Diffusion Algorithm Research Articles

A Bio-Inspired Goal-Directed Visual Navigation Model for Aerial Mobile Robots

Reliably navigating to a distant goal remains a major challenge in robotics. In contrast, animals such as rats and pigeons can perform goal-directed navigation with great reliability. Evidence from neural science and ethology suggests that various species represent the spatial space as a topological template, with which they can actively evaluate future navigation uncertainty and plan reliable/safe paths to distant goals. While topological navigation models have been deployed in mobile robots, relatively little inspiration has drawn upon biology in terms of topological mapping and active path planning. In this paper, we propose a novel bio-inspired topological navigation model, which consists of topological map construction, active path planning and path execution, for aerial mobile robots with visual landmark recognition and compass orientation capability. To mimic the topological spatial representation, the model firstly builds the topological nodes based on the reliability of visual landmarks, and constructs the edges based on the compass accuracy. Then a reward diffusion algorithm akin to animals’ path evaluation process is developed. The diffusion process takes the topological structure and landmark reliability into consideration, which helps the agent to construct the path with visually reliable nodes. In the path execution process, the agent combines orientation guidance and landmark recognition to estimate its position. To evaluate the performance of the proposed navigation model, a systematic series of experiments were conducted in a range of challenging and varied real-world visual environments. The results show that the proposed model generates animal-like navigation behaviours, which avoids travelling across large visually aliased areas, such as forest and water regions, and achieves higher localization accuracy than navigating on the shortest paths.

Identification of proliferative diabetic retinopathy-associated genes on the protein–protein interaction network by using heat diffusion algorithm

Diabetic retinopathy is a common complication of diabetes mellitus that causes pathogenic damage to the retina. Particularly, the proliferative diabetic retinopathy (PDR) state can cause abnormal angiogenesis in the retina tissues and trigger the retina destruction in advanced stage. In the clinic, the symptoms during the initiation and progression of PDR are relatively unrecognizable. Therefore, various studies have focused on the pathogenesis of PDR. According to published literature, genetic contributions play an irreplaceable role in the initiation and progression of PDR. Although many computational methods, such as shortest path- and random walk with restart-based methods, have been applied in screening the potential pathogenic factors of PDR, advanced computational methods, which may provide essential supplements for previous ones, are still widely needed. In this study, a novel computational method was presented to infer novel PDR-associated genes. Different from previous methods, the method used in this work employed a different network algorithm, that is, the Laplacian heat diffusion algorithm. This algorithm was applied on the protein–protein interaction network reported in the STRING database. Three screening tests were performed to filter the most likely inferred genes. A total of 26 genes were accessed using the proposed method. Compared with the two previous predictions, most of the identified genes were novel, and only one gene was shared. Several inferred genes, such as CSF3, COL18A1, CXCR2, CCR1, FGF23, CXCL11, and IL13, were related to the pathogenesis of PDR.

Complex-valued speckle effect and its suppression for high quality of phase unwrapping reconstruction in coherent digital holographic microscopy

In coherent digital holographic microscopy, speckle noise is incorporated into the hologram so that phase reconstruction may become worse in quality. Meanwhile, the previous speckle suppression algorithms are not satisfactory to solving this problem due to under- or over-filtering the image with a purely multiplicative or additive phase processing technique. In this paper, the complex-valued speckle effect on the phase reconstruction is first disclosed in theory by creating a multiplicative complex-valued model. A composite noise processing approach is subsequently proposed, which combines a revision of speckle reducing anisotropic diffusion algorithm and an improvement of dual-domain noise processing technique by developing a principal component analysis model for the image blocks to determine the true noise standard deviation in the algorithm for optimization of speckle suppression. The phase unwrapping algorithm with proposed speckle suppression processing is optimally combined to obtain better robustness and phase reconstruction quality of the image. The results are satisfactory. It verifies the validity of the proposed method especially for the objects with continuously-varied phases

Diffusion-based location-aware recommender systems

The recommendation is now part of our daily life. As the years pass by, companies collect more and more information about the users of their platforms. One question which could arise is: are the data collected useful for better predictions? In this paper, we investigate the performance impact on adding geographical positions on the performance of the prediction of users’ behavior using an existing diffusion-based recommender system. We show how we can improve the accuracy of the diffusion algorithm using the geographical position of users. The accuracy of the improved algorithm is compared with the state of art similar recommender algorithms. Moreover, we design a general framework to infer the position location of users based on the position of their activities.

