Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust Camera Pose Estimation via Consensus on Ray Bundle and Vector Field

Estimating the camera pose requires point correspondences. However, in practice, correspondences are inevitably corrupted by outliers, which affects the pose estimation. We propose a general and accurate outlier removal strategy for robust camera pose estimation. The proposed strategy can detect outliers by leveraging the fact that only inliers comply with two effective consensuses, i.e., 3D ray bundle consensus and 2D vector field consensus. Our strategy has a nested structure. First, the outer module utilizes the 3D ray bundle consensus. We define the likelihood based on the probabilistic mixture model and maximize it by the expectation-maximization (EM) algorithm. The inlier probability of each correspondence and the camera pose are determined alternately. Second, the inner module exploits the 2D vector field consensus to refine the probabilities obtained by the outer module. The refinement based on the Bayesian rule facilitates the convergence of the outer module and improves the accuracy of the entire framework. Our strategy can be integrated into various existing camera pose estimation methods which are originally vulnerable to outliers. Experiments on both synthesized data and real images have shown that our approach outperforms state-of-the-art outlier rejection methods in terms of accuracy and robustness.
XBotCloud: A Scalable Cloud Computing Infrastructure for XBot Powered Robots

Limitations with the on-board computational resources installed on untethered robots such as humanoids and mobile robots in general affects significantly the performance and capabilities of these machines. An approach to address this issue is to make use of the cloud robotics concept and take advantage of the extensive computational resources of the cloud. XBotCloud is a recently developed component of the XBot framework. It tackles the above challenges by introducing the tools and mechanisms to enable users and robots to exploit the computational resources of the cloud allowing the execution of services with low, soft or hard Real-Time execution/communication performance. The latter is ensured thanks to the functionality provided by the XBotCore Real-Time cross-robot software component of the XBot framework. XBotCloud addresses also one of the main challenges related with cloud robotics: security. To avoid remote attacks it takes advantage of the Amazon Web Services (AWS)Cloud Security and it uses an internal VPN Network to handle the connectivity between the robot and the cloud server. The full implementation of the framework is presented and its functionality is demonstrated in realistic tasks involving pipelines that mix the execution of cloud services with moderate execution time constraints and Real-Time modules running on the robot local control unit. XBotCloud performances and cross-robot flexibility are experimentally validated on two different robotic platforms, the WALK-MAN humanoid and the CENTAURO upper body / full-body.
Quality Evaluation and Penalty Function of Solar Power

The increasing capacity of the grid-connected solar system has negative influence on the power grid. This paper focuses on the quality evaluation of the solar power. The quality of the solar power is quantified by the penalty function, which considers the total output, the output fluctuation and the forecast error. Firstly, the K-means clustering technique to cluster the sample data of the indices is applied. Then, the significance test using the F-distribution is applied to validate the effectiveness of the proposed model. Finally, the penalty function is proposed. The logarithmic function, the power function, and the exponential function are applied to compare the influences of each index on the penalty function. The Analytic Hierarchy Process (AHP) and the entropy method are used to determine the weight coefficients. The feasibility and application value of the proposed model are validated by the numerical results.
Scalable and Anonymity Preserving Overlays for Voice over IP Communications

Anonymous Voice over IP (VoIP) communication is an important tool to provide freedom of speech. For achieving a good trade-off between quality of service (QoS) and anonymity, connecting mixes by padded links is a promising approach. However, constructing suitable overlay topologies for establishing padded links is not easy: existing approaches are either not scalable with regard to the number of mixes because of using full meshes or impractical due to computational complexity of the algorithms used to create optimal reduced topologies. This prevents them from being deployed as volunteer-based networks like Tor. In this paper, we propose and study different heuristic strategies for reduced topology construction and path selection with low computational complexity. We evaluate our approach using latency and bandwidth estimations from the Tor network and demonstrate that we can achieve appropriate performance and anonymity metrics for VoIP. Especially, the achieved properties are similar to those of a recently proposed approach that calculates optimal topologies for small networks.
Ransomware Encrypted Your Files but You Restored Them from Network Traffic

In a scenario where user files are stored in a network shared volume, a single computer infected by ransomware could encrypt the whole set of shared files, with a large impact on user productivity. On the other hand, medium and large companies maintain hardware or software probes that monitor the traffic in critical network links, in order to evaluate service performance, detect security breaches, account for network or service usage, etc. In this paper we suggest using the monitoring capabilities in one of these tools in order to keep a trace of the traffic between the users and the file server. Once the ransomware is detected, the lost files can be recovered from the traffic trace. This includes any user modifications posterior to the last snapshot of periodic backups. The paper explains the problems faced by the monitoring tool, which is neither the client nor the server of the file sharing operations. It also describes the data structures in order to process the actions of users that could be simultaneously working on the same file. A proof of concept software implementation was capable of successfully recovering the files encrypted by 18 different ransomware families.
From ICS Attacks' Analysis to the S.A.F.E. Approach: Implementation of Filters Based on Behavioral Models and Critical State Distance for ICS Cybersecurity

