Compression of surface texture acceleration signal based on spectrum characteristics

Background Adequate data collection can enhance the realism of online rendering or offline playback of haptic surface textures.A parallel challenge is to reduce communication delays and improve storage space utilization.Methods Based on the similarity of the short-term amplitude spectrum trend,this study proposes a frequency-domain compression method.A compression framework is designed,which first maps the amplitude spectrum into grayscale images,compresses them with a still image compression method,and then adaptively encodes the maximum amplitude and part of the initial phase for each time window to achieve the final compression.Results The comparison between the original signal and the recovered signal shows that when the time-frequency similarity is 90%,the average compression ratio of our method is 9.85%in the case of a single interaction point.The subjective score for similarity was found to be high,with an average of 87.85.Conclusions Our method can be used for offline compression of vibrotactile data.For multi-interaction points in space,the trend similarity grayscale image can be reused,and the compression ratio is further reduced.

基于MCS-SBL算法的配电网故障定位方法

配电网拓扑结构复杂,传统方法往往需要大量测点信息且难以实现快速有效的故障定位,本文提出基于少量测点信息的故障定位方法。首先,利用等效原理建立一个欠定的故障节点电压方程;其次,利用多重测量向量模型的贝叶斯压缩感知算法求解方程,根据重构稀疏电流矩阵的非零元素位置求解故障区域,实现故障定位;最后,在IEEE33节点配电系统上进行仿真实验,结果表明,所提方法仅需要少量测点的故障前后正序电压分量便可有效定位故障,计算速度较快,并且基本不受故障类型、过渡电阻的影响,同时适用于单故障和多重故障的场景,具有较强的抗噪能力。

Meso-damage evolution and mechanical characteristics of low-porosity sedimentary rocks under uniaxial compression

Low pore sedimentary rocks(from Guangxi, China) were subjected to uniaxial compression loading experiment under different initial stresses. The rock samples were investigated by nuclear magnetic resonance before and after the loading. The relationships between the mesoscopic rock damage and macroscopic mechanical parameters were established, and the initial damage stress of the low-porosity sedimentary rock was determined. The results showed that this type of rock has the initial stress of damage. When the initial loading stress is lower than the initial stress of damage, the T2 spectrum area of the rock sample gradually decreases, and the primary pores of the rock are further closed under the stress. The range of the initial stress of damage for this type of rock is 8-16 MPa. When the loading stress exceeds the initial stress of damage, the T2 spectrum area gradually increases, indicating that the porosity of the rock increases and microscopic damage of the rock appears. The rock damage degree is defined, and the nonlinear function between the rock damage degree and the initial loading stress is established.

A SPARSITY AND COMPRESSION RATIO JOINT ADJUSTMENT METHOD FOR COLLABORATIVE SPECTRUM SENSING

Spectrum sensing is the fundamental task for Cognitive Radio (CR). To overcome the challenge of high sampling rate in traditional spectral estimation methods, Compressed Sensing (CS) theory is developed. A sparsity and compression ratio joint adjustment algorithm for compressed spectrum sensing in CR network is investigated, with the hypothesis that the sparsity level is unknown as priori knowledge at CR terminals. As perfect spectrum reconstruction is not necessarily required during spectrum detection process, the proposed algorithm only performs a rough estimate of sparsity level. Meanwhile, in order to further reduce the sensing measurement, different compression ratios for CR terminals with varying Signal-to-Noise Ratio (SNR) are considered. The proposed algorithm, which optimizes the compression ratio as well as the estimated sparsity level, can greatly reduce the sensing measurement without degrading the detection performance. It also requires less steps of iteration for convergence. Corroborating simulation results are presented to testify the effectiveness of the proposed algorithm for collaborative spectrum sensing.

基于AAPC、CS与卡尔曼滤波的WiFi室内定位跟踪算法

针对基于位置指纹的WiFi室内定位技术的定位精度尚未达到实际应用要求的问题,提出一种融合自适应仿射传播(AAPC)、压缩感知(CS)与卡尔曼滤波的WiFi室内定位跟踪算法。其中,离线阶段使用AAPC算法生成具有最优聚类效应性能的聚类指纹,在线阶段采用CS与最近邻算法进行位置估计。最后,通过将卡尔曼滤波与物理限制相集成来进行定位跟踪。通过采集大量真实实验数据,证明了所开发的算法具有更高的定位精度和更准确的轨迹跟踪效果。

