Abstract: In this work we present a solution for DAGC (Digital
Automatic Gain Control) in WLAN receivers compatible to IEEE 802.11a/g standard. Those standards define communication in 5/2.4
GHz band using Orthogonal Frequency Division Multiplexing OFDM modulation scheme. WLAN Transceiver that we have used
enables gain control over Low Noise Amplifier (LNA) and a
Variable Gain Amplifier (VGA). The control over those signals is
performed in our digital baseband processor using dedicated hardware block DAGC. DAGC in this process is used to automatically control the VGA and LNA in order to achieve better
signal-to-noise ratio, decrease FER (Frame Error Rate) and hold the
average power of the baseband signal close to the desired set point.
DAGC function in baseband processor is done in few steps: measuring power levels of baseband samples of an RF signal,accumulating the differences between the measured power level and
actual gain setting, adjusting a gain factor of the accumulation, and
applying the adjusted gain factor the baseband values. Based on the measurement results of RSSI signal dependence to input power we have concluded that this digital AGC can be implemented applying
the simple linearization of the RSSI. This solution is very simple but also effective and reduces complexity and power consumption of the
DAGC. This DAGC is implemented and tested both in FPGA and in ASIC as a part of our WLAN baseband processor. Finally, we have integrated this circuit in a compact WLAN PCMCIA board based on MAC and baseband ASIC chips designed from us.
Abstract: Removing noise from the any processed images is very important. Noise should be removed in such a way that important information of image should be preserved. A decisionbased nonlinear algorithm for elimination of band lines, drop lines, mark, band lost and impulses in images is presented in this paper. The algorithm performs two simultaneous operations, namely, detection of corrupted pixels and evaluation of new pixels for replacing the corrupted pixels. Removal of these artifacts is achieved without damaging edges and details. However, the restricted window size renders median operation less effective whenever noise is excessive in that case the proposed algorithm automatically switches to mean filtering. The performance of the algorithm is analyzed in terms of Mean Square Error [MSE], Peak-Signal-to-Noise Ratio [PSNR], Signal-to-Noise Ratio Improved [SNRI], Percentage Of Noise Attenuated [PONA], and Percentage Of Spoiled Pixels [POSP]. This is compared with standard algorithms already in use and improved performance of the proposed algorithm is presented. The advantage of the proposed algorithm is that a single algorithm can replace several independent algorithms which are required for removal of different artifacts.
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: The ideal sinc filter, ignoring the noise statistics, is often
applied for generating an arbitrary sample of a bandlimited signal by
using the uniformly sampled data. In this article, an optimal interpolator is proposed; it reaches a minimum mean square error (MMSE)
at its output in the presence of noise. The resulting interpolator is
thus a Wiener filter, and both the optimal infinite impulse response
(IIR) and finite impulse response (FIR) filters are presented. The
mean square errors (MSE-s) for the interpolator of different length
impulse responses are obtained by computer simulations; it shows that
the MSE-s of the proposed interpolators with a reasonable length are
improved about 0.4 dB under flat power spectra in noisy environment with signal-to-noise power ratio (SNR) equal 10 dB. As expected,
the results also demonstrate the improvements for the MSE-s with various fractional delays of the optimal interpolator against the ideal
sinc filter under a fixed length impulse response.
Abstract: For about two decades scientists have been
developing techniques for enhancing the quality of medical images
using Fourier transform, DWT (Discrete wavelet transform),PDE
model etc., Gabor wavelet on hexagonal sampled grid of the images
is proposed in this work. This method has optimal approximation
theoretic performances, for a good quality image. The computational
cost is considerably low when compared to similar processing in the
rectangular domain. As X-ray images contain light scattered pixels,
instead of unique sigma, the parameter sigma of 0.5 to 3 is found to
satisfy most of the image interpolation requirements in terms of high
Peak Signal-to-Noise Ratio (PSNR) , lower Mean Squared Error
(MSE) and better image quality by adopting windowing technique.
