Average Running Time Research Articles

Dynamic Rapid Scheduling Algorithm for Vehicle Time Sensitive Communication based on CILP and AGA

Vehicle time-sensitive communication (VTSC) is an emerging paradigm where the Time-Sensitive Networking together with V2X provides connected vehicle with deterministic and low communication latency. Scheduling is a critical step to realize the determinism. Recently, as the scale and variety of VTSCs increase, the requirements of simultaneously achieve multicast supportment, short runtime, dynamic schduling ability and low resource overhead challenge the traditional TSN scheduling algorithms. This paper proposes a scheduling scheme with multiple stages to achieve both efficient and rapid scheduling. This paper first formulates the joint routing and scheduling problems by proposing a cluster-Integer Linear Programming (CILP). It aims to accelerate the static scheduling and realize real-time response for dynamic incremental scheduling. An improved topology pruning method and a clustering-aggregation ILP procedure are presented. Then, since the dynamic scheduling will gradually reduce the resource utilization, this paper proposes an adaptive genetic algorithm (AGA) to rapidly perform the resource optimization. The crossover operator, mutation operator, adaptive probability and fitness function are designed, supporting multicast TSN flows. Finally, this paper conducts extensive simulations to validate the proposed algorithms. The results show that, comparing with traditional methods, the proposed CILP method can reduce the average runtime by up to 86.7% for static scheduling and realize real-time dynamic scheduling. The proposed AGA method can reduce the resource optimization runtime by up to 70% while improving the schedulability. The overall multi-stage scheduling mechanism could satisfy the VTSC scheduling requirements.

XAtlas: scalable small variant calling across heterogeneous next-generation sequencing experiments.

The growing volume and heterogeneity of next-generation sequencing (NGS) data complicate the further optimization of identifying DNA variation, especially considering that curated high-confidence variant call sets frequently used to validate these methods are generally developed from the analysis of comparatively small and homogeneous sample sets. We have developed xAtlas, a single-sample variant caller for single-nucleotide variants (SNVs) and small insertions and deletions (indels) in NGS data. xAtlas features rapid runtimes, support for CRAM and gVCF file formats, and retraining capabilities. xAtlas reports SNVs with 99.11% recall and 98.43% precision across a reference HG002 sample at 60× whole-genome coverage in less than 2 CPU hours. Applying xAtlas to 3,202 samples at 30× whole-genome coverage from the 1000 Genomes Project achieves an average runtime of 1.7 hours per sample and a clear separation of the individual populations in principal component analysis across called SNVs. xAtlas is a fast, lightweight, and accurate SNV and small indel calling method. Source code for xAtlas is available under a BSD 3-clause license at https://github.com/jfarek/xatlas.

HCovDock: an efficient docking method for modeling covalent protein–ligand interactions

Covalent inhibitors have received extensive attentions in the past few decades because of their long residence time, high binding efficiency and strong selectivity. Therefore, it is valuable to develop computational tools like molecular docking for modeling of covalent protein-ligand interactions or screening of potential covalent drugs. Meeting the needs, we have proposed HCovDock, an efficient docking algorithm for covalent protein-ligand interactions by integrating a ligand sampling method of incremental construction and a scoring function with covalent bond-based energy. Tested on a benchmark containing 207 diverse protein-ligand complexes, HCovDock exhibits a significantly better performance than seven other state-of-the-art covalent docking programs (AutoDock, Cov_DOX, CovDock, FITTED, GOLD, ICM-Pro and MOE). With the criterion of ligand root-mean-squared distance < 2.0 Å, HCovDock obtains a high success rate of 70.5% and 93.2% in reproducing experimentally observed structures for top 1 and top 10 predictions. In addition, HCovDock is also validated in virtual screening against 10 receptors of three proteins. HCovDock is computationally efficient and the average running time for docking a ligand is only 5 min with as fast as 1 sec for ligands with one rotatable bond and about 18 min for ligands with 23 rotational bonds. HCovDock can be freely assessed at http://huanglab.phys.hust.edu.cn/hcovdock/.

