An Improved Minimum Distance Method Based on Artificial Neural Networks

MDM (minimum distance method) is a very popular algorithm in staterecognition. But it has a presupposition, that is, the distance within one class must be shorterenough than the distance between classes. When this presupposition is not satisfied, the method isno longer valid. In order to overcome the shortcomings of MDM, an improved minimum distance method(IMDM) based on ANN (artificial neural networks) is presented. The simulation results demonstratethat IMDM has two advantages, that is, the rate of recognition is faster and the accuracy ofrecognition is higher compared with MDM.

On the Computing of the Minimum Distance of Linear Block Codes by Heuristic Methods

The evaluation of the minimum distance of linear block codes remains an open problem in coding theory, and it is not easy to determine its true value by classical methods, for this reason the problem has been solved in the literature with heuristic techniques such as genetic algorithms and local search algorithms. In this paper we propose two approaches to attack the hardness of this problem. The first approach is based on genetic algorithms and it yield to good results comparing to another work based also on genetic algorithms. The second approach is based on a new randomized algorithm which we call 'Multiple Impulse Method (MIM)', where the principle is to search codewords locally around the all-zero codeword perturbed by a minimum level of noise, anticipating that the resultant nearest nonzero codewords will most likely contain the minimum Hamming-weight codeword whose Hamming weight is equal to the minimum distance of the linear code.

Solid rocket motor propellant grain burnback simulation based on fast minimum distance function calculation and improved marching tetrahedron method

To efficiently compute arbitrary propellant grain evolution of the burning surface with uniform and non-uniform burning rate for solid rocket motor,a unified framework of burning surface regression simulation has been developed based on minimum distance function.In order to speed up the computation of the mini-mum distance between grid nodes of grain and the triangular mesh of burning surface,a fast distance querying method based on the equal size cube voxel structure was employed.An improved marching tetrahedron method based on piecewise linear approximation was carried out on second-order tetrahedral elements,achieved high-efficiency and adequate accuracy of burning surface extraction simultaneously.The cases of star grain,finocyl grain,and non-uniform tube grain were studied to verify the proposed method.The observed result indicates that the grain burnback computation method could realize the accurate simulation on unstructured tetrahedral mesh with a desirable performance on computational time.

Minimum distances of three families of low-density parity-check codes based on finite geometries

Three families of low-density parity-check （LDPC） codes are constructed based on the totally isotropic subspaces of symplectic, unitary, and orthogonal spaces over finite fields, respectively. The minimum distances of the three families of LDPC codes in some special cases are settled.

2-Dimension Interleaver Design for IDMA Systems

A new design method interleavers, 2-dimension interleavers, are proposed for interleave division multiple access （IDMA） systems. With a same interleaving rule named I＇, the row indices and column indices of a traditional block interleaving matrix are scrambled to obtain an interleaver, which is marked as the master interleaver. F is produced by a loworder PN sequence generator. Two ways are provided for generating different interleavers. One is that all interleavers are generated by the circular shifting master interleaver. The other is that different inter leavers are generated by different Ts. Besides, we prove that the minimum distance between two adjacent bits resulted from 2-dimension interleaves is much larger than that of other schemes, such as random interleavers, power interleavers, and shiffting interleaves. The simulation results show that 2-dimension interleavers can achieve much better performance with much less resource consumption than random interleavers in IDMA systems.

Generalized Minimum Rank Distance of Variable-Rate Linear Network Error Correction Codes

By extending the notion of the minimum distance for linear network error correction code(LNEC), this paper introduces the concept of generalized minimum rank distance(GMRD) of variable-rate linear network error correction codes. The basic properties of GMRD are investigated. It is proved that GMRD can characterize the error correction/detection capability of variable-rate linear network error correction codes when the source transmits the messages at several different rates.

