Flexible,thermal processable,self-healing,and fully bio-based starch plastics by constructing dynamic imine network

The serious environmental threat caused by petroleum-based plastics has spurred more researches in developing substitutes from renewable sources.Starch is desirable for fabricating bioplastic due to its abundance and renewable nature.However,limitations such as brittleness,hydrophilicity,and thermal properties restrict its widespread application.To overcome these issues,covalent adaptable network was constructed to fabricate a fully bio-based starch plastic with multiple advantages via Schiff base reactions.This strategy endowed starch plastic with excellent thermal processability,as evidenced by a low glass transition temperature(T_(g)=20.15℃).Through introducing Priamine with long carbon chains,the starch plastic demonstrated superior flexibility(elongation at break=45.2%)and waterproof capability(water contact angle=109.2°).Besides,it possessed a good thermal stability and self-adaptability,as well as solvent resistance and chemical degradability.This work provides a promising method to fabricate fully bio-based plastics as alternative to petroleum-based plastics.

Computer vision-based limestone rock-type classification using probabilistic neural network

Proper quality planning of limestone raw materials is an essential job of maintaining desired feed in cement plant. Rock-type identification is an integrated part of quality planning for limestone mine. In this paper, a computer vision-based rock-type classification algorithm is proposed for fast and reliable identification without human intervention. A laboratory scale vision-based model was developed using probabilistic neural network（PNN） where color histogram features are used as input. The color image histogram-based features that include weighted mean, skewness and kurtosis features are extracted for all three color space red, green, and blue. A total nine features are used as input for the PNN classification model. The smoothing parameter for PNN model is selected judicially to develop an optimal or close to the optimum classification model. The developed PPN is validated using the test data set and results reveal that the proposed vision-based model can perform satisfactorily for classifying limestone rocktypes. Overall the error of mis-classification is below 6%. When compared with other three classification algorithms, it is observed that the proposed method performs substantially better than all three classification algorithms.

An Experimental Study of Network-based Language Learning in Less-developed Areas

The advent of the Age of Information brings about bright prospects to Network-based Language Learning(NBLL).The thesis adopts the Engagement Theory as guided principles.The purpose is to use the novel NBLL model effectively with the help of modern technology especially in less-developed areas.This thesis focuses on network-based experimental study.The research shows that the students under NBLL environment have cultivated the capabilities in information collection,computer operation,and information evaluation,as well as the abilities in problem solving,reasoning with criticism,and cooperating with others.

A long-term-based handover decision algorithm for dense macro-femto coexistence networks

For the dense macro-femto coexistence networks scenario, a long-term-based handover（LTBH） algorithm is proposed. The handover decision algorithm is jointly determined by the angle of handover（AHO） and the time-tostay（TTS） to reduce the unnecessary handover numbers.First, the proposed AHO parameter is used to decrease the computation complexity in multiple candidate base stations（CBSs） scenario. Then, two types of TTS parameters are given for the fixed base stations and mobile base stations to make handover decisions among multiple CBSs. The simulation results show that the proposed LTBH algorithm can not only maintain the required transmission rate of users, but also effectively reduce the unnecessary numbers of handover in the dense macro-femto networks with the coexisting mobile BSs.

A Path-Based Approach for Data Aggregation in Grid-Based Wireless Sensor Networks

Sensor nodes in a wireless sensor network （WSN） are typically powered by batteries, thus the energy is constrained. It is our design goal to efficiently utilize the energy of each sensor node to extend its lifetime, so as to prolong the lifetime of the whole WSN. In this paper, we propose a path-based data aggregation scheme （PBDAS） for grid-based wireless sensor networks. In order to extend the lifetime of a WSN, we construct a grid infrastructure by partitioning the whole sensor field into a grid of cells. Each cell has a head responsible for aggregating its own data with the data sensed by the others in the same cell and then transmitting out. In order to efficiently and rapidly transmit the data to the base station （BS）, we link each cell head to form a chain. Each cell head on the chain takes turn becoming the chain leader responsible for transmitting data to the BS. Aggregated data moves from head to head along the chain, and finally the chain leader transmits to the BS. In PBDAS, only the cell heads need to transmit data toward the BS. Therefore, the data transmissions to the BS substantially decrease. Besides, the cell heads and chain leader are designated in turn according to the energy level so that the energy depletion of nodes is evenly distributed. Simulation results show that the proposed PBDAS extends the lifetime of sensor nodes, so as to make the lifetime of the whole network longer.

