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Spiking Neural Networks (SNNs) are the new generation of artificial neural networks that closely mimic the time encoding and information processing aspects of the human brain. In this work, a multi-task autonomous learning paradigm is proposed for the mobile robot application, which employs a SNN to construct the controlling system of the mobile robot. The Reward-modulated Spiking-time-dependent Plasticity learning rule is developed for the SNN-based controller, which aims to achieve the capability of autonomous learning under multiple tasks. Reward signals are generated based on the instantaneous frequencies of pre- and post-synaptic spikes, which adapts to the sensory stimuli and environmental feedback. Meanwhile, inspired by lateral inhibition connections, a task switch mechanism is designed to enable the controller to switch the operations between multiple tasks. Two tasks of obstacle avoidance and target tracking are used for performance evaluation and results demonstrate that the mobile robot with the proposed paradigm is able to autonomously learn, switch and complete the tasks.
Junxiu Liu; Hao Lu; Yuling Luo; Su Yang. Spiking neural network-based multi-task autonomous learning for mobile robots. Engineering Applications of Artificial Intelligence 2021, 104, 104362 .
AMA StyleJunxiu Liu, Hao Lu, Yuling Luo, Su Yang. Spiking neural network-based multi-task autonomous learning for mobile robots. Engineering Applications of Artificial Intelligence. 2021; 104 ():104362.
Chicago/Turabian StyleJunxiu Liu; Hao Lu; Yuling Luo; Su Yang. 2021. "Spiking neural network-based multi-task autonomous learning for mobile robots." Engineering Applications of Artificial Intelligence 104, no. : 104362.
Spiking neural networks (SNNs) have the potential to closely mimic the information processing of biological brains, by using massive neurons that are interconnected in a complex network. Recent researches have considered using electronic hardware circuits to SNN implementations to meet real-time processing requirements. Network-on-Chips (NoCs) have been widely used to develop such SNN circuits as their interconnections can offer stable interconnectivity for neuron communications with high throughput and real-time execution. However, its scalability is limited due to expensive and complex NoC routers which leads to high energy consumption and large area utilization. Therefore, a minimally buffered deflection router (MBDR) is proposed in this work to address the scalability challenge of the hardware SNNs. It employs a deflection router technique to remove most of the inter-buffers and other expensive components of the conventional routers. Moreover, a novel flow controller is developed in MBDR to further reduce power consumption. Compared to existing approaches, experimental results show that based on 90-nm CMOS technology the area and power consumption of the proposed router are reduced by ~ 86% and ~ 88%, respectively. In the meantime, system throughput is maintained at a high level.
Junxiu Liu; Dong Jiang; Yuling Luo; Senhui Qiu; Yongchuang Huang. Minimally buffered deflection router for spiking neural network hardware implementations. Neural Computing and Applications 2021, 33, 11753 -11764.
AMA StyleJunxiu Liu, Dong Jiang, Yuling Luo, Senhui Qiu, Yongchuang Huang. Minimally buffered deflection router for spiking neural network hardware implementations. Neural Computing and Applications. 2021; 33 (18):11753-11764.
Chicago/Turabian StyleJunxiu Liu; Dong Jiang; Yuling Luo; Senhui Qiu; Yongchuang Huang. 2021. "Minimally buffered deflection router for spiking neural network hardware implementations." Neural Computing and Applications 33, no. 18: 11753-11764.
