Improved Adaptive Random Convolutional Network Coding Algorithm

To address the issue of field size in random network coding, we propose an Improved Adaptive Random Convolutional Network Coding (IARCNC) algorithm to considerably reduce the amount of occupied memory. The operation of IARCNC is similar to that of Adaptive Random Convolutional Network Coding (ARCNC), with the coefficients of local encoding kernels chosen uniformly at random over a small finite field. The difference is that the length of the local encoding kernels at the nodes used by IARCNC is constrained by the depth; meanwhile, increases until all the related sink nodes can be decoded. This restriction can make the code length distribution more reasonable. Therefore, IARCNC retains the advantages of ARCNC, such as a small decoding delay and partial adaptation to an unknown topology without an early estimation of the field size. In addition, it has its own advantage, that is, a higher reduction in memory use. The simulation and the example show the effectiveness of the proposed algorithm.

Novel joint encoding/decoding algorithms of fountain codes for underwater acoustic communication

Fountain codes are considered to be a promising coding technique in underwater acoustic communication（UAC） which is challenged with the unique propagation features of the underwater acoustic channel and the harsh marine environment. And Luby transform（LT） codes are the first codes fully realizing the digital fountain concept. However, in conventional LT encoding/decoding algorithms, due to the imperfect coverage（IC） of input symbols and short cycles in the generator matrix, stopping sets would occur and terminate the decoding. Thus, the recovery probability is reduced,high coding overhead is required and decoding delay is increased.These issues would be disadvantages while applying LT codes in underwater acoustic communication. Aimed at solving those issues, novel encoding/decoding algorithms are proposed. First,a doping and non-uniform selecting（DNS） encoding algorithm is proposed to solve the IC and the generation of short cycles problems. And this can reduce the probability of stopping sets occur during decoding. Second, a hybrid on the fly Gaussian elimination and belief propagation（OFG-BP） decoding algorithm is designed to reduce the decoding delay and efficiently utilize the information of stopping sets. Comparisons via Monte Carlo simulation confirm that the proposed schemes could achieve better overall decoding performances in comparison with conventional schemes.

Design and analysis of a network coding algorithm for ad hoc networks

Network coding is proved to have advantages in both wireline and wireless networks. Especially, appropriate network coding schemes are programmed for underlined networks. Considering the feature of strong node mobility in aviation communication networks, a hop-by-hop network coding algorithm based on ad hoc networks was proposed. Compared with COPE-like network coding algorithms, the proposed algorithm does not require overhearing from other nodes, which meets confidentiality requirements of aviation communication networks. Meanwhile, it does save resource consumption and promise less processing delay. To analyze the performance of the network coding algorithm in scalable networks with different traffic models, a typical network was built in a network simulator, through which receiving accuracy rate and receiving delay were both examined.The simulation results indicate that, by virtue of network coding, the proposed algorithm works well and improves performance significantly. More specifically, it has better performance in enhancing receiving accuracy rate and reducing receiving delay, as compared with any of the traditional networks without coding. It was applied to both symmetric and asymmetric traffic flows and, in particular, it achieves much better performance when the network scale becomes larger. Therefore, this algorithm has great potentials in large-scale multi-hop aviation communication networks.

A new multi-model coding algorithm based on MBE and spectral amplitude correlation between successive frames

The researches on spectral amplitude correlation between successive frames based on MBE (Multi-Band Excitation) are presented and a new MBE coding algorithm of muitimodels is proposed. It has been shown that the magnitude spectrum in MBE coding algorithm can be estimated in very high accuracy by using a simple linear predictive model of the spectral amplitude correlation between successive frames. This model employs oniy one gain coefficient and one predictive coefficient. The accuracy of estimated magnitude spectrum in this model is higher than that of the conventional MBE algorithm using the 10-th order LPC (Linear Predictive Coding) Inodel without the spectral amplitude correlation between successlve frames.This model is superior to the conventional MBE coding model in volced speech, but it is iess effective in unvoiced speech, so that the conventional MBE coding algorithm is still used for unvoiced speech. The new multi-model coding algorithm improves the quality of coding speech,and obtains good results

A speech coding algorithm based on harmonic-stochastic excitation

A very low bit rate algorithm for encoding speech signals at 825 bps based on a mixed harmonic and stochastic modeling of the excitation signal is presented. The algorithm is more robust in the V/UV decision, reliable pitch estimation, and excitation signals synthesis. The bit allocation schedules in every case and the analysis-by-synthesis processes of the parameters are also described. The Diagnostic Rhyme Test (DRT) results show that the performance of the proposed algorithm is comparable to that of the MELP algorithm at 2.4 kbps, and the speech distinctness is 90.25%.

