Global positioning system (GPS) technology is well suited for attitude determination. However, in land vehicle application, low-cost single frequency GPS receivers which have low measurement quality are often used, and external factors such as multipath and low satellite visibility in the densely built-up urban environment further degrade the quality of the GPS measurements. Due to the low-quality receivers used and the challenging urban environment, the success rate of the single epoch ambiguity resolution for dynamic attitude determination is usually quite low. In this paper, a micro-electro-mechanical system (MEMS)—inertial navigation system (INS)-aided ambiguity resolution method is proposed to improve the GPS attitude determination performance, which is particularly suitable for land vehicle attitude determination. First, the INS calculated baseline vector is augmented with the GPS carrier phase and code measurements. This improves the ambiguity dilution of precision (ADOP), resulting in better quality of the unconstrained float solution. Second, the undesirable float solutions caused by large measurement errors are further filtered and replaced using the INS-aided ambiguity function method (AFM). The fixed solutions are then obtained by the constrained least squares ambiguity decorrelation (CLAMBDA) algorithm. Finally, the GPS/MEMS-INS integration is realized by the use of a Kalman filter. Theoretical analysis of the ADOP is given and experimental results demonstrate that our proposed method can significantly improve the quality of the float ambiguity solution, leading to high success rate and better accuracy of attitude determination.

Band selection has become an effective method to reduce hyperspectral dimensionality. In this letter, an information-assisted density peak index (IaDPI) is proposed to prioritize the bands. Based on a clustering method by finding density peaks, IaDPI introduces the intraband information entropy into the local density and intercluster distance to ensure cluster centers with a high quality. Also, the band distance is integrated with channel proximity to control the compactness of local density. Owing to the intraband entropy and the interband weighted dissimilarity, the selected band set with top-ranked IaDPI scores can hold high local density, clear global distinction, and good informative quality. Experimental results on real hyperspectral data indicate the advantages of the proposed IaDPI in good selection quality, robust noise immunity, and high classification accuracy.

The adatom arrays on surfaces offer an ideal playground to explore the mechanisms of chemical bonding via changes in the local electronic tunneling spectra. While this information is readily available in hyperspectral scanning tunneling spectroscopy data, its analysis has been considerably impeded by a lack of suitable analytical tools. Here we develop a machine learning based workflow combining supervised feature identification in the spatial domain and unsupervised clustering in the energy domain to reveal the details of structure-dependent changes of the electronic structure in adatom arrays on the Co₃Sn₂S₂ cleaved surface. This approach, in combination with first-principles calculations, provides insight for using artificial neural networks to detect adatoms and classifies each based on their local neighborhood comprised of other adatoms. These structurally classified adatoms are further spectrally deconvolved. The unexpected inhomogeneity of electronic structures among adatoms in similar configurations is unveiled using this method, suggesting there is not a single atomic species of adatoms, but rather multiple types of adatoms on the Co₃Sn₂S₂ surface. This is further supported by a slight contrast difference in the images (or slight size variation) of the topography of the adatoms.

The aeronautical spectrum band for air-to-ground (A/G) communications is severely underutilized, known less than 12.5%, yet anticipating scarcity in the near future because of the explosive growth in aeronautical service data. Air-to-air (A/A) communications allow aircraft in close proximity to communicate with each other directly without ground stations or satellites, and are regarded as an effective complement for A/G communications in terms of the spectrum utilization efficiency. In this paper, to enable such spectral efficiency that A/A links reuse the spectrum of A/G communications, we propose a cognitive solution that A/A communications are regarded as secondary users which sense idle primary A/G communication channels and transmit data on these channels wherever possible, which brings two major issues: spectrum sensing and sharing between A/A and A/G communications. A coalitional manner is devised that some A/A links are grouping together to perform cooperative spectrum sensing and sharing while an interference graph is used to address the interference that arises from spectrum sharing within a group. We then formulate the cooperative spectrum sensing and sharing into a coalitional graph game played by all the A/A links involved. Finally, a distributed coalitional graph game algorithm is developed to maximize the utility so as to optimize the schedule of the A/A transmissions on the available A/G channels. Extensive simulations show that the achievable sum rate approaches to the results of the optimal exhausted searching with a relatively low complexity.