一种基于模型选择的PF-TBD算法

在目前的基于粒子滤波检测前跟踪(PF-TBD)算法中,对粒子的预测通常是根据单一直线运动模型实现的,在目标机动时由于与运动形式相差较大,影响了跟踪效果。为此,提出一种基于模型选择的粒子滤波检测前跟踪(MM-PF-TBD)算法。该算法由已估计出的目标位置,计算相对偏转角,并以此判定目标当前的运动模式,进而选择相应的运动模型对下一时刻的粒子进行预测,显著提高了对粒子预测的精度。理论分析和仿真实验表明,文中所提算法适用于目标不同的运动形式,有效提高了目标机动时的检测和跟踪性能。     

基于stm32智能上假肢手部姿态自平衡系统的设计

针对残疾人假肢手部需要保持平衡的环境比较多,但是要实现假肢手部在运动过程中的平衡非常不易,要实现假肢手部的姿态自平衡更是困难,所以该文提出了一种基于stm32智能上假肢手部姿态自平衡系统的设计方法。该系统由加速度传感器、陀螺仪、stm32微控制器、高性能舵机组成,由stm32实时采集加速度传感器、陀螺仪的数据经过互补滤波算法优化角度值并处理后,通过串口向舵机发送动作信号。实验结果表明,该系统能使假肢在无规则运动过程中保持手部平衡。     

Pedestrian trajectory prediction in crossing scenario using fuzzy logic and switching Kalman filter

Pedestrian trajectory prediction plays an important role in bothadvanced driving assistance system (ADAS) and autonomous vehicles. An algorithm for pedestrian trajectory prediction in crossing scenario is proposed. To obtain features of pedestrian motion, we develop a method for data labelling and pedestrian body orientation regression. Using the hierarchical features as domain of discourse, fuzzy logic rules are built to describe the transition between different pedestrian states and motion models. With derived probability of each type of motion model we further predict the pedestrian trajectory in the next 1.5 s using switching Kalman filter (KF). The proposed algorithm is further verified in our dataset, and the result indicates that the proposed algorithm successfully predicts pedestrian' s crossing behavior 0.4 s earlier before pedestrian moves. Meanwhile, the precision of predicted trajectory surpasses other methods including interacting multi-model KF and dynamic Bayesian network (DBN).     

基于热释电红外传感器的动作检测系统设计

人体动作检测广泛应用于人机交互、智能安防、军事情报等领域,该文利用热释电红外传感器对人体红外辐射灵敏的感应,通过设计信号调理及采集电路,搭建实验平台对传感器输出信号进行采集分析,在时域和频域中进行运动特征的提取算法设计,实现人体运动形态的检测。该文以踏步过程中跳跃动作的提取为例,介绍了整个设计过程。该人体检测方法具有成本低,抗干扰能力强等优势,对人体检测领域研究有重要的参考价值。     

An efficient scalable intra coding algorithm for spatial scalability in enhancement layer

Scalable video coding (SVC) is attractive due to the capability of reconstructing lower resolution or lower quality signals from partial bit streams, which allows for simple solutions adaptted to network and terminal capabilities. This article addresses the spatial scalability of SVC and proposes an efficient H.264-based scalable intra coding algorithm. In comparison with precious single layer intra prediction (SLIP) method, the proposed algorithm aims to improve the intra coding performance of the enhancement layer by a new inter layer intra prediction (ILIP) method. The main idea of ILIP is that up-sampled and reconstructed pixels of the base layer are very useful to predict and encode those pixels of the enhancement layer, especially when those neighbouring pixels are not available. Experimental results show that the peak signal to noise ratio (PSNR) data of luminance component of encoded frames are improved, and both bit-rates and computation complexity are maintained very well. For sequence Football, the average increase of PSNR is up to 0.21?dB, while for Foreman and Bus, they are 0.14?dB and 0.17?dB, respectively.     