New algorithm for variable-rate linear broadcast network coding

To adjust the variance of source rate in linear broadcast networks, global encoding kernels should have corresponding dimensions to instruct the decoding process. The algorithm of constructing such global encoding kernels is to adjust heterogeneous network to possible link failures. Linear algebra, graph theory and group theory are applied to construct one series of global encoding kernels which are applicable to all source rates. The effectiveness and existence of such global encoding kernels are proved. Based on 2 information flow, the algorithm of construction is explicitly given within polynomial time O（｜E｜ ｜T｜.ω^2max）, and the memory complexity of algorithm is O（｜E｜）. Both time and memory complexity of this algorithm proposed can be O（ωmax） less than those of algorithms in related works.

Totally Coded Algorithm for Switched-Current Network Analysis in Frequency Domain

Based on mirror-blocks, a totally coded algorithm （TCA） for switched-current （SI） network analysis in frequency domain is presented. The algorithm is simple, available, and suitable for any swltched-current networks. A basis of analysis and design for switched-current networks via this algorithm is provided.

Dynamic airspace sectorization via improved genetic algorithm

This paper deals with dynamic airspace sectorization （DAS） problem by an improved genetic algorithm （iGA）. A graph model is first constructed that represents the airspace static structure. Then the DAS problem is formulated as a graph-partitioning problem to balance the sector workload under the premise of ensuring safety. In the iGA, multiple populations and hybrid coding are applied to determine the optimal sector number and airspace sectorization. The sector constraints are well satisfied by the improved genetic operators and protect zones. This method is validated by being applied to the airspace of North China in terms of three indexes, which are sector balancing index, coordination workload index and sector average flight time index. The improvement is obvious, as the sector balancing index is reduced by 16.5 %, the coordination workload index is reduced by 11.2 %, and the sector average flight time index is increased by 11.4 % during the peak-hour traffic.

On grey relation projection model based on projection pursuit

Multidimensional grey relation projection value can be synthesized as one-dimensional projection value by using projection pursuit model. The larger the projection value is,the better the model. Thus,according to the projection value,the best one can be chosen from the model aggregation. Because projection pursuit modeling based on accelerating genetic algorithm can simplify the implementation procedure of the projection pursuit technique and overcome its complex calculation as well as the difficulty in implementing its program,a new method can be obtained for choosing the best grey relation projection model based on the projection pursuit technique.

Algebraic-Based Nonbinary LDPC Codes with Flexible Field Orders and Code Rates

In this paper, we study the rank of matrices over GF(2~p),and propose two construction methods for algebraic-based nonbinary LDPC codes from an existing LDPC code, referred to as the original code. By multiplying all elements of each column of the binary parity-check matrix H corresponding to the original code with the same nonzero element of any field, the first class of nonbinary LDPC codes with flexible field order is proposed. The second method is to replace the nonzero elements of some columns in H with different nonzero field elements in a given field, and then another class of nonbinary LDPC codes with various rates is obtained. Simulation results show that the proposed nonbinary LDPC codes perform well over the AWGN channel with the iterative decoding algorithms.

Real-Time Implementation for Reduced-Complexity LDPC Decoder in Satellite Communication

In this paper,it has proposed a realtime implementation of low-density paritycheck(LDPC) decoder with less complexity used for satellite communication on FPGA platform.By adopting a(2048.4096)irregular quasi-cyclic(QC) LDPC code,the proposed partly parallel decoding structure balances the complexity between the check node unit(CNU) and the variable node unit(VNU) based on min-sum(MS) algorithm,thereby achieving less Slice resources and superior clock performance.Moreover,as a lookup table(LUT) is utilized in this paper to search the node message stored in timeshare memory unit,it is simple to reuse and save large amount of storage resources.The implementation results on Xilinx FPGA chip illustrate that,compared with conventional structure,the proposed scheme can achieve at last 28.6%and 8%cost reduction in RAM and Slice respectively.The clock frequency is also increased to 280 MHz without decoding performance deterioration and convergence speed reduction.

