Search

Different thermal evolution degrees on pore development were studied by cryogenic nitrogen adsorption experiment and microscopic photometric analysis. The organic matter Ro, the pore volume, and th...

Coupling learning is designed to estimate, discover, and extract the interactions and relationships among learning components. It provides insights into complex interactive data, and has been extensively incorporated into recommender systems to enhance the interpretability of sophisticated relationships between users and items. Coupling learning can be further fostered once the trending collaborative learning can be engaged to take advantage of the cross-platform data. To facilitate this, privacy-preserving solutions are in high demand—it is desired that the collaboration should not expose either the private data of each individual owner or the model parameters trained on their datasets. In this article, we develop a distributed collaborative coupling learning system, which enables differential privacy. The proposed system defends against the adversary who has gained full knowledge of the training mechanism and the access to the model trained collaboratively. It also addresses the privacy-utility tradeoff by a provable tight sensitivity bound. Our experiments demonstrate that the proposed system guarantees favorable privacy gains at a modest cost in recommendation quality, even in scenarios with a large number of training epochs.

Semantic segmentation of point cloud data, an efficient 3-D scattered point representation, is a fundamental task for various applications, such as autonomous driving and 3-D telepresence. In recent years, deep learning techniques have achieved significant progress in semantic segmentation, especially in the 2-D image setting. However, due to the irregularity of point clouds, most of them cannot be applied to this special data representation directly. While recent works are able to handle the irregularity problem and maintain the permutation invariance, most of them fail to capture the valuable high-dimensional local feature in fine granularity. Inspired by fuzzy mathematical methods and the analysis on the drawbacks of current state-of-the-art works, in this article, we propose a novel deep neural model, Fuzzy3DSeg, that is able to directly feed in the point clouds while maintaining invariant to the permutation of the data feeding order. We deeply integrate the learning of the fuzzy neighborhood feature of each point into our network architecture, so as to perform operations on high-dimensional features. We demonstrate the effectiveness of this network architecture level integration, compared with methods of the fuzzy data preprocessing cascading neural network. Comprehensive experiments on two challenging datasets demonstrate that the proposed Fuzzy3DSeg significantly outperforms the state-of-the-art methods.

Existing research exploits the semantic information from reviews to complement user-item interactions for item recommendation. However, as these approaches either defer the user-item interactions until the prediction layer or simply concatenate all the reviews of a user/item into a single review, they fail to capture the complex correlations between each user-item pair or introduce noises. Thus, we propose a novel Hierarchical and Interactive Gate Network (HIGnet) model for rating prediction. Modeling local word informativeness and global review semantics in a hierarchical manner enable us to exploit textual features of users/items and capture complex semantic user-item correlations at different levels of granularities. Experiments on five challenging real-world datasets demonstrate the state-of-the-art performance of the proposed HIGnet model. To facilitate community research, the implementation of the proposed model is made publicly available (https://github.com/uqjwen/higan).

Deep Convolutional Neural Networks (DCNNs) have enhanced the performance of semantic image segmentation but many challenges still remain. Specifically, some details may be lost due to the downsampling operations in DCNNs. Furthermore, objects may appear in an image at different scales, and extracting features using convolutional filters with large sizes is costly in computation. Moreover, in many cases, contextual information, such as global and background features, is potentially useful for semantic segmentation. In this paper, we address these challenges by proposing a Multi-Receptive Atrous Convolutional Network (MRACN) for semantic image segmentation. The proposed MRACN captures the multi-receptive features and the global features at different receptive scales of the input. MRACN can serve as a module easily being integrated into existing models. We adapt the ResNet-101 model as the backbone network and further propose a MRACN segmentation model (MRACN-Seg). The experimental results demonstrate the effectiveness of the proposed model on two datasets: a benchmark dataset (PASCAL VOC 2012) and our industry dataset.