To diagnose Alzheimer's disease (AD), neuroimaging methods such as magnetic resonance imaging have been employed. Recent progress in computer vision with deep learning (DL) has further inspired research focused on machine learning algorithms. However, a few limitations of these algorithms, such as the requirement for large number of training images and the necessity for powerful computers, still hinder the extensive usage of AD diagnosis based on machine learning. In addition, large number of training parameters and heavy computation make the DL systems difficult in integrating with mobile embedded devices, for example the mobile phones. For AD detection using DL, most of the current research solely focused on improving the classification performance, while few studies have been done to obtain a more compact model with less complexity and relatively high recognition accuracy. In order to solve this problem and improve the efficiency of the DL algorithm, a deep separable convolutional neural network model is proposed for AD classification in this paper. The depthwise separable convolution (DSC) is used in this work to replace the conventional convolution. Compared to the traditional neural networks, the parameters and computing cost of the proposed neural network are found greatly reduced. The parameters and computational costs of the proposed neural network are found to be significantly reduced compared with conventional neural networks. With its low power consumption, the proposed model is particularly suitable for embedding mobile devices. Experimental findings show that the DSC algorithm, based on the OASIS magnetic resonance imaging dataset, is very successful for AD detection. Moreover, transfer learning is employed in this work to improve model performance. Two trained models with complex networks, namely AlexNet and GoogLeNet, are used for transfer learning, with average classification rates of 91.40%, 93.02% and a less power consumption.
Junxiu Liu; Mingxing Li; Yuling Luo; Su Yang; Wei Li; Yifei Bi. Alzheimer's disease detection using depthwise separable convolutional neural networks. Computer Methods and Programs in Biomedicine 2021, 203, 106032 .
AMA StyleJunxiu Liu, Mingxing Li, Yuling Luo, Su Yang, Wei Li, Yifei Bi. Alzheimer's disease detection using depthwise separable convolutional neural networks. Computer Methods and Programs in Biomedicine. 2021; 203 ():106032.
Chicago/Turabian StyleJunxiu Liu; Mingxing Li; Yuling Luo; Su Yang; Wei Li; Yifei Bi. 2021. "Alzheimer's disease detection using depthwise separable convolutional neural networks." Computer Methods and Programs in Biomedicine 203, no. : 106032.
When the chaotic block cryptographic algorithms are performed on hardware devices, the leakages of power consumption etc. are crucial information which can be used to analyse the security of the cryptosystems. Template Attack (TA) can recover the secret key. However, there are still some challenges for TA such as irreversible covariance matrix and exponentiation calculation overflow. Machine Learning-based Similarity Attacks (MLSAs) are proposed to effectively analyse the sensitive information of the chaotic block cryptosystem. The proposed method consists of three steps: parameter tuning, learning and attacking. For the parameter tuning, the profiling traces are categorised according to the Hamming weights of sensitive intermediate data. Then a 10-fold cross-validation is executed to determine the corresponding parameter settings for learning algorithms. In the learning step, the profiling traces and Hamming weight labels are used to train machine learning models, and in the attacking step different similarity measure methods are used to calculate similarities between actual and hypothetical Hamming weight labels to attack the secret keys. Performance analyses demonstrate that the proposed MLSAs have higher success rates than TA and lower computational time consumptions under most of scenarios. Therefore, the MLSAs can efficiently attack and analyse hardware security of chaotic block cryptosystems.
Junxiu Liu; Shunsheng Zhang; Yuling Luo; Lvchen Cao. Machine Learning-Based Similarity Attacks for Chaos-based Cryptosystems. IEEE Transactions on Emerging Topics in Computing 2020, PP, 1 -1.
AMA StyleJunxiu Liu, Shunsheng Zhang, Yuling Luo, Lvchen Cao. Machine Learning-Based Similarity Attacks for Chaos-based Cryptosystems. IEEE Transactions on Emerging Topics in Computing. 2020; PP (99):1-1.
Chicago/Turabian StyleJunxiu Liu; Shunsheng Zhang; Yuling Luo; Lvchen Cao. 2020. "Machine Learning-Based Similarity Attacks for Chaos-based Cryptosystems." IEEE Transactions on Emerging Topics in Computing PP, no. 99: 1-1.
Recent research showed that the chaotic maps are considered as alternative methods for generating pseudo-random numbers, and various approaches have been proposed for the corresponding hardware implementations. In this work, an efficient hardware pseudo-random number generator (PRNG) is proposed, where the one-dimensional logistic map is optimised by using the perturbation operation which effectively reduces the degradation of digital chaos. By employing stochastic computing, a hardware PRNG is designed with relatively low hardware utilisation. The proposed hardware PRNG is implemented by using a Field Programmable Gate Array device. Results show that the chaotic map achieves good security performance by using the perturbation operations and the generated pseudo-random numbers pass the TestU01 test and the NIST SP 800-22 test. Most importantly, it also saves 89% of hardware resources compared to conventional approaches.