Abstract: This paper study the high-level modelling and design
of delta-sigma (ΔΣ) noise shapers for audio Digital-to-Analog
Converter (DAC) so as to eliminate the in-band Signal-to-Noise-
Ratio (SNR) degradation that accompany one channel mismatch in
audio signal. The converter combines a cascaded digital signal
interpolation, a noise-shaping single loop delta-sigma modulator with
a 5-bit quantizer resolution in the final stage. To reduce sensitivity of
Digital-to-Analog Converter (DAC) nonlinearities of the last stage, a
high pass second order Data Weighted Averaging (R2DWA) is
introduced. This paper presents a MATLAB description modelling
approach of the proposed DAC architecture with low distortion and
swing suppression integrator designs. The ΔΣ Modulator design can
be configured as a 3rd-order and allows 24-bit PCM at sampling rate
of 64 kHz for Digital Video Disc (DVD) audio application. The
modeling approach provides 139.38 dB of dynamic range for a 32
kHz signal band at -1.6 dBFS input signal level.
Abstract: Machining is an important manufacturing process used to produce a wide variety of metallic parts. Among various machining processes, turning is one of the most important one which is employed to shape cylindrical parts. In turning, the quality of finished product is measured in terms of surface roughness. In turn, surface quality is determined by machining parameters and tool geometry specifications. The main objective of this study is to simultaneously model and optimize machining parameters and tool geometry in order to improve the surface roughness for AISI1045 steel. Several levels of machining parameters and tool geometry specifications are considered as input parameters. The surface roughness is selected as process output measure of performance. A Taguchi approach is employed to gather experimental data. Then, based on signal-to-noise (S/N) ratio, the best sets of cutting parameters and tool geometry specifications have been determined. Using these parameters values, the surface roughness of AISI1045 steel parts may be minimized. Experimental results are provided to illustrate the effectiveness of the proposed approach.
Abstract: In this paper we propose a method which improves the efficiency of video coding. Our method combines an adaptive GOP (group of pictures) structure and the shot cut detection. We have analyzed different approaches for shot cut detection with aim to choose the most appropriate one. The next step is to situate N frames to the positions of detected cuts during the process of video encoding. Finally the efficiency of the proposed method is confirmed by simulations and the obtained results are compared with fixed GOP structures of sizes 4, 8, 12, 16, 32, 64, 128 and GOP structure with length of entire video. Proposed method achieved the gain in bit rate from 0.37% to 50.59%, while providing PSNR (Peak Signal-to-Noise Ratio) gain from 1.33% to 0.26% in comparison to simulated fixed GOP structures.
Abstract: Tumor classification is a key area of research in the
field of bioinformatics. Microarray technology is commonly used in
the study of disease diagnosis using gene expression levels. The
main drawback of gene expression data is that it contains thousands
of genes and a very few samples. Feature selection methods are used
to select the informative genes from the microarray. These methods
considerably improve the classification accuracy. In the proposed
method, Genetic Algorithm (GA) is used for effective feature
selection. Informative genes are identified based on the T-Statistics,
Signal-to-Noise Ratio (SNR) and F-Test values. The initial candidate
solutions of GA are obtained from top-m informative genes. The
classification accuracy of k-Nearest Neighbor (kNN) method is used
as the fitness function for GA. In this work, kNN and Support Vector
Machine (SVM) are used as the classifiers. The experimental results
show that the proposed work is suitable for effective feature
selection. With the help of the selected genes, GA-kNN method
achieves 100% accuracy in 4 datasets and GA-SVM method
achieves in 5 out of 10 datasets. The GA with kNN and SVM
methods are demonstrated to be an accurate method for microarray
based tumor classification.
Abstract: Several combinations of the preprocessing algorithms,
feature selection techniques and classifiers can be applied to the data
classification tasks. This study introduces a new accurate classifier,
the proposed classifier consist from four components: Signal-to-
Noise as a feature selection technique, support vector machine,
Bayesian neural network and AdaBoost as an ensemble algorithm.
To verify the effectiveness of the proposed classifier, seven well
known classifiers are applied to four datasets. The experiments show
that using the suggested classifier enhances the classification rates for
all datasets.
Abstract: Photoplethysmography is a simple measurement of the
variation in blood volume in tissue. It detects the pulse signal of heart
beat as well as the low frequency signal of vasoconstriction and
vasodilation. The transmission type measurement is limited to only a
few specific positions for example the index finger that have a short
path length for light. The reflectance type measurement can be
conveniently applied on most parts of the body surface. This study
analyzed the factors that determine the quality of reflectance
photoplethysmograph signal including the emitter-detector distance,
wavelength, light intensity, and optical properties of skin tissue.
