Attribute Allocation and Retrieval Scheme for Large-Scale Sensor Networks

Wireless sensor network is an emerging technology that enables remote monitoring of large geographical regions. In this paper, we address the problem of distributing attributes over such a large-scale sensor network so that the cost of data retrieval is minimized. The proposed scheme is a data-centric storage scheme where the attributes are distributed over the network depending on the correlations between them. The problem addressed here is similar to the Allocation Problem of distributed databases. In this paper, we have defined the Allocation Problem in the context of sensor networks and have proposed a scheme for finding a good distribution of attributes to the sensor network. We also propose an architecture for query processing given such a distribution of attributes. We analytically determine the conditions under which the proposed architecture is beneficial and present simulation results to demonstrate the same. To the best of our knowledge, this is the first attempt to determine an allocation of attributes over a sensor network based on the correlations between attributes.     

Operating Penalties in Single-Fiber Operation 10-Gb/s, 1024-Way Split, 110-km Long-Reach Optical Access Networks

We report for the first time optical signal-to-noise penalties which lead to performance degradations in single-fiber long-reach optical access networks when compared to identical dual-fiber systems. A simplified architecture, with reduced optical amplifier count compared to previous work, for single-fiber operation of a symmetrical 10-Gb/s, 1024-way split, 110-km long-reach optical access network is presented and demonstrated. In addition, a possible solution to remove the optical signal-to-noise penalty is suggested     

Multi-level attention model for tracking and segmentation of objects under complex occlusion

A multi-level attention framework for tracking and segmentation of humans and objects under complex occlusions is investigated, featuring an effective probabilistic appearance-based technique for pixel reclassification during object grouping and splitting. A novel ’spatial-depth affinity metric’ is introduced in the conventional likelihood function, utilising information of both spatial locations of pixels and dynamic depth ordering of the component objects in grouping. Depth ordering estimation is achieved through a combination of top-down and bottom-up approach. Experiments on some realworld difficult scenarios of low quality and highly compressed videos demonstrate the very promising results achieved.     

A possibilistic clustering approach to novel fault detection and isolation

In this paper, a new approach for fault detection and isolation that is based on the possibilistic clustering algorithm is proposed. Fault detection and isolation (FDI) is shown here to be a pattern classification problem, which can be solved using clustering and classification techniques. A possibilistic clustering based approach is proposed here to address some of the shortcomings of the fuzzy c-means (FCM) algorithm. The probabilistic constraint imposed on the membership value in the FCM algorithm is relaxed in the possibilistic clustering algorithm. Because of this relaxation, the possibilistic approach is shown in this paper to give more consistent results in the context of the FDI tasks. The possibilistic clustering approach has also been used to detect novel fault scenarios, for which the data was not available while training. Fault signatures that change as a function of the fault intensities are represented as fault lines, which have been shown to be useful to classify faults that can manifest with different intensities. The proposed approach has been validated here through simulations involving a benchmark quadruple tank process and also through experimental case studies on the same setup. For large scale systems, it is proposed to use the possibilistic clustering based approach in the lower dimensional approximations generated by algorithms such as PCA. Towards this end, finally, we also demonstrate the key merits of the algorithm for plant wide monitoring study using a simulation of the benchmark Tennessee Eastman problem.     

COMBINED EFFECTS OF ENZYME DOSAGE AND REACTION TIME ON PAPAYA JUICE EXTRACTION WITH THE AID OF PECTIC ENZYMES – A PRELIMINARY REPORT

Pectic enzymes are widely used in the food industry for fruit juice extraction as well as in the clarification of cloudy juices. Our laboratory has been using pectic enzymes produced from a strain of Saccharomyces cerevisiae (ATCC 52712) in different applications including fruit juice extraction. The enzyme was produced in the laboratory by culturing the yeast in papaya juice supplemented with 1% pectin for 6 days. Known amounts of enzyme preparation (0–40 mg protein) were added to a measured weight of papaya mash for varying reaction periods (30–90 min) and the amount of free‐run juice obtained in each treatment compared with a control sample. Treatment of 200 g of papaya mash with different dosages of the pectic enzyme extract resulted in rapid increases in flow rate of free‐run juice. Mash treated with 32 mg of total protein extract with a 30‐min reaction time was the optimum for a maximum rate of juice flow (25 mL/min) when initial rates were measured. There was no significant difference (P > 0.05) between the red‐ and yellow‐fleshed varieties of the papaya used. When juice flow was monitored over 6 min, the treated samples gave a flow rate that was more than twice those of the untreated samples. This biotechnological approach could be adopted to enhance papaya‐juice production by local fruit juice processors when parameters for scale‐up processes are established.     

