Abstract: Intelligent technologies are increasingly facilitating
sustainable water management strategies in Australia. While this
innovation can present clear cost benefits to utilities through
immediate leak detection and deference of capital costs, the impact of
this technology on households is less distinct. By offering real-time
engagement and detailed end-use consumption breakdowns, there is
significant potential for demand reduction as a behavioural response
to increased information. Despite this potential, passive
implementation without well-planned residential engagement
strategies is likely to result in a lost opportunity. This paper begins
this research process by exploring the effect of smart water meters
through the lens of three behaviour change theories. The Theory of
Planned Behaviour (TPB), Belief Revision theory (BR) and Practice
Theory emphasise different variables that can potentially influence
and predict household water engagements. In acknowledging the
strengths of each theory, the nuances and complexity of household
water engagement can be recognised which can contribute to
effective planning for residential smart meter engagement strategies.
Abstract: Memory Errors Detection and Correction aim to secure the transaction of data between the central processing unit of a satellite onboard computer and its local memory. In this paper, the application of a double-bit error detection and correction method is described and implemented in Field Programmable Gate Array (FPGA) technology. The performance of the proposed EDAC method is measured and compared with two different EDAC devices, using the same FPGA technology. Statistical analysis of single-event upset (SEU) and multiple-bit upset (MBU) activity in commercial memories onboard the first Algerian microsatellite Alsat-1 is given.
Abstract: This article concerns the presentation of an integrated
method for detection of steganographic content embedded by new
unknown programs. The method is based on data mining and
aggregated hypothesis testing. The article contains the theoretical
basics used to deploy the proposed detection system and the
description of improvement proposed for the basic system idea.
Further main results of experiments and implementation details are
collected and described. Finally example results of the tests are
presented.
Abstract: Skin color is an important visual cue for computer
vision systems involving human users. In this paper we combine skin
color and optical flow for detection and tracking of skin regions. We
apply these techniques to gesture recognition with encouraging
results. We propose a novel skin similarity measure. For grouping
detected skin regions we propose a novel skin region grouping
mechanism. The proposed techniques work with any number of skin
regions making them suitable for a multiuser scenario.
Abstract: One approach to assess neural networks underlying the cognitive processes is to study Electroencephalography (EEG). It is relevant to detect various mental states and characterize the physiological changes that help to discriminate two situations. That is why an EEG (amplitude, synchrony) classification procedure is described, validated. The two situations are "eyes closed" and "eyes opened" in order to study the "alpha blocking response" phenomenon in the occipital area. The good classification rate between the two situations is 92.1 % (SD = 3.5%) The spatial distribution of a part of amplitude features that helps to discriminate the two situations are located in the occipital regions that permit to validate the localization method. Moreover amplitude features in frontal areas, "short distant" synchrony in frontal areas and "long distant" synchrony between frontal and occipital area also help to discriminate between the two situations. This procedure will be used for mental fatigue detection.
Abstract: Wrist pulse analysis for identification of health status
is found in Ancient Indian as well as Chinese literature. The preprocessing
of wrist pulse is necessary to remove outlier pulses and
fluctuations prior to the analysis of pulse pressure signal. This paper
discusses the identification of irregular pulses present in the pulse
series and intricacies associated with the extraction of time domain
pulse features. An approach of Dynamic Time Warping (DTW) has
been utilized for the identification of outlier pulses in the wrist pulse
series. The ambiguity present in the identification of pulse features is
resolved with the help of first derivative of Ensemble Average of
wrist pulse series. An algorithm for detecting tidal and dicrotic notch
in individual wrist pulse segment is proposed.
Abstract: Texture classification is a trendy and a catchy
technology in the field of texture analysis. Textures, the repeated
patterns, have different frequency components along different
orientations. Our work is based on Texture Classification and its
applications. It finds its applications in various fields like Medical
Image Classification, Computer Vision, Remote Sensing,
Agricultural Field, and Textile Industry. Weed control has a major
effect on agriculture. A large amount of herbicide has been used for
controlling weeds in agriculture fields, lawns, golf courses, sport
fields, etc. Random spraying of herbicides does not meet the exact
requirement of the field. Certain areas in field have more weed
patches than estimated. So, we need a visual system that can
discriminate weeds from the field image which will reduce or even
eliminate the amount of herbicide used. This would allow farmers to
not use any herbicides or only apply them where they are needed. A
machine vision precision automated weed control system could
reduce the usage of chemicals in crop fields. In this paper, an
intelligent system for automatic weeding strategy Multi Resolution
Combined Statistical & spatial Frequency is used to discriminate the
weeds from the crops and to classify them as narrow, little and broad
weeds.
