Uniquely Decomposable Constellation Group-Based Sparse Vector Coding for Short Packet Communications

Sparse vector coding(SVC)is emerging as a potential technology for short packet communications.To further improve the block error rate(BLER)performance,a uniquely decomposable constellation group-based SVC(UDCG-SVC)is proposed in this article.Additionally,in order to achieve an optimal BLER performance of UDCG-SVC,a problem to optimize the coding gain of UDCG-based superimposed constellation is formulated.Given the energy of rotation constellations in UDCG,this problem is solved by converting it into finding the maximized minimum Euclidean distance of the superimposed constellation.Simulation results demonstrate the validness of our derivation.We also find that the proposed UDCGSVC has better BLER performance compared to other SVC schemes,especially under the high order modulation scenarios.

Predicting and Classifying User Identification Code System Based on Support Vector Machines

In digital fingerprinting, preventing piracy of images by colluders is an important and tedious issue. Each image will be embedded with a unique User IDentification （UID） code that is the fingerprint for tracking the authorized user. The proposed hiding scheme makes use of a random number generator to scramble two copies of a UID, which will then be hidden in the randomly selected medium frequency coefficients of the host image. The linear support vector machine （SVM） will be used to train classifications by calculating the normalized correlation （NC） for the 2class UID codes. The trained classifications will be the models used for identifying unreadable UID codes. Experimental results showed that the success of predicting the unreadable UID codes can be increased by applying SVM. The proposed scheme can be used to provide protections to intellectual property rights of digital images aad to keep track of users to prevent collaborative piracies.

Changes in segment coordination variability and the impacts of the lower limb across running mileages in half marathons:Implications for running injuries

Background:Segment coordination variability(CV)is a movement pattern associated with running-related injuries.It can also be adversely affected by a prolonged run.However,research on this topic is currently limited.The purpose of this study was to investigate the effects of a prolonged run on segment CV and vertical loading rates during a treadmill half marathon.Methods:Fifteen healthy runners ran a half marathon on an instrumental treadmill in a biomechanical laboratory.Synchronized kinematic and kinetic data were collected every 2 km(from 2 km until 20 km),and the data were processed by musculoskeletal modeling.Segment CVs were computed from the angle-angle plots of selected pelvis-thigh,thigh-shank,and shank-rearfoot couplings using a modified vector coding technique.The loading rate of vertical ground reaction force was also calculated.A one-way MANOVA with repeated measures was performed on each of the outcome variables to examine the main effect of running mileage.Results:Significant effects of running mileage were found on segment CVs(p≤0.010)but not on loading rate(p=0.881).Notably,during the early stance phase,the CV of pelvis frontal thigh frontal was significantly increased at 20 km compared with the CV at 8 km(g=0.59,p=0.022).The CV of shank transverse vs.rearfoot frontal decreased from 2 km to 8 km(g=0.30,p=0.020)but then significantly increased at both 18 km(g=0.05,p<0.001)and 20 km(g=0.36,p<0.001).Conclusion:At the early stance,runners maintained stable CVs on the sagittal plane,which could explain the unchanged loading rate throughout the half marathon.However,increased CVs on the frontal/transverse plane may be an early sign of fatigue and indicative of possible injury risk.Further studies are necessary for conclusive statements in this regard.

Fast MATLAB assembly of FEM matrices in 2D and 3D using cell-array approach

We propose a MATLAB implementation of the P1 finite element method for the numerical solutions of the Poisson problem and the linear elasticity problem in two-dimensional(2D)and three-dimensional(3D).The code consists of vectorized(and short)assembling functions for the matrices(mass and stiffness)and the right-hand sides.Since for the P1 finite element,the element mass matrix and right-hand side are simple,the implementation uses only the MATLAB function sparse on the elements volume.For the stiffness matrix,to obtain a MATLAB implementation close to the standard form,cell-arrays are used to store the gradients of the element basis functions.The assembling procedure can then use matrix/vector products on small size cell-arrays.Numerical experiments show that our implementation is fast,scalable with respect to time,and outperforms existing vectorized MATLAB FEM codes.