Since the beginning of this century, Industrial Control Systems (ICSs) are the target of hackers which use vulnerabilities to perform attacks affecting the physical system. These cyberattacks can cause tremendous damages on its system, environment, product quality or equipment of production. Most approaches proposed in the literature are derived from Information Technology cybersecurity field. Unfortunately, most of them fail to fully secure the ICSs. In fact, either they do not consider their specificities or are not adequate as analyzing the industrial network. This paper provides two major contributions: (i) analyses of the main attacks against ICSs with an automation point of view and, (ii) the implementation of the S.A. F.E. (Security Approach based on Filter Execution) approach on a platform. The detection strategy analyzes the normal behavior model of ICSs with its current behavior by analyzing exchanges between control and operative parts through filters. To conclude this paper, the authors discuss about the main contributions, limitations and future research avenues from the proposed approach.
Energy-Optimal Collision-Free Motion Planning for Multiaxis Motion Systems: An Alternating Quadratic Programming Approach

This work investigates energy-optimal motion planning for a class of multiaxis motion systems where the system dynamics are linear time-invariant and decoupled in each axis. Solving the problem in a reliable and efficient manner remains challenging owing to the presence of various constraints on control and states, nonconvexity in its cost function, and obstacles. This paper shows how the cost function can be convexified by considering the system dynamics, while decomposing decision variables to obtain a convex representation of collision avoidance constraints. With the convexified cost function and constraints, the original problem is decomposed into two quadratic programming (QP) problems. An alternating quadratic programming (AQP) algorithm is proposed to solve both the QP problems alternatingly and iteratively till convergence. Requiring an initial feasible trajectory as a guess, AQP necessarily converges to an energy-efficient solution that is homotopic to the initial guess. Under certain circumstances, AQP is guaranteed to produce a local optimum. Simulation demonstrates that AQP is computationally efficient and reliable while claiming comparable energy saving as the mixed-integer QP approach. Note to Practitioners—This paper presents an energy-optimal motion planning algorithm that can be easily implemented on a class of multiaxis motion systems. Main advantages of the proposed algorithm are: 1) it produces a trajectory resulting in lower but comparable energy efficiency as the global optimum; 2) it is guaranteed to provide an energy-efficient and constraint-compliant trajectory, and thus is reliable; 3) it requires a low computational load and can be deployed on a wide range of applications; and 4) its implementation is straightforward to any engineer with basic knowledge of numerical methods.
Three-Phase Modes of the Frequency-Regulated Asynchronous Electric Drive for Pulse-Width Modulation with Carrier Frequency in the Determined Chaos Mode

The concept of deterministic chaos at the initial development stage was considered as an unusual phenomenon that never arose in the solution of practical problems and was of interest only for a narrow circle of scientists. However, later chaotic dynamics was discovered in a large number of different systems. Further development revealed a number of practical problems, when there were chaotic regimes, sometimes harmful, and sometimes useful. Practically important classes of problems have appeared, when a non-linear system must be controlled, reduced, or, on the contrary, increased in degree of randomness.
Efficient Map Representations for Multi-Dimensional Normal Distributions Transforms

Efficient 2D and 3D map representations of both static and dynamic, indoor and outdoor environments are crucial for navigation of driving and flying robots. In this paper, we propose a fast and accurate approach for 2D and 3D Normal Distributions Transform (NDT) mapping based on indexed kd-trees. Similar to other approaches, we also model free space, which allows us to obtain occupancy probabilities. Additionally, we provide optional visibility based updates to enhance map consistency in case of noisy data, e.g. from stereo cameras. Unlike other available implementations, our approach is natively applicable to large-scale environments and in real-time, because our maps are able to grow dynamically. This also offers applicability to exploration tasks. To evaluate our approach, we present experimental results on publicly available datasets and discuss the mapping efficiency in terms of accuracy, runtime and memory management. As an exemplary use case, we apply our maps to Monte Carlo Localization on a well-known large-scale dataset.
Using the graph-theoretic approach to solving the Role Mining problem