Rapid and robust endoscopic content area estimation: a lean GPU-based pipeline and curated benchmark dataset

ABSTRACT Endoscopic content area refers to the informative area enclosed by the dark, non-informative, border regions present in most endoscopic footage. The estimation of the content area is a common task in endoscopic image processing and computer vision pipelines. Despite the apparent simplicity of the problem, several factors make reliable real-time estimation surprisingly challenging. The lack of rigorous investigation into the topic combined with the lack of a common benchmark dataset for this task has been a long-lasting issue in the field. In this paper, we propose two variants of a lean GPU-based computational pipeline combining edge detection and circle fitting. The two variants differ by relying on handcrafted features, and learned features respectively to extract content area edge point candidates. We also present a first-of-its-kind dataset of manually annotated and pseudo-labelled content areas across a range of surgical indications. To encourage further developments, the curated dataset, and an implementation of both algorithms, has been made public (anonymised url https://doi.org/10.7303/syn32148000, https://github.com/charliebudd/torch-content-area). We compare our proposed algorithm with a state-of-the-art U-Net-based approach and demonstrate significant improvement in terms of both accuracy (Hausdorff distance: 6.3 px versus 118.1 px) and computational time (Average runtime per frame: 0.13 ms versus 11.2 ms).

The Surface Defects Detection of Citrus on Trees Based on a Support Vector Machine

Machine learning and image processing have been combined to identify and detect defects in mature citrus fruit at night, which has great research and development significance. First, a multi-light vision system was used to collect citrus UV images, and from these, 1500 samples were obtained, 80% of which were training and 20% were experimental sets. For a support vector machine (SVM) model with “2*Cb-Cr”, “4*a-b-l”, and “H” as the training features, the accuracy of the final training model in the experimental set is 99.67%. Then, the SVM model was used to identify mature citrus regions, detect defects, and output the defective citrus regions label. The average running time of the detection algorithm was 0.84097 s, the accuracy of citrus region detection was 95.32%, the accuracy of citrus defect detection was 96.32%, the precision was 95.24%, and the recall rate was 87.91%. The results show that the algorithm had suitable accuracy and real-time performance in recognition and defect detection in citrus in a natural environment at night.

Investigation on air conditioning load patterns and electricity consumption of typical residential buildings in tropical wet and dry climate in India

The residential sector accounts for around 24% of the total electricity consumption in India. Recent studies show that air conditioners (ACs) have become a significant contributor to residential electricity consumption. Further, it is predicted that by 2037, the demand for ACs will increase by four times due to their affordability and availability. Not many studies have been found on residential AC usage patterns and the factors (AC load, setpoint, hours of usage) that influence household electricity consumption. This paper investigates the residential AC usage patterns and AC’s contribution to total residential electricity consumption. Twenty-five urban homes from a wet and dry climatic region of India were monitored for nine months (in 2019) to determine overall household electricity consumption patterns, AC usage, and indoor environment during summer, monsoon, and winter. Analysis of seasonal consumption patterns shows a significant difference in electricity usage between homes with ACs and homes without ACs during the summer season. The average electricity consumption for AC homes was 15.1 kWh/day during summer, 6.6 kWh/day during monsoon, and 6.1 kWh/day during the winter season. Results showed that AC alone contributed to 39% of the total household consumption in summers. The peak AC usage in all homes is observed during sleep hours which was generally between 10:00 pm and 6:00 am and the average AC runtime was 6.2 h. The average indoor temperature was recorded as 26.9 °C during the AC ON period. The AC peak load, i.e., the maximum electricity demand during the AC ON period, is 1.7 kW on average during the study period. The average annual consumption of homes with ACs was 2881 kWh, and for non-AC homes, the consumption was 2230 kWh. Findings from our analysis provide a detailed understanding of AC consumption profiles and the difference in electricity consumption characteristics between AC and non-AC homes across different seasons.