Image encryption with cross colour field algorithm and improved cascade chaos systems

In this study, the critical factor of the chaos system has been analysed to improve the randomicity of the encryption keyspace. Several chaos systems have been integrated together with a linear function to form a much more efficient key sequence generator. This study also presents the cross colour field confusion method which scrambles the pixels among the R G B colour matrixes. In this way, the range of the pixels scrambling map is extended to three matrixes compared to the traditional pixels scrambling schemes which do pixels scrambling in its own colour matrix. The experiment results show that the improved cascade system has better bifurcation diagrams. The cross colour field diffusion algorithm makes the encrypted image has little pixel correlation and no information leakage. The experiment results justify that the novel cross colour confusion scheme with improved chaos systems has a good ability to resist brute force attack, known plain attack, chosen plain text attack, chosen ciphertext attack and differential attack. The security analysis demonstrates that the proposed approach has satisfactory properties in image encryption.

Colored Image Encryption and Decryption with a New Algorithm and a Hyperchaotic Electrical Circuit

In the present work, an encryption/decryption technique, using a new bit-level scrambling and a new diffusion algorithm is presented. The proposed system uses a modified Chua’s circuit (MCC) for the chaotic number generation for the first time to our knowledge. In 2006, the MCC, which exhibited a hyper-chaotic behavior for a wide parameter regime due to its double frequency excitation feature was suggested by one of the authors of the present paper. However, it has not been used for secure communication issues. According to present technique, the generated data are transformed to the developed algorithm for the encryption and decryption purposes. Following the encryption procedure, the encrypted colored images are evaluated by a variety of tests including the analyses of secret key size, secret key sensitivity, histogram, correlation, differential attack, information entropy, and noise attack. The results prove that the suggested colored image encryption/decryption technique is satisfactory for the secure communication issues in terms of efficiency and speed.

Information Diffusion Enhanced by Multi-Task Peer Prediction

Our study aims to strengthen truthfulness of the two-path mechanism: an information diffusion algorithm to find an influential node in non-cooperative directed acyclic graphs (DAGs). This subject is important because the two-path mechanism ensures only weak truthfulness (i.e., nodes are indifferent between reporting true or false out-edges), which restricts node selection accuracy. To enhance the mechanism, we employed an additional reward layer based on a multi-task peer prediction, where an informative equilibrium provides strictly higher rewards than any other equilibrium in virtually all cases (strong truthfulness). Rewards, which are derived from a comparison of each report, encourage a node to report true out-edges without affecting its own probability of being selected by the original two-path mechanism. We have also experimentally confirmed that our proposed {\em strongly truthful two-path mechanism} can sufficiently elicit true out-edges from each node.

TCP Throughput Achieved by a Folded Clos Network Controlled by Different Flow Diffusion Algorithms

TCP Throughput Achieved by a Folded Clos Network Controlled by Different Flow Diffusion Algorithms

A New Image Encryption Algorithm Based on Composite Chaos and Hyperchaos Combined with DNA Coding.

In order to obtain chaos with a wider chaotic scope and better chaotic behavior, this paper combines the several existing one-dimensional chaos and forms a new one-dimensional chaotic map by using a modular operation which is named by LLS system and abbreviated as LLSS. To get a better encryption effect, a new image encryption method based on double chaos and DNA coding technology is proposed in this paper. A new one-dimensional chaotic map is combined with a hyperchaotic Qi system to encrypt by using DNA coding. The first stage involves three rounds of scrambling; a diffusion algorithm is applied to the plaintext image, and then the intermediate ciphertext image is partitioned. The final encrypted image is formed by using DNA operation. Experimental simulation and security analysis show that this algorithm increases the key space, has high sensitivity, and can resist several common attacks. At the same time, the algorithm in this paper can reduce the correlation between adjacent pixels, making it close to 0, and increase the information entropy, making it close to the ideal value and achieving a good encryption effect.

