Abstract: The area of liberty, security and justice within the
European Union is still a work in progress. No one can deny that the
EU struggles between a monistic and a dualist approach.
The aim of our essay is to first review how the European law is
perceived by the rest of the international scene. It will then discuss
two main mechanisms at play: the interpretation of larger
international treaties and the penal mechanisms of European law.
Finally, it will help us understand the role of a penal Europe on the
international scene with concrete examples.
Special attention will be paid to cases that deal with fundamental
rights as they represent an interesting case study in Europe and in the
rest of the World. It could illustrate the aforementioned duality
currently present in the Union’s interpretation of international public
law. On the other hand, it will explore some specific European penal
mechanism through mutual recognition and the European arrest
warrant in the transnational criminality frame.
Concerning the interpretation of the treaties, it will first, underline
the ambiguity and the general nature of some treaties that leave the
EU exposed to tension and misunderstanding then it will review the
validity of an EU act (whether or not it is compatible with the rules of
International law).
Finally, it will focus on the most complete manifestation of liberty,
security and justice through the principle of mutual recognition. Used
initially in commercial matters, it has become “the cornerstone” of
European construction. It will see how it is applied in judicial
decisions (its main event and achieving success is via the European
arrest warrant) and how European member states have managed to
develop this cooperation.
Abstract: This research study aims to present a retrospective
study about speech recognition systems and artificial intelligence.
Speech recognition has become one of the widely used technologies,
as it offers great opportunity to interact and communicate with
automated machines. Precisely, it can be affirmed that speech
recognition facilitates its users and helps them to perform their daily
routine tasks, in a more convenient and effective manner. This
research intends to present the illustration of recent technological
advancements, which are associated with artificial intelligence.
Recent researches have revealed the fact that speech recognition is
found to be the utmost issue, which affects the decoding of speech. In
order to overcome these issues, different statistical models were
developed by the researchers. Some of the most prominent statistical
models include acoustic model (AM), language model (LM), lexicon
model, and hidden Markov models (HMM). The research will help in
understanding all of these statistical models of speech recognition.
Researchers have also formulated different decoding methods, which
are being utilized for realistic decoding tasks and constrained
artificial languages. These decoding methods include pattern
recognition, acoustic phonetic, and artificial intelligence. It has been
recognized that artificial intelligence is the most efficient and reliable
methods, which are being used in speech recognition.
Abstract: Theory of Mind (ToM) refers to the ability to infer
another’s mental state. With appropriate ToM, one can behave well in
social interactions. A growing body of evidence has demonstrated that
patients with temporal lobe epilepsy (TLE) may damage ToM by
affecting on regions of the underlying neural network of ToM.
However, the question of whether there is cerebral laterality for ToM
functions remains open. This study aimed to examine whether there is
cerebral lateralization for ToM abilities in TLE patients. Sixty-seven
adult TLE patients and 30 matched healthy controls (HC) were
recruited. Patients were classified into right (RTLE), left (LTLE), and
bilateral (BTLE) TLE groups on the basis of a consensus panel review
of their seizure semiology, EEG findings, and brain imaging results.
All participants completed an intellectual test and four tasks measuring
basic and advanced ToM. The results showed that, on all ToM tasks,
(1) each patient group performed worse than HC; (2) there were no
significant differences between LTLE and RTLE groups; and (3) the
BTLE group performed the worst. It appears that the neural network
responsible for ToM is distributed evenly between the cerebral
hemispheres.
Abstract: This study examined how individuals in their
respective teams contributed to innovation performance besides
defining the term of innovation in their own respective views. This
study also identified factors that motivated University staff to
contribute to the innovation products. In addition, it examined
whether there is a significant relationship between professional
training level and the length of service among university staff
towards innovation and to what extent do the two variables
contributed towards innovative products. The significance of this
study is that it revealed the strengths and weaknesses of the
university staff when contributing to innovation performance.
Stratified-random sampling was employed to determine the samples
representing the population of lecturers in the study, involving 123
lecturers in one of the local universities in Malaysia. The method
employed to analyze the data is through categorizing into themes for
the open-ended questions besides using descriptive and inferential
statistics for the quantitative data. This study revealed that two types
of definition for the term “innovation” exist among the university
staff, namely, creation of new product or new approach to do things
as well as value-added creative way to upgrade or improve existing
process and service to be more efficient. This study found that the
most prominent factor that propels them towards innovation is to
improve the product in order to benefit users, followed by selfsatisfaction
and recognition. This implies that the staff in the
organization viewed the creation of innovative products as a process
of growth to fulfill the needs of others and also to realize their
personal potential. This study also found that there was only a
significant relationship between the professional training level and
the length of service of 4 - 6 years among the university staff. The
rest of the groups based on the length of service showed that there
was no significant relationship with the professional training level
towards innovation. Moreover, results of the study on directional
measures depicted that the relationship for the length of service of 4-
6 years with professional training level among the university staff is
quite weak. This implies that good organization management lies on
the shoulders of the key leaders who enlighten the path to be
followed by the staff.
