Abstract: This paper presents a generalized form of the
mechanistic deconvolution technique (GMD) to modeling image sensors applicable in various pan–tilt planes of view. The mechanistic deconvolution technique (UMD) is modified with the
given angles of a pan–tilt plane of view to formulate constraint parameters and characterize distortion effects, and thereby, determine
the corrected image data. This, as a result, does not require experimental setup or calibration. Due to the mechanistic nature of
the sensor model, the necessity for the sensor image plane to be
orthogonal to its z-axis is eliminated, and it reduces the dependency on image data. An experiment was constructed to evaluate the
accuracy of a model created by GMD and its insensitivity to changes in sensor properties and in pan and tilt angles. This was compared
with a pre-calibrated model and a model created by UMD using two sensors with different specifications. It achieved similar accuracy
with one-seventh the number of iterations and attained lower mean error by a factor of 2.4 when compared to the pre-calibrated and
UMD model respectively. The model has also shown itself to be robust and, in comparison to pre-calibrated and UMD model, improved the accuracy significantly.
Abstract: Functional imaging procedures for the non-invasive assessment of tissue microcirculation are highly requested, but require a mathematical approach describing the trans- and intercapillary passage of tracer particles. Up to now, two theoretical, for the moment different concepts have been established for tracer kinetic modeling of contrast agent transport in tissues: pharmacokinetic compartment models, which are usually written as coupled differential equations, and the indicator dilution theory, which can be generalized in accordance with the theory of lineartime- invariant (LTI) systems by using a convolution approach. Based on mathematical considerations, it can be shown that also in the case of an open two-compartment model well-known from functional imaging, the concentration-time course in tissue is given by a convolution, which allows a separation of the arterial input function from a system function being the impulse response function, summarizing the available information on tissue microcirculation. Due to this reason, it is possible to integrate the open two-compartment model into the system-theoretic concept of indicator dilution theory (IDT) and thus results known from IDT remain valid for the compartment approach. According to the long number of applications of compartmental analysis, even for a more general context similar solutions of the so-called forward problem can already be found in the extensively available appropriate literature of the seventies and early eighties. Nevertheless, to this day, within the field of biomedical imaging – not from the mathematical point of view – there seems to be a trench between both approaches, which the author would like to get over by exemplary analysis of the well-known model.
Abstract: This paper aims to present a survey of object
recognition/classification methods based on image moments. We
review various types of moments (geometric moments, complex
moments) and moment-based invariants with respect to various
image degradations and distortions (rotation, scaling, affine
transform, image blurring, etc.) which can be used as shape
descriptors for classification. We explain a general theory how to
construct these invariants and show also a few of them in explicit
forms. We review efficient numerical algorithms that can be used
for moment computation and demonstrate practical examples of
using moment invariants in real applications.
Abstract: Mobile WiMAX is a broadband wireless solution that
enables convergence of mobile and fixed broadband networks
through a common wide area broadband radio access technology and
flexible network architecture. It adopts Orthogonal Frequency
Division Multiple Access (OFDMA) for improved multi-path
performance in Non-Line-Of-Sight (NLOS) environments. Scalable
OFDMA (SOFDMA) is introduced in the IEEE 802e[1]. WIMAX
system uses one of different types of channel coding but The
mandatory channel coding scheme is based on binary nonrecursive
Convolutional Coding (CC). There are other several optional channel
coding schemes such as block turbo codes, convolutional turbo
codes, and low density parity check (LDPC).
In this paper a comparison between the performance of WIMAX
using turbo code and using convolutional product code (CPC) [2] is
made. Also a combination between them had been done. The CPC
gives good results at different SNR values compared to both the
turbo system, and the combination between them. For example, at
BER equal to 10-2 for 128 subcarriers, the amount of improvement
in SNR equals approximately 3 dB higher than turbo code and equals
approximately 2dB higher than the combination respectively. Several
results are obtained at different modulating schemes (16QAM and
64QAM) and different numbers of sub-carriers (128 and 512).
