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Leaf area index (LAI) is an important biological factor reflecting vegetation growth and forest ecosystem. LAI can be used to obtain plant health status, carbon cycle, and surrounding ecological environment effectively. In this study, the smartphone was equipped with a fisheye lens, and the optimization method was used to estimate LAI, which was compared with digital hemispherical photography (DHP) to investigate the possibility of the new method for LAI estimation. The hemispherical image was divided into blocks, and the optimized Otsu method was used for algorithm segmentation, which can effectively distinguish vegetation from the sky. Concurrently, when the gap fraction inversion LAI was performed, the linear inversion algorithm was improved based on single-angle inversion, and the LAI was obtained by inversion through the linear fitting of the mul-tiangle gap fraction. The experimental sample was located in Olympic National Forest Park in Beijing. Three coniferous mixed forests and three broadleaved forests were selected from the experimental sample. LAI measurements from smartphones were compared with those from DHP. In the samples for mixed coniferous forests, the values for coefficients of determination R^2 were 0.835, 0.802, and 0.809, and root mean square errors (REMS) were 0.137, 0.120, and 0.147. For the broadleaf forest samples, the values for R² were 0.629, 0.679, and 0.758, and REMS were 0.144, 0.135, and 0.137. The R^2 and RMES for the overall data was 0.810 and 0.134, respectively, and a good agreement between the LAI measurements from the proposed method and those from the DHP supports an accurate estimation. The results show that the use of a fisheye lens on a smartphone can effectively and accurately obtain tree canopy LAI. This provides a fast and effective new method to measure LAI of forest vegetation near the ground, which is of great significance for studying the interaction between plant growth status, ecological environment, and phenological changes.

To improve the measurement accuracy of wood density and study the linear correlation between the drill feed resistance and wood density, a new micro drill instrument that can can simultaneously measure the rotation resistance and feed resistance of the drill needle was designed. The test wood samples included hardwood, softwood and conifer. The absolute dry density of each wood sample was measured. The drill resistance data was tested by using self-developed micro drill instrument and Resistogaph 650-SC. 4 linear models between drill resistance and the absolute dry density of wood . The results showed that: the statistical indicators of each model of the self-made micro drill resistance in-strument were better than the corresponding indicators of Resistogaph 650-SC; the coefficient of determination of the linear regression model between the feed resistance of the self-made micro drill resistance instrument and the absolute dry density of wood was 0.946; the statistical indicators of model including rotation resistance and feed resistance, were better than those of the model only including rotation resistance. Therefor, the design proposed in the article is reasonable, and increasing the feed resistance can improve the measurement accuracy of the micro drill resistance instrument for measuring wood density.

The tractor–trailer wheeled robot (TTWR) is a typical modular mobile robot system. It is widely used in industry and agriculture transportation because of its simple structure, heavy load, high efficiency, strong adaptability, and flexibility. Because it is a nonlinear and underactuated system subjected to nonholonomic constraints, the complexity of the system and influence of the inner wheel difference pose great challenges to its trajectory prediction and path planning. To solve this problem, this paper proposes a trajectory prediction algorithm for an unpowered trailer for the TTWR system. In contrast to traditional single-path planning, the algorithm combines the motion characteristics of the Ackermann chassis tractor, analyzes the trailer motion law, and fully considers the lateral deviation of the trailer during the turning process. The algorithm can plan a reasonable cooperative path for the TTWR system. To verify the feasibility and effectiveness of the algorithm, the algorithm is transplanted to the TTWR intelligent experimental platform. A comparison of the simulation and experimental results shows that under the control of the preview tracking algorithm, the maximum error between the actual trajectory of the trailer and the trajectory predicted by the algorithm is generally no more than 9 cm, and the average error is 3.7 cm. This algorithm can provide an effective basis for TTWR system trajectory planning and path tracking. This algorithm is of great significance to TTWRs in warehousing logistics, freight forwarding, and skidding transportation.

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Forestry mobile robots can effectively solve the problems of low efficiency and poor safety in the forestry operation process. To realize the autonomous navigation of forestry mobile robots, a vision system consisting of a monocular camera and two-dimensional LiDAR and its calibration method are investigated. First, the adaptive algorithm is used to synchronize the data captured by the two in time. Second, a calibration board with a convex checkerboard is designed for the spatial calibration of the devices. The nonlinear least squares algorithm is employed to solve and optimize the external parameters. The experimental results show that the time synchronization precision of this calibration method is 0.0082s, the communication rate is 23Hz, and the gradient tolerance of spatial calibration is 8.55e−07. The calibration results satisfy the requirements of real-time operation and accuracy of the forestry mobile robot vision system. Furthermore, the engineering applications of the vision system are discussed herein. This study lays the foundation for further forestry mobile robots research, which is relevant to intelligent forest machines.

Precise motion control is a challenging and important goal in the application of mobile robots. The mechanical structure of a novel mobile robot is presented. Using the support vector machine learning control method in statistical theory, the human control strategy is represented by the parametric model without knowledge of the actual robot mathematical model. Moreover, using the learning controller, the position motion control experiments of the robot are carried out. The results of the experiments show that this learning control method is feasible and valid for the precise position control of the mobile robot, and the maxim error is less than 32 cm in a 10 m linear movement.

This paper designs a plant leaf expansion pressure non-destructive detection device, aiming to promote plant leaf expansion pressure research and achieve precision irrigation. The design is based on leaf expansion pressure probe technology, which can effectively monitor the plant leaf expansion pressure by detecting the feedback of the leaf under constant pressure. In this paper, the stability of the sensor and the calibration model is tested. The calibration experiments showed that the coefficient of determination R2 of the sensor was over 0.99, the static test results showed that the range of the sensor was 0–300 kPa, and the fluctuation of the sensor was less than 0.2 kPa during the long-term stability test. The indoor comparison tests showed that there was a significant difference in the variation of leaf expansion pressure data between plants under drought conditions and normal conditions. The irrigation experiments showed that the leaf expansion pressure was very sensitive to irrigation. The correlation between the expansion pressure data and the environmental factors was analyzed. The correlation coefficient between expansion pressure and light intensity was found to be 0.817. The results of the outdoor experiments showed that there was a significant difference in the expansion pressure of plants under different weather conditions. The data show that the plant leaf expansion pressure non-destructive detection device designed in this paper can be used both as an effective means of detecting plant leaf expansion pressure and promoting the research of plant physiological feedback mechanisms and precision irrigation.

Due to global warming, changes in plant phenology such as an early leaf spreading period in spring, a late abscission period in autumn, and growing season extension are commonly seen. Here, near-earth remote sensing images were used to monitor the canopy phenology of Populus tomentosa B301 in planted forests under full drip irrigation, full furrow irrigation, and no irrigation (rain fed). Experiments were conducted to collect phenological data across a growing season. Continuous canopy images were used to calculate different vegetation indices; the key phenological period was determined via the double logistic model and the curvature method. The effects of irrigation methods and precipitation in the rainy season on tree growth changes and key phenological periods were analyzed. The results showed that: (1) The green chromatic coordinate (GCC) conformed to the vegetation index of the tree species canopy phenological study. (2) During the phenological period throughout the year, the GCC reaching peak time (MOE) of the canopy phenology of Populus tomentosa B301 was the same in the three methods, while the time of shedding at the end of the growing season without irrigation (preset point 1) was 8 days longer than with full drip irrigation (preset point 3), and 7 days faster than with full furrow irrigation (preset point 5). (3) In the preliminary rainy season, different irrigation volumes induced different growth changes and phenological periods of the trees, resulting in different data of vegetation indicators under different growth conditions. (4) During the rainy season, the precipitation had different effects on cultivating P. tomentosa B301 using the three methods, that is, high precipitation could increase the growth rate of the fully irrigated area, otherwise the growth rate of this tree species was increased in full drip irrigation areas. Precipitation was lower and irregular, and the growth rate of this species was faster than the other two irrigation methods in the non-irrigated area, which was more adaptable to external environmental changes. The internal growth mechanism of the phenological changes in different areas of the planted forests was influenced by the different cultivation methods. Moreover, the collected phenological data provide a basis for the study of plant phenology with large data sets and deepens our understanding of the phenology of planted forests in response to climate change.

C2H2 is a very promising fuel substitute in the future, but it is often accompanied by the presence of CH4 in its production process, and high-purity C2H2 cannot be obtained. In this work, the mixed metal-organic framework (M’MOF), [Fe(pyz)Ni(CN)4] (FeNi-M’MOF, pyz ​= ​pyrazine), was used to induce changes in Fe–N bonds at room temperature using spin-crossover (SCO) properties, and highly selective adsorption of C2H2/CH4 was carried out. The MOFs has a high C2H2 adsorption capacity (96.5 ​cm3 ​g−1 for HS state; 96.1 ​cm3 ​g−1 for LS state), and has a remarkable selectivity for C2H2/CH4 (198.9 for HS state; 273.5 for LS state) at room temperature and standard atmospheric pressure. Under the condition that the C2H2 adsorption capacity is less affected, the selectivity of the polymer to C2H2/CH4 in the LS state is much higher than that in the HS state. Moreover, the preparation method of this material is simple and the performance after synthesis is stable, which indicates that FeNi-M’MOF is a promising C2H2/CH4 separation material.