Abstract: This paper presents a dynamic model for mechanical
loads of an electric drive, including angular misalignment and
including load unbalance. The misalignment model represents the
effects of the universal joint between the motor and the mechanical
load. Simulation results are presented for an induction motor driving
a mechanical load with angular misalignment for both flexible and
rigid coupling. The models presented are very useful in the study of
mechanical fault detection in induction motors, using mechanical and
electrical signals already available in a drive system, such as speed,
torque and stator currents.
Abstract: Human immunodeficiency virus infection and
acquired immunodeficiency syndrome is a global pandemic with
cases reporting from virtually every country and continues to be a
common infection in developing country like India.
Microalbuminuria is a manifestation of human immunodeficiency
virus associated nephropathy. Therefore, microalbuminuria may be
an early marker of human immunodeficiency virus associated
nephropathy, and screening for its presence may be beneficial. A
strikingly high prevalence of microalbuminuria among human
immunodeficiency virus infected patients has been described in
various studies. Risk factors for clinically significant proteinuria
include African - American race, higher human immunodeficiency
virus ribonucleic acid level and lower CD4 lymphocyte count. The
cardiovascular risk factors of increased systolic blood pressure and
increase fasting blood sugar level are strongly associated with
microalbuminuria in human immunodeficiency virus patient. These
results suggest that microalbuminuria may be a sign of current
endothelial dysfunction and micro-vascular disease and there is
substantial risk of future cardiovascular disease events. Positive
contributing factors include early kidney disease such as human
immunodeficiency virus associated nephropathy, a marker of end
organ damage related to co morbidities of diabetes or hypertension,
or more diffuse endothelial cells dysfunction. Nevertheless after
adjustment for non human immunodeficiency virus factors, human
immunodeficiency virus itself is a major risk factor. The presence of
human immunodeficiency virus infection is independent risk to
develop microalbuminuria in human immunodeficiency virus patient.
Cardiovascular risk factors appeared to be stronger predictors of
microalbuminuria than markers of human immunodeficiency virus
severity person with human immunodeficiency virus infection and
microalbuminuria therefore appear to potentially bear the burden of
two separate damage related to known vascular end organ damage
related to know vascular risk factors, and human immunodeficiency
virus specific processes such as the direct viral infection of kidney
cells.The higher prevalence of microalbuminuria among the human
immunodeficiency virus infected could be harbinger of future
increased risks of both kidney and cardiovascular disease. Further
study defining the prognostic significance of microalbuminuria
among human immunodeficiency virus infected persons will be
essential. Microalbuminuria seems to be a predictor of cardiovascular
disease in diabetic and non diabetic subjects, hence it can also be
used for early detection of micro vascular disease in human
immunodeficiency virus positive patients, thus can help to diagnose
the disease at the earliest.
Abstract: Corner detection and optical flow are common techniques for feature-based video stabilization. However, these algorithms are computationally expensive and should be performed at a reasonable rate. This paper presents an algorithm for discarding irrelevant feature points and maintaining them for future use so as to improve the computational cost. The algorithm starts by initializing a maintained set. The feature points in the maintained set are examined against its accuracy for modeling. Corner detection is required only when the feature points are insufficiently accurate for future modeling. Then, optical flows are computed from the maintained feature points toward the consecutive frame. After that, a motion model is estimated based on the simplified affine motion model and least square method, with outliers belonging to moving objects presented. Studentized residuals are used to eliminate such outliers. The model estimation and elimination processes repeat until no more outliers are identified. Finally, the entire algorithm repeats along the video sequence with the points remaining from the previous iteration used as the maintained set. As a practical application, an efficient video stabilization can be achieved by exploiting the computed motion models. Our study shows that the number of times corner detection needs to perform is greatly reduced, thus significantly improving the computational cost. Moreover, optical flow vectors are computed for only the maintained feature points, not for outliers, thus also reducing the computational cost. In addition, the feature points after reduction can sufficiently be used for background objects tracking as demonstrated in the simple video stabilizer based on our proposed algorithm.
