We propose to combine a biomimetic navigation model which solves a simultaneous localization and mapping task with a biomimetic sonar mounted on a mobile robot to address two related questions. First, can robotic sonar sensing lead to intelligent interactions with complex environments? Second, can we model sonar based spatial orientation and the construction of spatial maps by bats? To address these questions we adapt the mapping module of RatSLAM, a previously published navigation system based on computational models of the rodent hippocampus. We analyze the performance of the proposed robotic implementation operating in the real world. We conclude that the biomimetic navigation model operating on the information from the biomimetic sonar allows an autonomous agent to map unmodified (office) environments efficiently and consistently. Furthermore, these results also show that successful navigation does not require the readings of the biomimetic sonar to be interpreted in terms of individual objects/landmarks in the environment. We argue that the system has applications in robotics as well as in the field of biology as a simple, first order, model for sonar based spatial orientation and map building.

Polymeric membranes are being studied for their potential use in post-combustion carbon capture on the premise that they could dramatically lower costs relative to mature technologies available today. Mixed matrix membranes (MMMs) are advanced materials formed by combining polymers with inorganic particles. Using metal–organic frameworks (MOFs) as the inorganic particles has been shown to improve selectivity and permeability over pure polymers. We have carried out high-throughput atomistic simulations on 112 888 real and hypothetical metal–organic framework structures in order to calculate their CO₂ permeabilities and CO₂/N₂ selectivities. The CO₂/H₂O sorption selectivity of 2 017 real MOFs was evaluated using the H₂O sorption data of Li et al. (S. Li, Y. G. Chung and R. Q. Snurr, Langmuir, 2016, 32, 10368–10376). Using experimentally measured polymer properties and the Maxwell model, we predicted the properties of all of the hypothetical mixed matrix membranes that could be made by combining the metal–organic frameworks with each of nine polymers, resulting in over one million possible MMMs. The predicted gas permeation of MMMs was compared to published gas permeation data in order to validate the methodology. We then carried out twelve individually optimized techno-economic evaluations of a three-stage membrane-based capture process. For each evaluation, capture process variables such as flow rate, capture fraction, pressure and temperature conditions were optimized and the resultant cost data were interpolated in order to assign cost based on membrane selectivity and permeability. This work makes a connection from atomistic simulation all the way to techno-economic evaluation for a membrane-based carbon capture process. We find that a large number of possible mixed matrix membranes are predicted to yield a cost of carbon capture less than $50 per tonne CO₂ removed, and a significant number of MOFs so identified have favorable CO₂/H₂O sorption selectivity.

Array beamforming techniques allow for the generation of 3-D spatial filters which can be used to localize objects in a large field of view (FOV) without the need for mechanical scanning. By combining broadband beamforming with a sparse, random array of receivers, we have constructed a low-cost, yet powerful, in-air sonar system, which is suited for a wide range of robotic applications. Experimental results in unmodified office environments show the performance of the sonar sensor. In particular, we document the sensor's capacity to produce 3-D location measurements in the presence of multiple highly overlapping echoes. We show how this capability makes possible the combination of a wide FOV with accurate 3-D localization, allowing the sensor to operate under real-time constraints in realistic environments. To demonstrate the use of this sensor, we describe an odometry application that estimates egomotion of a mobile robot using acoustic flow.

Density functional theory (DFT) calculations are used to characterize the interaction of mercury with copper, nickel, palladium, platinum, silver, and gold surfaces. Mercury binds relatively strongly to all the metal surfaces studied, with binding energies up to $\sim 1\mathrm{eV}$ for Pt and Pd. DFT calculations underestimate the energy of adsorption with respect to available experimental data. Plane-wave DFT results using the local density approximation and the Perdew-Wang 1991 and Perdew-Burke-Ernzerhof parametrizations of the generalized gradient approximation indicate that binding of mercury at hollow sites is preferred over binding at top or bridge sites. The interaction with mercury in order of increasing reactivity over the six metals studied is $\mathrm{Ag}<\mathrm{Au}<\mathrm{Cu}<\mathrm{Ni}<\mathrm{Pt}<\mathrm{Pd}$. Binding is stronger on the (001) faces of the metal surfaces, where mercury is situated in fourfold hollow sites as opposed to the threefold hollow sites on (111) faces. In general, mercury adsorption leads to decreases in the work function; adsorbate-induced work function changes are particularly dramatic on Pt.

In this paper, we introduce a comprehensive angle of arrival (AoA) estimation solution for the long range (LoRa) communication network. Termed the LoRa array (LoRay), the proposed system constitutes hardware and software solutions to estimate the AoA of the received signals in real life urban environments. The hardware solution is based on converting multiple individual software defined radios (SDR) into a single SDR that consists of multiple RF-channels. The proposed hardware is cost effective, flexible and generic. The software solution, on the other hand, utilizes the space alternating generalized expectation-maximization (SAGE) algorithm to estimate the AoA of highly correlated received signals. The proposed software exploits few samples of the received signal to estimate the AoA of the direct and reflected paths in an intensive multipath environment. The LoRay system has been validated in outdoor urban environments. The experimental results show that the proposed system provides stable and accurate AoA estimations for both the line-of-sight (LoS) and the non-line-of-sight (NLoS) conditions. The AoA of 80% of the received signals have been estimated within an estimation error below 2° and 10° for the LoS and the NLoS locations, respectively.