Zeitschriftenaufsätze

In outdoor RF localization systems, particularly where line of sight can not be guaranteed or where multipath effects are severe, information about the terrain may improve the position estimate's performance. Given the difficulties in obtaining real data, a ray-tracing fingerprint is a viable option. Nevertheless, although presenting good simulation results, the performance of systems trained with simulated features only suffer degradation when employed to process real-life data. This work intends to improve the localization accuracy when using ray-tracing fingerprints and a few field data obtained from an adverse environment where a large number of measurements is not an option. We employ a machine learning (ML) algorithm to explore the multipath information. We selected algorithms random forest and gradient boosting; both considered efficient tools in the literature. In a strict simulation scenario (simulated data for training, validating, and testing), we obtained the same good results found in the literature (error around 2 m). In a real-world system (simulated data for training, real data for validating and testing), both ML algorithms resulted in a mean positioning error around 100 ,m. We have also obtained experimental results for noisy (artificially added Gaussian noise) and mismatched (with a null subset of) features. From the simulations carried out in this work, our study revealed that enhancing the ML model with a few real-world data improves localization’s overall performance. From the machine ML algorithms employed herein, we also observed that, under noisy conditions, the random forest algorithm achieved a slightly better result than the gradient boosting algorithm. However, they achieved similar results in a mismatch experiment. This work’s practical implication is that multipath information, once rejected in old localization techniques, now represents a significant source of information whenever we have prior knowledge to train the ML algorithm.

"Industrie 4.0" ist für kleine und mittelständische Unternehmen (KMU), die sich mit dem Fügen, Trennen und Beschichten auskennen, künftig von großer Bedeutung. Dabei geht es aber weniger darum, konkrete Produktions-, Fertigungs- oder Schweißprozesse zu beschreiben, sondern kritisch zu prüfen, wo in kleinen und mittelständischen Unternehmen Lücken hinsichtlich Industrie 4.0 bestehen und wie sich diese schließen lassen. Denn nur so können Unternehmen langfristig den bestehenden, digitalen Vorsprung für sich nutzen. Aus diesem Grund beschäftigt sich die Abreitsgruppe "Industrie 4.0" im DVS aktuell mit diesem Thema. Sie stößt die Diskussion über die Digitalisierung in der Schweißtechnik an und initiiert unter dem Titel "Vom Fortschritt profitieren: Industrie 4.0 in der Schweißtechnik" eine Serie von Fachbeiträgen in dieser Zeitschrift, die mit dem zweiten Teil in der vorliegenden Ausgabe fortgesetzt wird.

The R-Mode system is a terrestrial navigation system currently under development, which exploits existing means of medium frequency radio transmission. The positioning and timing performance depends on the estimation of the signals' phase offset, from which the ranging information is derived. For an analogous problem such as the single-tone phase estimation, the Cramér-Rao bound (CRB) describes the minimal achievable performance in the mean squared error sense. For R-Mode, the problem involves the estimation of the phase offset for a beat signal, which can be described as the difference of phase estimation for the two aiding carriers next to the signal. This estimates are not statistically independent for finite observation, as we show in this paper. The effect becomes stronger for short observation times, which are important for a near real time application. In this contribution, we are interested in phase offset estimation for the signal models relevant to R-Mode: a beat signal and a beat signal combined with an MSK signal. A closed-form lower CRB is proposed for the aforementioned signal models phase estimation, as well as a generalization of the bound for the phase-difference estimation. Based on this derivation, optimized bit sequences are shown to improve performance of the estimates. The validity of the proposal is verified based on a simulation setup. Measurements acquired during a measurement campaign serve to further justify the usefulness of the bound. Some possible applications of such a bound are R-Mode coverage prediction and the associated phase estimators' performance.

Transition metal oxides have a great potential in sodium-ion capacitors (SICs) due to their pronouncedly higher capacity and low cost. However, their poor conductivity and fragile structure hinder their development. Herein, core-shell-like nickel-cobalt oxysulfide (NCOS) nanowires are synthesized and demonstrated as an advanced SICs anode. The bimetallic oxysulfide with multiple cation valence can promote the sodium ion adsorption and redox reaction, massive defects enable accommodation of the volume change in the sodiation/desodiation process, meanwhile the core-shell-like structure provides abundant channels for fast transfer of sodium ions, thereby synergistically making the NCOS electrode exhibit a high reversible sodium ion storage capacity (1468.5 mAh g^-1 at 0.1 A g^-1) and an excellent cyclability (90.5% capacity retention after 1000 cycles). The in-situ X-ray diffraction analysis unravels the insertion and conversion mechanism for sodium storage in NCOS, and the enhanced capability of NCOS is further verified by the kinetic analysis and theoretical calculations. Finally, SICs consisting of the NCOS anode and a boron-nitrogen co-doped carbon nanotubes cathode deliver an energy density of 205.7 Wh kg^-1, a power density of 22.5 kW kg^-1, and an outstanding cycling lifespan. These results indicate an efficient strategy in designing a high-performance anode for sodium storage based on bimetallic dianion compounds.

Since the first realization of two-photon direct laser writing (DLW) in Maruo et al. (Opt Lett 22:132-134, 1997), the manufacturing using direct laser writing techniques spread out in many laboratories all over the world. Photosensitive materials with different material properties open a new field for micro- and nanofabrication. The achievable structuring resolution using this technique is reported to be sub-100 nm (Paz et al. in J. Laser Appl. 24:042004, 2012), while a smallest linewidth of 25 nm could be shown in Tan et al. (Appl Phys Lett 90:071106, 2007). In our approach, the combination of DLW with the nanopositioning and nanomeasuring machine NMM-1 offers an improvement of the technique from the engineering side regarding the ultra-precise positioning (Weidenfeller et al. in Adv Fabr Technol Micro/Nano Opt Photon XI 10544:105440E, 2018). One big benefit besides the high positioning resolution of 0.1 nm is offered by the positioning range of 25 mm × 25 mm × 5 mm (Jäger et al. in Technisches Messen 67:319-323, 2000; Manske et al. in Meas Sci Technol 18:520-527, 2007). Thus, a trans-scale fabrication without any stitching or combination of different positioning systems is necessary. The immense synergy between the highly precise positioning and the DLW is demonstrated by the realization of resist lines and trenches whose center-to-center distance undergoes the modified diffraction limit for two-photon processes. The precise positioning accuracy enables a defined distance between illuminated lines. Hence, with a comparable huge width of the trenches of 1.655 [my]m due to a low effective numerical aperture of 0.16, a resist line of 30 nm between two written trenches could be achieved. Although the interrelationships for achieving such narrow trenches have not yet been clarified, much smaller resist lines and trench widths are possible with this approach in the near future.