The specification of distributed sensing and control

Logical Sensor System Specification (LSS) has been introduced as a convenient means for specifying multi-sensor systems and their implementations. In this article we demonstrate how control issues can be handled in the context of LSS. In particular, the Logical Sensor Specification is extended to include a control mechanism which permits control information to (1) flow from more centralized processing to more peripheral processes, and (2) be generated locally in the logical sensor by means of a micro-expert system specific to the interface represented by the given logical sensor. Examples are given including a proposed scheme for controlling the Utah/MIT dextrous hand.     

An Efficient Method for the Olefination of Aldehydes with Ethyl Diazoacetate Using Iron (II) Phthalocyanine as Catalyst

A variety of aliphatic and aromatic aldehydes were smoothly converted to the corresponding olefins in excellent yields with ethyldiazoacetate using iron (II) phthalocyanine as catalyst in the presence of triphenylphosphine as reducing agent.     

Metal Acetylacetonates as Highly Efficient and Cost Effective Catalysts for the Synthesis of Cyclic Carbonates from CO<Subscript>2</Subscript> and Epoxides

Abstract  

Metal acetylacetonates were found to be efficient and cost effective catalysts for the formation of cyclic carbonates by cycloaddition of carbon dioxide with epoxides, providing high to excellent yields of the corresponding carbonates. Among the various catalysts such as acetylacetonates of Co, Ni, Cu, Zn, Fe, Cr and VO studied, Ni(acac)₂ was found to be promising catalyst for this reaction. The present methodology was found to be superior due to the easy accessibility and comparatively inexpensive nature of metal acetylacetonates than salen complexes.     

Learning to Perceive Objects for Autonomous Navigation

Current machine perception techniques that typically use segmentation followed by object recognition lack the required robustness to cope with the large variety of situations encountered in real-world navigation. Many existing techniques are brittle in the sense that even minor changes in the expected task environment (e.g., different lighting conditions, geometrical distortion, etc.) can severely degrade the performance of the system or even make it fail completely. In this paper we present a system that achieves robust performance by using local reinforcement learning to induce a highly adaptive mapping from input images to segmentation strategies for successful recognition. This is accomplished by using the confidence level of model matching as reinforcement to drive learning. Local reinforcement learning gives rises to better improvement in recognition performance. The system is verified through experiments on a large set of real images of traffic signs.     

Visual learning by coevolutionary feature synthesis.

In this paper, a novel genetically inspired visual learning method is proposed. Given the training raster images, this general approach induces a sophisticated feature-based recognition system. It employs the paradigm of cooperative coevolution to handle the computational difficulty of this task. To represent the feature extraction agents, the linear genetic programming is used. The paper describes the learning algorithm and provides a firm rationale for its design. Different architectures of recognition systems are considered that employ the proposed feature synthesis method. An extensive experimental evaluation on the demanding real-world task of object recognition in synthetic aperture radar (SAR) imagery shows the ability of the proposed approach to attain high recognition performance in different operating conditions.     

Room Temperature Adsorptive Removal of Thiophene over Zinc Oxide-Based Adsorbents

This study investigated the room temperature adsorptive removal of thiophene over zinc oxide adsorbents in the presence of hydrogen. The bulk zinc oxide was prepared by precipitation method and calcined at different temperatures in the range of 300-550 °C. Supported zinc oxide was prepared by co-precipitation of 30 wt.% ZnO with alumina and calcined at 550 °C. Properties of the adsorbents were determined by various characterization techniques such as surface area and pore volume analysis, XRD, FESEM, EDX and TPR. The desulfurization process was carried out in a down-flow packed bed reactor at room temperature (30 °C). The BET surface area of bulk zinc oxide adsorbents decreased with the increase in calcination temperature from 300 to 550 °C. The surface area of bulk zinc oxide adsorbents was 30.5 and 14.6 m²/g when calcined at 300 and 550 °C, respectively. The surface are of supported zinc oxide adsorbents was 177 m²/g. The highest average pore size was obtained for bulk ZnO calcined at 550 °C (45 nm) compared to that calcined at 300 °C (42 nm) and supported ZnO (27 nm). The XRD peaks corresponded to the hexagonal structure of zinc oxide. The removal of thiophene was most significant for bulk ZnO calcined at 550 °C. The higher removal efficiency for this adsorbent in spite of lower surface area may be attributed to its higher percentage of larger pores and higher average pore size.