We report a novel method for the synthesis of a self-reducible (thermally reducible without a reducing atmosphere) and alcohol-soluble copper-based metal-organic decomposition (MOD) ink for printed electronics. Alcohol-solvent-based conductive inks are necessary for commercial printing processes such as reverse offset printing. We selected copper(II) formate as a precursor and alkanolamine (2-amino-2-methyl-1-propanol) as a ligand to make an alcohol-solvent-based conductive ink and to assist in the reduction reaction of copper(II) formate. In addition, a co-complexing agent (octylamine) and a sintering helper (hexanoic acid) were introduced to improve the metallic copper film. The specific resistivity of copper-based MOD ink (Cuf-AMP-OH ink) after heat treatment at 350 °C is 9.46 μΩ·cm, which is 5.5 times higher than the specific resistivity of bulk copper. A simple stamping transfer was conducted to demonstrate the potential of our ink for commercial printing processes.

Conventional geomagnetic field-based indoor positioning and localization techniques determine the user's position by comparing the database with the geomagnetic field strength collected by the user. However, the magnetic field strength collected from various devices varies significantly. So, the greater the difference between the geomagnetic field strength stored in the database and user collected geomagnetic field strength is, the lower the degree of location accuracy will be. The diversity of smartphone makes it impossible to develop a single database which can work with all the smartphones in the same fashion. Intending to solve these problems, this paper proposes the use of geomagnetic field patterns called MP (Magnetic Pattern) with CNN (Convolutional Neural Networks) to perform indoor localization. The database is constructed using the MP that occurs at the points of measurement while the location is calculated using CNN which matches the user collected MP with the database. A voting mechanism is contrived to combine the predictions from several CNNs and the user's position is finally estimated. To evaluate the performance of the proposed technique, Samsung Galaxy S8 and LG G6 are used in two buildings with different experimental environments and path geometry. The proposed approach is tested by two male and two female users for analyzing the impact of user heights. Experiment results show promising results; furthermore, the comparison analysis with other magnetic indoor localization approaches demonstrate that the proposed approach outperforms them.INDEX TERMS Indoor localization, convolutional neural networks, magnetic field data, pedestrian dead reckoning, deep learning.

Abstract. In this paper we obtain a closed form expression of the zeta function Z(X Γ , u) of a finite quotient X Γ of the Bruhat-Tits building of PGL 3 over a nonarchimedean local field F by a discrete cocompact torsion-free subgroup Γ of PGL 3 . Analogous to a graph zeta function, Z(X Γ , u) is a rational function with two different expressions and it satisfies the Riemann hypothesis if and only if X Γ is a Ramanujan complex.

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To achieve high-fidelity operations on a large-scale quantum computer, the parameters of the physical system must be efficiently characterized with high accuracy. For trapped ions, the entanglement between qubits are mediated by the motional modes of the ion chain, and thus characterizing the motional-mode parameters becomes essential. In this paper, we develop and explore physical models that accurately predict both magnitude and sign of the Lamb-Dicke parameters when the modes are probed in parallel. We further devise an advanced characterization protocol that shortens the characterization time by more than an order of magnitude, when compared to that of the conventional method that only uses mode spectroscopy. We discuss potential ramifications of our results to the development of a scalable trapped-ion quantum computer, viewed through the lens of system-level resource trade offs.