Differential Spatial Modulation Mapping Algorithms

Differential spatial modulation(DSM)is a multiple-input multiple-output(MIMO)transmission scheme.It has attracted extensive research interest due to its ability to transmit additional data without increasing any radio frequency chain.In this paper,DSM is investigated using two mapping algorithms:Look-Up Table Order(LUTO)and Permutation Method(PM).Then,the bit error rate(BER)performance and complexity of the two mapping algorithms in various antennas and modulation methods are verified by simulation experiments.The results show that PM has a lower BER than the LUTO mapping algorithm,and the latter has lower complexity than the former.

A Reduced Search Soft-Output Detection Algorithm and Its Application to Turbo-Equalization

To decrease the complexity of MAP algorithm, reduced state or reduced search techniques can be applied. In this paper we propose a reduced search soft output detection algorithm fully based on the principle of M algorithm for turbo equalization, which is a suboptimum version of the Lee algorithm. This algorithm is called soft output M algorithm (denoted as SO M algorithm), which applies the M strategy to both the forward recursion and the extended forward recursion of the Lee algorithm. Computer simulation results show that, by properly selecting and adjusting the breadth parameter and depth parameter during the iteration of turbo equalization, this algorithm can obtain good performance and complexity trade off.

Hybrid ToA and IMU indoor localization system by various algorithms

In this paper, we integrate inertial navigation system (INS) with wireless sensor network (WSN) to enhance the accuracy of indoor localization. Inertial measurement unit (IMU), the core of the INS, measures the accelerated and angular rotated speed of moving objects. Meanwhile, the ranges from the object to beacons, which are sensor nodes with known coordinates, are collected by time of arrival (ToA) approach. These messages are simultaneously collected and transmitted to the terminal. At the terminal, we set up the state transition models and observation models. According to them, several recursive Bayesian algorithms are applied to producing position estimations. As shown in the experiments, all of three algorithms do not require constant moving speed and perform better than standalone ToA system or standalone IMU system. And within them, two algorithms can be applied for the tracking on any path which is not restricted by the requirement that the trajectory between the positions at two consecutive time steps is a straight line.

S-box:six-dimensional compound hyperchaotic map and artificial bee colony algorithm

Being as unique nonlinear components of block ciphers,substitution boxes（S-boxes） directly affect the security of the cryptographic systems.It is important and difficult to design cryptographically strong S-boxes that simultaneously meet with multiple cryptographic criteria such as bijection,non-linearity,strict avalanche criterion（SAC）,bits independence criterion（BIC）,differential probability（DP） and linear probability（LP）.To deal with this problem,a chaotic S-box based on the artificial bee colony algorithm（CSABC） is designed.It uses the S-boxes generated by the six-dimensional compound hyperchaotic map as the initial individuals and employs ABC to improve their performance.In addition,it considers the nonlinearity and differential uniformity as the fitness functions.A series of experiments have been conducted to compare multiple cryptographic criteria of this algorithm with other algorithms.Simulation results show that the new algorithm has cryptographically strong S-box while meeting multiple cryptographic criteria.

Universally Composable Symbolic Analysis of Group Key Exchange Protocol

Canetti and Herzog have already proposed universally composable symbolic analysis(UCSA) to analyze mutual authentication and key exchange protocols. However,they do not analyze group key exchange protocol. Therefore,this paper explores an approach to analyze group key exchange protocols,which realize automation and guarantee the soundness of cryptography. Considered that there exist many kinds of group key exchange protocols and the participants’ number of each protocol is arbitrary. So this paper takes the case of Burmester-Desmedt(BD) protocol with three participants against passive adversary(3-BD-Passive) . In a nutshell,our works lay the root for analyzing group key exchange protocols automatically without sacrificing soundness of cryptography.

Performance analysis and code recognition for dual N-ary orthogonal hybrid modulation systems

A dual N-ary orthogonal hybrid modulation system is introduced in this paper, which can increase the data rate greatly compared with conventional N-ary orthogonal spread spectrum system, so it can be used for high rate data communication. Then, three code recognition algorithms are presented for dual N-ary orthogonal hybrid modulation system and the analytic bit error rate （BER） performance of the system in additive white Gaussian noise （AWGN） and fiat Rayleigh fading channel is derived. Finally, the computer simulation of the system with three code recognition algorithms is performed, which shows that the simplified maximum a posteriori （MAP） algorithm is the best for the system with a compromise between the performance and the complexity.

