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Image dehazing has evolved into an attractive research field in the computer vision community in the past few decades. Previous traditional approaches attempt to design energy-based objective functions. However, they cannot accurately express the intrinsic characteristics of the images, posing weak adaptation ability for real-world complex scenarios. More recently, deep learning techniques for image dehazing have matured and become more reliable, showing outstanding performance. Nevertheless, these methods heavily depend on training data, restricting their application ranges. More importantly, both traditional and deep learning approaches all ignore a common issue, noises/artifacts always appear in the recovery process. To this end, a new Hadamard-Product (HP) model is proposed, which consists of a series of data-driven priors. Based on this model, we derive a Learnable Hadamard-Product-Propagation (LHPP) by cascading a series of principle-inspired guidance and recovery modules. In which, the principle-inspired guidance related to transmission is endowed the smoothness property, the other recovery module satisfies the distribution of natural images. The Hadamard-product-based propagations is generated in our developed learnable framework for the task of image dehazing. In this way, we can eliminate noises/artifacts in the recovery procedure to obtain the ideal outputs. Subsequently, since the generality of our HP model, we successfully extend our LHPP to settle low-light image enhancement and underwater image enhancement problems. A series of analytical experiments are performed to verify our effectiveness. Plenty of performance evaluations on three complex tasks fully reveal our superiority against multiple state-of-the-art methods.

Many real-world critical systems, e.g., aircrafts, manned space flight systems and submarines, complex technological processes utilize mission aborts to enhance their survivability. Specifically, a mission can be aborted when a certain malfunction condition is met and a rescue or recovery procedure is then initiated. In this paper, we consider systems with observed degradation when a decision to abort a mission or to continue operation is executed at inspection. If this degradation is larger than the optimally predetermined level, then a mission is aborted, whereas if it is smaller, a system continues its operation either under the initial (full load) regime or under the lighter regime with the decreased load. The latter option also depends on the observed value of degradation at inspection. An optimal problem minimizing the expected costs with respect to the relevant levels of deterioration and inspection time is formulated and analytical relationships for the probabilities of interest and expected cost rates are derived. A new virtual age-based approach to age recalculation after the switching of regimes is proposed. A detailed illustrative example is presented.

Real-time positioning feature is becoming an essential part in a variety of military and civilian applications. In particular, the cooperation of multisensors helps to improve navigation accuracy and the universality of the system. In this article, we propose a novel robust fault-tolerant federated filter based on strapdown inertial navigation system (SINS), global navigation satellite system (GNSS), celestial navigation system (CNS), and doppler velocity log (DVL). In the proposed algorithm, the position information of the GNSS, the velocity information of the DVL, and the attitude information of the CNS are introduced as measurement information to correct the divergence error of SINS. The federated filter is exploited for its flexibility and capability in fault toleration. To amend fault and recover information in local filters, a simplified state Chi-square test (SSCST) is utilized as the fault detection, isolation, and recovery procedure. Meanwhile, a new adaptive information sharing factor algorithm based on the results of SSCST and the residuals between the actual observations and the predicted observations is designed, which can adaptively reflect the performance of each local filter. Experimental results show that the accuracy of the proposed integrated navigation and positioning algorithm can be improved and the system remains stable even when the error occurs.

The use of Internet of Things (IoT) is continuously growing, and its application areas henceforth reach all societal and economical fields. The number of connected IoT devices is increasing exponentially, and so is the amount of their exchanged data. To ensure the security and privacy of these data, several Group Key Management (GKM) schemes were proposed. These schemes assume the presence of a centralized Key Manager (KM), which is regarded as the central component of these schemes. However, the huge responsibilities carried out by the KM require that it meets several performance and security criteria. Moreover, the central role it plays in the GKM makes it a classic single point of failure. Solving this issue in an energy efficient way remains a difficult challenge. In this paper, we propose a highly flexible and decentralized election-based protocol for Key Management. We introduce new mechanisms to ensure the technical reliability of our scheme in terms of performance and security. We also describe how our solution tackles the issue of single point of failure, by introducing a simple failure recovery procedure. Our simulations show that our solution have a low energy consumption.