The actuation of a forestry logging harvester is a complex task that requires long and costly training periods because the qualification of the operator has a significant impact on productivity and safety. Simulation-based training that makes use of virtual reality is becoming a competitive alternative to traditional training due to the reduced costs and risks in the instruction of the harvester operators. Aiming at the working process and operating environment of the logging harvester, an interactive real-time dynamic virtual driving system for forestry logging harvesters is developed. The harvester model is built by using 3dsMAX together with the 3D modeling software SolidWorks. The models of the harvester and trees are imported into Unity3D to create a virtual operating environment. Parent–child relationships for each logging harvester component is set in accordance with the simulation needs, and a corresponding mathematical model for the motion of the manipulator is established by analyzing the motion principle of the manipulator. A proposed cutting algorithm is used to perform the cutting of the model tree. The test results show that the virtual driving system can meet the training needs of logging harvester operators with realistic immersion and good interaction.

The detection and recognition of unstructured roads in forest environments are critical for smart forestry technology. Forest roads lack effective reference objects and manual signs and have high degrees of nonlinearity and uncertainty, which pose severe challenges to forest engineering vehicles. This research aims to improve the automation and intelligence of forestry engineering and proposes an unstructured road detection and recognition method based on a combination of image processing and 2D lidar detection. This method uses the “improved SEEDS + Support Vector Machine (SVM)” strategy to quickly classify and recognize the road area in the image. Combined with the remapping of 2D lidar point cloud data on the image, the actual navigation requirements of forest unmanned navigation vehicles were fully considered, and road model construction based on the vehicle coordinate system was achieved. The algorithm was transplanted to a self-built intelligent navigation platform to verify its feasibility and effectiveness. The experimental results show that under low-speed conditions, the system can meet the real-time requirements of processing data at an average of 10 frames/s. For the centerline of the road model, the matching error between the image and lidar is no more than 0.119 m. The algorithm can provide effective support for the identification of unstructured roads in forest areas. This technology has important application value for forestry engineering vehicles in autonomous inspection and spraying, nursery stock harvesting, skidding, and transportation.

The combination of blockchain technology and Internet of Things (IoT) technology has brought many significant advantages and new development directions. With the development of embedded technology and 5G communication technology, the performance limitations and network limitations that are traditionally believed to restrict the application of blockchain technology to IoT devices have been broken. The development of &ldquo, blockchain + 5G + IoT&rdquo, provides reliable data from the source for the blockchain, linking the credible mapping of physical assets and digital assets. However, at the beginning of the blockchain design, the application of the IoT was not fully considered, so there have been some obvious defects in applying the blockchain technology in the IoT. In the Byzantine fault tolerance (BFT) consensus algorithm of traditional blockchain, the entire blockchain network will become paralyzed when more than 1/3 of the nodes in the network are offline. However, in IoT applications, this situation is likely to occur and greatly limits the security and stability of the application of blockchain technology in the IoT. In order to solve this problem, we proposed an IoT adaptive dynamic blockchain networking method based on discrete heartbeat signals. The feature of the method is to set a different monitoring time for each group of nodes, that is, discrete heartbeat signals monitoring. When the number of nodes gradually decreases, the IoT adaptive dynamic blockchain network can dynamically adapt to this process. Even when more than 1/3 of the IoT are offline, the adaptive dynamic IoT blockchain network can maintain stable running. This method also has the advantages of a short network expectation recovery time and avoids instantaneous system paralysis caused by the thundering herd effect. This research improves the security and stability of the application of blockchain technology in the IoT, and provides the necessary technical foundation for the better combination of blockchain technology and IoT technology.

A motion planner based on artificial potential field for unmanned tractor is proposed in this paper. In order to get the effective environment model, the model for condition of terrain and impact of ground to tractor is analyzed and built. The tractor usually brings a trailer behind, so the kinematics of tractor with a trailer is presented. The motion planner based on artificial potential field is designed for the unmanned tractor working in farmland. According to the characteristics of the unmanned tractor, the control algorithm and motion planner is optimized. The simulation of the improved motion planner for unmanned tractor is presented and analyzed. And the simulation results are presented to show the effectiveness of the proposed method.

