Enhancing visual security: An image encryption scheme based on parallel compressive sensing and edge detection embedding

A novel image encryption scheme based on parallel compressive sensing and edge detection embedding technology is proposed to improve visual security. Firstly, the plain image is sparsely represented using the discrete wavelet transform.Then, the coefficient matrix is scrambled and compressed to obtain a size-reduced image using the Fisher–Yates shuffle and parallel compressive sensing. Subsequently, to increase the security of the proposed algorithm, the compressed image is re-encrypted through permutation and diffusion to obtain a noise-like secret image. Finally, an adaptive embedding method based on edge detection for different carrier images is proposed to generate a visually meaningful cipher image. To improve the plaintext sensitivity of the algorithm, the counter mode is combined with the hash function to generate keys for chaotic systems. Additionally, an effective permutation method is designed to scramble the pixels of the compressed image in the re-encryption stage. The simulation results and analyses demonstrate that the proposed algorithm performs well in terms of visual security and decryption quality.

NOVEL METHOD OF MOVING TARGET DETECTION FOR DUAL-CHANNEL WAS RADAR BASED ON COMPRESSED SENSING

We propose a ground moving target detection method for dual-channel Wide Area Surveillance(WAS) radar based on Compressed Sensing(CS).Firstly,the method of moving target detection of the WAS radar is studied.In order to reduce the sample data quantity of the radar,the echo data is randomly sampled in the azimuth direction,then,the matched filter is used to perform the range direction focus.We can use the compressive sensing theory to recover the signal in the Doppler domain.At last,the phase difference between the two channels is compensated to suppress the clutter.The result of the simulated data verifies the effectiveness of the proposed method.

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.

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.

Impulse Radio UWB Signal Detection Based on Compressed Sensing

The extremely high sampling rate is a challenge for ultra-wideband (UWB) communication. In this paper, we study the compressed sensing (CS) based impulse radio UWB (IR-UWB) signal detection and propose an IR-UWB signal detection algorithm based on compressive sampling matching pursuit (CoSaMP). The proposed algorithm relies on the fact that UWB received signal is sparse in the time domain. The new algorithm can significantly reduce the sampling rate required by the detection and provides a better performance in case of the low signal-to-noise ratio when comparing with the existing matching pursuit (MP) based detection algorithm. Simulation results demonstrate the effectiveness of the proposed algorithm.

Compressed sensing and Otsu's method based binary CT image reconstruction technique in non-destructive detection

This paper tries to address the problem of binary CT image reconstruction in non-destructive detection with an algorithm based on compressed sensing(CS) and Otsu's method, which could reconstruct binary CT image of test object from incomplete detection data. According to binary CT image characteristics, we employ Splitbregman method based on L1/2regularization to solve piecewise constant region reconstruction. To improve the reconstructed image quality from incomplete detection data, we utilize a priori knowledge and Otsu's method as the optimization constraint. In our study, we make numerical simulation to investigate our proposed method,and compare reconstructed results from different reconstruction methods. Finally, the experimental results demonstrate that the proposed method could effectively reduce noise and suppress artifacts, and reconstruct high-quality binary image from incomplete detection data.

Compressive sensing for small moving space object detection in astronomical images

It is known that detecting small moving objects in as- tronomical image sequences is a significant research problem in space surveillance. The new theory, compressive sensing, pro- vides a very easy and computationally cheap coding scheme for onboard astronomical remote sensing. An algorithm for small moving space object detection and localization is proposed. The algorithm determines the measurements of objects by comparing the difference between the measurements of the current image and the measurements of the background scene. In contrast to reconstruct the whole image, only a foreground image is recon- structed, which will lead to an effective computational performance, and a high level of localization accuracy is achieved. Experiments and analysis are provided to show the performance of the pro- posed approach on detection and localization.

Soft measurement of wood defects based on LDA feature fusion and compressed sensor images

We proposed a detection method for wood defects based on linear discriminant analysis (LDA) and the use of compressed sensor images. Wood surface images were captured, using a camera Oscar F810C IRF camera, and then the image segmentation was performed, and the defect features were extracted from wood board images. To reduce the processing time, LDA algorithm was used to integrate these features and reduce their dimensions. Features after fusion were used to construct a data dictionary and a compressed sensor was designed to recognize the wood defects types. Of the three major defect types, 50 images live knots, dead knots, and cracks were used to test the effects of this method. The average time for feature fusion and classification was 0.446 ms with the classification accuracy of 94%.

Sampling adaptive block compressed sensing reconstruction algorithm for images based on edge detection

In this paper, a sampling adaptive for block compressed sensing with smooth projected Landweber based on edge detection （SA-BCS-SPL-ED） image reconstruction algorithm is presented. This algorithm takes full advantage of the characteristics of the block compressed sensing, which assigns a sampling rate depending on its texture complexity of each block. The block complexity is measured by the variance of its texture gradient, big variance with high sampling rates and small variance with low sampling rates. Meanwhile, in order to avoid over-sampling and sub-sampling, we set up the maximum sampling rate and the minimum sampling rate for each block. Through iterative algorithm, the actual sampling rate of the whole image approximately equals to the set up value. In aspects of the directional transforms, discrete cosine transform （DCT）, dual-tree discrete wavelet transform （DDWT）, discrete wavelet transform （DWT） and Contourlet （CT） are used in experiments. Experimental results show that compared to block compressed sensing with smooth projected Landweber （BCS-SPL）, the proposed algorithm is much better with simple texture images and even complicated texture images at the same sampling rate. Besides, SA-BCS-SPL-ED-DDWT is quite good for the most of images while the SA-BCS-SPL-ED-CT is likely better only for more-complicated texture images.

