Current Issue : April - June Volume : 2017 Issue Number : 2 Articles : 7 Articles
We proposed a crowd cloud routing protocol based on opportunistic computing to improve the data transmission\nefficiency, reliability, and reduce routing overhead in wireless sensor networks. Based on the analysis of the demand\nof big data processing in wireless sensor network, the data analysis and processing platform for wireless sensor\nnetwork are designed based on the combination with the cloud computing. The cloud platform includes the main\nnodes, the nodes, and the core nodes. There are the engine and the drive between the wireless sensor network\nand the cloud server. Secondly, aiming at the problem of data transmission in the cloud platform, we design an\nopportunistic computing model which is suitable for wireless sensor networks to minimize the weight of routing\nmanagement and network overhead. Then, we design an opportunistic calculation model to guarantee the data\ntransmission scheme of the cloud platform. Finally, by eliminating the factors that may cause the link instability, the\ncrowd cloud routing protocol is proposed. The experimental results show that the proposed crowd cloud routing\nprotocol has the functions of real-time and reliability and reduces the cost of routing request....
Medical ultrasonic imaging has been utilized in a variety of clinical diagnoses for many years. Recently, because of the\nneeds of portable and mobile medical ultrasonic diagnoses, the development of real-time medical ultrasonic imaging\nalgorithms on embedded computing platforms is a rising research direction. Typically, delay-and-sum beamforming\nalgorithm is implemented on embedded medical ultrasonic scanners. Such algorithm is the easiest to implement at\nreal-time frame rate, but the image quality of this algorithm is not high enough for complicated diagnostic cases. As a\nresult, minimum-variance adaptive beamforming algorithm for medical ultrasonic imaging is considered in this paper,\nwhich shows much higher image quality than that of delay-and-sum beamforming algorithm. However, minimumvariance\nadaptive beamforming algorithm is a complicated algorithm with O(n3) computational complexity.\nConsequently, it is not easy to implement such algorithm on embedded computing platform at real-time frame rate.\nOn the other hand, GPU is a well-known parallel computing platform for image processing. Therefore, embedded\nGPU computing platform is considered as a potential real-time implementation platform of minimum-variance\nbeamforming algorithm in this paper. By applying the described effective implementation strategies, the GPU\nimplementation of minimum-variance beamforming algorithm performed more than 100 times faster than the ARM\nimplementation on the same heterogeneous embedded platform. Furthermore, platform power consumptions,\ncomputation energy efficiency, and platform cost efficiency of the experimental heterogeneous embedded platforms\nwere also evaluated, which demonstrated that the investigated heterogeneous embedded computing platforms\nwere suitable for real-time portable or mobile high-quality medical ultrasonic imaging device constructions....
Optical principle embedded image analysis can effectively improve the accuracy of image recognition, but there is\na problem of low efficiency and high computational complexity. In view of the above problems, we design an\nimage fusion mechanism based on an optical embedded scheme. We have proposed the optical image space,\nwhich is a three-dimensional space coordinate system. Each sample point of the system represents a spot. Light\npoints in the image are described using three quantities, which are light intensity, image concentration, and light.\nAn embedded optical image analysis model is proposed. We use a mobile embedding scheme to map multiple\npoints of light into the same optical surface. Optical crowd block structure was proposed for increasing image data\nin an optical image system. The structure can improve the continuity of the image regions in different coordinate\nsystems. The mechanism of crowd fusion for mobile embedded images is proposed. The experimental results show\nthat the proposed mechanism is superior in the aspects of image recognition accuracy and algorithm execution\ncost....
For complexity and efficiency of the multi-objective optimization, we proposed the mobile distance field-driven adaptive\ncrowd optimization algorithm. In space, we modify the surface parameters based on the corresponding changes of the\ndistance field to obtain the moving target�s moving track and moving surface. When the curve of the moving\ntrack is changed, the x axis and the y axis of the moving track are adjusted adaptively. In this paper, the moving process\nis divided into three processes: the target dynamic crowd control, the crowd model algorithm, and the predictive control\nof linear time domain based on the moving target prediction and crowd control algorithm. Then, the multi-objective\noptimization algorithm of moving objects is proposed by using the crowd model to predict the status and the position\nof the target. The experimental results show the high accuracy, low complexity, and high efficiency of the proposed\noptimization algorithm....
It has become the hot research issue that solves the bottleneck of resource management in the development of\nInternet through virtualization. However, there are the challenges of mobility management, resource management,\nand network overhead management in the virtualization of the Internet. First, based on the mobile Internet\nnetwork construction and management mode, the mobile Internet virtual model was proposed for managing the\ndifferences of the port communication between the mobile Internet protocol layer and protocol layer. Secondly,\nbased on the network management cost control and the reconstruction of the Internet virtualization, we designed\nthe network overhead crowd optimization space and management vector. The network overhead crowd management\nmechanism is proposed, which will transport the mobile virtualization Internet topology to point-to-point structure.\nFinally, the simulation results verified the advantages of the network overhead management mechanism of the virtual\nmobile Internet in terms of the cost and real time of the network overhead management....
In order to eliminate the factors that restrict the performance of wireless network data transmission, we proposed\nthe optimal control mechanism of wireless network data transmission. The proposed mechanism solves the\nintelligence problem of the feedback loop, the mobility of relay nodes, and the feedback of the receiving end. On\nthe one hand, to eliminate the external interference factors, we established an optimal feedback loop control\nsystem between the sender and the receiver. On the other hand, in the time linear region, the crowd feedback\nmodule is added to the optimal feedback closed-loop control system based on the linear weight. On the basis of\nthe above schemes, we proposed an adaptive optimization model of mobile data transmission. The experiments\ncompared the proposed optimal crowd feedback optimal control scheme with the optimization strategy of the\ndata transmission. From the results of system efficiency and system throughput performance, the proposed optimal\ncrowd feedback optimal control scheme has an obvious advantage....
A hybrid life detection radar system which transmits a wideband chaotic signal containing\nan embedded single-tone is proposed. The chaotic signal is used for target localization by the\ntime-domain correlation method and synthetic aperture technique, and the single-tone signal is used\nto measure the frequencies of breathing and heartbeat based on an on-chip split-ring integrated sensor\nand Michelson interference principle. Experimental results in free space and in through-wall scenarios\ndemonstrate that the system can realize human detection and localization simultaneously with\nhigh range resolution, high sensitivity, and large dynamic range without complex signal processing.\nThe range resolution is about 10 cm, and the dynamic range is 35 dB for the respiration signal detection\nand 25 dB for the heartbeat signal detection. Due to its good immunity to interference/jamming and\nhigh spectrum efficiency, the proposed system is suitable for post-disaster rescue, elder/infant/patient\nvitality monitoring, and anti-terrorism enforcement applications....
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