Based on Internet-of-Things and multi-sensor technology, an intelligent wireless monitoring system was developed to obtain field ecological parameters and provide forest fire warning in real-time. The GPRS and China&rsquo, s Beidou satellite communication were selectively used for date transmission in the field with weak cell phone signals. This monitoring system is mainly composed of several field ecological monitoring stations, a cloud server, and online system software. Atmosphere, soil, sunlight and plant parameters of different regions are obtained real-time by sensors stably and reliably. This system has functions such as field ecological data storage, dynamic query, report generation, and data analysis. As an example of typical application, the forest fire weather grade, which was supplemented with the litter layer soil humidity, was calculated to realize the early warning of the local forest fire in this system through continuous experiments at Beijing Jiufeng National Forest Park from March to May 2017 and Inner Mongolia from March to June 2018. The success ratios of data transmission through Beidou satellite were 98.57%, 99.43%, 99.59%, and 98.85%, respectively, in Beijing, and through GPRS were 99.89% and 99.90% in Inner Mongolia. Long-term real-time field ecological monitoring and forest fire warning were successfully realized. This system can be widely used for big data field acquisition and analysis in forest and agriculture regions.

CryoEM data capture the dynamic character associated with biological macromolecular assemblies by preserving the various conformations of the individual specimens at the moment of flash freezing. Regions of high variation in the data set are apparent in the image reconstruction due to the poor density that results from the lack of superposition of these regions. These observations are qualitative and, to date, only preliminary efforts have been made to quantitate the heterogeneity in the ensemble of particles that are individually imaged. We developed and tested a quantitative method for simultaneously computing a reconstruction of the particle and a map of the space-varying heterogeneity of the particle based on an entire data set. The method uses a maximum likelihood algorithm that explicitly takes into account the continuous variability from one instance to another instance of the particle. The result describes the heterogeneity of the particle as a variance to be plotted at every voxel of the reconstructed density. The test, employing time resolved data sets of virus maturation, not only recapitulated local variations obtained with difference map analysis, but revealed a remarkable time dependent reduction in the overall particle dynamics that was unobservable with classical methods of analysis.

Space grippers are the key devices for accomplishing space non-cooperative target capture, which has a great significance for satellite services and space debris removal. This article proposes a novel mechanical gripper device for the capture of aluminum honeycomb panels of non-cooperative satellites. The gripper was modeled and assembled in the three-dimensional modeling platform UGNX. The model was imported into the simulation software ADAMS. ADAMS is capable of analyzing the kinematic feasibility of the gripper model. Collision and penetrating power analysis of the mechanical claws into an aluminum honeycomb plate were carried out in LS-DYNA. Through non-vertical piercing experiment, the maximum approaching angle tolerance is 10°. The established rigid connection can withstand a destructive force greater than 1000 N. A prototype of the mechanical gripper is built. A ground test was carried out with this prototype, in which a test-platform simulated the space microgravity environment. The results certified that the prototype could reach the target at a relative speed of 0.5 m/s and then quickly complete the grabbing motion and establish a rigid connection. The experiment shows that this mechanical gripper can accomplish the task of repeatedly capturing the surface of non-cooperative space satellites.

A good benchmarking methodology can save a tremendous amount of resources in terms of human effort, machine cycles, and cost. Such a methodology must consider the relevance and openness of the chosen codes, well-defined rules for executing and reporting the benchmarks, a review process to enforce the rules, and a public repository for the obtained information. For the methodology to be feasible, it must also be supported by adequate tools that enable the user to consistently execute the benchmarks and gather the requisite metrics. At the very least, reliable benchmarking results can help people make decisions about HPC acquisitions and assist scientists and engineers in system advances. By saving resources and enabling balanced designs and configurations, realistic benchmarking ultimately leads to increased competitiveness in both industry and academia.

In single-particle cryo electron microscopy, many electron microscope images each of a single instance of a biological particle such as a virus or a ribosome are measured and the 3-D electron scattering intensity of the particle is reconstructed by computation. Because each instance of the particle is imaged separately, it should be possible to characterize the heterogeneity of the different instances of the particle as well as a nominal reconstruction of the particle. In this paper, such an algorithm is described and demonstrated on the bacteriophage Hong Kong 97. The algorithm is a statistical maximum likelihood estimator computed by an expectation maximization algorithm implemented in Matlab software.

We present the design and evaluation of a distributed matrix-assembly abstraction for large-scale inverse problems in HPC environments: namely, physics-based Hessian estimation in full-waveform seismic inversion at the scale of the entire globe. Our solution to this data-assimilation problem relies on UPC++, a new PGAS extension to the C++ language, to implement one-sided asynchronous updates to distributed matrix elements, and allows us to tackle inverse problems well beyond our previous capabilities. Our evaluation includes scaling results for Hessian estimation on up to 12, 288 cores, typical of current production scientific runs and next-generation inversions. We also present comparisons with an alternative implementation based on MPI-3 remote memory access (RMA) operations, focusing on performance and code complexity. Interoperability between UPC++ and other parallel programming tools (e.g. MPI, OpenMP) allowed for incremental adoption of the PGAS model where most beneficial. Further, we note that this model of asynchronous assembly can generalize to other data-assimilation applications that accumulate updates into shared global state.