A fast mapping method to evaluate immersed boundary hydrodynamic forces

Immersed boundary method is a crucial method to deal with particle suspension flow.Particle shapes involved in such flow are usually simple geometry,such as sphere and ellipsoid,which can be conveniently represented by the triangular surface grid.When the number of particles and resolution of the surface grid increase,calculating the hydrodynamic force on the particle surface through integration can be time-consuming.Hence,the present paper establishes a fast mapping method to evaluate immersed boundary hydrodynamic force.Firstly,the particle surface grid is generated by an initial triangular element grid.Subsequently,the initial surface grid is refined by bisection refinement to the desired resolution.The final step is to find the triangular element index on the particle triangular surface grid,which contains the projective point.Test cases show that the present mapping algorithm has good accuracy and efficiency for calculating hydrodynamic forces of particles.

Tracking the events in the coverage of wireless sensor networks based on artificial neural-networks algorithms

Sensor deployment is an important problem in mobile wireless sensor networks.This paper presents a dis-tributed self-spreading deployment algorithm(SOMDA)for mobile sensors based on artificial neural-networks self-organizing maps algorithm.During the deployment,the nodes compete to track the event and cooperate to form an ordered topology.After going through the algorithm,the statistical distribution of the nodes approaches that of the events in the interest area.The performance of the algo-rithm is evaluated by the covered percentage of re-gion/events,the detecting ability and the energy equaliza-tion of the networks.The simulation results indicate that SOMDA outperforms uniform and random deployment with lossless coverage,enhancive detecting ability and signifi-cant energy equalization.

Hardware Acceleration for SLAM in Mobile Systems

The emerging mobile robot industry has spurred a flurry of interest in solving the simultaneous localization and mapping(SLAM)problem.However,existing SLAM platforms have difficulty in meeting the real-time and low-pow-er requirements imposed by mobile systems.Though specialized hardware is promising with regard to achieving high per-formance and lowering the power,designing an efficient accelerator for SLAM is severely hindered by a wide variety of SLAM algorithms.Based on our detailed analysis of representative SLAM algorithms,we observe that SLAM algorithms advance two challenges for designing efficient hardware accelerators:the large number of computational primitives and ir-regular control flows.To address these two challenges,we propose a hardware accelerator that features composable com-putation units classified as the matrix,vector,scalar,and control units.In addition,we design a hierarchical instruction set for coping with a broad range of SLAM algorithms with irregular control flows.Experimental results show that,com-pared against an Intel x86 processor,on average,our accelerator with the area of 7.41 mm^(2) achieves 10.52x and 112.62x better performance and energy savings,respectively,across different datasets.Compared against a more energy-efficient ARM Cortex processor,our accelerator still achieves 33.03x and 62.64x better performance and energy savings,respec-tively.

Coupled solid-fluid FE-analysis of an embankment dam

A coupled solid-fluid FE-model for partially saturated soils,characterized by modeling the soil as a three-phase material consisting of a deformable soil skeleton and the fluid phases water and air,is reviewed briefly.As a constitutive model for the soil skeleton,the well-known Barcelona Basic model(BBM)is employed,which is formulated in terms of net stress and matric suction.For the BBM,a computationally efficient return mapping algorithm is proposed,which only requires the solution of a scalar nonlinear equation at the integration point level.The coupled FE-model is applied to the coupled transient numerical simulation of the water flow and the deformations and stresses in an embankment dam.

A Multiscale Approach to Automatic Medical Image Segmentation Using Self-Organizing Map

In this paper, a new medical image classification scheme is proposed using selforganizing map (SOM) combined with multiscale technique. It addresses the problem of the handling of edge pixels in the traditional multiscale SOM classifiers. First, to solve the difficulty in manual selection of edge pixels, a multiscale edge detection algorithm based on wavelet transform is proposed. Edge pixels detected are then selected into the training set as a new class and a mu1tiscale SoM classifier is trained using this training set. In this new scheme, the SoM classifier can perform both the classification on the entire image and the edge detection simultaneously. On the other hand, the misclassification of the traditional multiscale SoM classifier in regions near edges is greatly reduced and the correct classification is improved at the same time.