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Recently, research intergrading medicine and Artificial Intelligence has attracted extensive attention. Mobile health has emerged as a promising paradigm for improving people's work and life in the future. However, high mobility of mobile devices and limited resources pose challenges for users to deal with the applications in mobile health that require large amount of computational resources. In this paper, a novel computation offloading mechanism is proposed in the environments combining of the Internet of Vehicles and Multi-Access Edge Computing. Through the proposed mechanism, mobile health applications are divided into several parts and can be offloaded to appropriate nearby vehicles while meeting the requirements of application completion time, energy consumption, and resource utilization. A particle swarm optimization based approach is proposed to optimize the the aforementioned computation offloading problem in a specific medical application. Evaluations of the proposed algorithms against local computing method serves as baseline method are conducted via extensive simulations. The average task completion time saved by our proposed task allocation scheme increases continually compared with the local solution. Specially, the global resource utilization rate increased from 71.8% to 94.5% compared with the local execution time.

This paper presents a parallel control method based on the expanded state observer (ESO) for aero-engine blade robot polishing. Aiming to reduce the fluctuation of polishing force caused by environmental noise and modeling errors. First, calibrate six-dimensional force sensor according to the maximum acceleration of the end effector during the polishing process. Then, build the gravity compensation and zero drift compensation model. Besides, use this model to compensate measurement error of the six-dimensional force sensor. Finally, calculate the error between the expected polishing force and the actual feedback value and its derivative value. Use calculation results to design the control boundary layer. The polishing force controller is divided into two parallel control loops to design. When the switching value is in the control boundary layer. A nonlinear active disturbance rejection control (ADRC) loop is used. When the switching value is outside the control boundary layer. An ESO-based sliding mode control (SMC) loop is used. Simulation and experimental results show that the proposed parallel control method based on ESO has a fast response and high robustness compared with FuzzyPID, PID, and ADRC. It can effectively suppress the force fluctuation in the polishing process and significantly improve the surface processing quality of the aero-engine blade.

Most large space debris has large residual angular momentum, and the de-tumbling and capturing operation can easily cause instability and failure of tracking satellites. Therefore, it is necessary to perform real-time dynamic parameter identification of space debris prior to the imminent de-tumbling and capture operation, thus improving the efficiency and success of active debris removal (ADR) missions. A method for identifying dynamic parameters based on the fusion of visual and inertial data is proposed. To obtain the inertial data, the inertial measurement units (IMU) with light markers were fixed on the debris surface by space harpoon, which has been experimentally proven in space, and the binocular vision was placed at the front of a tracking satellite to obtain coordinates of the light markers. A novel method for denoising inertial data is proposed, which will eliminate the interference from the space environment. Furthermore, based on the denoised data and coordinates of the light markers, the mass-center location is estimated. The normalized angular momentum is calculated using the Euler–Poinsot motion characteristics, and all active debris removal parameters are determined. Simulations with Gaussian noise and experiments in the controlled laboratory have been conducted, the results indicate that this method can provide accurate dynamic parameters for the ADR mission.

The growth of smart vehicles and computation-intensive applications poses new challenges in providing reliable and efficient vehicular services. Offloading such applications from vehicles to mobile edge cloud servers has been considered as a remedy, although resource limitations and coverage constraints of the cloud service may still result in unsatisfactory performance. Recent studies have shown that exploiting the unused resources of nearby vehicles for application execution can augment the computational capabilities of application owners while alleviating heavy on-board workloads. However, encouraging vehicles to share resources or execute applications for others remains a sensitive issue due to user selfishness. To address this issue, we establish a novel computation offloading marketplace in vehicular networks where a Vickrey–Clarke–Groves based reverse auction mechanism utilizing integer linear programming (ILP) problem is formulated while satisfying the desirable economical properties of truthfulness and individual rationality. As ILP has high computation complexity which brings difficulties in implementation under larger and fast changing network topologies, we further develop an efficient unilateral-matching-based mechanism, which offers satisfactory suboptimal solutions with polynomial computational complexity, truthfulness and individual rationality properties as well as matching stability. Simulation results show that, as compared with baseline methods, the proposed unilateral-matching-based mechanism can greatly improve the system efficiency of vehicular networks in all traffic scenarios.

NoSQL database can provide massive, high concurrency, and scalable services for storing different types of data. HBase, a type of NoSQL database, in which columns are grouped into column families, is very suitable for storing semi-structured or unstructured spatial vector data. However, since there are few rules and constraints to be followed for the NoSQL database, the design of storage schema for spatial data based on NoSQL is difficult. In this paper, based on our early work, an improved Z-curve storage schema is proposed for spatial vector data. According to our new schema, row key of a geometric object is the Z-curve code of the spatial grids intersected with the geometric object. Moreover, geometric objects with the same row key are stored in a column family. Our proposed method has two features. First, geometric objects adjacent in the location are adjacent in physical storage. Second, redundant exists in storage for improving query accuracy. In our experiments, we compare the improved Z-curve storage schema with a Quadtree storage schema, an R-tree storage schema, and the previous Z-curve storage schema. Query response time, memory usage, and the query accuracy of spatial query on point and range are used to verify the validity of our proposed method. The experimental results show that the two storage schemas based on Z-curve achieve higher query efficiency than the two storage schemas based on tree-the Quadtree storage schema and the R-tree storage schema. More importantly, the query results of the improved Z-curve schema are completely correct, while the query results of the previous Z-curve schema are not.

