Lateral ion implant straggle and mask proximity effect

Lateral scattering of retrograde well implants is shown to have an effect on the threshold voltage of nearby devices. The threshold voltage of both NMOSFETs and PMOSFETs increases in magnitude for conventional retrograde wells, but for triple-well isolated NMOSFETs the threshold voltage decreases for narrow devices near the edge of the well. Electrical data, SIMS, and SUPREM4 simulations are shown that elucidate the phenomenon.     

双缸式气动-手动楔式闸阀在柴油罐上的应用

文章简单介绍了气动阀的结构、工作原理及特点,并时堆修保养注意事项以及可能发生的故障和排除故障的方法做了详细讲解。最后,将气动阀在柴油罐上的应用情况与普通闸阀相比较,分析其使用性能和优点,进一步推广气动闽在锦西石化分公司的应用。     

Dispersion-tolerant 10-gb/s duobinary system employing heterodyne detection and MLSE

We experimentally demonstrate a 10.7-Gb/s duobinary transmission system operating over a chromatic-dispersion range of /spl sim/12 000 ps/nm. Heterodyne detection and maximum-likelihood sequence estimation are employed to achieve this result.     

Unified Systolic-Like Architecture for DCT and DST Using Distributed Arithmetic

A common computing-core representation of the discrete cosine transform and discrete sine transform is derived and a reduced-complexity algorithm is developed for computation of the proposed computing-core. A parallel architecture based on the principle of distributed arithmetic is designed further for the computation of these transforms using the common-core algorithm. The proposed scheme not only leads to a systolic-like regular and modular hardware for computing these transforms, but also offers significant improvement in area-time efficiency over the existing structures. The structure proposed here is devoid of complicated input/output mapping and does not involve any complex control. Unlike the convolution-based structures, it does not restrict the transform length to be a prime or multiple of prime and can be utilized as a reusable core for cost-effective, memory-efficient, high-throughput implementation of either of these transforms     

A New Hand-Held Microsystem Architecture for Biological Analysis

This paper presents a hand-held microsystem based on new fully integrated magnetoresistive biochips for biomolecular recognition (DNA hybridization, antibody antigen interaction, etc.). Magnetoresistive chip surfaces are chemically treated, enabling the immobilization of probe biomolecules such as DNA or antibodies. Fluid handling is also integrated in the biochip. The proposed microsystem not only integrates the biochip, which is an array of 16times16 magnetoresistive sensors, but it also provides all the electronic circuitry for addressing and reading out each transducer. The proposed architecture and circuits were specifically designed for achieving a compact, programmable and portable microsystem. The microsystem also integrates a hand-held analyzer connected through a wireless channel. A prototype of the system was already developed and detection of magnetic nanoparticles was obtained. This indicates that the system may be used for magnetic label based bioassays     

The radiation resistance of an arbitrarily oriented electric dipole exciting a corner antenna or a plane screen

Rigorous analytic expressions for radiation resistance R _Σ of an electric dipole are obtained for three mutually orthogonal orientations of the dipole’s axis with respect to a plane metal screen or right-angle corner of infinite dimensions. Radiation resistance R _Σ of the dipole oriented arbitrarily in space is calculated and analyzed as a function of the distance from the screen.     

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.