Designing Uncorrelated Address Constrain for DNA Storage by DMVO Algorithm

Cao, Ben; Li, Xue; Zhang, Xiaokang; Wang, Bin; Zhang, Qiang; Wei, Xiaopeng

doi:10.1109/tcbb.2020.3011582

Cited by 51 publications

(53 citation statements)

References 56 publications

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“…In addition, the increase of the lower bounds of the coding sets directly leads to improvements of the coding rate. The coding rate is defined as R = log 4 M / n ( Cao et al, 2020 ), where n is the length of coded DNA and M is the number of the DNA coding set. For example, the values used in previous work ( Limbachiya et al, 2018 ) are n = 9 and d = 4, R = log 4 199 /9 ≈ 0.42.…”

Section: Designing Of Lower Bounds Of Coding Setsmentioning

confidence: 99%

CLGBO: An Algorithm for Constructing Highly Robust Coding Sets for DNA Storage

Zheng

Wang

2021

Front. Genet.

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In the era of big data, new storage media are urgently needed because the storage capacity for global data cannot meet the exponential growth of information. Deoxyribonucleic acid (DNA) storage, where primer and address sequences play a crucial role, is one of the most promising storage media because of its high density, large capacity and durability. In this study, we describe an enhanced gradient-based optimizer that includes the Cauchy and Levy mutation strategy (CLGBO) to construct DNA coding sets, which are used as primer and address libraries. Our experimental results show that the lower bounds of DNA storage coding sets obtained using the CLGBO algorithm are increased by 4.3–13.5% compared with previous work. The non-adjacent subsequence constraint was introduced to reduce the error rate in the storage process. This helps to resolve the problem that arises when consecutive repetitive subsequences in the sequence cause errors in DNA storage. We made use of the CLGBO algorithm and the non-adjacent subsequence constraint to construct larger and more highly robust coding sets.

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Section: Designing Of Lower Bounds Of Coding Setsmentioning

confidence: 99%

CLGBO: An Algorithm for Constructing Highly Robust Coding Sets for DNA Storage

Zheng

Wang

2021

Front. Genet.

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“…It is also a significant research problem to effectively apply related techniques in the fields of medicine and healthcare. Cao et al combined Brownian multi-verse optimizer (BMVO) algorithm [ 7 ] and a Damping Multi-Verse Optimizer (DMVO) algorithm [ 8 ] with DNA storage to show us how closely future disease prediction is linked to DNA storage. Recently, deep-learning methods have been applied for ECG signal processing and heart disease diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Constrained transformer network for ECG signal processing and arrhythmia classification

Che

Zhang

Zhu

et al. 2021

BMC Med Inform Decis Mak

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Background Heart disease diagnosis is a challenging task and it is important to explore useful information from the massive amount of electrocardiogram (ECG) records of patients. The high-precision diagnostic identification of ECG can save clinicians and cardiologists considerable time while helping reduce the possibility of misdiagnosis at the same time.Currently, some deep learning-based methods can effectively perform feature selection and classification prediction, reducing the consumption of manpower. Methods In this work, an end-to-end deep learning framework based on convolutional neural network (CNN) is proposed for ECG signal processing and arrhythmia classification. In the framework, a transformer network is embedded in CNN to capture the temporal information of ECG signals and a new link constraint is introduced to the loss function to enhance the classification ability of the embedding vector. Results To evaluate the proposed method, extensive experiments based on real-world data were conducted. Experimental results show that the proposed model achieve better performance than most baselines. The experiment results also proved that the transformer network pays more attention to the temporal continuity of the data and captures the hidden deep features of the data well. The link constraint strengthens the constraint on the embedded features and effectively suppresses the effect of data imbalance on the results. Conclusions In this paper, an end-to-end model is used to process ECG signal and classify arrhythmia. The model combine CNN and Transformer network to extract temporal information in ECG signal and is capable of performing arrhythmia classification with acceptable accuracy. The model can help cardiologists perform assisted diagnosis of heart disease and improve the efficiency of healthcare delivery.

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“…Technology applied to image encryption includes chaotic systems [1][2][3][4][5][6][7][8], cellular automatons [9][10][11], substitution boxes [12][13][14], DNA encoding [15][16][17][18][19][20], elliptic curves [21][22][23][24], finite-precision error [25], Galois field [26] and quantum computing [27,28]. The chaotic system is most popular in the field of image encryption and is continuously improved, such as symmetric chaotic maps [29] and adaptive chaotic maps [30,31].…”

Section: Introductionmentioning

confidence: 99%

Image Encryption Scheme Based on Multiscale Block Compressed Sensing and Markov Model

Shi

Wang

2021

Entropy

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Many image encryption schemes based on compressed sensing have the problem of poor quality of decrypted images. To deal with this problem, this paper develops an image encryption scheme by multiscale block compressed sensing. The image is decomposed by a three-level wavelet transform, and the sampling rates of coefficient matrices at all levels are calculated according to multiscale block compressed sensing theory and the given compression ratio. The first round of permutation is performed on the internal elements of the coefficient matrices at all levels. Then the coefficient matrix is compressed and combined. The second round of permutation is performed on the combined matrix based on the state transition matrix. Independent diffusion and forward-backward diffusion between pixels are used to obtain the final cipher image. Different sampling rates are set by considering the difference of information between an image’s low- and high-frequency parts. So, the reconstruction quality of the decrypted image is better than that of other schemes, which set one sampling rate on an entire image. The proposed scheme takes full advantage of the randomness of the Markov model and shows an excellent encryption effect to resist various attacks.

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Designing Uncorrelated Address Constrain for DNA Storage by DMVO Algorithm

Cited by 51 publications

References 56 publications

CLGBO: An Algorithm for Constructing Highly Robust Coding Sets for DNA Storage

CLGBO: An Algorithm for Constructing Highly Robust Coding Sets for DNA Storage

Constrained transformer network for ECG signal processing and arrhythmia classification

Image Encryption Scheme Based on Multiscale Block Compressed Sensing and Markov Model

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