386 research outputs found
Wireless indoor positioning based on TDOA and DOA estimation techniques using IEEE 802.11 standards
Magdeburg, Univ., Fak. für Elektrotechnik und Informationstechnik, Diss., 2015von Abdo Nasser Ali Gabe
Beyond the noise : high fidelity MR signal processing
This thesis describes a variety of methods developed to increase the sensitivity and resolution of liquid state nuclear magnetic resonance (NMR) experiments. NMR is known as one of the most versatile non-invasive analytical techniques yet often suffers from low sensitivity. The main contribution to this low sensitivity issue is a presence of noise and level of noise in the spectrum is expressed numerically as “signal-to-noise ratio”. NMR signal processing involves sensitivity and resolution enhancement achieved by noise reduction using mathematical algorithms. A singular value decomposition based reduced rank matrix method, composite property mapping, in particular is studied extensively in this thesis to present its advantages, limitations, and applications. In theory, when the sum of k noiseless sinusoidal decays is formatted into a specific matrix form (i.e., Toeplitz), the matrix is known to possess k linearly independent columns. This information becomes apparent only after a singular value decomposition of the matrix. Singular value decomposition factorises the large matrix into three smaller submatrices: right and left singular vector matrices, and one diagonal matrix containing singular values. Were k noiseless sinusoidal decays involved, there would be only k nonzero singular values appearing in the diagonal matrix in descending order providing the information of the amplitude of each sinusoidal decay. The number of non-zero singular values or the number of linearly independent columns is known as the rank of the matrix. With real NMR data none of the singular values equals zero and the matrix has full rank. The reduction of the rank of the matrix and thus the noise in the reconstructed NMR data can be achieved by replacing all the singular values except the first k values with zeroes. This noise reduction process becomes difficult when biomolecular NMR data is to be processed due to the number of resonances being unknown and the presence of a large solvent peak
Configurable Input Devices for 3D Interaction using Optical Tracking
Three-dimensional interaction with virtual objects is one of the aspects that needs to be addressed
in order to increase the usability and usefulness of virtual reality. Human beings
have difficulties understanding 3D spatial relationships and manipulating 3D user interfaces,
which require the control of multiple degrees of freedom simultaneously. Conventional interaction
paradigms known from the desktop computer, such as the use of interaction devices as
the mouse and keyboard, may be insufficient or even inappropriate for 3D spatial interaction
tasks.
The aim of the research in this thesis is to develop the technology required to improve 3D
user interaction. This can be accomplished by allowing interaction devices to be constructed
such that their use is apparent from their structure, and by enabling efficient development of
new input devices for 3D interaction.
The driving vision in this thesis is that for effective and natural direct 3D interaction the
structure of an interaction device should be specifically tuned to the interaction task. Two
aspects play an important role in this vision. First, interaction devices should be structured
such that interaction techniques are as direct and transparent as possible. Interaction techniques
define the mapping between interaction task parameters and the degrees of freedom of
interaction devices. Second, the underlying technology should enable developers to rapidly
construct and evaluate new interaction devices.
The thesis is organized as follows. In Chapter 2, a review of the optical tracking field is
given. The tracking pipeline is discussed, existing methods are reviewed, and improvement
opportunities are identified.
In Chapters 3 and 4 the focus is on the development of optical tracking techniques of rigid
objects. The goal of the tracking method presented in Chapter 3 is to reduce the occlusion
problem. The method exploits projection invariant properties of line pencil markers, and the
fact that line features only need to be partially visible.
In Chapter 4, the aim is to develop a tracking system that supports devices of arbitrary
shapes, and allows for rapid development of new interaction devices. The method is based on
subgraph isomorphism to identify point clouds. To support the development of new devices
in the virtual environment an automatic model estimation method is used.
Chapter 5 provides an analysis of three optical tracking systems based on different principles.
The first system is based on an optimization procedure that matches the 3D device
model points to the 2D data points that are detected in the camera images. The other systems
are the tracking methods as discussed in Chapters 3 and 4.
In Chapter 6 an analysis of various filtering and prediction methods is given. These
techniques can be used to make the tracking system more robust against noise, and to reduce
the latency problem.
Chapter 7 focusses on optical tracking of composite input devices, i.e., input devices
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that consist of multiple rigid parts that can have combinations of rotational and translational
degrees of freedom with respect to each other. Techniques are developed to automatically
generate a 3D model of a segmented input device from motion data, and to use this model to
track the device.
In Chapter 8, the presented techniques are combined to create a configurable input device,
which supports direct and natural co-located interaction. In this chapter, the goal of the thesis
is realized. The device can be configured such that its structure reflects the parameters of the
interaction task.
In Chapter 9, the configurable interaction device is used to study the influence of spatial
device structure with respect to the interaction task at hand. The driving vision of this thesis,
that the spatial structure of an interaction device should match that of the task, is analyzed
and evaluated by performing a user study.
