Automatic 3D Building Detection and Modeling from Airborne LiDAR Point Clouds

Urban reconstruction, with an emphasis on man-made structure modeling, is an active research area with broad impact on several potential applications. Urban reconstruction combines photogrammetry, remote sensing, computer vision, and computer graphics. Even though there is a huge volume of work that has been done, many problems still remain unsolved. Automation is one of the key focus areas in this research. In this work, a fast, completely automated method to create 3D watertight building models from airborne LiDAR (Light Detection and Ranging) point clouds is presented. The developed method analyzes the scene content and produces multi-layer rooftops, with complex rigorous boundaries and vertical walls, that connect rooftops to the ground. The graph cuts algorithm is used to separate vegetative elements from the rest of the scene content, which is based on the local analysis about the properties of the local implicit surface patch. The ground terrain and building rooftop footprints are then extracted, utilizing the developed strategy, a two-step hierarchical Euclidean clustering. The method presented here adopts a divide-and-conquer scheme. Once the building footprints are segmented from the terrain and vegetative areas, the whole scene is divided into individual pendent processing units which represent potential points on the rooftop. For each individual building region, significant features on the rooftop are further detected using a specifically designed region-growing algorithm with surface smoothness constraints. The principal orientation of each building rooftop feature is calculated using a minimum bounding box fitting technique, and is used to guide the refinement of shapes and boundaries of the rooftop parts. Boundaries for all of these features are refined for the purpose of producing strict description. Once the description of the rooftops is achieved, polygonal mesh models are generated by creating surface patches with outlines defined by detected vertices to produce triangulated mesh models. These triangulated mesh models are suitable for many applications, such as 3D mapping, urban planning and augmented reality

Selective Combining for Hybrid Cooperative Networks

In this study, we consider the selective combining in hybrid cooperative networks (SCHCNs scheme) with one source node, one destination node and $N$ relay nodes. In the SCHCN scheme, each relay first adaptively chooses between amplify-and-forward protocol and decode-and-forward protocol on a per frame basis by examining the error-detecting code result, and $N_c$ ($1\leq N_c \leq N$) relays will be selected to forward their received signals to the destination. We first develop a signal-to-noise ratio (SNR) threshold-based frame error rate (FER) approximation model. Then, the theoretical FER expressions for the SCHCN scheme are derived by utilizing the proposed SNR threshold-based FER approximation model. The analytical FER expressions are validated through simulation results.Comment: 27 pages, 8 figures, IET Communications, 201

Golay Complementary Sequences Over the QAM Constellation

In this paper, we present new constructions for $M^{2}$ -QAM and $2M$ $Q$-$PAM$ Golay complementary sequences of length $2^n$ for integer $n$, where $M=2^{m}$ for integer $m$. New decision conditions are proposed to judge whether an offset pairs can be used to construct the Golay complementary sequences over constellation, and with the new decision conditions, we prove the conjecture 1 proposed by Ying Li~\cite{16}. We describe a new offset pairs and construct new $64$-$QAM$ Golay sequences based on this new offset pairs. We also study the $128$-$QAM$ Golay complementary sequences, and propose a new decision condition to judge whether the sequences are $128$-$QAM$ Golay complementary

New Methods to Construct Golay Complementary Sequences Over the QAMQAM Constellation

In this paper, based on binary Golay complementary sequences, we propose some methods to construct Golay complementary sequences of length $2^n$ for integer n, over the $M^2$-$QAM$ constellation and $2M$-$Q$-$PAM$ constellations, where $M=2^m$ for integer $m$. A method to judge whether a sequence constructed using the new general offset pairs over the $QAM$ constellation is Golay complementary sequence is proposed. Base on this judging rule, we can construct many new Golay complementary sequences. In particular, we study Golay complementary sequences over $16$-$QAM$ constellation and $64$-$QAM$ constellation,many new Golay complementary sequences over these constellations have been found

Prediction of World Crude Oil Price with the Method of Missing Data

As the fluctuation of oil price plays an important role in global political and economic situation, forecasting the price of oil is significant. In this paper, we analyze the data of the world crude oil price using ideas of treating with the missing data, i.e. we take the predictor as missing data and use the EM algorithm to establish time series model. We give the predictive values of weekly world crude oil price of January and February in 2011 using the data of 2009 and 2010. Meanwhile, we found that the method based on missing data is more effective than normal time series method by comparing the predictive value with reality data. In addition, this method is also applicable to the case that historical observations have missing data. Key words: World Crude Oil Price; Forecast; Missing Data; EM Algorithm; Time Serie