Various aspects of human activity recognition have been researched so far and a variety of methods have been used to address them. Most of this research assumed that the data sources used for the recognition task are static. In real environments, however, sensors can be added or can fail and be replaced by different types of sensors. It is therefore important to create an activity recognition model that is able to leverage dynamically available sensors. To approach this problem, we propose methods for activity learning and activity recognition adaptation in environments with dynamic sensor deployments. In our previous work, we proposed sensor and activity context models to address sensor heterogeneity and also a learning-to-rank method for activity learning and its adaptation based on the proposed context models. However, most of the existing solutions, including our previous work, require labelled data for training. To tackle this problem and further improve the recognition accuracy, in this paper, we propose a knowledge-based method for activity recognition and activity model adaptation with dynamically available contexts in an unsupervised manner. We also propose a semi-supervised data selection method for activity model adaptation, so the activity model can be adapted without labelled data. We use comprehensive datasets to demonstrate effectiveness of the proposed methods, and show their advantage over the conventional machine learning algorithms in terms of recognition accuracy.

In order to investigate how topographic factors and soil physicochemical properties influenced the distribution and fractionation of rare earth elements (REEs) in soil, Jiangjin district of Sichuan Basin, an area with mountainous topography, was selected as a study area. The concentration of REEs, pH and organic matter (OM) and major elements in 156 topsoil samples were measured and analyzed. The topographic factors considered were elevation, slope, and topographic wetness index (TWI), which were extracted by using the digital elevation model (DEM). The median concentration of total REEs in topsoil of the study area was 147 mg/kg, lower than the Chinese soil background value (164 mg/kg). The concentration of LREEs and HREEs, and the ratio of LREEs/HREEs and LaN/YbN indicated that the distribution and fractionation patterns of REEs in topsoil were LREEs-enriched. Significant Eu negative anomalies and weak Ce negative anomalies were observed in topsoil according to the median values of δEu (0.57) and δCe (0.89). The coefficient of weathering and eluviation (BA), an important factor affecting the distribution and fractionation of REEs, was substantially correlated with δEu (r = 0.344, p

Common use of smartphones is a compelling reason for performing activity recognition with on-board sensors as it is more practical than other approaches, such as wearable sensors and augmented environments. Many solutions have been proposed by academia, but practical use is limited to experimental settings. Ad hoc solutions exist with different degrees in recognition accuracy and efficiency. To ease the development of activity recognition for the mobile application eco-system, Google released an activity recognition service on their Android platform. In this paper, we present a systematic evaluation of this activity recognition service and share the lesson learnt. Through our experiments, we identified scenarios in which the recognition accuracy was barely acceptable. We analyze the cause of the inaccuracy and propose four practical and light-weight solutions to significantly improve the recognition accuracy and efficiency. Our evaluation confirmed the improvement. As a contribution, we released the proposed solutions as open-source projects for developers who want to incorporate activity recognition into their applications.

Social media has evolved into one of the most important channels to share micro-videos nowadays. The sheer volume of micro-videos available in social networks often undermines users’ capability to choose the micro-videos that best fit their interests. Recommendation appear as a natural solution to this problem. However, existing video recommendation methods only consider the users’ historical preferences on videos, without exploring any video contents. In this paper, we develop a novel latent genre aware micro-video recommendation model to solve the problem. First, we extract user-item interaction features, and auxiliary features describing both contextual and visual contents of micro-videos. Second, these features are fed into the neural recommendation model that simultaneously learns the latent genres of micro-videos and the optimal recommendation scores. Experiments on real-world dataset demonstrate the effectiveness and the efficiency of our proposed method compared with several state-of-the-art approaches.

A large volume of knowledge has been accumulated with the prevalence of social networks. Although extra knowledge can mitigate the problem of data sparsity, it is still a challenging task to integrate data across different information spaces for recommendation. In this paper, we propose to address recommendation that involves heterogeneous information spaces, namely interactive space (i.e. user-item), structural space (user-user) and semantic space (user-attribute). Instead of modeling each information space independently, we propose to seamlessly integrate information across heterogeneous spaces. To do this, we propose an attention mechanism in which the users/items attend differently to their structural neighbors (structural space) for learning compact representations. The attentions are parameterized by the interactions between user/item attributes (semantic space), and they are collaboratively learned for the recommendation task (interactive space). In this way, information across different spaces can be complementary to each other for boosting recommendation performance. We also prove that the proposed attentive embedding method is a generalization of traditional social regularization and network embedding methods. We validate the effectiveness of the proposed model with two real world datasets, and show that the proposed model is able to outperform state-of-the-art recommender models significantly.