Winter indoor thermal environment and heating demand of low-quality centrally heated houses in cold climates

In northern China, district heating systems without terminal control typically serve thousands of end-users, thus inevitably some users suffer from insufficient warmth and others are overheated indoors due to hydraulic imbalances. This paper aimed to understand the indoor thermal conditions and the actual heating demand in low-quality centrally heated dwellings. The driving force of heating demand was analyzed based on a smartphone survey of 233 households in cold climates, and field measurements of winter indoor thermal environment and AC supplemental heating behavior were conducted in a sample of 13 cool dwellings. Logistic regression and heteroskedasticity-robust Ordinary Least Squares were adopted to assess AC usage patterns and driving forces of heating demand, respectively. New knowledge has been gathered regarding average temperatures of 17.4 ℃ and 16.8 °C in cool housing living rooms and bedrooms, respectively, outside the human thermal comfort zone. Simultaneously, intermittent and partial AC usage patterns induced by time and air temperature were observed in these cool end-users for the comfort. The average daily running time of AC devices was 2.0 h for the living room and 1.8 h for the bedroom. There were statistically significant base linking occupants' heating demands to education level, household income, tenure type, presence of children, thermal experience and heating system. However, it was not a significant factor for household size. In addition, a rebound effect of building energy efficiency improvements on the interior temperature was also noticed. The results of this study shed light on better and target-oriented residential internal temperature or heating system design for both thermal comfort requirements and energy-savings.

The rat eustachian tube: Anatomical, histological, and radiological features

PurposeThis study investigated the anatomical and histological characteristics of the rat Eustachian tube (E-tube) and the feasibility of Eustachian tubography in a rat model. Materials and methodsFifteen male Wistar rats were used in this study, and the bilateral E-tubes of each rat were examined. Ten E-tubes were used for anatomical studies, another ten for histological analysis, and the other ten for Eustachian tubography. Five rats were euthanized and decapitated, and ten E-tubes were dissected to describe the anatomy of the E-tube. Ten E-tube specimens obtained from five other rats were sectioned to investigate E-tube histology. Eustachian tubography was performed on the bilateral E-tubes of the other five rats using the trans-tympanic approach. ResultsThe rat E-tubes consisted of bony and membranous parts. Cartilage and bone tissue covered only the bony part. The E-tubes’ mean diameter and overall length were 2.97 ​mm and 4.96 ​mm, respectively. The tympanic orifices’ mean diameter was 1.21 ​mm. The epithelium of E-tubes was mainly composed of pseudostratified ciliated and goblet cells. Eustachian tubography was successfully performed on both sides of the E-tube for each rat. The technical success rate was 100%, the average running time was 4.9 ​min, and no procedure-related complications occurred. On tubography images, the E-tube, tympanic cavity, and nasopharynx could be identified because of the visualization of bony landmarks. ConclusionIn this study, we described the anatomical and histological features of rat E-tubes. With the aid of these findings, E-tube angiography was successfully performed using a transtympanic approach. These results will facilitate further investigation of E-tube dysfunction.

Apple detection and instance segmentation in natural environments using an improved Mask Scoring R-CNN Model.

The accurate detection and segmentation of apples during growth stage is essential for yield estimation, timely harvesting, and retrieving growth information. However, factors such as the uncertain illumination, overlaps and occlusions of apples, homochromatic background and the gradual change in the ground color of apples from green to red, bring great challenges to the detection and segmentation of apples. To solve these problems, this study proposed an improved Mask Scoring region-based convolutional neural network (Mask Scoring R-CNN), known as MS-ADS, for accurate apple detection and instance segmentation in a natural environment. First, the ResNeSt, a variant of ResNet, combined with a feature pyramid network was used as backbone network to improve the feature extraction ability. Second, high-level architectures including R-CNN head and mask head were modified to improve the utilization of high-level features. Convolutional layers were added to the original R-CNN head to improve the accuracy of bounding box detection (bbox_mAP), and the Dual Attention Network was added to the original mask head to improve the accuracy of instance segmentation (mask_mAP). The experimental results showed that the proposed MS-ADS model effectively detected and segmented apples under various conditions, such as apples occluded by branches, leaves and other apples, apples with different ground colors and shadows, and apples divided into parts by branches and petioles. The recall, precision, false detection rate, and F1 score were 97.4%, 96.5%, 3.5%, and 96.9%, respectively. A bbox_mAP and mask_mAP of 0.932 and 0.920, respectively, were achieved on the test set, and the average run-time was 0.27 s per image. The experimental results indicated that the MS-ADS method detected and segmented apples in the orchard robustly and accurately with real-time performance. This study lays a foundation for follow-up work, such as yield estimation, harvesting, and automatic and long-term acquisition of apple growth information.

SkinnerMT

SkinnerMT is an adaptive query processing engine, specialized for multi-core platforms. SkinnerMT features different strategies for parallel processing that allow users to trade between average run time and performance robustness.First, SkinnerMT supports execution strategies that execute multiple query plans in parallel, thereby reducing the risk to find near-optimal plans late and improving robustness. Second, SkinnerMT supports data-parallel processing strategies. Its parallel multi-way join algorithm is sensitive to the assignment from tuples to threads. Here, SkinnerMT uses a cost-based optimization strategy, based on runtime feedback. Finally, SkinnerMT supports hybrid processing methods, mixing parallel search with data-parallel processing.The experiments show that parallel search increases robustness while parallel processing increases average-case performance. The hybrid approach combines advantages from both. Compared to traditional database systems, SkinnerMT is preferable for benchmarks where query optimization is hard. Compared to prior adaptive processing baselines, SkinnerMT exploits parallelism better.

Hybrid Analog-Digital Transceiver Design for RIS-Assisted mmWave MIMO Communications

Reconfigurable intelligent surface (RIS) is a cost-effective solution to support millimeter wave (mmWave) communications suffering from severe blockage. In this letter, we aim to maximize the spectral efficiency (SE) of the RIS-assisted mmWave multiple-input multiple-output (MIMO) system by jointly optimizing the hybrid transceiver and passive RIS under the nonconvex unit-modulus constraints. It is revealed that the considered system is equivalent to a conventional mmWave system with a Kronecker-structured hybrid transmit antenna array. Using this fact, we firstly propose to jointly design the RIS and hybrid transceiver based on the idea of unitary matching. Motivated by the asymptotic orthogonality of array steering vectors for very large systems, a low-complexity two-stage scheme is also developed by simply choosing the transmit/receive array steering vectors associated with the first several largest path gains as the analog precoder/combiner. Numerical results further demonstrate the superior performance of the proposed schemes in terms of the achievable SEs and average run time.

Trade-offs in muscle physiology in selectively bred high runner mice.

A trade-off between locomotor speed and endurance occurs in various taxa, and is thought to be underpinned by a muscle-level trade-off. Among four replicate high runner (HR) lines of mice, selectively bred for voluntary wheel-running behavior, a negative correlation between average running speed and time spent running has evolved. We hypothesize that this trade-off is due to changes in muscle physiology. We studied the HR lines at generation 90, at which time one line (L3) is fixed for the mini-muscle phenotype, another is polymorphic (L6) and the others (L7, L8) lack mini-muscle individuals. We used in situ preparations to quantify the contractile properties of the triceps surae muscle complex. Maximal shortening velocity varied significantly, being lowest in mini-muscle mice (L3 mini=25.2mms-1, L6 mini=25.5 mm s-1), highest in normal-muscle mice L6 and L8 (40.4 and 50.3 mm s-1, respectively) and intermediate in normal-muscle L7 mice (37.2 mm s-1). Endurance, measured both as the slope of the decline in force and the proportion of initial force that could be sustained, also varied significantly. The slope was shallowest in mini-muscle mice (L3 mini=-0.00348, L6 mini=-0.00238), steepest in lines L6 and L8 (-0.01676 and -0.01853), and intermediate in L7 (-0.01145). Normalized sustained force was highest in mini-muscle mice (L3 mini=0.98, L6 mini=0.92) and lowest in L8 (0.36). There were significant, negative correlations between velocity and endurance metrics, indicating a muscle-level trade-off. However, this muscle-level trade-off does not seem to underpin the organismal-level speed and endurance trade-off previously reported as the ordering of the lines is reversed: the lines that run the fastest for the least time have the lowest muscle complex velocity and highest endurance.