Junxiu Liu; Zhewei Liang; Yuling Luo; Lvchen Cao; Shunsheng Zhang; Yanhu Wang; Su Yang. A Hardware Pseudo-Random Number Generator Using Stochastic Computing and Logistic Map. Micromachines 2020, 12, 31 .
AMA StyleJunxiu Liu, Zhewei Liang, Yuling Luo, Lvchen Cao, Shunsheng Zhang, Yanhu Wang, Su Yang. A Hardware Pseudo-Random Number Generator Using Stochastic Computing and Logistic Map. Micromachines. 2020; 12 (1):31.
Chicago/Turabian StyleJunxiu Liu; Zhewei Liang; Yuling Luo; Lvchen Cao; Shunsheng Zhang; Yanhu Wang; Su Yang. 2020. "A Hardware Pseudo-Random Number Generator Using Stochastic Computing and Logistic Map." Micromachines 12, no. 1: 31.
Implementing a watermarking algorithm with high security and low computational complexity is a challenge, especially at a limited distortion level. A novel watermarking scheme is proposed in this paper, which is based on Tent-Logistic-Cosine Map (TLCM) and Direct Current (DC) coefficient modification. Firstly, the watermark is encrypted by a matrix obtained from TLCM. Then, the cover image is divided into non-overlapping \(4 \times 4\) sub-blocks and some blocks are selected randomly. Thereafter, the DC coefficients of selected blocks are calculated directly in the spatial domain without performing two-dimensional discrete cosine transform. Finally, using the proposed watermark embedding procedure, DC coefficients of selected blocks are updated according to the encrypted watermark bits. Results show that the proposed watermarking algorithm has high security and low computational complexity at a limited distortion.
Liangjia Li; Yuling Luo; Junxiu Liu; Senhui Qiu; Lanhang Li. A Robust Watermarking Scheme with High Security and Low Computational Complexity. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2020, 439 -454.
AMA StyleLiangjia Li, Yuling Luo, Junxiu Liu, Senhui Qiu, Lanhang Li. A Robust Watermarking Scheme with High Security and Low Computational Complexity. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. 2020; ():439-454.
Chicago/Turabian StyleLiangjia Li; Yuling Luo; Junxiu Liu; Senhui Qiu; Lanhang Li. 2020. "A Robust Watermarking Scheme with High Security and Low Computational Complexity." Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering , no. : 439-454.
Image watermarking technique is one of effective solutions to protect copyright, and it is applied to a variety of information security application domains. It needs to meet four requirements of imperceptibility, robustness, capacity and security. A multi-scale and secure image watermarking method is proposed in this work, which is based on the Integer Wavelet Transform (IWT) and Singular Value Decomposition (SVD). Four IWT sub-bands are firstly obtained after 1-level IWT on the host image, and the corresponding singular diagonal matrices of four sub-bands can be obtained using SVD. Then, each singular diagonal matrix is divided into four non-overlapping sections in terms of the size of embedding watermark. Particularly, the size of upper left part is same as the size of watermark. The watermark can be directly embedded into four upper left parts afterwards by multiplying different scaling factors to complete the final watermarking operation. Especially, a novel optimized authentication mechanism is designed to resolve the false positive problem, which exists in the SVD-based watermarking algorithms. In addition, three-dimensional optimal mapping algorithm is proposed to search the optimal scaling factors through a novel objective evaluation function, and it can significantly improve the imperceptibility and robustness. The experimental test and comparison analysis illustrate that the proposed watermark scheme demonstrates a high imperceptibility with peak signal to noise ratio values of 45 dB and strong robustness with average normalized correlation values of 0.92.
Yuling Luo; Liangjia Li; Junxiu Liu; Shunbin Tang; Lvchen Cao; Shunsheng Zhang; Senhui Qiu; Yi Cao. A multi-scale image watermarking based on integer wavelet transform and singular value decomposition. Expert Systems with Applications 2020, 168, 114272 .