Detecting roadside safety attributes and distances in point cloud data is a challenging task. The major problems are accurate detection of attributes and attribute centers for calculating safety distance among attributes. In this paper, we propose a point cloud classification framework for roadside safety attributes detection. In addition, we propose an object center approximation technique for distance calculation that has been integrated into the proposed framework. The proposed framework has been evaluated on large real-world point cloud data, and the experimental results are promising. The framework achieved 100% object-wise accuracy on detecting poles and trees, while the overall point-wise accuracy on detecting all seven attributes was 86%.

In recommender systems, the user-item interaction data is usually sparse and not sufficient for learning comprehensive user/item representations for recommendation. To address this problem, we propose a novel dual-bridging recommendation model (DBRec). DBRec performs latent user/item group discovery simultaneously with collaborative filtering, and interacts group information with users/items for bridging similar users/items. Therefore, a user's preference over an unobserved item, in DBRec, can be bridged by the users within the same group who have rated the item, or the user-rated items that share the same group with the unobserved item. In addition, we propose to jointly learn user-user group (item-item group) hierarchies, so that we can effectively discover latent groups and learn compact user/item representations. We jointly integrate collaborative filtering, latent group discovering and hierarchical modelling into a unified framework, so that all the model parameters can be learned toward the optimization of the objective function. We validate the effectiveness of the proposed model with two real datasets, and demonstrate its advantage over the state-of-the-art recommendation models with extensive experiments., Comment: 10 pages, 16 figures, The 28th ACM International Conference on Information and Knowledge Management (CIKM '19)

Graph learning has been demonstrated as one of the most effective methods for semi-supervised dimension reduction, as it can achieve label propagation between labeled and unlabeled samples to improve the feature projection performance. However, most existing methods perform this important label propagation process on the graph with sub-optimal structure, which will reduce the quality of the learned labels and thus affect the subsequent dimension reduction. To alleviate this problem, in this paper, we propose an effective Label Propagation with Structured Graph Learning (LPSGL) method for semi-supervised dimension reduction. In our model, label propagation, semi-supervised structured graph learning and dimension reduction are simultaneously performed in a unified learning framework. We propose a semi-supervised structured graph learning method to characterize the intrinsic semantic relations of samples more accurately. Further, we assign different importance scores for the given and learned labeled samples to differentiate their effects on learning the feature projection matrix. In our method, the semantic information can be propagated more effectively from labeled samples to the unlabeled samples on the learned structured graph. And a more discriminative feature projection matrix can be learned to perform the dimension reduction. An iterative optimization with the proved convergence is proposed to solve the formulated learning framework. Experiments demonstrate the state-of-the-art performance of the proposed method. The source codes and testing datasets are available at https://github.com/FWang-sdnu/LPSGL-code .

Neighborhood-based recommenders are a major class of Collaborative Filtering models. The intuition is to exploit neighbors with similar preferences for bridging unseen user-item pairs and alleviating data sparseness, in other words, learn the sub-graph representation of each user in a user graph. Many existing works propose neural attention networks to aggregate neighbors and place higher weights on the specific subsets of users for recommendation. However, the neighborhood information is not necessarily always informative, and the noises in the neighborhood can negatively affect the model performance. To address this issue, we propose a novel neighborhood-based recommender, where a hybrid gated network is designed to automatically separate similar neighbors from dissimilar (noisy) ones, and aggregate those similar neighbors to comprise neighborhood representations. The confidence in the neighborhood is also addressed by putting higher weights on the neighborhood representations if we are confident with the neighborhood information, and vice versa. In addition, a user-neighbor component is proposed to explicitly regularize user-neighbor proximity in latent space. These two components are combined into a unified model to complement each other for the recommendation task. Extensive experiments on three public datasets demonstrate that the proposed model consistently outperforms the state-of-the-art neighborhood-based recommenders. Furthermore, we study different variants of the proposed model to justify the underlying intuition of the proposed hybrid gated network and user-neighbor modeling components.

