Optical image compression and encryption transmission-based ondeep learning and ghost imaging

Zhang, Leihong; Xiong, Rui; Chen, Jian; Zhang, Dawei

doi:10.1007/s00340-019-7362-1

Cited by 26 publications

(20 citation statements)

References 20 publications

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“…[3][4][5] Later, many experimental efforts have been made to improve the low-temperature performance of n-type Mg 3+ Sb 2−x Bi x . [6][7][8][9][10][11][12][13] Theoretically, accurate electronic structure, [14,15] chemical bonding, [16][17][18] and phonon transport properties [19,20] have been revealed for understanding the outstanding TE properties of n-type Mg 3 Sb 2 -based TE materials. Despite many significant studies on ntype Te-doped Mg 3 Sb 2 -based compounds, the optimization of transport properties requires efficient n-type dopants for the broad tunability of carrier density.…”

Section: Doi: 101002/advs202002867mentioning

confidence: 99%

Probing Efficient N‐Type Lanthanide Dopants for Mg₃Sb₂ Thermoelectrics

2020

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The recent discovery of n-type Mg 3 Sb 2 thermoelectrics has ignited intensive research activities on searching for potential n-type dopants for this material. Using first-principles defect calculations, here, a systematic computational screening of potential efficient n-type lanthanide dopants is conducted for Mg 3 Sb 2. In addition to La, Ce, Pr, and Tm, it is found that high electron concentration (≳10 20 cm −3 at the growth temperature of 900 K) can be achieved by doping on the Mg sites with Nd, Gd, Ho, and Lu, which are generally more efficient than other lanthanide dopants and the anion-site dopant Te. Experimentally, Nd and Tm are confirmed as effective n-type dopants for Mg 3 Sb 2 since doping with Nd and Tm shows higher electron concentration and thermoelectric figure of merit zT than doping with Te. Through codoping with Nd (Tm) and Te, simultaneous power factor improvement and thermal conductivity reduction are achieved. As a result, high zT values of ≈1.65 and ≈1.75 at 775 K are obtained in n-type Mg 3.5 Nd 0.04 Sb 1.97 Te 0.03 and Mg 3.5 Tm 0.03 Sb 1.97 Te 0.03 , respectively, which are among the highest values for n-type Mg 3 Sb 2 without alloying with Mg 3 Bi 2. This work sheds light on exploring promising n-type dopants for the design of Mg 3 Sb 2 thermoelectrics. Thermoelectric (TE) materials show great promise in waste heat recovery and solid-state refrigeration applications since they can directly convert heat into electricity or vice versa purely by solidstate means. [1,2] The performance of TE materials is typically determined by the dimensionless figure of merit zT = 2 T/ , where is the Seebeck coefficient, is the electrical conductivity, T is the absolute temperature, and is the total thermal conductivity. Low-cost high-performance materials are required for the widespread application of TE technology. The recently discovered n-type Mg 3 Sb 2-based compound is one such material that

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Section: Doi: 101002/advs202002867mentioning

confidence: 99%

Probing Efficient N‐Type Lanthanide Dopants for Mg₃Sb₂ Thermoelectrics

2020

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“…e chaotic system has many unique features similar to the counterpart of the cryptosystem, such as bulky data capacity and high redundancy [4], which are difficult to handle by using text encryption techniques. In the last two decades, many new image encryption schemes were proposed, and researchers proposed many image encryption schemes by applying various technologies, such as compressive sensing [5][6][7], DNA coding [8][9][10][11][12][13], quantum theory [14][15][16][17], chaos systems [18][19][20][21][22][23][24][25][26][27][28], S-box [29], and networks [30,31].…”

Section: Introductionmentioning

confidence: 99%

A Novel Image Encryption Algorithm Based on the Delayed Maps and Permutation-Confusion-Diffusion Architecture

Sun

Zhu

2021

Security and Communication Networks

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To improve the dynamical behaviors of 1D chaotic maps, a new linear-delay-modulation method (LDM) is proposed. Derived from the Sine map, a delayed Sine map (DSM) is proposed based on the LDM. Then, we substitute the Sine map in the SIMM system with DSM and obtained a delayed SIMM system (DSIMM). Its chaotic performance is analyzed through the phase diagram, Lyapunov exponent spectrum, and complexity. The results show that the delayed chaotic map can generate more complex dynamical behaviors and more random sequences. Hence, we apply the two delayed systems to a novel image encryption algorithm with the permutation-confusion-diffusion architecture. Firstly, to permutate the pixel of the image efficiently, the plain-image is scrambled by using a multilayer of the nonlinear index. Secondly, the image is confused by using the chaotic matrix generated with two chaotic sequences, and then, the ciphertext is transformed into a 1D sequence. Finally, to improve the plaintext sensitivity and facilitate key management, we enhance the sensitivity by applying a novel diffusion algorithm instead of using plaintext-related keystream. The diffusion equation contains the sum of undiffused pixels and the operation of cyclic bit-shift. Simulation results for the gray image illustrate the effectiveness of the proposed encryption algorithm.

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“…al. reported a new scheme for image compression and encryption, and they demonstrated that the degraded image caused by ghost-imaging transmission can be substantially improved by DL [25]. In particular, because the optical ciphertext is of more randomness than natural image or hologram, the compression of it becomes extremely difficult.…”

Section: Introductionmentioning

confidence: 99%

Optical Compressive Encryption via Deep Learning

Qin

Wan

et al. 2021

IEEE Photonics J.

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The compression of the ciphertext of a cryptosystem is desirable considering the dramatic increase in secure data transfer via Internet. In this paper, we propose a simple and universal scheme to compress and decompress the ciphertext of an optical cryptosystem by the aid of deep learning (DL). For compression, the ciphertext is first resized to a relatively small dimension by bilinear interpolation and thereafter condensed by the JPEG2000 standard. For decompression, a well-trained deep neural network (DNN) can be employed to perfectly recover the original ciphertext, in spite of the severe information loss suffered by the compressed file. In contrast with JPEG2000 and JPEG, our proposal can achieve a far smaller size of the compressed file (SCF) while offering comparable decompression quality. In addition, the SCF can be further reduced by compromising the quality of the recovered plaintext. It is also shown that the compression procedure can provide an additional security level, and this may offer new insight into the compressive encryption in optical cryptosystems. Both simulation and experimental results are presented to demonstrate the proposal.

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Optical image compression and encryption transmission-based ondeep learning and ghost imaging

Cited by 26 publications

References 20 publications

Probing Efficient N‐Type Lanthanide Dopants for Mg₃Sb₂ Thermoelectrics

Probing Efficient N‐Type Lanthanide Dopants for Mg₃Sb₂ Thermoelectrics

A Novel Image Encryption Algorithm Based on the Delayed Maps and Permutation-Confusion-Diffusion Architecture

Optical Compressive Encryption via Deep Learning

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Optical image compression and encryption transmission-based ondeep learning and ghost imaging

Cited by 26 publications

References 20 publications

Probing Efficient N‐Type Lanthanide Dopants for Mg3Sb2 Thermoelectrics

Probing Efficient N‐Type Lanthanide Dopants for Mg3Sb2 Thermoelectrics

A Novel Image Encryption Algorithm Based on the Delayed Maps and Permutation-Confusion-Diffusion Architecture

Optical Compressive Encryption via Deep Learning

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Probing Efficient N‐Type Lanthanide Dopants for Mg₃Sb₂ Thermoelectrics

Probing Efficient N‐Type Lanthanide Dopants for Mg₃Sb₂ Thermoelectrics