictive Control for Linear Discrete-Time Systems with Random Dither Quantization
Recently, feedback control systems using random dither
quantizers have been proposed for linear discrete-time systems.
However, the constraints imposed on state and control variables
have not yet been taken into account for the design of feedback
control systems with random dither quantization. Model predictive
control is a kind of optimal feedback control in which control
performance over a finite future is optimized with a performance
index that has a moving initial and terminal time. An important
advantage of model predictive control is its ability to handle
constraints imposed on state and control variables. Based on the
model predictive control approach, the objective of this paper is to
present a control method that satisfies probabilistic state constraints
for linear discrete-time feedback control systems with random dither
quantization. In other words, this paper provides a method for
solving the optimal control problems subject to probabilistic state
constraints for linear discrete-time feedback control systems with
random dither quantization.
Development of a Real-Time Brain-Computer Interface for Interactive Robot Therapy: An Exploration of EEG and EMG Features during Hypnosis
This study presents a framework for development of a new generation of therapy robots that can interact with users by monitoring their physiological and mental states. Here, we focused on one of the controversial methods of therapy, hypnotherapy. Hypnosis has shown to be useful in treatment of many clinical conditions. But, even for healthy people, it can be used as an effective technique for relaxation or enhancement of memory and concentration. Our aim is to develop a robot that collects information about user’s mental and physical states using electroencephalogram (EEG) and electromyography (EMG) signals and performs cost-effective hypnosis at the comfort of user’s house. The presented framework consists of three main steps: (1) Find the EEG-correlates of mind state before, during, and after hypnosis and establish a cognitive model for state changes, (2) Develop a system that can track the changes in EEG and EMG activities in real time and determines if the user is ready for suggestion, and (3) Implement our system in a humanoid robot that will talk and conduct hypnosis on users based on their mental states. This paper presents a pilot study in regard to the first stage, detection of EEG and EMG features during hypnosis.
An Improvement of Multi-Label Image Classification Method Based on Histogram of Oriented Gradient
Image Multi-label Classification (IMC) assigns a label or a set of labels to an image. The big demand for image annotation and archiving in the web attracts the researchers to develop many algorithms for this application domain. The existing techniques for IMC have two drawbacks: The description of the elementary characteristics from the image and the correlation between labels are not taken into account. In this paper, we present an algorithm (MIML-HOGLPP), which simultaneously handles these limitations. The algorithm uses the histogram of gradients as feature descriptor. It applies the Label Priority Power-set as multi-label transformation to solve the problem of label correlation. The experiment shows that the results of MIML-HOGLPP are better in terms of some of the evaluation metrics comparing with the two existing techniques.
Privacy-Preserving Location Sharing System with Client/Server Architecture in Mobile Online Social Network
Location sharing is a fundamental service in mobile Online Social Networks (mOSNs), which raises significant privacy concerns in recent years. Now, most location-based service applications adopt client/server architecture. In this paper, a location sharing system, named CSLocShare, is presented to provide flexible privacy-preserving location sharing with client/server architecture in mOSNs. CSLocShare enables location sharing between both trusted social friends and untrusted strangers without the third-party server. In CSLocShare, Location-Storing Social Network Server (LSSNS) provides location-based services but do not know the users’ real locations. The thorough analysis indicates that the users’ location privacy is protected. Meanwhile, the storage and the communication cost are saved. CSLocShare is more suitable and effective in reality.
