36,291 research outputs found
Minimal Paths for Tubular Structure Segmentation with Coherence Penalty and Adaptive Anisotropy
The minimal path method has proven to be particularly useful and efficient in
tubular structure segmentation applications. In this paper, we propose a new
minimal path model associated with a dynamic Riemannian metric embedded with an
appearance feature coherence penalty and an adaptive anisotropy enhancement
term. The features that characterize the appearance and anisotropy properties
of a tubular structure are extracted through the associated orientation score.
The proposed dynamic Riemannian metric is updated in the course of the geodesic
distance computation carried out by the efficient single-pass fast marching
method. Compared to state-of-the-art minimal path models, the proposed minimal
path model is able to extract the desired tubular structures from a complicated
vessel tree structure. In addition, we propose an efficient prior path-based
method to search for vessel radius value at each centerline position of the
target. Finally, we perform the numerical experiments on both synthetic and
real images. The quantitive validation is carried out on retinal vessel images.
The results indicate that the proposed model indeed achieves a promising
performance.Comment: This manuscript has been accepted by IEEE Trans. Image Processing,
201
Speech Recognition by Machine, A Review
This paper presents a brief survey on Automatic Speech Recognition and
discusses the major themes and advances made in the past 60 years of research,
so as to provide a technological perspective and an appreciation of the
fundamental progress that has been accomplished in this important area of
speech communication. After years of research and development the accuracy of
automatic speech recognition remains one of the important research challenges
(e.g., variations of the context, speakers, and environment).The design of
Speech Recognition system requires careful attentions to the following issues:
Definition of various types of speech classes, speech representation, feature
extraction techniques, speech classifiers, database and performance evaluation.
The problems that are existing in ASR and the various techniques to solve these
problems constructed by various research workers have been presented in a
chronological order. Hence authors hope that this work shall be a contribution
in the area of speech recognition. The objective of this review paper is to
summarize and compare some of the well known methods used in various stages of
speech recognition system and identify research topic and applications which
are at the forefront of this exciting and challenging field.Comment: 25 pages IEEE format, International Journal of Computer Science and
Information Security, IJCSIS December 2009, ISSN 1947 5500,
http://sites.google.com/site/ijcsis
Block-Matching Convolutional Neural Network for Image Denoising
There are two main streams in up-to-date image denoising algorithms:
non-local self similarity (NSS) prior based methods and convolutional neural
network (CNN) based methods. The NSS based methods are favorable on images with
regular and repetitive patterns while the CNN based methods perform better on
irregular structures. In this paper, we propose a block-matching convolutional
neural network (BMCNN) method that combines NSS prior and CNN. Initially,
similar local patches in the input image are integrated into a 3D block. In
order to prevent the noise from messing up the block matching, we first apply
an existing denoising algorithm on the noisy image. The denoised image is
employed as a pilot signal for the block matching, and then denoising function
for the block is learned by a CNN structure. Experimental results show that the
proposed BMCNN algorithm achieves state-of-the-art performance. In detail,
BMCNN can restore both repetitive and irregular structures.Comment: 11 pages, 9 figure
A Journey from Improper Gaussian Signaling to Asymmetric Signaling
The deviation of continuous and discrete complex random variables from the
traditional proper and symmetric assumption to a generalized improper and
asymmetric characterization (accounting correlation between a random entity and
its complex conjugate), respectively, introduces new design freedom and various
potential merits. As such, the theory of impropriety has vast applications in
medicine, geology, acoustics, optics, image and pattern recognition, computer
vision, and other numerous research fields with our main focus on the
