Preamble design using embedded signalling for OFDM broadcast systems based on reduced-complexity distance detection

The second generation digital terrestrial television broadcasting standard (DVB-T2) adopts the so-called P1 symbol as the preamble for initial synchronization. The P1 symbol also carries a number of basic transmission parameters, including the fast Fourier transform size and the single-input/single-output as well as multiple-input/single-output mode, in order to appropriately configure the receiver for carrying out the subsequent processing. In this contribution, an improved preamble design is proposed, where a pair of training sequences is inserted in the frequency domain and their distance is used for transmission parameter signalling. At the receiver, only a low-complexity correlator is required for the detection of the signalling. Both the coarse carrier frequency offset and the signalling can be simultaneously estimated by detecting the above-mentioned correlation. Compared to the standardised P1 symbol, the proposed preamble design significantly reduces the complexity of the receiver while retaining high robustness in frequency-selective fading channels. Furthermore, we demonstrate that the proposed preamble design achieves a better signalling performance than the standardised P1 symbol, despite reducing the numbers of multiplications and additions by about 40% and 20%, respectively

Potential destination discovery for low predictability individuals based on knowledge graph

Travelers may travel to locations they have never visited, which we call potential destinations of them. Especially under a very limited observation, travelers tend to show random movement patterns and usually have a large number of potential destinations, which make them difficult to handle for mobility prediction (e.g., destination prediction). In this paper, we develop a new knowledge graph-based framework (PDPFKG) for potential destination discovery of low predictability travelers by considering trip association relationships between them. We first construct a trip knowledge graph (TKG) to model the trip scenario by entities (e.g., travelers, destinations and time information) and their relationships, in which we introduce the concept of private relationship for complexity reduction. Then a modified knowledge graph embedding algorithm is implemented to optimize the overall graph representation. Based on the trip knowledge graph embedding model (TKGEM), the possible ranking of individuals' unobserved destinations to be chosen in the future can be obtained by calculating triples' distance. Empirically. PDPFKG is tested using an anonymous vehicular dataset from 138 intersections equipped with video-based vehicle detection systems in Xuancheng city, China. The results show that (i) the proposed method significantly outperforms baseline methods, and (ii) the results show strong consistency with traveler behavior in choosing potential destinations. Finally, we provide a comprehensive discussion of the innovative points of the methodology

Traffic Impact Analysis of Urban Intersections with Comprehensive Waiting Area on Urban Intersection based on PARAMICS

AbstractTo improve the traffic operation and take advantage of the space resource, left-turn vehicles waiting area and straight vehicles waiting area are adopted separately at intersection at home and abroad, but there is not much research for using left-turn and straight waiting area together at the same intersection. Research using simulation software PARAMICS combining with programming API to simulate a specific comprehensive waiting area in Guangzhou city under nine different traffic volumes for three conditions: following control strategy, underutilizing control strategy and without control strategy. By evaluating the following four indexes: link delay, queue length, link average speed and passing vehicles, the simulation results indicate that the improving effect of following control strategy is superior to the underutilizing control strategy. Implementing comprehensive waiting area and in conjunction with the following control strategy can improve traffic operation when traffic volume is larger than the surveyed situation volume while the improvement is more effectively when traffic volume continues to increase. Setting comprehensive waiting area cannot improve intersection traffic operation but will worsen the traffic operation when traffic volume is less than the surveyed situation volume

An Assessment of Anthropogenic CO_2 Emissions by Satellite-Based Observations in China

Carbon dioxide (CO_2) is the most important anthropogenic greenhouse gas and its concentration in atmosphere has been increasing rapidly due to the increase of anthropogenic CO_2 emissions. Quantifying anthropogenic CO_2 emissions is essential to evaluate the measures for mitigating climate change. Satellite-based measurements of greenhouse gases greatly advance the way of monitoring atmospheric CO2 concentration. In this study, we propose an approach for estimating anthropogenic CO_2 emissions by an artificial neural network using column-average dry air mole fraction of CO_2 (XCO_2) derived from observations of Greenhouse gases Observing SATellite (GOSAT) in China. First, we use annual XCO_2 anomalies (dXCO_2) derived from XCO_2 and anthropogenic emission data during 2010–2014 as the training dataset to build a General Regression Neural Network (GRNN) model. Second, applying the built model to annual dXCO_2 in 2015, we estimate the corresponding emission and verify them using ODIAC emission. As a results, the estimated emissions significantly demonstrate positive correlation with that of ODIAC CO_2 emissions especially in the areas with high anthropogenic CO_2 emissions. Our results indicate that XCO_2 data from satellite observations can be applied in estimating anthropogenic CO_2 emissions at regional scale by the machine learning. This developed method can estimate carbon emission inventory in a data-driven way. In particular, it is expected that the estimation accuracy can be further improved when combined with other data sources, related CO_2 uptake and emissions, from satellite observations

