Decorrelation Operation Research Articles

A novel image encryption scheme based on ccnn

With the advancement of computational capacity, the key space will become one of the crucial factors influencing the security of digital cryptographic systems. Despite chaotic-based digital cryptographic systems possessing large key spaces, the post-Moore’s era rapid growth in computational capacity continues to pose challenges to the security of chaotic-based cryptographic systems. To address this issue, a novel image encryption scheme based on non-autonomous chaotic system is presented in this paper. In particular, a brain inspired neuron called continuous-coupled neural network (CCNN) is utilized to design image encryption scheme. To achieve the efficient image encryption scheme, firstly, the CCNN model is simplified to uncoupled-linking neuron model. The dynamic behavior under various driving signals is studied. The analysis showed that uncoupled-linking CCNN neuron exhibit various dynamic behavior under sine waves, triangular waves, sawtooth, superimposed sine waves, etc. Secondly, the decorrelation operation method is utilized to enhance the pseudo-randomness of the sequence. On this basis, thirdly, the image encryption scheme is proposed. It uses bit-level pixel scrambling, row scrambling, column scrambling and diffusion to modify the pixel value and the pixel position of the image. Security analysis shows that the proposed scheme is able to resist differential attack, statistics attack, known-plaintext attack and brute force attack. Moreover, the key space of the proposed scheme can be extended by the combination of drive signals. This unique feature makes the key space of the proposed scheme to be infinite, leading this kind of chaos-based cryptographic system to be a competitive candidate in post-Moore’s era.

A New Secure Communications Scheme Based on a Synchronisation of Hybrid Chaotic System

This paper presents a novel approach for secure communication utilizing a chaotic hybrid optical bistable system and chaotic modulation. The proposed crypto system encrypts the message at the transmitter using the chaotic hybrid optical bistable system with decorrelation operation to improve the chaotic sequence's performance. The encoded message is then injected into the dynamics of the chaotic memristor system. At the receiver, the synchronization of the two chaotic systems with passive control and predictive control allows for the recovery of the message through chaotic demodulation. The effectiveness of this approach is demonstrated through numerical simulation using medical images.

Direction of Arrival Estimation of Coherent Wideband Sources Using Nested Array.

Due to their ability to achieve higher DOA estimation accuracy and larger degrees of freedom (DOF) using a fixed number of antennas, sparse arrays, etc., nested and coprime arrays have attracted a lot of attention in relation to research into direction of arrival (DOA) estimation. However, the usage of the sparse array is based on the assumption that the signals are independent of each other, which is hard to guarantee in practice due to the complex propagation environment. To address the challenge of sparse arrays struggling to handle coherent wideband signals, we propose the following method. Firstly, we exploit the coherent signal subspace method (CSSM) to focus the wideband signals on the reference frequency and assist in the decorrelation process, which can be implemented without any pre-estimations. Then, we virtualize the covariance matrix of sparse array due to the decorrelation operation. Next, an enhanced spatial smoothing algorithm is applied to make full use of the information available in the data covariance matrix, as well as to improve the decorrelation effect, after which stage the multiple signal classification (MUSIC) algorithm is used to obtain DOA estimations. In the simulation, with reference to the root mean square error (RMSE) that varies in tandem with the signal-to-noise ratio (SNR), the algorithm achieves satisfactory results compared to other state-of-the-art algorithms, including sparse arrays using the traditional incoherent signal subspace method (ISSM), the coherent signal subspace method (CSSM), spatial smoothing algorithms, etc. Furthermore, the proposed method is also validated via real data tests, and the error value is only 0.2 degrees in real data tests, which is lower than those of the other methods in real data tests.