communication systems. The journey begins from the design of improper Gaussian
signaling in the interference-limited communications and leads to a more
elaborate and practically feasible asymmetric discrete modulation design. Such
asymmetric shaping bridges the gap between theoretically and practically
achievable limits with sophisticated transceiver and detection schemes in both
coded/uncoded wireless/optical communication systems. Interestingly,
introducing asymmetry and adjusting the transmission parameters according to
some design criterion render optimal performance without affecting the
bandwidth or power requirements of the systems. This dual-flavored article
initially presents the tutorial base content covering the interplay of
reality/complexity, propriety/impropriety and circularity/noncircularity and
then surveys majority of the contributions in this enormous journey.Comment: IEEE COMST (Early Access
Non-Uniform Wavelet Sampling for RF Analog-to-Information Conversion
Feature extraction, such as spectral occupancy, interferer energy and type,
or direction-of-arrival, from wideband radio-frequency~(RF) signals finds use
in a growing number of applications as it enhances RF transceivers with
cognitive abilities and enables parameter tuning of traditional RF chains. In
power and cost limited applications, e.g., for sensor nodes in the Internet of
Things, wideband RF feature extraction with conventional, Nyquist-rate
analog-to-digital converters is infeasible. However, the structure of many RF
features (such as signal sparsity) enables the use of compressive sensing (CS)
techniques that acquire such signals at sub-Nyquist rates. While such CS-based
analog-to-information (A2I) converters have the potential to enable low-cost
and energy-efficient wideband RF sensing, they suffer from a variety of
real-world limitations, such as noise folding, low sensitivity, aliasing, and
limited flexibility.
This paper proposes a novel CS-based A2I architecture called non-uniform
wavelet sampling (NUWS). Our solution extracts a carefully-selected subset of
wavelet coefficients directly in the RF domain, which mitigates the main issues
of existing A2I converter architectures. For multi-band RF signals, we propose
a specialized variant called non-uniform wavelet bandpass sampling (NUWBS),
which further improves sensitivity and reduces hardware complexity by
leveraging the multi-band signal structure. We use simulations to demonstrate
that NUWBS approaches the theoretical performance limits of -norm-based
sparse signal recovery. We investigate hardware-design aspects and show ASIC
measurement results for the wavelet generation stage, which highlight the
efficacy of NUWBS for a broad range of RF feature extraction tasks in cost- and
power-limited applications.Comment: To appear in the IEEE Transactions on Circuits and Systems I: Regular
Paper
Solar Potential Analysis of Rooftops Using Satellite Imagery
Solar energy is one of the most important sources of renewable energy and the
cleanest form of energy. In India, where solar energy could produce power
around trillion kilowatt-hours in a year, our country is only able to produce
power of around in gigawatts only. Many people are not aware of the solar
potential of their rooftop, and hence they always think that installing solar
panels is very much expensive. In this work, we introduce an approach through
which we can generate a report remotely that provides the amount of solar
potential of a building using only its latitude and longitude. We further
evaluated various types of rooftops to make our solution more robust. We also
provide an approximate area of rooftop that can be used for solar panels
placement and a visual analysis of how solar panels can be placed to maximize
the output of solar power at a location
Real time expert system for anomaly detection of aerators based on computer vision technology and existing surveillance cameras
Aerators are essential and crucial auxiliary devices in intensive culture,
especially in industrial culture in China. The traditional methods cannot
accurately detect abnormal condition of aerators in time. Surveillance cameras
are widely used as visual perception modules of the Internet of Things, and
then using these widely existing surveillance cameras to realize real-time
anomaly detection of aerators is a cost-free and easy-to-promote method.
However, it is difficult to develop such an expert system due to some technical
and applied challenges, e.g., illumination, occlusion, complex background, etc.
