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Pneumonia is a common infectious disease in the world. Its main diagnostic method is chest X-ray (CXR) examination. However, the high visual similarity between a large number of pathologies in CXR makes the interpretation and differentiation of pneumonia a challenge. In this paper, we propose an improved convolutional neural network (CNN) model for pneumonia detection. In order to guide the CNN to focus on disease-specific attended region, the pneumonia area of image is erased and marked as a non-pneumonia sample. In addition, transfer learning is used to segment the interest region of lungs to suppress background interference. The experimental results show that the proposed method is superior to the state-of-the-art object detection model in terms of accuracy and false positive rate.

Hypertension is one of the most important contributors to heart disease and stroke. Multimodality medical image fusion plays an important role in the precise diagnosis, treatment planning and follow-up studies of various diseases. In this paper, we propose an image fusion framework in patients with hypertension, which is based on Frei-Chen operators in generalized reisz-wavelet transform (GRWT) domain. The proposed method is tested on two cases of MRI/CT and MRI/SPECT images. The input medical images are first transformed by GRWT into basic and detail components. Further, they are fused respectively by Frei-Chen operators which extracts ripples, edges, lines and points well. Finally, the fused image is constructed by the inverse GRWT and evaluated by indicators such as average gradient and spatial frequency etc. The visual and quantitative evaluation of the results demonstrated the superior performance of the proposed image fusion method which assists doctors for precise and sufficient diagnosis.

In this paper, we consider a cluster-to-cluster (C2C) multicast scenario where multiple transmitters in a sending cluster cooperatively transmit a common known data packet to multiple receivers of the receiving cluster. The maximization of transmission rate is firstly formulated as a max-min fairness problem given the set of receivers, and an iterative transmit beamforming optimization algorithm is proposed to obtain the optimal transmit beamforming vector. Furthermore, in order to maximize the C2C system throughput, a joint optimization algorithm of transmit beamforming and receiver selection is proposed to search the optimal set of receivers based on the above transmit beamforming algorithm. The simulation results prove that the proposed algorithm has achieved a higher throughput than the conventional schemes in a lower complexity.

Combining cooperative communications and opportunistic routing, an opportunistic cooperative packet transmission scheme is proposed to enhance the end-to-end performance of the wireless multi-hop networks. The proposed scheme divides the multi-hop route into multiple virtual multiple-input-multiple-output (MIMO) transmissions. Before each transmission, based on the idea of opportunistic routing, a cluster head (CH) is introduced to determine the multiple transmitters and multiple receivers within the transmission range of CH. Then, the CH organizes the cooperative packet transmission, in which multiple transmitters cooperatively beamform to multiple receivers simultaneously. According to the transmission results, update the CH node and start the new cooperative transmission. Iteratively, the transmission lasts until the destination has successfully received the packet. The simulation results demonstrate that the proposed opportunistic cooperative packet transmission scheme is effective on increasing the number of successful packet transmission, shortening the end-to-end transmission delay and improving the packet arrival ratio and transmission efficiency in comparison with other existing routing schemes.

The key technique of cognitive radio(CR) is spectrum sensing. It shows that the cooperative spectrum sensing(CSS) can improve the detection probability. However, in the CSS of high real-time scene of internet of things(IoT), sensing and reporting period cannot be neglected, which leads to the high latency and low throughput. In this paper, we consider an improved CSS scheme, where each cognitive node employs the multi-taper sensing method(MTM) on every collected observation vector and performs truncated sequential probability ratio test (T-SPRT) on MTM results. Theoretical analysis and expressions for latency and throughput are derived based on the designed frame structure. Simulation results demonstrate that the proposed scheme decreases sensing latency and improves throughput with high detection probability.

