Abstract: In this paper, we present a wavelet coefficients masking
based on Local Binary Patterns (WLBP) approach to enhance the
temporal spectra of the wavelet coefficients for speech enhancement.
This technique exploits the wavelet denoising scheme, which splits
the degraded speech into pyramidal subband components and extracts
frequency information without losing temporal information. Speech
enhancement in each high-frequency subband is performed by binary
labels through the local binary pattern masking that encodes the ratio
between the original value of each coefficient and the values of the
neighbour coefficients. This approach enhances the high-frequency
spectra of the wavelet transform instead of eliminating them through
a threshold. A comparative analysis is carried out with conventional
speech enhancement algorithms, demonstrating that the proposed
technique achieves significant improvements in terms of PESQ, an
international recommendation of objective measure for estimating
subjective speech quality. Informal listening tests also show that
the proposed method in an acoustic context improves the quality
of speech, avoiding the annoying musical noise present in other
speech enhancement techniques. Experimental results obtained with a
DNN based speech recognizer in noisy environments corroborate the
superiority of the proposed scheme in the robust speech recognition
scenario.
Abstract: Captured images may suffer from Gaussian blur due to poor lens focus or camera motion. Unsharp masking is a simple and effective technique to boost the image contrast and to improve digital images suffering from Gaussian blur. The technique is based on sharpening object edges by appending the scaled high-frequency components of the image to the original. The quality of the enhanced image is highly dependent on the characteristics of both the high-frequency components and the scaling/gain factor. Since the quality of an image may not be the same throughout, we propose an adaptive unsharp masking method in this paper. In this method, the gain factor is computed, considering the gradient variations, for individual pixels of the image. Subjective and objective image quality assessments are used to compare the performance of the proposed method both with the classic and the recently developed unsharp masking methods. The experimental results show that the proposed method has a better performance in comparison to the other existing methods.
Abstract: In this paper, we propose a method to model the
relationship between failure time and degradation for a simple step
stress test where underlying degradation path is linear and different
causes of failure are possible. It is assumed that the intensity function
depends only on the degradation value. No assumptions are made
about the distribution of the failure times. A simple step-stress test
is used to shorten failure time of products and a tampered failure
rate (TFR) model is proposed to describe the effect of the changing
stress on the intensities. We assume that some of the products that
fail during the test have a cause of failure that is only known to
belong to a certain subset of all possible failures. This case is known
as masking. In the presence of masking, the maximum likelihood
estimates (MLEs) of the model parameters are obtained through an
expectation-maximization (EM) algorithm by treating the causes of
failure as missing values. The effect of incomplete information on the
estimation of parameters is studied through a Monte-Carlo simulation.
Finally, a real example is analyzed to illustrate the application of the
proposed methods.
Abstract: Voting algorithms are extensively used to make
decisions in fault tolerant systems where each redundant module
gives inconsistent outputs. Popular voting algorithms include
majority voting, weighted voting, and inexact majority voters. Each
of these techniques suffers from scenarios where agreements do not
exist for the given voter inputs. This has been successfully overcome
in literature using fuzzy theory. Our previous work concentrated on a
neuro-fuzzy algorithm where training using the neuro system
substantially improved the prediction result of the voting system.
Weight training of Neural Network is sub-optimal. This study
proposes to optimize the weights of the Neural Network using
Artificial Bee Colony algorithm. Experimental results show the
proposed system improves the decision making of the voting
algorithms.
Abstract: In this paper, a model is proposed to determine the life
distribution parameters of the useful life region for the PV system
utilizing a combination of non-parametric and linear regression
analysis for the failure data of these systems. Results showed that this
method is dependable for analyzing failure time data for such reliable
systems when the data is scarce.
Abstract: In this paper, design and fabrication of an actuated parallel-plate mirror based on a 3D-printer is described. The mirror and electrode layers are fabricated separately and assembled thereafter. The alignment is performed by dowel pin-hole pairs fabricated on the respective layers. The electrodes are formed on the surface of the electrode layer by Au ion sputtering using a suitable mask, which is also fabricated by a 3D-printer.For grounding the mirror layer, except the contact area with the electrode paths, all the surface is Au ion sputtered. 3D-printers are widely used for creating 3D models or mock-ups. The authors have recently proposed that these models can perform electromechanical functions such as actuators by suitably masking them followed by metallization process. Since the smallest possible fabrication size is in the order of sub-millimeters, these electromechanical devices are named by the authors as SMEMS (Sub-Milli Electro-Mechanical Systems) devices. The proposed mirror described in this paper which consists of parallel-plate electrostatic actuators is also one type of SMEMS devices. In addition, SMEMS is totally environment-clean compared to MEMS (Micro Electro-Mechanical Systems) fabrication processes because any hazardous chemicals or gases are utilized.