This paper proposes a real-time path-following control system for a multi-joint robotic fish. The mechanical structure and dynamic model of the robotic fish for pathfollowing are first described. Then, the framework of the path-following control algorithm is established based on the built dynamic model, including a modified line-of-sight (LOS) guidance law, an active disturbance rejection control (ADRC)based heading controller and a proportional-integral-derivative (PID)-based speed controller. Specially, the modified LOS strategy is designed to select the tracking points and also provide the desired heading angle. Afterwards, to overcome systematic uncertainties and environmental disturbances, the ADRC method is adopted to design the heading controller. Meanwhile, the PID controller is also developed to maintain an appropriate swimming speed. Finally, simulations in both linear-and circular-path following are presented to validate the effectiveness of the proposed method.

In the traditional gait skeleton model, the fixed length proportions among different bones cause the loss of model personality, and the shoulder and hip points that are blocked by the body are difficult to be extracted. For those problems, a method based on the spatial and temporal fusion between vision and tactility is proposed, by which a equal proportion 3D gait skeleton model can be restructured in the camera coordinate system accurately through a single-frame image. In the geometric analysis process, some logical assumptions are proposed according to human anatomy and the laws of human movement, and an effective method is proposed for the extraction of thigh slope. The experimental result shows that the consistent equal proportion models, in which single bone length error is eventually controlled within $$\pm \,5\,{\text {mm}}$$, can be extracted in different position under the premise of rapidity with the aid of this method. The extracted shoulder and hip points also meet the real human body skeleton structure, which lays the foundation for the integrity and rationality of the entire skeletal model.

The 5G Internet of Vehicles has become a new paradigm alongside the growing popularity and variety of computation-intensive applications with high requirements for computational resources and analysis capabilities. Existing network architectures and resource management mechanisms may not sufficiently guarantee satisfactory Quality of Experience and network efficiency, mainly suffering from the coverage limitation of road side units, unsatisfactory computational resources and capabilities of onboard equipment, frequently changing network topologies, and ineffective resource management schemes. To meet the demands of such applications, in this article we first establish a novel architecture by integrating the satellite network with the 5G cloud-enabled Internet of Vehicles to efficiently support seamless coverage and efficient resource management. An incentive mechanism based joint optimization problem of opportunistic computation offloading under delay and cost constraints is formulated under the proposed 5G integrated satellite-ground framework, where a vehicular user can either be a service requestor allowed to offload workload to nearby vehicles via vehicle-to-vehicle channels while effectively controlling the cost, or a service provider who provides computing services while protecting profits. As the optimization problem is non-convex and NP-hard, simulated annealing based on the Markov Chain Monte Carlo as well as the metropolis algorithm is applied which can efficaciously obtain both high-quality and cost-effective approximations of global optimal solutions. The effectiveness of the proposed mechanism is corroborated through simulation results. - 2002-2012 IEEE. AcknowledgMent This work is supported in part by the Major Research Plan of the National Natural Science Foundation of China (grant no. 91638204); the 2015 National Natural Science Foundation of China (grant no. 61401381); the State Key Program of the National Natural Science Foundation of China (grant no. 61731012); the 2018 National Natural Science Foundation of China (grant no. 61871339); the Key Laboratory of Digital Fujian on IoT Communication, Architecture and Safety Technology (grant no. 2010499); and the US National Science Foundation (grant no. ECCS-1444009 and CNS-1824518). Scopus

Mobile edge computing (MEC) is a technology that transfers resource to the edge of network, which spares more attention to giving users easier access to network and computation resources. Due to the large amount of data computation needed for devices in Internet of Things, MEC technology is applied to improve computing efficiency. Though MEC can be applied to the Internet of Things, it needs further consideration on how to efficiently and reasonably allocate computing resources, and how to minimize the computing time of all users. This paper proposes a computing resources allocation scheme based on hybrid quantum-behaved particle swarm optimization. Simulation experiments with the network environment based on the Internet of Things is carried out. The results show that this algorithm can accelerate the whole computing process and reduce the number of iterations.

With the rapid development of economic globa-lization, the method of material identification in customs is behind the times. The higher identification efficiency is required by the increase of imports. In this paper, a new hyperspectral imaging intelligent material identifying system based on instantly scanning technique is proposed. The system is investigated by an identification experiment with 5 kinds of flour participated. The spectrum imaging device adopted is from MeiyaPico coded CP-2000B. 250 groups of spectral feature (400 to 750 nm) data are collected by the device. Sample sets retreated by removing the envelope using the radial basis function for kernel are transformed into a subspace. The database building and the identification are accompanied by SVM classification algorithm. In the experiments, the accuracy of system proposed is above 95%.

A new recognizing obstacle method based on the visual information with the different voice was proposed in this paper, which overcame the flaw of the former ETA system to be unable to distinguish the outside obstacle concrete information. This method extraction goal object's color and the shape characteristic, with the established database retrieval math, distinguishes the goal object and then sends out the corresponding pronunciation. After a period of time's training, the blind person can skilled listen to the sound to know the obstacle information.

The visual tracking technology is one of the important parts of robot visual technologies. It plays important actions in the research of intelligent robots, intelligent building and robot football. It extracts parameters of object by employing methods of image processing, and then tracks the object. An object tracking method based on image segmentation with RGB and HSV models and rough set minimal rule set achieved by using reduction algorithm of rough set theory,which is used to control the camera, is presented for tracking a particular object. The experimental results show that the method is effective and robust.

In order to enhance the speed of image processing we apply the optical wavelet transform to vision system by the method of photoelectric hybrid implementation. The state-of-the-art liquid crystal on silicon (LCoS) technology is applied to improve the signal-to-noise ratio of the wavelet transform. A fan out grating implemented by a phase-only LCoS is used to implement multiple channel optical processing. Therefore the parallelism of the vision system is improved further. The research results shows that the optical wavelet transform based vision system is reasonable and feasible. The image feature extraction by optical information processing can enhance the speed of vision processing.