Multisource forest point cloud registration with semantic-guided keypoints and robust RANSAC mechanisms

• The proposed method was independent of single tree detection and ground filtering. • A new type of keypoint was proposed for forest point cloud registration. • Robust RANSAC mechanism was introduced to enhance registration performance. The Increasing availabilities of Terrestrial Laser Scanning (TLS) and Unmanned Aerial Vehicle Laser Scanning (ULS) have advanced accurate and detailed forest structural measurements. Registration of multi-perspective observations from different platforms is a prerequisite for a comprehensive forest structure understanding. Currently, forest point cloud registrations are typically done by single tree attribute-based (e.g. tree location, stem diameter) methods, which suffer from various and complex forest compositions and terrains, and can be unreliable for forests with regular tree layouts and insufficient common trees. Therefore, this study presents a unified and marker-free framework for both TLS-TLS and ULS-TLS point cloud registrations in forest areas. First, motivated by wood-leaf separation, semantic-guided keypoints from TLS and ULS point clouds are detected by utilizing a wood response indicator (WRI), which represents reliable repeatability between different views of forest point clouds. Second, an initial correspondence set is generated by using the WRI filter and Binary Shape Context (BSC) descriptor matching. Finally, the initial correspondence set is trimmed and optimized through a robust RANSAC mechanism, consisting of a geometric compatibility filter to guide and trim the hypothesis generation process and a modified hypothesis evaluation step to further optimize the transformation. Experiments have been conducted on six plots involving diverse stem densities and tree species located in forest farms in Guangxi, China. The resulting average registration residual and runtime are 0.049 m and 93 s for TLS-TLS scenes, 0.299 m and 242 s for ULS-TLS scenes, respectively. Comprehensive comparisons demonstrate that the proposed method outperforms other baselines. It is demonstrated that the proposed framework can improve the practicality and efficiency of multi-source data collection and registration, thus facilitating the application of TLS and ULS in forest ecosystem science.

MIP-based solution approaches for multi-site resource-constrained project scheduling

The execution of a project is often distributed among multiple sites. The planning of such a project includes selecting a specific site for the execution of each of the project’s activities and allocating the available resource units to the execution of these activities over time. While some resource units are available at a certain site only, others can be moved across sites. Given the spatial distance between sites, transportation times arise if a resource unit must be transported from one site to another or if the output of an activity must be transported to another site. This planning problem has been introduced in recent literature as the multi-site resource-constrained project scheduling problem. We present a continuous-time model and devise a matheuristic for this planning problem. The continuous-time model uses, among others, binary variables to impose a sequence between activities assigned to the same resource units. In the matheuristic, the binary restrictions on these variables are initially relaxed and iteratively restored for the subset of activities scheduled in the current iteration. We compare the performance of the continuous-time model and the matheuristic to the performance of a discrete-time model and several metaheuristics from the literature using two sets of test instances from the literature. Both the continuous-time model and the matheuristic derive on average superior solutions in shorter average running times than the reference approaches.

Learning to Branch-and-Bound to Route an Autonomous Mobility on Demand System

Autonomous Mobility on Demand (AMoD) is a system consisting of a fleet of centrally-controlled, autonomous vehicles that take customers from their requested origins to their requested destinations. In order to minimize the distance traveled by the fleet, a routing scheme must be developed to service all customer requests. This paper investigates the usage of the branch-and-bound algorithm (BB) to find such a scheme, as well as the usage of a neural network (NN) to speed up BB. Given a fixed road network, sets of randomly generated requests were passed into BB to obtain the ordering of each set that minimized the number of computations, and a NN was trained on this data. New randomly generated request sets were then passed into the trained NN, and runtimes given the NN-predicted orderings were compared to average runtimes over all permutations. For a NN with 1024 nodes in the first and 512 in the second hidden layer and a learning rate of 0.1, using the NN resulted in an average 40% decrease from average runtimes; furthermore, NN-orderings never resulted in increased runtime. Other combinations of NN parameters resulted in around 25% decrease in runtime. Also, BB performed the same number of computations for all permutations with the same first request, simplifying the problem to only finding the next request. These results show that training the NN results in a more efficient, faster routing algorithm that is therefore easier to scale up, enabling at-scale adoption of AMoD.