Laterally coupled buried heterostructure high-Q ring resonators

All-buried InP-InGaAsP ring resonators laterally coupled to bus waveguides are demonstrated. The buried configurations offer a lower built-in refractive index step along the resonator periphery, which affords enhanced optical coupling coefficients between the waveguides and reduced scattering losses caused by the resonator sidewall imperfections. Very low optical intensity attenuations of 0.4 cm/sup -1/ and coupling-limited quality factors of greater than 10/sup 5/ are observed from 200-/spl mu/m-radii ring resonators. The measured spectral linewidth is as narrow as 0.0145 nm.     

Fabrication of Schottky barrier MOSFETs using self-assembly CoSi2 nanopatterning and spacer gate technologies

A self-assembly patterning method for generation of epitaxial CoSi₂ nanostructures was used to fabricate 50 nm channel-length MOSFETs. The transistors have either a symmetric structure with Schottky source and drain or an asymmetric structure with n⁺-source and Schottky drain. The patterning technique is based on anisotropic diffusion of Co/Si atoms in a strain field during rapid thermal oxidation. The strain field is generated along the edges of a mask consisting of 20 nm SiO₂ and 300 nm Si₃N₄. During rapid thermal oxinitridation (RTON) of the masked silicide structure, a well-defined separation of the silicide layer forms along the edge of the mask. These highly uniform gaps define the channel region of the fabricated device. The separated silicide layers act as metal source and drain. A poly-Si spacer was used as the gate contact. The asymmetric transistor was fabricated by ion implantation into the unprotected CoSi₂ layer and a subsequent out-diffusion process to form the n⁺-source. I–V characteristics of both the symmetric and asymmetric transistor structures have been investigated.     

Electrical Properties of GeO2–NiO Materials Containing Ni2GeO4

The GeO₂–NiO system is studied in the stability range of Ni₂GeO₄ by temperature-dependent resistivity measurements, in combination with x-ray diffraction, differential thermal analysis, and thermogravimetry. The resistivity of the starting mixtures and heat-treated samples is measured in the composition range 55–90 mol % NiO. The effect of air humidity on the resistivity of the samples is examined.     

A multigrid accelerated hybrid unstructured mesh method for 3D compressible turbulent flow

 A cell vertex finite volume method for the solution of steady compressible turbulent flow problems on unstructured hybrid meshes of tetrahedra, prisms, pyramids and hexahedra is described. These hybrid meshes are constructed by firstly discretising the computational domain using tetrahedral elements and then by merging certain tetrahedra. A one equation turbulence model is employed and the solution of the steady flow equations is obtained by explicit relaxation. The solution process is accelerated by the addition of a multigrid method, in which the coarse meshes are generated by agglomeration, and by parallelisation. The approach is shown to be effective for the simulation of a number of 3D flows of current practical interest. Sponsored by The Research Council of Norway, project number 125676/410 Dedicated to the memory of Prof. Mike Crisfield, a respected colleague     

Sequential testing of sorted and transformed data as an efficient way to implement long GLRTs

It is often required to detect a long weak signal in Gaussian noise, and frequently, the exact form of that signal is parameterized but not known. A bank of matched filters provides an appropriate detector. However, in some practical applications, there are very many matched filters, and most are quite long. The consequent computational needs may render the classical bank-of-filters approach infeasibly expensive. One example, and our original motivation, is the detection of chirp gravitational waves by an Earth-based interferometer. In this paper, we provide a computational approach to this problem via sequential testing. Since the sequential tests to be used are not for constant signals, we develop the theory in terms of average sample number (ASN) for this case. Specifically, we propose two easily calculable expressions for the ASN: one a bound and the other an approximation. The sequential approach does yield moderate computational savings, but we find that by preprocessing the data using short/medium fast Fourier transforms (FFTs) and an appropriate sorting of these FFT outputs such that the most informative samples are entered to a sequential test first, quite high numerical efficiency can be realized. The idea is simple but appears to be quite successful: Examples are presented in which the computational load is reduced by several orders of magnitude. The FFT is an example of an energy-agglomerating transform, but of course, there are many others. The point here is that the transform need not match the sought signal exactly in the sense that all energy becomes confined to a single sample; it is enough that the energy becomes concentrated, and the more concentrated the better.