Three-dimensional (3D) reconstruction based on aerial images of unmanned aerial vehicles (UAVs) is important for surveying and monitoring natural resources, supporting land cover analysis and geological hazard mapping. However, the performance of a fixed UAV docking station and manual deployments in large areas is limited by slow data acquisition and transmission. To accelerate data acquisition, emerging mobile UAV docking stations can collaboratively capture images in wide areas. Moreover, by leveraging edge computing power on UAVs and docking stations, the computations of 3D reconstruction can be performed locally, eliminating the need for data transmission. This paper builds a mathematical model for the entire process. The model can be decomposed into three associated problems: area partitioning, computation offloading, and mobile docking station path planning. The optimization objective is to minimize the execution time under the constraint of UAV battery power. A suboptimal solution is first derived using an enumeration-genetic algorithm and then fine-tuned using deep reinforcement learning. The experimental results validate the feasibility of using mobile docking stations for 3D reconstruction. In addition, the numerical results indicate that our proposed solution reduces execution time compared to the benchmark solution.

One of the fundamental tasks in autonomous driving is environment perception for pedestrian detection, where the fused pedestrian detection using camera and light detection and ranging (LiDAR) information imposes challenges since the data alignment, compensation, and fusion between different data modes are challenging and the simultaneous acquisition of data from two different modalities also increases the difficulty. This work addresses the above challenges from both of the hardware and software dimensions. First, a multimodal pedestrian data acquisition platform is designed and constructed using an RGB camera, sparse LiDAR, and data processing module including hardware connection and deployment, sensor distortion correction and joint calibration, and data acquisition synchronization. Pedestrian data from multiple scenes are then collected using this platform to produce and form a dedicated multimodal pedestrian detection dataset. Further, a two-branch multimodal multilevel fusion pedestrian detection network (MM-Net) is proposed, which includes a two-branch feature extraction module and a feature-level data fusion module. Extensive experiments are performed on the multimodal pedestrian detection dataset and KITTI dataset for the comparison with the existing models. The experimental results demonstrate the superior performance of MM-Net.

A high-intensity storm surge hazard exposes residents and facilities to inundation danger. A continuously operating robust inundation emergency logistics system is vital to guarantee lives and support the control of hazardous materials. A massive inundation area, large number of affected facilities, and instantly changing flooding situation bring tremendous challenges to rescue resource allocation. In this article, a rescue resource distribution scheduling of storm surge inundation logistics is proposed to quantitatively formulate the rescue time minimization problem in emergency logistics. The mixed-integer linear programming (MILP) method is proposed for the emergency logistics scheduling model validation and optimality comparison. To enhance the efficiency of creating a good quality allocation strategy when facing large-scale problems, a deep reinforcement learning algorithm—deep deterministic policy gradient (DDPG)—is utilized to search the solutions. Based on a rescue resource scheduling model of storm surge inundation logistics targeting storm surge Mangkhut in September, 2018, a case study of the Futian District, Shenzhen, China, was conducted to verify the correctness and efficiency of the MILP and the DDPG. The optimal schedule solution designed by the MILP had a duration of 3.5138 h, while the solution calculated by DDPG had a duration of 5.065 h for the rescue. The execution time of DDPG was stable and under a second, while the execution time of the MILP was over two hours.

Due to the trade-off of temporal resolution and spatial resolution, spatiotemporal image-fusion uses existing high-spatial-low-temporal (HSLT) and high-temporal-low-spatial (HTLS) images as prior knowledge to reconstruct high-temporal-high-spatial (HTHS) images. However, some existing spatiotemporal image-fusion algorithms ignore the issue that the spatial information of HTLS images is insufficient to support the acquisition of spatial information, which leads to the unsatisfactory accuracy of the fusion result. To introduce more spatial information, the algorithm in this article uses Cycle-generative adversarial networks (GANs) to simulate the change process of two HSLT images at k-1 and k+1, and to generate some simulated images between k-1 and k+1. Then, the generated images are selected under the help of HTLS images, and the selected ones are then enhanced with wavelet transform. Finally, the image with spatial information is introduced into the Flexible Spatiotemporal DAta Fusion (FSDAF) framework to improve the performance of spatiotemporal image-fusion. Extensive experiments on two real data sets demonstrate that our proposed method outperforms current state-of-the-art spatiotemporal image-fusion methods.