Abstract: A hardware efficient, multi mode, re-configurable
architecture of interleaver/de-interleaver for multiple standards,
like DVB, WiMAX and WLAN is presented. The interleavers
consume a large part of silicon area when implemented by using
conventional methods as they use memories to store permutation
patterns. In addition, different types of interleavers in different
standards cannot share the hardware due to different construction
methodologies. The novelty of the work presented in this paper is
threefold: 1) Mapping of vital types of interleavers including
convolutional interleaver onto a single architecture with flexibility
to change interleaver size; 2) Hardware complexity for channel
interleaving in WiMAX is reduced by using 2-D realization of the
interleaver functions; and 3) Silicon cost overheads reduced by
avoiding the use of small memories. The proposed architecture
consumes 0.18mm2 silicon area for 0.12μm process and can
operate at a frequency of 140 MHz. The reduced complexity helps
in minimizing the memory utilization, and at the same time
provides strong support to on-the-fly computation of permutation
patterns.
Abstract: This work explores blind image deconvolution by recursive function approximation based on supervised learning of neural networks, under the assumption that a degraded image is linear convolution of an original source image through a linear shift-invariant (LSI) blurring matrix. Supervised learning of neural networks of radial basis functions (RBF) is employed to construct an embedded recursive function within a blurring image, try to extract non-deterministic component of an original source image, and use them to estimate hyper parameters of a linear image degradation model. Based on the estimated blurring matrix, reconstruction of an original source image from a blurred image is further resolved by an annealed Hopfield neural network. By numerical simulations, the proposed novel method is shown effective for faithful estimation of an unknown blurring matrix and restoration of an original source image.
Abstract: In this paper, the statistical properties of filtered or convolved signals are considered by deriving the resulting density functions as well as the exact mean and variance expressions given a prior knowledge about the statistics of the individual signals in the filtering or convolution process. It is shown that the density function after linear convolution is a mixture density, where the number of density components is equal to the number of observations of the shortest signal. For circular convolution, the observed samples are characterized by a single density function, which is a sum of products.
Abstract: The modern telecommunication industry demands
higher capacity networks with high data rate. Orthogonal frequency
division multiplexing (OFDM) is a promising technique for high data
rate wireless communications at reasonable complexity in wireless
channels. OFDM has been adopted for many types of wireless
systems like wireless local area networks such as IEEE 802.11a, and
digital audio/video broadcasting (DAB/DVB). The proposed research
focuses on a concatenated coding scheme that improve the
performance of OFDM based wireless communications. It uses a
Redundant Residue Number System (RRNS) code as the outer code
and a convolutional code as the inner code. Here, a direct conversion
of analog signal to residue domain is done to reduce the conversion
complexity using sigma-delta based parallel analog-to-residue
converter. The bit error rate (BER) performances of the proposed
system under different channel conditions are investigated. These
include the effect of additive white Gaussian noise (AWGN),
multipath delay spread, peak power clipping and frame start
synchronization error. The simulation results show that the proposed
RRNS-Convolutional concatenated coding (RCCC) scheme provides
significant improvement in the system performance by exploiting the
inherent properties of RRNS.
Abstract: With the rapid popularization of internet services, it is apparent that the next generation terrestrial communication systems must be capable of supporting various applications like voice, video, and data. This paper presents the performance evaluation of turbo- coded mobile terrestrial communication systems, which are capable of providing high quality services for delay sensitive (voice or video) and delay tolerant (text transmission) multimedia applications in urban and suburban areas. Different types of multimedia information require different service qualities, which are generally expressed in terms of a maximum acceptable bit-error-rate (BER) and maximum tolerable latency. The breakthrough discovery of turbo codes allows us to significantly reduce the probability of bit errors with feasible latency. In a turbo-coded system, a trade-off between latency and BER results from the choice of convolutional component codes, interleaver type and size, decoding algorithm, and the number of decoding iterations. This trade-off can be exploited for multimedia applications by using optimal and suboptimal performance parameter amalgamations to achieve different service qualities. The results are therefore proposing an adaptive framework for turbo-coded wireless multimedia communications which incorporate a set of performance parameters that achieve an appropriate set of service qualities, depending on the application's requirements.