Abstract: Neuroplasticity or the flexibility of the neural system
is the ability of the brain to adapt to the lack or deterioration of sense
and the capability of the neural system to modify itself through
changing shape and function. Not only have studies revealed that
neuroplasticity does not end in childhood, but also they have proven
that it continues till the end of life and is not limited to the neural
system and covers the cognitive system as well. In the field of
cognition, neuroplasticity is defined as the ability to change old
thoughts according to new conditions and the individuals' differences
in using various styles of cognitive regulation inducing several social,
emotional and cognitive outcomes. This paper attempts to discuss and
define major theories and principles of neuroplasticity and elaborate
on nature or nurture.
Abstract: The detection of moving objects from a video image
sequences is very important for object tracking, activity recognition,
and behavior understanding in video surveillance.
The most used approach for moving objects detection / tracking is
background subtraction algorithms. Many approaches have been
suggested for background subtraction. But, these are illumination
change sensitive and the solutions proposed to bypass this problem
are time consuming.
In this paper, we propose a robust yet computationally efficient
background subtraction approach and, mainly, focus on the ability to
detect moving objects on dynamic scenes, for possible applications in
complex and restricted access areas monitoring, where moving and
motionless persons must be reliably detected. It consists of three
main phases, establishing illumination changes invariance,
background/foreground modeling and morphological analysis for
noise removing.
We handle illumination changes using Contrast Limited Histogram
Equalization (CLAHE), which limits the intensity of each pixel to
user determined maximum. Thus, it mitigates the degradation due to
scene illumination changes and improves the visibility of the video
signal. Initially, the background and foreground images are extracted
from the video sequence. Then, the background and foreground
images are separately enhanced by applying CLAHE.
In order to form multi-modal backgrounds we model each channel
of a pixel as a mixture of K Gaussians (K=5) using Gaussian Mixture
Model (GMM). Finally, we post process the resulting binary
foreground mask using morphological erosion and dilation
transformations to remove possible noise.
For experimental test, we used a standard dataset to challenge the
efficiency and accuracy of the proposed method on a diverse set of
dynamic scenes.
Abstract: This paper presents two techniques, local feature
extraction using image spectrum and low frequency spectrum
modelling using GMM to capture the underlying statistical
information to improve the performance of face recognition
system. Local spectrum features are extracted using overlap sub
block window that are mapped on the face image. For each of this
block, spatial domain is transformed to frequency domain using
DFT. A low frequency coefficient is preserved by discarding high
frequency coefficients by applying rectangular mask on the
spectrum of the facial image. Low frequency information is non-
Gaussian in the feature space and by using combination of several
Gaussian functions that has different statistical properties, the best
feature representation can be modelled using probability density
function. The recognition process is performed using maximum
likelihood value computed using pre-calculated GMM components.
The method is tested using FERET datasets and is able to achieved
92% recognition rates.
Abstract: The performance and analysis of speech recognition
system is illustrated in this paper. An approach to recognize the
English word corresponding to digit (0-9) spoken by 2 different
speakers is captured in noise free environment. For feature extraction,
speech Mel frequency cepstral coefficients (MFCC) has been used
which gives a set of feature vectors from recorded speech samples.
Neural network model is used to enhance the recognition
performance. Feed forward neural network with back propagation
algorithm model is used. However other speech recognition
techniques such as HMM, DTW exist. All experiments are carried
out on Matlab.
Abstract: The goal of image segmentation is to cluster pixels
into salient image regions. Segmentation could be used for object
recognition, occlusion boundary estimation within motion or stereo
systems, image compression, image editing, or image database lookup.
