Design and Fabrication of Low-microwave loss Coplannar Waveguide and precise V groove on Silicon Substrate for Optoelectronic Packaging - art. no. 601938

Optoelectronic packaging has become a most important factor that influences the final performance and cost of the module. In this paper, low microwave loss coplanar waveguide(CPW) on high resistivity silicon(HRS) and precise V groove in silicon substrate were successfully fabricated. The microwave attenuation of the CPW made on HRS with the simple process is lower than 2 dB/cm in the frequency range of 0 similar to 26GHz, and V groove has the accuracy in micro level and smooth surface. These two techniques built a good foundation for high frequency packaging and passive coupling of the optoelectronic devices. Based on these two techniques, a simple high resistivity silicon substrate that integrated V groove and CPW for flip-chip packaging of lasers was completed. It set a good example for more complicate optoelectronic packaging.

Nonradiative recombination effect on photoluminescence decay dynamics in GaInNAs/GaAs quantum wells - art. no. 61180Z

The nonradiative recombination effect on the photoluminescence (PL) decay dynamics in GaInNAs/GaAs quantum wells is studied by photoluminescence and time-resolved photoluminescence under various excitation intensities and temperatures. It is found that the PL decay dynamics strongly depends on the excitation intensity. In particular, under the moderate excitation levels the PL decay curves exhibit unusual non-exponential behavior and show a convex shape. By introducing a new concept of the effective concentration of nonradiative recombination centers into a rate equation, the observed results are well simulated. In the cw PL measurement, a rapid PL quenching is observed even at very low temperature and is of the excitation power dependence. These results further demonstrate that the non-radiative recombination process plays a very important role on the optical properties of GaInNAs/GaAs quantum wells.

Narrow stripe selective growth of InGaAlAs waveguides used for buried heterostructure lasers - art. no. 63210I

The narrow stripe selective growth of the InGaAlAs bulk waveguides and InGaAlAs MQW waveguides was first investigated. Flat and clear interfaces were obtained for the selectively grown InGaAlAs waveguides under optimized growth conditions. These selectively grown InGaAlAs waveguides were covered by specific InP layers, which can keep the waveguides from being oxidized during the fabrication of devices. PL peak wavelength shifts of 70 nm for the InGaAlAs bulk waveguides and 73 nm for the InGaAlAs MQW waveguides were obtained with a small mask stripe width varying from 0 to 40 gm, and were interpreted in considering both the migration effect from the masked region (MMR) and the lateral vapor diffusion effect (LVD). The quality of the selectively grown InGaAlAs MQW waveguides was confirmed by the PL peak intensity and the PL FWHM. Using the narrow stripe selectively grown InGaAlAs MQW waveguides, then the buried heterostructure (BH) lasers were fabricated by a developed unselective regrowth method, instead of conventional selective regrowth. The InGaAlAs MQW BH lasers exhibit good performance characteristics, with a high internal differential quantum efficiency of about 85% and an internal loss of 6.7 cm(-1).

用改进的ART-2网络建立移动机器人环境模型的研究

本文应用自适应共振理论中ART-2神经网络进行移动机器人环境障碍模式识别。ART-2神经网络在处理单方向渐变的模式输入时具有模式漂移的特点，机器人在静态环境中运动依赖这种特点，但在动态环境中模式漂移的特点却会对机器人的安全造成威胁。为此，设计了一种改进的ART-2神经网络，使得移动机器人同时适应在静态和动态环境中安全运动。

Tactile Sensing for Mechatronics: A State of the Art Survey

Lee M.H. and Nicholls H.R., Tactile Sensing for Mechatronics: A State of the Art Survey, Mechatronics, 9, Jan 1999, pp1-31.

ART: An ontology based tool for the translation of papers into Semantic Web format

To be presented at SIG/ISMB07 ontology workshop: http://bio-ontologies.org.uk/index.php To be published in BMC Bioinformatics. Sponsorship: JISC

From Art to Avant Garde? Television, Formalism and the Arts Documentary in 1960's Britain

Sexton, J. (2008). From Art to Avant Garde? Television, Formalism and the Arts Documentary in 1960's Britain. In L. Mulvey and J. Sexton (Eds.), Experimental British Television (pp.89-105). Manchester: Manchester University Press. RAE2008

Bring ART into the ACT

ACT is compared with a particular type of connectionist model that cannot handle symbols and use non-biological operations that cannot learn in real time. This focus continues an unfortunate trend of straw man "debates" in cognitive science. Adaptive Resonance Theory, or ART, neural models of cognition can handle both symbols and sub-symbolic representations, and meets the Newell criteria at least as well as these models.