Secure Communication Scheme for Brain-Computer Interface Systems Based on High-Dimensional Hyperbolic Sine Chaotic System

Brain-Computer Interface (BCI) is a direct communication pathway between the brain and the external environment without using peripheral nerves and muscles. This emerging topic is suffering from serious issues such as malicious tampering and privacy leakage. To address this issue, we propose a novel communication scheme for BCI Systems. In particular, this scheme first utilizes high-dimensional chaotic systems with hyperbolic sine nonlinearity as the random number generator, then decorrelation operation is used to remove the physical characteristics of the output sequences. Finally, each of the sequences is applied in differential chaos shift keying (DCSK). Since each output sequence corresponds to a unique electrode, the communication data of different electrodes will not interfere with each other. Compared with popular multi-user DSCK schemes using Walsh code sequences, this scheme does not require the channel data of all electrodes while decoding. Therefore, this scheme has higher efficiency. Experimental results on communication data indicate that the proposed scheme can provide a high level of security.

Channel-Equalization-HAR: A Light-weight Convolutional Neural Network for Wearable Sensor Based Human Activity Recognition

Recently, human activity recognition (HAR) that uses wearable sensors has become a research hotspot because its wide applications in real-world scenarios. Essentially, HAR can be treated as multi-channel time series classification problem, where different channels may come from heterogeneous sensor modalities. Deep learning, especially convolutional neural networks (CNNs) have made breakthroughs in ubiquitous HAR scenario. Various normalization methods enable layers of networks to learn more independently by normalizing hybrid sensor features. However, normalization tends to produce a channel collapse phenomenon, where many channels generates tiny values. Most channels are inhibited and contribute very little to output. As a result, the network has to rely on only a few valid channels, which inevitably impair the generality ability. In this paper, we provide an alternative called Channel Equalization to reactivate these inhibited channels by performing whitening or decorrelation operation, which compels all channels to contribute more or less to feature representation. Extensive experiments are conducted on several public HAR benchmarks, which indicate that the proposed method significantly surpasses recent SOTA at negligible computational overhead. To our knowledge, the Channel Equalization is for the first time to be applied in multimodal HAR scenario. Finally, the actual operation is evaluated on an embedded platform.

Reversible Data Hiding for JPEG Images Based on Multiple Two-Dimensional Histograms

Reversible data hiding (RDH) for JPEG images has attracted extensive attentions in recent years. However, since DCT is a de-correlation operation, it is difficult for JPEG images to employ the image redundancy for RDH design like uncompressed images. In this paper, considering better utilizing the special properties of quantized DCT coefficients, a new RDH scheme for JPEG images based on multiple two-dimensional (2D) histograms modification is proposed. Firstly, by combining every two nonzero alternating current (AC) coefficients of adjacent DCT blocks in each band as a pair, a new coefficients pairing strategy is proposed for a sharper 2D histogram. Then, with a classification process, multiple 1D and 2D histograms are generated, in which the 2D histograms are selected for data embedding and the bins in 1D histograms are shifted for reversibility. Finally, to further enhance the embedding performance, the 2D mappings are adaptively determined for different 2D histograms through a formulated rate-distortion model. Experimental results demonstrate the superiority of the proposed scheme both in visual quality and file size preservation.

Deep-learning-based ciphertext-only attack on optical double random phase encryption

Optical cryptanalysis is essential to the further investigation of more secure optical cryptosystems. Learning-based attack of optical encryption eliminates the need for the retrieval of random phase keys of optical encryption systems but it is limited for practical applications since it requires a large set of plaintext-ciphertext pairs for the cryptosystem to be attacked. Here, we propose a two-step deep learning strategy for ciphertext-only attack (COA) on the classical double random phase encryption (DRPE). Specifically, we construct a virtual DRPE system to gather the training data. Besides, we divide the inverse problem in COA into two more specific inverse problems and employ two deep neural networks (DNNs) to respectively learn the removal of speckle noise in the autocorrelation domain and the de-correlation operation to retrieve the plaintext image. With these two trained DNNs at hand, we show that the plaintext can be predicted in real-time from an unknown ciphertext alone. The proposed learning-based COA method dispenses with not only the retrieval of random phase keys but also the invasive data acquisition of plaintext-ciphertext pairs in the DPRE system. Numerical simulations and optical experiments demonstrate the feasibility and effectiveness of the proposed learning-based COA method.

