Abstract: Multispectral screening systems are becoming more
popular because of their very interesting properties and applications.
One of the most significant applications of multispectral screening
systems is prevention of terrorist attacks. There are many kinds of
threats and many methods of detection. Visual detection of objects
hidden under clothing of a person is one of the most challenging
problems of threats detection. There are various solutions of the
problem; however, the most effective utilize multispectral
surveillance imagers. The development of imaging devices and
exploration of new spectral bands is a chance to introduce new
equipment for assuring public safety. We investigate the possibility
of long lasting detection of potentially dangerous objects covered
with various types of clothing. In the article we present the results of
comparative studies of passive imaging in three spectrums – visible,
infrared and terahertz.
Abstract: In this paper two approaches to joint signal detection,
time of arrival (ToA) and angle of arrival (AoA) estimation in
multi-element antenna array are investigated. Two scenarios were
considered: first one, when the waveform of the useful signal
is known a priori and, second one, when the waveform of the
desired signal is unknown. For first scenario, the antenna array
signal processing based on multi-element matched filtering (MF)
with the following non-coherent detection scheme and maximum
likelihood (ML) parameter estimation blocks is exploited. For second
scenario, the signal processing based on the antenna array elements
covariance matrix estimation with the following eigenvector analysis
and ML parameter estimation blocks is applied. The performance
characteristics of both signal processing schemes are thoroughly
investigated and compared for different useful signals and noise
parameters.
Abstract: The parameters of a two-layer soil can be determined by
processing resistivity data obtained from resistivity measurements
carried out on the soil of interest. The processing usually entails
applying the resistivity data as inputs to an optimisation function.
This paper proposes an algorithm which utilises the square error as an
optimisation function. Resistivity data from previous works were
applied to test the accuracy of the new algorithm developed and the
result obtained conforms significantly to results from previous works.
Abstract: Web-based Cognitive Writing Instruction (WeCWI) is
a hybrid e-framework for the development of a web-based instruction
(WBI), which contributes towards instructional design and language
development. WeCWI divides its contribution in instructional design
into macro and micro perspectives. In macro perspective, being a 21st
century educator by disseminating knowledge and sharing ideas with
the in-class and global learners is initiated. By leveraging the virtue
of technology, WeCWI aims to transform an educator into an
aggregator, curator, publisher, social networker and ultimately, a
web-based instructor. Since the most notable contribution of
integrating technology is being a tool of teaching as well as a
stimulus for learning, WeCWI focuses on the use of contemporary
web tools based on the multiple roles played by the 21st century
educator. The micro perspective in instructional design draws
attention to the pedagogical approaches focusing on three main
aspects: reading, discussion, and writing. With the effective use of
pedagogical approaches through free reading and enterprises,
technology adds new dimensions and expands the boundaries of
learning capacity. Lastly, WeCWI also imparts the fundamental
theories and models for web-based instructors’ awareness such as
interactionist theory, cognitive information processing (CIP) theory,
computer-mediated communication (CMC), e-learning interactionalbased
model, inquiry models, sensory mind model, and leaning styles
model.
Abstract: This study aimed at investigating whether the
functional brain networks constructed using the initial EEG (obtained
when patients first visited hospital) can be correlated with the
progression of cognitive decline calculated as the changes of
mini-mental state examination (MMSE) scores between the latest and
initial examinations. We integrated the time–frequency cross mutual
information (TFCMI) method to estimate the EEG functional
connectivity between cortical regions, and the network analysis based
on graph theory to investigate the organization of functional networks
in aMCI. Our finding suggested that higher integrated functional
network with sufficient connection strengths, dense connection
between local regions, and high network efficiency in processing
information at the initial stage may result in a better prognosis of the
subsequent cognitive functions for aMCI. In conclusion, the functional
connectivity can be a useful biomarker to assist in prediction of
cognitive declines in aMCI.