In this paper, we present a color image segmentation using
support vector machine (SVM) pixel classification. Firstly, the pixel
level color and texture features of the image are extracted and they
are used as input to the SVM classifier. These features are extracted
using the homogeneity model and Gabor Filter. With the extracted
pixel level features, the SVM Classifier is trained by using FCM
(Fuzzy C-Means).The image segmentation takes the advantage of
both the pixel level information of the image and also the ability of
the SVM Classifier. The Experiments show that the proposed method
has a very good segmentation result and a better efficiency, increases
the quality of the image segmentation compared with the other
segmentation methods proposed in the literature.
Abstract: Human motion capture has become one of the major
area of interest in the field of computer vision. Some of the major
application areas that have been rapidly evolving include the
advanced human interfaces, virtual reality and security/surveillance
systems. This study provides a brief overview of the techniques and
applications used for the markerless human motion capture, which
deals with analyzing the human motion in the form of mathematical
formulations. The major contribution of this research is that it
classifies the computer vision based techniques of human motion
capture based on the taxonomy, and then breaks its down into four
systematically different categories of tracking, initialization, pose
estimation and recognition. The detailed descriptions and the
relationships descriptions are given for the techniques of tracking and
pose estimation. The subcategories of each process are further
described. Various hypotheses have been used by the researchers in
this domain are surveyed and the evolution of these techniques have
been explained. It has been concluded in the survey that most
researchers have focused on using the mathematical body models for
the markerless motion capture.
Abstract: This paper presents an efficient fusion algorithm for
iris images to generate stable feature for recognition in unconstrained
environment. Recently, iris recognition systems are focused on real
scenarios in our daily life without the subject’s cooperation. Under
large variation in the environment, the objective of this paper is to
combine information from multiple images of the same iris. The
result of image fusion is a new image which is more stable for further
iris recognition than each original noise iris image. A wavelet-based
approach for multi-resolution image fusion is applied in the fusion
process. The detection of the iris image is based on Adaboost
algorithm and then local binary pattern (LBP) histogram is then
applied to texture classification with the weighting scheme.
Experiment showed that the generated features from the proposed
fusion algorithm can improve the performance for verification system
through iris recognition.
Abstract: The inherent skin patterns created at the joints in the
finger exterior are referred as finger knuckle-print. It is exploited to
identify a person in a unique manner because the finger knuckle print
is greatly affluent in textures. In biometric system, the region of
interest is utilized for the feature extraction algorithm. In this paper,
local and global features are extracted separately. Fast Discrete
Orthonormal Stockwell Transform is exploited to extract the local
features. Global feature is attained by escalating the size of Fast
Discrete Orthonormal Stockwell Transform to infinity. Two features
are fused to increase the recognition accuracy. A matching distance is
calculated for both the features individually. Then two distances are
merged mutually to acquire the final matching distance. The
proposed scheme gives the better performance in terms of equal error
rate and correct recognition rate.
Abstract: The 3D body movement signals captured during
human-human conversation include clues not only to the content of
people’s communication but also to their culture and personality.
This paper is concerned with automatic extraction of this information
from body movement signals. For the purpose of this research, we
collected a novel corpus from 27 subjects, arranged them into groups
according to their culture. We arranged each group into pairs and
each pair communicated with each other about different topics.
A state-of-art recognition system is applied to the problems of
person, culture, and topic recognition. We borrowed modeling,
classification, and normalization techniques from speech recognition.
We used Gaussian Mixture Modeling (GMM) as the main technique
for building our three systems, obtaining 77.78%, 55.47%, and
39.06% from the person, culture, and topic recognition systems
respectively. In addition, we combined the above GMM systems with
Support Vector Machines (SVM) to obtain 85.42%, 62.50%, and
40.63% accuracy for person, culture, and topic recognition
respectively.
Although direct comparison among these three recognition
systems is difficult, it seems that our person recognition system
performs best for both GMM and GMM-SVM, suggesting that intersubject
differences (i.e. subject’s personality traits) are a major
source of variation. When removing these traits from culture and
topic recognition systems using the Nuisance Attribute Projection
(NAP) and the Intersession Variability Compensation (ISVC)
techniques, we obtained 73.44% and 46.09% accuracy from culture
and topic recognition systems respectively.
Abstract: The study of the electrical signals produced by neural
activities of human brain is called Electroencephalography. In this
paper, we propose an automatic and efficient EEG signal
classification approach. The proposed approach is used to classify the
EEG signal into two classes: epileptic seizure or not. In the proposed
approach, we start with extracting the features by applying Discrete
Wavelet Transform (DWT) in order to decompose the EEG signals
into sub-bands. These features, extracted from details and
approximation coefficients of DWT sub-bands, are used as input to
Principal Component Analysis (PCA). The classification is based on
reducing the feature dimension using PCA and deriving the supportvectors
using Support Vector Machine (SVM). The experimental are
performed on real and standard dataset. A very high level of
classification accuracy is obtained in the result of classification.