The Zienkiewicz–Zhu (ZZ) super-convergent patch recovery technique based on a node neighborhood patch configuration is used most widely for recovery of the stress field of a finite element analysis. In this study, an improved ZZ recovery technique using element neighborhood patch configuration is proposed. The improved recovery procedure is based on recovery of the stress field in the least-squares sense over an element patch that consists of the union of the elements surrounding the element under consideration. The proposed patch configuration provides more sampling points and improves the performance of the standard ZZ recovery technique. The effectiveness and reliability of the improved ZZ recovery approach is demonstrated through plane elastic and plastic plate problems. The problem domain is discretized with triangular and quadrilateral elements of different sizes. A comparison of the quality of error estimation using the ZZ recovery of derivative field and recovery of the displacement field using similar element neighborhood patch configurations is also presented. The numerical results show that the ZZ recovery technique and the displacement recovery technique, using a modified patch configuration, yield better results, convergence rate, and effectivity as compared with the standard ZZ super-convergent patch recovery technique. It is concluded that the improved ZZ recovery technique-based adaptive finite element analysis is very effective for converging a predefined accuracy with a significantly smaller number of degrees of freedom, especially in an elastic problem. It is also concluded that the improved ZZ recovery technique captures the plastic deformation problem solution errors more reliably than the standard ZZ recovery technique.

This paper is concerned with the problem of recovering a structured signal from a relatively small number of corrupted random measurements. Sharp phase transitions have been numerically observed in practice when different convex programming procedures are used to solve this problem. This paper is devoted to presenting theoretical explanations for these phenomenons by employing some basic tools from Gaussian process theory. Specifically, we identify the precise locations of the phase transitions for both constrained and penalized recovery procedures. Our theoretical results show that these phase transitions are determined by some geometric measures of structure, e.g., the spherical Gaussian width of a tangent cone and the Gaussian (squared) distance to a scaled subdifferential. By utilizing the established phase transition theory, we further investigate the relationship between these two kinds of recovery procedures, which also reveals an optimal strategy (in the sense of Lagrange theory) for choosing the tradeoff parameter in the penalized recovery procedure. Numerical experiments are provided to verify our theoretical results., 34 pages, 6 figures

We present a patch-based equilibrated flux recovery procedure for the conforming finite element approximation to diffusion problems. The recovered flux is computed as the solution to a local constraint-free minimization problem on each patch. The approach is valid for higher order conforming elements in both two and three dimensions. The resulting estimator admits guaranteed reliability and the robust local efficiency is proved under the quasi-monotonicity condition of the diffusion coefficient. Numerical experiments are given to confirm the theoretical results.

The possible optimization of metal recovery from Printed Circuit Boards (PCBs) and Central Processing Units (CPUs) has been investigated. Usual practice is to recover primarily the metals with the highest market price. In contrast, the present work shows how strategic considerations of the value share (%) of metals content and data regarding the environmental impact of their recovery can instruct about the best strategies to adopt, pointing at the metals to be recovered as a priority depending on the case. An accurate PCBs’ characterization carried out through microwave digestion with a mixture of HNO3, HF and HCl, is a first essential step of the procedure. Then, metals are recovered through chemical leaching with different chemical substances, exploiting both chemical and physical steps. A proposal is presented to improve the environmental and economic sustainability of the treatment of PCBs, which provides for the initial recovery of Cu, Pb and Sn from the whole boards, through leaching with 6M HNO3, followed by the recovery of gold and other precious metals from the board components once removed and appropriately crushed. Although unusual, the recovery procedure can be adapted accordingly, allowing greater profits, easier management and higher metals recovery rates.

Many real-world systems use mission aborts to enhance their survivability. Specifically, a mission can be aborted when a certain malfunction condition is met and a risk of a system loss in the case of a mission continuation becomes too high. Usually, the rescue or recovery procedure is initiated upon the mission abort. Previous works have discussed a setting when only one attempt to complete a mission is allowed and this attempt can be aborted. However, missions with a possibility of multiple attempts can occur in different real-world settings when accomplishing a mission is really important and the cost-related and the time-wise restrictions for this are not very severe. The probabilistic model for the multiattempt case is suggested and the tradeoff between the overall mission success probability (MSP) and a system loss probability is discussed. The corresponding optimization problems are formulated. For the considered illustrative example, a detailed sensitivity analysis is performed that shows specifically that even when the system's survival is not so important, mission aborting can be used to maximize the multiattempt MSP.