AMA StyleYuling Luo, Liangjia Li, Junxiu Liu, Shunbin Tang, Lvchen Cao, Shunsheng Zhang, Senhui Qiu, Yi Cao. A multi-scale image watermarking based on integer wavelet transform and singular value decomposition. Expert Systems with Applications. 2020; 168 ():114272.
Chicago/Turabian StyleYuling Luo; Liangjia Li; Junxiu Liu; Shunbin Tang; Lvchen Cao; Shunsheng Zhang; Senhui Qiu; Yi Cao. 2020. "A multi-scale image watermarking based on integer wavelet transform and singular value decomposition." Expert Systems with Applications 168, no. : 114272.
A novel method of using the spiking neural networks (SNNs) and the electroencephalograph (EEG) processing techniques to recognize emotion states is proposed in this paper. Three algorithms including discrete wavelet transform (DWT), variance and fast Fourier transform (FFT) are employed to extract the EEG signals, which are further taken by the SNN for the emotion classification. Two datasets, i.e., DEAP and SEED, are used to validate the proposed method. For the former dataset, the emotional states include arousal, valence, dominance and liking where each state is denoted as either high or low status. For the latter dataset, the emotional states are divided into three categories (negative, positive and neutral). Experimental results show that by using the variance data processing technique and SNN, the emotion states of arousal, valence, dominance and liking can be classified with accuracies of 74%, 78%, 80% and 86.27% for the DEAP dataset, and an overall accuracy is 96.67% for the SEED dataset, which outperform the FFT and DWT processing methods. In the meantime, this work achieves a better emotion classification performance than the benchmarking approaches, and also demonstrates the advantages of using SNN for the emotion state classifications.
Yuling Luo; Qiang Fu; Juntao Xie; Yunbai Qin; Guopei Wu; Junxiu Liu; Frank Jiang; Yi Cao; Xuemei Ding. EEG-Based Emotion Classification Using Spiking Neural Networks. IEEE Access 2020, 8, 46007 -46016.
AMA StyleYuling Luo, Qiang Fu, Juntao Xie, Yunbai Qin, Guopei Wu, Junxiu Liu, Frank Jiang, Yi Cao, Xuemei Ding. EEG-Based Emotion Classification Using Spiking Neural Networks. IEEE Access. 2020; 8 (99):46007-46016.
Chicago/Turabian StyleYuling Luo; Qiang Fu; Juntao Xie; Yunbai Qin; Guopei Wu; Junxiu Liu; Frank Jiang; Yi Cao; Xuemei Ding. 2020. "EEG-Based Emotion Classification Using Spiking Neural Networks." IEEE Access 8, no. 99: 46007-46016.
In this paper, a color image encryption method using the memristive hyperchaotic system and deoxyribonucleic acid (DNA) encryption is proposed. First, the pseudo-random sequences are generated by a keystream generation mechanism based on a memristive hyperchaotic system and the plain image. Due to this, the memristive hyperchaotic system has a complex dynamical behavior and is highly sensitive to initial conditions, the proposed keystream generation mechanism is highly random which is also dependent on the plain images. Second, a permutation based on the cycle-shift operation is designed to eliminate the correlations between adjacent pixels in the plain images. Then, the scrambled sequences are processed by DNA encryption to increase the system ability to defense the brute force attacks. Finally, the cipher image is obtained after the diffusion and interaction among red, green and blue components. Experimental analysis and performance comparisons show that the proposed method has high security, good efficiency and strong robustness under different attacks.
Xue Ouyang; Yuling Luo; Junxiu Liu; Lvchen Cao; Yunqi Liu. A color image encryption method based on memristive hyperchaotic system and DNA encryption. International Journal of Modern Physics B 2020, 34, 1 .
AMA StyleXue Ouyang, Yuling Luo, Junxiu Liu, Lvchen Cao, Yunqi Liu. A color image encryption method based on memristive hyperchaotic system and DNA encryption. International Journal of Modern Physics B. 2020; 34 (4):1.
