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Cooperative communication techniques are widely used in large-scale wireless networks for improving network throughput. This is achieved through the creation of transmission diversity by forming multiple cooperative transmission links which involve several relaying nodes as helpers for an end-to-end data transmission. Consequently, link interference range will inevitably be enlarged, which will adversely hamper the diversity created and offset the network throughput improvement as a result. This problem has not been properly addressed by most cooperative communication protocols reported so far. In this paper, a novel protocol that performs cooperative medium access control as well as minimizes the interference range, namely MIR-CMAC, is proposed to directly address the enlarged link interference range problem introduced by cooperative communication. Specifically, functions like channel reservation, cooperative priority differentiation and contention for relay selection are collectively enabled in MIR-CMAC to reduce channel access collision and select the suitable helper node that can maximize the integrated link data rate and minimize the link interference concerned. In addition, a full interference model is established to evaluate the effect of interference across the whole process from channel access to packet transmission and derive a new network throughput analysis method for MIR-CMAC. Both numerical and simulation results demonstrate the effectiveness of MIR-CMAC and its performance improvements over other protocols.

Clustering objects remains challenges in seeking an under-lying partition by exploiting multiple views. Popular clustering algorithms focus on designing various constraints to handle particular representation tasks, all of which rely on a predefined pairwise similarity (sample-to-sample). However, the pairwise similarity is notoriously vulnerable to noise or outliers contaminations, resulting in sub-optimal clustering performances. To tackle the issue, this paper proposes to enhance multi-view clustering by exploring varieties of high-order statistics within multi-view data, named by HIgh-order Similarity and essential Tensor clustering method (HIST). The HIST incorporates both high-order similarity (samples-to-samples) and high-order correlation (view-to-view) into an adaptive learning model to comprehensively exploit the inherent clustering structure. Experimental results on six real datasets show the superiority of our approach over the ten popular methods.

In this paper, we propose a SWIPT-based distributed node sleep scheduling method combining with clustering technology. The information transmission layer adopts a distributed topology structure. Active nodes are divided into cluster head nodes and cluster member nodes. The cluster head node acts as a relay of cluster member nodes, which can reduce the single-hop communication distance and greatly reduce the energy consumption of information transmission. The energy-carrying communication process is divided into three time slots: the first time slot, the sensor node collects energy, according to the residual energy of the node, the energy collected in the first time slot, and the requirements of network connectivity, the sleep scheduling optimization algorithm will divide the nodes into sleeping nodes and active nodes, and the active nodes are clustered according to the relative position of the information sending rate of the node; in the second time slot, the cluster head node receives the information sent by the cluster member node, and the sleeping node continues to collect energy; In three time slots, the cluster head node processes the collected data and sends it to the sink node, and the cluster member nodes and sleeping nodes continue to collect energy. Finally, according to the user's requirements for energy utilization and network coverage, an appropriate time slot division factor is selected. Compared with the centralized sleep scheduling method, the energy utilization and coverage of the network are improved.

To reveal fault propagation paths is one of the most critical studies for the analysis of power system security; however, it is rather dif cult. This paper proposes a new framework for the fault propagation path modeling method of power systems based on membrane computing.We rst model the fault propagation paths by proposing the event spiking neural P systems (Ev-SNP systems) with neurotransmitter concentration, which can intuitively reveal the fault propagation path due to the ability of its graphics models and parallel knowledge reasoning. The neurotransmitter concentration is used to represent the probability and gravity degree of fault propagation among synapses. Then, to reduce the dimension of the Ev-SNP system and make them suitable for large-scale power systems, we propose a model reduction method for the Ev-SNP system and devise its simpli ed model by constructing single-input and single-output neurons, called reduction-SNP system (RSNP system). Moreover, we apply the RSNP system to the IEEE 14- and 118-bus systems to study their fault propagation paths. The proposed approach rst extends the SNP systems to a large-scaled application in critical infrastructures from a single element to a system-wise investigation as well as from the post-ante fault diagnosis to a new ex-ante fault propagation path prediction, and the simulation results show a new success and promising approach to the engineering domain.