The problem of Roles Mining is to build a role-based security policy for large information systems based on an analysis of the set of allowed user permissions. The article presents an algorithm for analyzing the matrix of authorized user permissions for optimal role formation. A set of permissions for an individual user is treated as a transaction. As a transaction record, used authority to act on the system. After that, the algorithm of searching for associative rules for the set of authorities is applied. Only associative rules are considered, including one power as a prerequisite and one as a consequence. A graph of the relationship of authority in the given system is being constructed. This graph is oriented weighted. The weight of the arc is determined by the support of the corresponding associative rule. For the graph constructed, clustering is performed using the community search algorithm. A hierarchy of communities is being built. Powers unite in roles if they are part of the same community. The hierarchy of communities defines the hierarchy of roles. The proposed algorithm can be used both for building a role-based security policy, and for optimizing an already existing role hierarchy.
Improved Quasi-PCI Control Strategy of Inverter with Unbalanced Load

Since there is no grid supply, the microgrid inverters have to provide high-quality power for various load. However, the output characteristics of the inverter are easily affected by the load, especially under unbalanced load conditions, the three-phase output voltages will be asymmetrical without a good control strategy. In order to solve the influence of unbalanced load on the inverter, this paper proposes an improved quasi proportional complex integral (IQPCI) control strategy, which can not only reduce the steady-state error of tracking ac signals, but also effectively suppress voltage unbalance components. On the one hand, the mathematical model of the four-leg inverter is established. The double closed-loop control method is used in $\alpha\beta$0 stationary frame, and the working characteristics of the IQPCI controller are analyzed. On the other hand, the influences of controller parameters on the system performance are analyzed by the bode diagrams, and the design and verification of the parameters are also carried out. Finally, simulation of the inverter with unbalanced load is carried out, moreover, the correctness of the proposed control strategy and the selected controllers parameters are verified.
Real Time Incremental Foveal Texture Mapping for Autonomous Vehicles

We propose an end-to-end real time framework to generate high resolution graphics grade textured 3D map of urban environment. The generated detailed map finds its application in the precise localization and navigation of autonomous vehicles. It can also serve as a virtual test bed for various vision and planning algorithms as well as a background map in the computer games. In this paper, we focus on two important issues: (i) incrementally generating a map with coherent 3D surface, in real time and (ii) preserving the quality of color texture. To handle the above issues, firstly, we perform a pose-refinement procedure which leverages camera image information, Delaunay triangulation and existing scan matching techniques to produce high resolution 3D map from the sparse input LIDAR scan. This 3D map is then texturized and accumulated by using a novel technique of ray-filtering which handles occlusion and inconsistencies in pose-refinement. Further, inspired by human fovea, we introduce foveal-processing which significantly reduces the computation time and also assists ray-filtering to maintain consistency in color texture and coherency in 3D surface of the output map. Moreover, we also introduce texture error (TE) and mean texture mapping error (MTME), which provides quantitative measure of texturing and overall quality of the textured maps.
Riding and Speed Governing for Parallel Two-Wheeled Scooter Based on Sequential Online Learning Control by Humanoid Robot

The sequential online tuning for controller gains is required for the continuous action of the riding into parallel two-wheeled scooter and the speed governing after riding by humanoid robot. The implemented controllers are different between the riding and the speed governing, and these tuning strategies are also different. In particular, the riding requires the immediate tuning in the short riding phase and the speed governing requires the accurate tuning to regulate the speed of humanoid robot. To the above requirements, this paper proposes the Sequential Online Learning Control (SOLC)method composed of the cascade connection of SGD-based open-loop Learning Control (SLC)and Mini-batch-based closed-loop Learning Control (MLC). SLC contributes the damping gain online tuning for the foot torque control during execution of riding, and MLC contributes the PID gains online tuning for the speed governing control. Finally, we show the validity of SOLC through the sequential experiment of riding and speed governing for parallel two-wheeled scooter by life-sized humanoid robot HRP2-JSK.
Radio Frequency Interference Suppression for SAR via Block Sparse Bayesian Learning

Radio frequency interference (RFI) can severely degrade the image quality for synthetic aperture radar (SAR). Traditional sparse recovery-based RFI suppression methods construct a joint dictionary to represent both the time-domain RFI and the useful target echo (UTE), thus, transforming the task of RFI suppression into a signal recovery problem. However, these methods generally have poor performance when the one-dimensional (1-D) range profiles are nonsparse. To overcome this problem, two RFI suppression algorithms are proposed based on a modified block sparse Bayesian learning (BSBL). In the first algorithm, the vector to be recovered is divided into several blocks of identical size. The Bayesian hyper-parameters corresponding to the RFI and UTE are learned separately by exploiting the temporal intrablock correlation, and then the nonsparse vector can be successfully recovered. In the second algorithm, the dictionary is adaptively tuned during the iteration process of BSBL, thus reducing the computational load. After recovering the UTE, a well-focused 2-D image can be obtained by traditional SAR imaging algorithms. With the use of properties of both the RFI and UTE, and by exploiting the intrablock correlation, the proposed RFI suppression methods outperform other sparse recovery methods, especially when the range profile is nonsparse. Simulation results demonstrate the superior performance of the proposed algorithms.
Blockchain-Based Solutions to Security and Privacy Issues in the Internet of Things