Chicago/Turabian StyleXue Ouyang; Yuling Luo; Junxiu Liu; Lvchen Cao; Yunqi Liu. 2020. "A color image encryption method based on memristive hyperchaotic system and DNA encryption." International Journal of Modern Physics B 34, no. 4: 1.
The echo state network (ESN) is a powerful recurrent neural network for time series modelling. ESN inherits the simplified structure and relatively straightforward training process of conventional neural networks, and shows strong computational capabilities to solve nonlinear problems. It is able to map low-dimensional input signals to high-dimensional space for information extraction, but it is found that not every dimension of the reservoir output directly contributes to the model generalization. This work aims to improve the generalization capabilities of the ESN model by reducing the redundant reservoir output features. A novel hybrid model, namely binary grey wolf echo state network (BGWO-ESN), is proposed which optimises the ESN output connection by the feature selection scheme. Specially, the feature selection scheme of BGWO is developed to improve the ESN output connection structure. The proposed method is evaluated using synthetic and financial data sets. Experimental results demonstrate that the proposed BGWO-ESN model is more effective than other benchmarks, and obtains the lowest generalization error.
Junxiu Liu; Tiening Sun; Yuling Luo; Su Yang; Yi Cao; Jia Zhai. Echo state network optimization using binary grey wolf algorithm. Neurocomputing 2019, 385, 310 -318.
AMA StyleJunxiu Liu, Tiening Sun, Yuling Luo, Su Yang, Yi Cao, Jia Zhai. Echo state network optimization using binary grey wolf algorithm. Neurocomputing. 2019; 385 ():310-318.
Chicago/Turabian StyleJunxiu Liu; Tiening Sun; Yuling Luo; Su Yang; Yi Cao; Jia Zhai. 2019. "Echo state network optimization using binary grey wolf algorithm." Neurocomputing 385, no. : 310-318.
Quantum neural network (QNN) is developed based on two classical theories of quantum computation and artificial neural networks. It has been proved that quantum computing is an important candidate for improving the performance of traditional neural networks. In this work, inspired by the QNN, the quantum computation method is combined with the echo state networks (ESNs), and a hybrid model namely quantum echo state network (QESN) is proposed. Firstly, the input training data is converted to quantum state, and the internal neurons in the dynamic reservoir of ESN are replaced by qubit neurons. Then in order to maintain the stability of QESN, the particle swarm optimization (PSO) is applied to the model for the parameter optimizations. The synthetic time series and real financial application datasets (Standard & Poor's 500 index and foreign exchange) are used for performance evaluations, where the ESN, autoregressive integrated moving average (ARIMAX) are used as the benchmarks. Results show that the proposed PSO-QESN model achieves a good performance for the time series predication tasks and is better than the benchmarking algorithms. Thus, it is feasible to apply quantum computing to the ESN model, which provides a novel method to improve the ESN performance.
Junxiu Liu; Tiening Sun; Yuling Luo; Su Yang; Yi Cao; Jia Zhai. An echo state network architecture based on quantum logic gate and its optimization. Neurocomputing 2019, 371, 100 -107.
AMA StyleJunxiu Liu, Tiening Sun, Yuling Luo, Su Yang, Yi Cao, Jia Zhai. An echo state network architecture based on quantum logic gate and its optimization. Neurocomputing. 2019; 371 ():100-107.
Chicago/Turabian StyleJunxiu Liu; Tiening Sun; Yuling Luo; Su Yang; Yi Cao; Jia Zhai. 2019. "An echo state network architecture based on quantum logic gate and its optimization." Neurocomputing 371, no. : 100-107.
Recently, the activities of elder people are monitored to support them live independently and safely, where the embedded hardware systems such as wearable devices are widely used. It is a research challenge to deploy deep learning algorithms on embedded devices to recognize the human activities, with the hardware constraints of limited computing resources and low power consumption. In this paper, human body posture recognition methods are proposed for the wearable embedded systems, where back propagation neural network (BPNN) and binary neural network (BNN) are employed to classify the human body postures. The BNN quantizes the synaptic weights and activation values to +1 or −1 based on the BPNN, and is able to achieve a good trade-off between the performance and cost for the embedded systems. In the experiments, the proposed methods are deployed on embedded device of Raspberry Pi 3 for real application of body postures recognition. Results show that compared with BPNN, the BNN can achieve a better trade-off between classification accuracy and cost including required computing resource, power consumption and processing time, e.g. it uses 85.29% less memory, 8.86% less power consumption, and has 5.19% faster classification speed. Therefore, the BNN is more suitable for deployment to resource constrained embedded hardware devices, which is of great significance for the application of human body posture recognition using wearable devices.
