Compressing Inertial Motion Data in Wireless Sensing Systems – An Initial Experiment

The use of wireless inertial motion sensors, such as accelerometers, for supporting medical care and sport’s training, has been under investigation in recent years. As the number of sensors (or their sampling rates) increases, compressing data at source(s) (i.e. at the sensors), i.e. reducing the quantity of data that needs to be transmitted between the on-body sensors and the remote repository, would be essential especially in a bandwidth-limited wireless environment. This paper presents a set of compression experiment results on a set of inertial motion data collected during running exercises. As a starting point, we selected a set of common compression algorithms to experiment with. Our results show that, conventional lossy compression algorithms would achieve a desirable compression ratio with an acceptable time delay. The results also show that the quality of the decompressed data is within acceptable range

Data Compression in the Petascale Astronomy Era: a GERLUMPH case study

As the volume of data grows, astronomers are increasingly faced with choices on what data to keep -- and what to throw away. Recent work evaluating the JPEG2000 (ISO/IEC 15444) standards as a future data format standard in astronomy has shown promising results on observational data. However, there is still a need to evaluate its potential on other type of astronomical data, such as from numerical simulations. GERLUMPH (the GPU-Enabled High Resolution cosmological MicroLensing parameter survey) represents an example of a data intensive project in theoretical astrophysics. In the next phase of processing, the ~27 terabyte GERLUMPH dataset is set to grow by a factor of 100 -- well beyond the current storage capabilities of the supercomputing facility on which it resides. In order to minimise bandwidth usage, file transfer time, and storage space, this work evaluates several data compression techniques. Specifically, we investigate off-the-shelf and custom lossless compression algorithms as well as the lossy JPEG2000 compression format. Results of lossless compression algorithms on GERLUMPH data products show small compression ratios (1.35:1 to 4.69:1 of input file size) varying with the nature of the input data. Our results suggest that JPEG2000 could be suitable for other numerical datasets stored as gridded data or volumetric data. When approaching lossy data compression, one should keep in mind the intended purposes of the data to be compressed, and evaluate the effect of the loss on future analysis. In our case study, lossy compression and a high compression ratio do not significantly compromise the intended use of the data for constraining quasar source profiles from cosmological microlensing.Comment: 15 pages, 9 figures, 5 tables. Published in the Special Issue of Astronomy & Computing on The future of astronomical data format

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

Volumetric Medical Images Visualization on Mobile Devices

Volumetric medical images visualization is an important tool in the diagnosis and treatment of diseases. Through history, one of the most dificult tasks for Medicine Specialists has been the accurate location of broken bones and of the damaged tissues during Chemotherapy treatment, among other applications; like techniques used in Neurological Studies. Thus these situations enhance the need of visualization in Medicine. New technologies, the improvement and development of new hardware as well as software and the updating of old ones for graphic applications have resulted in specialized systems for medical visualization. However the use of these techniques in mobile devices has been poor due to its low performance. In our work, we propose a client-server scheme, where the model is compressed in the server side and is reconstructed in a nal thin-client device. The technique restricts the natural density values to achieve good bone visualization in medical models, transforming the rest of the data to zero. Our proposal uses a tridimensional Haar Wavelet Function locally applied inside units blocks of 16x16x16, similar to the Wavelet Based 3D Compression Scheme for Interactive Visualization of Very Large Volume Data approach. We also implement a quantization algorithm which handles error coeficients according to the frequency distributions of these coe cients. Finally, we made an evaluation of the volume visualization; on current mobile devices .We present the speci cations for the implementation of our technique in the Nokia n900 Mobile Phone

GPU acceleration of predictive partitioned vector quantization for ultraspectral sounder data compression

[[abstract]]For the large-volume ultraspectral sounder data, compression is desirable to save storage space and transmission time. To retrieve the geophysical paramters without losing precision the ultraspectral sounder data compression has to be lossless. Recently there is a boom on the use of graphic processor units (GPU) for speedup of scientific computations. By identifying the time dominant portions of the code that can be executed in parallel, significant speedup can be achieved by using GPU. Predictive partitioned vector quantization (PPVQ) has been proven to be an effective lossless compression scheme for ultraspectral sounder data. It consists of linear prediction, bit depth partitioning, vector quantization, and entropy coding. Two most time consuming stages of linear prediction and vector quantization are chosen for GPU-based implementation. By exploiting the data parallel characteristics of these two stages, a spatial division design shows a speedup of 72x in our four-GPU-based implementation of the PPVQ compression scheme.[[notice]]補正完畢[[journaltype]]國外[[incitationindex]]SCI[[booktype]]紙本[[countrycodes]]US