Biomechanics study of interal fixation with hol-low-compression-screw and composite calcium phosphate cement of osteoporotic femoral neck

Objective To evaluate the biomechanics of hollow-compression-screw in the osteoporotic femoral neck with composite c alcium phosphate cement (CCPC).Methods Sixteen femurs of superior segment were randomly divided into two groups:augmentation group and non-augmentation group.CCPC was used in augmentation group.Result Augmentation with CCPC would improve the initial mobile force of hollow-compression-screw,the ini tial mobile force and the maximal axial pull-out strength for augmentation group,non-augmentation group in-creased from(192.7±14.0)N and(202.8±14.0)N to(328.5±34.7)N and(347.8±31.2)N.There was significant difference of two groups(P <0.01).Conclusion CCPC can enhance hollow-compressio n-screw fixation in osteoporotic fe moral neck.

变化信道稀疏度的CSI反馈方法

在大规模多输入多输出(multiple input multiple output,MIMO)系统中,压缩感知(compressed sensing,CS)技术常用于具有稀疏特性的信道状态信息(channel state information,CSI)反馈。针对CS重构时信道稀疏度通常未知的问题,基于深度展开技术提出了一种变化信道稀疏度的CSI反馈方法(a CSI-feedback method for varying channel sparsity,AVCS)。AVCS将信道稀疏度作为训练参数,学习得到通用的网络架构。随着天线数量增大导致信道(矩阵)维度激增,学习网络所得的相互抑制矩阵会呈现二次增长问题,AVCS利用相互抑制矩阵托普利兹(Toeplitz)特性设计了降维卷积网络,解决CSI反馈时的计算复杂度问题。仿真结果表明,所提方法提高了在大规模MIMO系统下CSI重构的适用性,减少了反馈开销且对信道稀疏度具有鲁棒性。

COOPERATIVE WIDEBAND SPECTRUM SENSING BASED ON SEQUENTIAL COMPRESSED SENSING

Compressed sensing offers a new wideband spectrum sensing scheme in Cognitive Radio (CR). A major challenge of this scheme is how to determinate the required measurements while the signal sparsity is not known a priori. This paper presents a cooperative sensing scheme based on se-quential compressed sensing where sequential measurements are collected from the analog-to-information converters. A novel cooperative compressed sensing recovery algorithm named Simul-taneous Sparsity Adaptive Matching Pursuit (SSAMP) is utilized for sequential compressed sensing in order to estimate the reconstruction errors and determinate the minimal number of required meas-urements. Once the fusion center obtains enough measurements, the reconstruction spectrum sparse vectors are then used to make a decision on spectrum occupancy. Simulations corroborate the effec-tiveness of the estimation and sensing performance of our cooperative scheme. Meanwhile, the per-formance of SSAMP and Simultaneous Orthogonal Matching Pursuit (SOMP) is evaluated by Mean-Square estimation Errors (MSE) and sensing time.

AN ADAPTIVE MEASUREMENT SCHEME BASED ON COMPRESSED SENSING FOR WIDEBAND SPECTRUM DETECTION IN COGNITIVE WSN

An Adaptive Measurement Scheme (AMS) is investigated with Compressed Sensing (CS) theory in Cognitive Wireless Sensor Network (C-WSN). Local sensing information is collected via energy detection with Analog-to-Information Converter (AIC) at massive cognitive sensors, and sparse representation is considered with the exploration of spatial temporal correlation structure of detected signals. Adaptive measurement matrix is designed in AMS, which is based on maximum energy subset selection. Energy subset is calculated with sparse transformation of sensing information, and maximum energy subset is selected as the row vector of adaptive measurement matrix. In addition, the measurement matrix is constructed by orthogonalization of those selected row vectors, which also satisfies the Restricted Isometry Property (RIP) in CS theory. Orthogonal Matching Pursuit (OMP) reconstruction algorithm is implemented at sink node to recover original information. Simulation results are performed with the comparison of Random Measurement Scheme (RMS). It is revealed that, signal reconstruction effect based on AMS is superior to conventional RMS Gaussian measurement. Moreover, AMS has better detection performance than RMS at lower compression rate region, and it is suitable for large-scale C-WSN wideband spectrum sensing.

