Existing urban boundaries are usually defined by government agencies for administrative, economic, and political purposes. However, it is not clear whether the boundaries truly reflect human interactions with urban space in intraand inter-regional activities. Defining urban boundaries that consider socioeconomic relationships and citizen commute patterns is important for many aspects of urban and regional planning. In this paper, we describe a method to delineate urban boundaries based upon human interactions with physical space inferred from social media. Specifically, we depicted the urban boundaries of Great Britain using a mobility network of Twitter user spatial interactions, which was inferred from over 69 million geo-located tweets. We define the nonadministrative anthropographic boundaries in a hierarchical fashion based on different physical movement ranges of users derived from the collective mobility patterns of Twitter users in Great Britain. The results of strongly connected urban regions in the form of communities in the network space yield geographically cohesive, non-overlapping urban areas, which provide a clear delineation of the non-administrative anthropographic urban boundaries of Great Britain. The method was applied to both national (Great Britain) and municipal scales (the London metropolis). While our results corresponded well with the administrative boundaries, many unexpected and interesting boundaries were identified. Importantly, as the depicted urban boundaries exhibited a strong instance of spatial proximity, we employed a gravity model to understand the distance decay effects in shaping the delineated urban boundaries. The model explains how geographical distances found in the mobility patterns affect the interaction intensity among different non-administrative anthropographic urban areas, which provides new insights into human spatial interactions with urban space.

A number of recent studies showed that digital footprints around built environments, such as geo-located tweets, are promising data sources for characterizing urban land use. However, challenges for achieving this purpose exist due to the volume and unstructured nature of geo-located social media. Previous studies focused on analyzing Twitter data collectively resulting in coarse resolution maps of urban land use. We argue that the complex spatial structure of a large collection of tweets, when viewed through the lens of individual-level human mobility patterns, can be simplified to a series of key locations for each user, which could be used to characterize urban land use at a higher spatial resolution. Contingent issues that could affect our approach, such as Twitter users’ biases and tendencies at locations where they tweet the most, were systematically investigated using 39 million geo-located Tweets and two independent datasets of the City of Chicago: 1) travel survey and 2) parcel-level land use map. Our results support that the majority of Twitter users show a preferential return, where their digital traces are clustered around a few key locations. However, we did not find a general relation among users between the ranks of locations for an individual—based on the density of tweets—and their land use types. On the contrary, temporal patterns of tweeting at key locations were found to be coherent among the majority of users and significantly associated with land use types of these locations. Furthermore, we used these temporal patterns to classify key locations into generic land use types with an overall classification accuracy of 0.78. The contribution of our research is twofold: a novel approach to resolving land use types at a higher resolution, and in-depth understanding of Twitter users’ location-related and temporal biases, promising to benefit human mobility and urban studies in general.

This proposed e-diary with its intuitive interface has overcome previous deficiencies in electronic diaries with most patients finding the format user-friendly. Electronic reports make analysis and interpretation by HP quicker and more accurate.

Models and observations show that the Arctic is experiencing the most rapid changes in global near-surface air temperature. We developed novel EASE-grid Level 3 (L3) land surface temperature (LST) products from Level 2 (L2) AATSR and MODIS data to provide weekly, monthly and annual LST means over the pan-Arctic region at various grid resolutions (1-25 km) for the past decade (2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010). In this paper, we provide: (1) a review of previous validation of MODIS/AATSR L2; (2) a description of the processing chain of L3 products; (3) an assessment of the 25 km products uncertainty, and; (4) a quantification of the bias introduced by over-representing clear-sky days in MODIS L3 products. In addition, we generated uncertainty maps by comparing L3 products with LST from passive microwave sensors (AMSR-E and SSM/I) and the North American Regional Reanalysis (NARR). Results show a close correspondence between MODIS and AATSR monthly products with a mean-difference (MD) of −1.1 K. Comparing L3 products with NARR indicates a close agreement in summer and a systematic bias in winter, which is entirely negative with respect to MODIS L3 (MD: −3.6, Min: −6.8, Max: −1 K). Comparing monthly averaged MODIS L3 to NARR clear-sky to quantify over-representing clear-sky days indicates a decrease of winter and an increase of summer difference compared to NARR all-sky. Finally, we provide suggestions to improve LST retrieval over Arctic regions.

