New Distance Transforms for Gray Level Image Compression

This thesis deals with distance transforms which are a fundamental issue in image processing and computer vision. In this thesis, two new distance transforms for gray level images are presented. As a new application for distance transforms, they are applied to gray level image compression. The new distance transforms are both new extensions of the well known distance transform algorithm developed by Rosenfeld, Pfaltz and Lay. With some modification their algorithm which calculates a distance transform on binary images with a chosen kernel has been made to calculate a chessboard like distance transform with integer numbers (DTOCS) and a real value distance transform (EDTOCS) on gray level images. Both distance transforms, the DTOCS and EDTOCS, require only two passes over the graylevel image and are extremely simple to implement. Only two image buffers are needed: The original gray level image and the binary image which defines the region(s) of calculation. No other image buffers are needed even if more than one iteration round is performed. For large neighborhoods and complicated images the two pass distance algorithm has to be applied to the image more than once, typically 3 10 times. Different types of kernels can be adopted. It is important to notice that no other existing transform calculates the same kind of distance map as the DTOCS. All the other gray weighted distance function, GRAYMAT etc. algorithms find the minimum path joining two points by the smallest sum of gray levels or weighting the distance values directly by the gray levels in some manner. The DTOCS does not weight them that way. The DTOCS gives a weighted version of the chessboard distance map. The weights are not constant, but gray value differences of the original image. The difference between the DTOCS map and other distance transforms for gray level images is shown. The difference between the DTOCS and EDTOCS is that the EDTOCS calculates these gray level differences in a different way. It propagates local Euclidean distances inside a kernel. Analytical derivations of some results concerning the DTOCS and the EDTOCS are presented. Commonly distance transforms are used for feature extraction in pattern recognition and learning. Their use in image compression is very rare. This thesis introduces a new application area for distance transforms. Three new image compression algorithms based on the DTOCS and one based on the EDTOCS are presented. Control points, i.e. points that are considered fundamental for the reconstruction of the image, are selected from the gray level image using the DTOCS and the EDTOCS. The first group of methods select the maximas of the distance image to new control points and the second group of methods compare the DTOCS distance to binary image chessboard distance. The effect of applying threshold masks of different sizes along the threshold boundaries is studied. The time complexity of the compression algorithms is analyzed both analytically and experimentally. It is shown that the time complexity of the algorithms is independent of the number of control points, i.e. the compression ratio. Also a new morphological image decompression scheme is presented, the 8 kernels' method. Several decompressed images are presented. The best results are obtained using the Delaunay triangulation. The obtained image quality equals that of the DCT images with a 4 x 4

Potential and Challenges of Analog Reconfigurable Computation in Modern and Future CMOS

In this work, the feasibility of the floating-gate technology in analog computing platforms in a scaled down general-purpose CMOS technology is considered. When the technology is scaled down the performance of analog circuits tends to get worse because the process parameters are optimized for digital transistors and the scaling involves the reduction of supply voltages. Generally, the challenge in analog circuit design is that all salient design metrics such as power, area, bandwidth and accuracy are interrelated. Furthermore, poor flexibility, i.e. lack of reconfigurability, the reuse of IP etc., can be considered the most severe weakness of analog hardware. On this account, digital calibration schemes are often required for improved performance or yield enhancement, whereas high flexibility/reconfigurability can not be easily achieved. Here, it is discussed whether it is possible to work around these obstacles by using floating-gate transistors (FGTs), and analyze problems associated with the practical implementation. FGT technology is attractive because it is electrically programmable and also features a charge-based built-in non-volatile memory. Apart from being ideal for canceling the circuit non-idealities due to process variations, the FGTs can also be used as computational or adaptive elements in analog circuits. The nominal gate oxide thickness in the deep sub-micron (DSM) processes is too thin to support robust charge retention and consequently the FGT becomes leaky. In principle, non-leaky FGTs can be implemented in a scaled down process without any special masks by using “double”-oxide transistors intended for providing devices that operate with higher supply voltages than general purpose devices. However, in practice the technology scaling poses several challenges which are addressed in this thesis. To provide a sufficiently wide-ranging survey, six prototype chips with varying complexity were implemented in four different DSM process nodes and investigated from this perspective. The focus is on non-leaky FGTs, but the presented autozeroing floating-gate amplifier (AFGA) demonstrates that leaky FGTs may also find a use. The simplest test structures contain only a few transistors, whereas the most complex experimental chip is an implementation of a spiking neural network (SNN) which comprises thousands of active and passive devices. More precisely, it is a fully connected (256 FGT synapses) two-layer spiking neural network (SNN), where the adaptive properties of FGT are taken advantage of. A compact realization of Spike Timing Dependent Plasticity (STDP) within the SNN is one of the key contributions of this thesis. Finally, the considerations in this thesis extend beyond CMOS to emerging nanodevices. To this end, one promising emerging nanoscale circuit element - memristor - is reviewed and its applicability for analog processing is considered. Furthermore, it is discussed how the FGT technology can be used to prototype computation paradigms compatible with these emerging two-terminal nanoscale devices in a mature and widely available CMOS technology.

Synthesis and characterization of nanoperforated metal oxide thin films

For advanced devices in the application fields of data storage, solar cell and biosensing, one of the major challenges to achieve high efficiency is the fabrication of nanopatterned metal oxide surfaces. Such surfaces often require both precise structure at the nanometer scale and controllable patterned structure at the macro scale. Nowadays, the dominating candidates to fabricate nanopatterned surfaces are the lithographic technique and block-copolymer masks, most of which are unfortunately costly and inefficient. An alternative bottom-up approach, which involves organic/inorganic self-assembly and dip-coating deposition, has been studied intensively in recent years and has proven to be an effective technique for the fabrication of nanoperforated metal oxide thin films. The overall objective of this work was to optimize the synthesis conditions of nanoperforated TiO2 (NP-TiO2) thin films, especially to be compatible with mixed metal oxide systems. Another goal was to develop fabrication and processing of NP-TiO2 thin films towards largescale production and seek new applications for solar cells and biosensing. Besides the traditional dip-coating and drop-casting methods, inkjet printing was used to prepare thin films of metal oxides, with the advantage of depositing the ink onto target areas, further enabling cost-effective fabrication of micro-patterned nanoperforated metal oxide thin films. The films were characterized by water contact angle determination, Atomic Force Microscopy, Scanning Electron Microscopy, X-ray Photoelectron Spectroscopy and Grazing Incidence XRay Diffraction. In this study, well-ordered zinc titanate nanoperforated thin films with different Zn/Ti ratios were produced successfully with zinc precursor content up to 50 mol%, and the dominating phase was Zn2Ti3O8. NP-TiO2 structures were also obtained by a cost-efficient means, namely inkjet printing, at both ambient temperature and 60 °C. To further explore new biosensing applications of nanoperforated oxide thin films, inkjet printing was used for the fabrication of both continuous and patterned polymeric films onto NP-TiO2 and perfluorinated phosphate functionalized NP-TiO2 substrates, respectively. The NP-TiO2 films can be also functionalized with a fluoroalkylsilane, resulting in hydrophobic surfaces on both titania and silica. The surface energy contrast in the nanoperforations can be tuned by irradiating the films with UV light, which provides ideal model systems for wettability studies.