This article studies the approximate recovery of low-rank matrices acquired through binary measurements. Two types of recovery algorithms are considered, one based on hard singular value thresholding and the other one based on semidefinite programming. In case no thresholds are introduced before binary quantization, it is first shown that the direction of the low-rank matrices can be well approximated. Then, in case nonadaptive thresholds are incorporated, it is shown that both the direction and the magnitude can be well approximated. Finally, by allowing the thresholds to be chosen adaptively, we exhibit a recovery procedure for which low-rank matrices are fully approximated with error decaying exponentially with the number of binary measurements. In all cases, the procedures are robust to prequantization error. The underlying arguments are essentially deterministic: they rely only on an unusual restricted isometry property of the measurement process, which is established once and for all for Gaussian measurement processes.

A Metamorphic Robots System is a modular self-reconfigurable robotic system composed of autonomous mobile modules in a 2D (or 3D) regular grid. The modules have limited computational capabilities, interact only with neighboring modules, and can move around adjacent modules from a cell to an empty neighboring cell under specific conditions. An important well-studied problem for these robotic systems is Motion Planning, also known as Shape Formationor Self-reconfiguration, requiring the modules to organize themselves into a pre-determined final configuration (i.e., shape); basic shapes such as the line (or chain) are especially important as they are utilized as a foundation for constructing more complicated shapes and are an initial measure for handling complicated tasks. A metamorphic robots system could offer a higher degree of reliability and robustness compared to fixed-architecture robots due to its capacity to self-repair: should some modules fail and no longer move, the shape could be reconstructed by the non-faulty modules. To do this correctly, efficiently, and without restricting the autonomy of the modules is a non-trivial task. In this paper, we study the Line Recoveryproblem, requiring the non-faulty modules to reconstruct the line without violation of connectivity requirements at any time during the recovery procedure. A thorough feasibility characterization of the problem, the necessary conditions for its solvability, and an algorithm that solves the problem, regardless of the number and distribution of faults, are provided.

In the current world of the Internet-of-things and edge computing, computations are increasingly performed locally on small connected systems. As such, those devices are often vulnerable to adversarial physical access, enabling a plethora of physical attacks which is a challenge even if such devices are built for security. As cryptography is one of the cornerstones of secure communication among devices, the pertinence of fault attacks is becoming increasingly apparent in a setting where a device can be easily accessed in a physical manner. In particular, two recently proposed fault attacks, Statistical Ineffective Fault Attack (SIFA) and the Fault Template Attack (FTA) are shown to be formidable due to their capability to bypass the common duplication based countermeasures. Duplication based countermeasures, deployed to counter the Differential Fault Attack (DFA), work by duplicating the execution of the cipher followed by a comparison to sense the presence of any effective fault, followed by an appropriate recovery procedure. While a handful of countermeasures are proposed against SIFA, no such countermeasure is known to thwart FTA to date. In this work, we propose a novel countermeasure based on duplication, which can protect against both SIFA and FTA. The proposal is also lightweight with only a marginally additional cost over simple duplication based countermeasures. Our countermeasure further protects against all known variants of DFA, including Selmke, Heyszl, Sigl's attack from FDTC 2016. It does not inherently leak side-channel information and is easily adaptable for any symmetric key primitive. The validation of our countermeasure has been done through gate-level fault simulation.

In this paper, we consider millimeter Wave (mmWave) technology to provide reliable wireless network service within factories where links may experience rapid and temporary fluctuations of the received signal power due to dynamic blockers, such as humans and robots, moving in the environment. We propose a novel beam recovery procedure that leverages Machine Learning (ML) tools to predict the starting and finishing of blockage events. This erases the delay introduced by current 5G New Radio (5G-NR) procedures when switching to an alternative serving base station and beam, and then re-establish the primary connection after the blocker has moved away. Firstly, we generate synthetic data using a detailed system-level simulator that integrates the most recent 3GPP 3D Indoor channel models and the geometric blockage Model-B. Then, we use the generated data to train offline a set of beam-specific Deep Neural Network (DNN) models that provide predictions about the beams' blockage states. Finally, we deploy the DNN models online into the system-level simulator to evaluate the benefits of the proposed solution. Our prediction-based beam recovery procedure guarantee higher signal level stability and up to $82\%$ data rate improvement with respect detection-based methods when blockers move at speed of $2$ m/s., Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible