Robust surface segmentation and edge feature lines extraction from fractured fragments of relics

AbstractSurface segmentation and edge feature lines extraction from fractured fragments of relics are essential steps for computer assisted restoration of fragmented relics. As these fragments were heavily eroded, it is a challenging work to segment surface and extract edge feature lines. This paper presents a novel method to segment surface and extract edge feature lines from triangular meshes of irregular fractured fragments. Firstly, a rough surface segmentation is accomplished by using a clustering algorithm based on the vertex normal vector. Secondly, in order to differentiate between original and fracture faces, a novel integral invariant is introduced to compute the surface roughness. Thirdly, an accurate surface segmentation is implemented by merging faces based on face normal vector and roughness. Finally, edge feature lines are extracted based on the surface segmentation. Some experiments are made and analyzed, and the results show that our method can achieve surface segmentation and edge extraction effectively

A novel statistical cerebrovascular segmentation algorithm with particle swarm optimization

AbstractWe present an automatic statistical intensity-based approach to extract the 3D cerebrovascular structure from time-of flight (TOF) magnetic resonance angiography (MRA) data. We use the finite mixture model (FMM) to fit the intensity histogram of the brain image sequence, where the cerebral vascular structure is modeled by a Gaussian distribution function and the other low intensity tissues are modeled by Gaussian and Rayleigh distribution functions. To estimate the parameters of the FMM, we propose an improved particle swarm optimization (PSO) algorithm, which has a disturbing term in speeding updating the formula of PSO to ensure its convergence. We also use the ring shape topology of the particles neighborhood to improve the performance of the algorithm. Computational results on 34 test data show that the proposed method provides accurate segmentation, especially for those blood vessels of small sizes

A Dense Point-to-Point Alignment Method for Realistic 3D Face Morphing and Animation

We present a new point matching method to overcome the dense point-to-point alignment of scanned 3D faces. Instead of using the rigid spatial transformation in the traditional iterative closest point (ICP) algorithm, we adopt the thin plate spline (TPS) transformation to model the deformation of different 3D faces. Because TPS is a non-rigid transformation with good smooth property, it is suitable for formulating the complex variety of human facial morphology. A closest point searching algorithm is proposed to keep one-to-one mapping, and to get good efficiency the point matching method is accelerated by a KD-tree method. Having constructed the dense point-to-point correspondence of 3D faces, we create 3D face morphing and animation by key-frames interpolation and obtain realistic results. Comparing with ICP algorithm and the optical flow method, the presented point matching method can achieve good matching accuracy and stability. The experiment results have shown that our method is efficient for dense point objects registration

Reconstruction of Daily 30 m Data from HJ CCD, GF-1 WFV, Landsat, and MODIS Data for Crop Monitoring

With the recent launch of new satellites and the developments of spatiotemporal data fusion methods, we are entering an era of high spatiotemporal resolution remote-sensing analysis. This study proposed a method to reconstruct daily 30 m remote-sensing data for monitoring crop types and phenology in two study areas located in Xinjiang Province, China. First, the Spatial and Temporal Data Fusion Approach (STDFA) was used to reconstruct the time series high spatiotemporal resolution data from the Huanjing satellite charge coupled device (HJ CCD), Gaofen satellite no. 1 wide field-of-view camera (GF-1 WFV), Landsat, and Moderate Resolution Imaging Spectroradiometer (MODIS) data. Then, the reconstructed time series were applied to extract crop phenology using a Hybrid Piecewise Logistic Model (HPLM). In addition, the onset date of greenness increase (OGI) and greenness decrease (OGD) were also calculated using the simulated phenology. Finally, crop types were mapped using the phenology information. The results show that the reconstructed high spatiotemporal data had a high quality with a proportion of good observations (PGQ) higher than 0.95 and the HPLM approach can simulate time series Normalized Different Vegetation Index (NDVI) very well with R2 ranging from 0.635 to 0.952 in Luntai and 0.719 to 0.991 in Bole, respectively. The reconstructed high spatiotemporal data were able to extract crop phenology in single crop fields, which provided a very detailed pattern relative to that from time series MODIS data. Moreover, the crop types can be classified using the reconstructed time series high spatiotemporal data with overall accuracy equal to 0.91 in Luntai and 0.95 in Bole, which is 0.028 and 0.046 higher than those obtained by using multi-temporal Landsat NDVI data

