Abstract: In order to protect data privacy, image with sensitive or
private information needs to be encrypted before being outsourced to
the cloud. However, this causes difficulties in image retrieval and data
management. A secure image retrieval method based on orthogonal
decomposition is proposed in the paper. The image is divided into two
different components, for which encryption and feature extraction are
executed separately. As a result, cloud server can extract features from
an encrypted image directly and compare them with the features of the
queried images, so that the user can thus obtain the image. Different
from other methods, the proposed method has no special requirements
to encryption algorithms. Experimental results prove that the proposed
method can achieve better security and better retrieval precision.
Abstract: The biodegradable family of polymers
polyhydroxyalkanoates is an interesting substitute for convectional
fossil-based plastics. However, the manufacturing and environmental
impacts associated with their production via intracellular bacterial
fermentation are strongly dependent on the raw material used and on
energy consumption during the extraction process, limiting their
potential for commercialization. Industrial wastewater is studied in
this paper as a promising alternative feedstock for waste valorization.
Based on results from laboratory and pilot-scale experiments, a
conceptual process design, techno-economic analysis and life cycle
assessment are developed for the large-scale production of the most
common type of polyhydroxyalkanoate, polyhydroxbutyrate.
Intracellular polyhydroxybutyrate is obtained via fermentation of
microbial community present in industrial wastewater and the
downstream processing is based on chemical digestion with
surfactant and hypochlorite. The economic potential and
environmental performance results help identifying bottlenecks and
best opportunities to scale-up the process prior to industrial
implementation. The outcome of this research indicates that the
fermentation of wastewater towards PHB presents advantages
compared to traditional PHAs production from sugars because the
null environmental burdens and financial costs of the raw material in
the bioplastic production process. Nevertheless, process optimization
is still required to compete with the petrochemicals counterparts.
Abstract: The aim of software maintenance is to maintain
the software system in accordance with advancement in software
and hardware technology. One of the early works on software
maintenance is to extract information at higher level of abstraction. In
this paper, we present the process of how to design an information
extraction tool for software maintenance. The tool can extract the
basic information from old programs such as about variables, based
classes, derived classes, objects of classes, and functions. The tool
have two main parts; the lexical analyzer module that can read the
input file character by character, and the searching module which
users can get the basic information from the existing programs. We
implemented this tool for a patterned sub-C++ language as an input
file.
Abstract: Essential oils have a significant antimicrobial activity.
These oils can successfully replace the antibiotics. So, the
microorganisms show their inefficiencies resistant for the antibiotics.
For this reason, we study the physicochemical analysis and
antimicrobial activity of the essential oil of Daucus carota. The
extraction is done by steam distillation of water which brought us a
very significant return of 4.65%. The analysis of the essential oil is
performed by GC / MS and has allowed us to identify 32 compounds
in the oil of D. carota flowering tops of Bouira. Three of which are in
the majority are the α-Pinene (22.3%), the carotol (21.7%) and the
limonene (15.8%).
Abstract: This paper presents system level CMOS solid-state
nanopore techniques enhancement for speedup next generation
molecular recording and high throughput channels. This discussion
also considers optimum number of base-pair (bp) measurements
through channel as an important role to enhance potential read
accuracy. Effective power consumption estimation offered suitable
range of multi-channel configuration. Nanopore bp extraction model
in statistical method could contribute higher read accuracy with
longer read-length (200 < read-length). Nanopore ionic current
switching with Time Multiplexing (TM) based multichannel readout
system contributed hardware savings.
Abstract: The Algeria by its location offers a rich and diverse
vegetation. A large number of aromatic and medicinal plants grow
spontaneously. The interest in these plants has continued to grow in
recent years. Their particular properties due to the essential oil
fraction can be utilized to treat microbial infections. To this end, and
in the context of the valuation of the Algerian flora, we became
interested in the species of the family Lamiaceae which is one of the
most used as a global source of spices. The plant on which we have
based our choice is a species of sage "Salvia officinalis" from the
Isser localized region within the province of Boumerdes. This work
focuses on the study of the antimicrobial activity of essential oil
extracted from the leaves of Salvia officinalis. The extraction is
carried out by essential oil hydrodistillation and reveals a yield of
1.06℅. The study of the antimicrobial activity of the essential oil by
the method of at aromatogramme shown that Gram positive bacteria
are most susceptible (Staphylococcus aureus and Bacillus subtilis)
with a strong inhibition of growth. The yeast Candida albicans
fungus Aspergillus niger and have shown moderately sensitive.
Abstract: Ionic liquids consisting of a phosphonium cationic
moiety and a saccharinate anion are synthesized and compared with
their precursors, phosphonium chlorides, in reference to their
extraction efficiency towards L-lactic acid. On the base of
measurements of the acid and the water partitioning in the
equilibrium biphasic systems, the molar ratios between acid, water
and ionic liquid are estimated which allows to deduce the lactic acid
extractive pathway. The effect of a salting-out addition that
strengthens hydrophobicity in both phases is studied in view to reveal
the best biphasic system with respect to IL low toxicity and high
extraction efficiency.