Regional uncertainty of GOSAT XCO_2 retrievals in China: quantification and attribution

The regional uncertainty of the column-averaged dry air mole fraction of CO_2 (XCO_2) retrieved using different algorithms from the Greenhouse gases Observing SATellite (GOSAT) and its attribution are still not well understood. This paper investigates the regional performance of XCO_2 within a latitude band of 37–42° N segmented into 8 cells in a grid of 5° from west to east (80–120° E) in China, where typical land surface types and geographic conditions exist. The former includes desert, grassland and built-up areas mixed with cropland; and the latter includes anthropogenic emissions that change from small to large from west to east, including those from the megacity of Beijing. For these specific cells, we evaluate the regional uncertainty of GOSAT XCO_2 retrievals by quantifying and attributing the consistency of XCO_2 retrievals from four algorithms (ACOS, NIES, OCFP and SRFP) by intercomparison. These retrievals are then specifically compared with simulated XCO_2 from the high-resolution nested model in East Asia of the Goddard Earth Observing System 3-D chemical transport model (GEOS-Chem). We also introduce the anthropogenic CO_2 emissions data generated from the investigation of surface emitting point sources that was conducted by the Ministry of Environmental Protection of China to GEOS-Chem simulations of XCO_2 over the Chinese mainland. The results indicate that (1) regionally, the four algorithms demonstrate smaller absolute biases of 0.7–1.1 ppm in eastern cells, which are covered by built-up areas mixed with cropland with intensive anthropogenic emissions, than those in the western desert cells (1.0–1.6 ppm) with a high-brightness surface from the pairwise comparison results of XCO_2 retrievals. (2) Compared with XCO_2 simulated by GEOS-Chem (GEOS-XCO_2), the XCO_2 values from ACOS and SRFP have better agreement, while values from OCFP are the least consistent with GEOS-XCO_2. (3) Viewing attributions of XCO_2 in the spatio-temporal pattern, ACOS and SRFP demonstrate similar patterns, while OCFP is largely different from the others. In conclusion, the discrepancy in the four algorithms is the smallest in eastern cells in the study area, where the megacity of Beijing is located and where there are strong anthropogenic CO_2 emissions, which implies that XCO_2 from satellite observations could be reliably applied in the assessment of atmospheric CO_2 enhancements induced by anthropogenic CO_2 emissions. The large inconsistency among the four algorithms presented in western deserts which displays a high albedo and dust aerosols, moreover, demonstrates that further improvement is still necessary in such regions, even though many algorithms have endeavored to minimize the effects of aerosols scattering and surface albedo

Simultaneous Incomplete Traffic Data Imputation and Similarity Pattern Discovery with Bayesian Nonparametric Tensor Decomposition

A crucial task in traffic data analysis is similarity pattern discovery, which is of great importance to urban mobility understanding and traffic management. Recently, a wide range of methods for similarities discovery have been proposed and the basic assumption of them is that traffic data is complete. However, missing data problem is inevitable in traffic data collection process due to a variety of reasons. In this paper, we propose the Bayesian nonparametric tensor decomposition (BNPTD) to achieve incomplete traffic data imputation and similarity pattern discovery simultaneously. BNPTD is a hierarchical probabilistic model, which is comprised of Bayesian tensor decomposition and Dirichlet process mixture model. Furthermore, we develop an efficient variational inference algorithm to learn the model. Extensive experiments were conducted on a smart card dataset collected in Guangzhou, China, demonstrating the effectiveness of our methods. It should be noted that the proposed BNPTD is universal and can also be applied to other spatiotemporal traffic data

Optimal number and locations of automatic vehicle identification sensors considering link travel time estimation

Abstract The problem of optimally locating Automatic Vehicle Identification (AVI) sensors on a traffic network for travel time estimation has been a topic of growing interests in recent years. Even though great progresses have been made on AVI sensor deployment for path‐level travel time estimation, very few contributions exist in the literatures that address the AVI sensor deployment for link‐level travel time estimation on an urban network. In this paper, considering the link travel time estimation, two deployment sub‐problems are addressed: (1) where to deploy a certain number of AVI sensors? (2) What is a cost‐effective number of AVI sensors to deploy? To address the first problem, a potential game of sensors is developed to find their optimal locations which maximize the objective function that consists of estimation coverage and estimation accuracy. Then, based on the optimal locations, an incremental search method is proposed to find the optimal number of sensors considering the cost. The case in Shanghai shows the proposed game‐theoretic method is superior to other two heuristic algorithms. Moreover, compared to the real‐world sensor locations, the optimally redeployed locations improve both the estimation coverage and estimation accuracy. The case in Xuancheng City validates the proposed incremental search uses less computations to find an optimal number that close to the global optimal number solved from the brute‐force search