Discrete Wavelet Transforms

The discrete wavelet transform (DWT) algorithms have a firm position in processing of signals in several areas of research and industry. As DWT provides both octave-scale frequency and spatial timing of the analyzed signal, it is constantly used to solve and treat more and more advanced problems. The present book: Discrete Wavelet Transforms: Algorithms and Applications reviews the recent progress in discrete wavelet transform algorithms and applications. The book covers a wide range of methods (e.g. lifting, shift invariance, multi-scale analysis) for constructing DWTs. The book chapters are organized into four major parts. Part I describes the progress in hardware implementations of the DWT algorithms. Applications include multitone modulation for ADSL and equalization techniques, a scalable architecture for FPGA-implementation, lifting based algorithm for VLSI implementation, comparison between DWT and FFT based OFDM and modified SPIHT codec. Part II addresses image processing algorithms such as multiresolution approach for edge detection, low bit rate image compression, low complexity implementation of CQF wavelets and compression of multi-component images. Part III focuses watermaking DWT algorithms. Finally, Part IV describes shift invariant DWTs, DC lossless property, DWT based analysis and estimation of colored noise and an application of the wavelet Galerkin method. The chapters of the present book consist of both tutorial and highly advanced material. Therefore, the book is intended to be a reference text for graduate students and researchers to obtain state-of-the-art knowledge on specific applications

Lossless and low-cost integer-based lifting wavelet transform

Discrete wavelet transform (DWT) is a powerful tool for analyzing real-time signals, including aperiodic, irregular, noisy, and transient data, because of its capability to explore signals in both the frequency- and time-domain in different resolutions. For this reason, they are used extensively in a wide number of applications in image and signal processing. Despite the wide usage, the implementation of the wavelet transform is usually lossy or computationally complex, and it requires expensive hardware. However, in many applications, such as medical diagnosis, reversible data-hiding, and critical satellite data, lossless implementation of the wavelet transform is desirable. It is also important to have more hardware-friendly implementations due to its recent inclusion in signal processing modules in system-on-chips (SoCs). To address the need, this research work provides a generalized implementation of a wavelet transform using an integer-based lifting method to produce lossless and low-cost architecture while maintaining the performance close to the original wavelets. In order to achieve a general implementation method for all orthogonal and biorthogonal wavelets, the Daubechies wavelet family has been utilized at first since it is one of the most widely used wavelets and based on a systematic method of construction of compact support orthogonal wavelets. Though the first two phases of this work are for Daubechies wavelets, they can be generalized in order to apply to other wavelets as well. Subsequently, some techniques used in the primary works have been adopted and the critical issues for achieving general lossless implementation have solved to propose a general lossless method. The research work presented here can be divided into several phases. In the first phase, low-cost architectures of the Daubechies-4 (D4) and Daubechies-6 (D6) wavelets have been derived by applying the integer-polynomial mapping. A lifting architecture has been used which reduces the cost by a half compared to the conventional convolution-based approach. The application of integer-polynomial mapping (IPM) of the polynomial filter coefficient with a floating-point value further decreases the complexity and reduces the loss in signal reconstruction. Also, the “resource sharing” between lifting steps results in a further reduction in implementation costs and near-lossless data reconstruction. In the second phase, a completely lossless or error-free architecture has been proposed for the Daubechies-8 (D8) wavelet. Several lifting variants have been derived for the same wavelet, the integer mapping has been applied, and the best variant is determined in terms of performance, using entropy and transform coding gain. Then a theory has been derived regarding the impact of scaling steps on the transform coding gain (GT). The approach results in the lowest cost lossless architecture of the D8 in the literature, to the best of our knowledge. The proposed approach may be applied to other orthogonal wavelets, including biorthogonal ones to achieve higher performance. In the final phase, a general algorithm has been proposed to implement the original filter coefficients expressed by a polyphase matrix into a more efficient lifting structure. This is done by using modified factorization, so that the factorized polyphase matrix does not include the lossy scaling step like the conventional lifting method. This general technique has been applied on some widely used orthogonal and biorthogonal wavelets and its advantages have been discussed. Since the discrete wavelet transform is used in a vast number of applications, the proposed algorithms can be utilized in those cases to achieve lossless, low-cost, and hardware-friendly architectures

Mengenal pasti tahap pengetahuan pelajar tahun akhir Ijazah Sarjana Muda Kejuruteraan di KUiTTHO dalam bidang keusahawanan dari aspek pengurusan modal