Optimal and suboptimal structured algorithms of binary linear block codes

The optimal and suboptimal structured algorithms of linear block codes from the geometrical perspective are represented.The minimum distance and weight property lemmas and the theorem are proved for the generator matrix.Based upon the property of generator matrix,the structured algorithms of linear block codes are demonstrated.Since the complexity of optimal structured algorithm is very high,the binary linear block codes is searched by using the suboptimal structured algorithm.The comparison with Bose-Chaudhuri-Hocquenqhem（BCH） codes shows that the searched linear block codes are equivalent on minimum distance and can be designed for more block lengths.Because the linear block codes are used widely in communication systems and digital applications,the optimal and suboptimal structured algorithms must have great future being widely used in many applications and perspectives.

Finding Starting Points for Tracing Surface Intersections

To find starting points for all the intersection curves, one of the surfaces is subdivided into some small surface patches. Based on a correlative algorithm of computing the minimum distance of two surfaces, the intersections of every patch with another surface are detected, and starting points are calculated by dichotomy. This algorithm shows superior efficiency in the computational complexity and number of iterations needed. It can be used to determine exact starting points on all possible solution curves between any kinds of parametric sculptured surfaces.

Pseudo minimum translational distance between convex polyhedra(Ⅰ)——Definition and properties

Based on the concept of gauge function, the pseudo minimum translational distance (PMTD) between two convex objects is defined in this paper. PMTD not only provides a measure of the clearance between two separating objects, but also quantifies the penetration of two objects intersecting each other. It is proved that the PMTD is differentiate almost everywhere w. r.t. the configuration variable of the objects. Algorithms for calculating PMTD and its derivative are also presented.

Coded Modulation Faster-than-Nyquist Transmission with Precoder and Channel Shortening Optimization

Faster-than-Nyquist(FTN)signaling can improve the spectrum efficiency(SE)of the transmission system.In this paper,we propose a coded modulation FTN(CM-FTN)transmission scheme with precoder and channel shortening(CS)optimization to improve bit error rate(BER)performance and reduce the complexity of FTN equalizer.In our proposal,the information rate(IR)or spectral efficiency(SE)is employed and verified as a better performance metric for CM-FTN than the minimum Euclidian distance(MED).The precoder of CM-FTN is optimized for maximizing the IR criterion using the bare-bones particle swarm optimization(BB-PSO)algorithm.Further,a three-carrier CM-FTN system model is used to capture the broadening effect of precoder.Also targeting for the IR maximization,the inter-symbol interference(ISI)length for CS is optimized to reduce the receiver complexity without performance loss.Simulation results demonstrate that our method has a 0.6dB precoding gain compared with the nonprecoding scheme and a maximum of 87.5%of the complexity of FTN equalizer is reduced without BER loss.

Landscape Pattern Optimization of Chengdu Based on Modern Garden City

TOptimization of regional landscape pattern is significant for improving function and value of ecosystem,and restraining the expansion of urban layout.Taking Chengdu City for example,this paper applied RS and GIS techniques,landscape indexes and ecological service function evaluation to further analyze the temporal and spatial characteristics of landscape pattern and spatial differences of regional ecological functions,and on this basis,identified the spatial distribution of ecological source lands.Based on the long-term objective of building Chengdu into a modem garden city,this paper applied the accumulative cost distance model and introduced garden city theory to construct regional ecological corridors and ecological nodes,and explored the approaches of optimizing landscape pattern of modem garden city.The results showed that a great deal of arable land has been transferred to construction land in the urbanization;intensity of regional ecological functions showed obvious spatial differences;ecological source lands were mainly distributed in the Longmen Mountain,the Qionglai Mountain,the Changqiu Mountain and the Longquan Mountain;according to actual conditions of the study area,the road ecological corridors,river corridors and agricultural corridors in the layout of "four rings and six radial corridors" were constructed;ecological nodes dominated by intersection,wetland and forest park were formed.This research method and results are significant references for building Chengdu into a modem garden

Four-Dimensional Signal Constellations Based on Binary Frequency-Shift Keying and <i>M</i>-ary Amplitude-Phase-Shift Keying