Mu opioid receptor mRNA overexpression predicts poor prognosis among 18 common solid cancers: A pan-cancer analysis

BackgroundOpioids are widely used for patients with solid tumors during surgery and for cancer pain relief. We conducted a pan-cancer genomic analysis to investigate the prognostic features of Mu opioid receptor (MOR) mRNA expression across 18 primary solid cancers.MethodsAll the data of cancer with MOR mRNA were retrieved from cBioPortal for Cancer Genomics. Logistic regression was used to determine the associations between MOR mRNA expression and clinicopathological features. Log-rank test and Cox regression was used for survival analysis. Subgroup analysis and propensity score matching were also carried out.Results7,274 patients, including 1,112 patients with positive MOR mRNA expression, were included for data analyses. Positive MOR mRNA expression was associated with more advanced stage of T (adjusted Odds ratio [OR], 1.176; 95% confidence interval [CI], 1.022-1.354; P=0.024), M (adjusted OR, 1.548; 95% CI, 1.095-2.189; P=0.013) except N (adjusted OR, 1.145; 95% CI, 0.975-1.346; P=0.101), and worse prognosis for overall survival (Hazard ratio [HR] 1.347, 95% CI 1.200-1.512, P<0.001), progression-free survival (HR 1.359, 95% CI 1.220-1.513, P<0.001), disease-free survival (HR 1.269, 95% CI 1.016-1.585, P<0.001) and disease-specific survival (HR 1.474, 95% CI 1.284-1.693, P<0.001). Patients with positive MOR mRNA expression tended to be classified as tumor microenvironment immune types II, representing low PD-L1 and low CD8A expression.ConclusionMOR mRNA overexpression is associated with poor prognosis and poor response to PD-L1 therapy

DisCo: Remedy Self-supervised Learning on Lightweight Models with Distilled Contrastive Learning

While self-supervised representation learning (SSL) has received widespread attention from the community, recent research argue that its performance will suffer a cliff fall when the model size decreases. The current method mainly relies on contrastive learning to train the network and in this work, we propose a simple yet effective Distilled Contrastive Learning (DisCo) to ease the issue by a large margin. Specifically, we find the final embedding obtained by the mainstream SSL methods contains the most fruitful information, and propose to distill the final embedding to maximally transmit a teacher's knowledge to a lightweight model by constraining the last embedding of the student to be consistent with that of the teacher. In addition, in the experiment, we find that there exists a phenomenon termed Distilling BottleNeck and present to enlarge the embedding dimension to alleviate this problem. Our method does not introduce any extra parameter to lightweight models during deployment. Experimental results demonstrate that our method achieves the state-of-the-art on all lightweight models. Particularly, when ResNet-101/ResNet-50 is used as teacher to teach EfficientNet-B0, the linear result of EfficientNet-B0 on ImageNet is very close to ResNet-101/ResNet-50, but the number of parameters of EfficientNet-B0 is only 9.4\%/16.3\% of ResNet-101/ResNet-50. Code is available at https://github. com/Yuting-Gao/DisCo-pytorch.Comment: ECCV 202

Attenuation by a Human Body and Trees as well as Material Penetration Loss in 26 and 39 GHz Millimeter Wave Bands

This paper investigates the attenuation by a human body and trees as well as material penetration loss at 26 and 39 GHz by measurements and theoretical modeling work. The measurements were carried out at a large restaurant and a university campus by using a time domain channel sounder. Meanwhile, the knife-edge (KE) model and one-cylinder and two-cylinder models based on uniform theory of diffraction (UTD) are applied to model the shape of a human body and predict its attenuation in theory. The ITU (International Telecommunication Union) and its modified models are used to predict the attenuation by trees. The results show that the upper bound of the KE model is better to predict the attenuation by a human body compared with UTD one-cylinder and two-cylinder models at both 26 and 39 GHz. ITU model overestimates the attenuation by willow trees, and a modified attenuation model by trees is proposed based on our measurements at 26 GHz. Penetration loss for materials such as wood and glass with different types and thicknesses is measured as well. The measurement and modeling results in this paper are significant and necessary for simulation and planning of fifth-generation (5G) mm-wave radio systems in ITU recommended frequency bands at 26 and 39 GHz