Abstract: Mostly transforms are used for speech data
compressions which are lossy algorithms. Such algorithms are
tolerable for speech data compression since the loss in quality is not
perceived by the human ear. However the vector quantization (VQ)
has a potential to give more data compression maintaining the same
quality. In this paper we propose speech data compression algorithm
using vector quantization technique. We have used VQ algorithms
LBG, KPE and FCG. The results table shows computational
complexity of these three algorithms. Here we have introduced a new
performance parameter Average Fractional Change in Speech
Sample (AFCSS). Our FCG algorithm gives far better performance
considering mean absolute error, AFCSS and complexity as
compared to others.
Abstract: This paper presents the 20-GHz fractional PLL (Phase
Locked Loop) circuit for the next generation Wi-Fi by using 90 nm
TSMC process. The newly suggested millimeter wave 16/17
pre-scalar is designed and verified by measurement to make the
fractional PLL having a low quantization noise. The operational
bandwidth of the 60 GHz system is 15 % of the carrier frequency
which requires large value of Kv (VCO control gain) resulting in
degradation of phase noise. To solve this problem, this paper adopts
AFC (Automatic Frequency Controller) controlled 4-bit millimeter
wave VCO with small value of Kv. Also constant Kv is implemented
using 4-bit varactor bank. The measured operational bandwidth is 18.2
~ 23.2 GHz which is 25 % of the carrier frequency. The phase noise of
-58 and -96.2 dBc/Hz at 100 KHz and 1 MHz offset is measured
respectively. The total power consumption of the PLL is only 30 mW.
Abstract: Because of the great advance in multimedia
technology, digital multimedia is vulnerable to malicious
manipulations. In this paper, a public key self-recovery block-based
video authentication technique is proposed which can not only
precisely localize the alteration detection but also recover the missing
data with high reliability. In the proposed block-based technique,
multiple description coding MDC is used to generate two codes (two
descriptions) for each block. Although one block code (one
description) is enough to rebuild the altered block, the altered block
is rebuilt with better quality by the two block descriptions. So using
MDC increases the ratability of recovering data. A block signature is
computed using a cryptographic hash function and a doubly linked
chain is utilized to embed the block signature copies and the block
descriptions into the LSBs of distant blocks and the block itself. The
doubly linked chain scheme gives the proposed technique the
capability to thwart vector quantization attacks. In our proposed
technique , anyone can check the authenticity of a given video using
the public key. The experimental results show that the proposed
technique is reliable for detecting, localizing and recovering the
alterations.
Abstract: Several studies have been carried out, using various techniques, including neural networks, to discriminate vigilance states in humans from electroencephalographic (EEG) signals, but we are still far from results satisfactorily useable results. The work presented in this paper aims at improving this status with regards to 2 aspects. Firstly, we introduce an original procedure made of the association of two neural networks, a self organizing map (SOM) and a learning vector quantization (LVQ), that allows to automatically detect artefacted states and to separate the different levels of vigilance which is a major breakthrough in the field of vigilance. Lastly and more importantly, our study has been oriented toward real-worked situation and the resulting model can be easily implemented as a wearable device. It benefits from restricted computational and memory requirements and data access is very limited in time. Furthermore, some ongoing works demonstrate that this work should shortly results in the design and conception of a non invasive electronic wearable device.
Abstract: In high bitrate information hiding techniques, 1 bit is
embedded within each 4 x 4 Discrete Cosine Transform (DCT)
coefficient block by means of vector quantization, then the hidden bit
can be effectively extracted in terminal end. In this paper high bitrate
information hiding algorithms are summarized, and the scheme of
video in video is implemented. Experimental result shows that the host
video which is embedded numerous auxiliary information have little
visually quality decline. Peak Signal to Noise Ratio (PSNR)Y of host
video only degrades 0.22dB in average, while the hidden information
has a high percentage of survives and keeps a high robustness in
H.264/AVC compression, the average Bit Error Rate(BER) of hiding
information is 0.015%.