Human motion prediction using optimized sliding window polynomial fitting and recursive least squares

Human motion prediction is a critical issue in human-robot collaboration (HRC) tasks. In order to reduce thelocal error caused by the limitation of the capture range and sampling frequency of the depth sensor, a hybrid human motion prediction algorithm, optimized sliding window polynomial fitting and recursive least squares (OSWPF-RLS) was proposed. The OSWPF-RLS algorithm uses the human body joint data obtained under the HRC task as input, and uses recursive least squares (RLS) to predict the human movement trajectories within the time window. Then, the optimized sliding window polynomial fitting (OSWPF) is used to calculate the multi-step prediction value, and the increment of multi-step prediction value was appropriately constrained. Experimental results show that compared with the existing benchmark algorithms, the OSWPF-RLS algorithm improved the multi-     

基于灰度理论模型的截肢受试者手势分类方法研究

针对截肢者手势动作特征提取复杂、动作识别率较低的问题,该文提出一种基于灰度模型的特征提取方法。首先对预处理后的肌电信号与加速度信号经滑动窗信号截取。然后提取表面肌电信号均值、灰度模型的驱动项系数和加速度信号的绝对值均值构成特征向量,最后对滑动窗截取信号特征进行连续的识别。该文采用NinaPro(Non invasive adaptive Prosthetics)公开数据集对提出的方法进行验证,实验表明该文算法能够有效提取肌电和加速度信号的特征,对9名截肢受试者的17类手势动作的平均识别率达到91.14%,提高了17类手势的识别准确率,为仿生假肢人机交互控制算法提供了一种新的思路。     

一种新型GM(1,1)-AR预测模型在陀螺漂移中的应用

为了对陀螺漂移趋势进行更有效的预测,提出一种基于小波分析的新型GM(1,1)-AR时间序列预测算法。该算法通过运用小波分解算法将陀螺漂移数据中的趋势项和随机项进行分离,然后分别运用GM(1,1)模型和AR时间序列预测模型对趋势项和随机项进行预测,最后用小波重构算法得出最终的预测值。给出了一种算法及具体步骤,最后用某型导弹陀螺漂移数据进行仿真实验,以检验这种算法的有效性和可行性,结果表明这种预测算法应用于陀螺漂移趋势预测是可行的。     

一种新的无线异构网络的自适应垂直切换算法

针对当前无线异构网络融合技术中的垂直切换机制,提出了一种以平均接收信号强度为评价指标,并由此指标判断可能的运动趋势的自适应垂直切换算法。仿真结果表明:相比于传统的固定门限的平均接收信号强度垂直切换算法,提出的方法能较好地预测并提前触发该发生的切换,改善切换性能。     

An Adaptive Hard Handoff Algorithm for Mobile Cellular Communication Systems

In this letter, we propose an adaptive hard handoff algorithm with a dynamic hysteresis value based on the received signal strength from the serving base station, for mobile cellular communication systems. A discrete‐time method is presented to evaluate handoff algorithms analytically. Performance is evaluated in terms of the average number of handoffs, the probability of link degradation, and the average handoff delay. Numerical results and simulations demonstrate that the proposed algorithm outperforms the handoff algorithm with fixed hysteresis values and the handoff algorithm using both threshold and hysteresis.     

Application of a neuro-fuzzy network for gait event detection using electromyography in the child with cerebral palsy