To tackle these aforementioned challenges, we propose a real-time expert system
based on computer vision technology and existing surveillance cameras for
anomaly detection of aerators, which consists of two modules, i.e., object
region detection and working state detection. First, it is difficult to detect
the working state for some small object regions in whole images, and the time
complexity of global feature comparison is also high, so we present an object
region detection method based on the region proposal idea. Moreover, we propose
a novel algorithm called reference frame Kanade-Lucas-Tomasi (RF-KLT) algorithm
for motion feature extraction in fixed regions. Then, we present a dimension
reduction method of time series for establishing a feature dataset with obvious
boundaries between classes. Finally, we use machine learning algorithms to
build the feature classifier. The experimental results in both the actual video
dataset and the augmented video dataset show that the accuracy for detecting
object region and working state of aerators is 100% and 99.9% respectively, and
the detection speed is 77-333 frames per second (FPS) according to the
different types of surveillance cameras.Comment: 17 figure
Estimating Blood Pressure from Photoplethysmogram Signal and Demographic Features using Machine Learning Techniques
Hypertension is a potentially unsafe health ailment, which can be indicated
directly from the Blood pressure (BP). Hypertension always leads to other
health complications. Continuous monitoring of BP is very important; however,
cuff-based BP measurements are discrete and uncomfortable to the user. To
address this need, a cuff-less, continuous and a non-invasive BP measurement
system is proposed using Photoplethysmogram (PPG) signal and demographic
features using machine learning (ML) algorithms. PPG signals were acquired from
219 subjects, which undergo pre-processing and feature extraction steps. Time,
frequency and time-frequency domain features were extracted from the PPG and
their derivative signals. Feature selection techniques were used to reduce the
computational complexity and to decrease the chance of over-fitting the ML
algorithms. The features were then used to train and evaluate ML algorithms.
The best regression models were selected for Systolic BP (SBP) and Diastolic BP
(DBP) estimation individually. Gaussian Process Regression (GPR) along with
ReliefF feature selection algorithm outperforms other algorithms in estimating
SBP and DBP with a root-mean-square error (RMSE) of 6.74 and 3.59 respectively.
This ML model can be implemented in hardware systems to continuously monitor BP
and avoid any critical health conditions due to sudden changes.Comment: Accepted for publication in Sensor, 14 Figures, 14 Table
Biosignal Analysis with Matching-Pursuit Based Adaptive Chirplet Transform
Chirping phenomena, in which the instantaneous frequencies of a signal change
with time, are abundant in signals related to biological systems. Biosignals
are non-stationary in nature and the time-frequency analysis is a viable tool
to analyze them. It is well understood that Gaussian chirplet function is
critical in describing chirp signals. Despite the theory of adaptive chirplet
transform (ACT) has been established for more than two decades and is well
accepted in the community of signal processing, application of ACT to
bio-/biomedical signal analysis is still quite limited, probably because that
the power of ACT, as an emerging tool for biosignal analysis, has not yet been
fully appreciated by the researchers in the field of biomedical engineering. In
this paper, we describe a novel ACT algorithm based on the "coarse-refinement"
scheme. Namely, the initial estimate of a chirplet is implemented with the
matching-pursuit (MP) algorithm and subsequently it is refined using the
expectation-maximization (EM) algorithm, which we coin as MPEM algorithm. We
emphasize the robustness enhancement of the algorithm in face of noise, which
is important to biosignal analysis, as they are usually embedded in strong
background noise. We then demonstrate the capability of our algorithm by
applying it to the analysis of representative biosignals, including visual
evoked potentials (bioelectrical signals), audible heart sounds and bat
ultrasonic echolocation signals (bioacoustic signals), and human speech. The
results show that the MPEM algorithm provides more compact representation of
signals under investigation and clearer visualization of their time-frequency
structures, indicating considerable promise of ACT in biosignal analysis. The
MATLAB code repository is hosted on GitHub for free download
(https://github.com/jiecui/mpact).Comment: 27 pages, 8 figure
Computational Intelligence for Condition Monitoring
Condition monitoring techniques are described in this chapter. Two aspects of
condition monitoring process are considered: (1) feature extraction; and (2)
condition classification. Feature extraction methods described and implemented
are fractals, Kurtosis and Mel-frequency Cepstral Coefficients. Classification
methods described and implemented are support vector machines (SVM), hidden
Markov models (HMM), Gaussian mixture models (GMM) and extension neural
networks (ENN). The effectiveness of these features were tested using SVM, HMM,
GMM and ENN on condition monitoring of bearings and are found to give good
results.Comment: 23 page
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