Object detection, aiming to recognize and locate objects of interest in aerial images, has historically played a significant role in the remote sensing community. Following remarkable improvements in Earth observation technologies, high-resolution remote sensing (HRRS) images with a bird’s eye view perspective have revealed many categories of objects with sufficient variations in appearance and on complex backgrounds that make HRRS object detection an active but challenging task. The selection of positive samples and negative training instances is an essential factor in influencing detectors’ performance. Related studies have found that many low-quality negative samples in the detectors’ training process have caused training instability and low detection accuracy. In this work, a novel sampling-balance-based multistage network (SB-MSN) is presented to adaptively mine high-quality positive and negative instances for training an accurate detector. It has a series of components to ensure the selection and generation of high-quality examples for training an accurate detector, including a multiscale information retention module, an intersection over union balance sampling strategy, a balance L1 loss, and a multistage network. The proposed detector has been evaluated on three representative HRRS data sets. The extensive experimental results show that our detector can solve the problem of low-quality samples and significantly improve the detection performance of the mAP by 1.4% with the NWPU VHR-10 data set, 3.5% with the high-resolution remote sensing detection (HRRSD) data set, and 4.2% with the detection in the optical remote (DIOR) data set.1

Storm surge hazard annually costs hundreds of lives and billions of dollars for damages in coastal countries. Simulating the storm surge events and alerting flood areas in advance is essential to guarantee the safety of citizens and urban infrastructure. However, due to the complexity of the urban environment, the accuracy of simulation faces challenges. By leveraging the facility of the Internet-of-Things (IoT) technique, the urban flood simulation procedure under storm surge hazard can be improved. In this article, targeting a coastal city usually under the threat of the storm surge, a deep-reinforcement-learning-based storm surge flood simulation approach is proposed to simulate the flood situation under the specific level of the storm surge. By analyzing real-time urban flood data and weather data collected by the IoT system, the model of the urban flood can be constructed and refined. A case study based on storm surge took place on July 1, 2019 in Shenzhen and was conducted to evaluate the effectiveness of the proposed model. The results demonstrate that the proposed method can effectively simulate the storm surge flood, and the achieved accuracy is up to 97.22%.

Big service is an extremely important application of service computing to provide predictive and needed services to humans. To operationalize big services, the heterogeneous data collected from Cyber-Physical-Social Systems (CPSS) must be processed efficiently. However, because of the rapid rise in the volume of data, faster and more efficient computational techniques are required. Therefore, in this paper, we propose a multi-order distributed high-order singular value decomposition method (MDHOSVD) with its incremental computational algorithm. To realize the MDHOSVD, a tensor blocks unfolding integration regulation is proposed. This method allows for the efficient analysis of large-scale heterogeneous data in blocks in an incremental fashion. Using simulation and experimental results from real-life, the high-efficiency of the proposed data processing and computational method, is demonstrated. Further, a case study about cyber-physical-social system data processing is illustrated. The proposed MDHOSVD method speeds up data processing, scales with data volume, improves the adaptability and extensibility over data diversity and converts low-level data into actionable knowledge.

Cloud computing is now a well-adopted computing paradigm. With unprecedented scalability and flexibility, the computational cloud is able to carry out large scale computing tasks in parallel. The datacenter cloud is a new cloud computing model that uses multi-datacenter architectures for large scale massive data processing or computing. In datacenter cloud computing, the overall efficiency of the cloud depends largely on the workload scheduler, which allocates clients' tasks to different Cloud datacenters. Developing high performance workload scheduling techniques in Cloud computing imposes a great challenge which has been extensively studied. Most previous works aim only at minimizing the completion time of all tasks. However, timeliness is not the only concern, reliability and security are also very important. In this work, a comprehensive Quality of Service (QoS) model is proposed to measure the overall performance of datacenter clouds. An advanced Cross-Entropy based stochastic scheduling (CESS) algorithm is developed to optimize the accumulative QoS and sojourn time of all tasks. Experimental results show that our algorithm improves accumulative QoS and sojourn time by up to 56.1 and 25.4 percent respectively compared to the baseline algorithm. The runtime of our algorithm grows only linearly with the number of Cloud datacenters and tasks. Given the same arrival rate and service rate ratio, our algorithm steadily generates scheduling solutions with satisfactory QoS without sacrificing sojourn time.

