Development,integration, and field evaluation of an autonomous citrus-harvesting robot

Citrus harvesting is a labor-intensive and time-intensive task. As the global population continues to age, labor costs are increasing dramatically. Therefore, the citrus-harvesting robot has attracted considerable attention from the business and academic communities. However, robotic harvesting in unstructured and natural citrus orchards remains a challenge. This study aims to address some challenges faced in commercializing citrus-harvesting robots. We present a fully integrated, autonomous, and innovative solution for citrus-harvesting robots to overcome the harvesting difficulties derived from the natural growth characteristics of citrus. This solution uses a fused simultaneous localization and mapping algorithm based on multiple sensors to perform high-precision localization and navigation for the robot in the field orchard. Besides, a novel visual method for estimating fruit poses is proposed to cope with the randomization of citrus growth orientations. Further, a new end-effector is designed to improve the success and conformity rate of citrus stem cutting. Finally, a fully autonomous harvesting robot system has been developed and integrated. Field evaluations showed that the robot could harvest citrus continuously with an overall success rate of 87.2% and an average picking time of 10.9 s/fruit. These efforts provide a solid foundation for the future commercialization of citrus-harvesting robots.     

Effect of Nano Potassium Fertilizer on Cucumber Amino Acid Component,Volatile Metabolite Components and GLN Family Genes Expression

Wireless Personal Communications - To investigate the effect of nano-potassium silicate on the expression of glutamine (GLN) family gene, amino acid (AAs) componentand volatile metabolites (VMs)...     

Global contextual guided residual attention network for salient object detection

High-level semantic features and low-level detail features matter for salient object detection in fully convolutional neural networks (FCNs). Further integration of low-level and high-level features increases the ability to map salient object features. In addition, different channels in the same feature are not of equal importance to saliency detection. In this paper, we propose a residual attention learning strategy and a multistage refinement mechanism to gradually refine the coarse prediction in a scale-by-scale manner. First, a global information complementary (GIC) module is designed by integrating low-level detailed features and high-level semantic features. Second, to extract multiscale features of the same layer, a multiscale parallel convolutional (MPC) module is employed. Afterwards, we present a residual attention mechanism module (RAM) to receive the feature maps of adjacent stages, which are from the hybrid feature cascaded aggregation (HFCA) module. The HFCA aims to enhance feature maps, which reduce the loss of spatial details and the impact of varying the shape, scale and position of the object. Finally, we adopt multiscale cross-entropy loss to guide network learning salient features. Experimental results on six benchmark datasets demonstrate that the proposed method significantly outperforms 15 state-of-the-art methods under various evaluation metrics.

     

Pulse colliding in a self-mode-locked ring-cavity ti:sapphire laser

A new self-mode-locked ring-cavity Ti:sapphire laser is described that is self-mode locked in both unidirectional and bidirectional operations. We found that clockwise and counterclockwise pulses collide with each other at the Ti:sapphire rod when the laser is mode locked in a bidirectional operation. Spectrum narrowing and pulse broadening were found in bidirectional rather than unidirectional mode-locked operation. This is explained by the performance of a transient grating in the gain medium that restricts oscillation to a narrow spectral range.     

Clearance of iohexol, chromium-51-ethylenediaminetetraacetic acid, and creatinine for determining the glomerular filtration rate in pigs with normal renal function: comparison of different clearance techniques