Abstract: Brain functional networks based on resting-state EEG
data were compared between patients with mild Alzheimer’s disease
(mAD) and matched patients with amnestic subtype of mild cognitive
impairment (aMCI). We integrated the time–frequency cross mutual
information (TFCMI) method to estimate the EEG functional
connectivity between cortical regions and the network analysis based
on graph theory to further investigate the alterations of functional
networks in mAD compared with aMCI group. We aimed at
investigating the changes of network integrity, local clustering,
information processing efficiency, and fault tolerance in mAD brain
networks for different frequency bands based on several topological
properties, including degree, strength, clustering coefficient, shortest
path length, and efficiency. Results showed that the disruptions of
network integrity and reductions of network efficiency in mAD
characterized by lower degree, decreased clustering coefficient, higher
shortest path length, and reduced global and local efficiencies in the
delta, theta, beta2, and gamma bands were evident. The significant
changes in network organization can be used in assisting
discrimination of mAD from aMCI in clinical.
Abstract: Segmentation is one of the essential tasks in image
processing. Thresholding is one of the simplest techniques for
performing image segmentation. Multilevel thresholding is a simple
and effective technique. The primary objective of bi-level or
multilevel thresholding for image segmentation is to determine a best
thresholding value. To achieve multilevel thresholding various
techniques has been proposed. A study of some nature inspired
metaheuristic algorithms for multilevel thresholding for image
segmentation is conducted. Here, we study about Particle swarm
optimization (PSO) algorithm, artificial bee colony optimization
(ABC), Ant colony optimization (ACO) algorithm and Cuckoo
search (CS) algorithm.
Abstract: The manufacturing technology of band cotton is very
delicate and depends to choice of certain parameters such as torsion
of warp yarn.
The fabric elasticity is achieved without the use of any elastic
material, chemical expansion, artificial or synthetic and it’s capable
of creating pressures useful for therapeutic treatments.
Before use, the band is subjected to treatments of specific
preparation for obtaining certain elasticity, however, during its
treatment, there are some regression parameters. The dependence of
manufacturing parameters on the quality of the chemical treatment
was confirmed.
The aim of this work is to improve the properties of the fabric
through the development of manufacturing technology appropriately.
Finally for the treatment of the strip pancake 100% cotton, a
treatment method is recommended.
Abstract: Due to reduced stiffness, research on second
generation titanium alloys for implant applications, like the
metastable β-titanium alloy Ti-15Mo, become more and more
important in the recent years. The machinability of these alloys is
generally poor leading to problems during implant production and
comparably large production costs. Therefore, in the present study,
Ti-15Mo was alloyed with 0.8 wt.-% of the rare earth metals
lanthanum (Ti-15Mo+0.8La) and neodymium (Ti-15Mo+0.8Nd) to
improve its machinability. Their microstructure consisted of a
titanium matrix and micrometer-size particles of the rare earth metals
and two of their oxides. The particles stabilized the microstructure as
grain growth was minimized. As especially the ductility might be
affected by the precipitates, the behavior of Ti-15Mo+0.8La and Ti-
15Mo+0.8Nd was investigated during static and dynamic
deformation at elevated temperature to develop a processing route.
The resulting mechanical properties (static strength and ductility)
were similar in all investigated alloys.
Abstract: Copper being one of the major intrinsic residual
impurities in steel possesses the tendency to induce severe
microstructural distortions if not controlled within certain limits.
Hence, this paper investigates the effect of this element on the
mechanical properties of construction steel with a view to ascertain
its safe limits for effective control. The experiment entails collection
of statistically scheduled samples of hot rolled profiles with varied
copper concentrations in the range of 0.12-0.39 wt. %. From these
samples were prepared standard test specimens subjected to tensile,
impact, hardness and microstructural analyses. Results show a rather
huge compromise in mechanical properties as the specimens
demonstrated 54.3%, 74.2% and 64.9% reduction in tensile strength,
impact energy and hardness respectively as copper content increases
from 0.12 wt. % to 0.39 wt. %. The steel’s abysmal performance is
due to the severe distortion of the microstructure occasioned by the
development of incoherent complex compounds which weaken the
pearlite reinforcing phase. It is concluded that the presence of copper
above 0.22 wt. % is deleterious to construction steel performance.