Abstract: In this paper, we propose ablock-wise watermarking scheme for color image authentication to resist malicious tampering of digital media. The thresholding technique is incorporated into the scheme such that the tampered region of the color image can be recovered with high quality while the proofing result is obtained. The watermark for each block consists of its dual authentication data and the corresponding feature information. The feature information for recovery iscomputed bythe thresholding technique. In the proofing process, we propose a dual-option parity check method to proof the validity of image blocks. In the recovery process, the feature information of each block embedded into the color image is rebuilt for high quality recovery. The simulation results show that the proposed watermarking scheme can effectively proof the tempered region with high detection rate and can recover the tempered region with high quality.
Abstract: This paper presents a new STAKCERT KDD
processes for worm detection. The enhancement introduced in the
data-preprocessing resulted in the formation of a new STAKCERT
model for worm detection. In this paper we explained in detail how
all the processes involved in the STAKCERT KDD processes are
applied within the STAKCERT model for worm detection. Based on
the experiment conducted, the STAKCERT model yielded a 98.13%
accuracy rate for worm detection by integrating the STAKCERT
KDD processes.
Abstract: X-ray mammography is the most effective method for
the early detection of breast diseases. However, the typical diagnostic
signs such as microcalcifications and masses are difficult to detect
because mammograms are of low-contrast and noisy. In this paper, a
new algorithm for image denoising and enhancement in Orthogonal
Polynomials Transformation (OPT) is proposed for radiologists to
screen mammograms. In this method, a set of OPT edge coefficients
are scaled to a new set by a scale factor called OPT scale factor. The
new set of coefficients is then inverse transformed resulting in
contrast improved image. Applications of the proposed method to
mammograms with subtle lesions are shown. To validate the
effectiveness of the proposed method, we compare the results to
those obtained by the Histogram Equalization (HE) and the Unsharp
Masking (UM) methods. Our preliminary results strongly suggest
that the proposed method offers considerably improved enhancement
capability over the HE and UM methods.
Abstract: Early detection of lung cancer through chest radiography is a widely used method due to its relatively affordable cost. In this paper, an approach to improve lung nodule visualization on chest radiographs is presented. The approach makes use of linear phase high-frequency emphasis filter for digital filtering and
histogram equalization for contrast enhancement to achieve improvements. Results obtained indicate that a filtered image can
reveal sharper edges and provide more details. Also, contrast enhancement offers a way to further enhance the global (or local) visualization by equalizing the histogram of the pixel values within
the whole image (or a region of interest). The work aims to improve lung nodule visualization of chest radiographs to aid detection of lung cancer which is currently the leading cause of cancer deaths worldwide.
Abstract: Three sulphonic acid-doped polyanilines were
synthesized through chemical oxidation at low temperature (0-5 oC)
and potential of these polymers as sensing agent for O2 gas detection
in terms of fluorescence quenching was studied. Sulphuric acid,
dodecylbenzene sulphonic acid (DBSA) and camphor sulphonic acid
(CSA) were used as doping agents. All polymers obtained were dark
green powder. Polymers obtained were characterized by Fourier
transform infrared spectroscopy, ultraviolet-visible absorption
spectroscopy, thermogravimetry analysis, elemental analysis,
differential scanning calorimeter and gel permeation
chromatography. Characterizations carried out showed that polymers
were successfully synthesized with mass recovery for sulphuric aciddoped
polyaniline (SPAN), DBSA-doped polyaniline (DBSA-doped
PANI) and CSA-doped polyaniline (CSA-doped PANI) of 71.40%,
75.00% and 39.96%, respectively. Doping level of SPAN, DBSAdoped
PANI and CSA-doped PANI were 32.86%, 33.13% and
53.96%, respectively as determined based on elemental analysis.
Sensing test was carried out on polymer sample in the form of
solution and film by using fluorescence spectrophotometer. Samples
of polymer solution and polymer film showed positive response
towards O2 exposure. All polymer solutions and films were fully
regenerated by using N2 gas within 1 hour period. Photostability
study showed that all samples of polymer solutions and films were
stable towards light when continuously exposed to xenon lamp for 9
hours. The relative standard deviation (RSD) values for SPAN
solution, DBSA-doped PANI solution and CSA-doped PANI
solution for repeatability were 0.23%, 0.64% and 0.76%,
respectively. Meanwhile RSD values for reproducibility were 2.36%,
6.98% and 1.27%, respectively. Results for SPAN film, DBSAdoped
PANI film and CSA-doped PANI film showed the same
pattern with RSD values for repeatability of 0.52%, 4.05% and
0.90%, respectively. Meanwhile RSD values for reproducibility were
2.91%, 10.05% and 7.42%, respectively. The study on effect of the
flow rate on response time was carried out using 3 different rates
which were 0.25 mL/s, 1.00 mL/s and 2.00 mL/s. Results obtained
showed that the higher the flow rate, the shorter the response time.
