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Bio-inspired cryptosystem on the reciprocal domain: DNA strands mutate to secure health data Research Articles

S. Aashiq Banu, Rengarajan Amirtharajan,aashiqbanu@sastra.ac.in,amir@ece.sastra.edu

Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 7,   Pages 940-956 doi: 10.1631/FITEE.2000071

Abstract: Healthcare and telemedicine industries are relying on technology that is connected to the Internet. Digital health data are more prone to cyber attacks because of the treasure trove of personal data they possess. This necessitates protection of digital medical images and their secure transmission. In this paper, an encryption technique based on mutated with Lorenz and Lü is employed to generate high pseudo-random key streams. The proposed chaos- cryptic system operates on the integer wavelet transform (IWT) domain and a bio-inspired , unit for enhancing the confusion and diffusion phase in an approximation coefficient. Finally, an XOR operation is performed with a quantised chaotic set from the developed combined attractors. The algorithm attains an average entropy of 7.9973, near-zero correlation with an NPCR of 99.642%, a UACI of 33.438%, and a keyspace of 10. Further, the experimental analyses and NIST statistical test suite have been designed such that the proposed technique has the potency to withstand any statistical, differential, and brute force attacks.

Keywords： 医学图像加密；DNA；混沌吸引子；交叉；突变；电子医疗    

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Dynamic analysis, FPGA implementation, and cryptographic application of an autonomous 5D chaotic system with offset boosting Research Articles

Sifeu Takougang Kingni, Karthikeyan Rajagopal, Serdar Çiçek, Ashokkumar Srinivasan, Anitha Karthikeyan,stkingni@gmail.com

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 6,   Pages 809-962 doi: 10.1631/FITEE.1900167

Abstract: An autonomous five-dimensional (5D) system with is constructed by modifying the well-known three-dimensional autonomous Liu and Chen system. Equilibrium points of the proposed autonomous 5D system are found and its stability is analyzed. The proposed system includes Hopf bifurcation, periodic attractors, quasi-periodic attractors, a one-scroll chaotic attractor, a double-scroll chaotic attractor, coexisting attractors, the bistability phenomenon, with partial amplitude control, reverse period-doubling, and an intermittency route to chaos. Using a field programmable gate array (FPGA), the proposed autonomous 5D system is implemented and the phase portraits are presented to check the numerical simulation results. The chaotic attractors and coexistence of the attractors generated by the of the proposed system have good qualitative agreement with those found during the numerical simulation. Finally, a sound data encryption and communication system based on the proposed autonomous 5D is designed and illustrated through a numerical example.

Keywords： 混沌系统；霍普夫分岔；共存吸引子；偏置增强；FPGA实现；声音加密    

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novel color image encryption algorithm based on a fractional-order discrete chaotic neural network and DNA Research Articles

Li-ping Chen, Hao Yin, Li-guo Yuan, António M. Lopes, J. A. Tenreiro Machado, Ran-chao Wu,lip_chenhut@126.com

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 6,   Pages 809-962 doi: 10.1631/FITEE.1900709

Abstract: A novel algorithm based on dynamic deoxyribonucleic acid (DNA) encoding and chaos is presented.DNA encoding and diffusion are used to diffuse the image information.

Keywords： 分数阶离散系统；神经网络；DNA加密；彩色图像加密    

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Cellular automata based multi-bit stuck-at fault diagnosis for resistive memory Research Article

Sutapa SARKAR, Biplab Kumar SIKDAR, Mousumi SAHA

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 7,   Pages 1110-1126 doi: 10.1631/FITEE.2100255

Abstract: This paper presents a group-based dynamic scheme intended for resistive random-access memory (ReRAM). Traditional static random-access memory, dynamic random-access memory, NAND, and NOR flash memory are limited by their scalability, power, package density, and so forth. Next-generation memory types like ReRAMs are considered to have various advantages such as high package density, non-volatility, scalability, and low power consumption, but has been a problem. Unreliable memory operation is caused by permanent stuck-at faults due to extensive use of write- or memory-intensive workloads. An increased number of stuck-at faults also prematurely limit chip lifetime. Therefore, a cellular automaton (CA) based dynamic stuck-at fault-tolerant design is proposed here to combat unreliable cell functioning and variable cell lifetime issues. A scalable, block-level fault diagnosis and recovery scheme is introduced to ensure readable data despite multi-bit stuck-at faults. The scheme is a novel approach because its goal is to remove all the restrictions on the number and nature of stuck-at faults in general fault conditions. The proposed scheme is based on Wolfram's null boundary and periodic boundary CA theory. Various special classes of CAs are introduced for 100% fault tolerance: (SACAs), (TACAs), and (MACAs). The target micro-architectural unit is designed with optimal space overhead.

Keywords： Resistive memory     Cell reliability     Stuck-at fault diagnosis     Single-length-cycle single-attractor cellular automata     Single-length-cycle two-attractor cellular automata     Single-length-cycle multiple-attractor cellular automata    

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A joint image compression and encryption scheme based on a novel coupled map lattice system and DNA operations Research Article

Yuanyuan LI, Xiaoqing YOU, Jianquan LU, Jungang LOU,jqluma@seu.edu.cn

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 6,   Pages 813-827 doi: 10.1631/FITEE.2200645

Abstract: Finally, the compressed coefficient matrix is diffused by DNA random encoding, DNA addition, and bit

Keywords： Compressive sensing     Coupled map lattice (CML)     DNA operations     Semi-tensor product    

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Optimal one-bit perturbation in Boolean networks based on cascading aggregation Research Articles

Jin-feng PAN, Min MENG

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 2,   Pages 294-303 doi: 10.1631/FITEE.1900411

Abstract: We investigate the problem of finding optimal one-bit perturbation that maximizes the size of the basin of attractions (BOAs) of desired attractors and minimizes the size of the BOAs of undesired attractors for large-scale Boolean networks by cascading aggregation. First, via the aggregation, a necessary and sufficient condition is given to ensure the invariance of desired attractors after one-bit perturbation. Second, an algorithm is proposed to identify whether the one-bit perturbation will cause the emergence of new attractors or not. Next, the change of the size of BOAs after one-bit perturbation is provided in an algorithm. Finally, the efficiency of the proposed method is verified by a T-cell receptor network.

Keywords： Large-scale Boolean network     Attractor     Cascading aggregation     One-bit perturbation    

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Learning-based parameter prediction for quality control in three-dimensional medical image compression Research Articles

Yuxuan Hou, Zhong Ren, Yubo Tao, Wei Chen,3140104190@zju.edu.cn,renzhong@cad.zju.edu.cn

Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 9,   Pages 1169-1178 doi: 10.1631/FITEE.2000234

Abstract: is of vital importance in compressing three-dimensional (3D) medical imaging data. Optimal compression parameters need to be determined based on the specific quality requirement. In , regarded as the state-of-the-art compression tool, the quantization parameter (QP) plays a dominant role in controlling quality. The direct application of a video-based scheme in predicting the ideal parameters for 3D cannot guarantee satisfactory results. In this paper we propose a parameter prediction scheme to achieve efficient . Its kernel is a support vector regression (SVR) based learning model that is capable of predicting the optimal QP from both video-based and structural image features extracted directly from raw data, avoiding time-consuming processes such as pre-encoding and iteration, which are often needed in existing techniques. Experimental results on several datasets verify that our approach outperforms current video-based methods.

Keywords： 医学图像压缩；高效视频编码（HEVC）；质量控制；基于学习方法    

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A novel hybrid cryptosystem based on DQFrFT watermarking and 3D-CLM encryption for healthcare services Research Article

Fatma KHALLAF, Walid EL-SHAFAI, El-Sayed M. EL-RABAIE, Naglaa F. SOLIMAN, Fathi E. Abd EL-SAMIE,fatma.mohammed333@gmail.com,eng.waled.elshafai@gmail.com,elsayedelrabaie@gmail.com,nfsoliman@pnu.edu.sa,fathi_sayed@yahoo.com

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 7,   Pages 1045-1061 doi: 10.1631/FITEE.2200372

Abstract: algebra has been used to apply the fractional Fourier transform (FrFT) to color images in a comprehensive approach. However, the discrete fractional random transform (DFRNT) with adequate basic randomness remains to be examined. This paper presents a novel multistage privacy system for s based on discrete fractional Fourier transform (DQFrFT) watermarking and . First, we describe DFRNT (QDFRNT), which generalizes DFRNT to handle signals effectively, and then use QDFRNT to perform . To efficiently evaluate QDFRNT, this study derives the relationship between the QDFRNT of a signal and the four components of the DFRNT signal. Moreover, it uses the human vision system’s (HVS) masking qualities of edge, texture, and color tone immediately from the color host image to adaptively modify the watermark strength for each block in the using the QDFRNT-based and support vector machine (SVM) techniques. The limitations of watermark embedding are also explained to conserve watermarking energy. Second, 3D-CLM is employed to improve the system’s security and efficiency, allowing it to be used as a multistage privacy system. The proposed security system is effective against many types of channel noise attacks, according to simulation results.

Keywords： Color medical image     Quaternion     Adaptive watermarking     Encryption     Fractional transform     Three-dimensional chaotic logistic map (3D-CLM)    

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Deep Learning in Medical Ultrasound Analysis: A Review Review

Shengfeng Liu, Yi Wang, Xin Yang, Baiying Lei, Li Liu, Shawn Xiang Li, Dong Ni, Tianfu Wang

Engineering 2019, Volume 5, Issue 2,   Pages 261-275 doi: 10.1016/j.eng.2018.11.020

Abstract:

Ultrasound (US) has become one of the most commonly performed imaging modalities in clinical practice. It is a rapidly evolving technology with certain advantages and with unique challenges that include low imaging quality and high variability. From the perspective of image analysis, it is essential to develop advanced automatic US image analysis methods to assist in US diagnosis and/or to make such assessment more objective and accurate. Deep learning has recently emerged as the leading machine learning tool in various research fields, and especially in general imaging analysis and computer vision. Deep learning also shows huge potential for various automatic US image analysis tasks. This review first briefly introduces several popular deep learning architectures, and then summarizes and thoroughly discusses their applications in various specific tasks in US image analysis, such as classification, detection, and segmentation. Finally, the open challenges and potential trends of the future application of deep learning in medical US image analysis are discussed.

Keywords： Deep learning     Medical ultrasound analysis     Classification     Segmentation     Detection    

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On Balancing Mechanism Between Supply of Medical Education and Demand for Clinicians in China

Li Na , Du Jian , Tang Xiaoli , Gao Baihong , Zhan Qimin

Strategic Study of CAE 2019, Volume 21, Issue 2,   Pages 89-92 doi: 10.15302/J-SSCAE-2019.02.008

Abstract:

The imbalance between supply and demand in terms of talent cultivation scale, structure, and quality is one of the key issues facing the cultivation of medical personnel in China. The supply of medical talents in China cannot meet the growing demand for health, the quality of personnel training is uneven, and the talent supply structure does not match social needs. Through the research and discussion on the forms and contents of medical education in the United States and the United Kingdom, this paper proposes the enlightenment on how to maintain the balance between supply and demand of medical education in China and how to improve the efficiency of medical training. It is suggested to gradually establish a talent supply and demand balance mechanism where the number and structure of post-graduate education posts determines the enrollment scale and structure of medical colleges and universities, and establish a dynamic monitoring and early warning mechanism for the demand for medical talents. Furthermore, medical personnel training requires cross-departmental macro-coordination and regulation, and quality requirements such as a high threshold for doctors should be set to limit the cultivation scale of medical talents.

Keywords： medical education     clinical training     supply-demand balancing mechanism     policy implications    

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Development Strategies for Artificial Intelligence and Robotics in Medicine

Han Xiaoguang, Zhu Xiaolong, Jiang Yuzhen, He Da, Liu Wenyong,Duan Xingguang, Tian Wei

Strategic Study of CAE 2023, Volume 25, Issue 5,   Pages 43-54 doi: 10.15302/J-SSCAE-2023.07.031

Abstract:

