Accurate wireless timing synchronization has been an extremely important topic in wireless sensor networks, required in applications ranging from distributed beam forming to precision localization and navigation. However, it is very challenging to realize, in particular when the required accuracy should be better than the runtime between the nodes. This work presents, to our knowledge for the first time, an experimental timing synchronization scheme that achieves a timing accuracy better than 5-ns rms in a network with 4 nodes. The experimental hardware is built from commercially available components and based on software-defined ultra-wideband transceivers. The protocol for establishing the synchronization is based on our recently developed “blink” protocol that can scale from the small network demonstrated here to larger networks of hundreds or thousands of nodes.

MOTIVATION: Often during the analysis of biological data, it is of importance to interpret the correlation structure that exists between variables. Such correlations may reveal patterns of co-regulation that are indicative of biochemical pathways or common mechanisms of response to a related set of treatments. However, analyses of correlations are usually conducted by either subjective interpretation of the univariate covariance matrix or by applying multivariate modeling techniques, which do not take prior biological knowledge into account. Over-representation analysis (ORA) is a simple method for objectively deciding whether a set of variables of known or suspected biological relevance, such as a gene set or pathway, is more prevalent in a set of variables of interest than we expect by chance. However, ORA is usually applied to a set of variables differentiating a single experimental variable and does not take into account correlations. RESULTS: Over-representation of correlation analysis (ORCA) is a novel combination of ORA and correlation analysis that provides a means to test whether more associations exist between two specific groups of variables than expected by chance. The method is exemplified by application to drug sensitivity and microRNA expression data from a panel of cancer cell lines (NCI60). ORCA highlighted a previously reported correlation between sensitivity to alkylating anticancer agents and topoisomerase inhibitors. We also used this approach to validate microRNA clusters predicted by mRNA correlations. These observations suggest that ORCA has the potential to reveal novel insights from these data, which are not readily apparent using classical ORA. AVAILABILITY AND IMPLEMENTATION: The R code of the method is available at https://github.com/ORCABioinfo/ORCAcode.

A self-localized Ultra-Wide-Band (UWB) system that is suitable to navigate mobile robots in indoor environments is introduced. In impulse-based UWB systems, positional accuracy is inversely proportional to the signal bandwidth. In the work, a number of anchor nodes are located at fixed positions in an indoor environment transmitting synchronized 2 ns pulses with Differential Binary Phase Shift Keying (DBPSK) modulation. An UWB receiver mounted on a mobile robot utilizes Time Difference of Arrival (TDOA) between pairs of synchronized transmitting anchor nodes for localization. Self-localization implies that position estimation algorithms run locally on the mobile robot. A prototype non-coherent UWB system using off-the-shelf components is implemented where signal acquisition runs on a Field Programmable Gate Array (FPGA). Measurement results indicate sub-20 cm positional accuracy with Line Of Sight (LOS) and Non-Line of Sight (NLOS) conditions relative to fixed anchor nodes in a typical indoor environment.