Traditional recommender systems encounter several challenges such as data sparsity and unexplained recommendation. To address these challenges, many works propose to exploit semantic information from review data. However, these methods have two major limitations in terms of the way to model textual features and capture textual interaction. For textual modeling, they simply concatenate all the reviews of a user/item into a single review. However, feature extraction at word/phrase level can violate the meaning of the original reviews. As for textual interaction, they defer the interactions to the prediction layer, making them fail to capture complex correlations between users and items. To address those limitations, we propose a novel Hierarchical Text Interaction model (HTI) for rating prediction. In HTI, we propose to model low-level word semantics and high-level review representations hierarchically. The hierarchy allows us to exploit textual features at different granularities. To further capture complex user–item interactions, we propose to exploit semantic correlations between each user–item pair at different hierarchies. At word level, we propose an attention mechanism specialized to each user–item pair, and capture the important words for representing each review. At review level, we mutually propagate textual features between the user and item, and capture the informative reviews. The aggregated review representations are integrated into a collaborative filtering framework for rating prediction. Experiments on five real-world datasets demonstrate that HTI outperforms state-of-the-art models by a large margin. Further case studies provide a deep insight into HTI’s ability to capture semantic correlations at different levels of granularities for rating prediction.

The human genome can reveal sensitive information and is potentially re-identifiable, which raises privacy and security concerns about sharing such data on wide scales. In this work, we propose a preventive approach for privacy-preserving sharing of genomic data in decentralized networks for Genome-wide association studies (GWASs), which have been widely used in discovering the association between genotypes and phenotypes. The key components of this work are: a decentralized secure network, with a privacy- preserving sharing protocol, and a gene fragmentation framework that is trainable in an end-to-end manner. Our experiments on real datasets show the effectiveness of our privacy-preserving approaches as well as significant improvements in efficiency when compared with recent, related algorithms.

Identifying Australian Road Assessment Programme (AusRAP) attributes, such as speed signs, trees and electric poles, is the focus of road safety management. The major challenges are accurately segmenting and classifying AusRAP attributes. Researchers have focused on sematic segmentation and object classification to address the challenges mostly in 2D image setting, and few of them have recently extended techniques from 2D to 3D setting. However, most of them are designed for general objects and small scenes rather than large roadside scenes, and their performance on identifying AusRAP attributes, such as poles and trees, is limited. In this paper, we investigate segmentation and classification in roadside 3D setting, and propose an automatic 3D segmentation and classification framework for identifying AusRAP attributes. The proposed framework is able to directly take large raw 3D point cloud data collected by Light Detection and Ranging technique as input. We evaluate the proposed framework on real-world point cloud data provided by the Queensland Department of Transport and Main Roads.

As the sheer volume of available micro-videos often undermines the users’ capability to choose the micro-videos, in this paper, we propose a multi-source multi-net micro-video recommendation model that recommends micro-videos fitting users’ best interests. Different from existing works, as micro-video inherits the characteristics of social platforms, we simultaneously incorporate multi-source content data of items and multi-networks of users to learn user and item representations for recommendation. This information can be complementary to each other in a way that multi-modality data can bridge the semantic gap among items, while multi-type user networks, such as following and reposting, are able to propagate the preferences among users. Furthermore, to discover the hidden categories of micro-videos that properly match users’ interests, we interactively learn the user-item representations. The resulted categorical representations are interacted with user representations to model user preferences at different level of hierarchies. Finally, multi-source content item data, multi-type user networks and hidden item categories are jointly modelled in a unified recommender, and the parameters of the model are collaboratively learned to boost the recommendation performance. Experiments on a real dataset demonstrate the effectiveness of the proposed model and its advantage over the state-of-the-art baselines.

