scholarly journals Image Parallel Encryption Technology Based on Sequence Generator and Chaotic Measurement Matrix

Entropy ◽  
2020 ◽  
Vol 22 (1) ◽  
pp. 76 ◽  
Author(s):  
Jiayin Yu ◽  
Shiyu Guo ◽  
Xiaomeng Song ◽  
Yaqin Xie ◽  
Erfu Wang
Keyword(s):  
Compressed Sensing ◽  
Circuit Simulation ◽  
Sampling Rate ◽  
Image Transmission ◽  
Transmission Efficiency ◽  
Data Sampling ◽  
Measurement Matrix ◽  
Chaotic Cryptography ◽  
Parallel Mode ◽  
Sequence Generator

In this paper, a new image encryption transmission algorithm based on the parallel mode is proposed. This algorithm aims to improve information transmission efficiency and security based on existing hardware conditions. To improve efficiency, this paper adopts the method of parallel compressed sensing to realize image transmission. Compressed sensing can perform data sampling and compression at a rate much lower than the Nyquist sampling rate. To enhance security, this algorithm combines a sequence signal generator with chaotic cryptography. The initial sensitivity of chaos, used in a measurement matrix, makes it possible to improve the security of an encryption algorithm. The cryptographic characteristics of chaotic signals can be fully utilized by the flexible digital logic circuit. Simulation experiments and analyses show that the algorithm achieves the goal of improving transmission efficiency and has the capacity to resist illegal attacks.

scholarly journals Parallel Encryption of Noisy Images Based on Sequence Generator and Chaotic Measurement Matrix

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Jiayin Yu ◽  
Yaqin Xie ◽  
Shiyu Guo ◽  
Yanqi Zhou ◽  
Erfu Wang
Keyword(s):  
Compressed Sensing ◽  
Information Transmission ◽  
Image Encryption ◽  
Rapid Development ◽  
Chaotic Map ◽  
Image Transmission ◽  
Measurement Matrix ◽  
Sensing Technology ◽  
Sequence Generator ◽  
Parallel Encryption

With the rapid development of information technology in today’s society, the security of transmission and the storage capacity of hardware are increasingly required in the process of image transmission. Compressed sensing technology can achieve data sampling and compression at the rate far lower than that of the Nyquist sampling theorem and can effectively improve the efficiency of information transmission. Aiming at the problem of weak security of compressed sensing, this study combines the cryptographic characteristics of chaotic systems with compressed sensing technology. In the actual research process, the existing image encryption technology needs to be applied to the hardware. This paper focuses on the combination of image encryption based on compressed sensing and digital logic circuits. We propose a novel technology of parallel image encryption based on a sequence generator. It uses a three-dimensional chaotic map with multiple stability to generate a measurement matrix. This study also analyzes the effectiveness, reliability, and security of the parallel encryption algorithm for source noise pollution with different distribution characteristics. Simulation results show that parallel encryption technology can effectively improve the efficiency of information transmission and greatly enhance its security through key space expansion.

scholarly journals Low-Complexity Rate-Distortion Optimization of Sampling Rate and Bit-Depth for Compressed Sensing of Images

Entropy ◽  
2020 ◽  
Vol 22 (1) ◽  
pp. 125
Author(s):  
Qunlin Chen ◽  
Derong Chen ◽  
Jiulu Gong ◽  
Jie Ruan
Keyword(s):  
Compressed Sensing ◽  
Signal To Noise Ratio ◽  
Sampling Rate ◽  
Rate Distortion ◽  
Image Transmission ◽  
Low Complexity ◽  
Rate Model ◽  
Bit Rate ◽  
Compression Performance ◽  
The Relationship

Compressed sensing (CS) offers a framework for image acquisition, which has excellent potential in image sampling and compression applications due to the sub-Nyquist sampling rate and low complexity. In engineering practices, the resulting CS samples are quantized by finite bits for transmission. In circumstances where the bit budget for image transmission is constrained, knowing how to choose the sampling rate and the number of bits per measurement (bit-depth) is essential for the quality of CS reconstruction. In this paper, we first present a bit-rate model that considers the compression performance of CS, quantification, and entropy coder. The bit-rate model reveals the relationship between bit rate, sampling rate, and bit-depth. Then, we propose a relative peak signal-to-noise ratio (PSNR) model for evaluating distortion, which reveals the relationship between relative PSNR, sampling rate, and bit-depth. Finally, the optimal sampling rate and bit-depth are determined based on the rate-distortion (RD) criteria with the bit-rate model and the relative PSNR model. The experimental results show that the actual bit rate obtained by the optimized sampling rate and bit-depth is very close to the target bit rate. Compared with the traditional CS coding method with a fixed sampling rate, the proposed method provides better rate-distortion performance, and the additional calculation amount amounts to less than 1%.