The concepts and techniques developed in this thesis allow researchers to rapidly construct
and apply new interaction devices for 3D interaction in virtual environments. Devices
can be constructed such that their spatial structure reflects the 3D parameters of the interaction
task at hand. The interaction technique then becomes a transparent one-to-one mapping
that directly mediates the functions of the device to the task. The developed configurable interaction
devices can be used to construct intuitive spatial interfaces, and allow researchers to
rapidly evaluate new device configurations and to efficiently perform studies on the relation
between the spatial structure of devices and the interaction task
Target Recognition Using Late-Time Returns from Ultra-Wideband, Short-Pulse Radar
The goal of this research is to develop algorithms that recognize targets by exploiting properties in the late-time resonance induced by ultra-wide band radar signals. A new variant of the Matrix Pencil Method algorithm is developed that identifies complex resonant frequencies present in the scattered signal. Kalman filters are developed to represent the dynamics of the signals scattered from several target types. The Multiple Model Adaptive Estimation algorithm uses the Kalman filters to recognize targets. The target recognition algorithm is shown to be successful in the presence of noise. The performance of the new algorithms is compared to that of previously published algorithms
Channel Prediction for Mobile MIMO Wireless Communication Systems
Temporal variation and frequency selectivity of wireless channels constitute
a major drawback to the attainment of high gains in capacity
and reliability offered by multiple antennas at the transmitter and receiver
of a mobile communication system. Limited feedback and adaptive transmission
schemes such as adaptive modulation and coding, antenna selection,
power allocation and scheduling have the potential to provide the platform
of attaining the high transmission rate, capacity and QoS requirements in
current and future wireless communication systems. Theses schemes require
both the transmitter and receiver to have accurate knowledge of Channel
State Information (CSI). In Time Division Duplex (TDD) systems, CSI at
the transmitter can be obtained using channel reciprocity. In Frequency Division
Duplex (FDD) systems, however, CSI is typically estimated at the
receiver and fed back to the transmitter via a low-rate feedback link. Due to
the inherent time delays in estimation, processing and feedback, the CSI obtained
from the receiver may become outdated before its actual usage at the
transmitter. This results in significant performance loss, especially in high
mobility environments. There is therefore a need to extrapolate the varying
channel into the future, far enough to account for the delay and mitigate the
performance degradation.
The research in this thesis investigates parametric modeling and prediction
of mobile MIMO channels for both narrowband and wideband systems.
The focus is on schemes that utilize the additional spatial information offered
by multiple sampling of the wave-field in multi-antenna systems to
aid channel prediction. The research has led to the development of several
algorithms which can be used for long range extrapolation of time-varyingchannels. Based on spatial channel modeling approaches, simple and efficient
methods for the extrapolation of narrowband MIMO channels are proposed.
Various extensions were also developed. These include methods for wideband
channels, transmission using polarized antenna arrays, and mobile-to-mobile
systems.
Performance bounds on the estimation and prediction error are vital when
evaluating channel estimation and prediction schemes. For this purpose, analytical
expressions for bound on the estimation and prediction of polarized
and non-polarized MIMO channels are derived. Using the vector formulation
of the Cramer Rao bound for function of parameters, readily interpretable
closed-form expressions for the prediction error bounds were found for cases
with Uniform Linear Array (ULA) and Uniform Planar Array (UPA). The
derived performance bounds are very simple and so provide insight into system
design.
The performance of the proposed algorithms was evaluated using standardized
channel models. The effects of the temporal variation of multipath
parameters on prediction is studied and methods for jointly tracking the
channel parameters are developed. The algorithms presented can be utilized
to enhance the performance of limited feedback and adaptive MIMO
transmission schemes
Microwave Imaging to Improve Breast Cancer Diagnosis
Breast cancer is the most prevalent type of cancer worldwide. The correct diagnosis of Axillary Lymph Nodes (ALNs) is important for an accurate staging of breast cancer. The performance of current imaging modalities for both breast cancer detection and staging is still unsatisfactory. Microwave Imaging (MWI) has been studied to aid breast cancer diagnosis. This thesis addresses several novel aspects of the development of air-operated MWI systems for both breast cancer detection and staging.
Firstly, refraction effects in air-operated setups are evaluated to understand whether refraction calculation should be included in image reconstruction algorithms. Then, the research completed towards the development of a MWI system to detect the ALNs is presented. Anthropomorphic numerical phantoms of the axillary region are created, and the dielectric properties of ALNs are estimated from Magnetic Resonance Imaging exams. The first pre-clinical MWI setup tailored to detect ALNs is numerically and experimentally tested. To complement MWI results, the feasibility of using machine learning algorithms to classify healthy and metastasised ALNs using microwave signals is analysed. Finally, an additional study towards breast cancer detection is presented by proposing a prototype which uses a focal system to focus the energy into the breast and decrease the coupling between antennas.