Light emitting diodes (LEDs) with four different visible
wavelengths were used as the light emitters. A phototransistor was
used as the light detector. A micro translation stage adjusts the
emitter-detector distance from 2 mm to 15 mm.
The reflective photoplethysmograph signals were measured on
different sites. The optimal emitter-detector distance was chosen to
have a large dynamic range for low frequency drifting without signal
saturation and a high perfusion index. Among these four wavelengths,
a yellowish green (571nm) light with a proper emitter-detection
distance of 2mm is the most suitable for obtaining a steady and reliable
reflectance photoplethysmograph signal
Abstract: In wavelet regression, choosing threshold value is a crucial issue. A too large value cuts too many coefficients resulting in over smoothing. Conversely, a too small threshold value allows many coefficients to be included in reconstruction, giving a wiggly estimate which result in under smoothing. However, the proper choice of threshold can be considered as a careful balance of these principles. This paper gives a very brief introduction to some thresholding selection methods. These methods include: Universal, Sure, Ebays, Two fold cross validation and level dependent cross validation. A simulation study on a variety of sample sizes, test functions, signal-to-noise ratios is conducted to compare their numerical performances using three different noise structures. For Gaussian noise, EBayes outperforms in all cases for all used functions while Two fold cross validation provides the best results in the case of long tail noise. For large values of signal-to-noise ratios, level dependent cross validation works well under correlated noises case. As expected, increasing both sample size and level of signal to noise ratio, increases estimation efficiency.
Abstract: To compress, improve bit error performance and also enhance 2D images, a new scheme, called Iterative Cellular-Turbo System (IC-TS) is introduced. In IC-TS, the original image is partitioned into 2N quantization levels, where N is denoted as bit planes. Then each of the N-bit-plane is coded by Turbo encoder and transmitted over Additive White Gaussian Noise (AWGN) channel. At the receiver side, bit-planes are re-assembled taking into consideration of neighborhood relationship of pixels in 2-D images. Each of the noisy bit-plane values of the image is evaluated iteratively using IC-TS structure, which is composed of equalization block; Iterative Cellular Image Processing Algorithm (ICIPA) and Turbo decoder. In IC-TS, there is an iterative feedback link between ICIPA and Turbo decoder. ICIPA uses mean and standard deviation of estimated values of each pixel neighborhood. It has extra-ordinary satisfactory results of both Bit Error Rate (BER) and image enhancement performance for less than -1 dB Signal-to-Noise Ratio (SNR) values, compared to traditional turbo coding scheme and 2-D filtering, applied separately. Also, compression can be achieved by using IC-TS systems. In compression, less memory storage is used and data rate is increased up to N-1 times by simply choosing any number of bit slices, sacrificing resolution. Hence, it is concluded that IC-TS system will be a compromising approach in 2-D image transmission, recovery of noisy signals and image compression.
Abstract: This paper presents a study of the Taguchi design
application to optimize surface quality in damper inserted end milling
operation. Maintaining good surface quality usually involves
additional manufacturing cost or loss of productivity. The Taguchi
design is an efficient and effective experimental method in which a
response variable can be optimized, given various factors, using
fewer resources than a factorial design. This Study included spindle
speed, feed rate, and depth of cut as control factors, usage of different
tools in the same specification, which introduced tool condition and
dimensional variability. An orthogonal array of L9(3^4)was used;
ANOVA analyses were carried out to identify the significant factors
affecting surface roughness, and the optimal cutting combination was
determined by seeking the best surface roughness (response) and
signal-to-noise ratio. Finally, confirmation tests verified that the
Taguchi design was successful in optimizing milling parameters for
surface roughness.
Abstract: The paper presents the study of synthetic transmit
aperture method applying the Golay coded transmission for medical
ultrasound imaging. Longer coded excitation allows to increase the
total energy of the transmitted signal without increasing the peak
pressure. Signal-to-noise ratio and penetration depth are improved
maintaining high ultrasound image resolution.
In the work the 128-element linear transducer array with 0.3 mm
inter-element spacing excited by one cycle and the 8 and 16-bit
Golay coded sequences at nominal frequencies 4 MHz was used.