In this article, we give the construction of new four-dimensional signal constellations in the Euclidean space, which represent a certain combination of binary frequency-shift keying (BFSK) and M-ary amplitude-phase-shift keying (MAPSK). Description of such signals and the formulas for calculating the minimum squared Euclidean distance are presented. We have developed an analytic building method for even and odd values of M. Hence, no computer search and no heuristic methods are required. The new optimized BFSK-MAPSK (M = 5,6,···,16) signal constructions are built for the values of modulation indexes h =0.1,0.15,···,0.5 and their parameters are given. The results of computer simulations are also provided. Based on the obtained results we can conclude, that BFSK-MAPSK systems outperform similar four-dimensional systems both in terms of minimum squared Euclidean distance and simulated symbol error rate.

Multiple mobile-obstacle avoidance algorithm for redundant manipulator

In order to overcome the shortcomings of the previous obstacle avoidance algorithms,an obstacle avoidance algorithm applicable to multiple mobile obstacles was proposed.The minimum prediction distance between obstacles and a manipulator was obtained according to the states of obstacles and transformed to escape velocity of the corresponding link of the manipulator.The escape velocity was introduced to the gradient projection method to obtain the joint velocity of the manipulator so as to complete the obstacle avoidance trajectory planning.A7-DOF manipulator was used in the simulation,and the results verified the effectiveness of the algorithm.

Region-based classification of PolSAR data using radial basis kernel functions with stochastic distances

Region-based classification of PolSAR data can be effectively performed by seeking for the assignment that minimizes a distance between prototypes and segments.Silva et al.[“Classification of segments in PolSAR imagery by minimum stochastic distances between wishart distributions.”IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 6(3):1263–1273]used stochastic distances between complex multivariate Wishart models which,differently from other measures,are computationally tractable.In this work we assess the robustness of such approach with respect to errors in the training stage,and propose an extension that alleviates such problems.We introduce robustness in the process by incorporating a combination of radial basis kernel functions and stochastic distances with Support Vector Machines(SVM).We consider several stochastic distances between Wishart:Bhatacharyya,Kullback-Leibler,Chi-Square,Rényi,and Hellinger.We perform two case studies with PolSAR images,both simulated and from actual sensors,and different classification scenarios to compare the performance of Minimum Distance and SVM classification frameworks.With this,we model the situation of imperfect training samples.We show that SVM with the proposed kernel functions achieves better performance with respect to Minimum Distance,at the expense of more computational resources and the need of parameter tuning.Code and data are provided for reproducibility.

Two constructions of LDPC codes based on pseudo-symplectic geometry over finite fields

F（2v＋ 1） q Based on the pseudo-symplectic space over q of characteristics 2, combining the definition of low density parity check （LDPC） codes with the knowledge of graph theory, two kinds of LDPC codes with larger girth are constructed. By the knowledge of bipartite graph and the girth properties of LDPC codes, both the girth of the code C（m1 ,2v ＋ 1 ,q） and the code C（m2 ,2v ＋ 1 ,q） are computed are 8. The girth is larger, the performance of LDPC codes is better. Finally, according to the properties of the check matrix and the linear relation between the column vectors of the check matrix, both the minimum distances of the two codes are obtained are 2q ＋ 2.

Construction of LDPC codes over GF(q) with modified progressive edge growth

A parity check matrix construction method for constructing a low-density parity-check （LDPC） codes over GF（q） （q〉2） based on the modified progressive edge growth （PEG） algorithm is introduced. First, the nonzero locations of the parity check matrix are selected using the PEG algorithm. Then the nonzero elements are defined by avoiding the definition of subcode. A proof is given to show the good minimum distance property of constructed GF（q）-LDPC codes. Simulations are also presented to illustrate the good error performance of the designed codes.