Junxiu Liu; Mingxing Li; Yuling Luo; Su Yang; Senhui Qiu. Human Body Posture Recognition Using Wearable Devices. Transactions on Petri Nets and Other Models of Concurrency XV 2019, 326 -337.
AMA StyleJunxiu Liu, Mingxing Li, Yuling Luo, Su Yang, Senhui Qiu. Human Body Posture Recognition Using Wearable Devices. Transactions on Petri Nets and Other Models of Concurrency XV. 2019; ():326-337.
Chicago/Turabian StyleJunxiu Liu; Mingxing Li; Yuling Luo; Su Yang; Senhui Qiu. 2019. "Human Body Posture Recognition Using Wearable Devices." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 326-337.
For the Spiking Neuron Network (SNN) systems, the hardware implementation has unique advantages in terms of performance, energy, and scalability. The Networks-on-Chip (NoC) interconnection strategy has been widely used in hardware SNNs as it provides excellent interconnection mechanism for interneuronal communications. However, the mapping between the SNN models and NoC hardware systems remains a research challenge. In this paper, a multi-objective immune genetic algorithm is proposed for the mapping of SNN hardware system, which is based on the Immune Algorithm (IA) and Genetic Algorithm (GA). It can optimize the SNN hardware systems by reducing the energy consumption and communication delays. In the experiments, the spiking astrocyte neuron network model and the Star-Subnet-Based-3D Mesh (3D-SSBM) NoC hardware system are used for testing. Results demonstrate that the proposed algorithm provides an effective mapping solution for hardware SNNs with low energy consumption and communication delay.
Junxiu Liu; Xingyue Huang; Yongchuang Huang; Yuling Luo; Su Yang. Multi-objective Spiking Neural Network Hardware Mapping Based on Immune Genetic Algorithm. Transactions on Petri Nets and Other Models of Concurrency XV 2019, 745 -757.
AMA StyleJunxiu Liu, Xingyue Huang, Yongchuang Huang, Yuling Luo, Su Yang. Multi-objective Spiking Neural Network Hardware Mapping Based on Immune Genetic Algorithm. Transactions on Petri Nets and Other Models of Concurrency XV. 2019; ():745-757.
Chicago/Turabian StyleJunxiu Liu; Xingyue Huang; Yongchuang Huang; Yuling Luo; Su Yang. 2019. "Multi-objective Spiking Neural Network Hardware Mapping Based on Immune Genetic Algorithm." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 745-757.
In this paper, a novel image watermarking method is proposed which is based on discrete wave transformation (DWT), Hessenberg decomposition (HD) and singular value decomposition (SVD). In the embedding process, the host image is firstly decomposed into a number of sub-bands through multi-level DWT, and the resulting coefficients of which are then used as the input for HD. The watermark is operated on SVD at the same time. The watermark is finally embedded into the host image by the scaling factor. Fruit fly optimization algorithm, one of the natural inspired optimization algorithms is devoted to find the scaling factor through the proposed objective evaluation function. The proposed method is compared to other research works under various spoof attacks, such as the filter, noise, JPEG compression, JPEG2000 compression and sharpening attacks. The experimental results show that the proposed image watermarking method has a good trade-off between robustness and invisibility even for the watermarks with multiple sizes.
Junxiu Liu; Jiadong Huang; Yuling Luo; Lvchen Cao; Su Yang; Duqu Wei; Ronglong Zhou. An Optimized Image Watermarking Method Based on HD and SVD in DWT Domain. IEEE Access 2019, 7, 80849 -80860.