An Approach for Vocal Register Recognition Based on Spectral Analysis of Singing
Recognizing and controlling vocal registers during
singing is a difficult task for beginner vocalist. It requires among
others identifying which part of natural resonators is being used
when a sound propagates through the body. Thus, an application
has been designed allowing for sound recording, automatic vocal
register recognition (VRR), and a graphical user interface providing
real-time visualization of the signal and recognition results. Six
spectral features are determined for each time frame and passed to the
support vector machine classifier yielding a binary decision on the
head or chest register assignment of the segment. The classification
training and testing data have been recorded by ten professional
female singers (soprano, aged 19-29) performing sounds for both
chest and head register. The classification accuracy exceeded 93%
in each of various validation schemes. Apart from a hard two-class
clustering, the support vector classifier returns also information on
the distance between particular feature vector and the discrimination
hyperplane in a feature space. Such an information reflects the level
of certainty of the vocal register classification in a fuzzy way. Thus,
the designed recognition and training application is able to assess and
visualize the continuous trend in singing in a user-friendly graphical
mode providing an easy way to control the vocal emission.
An Adaptive Dimensionality Reduction Approach for Hyperspectral Imagery Semantic Interpretation
With the development of HyperSpectral Imagery
(HSI) technology, the spectral resolution of HSI became denser,
which resulted in large number of spectral bands, high correlation
between neighboring, and high data redundancy. However, the
semantic interpretation is a challenging task for HSI analysis
due to the high dimensionality and the high correlation of the
different spectral bands. In fact, this work presents a dimensionality
reduction approach that allows to overcome the different issues
improving the semantic interpretation of HSI. Therefore, in order
to preserve the spatial information, the Tensor Locality Preserving
Projection (TLPP) has been applied to transform the original HSI.
In the second step, knowledge has been extracted based on the
adjacency graph to describe the different pixels. Based on the
transformation matrix using TLPP, a weighted matrix has been
constructed to rank the different spectral bands based on their
contribution score. Thus, the relevant bands have been adaptively
selected based on the weighted matrix. The performance of the
presented approach has been validated by implementing several
experiments, and the obtained results demonstrate the efficiency
of this approach compared to various existing dimensionality
reduction techniques. Also, according to the experimental results,
we can conclude that this approach can adaptively select the
relevant spectral improving the semantic interpretation of HSI.
Neuron-Based Control Mechanisms for a Robotic Arm and Hand
A robotic arm and hand controlled by simulated
neurons is presented. The robot makes use of a biological neuron
simulator using a point neural model. The neurons and synapses are
organised to create a finite state automaton including neural inputs
from sensors, and outputs to effectors. The robot performs a simple
pick-and-place task. This work is a proof of concept study for a
longer term approach. It is hoped that further work will lead to
more effective and flexible robots. As another benefit, it is hoped that
further work will also lead to a better understanding of human and
other animal neural processing, particularly for physical motion. This
is a multidisciplinary approach combining cognitive neuroscience,
robotics, and psychology.
Robust Control of a Dynamic Model of an F-16 Aircraft with Improved Damping through Linear Matrix Inequalities
This work presents an application of Linear Matrix
Inequalities (LMI) for the robust control of an F-16 aircraft through
an algorithm ensuring the damping factor to the closed loop system.
The results show that the zero and gain settings are sufficient to ensure
robust performance and stability with respect to various operating
points. The technique used is the pole placement, which aims to put
the system in closed loop poles in a specific region of the complex
plane. Test results using a dynamic model of the F-16 aircraft are
presented and discussed.
Incremental Learning of Independent Topic Analysis
In this paper, we present a method of applying
Independent Topic Analysis (ITA) to increasing the number of
document data. The number of document data has been increasing
since the spread of the Internet. ITA was presented as one method
to analyze the document data. ITA is a method for extracting the
independent topics from the document data by using the Independent
Component Analysis (ICA). ICA is a technique in the signal
processing; however, it is difficult to apply the ITA to increasing
number of document data. Because ITA must use the all document
data so temporal and spatial cost is very high. Therefore, we
present Incremental ITA which extracts the independent topics from
increasing number of document data. Incremental ITA is a method
of updating the independent topics when the document data is added
after extracted the independent topics from a just previous the data.
In addition, Incremental ITA updates the independent topics when the
document data is added. And we show the result applied Incremental
ITA to benchmark datasets.