CSI-based passive intrusion detection bound estimation in indoor NLoS scenario

Passive intrusion detection is important for many indoor safety applications.By utilizing widespread WiFi infrastructures,channel state information(CSI)based intrusion detection methods have attractive advantages such as low cost,non-line-of-sight(NLoS)support,and privacy protection.However,existing CSI-based methods lack in-depth and intensive analysis of intrusion detection bound.To the best of our knowledge,this is the first work that precisely characterizes and models CSI-based intrusion detection bound in typical indoor NLoS scenario.A bound is defined as an intruder’s farthest position from the transmitter during intrusion detection.To derive a model for the bound,we first derived an intrusion-disturbed NLoS channel model by analyzing the influence of human intrusion on the NLoS wireless channel.Subsequently,based on the channel model,we further derived an intrusion detection bound model.Based on the derived bound model,we proposed a practical system to estimate the real intrusion detection bound.Extensive experiments were conducted based on practical systems.The experimental and simulation results verified and demonstrated the effectiveness of the derived bound model.Our work not only reveals the fundamental performance limit of the basic intrusion detection method in an indoor NLoS scenario,but also provides a valuable reference for bound estimation for other fine-grained wireless sensing applications.

基于小波变换和压缩感知的工频磁异常信号降噪方法

针对强噪声背景下水下目标微弱工频磁异常信号提取困难的问题,提出一种基于小波变换的压缩感知降噪方法。该方法利用噪声信号不具有稀疏性的特点,将含噪信号在小波域内分解,并在此基础上进行压缩感知的重构与降噪处理,实现微弱工频磁异常信号与噪声最大限度地分离。仿真及试验表明,该方法得到很好的降噪效果,避免了小波阈值滤波中阈值选取对降噪效果的影响,能够最大程度地保留原始信号的固有特征。

基于稀疏恢复的雷达信号处理研究综述

随着雷达目标探测需求的增加,基于压缩感知(CS)模型的稀疏恢复(SR)技术被广泛应用于雷达信号处理领域。该文首先对压缩感知的基本理论进行梳理;接着从场景稀疏以及稀疏观测两个角度介绍了雷达信号处理中的稀疏特性;然后基于稀疏特性,从空域处理、脉冲压缩、相参处理、雷达成像以及目标检测等角度概述了压缩感知技术在雷达信号处理中的应用。最后,对压缩感知技术在雷达信号处理中的应用进行了总结。

基于压缩感知的兰姆波下采样和损伤定位

一种基于压缩感知和兰姆波的损伤检测方法利用兰姆波下采样信号构造测量矩阵,需要恰当的采样率才能使测量矩阵行数小于列数,从而满足压缩感知的适用条件。但是,过低的采样率会导致采集到的接收信号与真实信号严重失真。针对这一问题,提出基于插值的计算下采样信号与真实信号相关系数的方法,判断下采样信号与真实信号的失真程度。并结合压缩比,选择合适的采样间隔,确定测量矩阵。通过对测量矩阵做行正交规范化处理,优化测量矩阵,大大提高重构效果和损伤定位精度及能力。优化后,重构概率为1的稀疏度由1增长到12左右。

压缩感知辅助的低复杂度SCMA系统优化设计

稀疏码多址接入(SCMA)技术是一项备受重视的基于码域的非正交多址接入(NOMA)技术。针对现有SCMA码本设计中未能结合数据和解码器性质以及MPA复杂度较高的问题,该文提出一种压缩感知辅助的低复杂度SCMA系统优化设计方案。首先以系统误码率为优化目标,设计一种码本自更新方法用于实现低复杂度检测器,该方法在稀疏向量重构训练过程中使用梯度下降法实现码本的自更新。其次,设计一种压缩感知辅助的多用户检测算法:符号判决正交匹配追踪(SD-OMP)算法。通过在发射端对发射信号进行稀疏化处理,在接收端利用压缩感知技术对多用户的稀疏信号进行高效的检测和重构,达到减少用户间的冲突和降低系统复杂度的目的。仿真结果表明,在高斯信道条件下,压缩感知辅助的低复杂度SCMA系统优化设计方案能够有效降低多用户检测的复杂度,且在系统用户部分活跃时能够表现出较好的误码率性能。