Adaptive swarm-based routing in communication networks

Swarm intelligence inspired by the social behavior of ants boasts a number of attractive features, including adaptation, robustness and distributed, decentralized nature, which are well suited for routing in modern communication networks. This paper describes an adaptive swarm-based routing algorithm that increases convergence speed, reduces routing instabilities and oscillations by using a novel variation of reinforcement learning and a technique called momentum.Experiment on the dynamic network showed that adaptive swarm-based routing learns the optimum routing in terms of convergence speed and average packet latency.

Game theory-based attack and defense analysis in virtual wireless networks with jammers and eavesdroppers

Dynamic spectrum sharing and cognitive radio networks were proposed to enhance the Radio Frequency(RF)spectrum utilization.However,there are several challenges to realize them in real systems,such as sensing uncertainty causing issues to licensed users,business models for licensed service providers.Wireless virtualization is regarded as a technology that leverages service level agreements to sublease unused or underutilized RF spectrum that addresses aforementioned issues and helps to significantly enhance the utilization of the RF spectrum,offer improved coverage and capacity of networks,enhance network security and reduce energy consumption.With wireless virtualization,wireless networks'physical substrate is shared and reconfigured dynamically between virtual wireless networks through Mobile Virtual Network Operations(MVNOs).Wireless virtualization with dynamic configurable features of Wireless Infrastructure Providers(WIPs),virtualized wireless networks are vulnerable to a multitude of attacks,including jamming attacks and eavesdropping attacks.This paper investigates a means of defense through the employment of coalition game theory when jammers try to degrade the signal quality of legitimate users,and eavesdroppers aim to reduce secrecy rates.Specifically,we consider a virtual wireless network where MVNO users'job is to improve their Signal to Interference plus Noise Ratio(SINR)while the jammers target to collectively enhance their Jammer Received Signal Strength(JRSS),and an eavesdropper's goal is to reduce the overall secrecy rate.Numerical results have demonstrated that the proposed game strategies are effective(in terms of data rate,secrecy rate and latency)against such attackers compared to the traditional approaches.

Proposed Caching Scheme for Optimizing Trade-off between Freshness and Energy Consumption in Name Data Networking Based IoT

Over the last few years, the Internet of Things (IoT) has become an omnipresent term. The IoT expands the existing common concepts, anytime and anyplace to the connectivity for anything. The proliferation in IoT offers opportunities but may also bear risks. A hitherto neglected aspect is the possible increase in power consumption as smart devices in IoT applications are expected to be reachable by other devices at all times. This implies that the device is consuming electrical energy even when it is not in use for its primary function. Many researchers’ communities have started addressing storage ability like cache memory of smart devices using the concept called—Named Data Networking (NDN) to achieve better energy efficient communication model. In NDN, memory or buffer overflow is the common challenge especially when internal memory of node exceeds its limit and data with highest degree of freshness may not be accommodated and entire scenarios behaves like a traditional network. In such case, Data Caching is not performed by intermediate nodes to guarantee highest degree of freshness. On the periodical updates sent from data producers, it is exceedingly demanded that data consumers must get up to date information at cost of lease energy. Consequently, there is challenge in maintaining tradeoff between freshness energy consumption during Publisher-Subscriber interaction. In our work, we proposed the architecture to overcome cache strategy issue by Smart Caching Algorithm for improvement in memory management and data freshness. The smart caching strategy updates the data at precise interval by keeping garbage data into consideration. It is also observed from experiment that data redundancy can be easily obtained by ignoring/dropping data packets for the information which is not of interest by other participating nodes in network, ultimately leading to optimizing tradeoff between freshness and energy required.

Development of an Ontology-Based Knowledge Network by Interconnecting Soil/Water Concepts/Properties, Derived from Standards Methods and Published Scientific References Outlining Infiltration/Percolation Process of Contaminated Water

The present work deals with the development of an Ontology-Based Knowledge Network of soil/water physicochemical & biological properties (soil/water concepts), derived from ASTM Standard Methods (ASTMi,n) and relevant scientific/applicable references (published papers—PPi,n) to fill up/bridge the gap of the information science between cited Standards and infiltration discipline conceptual vocabulary providing accordingly a dedicated/internal Knowledge Base (KB). This attempt constitutes an innovative approach, since it is based on externalizing domain knowledge in the form of Ontology-Based Knowledge Networks, incorporating standardized methodology in soil engineering. The ontology soil/water concepts (semantics) of the developed network correspond to soil/water physicochemical & biological properties, classified in seven different generations that are distinguished/located in infiltration/percolation process of contaminated water through soil porous media. The interconnections with arcs between corresponding concepts/properties among the consecutive generations are defined by the relationship of dependent and independent variables. All these interconnections are documented according to the below three ways: 1) dependent and independent variables interconnected by using the logical operator “depends on” quoting existent explicit functions and equations;2) dependent and independent variables interconnected by using the logical operator “depends on” quoting produced implicit functions, according to Rayleigh’s method of indices;3) dependent and independent variables interconnected by using the logical operator “related to” based on a logical dependence among the examined nodes-concepts-variables. The aforementioned approach provides significant advantages to semantic web developers and web users by means of prompt knowledge navigation, tracking, retrieval and usage.