Abstract: Array-based gene expression analysis is a powerful
tool to profile expression of genes and to generate information on
therapeutic effects of new anti-cancer compounds. Anti-apoptotic
effect of thymoquinone was studied in MCF7 breast cancer cell line
using gene expression profiling with cDNA microarray. The purity
and yield of RNA samples were determined using RNeasyPlus Mini
kit. The Agilent RNA 6000 NanoLabChip kit evaluated the quantity
of the RNA samples. AffinityScript RT oligo-dT promoter primer
was used to generate cDNA strands. T7 RNA polymerase was used to
convert cDNA to cRNA. The cRNA samples and human universal
reference RNA were labelled with Cy-3-CTP and Cy-5-CTP,
respectively. Feature Extraction and GeneSpring softwares analysed
the data. The single experiment analysis revealed involvement of 64
pathways with up-regulated genes and 78 pathways with downregulated
genes. The MAPK and p38-MAPK pathways were
inhibited due to the up-regulation of PTPRR gene. The inhibition of
p38-MAPK suggested up-regulation of TGF-ß pathway. Inhibition of
p38-MAPK caused up-regulation of TP53 and down-regulation of
Bcl2 genes indicating involvement of intrinsic apoptotic pathway.
Down-regulation of CARD16 gene as an adaptor molecule regulated
CASP1 and suggested necrosis-like programmed cell death and
involvement of caspase in apoptosis. Furthermore, down-regulation
of GPCR, EGF-EGFR signalling pathways suggested reduction of
ER. Involvement of AhR pathway which control cytochrome P450
and glucuronidation pathways showed metabolism of Thymoquinone.
The findings showed differential expression of several genes in
apoptosis pathways with thymoquinone treatment in estrogen
receptor-positive breast cancer cells.
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: In this paper, we used data mining to extract
biomedical knowledge. In general, complex biomedical data
collected in studies of populations are treated by statistical methods,
although they are robust, they are not sufficient in themselves to
harness the potential wealth of data. For that you used in step two
learning algorithms: the Decision Trees and Support Vector Machine
(SVM). These supervised classification methods are used to make the
diagnosis of thyroid disease. In this context, we propose to promote
the study and use of symbolic data mining techniques.
Abstract: Electroencephalogram (EEG) is a noninvasive
technique that registers signals originating from the firing of neurons
in the brain. The Emotiv EEG Neuroheadset is a consumer product
comprised of 14 EEG channels and was used to record the reactions
of the neurons within the brain to two forms of stimuli in 10
participants. These stimuli consisted of auditory and visual formats
that provided directions of ‘right’ or ‘left.’ Participants were
instructed to raise their right or left arm in accordance with the
instruction given. A scenario in OpenViBE was generated to both
stimulate the participants while recording their data. In OpenViBE,
the Graz Motor BCI Stimulator algorithm was configured to govern
the duration and number of visual stimuli. Utilizing EEGLAB under
the cross platform MATLAB®, the electrodes most stimulated during
the study were defined. Data outputs from EEGLAB were analyzed
using IBM SPSS Statistics® Version 20. This aided in determining
the electrodes to use in the development of a brain-machine interface
(BMI) using real-time EEG signals from the Emotiv EEG
Neuroheadset. Signal processing and feature extraction were
accomplished via the Simulink® signal processing toolbox. An
Arduino™ Duemilanove microcontroller was used to link the Emotiv
EEG Neuroheadset and the right and left Mecha TE™ Hands.
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: Over the past few years, the online multimedia
collection has grown at a fast pace. Several companies showed
interest to study the different ways to organise the amount of audio
information without the need of human intervention to generate
metadata. In the past few years, many applications have emerged on
the market which are capable of identifying a piece of music in a
short time. Different audio effects and degradation make it much
harder to identify the unknown piece. In this paper, an audio
fingerprinting system which makes use of a non-parametric based
algorithm is presented. Parametric analysis is also performed using
Gaussian Mixture Models (GMMs). The feature extraction methods
employed are the Mel Spectrum Coefficients and the MPEG-7 basic
descriptors. Bin numbers replaced the extracted feature coefficients
during the non-parametric modelling. The results show that nonparametric
analysis offer potential results as the ones mentioned in
the literature.
Abstract: Due to the rapid increase of Internet, web opinion
sources dynamically emerge which is useful for both potential
customers and product manufacturers for prediction and decision
purposes. These are the user generated contents written in natural
languages and are unstructured-free-texts scheme. Therefore, opinion
mining techniques become popular to automatically process customer
reviews for extracting product features and user opinions expressed
over them. Since customer reviews may contain both opinionated and
factual sentences, a supervised machine learning technique applies
for subjectivity classification to improve the mining performance. In
this paper, we dedicate our work is the task of opinion
summarization. Therefore, product feature and opinion extraction is
critical to opinion summarization, because its effectiveness
significantly affects the identification of semantic relationships. The
polarity and numeric score of all the features are determined by
Senti-WordNet Lexicon. The problem of opinion summarization
refers how to relate the opinion words with respect to a certain
feature. Probabilistic based model of supervised learning will
improve the result that is more flexible and effective.