Emotion, as an advanced function of the human brain, affects kinds of human behaviors. Electroencephalographs (EEG) are widely used in the field of emotion classification owing to their low cost and portability. In this work, we study the effects of a non-linear EEG feature and a channel selection method on emotion recognition. First, the fractal dimension(FD) which could reflect the state of the brain is extracted with a sliding window. The top seven channels are screened out by calculating the F-score from the whole samples. Then, based on the signals from forehead channels, filtered channels and associated channels, emotions on valence and arousal are classified by Support Vector Machine(SVM) and K Nearest Neighbours(KNN). The result shows that the forehead channels Fp2, AF8, Fpz play an important role in valence classification. When combining the forehead channels with other channels that have higher F-score, the SVM classifier has a better accuracy on the whole set with 89.37% on valence and 87.07% on arousal. Besides, the overall accuracy calculated on each participants with associated channels get significant improvement. Especially, the KNN classifier has a much better result on every subject. This phenomenon indicates that by combining the higher F-score channels with the forehead channels, the associated channels can not only take advantage of the forehead channels' ability to categorize emotions but also consider individual differences.

As the occurrence of depression in society becomes increasingly more common, it is an urgent task to find more objective and effective tools for real-time depression assessment. Gait analysis offers a new low-cost and contactless method for depression diagnosis. Therefore, interest in gait-based depression detection using depth sensors, such as Kinect, has grown rapidly in recent years. In this paper, a pseudo-velocity model is built to analyze the abnormal gait related to the depression by combining the velocity and angular velocity of the joints. Subsequently, we extract some features in time and frequency domain from our model to establish the classification model for depression detection. Experimental results on depression gait data recordings from 43 scored-depressed and 52 non-depressed individuals show that the proposed method achieves a good classification accuracy of 92.35 % and is superior to other existing methods. The outstanding classification performance suggests that the proposed method has notential clinical value in depression detection.

Tens of millions of people suffer from depression worldwide. It is urgent to explore an effective method for diagnosing depression. This study developed a novel of multimodal feature fusion depression recognition method based on functional near-infrared spectroscopy (fNIRS). Sixty volunteers, including thirty patients with depression and thirty healthy controls, participated in the study. The 22-channel fNIRS device recorded the participants’ brain oxyhemoglobin (HbO) and deoxyhemoglobin (HbR) concentration changes in the positive, neutral and negative affective words’ stimulation. K-nearest neighbors (KNN) and support vector machine (SVM) classifiers were used to recognize depressed patients from normal people, and 10-fold cross-validation was used to verify the classification result. Under the three single-mode features, the accuracy rates were 85.69%, 88.32% and 86.77%, corresponding to the positive condition, neutral condition and negative condition. Then, we used concatenation and linear combination for feature fusion. For the concatenation fusion method, the principal component analysis (PCA) was used to reduce the dimension. The result showed that feature fusion can relatively improve the recognition rate of people with depression, compared with single-model features. The optimal feature fusion method is to concatenate the neutral features and negative features, and the best accuracy reaches 94.45%. The study may provide a more accurate and convenient method for depression detection.

Multi-view clustering aims to partition the data into their underlying clusters via leveraging multiple views information. To exploit cross-view information, existed approaches in tensor-based subspace learning attract much attention. In order to explore essential tensor, the most recent work mainly focuses on capturing representation tensor with sparse and low-rank constraints. However, one shortcoming is that this process may suffer from instability since it did not consider retaining local structure between samples. To tackle the issue, we introduce a novel self-expressive tensor learning method considering both global and local constraints to promote the learning of representation tensor. In particular, we construct a tensor-based subspace representation that joint low-rank and graph-regularized tensor learning to a united optimization problem. The essential global structure and high-order correlations can be naturally captured through low-rank self-expressive tensor learning. Meanwhile, the local structures can be preserved by introducing graph regularized terms on representation tensor, thus bring benefits to subsequent clustering task. An effective optimization procedure for solving the proposed model is presented. We conduct extensive experiments on text, object, and gene expression datasets. The experimental results well demonstrate that the proposed method, named by TLGRL, achieves superiority over benchmark methods.