In uplink power-domain NOMA, multiple users are divided into some groups and share the same channel resources. User clustering is a key factor on the performance of uplink NOMA. A novel user clustering algorithm called interval clustering algorithm is studied in this paper. The proposed scheme allows a user belong to different NOMA groups, which can improve the efficiency of channel resources. In addition, the proposed algorithm has lower implementation complexity as it does not need the order of NOMA UEs. Simulation results indicate that the proposed algorithm can achieve better capacity performance than that of conventional algorithms, such as greedy pairing algorithm, sub-optimal clustering algorithm and hybrid pairing algorithm when multiple users have similar channel conditions.

In Narrowband Internet-of-Things (NB-IoT) systems, resource units (RUs) including five forms are defined as the scheduling units in narrowband physical uplink shared channel (NPUSCH), which causes resource fragmentation and intolerable delay when the multiple RUs are scheduled in arbitrary manner. In this paper, we propose a multiple RUs allocation and scheduling scheme with quality-of-service (QoS) guarantee for NB-IoT systems, which achieves higher resource efficiency and satisfies access delay requirements in a co-existing environment of both delay-sensitive and delay-tolerant services. Firstly, a multiple RUs allocation criterion depending on the QoS requirements for five types of services is proposed. Secondly, based on the proposed criterion, the multiple RUs scheduling is formulated as a two-dimensional packing problem and an effective multiple RUs placement algorithm with QoS guarantee is designed as an approximate solution to maximize resource efficiency. Finally, simulation results demonstrate that our scheme can significantly improve the resource efficiency and satisfy the access delay for each type of service.

In a wireless broadcast channel, each packet is typically associated with a decoding delay constraint. Meeting the constraints of packets under the imperfect feedback is a challenging issue for any online network coding scheme. Thus motivated, in this paper, we investigate the throughput performance of the single-hop network with the decoding delay constraints and imperfect feedback, and have proposed an Upper Triangle Network Coding (UTNC) scheme. The proposed scheme has designed an upper triangular generation matrix G, based on which the original packets in the transmission queue are encoded by the sender and broadcast to multiple receivers. We have evaluated the throughput performance of the UTNC scheme with the different decoding delay constraints, feedback erasure probabilities and the number of receivers. Simulation results show that the proposed scheme has effectively accelerated the packet decoding and avoided the meaningless retransmission resulting from the imperfect feedback. More importantly, the UTNC scheme has achieved higher throughput compared with the conventional ARQ for network coding (ANC) scheme.

This paper proposes an improved scheme for random access procedure, especially for fixed location Machine-to-Machine devices. Based on a random access scheme with timing alignment information, the scheme improved the fairness of the procedure by dividing all the devices into spatial groups. According to the group number and the timing alignment parameter, the base station will fairly give devices the authority to access to the system even it is located at the edge of the cellular. The paper makes efforts with different gaps on collision probability and device access rate. Simulation results validate that the fairness of the access rating is distinctly improved by the proposed scheme.

In this paper, we propose a dynamic resource allocation with quality-of-service (Qos) guarantee for clustered machine-to-machine (M2M) communications, which provides higher throughput as well as higher resource efficiency and lower access delay in a co-existing environment of both delaysensitive and delay-tolerant services. We develop the dynamic resource allocation into four cases. In each case, the total throughput is improved under the condition of satisfying the access delay constrain for each cluster by two folds. First, a reasonable resource tradeoff between physical random access channel (PRACH) and physical uplink shared channel (PUSCH) is achieved. Second, the PRACH resource are separated for each cluster, and the access class barring (ACB) schemes separately control the number of devices belonging to different clusters. The case whose the total throughput is maximum is selected from the four cases as the optimal resource allocation scheme. Simulation results demonstrate our scheme can significantly improve the total throughput as well as the resource efficiency and reduce the access delay for each cluster.