A distributed routing algorithm based-on simplified topology in LEO satellite networks

In this paper, a distributed muting strategy based on simplified topology （DRBST） was proposed for LEO satellite networks. The topology of LEO satellite networks was simplified aiming at minimizing intersatellite links handover number. To optimize the route based on the simplified topology, we considered not only the transmission delay but also the queuing delay and the processing delay, which were analyzed using Markov chain and determined using a novel methodology. The DRBST algorithm was simulated in a LEO satellite networks model built using OPNET. The simulation results demonstrate that the low complexity DRBST algorithm can guarantee end-to-end delay bound. Moreover, the muting protocol cost is much less than traditional algorithms.

A new algorithm for wireless sensor network based on NS-2

Considering wireless sensor network characteristics,this paper uses network simulator,version2(NS-2)algorithm to improve Ad hoc on-demand distance vector(AODV)routing algorithm,so that it can be applied to wireless sensor networks.After studying AODV routing protocol,a new algorithm called Must is brought up.This paper introduces the background and algorithm theory of Must,and discusses the details about how to implement Must algorithm.At last,using network simulator(NS-2),the performance of Must is evaluated and compared with that of AODV.Simulation results show that the network using Must algorithm has perfect performance.

Experts' Knowledge Fusion in Model-Based Diagnosis Based on Bayes Networks

In previous researches on a model-based diagnostic system, the components are assumed mutually independent. Howerver , the assumption is not always the case because the information about whether a component is faulty or not usually influences our knowledge about other components. Some experts may draw such a conclusion that 'if component m 1 is faulty, then component m 2 may be faulty too'. How can we use this experts' knowledge to aid the diagnosis? Based on Kohlas's probabilistic assumption-based reasoning method, we use Bayes networks to solve this problem. We calculate the posterior fault probability of the components in the observation state. The result is reasonable and reflects the effectiveness of the experts' knowledge.

A Network-Based VPN Architecture Using Virtual Routing

A network-based Virtual Private Network (VPN) architecture by using fundamental routing mechanism is proposed. This network is a virtual overlay network based on the relay of IP-in-IP tunneling of virtual routing modules. The packet format employs the encapsulation of IPSec ESP(Encapsulating Security Payload), an impact path code and an extended DS(Differentiated Services) code to support multi-path routing and QoS. Comparing with other models of VPN, this network system can be deployed in the current network with little investment, and it is easy to implement. The simulation result shows its performance is better than the traditional VPN system of black box mode.

Encoding-Decoding-Based Control and Filtering of Networked Systems： Insights, Developments and Opportunities

In order to make the information transmission more efficient and reliable in a digital communication channel with limited capacity, various encoding-decoding techniques have been proposed and widely applied in many branches of the signal processing including digital communications, data compression,information encryption, etc. Recently, due to its promising application potentials in the networked systems(NSs), the analysis and synthesis issues of the NSs under various encoding-decoding schemes have stirred some research attention. However, because of the network-enhanced complexity caused by the limited network resources, it poses new challenges to the design of suitable encoding-decoding procedures to meet certain control or filtering performance for the NSs. In this survey paper, our aim is to present a comprehensive review of the encoding-decodingbased control and filtering problems for different types of NSs.First, some basic introduction with respect to the coding-decoding mechanism is presented in terms of its engineering insights,specific properties and theoretical formulations. Then, the recent representative research progress in the design of the encodingdecoding protocols for various control and filtering problems is discussed. Some possible further research topics are finally outlined for the encoding-decoding-based NSs.

Parallel Neural Network-Based Motion Controller for Autonomous Underwater Vehicles

A parallel neural network-based controller （PNNC） is presented for the motion control of underwater vehicles in this paper. It consists of a real-time part, a self-learning part and a desired-state programmer, and it is different from normal adaptive neural network controller in structure. Owing to the introduction of the self-learning part, on-line learning can be performed without sample data in several sample periods, resulting in high learning speed of the controller and good control performance. The desired-state programmer is utilized to obtain better learning samples of the neural network to keep the stability of the controller. The developed controller is applied to the 4-degree of freedom control of the AUV “IUV- IV” and is successful on the simulation platform. The control performance is also compared with that of neural network controller with different structures such as normal adaptive neural network and different learning methods. Current effects and surge velocity control are also included to demonstrate the controller＇ s performance. It is shown that the PNNC has a great possibility to solve the problems in the control system design of underwater vehicles.