Developing an Inkjet Printer I: RGB Image to CMY Ink Amounts - Image Processing and Color Management

In this paper, we propose a novel error diffusion halftoning algorithm for the conversion of cyan, magenta and yellow (CMY) ink amounts to a multi-bit halftone image. We adopt the idea of allowing multiple drops of colorants in order to achieve print results with better saturation, which is implemented by modifying the classic Floyd-Steinberg error diffusion algorithm. For better halftone texture and more efficient use of colorants, we add a dot-off-dot feature to the classic Floyd-Steinberg error diffusion. Blending-in the original input image with its DBS screened multilevel halftone image as a preprocessing step to dot-off-dot error diffusion is proposed as a measure to reduce halftone artifacts. Digitally simulated halftone images will be presented to illustrate the better halftone texture that can be achieved by applying the proposed algorithm.

Diffusion fused sparse LMS algorithm over networks

The problem of distributed estimation for sparse parameter has attracted much attention in recent years due to its wide applications. In some cases, the sparsity feature may also be found in the differences of successive components of the true parameter, i.e. the non-zero components may be assembled in one or more regions of the true parameter. In this paper, we propose the fused sparse diffusion LMS algorithm for recovering such parameter. The proposed algorithm relies on a sparse regularization term to enforce sparsity of components themselves, and fused sparse regularization term to enforce similarity of adjacent non-zero components. We then provide the condition of the proposed algorithm converging in the mean sense and study mean-square behaviors, which include mean square deviation (MSD) and individual weight error variance (IWEV). To further enhance estimation performance of the fused sparse diffusion algorithm, we provide an adaptive strategy in which the optimal regularization factors can be adjusted at each iteration. Finally, numerical simulations are conducted to show the superiority of our proposed algorithm over several other algorithms.

Routing Using Genetic Algorithm in a Wireless Sensor Network

In this paper we probe the routing algorithm that maximizes the quality of the network. In this regard, we present various scenarios for comparisons among different routing algorithms in a wireless sensor network. Using simulations conducted in NS-2, we compare the performance of genetic algorithm (GA) to the Dijkstra algorithm, Ad hoc On-Demand Distance Vector (AODV), GA-based AODV Routing (GA-AODV), grade diffusion (GD) algorithm, directed diffusion algorithm and GA combined with the GD algorithm. We assume the presence of faulty nodes and work on finding out the performance that enhances the lifespan of the sensor network. In this regard, we have simulated routing algorithms while considering faulty nodes up to 50% of the functioning nodes. Nodes are considered to be dynamic and we assumed different mobility speeds of the nodes. Our results demonstrate that GA can be used in different network configurations as it shows a better performance in the wireless sensor network.

Leader-Following Diffusion-Based Reactive Power Coordination and Voltage Control of Offshore Wind Farm

This paper proposed a distributed reactive power coordination and voltage control of offshore wind farm based on the leader-following diffusion algorithm. Designating several wind turbine generators (WTGs) as the leaders to receive the information of voltage at point of common coupling (PCC), the reactive power generations required to minimize the voltage deviation could be computed by these leaders. The required reactive power generations are diffused throughout all WTGs by the diffusion algorithm, resulting in the coordinated operation of WTGs to regulate the PCC voltage. The proposed offshore wind farm controller is based on the hierarchical control strategy, which consists of primary and secondary layers. The primary layer is responsible for the inner current, voltage or power regulations whereas the secondary layer is based on the proposed diffusion algorithm to achieve the coordinated operation among WTGs. The proposed strategy could maintain accurate reactive power sharing among WTGs and regulate the PCC voltage. A comparison study with the conventional consensus-based control is presented to show the effectiveness of the proposed diffusion controller. The comparison results show the better performance of the proposed method in terms of dynamic responses of PCC voltage and reactive power coordination. Simulation scenarios of constant wind speed, variable wind speed, and voltage sag in the utility grid are carried out to evaluate the performance of proposed method. The proposed diffusion control is tested either in a small or large offshore wind farm systems. Effect of communication delay on the performance of proposed diffusion control is also described. An experiment of the small-scale wind farm system is conducted to show the feasibility of proposed diffusion strategy.