RATIONALE AND OBJECTIVES: We wanted to improve determination of the glomerular filtration rate (GFR) with plasma clearance techniques because the alternative-renal clearance techniques-may involve inaccurate urine sampling or risk of urinary tract infection when bladder catheterization becomes necessary. Therefore, we compared the renal and plasma clearances of iohexol and chromium-51-ethylenediaminetetraacetic acid (51Cr-EDTA), as well as endogenous creatinine clearance, in 19 normal pigs using different techniques. METHODS: After an intravenous bolus injection of the GFR markers, 16 plasma samples were used to plot the marker concentrations versus time for 4.5 hr. Urine was collected during nine 30-min periods. Plasma clearance was calculated by dividing the dose of marker with the area under the plasma concentration curve (AUC) from the time of injection to infinity using one-compartment (ClAUC-slope) and three-compartment (ClAUC-3comp) models. The renal clearance was calculated by dividing the amount of marker excreted in the urine in a period with the AUC in the same period. This AUC was determined by integrating the total area in the period (Clren adv)--our reference method representing the "true" GFR--or by using the arithmetic mean of the plasma concentrations of the marker at the beginning and end of the urine collection period (Clren simple). Creatinine clearance was determined according to Clren simple. RESULTS: Renal clearances of iohexol and 51Cr-EDTA were significantly higher than creatinine clearance (P = .0002). There was no significant difference between the renal clearances of iohexol and 51Cr-EDTA or between their plasma clearances. The two mathematical methods of calculating the renal clearance of iohexol were highly correlated (rs = .99), as were the two methods of calculating its plasma clearance (rs = .95). Because of the extrarenal clearance of the markers, the plasma clearance methods for iohexol and 51Cr-EDTA always overestimated the true GFR. ClAUC-3comp was the method closest to the true GFR. For iohexol, the median overestimation of the GFR was higher with ClAUC-slope when early plasma samples (30-120 min) after injection of the marker were used (5.5 ml.min-1.10 kg-1) than when late samples (180-270 min) were used (4.0 ml.min-1.10 kg-1). After subtracting the median extrarenal clearances of iohexol and 51Cr-EDTA (previously determined in nephrectomized pigs) from their plasma clearances (ClAUC-3comp), the median overestimation of the true GFR was reduced from 2.0 to 1.1 ml.min-1.10 kg-1 with iohexol and from 2.1 to 1.3 ml.min-1.10 kg-1 with 51Cr-EDTA. CONCLUSION: GFR determination with plasma clearance techniques can be improved in three- and one-compartment models by taking late plasma samples and by subtracting the extrarenal plasma clearance of the species. One-compartment models can be improved by determining a correction formula in the species for the early parts of the decay curve of the plasma concentration of the marker.     

Revisiting the security of secure direct communication based on ping-pong protocol[Quantum Inf. Process. 8, 347 (2009)]

A. Chamoli and C.M. Bhandari presented a secure direct communication based on ping-pong protocol[Quantum Inf. Process. 8, 347 (2009)]. M.Naseri analyzed its security and pointed out that in this protocol any dishonest party can obtain all the other one’s secret message with zero risk of being detected by using fake entangled particles (FEP attack) [M. Naseri, Quantum Inf. Process. online]. In this letter, we reexamine the protocol’s security and discover that except the FEP attack, using a special property of GHZ states, any one dishonest party can also take a special attack, i.e., double-CNOT(Controlled NOT) attack. Finally, a denial-of-service attack is also discussed.     

VPDN在虚拟校园网的应用

本文主要介绍利用虚拟专用拨号网(VPDN)实现虚拟校园网的技术方案,并讨论了虚拟校园网系统的安全性。     

关联规则挖掘在教师成长中的应用

本文在对数据挖掘相关技术、关联规则挖掘算法进行深入研究的基础上,归纳总结了基于粗糙集理论的关联规则挖掘模型和属性约简算法,并将其应用于高校教师的成长中。     

Windows操作系统EFS加密方式应用及解析

本文介绍了Windows操作系统中EFS加密方式的优点、操作步骤、加密原理及解密方法,并详细列举了5个重点注意事项,为全面理解Windows操作系统中使用的EFS加密手段提供了帮助。     

An improved constraint satisfaction adaptive neural network for job-shop scheduling

This paper presents an improved constraint satisfaction adaptive neural network for job-shop scheduling problems. The neural network is constructed based on the constraint conditions of a job-shop scheduling problem. Its structure and neuron connections can change adaptively according to the real-time constraint satisfaction situations that arise during the solving process. Several heuristics are also integrated within the neural network to enhance its convergence, accelerate its convergence, and improve the quality of the solutions produced. An experimental study based on a set of benchmark job-shop scheduling problems shows that the improved constraint satisfaction adaptive neural network outperforms the original constraint satisfaction adaptive neural network in terms of computational time and the quality of schedules it produces. The neural network approach is also experimentally validated to outperform three classical heuristic algorithms that are widely used as the basis of many state-of-the-art scheduling systems. Hence, it may also be used to construct advanced job-shop scheduling systems.