Artificial Intelligence-Driven Fog-Computing-Based Radio Access Networks

The edge cache is an effective way to reduce the heavy traffic load and the end-to-end latency in radio access networks(RANs)for supporting a number of critical Internet of Things(IoT)services and applications.It has been verified to provide high spectral efficiency,high energy efficiency,and low latency.To exploit the advantages of edge cache,a paradigm of fog computing-based radio access networks(F-RANs)has emerged to provide great flexibility to satisfy quality-of-service requirements of various IoT applications in the fifth generation(5G)wireless systems.

Artificial Neural Networks for Event Based Rainfall-Runoff Modeling

The Artificial Neural Network (ANN) approach has been successfully used in many hydrological studies especially the rainfall-runoff modeling using continuous data. The present study examines its applicability to model the event-based rainfall-runoff process. A case study has been done for Ajay river basin to develop event-based rainfall-runoff model for the basin to simulate the hourly runoff at Sarath gauging site. The results demonstrate that ANN models are able to provide a good representation of an event-based rainfall-runoff process. The two important parameters, when predicting a flood hydrograph, are the magnitude of the peak discharge and the time to peak discharge. The developed ANN models have been able to predict this information with great accuracy. This shows that ANNs can be very efficient in modeling an event-based rainfall-runoff process for determining the peak discharge and time to the peak discharge very accurately. This is important in water resources design and management applications, where peak discharge and time to peak discharge are important input

Trusted Routing Based on Identity-Based Threshold Signature for Opportunistic Networks

In opportunistic Networks,compromised nodes can attack social context-based routing protocols by publishing false social attributes information.To solve this problem,we propose a security scheme based on the identity-based threshold signature which allows mobile nodes to jointly generate and distribute the secrets for social attributes in a totally self-organized way without the need of any centralized authority.New joining nodes can reconstruct their own social attribute signatures by getting enough partial signature services from encounter opportunities with the initial nodes.Mobile nodes need to testify whether the neighbors can provide valid attribute signatures for their routing advertisements in order to resist potential routing attacks.Simulation results show that:by implementing our security scheme,the network delivery probability of the social context-based routing protocol can be effectively improved when there are large numbers of compromised nodes in opportunistic networks.

A New SDH-Based ATM Network Survivability Escalation Mechanism

This paper investigates survivability escalation strategies in multi layers transport networks such as ATM/SDH/WDM networks, and presents oriented failures and oriented traffic escalation mechanisms. Furthermore, We present a new survivability Escalation strategy for SDH Based ATM transport networks, which addresses difficult problem for resources sharing pool(RSP) among different layers restoration mechanisms. In this paper, we also present integer programming (IP) model for the resources sharing pool (RSP) design problem and the node simulation model for escalation Node. The simulation results show that the proposed ESP is very efficient. The proposed model can be easily extended for other types of multi layer networks, such as WDM based ATM networks or WDM based SDH networks.

An Improved Scene-based Nonuniformity Correction Algorithm for Infrared Focal Plane Arrays Using Neural Networks

The improved scene-based adaptive nonuniformity correction (NUC) algorithms using a neural network (NNT) approach for infrared image sequences are presented and analyzed. The retina-like neural networks using steepest descent model was the first proposed infrared focal plane arrays (IRFPA) nonuniformity compensation method,which can perform parameter estimation of the sensors over time on a frame by frame basis. To increase the strength and the robustness of the NNT algorithm and to avoid the presence of ghosting artifacts,some optimization techniques,including momentum term,regularization factor and adaptive learning rate,were executed in the parameter learning process. In this paper,the local median filtering result of AX^U_ ij (n) is proposed as an alternative value of desired network output of neuron X_ ij (n),denoted as T_ ij (n),which is the local spatial average of AX^U_ ij (n) in traditional NNT methods. Noticeably,the NUC algorithm is inter-frame adaptive in nature and does not rely on any statistical assumptions on the scene data in the image sequence. Applications of this algorithm to the simulated video sequences and real infrared data taken with PV320 show that the correction results of image sequence are better than that of using original NNT approach,especially for the short-time image sequences (several hundred frames) subjected to the dense impulse noises with a number of dead or saturated pixels.