Abstract: Speech enhancement is a long standing problem with
numerous applications like teleconferencing, VoIP, hearing aids and
speech recognition. The motivation behind this research work is to
obtain a clean speech signal of higher quality by applying the optimal
noise cancellation technique. Real-time adaptive filtering algorithms
seem to be the best candidate among all categories of the speech
enhancement methods. In this paper, we propose a speech
enhancement method based on Recursive Least Squares (RLS)
adaptive filter of speech signals. Experiments were performed on
noisy data which was prepared by adding AWGN, Babble and Pink
noise to clean speech samples at -5dB, 0dB, 5dB and 10dB SNR
levels. We then compare the noise cancellation performance of
proposed RLS algorithm with existing NLMS algorithm in terms of
Mean Squared Error (MSE), Signal to Noise ratio (SNR) and SNR
Loss. Based on the performance evaluation, the proposed RLS
algorithm was found to be a better optimal noise cancellation
technique for speech signals.
Abstract: The increasing demand of gallium, indium and
rare-earth elements for the production of electronics, e.g. solid
state-lighting, photovoltaics, integrated circuits, and liquid crystal
displays, will exceed the world-wide supply according to current
forecasts. Recycling systems to reclaim these materials are not yet in
place, which challenges the sustainability of these technologies. This
paper proposes a multispectral imaging system as a basis for a vision
based recognition system for valuable components of electronics
waste. Multispectral images intend to enhance the contrast of images
of printed circuit boards (single components, as well as labels) for
further analysis, such as optical character recognition and entire
printed circuit board recognition. The results show, that a higher
contrast is achieved in the near infrared compared to ultraviolett and
visible light.
Abstract: Iris codes contain bits with different entropy. This
work investigates different strategies to reduce the size of iris
code templates with the aim of reducing storage requirements and
computational demand in the matching process. Besides simple subsampling
schemes, also a binary multi-resolution representation as
used in the JBIG hierarchical coding mode is assessed. We find that
iris code template size can be reduced significantly while maintaining
recognition accuracy. Besides, we propose a two-stage identification
approach, using small-sized iris code templates in a pre-selection
stage, and full resolution templates for final identification, which
shows promising recognition behaviour.
Abstract: In this study, data loss tolerance of Support Vector Machines (SVM) based activity recognition model and multi activity classification performance when data are received over a lossy wireless sensor network is examined. Initially, the classification algorithm we use is evaluated in terms of resilience to random data loss with 3D acceleration sensor data for sitting, lying, walking and standing actions. The results show that the proposed classification method can recognize these activities successfully despite high data loss. Secondly, the effect of differentiated quality of service performance on activity recognition success is measured with activity data acquired from a multi hop wireless sensor network, which introduces high data loss. The effect of number of nodes on the reliability and multi activity classification success is demonstrated in simulation environment. To the best of our knowledge, the effect of data loss in a wireless sensor network on activity detection success rate of an SVM based classification algorithm has not been studied before.
Abstract: In this study, we propose a novel technique for acoustic
echo suppression (AES) during speech recognition under barge-in
conditions. Conventional AES methods based on spectral subtraction
apply fixed weights to the estimated echo path transfer function
(EPTF) at the current signal segment and to the EPTF estimated until
the previous time interval. However, the effects of echo path changes
should be considered for eliminating the undesired echoes. We
describe a new approach that adaptively updates weight parameters in
response to abrupt changes in the acoustic environment due to
background noises or double-talk. Furthermore, we devised a voice
activity detector and an initial time-delay estimator for barge-in speech
recognition in communication networks. The initial time delay is
estimated using log-spectral distance measure, as well as
cross-correlation coefficients. The experimental results show that the
developed techniques can be successfully applied in barge-in speech
recognition systems.
Abstract: With a long history, dual-task has become one of the
most intriguing research fields regarding human brain functioning
and cognition. However, findings considering effects of taskinterrelations
are limited (especially, in combined motor and
cognitive tasks). Therefore, we aimed at developing a measurement
system in order to analyse interrelation effects of cognitive and motor
tasks. On the one hand, the present study demonstrates the
applicability of the measurement system and on the other hand first
results regarding a systematisation of different task combinations are
shown. Future investigations should combine imagine technologies
and this developed measurement system.