Accurate nasopharyngeal carcinoma (NPC) segmentation in magnetic resonance image (MRI) is crucial for diagnosis and treatment. However, most existing deep learning methods performed unsatisfactorily, since NPC is infiltrative and typically has a small or even tiny volume with indistinguishable boundary, making it indiscernible from tightly connected surrounding tissue in immense and complex background. To address the background dominant problem, this paper proposes a coarse-to-fine deep model. The proposed model starts with predicting a coarse mask with a well-designed segmentation module, followed by a boundary rendering module, which exploits semantic information from different layers of feature maps to refine the boundary of the coarse mask. The designed rendering module is shown to achieve superior performance with dramatically fewer parameters by operating only on the segmented mask, rather than on the whole feature maps as the popular methods do. Extensive experiments are conducted on a collected dataset consisting of 2000 MRI slices from 596 patients. Experimental results demonstrate that the proposed model not only outperforms six popular segmentation models but also has a considerable generalization capability on existing models.

In this paper, a four-element MIMO antenna with slots and partial ground plane for 5G communication systems operating at C-band (3.4GHz - 3.8GHz) is presented. It is designed on Rogers RT/Duriod 5880 with a dielectric constant and thickness of 2.2 and 0.787mm, respectively. The dimensions of the slots and the partial ground plane are optimized by applying a heuristic search algorithm, the Hill Climbing, to increase the impedance bandwidth and reduce the mutual coupling between the antenna elements without using a decoupling structure or extra spacing. It achieves a huge bandwidth of 539.4MHz covering 3.2926GHz to 3.832GHz at S 11 ≤ -10dB and resonates at 3.595GHz with a reflection coefficient and mutual coupling of -46.45dB and < -16dB, respectively. It also achieves an envelope correlation coefficient (ECC) of < 0.017, diversity gain (DG) of ~10dB and channel capacity loss (CCL) of < 0.18bits/sec/Hz throughout the operating frequency band, respectively.

In this paper, we investigate the fading characteristics in intra-vehicle channels based on a vehicle testbed and measurements which reflect the channel loss behavior contributed by attenuation and fading (large-scale and small-scale). We show that small-scale fading is dominant over other factors such as large-scale fading, which can be described with the Rician model. By applying the moment-based estimator method, we obtain the Rician K-factor that is correlated with the transmission distance given and follows the power law. We also show that the intra-vehicle channel can be described as near-Rayleigh for most of locations inside a vehicle. In addition, we derive an upper bound of the signal length based on our measurements for analyzing fading properties in vehicles.

In this paper we study interference reducing and resource allocation in Unmanned aerial vehicle (UAV) wireless powered communication system with two UAVs and two ground nodes (GNs). In existing scenarios interference exists at the receiver because multiple GNs transmit information at the same time. In order to reduce interference at the receiver, a new scenario is proposed in this paper. In the proposed scenario, one GN transmit information while another is receiving energy. Minimum uplink throughput is maximized by optimizing trajectory of UAVs and resource allocation. The optimization problem is decomposed into three subproblems which are approximated to convex optimization problems. Simulation results show that the new scenario achieves larger minimum uplink throughput than original scenario.

In this paper we investigate the performance of Dedicated Short-Range Communications (DSRC) or IEEE 802. 11p/OCB transceiver gathering performance statistics and relating its usage for ensuring safety and mitigating collision avoidance in Vehicle-to-Vehicle (V2V) communications. To achieve this, we created a low-cost Software Defined Radio (SDR) based DSRC testbed. We focus on quality of service (QoS) and robustness of such a system to assess how it could be used to support road safety applications and assist cooperative awareness in smart cities. To deal with real-world scenarios, we established a testbed using SDR devices and tested in varied scenarios. Our experiments are tailored to give insights into the fundamental challenges regarding how the practical driving safety issues can be effectively addressed by different technologies such as SDR, V2V communications, sensing and their combination. The results we collected and analysed can be used to support future V2V experimentation.

In this paper, we investigate the intra-vehicle multipath channel properties in non-line-of-sight conditions based on field measurements. The test was conducted using a software-defined-radio (SDR) based testbed within a frequency range of 2 GHz to 3 GHz. The results reveals the property of the power delay profile for 49 different testing locations and shows the channel behavior across the passenger and boot compartments of the vehicle investigated. The coherence bandwidth and delay spread of the channel are also shown to vary between 8.7 MHz to 24 MHz and between 50 – 75 ns, respectively. In addition, the dependency of coherence bandwidth and delay spread on distance and location are also analyzed.