This paper investigates a power allocation problem for maximizing energy efficiency (EE) in downlink Coordinated Multi-Point (CoMP) systems with non- orthogonal multiple access (NOMA). First, users' achievable data rate and network throughput are analysed under three transmission schemes: 1) all users' signals are jointly transmitted by coordinated base stations (BSs); 2) only cell-edge users' signals are jointly transmitted by coordinated BSs; 3) each user's signals are transmitted by only one BS. Next, we formulate EE maximization problems for the three schemes under the constraints of minimum users' data rate and maximum BS transmit power. The considered problem is non-convex and hard to tackle. To address it, an iterative sub-optimal algorithm is proposed by adopting fractional programming and difference of convex programming. Numerical results show that the near optimality performance of EE can be achieved by using the proposed algorithm with advantages of fast convergence and low complexity. Three transmission schemes of NOMA have superior EE performance compared with conventional orthogonal multiple access scheme in the same CoMP networks.

Motivated by improving the number of successful access for machine-to-machine (M2M) communications, and realizing resource allocation for any number of active M2M UEs with less computation, a computationally efficient adaptive resource allocation scheme is given in this paper. eNB first learns the resource allocation for few active M2M UEs, which is adaptively changed with the number of uplink resource blocks. Then the resource allocation for other M2M UEs is derived from those active M2M UEs. In addition, the access barring scheme is incorporated to improve the number of successful access. Experimental simulations demonstrate our scheme can significantly improve the number of successful access and reduce resource allocation computation.

Spatially coupling data transmission (SCDT) is a multiple access technique. Since both SCDT and low density parity-check (LDPC) codes can be represented by a factor graph, a joint sparse graph (JSG) including the single graphs of SCDT and LDPC codes is constructed. Based on the JSG, the joint demodulation and decoding (JDD) by applying belief propagation (BP) algorithm in the parallel schedule is performed, but its convergence rate is slow. In order to accelerate convergence rate, a new serial schedule is proposed. The extrinsic information transfer (EXIT) charts of iterative JDD are investigated for the two schedules and employed to evaluate their converge behaviour. EXIT analysis and simulation results demonstrate that about half iteration number can be saved at the cost of marginal system performance loss. Furthermore, compared with separate demodulation and decoding, turbo-structured JDD and the uncoupling structured JDD, the JDD based on JSG for SCDT by utilizing serial schedule achieves the best performance.

This paper proposes an improved random access (RA) procedure for machine to machine (M2M) communications. The proposed RA procedure makes evolved nodeB (eNB) detect if a preamble is selected by multiple user equipments (UEs) in the first step. To avoid false detection, an extended guard band is provided in PRACH. Based on the improved RA procedure, theoretical analysis on the probability of preamble collision and the number of successful access are addressed. In the simulation, the simulation of the probability of false detection with different number of bits of UE ID, the number of successful access and the impact of preamble collision are presented. Simulation results show that the new RA procedure outperforms traditional RA procedure on both probability of preamble collision and the number of successful access.

Classification algorithms based on association rules have been proven with higher accuracy and better understandability when compared with classical classifiers. These advantages make them be widely used in the application of intelligent decision systems. However, in some specific fields, such as health care field, people hope to use more prior knowledge and focus more attentions on properties owning strong correlation with class labels in the process of modeling. In this paper, a fuzzy weighted associative classifier based on information gain is proposed. This associative classifier employs an attribute selection strategy based on information gain to determine attribute importance degree and assigns corresponding weights such that the more important attributes are paid more attentions. In addition, the proposed algorithm applies the fuzzy sets to discretizing the numeric variables instead of partitioning directly for avoiding the sharp boundary issues. After implementation, the new classifier is tested with benchmark data from the UCI machine learning repository. Experimental results show that there is an improvement in classification accuracy and reduction in rules redundancy.

With the widespread application of wireless networks and the requirements of different user equipments (UEs), energy has become a scarcer resource as well as spectrum. In this paper, considering the actual scenarios of imperfect channel state information (CSI), we study a resource allocation scheme in the downlink orthogonal frequency division multiple access (OFDMA) systems. To balance between energy efficiency (EE) and proportional fairness (PF), the problem is formulated as maximizing average achievable EE with the constraints of PF of users and QoS assurance. To solve the optimal problem, we divide it into two layers. The sub-problem P1 of inner layer is solved to maximize PF, with the parameter of total transmit power which is updated by the sub-problem P2 of outer layer. In outer layer, with the allocation scheme from P1, a gradient-based adaptation resource allocation algorithm is proposed to achieve the maximum EE with total transmit power updated in every gradient iteration. Moreover, the impacts of the imperfect CSI on EE and PF are analysed. Simulation results are presented to show the superior performance of the proposed algorithms and verify the analytical findings.

