Architectural Methodology Based on Intentional Configuration of Behaviors

Intelligence has been an object of study for a long time. Different architectures try to capture and reproduce these aspects into artificial systems (or agents), but there is still no agreement on how to integrate them into a general framework. With this objective in mind, we propose an architectural methodology based on the idea of intentional configuration of behaviors. Behavior‐producing modules are used as basic control components that are selected and modified dynamically according to the intentions of the agent. These intentions are influenced by the situation perceived, knowledge about the world, and internal variables that monitor the state of the agent. The architectural methodology preserves the emergence of functionality associated with the behavior‐based paradigm in the more abstract levels involved in configuring the behaviors. Validation of this architecture is done using a simulated world for mobile robots, in which the agent must deal with various goals such as managing its energy and its well‐being, finding targets, and acquiring knowledge about its environment. Fuzzy logic, a topologic map learning algorithm, and activation variables with a propagation mechanism are used to implement the architecture for this agent.     

Performance evaluation of priority CSMA-CA mechanism on ISA100.11a wireless network

The International Society of Automation recently released ISA100.11a as an open standard for reliable wireless networks for industrial automation. ISA100.11a uses the TDMA scheme in the medium access layer to provide deterministic services. However, ISA100.11a adopts the CSMA-CA mechanism with priorities for retransmission from failure on dedicated links, sporadic data, and network configuration.This paper evaluates ISA100.11a CSMA-CA by simulation, considering the effects of backoff procedures and priority settings to probability of collision and successful use of slots. It's demonstrated that a high number of priority classes enable better network utilization resulting in less number of packets exceeding their lifetime.     

A Knowledge Discovery System with Support for Model Selection and Visualization

The process of knowledge discovery in databases consists of several steps that are iterative and interactive. In each application, to go through this process the user has to exploit different algorithms and their settings that usually yield multiple models. Model selection, that is, the selection of appropriate models or algorithms to achieve such models, requires meta-knowledge of algorithm/model and model performance metrics. Therefore, model selection is usually a difficult task for the user. We believe that simplifying the process of model selection for the user is crucial to the success of real-life knowledge discovery activities. As opposed to most related work that aims to automate model selection, in our view model selection is a semiautomatic process, requiring an effective collaboration between the user and the discovery system. For such a collaboration, our solution is to give the user the ability to try various alternatives and to compare competing models quantitatively by performance metrics, and qualitatively by effective visualization. This paper presents our research on model selection and visualization in the development of a knowledge discovery system called D2MS. The paper addresses the motivation of model selection in knowledge discovery and related work, gives an overview of D2MS, and describes its solution to model selection and visualization. It then presents the usefulness of D2MS model selection in two case studies of discovering medical knowledge in hospital data—on meningitis and stomach cancer—using three data mining methods of decision trees, conceptual clustering, and rule induction.     

Enhanced Solar‐to‐Hydrogen Generation with Broadband Epsilon‐Near‐Zero Nanostructured Photocatalysts

The direct conversion of solar energy into fuels or feedstock is an attractive approach to address increasing demand of renewable energy sources. Photocatalytic systems relying on the direct photoexcitation of metals have been explored to this end, a strategy that exploits the decay of plasmonic resonances into hot carriers. An efficient hot carrier generation and collection requires, ideally, their generation to be enclosed within few tens of nanometers at the metal interface, but it is challenging to achieve this across the broadband solar spectrum. Here the authors demonstrate a new photocatalyst for hydrogen evolution based on metal epsilon‐near‐zero metamaterials. The authors have designed these to achieve broadband strong light confinement at the metal interface across the entire solar spectrum. Using electron energy loss spectroscopy, the authors prove that hot carriers are generated in a broadband fashion within 10 nm in this system. The resulting photocatalyst achieves a hydrogen production rate of 9.5 µmol h^?1 cm^?2 that exceeds, by a factor of 3.2, that of the best previously reported plasmonic‐based photocatalysts for the dissociation of H₂ with 50 h stable operation.     

