基于点对点WiFi通信实现移动数据流量卸载的架构:预定-发送(英文)

Currently cellular networks do not have sufficient capacity to accommodate the exponential growth of mobile data requirements.Data can be delivered between mobile terminals through peer-to-peer WiFi communications(e.g.WiFi direct),but contacts between mobile terminals are frequently disrupted because of the user mobility.In this paper,we propose a Subscribe-and-Send architecture and an opportunistic forwarding protocol for it called HPRO.Under Subscribe-and-Send,a user subscribes contents on the Content Service Provider(CSP) but does not download the subscribed contents.Some users who have these contents deliver them to the subscribers through WiFi opportunistic peer-to-peer communications.Numerical simulations provide a robust evaluation of the forwarding performance and the traffic offloading performance of Subscribe-and-Send and HPRO.

Feature Extraction by Multi-Scale Principal Component Analysis and Classification in Spectral Domain

Feature extraction of signals plays an important role in classification problems because of data dimension reduction property and potential improvement of a classification accuracy rate. Principal component analysis (PCA), wavelets transform or Fourier transform methods are often used for feature extraction. In this paper, we propose a multi-scale PCA, which combines discrete wavelet transform, and PCA for feature extraction of signals in both the spatial and temporal domains. Our study shows that the multi-scale PCA combined with the proposed new classification methods leads to high classification accuracy for the considered signals.

On the Energy Self-Sustainability of IoT via Distributed Compressed Sensing

This paper advocates the use of the distributed compressed sensing(DCS)paradigm to deploy energy harvesting(EH)Internet of Thing(IoT)devices for energy self-sustainability.We consider networks with signal/energy models that capture the fact that both the collected signals and the harvested energy of different devices can exhibit correlation.We provide theoretical analysis on the performance of both the classical compressive sensing(CS)approach and the proposed distributed CS(DCS)-based approach to data acquisition for EH IoT.Moreover,we perform an in-depth comparison of the proposed DCSbased approach against the distributed source coding(DSC)system.These performance characterizations and comparisons embody the effect of various system phenomena and parameters including signal correlation,EH correlation,network size,and energy availability level.Our results unveil that,the proposed approach offers significant increase in data gathering capability with respect to the CS-based approach,and offers a substantial reduction of the mean-squared error distortion with respect to the DSC system.

Combinations of Feature Descriptors for Texture Image Classification

Texture recognition and classification is a widely applicable task in computer vision. A key stage in performing this task is feature extraction, which identifies sets of features that describe the visual texture of an image. Many descriptors can be used to perform texture classification;among the more common of these are the grey level co-occurrence matrix, Gabor wavelets, steerable pyramids and SIFT. We analyse and compare the effectiveness of these methods on the Brodatz, UIUCTex and KTH-TIPS texture image datasets. The efficacy of the descriptors is evaluated both in isolation and by combining several of them by means of machine learning approaches such as Bayesian networks, support vector machines, and nearest-neighbour approaches. We demonstrate that using a combination of features improves reliability over using a single feature type when multiple datasets are to be classified. We determine optimal combinations for each dataset and achieve high classification rates, demonstrating that relatively simple descriptors can be made to perform close to the very best published results. We also demonstrate the importance of selecting the optimal descriptor set and analysis techniques for a given dataset.

Big Data: Where Dreams Take Flight

From academia to industry, big data has become a buzzword in information technology. The US Federal Government is paying much attention to the big-data revolution. In 2012, fourteen US government departments allocated funds to 87 big-data projects [1].