Provided is a node and a communication method of the node in a content oriented network (CON) in which the communication method of the node included in the CON may include receiving, at a receiver, a content request packet having a content name comprising hierarchical name components and flat name components, extracting the hierarchical name components and the flat name components from the content request packet, and processing the content request packet using the hierarchical name components and the flat name components.

Node and communication method for supporting content mobility

This research proposes an intelligent decision support system for acute lymphoblastic leukaemia diagnosis from microscopic blood images. A novel clustering algorithm with stimulating discriminant measures (SDM) of both within- and between-cluster scatter variances is proposed to produce robust segmentation of nucleus and cytoplasm of lymphocytes/lymphoblasts. Specifically, the proposed between-cluster evaluation is formulated based on the trade-off of several between-cluster measures of well-known feature extraction methods. The SDM measures are used in conjuction with Genetic Algorithm for clustering nucleus, cytoplasm, and background regions. Subsequently, a total of eighty features consisting of shape, texture, and colour information of the nucleus and cytoplasm sub-images are extracted. A number of classifiers (multi-layer perceptron, Support Vector Machine (SVM) and Dempster-Shafer ensemble) are employed for lymphocyte/lymphoblast classification. Evaluated with the ALL-IDB2 database, the proposed SDM-based clustering overcomes the shortcomings of Fuzzy C-means which focuses purely on within-cluster scatter variance. It also outperforms Linear Discriminant Analysis and Fuzzy Compactness and Separation for nucleus-cytoplasm separation. The overall system achieves superior recognition rates of 96.72% and 96.67% accuracies using bootstrapping and 10-fold cross validation with Dempster-Shafer and SVM, respectively. The results also compare favourably with those reported in the literature, indicating the usefulness of the proposed SDM-based clustering method.

/pdf/an-intelligent-decision-support-system-for-leukaemia-3yhff0ppv8.pdf

An Intelligent Decision Support System for Leukaemia Diagnosis using Microscopic Blood Images.

Acute myelogenous leukemia (AML) is a subtype of acute leukemia, which is characterized by the accumulation of myeloid blasts in the bone marrow. Careful microscopic examination of stained blood smear or bone marrow aspirate is still the most significant diagnostic methodology for initial AML screening and considered as the first step toward diagnosis. It is time-consuming and due to the elusive nature of the signs and symptoms of AML; wrong diagnosis may occur by pathologists. Therefore, the need for automation of leukemia detection has arisen. In this paper, an automatic technique for identification and detection of AML and its prevalent subtypes, i.e., M2-M5 is presented. At first, microscopic images are acquired from blood smears of patients with AML and normal cases. After applying image preprocessing, color segmentation strategy is applied for segmenting white blood cells from other blood components and then discriminative features, i.e., irregularity, nucleus-cytoplasm ratio, Hausdorff dimension, shape, color, and texture features are extracted from the entire nucleus in the whole images containing multiple nuclei. Images are classified to cancerous and noncancerous images by binary support vector machine (SVM) classifier with 10-fold cross validation technique. Classifier performance is evaluated by three parameters, i.e., sensitivity, specificity, and accuracy. Cancerous images are also classified into their prevalent subtypes by multi-SVM classifier. The results show that the proposed algorithm has achieved an acceptable performance for diagnosis of AML and its common subtypes. Therefore, it can be used as an assistant diagnostic tool for pathologists.

Automatic Recognition of Acute Myelogenous Leukemia in Blood Microscopic Images Using K-means Clustering and Support Vector Machine.

Android has become the most prevalent mobile system, but in the meanwhile malware on this platform is widespread. System call sequences are studied to detect malware. However, malware detection with these approaches relies on common system-call-subsequences. It is not so efficient because it is difficult to decide the appropriate length of the common subsequences. To address this issue, the authors propose a new approach, back-propagation neural network on Markov chains from system call sequences (BMSCS). It treats one system call sequence as a homogeneous stationary Markov chain and applies back-propagation neural network (BPNN) to detect malware by comparing transition probabilities in the chain. Since transition probabilities from one system call to another in malware are significantly different from those in benign applications, BMSCS can efficiently detect malware by capturing the anomaly in state transitions with the help of BPNN. The authors evaluate the performance of BMSCS by experiments with real application samples. The experiment results show that the F 
-score of BMSCS achieves up to 0.982773, which is higher than the other methods in the literature.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-ifs.2015.0211

Back-propagation neural network on Markov chains from system call sequences: a new approach for detecting Android malware with system call sequences

Next generation wireless systems will be able to handle both real time multimedia services like voice and non-real time data services in an efficient manner. In order to provide efficient transmission rates to each terminal, efficient use of system resources requires rate control and in addition to power control. In this paper transmission power control problem for uplink in 3GPP LTE-Advanced networks can be identified and minimized by developing a Generalized Power control Algorithm. It is developed by using an economic theory based utility function. This Algorithm is distributed, which is to be installed in mobile phones those are operating at LTE-A networks. The utility function for power control can be solved with the help of Lagrangian equation. It is proved that utility function is applicable to this work and results obtained are unique depends upon the user equipment and power control is obtained by making their utility function maximum. Numerical results are presented for the algorithm.

A utility function based uplink power control algorithm in LTE-Advanced networks

In this paper, transmission power control problem for uplink LTE network is investigated and a new autonomic uplink power control scheme was proposed based on utility function, which is a self-organized algorithm. The whole approach is based on the economic concept named utility function. Then a self-organized algorithm is distributed in each mobile users to control the transmission power and to maximize the transmission utility. The proposed scheme is solved through the Lagrange multiplier technique. It is proved that the utility function based algorithm is applicable and the optimal power level can be achieved based on our model.

http://ieeexplore.ieee.org/iel7/6245522/7085375/07085389.pdf

Self-organized algorithm in LTE networks: A utility function based optimal power control scheme

With the developments of cyberspace, more and more technologies can be used in cyberspace, designing a new architecture for cyberspace is extremely urgent. The research of cyberspace will be important significance. In this paper, we will research the architecture of cyberspace, and construct a new architecture based on wisdom. Then the security services supplied by cyberspace is given based on the wisdom architecture.

Research of cyberspace architecture

With the launch and development of the Android system, it is urgent to solve its terminal security protection problem. Based on three aspects, i.e., Android features, security architecture and security mechanisms, this paper analyses the security threats and the corresponding risks faced by the terminal from the technical level and management level respectively. Moreover, a security ecosystem architecture based on Android platform is designed reasonably and effectively to regulate the Android management system. The proposed ecosystem is benefit to solve the security problems such as open-source vulnerability and App store management chaos. The achievement of this paper promotes researches of security risk issues positively.

http://ieeexplore.ieee.org/iel7/6745500/6748549/06748619.pdf

Research of secure ecosystem based on Android platform

Data-centric network plays an important part in the next generation network architecture. And name system is one of basic elements in data-centric network. This paper describes an approach which achieves the name system design criterion. In naming, a combination of three entities is used to achieve security, scalability, and flexibility. In name resolution, a three-layer structure is proposed to achieve precise and semantic resolutions.

Jianwei An

Papers

Self-organized algorithm in LTE networks: A utility function based optimal power control scheme

Research of cyberspace architecture

Research of secure ecosystem based on Android platform

A Hierarchical Name System Based on Hybrid P2P for Data-Centric Networks

Fibonacci Sequence and Golden Section-Based on A Single-rate Multicast Congestion Control Scheme