Wideband Cognitive Radio Networks Based Compressed Spectrum Sensing: A Survey

Spectrum sensing is a core function at cognitive radio systems to have spectrum awareness. This could be achieved by collecting samples from the frequency band under observation to make a conclusion whether the band is occupied, or it is a spectrum hole. The task of sensing is becoming more challenging especially at wideband spectrum scenario. The difficulty is due to conventional sampling rate theory which makes it infeasible to sample such very wide range of frequencies and the technical requirements are very costly. Recently, compressive sensing introduced itself as a pioneer solution that relaxed the wideband sampling rate requirements. It showed the ability to sample a signal below the Nyquist sampling rate and reconstructed it using very few measurements. In this paper, we discuss the approaches used for solving compressed spectrum sensing problem for wideband cognitive radio networks and how the problem is formulated and rendered to improve the detection performance.

Femtosecond parabolic pulse nonlinear compression with gas-filled hollow-core fiber

We study theoretically the spectral intensity evolutions of the femtosecond Gaussian and parabolic pulses with different initial pulse energies and compare the nonlinear compressions of these pulses based on a meter-long hollow-core fiber filled with neon for different initial pulse durations. The pulses are first coupled into gas-filled hollow-core fiber for spectrum broadening, then compressed by the optimal chirp compensation. The parabolic pulse possesses a shorter pulse duration, larger peak power, and cleaner wings than Gaussian pulse. The properties are useful for compressing the pulses and thus generating the high-energy, short-duration pulses.

Joint compressive spectrum sensing scheme in wideband cognitive radio networks

In this paper,a distributed compressive spectrum sensing scheme in wideband cognitive radio networks is investigated.An analog-to-information converters（AIC） RF front-end sampling structure is proposed which use parallel low rate analog to digital conversions（ADCs） and fewer storage units for wideband spectrum signal sampling.The proposed scheme uses multiple low rate congitive radios（CRs） collecting compressed samples through AICs distritbutedly and recover the signal spectrum jointly.A general joint sparsity model is defined in this scenario,along with a universal recovery algorithm based on simultaneous orthogonal matching pursuit（S-OMP）.Numerical simulations show this algorithm outperforms current existing algorithms under this model and works competently under other existing models.

Cooperative Compressive Spectrum Sensing in Cognitive Underw ater Acoustic Communication Networks

Because of the specific of underwater acoustic channel,spectrum sensing entails many difficulties in cognitive underwater acoustic communication( CUAC) networks, such as severe frequency-dependent attenuation and low signal-to-noise ratios. To overcome these problems, two cooperative compressive spectrum sensing( CCSS) schemes are proposed for different scenarios( with and without channel state information). To strengthen collaboration among secondary users( SUs),cognitive central node( CCN) is provided to collect data from SUs. Thus,the proposed schemes can obtain spatial diversity gains and exploit joint sparse structure to improve the performance of spectrum sensing. Since the channel occupancy is sparse,we formulate the spectrum sensing problems into sparse vector recovery problems,and then present two CCSS algorithms based on path-wise coordinate optimization( PCO) and multi-task Bayesian compressive sensing( MT-BCS),respectively.Simulation results corroborate the effectiveness of the proposed methods in detecting the spectrum holes in underwater acoustic environment.

Spectral Imagers with Linear Detector Imager Systems Based on Spectrum Compressed

Traditional spectral imagers require 2-dimensional detectors. We present a new method to implement spectral imagers with linear detector imager systems based on spectrum compressed. Using 1-dimension detectors instead of 2-dimension detectors to get 3-dimensional data cubes, the spectral imagers could get both the spectral information and the spatial information of each ground object. By the method of characteristics decoupling, we make high precision reconstruction of compressed data. Theoretical analysis and simulations show that it not only ensures the imaging quality but also reduces the dimension of the detectors and complexity of imaging system greatly.

Intelligent Spectrum Detection Model Based on Compressed Sensing in Cognitive Radio Network

In view of the uncertainty of the status of primary users in cognitive networks and the fact that the random detection strategy cannot guarantee cognitive users to accurately find available channels,this paper proposes a joint random detection strategy using the idle cognitive users in cognitive wireless networks.After adding idle cognitive users for detection,the compressed sensing model is employed to describe the number of available channels obtained by the cognitive base station to derive the detection performance of the cognitive network at this time.Both theoretical analysis and simulation results show that using idle cognitive users can reduce service delay and improve the throughput of cognitive networks.After considering the time occupied by cognitive users to report detection information,the optimal participation number of idle cognitive users in joint detection is obtained through the optimization algorithm.

