Combining interest points and edges for content-based image retrieval

This paper presents a novel approach using combined features to retrieve images containing specific objects, scenes or buildings. The content of an image is characterized by two kinds of features: Harris-Laplace interest points described by the SIFT descriptor and edges described by the edge color histogram. Edges and corners contain the maximal amount of information necessary for image retrieval. The feature detection in this work is an integrated process: edges are detected directly based on the Harris function; Harris interest points are detected at several scales and Harris-Laplace interest points are found using the Laplace function. The combination of edges and interest points brings efficient feature detection and high recognition ratio to the image retrieval system. Experimental results show this system has good performance. © 2005 IEEE.

A surface acoustic wave-based immunosensing device using a nanocrystalline ZnO film on Si

The novelty of this study resides in the fabrication of a bio-sensing device, based on the surface acoustic wave (SAW) on a nanocrystalline ZnO film. The ZnO film was deposited using an rf magnetron sputtering at room temperature on silicon. The deposited films showed the c-axisoriented crystallite with grain size of ∼40 nm. The immunosensing device was fabricated using photolithographic protocols on the film. As a model biomolecular recognition and immunosensing, biospecific interaction between a 6-(2,4-dinitrophenyl) aminohexanoic acid (DNP) antigen and its antibody was employed, demonstrating the shifts of resonant frequencies on SAW immunosensing device. The device exhibited a linearity as a function of the antibody concentration in the range of 20∼20,000 ng/ml. © 2009 American Scientific Publishers. All rights reserved.

Suspended multiwalled carbon nanotubes as self-aligned evaporation masks

We describe the nanofabrication study of self-aligned electrodes on suspended multiwalled carbon nanotube structures. When metal is deposited on a suspended multiwalled carbon nanotube structure, the nanotube acts as an evaporation mask, resulting in the formation of discontinuous electrodes. The metal deposits on the nanotubes are removed with lift-off. Using Al sacrificial layers, it was possible to fabricate self-aligned contact electrodes and control electrodes nanometers from the suspended carbon nanotubes with a single lithography step. It was also shown that the fabrication technique may also be used to form nano-gapped contact electrodes. The technique should prove useful for the fabrication of nano-electromechanical systems.

High sensitive NO2 gas sensor with low power consumption using selectively grown ZnO nanorods.

The noble gas sensor using multiple ZnO nanorods was fabricated with CMOS compatible process and sol-gel growth method on selective area and gas response characteristics to NO2 gas of the sensor device were investigated. We confirmed the sensors had high sensitive response denoted by the sensitivity of several tens for NO2 gas sensing and also showed pretty low power consumption close to 20 mW even though the recovery of resistance come up to almost the initial value.

Thermoelastic dissipation in MEMS/NEMS flexural mode resonators.

Understanding the energy dissipation mechanisms in single-crystal silicon MEMS/NEMS resonators are particularly important to maximizing an important figure of merit relevant for miniature sensor and signal processing applications: the Quality factor (Q) of resonance. This paper discusses thermoelastic dissipation (TED) as the dominant internal-friction mechanism in flexural mode MEMS/NEMS resonators. Criteria for optimizing the geometrical design of flexural mode MEMS/NEMS resonators are theoretically established with a view towards minimizing the TED for single-crystal silicon MEMS/NEMS flexural mode resonators.

Growth of carbon nanotubes on fully processed silicon-on-insulator CMOS substrates.