© 2015 IEEE. Structured grid linear solvers often require manually packing and unpacking of communication data to achieve high performance.Orchestrating this process efficiently is challenging, labor-intensive, and potentially error-prone.In this paper, we explore an alternative approach that communicates the data with naturally grained messagesizes without manual packing and unpacking. This approach is the distributed analogue of shared-memory programming, taking advantage of the global addressspace in PGAS languages to provide substantial programming ease. However, its performance may suffer from the large number of small messages. We investigate theruntime support required in the UPC++library for this naturally grained version to close the performance gap between the two approaches and attain comparable performance at scale using the High-Performance Geometric Multgrid (HPGMG-FV) benchmark as a driver.

The storage needs of modern scientific applications are growing exponentially, and designing economical storage solutions for such applications – especially in Grid environments – is an important research topic. This work presents Kosha, a system that aims to harvest redundant storage space on cluster nodes and user desktops to provide a reliable, shared file system that acts as a large distributed storage. Kosha utilizes peer-to-peer (p2p) mechanisms to enhance the widely-used Network File System (NFS). P2P storage systems provide location transparency, mobility transparency, load balancing, and file replication – features that are not available in NFS. On the other hand, NFS provides hierarchical file organization, directory listings, and file permissions, which are missing from p2p storage systems. By blending the strengths of NFS and p2p storage systems, Kosha provides a low overhead storage solution. Our experiments show that compared to unmodified NFS, Kosha introduces a 3.3% fixed overhead and 4.5% additional overhead as nodes are increased from two to sixteen. For larger number of nodes, the additional overhead increases slowly. Kosha achieves load balancing in distributed directories, and guarantees $99.99\%$ or better file availability.

The scattering density of the virus is represented as a truncated weighted sum of orthonormal basis functions in spherical coordinates, where the angular dependence of each basis function has icosahedral symmetry. A statistical model of the image formation process is proposed and the maximum likelihood estimation method computed by an expectation-maximization algorithm is used to estimate the weights in the sum and thereby compute a 3-D reconstruction of the virus particle. If multiple types of virus particle are represented in the boxed images then multiple 3-D reconstructions are computed simultaneously without first requiring that the type of particle shown in each boxed image be determined. Examples of the procedure are described for viruses with known structure: (1). 3-D reconstruction of Flockhouse Virus from experimental images, (2). 3-D reconstruction of the capsid of Nudaurelia Omega Capensis Virus from synthetic images, and (3). 3-D reconstruction of both the capsid and the procapsid of Nudaurelia Omega Capensis Virus from a mixture of unclassified synthetic images.

Copyright 2014 ACM 978-1-4503-3247-7/14/10$15.00. Partitioned Global Address Space (PGAS) languages and one-sided communication enable application developers to select the communication paradigm that balances the performance needs of applications with the productivity desires of programmers. In this paper, we evaluate three different one-sided communication paradigms in the context of geometric multigrid using the miniGMG benchmark. Although miniGMG's static, regular,and predictable communication does not exploit the ultimate potential of PGAS models, multigrid solvers appear in many contemporary applications and represent one of the most important communication patterns. We use UPC++, a PGAS extension of C++, as the vehicle for our evaluation, though our work is applicable to any of the existing PGAS languages and models. We compare performance with the highly tuned MPI baseline, and the results indicate that the most promising approach towards achieving performance and ease of programming is to use high-level abstractions, such as the multidimensional arrays provided by UPC++, that hide data aggregation and messaging in the runtime library. (c) 2014 Association for Computing Machinery.

The Partitioned Global Address Space (PGAS) programming models combine shared and distributed memory features, providing the basis for high performance and high productivity parallel programming environments. UPC++ [39] is a very recent PGAS implementation that takes a library-based approach and avoids the complexities associated with compiler transformations. However, this implementation does not support dynamic task parallelism and only relies on other threading models (e.g., OpenMP or pthreads) for exploiting parallelism within a PGAS place.In this paper, we introduce a compiler-free PGAS library called HabaneroUPC++, which supports a tighter integration of intra-place and inter-place parallelism than standard hybrid programming approaches. The library makes heavy use of C++11 lambda functions in its APIs. C++11 lambdas avoid the need for compiler support while still retaining the syntactic convenience of language-based approaches. The HabaneroUPC++ library implementation is based on a tight integration of the UPC++ library and the Habanero-C++ library, with new extensions to support the integration. The UPC++ library is used to provide PGAS communication and function shipping support using GASNet, and the Habanero-C++ library is used to provide support for intra-place work-stealing integrated with function shipping. We demonstrate the programmability and performance of our implementation using two benchmarks, scaled up to 6K cores. The insights developed in this paper promise to further enhance the usability and popularity of PGAS programming models.