IoT is leading a digital revolution in both academia and industry. It brings convenience to people's daily lives; however, the issues of security and privacy of IoT become challenges. Blockchain, a decentralized database based on cryptographic techniques, is promising for IoT security, which may influence a variety of areas including manufacture, finance, and trading. The blockchain framework in an IoT system is an intriguing alternative to the traditional centralized model, which is struggling to meet some specified demands in IoT. In this article, we investigate typical security and privacy issues in IoT and develop a framework to integrate blockchain with IoT, which can provide great assurance for IoT data and various functionalities and desirable scalability including authentication, decentralized payment, and so on. We also suggest some possible solutions to these security and privacy issues in IoT based on blockchain and Ethereum to show how blockchain contributes to IoT.
A Clustering-Based Energy Saving Scheme for Dense Small Cell Networks

The network traffic of mobile wireless communication grows exponentially in recent years. In order to serve intense traffic areas, such as train stations, airports, or hot-spot buildings, the investigation on 5G networks for the next-generation mobile telecommunications has been triggered. One promising solution is the dense small cell networks which consist of a large number of small cells. Base stations (BSs) are one of the major contributors to the power consumption in cellular networks. To address the power consumption issue, the BS ON/OFF control mechanism is proposed by the 3GPP. Some previous solutions for BS ON/OFF control try to save energy by switching off as many BSs as possible. In this paper, we aim to minimize the total power consumption of a dense small cell network while offering QoS guarantees to all its user equipment (UEs). The BS ON/OFF control for minimal total power consumption of an entire network is an NP-hard problem. To this problem, we propose a clustering-based scheme for BS ON/OFF control which in the past was applied to mitigate interference. The proposed scheme is a three-phase approach, consisting of BS clustering algorithm, intra-cluster BS ON/OFF control algorithm, and inter-cluster BS ON/OFF control algorithm. By the clustering technique, we reduce an NP-hard problem to a number of manageable sub-problems. For the possibility of further power saving, we also consider the ON/OFF control of BSs residing on the boundaries of clusters. The time complexity is analyzed. Finally, experimental results reveal that the proposed scheme outperforms existing approaches in power saving and is capable of retaining QoS guarantees for all UEs.
Proactive Cache-Based Location Privacy Preserving for Vehicle Networks

With the diversification of location-based services in vehicle networks, users can obtain such services through submitting searching locations and points of interest. However, users may worry that their real locations and other privacy information will leak out when they get such services, so appropriate location privacy protection measures are necessary. Traditional location privacy protection, such as K-anonymous, cannot be carried out directly because of the characteristics of vehicle networks, such as high mobility. In order to solve such problems, this article proposes a strategy combining cache strategy with K-anonymous that can not only satisfy users' demand on obtaining required services with lowest cost, but also protect the location privacy of users. Specifically, on one hand, cache strategy that deploys part of services on roadside units in advance is used to maximize the satisfaction of users' service requests. On the other hand, each user is set with a K value to meet the need of K-anonymous. The trade-off of these two aspects guarantees users' services and the location privacy. The experiments show that the strategy proposed in this article is better than other strategies.
Integrated Approach for Lithological Classification Using ASTER Imagery in a Shallowly Covered Region—The Eastern Yanshan Mountain of China

This paper studied the applicability of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data for lithological classification in the shallowly covered Eastern Yanshan Mountain of Eastern China. The interpreted objects include quartz sandstone, carbonate rock, gneiss, and andesite. An integrated approach was employed to process the remote sensing data: First, the matched filtering method, accompanied by the ASTER library and imagery spectra as reference, was used to enhance the targeted information, namely the remotely visible (typical) rock outcrops. As the andesite is frequently weathered to iron-rich minerals in the field, so the high-resolution SPOT6 3/1 image, instead of the ASTER image, was used to locate the outcrops. Second, the fractal digital number-frequency algorithm was developed to preliminarily extract the lithology anomaly patches; and the obtained geological anomalies were generalized into three types: anomalies related to outcrops, anomalies caused by different forms of the same lithology, and random noise. Third, digital elevation model derived slope masks, in combination with the spatial intersection operation, was used to eliminate the pseudo-outcrop anomalies and noise. Finally, the accuracy assessment was conducted by referencing the local rock-outcrop database, and the misclassification rates for quartz sandstone, carbonate rock, gneiss, and andesite are 8.9%, 12.5%, 23%, and 48.3%, respectively. This study has contributed a useful case study for remote-sensing lithology mapping in shallowly covered areas, and the proposed method should have a great potential to be applied to many similar cases.