An adaptive neuro-fuzzy inference system (ANFIS) with a supervisory control system (SCS) was used to predict the occurrence of gait events using the electromyographic (EMG) activity of lower extremity muscles in the child with cerebral palsy (CP). This is anticipated to form the basis of a control algorithm for the application of electrical stimulation (ES) to leg or ankle muscles in an attempt to improve walking ability. Either surface or percutaneous intramuscular electrodes were used to record the muscle activity from the quadriceps muscles, with concurrent recording of the gait cycle performed using a VICON motion analysis system for validation of the ANFIS with SCS. Using one EMG signal and its derivative from each leg as its inputs, the ANFIS with SCS was able to predict all gait events in seven out of the eight children, with an average absolute time differential between the VICON recording and the ANFIS prediction of less than 30 ms. Overall accuracy in predicting gait events ranged from 98.6% to 95.3% (root mean-squared error between 0.7 and 1.5). Application of the ANFIS with the SCS to the prediction of gait events using EMG data collected two months after the initial data demonstrated comparable results, with no significant differences between gait event detection times. The accuracy rate and robustness of the ANFIS with SCS with two EMG signals suggests its applicability to ES control.     

基于预测的差分信号压缩感知算法

基于压缩感知的频谱感知方法可以较低的采样速率快速获取信号,并利用获得的稀疏数据样本来判断信道的占用情况。然而,压缩感知技术中信号重构算法的复杂度很高,难以满足无线通信中的实时性要求。本文提出一种基于预测的差分信号压缩感知算法,该算法利用信道占用时间上的相关性,建立了一种信道占用情况的预测模型,依此模型预测出信道占用的变化情况;基于预测结果,在重构信号时可减少频点的搜索范围,两次降低重构算法的运算量。仿真结果表明,在保证感知性能的前提下,新算法可大幅降低迭代次数,减少算法复杂度。      

Bottom-up motion compensated prediction in wavelet domain for spatially scalable video coding

A new in-band motion compensation algorithm for wavelet-based video coding is proposed: the bottom-up prediction algorithm (BUP). This algorithm overcomes the periodic shift-invariance of the discrete wavelet transform (DWT) and is formalised into prediction rules using filtering operations. The combination of all prediction rules of the BUP algorithm defines a new transform: the bottom-up overcomplete DWT or BUP ODWT, which is shift-invariant. The envisaged application for the BUP algorithm is spatially scalable wavelet video coding.     

可控测角精度和范围的数字阵列单脉冲和差波束优化

针对大角度覆盖范围目标快速定位以及机动目标精确角度跟踪的需要,该文提出一种基于可控测角精度和范围的数字阵列单脉冲和差波束迭代快速优化算法。算法可以根据期望的测角精度或期望测角区间,以函数形式给出修正的期望和差波束方向图主瓣区域,然后采用所提出的快速区域加权方向图综合算法闭式优化得到和差波束方向图。该算法通过迭代优化可以在给定输入信噪比和测角精度需求的前提下,最大化单脉冲角度估计的有效测角区间,或者在给定的测角范围内,最优化测角精度。     

基于自适应重采样次数的协方差角度估计算法

针对基于Hermitian分解的协方差角度估计算法在低信噪比(SNR)时性能差的问题,提出了基于自适应重采样次数的协方差角度估计算法。该算法首先用接收信号扫描角度空间内的所有导向矢量,划定正确的角度估计区间,并给出判断角度估计结果是否可靠的标准。然后构造协方差矩阵,得到角度估计结果,接着以角度估计结果是否可靠为判断标准自适应地对原始信号进行重采样。若重采样后对应某一信号的多个角度估计结果都不可靠,则对原信号重采样并估计到达角,直到所有信号均有可靠结果为止。仿真结果表明,所提算法修正异常的角度估计结果,提高协方差角度估计算法在低信噪比时的精确度。     

Adaptive prediction and cancellation digitization method for wideband multistandard software radio base-station receivers