Malaysia ialah sebuah negara membangun di dunia. Dalam proses pembangunan ini, hasrat negara untuk melahirkan bakal usahawan beijaya tidak boleh dipandang ringan. Oleh itu, pengetahuan dalam bidang keusahawanan perlu diberi perhatian dengan sewajarnya; antara aspek utama dalam keusahawanan ialah modal. Pengurusan modal yang tidak cekap menjadi punca utama kegagalan usahawan. Menyedari hakikat ini, kajian berkaitan Pengurusan Modal dijalankan ke atas 100 orang pelajar Tahun Akhir Kejuruteraan di KUiTTHO. Sampel ini dipilih kerana pelajar-pelajar ini akan menempuhi alam pekeijaan di mana mereka boleh memilih keusahawanan sebagai satu keijaya. Walau pun mereka bukanlah pelajar dari jurusan perniagaan, namun mereka mempunyai kemahiran dalam mereka cipta produk yang boleh dikomersialkan. Hasil dapatan kajian membuktikan bahawa pelajar-pelajar ini berminat dalam bidang keusahawanan namun masih kurang pengetahuan tentang pengurusan modal terutamanya dalam menentukan modal permulaan, pengurusan modal keija dan caracara menentukan pembiayaan kewangan menggunakan kaedah jualan harian. Oleh itu, satu garis panduan Pengurusan Modal dibina untuk memberi pendedahan kepada mereka

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

Distributed Transforms for Efficient Data Gathering in Sensor Networks

Devices, systems, and techniques for data collecting network such as wireless sensors are disclosed. A described technique includes detecting one or more remote nodes included in the wireless sensor network using a local power level that controls a radio range of the local node. The technique includes transmitting a local outdegree. The local outdegree can be based on a quantity of the one or more remote nodes. The technique includes receiving one or more remote outdegrees from the one or more remote nodes. The technique includes determining a local node type of the local node based on detecting a node type of the one or more remote nodes, using the one or more remote outdegrees, and using the local outdegree. The technique includes adjusting characteristics, including an energy usage characteristic and a data compression characteristic, of the wireless sensor network by selectively modifying the local power level and selectively changing the local node type

Novel implementation technique for a wavelet-based broadband signal detection system

This thesis reports on the design, simulation and implementation of a novel Implementation for a Wavelet-based Broadband Signal Detection System. There is a strong interest in methods of increasing the resolution of sonar systems for the detection of targets at sea. A novel implementation of a wideband active sonar signal detection system is proposed in this project. In the system the Continuous Wavelet Transform is used for target motion estimation and an Adaptive-Network-based Fuzzy inference System (ANFIS) is adopted to minimize the noise effect on target detection. A local optimum search algorithm is introduced in this project to reduce the computation load of the Continuous Wavelet Transform and make it suitable for practical applications. The proposed system is realized on a Xilinx University Program Virtex-II Pro Development System which contains a Virtex II pro XC2VP30 FPGA chip with 2 powerPC 405 cores. Testing for single target detection and multiple target detection shows the proposed system is able to accurately locate targets under reverberation-limited underwater environment with a Signal-Noise-Ratio of up to -30db, with location error less than 10 meters and velocity estimation error less than 1 knot. In the proposed system the combination of CWT and local optimum search algorithm significantly saves the computation time for CWT and make it more practical to real applications. Also the implementation of ANFIS on the FPGA board indicates in the future a real-time ANFIS operation with VLSI implementation would be possible

Image up-sampling using the discrete wavelet transform

Image up-sampling is an effective technique, useful in today\u27s digital image processing applications and rendering devices. In image up-sampling, an image is enhanced from a lower resolution to a higher resolution with the degree of enhancement depending upon application requirements. It is known that the traditional interpolation based approaches for up-sampling, such as the Bilinear or Bicubic interpolations, blur the resultant images along edges and image features. Furthermore, in color imagery, these interpolation-based up-sampling methods may have color infringing artifacts in the areas where the images contain sharp edges and fine textures. We present an interesting up-sampling algorithm based on the Discrete Wavelet Transform (DWT). The proposed method preserves much of the sharp edge features in the image, and lessens the amount of color artifacts. Effectiveness of the proposed algorithm has been demonstrated based on comparison of PSNR and Δ E * ab quality metrics between the original and reconstructed images