China is facing significant challenges in healthcare owing to a severe aging population, continuously increasing healthcare demands, and uneven distribution of healthcare resources. The application of artificial intelligence (AI) and robotics in the human medical and health field can provide innovative support in theory and diagnosis for surgeons, researchers, and patients, contributing to improved clinical outcomes, reduced medical costs, and the promotion of balanced distribution of healthcare resources. This paper starts from four aspects: surgical robots, rehabilitation and nursing robots, assisted telemedicine, and medical AI, extracts the typical applications of AI and robots in the medical field, assesses the current status of AI and robotics in medicine, and evaluates pertinent policies, laws, and regulatory frameworks. Based on the understanding of the challenges faced in the development of AI and robotics in medicine in China, it delineates phased  development goals and identifies future key directions: advancing toward differentiated, miniaturized, and intelligent surgical robotics; emphasizing patient-centered rehabilitation and nursing robotics; achieving multi-tasking and high situational awareness in assisted  telemedicine; and promoting medical AI for healthcare innovation. This paper suggests determining promising research directions for surgical robotics, promoting domestic production of core components, exploring commercially viable models that align with national conditions,  expanding the coverage of product applications, focusing on the development of specialized talent pools, implementing technology innovation driven by clinical demands, and strengthening regulations to reduce medical accidents and safeguard data privacy. These measures are expected to foster high-quality development of AI and robotics in medicine in China.

Keywords： smart healthcare     artificial intelligence     surgical robotics     rehabilitation and nursing robotics     assisted telemedicine    

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Interactive medical image segmentation with self-adaptive confidence calibration

沈楚云,李文浩,徐琪森,胡斌,金博,蔡海滨,朱凤平,李郁欣,王祥丰

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 9,   Pages 1332-1348 doi: 10.1631/FITEE.2200299

Abstract: Interactive medical image segmentation based on human-in-the-loop machine learning is a novel paradigm that draws on human expert knowledge to assist medical image segmentation. However, existing methods often fall into what we call interactive misunderstanding, the essence of which is the dilemma in trading off short- and long-term interaction information. To better use the interaction information at various timescales, we propose an interactive segmentation framework, called interactive MEdical image segmentation with self-adaptive Confidence CAlibration (MECCA), which combines action-based confidence learning and multi-agent reinforcement learning. A novel confidence network is learned by predicting the alignment level of the action with short-term interaction information. A confidence-based reward-shaping mechanism is then proposed to explicitly incorporate confidence in the policy gradient calculation, thus directly correcting the model’s interactive misunderstanding. MECCA also enables user-friendly interactions by reducing the interaction intensity and difficulty via label generation and interaction guidance, respectively. Numerical experiments on different segmentation tasks show that MECCA can significantly improve short- and long-term interaction information utilization efficiency with remarkably fewer labeled samples. The demo video is available at https://bit.ly/mecca-demo-video.

Keywords： Medical image segmentation     Interactive segmentation     Multi-agent reinforcement learning     Confidence learning     Semi-supervised learning    

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Discrete fractional watermark technique Correspondence

Zai-rong Wang, Babak Shiri, Dumitru Baleanu,wangzr@njtc.edu.cn,shire_babak@yahoo.com,dumitru@cankaya.edu.tr

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 6,   Pages 809-962 doi: 10.1631/FITEE.2000133

Abstract: The fractional logistic map holds rich dynamics and is adopted to generate chaotic series. A image is then encrypted and inserted into the original images. Since the encryption image takes the fractional order within (0, 1], it increases the key space and becomes difficult to attack. This study provides a robust method in the protection of the copyright of hardware, images, and other electronic files.

Keywords： 离散分数阶微积分；图像加密；水印    

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Chaotic digital cryptosystem using serial peripheral interface protocol and its dsPIC implementation None

Rodrigo MÉNDEZ-RAMÍREZ, Adrian ARELLANO-DELGADO, César CRUZ-HERNÁNDEZ, Fausto ABUNDIZ-PÉREZ, Rigoberto MARTÍNEZ-CLARK

Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 2,   Pages 165-179 doi: 10.1631/FITEE.1601346

Abstract: decryption is based on the chaotic Hénon map and two methods for blur and permute (in combination with DNA

Keywords： Chaotic systems     Statistical tests     Embedded systems     dsPIC microcontroller     Serial peripheral interface (SPI) protocol    

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Big Data for Precision Medicine

Daniel Richard Leff, Guang-Zhong Yang

Engineering 2015, Volume 1, Issue 3,   Pages 277-279 doi: 10.15302/J-ENG-2015075

Abstract:

This article focuses on the potential impact of big data analysis to improve health, prevent and detect disease at an earlier stage, and personalize interventions. The role that big data analytics may have in interrogating the patient electronic health record toward improved clinical decision support is discussed. We examine developments in pharmacogenetics that have increased our appreciation of the reasons why patients respond differently to chemotherapy. We also assess the expansion of online health communications and the way in which this data may be capitalized on in order to detect public health threats and control or contain epidemics. Finally, we describe how a new generation of wearable and implantable body sensors may improve wellbeing, streamline management of chronic diseases, and improve the quality of surgical implants.