Abstract: The purpose of this paper is to assess the value of neural networks for classification of cancer and noncancer prostate cells. Gauss Markov Random Fields, Fourier entropy and wavelet average deviation features are calculated from 80 noncancer and 80 cancer prostate cell nuclei. For classification, artificial neural network techniques which are multilayer perceptron, radial basis function and learning vector quantization are used. Two methods are utilized for multilayer perceptron. First method has single hidden layer and between 3-15 nodes, second method has two hidden layer and each layer has between 3-15 nodes. Overall classification rate of 86.88% is achieved.
Abstract: Discrete Cosine Transform (DCT) based transform coding is very popular in image, video and speech compression due to its good energy compaction and decorrelating properties. However, at low bit rates, the reconstructed images generally suffer from visually annoying blocking artifacts as a result of coarse quantization. Lapped transform was proposed as an alternative to the DCT with reduced blocking artifacts and increased coding gain. Lapped transforms are popular for their good performance, robustness against oversmoothing and availability of fast implementation algorithms. However, there is no proper study reported in the literature regarding the statistical distributions of block Lapped Orthogonal Transform (LOT) and Lapped Biorthogonal Transform (LBT) coefficients. This study performs two goodness-of-fit tests, the Kolmogorov-Smirnov (KS) test and the 2- test, to determine the distribution that best fits the LOT and LBT coefficients. The experimental results show that the distribution of a majority of the significant AC coefficients can be modeled by the Generalized Gaussian distribution. The knowledge of the statistical distribution of transform coefficients greatly helps in the design of optimal quantizers that may lead to minimum distortion and hence achieve optimal coding efficiency.
Abstract: In this paper, a novel multipurpose audio watermarking
algorithm is proposed based on Vector Quantization (VQ) in Discrete
Cosine Transform (DCT) domain using the codeword labeling and
index-bit constrained method. By using this algorithm, it can fulfill the
requirements of both the copyright protection and content integrity
authentication at the same time for the multimedia artworks. The
robust watermark is embedded in the middle frequency coefficients of
the DCT transform during the labeled codeword vector quantization
procedure. The fragile watermark is embedded into the indices of the
high frequency coefficients of the DCT transform by using the
constrained index vector quantization method for the purpose of
integrity authentication of the original audio signals. Both the robust
and the fragile watermarks can be extracted without the original audio
signals, and the simulation results show that our algorithm is effective
with regard to the transparency, robustness and the authentication
requirements
Abstract: In the framework of the image compression by
Wavelet Transforms, we propose a perceptual method by
incorporating Human Visual System (HVS) characteristics in the
quantization stage. Indeed, human eyes haven-t an equal sensitivity
across the frequency bandwidth. Therefore, the clarity of the
reconstructed images can be improved by weighting the quantization
according to the Contrast Sensitivity Function (CSF). The visual
artifact at low bit rate is minimized. To evaluate our method, we use
the Peak Signal to Noise Ratio (PSNR) and a new evaluating criteria
witch takes into account visual criteria. The experimental results
illustrate that our technique shows improvement on image quality at
the same compression ratio.
Abstract: Iris-based biometric authentication is gaining importance
in recent times. Iris biometric processing however, is a complex
process and computationally very expensive. In the overall processing
of iris biometric in an iris-based biometric authentication system,
feature processing is an important task. In feature processing, we extract
iris features, which are ultimately used in matching. Since there
is a large number of iris features and computational time increases
as the number of features increases, it is therefore a challenge to
develop an iris processing system with as few as possible number of
features and at the same time without compromising the correctness.