Single element transmission aperture was used to generate a spherical
wave covering the full image region and all the elements received the
echo signals. The comparison of 2D ultrasound images of the wire
phantom as well as of the tissue mimicking phantom is presented to
demonstrate the benefits of the coded transmission. The results were
obtained using the synthetic aperture algorithm with transmit and
receive signals correction based on a single element directivity
function.
Abstract: In diversity rich environments, such as in Ultra-
Wideband (UWB) applications, the a priori determination of the
number of strong diversity branches is difficult, because of the considerably large number of diversity paths, which are characterized
by a variety of power delay profiles (PDPs). Several
Rake implementations have been proposed in the past, in order to reduce the number of the estimated and combined paths. To this
aim, we introduce two adaptive Rake receivers, which combine
a subset of the resolvable paths considering simultaneously the
quality of both the total combining output signal-to-noise ratio (SNR) and the individual SNR of each path. These schemes achieve
better adaptation to channel conditions compared to other known receivers, without further increasing the complexity. Their performance
is evaluated in different practical UWB channels, whose models are based on extensive propagation measurements. The
proposed receivers compromise between the power consumption,
complexity and performance gain for the additional paths, resulting in important savings in power and computational resources.
Abstract: In this study, a classification-based video
super-resolution method using artificial neural network (ANN) is
proposed to enhance low-resolution (LR) to high-resolution (HR)
frames. The proposed method consists of four main steps:
classification, motion-trace volume collection, temporal adjustment,
and ANN prediction. A classifier is designed based on the edge
properties of a pixel in the LR frame to identify the spatial information.
To exploit the spatio-temporal information, a motion-trace volume is
collected using motion estimation, which can eliminate unfathomable
object motion in the LR frames. In addition, temporal lateral process is
employed for volume adjustment to reduce unnecessary temporal
features. Finally, ANN is applied to each class to learn the complicated
spatio-temporal relationship between LR and HR frames. Simulation
results show that the proposed method successfully improves both
peak signal-to-noise ratio and perceptual quality.
Abstract: In this paper, a residue number arithmetic is used in
direct sequence spread spectrum system, this system is evaluated and
the bit error probability of this system is compared to that of non
residue number system. The effect of channel bandwidth, PN
sequences, multipath effect and modulation scheme are studied. A
Matlab program is developed to measure the signal-to-noise ratio
(SNR), and the bit error probability for the various schemes.
Abstract: Automated operations based on voice commands will become more and more important in many applications, including robotics, maintenance operations, etc. However, voice command recognition rates drop quite a lot under non-stationary and chaotic noise environments. In this paper, we tried to significantly improve the speech recognition rates under non-stationary noise environments. First, 298 Navy acronyms have been selected for automatic speech recognition. Data sets were collected under 4 types of noisy environments: factory, buccaneer jet, babble noise in a canteen, and destroyer. Within each noisy environment, 4 levels (5 dB, 15 dB, 25 dB, and clean) of Signal-to-Noise Ratio (SNR) were introduced to corrupt the speech. Second, a new algorithm to estimate speech or no speech regions has been developed, implemented, and evaluated. Third, extensive simulations were carried out. It was found that the combination of the new algorithm, the proper selection of language model and a customized training of the speech recognizer based on clean speech yielded very high recognition rates, which are between 80% and 90% for the four different noisy conditions. Fourth, extensive comparative studies have also been carried out.
Abstract: The present work analyses different parameters of pressure die casting to minimize the casting defects. Pressure diecasting is usually applied for casting of aluminium alloys. Good surface finish with required tolerances and dimensional accuracy can be achieved by optimization of controllable process parameters such as solidification time, molten temperature, filling time, injection pressure and plunger velocity. Moreover, by selection of optimum process parameters the pressure die casting defects such as porosity, insufficient spread of molten material, flash etc. are also minimized. Therefore, a pressure die casting component, carburetor housing of aluminium alloy (Al2Si2O5) has been considered. The effects of selected process parameters on casting defects and subsequent setting of parameters with the levels have been accomplished by Taguchi-s parameter design approach. The experiments have been performed as per the combination of levels of different process parameters suggested by L18 orthogonal array. Analyses of variance have been performed for mean and signal-to-noise ratio to estimate the percent contribution of different process parameters. Confidence interval has also been estimated for 95% consistency level and three conformational experiments have been performed to validate the optimum level of different parameters. Overall 2.352% reduction in defects has been observed with the help of suggested optimum process parameters.