Keywords： big data     biosensors     body-sensing networks     implantable sensors     clinical decision support systems     pharmacogenetics     mHealth    

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Title Author Date Type Operation

Bio-inspired cryptosystem on the reciprocal domain: DNA strands mutate to secure health data

S. Aashiq Banu, Rengarajan Amirtharajan,aashiqbanu@sastra.ac.in,amir@ece.sastra.edu

Journal Article

Dynamic analysis, FPGA implementation, and cryptographic application of an autonomous 5D chaotic system with offset boosting

Sifeu Takougang Kingni, Karthikeyan Rajagopal, Serdar Çiçek, Ashokkumar Srinivasan, Anitha Karthikeyan,stkingni@gmail.com

Journal Article

novel color image encryption algorithm based on a fractional-order discrete chaotic neural network and DNA

Li-ping Chen, Hao Yin, Li-guo Yuan, António M. Lopes, J. A. Tenreiro Machado, Ran-chao Wu,lip_chenhut@126.com

Journal Article

Cellular automata based multi-bit stuck-at fault diagnosis for resistive memory

Sutapa SARKAR, Biplab Kumar SIKDAR, Mousumi SAHA

Journal Article

A joint image compression and encryption scheme based on a novel coupled map lattice system and DNA operations

Yuanyuan LI, Xiaoqing YOU, Jianquan LU, Jungang LOU,jqluma@seu.edu.cn

Journal Article

Optimal one-bit perturbation in Boolean networks based on cascading aggregation

Jin-feng PAN, Min MENG

Journal Article

Learning-based parameter prediction for quality control in three-dimensional medical image compression

Yuxuan Hou, Zhong Ren, Yubo Tao, Wei Chen,3140104190@zju.edu.cn,renzhong@cad.zju.edu.cn

Journal Article

A novel hybrid cryptosystem based on DQFrFT watermarking and 3D-CLM encryption for healthcare services

Fatma KHALLAF, Walid EL-SHAFAI, El-Sayed M. EL-RABAIE, Naglaa F. SOLIMAN, Fathi E. Abd EL-SAMIE,fatma.mohammed333@gmail.com,eng.waled.elshafai@gmail.com,elsayedelrabaie@gmail.com,nfsoliman@pnu.edu.sa,fathi_sayed@yahoo.com

Journal Article

Deep Learning in Medical Ultrasound Analysis: A Review

Shengfeng Liu, Yi Wang, Xin Yang, Baiying Lei, Li Liu, Shawn Xiang Li, Dong Ni, Tianfu Wang

Journal Article

On Balancing Mechanism Between Supply of Medical Education and Demand for Clinicians in China

Li Na , Du Jian , Tang Xiaoli , Gao Baihong , Zhan Qimin

Journal Article

Development Strategies for Artificial Intelligence and Robotics in Medicine

Han Xiaoguang, Zhu Xiaolong, Jiang Yuzhen, He Da, Liu Wenyong,Duan Xingguang, Tian Wei

Journal Article

Interactive medical image segmentation with self-adaptive confidence calibration

沈楚云,李文浩,徐琪森,胡斌,金博,蔡海滨,朱凤平,李郁欣,王祥丰

Journal Article

Discrete fractional watermark technique

Zai-rong Wang, Babak Shiri, Dumitru Baleanu,wangzr@njtc.edu.cn,shire_babak@yahoo.com,dumitru@cankaya.edu.tr

Journal Article

Chaotic digital cryptosystem using serial peripheral interface protocol and its dsPIC implementation

Rodrigo MÉNDEZ-RAMÍREZ, Adrian ARELLANO-DELGADO, César CRUZ-HERNÁNDEZ, Fausto ABUNDIZ-PÉREZ, Rigoberto MARTÍNEZ-CLARK

Journal Article

Big Data for Precision Medicine

Daniel Richard Leff, Guang-Zhong Yang

Journal Article