In this paper, we address this issue and present an approach to feature
extraction and feature matching process. We apply Daubechies D4
wavelet with 4 levels to extract features from iris images. These
features are encoded with 2 bits by quantizing into 4 quantization
levels. With our proposed approach it is possible to represent an
iris template with only 304 bits, whereas existing approaches require
as many as 1024 bits. In addition, we assign different weights to
different iris region to compare two iris templates which significantly
increases the accuracy. Further, we match the iris template based on
a weighted similarity measure. Experimental results on several iris
databases substantiate the efficacy of our approach.
Abstract: This paper presents an evaluation for a wavelet-based
digital watermarking technique used in estimating the quality of
video sequences transmitted over Additive White Gaussian Noise
(AWGN) channel in terms of a classical objective metric, such as
Peak Signal-to-Noise Ratio (PSNR) without the need of the original
video. In this method, a watermark is embedded into the Discrete
Wavelet Transform (DWT) domain of the original video frames
using a quantization method. The degradation of the extracted
watermark can be used to estimate the video quality in terms of
PSNR with good accuracy. We calculated PSNR for video frames
contaminated with AWGN and compared the values with those
estimated using the Watermarking-DWT based approach. It is found
that the calculated and estimated quality measures of the video
frames are highly correlated, suggesting that this method can provide
a good quality measure for video frames transmitted over AWGN
channel without the need of the original video.
Abstract: Wireless Sensor Network (WSN) comprises of sensor
nodes which are designed to sense the environment, transmit sensed
data back to the base station via multi-hop routing to reconstruct
physical phenomena. Since physical phenomena exists significant
overlaps between temporal redundancy and spatial redundancy, it is
necessary to use Redundancy Suppression Algorithms (RSA) for sensor
node to lower energy consumption by reducing the transmission
of redundancy. A conventional algorithm of RSAs is threshold-based
RSA, which sets threshold to suppress redundant data. Although
many temporal and spatial RSAs are proposed, temporal-spatial RSA
are seldom to be proposed because it is difficult to determine when
to utilize temporal or spatial RSAs. In this paper, we proposed a
novel temporal-spatial redundancy suppression algorithm, Codebookbase
Redundancy Suppression Mechanism (CRSM). CRSM adopts
vector quantization to generate a codebook, which is easily used to
implement temporal-spatial RSA. CRSM not only achieves power
saving and reliability for WSN, but also provides the predictability
of network lifetime. Simulation result shows that the network lifetime
of CRSM outperforms at least 23% of that of other RSAs.
Abstract: The existing image coding standards generally degrades at low bit-rates because of the underlying block based Discrete Cosine Transform scheme. Over the past decade, the success of wavelets in solving many different problems has contributed to its unprecedented popularity. Due to implementation constraints scalar wavelets do not posses all the properties such as orthogonality, short support, linear phase symmetry, and a high order of approximation through vanishing moments simultaneously, which are very much essential for signal processing. New class of wavelets called 'Multiwavelets' which posses more than one scaling function overcomes this problem. This paper presents a new image coding scheme based on non linear approximation of multiwavelet coefficients along with multistage vector quantization. The performance of the proposed scheme is compared with the results obtained from scalar wavelets.
Abstract: Discrete Wavelet Transform (DWT) has demonstrated
far superior to previous Discrete Cosine Transform (DCT) and
standard JPEG in natural as well as medical image compression. Due
to its localization properties both in special and transform domain,
the quantization error introduced in DWT does not propagate
globally as in DCT. Moreover, DWT is a global approach that avoids
block artifacts as in the JPEG. However, recent reports on natural
image compression have shown the superior performance of
contourlet transform, a new extension to the wavelet transform in two
dimensions using nonseparable and directional filter banks,
compared to DWT. It is mostly due to the optimality of contourlet in
representing the edges when they are smooth curves. In this work, we
investigate this fact for medical images, especially for CT images,
which has not been reported yet. To do that, we propose a
compression scheme in transform domain and compare the
performance of both DWT and contourlet transform in PSNR for
different compression ratios (CR) using this scheme. The results
obtained using different type of computed tomography images show
that the DWT has still good performance at lower CR but contourlet
transform performs better at higher CR.
