Modeling of Mobile Communication Systems by Electromagnetic Theory in the Direct and Single Reflected Propagation Scenario

Guo, Sheng; Dang, Shuping; Hossain, Nadim; Zhang, Xu

doi:10.1007/978-3-662-48683-2_25

Cited by 14 publications

(5 citation statements)

References 10 publications

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“…And concerns about personal data security and anonymity still hinders the application massive application of mHealth [58]. The new contributions in cloud computing, bigdata and network will bring more new production to mHealth field [83] [69]. The connection with braincomputer interface (BCI) application is also a promising topic in future [84] [85].…”

Section: Resultsmentioning

confidence: 99%

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Bigdata Oriented Multimedia Mobile Health Applications

Lv¹,

Chirivella²,

Gagliardo³

2016

J Med Syst

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In this paper, two mHealth applications are introduced, which can be employed as the terminals of bigdata based health service to collect information for electronic medical records (EMRs). The first one is a hybrid system for improving the user experience in the hyperbaric oxygen chamber by 3D stereoscopic virtual reality glasses and immersive perception. Several HMDs have been tested and compared. The second application is a voice interactive serious game as a likely solution for providing assistive rehabilitation tool for therapists. The recorder of the voice of patients could be analysed to evaluate the long-time rehabilitation results and further to predict the rehabilitation process.

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Section: Resultsmentioning

confidence: 99%

“…[75] [86][36] [73] [74][69]. The connection with braincomputer interface (BCI) application is also a promising topic in future[84] [85].…”

mentioning

confidence: 99%

Bigdata Oriented Multimedia Mobile Health Applications

Lv¹,

Chirivella²,

Gagliardo³

2016

J Med Syst

View full text Add to dashboard Cite

show abstract

“…Thereby, it has been widely applied into many research domains [21,28,32,35], such as network routing [11,16], multicast and QoS problem, etc. [15,29].…”

Section: Artificial Fish-swarm Algorithm and Virtual Streammentioning

confidence: 98%

Coverage-enhancing approach in multimedia directional sensor networks for smart transportation

Zhang

Jia

2016

Multimed Tools Appl

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Smart transportation has a significantly impact on city management and city planning, which has received extensive attentions from academic and industrial communities. Different from omni-directional sensing system, as a directional sensing system, the multimedia-directional sensor network holds the special coverage scheme, which is usually used for smart cities, smart transportation, and harsh environment surveillance, for instance, nuclear-pollution regions where are inhospitable for people. This paper advances Virtual Stream Artificial Fish-swarm based Coverage-Enhancing Algorithm (VSAFCEA) as a coverage-enhancing means in multimedia directional sensor networks. Firstly, a concept of virtual streams, based on traditional artificial fish-swarm algorithm, is proposed. Then, the traditional behaviors of fishes in artificial fish-swarm algorithm are modified and expanded with several new behaviors. Finally, the presented VSAFCEA is adopted for coverage-enhancing issue in the situation of directional sensor networks with rotational direction-adjustable model. With a sequence of steps of artificial fishes in virtual stream, the presented VSAFCEA can figure out the approximation to the highest area coverage rate. Based on comparison of these simulation results (results of presented VSAFCEA and that of other typical coverage-enhancing ways in directional sensor networks), the conclusion can be drawn that VSAFCEA could attain higher area coverage rate of directional sensor networks with fewer iterative computing times.

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“…Since wavelet transform cannot realize the best approximation, this paper combines the advantage of curvelet transformsuitable for expressing edge detail information and curve information, and adopts curvelet transform for sparse representation of MRI image. Because l0 norm in Formula ( 6) is non-convex, sparse reconstruction of MRI image is actually the problem of solving l1 norm optimization [16,7]: min || α ||1 such that y= ϕFU α (8) α…”

Section: Compressed Sensing -Mrimentioning

confidence: 99%

Sparse Sampling MRI Reconstruction Technique via ANN

Christilin

2023

Preprint

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MRI is a viable tissue of an image. But in slow processing of image acquisition, MRI creates some noisy data. Compressed Sensing is a join mechanism of compression and sensing. So Compressed Sensing method is guiding to decrease the pattern recognition and suppress the some artifacts in MRI. However, MRI has some sparsity image which is obtained by the curvelet transformation and the reconstructed the noise data. But the issue of curvelet transformation is that it could not manipulate the sparse image. So the Patch Based Directional Curvelet Transformation (PBDCT) recovered the problem of that transformation in this research. But sparse sampling is built a hardware which is very difficult. Hence the proposed sparse sampling process is utilized for Artificial Neural Network (ANN) Experimentally, this proposed method is compared with SIDCT and PBDCT based on Signal-to-Noise Ratio (SNR) and Relative L2 Norm Error (RLNE)

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Modeling of Mobile Communication Systems by Electromagnetic Theory in the Direct and Single Reflected Propagation Scenario

Cited by 14 publications

References 10 publications

Bigdata Oriented Multimedia Mobile Health Applications

Bigdata Oriented Multimedia Mobile Health Applications

Coverage-enhancing approach in multimedia directional sensor networks for smart transportation

Sparse Sampling MRI Reconstruction Technique via ANN

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