Detection of low molecular weight toxins using an optical phase method of ellipsometry

The method of total internal reflection ellipsometry (TIRE), which records simultaneously two parameters Δ and Ψ related to the amplitudes and phases of p- and s-components of polarised light, respectively, was exploited here for detection of mycotoxins (T-2 and zearalenone). The above mycotoxins in sub-ppb concentrations were detected using TIRE in both direct and competitive immunoassay. The study of immune reaction kinetics allowed the evaluation of adsorption/desorption rates and the association constant for zearalenone. The investigation of grain products samples revealed the presence of high concentrations of T-2 (>600 ng/ml) in mould products.     

A photoluminescence-based quantum semiconductor biosensor for rapid in situ detection of Escherichia coli

This work describes a novel method of detecting Escherichia coli using photoluminescence (PL) emission from III–V quantum semiconductor (QS) devices functionalized with two different antibody-based architectures. The first approach employed self-assembled monolayers of biotinylated polyethylene glycol thiols to immobilize biotinylated antibody via neutravidin. In the second approach, we used QS microstructures coated with a thin layer of Si₃N₄ allowing direct functionalization with E. coli antibodies through hydrofluoric acid etching and glutaraldehyde-based reticulation. Atomic force, optical and fluorescence microscopy measurements were used to assess the immobilization process. Depending on the biosensing architecture, density of the immobilized bacteria was observed in the range of 0.5–0.7 bacteria/100 μm². The detection of E. coli at 10⁴ CFU/ml was achieved within less than 120 min of the bacteria exposure. It is expected that an even better sensitivity threshold could be achieved following further optimization of the method.     

High responsivity silicon MOS phototransistors

We describe a metal-oxide silicon (MOS) phototransistor that relies on a novel lateral doping scheme that creates a p-i-n junction configuration for light detection. This is essentially a hybrid device with the horizontal structure of a p-i-n diode and the vertical structure of a MOS field-effect transistor. The lateral p-i-n diode detects light whereas the gate can be used to change the current flowing through the device; making it appear as a MOSFET. This feature makes it easy to integrate it with other conventional MOSFETs on a CMOS process flow. The device shows high optical responsivities that persist to wavelengths in the near-ultraviolet region. The fabrication of the device as well as its electrical and optical characteristics is described.     

From embedded sensors to sensorial materials—The road to function scale integration

Ubiquitous computing is about to become part of our everyday lives by integrating hundreds of “invisible” to us computing devices in our environment, so that they can unobtrusively and constantly assist us. This will imply more and smaller “invisible” sensors, homogeneously distributed and at the same time densely packed in host materials, responding to various stimuli and immediately delivering information. In order to reach this aim, the embedded sensors should be integrated within the host material, heading towards sensorial materials. The first step is to omit all parts that are not needed for the sensorial task and to find new solutions for a gentle integration. This is what we call function scale integration. The paper discusses sensor embedding in the human hand as an example of integration in nature, new technological applications and main challenges associated with this approach.     

Integrated metallic gauge in a piezoelectric cantilever

In this study, a self-sensitive piezoelectric cantilever with a new design for sensing is presented. Micro-cantilevers were actuated by a 120 nm-thick Lead Zirconate Titanate Oxide (PZT) layer. Detection was done by measuring the resistance of a metallic gauge integrated on the surface of the micro-piezoelectric cantilevers. Some metallic gauges were embedded in a Wheatstone bridge to improve the measured resistance accuracy. Devices with direct resistance measurement were also realized. Gauge resistance responses were compared with an optical measurement by a White Light Interferometer. By biasing the PZT between −5 V and 5 V, cyclic deflection of the cantilever was detected with both optical and metallic gauge resistance systems. The two measurements fit. This self-sensitive piezoelectric cantilever can be used for fatigue test with packaged devices.     

Controlling the initial phase of PECVD growth of vertically aligned carbon nanofibers on TiN

We explore the growth of vertically aligned carbon nanofibers by plasma enhanced chemical vapor deposition, using lithographically defined Ni catalyst seeds on TiN. TiN is selected for being an electrically conducting diffusion barrier suitable for the realization of electronic devices. We show that the rate of Ni diffusion correlates to both the level of oxygen content in the TiN film and to the film resistivity. The synthesis of the nanofibers was characterized using electron microscopy with an emphasis on three growth parameters: substrate temperature, plasma power, and chamber pressure. We propose that a catalyst surface free from carbon deposits throughout the process will induce diffusion-limited growth. The growth will shift towards a supply-limited process when the balance between acetylene, as the effective carbon bearing gas, and atomic hydrogen, as the main etching agent, is skewed in favor of acetylene. This determines whether the dominating growth mode will be vertically aligned ‘tip-type’ or disordered ‘base-type’, by affecting the competition between the formation of the first graphitic sheets on the catalyst surface and at the catalyst-substrate interface.     

Image segmentation and bias correction via an improved level set method

Intensity inhomogeneity causes considerable difficulty in the quantitative analysis of magnetic resonance (MR) images. Thus bias field estimation is a necessary pre-processing step before quantitative analysis of MR data. This paper presents a variational level set approach for bias correction and segmentation for images with intensity inhomogeneities. Our method is based on the observation that local intensity variations in relatively smaller regions are separable, despite the inseparability of the whole image. In the beginning we define a function for clustering the image pixels in a smaller neighborhood. The cluster centers in this objective function have a multiplicative factor that estimates the bias within the neighborhood. Generally the local intensity variations are described by the Gaussian distributions with different means and variances. In this work the objective functions are integrated over the entire domain with local Gaussian distribution of fitting energy, ultimately analyzing the data with a level set framework. Our method is able to capture bias of quite general profiles. Moreover, our model can also distinguish regions with similar intensity distribution with different variances. The proposed method has been rigorously validated with images acquired on variety of imaging modalities with promising results.     

Stability and synchronization for Markovian jump neural networks with partly unknown transition probabilities

This paper addresses the problems of stability and synchronization for a class of Markovian jump neural networks with partly unknown transition probabilities. We first study the stability analysis problem for a single neural network and present a sufficient condition guaranteeing the mean square asymptotic stability. Then based on the Lyapunov functional method and the Kronecker product technique, the chaos synchronization problem of an array of coupled networks is considered. Both the stability and the synchronization conditions are delay-dependent, which are expressed in terms of linear matrix inequalities. The effectiveness of the developed methods is shown by simulation examples.     

Estimating the fundamental matrix based on least absolute deviation

The epipolar geometry is the intrinsic projective geometry between two views, and the algebraic representation of it is the fundamental matrix. Estimating the fundamental matrix requires solving an over-determined equation. Many classical approaches assume that the error values of the over-determined equation obey a Gaussian distribution. However, the performances of these approaches may decrease significantly when the noise is large and heterogeneous. This paper proposes a novel technique for estimating the fundamental matrix based on least absolute deviation (LAD), which is also known as the L₁ method. Then a linear iterative algorithm is presented. The experimental results on some indoor and outdoor scenes show that the proposed algorithm yields the accurate and robust estimates of the fundamental matrix when the noise is non-Gaussian.