Abstract: A set of Artificial Neural Network (ANN) based methods
for the design of an effective system of speech recognition of
numerals of Assamese language captured under varied recording
conditions and moods is presented here. The work is related to
the formulation of several ANN models configured to use Linear
Predictive Code (LPC), Principal Component Analysis (PCA) and
other features to tackle mood and gender variations uttering numbers
as part of an Automatic Speech Recognition (ASR) system in
Assamese. The ANN models are designed using a combination of
Self Organizing Map (SOM) and Multi Layer Perceptron (MLP)
constituting a Learning Vector Quantization (LVQ) block trained in a
cooperative environment to handle male and female speech samples
of numerals of Assamese- a language spoken by a sizable population
in the North-Eastern part of India. The work provides a comparative
evaluation of several such combinations while subjected to handle
speech samples with gender based differences captured by a microphone
in four different conditions viz. noiseless, noise mixed, stressed
and stress-free.
Abstract: In present paper we proposed a simple and effective method to compress an image. Here we found success in size reduction of an image without much compromising with it-s quality. Here we used Haar Wavelet Transform to transform our original image and after quantization and thresholding of DWT coefficients Run length coding and Huffman coding schemes have been used to encode the image. DWT is base for quite populate JPEG 2000 technique.
Abstract: Naive Bayes Nearest Neighbor (NBNN) and its variants, i,e., local NBNN and the NBNN kernels, are local feature-based classifiers that have achieved impressive performance in image classification. By exploiting instance-to-class (I2C) distances (instance means image/video in image/video classification), they avoid quantization errors of local image descriptors in the bag of words (BoW) model. However, the performances of NBNN, local NBNN and the NBNN kernels have not been validated on video analysis. In this paper, we introduce these three classifiers into human action recognition and conduct comprehensive experiments on the benchmark KTH and the realistic HMDB datasets. The results shows that those I2C based classifiers consistently outperform the SVM classifier with the BoW model.
Abstract: In this work we introduce an efficient method to limit
the impact of the hiding process on the quality of the cover speech.
Vector quantization of the speech spectral information reduces drastically
the number of the secret speech parameters to be embedded
in the cover signal. Compared to scalar hiding, vector quantization
hiding technique provides a stego signal that is indistinguishable from
the cover speech. The objective and subjective performance measures
reveal that the current hiding technique attracts no suspicion about the
presence of the secret message in the stego speech, while being able
to recover an intelligible copy of the secret message at the receiver
side.
Abstract: To model the human visual system (HVS) in the region of interest, we propose a new objective metric evaluation adapted to wavelet foveation-based image compression quality measurement, which exploits a foveation setup filter implementation technique in the DWT domain, based especially on the point and region of fixation of the human eye. This model is then used to predict the visible divergences between an original and compressed image with respect to this region field and yields an adapted and local measure error by removing all peripheral errors. The technique, which we call foveation wavelet visible difference prediction (FWVDP), is demonstrated on a number of noisy images all of which have the same local peak signal to noise ratio (PSNR), but visibly different errors. We show that the FWVDP reliably predicts the fixation areas of interest where error is masked, due to high image contrast, and the areas where the error is visible, due to low image contrast. The paper also suggests ways in which the FWVDP can be used to determine a visually optimal quantization strategy for foveation-based wavelet coefficients and to produce a quantitative local measure of image quality.
Abstract: This paper presents a new method to detect high impedance faults in radial distribution systems. Magnitudes of third and fifth harmonic components of voltages and currents are used as a feature vector for fault discrimination. The proposed methodology uses a learning vector quantization (LVQ) neural network as a classifier for identifying high impedance arc-type faults. The network learns from the data obtained from simulation of a simple radial system under different fault and system conditions. Compared to a feed-forward neural network, a properly tuned LVQ network gives quicker response.