The results show refraction calculation may be neglected in low to moderate permittivity media. Moreover, MWI has the potential as an imaging technique to assess ALN diagnosis as estimation of dielectric properties indicate there is sufficient contrast between healthy and metastasised ALNs, and the imaging results obtained in this thesis are promising for ALN detection. The performance of classification models shows these models may potentially give complementary information to imaging results. The proposed breast imaging prototype also shows promising results for breast cancer detection
Structure-Preserving Model Reduction of Physical Network Systems
This paper considers physical network systems where the energy storage is naturally associated to the nodes of the graph, while the edges of the graph correspond to static couplings. The first sections deal with the linear case, covering examples such as mass-damper and hydraulic systems, which have a structure that is similar to symmetric consensus dynamics. The last section is concerned with a specific class of nonlinear physical network systems; namely detailed-balanced chemical reaction networks governed by mass action kinetics. In both cases, linear and nonlinear, the structure of the dynamics is similar, and is based on a weighted Laplacian matrix, together with an energy function capturing the energy storage at the nodes. We discuss two methods for structure-preserving model reduction. The first one is clustering; aggregating the nodes of the underlying graph to obtain a reduced graph. The second approach is based on neglecting the energy storage at some of the nodes, and subsequently eliminating those nodes (called Kron reduction).</p
Non-Contact Human Motion Sensing Using Radar Techniques
Human motion analysis has recently gained a lot of interest in the research community due to its widespread applications. A full understanding of normal motion from human limb joint trajectory tracking could be essential to develop and establish a scientific basis for correcting any abnormalities. Technology to analyze human motion has significantly advanced in the last few years. However, there is a need to develop a non-invasive, cost effective gait analysis system that can be functional indoors or outdoors 24/7 without hindering the normal daily activities for the subjects being monitored or invading their privacy. Out of the various methods for human gait analysis, radar technique is a non-invasive method, and can be carried out remotely. For one subject monitoring, single tone radars can be utilized for motion capturing of a single target, while ultra-wideband radars can be used for multi-subject tracking. But there are still some challenges that need to be overcome for utilizing radars for motion analysis, such as sophisticated signal processing requirements, sensitivity to noise, and hardware imperfections. The goal of this research is to overcome these challenges and realize a non-contact gait analysis system capable of extracting different organ trajectories (like the torso, hands and legs) from a complex human motion such as walking. The implemented system can be hugely beneficial for applications such as treating patients with joint problems, athlete performance analysis, motion classification, and so on
Development of a text reading system on video images
Since the early days of computer science researchers sought to devise a machine which could automatically read text to help people with visual impairments. The problem of extracting and recognising text on document images has been largely resolved, but reading text from images of natural scenes remains a challenge. Scene text can present uneven lighting, complex backgrounds or perspective and lens distortion; it usually appears as short sentences or isolated words and shows a very diverse set of typefaces. However, video sequences of natural scenes provide a temporal redundancy that can be exploited to compensate for some of these deficiencies. Here we present a complete end-to-end, real-time scene text reading system on video images based on perspective aware text tracking.
The main contribution of this work is a system that automatically detects, recognises and tracks text in videos of natural scenes in real-time. The focus of our method is on large text found in outdoor environments, such as shop signs, street names and billboards. We introduce novel efficient techniques for text detection, text aggregation and text perspective estimation. Furthermore, we propose using a set of Unscented Kalman Filters (UKF) to maintain each text region¿s identity and to continuously track the homography transformation of the text into a fronto-parallel view, thereby being resilient to erratic camera motion and wide baseline changes in orientation. The orientation of each text line is estimated using a method that relies on the geometry of the characters themselves to estimate a rectifying homography. This is done irrespective of the view of the text over a large range of orientations. We also demonstrate a wearable head-mounted device for text reading that encases a camera for image acquisition and a pair of headphones for synthesized speech output.
Our system is designed for continuous and unsupervised operation over long periods of time. It is completely automatic and features quick failure recovery and interactive text reading. It is also highly parallelised in order to maximize the usage of available processing power and to achieve real-time operation. We show comparative results that improve the current state-of-the-art when correcting perspective deformation of scene text. The end-to-end system performance is demonstrated on sequences recorded in outdoor scenarios. Finally, we also release a dataset of text tracking videos along with the annotated ground-truth of text regions
Agnostic Tracking: Nanoscale, High Bandwidth, 3D Particle Tracking for Biology
The ability to detect biological events at single molecule level provides unique insights in the field of biophysics. Back-focal-plane laser interferometry is a promising technique for single-molecule-scale, 3D position measurements at rates far beyond the capability of video. I present an in-situ calibration method for the back-focal-plane, low-power (non-trapping) laser interferometry. The software-based technique does not rely on any a priori model or calibration knowledge; hence the name Agnostic. The technique is sufficiently fast and non-invasive that the calibration can be performed on the fly, without interrupting or compromising the on-going experiment. The technique can be applied to track 3D, long range motion (up to 100 um) of a broad variety of microscopic biological objects. The spatiotemporal resolution achieved is of the order of a few nanometers and tens of microseconds. Three biological applications enabled by the technique are presented: firstly, a prototype of an oscillating-bead high-bandwidth frequency-response analyzer for biology, based on Agnostic Tracking as implemented in our custom-built 3D Magnetic Force Microscope (3DFM); secondly, a magnetic-force study that revealed a previously-unknown anchoring-dependent nonlinear response of a cellular membrane; last, a rheological study that revealed a novel grouping of motion characteristics of individual vesicles diffusing inside live cytoplasm
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