Petroleum industry has started to embrace the advanced Petroleum Cyber-Physical System (CPS) technologies. Offshore petroleum CPS is particularly difficult to build, mainly due to the challenge in detecting and preventing offshore oil leaking. During the oil exploration and transportation process, the remote multi-sensing technology is typically used for leak detection, enabling the underwater modeling of an offshore petroleum CPS. However, such a technology suffers from insufficient remote sensing resources and large computational overhead. In this work, a cross entropy optimization based leak detection technique is proposed to detect the oil leak, which also facilitates the understanding of the oil leak induced marine pollution. Experimental results on a real Penglai oil spill event demonstrate that the proposed technique can effectively identify the sources of oil spills with accuracy of up to 90.78%.

Summary PM2.5 time series have the features of non‐stationary and nonlinear. Existing forecasting methods for PM2.5 cannot achieve high accuracy for they have ignored the potential characteristics of PM2.5 time series. Aiming at this problem, a hybrid approach using local mean decomposition and Support Vector Regression (SVR)‐Elman (LSE) is firstly proposed in this paper to analyse 5days ahead PM2.5 concentrations for forecasting in Wuhan, China: (1) the meaningful PF1‐PF5 components are extracted from original PM2.5 time series by local mean decomposition; (2) the first high‐frequency product function is managed by using the SVR model, such that the relationship between PM2.5 and other air quality data can be revealed accurately; (3) the other components are trained by Elman model with the sliding window method. Experimental results show that, compared with multiple linear regression, autoregressive integrated moving average, BP neural network, and SVR models, the proposed hybrid LSE model‐based approach exhibits the best performance in terms of R2, MAE, MAPE, RMSE, while it is applied for forecasting in real datasets. Copyright © 2016 John Wiley & Sons, Ltd.

In a petroleum cyber-physical system (CPS), interwell connectivity estimation is critical for improving petroleum production. An accurately estimated connectivity topology facilitates reduction in the production cost and improvement in the waterflood management. This paper presents the first study focused on computer-aided design for a petroleum CPS. A new CPS framework is developed to estimate the petroleum well connectivities. Such a framework explores an innovative water/oil index integrated with the advanced cross-entropy optimization. It is applied to a real industrial petroleum field with massive petroleum CPS data. The experimental results demonstrate that our automated estimations well match the expensive tracer-based true observations. This demonstrates that our framework is highly promising.

The fusion of remote sensing images with different spatial and temporal resolutions is needed for diverse Earth observation applications. A small number of spatiotemporal fusion methods that use sparse representation appear to be more promising than weighted- and unmixing-based methods in reflecting abruptly changing terrestrial content. However, none of the existing dictionary-based fusion methods consider the downsampling process explicitly, which is the degradation and sparse observation from high-resolution images to the corresponding low-resolution images. In this paper, the downsampling process is described explicitly under the framework of compressed sensing for reconstruction. With the coupled dictionary to constrain the similarity of sparse coefficients, a new dictionary-based spatiotemporal fusion method is built and named compressed sensing for spatiotemporal fusion, for the spatiotemporal fusion of remote sensing images. To deal with images with a high-resolution difference, typically Landsat-7 and Moderate Resolution Imaging Spectrometer (MODIS), the proposed model is performed twice to shorten the gap between the small block size and the large resolution rate. In the experimental procedure, the near-infrared, red, and green bands of Landsat-7 and MODIS are fused with root mean square errors to check the prediction accuracy. It can be concluded from the experiment that the proposed methods can produce higher quality than five state-of-the-art methods, which prove the feasibility of incorporating the downsampling process in the spatiotemporal model under the framework of compressed sensing.