A novel fast image encryption algorithm for embedded systems

Nowadays, embedded systems can be found everywhere in daily life. In the development of embedded systems, data security is one of the critical factors. Encryption is an effective way to protect data from threats. Among encryption algorithms, chaos-based methods have strong cryptographic properties since chaotic systems are sensitive to initial conditions and parameters. However, most of these algorithms cannot be applied in practice because their encryption speed is not fast enough. In this paper, a fast image encryption algorithm is proposed. Compared with traditional chaos-based image encryption algorithms, the proposed method utilizes mixed-sequence and decorrelation operation to enhance the randomness of chaotic sequence. Moreover, it used minimum length of the sequence which is determined by experiments. Therefore, the proposed scheme spends much less computation time, which is an important advantage for being applied in practice. Testing results have shown that this algorithm has good performance in resisting known attacks, such as known-plaintext attacks, chosen ciphertext attacks, statistical attacks, differential attacks, and various brute-force attacks.

Self-learning for face clustering

In this paper, we simulate the learning way of human to propose a self-learning framework for face clustering. Specifically, we first perform a decorrelation operation on face images through patch-based two-dimensional reconstruction, which has a similar function to the retina. Then we group the semantically similar faces by using a novel self-paced learning model, which is inspired by three major observations: (i) The learning process of human gradually proceeds from easy to complex tasks; (ii) The prior knowledge of human might change with the increase of learned experience; (iii) More prior knowledge usually leads to better prediction accuracy. Experiments on benchmark face databases demonstrate the effectiveness and efficiency of the proposed framework.

A new simple chaotic system and its application in medical image encryption

Today, medical imaging suffers from serious issues such as malicious tampering and privacy leakage. Encryption is an effective way to protect these images from security threats. Among the available encryption algorithms, chaos-based methods have strong cryptographic properties, because chaotic systems are sensitive to initial conditions and parameters. However, traditional chaotic systems are easy to build, analyze, predict and can be re-scaled to any desired frequency. Thus, encryption schemes using traditional chaotic systems have low security levels. In this work, we propose a new simple chaotic system that utilizes a hyperbolic sine as its nonlinearity; this nonlinearity has rarely appeared in previous studies. Furthermore, the new chaotic system uses a decorrelation operation to enhance its performance. Statistical testing verifies that the chaotic sequence has good pseudorandom characteristics. In this study, we propose a scheme for medical image encryption based on this new chaotic system. The results of tests show that this encryption method can encrypt images effectively in a single round and that the proposed scheme provides sufficient security against known attacks.

Blind beamforming using fractional Fourier transform domain cyclostationarity

A new scheme of blind beamforming algorithm is proposed in this paper. The beamformer efficiently filters out the compact desired chirp signal and significantly suppress the cyclically uncorrelated nonstationary interference and stationary (colored) Gaussian noise by identifying and utilizing the optimum fractional Fourier transform (FrFT) domain second-order cyclostationarity. This greatly improves the beamformer output signal-to-interference plus noise ratio (SINR) by effectively reducing the optimum FrFT domain noncyclic signal-colored noise cross-terms in the presence of finite data length de-correlation operation. Simulation results show that the new method works well under a wide range of signal-to-noise ratio and signal-to-interference ratio.