Identification of Minimal Residual Disease in B-Acute Lymphoblastic Leukaemia Using Uniform Manifold Approximation Projection

Background and objective: Acute Lymphoblastic Leukaemia (ALL) is the most common childhood cancer, with B lineage ALL accounting for approximately 85% of cases. Minimal residual disease (MRD) levels during induction therapy are important prognostic biomarkers that allow patient stratification dependent on their risk of relapse. MRD is quantified by molecular analyses of antigen receptor gene rearrangements or by flow cytometric evaluation of aberrant immunophenotypes. The latter method is increasingly being used worldwide, due to its reduced cost, its provision of predictive biomarkers for antigen therapies and the inclusion of both MRD methodologies in contemporary clinical trials. However, accurate detection of MRD by flow cytometry is complex, time-consuming and relies heavily on user training and expertise. Machine learning has been evolving in the field, with the aim of superseding manual analysis of Flow MRD data. This study aimed to test the ability of the unsupervised machine learning dimensional reduction algorithm, Uniform Manifold Approximation Projection, (UMAP) to visualise and accurately quantify 'MRD islands' from normal cell populations with minimal human intervention. Methods: Flow cytometric data from bone marrow samples from children entered on the UKALL2011 clinical trial were used in the study. For B lineage ALL, the protocol included the identification of a leukaemia associated immunophenotype at diagnosis using 2 antibody combinations and 'on treatment' samples were then ran with the most appropriate antibody tube. In this study, we used data from Euro tube 1 which included the antibodies, CD20 FITC, CD38 PE, CD34 PerCP, CD10 PE-Cy7, CD19 APC, CD45 APC-Cy7 and the nuclear dye, Syto 41, tagged with Pacific Blue. FCS files were uploaded to Cytobank and pre-gated for CD19 positive cells prior to running the UMAP algorithm. Each data set was ran alongside data from 10 normal marrows stained with Euro tube 1. Results: Initially, a selection of samples with varying MRD levels (n=6) were ran using UMAP with a range of minimum distance and number of neighbours parameters. A minimum distance of 0.1 and number of neighbours of 13 was the optimal UMAP settings to produce a very clear separation of MRD islands from healthy cells populations across all samples. (Figure 1). These setting were then used for 341 B-ALL MRD samples at different time points day 8 (n=116), day 15 (n=32), days 29 (n=118), weeks 6-20 (n= 75). MRD levels from UMAP were compared to those generated by the gold standard sequential gating. Excellent concordance was seen, as demonstrated by an R2 value of 0.99 (linear correlation). For 'on treatment samples', UMAP showed excellent accuracy with sensitivity and specificity values of 93% and 100%, respectively. Positive predictive values were 100% while negative predictive values were 94% due to UMAP analysis failing to identify an MRD island in 11 'on treatment' samples, 9 at day 8 and 2 at day 28. The average UMAP run time was less than 2 minutes. Conclusion: Detection of MRD using the UMAP algorithm showed an excellent correlation with the gold standard sequential gating technique. False negatives were due to overlap of antigen expression with normal cells and/or low ALL cell numbers. Current Flow MRD methodologies use 8 or more antibodies and higher target cell numbers, which may reduce the false negative UMAP call rate. In summary, UMAP has promising ability to simplify the downstream data analyses of Flow MRD, overcoming the extensive training needed, human subjectivity and will be time and thus cost saving. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Efficient co-registration of UAV and ground LiDAR forest point clouds based on canopy shapes