Thermal remote sensing of soil moisture in vineyards is a challenge. The grass-covered soil, in addition to a standing grape canopy, create complex patterns of heating and cooling and increase the surface temperature variability between vine rows. In this study, we evaluate the strength of relationships between soil moisture, mechanical resistance and thermal inertia calculated from the drop of surface temperature during a clear sky night over a vineyard in the Niagara region. We utilized data from two sensors, an airborne thermal camera (height ≈ 500 m a.g.l.) and a handheld thermal gun (height ≈ 1 m a.g.l.), to explore the effects of different field of views and the high inter-row temperature variability. Spatial patterns of soil moisture correlated more with estimated thermal inertia than with surface temperature recorded at sunrise or sunset. Despite the coarse resolution of airborne thermal inertia images, it performed better than estimates from the handheld thermal gun. Between-row variation was further analyzed using a linear mixed-effects model. Despite the limited spatial variability of soil properties within a single vineyard, the magnitudes of the model coefficients for soil moisture and mechanical resistance are encouraging indicators of the utility of thermal inertia in vineyard management. OPEN ACCESSRemote Sens. 2013, 5 3730

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Hardware implementation of the proposed direction of arrival (DOA) estimation algorithms based on Cholesky and LDL decomposition is presented in this paper. The proposed algorithms are implemented for execution on a field programmable gate array (FPGA) as well as a PC (running LabVIEW) for the multiple non-coherent sources located in the far-field region of a uniform linear array (ULA). Prototype testbeds built using the national instruments (NI) universal software radio peripheral (USRP) software defined radio (SDR) platform and Xilinx Virtex-5 FPGA are originally constructed for the experimental validation of the proposed algorithms. The results from LabVIEW simulations and real-time hardware experiments demonstrate the effectiveness of the proposed algorithms. Specifically, the implementation of the proposed algorithms on a Xilinx Virtex-5 FPGA using the LabVIEW software clarifies their efficiency in terms of computation time and resource utilization, which make them suitable for the real-time practical applications. Moreover, the performance comparison with the QR decomposition-based DOA algorithms as well as similar FPGA-based implementations reported in the literature is conducted in terms of the estimation accuracy, computation speed, and FPGA resources consumed.

Non-orthogonal multiple access (NOMA) is the technique proposed for multiple access in the fifth generation (5G) cellular network. In NOMA, different users are allocated different power levels and are served using the same time/frequency resource blocks (RBs). The main challenges in existing NOMA systems are the limited channel feedback and the difficulty of merging it with advanced adaptive coding and modulation schemes. Unlike formerly proposed solutions, in this paper, we propose an effective channel estimation (CE) algorithm based on the long-short term memory (LSTM) neural network. The LSTM has the advantage of adapting dynamically to the behavior of the fluctuating channel state. On average, the use of LSTM results in a 10% lower outage probability and a 37% increase in the user sum rate as well as a maximal reduction in the bit error rate (BER) of 50% in comparison to the conventional NOMA system. Furthermore, we propose a novel power coefficient allocation algorithm based on binomial distribution and Pascal’s triangle. This algorithm is used to divide power among N users according to each user’s channel condition. In addition, we introduce adaptive code rates and rotated constellations with cyclic Q-delay in the quadri-phase shift keying (QPSK) and quadrature amplitude modulation (QAM) modulators. This modified modulation scheme overcomes channel fading effects and helps to restore the transmitted sequences with fewer errors. In addition to the initial LSTM stage, the added adaptive coding and modulation stages result in a 73% improvement in the BER in comparison to the conventional NOMA system.