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.

基于YOLOv8改进的脑癌检测算法

自动检测脑部肿瘤在磁共振成像中的位置是一个复杂、繁重的任务,需要耗费大量时间和资源。传统识别方案经常出现误解、遗漏和误导的情况,从而影响患者的治疗进度,对患者的生命安全产生影响。为了进一步提高鉴定的效果,引入了4项关键改进措施。首先,采用了高效的多尺度注意力EMA(Efficient Multi-scale Attention),这种方法既可以对全局信息进行编码,也可以对信息进行重新校准,同时通过并行的分支输出特征进行跨维度的交互,使信息进一步聚合。其次,引入了BiFPN(Bidirectional Feature Pyramid Network)模块,并对其结构进行改进,以便缩短每一次检测所需要的时间,同时提升图像识别效果。然后采用MDPIoU损失函数和Mish激活函数进行改进,进一步提高检测的准确度。最后进行仿真实验,实验结果表明,改进的YOLOv8算法在脑癌检测中的精确率、召回率、平均精度均值均有提升,其中Precision提高了4.48%,Recall提高了2.64%,mAP@0.5提高了2.6%,mAP@0.5:0.9提高了7.0%。

Car-following model featured with factors reflecting emergency evacuation situation

A new emergency evacuation car-following model （EECM） is proposed. The model aims to capture the main characteristics of traffic flow and driver behavior under an emergency evacuation, and it is developed on the basis of minimum safety distances with parts of the drivers＇ abnormal behavior in a panic emergency situation. A thorough questionnaire survey is undertaken among drivers of different ages. Based on the results from the survey, a safety-distance car-following model is formulated by taking into account two new parameters： a differential distributing coefficient and a driver＇ s experiential decision coefficient, which are used to reflect variations of driving behaviors under an emergency evacuation situation when compared with regular conditions. The formulation and derivation of the new model, as well as its properties and applicability are discussed. A case study is presented to compare the car-following trajectories using observed data under regular peak-hour traffic conditions and theoretical EECM results. The results indicate the consistency of the analysis of assumptions on the EECM and observations.

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.

Local information enhanced LBP

Based on the observation that there exists multiple information in a pixel neighbor,such as distance sum and gray difference sum,local information enhanced LBP(local binary pattern)approach,i.e.LE-LBP,is presented.Geometric information of the pixel neighborhood is used to compute minimum distance sum.Gray variation information is used to compute gray difference sum.Then,both the minimum distance sum and the gray difference sum are used to build a feature space.Feature spectrum of the image is computed on the feature space.Histogram computed from the feature spectrum is used to characterize the image.Compared with LBP,rotation invariant LBP,uniform LBP and LBP with local contrast,it is found that the feature spectrum image from LE-LBP contains more details,however,the feature vector is more discriminative.The retrieval precision of the system using LE-LBP is91.8%when recall is 10%for bus images.

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

A 3-D Polarization Quadrature Amplitude Modulation Method and Constellation Mapping

As a new three-dimensional(3-D)modulation,Polarization Quadrature Amplitude Modulation(PQAM) can be regarded as the combination of Pulse amplitude modulation(PAM) and Quadrature Amplitude Modulation(QAM) Modulation.It can better improve the digital communication efficiency and reduce the Symbol error rate(SER) of the system than one-dimensional or two-dimensional modulation scheme.How to design a feasible constellation is the most concerned problem of PQAM currently.This paper first studies the relationship between the SER theoretical value of PQAM and the distribution of M and N,proposes a new M,N allocation scheme.Secondly,a new and straightforward design method of constructing higher-level 3-D signal constellations,which can be matched with the PQAM,and the constellation can divided into three different structures according to the ary for PQAM.Finally,the simulation results show that:in PQAM system,the modulation scheme and the constellation mapping scheme are proposed in this paper which can effectively reduce the system SER and improve the anti-noise performance of the system.

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.

A novel mapping scheme and modified decoding algorithm for BICM-ID

This paper proposes a novel mapping scheme for bit-interleaved coded modulation with iterative decoding(BICM-ID).The symbol mapping is composed of two QPSK with different radiuses and phases,called cross equalization-8PSK-quasi-semi set partitioning(CE-8PSK-Quasi-SSP).Providing the same average power,the proposed scheme can increase the minimum squared Euclidean distance(MSED)and then improve the receiving performance of BICM-ID compared with conventional symbol mapping schemes.Simultaneously,a modified iteration decoding algorithm is proposed in this paper.In the process of iteration decoding,different proportion of the extrinsic information to the systematic observations results in distinct decoding performance.At high SNR(4~9dB),the observation information plays a more important role than the extrinsic information.Simulation results show that the proportion set at 1.2 is more suitable for the novel mapping in BICM-ID.When the BER is 10^(-4),more than 0.9dB coding gain over Rayleigh channels can be achieved for the improved mapping and decoding scheme.

A NEW LABELING SEARCH ALGORITHM FOR BIT-INTERLEAVED CODED MODULATION WITH ITERATIVE DECODING

Bit-Interleaved Coded Modulation with Iterative Decoding (BICM-ID) is a bandwidth ef- ficient transmission, where the bit error rate is reduced through the iterative information exchange between the inner demapper and the outer decoder. The choice of the symbol mapping is the crucial design parameter. This paper indicates that the Harmonic Mean of the Minimum Squared Euclidean (HMMSE) distance is the best criterion for the mapping design. Based on the design criterion of the HMMSE distance, a new search algorithm to find the optimized labeling maps for BICM-ID system is proposed. Numerical results and performance comparison show that the new labeling search method has a low complexity and outperforms other labeling schemes using other design criterion in BICM-ID system, therefore it is an optimized labeling method.

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.