Hardware and software architecture for power quality analysis

This paper presents a novel concept for power quality hardware and software architecture. Software tools related to detection, classification and characterization of power quality events can be integrated into a Distribution Control Center in which the interconnection between different subsystems is being carried out through the Internet allowing flexibility to the system. This system is implemented using wavelet analysis applied in all proposed algorithms. Different power quality events are taken as examples to illustrate the capabilities of the proposed method. The obtained results reveal that the proposed architecture is feasible to be implemented as an integral part of a control center system.The proposed system has the property of fast and accurate detection and classification of any power quality disturbance event and introduces a new PQ index determination that allows characterizing any type of disturbance including the non-periodic signals.     

Synthesis and applications of heterobifunctional poly(ethylene oxide) oligomers

Poly(ethylene oxide) (PEO) oligomers are employed extensively in pharmaceutical and biomedical arenas mainly due to their excellent physical and biological properties, including solubility in water and organic solvents, lack of toxicity, and absence of immunogenicity. PEO can be chemically modified and reacted with, or adsorbed onto, other molecules and surfaces. Sophisticated applications for PEO have increased the demand for PEO oligomers with tailored functionalities, and heterobifunctional PEOs are often needed. This review discusses the synthesis and applications of heterobifunctional PEO oligomers possessing amine, carboxylate, thiol, and maleimide functional groups.     

Target differentiation with simple infrared sensors using statistical pattern recognition techniques

This study compares the performances of various statistical pattern recognition techniques for the differentiation of commonly encountered features in indoor environments, possibly with different surface properties, using simple infrared (IR) sensors. The intensity measurements obtained from such sensors are highly dependent on the location, geometry, and surface properties of the reflecting feature in a way that cannot be represented by a simple analytical relationship, therefore complicating the differentiation process. We construct feature vectors based on the parameters of angular IR intensity scans from different targets to determine their geometry and/or surface type. Mixture of normals classifier with three components correctly differentiates three types of geometries with different surface properties, resulting in the best performance (100%) in geometry differentiation. Parametric differentiation correctly identifies six different surface types of the same planar geometry, resulting in the best surface differentiation rate (100%). However, this rate is not maintained with the inclusion of more surfaces. The results indicate that the geometrical properties of the targets are more distinctive than their surface properties, and surface recognition is the limiting factor in differentiation. The results demonstrate that simple IR sensors, when coupled with appropriate processing and recognition techniques, can be used to extract substantially more information than such devices are commonly employed for.     

A Non-Line-of-Sight mitigation localization algorithm for sensor networks using clustering analysis

Non-Line-of-Sight propagation of wireless signal has an impact on measured distances in range-based localization and will bias the final localization results. A new localization algorithm is proposed in this paper to mitigate Non-Line-of-Sight errors when there are more than enough anchor nodes deployed around the node to be located. This algorithm utilizes multi-round clustering analysis to filter the pre-located estimators which derive from all possible subsets of measured distances. In each round, the method density-based spatial clustering of applications with noise is adopted. Simulations show that the proposed algorithm can effectively improve localization accuracy not only when the measured distances with Non-Line-of-Sight error are minor but also under the condition that all of them suffer random Non-Line-of-Sight error.     

Telerobot-enabled HUB-CI model for collaborative lifecycle management of design and prototyping

The HUB-CI model is investigated in a telerobotic system in a client/server network to manage the lifecycle of engineering design and prototyping. The purpose of this platform is to support collaborative engineering design and proof of concept to enhance distributed team collaboration and resource utilization. The suggested platform is exemplified in two collaboration support tools and a physical prototyping platform. Structured Co-Insight Management is developed to support innovative idea exchanges and the consensus decision-making during the design process. Conflict/error detection management helps preventing conflicts and errors during the lifecycle of design and development. Physical collaboration over the network occurs when a team controls the telerobot operation during prototyping and testing in design cycles. A pilot system is implemented with a group project for the design of an electronic circuit (including both hardware and software designs). The functional assessment method is used to compare this platform to other collaborative design tools. The system presented offers unique qualitative advantages as an integrated collaboration support system.