管道入侵报警和泄漏检测的智能化发展

第三方破坏、自然灾害破坏和管道的老化及腐蚀是天然气长输管道线路日常运行的安全隐患。为保障管道的安全运行,综合应用入侵报警、泄漏检测和其他安防技术,搭建综合智能安防监控平台,分析了几种常用的入侵报警和泄漏检测技术,明确了天然气长输管道行业以光纤传感技术作为入侵报警和泄漏检测系统的发展方向。以阿联酋天然气管网为例,分析了各种技术在特定环境中应用的情况,探讨我国输气管道项目实际应用的问题。入侵报警和泄漏检测在天然气管道上的应用越来越多,需要进一步开放平台接口,接入门禁系统、扩音对讲系统、巡更系统、动力环境监测系统,共同组成多系统融合的综合智能安防监控平台。依托5G网络、人工智能、云计算、大数据、物联网等智能技术的优势,推进管道运营体系改革,实现管道行业由传统运行模式逐步向数字化、智能化发展。

Cluster-Based Massive Access for Massive MIMO Systems

Massive machine type communication aims to support the connection of massive devices,which is still an important scenario in 6G.In this paper,a novel cluster-based massive access method is proposed for massive multiple input multiple output systems.By exploiting the angular domain characteristics,devices are separated into multiple clusters with a learned cluster-specific dictionary,which enhances the identification of active devices.For detected active devices whose data recovery fails,power domain nonorthogonal multiple access with successive interference cancellation is employed to recover their data via re-transmission.Simulation results show that the proposed scheme and algorithm achieve improved performance on active user detection and data recovery.

Optimized Binary Neural Networks for Road Anomaly Detection:A TinyML Approach on Edge Devices

Integrating Tiny Machine Learning(TinyML)with edge computing in remotely sensed images enhances the capabilities of road anomaly detection on a broader level.Constrained devices efficiently implement a Binary Neural Network(BNN)for road feature extraction,utilizing quantization and compression through a pruning strategy.The modifications resulted in a 28-fold decrease in memory usage and a 25%enhancement in inference speed while only experiencing a 2.5%decrease in accuracy.It showcases its superiority over conventional detection algorithms in different road image scenarios.Although constrained by computer resources and training datasets,our results indicate opportunities for future research,demonstrating that quantization and focused optimization can significantly improve machine learning models’accuracy and operational efficiency.ARM Cortex-M0 gives practical feasibility and substantial benefits while deploying our optimized BNN model on this low-power device:Advanced machine learning in edge computing.The analysis work delves into the educational significance of TinyML and its essential function in analyzing road networks using remote sensing,suggesting ways to improve smart city frameworks in road network assessment,traffic management,and autonomous vehicle navigation systems by emphasizing the importance of new technologies for maintaining and safeguarding road networks.

Coded aperture compressive imaging array applied for surveillance systems

This paper proposes an application of compressive imaging systems to the problem of wide-area video surveillance systems. A parallel coded aperture compressive imaging system and a corresponding motion target detection algorithm in video using compressive image data are developed. Coded masks with random Gaussian, Toeplitz and random binary are utilized to simulate the compressive image respectively. For compressive images, a mixture of the Gaussian distribution is applied to the compressed image field to model the background. A simple threshold test in compressive sampling image is used to declare motion objects. Foreground image retrieval from underdetermined measurement using the total variance optimization algorithm is explored. The signal-to-noise ratio （SNR） is employed to evaluate the image quality recovered from the compressive sampling signals, and receiver operation characteristic （ROC） curves are used to quantify the performance of the motion detection algorithm. Experimental results demonstrate that the low dimensional compressed imaging representation is sufficient to determine spatial motion targets. Compared with the random Gaussian and Toeplitz mask, motion detection algorithms using the random binary phase mask can yield better detection results. However using the random Gaussian and Toeplitz phase mask can achieve high resolution reconstructed images.

基于卡尔曼滤波改进压缩感知算法的车辆目标跟踪

针对传统的基于压缩感知技术的目标跟踪算法存在的跟踪漂移问题,提出了一种采用改进压缩感知算法和卡尔曼滤波方法相结合的车辆目标跟踪算法.首先,通过传统压缩感知目标跟踪算法识别出本帧目标存在概率最大的区域得到观测值;其次,利用卡尔曼滤波预测本帧的跟踪轨迹得到预测值,通过卡尔曼滤波增益系数对预测值与观测值进行修正,获得最终目标跟踪结果;最后,在修正后的目标区域周围进行正负样本采样以实现朴素贝叶斯分类器更新,进而实现目标跟踪轨迹的实时更新.通过实验室试验以及野外实测验证了所提方法的可行性,相较于基于压缩感知技术的目标跟踪算法,本文所提方法的跟踪结果平均误差分别降低了48%和89%,跟踪轨迹更加趋近车辆真实运动轨迹.

联合波形选择和PRI捷变探通一体化波形设计

针对应用相位调制于线性调频(LFM)的探通一体化波形设计方法中自相关旁瓣水平高且误符号率较高的问题,文中提出了联合正交波形选择和脉冲重复间隔(PRI)捷变的双重索引调制方法。首先,通过对LFM波形附加扰动相位实现有良好自相关和互相关性能的正交波形集。其次,详细给出了通信信息的调制和解调方法。最后,结果表明设计的一体化波形不仅能满足雷达探测性能而且具有良好的自相关特性,此外在低信噪比条件下误符号率低。