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.     

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.     

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.     

A 3–10 GHz Ultra Wideband Receiver LNA in 0.13 \mu m CMOS

A fully integrated low-power, low-complexity ultra wideband (UWB) 3–10 GHz receiver front-end in standard 130 nm CMOS technology is proposed for UWB radar sensing applications. The receiver front-end consists of a full UWB band low-noise amplifier and an on-chip diplexer. The on-chip diplexer has a 1 dB insertion loss and provides a \(-\) 30 dB isolation. The diplexer switch was co-designed with the receiver input matching network to optimize the power matching while simultaneously achieving good noise matching performance. The receiver low-noise amplifier provides a 3–10 GHz bandwidth input matching and a power gain of 17 dB. The overall receiver front-end consumes an average power of 13 mW. The core area of the transceiver circuit is 500 \(\mu \) m by 700 \(\mu \) m.     

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.     

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′.     

Photonic Hydrogels from Chiral Nematic Mesoporous Chitosan Nanofibril Assemblies

Iridescence in animals and plants often arises from structural coloration, which involves hierarchical organization of minerals and biopolymers over length scales of the visible spectrum, leading to diffraction of light. In this work, discarded crustacean shells that are not known for their structural colors are used to produce photonic nanostructures of large, freestanding chiral nematic mesoporous chitosan membranes with tunable iridescent color. Bioinspired by colorful nanostructures in nature, photonic hydrogels with Bouligand‐type organization are fabricated from the twisted mesoporous membranes, where the chitosan nanofibrils are a novel precursor for surface acetylation and are also a biotemplate for polymerizing methyl methacrylate. The colors of the hydrogels can be tailored by swelling as they show large volume changes in response to changes in solvent environment.     

Actor-oriented directional anycast routing in wireless sensor and actor networks with smart antennas

Current routing protocols in wireless sensor and actor networks (WSANs) shows a lack of unification for different traffic patterns because the communication for sensor to actor and that for actor to actor are designed separately. Such a design poses a challenge for interoperability between sensors and actors. With the presence of rich-resource actor nodes, we argue that to improve network lifetime, the problem transforms from reducing overall network energy consumption to reducing energy consumption of constrained sensor nodes. To reduce energy consumption of sensor nodes, especially in challenging environments with coverage holes/obstacles, we propose that actor nodes should share forwarding tasks with sensor nodes. To enable such a feature, efficient interoperability between sensors and actors is required, and thus a unified routing protocol for both sensors and actors is needed. This paper explores capabilities of directional transmission with smart antennas and rich-resource actors to design a novel unified actor-oriented directional anycast routing protocol (ADA) which supports arbitrary traffic in WSANs. The proposed routing protocol exploits actors as main routing anchors as much as possible because they have better energy and computing power compared to constraint sensor nodes. In addition, a directional anycast routing approach is also proposed to further reduce total delay and energy consumption of overall network. Through extensive experiments, we show that ADA outperforms state-of-the-art protocols in terms of packet delivery latency, network lifetime, and packet reliability. In addition, by offer fault tolerant features, ADA also performs well in challenging environments where coverage holes and obstacles are of concerns.