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GoAhead WebServer 2.1.8 Release Notes
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We consider the Problem of target localization by a
network of passive sensors. When an unknown target emits an
acoustic or a radio signal, its position can be localized with multiple
sensors using the time difference of arrival (TDOA) information.
In this paper, we consider the maximum likelihood formulation
of this target localization Problem and provide efficient convex
relaxations for this nonconvex optimization Problem.We also propose
a formulation for robust target localization in the presence of
sensor location errors. Two Cramer-Rao bounds are derived corresponding
to situations with and without sensor node location errors.
Simulation results confirm the efficiency and superior performance
of the convex relaxation approach as compared to the
existing least squares based approach when large sensor node location
errors are present.
Lithium–sulfur batteries are a promising energy-storage technology due to their relatively low cost and high theoretical energy density. However, one of their major technical Problems is the shuttling of soluble polysulfides between electrodes, resulting in rapid capacity fading. Here, we present a metal–organic framework (MOF)-based battery separator to mitigate the shuttling Problem. We show that the MOF-based separator acts as an ionic sieve in lithium–sulfur batteries, which selectively sieves Li+ ions while e ciently suppressing undesired polysulfides migrating to the anode side. When a sulfur-containing mesoporous carbon material (approximately 70 wt% sulfur content) is used as a cathode composite without elaborate synthesis or surface modification, a lithium–sulfur battery with a MOF-based separator exhibits a low capacity decay rate (0.019% per cycle over 1,500 cycles). Moreover, there is almost no capacity fading after the initial 100 cycles. Our approach demonstrates the potential for MOF-based materials as separators for energy-storage applications.
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.
Without conceding a blemish in the first edition, I think I had best come clean
and admit that I embarked on a second edition largely to adopt a more geometric
approach to the detection of signals in white Gaussian noise. Equally rigorous, yet
more intuitive, this approach is not only student-friendly, but also extends more
easily to the detection Problem with random parameters and to the radar Problem
In this paper, we consider the channel estimation
Problem in Millimeter wave (mmWave) wireless systems with
large antenna arrays. By exploiting the inherent sparse nature of
the mmWave channel, we develop a novel rate-adaptive channel
estimation (RACE) algorithm, which can adaptively adjust the
number of required channel measurements based on an expected
probability of estimation error (PEE).
Evolutionary Computation (EC) deals with Problem solving, optimization, and
machine learning techniques inspired by principles of natural evolution and ge-
netics. Just from this basic definition, it is clear that one of the main features of
the research community involved in the study of its theory and in its applications
is multidisciplinarity. For this reason, EC has been able to draw the attention of
an ever-increasing number of researchers and practitioners in several fields.
In cellular networks, it is estimated that
2
3
of calls and over 90% of data services occur
indoors. However, some surveys show that many households and businesses experience
a poor indoor coverage Problem. It has been identified that poor coverage is the main
reason for churn, which is very costly for operators in saturated markets. How to provide
good indoor coverage cost effectively is thus a demanding challenge for operators.
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
