Combining nonlinear multiresolution system and vector quantization for still image compression

It is popular to use multiresolution systems for image coding and compression. However, general-purpose techniques such as filter banks and wavelets are linear. While these systems are rigorous, nonlinear features in the signals cannot be utilized in a single entity for compression. Linear filters are known to blur the edges. Thus, the low-resolution images are typically blurred, carrying little information. We propose and demonstrate that edge- preserving filters such as median filters can be used in generating a multiresolution system using the Laplacian pyramid. The signals in the detail images are small and localized in the edge areas. Principal component vector quantization (PCVQ) is used to encode the detail images. PCVQ is a tree-structured VQ which allows fast codebook design and encoding/decoding. In encoding, the quantization error at each level is fed back through the pyramid to the previous level so that ultimately all the error is confined to the first level. With simple coding methods, we demonstrate that images with PSNR 33 dB can be obtained at 0.66 bpp without the use of entropy coding. When the rate is decreased to 0.25 bpp, the PSNR of 30 dB can still be achieved. Combined with an earlier result, our work demonstrate that nonlinear filters can be used for multiresolution systems and image coding

Image enhancement by edge-preserving filtering

Image enhancement is useful when the details in an image are lost due to various reasons. It is common to subtract a mask from a given image to enhance the details. The trick is how to obtain a good mask. We describe here how an edge-preserving filter can be used to generate a mask which is smooth over areas with fine details, yet preserving most of the edges. Experiments with real images show that our scheme is very effective

Clustering data by melting

We derive a new clustering algorithm based on information theory and statistical mechanics, which is the only algorithm that incorporates scale. It also introduces a new concept into clustering: cluster independence. The cluster centers correspond to the local minima of a thermodynamic free energy, which are identified as the fixed points of a one-parameter nonlinear map. The algorithm works by melting the system to produce a tree of clusters in the scale space. Melting is also insensitive to variability in cluster densities, cluster sizes, and ellipsoidal shapes and orientations. We tested the algorithm successfully on both simulated data and a Synthetic Aperture Radar image of an agricultural site with 12 attributes for crop identification

Combining nonlinear multiresolution system and vector quantization for still image compression

It is popular to use multiresolution systems for image coding and compression. However, general-purpose techniques such as filter banks and wavelets are linear. While these systems are rigorous, nonlinear features in the signals cannot be utilized in a single entity for compression. Linear filters are known to blur the edges. Thus, the low-resolution images are typically blurred, carrying little information. We propose and demonstrate that edge- preserving filters such as median filters can be used in generating a multiresolution system using the Laplacian pyramid. The signals in the detail images are small and localized in the edge areas. Principal component vector quantization (PCVQ) is used to encode the detail images. PCVQ is a tree-structured VQ which allows fast codebook design and encoding/decoding. In encoding, the quantization error at each level is fed back through the pyramid to the previous level so that ultimately all the error is confined to the first level. With simple coding methods, we demonstrate that images with PSNR 33 dB can be obtained at 0.66 bpp without the use of entropy coding. When the rate is decreased to 0.25 bpp, the PSNR of 30 dB can still be achieved. Combined with an earlier result, our work demonstrate that nonlinear filters can be used for multiresolution systems and image coding

Parallel Algorithms for One and Two-Vehicle Navigation

A two vehicle navigator on a descrete space is analyzed. The concept of linking time maps as source to optimal path planning is discussed. The rules for constructing these maps are given in a cellular automata mode. The implementation of these rules on a parallel computer is presented

A Neural Network Approach to Multi-Vehicle Navigation

We develop a neural network formulation for multii-vehicle navigation on a two-dimensional surface here. A time-linking map is generated for each individual vehicle using techniques similar to the known shortest path algorithms for an isolated vehicle. Neural networks are then applied to generate non-conflicting paths minimizing the time of travel

Neural Networks for Template Matching: Application to Real-Time Classification of the Action Potentials of Real Neurons

Much experimental study of real neural networks relies on the proper classification of extracellulary sampled neural signals (i .e. action potentials) recorded from the brains of experimental animals. In most neurophysiology laboratories this classification task is simplified by limiting investigations to single, electrically well-isolated neurons recorded one at a time. However, for those interested in sampling the activities of many single neurons simultaneously, waveform classification becomes a serious concern. In this paper we describe and constrast three approaches to this problem each designed not only to recognize isolated neural events, but also to separately classify temporally overlapping events in real time. First we present two formulations of waveform classification using a neural network template matching approach. These two formulations are then compared to a simple template matching implementation. Analysis with real neural signals reveals that simple template matching is a better solution to this problem than either neural network approach

Biorefining Waste Sludge From Water and Sewage Treatment Plants Into Eco-Construction Material

This study aims to investigate the feasibility of using different waste sludge and coal combustion residuals in eco-concrete block production. The compressive strength of the eco-concrete blocks produced by waterworks sludge, bottom and fly ashes were 36 MPa, which comply with the standard specifications for paving blocks in Hong Kong. The optimal mixing proportion (by weight) of different materials in the blocks, such as aggregates, cementitious materials, water, and fly ash was 1.1:1.0:0.5:0.22, respectively. The environmental and toxicological impacts of the final products were then evaluated according to the toxicity characteristic leaching procedure (TCLP). While several heavy metals (i.e., Hg, Cu, and Pb) have been identified in the specimens, the levels of these contaminants complied with Standards (US 40 CFR 268.48). Waste materials generated from water and sewage treatment processes and power plants are feasible to be used as ingredients for paving concrete block production. These products are environmentally acceptable and mechanically suitable for resource recovery of waste materials

Bacterial Microbiota Profiling in Gastritis without Helicobacter pylori Infection or Non-Steroidal Anti-Inflammatory Drug Use

Recent 16S ribosomal RNA gene (rRNA) molecular profiling of the stomach mucosa revealed a surprising complexity of microbiota. Helicobacter pylori infection and non-steroidal anti-inflammatory drug (NSAID) use are two main contributors to gastritis and peptic ulcer. However, little is known about the association between other members of the stomach microbiota and gastric diseases. In this study, cloning and sequencing of the 16S rRNA was used to profile the stomach microbiota from normal and gastritis patients. One hundred and thirty three phylotypes from eight bacterial phyla were identified. The stomach microbiota was found to be closely adhered to the mucosa. Eleven Streptococcus phylotypes were successfully cultivated from the biopsies. One to two genera represented a majority of clones within any of the identified phyla. We further developed two real-time quantitative PCR assays to quantify the relative abundance of the Firmicutes phylum and the Streptococcus genus. Significantly higher abundance of the Firmicutes phylum and the Streptococcus genus within the Firmicutes phylum was observed in patients with antral gastritis, compared with normal controls. This study suggests that the genus taxon level can largely represent much higher taxa such as the phylum. The clinical relevance and the mechanism underlying the altered microbiota composition in gastritis require further functional studies