Abstract: In this paper, we present a non-blind technique of
adding the watermark to the Fourier spectral components of audio
signal in a way such that the modified amplitude does not exceed the
maximum amplitude spread (MAS). This MAS is due to individual
Discrete fourier transform (DFT) coefficients in that particular frame,
which is derived from the Energy Spreading function given by
Schroeder. Using this technique one can store double the information
within a given frame length i.e. overriding the watermark on the
host of equal length with least perceptual distortion. The watermark
is uniformly floating on the DFT components of original signal.
This helps in detecting any intentional manipulations done on the
watermarked audio. Also, the scheme is found robust to various signal
processing attacks like presence of multiple watermarks, Additive
white gaussian noise (AWGN) and mp3 compression.
Abstract: The aim of this study was to test whether the Attention
Networks Test (ANT) showed temporal decrements in performance.
Vigilance tasks typically show such decrements, which may reflect
impairments in executive control resulting from cognitive fatigue.
The ANT assesses executive control, as well as alerting and
orienting. Thus, it was hypothesized that ANT executive control
would deteriorate over time. Manipulations including task condition
(trial composition) and masking were included in the experimental
design in an attempt to increase performance decrements. However,
results showed that there is no temporal decrement on the ANT. The
roles of task demands, cognitive fatigue and participant motivation in
producing this result are discussed. The ANT may not be an effective
tool for investigating temporal decrement in attention.
Abstract: The electrical potentials generated during eye movements and blinks are one of the main sources of artifacts in Electroencephalogram (EEG) recording and can propagate much across the scalp, masking and distorting brain signals. In recent times, signal separation algorithms are used widely for removing artifacts from the observed EEG data. In this paper, a recently introduced signal separation algorithm Mutual Information based Least dependent Component Analysis (MILCA) is employed to separate ocular artifacts from EEG. The aim of MILCA is to minimize the Mutual Information (MI) between the independent components (estimated sources) under a pure rotation. Performance of this algorithm is compared with eleven popular algorithms (Infomax, Extended Infomax, Fast ICA, SOBI, TDSEP, JADE, OGWE, MS-ICA, SHIBBS, Kernel-ICA, and RADICAL) for the actual independence and uniqueness of the estimated source components obtained for different sets of EEG data with ocular artifacts by using a reliable MI Estimator. Results show that MILCA is best in separating the ocular artifacts and EEG and is recommended for further analysis.
Abstract: Effective estimation of just noticeable distortion (JND) for images is helpful to increase the efficiency of a compression algorithm in which both the statistical redundancy and the perceptual redundancy should be accurately removed. In this paper, we design a DCT-based model for estimating JND profiles of color images. Based on a mathematical model of measuring the base detection threshold for each DCT coefficient in the color component of color images, the luminance masking adjustment, the contrast masking adjustment, and the cross masking adjustment are utilized for luminance component, and the variance-based masking adjustment based on the coefficient variation in the block is proposed for chrominance components. In order to verify the proposed model, the JND estimator is incorporated into the conventional JPEG coder to improve the compression performance. A subjective and fair viewing test is designed to evaluate the visual quality of the coding image under the specified viewing condition. The simulation results show that the JPEG coder integrated with the proposed DCT-based JND model gives better coding bit rates at visually lossless quality for a variety of color images.
Abstract: A new analysis of perceptual speech enhancement is
presented. It focuses on the fact that if only noise above the masking
threshold is filtered, then noise below the masking threshold, but
above the absolute threshold of hearing, can become audible after the
masker filtering. This particular drawback of some perceptual filters,
hereafter called the maskee-to-audible-noise (MAN) phenomenon,
favours the emergence of isolated tonals that increase musical noise.
Two filtering techniques that avoid or correct the MAN phenomenon
are proposed to effectively suppress background noise without introducing
much distortion. Experimental results, including objective
and subjective measurements, show that these techniques improve
the enhanced speech quality and the gain they bring emphasizes the
importance of the MAN phenomenon.
Abstract: The nonlinear chaotic non-autonomous fourth order
system is algebraically simple but can generate complex chaotic
attractors. In this paper, non-autonomous fourth order chaotic
oscillator circuits were designed and simulated. Also chaotic nonautonomous
Attractor is addressed suitable for chaotic masking
communication circuits using Matlab® and MultiSIM® programs.
We have demonstrated in simulations that chaos can be synchronized
and applied to signal masking communications. We suggest that this
phenomenon of chaos synchronism may serve as the basis for little
known chaotic non-autonomous Attractor to achieve signal masking
communication applications. Simulation results are used to visualize
and illustrate the effectiveness of non-autonomous chaotic system in
signal masking. All simulations results performed on nonautonomous
chaotic system are verify the applicable of secure
communication.
Abstract: We introduce an algorithm based on the
morphological shared-weight neural network. Being nonlinear and
translation-invariant, the MSNN can be used to create better
generalization during face recognition. Feature extraction is
performed on grayscale images using hit-miss transforms that are
independent of gray-level shifts. The output is then learned by
interacting with the classification process. The feature extraction and
classification networks are trained together, allowing the MSNN to
simultaneously learn feature extraction and classification for a face.