On dispersing different base matrices combined with masking, different kinds of quasi-cyclic low-density parity check (QC-LDPC) codes are constructed, which may achieve good error performances. Two new algebraic constructions of regular QC-LDPC codes are presented in this paper. The two constructions are based on two base matrices by dispersing and masking. The two base matrices are based on the generators of the cyclic group of a prime field. Results show that the two classes of codes presented in this paper perform well with iterative decoding over AWGN channel and have advantages over MacKay codes in some aspects of code performances. We find that one of the constructed codes converges very fast with iterative decoding, which is a critical property in ultra-high throughput communication systems.

Fast deploying and networking in a disaster area are of great importance. Firstly, we propose an architecture of Emergency Medical Networks in this paper. The objective of this study is to achieve the optimality of heterogeneous sensor nodes assignment in this network. To obtain the needed coverage, network designers initially need to determine how many sensor nodes should be deployed in the disaster area. In consideration of finite energy and sensing ranges in both sensors nodes and sink nodes, we apply the coverage process theory into the assignment of wireless sensor networks to improve the coverage ratio during a given time period. Further, the simulation data show that the reasonable assignment could get the precise estimation of the coverage ratio, which reflects the validity of this assignment. The optimality design provides helpful configuration guidelines for the practical Emergency Medical Networks deployment.

Chronic diseases are gradually becoming the principal factors of harm to people's health. Fortunately, the development of e-health provides a novel thought for chronic disease prevention and treatment. This paper focuses on the research of cardiovascular disease (CVDs) prevention and early warning techniques using e-health and data mining. In this paper, we will use weighted associative classification algorithm to model the data in healthcare database to determine the level of cardiovascular risk. Besides, on the basis of data mining and knowledge discovery, intelligent warning mechanisms are proposed to provide different services to patients with different levels of risk. The experimental results show that the used classification algorithm is a more effective mining algorithm in the field of healthcare with higher accuracy and better comprehension. Our study is of definite significance to help control risk level of CVDs patients.

This paper presents a healthcare monitoring architecture coupled with a wireless sensor network and wearable sensor systems which monitor chronic patients in nursing house or the elderly in their home. With this architecture, we investigate how sensor nodes are deployed in the three-dimension (3D) monitoring region to achieve uniform distribution, which directly determines the Quality of Service (QoS). Based on the existing two-dimension (2D) coverage-enhancing algorithms for wireless sensor networks, a 3D sensing model and a coverage optimization algorithm are proposed in this paper. Firstly, an intelligent optimization algorithm is utilized to adjust the position of sensor nodes. Then, we pick out the redundant nodes with the set coverage algorithm, and move them into the uncovered area to increase the coverage ratio. The simulation results show that the coverage ratio increased by the coverage optimization algorithm compared with the other coverage algorithms.

Energy is one of the crucial factors in wireless sensor network. In this paper, we consider energy fairness problem in heterogeneous wireless sensor network. Firstly, we propose a nodes' deployment model in which we deploy heterogeneous sensor nodes judiciously to conserve energy. This prevents the problem of occurrence of 'energy holes' and ensures prolonged network lifetime. Then, we use Game Theory to model the packet transmission among sensor nodes, and get the Nash equilibrium by properly designing the measurement function. Finally, the simulation results fully prove the effectiveness of the proposed scheme in this paper, which not only make the energy consumption of the network balanced but the network lifetime also improved.

