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.
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.
Homogeneous Partitioning of the Surveillance Volume discusses the
implementation of the first of three sequentially complementary approaches for
increasing the probability of target detection within at least some of the cells of
the surveillance volume for a spatially nonGaussian or Gaussian “noise”
environment that is temporally Gaussian. This approach, identified in the Preface
as Approach A, partitions the surveillance volume into homogeneous contiguous
subdivisions.
When joining Siemens in 2001, I also extended my research interest towards radio net-
work planning methodologies. This area of research brought together my personal interest
in mobile communications and in the design of efficient algorithms and data structures.
Between 2001 and 2003, I participated in the EU project Momentum, which was target-
ing the performance evaluation and optimization of UMTS radio networks. I
Since the 1990s the EU has been pursuing climate change mitigation targets. Following the
international commitment to the legally binding greenhouse gas reduction under the Kyoto
Protocol, the 2020 policy package consists of a set of binding legislation to ensure that the EU
meets its climate and energy targets for the year 2020. The package sets three key targets: 20%
reduction in greenhouse gas emissions (from 1990 levels), 20% of EU energy from renewables (as
well as a 10% target for renewable fuels) and 20% improvement in energy efficiency. The targets
were set by EU leaders in 2007 and enacted in legislation in 2009 3 . They are also headline targets of
the Europe 2020 strategy for smart, sustainable and inclusive growth.
Identification is pervasive nowadays in daily life due to many complicated activities such as
bank and library card reading, asset tracking, toll collecting, restricted access to sensitive data
and procedures and target identification. This kind of task can be realized by passwords, bio-
metric data such as fingerprints, barcode, optical character recognition, smart cards and radar.
Radiofrequencyidentification(RFID)isatechniquetoidentifyobjectsbyusingradiosystems.
It is a contactless, usually short distance, wireless data transmission and reception technique
for identification of objects. An RFID system consists of two components: the tag (also called
transponder) and the reader (also called interrogator).
Radio frequency identification (RFID) technology is witnessing a recent explosion of
development in both industry and academia. A number of applications include supply
chain management, electronic payments, RFID passports, environmental monitoring
and control, office access control, intelligent labels, target detection and tracking, port
management, food production control, animal identification, and so on. RFID is also
an indispensable foundation to realize the pervasive computing paradigm—“Internet of
things.” It is strongly believed that many more scenarios will be identified when the
principles of RFID are thoroughly understood, cheap components available, and when
RFID security is guaranteed.
