Design of Lattice Structures Using Local Relative Density Mapping Method

In order to solve the problem of substantial computational resources of lattice structure during optimization, a local relative density mapping(LRDM) method is proposed. The proposed method uses solid isotropic microstructures with penalization to optimize a model at the macroscopic scale. The local relative density information is obtained from the topology optimization result. The contour lines of an optimized model are extracted using a density contour approach, and the triangular mesh is generated using a mesh generator. A local mapping relationship between the elements’ relative density and the struts’ relative cross?sectional area is established to automatically determine the diameter of each individual strut in the lattice structures. The proposed LRDM method can be applied to local finite element meshes and local density elements, but it is also suitable for global ones. In addition, some cases are con?sidered in order to test the e ectiveness of the LRDM method. The results show that the solution time of the LRDM is lower than the RDM method by approximately 50%. The proposed method provides instructions for the design of more complex lattice structures.

Applying Density and Hotspot Analysis for Indigenous Traditional Land Use: Counter-Mapping with Wasagamack First Nation, Manitoba, Canada

Traditional land-use studies display specific locations used and occupied by Indigenous Peoples in their ancestral lands to sustain their land-based livelihoods. Indigenous communities use these maps to reclaim their territories by demonstrating their current land-use and occupancy that extends vast distances beyond their reserves. To support the protection of ancestral territory against the threats of resource extraction by outsiders, we applied the density and hotspot mapping approaches to display the concentrated land use areas of 49 harvesters of Wasagamack First Nation in Manitoba, Canada. In contrast to the conventional land use mapping, which presents the land use areas as points or spots on the map, density and hotspot mapping shows areas of intensive land use and cultural significance. This paper reinforces Wasagamack Anishininews’ view that their entire ancestral territory is sacred and vital to the Wasagamack First Nation and supports their case for their traditional territory’s self-governance. If integrated with Wasagamack Anishininews’ community development goals, the density and hotspot mapping approach can facilitate land use planning for sustainable conservation of important areas for the well-being of Wasagamack First Nation.

A Deep Learning-Based Crowd Counting Method and System Implementation on Neural Processing Unit Platform

In this paper, a deep learning-based method is proposed for crowdcountingproblems. Specifically, by utilizing the convolution kernel densitymap, the ground truth is generated dynamically to enhance the featureextractingability of the generator model. Meanwhile, the “cross stage partial”module is integrated into congested scene recognition network (CSRNet) toobtain a lightweight network model. In addition, to compensate for the accuracydrop owing to the lightweight model, we take advantage of “structuredknowledge transfer” to train the model in an end-to-end manner. It aimsto accelerate the fitting speed and enhance the learning ability of the studentmodel. The crowd-counting system solution for edge computing is alsoproposed and implemented on an embedded device equipped with a neuralprocessing unit. Simulations demonstrate the performance improvement ofthe proposed solution in terms of model size, processing speed and accuracy.The performance on the Venice dataset shows that the mean absolute error(MAE) and the root mean squared error (RMSE) of our model drop by32.63% and 39.18% compared with CSRNet. Meanwhile, the performance onthe ShanghaiTech PartB dataset reveals that the MAE and the RMSE of ourmodel are close to those of CSRNet. Therefore, we provide a novel embeddedplatform system scheme for public safety pre-warning applications.

Wavelet analysis and interpretation of gravity data in Sichuan-Yunnan region, China

The Bouguer gravity anomaly data of Sichuan-Yunnan region and its vicinity were analyzed with wavelet trans- formation method. In the process, complete orthogonal wavelet function system with good symmetry and higher vanishing moment was selected to decompose the gravity anomaly into two parts. With the power spectral analysis on the decomposed anomalies, we interpreted that the two parts of anomalies represent the density variation in upper and middle crust, and in deep crust and uppermost mantle, respectively. The two parts of anomalies indicate the difference between shallow and deep tectonics. The results of shallow-layer apparent density mapping reveal that: a) the crustal density in Sichuan basin is higher than that in Songpan-Garze orogenic zone; b) the density of Kangdian rhombic block is heterogeneous; c) the boundary faults of Kangdian block are of different density fea- tures, suggesting different tectonic signification. The results of deep-layer apparent density mapping show a similar, but not the same, density distribution pattern as the shallow results, and indicate that the tectonics of shallow and deep crust are different, they may be in a status of incomplete coupling. Our results also show that the earthquakes in this area are controlled not only by the fracture zones but also by the deep density distribution.

Looking at the Statistical Texture Approach Applied to Weather Radar Rainfall Fields

Texture analysis methods have been used in a variety of applications, for instance in remote sensing. Though widely used in electrical engineering, its application in atmospheric sciences is still limited. This paper reviews some concepts of digital texture and statistical texture approach, applying them to a set of specific maps to analyze the correlation between texture measurements used in most papers. It is also proposed an improvement of the method by setting free a distance parameter and the use of a new texture measurement based on the Kullback-Leibler divergence. Eight statistical measurements were used: mean, contrast, standard deviation, cluster shade, cluster prominence, angular second moment, local homogeneity and Shannon entropy. The above statistical measurements were applied to simple maps and a set of rainfall fields measured with weather radar. The results indicate some high correlations, e.g. between the mean and the contrast or between the angular second moment, local homogeneity and the Shannon entropy, besides the potentiality of the method to discriminate maps.