Abstract: Because of increasing demands for security in today-s
society and also due to paying much more attention to machine
vision, biometric researches, pattern recognition and data retrieval in
color images, face detection has got more application. In this article
we present a scientific approach for modeling human skin color, and
also offer an algorithm that tries to detect faces within color images
by combination of skin features and determined threshold in the
model. Proposed model is based on statistical data in different color
spaces. Offered algorithm, using some specified color threshold, first,
divides image pixels into two groups: skin pixel group and non-skin
pixel group and then based on some geometric features of face
decides which area belongs to face.
Two main results that we received from this research are as follow:
first, proposed model can be applied easily on different databases and
color spaces to establish proper threshold. Second, our algorithm can
adapt itself with runtime condition and its results demonstrate
desirable progress in comparison with similar cases.
Abstract: Large scale climate signals and their teleconnections can influence hydro-meteorological variables on a local scale. Several extreme flow and timing measures, including high flow and low flow measures, from 62 hydrometric stations in Canada are investigated to detect possible linkages with several large scale climate indices. The streamflow data used in this study are derived from the Canadian Reference Hydrometric Basin Network and are characterized by relatively pristine and stable land-use conditions with a minimum of 40 years of record. A composite analysis approach was used to identify linkages between extreme flow and timing measures and climate indices. The approach involves determining the 10 highest and 10 lowest values of various climate indices from the data record. Extreme flow and timing measures for each station were examined for the years associated with the 10 largest values and the years associated with the 10 smallest values. In each case, a re-sampling approach was applied to determine if the 10 values of extreme flow measures differed significantly from the series mean. Results indicate that several stations are impacted by the large scale climate indices considered in this study. The results allow the determination of any relationship between stations that exhibit a statistically significant trend and stations for which the extreme measures exhibit a linkage with the climate indices.
Abstract: The security of power systems against malicious cyberphysical
data attacks becomes an important issue. The adversary
always attempts to manipulate the information structure of the power
system and inject malicious data to deviate state variables while
evading the existing detection techniques based on residual test. The
solutions proposed in the literature are capable of immunizing the
power system against false data injection but they might be too costly
and physically not practical in the expansive distribution network.
To this end, we define an algebraic condition for trustworthy power
system to evade malicious data injection. The proposed protection
scheme secures the power system by deterministically reconfiguring
the information structure and corresponding residual test. More
importantly, it does not require any physical effort in either microgrid
or network level. The identification scheme of finding meters being
attacked is proposed as well. Eventually, a well-known IEEE 30-bus
system is adopted to demonstrate the effectiveness of the proposed
schemes.
Abstract: SDMA (Space-Division Multiple Access) is a MIMO
(Multiple-Input and Multiple-Output) based wireless communication
network architecture which has the potential to significantly increase
the spectral efficiency and the system performance. The maximum
likelihood (ML) detection provides the optimal performance, but its
complexity increases exponentially with the constellation size of
modulation and number of users. The QR decomposition (QRD)
MUD can be a substitute to ML detection due its low complexity and
near optimal performance. The minimum mean-squared-error
(MMSE) multiuser detection (MUD) minimises the mean square
error (MSE), which may not give guarantee that the BER of the
system is also minimum. But the minimum bit error rate (MBER)
MUD performs better than the classic MMSE MUD in term of
minimum probability of error by directly minimising the BER cost
function. Also the MBER MUD is able to support more users than
the number of receiving antennas, whereas the rest of MUDs fail in
this scenario. In this paper the performance of various MUD
techniques is verified for the correlated MIMO channel models based
on IEEE 802.16n standard.
Abstract: This paper presents a integer frequency offset (IFO)
estimation scheme for the 3GPP long term evolution (LTE) downlink
system. Firstly, the conventional joint detection method for IFO and
sector cell index (CID) information is introduced. Secondly, an IFO
estimation without explicit sector CID information is proposed, which
can operate jointly with the proposed IFO estimation and reduce
the time delay in comparison with the conventional joint method.
Also, the proposed method is computationally efficient and has almost
similar performance in comparison with the conventional method over
the Pedestrian and Vehicular channel models.