Airborne sparse flight array SAR 3D imaging based on compressed sensing in frequency domain

In airborne array synthetic aperture radar(SAR), the three-dimensional(3D) imaging performance and cross-track resolution depends on the length of the equivalent array. In this paper, Barker sequence criterion is used for sparse flight sampling of airborne array SAR, in order to obtain high cross-track resolution in as few times of flights as possible. Under each flight, the imaging algorithm of back projection(BP) and the data extraction method based on modified uniformly redundant arrays(MURAs) are utilized to obtain complex 3D image pairs. To solve the side-lobe noise in images, the interferometry between each image pair is implemented, and compressed sensing(CS) reconstruction is adopted in the frequency domain. Furthermore, to restore the geometrical relationship between each flight, the phase information corresponding to negative MURA is compensated on each single-pass image reconstructed by CS. Finally,by coherent accumulation of each complex image, the high resolution in cross-track direction is obtained. Simulations and experiments in X-band verify the availability.

Compressive Wideband Spectrum Sensing Based on Random Matrix Theory

Spectrum sensing in a wideband regime for cognitive radio network(CRN) faces considerably technical challenge due to the constraints on analog-to-digital converters(ADCs).To solve this problem,an eigenvalue-based compressive wideband spectrum sensing(ECWSS) scheme using random matrix theory(RMT) was proposed in this paper.The ECWSS directly utilized the compressive measurements based on compressive sampling(CS) theory to perform wideband spectrum sensing without requiring signal recovery,which could greatly reduce computational complexity and data acquisition burden.In the ECWSS,to alleviate the communication overhead of secondary user(SU),the sensors around SU carried out compressive sampling at the sub-Nyquist rate instead of SU.Furthermore,the exact probability density function of extreme eigenvalues was used to set the threshold.Theoretical analyses and simulation results show that compared with the existing eigenvalue-based sensing schemes,the ECWSS has much lower computational complexity and cost with no significant detection performance degradation.

Compression and reconstruction of speech signals based on compressed sensing

Based on the approximate sparseness of speech in wavelet basis,a compressed sensing theory is applied to compress and reconstruct speech signals.Compared with one-dimensional orthogonal wavelet transform(OWT),two-dimensional OWT combined with Dmeyer and biorthogonal wavelet is firstly proposed to raise running efficiency in speech frame processing,furthermore,the threshold is set to improve the sparseness.Then an adaptive subgradient projection method(ASPM)is adopted for speech reconstruction in compressed sensing.Meanwhile,mechanism which adaptively adjusts inflation parameter in different iterations has been designed for fast convergence.Theoretical analysis and simulation results conclude that this algorithm has fast convergence,and lower reconstruction error,and also exhibits higher robustness in different noise intensities.

Compressive near-field millimeter wave imaging algorithm based on Gini index and total variation mixed regularization

A compressive near-field millimeter wave(MMW)imaging algorithm is proposed.From the compressed sensing(CS)theory,the compressive near-field MMW imaging process can be considered to reconstruct an image from the under-sampled sparse data.The Gini index(GI)has been founded that it is the only sparsity measure that has all sparsity attributes that are called Robin Hood,Scaling,Rising Tide,Cloning,Bill Gates,and Babies.By combining the total variation(TV)operator,the GI-TV mixed regularization introduced compressive near-field MMW imaging model is proposed.In addition,the corresponding algorithm based on a primal-dual framework is also proposed.Experimental results demonstrate that the proposed GI-TV mixed regularization algorithm has superior convergence and stability performance compared with the widely used l1-TV mixed regularization algorithm.

自适应压缩频谱感知的采样率动态调整策略

受奈奎斯特-香农采样定理的限制,宽带频谱感知(wideband spectrum sensing,WBSS)的首要难点是采集宽带信号。根据变步长自适应滤波算法原理,基于自适应压缩频谱感知(adaptive compressed spectrum sensing,ACSS)提出了一种动态调整采样率的压缩频谱感知方法(dynamically adjust sampling rate ACSS,DASR-ACSS)。采用调制宽带转换器结构,针对传统ACSS固定步长的问题设计了步长动态调整策略。针对原始信号难以获取的问题,通过对信道占用状态相关性和检测率的变化趋势进行数据统计分析,设计了利用信道占用状态相关性的停止准则;通过对停止准则的实验数据分析,以sigmoid函数为基础设计了分段函数作为步长调整策略,利用步长补偿因子充分提升性能。仿真结果表明,与传统ACSS相比,DASR-ACSS能更快达到高检测率,能够平衡算法的实时性能和采样率压缩比性能,当信噪比在-30~30 dB时,DASR-ACSS表现出更好的检测性能。