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The signal processing (SP) landscape has been enriched by recent
advances in artificial intelligence (AI) and machine learning (ML),
yielding new tools for signal estimation, classification,
prediction, and manipulation. Layered signal representations,
nonlinear function approximation and nonlinear signal prediction
are now feasible at very large scale in both dimensionality and
data size. These are leading to significant performance gains in a
variety of long-standing problem domains like speech and Image
analysis. As well as providing the ability to construct new classes
of nonlinear functions (e.g., fusion, nonlinear filtering). This
book will help academics, researchers, developers, graduate and
undergraduate students to comprehend complex SP data across a wide
range of topical application areas such as social multimedia data
collected from social media networks, medical imaging data, data
from Covid tests etc. This book focuses on AI utilization in the
speech, image, communications and yirtual reality domains.
This book presents various application areas of computing in the
automotive sector. The authors explain how computing enhances the
performance of vehicles, covering the applications of computing in
smart transportation and the future scope. The authors focus on
computing for vehicle safety in conjunction with the latest
technologies in Internet of Things (IoT). The book provides a
holistic approach to computing in an inter-disciplinary and unified
view. Topics covered include driverless automated navigation
systems, smart transportation, self-learning systems, in-vehicle
intelligent systems, and off-road vehicle diagnosis and
maintenance, among others. The authors include simulated examples
and case studies for better understanding of the technologies and
applications. The book is intended for a wide range of readers from
students to researchers and industry practitioners and is a useful
resource for those planning to pursue research in the area of
computing and autonomous driving vehicles.
Electromyography (EMG) signal gives an electrical representation of
neuromuscular activation associated with contracting muscle
provides information about the performance of muscles and nerves.
EMG signal acquires noise while traveling through different
tissues. With the appropriate choice of the Wavelet Function (WF),
it is possible to remove interference noise. Higher Order
Statistics (HOS) can suppress white Gaussian noise in detection,
parameter estimation and solve classification problems. Based on
the RMS error, it is noticed that WF db2 can perform denoising most
effectively among the other WFs (db6, db8, dmey). Power spectrum
analysis is performed to the denoised EMG where mean power
frequency is calculated to indicate changes in muscle contraction.
Gaussianity and linearity tests are conducted to understand changes
in muscle contraction. According to the results, increase in muscle
contraction provides significant increase in EMG mean power
frequency. The study also verifies that the power spectrum of EMG
shows a shift to lower frequencies during fatigue. The bispectrum
analysis shows that the signal becomes less Gaussian and more
linear with increasing muscle force.
Fetal Electrocardiogram (FECG) signal contains potentially precise
information that could assist clinicians in making more appropriate
and timely decisions during labor. A Back-propagation Neural
Network and Adaptive Linear Neural Network have been designed to
extract the FECG from the abdominal ECG to assess the fetus during
the pregnancy and labor. The neural network was trained to
recognize the normal waveform and filtered out the unnecessary
artifacts including noises in the ECG signal, including power line
interference, motion artifacts, baseline drift, ECG amplitude
modulation with respiration and other composite noises. The
performance of the designed algorithm for FHR extraction is 93.75%.
The algorithm has been modeled using VHDL for hardware modeling of
FHR monitoring system, which has been synthesized and fitted into
Altera's Stratix II EP2S15F484C3 using the Quartus II version 7.2
Web Edition where the logic and DSP block utilization were 89% and
50% respectively. This research will open up a passage to
biomedical researchers and physicians to advocate an excellent
understanding of FECG signal and its analysis procedures for FHR
monitoring system.
The Set Partitioning in Hierarchical Trees (SPIHT) is modified and
a new algorithm is developed, called Modified SPIHT (MSPIHT) using
one list to store the co-ordinates of wavelet coefficients. When a
coefficient of DWT is found as significant or insignificant, its
last error bits will be omitted and rest of the bits will be
outputted. MSPIHT is the low memory solution of SPIHT algorithm by
eliminating the temporary list LSP and LIP. Absolute zerotree is a
good solution of the rapid extension of LIS. The MATLAB simulation
result shows that for coding a 512x512, gray-level image, MSPIHT
reduce execution time at most 7 times and for decoding at most 11
times at low bit rate, saves at least 0.5625 MBytes of memory. The
PSNR value of the reconstructed image using MSPIHT algorithm is
reduced by 0.787% with respect to original SPIHT algorithm. This
system is implemented on Altera EP2S60F1020C4 FPGA using the
software Quartus II 6.0. The results from hardware implementation
show that this design has speeded up at most 7129 times faster than
that of the results obtained from MATLAB simulations; thereby
making it highly promising for real-time and memory limited mobile
communication.
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