In this paper, we propose solutions to advance answer selection in Community Question Answering (CQA). Unlike previous works, we propose a hybrid attention mechanism to model question-answer pairs. Specifically, for each word, we calculate the intra-sentence attention indicating its local importance and the inter-sentence attention implying its importance to the counterpart sentence. The inter-sentence attention is based on the interactions between question-answer pairs, and the combination of these two attention mechanisms enables us to align the most informative parts in question-answer pairs for sentence matching. Additionally, we exploit user information for answer selection due to the fact that users are more likely to provide correct answers in their areas of expertise. We model users from their written answers to alleviate data sparsity problem, and then learn user representations according to the informative parts in sentences that are useful for question-answer matching task. This mean of modelling users can bridge the semantic gap between different users, as similar users may have the same way of wording their answers. The representations of users, questions and answers are learnt in an end-to-end neural network in a mean that best explains the interrelation between question-answer pairs. We validate the proposed model on a public dataset, and demonstrate its advantages over the baselines with thorough experiments.

We propose an efficient frequent activity patterns mining in smart environments.We build an accurate activity classifier based on the mined frequent patterns.We distinguish overlapped activities with global and local weights of sensor events.We use publicly available dataset of smart environments to validate our methods. In the past decades, activity recognition has aroused a great interest for the research groups majoring in context-awareness computing and human behaviours monitoring. However, the correlations between the activities and their frequent patterns have never been directly addressed by traditional activity recognition techniques. As a result, activities that trigger the same set of sensors are difficult to differentiate, even though they present different patterns such as different frequencies of the sensor events. In this paper, we propose an efficient association rule mining technique to find the association rules between the activities and their frequent patterns, and build an activity classifier based on these association rules. We also address the classification of overlapped activities by incorporating the global and local weight of the patterns. The experiment results using publicly available dataset demonstrate that our method is able to achieve better performance than traditional recognition methods such as Decision Tree, Naive Bayesian and HMM. Comparison studies show that the proposed association rule mining method is efficient, and we can further improve the activity recognition accuracy by considering global and local weight of frequent patterns of activities.

We propose an activity recognition model balancing accuracy, overhead, data labelling.We propose a similarity measurement method to effectively discover activity patterns.We perform comprehensive experimental and comparison studies to validate our method. In the past decades, activity recognition had aroused great interest for the community of context-awareness computing and human behaviours monitoring. However, most of the previous works focus on supervised methods in which the data labelling is known to be time-consuming and sometimes error-prone. In addition, due to the randomness and erratic nature of human behaviours in realistic environments, supervised models trained with data from certain subject might not be scaled to others. Further more, unsupervised methods, with little knowledge about the activities to be recognised, might result in poor performance and high clustering overhead. To this end, we propose an activity recognition model with labelled and unlabelled data in smart environments. With small amount of labelled data, we discover activity patterns from unlabelled data based on proposed similarity measurement algorithm. Our system does not require large amount of data to be labelled while the proposed similarity measurement method is effective to discover length-varying, disordered and discontinuous activity patterns in smart environments. Therefore, our methods yield comparable performance with much less labelled data when compared with traditional supervised activity recognition, and achieve higher accuracy with lower clustering overhead compared with unsupervised methods. The experiments based on real datasets from the smart environments demonstrate the effectiveness of our method, being able to discover more than 90% of original activities from the unlabelled data, and the comparative experiments show that our methods are capable of providing a better trade-off, regarding the accuracy, overhead and labelling efforts, between the supervised and unsupervised methods.