The Wireless Local Area Network (WLAN) has experienced tremendous changes through these days, both in usage and technological aspects. Recently, 802.11ac and 802.11ad protocols for WLAN have been standardized by IEEE. They are both amendments to the 802.11-2012 protocol and their primary Medium Access Control (MAC) technique is Distributed Coordination Function (DCF). What's more, in order to improve high speed throughput especially in MAC layer, 802.11ac and 802.11ad both extend frame aggregation mechanisms utilizing the MAC Protocol Service Unit Aggregation (A-MSDU) and MAC Protocol Data Unit Aggregation (A-MPDU) schemes and block acknowledgement (Block ACK) schemes. In this paper, we investigate the traditional analytical model of DCF. Then based on that model, we propose an adaptive Block ACK scheme to achieve higher throughput by reducing package loss. A MATLAB simulation is established to show that the proposed Block ACK enhancement can improve system performance.

Energy-efficient transmission is an important issue for wireless communication systems, due to the increased energy consumption and limited battery capacity. In this paper, we study the energy-efficient transmission in a single cellular downlink multi-user Orthogonal Frequency Division Multiple Access (OFDMA) system. The transmit power consumption and the circuit power consumption are both taken into consideration, when optimizing the total bits transmitted per Joule of energy. This optimization problem has nonlinear constraints, which are commonly solved by Lagrange-based algorithm. However, these methods are time-consuming and are not optimal. To address this issue, we propose an adaptive subcarrier, bit, and power allocation scheme to optimize energy-efficient transmissions based on genetic algorithms. Moreover, we improve the genetic algorithm with a new elitist method and a penalty handling method that are specific to our optimization problem. The simulation results show that the proposed scheme can obtain more satisfied solutions in a shorter time, compared to the traditional methods.

This paper studies the design and implementation of MAC (Media Access Control) layer in very-high-throughput WLAN(Wireless Local Area Network), which is based on the latest IEEE 802.11ac protocol. As an intermediate layer between service layer and physical layer, MAC layer is responsible for deployment and reorganization of data packets during sending and receiving process. Meanwhile, system implementation is successfully improved in both throughput and QoS (Quality of Service) aspects. Implementation results show that the MAC layer can deal with data beyond Gbps (Gigabit) stably, and deliver real-time HD (high-definition) videos in QoS aspect, while block error rate (BLER) being controlled within 10−3.

In many practical systems, perfect channel state information (CSI) is difficult to obtain at the base station (BS), we usually use limited feedback CSI to reduce the symbol error rate (SER). To analyze the SER performance of different transmit distributed antenna (DA) subset in limited feedback distributed antenna systems (DAS), the closed-form SER expressions for both binary phase shift keying (BPSK) and M-phase shift keying (MPSK) are derived in this paper. From these expressions, we find there exists an appropriate transmit DA subset which can obtain the best SER performance. Then, an adaptive transmit DA selection scheme with the minimum SER is proposed. Simulation results show that the proposed scheme can achieve SER performance gain than blanket transmission scheme and selection diversity scheme.

Distributed multiple-input multiple-output (DMIMO) system is a combination of distributed antenna systems (DAS) and co-located antenna systems (CAS). Power allocation between different antennas is a very important aspect in both DAS and CAS. In this paper, we investigate the downlink power allocation scheme in D-MIMO systems based on minimum symbol error rate (SER) criteria. We propose a power allocation scheme which can effectively approximate the optimal allocation scheme. In the proposed scheme, both Large-scale fading and transceiver antenna correlation are taken into consideration. Moreover, mobile terminal (MT) only needs to feedback largescale fading information and the rank of transmit antenna correlation matrix periodically, which significantly reduces the feedback overhead. Simulation results verify the capacity improvement based on the proposed power allocation scheme.

