3,610 research outputs found
Efficient Quantum Algorithms for State Measurement and Linear Algebra Applications
We present an algorithm for measurement of -local operators in a quantum
state, which scales logarithmically both in the system size and the output
accuracy. The key ingredients of the algorithm are a digital representation of
the quantum state, and a decomposition of the measurement operator in a basis
of operators with known discrete spectra. We then show how this algorithm can
be combined with (a) Hamiltonian evolution to make quantum simulations
efficient, (b) the Newton-Raphson method based solution of matrix inverse to
efficiently solve linear simultaneous equations, and (c) Chebyshev expansion of
matrix exponentials to efficiently evaluate thermal expectation values. The
general strategy may be useful in solving many other linear algebra problems
efficiently.Comment: 17 pages, 3 figures (v2) Sections reorganised, several clarifications
added, results unchange
Pulse shaping approach to PAPR reduction for OFDM communication systems
One of the main drawbacks of the OFDM communication system is the high peak-to-average-power ratio (PAPR) of the transmitted signal. In this thesis: (i ) Optimal pulse shaping filter design is proposed to reduce the PAPR of the OFDM signal; (ii ) The level crossing rate theorem is used to derive an upper bound for the CCDF of PAPR of OFDM signal with pulse shaping; (iii ) The multiple filter design is proposed to reduce the PAPR of multiuser OFDM signal
Sparse octree algorithms for scalable dense volumetric tracking and mapping
This thesis is concerned with the problem of Simultaneous Localisation and Mapping (SLAM), the task of localising an agent within an unknown environment and at the same time building a representation of it. In particular, we tackle the fundamental scalability limitations of dense volumetric SLAM systems. We do so by proposing a highly efficient hierarchical data-structure based on octrees together with a set of algorithms to support the most compute-intensive operations in typical volumetric reconstruction pipelines.
We employ our hierarchical representation in a novel dense pipeline based on occupancy probabilities. Crucially, the complete space representation encoded by the octree enables to demonstrate a fully integrated system in which tracking, mapping and occupancy queries can be performed seamlessly on a single coherent representation. While achieving accuracy either at par or better than the current state-of-the-art, we demonstrate run-time performance of at least an order of magnitude better than currently available hierarchical data-structures.
Finally, we introduce a novel multi-scale reconstruction system that exploits our octree hierarchy. By adaptively selecting the appropriate scale to match the effective sensor resolution in both integration and rendering, we demonstrate better reconstruction results and tracking accuracy compared to single-resolution grids. Furthermore, we achieve much higher computational performance by propagating information up and down the tree in a lazy fashion, which allow us to reduce the computational load when updating distant surfaces.
We have released our software as an open-source library, named supereight, which is freely available for the benefit of the wider community. One of the main advantages of our library is its flexibility. By carefully providing a set of algorithmic abstractions, supereight enables SLAM practitioners to freely experiment with different map representations with no intervention on the back-end library code and crucially, preserving performance. Our work has been adopted by robotics researchers in both academia and industry.Open Acces
Efficient reconfigurable architectures for 3D medical image compression
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Recently, the more widespread use of three-dimensional (3-D) imaging modalities,
such as magnetic resonance imaging (MRI), computed tomography (CT), positron
emission tomography (PET), and ultrasound (US) have generated a massive amount
of volumetric data. These have provided an impetus to the development of other
applications, in particular telemedicine and teleradiology. In these fields, medical
image compression is important since both efficient storage and transmission of data
through high-bandwidth digital communication lines are of crucial importance.
Despite their advantages, most 3-D medical imaging algorithms are computationally intensive with matrix transformation as the most fundamental operation involved in the transform-based methods. Therefore, there is a real need for high-performance systems, whilst keeping architectures exible to allow
for quick upgradeability with real-time applications. Moreover, in order to obtain
efficient solutions for large medical volumes data, an efficient implementation of
these operations is of significant importance. Reconfigurable hardware, in the form of field programmable gate arrays (FPGAs) has been proposed as viable system
building block in the construction of high-performance systems at an economical price.