Distributed Diffusion Unscented Kalman Filtering Algorithm with Application to Object Tracking

In this paper, a distributed diffusion unscented Kalman fltering algorithm based on covariance intersection strategy (DDUKF-CI) is proposed for object tracking. By virtue of the pseudo measurement matrix, the standard unscented Kalman fltering (UKF) is transformed to the information form that can be fused by the diffusion strategy. Then, intermediate information from neighbors are fused based on the diffusion framework to attain better estimation performance. Considering the unknown correlations in sensor networks, covariance intersection (CI) strategy is combined with the diffusion algorithm. Moreover, it is proved that the estimation error of the proposed DDUKF-CI is exponentially bounded in mean square using the stochastic stability theory. Finally, the performances of the proposed algorithm and the weighted average consensus unscented Kalman fltering (CUKF) are compared in a target tracking problem with a sensor network.

Quadratic Function Chaotic System and its Application on Digital Image Encryption

To obtain chaotic systems with good properties to help chaos based image encryption, this paper first study the condition under which quadratic functions can be used as pseudo-random sequence generator. We give a necessary and sufficient condition for quadratic functions topologically conjugate with the most frequently used Logistic map and Tent map. Since topological conjugacy maintains chaotic characteristics, more quadratic functions can be used as pseudo-random sequence generators in the digital image encryption. Then, it analyzes a diffusion algorithm with a good effect but poor in resisting the chosen plaintext attack. It is also verified by simulation. The quadratic function chaotic system is applied in the improvement of the diffusion algorithm. Furthermore, the improved algorithm contains plaintext related and cyclic shift operations. Finally, the performance analysis shows that the improved scheme has good security and high efficiency.

CYP1B1, VEGFA, BCL2, and CDKN1A Affect the Development of Chronic Obstructive Pulmonary Disease.

PurposeChronic obstructive pulmonary disease (COPD) is a progressive lung disease characterized by poor airflow. The purpose of this study was to explore the mechanisms involved in the development of COPD.Patients and MethodsThe mRNA expression profile GSE100281, consisting of 79 COPD and 16 healthy samples, was acquired from the Gene Expression Omnibus database. The differentially expressed genes (DEGs) between COPD samples and healthy samples were analyzed using the limma package. Functional enrichment analysis for the DEGs was carried out using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) tool. Furthermore, DEG-compound pairs were predicted using the Comparative Toxicogenomics Database. The KEGG metabolite IDs corresponding to the compounds were also obtained through the MetaboAnalyst pipeline. Based on the diffusion algorithm, the metabolite network was constructed. Finally, the expression levels of key genes were determined using quantitative PCR (qPCR).ResultsThere were 594 DEGs identified between the COPD and healthy samples, including 242 upregulated and 352 downregulated genes. A total of 696 DEG-compound pairs, such as BCL2-C00469 (ethanol) and BCL2-C00389 (quercetin) pairs, were predicted. CYP1B1, VEGFA, BCL2, and CDKN1A were included in the top 10 DEG-compound pairs. Additionally, 57 metabolites were obtained. In particular, hsa04750 (inflammatory mediator regulation of TRP channels)-C00469 (ethanol) and hsa04152 (AMPK signaling pathway)-C00389 (quercetin) pairs were found in the metabolite network. The results of qPCR showed that the expression of CYP1B1, VEGFA, BCL2, and CDKN1A was consistent with that predicted using bioinformatic analysis.ConclusionCYP1B1, VEGFA, BCL2, and CDKN1A may play important functions in the development and progression of COPD.