In many applications, such as network congestion monitoring, accounting and network anomaly detection, identifying heavy-hitter flows is very important and imperative. Recent research work on extracting heavy-hitter flows in high-speed network attracts quite a few researchers because of the importance of finding out heavy-hitter flows. However, due to the high speed of network and the ever-growing of flow amount, it's of great significance to identify heavy-hitter flows in high-speed network with much less memory and computational overhead apart from high measurement accuracy. Moreover, it's infeasible to track per-flow statistics to achieve high accuracy because of the limits of both the computational and memory requirements. Consequently, finding a rational way to solve this problem is of great significance. In this paper, a novel scheme named CHHFR (Caching Heavy-hitter Flows with Replacement) is proposed. CHHFR algorithm is based on LRU (Least Recently Used) replacement mechanism but different from it because CHHFR does not only care about the update time of traffic flows when it find a flow to replace. Another parameter named Ctr is added to LRU replacement mechanism to avoid its shortcomings. Through both theoretical analysis and experimental validation of different Internet traces, CHHFR algorithm can achieve a good measurement performance with a higher accuracy at a faster speed compared with the existing methods.

In this paper, the pilot design aiming at mitigating the inter-cell interference in MIMO OFDM systems is addressed. We first compute the mean square error of the frequency domain LS joint channel estimation with the consideration of interfering cell. We then overview the optimal pilot sequences within a cell, which can cancel the intra-cell multiple access interference. Based on the mean square error and variation coefficient, we propose two conditions for the pilot design to mitigate the inter- cell interference. It is shown that if an ideal channel between interferer and receiver is assumed, we can obtain the pilots resulting in a smaller value of mean square error based on the variation coefficient of zero, which make the power of inter-cell interference distribute at all paths of the channel more uniformly and less, therefore, the performance on system level would be improved. A further discussion about proposed guidelines in practical system is made, and numerical calculations as well as simulation results are presented to verify the theoretical analysis. Index Terms—pilot design, inter-cell interference, MIMO, OFDM.

As the development of Internet, it is more and more difficult to detect anomaly promptly and precisely. In this paper, we proposed an anomaly detection algorithm based on the predicting of multi-level wavelet detail signals synchronously. Firstly, process the time series of traffic with non-decimated Haar wavelet transform and produce detail signals. Secondly, predict the detail signals of wavelet transform and get the residual ratio series, which can expose the anomaly more obviously than original signal. Finally, based on principal of “3σ” of normal distribution, abrupt changes can be detected. Along with the arriving of traffic data, this algorithm detects anomaly on several time-scales recursively without delay. So this algorithm can detect traffic anomaly more precisely and promptly. Analysis and experiments reveal that this algorithm can detect anomalies effectively.

In this paper, we consider the multiuser MIMO downlink channel with multiple antennas at both the base station and the users. We concentrate on the case where the transmitter has imperfect channel state information due to estimation error and feedback delay. A channel modification matrix and a scalar factor based on minimum mean square error criterion are derived to form an equivalent channel matrix which has a lower variance of error caused by imperfect estimation and feedback delay, and then used to design the precoding vectors based on maximizing the so-called signal-to-leakage-plus-noise-ratio. We present a scheme that combines co-channel interference suppression and channel modification in an appropriate way. Simulation results validate the presented scheme.

Aiming at the problems of Information Island and Cooperation between governments' system in E-Government (EG), this paper proposed a One-Station EG Service Model (OS-SM) which is based on ontology and SOA (service-oriented architecture). Firstly, this model realized the common data exchange platform among the departments of government and designed an ontology base for data exchange by making use of the advantage of ontology's semantic expression ability and the achievements of ontology mapping, so that the problem of Information Island is resolved. Secondly, this model implemented the cooperation between the government departments by use of the technology of Web service and the characterization of SOA's Loosely Couple, and with this, the improvement method for the intranet of EG is given also. The experiment and practice proved that this model is effective and operative.

Because the ontology-based knowledge base is distributed and heterogeneous, a logic inference-based semantic Web service is presented in this paper. By use of the query converter, the service can retrieve the knowledge base by logic inference. Then the service can retrieve each distributed knowledge base independently and by the unified interfaces supplied by the service, the problem of heterogeneity becomes transparent for other services. The experiments show that this service is very perfect in retrieving the knowledge base.