Research on Compressed Sensing Matrix Based on Data Compressing of Insulator Leakage Current

2013 ◽  
Vol 475-476 ◽  
pp. 451-454
Author(s):  
Xue Ming Zhai ◽  
Xiao Bo You ◽  
Ruo Chen Li ◽  
Yu Jia Zhai ◽  
De Wen Wang
Keyword(s):  
Compressed Sensing ◽  
Data Compression ◽  
Leakage Current ◽  
Sparse Matrix ◽  
Circulant Matrix ◽  
Measurement Matrix ◽  
Sensing Matrix ◽  
Significant Step ◽  
On Line

Insulator fault may lead to the accident of power network,thus the on-line monitoring of insulator is very significant. Low rates wireless network is used for data transmission of leakage current. Making data compression and reconstruction of leakage current with the compressed sensing theory can achieve pretty good results. Determination of measurement matrix is the significant step for realizing the compressed sensing theory. This paper compares multiple measurement matrix of their effect via experiments, putting forward to make data compression and reconstruction of leakage current using Toeplitz matrix, circulant matrix and sparse matrix as measurement matrix, of which the reconstitution effect is almost the same as classical measurement matrix and depletes computational complexity and workload.

An improved measurement matrix of compressed sensing for synthetic aperture ultrasound imaging

2022 ◽  
Vol 188 ◽  
pp. 108592
Author(s):  
Ping Wang ◽  
Xuegong Liu ◽  
Xitao Li ◽  
Dawod Al-Qadasi ◽  
Linhong Wang
Keyword(s):  
Compressed Sensing ◽  
Ultrasound Imaging ◽  
Synthetic Aperture ◽  
Measurement Matrix

Biocompatible Cardiac Sensor with Continuous Arrhythmia Monitoring for Human Wellness

2016 ◽  
Vol 835 ◽  
pp. 71-77
Author(s):  
Jong Ha Lee
Keyword(s):  
Atrial Fibrillation ◽  
Power Transmission ◽  
Sampling Rate ◽  
Animal Experiments ◽  
Small Animal ◽  
The Body ◽  
Major Surgery ◽  
Data Sampling ◽  
Integrated Sensor ◽  
Sensor Module

Prolonged monitoring is more likely to result in an accurate diagnosis of atrial fibrillation patients than intermittent or short-term monitoring. In this study, we present an implantable ECG sensor to monitor atrial fibrillation patients in real time. The developed implantable sensor is composed of a micro controller unit, analog to digital converter, signal transmitter, antenna, and two electrodes. The sensor detects ECG signals from the two electrodes and transmits these signals to the external receiver that is carried by the patient. The sensor continuously transmits signals, so its battery consumption rate is extremely high. To overcome this problem, we consider using a wireless power transmission module in the sensor module. This module helps the sensor charge power wirelessly without holding the battery in the body. The size of the integrated sensor is approximately 0.12 inch x 1.18 inch x 0.19 inch. This sensor size is appropriate enough for cardiologists to insert the sensor into patients without the need for a major surgery. The data sampling rate was 300 samples/sec, and the frequency was 430 Hz for signal and power transmission. To verify the validation of the developed sensor, the small animal experiments were conducted.

scholarly journals Signal-Processing Framework for Ultrasound Compressed Sensing Data: Envelope Detection and Spectral Analysis

2020 ◽  
Vol 10 (19) ◽  
pp. 6956
Author(s):  
Yisak Kim ◽  
Juyoung Park ◽  
Hyungsuk Kim
Keyword(s):  
Signal Processing ◽  
Compressed Sensing ◽  
Frequency Domain ◽  
Time Domain ◽  
Sampling Rate ◽  
Processing Technique ◽  
Original Form ◽  
Basis Matrix ◽  
Full Reconstruction ◽  
Processing Framework

Acquisition times and storage requirements have become increasingly important in signal-processing applications, as the sizes of datasets have increased. Hence, compressed sensing (CS) has emerged as an alternative processing technique, as original signals can be reconstructed using fewer data samples collected at frequencies below the Nyquist sampling rate. However, further analysis of CS data in both time and frequency domains requires the reconstruction of the original form of the time-domain data, as traditional signal-processing techniques are designed for uncompressed data. In this paper, we propose a signal-processing framework that extracts spectral properties for frequency-domain analysis directly from under-sampled ultrasound CS data, using an appropriate basis matrix, and efficiently converts this into the envelope of a time-domain signal, avoiding full reconstruction. The technique generates more accurate results than the traditional framework in both time- and frequency-domain analyses, and is simpler and faster in execution than full reconstruction, without any loss of information. Hence, the proposed framework offers a new standard for signal processing using ultrasound CS data, especially for small and portable systems handling large datasets.

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