To address challenges on enabling reliable wireless communications inside vehicles, we initialize an investigation on the impact of intra-vehicle channel behavior and potential Electromagnetic Interference (EMI) that could hamper the performance required for wireless transmission in this environment. The investigation carried out over a vehicle testbed produces real-world data showing the loss performance and EMI across different compartments of the vehicle. The results reveal how the fading related loss, together with EMI at both peak and average levels contribute to the impairment of the intra-vehicle wireless channel.

This paper analyzes the present situation and development trend of charging blasting process in underground metal mines in China, introduces the key technology of intelligent explosive field mixed loading vehicle for underground mine. The research focuses on the autonomous driving and barrier technology of the underground intelligent explosive vehicle, the remote (range of visibility) wireless remote control and automatic hole finding technology of the working arm, the automatic sending and returning of the explosive pipe and the identification technology of the bottom of the hole. The industrial application experiment of underground intelligent explosive mixed loading vehicle in Fankou Lead Zinc mine is introduced. The application results show that the underground explosive vehicle in China has fully realized the automation and intelligence of underground charging. This equipment effectively improves the charging efficiency and safety.

Nowadays, online social media have become an integral part of people daily life. To provide high-quality contents, a variety of recommender systems have been applied to social platforms. On the framework of epidemic theory, in this paper we present a novel node-level rumor propagation model that takes into account the influence of recommender systems. With the proposed model, we perform a series of numerical simulations to investigate the dynamics of rumor propagation. The result shows that with a recommendation mechanism rumors can fast spread through a social network. Also, we examine the effects of some factors in the proposed model. This paper provides theoretical insight into the influence of recommender systems on rumor propagation.

There are a large number of application for the speaker recognition with a small size of speakers. A characteristic for this type of application is that the number of speakers is often changed slightly. To adapt this characteristic, we apply one-versus-rest(OvR) and neural network to implement speaker recognition. The task is first split into a series of binary classification problems by OvR strategy. The binary classification problems are then solved by neural network and the recognition result is obtained by the simple majority voting. When a few speakers are added to the set of speakers, we can only train the binary classifiers for the newly added speakers. The experiment on a dataset with small-scale speakers shows that both the recognition accuracy and the time cost is satisfactory, which indicates that the method is effective and feasible.

In this paper, we study energy efficiency optimization in OFDM based two-way DF relaying network with energy harvesting. Instead of time switching (TS) and power splitting (PS) schemes, we adopt OFDM modulation method, where subcarriers are divided into two groups to achieve information decoding (ID) and energy harvesting (EH) separately. The formulated EE optimization problem is non-convex constrained by the minimum information rate and the maximum transmission power. By exploiting fractional programming, an iterative resource allocation algorithm is proposed to solve the problem. Then we adopt dual decomposition and sub-gradient method to obtain the optimal variables, where subcarrier grouping and power allocation are jointly optimized to maximize the system energy efficiency.

In order to improve the efficiency of information transmission, this paper proposes an energy trading method based on wireless power supply. In the proposed method, the system consists of a source node, a relay node, a destination node and several energy supply points. Since the relay node is selfish, it does not consume its own energy to help the source node forward information. So this paper designed a series of energy-reward pairs. Through the contract theory designed in this paper, we study how to optimize the rewards that the destination node pays to the ESP and the energy provided by the ESP to the relay node to maximize the utility of the social welfare. The simulation results show that the energy trading method proposed in this paper can make the utility of the destination node reach the optimal value and effectively improve the information transmission efficiency.

This paper considers a single user uplink transmission system based on rateless code in Cloud Radio Access Network (C-RAN) under block fading channel. The system consists of a single user, a cluster of remote radio heads (RRHs), and a building baseband unit (BBU) pool. The user encodes the messages with rateless code and sends them to RRHs. Each RRH quantizes the received signals and uploads them to the BBU pool through the high speed fronthaul link. BBU pool applies the belief propagation (BP) algorithm for joint decompression and decoding of the received signals. With only the knowledge of the statistics of the channel states, we design the universally good degree profile adopted at the user based on the extrinsic information transfer (EXIT) analysis for the decoding process at the BBU. The simulation results show that, compared with the conventional BEC degree profile, the rateless code with the optimized degree profile achieves a better performance in the metric of average system throughput.