Constructions of Sidon spaces and cyclic subspace codes

In this paper,we firstly construct several new kinds of Sidon spaces and Sidon sets by investigating some known results.Secondly,using these Sidon spaces,we will present a construction of cyclic subspace codes with cardinality τ,q^(n)-1/q-1 and minimum distance 2k-2,whereτis a positive integer.We further-more give some cyclic subspace codes with size 2τ·q^(n)-1/q-1 and without changing the minimum distance 2k-2.

Aggregation of preferences in attitude scales

The problem of decision-making when evaluating alternatives according to several quality criteria is considered. Information is used about the pairwise comparison of alternatives by criteria: how many times one alternative is preferable to the other. The criteriamay have nonuniform scales. The method to form preferences matrices for representing numerical estimates of alternatives in attitude scales is proposed. The properties ofthe constructed relation are investigated. The algorithms for constructing the relationwith the minimum distance from the preferences by criteria are developed. An algorithm for constructing an aggregated relation based on the summation of preferencesby criteria is developed. Aggregate relation depends on the method of specifying thedistance between the matrices of preferences. The proposed algorithms for constructing an aggregated relation can use coefficients of importance of criteria. The methodcan be applied in collective choice problems when assigning estimates to alternativesby experts. A comparison of the proposed algorithms with each other and with knowndecision-making methods is carried out. The software system of multi-criteria choiceis developed. The task of choosing start-up projects for the purpose of investing by aventure fund has been solved.

Radar automatic target recognition based on feature extraction for complex HRRP

Radar high-resolution range profile （HRRP） has received intensive attention from the radar automatic target recognition （RATR） community. Usually, since the initial phase of a complex HRRP is strongly sensitive to target position variation, which is referred to as the initial phase sensitivity in this paper, only the amplitude information in the complex HRRP, called the real HRRP in this paper, is used for RATR, whereas the phase information is discarded. However, the remaining phase information except for initial phases in the complex HRRP also contains valuable target discriminant information. This paper proposes a novel feature extraction method for the complex HRRP. The extracted complex feature vector, referred to as the complex feature vector with difference phases, contains the difference phase information between range cells but no initial phase information in the complex HRRR According to the scattering center model, the physical mechanism of the proposed complex feature vector is similar to that of the real HRRP, except for reserving some phase information independent of the initial phase in the complex HRRP. The recognition algorithms, frame-template establishment methods and preprocessing methods used in the real HRRP-based RATR can also be applied to the proposed complex feature vector-based RATR. Moreover, the components in the complex feature vector with difference phases approximate to follow Gaussian distribution, which make it simple to perform the statistical recognition by such complex feature vector. The recognition experiments based on measured data show that the proposed complex feature vector can obtain better recognition performance than the real HRRP if only the cell interval parameters are properly selected.

Data recovery with sub-Nyquist sampling:fundamental limit and a detection algorithm

While the Nyquist rate serves as a lower bound to sample a general bandlimited signal with no information loss,the sub-Nyquist rate may also be sufficient for sampling and recovering signals under certain circumstances.Previous works on sub-Nyquist sampling achieved dimensionality reduction mainly by transforming the signal in certain ways.However,the underlying structure of the sub-Nyquist sampled signal has not yet been fully exploited.In this paper,we study the fundamental limit and the method for recovering data from the sub-Nyquist sample sequence of a linearly modulated baseband signal.In this context,the signal is not eligible for dimension reduction,which makes the information loss in sub-Nyquist sampling inevitable and turns the recovery into an under-determined linear problem.The performance limits and data recovery algorithms of two different sub-Nyquist sampling schemes are studied.First,the minimum normalized Euclidean distances for the two sampling schemes are calculated which indicate the performance upper bounds of each sampling scheme.Then,with the constraint of a finite alphabet set of the transmitted symbols,a modified time-variant Viterbi algorithm is presented for efficient data recovery from the sub-Nyquist samples.The simulated bit error rates(BERs)with different sub-Nyquist sampling schemes are compared with both their theoretical limits and their Nyquist sampling counterparts,which validates the excellent performance of the proposed data recovery algorithm.