Resilient Distributed Diffusion in Networks With Adversaries

In this paper, we study resilient distributed diffusion for multi-task estimation in the presence of adversaries where networked agents must estimate distinct but correlated states of interest by processing streaming data. We show that in general diffusion strategies are not resilient to malicious agents that do not adhere to the diffusion-based information processing rules. In particular, by exploiting the adaptive weights used for diffusing information, we develop time-dependent attack models that drive normal agents to converge to states selected by the attacker. We show that an attacker that has complete knowledge of the system can always drive its targeted agents to its desired estimates. Moreover, an attacker that does not have complete knowledge of the system including streaming data of targeted agents or the parameters they use in diffusion algorithms, can still be successful in deploying an attack by approximating the needed information. The attack models can be used for both stationary and non-stationary state estimation.In addition, we present and analyze a resilient distributed diffusion algorithm that is resilient to any data falsification attack in which the number of compromised agents in the local neighborhood of a normal agent is bounded. The proposed algorithm guarantees that all normal agents converge to their true target states if appropriate parameters are selected. We also analyze trade-off between the resilience of distributed diffusion and its performance in terms of steady-state mean-square-deviation (MSD) from the correct estimates. Finally, we evaluate the proposed attack models and resilient distributed diffusion algorithm using stationary and non-stationary multi-target localization.

A 3D Image Encryption Algorithm Based on the Chaotic System and the Image Segmentation

In this paper, a novel 3D image file encryption algorithm based on a new discrete chaotic system is presented. Chaotic characteristics of the novel chaotic system are analyzed by phase diagram, Lyapunov exponent, bifurcation diagram. Based on the analysis results, an encryption scheme is designed for 3D image file. The hash value which is calculated from the SHA-256 hash function is used to change the initial condition of the discrete system firstly. And then, the chaotic sequences are used to scramble and spread coordinate value for 3D image file by Arnold matrix and DNA diffusion algorithm. The security analysis shows that the proposed 3D image file encryption algorithm possesses higher security features to preserve the subject and resist conventional attack. The results of this paper give a different application scheme of the 3D image encryption algorithm.

A Robust Image Encryption Algorithm Based on a 32-bit Chaotic System

In these years, image encryption systems based on chaotic maps have received considerable attention from researchers. For the security of encryption algorithms, it is important to ensure that chaotic systems can produce high quality pseudo-random sequences. However, when a chaotic system that theoretically has good random performance is deployed in a limited precision platform, its random performance will deteriorate. We prove that even if the precision reaches 256-bit, the piecewise linear chaotic map still fails to achieve the desired random performance. Moreover, in the case of low precision, the cascade structure commonly used in mixed chaotic maps will aggravate the problem of random degeneration. To generate good pseudo-random numbers in low-precision platforms, we propose a positive integer random sequence generator, called 32-bit PL_PWLCM&LM, which comprises two 32-bit PL_PWLCMs and one 32-bit logistic map in parallel. In our proposed image encryption system, we adopt the permutation algorithm with four pixels as a permutation unit and the diffusion algorithm based on rows and columns. The security and performance analyses prove that the proposed scheme is a secure, robust, and fast image encryption algorithm. Source codes are shared on GitHub: https://github.com/cuclihao/Pic_Enc_V3.

Diffusion Normalized Least Mean M-estimate Algorithms: Design and Performance Analysis

This work proposes diffusion normalized least mean M-estimate algorithm based on the modified Huber function, which can equip distributed networks with robust learning capability in the presence of impulsive interference. In order to exploit the system's underlying sparsity to further improve the learning performance, a sparse-aware variant is also developed by incorporating the l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> -norm of the estimates into the update process. We then analyze the transient, steady-state and stability behaviors of the algorithms in a unified framework. In particular, we present an analytical method that is simpler than conventional approaches to deal with the score function since it removes the requirements of integrals and Price's theorem. Simulations in various impulsive noise scenarios show that the proposed algorithms are superior to some existing diffusion algorithms and the theoretical results are verifiable.