Abstract: Identity verification of authentic persons by their multiview faces is a real valued problem in machine vision. Multiview faces are having difficulties due to non-linear representation in the feature space. This paper illustrates the usability of the generalization of LDA in the form of canonical covariate for face recognition to multiview faces. In the proposed work, the Gabor filter bank is used to extract facial features that characterized by spatial frequency, spatial locality and orientation. Gabor face representation captures substantial amount of variations of the face instances that often occurs due to illumination, pose and facial expression changes. Convolution of Gabor filter bank to face images of rotated profile views produce Gabor faces with high dimensional features vectors. Canonical covariate is then used to Gabor faces to reduce the high dimensional feature spaces into low dimensional subspaces. Finally, support vector machines are trained with canonical sub-spaces that contain reduced set of features and perform recognition task. The proposed system is evaluated with UMIST face database. The experiment results demonstrate the efficiency and robustness of the proposed system with high recognition rates.
Abstract: In this paper, the implementation of low power,
high throughput convolutional filters for the one dimensional
Discrete Wavelet Transform and its inverse are presented. The
analysis filters have already been used for the implementation of a
high performance DWT encoder [15] with minimum memory
requirements for the JPEG 2000 standard. This paper presents the
design techniques and the implementation of the convolutional filters
included in the JPEG2000 standard for the forward and inverse DWT
for achieving low-power operation, high performance and reduced
memory accesses. Moreover, they have the ability of performing
progressive computations so as to minimize the buffering between
the decomposition and reconstruction phases. The experimental
results illustrate the filters- low power high throughput characteristics
as well as their memory efficient operation.
Abstract: This paper presents a VLSI design approach of a highspeed
and real-time 2-D Discrete Wavelet Transform computing. The
proposed architecture, based on new and fast convolution approach,
reduces the hardware complexity in addition to reduce the critical
path to the multiplier delay. Furthermore, an advanced twodimensional
(2-D) discrete wavelet transform (DWT)
implementation, with an efficient memory area, is designed to
produce one output in every clock cycle. As a result, a very highspeed
is attained. The system is verified, using JPEG2000
coefficients filters, on Xilinx Virtex-II Field Programmable Gate
Array (FPGA) device without accessing any external memory. The
resulting computing rate is up to 270 M samples/s and the (9,7) 2-D
wavelet filter uses only 18 kb of memory (16 kb of first-in-first-out
memory) with 256×256 image size. In this way, the developed design
requests reduced memory and provide very high-speed processing as
well as high PSNR quality.
Abstract: The characterisation of agro-wastes fibres for composite applications from Nigeria using X-ray diffraction (XRD) and Scanning Electron Microscopy (SEM) has been done. Fibres extracted from groundnut shell, coconut husk, rice husk, palm fruit bunch and palm fruit stalk are processed using two novel cellulose fibre production methods developed by the authors. Cellulose apparent crystallinity calculated using the deconvolution of the diffractometer trace shows that the amorphous portion of cellulose was permeable to hydrolysis yielding high crystallinity after treatment. All diffratograms show typical cellulose structure with well-defined 110, 200 and 040 peaks. Palm fruit fibres had the highest 200 crystalline cellulose peaks compared to others and it is an indication of rich cellulose content. Surface examination of the resulting fibres using SEM indicates the presence of regular cellulose network structure with some agglomerated laminated layer of thin leaves of cellulose microfibrils. The surfaces were relatively smooth indicating the removal of hemicellulose, lignin and pectin.
Abstract: In this paper, the performance of three types of serial
concatenated convolutional codes (SCCC) is compared and analyzed
in additive white Gaussian noise (AWGN) channel. In Type I, only the
parity bits of outer encoder are passed to inner encoder. In Type II and
Type III, both the information bits and the parity bits of outer encoder
are transferred to inner encoder. As results of simulation, Type I shows
the best bit error rate (BER) performance at low signal-to-noise ratio
(SNR). On the other hand, Type III shows the best BER performance
at high SNR in AWGN channel. The simulation results are analyzed
using the distance spectrum.