Multidimensional arrays are an important data structure in many scientific applications. Unfortunately, built-in support for such arrays is inadequate in C++, particularly in the distributed setting where bulk communication operations are required for good performance. In this paper, we present a multidimensional library for partitioned global address space (PGAS) programs, supporting the one-sided remote access and bulk operations of the PGAS model. The library is based on Titanium arrays, which have proven to provide good productivity and performance. These arrays provide a local view of data, where each rank constructs its own portion of a global data structure, matching the local view of execution common to PGAS programs and providing maximum flexibility in structuring global data. Unlike Titanium, which has its own compiler with array-specific analyses, optimizations, and code generation, we implement multidimensional arrays solely through a C++ library. The main goal of this effort is to provide a library-based implementation that can match the productivity and performance of a compiler-based approach. We implement the array library as an extension to UPC++, a C++ library for PGAS programs, and we extend Titanium arrays with specializations to improve performance. We evaluate the array library by porting four Titanium benchmarks to UPC++, demonstrating that it can achieve up to 25% better performance than Titanium without a significant increase in programmer effort..

Partitioned Global Address Space (PGAS) languages are convenient for expressing algorithms with large, random-access data, and they have proven to provide high performance and scalability through lightweight one-sided communication and locality control. While very convenient for moving data around the system, PGAS languages have taken different views on the model of computation, with the static Single Program Multiple Data (SPMD) model providing the best scalability. In this paper we present UPC++, a PGAS extension for C++ that has three main objectives: 1) to provide an object-oriented PGAS programming model in the context of the popular C++ language, 2) to add useful parallel programming idioms unavailable in UPC, such as asynchronous remote function invocation and multidimensional arrays, to support complex scientific applications, 3) to offer an easy on-ramp to PGAS programming through interoperability with other existing parallel programming systems (e.g., MPI, OpenMP, CUDA). We implement UPC++ with a 'compiler-free' approach using C++ templates and runtime libraries. We borrow heavily from previous PGAS languages and describe the design decisions that led to this particular set of language features, providing significantly more expressiveness than UPC with very similar performance characteristics. We evaluate the programmability and performance of UPC++ using five benchmarks on two representative supercomputers, demonstrating that UPC++ can deliver excellent performance at large scale up to 32K cores while offering PGAS productivity features to C++ applications. © 2014 IEEE.

Electron microscopy provides images of macromolecular complexes from which the 3-D structure of the complex can be computed when all instances of a complex are identical. An algorithm for characterizing the 3-D spatial statistics of the complex is described and demonstrated for the important case when different instances are not identical.

This paper describes a small range six-axis accelerometer (the measurement range of the sensor is ±g) with high sensitivity DCB (Double Cantilever Beam) elastic element. This sensor is developed based on a parallel mechanism because of the reliability. The accuracy of sensors is affected by its sensitivity characteristics. To improve the sensitivity, a DCB structure is applied as the elastic element. Through dynamic analysis, the dynamic model of the accelerometer is established using the Lagrange equation, and the mass matrix and stiffness matrix are obtained by a partial derivative calculation and a conservative congruence transformation, respectively. By simplifying the structure of the accelerometer, a model of the free vibration is achieved, and the parameters of the sensor are designed based on the model. Through stiffness analysis of the DCB structure, the deflection curve of the beam is calculated. Compared with the result obtained using a finite element analysis simulation in ANSYS Workbench, the coincidence rate of the maximum deflection is 89.0% along the x-axis, 88.3% along the y-axis and 87.5% along the z-axis. Through strain analysis of the DCB elastic element, the sensitivity of the beam is obtained. According to the experimental result, the accuracy of the theoretical analysis is found to be 90.4% along the x-axis, 74.9% along the y-axis and 78.9% along the z-axis. The measurement errors of linear accelerations ax, ay and az in the experiments are 2.6%, 0.6% and 1.31%, respectively. The experiments prove that accelerometer with DCB elastic element performs great sensitive and precision characteristics.