Chemical lithiation of amorphous FePO4 with LiI in acetonitrile is performed to form amorphous LiFePO4. The amorphous FePO4·2H2O precursor is synthesized by co-precipitation method from equimolar aqueous solutions of FeSO4·7H2O and NH4H2PO4, using H2O2 (hydrogen peroxide) as the oxidizing agent. The nanocrystalline LiFePO4/C is obtained by annealing the amorphous LiFePO4 and in situ carbon coating with sucrose in a reducing atmosphere. The particle size of FePO4·2H2O precursor decreases with increasing reaction temperature. The final LiFePO4/C products completely maintain the shape and size of the precursor even after annealing at 700 °C for 2 h. The excellent electrochemical properties of these nanocrystalline LiFePO4/C composites suggest that to decrease the particle size of LiFePO4 is very effective in enhancing the rate capability and cycle performance. The specific discharge capacities of LiFePO4/C obtained from the FePO4·2H2O precursor synthesized at 75 °C are 151.8 and 133.5 mAh g−1 at 0.1 and 1 C rates, with a low capacity fading of about 0.075 % per cycle over 50 cycles at 0.5 C rate.

With the rapid development of theory and practice in Genomics, research on Public Health Genomics, as a new field is beginning to contribute to people's life. A large volume of genomics data is available but not yet readily used in clinical services. A gap exists between genomics research and public healthcare genomics applications. We believe that machine intelligence can play an important role in transferring genomics knowledge to practical use. As a vision of our research, in this paper we present the usefulness of applying machine intelligence to public health genomics.

Numerous methods have been proposed to address different aspects of human activity recognition. However, most of the previous approaches are static in terms of the data sources used for the recognition task. As sensors can be added or can fail and be replaced by different types of sensors, creating an activity recognition model that is able to leverage dynamically available sensors becomes important. In this paper, we propose methods for activity learning and activity recognition adaptation in environments with dynamic sensor deployments. Specifically, we propose sensor and activity context models to address the problem of sensor heterogeneity, so that sensor readings can be pre-processed and populated into the recognition system properly. Based on those context models, we propose the learning-to-rank method for activity learning and its adaptation. To model the temporal characteristics of the human behaviours, we add temporal regularization into the learning and prediction phases. We use comprehensive datasets to demonstrate effectiveness of the proposed method, and show its advantage over the conventional machine learning algorithms in terms of recognition accuracy. Our method outperforms hybrid models that combine typical machine learning methods with graphical models (i.e. HMM, CRF) for temporal smoothing.

Core-shell LiFePO 4 @C composites were synthesized successfully from FePO 4 /C precursor using the polyvinyl alcohol (PVA) as the reducing agent, followed by a chemical vapor deposition (CVD) assisted solid-state reaction in the presence of Li 2 CO 3 . Some physical and chemical properties of the products were characterized by X-ray powder diffraction (XRD), Raman, SEM, TEM techniques. The effect of morphology and electrochemical properties of the composites were thoroughly investigated. XRD patterns showed that LiFePO 4 has an order olivine structure with space group of Pnma . TEM micrographs exhibited that the LiFePO 4 particles encapsulated with 3-nm thick carbon shells. The powders were homogeneous with grain size of about 0.8 μm. Compared with those synthesized by traditional organic carbon source mixed method, LiFePO 4 @C composite synthesized by CVD method exhibited better discharge capacity at initial 155.4 and 135.8 mAh g −1 at 0.1C and 1C rate, respectively. It is revealed that the carbon layer coated on the surface of LiFePO 4 and the amorphous carbon wrapping and connecting the particles enhanced the electronic conductivity and rate performances of the cathode materials.

The rapid market shift to multi-functional mobile devices has created an opportunity to support activity recognition using the on-board sensors of these devices. Over the last decade, many activity recognition approaches have been proposed for various activities in different settings. Wearable sensors and augmented environments potentially have better accuracy, however performing activity recognition on user mobile devices has also attracted significant attention. This is because of less requirements on the environments and easier application deployment. Many solutions have been proposed by academia, but practical use is limited to testbed experiments. In 2013, Google released an activity recognition service on Android, putting this technology to the test. With its enormous market share, the impact is significant. In this paper, we present a systematic evaluation of this activity recognition service and share the lesson learnt. Through our experiments, we found scenarios in which the recognition accuracy was barely acceptable. To improve its accuracy, we developed ARshell in which we apply a Markov smoother to post-process the results generated by the recognition service. Our evaluation experiments show significant improvement in accuracy when compared to the original results. As a contribution to the community, we open-sourced ARshell on GitHub for application developers who are interested in this activity recognition service.