• Time-efficient co-registration of UAV and ground LiDAR data is proposed for plantation forests. • Coarse alignment of forest point clouds is divided into ground alignment and canopy image matching. • Canopy images are matched by two pairs of canopy corner points and the canopy overlap rate. • The runtime of coarse alignment is less than 1 s for millions of forest points. Registration of unmanned aerial vehicle laser scanning (ULS) and ground light detection and ranging (LiDAR) point clouds in forests is critical to create a detailed representation of a forest structure and an accurate retrieval of forest parameters. However, tree occlusion poses challenges for those registration methods used artificial markers, and some automated registration methods have low time-efficiency due to the process of object (e.g., tree, crown) segmentation. In this study, we propose an automated and time-efficient method to register ULS and ground LiDAR (including terrestrial and backpack laser scanning) forest point clouds. Registration involves coarse alignment and fine registration, where the coarse alignment is divided into vertical and horizontal alignment. The vertical alignment is implemented by rotating grounds to the horizontal plane, and the horizontal alignment is achieved by canopy image matching. During image matching, vegetation points are projected onto the horizontal plane to obtain two binary images, and then, canopy shape feature, which is described by a two-point congruent set and canopy overlap, is used to match the binary images. Finally, we implement coarse alignment of ULS and ground LiDAR datasets by combining the results of ground alignment and image matching and finish fine registration in six plantation forest plots with sizes of 0.03 ha to 0.25 ha. Experimental results show that the ULS and ground LiDAR data in different plots are registered, of which the coarse alignment errors are less than 0.20 m in the horizontal direction, the final registration accuracy is less than 0.15 m, and the average runtime is less than 1 s. Our study demonstrates the effectiveness of the proposed strategy and has able to perform accurate and quick registration of ULS and ground LiDAR data from plantation forests with different attributes.

A Heuristic Remote Entanglement Distribution Algorithm on Memory-Limited Quantum Paths

Remote entanglement distribution plays a crucial role in large-scale quantum networks, and the key enabler for entanglement distribution is quantum routers (or repeaters) that can extend the entanglement transmission distance. However, the performance of quantum routers is far from perfect yet. Amongst the causes, the limited quantum memories in quantum routers largely affect the rate and efficiency of entanglement distribution. To overcome this challenge, this paper presents a new modeling for the maximization of entanglement distribution rate (EDR) on a memory-limited path, which is then transformed into entanglement generation and swapping sub-problems. We propose a greedy algorithm for short-distance entanglement generation so that the quantum memories can be efficiently used. As for the entanglement swapping sub-problem, we model it using an Entanglement Graph (EG), whose solution is yet found to be at least NP-complete. In light of it, we propose a heuristic algorithm by dividing the original EG into several sub-problems, each of which can be solved using dynamic programming (DP) in polynomial time. By conducting simulations, the results show that our proposed scheme can achieve a high EDR, and the developed algorithm has a polynomial-time upper bound and reasonable average runtime complexity.

Point cloud registration of arrester based on scale-invariant points feature histogram

Arrester is an important lightning protection device in the electrical field. The parameters of arrester such as creepage distance, umbrella distance and diameter are important for product quality, but they are difficult to measure because the shape of arrester is irregular. However, the three-dimensional (3D) reconstruction technique is efficient in measuring arrester parameters. The uniform distributed structure of umbrella skirt on the arrester surface restrict the registration of point cloud. In this paper, a scale-invariant points features histogram (SIPFH) descriptor is proposed; the descriptor combines the characteristics of Scale-invariant Feature Transform (SIFT) and fast point feature histogram (FPFH). Moreover, the improved Levenberg-Marquardt (LM) algorithm is presented, the maximum distance of corresponding points in the iterative process is adjusted to realize the local optimization. The point cloud registration method consists of two parts: primary registration method based on SIPFH, and secondary registration method based on improved LM algorithm. Point clouds of different arresters are collected to establish datasets, some of which have interference. Experimental results indicate that the root mean square error of the method is less than 0.02 m; the average running time is 2.7 s, which is 41.4% of the conventional method based on FPFH.

Secure Communication Model for Dynamic Task Offloading in Multi-Cloud Environment

As the data is increasing day-by-day, the mobile device storage space is not sufficient to store the complete information and also the computation capacity also is a limited resource which is not sufficient for performing all the required computations. Hence, cloud computing technology is used to overcome these limitations of the mobile device. But security is the main concern in the cloud server. Hence, secure communication model for dynamic task offloading in multi-cloud environment is proposed in this paper. Cloudlet also is used in this model. Triple DES with 2 keys is used during the communication process between the mobile device and cloudlet. Triple DES with 3 keys is used by the cloudlet while offloading the data to cloud server. AES is used by the mobile device while offloading the data to the cloud server. Computation time, communication time, average running time, and energy consumed by the mobile device are the parameters which are used to evaluate the performance of the proposed system, SCM_DTO. The performance of the proposed system, SCM_DTO is compared with ECDH-SAHE and is proved to be performing better.