AMA StyleJunxiu Liu, Jiadong Huang, Yuling Luo, Lvchen Cao, Su Yang, Duqu Wei, Ronglong Zhou. An Optimized Image Watermarking Method Based on HD and SVD in DWT Domain. IEEE Access. 2019; 7 (99):80849-80860.
Chicago/Turabian StyleJunxiu Liu; Jiadong Huang; Yuling Luo; Lvchen Cao; Su Yang; Duqu Wei; Ronglong Zhou. 2019. "An Optimized Image Watermarking Method Based on HD and SVD in DWT Domain." IEEE Access 7, no. 99: 80849-80860.
Due to the potential security problem about key management and distribution for the symmetric image encryption schemes, a novel asymmetric image encryption method is proposed in this work, which is based on the elliptic curve ElGamal (EC-ElGamal) cryptography and chaotic theory. Specifically, the SHA-512 hash is firstly adopted to generate the initial values of chaotic system, and a crossover permutation in terms of chaotic index sequence is used to scramble the plain-image. Furthermore, the generated scrambled image is embedded into the elliptic curve for the encrypted byelliptic curve ElGamal which can not only improve the security but also can help solve the key management problems. Finally, the diffusion combined chaos game with DNA sequence is executed to get the cipher image. Experimental analysis and performance comparisons demonstrate that the proposed method has high security, good efficiency, and strong robustness against chosen-plaintext attack which make it have potential applications for the image secure communications.
Yuling Luo; Xue Ouyang; Junxiu Liu; Lvchen Cao. An Image Encryption Method Based on Elliptic Curve Elgamal Encryption and Chaotic Systems. IEEE Access 2019, 7, 38507 -38522.
AMA StyleYuling Luo, Xue Ouyang, Junxiu Liu, Lvchen Cao. An Image Encryption Method Based on Elliptic Curve Elgamal Encryption and Chaotic Systems. IEEE Access. 2019; 7 (99):38507-38522.
Chicago/Turabian StyleYuling Luo; Xue Ouyang; Junxiu Liu; Lvchen Cao. 2019. "An Image Encryption Method Based on Elliptic Curve Elgamal Encryption and Chaotic Systems." IEEE Access 7, no. 99: 38507-38522.
Junxiu Liu; Jinlei Zhang; Yuling Luo; Su Yang; Jinling Wang; Qiang Fu. Mass Spectral Substance Detections Using Long Short-Term Memory Networks. IEEE Access 2019, 7, 10734 -10744.
AMA StyleJunxiu Liu, Jinlei Zhang, Yuling Luo, Su Yang, Jinling Wang, Qiang Fu. Mass Spectral Substance Detections Using Long Short-Term Memory Networks. IEEE Access. 2019; 7 ():10734-10744.
Chicago/Turabian StyleJunxiu Liu; Jinlei Zhang; Yuling Luo; Su Yang; Jinling Wang; Qiang Fu. 2019. "Mass Spectral Substance Detections Using Long Short-Term Memory Networks." IEEE Access 7, no. : 10734-10744.
Recent studies have shown that the electronic hardware devices can be compromised by the faults and fault tolerance is a crucial capability. This paper addresses the challenge of fault detection in the CMOS circuits, using two bio-inspired structures based on the HP lab's memristor and the BSIM3v3.2.2 transistor models. The first fault detection circuit (FDC) includes the memristor-based synapses and a modified leaky integrate-and-fire (LIF)-based neuron. The memristor-based synapse circuits can be further optimized which is the proposed second fault detection method (O-FDC), and it has a lower hardware overhead and power consumption compared to the former. Experimental results demonstrate that the proposed structures can detect the circuit faults under the inputs of direct current (DC), alternating current (AC) voltage sources, and pulse signals. Under the input of DC, the fault detection times for the two proposed structures are about 0.16ms and 1.2ms, respectively; when the input source is AC, the corresponding fault detection times are about 0.206ms and 0.758ms; and it takes only 6.47us for fault detection under the input of pulse signals. This work provides an alternative solution to enhance the fault-tolerant capability of the hardware systems.