FPGA Implementation of a Power Amplifier Linearizer for an ETSI-SDR OFDM Transmitter

Most satellite digital radio （SDR） systems use orthogonal frequency-division multiplexing （OFDM） transmission, which means that variable envelope signals are distorted by the RF power amplifier （PA）. It is customary to back off the input power to the PA to avoid the PA nonlinear region of operation. In this way, linearity can be achieved at the cost of power efficiency. Another attractive option is to use a linearizer, which compensates for the nonlinear effects of the PA. In this paper, an OFDM transmitter conforming to European Telecommunications Standard Institute SDR Technical Specifications 2007-2008 was designed and implemented on a low-cost field-programmable gate array （FPGA） platform. A weakly nonlinear PA, operating in the L-band SDR frequency, was used for signal transmission. An adaptive linearizer was designed and implemented on the same FPGA device using digital predistortion to correct the undesired effects of the PA on the transmitted signal. Test results show that spectral distortion can be suppressed between 6-9 dB using the designed linearizer when the PA is driven close to its saturation region.     

A 2D quadrangular pyramid photoelectric autocollimator with extended angle measurement range

A photoelectric autocollimator with high accuracy and extended measurement range based on the quadrangular pyramid is proposed, and the corresponding algorithms are also deduced. A new image processing algorithm has been proposed to improve the accuracy, and the corresponding errors are also estimated, the error does not exceed half a pixel when the distance between the marks more than two radii. The experimental results have verified that the measurement range of the proposed two-dimensional (2D) quadrangular pyramid photoelectric autocollimator can be increased times than that of the flat mirror photoelectric autocollimator from 10′ to 15′. The accuracy is better than 1″ when the deflection is less than 15′.     

CdS Quantum Dots Encapsulated in Chiral Nematic Mesoporous Silica: New Iridescent and Luminescent Materials

Simultaneous integration of light emission and iridescence into a semiconducting photonic material is attractive for the design of new optical devices. Here, a straightforward, one‐pot approach for liquid crystal self‐assembly of semiconductor quantum dots into cellulose nanocrystal‐templated silica is developed. Through a careful balance of the intermolecular interactions between a lyotropic tetraalkoxysilane/cellulose nanocrystal dispersion and water‐soluble polyacrylic acid/mercaptopropionic acid‐stabilized CdS quantum dots, CdS/silica/nanocellulose composites that retain both chiral nematic order of the cellulose nanocrystals and emission of the quantum dots are successfully co‐assembled. Subsequent removal of the cellulose template and organic stabilizers in the composites by controlled calcination generates new freestanding iridescent, luminescent chiral nematic mesoporous silica‐encapsulated CdS films. The pores of these materials are accessible to analytes and, consequently, the CdS quantum dots undergo strong luminescence quenching when exposed to TNT solutions or vapor.     

Detection of cross-channel anomalies

The data deluge has created a great challenge for data mining applications wherein the rare topics of interest are often buried in the flood of major headlines. We identify and formulate a novel problem: cross-channel anomaly detection from multiple data channels. Cross-channel anomalies are common among the individual channel anomalies and are often portent of significant events. Central to this new problem is a development of theoretical foundation and methodology. Using the spectral approach, we propose a two-stage detection method: anomaly detection at a single-channel level, followed by the detection of cross-channel anomalies from the amalgamation of single-channel anomalies. We also derive the extension of the proposed detection method to an online settings, which automatically adapts to changes in the data over time at low computational complexity using incremental algorithms. Our mathematical analysis shows that our method is likely to reduce the false alarm rate by establishing theoretical results on the reduction of an impurity index. We demonstrate our method in two applications: document understanding with multiple text corpora and detection of repeated anomalies in large-scale video surveillance. The experimental results consistently demonstrate the superior performance of our method compared with related state-of-art methods, including the one-class SVM and principal component pursuit. In addition, our framework can be deployed in a decentralized manner, lending itself for large-scale data stream analysis.     

Regularized nonnegative shared subspace learning

Joint modeling of related data sources has the potential to improve various data mining tasks such as transfer learning, multitask clustering, information retrieval etc. However, diversity among various data sources might outweigh the advantages of the joint modeling, and thus may result in performance degradations. To this end, we propose a regularized shared subspace learning framework, which can exploit the mutual strengths of related data sources while being immune to the effects of the variabilities of each source. This is achieved by further imposing a mutual orthogonality constraint on the constituent subspaces which segregates the common patterns from the source specific patterns, and thus, avoids performance degradations. Our approach is rooted in nonnegative matrix factorization and extends it further to enable joint analysis of related data sources. Experiments performed using three real world data sets for both retrieval and clustering applications demonstrate the benefits of regularization and validate the effectiveness of the model. Our proposed solution provides a formal framework appropriate for jointly analyzing related data sources and therefore, it is applicable to a wider context in data mining.