Petroleum Cyber-Physical System (CPS) marks the beginning of a new chapter of the oil and gas industry. Combining vast computational power with intelligent Computer Aided Design (CAD) algorithms, petroleum CPS is capable of precisely modeling the flow of fluids over the entire petroleum reservoir and leveraging the massive field data remotely collected at the production wells. It provides field operators with valuable insights into the geological structure and remaining reserves of the reservoir for optimizing their operational strategies. Despite such benefits, petroleum CPS is vulnerable to various cyberattacks that jeopardize the integrity of the field data collected at production wells. Given manipulated field data, CAD software would generate an inaccurate reservoir model which misleads the field operators.

More and more real-time applications need to handle dynamic continuous queries over streaming data of high density. Conventional data and query indexing approaches generally do not apply for excessive costs in either maintenance or space. Aiming at these problems, this study first proposes a new indexing structure by fusing an adaptive cell and KDB-tree, namely CKDB-tree. A cell-tree indexing approach has been developed on the basis of the CKDB-tree that supports dynamic continuous queries. The approach significantly reduces the space costs and scales well with the increasing data size. Towards providing a scalable solution to filtering massive steaming data, this study has explored the feasibility to utilize the contemporary general-purpose computing on the graphics processing unit (GPGPU). The CKDB-tree-based approach has been extended to operate on both the CPU (host) and the GPU (device). The GPGPU-aided approach performs query indexing on the host while perform streaming data filtering on the device in a massively parallel manner. The two heterogeneous tasks execute in parallel and the latency of streaming data transfer between the host and the device is hidden. The experimental results indicate that (1) CKDB-tree can reduce the space cost comparing to the cell-based indexing structure by 60 percent on average, (2) the approach upon the CKDB-tree outperforms the traditional counterparts upon the KDB-tree by 66, 75 and 79 percent in average for uniform, skewed and hyper-skewed data in terms of update costs, and (3) the GPGPU-aided approach greatly improves the approach upon the CKDB-tree with the support of only a single Kepler GPU, and it provides real-time filtering of streaming data with 2.5M data tuples per second. The massively parallel computing technology exhibits great potentials in streaming data monitoring.

Voltage partitioning on functional units/blocks targeting peak power minimization has been demonstrated to be effective for energy reduction considering voltage island shutdown impact. However, the existing technique can only solve this NP-hard problem efficiently on small designs. In this paper, a much faster linear time approximation scheme is proposed, which can approximate the optimal voltage partitioning solution within a factor 1 + ε, for any 0 < ε < 1, and runs in O(n + 1/εO(1)) time, where n is the number of functional units. There are multiple ingredients in such a surprisingly low time complexity algorithm. It first categorizes all the functional units into big functional units and small functional units using an ε related threshold. Subsequently, a rounding based dynamic programming procedure is performed to handle big functional units and a linear programming based algorithm is performed to handle small functional units, which is followed by the discretization of the continuous linear programming solution. Moreover, through the exploration of the unique nature of our problem, a greedy algorithm is proposed to optimally solve the linear programming formulation in a combinatorial fashion. Further, since patching a salient partitioning solution of big functional units with that of small functional units could lead to a much worse combined solution, a highly efficient enumeration process running in time independent of n is proposed. The experimental results demonstrate that our algorithm runs very fast. It needs only 0.3 second to partition a testcase with 5000 functional units which is more than 10000X faster than the existing algorithm while still reducing the peak power by 7.4%.