In this study, a new method for digitizing a combination of different analog signals occupying significantly different bandwidths and having a very high dynamic range is proposed and analyzed. Since it is based upon signal-prediction/cancellation principles, it is referred to as adaptive prediction and cancellation digitization (APCD) method and is applied to various families of signals simultaneously received by a multistandard software radio (SWR) base-station receiver. It is shown theoretically and by means of computer simulations that the APCD method can effectively reduce the high dynamic range of the signals before digitization takes place. Hence, the stringent analog-to-digital-converter (ADC) resolution requirements imposed by the operation of such SWR base-station receivers can be significant relaxed. The signal dynamic-range reduction is achieved by applying appropriate signal processing techniques, e.g., autoregressive (AR) and periodic autoregressive (PAR) prediction. Such techniques allow accurate prediction and subsequent cancellation of high-power narrowband signals present among the composite received analog signal. As these signals usually have cyclostationary statistical characteristics, analysis and performance evaluation of AR and PAR predictors, when used to predict cyclostationary signals, were presented. A new adaptive algorithm for implementing the PAR predictor is also proposed, and its validity is justified by theoretical analysis as well as by various performance evaluation results obtained by means of computer simulations.     

一种基于机器学习的卫星重叠隐蔽通信方法

针对传统卫星重叠通信中单个掩护信号带宽以及功率容限不够的问题,利用卫星转发器频谱环境中多个掩护信号提出了一种频域分割-子谱功率控制联合优化的多掩护信号重叠通信方法,建立了隐蔽通信信号传输性能和隐蔽性能的双目标优化问题,信关站侧采用感知的历史频谱数据训练生成支持向量机回归预测模型,用来预测不同转发器频谱环境下隐蔽信号的通信性能和隐蔽性能,并将训练好的预测模型下载到通信终端;终端侧利用双目标背包算法将支持向量机回归预测模型预测的隐蔽信号的通信性能和隐蔽性能作为价值因素、掩护信号个数作为背包重量来选择转发器频谱环境中的掩护信号,并且求解出隐蔽信号的频域分割和子频谱的功率控制参数,从而实现终端通信信号隐藏在卫星转发器的频谱环境中的目的。     

H.264帧内预测模式快速判定算法研究

帧内预测作为H.264中提高编码效率的重要部分,其采用了率失真优化技术（RDO）进行预测模式的选择,但同时编码复杂度和计算量也明显增加.为此,提出一种快速判定算法.该算法利用区域图像的质心坐标对区域图像的平坦性和方向性进行判断,算法通过判断宏块的平坦性提前选定块大小,根据4×4块的纹理方向,确定预测模式集,降低算法复杂度.实验结果表明,与H.264标准参考编解码器JM8.6相比,所提算法编码时间节省50％以上,峰值信噪比和码率变化不大.     

Spectral Analysis of Electrical Impedance Measurements on the Lower Limbs

Electrical impedance signals from the lower limbs of normal individuals and patients with atherosclerosis have been studied in the frequency domain. A current of 1 mA at 40 kHz was introduced with strip electrodes around the ankles, and voltage differences were measured with electrodes above the knee and above the current electrode of the same limb. The impedance signal AZ and the ECG were recorded on magnetic tape and transferred to a digital computer. The first derivative of the impedance signal, dZ/dt, was calculated by numerical methods from the coherent average of at least 20 AZ signals, and the spectra were obtained by an FFT algorithm. For young normal individuals, only 1 percent of the dZ/dt signal energy lies above 12 Hz. Patients with arterial obstructions present a greater attenuation in the lower harmonics, stretching the 99 percent energy bandwidth to 32 Hz. Although the amplitude of some spectral components can be used for noninvasive evaluation of arterial disease with a precision of up to 90 percent, it was found that the phase spectra are very little influenced by atherosclerosis.     

一种基于关节角度的步态识别新方法

基于行走运动的关节角度变化包含更丰富的个体识别信息的观点,提出利用下肢关节角度进行步态识别的新方法。依据人体解剖学的先验知识,通过对下肢运动分析定位盆骨、左右膝、左右踝关节点,提取相邻关节点连线与竖直线的夹角作为运动关节角度。识别时,考虑到NN,KNN等传统步态分类器分类能力较弱的缺点,采用针对小样本问题具有很好分类效果的支持向量机对步态特征向量进行分类。CASIA步态数据库上的仿真结果证明该方法具有较高的识别性能。