This is a post-peer-review, pre-copyedit version. The final authenticated version is available online at: http://dx.doi.org/10.1109/SC.2012.32 [Abstract] To efficiently scale dense linear algebra problems to future exascale systems, communication cost must be avoided or overlapped. Communication-avoiding 2.5D algorithms improve scalability by reducing inter-processor data transfer volume at the cost of extra memory usage. Communication overlap attempts to hide messaging latency by pipelining messages and overlapping with computational work. We study the interaction and compatibility of these two techniques for two matrix multiplication algorithms (Cannon and SUMMA), triangular solve, and Cholesky factorization. For each algorithm, we construct a detailed performance model that considers both critical path dependencies and idle time. We give novel implementations of 2.5D algorithms with overlap for each of these problems. Our software employs UPC, a partitioned global address space (PGAS) language that provides fast one-sided communication. We show communication avoidance and overlap provide a cumulative benefit as core counts scale, including results using over 24K cores of a Cray XE6 system. Office of Science of the U.S. Department of Energy; DE-AC02-05CH11231 Office of Science of the U.S. Department of Energy; DARPA HR0011-10-9-0008 Ministerio de Ciencia e Innovación; TIN2010-16735 Ministerio de Educación; AP2008-01578 Krell Department of Energy Computational Science Graduate Fellowship; DE-FG02-7ER25308 Office of Science of the U.S. Department of Energy; DE-AC02-05CH11231.

An estimation problem for statistical reconstruction of heterogeneous three-dimensional objects from two-dimensional tomographic data (single-particle cryoelectron microscope images) is posed as the problem of estimating class probabilities, means, and covariances for a Gaussian mixture where both the mean and covariance are stochastically structured. Both discrete (i.e., classes) and continuous heterogeneity is included. A maximum likelihood solution computed by a generalized expectation-maximization algorithm is presented and demonstrated on experimental images of Flock House Virus.

Instances of biological macromolecular complexes that have identical chemical constituents may not have the same geometry due to, for example, flexibility. Cryo electron microscopy provides one noisy projection image of each of many instances of a complex where the projection directions for the different instances are random. The noise is sufficient severe (SNR << 1) that the projection direction for a particular image cannot be easily estimated from the individual image. The goal is to determine the 3-D geometry of the complex (the 3-D distribution of electron scattering intensity) which requires fusing information from these many images of many complexes. In order to describe the geometric heterogeneity of the complexes, the complex is described as a weighted sum of basis functions where the weights are random. In order to get tractable algorithms, the weights are modeled as Gaussian random variables with unknown statistics and the noise is modeled as additive Gaussian random variables with unknown covariance. The statistics of the weights and the statistics of the noise are jointly estimated by maximum likelihood by a generalized expectation maximization algorithm. The method has been developed to the point where it appears able to solve problems of interest to the structural biology community.

Partitioned Global Address Space (PGAS) languages offer programmers the convenience of a shared memory programming style combined with locality control necessary to run on large-scale distributed memory systems. Even within a PGAS language programmers often need to perform global communication operations such as broadcasts or reductions, which are best performed as collective operations in which a group of threads work together to perform the operation. In this paper we consider the problem of implementing collective communication within PGAS languages and explore some of the design trade-offs in both the interface and implementation. In particular, PGAS collectives have semantic issues that are different than in send-receive style message passing programs, and different implementation approaches that take advantage of the one-sided communication style in these languages. We present an implementation framework for PGAS collectives as part of the GASNet communication layer, which supports shared memory, distributed memory and hybrids. The framework supports a broad set of algorithms for each collective, over which the implementation may be automatically tuned. Finally, we demonstrate the benefit of optimized GASNet collectives using application benchmarks written in UPC, and demonstrate that the GASNet collectives can deliver scalable performance on a variety of state-of-the-art parallel machines including a Cray XT4, an IBM BlueGene/P, and a Sun Constellation system with InfiniBand interconnect. © 2011 Elsevier B.V. All rights reserved.

The analysis of Cosmic Microwave Background (CMB) observations is a long-standing computational challenge, driven by the exponential growth in the size of the data sets being gathered. Since this growth is projected to continue for at least the next decade, it will be critical to extend the analysis algorithms and their implementations to peta-scale high performance computing (HPC) systems and beyond. The most computationally intensive part of the analysis is generating and reducing Monte Carlo realizations of an experiment's data. In this work we take the current state-of-the-art simulation and mapping software and investigate its performance when pushed to tens of thousands of cores on a range of leading HPC systems, in particular focusing on the communication bottleneck that emerges at high concurrencies. We present a new communication strategy that removes this bottleneck, allowing for CMB analyses of unprecedented scale and hence fidelity. Experimental results show a communication speedup of up to 116x using our alternative strategy.

Different instances of a biological macromolecular complex need not share the same 3-D electron scattering distribution function due to stoichiometric variability, flexibility and vibrations, etc. Cryo electron microscopy provides 2-D images of each of many such complexes where each image is roughly a projection. A statistical estimation problem is described and demonstrated for determining a statistical description of the complex from such imagery.

