Reconstruction- and example-based super-resolution
(SR) methods are promising for restoring a high-resolution
(HR) image from low-resolution (LR) image(s). Under large
magnification, reconstruction-based methods usually fail
to hallucinate visual details while example-based methods
sometimes introduce unexpected details. Given a generic
LR image, to reconstruct a photo-realistic SR image and
to suppress artifacts in the reconstructed SR image, we
introduce a multi-scale dictionary to a novel SR method
that simultaneously integrates local and non-local priors.
The local prior suppresses artifacts by using steering kernel regression to predict the target pixel from a small local
area. The non-local prior enriches visual details by taking
a weighted average of a large neighborhood as an estimate
of the target pixel. Essentially, these two priors are complementary to each other. Experimental results demonstrate
that the proposed method can produce high quality SR recovery both quantitatively and perceptually.
Accurate pose estimation plays an important role in solution of simultaneous localization and mapping (SLAM) problem, required for many robotic applications. This paper presents a new approach called R-SLAM, primarily to overcome systematic and non-systematic odometry errors which are generally caused by uneven floors, unexpected objects on the floor or wheel-slippage due to skidding or fast turns.The hybrid approach presented here combines the strengths of feature based and grid based methods to produce globally consistent high resolution maps within various types of environments.
The recent developments in full duplex (FD) commu-
nication promise doubling the capacity of cellular networks using
self interference cancellation (SIC) techniques. FD small cells
with device-to-device (D2D) communication links could achieve
the expected capacity of the future cellular networks (5G). In
this work, we consider joint scheduling and dynamic power
algorithm (DPA) for a single cell FD small cell network with
D2D links (D2DLs). We formulate the optimal user selection and
power control as a non-linear programming (NLP) optimization
problem to get the optimal user scheduling and transmission
power in a given TTI. Our numerical results show that using
DPA gives better overall throughput performance than full power
transmission algorithm (FPA). Also, simultaneous transmissions
(combination of uplink (UL), downlink (DL), and D2D occur
80% of the time thereby increasing the spectral efficiency and
network capacity
Rapid growth of wireless communication services in recent decades has created
a huge demand of radio spectrum. Spectrum scarcity and utilization inefficiency
limit the development of wireless networks. Cognitive radio is a promising tech-
nology that allows secondary users to reuse the underutilized licensed spectrum of
primary users. The major challenge for spectrum sharing is to achieve high spectrum
efficiency while making non-intrusive access to the licensed bands. This requires in-
formation of availability and quality of channel resources at secondary transmitters,
however, is difficult to be obtained perfectly in practice.
Mobile operators must continuously pursue cost‐
effective and efficient solutions to meet the high data
demand requirements of their subscribers. Limited spectrum
allocations and non‐contiguous spectrum blocks continue
to pose challenges for mobile operators supporting large
data uploads and downloads across their networks. With the
increase in video and social media content, the challenges
have increased exponentially.
This paper presents a Hidden Markov Model (HMM)-based speech
enhancement method, aiming at reducing non-stationary noise from speech
signals. The system is based on the assumption that the speech and the noise
are additive and uncorrelated. Cepstral features are used to extract statistical
information from both the speech and the noise. A-priori statistical
information is collected from long training sequences into ergodic hidden
Markov models. Given the ergodic models for the speech and the noise, a
compensated speech-noise model is created by means of parallel model
combination, using a log-normal approximation. During the compensation, the
mean of every mixture in the speech and noise model is stored. The stored
means are then used in the enhancement process to create the most likely
speech and noise power spectral distributions using the forward algorithm
combined with mixture probability. The distributions are used to generate a
Wiener filter for every observation. The paper includes a performance
evaluation of the speech enhancer for stationary as well as non-stationary
noise environment.