Biased ART: A Neural Architecture that Shifts Attention Toward Previously Disregarded Features Following an Incorrect Prediction

Memories in Adaptive Resonance Theory (ART) networks are based on matched patterns that focus attention on those portions of bottom-up inputs that match active top-down expectations. While this learning strategy has proved successful for both brain models and applications, computational examples show that attention to early critical features may later distort memory representations during online fast learning. For supervised learning, biased ARTMAP (bARTMAP) solves the problem of over-emphasis on early critical features by directing attention away from previously attended features after the system makes a predictive error. Small-scale, hand-computed analog and binary examples illustrate key model dynamics. Twodimensional simulation examples demonstrate the evolution of bARTMAP memories as they are learned online. Benchmark simulations show that featural biasing also improves performance on large-scale examples. One example, which predicts movie genres and is based, in part, on the Netflix Prize database, was developed for this project. Both first principles and consistent performance improvements on all simulation studies suggest that featural biasing should be incorporated by default in all ARTMAP systems. Benchmark datasets and bARTMAP code are available from the CNS Technology Lab Website: http://techlab.bu.edu/bART/.

On the Relationship Between the Generalized Equality Classifier and ART 2 Neural Networks

In this paper, we introduce the Generalized Equality Classifier (GEC) for use as an unsupervised clustering algorithm in categorizing analog data. GEC is based on a formal definition of inexact equality originally developed for voting in fault tolerant software applications. GEC is defined using a metric space framework. The only parameter in GEC is a scalar threshold which defines the approximate equality of two patterns. Here, we compare the characteristics of GEC to the ART2-A algorithm (Carpenter, Grossberg, and Rosen, 1991). In particular, we show that GEC with the Hamming distance performs the same optimization as ART2. Moreover, GEC has lower computational requirements than AR12 on serial machines.

ART-EMAP: A Neural Network Architecture for Learning and Prediction by Evidence Accumulation

This paper introduces ART-EMAP, a neural architecture that uses spatial and temporal evidence accumulation to extend the capabilities of fuzzy ARTMAP. ART-EMAP combines supervised and unsupervised learning and a medium-term memory process to accomplish stable pattern category recognition in a noisy input environment. The ART-EMAP system features (i) distributed pattern registration at a view category field; (ii) a decision criterion for mapping between view and object categories which can delay categorization of ambiguous objects and trigger an evidence accumulation process when faced with a low confidence prediction; (iii) a process that accumulates evidence at a medium-term memory (MTM) field; and (iv) an unsupervised learning algorithm to fine-tune performance after a limited initial period of supervised network training. ART-EMAP dynamics are illustrated with a benchmark simulation example. Applications include 3-D object recognition from a series of ambiguous 2-D views.

Sequential Memory with ART: A Self-Organizing Network Capable of Learning Sequences of Patterns

A model which extends the adaptive resonance theory model to sequential memory is presented. This new model learns sequences of events and recalls a sequence when presented with parts of the sequence. A sequence can have repeated events and different sequences can share events. The ART model is modified by creating interconnected sublayers within ART's F2 layer. Nodes within F2 learn temporal patterns by forming recency gradients within LTM. Versions of the ART model like ART I, ART 2, and fuzzy ART can be used.

ART-EMAP: A Neural Network Architecture for Object Recognition by Evidence Accumulation

A new neural network architecture is introduced for the recognition of pattern classes after supervised and unsupervised learning. Applications include spatio-temporal image understanding and prediction and 3-D object recognition from a series of ambiguous 2-D views. The architecture, called ART-EMAP, achieves a synthesis of adaptive resonance theory (ART) and spatial and temporal evidence integration for dynamic predictive mapping (EMAP). ART-EMAP extends the capabilities of fuzzy ARTMAP in four incremental stages. Stage 1 introduces distributed pattern representation at a view category field. Stage 2 adds a decision criterion to the mapping between view and object categories, delaying identification of ambiguous objects when faced with a low confidence prediction. Stage 3 augments the system with a field where evidence accumulates in medium-term memory (MTM). Stage 4 adds an unsupervised learning process to fine-tune performance after the limited initial period of supervised network training. Each ART-EMAP stage is illustrated with a benchmark simulation example, using both noisy and noise-free data. A concluding set of simulations demonstrate ART-EMAP performance on a difficult 3-D object recognition problem.