Abstract: A frequency grouping approach for multi-channel
instantaneous blind source separation (I-BSS) of convolutive
mixtures is proposed for a lower net residual inter-symbol
interference (ISI) and inter-channel interference (ICI) than the
conventional short-time Fourier transform (STFT) approach. Starting
in the time domain, STFTs are taken with overlapping windows to
convert the convolutive mixing problem into frequency domain
instantaneous mixing. Mixture samples at the same frequency but
from different STFT windows are grouped together forming unique
frequency groups.
The individual frequency group vectors are input to the I-BSS
algorithm of choice, from which the output samples are dispersed
back to their respective STFT windows. After applying the inverse
STFT, the resulting time domain signals are used to construct the
complete source estimates via the weighted overlap-add method
(WOLA). The proposed algorithm is tested for source deconvolution
given two mixtures, and simulated along with the STFT approach to
illustrate its superiority for fairly motionless sources.
Abstract: Among other factors that characterize satellite communication
channels is their high bit error rate. We present a system for
still image transmission over noisy satellite channels. The system
couples image compression together with error control codes to
improve the received image quality while maintaining its bandwidth
requirements. The proposed system is tested using a high resolution
satellite imagery simulated over the Rician fading channel. Evaluation
results show improvement in overall system including image quality
and bandwidth requirements compared to similar systems with different
coding schemes.
Abstract: Image convolution similar to the receptive fields
found in mammalian visual pathways has long been used in
conventional image processing in the form of Gabor masks.
However, no VLSI implementation of parallel, multi-layered pulsed
processing has been brought forward which would emulate this
property. We present a technical realization of such a pulsed image
processing scheme. The discussed IC also serves as a general testbed
for VLSI-based pulsed information processing, which is of interest
especially with regard to the robustness of representing an analog
signal in the phase or duration of a pulsed, quasi-digital signal, as
well as the possibility of direct digital manipulation of such an
analog signal. The network connectivity and processing properties
are reconfigurable so as to allow adaptation to various processing
tasks.
Abstract: The new concept of two–dimensional (2D) image
processing implementation for auto-guiding system is shown in this
paper. It is dedicated to astrophotography and operates with
astronomy CCD guide cameras or with self-guided dual-detector
CCD cameras and ST4 compatible equatorial mounts. This idea was
verified by MATLAB model, which was used to test all procedures
and data conversions. Next the circuit prototype was implemented at
Altera MAX II CPLD device and tested for real astronomical object
images. The digital processing speed of CPLD prototype board was
sufficient for correct equatorial mount guiding in real-time system.
Abstract: Iris localization is a very important approach in
biometric identification systems. Identification process usually is
implemented in three levels: iris localization, feature extraction, and
pattern matching finally. Accuracy of iris localization as the first step
affects all other levels and this shows the importance of iris
localization in an iris based biometric system. In this paper, we
consider Daugman iris localization method as a standard method,
propose a new method in this field and then analyze and compare the
results of them on a standard set of iris images. The proposed method
is based on the detection of circular edge of iris, and improved by
fuzzy circles and surface energy difference contexts. Implementation
of this method is so easy and compared to the other methods, have a
rather high accuracy and speed. Test results show that the accuracy of
our proposed method is about Daugman method and computation
speed of it is 10 times faster.
Abstract: The aim of this article is to narrate the utility of novel simulation approach i.e. convolution method to predict blood concentration of drug utilizing dissolution data of salbutamol sulphate microparticulate formulations with different release patterns (1:1, 1:2 and 1:3, drug:polymer). Dissolution apparatus II USP 2007 and 900 ml double distilled water stirrd at 50 rpm was employed for dissolution analysis. From dissolution data, blood drug concentration was determined, and in return predicted blood drug concentration data was used to calculate the pharmacokinetic parameters i.e. Cmax, Tmax, and AUC. Convolution is a good biwaiver technique; however its better utility needs it application in the conditions where biorelevant dissolution media are used.