Junxiu Liu; Yongchuang Huang; Yuling Luo; Jim Harkin; Liam McDaid. Bio-inspired fault detection circuits based on synapse and spiking neuron models. Neurocomputing 2018, 331, 473 -482.
AMA StyleJunxiu Liu, Yongchuang Huang, Yuling Luo, Jim Harkin, Liam McDaid. Bio-inspired fault detection circuits based on synapse and spiking neuron models. Neurocomputing. 2018; 331 ():473-482.
Chicago/Turabian StyleJunxiu Liu; Yongchuang Huang; Yuling Luo; Jim Harkin; Liam McDaid. 2018. "Bio-inspired fault detection circuits based on synapse and spiking neuron models." Neurocomputing 331, no. : 473-482.
Yuling Luo; Shunbin Tang; Xingsheng Qin; Lvchen Cao; Frank Jiang; Junxiu Liu. A Double-Image Encryption Scheme Based on Amplitude-Phase Encoding and Discrete Complex Random Transformation. IEEE Access 2018, 6, 77740 -77753.
AMA StyleYuling Luo, Shunbin Tang, Xingsheng Qin, Lvchen Cao, Frank Jiang, Junxiu Liu. A Double-Image Encryption Scheme Based on Amplitude-Phase Encoding and Discrete Complex Random Transformation. IEEE Access. 2018; 6 ():77740-77753.
Chicago/Turabian StyleYuling Luo; Shunbin Tang; Xingsheng Qin; Lvchen Cao; Frank Jiang; Junxiu Liu. 2018. "A Double-Image Encryption Scheme Based on Amplitude-Phase Encoding and Discrete Complex Random Transformation." IEEE Access 6, no. : 77740-77753.
Junxiu Liu; Tiening Sun; Yuling Luo; Qiang Fu; Yi Cao; Jia Zhai; Xuemei Ding. Financial Data Forecasting Using Optimized Echo State Network. Lecture Notes in Computer Science 2018, 138 -149.
AMA StyleJunxiu Liu, Tiening Sun, Yuling Luo, Qiang Fu, Yi Cao, Jia Zhai, Xuemei Ding. Financial Data Forecasting Using Optimized Echo State Network. Lecture Notes in Computer Science. 2018; ():138-149.
Chicago/Turabian StyleJunxiu Liu; Tiening Sun; Yuling Luo; Qiang Fu; Yi Cao; Jia Zhai; Xuemei Ding. 2018. "Financial Data Forecasting Using Optimized Echo State Network." Lecture Notes in Computer Science , no. : 138-149.
Tourist arrival and tourist demand forecasting are a crucial issue in tourism economy and the community economic development as well. Tourist demand forecasting has attracted much attention from tourism academics as well as industries. In recent year, it attracts increasing attention in the computational literature as advances in machine learning method allow us to construct models that significantly improve the precision of tourism prediction. In this paper, we draw upon both strands of the literature and propose a novel paired neural network model. The tourist arrival data is decomposed by two low-pass filters into long-term trend and short-term seasonal components, which are then modelled by a pair of autoregressive neural network models as a parallel structure. The proposed model is evaluated by the tourist arrival data to United States from twelve source markets. The empirical studies show that our proposed paired neural network model outperforming the selected benchmark model across all error measures and over different horizons.
Yuan Yao; Yi Cao; Xuemei Ding; Jia Zhai; Junxiu Liu; Yuling Luo; Shuai Ma; Kailin Zou. A paired neural network model for tourist arrival forecasting. Expert Systems with Applications 2018, 114, 588 -614.
AMA StyleYuan Yao, Yi Cao, Xuemei Ding, Jia Zhai, Junxiu Liu, Yuling Luo, Shuai Ma, Kailin Zou. A paired neural network model for tourist arrival forecasting. Expert Systems with Applications. 2018; 114 ():588-614.
Chicago/Turabian StyleYuan Yao; Yi Cao; Xuemei Ding; Jia Zhai; Junxiu Liu; Yuling Luo; Shuai Ma; Kailin Zou. 2018. "A paired neural network model for tourist arrival forecasting." Expert Systems with Applications 114, no. : 588-614.