Off-grid DOA estimation under nonuniform noise via variational sparse Bayesian learning

In this paper, the problem of direction-of-arrival (DOA) estimation in the presence of nonuniform noise is investigated, where the inherent off-grid effects in traditional sparsity-inducing algorithms are also considered. By formulating a sparse signal recovery problem for weighted partial virtual array (PVA) response, we develop a sparse Bayesian learning based method by exploiting joint sparsity between the power distribution of incident signals and the off-grid difference. In our proposed algorithm, a weighted partial covariance vector is obtained through the deliberate projection and decorrelation operations, which facilitates a sparse representation free from the nonuniform noise variances. Meanwhile, a variational Bayesian inference is implemented upon a hierarchical Bayesian learning model with an almost Jeffrey’s prior adopted, which strongly induces the sparsity and involves adaptively tuning sparseness-controlling parameters. Moreover, the proposed method works without the knowledge of the number of sources. Simulation results demonstrate it provides superiority in estimation precision and robustness against nonuniform noise.

SEGMENTATION AND CLASSIFICATION OF CERVICAL CYTOLOGY IMAGES USING MORPHOLOGICAL AND STATISTICAL OPERATIONS

Cervical cancer that is a disease, in which malignant (cancer) cells form in the tissues of the cervix, is one of the fourth leading causes of cancer death in female community worldwide. The cervical cancer can be prevented and/or cured if it is diagnosed in the pre-cancerous lesion stage or earlier. A common physical examination technique widely used in the screening is called Papanicolaou test or Pap test which is used to detect the abnormality of the cell. Due to intricacy of the cell nature, automating of this procedure is still a herculean task for the pathologist. This paper addresses solution for the challenges in terms of a simple and novel method to segment and classify the cervical cell automatically. The primary step of this procedure is pre-processing in which de-nosing, de-correlation operation and segregation of colour components are carried out, Then, two new techniques called Morphological and Statistical Edge based segmentation and Morphological and Statistical Region Based segmentation Techniques- put forward in this paper, and that are applied on the each component of image to segment the nuclei from cervical image. Finally, all segmented colour components are combined together to make a final segmentation result. After extracting the nuclei, the morphological features are extracted from the nuclei. The performance of two techniques mentioned above outperformed than standard segmentation techniques. Besides, Morphological and Statistical Edge based segmentation is outperformed than Morphological and Statistical Region based Segmentation. Finally, the nuclei are classified based on the morphological value. The segmentation accuracy is echoed in classification accuracy. The overall segmentation accuracy is 97%.

Blind beamforming technique for reception of multipath coherent signals

This letter deals with the problem of blind adaptive beamforming for receiving multipath coherent signals. Firstly, an uncorrelated interferences-blocking transformation is developed in conjunction with decorrelation operations. A novel blind beamforming technique is then proposed based on the transformed data to produce the maximum output signal-to-interference-plus-noise ratio (SINR). Both theoretical analysis and simulation examples confirm the effectiveness of the proposed method in desired coherent signals reception and interferences suppression.

Coherent angle estimation based on Hankel matrix construction in bistatic MIMO radar

In this article, a novel coherent angle estimation method is proposed for bistatic multiple-input multiple-output radar. First, a decorrelation operation is taken by constructing a Hankel block matrix whose rank equals the number of coherent targets and is independent of the coherency between these targets. Therefore, the signal and noise subspaces of the Hankel block matrix can be estimated properly. Then, in conjunction with the ESPRIT algorithm, the shift invariance properties of the transmit and receive arrays are exploited to estimate the angles of coherent targets with automatic pairing. The numerical simulations demonstrate its validity.

Order selection in fractional Fourier transform based beamforming

Traditionally, beamforming using fractional Fourier transform (FrFT) involves a trial-and-error based FrFT order selec- tion which is impractical. A new numerical order selection scheme is presented based on fractional power spectra (FrFT moment) of the linear chirp signal. This method can adaptively determine the optimum FrFT order by maximizing the second-order central FrFT moment. This makes the desired chirp signal substantially concentrated whereas the noise is rejected considerably. This improves the mean square error minimization beamformer by re- ducing effectively the signal-noise cross terms due to the finite data length de-correlation operation. Simulation results show that the new method works well under a wide range of signal to noise ratio and signal to interference ratio.