Beam shaping with limited amplitude weight values for satellite active phased array antenna

To gain the tradeoff between lower sidelobe and higher power amplifiers efficiency,a transmitting beam shaping scheme with limited amplitude weight values for satellite active phased array antenna is presented. The scheme is implemented by a dual coding genetic algorithm(GA). Phase and amplitude of array weight vectors for beam shaping are encoded by real coding and finite length binary coding,respectively,which,maintaining accuracy of results,reduces the amplitude dynamic range and improves the efficiency of power amplifiers. The presented algorithm,compared with complex-coded GA,increases the convergence rate due to the search space's decrease. In order to overcome the prematurity and obtain better global optimization or quasi-global optimization,a new dual coding GA based on "species diversity retention" strategy and adaptive crossover and mutation probability are presented.

A Method of Emergency Volunteer Team Internal Participation in Rescue Decision-Making Considering Psychological Behavior

A method for internal participation in rescue decision-making of emergency volunteer teams considering psychological behavior is proposed to address the time sequence of rescue tasks.Firstly,the problem of multi-tasking and multi-operation within the emergency volunteer team is described.Secondly,considering that task leaders are influenced by behavioral and psychological factors in the evaluation,the required time for the job is used as a reference point,and the expected time that volunteers can complete the job is used as an attribute value.The task leader's prospect satisfaction value for each volunteer is calculated based on prospect theory,and the perceived utility values of disappointment theory and regret theory are calculated to measure the task leader's satisfaction with each volunteer.Furthermore,a multilayer coded genetic algorithm is used to construct an optimization model for emergency volunteer decision-making with the objective of maximizing the satisfaction value.Finally,the feasibility and effectiveness of this method are illustrated by an example analysis.The result shows that the efficiency of rescue tasks can be improved through decision optimization within the volunteer team.

Stability enhancement of wind energy integrated hybrid system with the help of static synchronous compensator and symbiosis organisms search algorithm

Conventional proportional integral derivative(PID)controllers are being used in the industries for control purposes.It is very simple in design and low in cost but it has less capability to minimize the low frequency noises of the systems.Therefore,in this study,a low pass filter has been introduced with the derivative input of the PID controller to minimize the noises and to improve the transient stability of the system.This paper focuses upon the stability improvement of a wind-diesel hybrid power system model(HPSM)using a static synchronous compensator(STATCOM)along with a secondary PID controller with derivative filter(PIDF).Under any load disturbances,the reactive power mismatch occurs in the HPSM that affects the system transient stability.STATCOM with PIDF controller is used to provide reactive power support and to improve stability of the HPSM.The controller parameters are also optimized by using soft computing technique for performance improvement.This paper proposes the effectiveness of symbiosis organisms search algorithm for optimization purpose.Binary coded genetic algorithm and gravitational search algorithm are used for the sake of comparison.

Optimal Design of Fuzzy-AGC Based on PSO&RCGA to Improve Dynamic Stability of Interconnected Multi-area Power Systems

Quickly getting back the synchronism of a disturbed interconnected multi-area power system due to variations in loading condition is recognized as prominent issue related to automatic generation control(AGC).In this regard,AGC system based on fuzzy logic,i.e.,so-called FLAGC can introduce an effectual performance to suppress the dynamic oscillations of tie-line power exchanges and frequency in multi-area interconnected power system.Apart from that,simultaneous coordination scheme based on particle swarm optimization(PSO)along with real coded genetic algorithm(RCGA)is suggested to coordinate FLAGCs of the all areas.To clarify the high efficiency of aforementioned strategy,two different interconnected multi-area power systems,i.e.,three-area hydro-thermal power system and five-area thermal power system have been taken into account for relevant studies.The potency of this strategy has been thoroughly dealt with by considering the step load perturbation(SLP)in both the under study power systems.To sum up,the simulation results have plainly revealed dynamic performance of FLAGC as compared with conventional AGC(CAGC)in each power system in order to damp out the power system oscillations.