As VLSI technology enters the nanoscale regime, design reliability is becoming increasingly important. A major design reliability concern arises from electromigration which refers to the transport of material caused by ion movement in interconnects. Since the lifetime of an interconnect drastically depends on the current flowing through it, the electromigration problem aggravates with increasingly growing thinner wires. Further, the current-density-induced interconnect thermal issue becomes much more severe with larger current. To mitigate the electromigration and the current-density-induced thermal effects, interconnect current density needs to be reduced. Assigning wires to thick metals increases wire volume, and thus, reduces the current density. However, overstretching thick-metal assignment may hurt routability. Thus, it is highly desirable to minimize the thick-metal usage, or total wire cost, subject to the reliability constraint. In this paper, the minimum cost reliability-driven routing, which consists of Steiner tree construction and layer assignment, is considered. The problem is proven to be NP-hard and a highly effective iterative rounding-based integer linear programming algorithm is proposed. In addition, a unified routing technique is proposed to directly handle multiple current levels, which is critical in analog VLSI design. Further, the new algorithm is extended to handle blockage. Our experiments on 450 nets demonstrate that the new algorithm significantly outperforms the state-of-the-art work [CHECK END OF SENTENCE] with up to 14.7 percent wire reduction. In addition, the new algorithm can save 11.4 percent wires over a heuristic algorithm for handling multiple currents.

With advancing technology, large dynamic power consumption has significantly limited circuit miniaturization. Minimizing peak power consumption, which is defined as the maximum power consumption among all voltage partitions, is important since it enables energy saving from the voltage island shutdown mechanism. In this paper, we prove that the peak power driven voltage partitioning problem is NP-complete and propose an efficient provably good fully polynomial time approximation scheme for it. The new algorithm can approximate the optimal peak power driven voltage partitioning solution in O(m2(mn/ϵ4)) time within a factor of (1 + ϵ) for sufficiently small positive e, where n is the number of circuit blocks and m is the number of partitions which is a small constant in practice. Our experimental results demonstrate that the dynamic programming cannot finish for even 20 blocks while our new approximation algorithm runs fast. In particular, varying e, orders of magnitude speedup can be obtained with only 0.6% power increase. The tradeoff between the peak power minimization and the total power minimization is also investigated. We demonstrate that the total power minimization algorithm obtains good results in total power but with quite large peak power, while our peak power optimization algorithm can achieve on average 26.5% reduction in peak power with only 0.46% increase in total power. Moreover, our peak power driven voltage partitioning algorithm is integrated into a simulated annealing based floorplanning technique. Experimental results demonstrate that compared to total power driven floorplanning, the peak power driven floorplanning can significantly reduce peak power with only little impact in total power, HPWL, estimated power ground routing cost, level shifter cost and runtime. Further, when the voltage island shutdown is performed, peak power driven voltage partitioning can lead to over 10% more energy saving than a greedy frequency based voltage partitioning when multiple idle block sequences are considered.

This paper proposes a reliability-driven task scheduling scheme for multiprocessor real-time embedded systems that optimizes system energy consumption under stochastic fault occurrences. The task scheduling problem is formulated as an integer linear program where a novel fault adaptation variable is introduced to model the uncertainties of fault occurrences. The proposed scheme, which considers both the dynamic power and the leakage power, is able to handle the scheduling of independent tasks and tasks with precedence constraints, and is capable of scheduling tasks with varying deadlines. Experimental results have demonstrated that the proposed reliability-driven parallel scheduling scheme achieves energy savings of more than 15% when compared to the approach of designing for the corner case of fault occurrences.

Decoupling capacitor (decap) has been widely used to effectively reduce dynamic power supply noise. Traditional decap budgeting algorithms usually explore the sensitivity-based nonlinear optimizations or conjugate gradient (CG) methods, which can be prohibitively expensive for large-scale decap budgeting problems and cannot be easily parallelized. In this paper, we propose a hierarchical cross-entropy based optimization technique which is more efficient and parallel-friendly. Cross-entropy (CE) is an advanced optimization framework which explores the power of rare event probability theory and importance sampling. To achieve the high efficiency, a sensitivity-guided cross-entropy (SCE) algorithm is introduced which integrates CE with a partitioning-based sampling strategy to effectively reduce the solution space in solving the large-scale decap budgeting problems. Compared to improved CG method and conventional CE method, SCE with Latin hypercube sampling method (SCE-LHS) can provide 2× speedups, while achieving up to 25% improvement on power supply noise. To further improve decap optimization solution quality, SCE with sequential importance sampling (SCE-SIS) method is also studied and implemented. Compared to SCE-LHS, in similar runtime, SCE-SIS can lead to 16.8% further reduction on the total power supply noise.