For evaluation, we test for robustness under variations in gray levels
and noise while varying the network-s configuration to optimize
recognition efficiency and processing time. Results show that the
MSNN performs better for grayscale image pattern classification
than ordinary neural networks.
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: In this work we evaluate the possibility of predicting
the emotional state of a person based on the EEG. We investigate
the problem of classifying valence from EEG signals during
the presentation of affective pictures, utilizing the "frontal EEG
asymmetry" phenomenon. To distinguish positive and negative
emotions, we applied the Common Spatial Patterns algorithm.
In contrast to our expectations, the affective pictures did not
reliably elicit changes in frontal asymmetry. The classifying task
thereby becomes very hard as reflected by the poor classifier
performance. We suspect that the masking of the source of the
brain activity related to emotions, coming mostly from deeper
structures in the brain, and the insufficient emotional engagement
are among main reasons why it is difficult to predict the emotional
state of a person.
Abstract: Shadow detection is still considered as one of the
potential challenges for intelligent automated video surveillance
systems. A pre requisite for reliable and accurate detection and
tracking is the correct shadow detection and classification. In such a
landscape of conditions, privacy issues add more and more
complexity and require reliable shadow detection.
In this work the intertwining between security, accuracy,
reliability and privacy is analyzed and, accordingly, a novel
architecture for Privacy Enhancing Video Surveillance (PEVS) is
introduced. Shadow detection and masking are dealt with through the
combination of two different approaches simultaneously. This results
in a unique privacy enhancement, without affecting security.
Subsequently, the methodology was employed successfully in a
large-scale wireless video surveillance system; privacy relevant
information was stored and encrypted on the unit, without
transferring it over an un-trusted network.
Abstract: In this paper, we propose a Perceptually Optimized Foveation based Embedded ZeroTree Image Coder (POEFIC) that introduces a perceptual weighting to wavelet coefficients prior to control SPIHT encoding algorithm in order to reach a targeted bit rate with a perceptual quality improvement with respect to a given bit rate a fixation point which determines the region of interest ROI. The paper also, introduces a new objective quality metric based on a Psychovisual model that integrates the properties of the HVS that plays an important role in our POEFIC quality assessment. Our POEFIC coder is based on a vision model that incorporates various masking effects of human visual system HVS perception. Thus, our coder weights the wavelet coefficients based on that model and attempts to increase the perceptual quality for a given bit rate and observation distance. The perceptual weights for all wavelet subbands are computed based on 1) foveation masking to remove or reduce considerable high frequencies from peripheral regions 2) luminance and Contrast masking, 3) the contrast sensitivity function CSF to achieve the perceptual decomposition weighting. The new perceptually optimized codec has the same complexity as the original SPIHT techniques. However, the experiments results show that our coder demonstrates very good performance in terms of quality measurement.
Abstract: Most of the image watermarking methods, using the properties of the human visual system (HVS), have been proposed in literature. The component of the visual threshold is usually related to either the spatial contrast sensitivity function (CSF) or the visual masking. Especially on the contrast masking, most methods have not mention to the effect near to the edge region. Since the HVS is sensitive what happens on the edge area. This paper proposes ultrasound image watermarking using the visual threshold corresponding to the HVS in which the coefficients in a DCT-block have been classified based on the texture, edge, and plain area. This classification method enables not only useful for imperceptibility when the watermark is insert into an image but also achievable a robustness of watermark detection. A comparison of the proposed method with other methods has been carried out which shown that the proposed method robusts to blockwise memoryless manipulations, and also robust against noise addition.
Abstract: In this paper, a robust digital image watermarking
scheme for copyright protection applications using the singular value
decomposition (SVD) is proposed. In this scheme, an entropy
masking model has been applied on the host image for the texture
segmentation. Moreover, the local luminance and textures of the host
image are considered for watermark embedding procedure to
increase the robustness of the watermarking scheme. In contrast to all
existing SVD-based watermarking systems that have been designed
to embed visual watermarks, our system uses a pseudo-random
sequence as a watermark. We have tested the performance of our
method using a wide variety of image processing attacks on different
test images. A comparison is made between the results of our
proposed algorithm with those of a wavelet-based method to
demonstrate the superior performance of our algorithm.
Abstract: A new supported liquid membrane (SLM) system for
the selective transport of VO2
+ ions was prepared in this present
work. The SLM was a thin porous polyvinylidene difluoride
(PVDF) membrane soaked with Di-(2-ethylhexyl) phosphoric acid
(D2EHPA) as mobile carrier in Xylene as organic solvent.
D2EHPA acts as a highly selective carrier for the uphill transport of
VO2
+ ions through the SLM. The transport of VO2
+ ions reached to
64%. In the presence of P2O7-2 ion as suitable masking agent in the
feed solution, the interfering effects of other cations were eliminated.