Optimal Temporal Decoding of Neural Population Responses in a Reaction-Time Visual Detection Task

Behavioral performance in detection and discrimination tasks is likely to be limited by the quality and nature of the signals carried by populations of neurons in early sensory cortical areas. Here we used voltage-sensitive dye imaging (VSDI) to directly measure neural population responses in the primary visual cortex (V1) of monkeys performing a reaction-time detection task. Focusing on the temporal properties of the population responses, we found that V1 responses are consistent with a stimulus-evoked response with amplitude and latency that depend on target contrast and a stimulus-independent additive noise with long-lasting temporal correlations. The noise had much lower amplitude than the ongoing activity reported previously in anesthetized animals. To understand the implications of these properties for subsequent processing stages that mediate behavior, we derived the Bayesian ideal observer that specifies how to optimally use neural responses in reaction time tasks. Using the ideal observer analysis, we show that 1) the observed temporal correlations limit the performance benefit that can be attained by accumulating V1 responses over time, 2) a simple temporal decorrelation operation with time-lagged excitation and inhibition minimizes the detrimental effect of these correlations, 3) the neural information relevant for target detection is concentrated in the initial response following stimulus onset, and 4) a decoder that optimally uses V1 responses far outperforms the monkey in both speed and accuracy. Finally, we demonstrate that for our particular detection task, temporal decorrelation followed by an appropriate running integrator can approach the speed and accuracy of the optimal decoder.

Multiple finger expansion for blind interference canceller in the presence of subchip-spaced multipath components

A blind interference canceller in the presence of subchip-spaced multipath channels for direct-sequence code division multiple access (DS-CDMA) down-link system is considered. This technique is based on combining the existing blind interference canceller with a technique that involves assigning subchip-tap spacing to the Rake receiver. The proposed receiver minimizes the receiver's output energy subject to a constraint in order to mitigate the multiple access interference (MAI) along each multipath component, and then suboptimally combining all the multipath components. Moreover, it is able to mitigate the mismatch problem when subchip-spaced multipath components arrive at the blind interference canceller. It is known that optimal combining techniques perform a decorrelation operation before combining, which requires both knowledge and computational complexity. In the following, we have adopted a simpler but suboptimum approach in the combining of the suppressed signals at the output of our proposed receiver. Computer simulation results verify the effectiveness of the proposed receiver to handle subchip-spaced multipath components and still suppresses MAI significantly.

Internal-state analysis in a layered artificial neural network trained to categorize lung sounds

In regular use of artificial neural networks, only input and output states of the network are known to the user. Weight and bias values can be extracted but are difficult to interpret. We analyzed internal states of networks trained to map asthmatic lung sound spectra onto lung function parameters. Decorrelation of the spectral data revealed that the spectra can be seen as composed of distinct intracorrelated frequency bands. The effective pitch shifts with increasing degree of airways obstruction. By comparing internal state analysis and decorrelation analysis, we concluded that our neural network performs a simulation of a decorrelation operation.

Specular coherent and noncoherent optimal detection for unresolved multipath Ricean fading channels

This paper considers specular coherent and noncoherent optimal detection for unresolved multipath Ricean fading channels with known delays. The focus is on receiver structures and performance. Specular coherent detection employs the carrier phase of the Ricean specular component, while noncoherent detection does not. Therefore, a specular coherent detector must be augmented with a carrier phase estimator for the specular component. The structures considered are generalization of the well-known RAKE receiver to the unresolved multipath case. It is shown that both optimal structures perform a decorrelation operation before combining, which is essential to eliminating error floors under multipath unresolvability conditions. Furthermore, the noncoherent optimal receiver includes an inherent estimator for the specular component phasor. It is shown that the specular coherent and noncoherent structures converge at high SNR. This result is confirmed through analytical and numerical performance evaluation. Little performance gains can be obtained by the use of specular coherent detection for orthogonal frequency-shift keying and to a lesser extent for differential phase-shift keying over mixed mode Ricean/Rayleigh fading channels, making noncoherent demodulation attractive in these cases.