Abstract: This paper presents the results and findings from a
parametric study on the water surface elevation at upstream of bridge
constriction for subcritical flow. In this study, the influence of
Manning's Roughness Coefficient of main channel (nmc) and
floodplain (nfp), and bridge opening (b) flow rate (Q), contraction
(kcon) and expansion coefficients (kexp) were investigated on
backwater level. The DECK bridge models with different span widths
and without any pier were investigated within the two stage channel
having various roughness conditions. One of the most commonly
used commercial one-dimensional HEC-RAS model was used in this
parametric study. This study showed that the effects of main channel
roughness (nmc) and flow rate (Q) on the backwater level are much
higher than those of the floodplain roughness (nfp). Bridge opening
(b) with contraction (kcon) and expansion coefficients (kexp) have very
little effect on the backwater level within this range of parameters.
Abstract: Blueberries are widely valued for their high content in
phenolic compounds with antioxidant activity, and hence beneficial
for the human health. In this way, a study was done to determine the
phenolic composition (total phenols, anthocyanins and tannins) and
antioxidant activity of blueberries from three cultivars (Duke,
Bluecrop, and Ozarkblue) grown in two different Portuguese farms.
Initially two successive extractions were done with methanol
followed by two extractions with aqueous acetone solutions. These
extracts obtained were then used to evaluate the amount of phenolic
compounds and the antioxidant activity. The total phenols were
observed to vary from 4.9 to 8.2 mg GAE/g fresh weight, with
anthocyanin’s contents in the range 1.5-2.8 mg EMv3G/g and tannins
contents in the range 1.5- 3.8 mg/g. The results for antioxidant
activity ranged from 9.3 to 23.2 molTE/g and from 24.7 to 53.4molTE/g, when measured, respectively, by DPPH and ABTS
methods. In conclusion it was observed that, in general, the cultivar
had a visible effect on the phenols present, and furthermore, the
geographical origin showed relevance either in the phenols contents
or the antioxidant activity.
Abstract: Analysis of the properties of coconut (Cocos nucifera)
and its oil was evaluated in this work using standard analytical
techniques. The analyses carried out include proximate composition
of the fruit, extraction of oil from the fruit using different process
parameters and physicochemical analysis of the extracted oil. The
results showed the percentage (%) moisture, crude lipid, crude
protein, ash and carbohydrate content of the coconut as 7.59, 55.15,
5.65, 7.35 and 19.51 respectively. The oil from the coconut fruit was
odourless and yellowish liquid at room temperature (30oC). The
treatment combinations used (leaching time, leaching temperature
and solute: solvent ratio) showed significant differences (P
Abstract: Development of a method to estimate gene functions is
an important task in bioinformatics. One of the approaches for the
annotation is the identification of the metabolic pathway that genes are
involved in. Since gene expression data reflect various intracellular
phenomena, those data are considered to be related with genes’
functions. However, it has been difficult to estimate the gene function
with high accuracy. It is considered that the low accuracy of the
estimation is caused by the difficulty of accurately measuring a gene
expression. Even though they are measured under the same condition,
the gene expressions will vary usually. In this study, we proposed a
feature extraction method focusing on the variability of gene
expressions to estimate the genes' metabolic pathway accurately. First,
we estimated the distribution of each gene expression from replicate
data. Next, we calculated the similarity between all gene pairs by KL
divergence, which is a method for calculating the similarity between
distributions. Finally, we utilized the similarity vectors as feature
vectors and trained the multiclass SVM for identifying the genes'
metabolic pathway. To evaluate our developed method, we applied the
method to budding yeast and trained the multiclass SVM for
identifying the seven metabolic pathways. As a result, the accuracy
that calculated by our developed method was higher than the one that
calculated from the raw gene expression data. Thus, our developed
method combined with KL divergence is useful for identifying the
genes' metabolic pathway.
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 aim of research was to define the relations
between volatile compounds, some parameters (pH, titratable acidity
(TA), total soluble solid (TSS), lactic acid bacteria count) and
consumer preference of commercial fermented milks. These relations
tend to be used for controlling and developing new fermented milk
product. Three leading commercial brands of fermented milks in
Thailand were evaluated by consumers (n=71) using hedonic scale
for four attributes (sweetness, sourness, flavour, and overall liking),
volatile compounds using headspace-solid phase microextraction
(HS-SPME) GC-MS, pH, TA, TSS and LAB count. Then the
relations were analyzed by principal component analysis (PCA). The
PCA data showed that all of four attributes liking scores were related
to each other. They were also related to TA, TSS and volatile
compounds. The related volatile compounds were mainly on
fermented produced compounds including acetic acid, furanmethanol,
furfural, octanoic acid and the volatiles known as artificial fruit
flavour (beta pinene, limonene, vanillin, and ethyl vanillin). These
compounds were provided the information about flavour addition in
commercial fermented milk in Thailand.