A statistical estimation problem for determining 3-D reconstructions from a single 2-D projection image of each of multiple objects when the objects are heterogeneous is described. The method is based on a Gaussian mixture description of the heterogeneity and is motivated by cryo electron microscopy of biological objects.

Cryo electron microscopy is frequently used on biological specimens that show a mixture of different types of object. Because the electron beam rapidly destroys the specimen, the beam current is minimized which leads to noisy images (SNR substantially less than 1) and only one projection image per object (with an unknown projection direction) is collected. For situations where the objects can reasonably be described as coming from a finite set of classes, an approach based on joint maximum likelihood estimation of the reconstruction of each class and then use of the reconstructions to label the class of each image is described and demonstrated on two challenging problems: an assembly mutant of Cowpea Chlorotic Mottle Virus and portals of the bacteriophage P22.

Existing multicore systems already provide deep levels of thread parallelism; hybrid programming models and composability of parallel libraries are very active areas of research within the scientific programming community. As more applications and libraries become parallel, scenarios where multiple threads compete for a core are unavoidable. In this paper we evaluate the impact of task oversubscription on the performance of MPI, OpenMP and UPC implementations of the NAS Parallel Benchmarks on UMA and NUMA multi-socket architectures. We evaluate explicit thread affinity management against the default Linux load balancing and discuss sharing and partitioning system management techniques. Our results indicate that oversubscription provides beneficial effects for applications running in competitive environments. Sharing all the available cores between applications provides better throughput than explicit partitioning. Modest levels of oversubscription improve system throughput by 27% and provide better performance isolation of applications from their co-runners: best overall throughput is always observed when applications share cores and each is executed with multiple threads per core. Rather than “resource” symbiosis, our results indicate that the determining behavioral factor when applications share a system is the granularity of the synchronization operations.

Two algorithms are described for electron microscopy tasks: (1) 3-D reconstruction of an infectious bacteriophage, including the tail, based on a set of single-particle cryo electron microscopy images of identical phage particles with one image per particle and (2) restoration, resolution enhancement, and classification of spherical viruses in a cell based on a tomographic reconstruction of the entire cell. While different tasks, the statistical algorithms, which are maximum likelihood estimators, have extensive commonality. In order to provide solutions for such tasks in a timely fashion, a software toolkit has been created, based on the commonality, for writing parallel software systems to solve these problems.

Statistical algorithms are described for 2- and 3-D signal reconstruction and classification problems based on electron microscopy images where the objects displayed in the images are heterogeneous. Both discrete heterogeneity (there exist classes of objects but all objects within one class are identical) and continuous heterogeneity (there exist classes of objects but, in addition, the objects within a class differ). With continuous heterogeneity, the idea of reconstruction is generalized from reconstruction of a unique 2- or 3-D object to reconstruction of the statistics of the 2- or 3-D object. The basic process is reconstruction by maximum likelihood estimation computed by an expectation-maximization algorithm with classification following as postprocessing given the reconstruction. Two examples are considered, one focusing on classification and one on reconstruction.

A statistical estimation problem for determining 3-D reconstructions from a single 2-D projection image of each of multiple objects when the objects are heterogeneous is described.

A novel omni-directional rolling spherical robot equipped with a high-rate flywheel (BYQ-V) is presented; the mechanical structure of the robot are given, and the gyroscopic effects of high-rate flywheel can improve the dynamic stability of the robot. The simplified dynamic model of the robot is derived based on the constrained Lagrangian method. Moreover, a Linear Quadratic Regulator (LQR) controller and a Percentage Derivative (PD) controller are designed to implement the pose and velocity control of the robot respectively, Finally, the control method are validated through continuous circle motion experiment. This robot is designed for territory or lunar exploration in the future.Copyright © 2009 by ASME

Maximum likelihood statistical algorithms are described for estimating the 3-D variation of the electron scattering intensity of biological objects from cryo electron microscopy images of multiple instances of the object. Three virus objects, two spherical and one helical, are considered. Solution of the maximum likelihood problem by expectation maximization algorithms or by direct maximization of the log likelihood requires large scale computing and end-to-end codesign of biological problem formulation, statistical models, algorithms, and software design and implementation have contributed to achieving practical results.

A statistical approach for computing 3-D reconstructions of virus particles from cryo electron microscope images and minimal prior information has been developed which can solve a range of specific problems. The statistical approach causes high computation and storage complexity in the software implementation. A parallel software toolkit is described which allows the construction of software targeted at commodity PC clusters which is modular, reusable, and user-transparent while at the same time delivering nearly linear speedup on practical problems.