Abstract

Abstract
Conventional wireless communication systems are typically designed assuming a single transmitter-receiver pair for each link. In Low-Power Wide-Area Networks (LP-WANs), this one-to-one design paradigm is often overly pessimistic in terms of link budget because client packets are frequently detected by multiple gateways (i.e. one-to-many). Prior work has shown massive improvement in performance when specialized hardware is used to coherently combine signals at the physical layer. In this paper, we explore the potential of using multiple receivers at the MAC and link layer where these performance gains are often neglected. We present an approach called Opportunistic Packet Recovery (OPR) that targets the most likely corrupt bits across a set of packets that suffered failed CRCs at multiple LoRa LP-WAN base-stations. We see that bit errors are often disjoint across receivers, which aids in collaborative error detection. OPR leverages this to provide increasing gain in error recovery as a function of the number of receiving gateways. Since LP-WAN networks can easily offload packet processing to the cloud, there is ample compute time per packet (order of seconds) to search for bit permutations that would restore packet integrity. Link layer corrections have the advantage of being immediately applicable to the millions of already deployed LP-WAN systems without additional hardware or expensive RF front-ends. We experimentally demonstrate that OPR can correct up to 72% of packets that would normally have failed, when they are captured by multiple gateways.

Abstract

Abstract
This paper presents a methodology for the optimal placement and sizing of reactive power compensation devices in a distribution system (DS) with distributed generation. Quasi-static time series is embedded in an optimization method based on a genetic algorithm to adequately represent the uncertainty introduced by solar photovoltaic generation and electricity demand and its effect on DS operation. From the analysis of a typical DS, the reactive power compensation rating power results in an increment of 24.9% when compared to the classical genetic algorithm model. However, the incorporation of quasi-static time series analysis entails an increase of 26.8% on the computational time required.

Abstract

Abstract
To study how emotions affect people in expressive writing, scientists require tools to aid them in their research. The researchers at M-BW use an Experiment Management System, called HandSpy to store and analyze the hand-written productions of participants. The input is stored as digital ink and then displayed on a web-based interface. To assist the project, HandSpy integrates with new sources of information to help researchers visualize the link between psychophysiological data and written input. The newly acquired data is synchronized with the existing burst-pause interval model and represented on the user interface of the platform together with the already existing information.