Voice activity detection (VAD) is an essential part of speech processing system, which aims at extracting active speech information from continuous speech signals. In this paper, we firstly treat VAD as a two-class problem: active speech frame and nonspeech frame, in which the active speech frame is composed of information frame and noise frame. Support vector machine is then applied to solve the two-class problem. Finally, noise frames are removed from the active speech frames by the prior knowledge of noise. An experiment on a speech dataset of isolated numbers shows the accuracy and recall rate on test set reaches 99.49% and 98.06% respectively, which indicates that the proposed method is effective for isolated-word VAD task.

The prediction of price trend in stock market is a challenging task due to the inherent complexity and dynamics in price movement. Many machine learning algorithms, such as Support Vector Machine, Artificial Neural Network, and Hidden Markov Model, have been applied to it and achieved positive results. Long Short-Term Memory (LSTM), as a variant of RNN, can obtain hidden dependencies in data and has shown a significant performance in processing time series data. In this paper, we apply LSTM networks to predict the price movement of a short-term and test it by an experiment on some stocks randomly selected from CSI 300 constituent stocks. The experiment shows that the precision, recall rate and critical error of LSTM are all better than that of the random prediction. It indicates that LSTM can be used in the trend prediction of stock price. We also notice that many improvements need to be done in future.

Road accidents and traffic congestion are challenging issues for global transport systems. Autonomous vehicles and connected vehicles are promising technologies to improve road safety and reduce traffic congestion. In this paper we investigate the performance of connected vehicles and assess the positive impact of connected vehicles on the collaborative road safety applications. To achieve the research goal, we first develop a low cost and scalable connected vehicles testbed, which consists of laptops equipped with Wi-Fi transceivers. The main concern is that collaborative road safety applications need to be widely tested with vehicle to vehicle (V2V) communications. However, the commercial dedicated short-range communications (DSRC) devices are very expensive, which makes it difficult to set up a large connected vehicles testbed. Based on the experiments on the V2V communications, the performance of forward collision avoidance under emergency events, which is a representative collaborative road safety application, is analysed. Interesting insights to the performance of V2V and the improvement of collision avoidance with V2V are obtained. The research works could be used for the analysis of connected vehicles impact on other collaborative roads safety applications.

In this paper, we propose a cooperative spectrum sharing protocol based on OFDM two-way relaying with joint optimal subcarrier and power allocation. Specifically, the secondary system helps the primary system achieve their target rates through OFDM two-way relaying, where the secondary system forwards the primary signal by using a fraction of subcarriers and power. In return, the secondary system can gain spectrum access by using the remaining subcarriers and power to transmit its own signal. Joint optimal subcarrier and power allocation is derived aiming to maximize secondary transmission rate with primary transmission rate constraint. Simulation results demonstrate a significant enhancement in spectrum efficiency compared with several benchmark schemes.

Heart sounds contains a large amount of information which is useful for physicians to diagnose heart and cardiovascular diseases. One low-power heart sound monitoring system will be introduced in this paper. The system contains three parts: heart sound acquisition module, smart phone node and heart sound care center server. Heart sound data is gathered by acquisition module and then transmitted to smart phone node via Bluetooth low energy 5.0. The smart phone node monitors the user's heart status by analyzing phonocardiography and eigenvalue, and implements data exchange with the server-side. An 8th order 50Hz IIR digital notch filter is used to eliminate the 50Hz power-frequency interference and a 5-layer db6 wavelet decomposition with fixed threshold being used to remove the lung tone and breathing sound. On the heart sound care center server-side, experts can evaluate the user's heart status based on the received heart sound signals and eigenvalues, and then send the evaluation results back to the user's smart phone.

Aiming at the problem of time-consuming and low accuracy in dealing with massive data using traditional collaborative filtering algorithm, this paper analyzes the principle of the Item-Based algorithm and the ALS algorithm and parallelizes the two algorithms based on the Spark platform. And fuse the two algorithms. Considering the parallelization performance of recommended algorithm and recommended quality, the comparison experiment were implemented on the open data set MovieLens. Experimental results show that even on a single node, the running time of each algorithm is far less than the traditional single machine implementation. The fusion of two kinds of collaborative filtering algorithms can further improve the recommendation accuracy on the basis of the existing algorithms.