Consequently, FPGAs seem an ideal candidate to harness and exploit their inherent
advantages such as massive parallelism capabilities, multimillion gate counts, and
special low-power packages. The key achievements of the work presented in this thesis are summarised as follows. Two architectures for 3-D Haar wavelet transform (HWT) have been proposed based on transpose-based computation and partial reconfiguration suitable for 3-D medical imaging applications. These applications require continuous hardware servicing, and as a result dynamic partial reconfiguration (DPR) has been introduced. Comparative study for both non-partial and partial reconfiguration implementation has shown that DPR offers many advantages and leads to a compelling solution for implementing computationally intensive applications such as 3-D medical image compression. Using DPR, several large systems are mapped to small hardware resources, and the area, power consumption as well as maximum frequency are
optimised and improved. Moreover, an FPGA-based architecture of the finite Radon transform (FRAT)with three design strategies has been proposed: direct implementation of pseudo-code with a sequential or pipelined description, and block random access memory (BRAM)- based method. An analysis with various medical imaging modalities has been carried out. Results obtained for image de-noising implementation using FRAT exhibits
promising results in reducing Gaussian white noise in medical images. In terms of
hardware implementation, promising trade-offs on maximum frequency, throughput
and area are also achieved. Furthermore, a novel hardware implementation of 3-D medical image compression system with context-based adaptive variable length coding (CAVLC)
has been proposed. An evaluation of the 3-D integer transform (IT) and the discrete
wavelet transform (DWT) with lifting scheme (LS) for transform blocks reveal that
3-D IT demonstrates better computational complexity than the 3-D DWT, whilst
the 3-D DWT with LS exhibits a lossless compression that is significantly useful for
medical image compression. Additionally, an architecture of CAVLC that is capable
of compressing high-definition (HD) images in real-time without any buffer between
the quantiser and the entropy coder is proposed. Through a judicious parallelisation, promising results have been obtained with limited resources. In summary, this research is tackling the issues of massive 3-D medical volumes data that requires compression as well as hardware implementation to accelerate the
slowest operations in the system. Results obtained also reveal a significant achievement in terms of the architecture efficiency and applications performance.Ministry of Higher Education Malaysia (MOHE),
Universiti Tun Hussein Onn Malaysia (UTHM) and the British Counci
Motion Scalability for Video Coding with Flexible Spatio-Temporal Decompositions
PhDThe research presented in this thesis aims to extend the scalability range of the
wavelet-based video coding systems in order to achieve fully scalable coding with a
wide range of available decoding points. Since the temporal redundancy regularly
comprises the main portion of the global video sequence redundancy, the techniques
that can be generally termed motion decorrelation techniques have a central role in
the overall compression performance. For this reason the scalable motion modelling
and coding are of utmost importance, and specifically, in this thesis possible
solutions are identified and analysed.
The main contributions of the presented research are grouped into two
interrelated and complementary topics. Firstly a flexible motion model with rateoptimised
estimation technique is introduced. The proposed motion model is based
on tree structures and allows high adaptability needed for layered motion coding. The
flexible structure for motion compensation allows for optimisation at different stages
of the adaptive spatio-temporal decomposition, which is crucial for scalable coding
that targets decoding on different resolutions. By utilising an adaptive choice of
wavelet filterbank, the model enables high compression based on efficient mode
selection. Secondly, solutions for scalable motion modelling and coding are
developed. These solutions are based on precision limiting of motion vectors and
creation of a layered motion structure that describes hierarchically coded motion.
The solution based on precision limiting relies on layered bit-plane coding of motion
vector values. The second solution builds on recently established techniques that
impose scalability on a motion structure. The new approach is based on two major
improvements: the evaluation of distortion in temporal Subbands and motion search
in temporal subbands that finds the optimal motion vectors for layered motion
structure.
Exhaustive tests on the rate-distortion performance in demanding scalable video
coding scenarios show benefits of application of both developed flexible motion
model and various solutions for scalable motion coding
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