This paper describes the growth of Carbon Nanotubes (CNTs) both aligned and non-aligned on fully processed CMOS substrates containing high temperature tungsten metallization. While the growth method has been demonstrated in fabricating CNT gas sensitive layers for high temperatures SOI CMOS sensors, it can be employed in a variety of applications which require the use of CNTs or other nanomaterials with CMOS electronics. In our experiments we have grown CNTs both on SOI CMOS substrates and SOI CMOS microhotplates (suspended on membranes formed by post-CMOS deep RIE etching). The fully processed SOI substrates contain CMOS devices and circuits and additionally, some wafers contained high current LDMOSFETs and bipolar structures such as Lateral Insulated Gate Bipolar Transistors. All these devices were used as test structures to investigate the effect of additional post-CMOS processing such as CNT growth, membrane formation, high temperature annealing, etc. Electrical characterisation of the devices with CNTs were performed along with SEM and Raman spectroscopy. The CNTs were grown both at low and high temperatures, the former being compatible with Aluminium metallization while the latter being possible through the use of the high temperature CMOS metallization (Tungsten). In both cases we have found that there is no change in the electrical behaviour of the CMOS devices, circuits or the high current devices. A slight degradation of the thermal performance of the CMOS microhotplates was observed due to the extra heat dissipation path created by the CNT layers, but this is expected as CNTs exhibit a high thermal conductance. In addition we also observed that in the case of high temperature CNT growth a slight degradation in the manufacturing yield was observed. This is especially the case where large area membranes with a diameter in excess of 500 microns are used.

Coarse-level object recognition using interlevel products of complex wavelets

This paper introduces the Interlevel Product (ILP) which is a transform based upon the Dual-Tree Complex Wavelet. Coefficients of the ILP have complex values whose magnitudes indicate the amplitude of multilevel features, and whose phases indicate the nature of these features (e.g. ridges vs. edges). In particular, the phases of ILP coefficients are approximately invariant to small shifts in the original images. We accordingly introduce this transform as a solution to coarse scale template matching, where alignment concerns between decimation of a target and decimation of a larger search image can be mitigated, and computational efficiency can be maintained. Furthermore, template matching with ILP coefficients can provide several intuitive "near-matches" that may be of interest in image retrieval applications. © 2005 IEEE.

Semantic Discriminant mapping for classification and browsing of remote sensing textures and objects

We present a new approach based on Discriminant Analysis to map a high dimensional image feature space onto a subspace which has the following advantages: 1. each dimension corresponds to a semantic likelihood, 2. an efficient and simple multiclass classifier is proposed and 3. it is low dimensional. This mapping is learnt from a given set of labeled images with a class groundtruth. In the new space a classifier is naturally derived which performs as well as a linear SVM. We will show that projecting images in this new space provides a database browsing tool which is meaningful to the user. Results are presented on a remote sensing database with eight classes, made available online. The output semantic space is a low dimensional feature space which opens perspectives for other recognition tasks. © 2005 IEEE.

Restoration of images and 3D data to higher resolution by deconvolution with sparsity regularization

Image convolution is conventionally approximated by the LTI discrete model. It is well recognized that the higher the sampling rate, the better is the approximation. However sometimes images or 3D data are only available at a lower sampling rate due to physical constraints of the imaging system. In this paper, we model the under-sampled observation as the result of combining convolution and subsampling. Because the wavelet coefficients of piecewise smooth images tend to be sparse and well modelled by tree-like structures, we propose the L0 reweighted-L2 minimization (L0RL2 ) algorithm to solve this problem. This promotes model-based sparsity by minimizing the reweighted L2 norm, which approximates the L0 norm, and by enforcing a tree model over the weights. We test the algorithm on 3 examples: a simple ring, the cameraman image and a 3D microscope dataset; and show that good results can be obtained. © 2010 IEEE.

Convex approaches to model wavelet sparsity patterns

Statistical dependencies among wavelet coefficients are commonly represented by graphical models such as hidden Markov trees (HMTs). However, in linear inverse problems such as deconvolution, tomography, and compressed sensing, the presence of a sensing or observation matrix produces a linear mixing of the simple Markovian dependency structure. This leads to reconstruction problems that are non-convex optimizations. Past work has dealt with this issue by resorting to greedy or suboptimal iterative reconstruction methods. In this paper, we propose new modeling approaches based on group-sparsity penalties that leads to convex optimizations that can be solved exactly and efficiently. We show that the methods we develop perform significantly better in de-convolution and compressed sensing applications, while being as computationally efficient as standard coefficient-wise approaches such as lasso. © 2011 IEEE.