Density Map Guided Region Localization for End-to-End Small Object Detection

With the advancement of society and science and technology, the demand for detecting small objects in practical scenarios becomes stronger. Such objects are only represented by relatively small coverage of pixels, and the features are degraded severely after being extracted by a deep convolutional neural network, which is detrimental to the detection performance for small objects. Therefore, an intuitive solution is to increase the resolution of small objects by cropping the original image. In this paper, we propose a simple but effective object density map guided region localization module (DMGRL) to locate and crop the regions of interest where small objects may exist. Firstly, the density map of the objects is estimated by object density map estimation network, and then the coordinates of the small object regions are calculated;Secondly, the continuous differentiable affine transformation is utilized to crop these regions so that the detector with DMGRL can be trained end-to-end instead of two-stage training. Finally, the all prediction results of input image and cropped region images are merged together to output the final detection results by non maximum suppression (NMS). Extensive experiments demonstrate the superior performance of the detector incorporated DMGRL.

Rethinking Global Context in Crowd Counting

This paper investigates the role of global context for crowd counting.Specifically,a pure transformer is used to extract features with global information from overlapping image patches.Inspired by classification,we add a context token to the input sequence,to facilitate information exchange with tokens corresponding to image patches throughout transformer layers.Due to the fact that transformers do not explicitly model the tried-and-true channel-wise interactions,we propose a token-attention module(TAM)to recalibrate encoded features through channel-wise attention informed by the context token.Beyond that,it is adopted to predict the total person count of the image through regression-token module(RTM).Extensive experiments on various datasets,including ShanghaiTech,UCFQNRF,JHU-CROWD++and NWPU,demonstrate that the proposed context extraction techniques can significantly improve the performanceover the baselines.

Electron densitymap evaluation functions for determining the quality of protein crystal structures

Background Finding methods to judge the quality of X-ray crystallographic information is an active research topic.The quality of electron density maps reconstructed by Fourier transform is always limited by the finite resolution,the amplitude/phase error and the completeness of diffraction data.At present,the R value and effective resolution are common ways of evaluating the quality of electron density maps.Unfortunately,the current evaluation methods are only dependent on diffraction amplitude,without any phase information.Methods Advanced evaluation functions in real space are designed to estimate the electron density map quality.The electron density map definition evaluation function relies on the atomicity of the electron density distribution.We use the power spectrum electron density entropy in protein crystallography for the first time.These two functions include both structure factor amplitudes and phases via the Fourier transform of electron density map.Results We carry out tests on synthetic data sets of known structures,varying the resolution and error,and draw the quality curves of electron density maps with theoretical,noisy and experimental diffraction data by two evaluation functions at different resolutions.The curves reveal the optimum structure and resolution of proteins clearly.Conclusions The work presented here offers new methods to evaluate the qualities of the electron density maps of proteins with slight differences,and brand new indicators to select the optimum diffraction resolution of protein structures.

Complex atrial tachycardia with alternating cycle length:What is the mechanism?

Background Atrial tachycardia(AT)with cycle length(CL)alternans is uncommon and conventional mapping of this AT remains challenging. We used an ultrahigh density mapping system to rapidly map complicated circuits with sufficient spatial resolution and electrogram quality to elucidate the precise mechanism of this special ATs. Methods Of 210 consecutive patients with clinical ATs who underwent catheter ablation with the ultrahigh density mapping system,4 patients(1.9%)with CL alternans were identified. The AT alternating cycles mapped by the Rhythmia mapping system for long CL were 317±51(range 245-355)ms and for short CL were 282±51(range 235-333)ms. Both long and short cycles followed in 1∶1 sequence in all 4 patients(longshort-long-short). Results By comparing the separate maps with long and short CL,we classified ATs with CL alternans into 2 types. In type 1,CL alternans resulted from an intermittent 2∶1 conduction block through the slow conduction channel in the small circuit. In type 2,CL alternans caused by the alternated conduction velocity passing through the conduction gap were manifested. Ablation at the fractionated potentials contributes to the termination of AT in 3 of the 4 patients. Conclusions Ultrahigh density mapping system can accurately identify the mechanism of complex ATs with alternating CL. The CL alternans may be related to the intermittent conduction block within the channel of the small circuitor different conduction velocity through the identical channel. Fractionated electrogram recorded in the common isthmus or some"conduction gaps"may be a reasonable approach to terminate these ATs.

Diagnostic value of magnetocardiography in patients with coronary heart disease and in-stent restenosis

Background In-stent restenosis （ISR） has become one of the most challenging problems in patients with coronary heart disease. At present, using non-invasive methods to assess ISR is a hot topic. In this investigation we attempted to explore the potential of magnetocardiography （MCG） in diagnosis of in-stent restenosis. Methods MCG was analyzed in 52 patients with coronary artery disease for three times： before stenting, one month and 7 months after successful intracoronary stenting. Results The average classification of total maps （ACTM） and the ratio of abnormal maps （RAM） were lower in 1 month after intracoronary stenting compared with that obtained before stent planting （2.91 vs 2.52, 65.74% vs 42.80%, P〈0.01）, while complex ventricular excitation index （CVEI） increased from -42.63 to -20.05 （P〈0.01）. In ISR subgroup （n=16）, RAM decreased in 1 month after intracoronary stenting compared to it before stenting （68.99% vs 45.26%, P〈0.05）. ACTM increased in 7 months compared to that obtained in 1 month after stenting （3.15 vs 2.51, P〈0.05）. According to the ROC curve, ACTM showed its unique diagnostic value in restenosis patients. The sensitivity and specificity of ACTM were 80.0%, 69.40%, respectively. Its positive predictive value and negative predictive value were 54.6% and 88.5%, respectively. Conclusions After successful intracoronary stenting, most parameters of MCG were improved. ACTM was of prognostic value in diagnosing ISR.