Since people perform activities differently, to avoid overfitting, creating a general model with activity data of various users is required before the deployment for personal use. However, annotating a large amount of activity data is expensive and time-consuming. In this paper, we create a general model for activity recognition with a limited amount of labelled data. We combine Latent Dirichlet Allocation (LDA) and AdaBoost to jointly train a general activity model with partially labelled data. After that, when AdaBoost is used for online prediction, we combine it with graphical models (such as HMM and CRF) to exploit the temporal information in human activities to smooth out accidental misclassifications. Experiments on publicly available datasets show that we are able to obtain the accuracy of more than 90% with 1% labelled data.

Advances in the 3G and LTE technologies made video on-demand a very popular entertainment for people on the go. However, uniform coverage of mobile networks is almost impossible and, in addition, we often experience drops in bandwidth due to handovers between cellular stations, interference and admission control. In this paper, we analyse the state-of-the-art approaches to streaming on-demand videos, describe the lesson learnt and develop a solution based on our earlier proposal (Col Stream) to improve the user experience. The new Col Stream solution includes an incentive mechanism that mimics a share market in which users can trade their virtual tokens for bandwidth and vice versa. We also describe the prototype developed for the Android phones.

The rapid adoption of mobile phones and growing number of mobile services (like Whatsapp) have created high volume of mobile data traffic. Compared to the traditional voice and SMS services, mobile data is becoming more and more important. Bandwidth is in high demand and is becoming critical for user experiences with mobile contents. Due to a variety of factors (such as admission control policies, signal fading), bandwidth available to a device cannot be directly mapped to the signal strength. This creates a need for bandwidth estimation on mobile phones, similar to the signal strength indicator, to show the estimated bandwidth at the instant. Due to the complexity of bandwidth measurement and/or limitations of earlier generations of mobile phones, there is a small amount of research on bandwidth estimation for mobile phones. This paper revisits a variety of bandwidth estimation methods for wireless and mobile networks, analyses why most of the solutions fail, studies the accuracy limitation of bandwidth estimation in mobile networks, and finds a feasible solution for dynamically estimating bandwidth on mobile phones. We propose GPing-Pair, a low-cost bandwidth approximation for cellular connections that estimates bandwidth on the mobile device without server support. We implemented the proposed method as an application for Android phones.

The number of mobile users of on demand video is growing rapidly. However, bandwidth fluctuation in the 3G/LTE technologies is an obstacle in providing high quality smooth video playout for users on the go. In this paper, we present ColStream that can aggregate bandwidth from ubiquitous devices to ensure high quality video streaming with minimal stalling time. ColStream dynamically adjusts the set of collaborators and the size of video chunks that the collaborators need to pre-fetch ahead of the video chunks playout time to provide smooth video playout. ColStream uses a multi-objective optimisation method to maximise bandwidth and minimise cost. ColStream requires neither external servers nor proxies to provide its functionality. The paper describes the ColStream functionality, architecture, applied algorithms, ColStream prototype, and evaluation of its suitability for effective video streaming in mobile environments.

MAC protocol is an important topic in Wireless Sensor Network, which plays a decisive role in network performance. The current MAC protocol has many problems such as highly dependent on time synchronization, low throughputs or data delay. With multi-channel allocation strategy, the paper put forward a dynamic route allocation algorithm, which reduced data delay and increased the throughputs. The simulation experiment show that the algorithm improves the throughputs, reduces the collision and data delay.

Mobile Ad hoc network is a multihop and provisional automous networks without support of infrastructure, which is composed of a group of mobile nodes with wireless transceiver. Based on MAODV, the paper proposes NMP-MAODV multicast routing protocol for the link disconnection problem caused by node moving so that the node is out of its upstream node's signal range. The protocol improves the packet delivery ratio and average delay in highly mobile network using node mobility prediction and active-link switch. Simulation results prove the feasibility and effectiveness of NMP-MAODV in Ad hoc networks.