Abstract: In this research work, neural networks were applied to
classify two types of hip joint implants based on the relative hip joint
implant side speed and three components of each ground reaction
force. The condition of walking gait at normal velocity was used and
carried out with each of the two hip joint implants assessed. Ground
reaction forces’ kinetic temporal changes were considered in the first
approach followed but discarded in the second one. Ground reaction
force components were obtained from eighteen patients under such
gait condition, half of which had a hip implant type I-II, whilst the
other half had the hip implant, defined as type III by Orthoload®.
After pre-processing raw gait kinetic data and selecting the time
frames needed for the analysis, the ground reaction force components
were used to train a MLP neural network, which learnt to distinguish
the two hip joint implants in the abovementioned condition. Further
to training, unknown hip implant side and ground reaction force
components were presented to the neural networks, which assigned
those features into the right class with a reasonably high accuracy for
the hip implant type I-II and the type III. The results suggest that
neural networks could be successfully applied in the performance
assessment of hip joint implants.
Abstract: Over the past era, there have been a lot of efforts and
studies are carried out in growing proficient tools for performing
various tasks in big data. Recently big data have gotten a lot of
publicity for their good reasons. Due to the large and complex
collection of datasets it is difficult to process on traditional data
processing applications. This concern turns to be further mandatory
for producing various tools in big data. Moreover, the main aim of
big data analytics is to utilize the advanced analytic techniques
besides very huge, different datasets which contain diverse sizes from
terabytes to zettabytes and diverse types such as structured or
unstructured and batch or streaming. Big data is useful for data sets
where their size or type is away from the capability of traditional
relational databases for capturing, managing and processing the data
with low-latency. Thus the out coming challenges tend to the
occurrence of powerful big data tools. In this survey, a various
collection of big data tools are illustrated and also compared with the
salient features.
Abstract: With demand for primary energy continuously
growing, search for renewable and efficient energy sources has been
high on agenda of our society. One of the most promising energy
sources is biogas technology. Residues coming from dairy industry
and milk processing could be used in biogas production; however,
low efficiency and high cost impede wide application of such
technology. One of the main problems is management and conversion
of organic residues through the anaerobic digestion process which is
characterized by acidic environment due to the low whey pH (
Abstract: Color Histogram is considered as the oldest method
used by CBIR systems for indexing images. In turn, the global
histograms do not include the spatial information; this is why the
other techniques coming later have attempted to encounter this
limitation by involving the segmentation task as a preprocessing step.
The weak segmentation is employed by the local histograms while
other methods as CCV (Color Coherent Vector) are based on strong
segmentation. The indexation based on local histograms consists of
splitting the image into N overlapping blocks or sub-regions, and
then the histogram of each block is computed. The dissimilarity
between two images is reduced, as consequence, to compute the
distance between the N local histograms of the both images resulting
then in N*N values; generally, the lowest value is taken into account
to rank images, that means that the lowest value is that which helps to
designate which sub-region utilized to index images of the collection
being asked. In this paper, we make under light the local histogram
indexation method in the hope to compare the results obtained against
those given by the global histogram. We address also another
noteworthy issue when Relying on local histograms namely which
value, among N*N values, to trust on when comparing images, in
other words, which sub-region among the N*N sub-regions on which
we base to index images. Based on the results achieved here, it seems
that relying on the local histograms, which needs to pose an extra
overhead on the system by involving another preprocessing step
naming segmentation, does not necessary mean that it produces better
results. In addition to that, we have proposed here some ideas to
select the local histogram on which we rely on to encode the image
rather than relying on the local histogram having lowest distance with
the query histograms.