Annealing tunable charge density wave order in a magnetic kagome material FeGe

In the magnetic kagome metal FeGe, a charge density wave (CDW) order emerges inside the antiferromagnetic phase, providing a fertile playground to investigate the interplay between charge and magnetic orders. Here, we demonstrate that the CDW order, as well as magnetic properties, can be reversibly tuned on a large scale through post-growth annealing treatments. The antiferromagnetic and CDW transitions vary systematically as functions of both the temperature and the time period of annealing. Long-range CDW order with a maximum $T_{\mathrm{CDW}}$ and a minimum $T_{\mathrm{N}}$ can be realized in crystals annealed at \SI{320}{\degreeCelsius} for over 48 h. Using magnetization and magnetostrictive coefficient measurements, it is found that the CDW transition is rather stable against an external magnetic field and spin-flop transition. On the other hand, the critical field for spin-flop transition is significantly reduced in the long-range ordered CDW phase. Our results indicate that the CDW in FeGe is immune to variations in magnetic orders, while the magnetocrystalline anisotropy energy and the corresponding magnetic ground state can be altered significantly by the charge order. These findings provide crucial clues for further investigation and a better understanding of the nature of the CDW order in FeGe.Comment: 8 pages, 4 figure

TCP-Mobile Edge: Accelerating Delivery in Mobile Networks

Abstract-Owing to the imminent fixed mobile convergence, Internet applications are frequently accessed through mobile nodes. However, service delivery latency is too high to satisfy user expectations. In this paper, we design a new TCP algorithm, TCP-ME (Mobile Edge), to accelerate the service delivery in mobile networks. Considering the QoS (Quality of Service) mechanisms of mobile networks, TCP-ME is designed to differentiate the packet loss caused by wireless errors, traffic conditioning of mobile core networks, and Internet congestion, as well as to react to the packet loss accordingly. To detect wireless errors, we mark the ACK (Acknowledge) packets in the uplink direction at the base station, and the marking threshold is a function of the instantaneous downlink queue length and the number of consecutive HARQ retransmissions. We modify the ECN mechanism with deterministic marking to detect Internet congestion. The packet loss caused by traffic conditioners of mobile networks is detected by whether the incoming DUPACK is marked or not. TCP-ME adapts the inter-packet interval when the packet loss is caused by wireless errors or the admission control mechanism. If the packet loss is due to Internet congestion, TCP-ME applies the TCP-New Reno's congestion window adaptation algorithm. Simulation results show that TCP-ME can speed up web service response time in mobile networks by about 80%

Exploiting MIMO antennas in cooperative cognitive radio networks

Abstract—Recently, a new paradigm for cognitive radio net-works has been advocated, where primary users (PUs) recruit some secondary users (SUs) to cooperatively relay the primary traffic. However, all existing work on such cooperative cognitive radio networks (CCRNs) operate in the temporal domain. The PU needs to give out a dedicated portion of channel access time to the SUs for transmitting the secondary data in exchange for the SUs ’ cooperation, which limits the performance of both PUs and SUs. On the other hand, Multiple Input Multiple Output (MIMO) enables transmission of multiple independent data streams and suppression of interference via beam-forming in the spatial domain over MIMO antenna elements to provide significant performance gains. Researches have not yet explored how to take advantage of the MIMO technique in CCRNs. In this paper, we propose a novel MIMO-CCRN framework, which enables the SUs to utilize the capability provided by the MIMO to cooperatively relay the traffic for the PUs while concurrently accessing the same channel to transmit their own traffic. We design the MIMO-CCRN architecture by considering both the temporal and spatial domains to improve spectrum efficiency. Further we provide theoretical analysis for the primary and secondary transmission rate under MIMO cooperation and then formulate an optimization model based on a Stackelberg game to maximize the utilities of PUs and SUs. Evaluation results show that both primary and secondary users achieve higher utility by leveraging MIMO spatial cooperation in MIMO-CCRN than with conventional schemes. I

Health monitoring of rolling element bearing using a spectrum searching strategy

Aiming at achieving early fault diagnosis and tracking the degradation process of bearings, we propose a novel monitoring methodology using a spectrum searching strategy in this paper. Firstly, a vibration signal is collected with appropriate sampling frequency and length. Secondly, the structural information of spectrum (SIOS) on a predefined frequency grid is constructed through a searching algorithm after deriving the single-sided FFT spectrum. Finally, the two-dimensional (2-D) line plot of the frequency grid versus the average power in SIOS is employed to conduct fault detection and the sum of the largest six total-power (SLSTP) of the frequency grid in SIOS is calculated as a health indication to demonstrate the changes in the bearing’s health status. The performance of the proposed scheme is validated with both simulation and bearing data. Experimental results show that the monitoring algorithm could manifest satisfactory behaviors in early fault diagnosis and health assessment of bearings

A Smoothed Finite Element-Based Elasticity Model for Soft Bodies

One of the major challenges in mesh-based deformation simulation in computer graphics is to deal with mesh distortion. In this paper, we present a novel mesh-insensitive and softer method for simulating deformable solid bodies under the assumptions of linear elastic mechanics. A face-based strain smoothing method is adopted to alleviate mesh distortion instead of the traditional spatial adaptive smoothing method. Then, we propose a way to combine the strain smoothing method and the corotational method. With this approach, the amplitude and frequency of transient displacements are slightly affected by the distorted mesh. Realistic simulation results are generated under large rotation using a linear elasticity model without adding significant complexity or computational cost to the standard corotational FEM. Meanwhile, softening effect is a by-product of our method