Holographic sensors for the determination of ionic strength

Holographic sensors for monitoring ionic strength have been fabricated from charged sulphonate and quaternary ammonium monomers, incorporated into thin, polymeric hydrogel films which were transformed into volume holograms. The diffraction wavelength or reflected colour of the holograms was used to characterise their swelling or de-swelling behaviour as a function of ionic strength in various media. The effects of co-monomer structure, buffer composition, ion composition, pH and temperature were evaluated, whilst the reversibility and reproducibility of the sensor was also assessed. An acrylamide-based hologram containing equal molar amounts of negatively and positively charged monomers was shown to be able to quantify ionic strength independent of the identity of the ionic species present in the test solution. The sensor was fully reversible, free of hysteresis and exhibited little response to pH between 3 and 9 and temperature within the range 20-45 °C. The system was successfully used to quantify the ionic strength of milk solutions, which contain a complex mixture of ions and biological components.     

Adaptive agents and multiagent systems

New information systems and recent applications (grid computing, Web Services, and so on) are often distributed, large-scale, open, heterogeneous, and characterized by a dynamic environment. To model these complex systems, researchers have spent much effort during the last few years on multiagent systems. The aim is to model complex distributed systems as a set of (possibly organized) software agents that interact in a common environment. The decomposition of a system into a number of agents lets the system react and adapt better in a changing environment. Moreover, organized structures ("social" structures) can emerge from interactions between agents, which in turn constrain and coordinate the agents' behavior. A multiagent system takes its metaphors of interaction from social systems rather than using the metaphor of the isolated thinker that early artificial intelligence researchers preferred. An important issue when dealing with this increasing complexity is to build adaptive agents and multiagent systems. Agents and multiagent systems must be aware of their own capabilities and of changes to other agents and their environment. To remain effective, agents must be able to adapt their structures and knowledge while they execute.     

Effect of Sb implantation on copper silicides formation and morphology after annealing of Cu/Si structures

In this work, the solid state reaction between a thin film of copper and silicon has been studied using Rutherford backscattering spectroscopy, X-ray diffraction, scanning electron microscopy and microprobe analysis. Cu films of 400 and 900 Å thicknesses are thermally evaporated on Si(1 1 1) substrates, part of them had previously been implanted with antimony ions of 5×10¹⁴ or 5×10¹⁵ at. cm⁻² doses. The samples are heat-treated in vacuum at temperatures in the range 200–700 °C for various times. The results show the growth and formation of Cu₃Si and Cu₄Si silicides under crystallites shape dispatched on the sample surface, independently of the implantation dose. On the other hand, it is established that the copper layer is less and less consumed as the antimony dose increases, resulting in the accumulation of Sb⁺ ions at silicide/Si interface and in the silicide layer close to surface. The exposure of samples to air at room temperature shows the stability of Cu₄Si phase whereas the Cu₃Si silicide disappears to the benefit of the silicon dioxide formation. The observed phenomena are discussed.     

量子密码技术开辟通信安全新时代

量子密码技术被认为是绝对安全的加密技术。近年来,在美国、德国、日本和中国,相关研究都取得了明显进展。2004年6月,世界上第一个量子密码通信网络在美国马萨诸塞州剑桥城正式投入运行,标志着这一技术迈上了新台阶。据相关机构估算,量子保密通信系统一旦商用,将形成高达10亿美元的市场。     

Logarithmic wavelength demultiplexers

A general approach for a full 1 /spl times/ N demultiplexer using a tree of filter stages is proposed. The device architecture is compact, requiring at least N - 1 filter stages, and flexible, as each filter stage can be arbitrarily designed, with the only constraint of the half-band power property. The filters can be realized using any optical filtering techniques as thin-film interference, Bragg gratings, or planar delay-line circuits. The performances of the proposed architecture are illustrated with respect to different lattice-form finite impulse response (FIR) and infinite impulse response (IIR) filter stages, showing that the demultiplexer inherently presents low crosstalk and flat passband. A design example of a 1 /spl times/ 4 demultiplexer consisting of three all-pass (AP) filters is compared with a generalized Mach-Zehnder interferometer (MZI) with four AP filters in its arms, showing that the two approaches achieve similar results.     

Evaluation and optimization of package processing and design through solder joint profile prediction

Solder joints are generated using a variety of methods to provide both mechanical and electrical connection for applications such as flip-chip, wafer level packaging, fine pitch, ball-grid array, and chip scale packages. Solder joint shape prediction has been incorporated as a key tool to aid in process development, wafer level and package level design and development, assembly, and reliability enhancement. This work demonstrates the application of an analytical model and the Surface Evolver software in analyzing a variety of solder processing methods and package types. Bump and joint shape prediction was conducted for the design of wafer level bumping, flip-chip assembly, and wafer level packaging. The results from the prediction methodologies are validated with experimentally measured geometries at each level of design.     

Training and optimization of an artificial neural network controlling a hybrid power filter

A hybrid power compensator (HPC) consisting of a static VAr compensator and a dynamic compensator needs to be optimally controlled during the compensation of nonlinear loads. The HPC must be controlled to meet minimum requirements in terms of power factor and harmonic distortion, while at the same time minimizing its total cost. An artificial neural network (ANN) is used to control the HPC amidst a very dynamic power system environment. The performance of a reference ANN is evaluated while controlling an HPC connected to a typical nonlinear industrial load. The training and performance of the ANN is then optimized in terms of training set size, training set packing and ANN topology and the performance compared to the reference ANN. This paper highlights the importance of optimising the mentioned ANN parameters to achieve optimum ANN training and modeling accuracy. The results obtained reveals that the application of an ANN in controlling an HPC is feasible given that the ANN parameters are chosen appropriately.     

Analytic models for the latency and steady-state throughput of TCP Tahoe, Reno, and SACK

Continuing the process of improvements made to TCP through the addition of new algorithms in Tahoe and Reno, TCP SACK aims to provide robustness to TCP in the presence of multiple losses from the same window. In this paper we present analytic models to estimate the latency and steady-state throughput of TCP Tahoe, Reno, and SACK and validate our models using both simulations and TCP traces collected from the Internet. In addition to being the first models for the latency of finite Tahoe and SACK flows, our model for the latency of TCP Reno gives a more accurate estimation of the transfer times than existing models. The improved accuracy is partly due to a more accurate modeling of the timeouts, evolution of cwnd during slow start and the delayed ACK timer. Our models also show that, under the losses introduced by the droptail queues which dominate most routers in the Internet, current implementations of SACK can fail to provide adequate protection against timeouts and a loss of roughly more than half the packets in a round will lead to timeouts. We also show that with independent losses SACK performs better than Tahoe and Reno and, as losses become correlated, Tahoe can outperform both Reno and SACK.     

A VQ-based blind image restoration algorithm

Learning-based algorithms for image restoration and blind image restoration are proposed. Such algorithms deviate from the traditional approaches in this area, by utilizing priors that are learned from similar images. Original images and their degraded versions by the known degradation operator (restoration problem) are utilized for designing the VQ codebooks. The codevectors are designed using the blurred images. For each such vector, the high frequency information obtained from the original images is also available. During restoration, the high frequency information of a given degraded image is estimated from its low frequency information based on the codebooks. For the blind restoration problem, a number of codebooks are designed corresponding to various versions of the blurring function. Given a noisy and blurred image, one of the codebooks is chosen based on a similarity measure, therefore providing the identification of the blur. To make the restoration process computationally efficient, the principal component analysis (PCA) and VQ-nearest neighbor approaches are utilized. Simulation results are presented to demonstrate the effectiveness of the proposed algorithms.