Abstract: In many communication and signal processing
systems, it is highly desirable to implement an efficient narrow-band
filter that decimate or interpolate the incoming signals. This paper
presents hardware efficient compensated CIC filter over a narrow
band frequency that increases the speed of down sampling by using
multiplierless decimation filters with polyphase FIR filter structure.
The proposed work analyzed the performance of compensated CIC
filter on the bases of the improvement of frequency response with
reduced hardware complexity in terms of no. of adders and
multipliers and produces the filtered results without any alterations.
CIC compensator filter demonstrated that by using compensation
with CIC filter improve the frequency response in passed of interest
26.57% with the reduction in hardware complexity 12.25%
multiplications per input sample (MPIS) and 23.4% additions per
input sample (APIS) w.r.t. FIR filter respectively.
Abstract: Application of hulls processing technologies, based on high-concentrated energy sources (laser and plasma technologies), allow improve shipbuilding production. It is typical for high-speed vessels construction using steel and aluminum alloys with high precision hulls required. Report describes high-performance technologies for plasma welding (using direct current of reversed polarity), laser, and hybrid laser-arc welding of hulls structures developed by JSC “SSTC”
Abstract: This paper presents the application of finite dynamic
programming, specifically the "Markov Chain" model, as part of the
decision making process of a company in the cosmetics sector located
in the vicinity of Bogota DC. The objective of this process was to
decide whether the company should completely reconstruct its
wastewater treatment plant or instead optimize the plant through the
addition of equipment. The goal of both of these options was to make
the required improvements in order to comply with parameters
established by national legislation regarding the treatment of waste
before it is released into the environment. This technique will allow
the company to select the best option and implement a solution for
the processing of waste to minimize environmental damage and the
acquisition and implementation costs.
Abstract: Image segmentation process based on mathematical morphology has been studied in the paper. It has been established from the first principles of the morphological process, the entire segmentation is although a nonlinear signal processing task, the constituent wise, the intermediate steps are linear, bilinear and conformal transformation and they give rise to a non linear affect in a cumulative manner.
Abstract: Advances in the field of image processing envision a
new era of evaluation techniques and application of procedures in
various different fields. One such field being considered is the
biomedical field for prognosis as well as diagnosis of diseases. This
plethora of methods though provides a wide range of options to select
from, it also proves confusion in selecting the apt process and also in
finding which one is more suitable. Our objective is to use a series of
techniques on bone scans, so as to detect the occurrence of
rheumatoid arthritis (RA) as accurately as possible. Amongst other
techniques existing in the field our proposed system tends to be more
effective as it depends on new methodologies that have been proved
to be better and more consistent than others. Computer aided
diagnosis will provide more accurate and infallible rate of
consistency that will help to improve the efficiency of the system.
The image first undergoes histogram smoothing and specification,
morphing operation, boundary detection by edge following algorithm
and finally image subtraction to determine the presence of
rheumatoid arthritis in a more efficient and effective way. Using preprocessing
noises are removed from images and using segmentation,
region of interest is found and Histogram smoothing is applied for a
specific portion of the images. Gray level co-occurrence matrix
(GLCM) features like Mean, Median, Energy, Correlation, Bone
Mineral Density (BMD) and etc. After finding all the features it
stores in the database. This dataset is trained with inflamed and noninflamed
values and with the help of neural network all the new
images are checked properly for their status and Rough set is
implemented for further reduction.
Abstract: Analyzing brain signals of the patients suffering from the state of depression may lead to interesting observations in the signal parameters that is quite different from a normal control. The present study adopts two different methods: Time frequency domain and nonlinear method for the analysis of EEG signals acquired from depression patients and age and sex matched normal controls. The time frequency domain analysis is realized using wavelet entropy and approximate entropy is employed for the nonlinear method of analysis. The ability of the signal processing technique and the nonlinear method in differentiating the physiological aspects of the brain state are revealed using Wavelet entropy and Approximate entropy.