Architecture For Recovery Research Articles

A multidimensional Bayesian architecture for real-time anomaly detection and recovery in mobile robot sensory systems

For mobile robots to operate in an autonomous and safe manner they must be able to adequately perceive their environment despite challenging or unpredictable conditions in their sensory apparatus. Usually, this is addressed through ad-hoc, not easily generalizable Fault Detection and Diagnosis (FDD) approaches. In this work, we leverage Bayesian Networks (BNs) to propose a novel probabilistic inference architecture that provides generality, rigorous inferences and real-time performance for the detection, diagnosis and recovery of diverse and multiple sensory failures in robotic systems. Our proposal achieves all these goals by structuring a BN in a multidimensional setting that up to our knowledge deals coherently and rigorously for the first time with the following issues: modeling of complex interactions among the components of the system, including sensors, anomaly detection and recovery; representation of sensory information and other kinds of knowledge at different levels of cognitive abstraction; and management of the temporal evolution of sensory behavior. Real-time performance is achieved through the compilation of these BNs into feedforward neural networks. Our proposal has been implemented and tested for mobile robot navigation in environments with human presence, a complex task that involves diverse sensor anomalies. The results obtained from both simulated and real experiments prove that our architecture enhances the safety and robustness of robotic operation: among others, the minimum distance to pedestrians, the tracking time and the navigation time all improve statistically in the presence of anomalies, with a diversity of changes in medians ranging from ≃20% to ≃500%.

Dual-Filter Architecture for Blind Feedback Recovery of the Symbol Timing in Bandlimited Optical Intensity Channels

Reliable recovery of transmission parameters is of paramount importance not only for radio frequency receivers, but also for optical wireless communication solutions. This paper discusses a feedback algorithm for recovering the symbol timing in the context of a bandlimited optical intensity link. The link is determined by a PAM scheme and pulse shaping with squared raised cosine or double jump functions. When preambles or pilot sequences are not available to the receiver, non-data-aided (blind) recovery concepts are useful for tracking smaller variations of the timing error affecting the payload data. The current state-of-the-art solution uses a Gardner detector with two samples per symbol, although this introduces a non-negligible amount of inter-symbol interference resulting in an error floor. To avoid this drawback, the authors propose a dual-filter approach which includes an appropriately designed filter operated in parallel to the receiver filter. Despite the additional complexity of the dual-filter architecture, the recovery loop is very simple and requires only one sample per symbol, representing the lowest degree of complexity in this respect. Open-loop characteristic, jitter performance, and acquisition behavior of the suggested recovery loop are investigated.

Optimization strategies of different SCO2 architectures for gas turbine bottoming cycle applications

Cycle architecture, fluid parameter selection, and component design of an exhaust/waste heat recovery cycle require an integral approach. The exhaust/waste heat shall be utilized to a maximum, at minimum costs. The bottoming cycle needs to be aligned with the topping cycle regarding operational behavior, especially for a part load. To analyze potentials of exhaust heat recovery in a combined gas turbine sCO2 cycle, the bottoming cycle's optimum cycle architecture and fluid parameters have to be determined. A thermo-physical model of the sCO2 bottoming cycle, including knowledge of component design, component behavior, and costs, is based on the optimization procedure. As part of the CARBOSOLA project, techno-economic optimizations for a use case of exhaust heat recovery have been carried out. The paper aims to present the optimization methodology followed by the specific use case's boundary conditions, investigated sCO2 cycle architectures, and results of optimum cycle architecture and fluid parameters for maximum heat recovery and minimum costs. Attention will also be paid to accurate modeling of heat exchangers operating near the critical point.

Control System Architecture for Automatic Recovery of Fixed-Wing Unmanned Aerial Vehicles in a Moving Arrest System

Automatic recovery is an important step in enabling fully autonomous missions using fixed-wing unmanned aerial vehicles (UAVs) operating from ships or other moving platforms. However, automatic recovery in moving arrest systems is only briefly studied in the research literature, and is not yet an option when using low-cost, commercial off-the-shelf (COTS) autopilots. Acknowledging the reliability and low cost of COTS avionics, this paper adds recovery functionality as a modular extension based on non-intrusive additions to an autopilot with very general assumptions on its interface. This is achieved by line-of-sight guidance, which sends an augmented desired position to the autopilot, to ensure line-following along a virtual runway that guides the UAV into the arrest system. The translation and rotation of this line is determined by the pose of the arrest system, determined using two Global Navigation Satellite System (GNSS) receivers, where one is configured as a Real-Time Kinematic (RTK) base station. The relative position of the UAV and arrest system is also precisely estimated using RTK GNSS. Through extensive field testing, on two different fixed-wing UAVs, the system has shown its performance and reliability; 43 recovery attempts in a stationary net hit 0.01 ± 0.25m to the right and 0.07 ± 0.20m below the target in calm conditions. Further, 15 recoveries in a barge-mounted, ship-towed net hit 0.06 ± 0.53m to the right and 0.98 ± 0.27m below the target in winds up to 4 m/s. The remaining error is largely systematic, caused by communication delays, and could be reduced with more integral effect or through direct compensation.

A DSP Architecture for Distortion-Free Evoked Compound Action Potential Recovery in Neural Response Telemetry System.

This paper presents a digital signal processing (DSP) architecture for real-time and distortion-free recovery of electrically-evoked compound action potentials (ECAPs) from stimulus artifacts and periodic noises in bidirectional neural response telemetry (NRT) system. In this DSP architecture, a low computation-cost bidirectional-filtered coherent averaging (BFCA) method is proposed for programmable linear-phase filtering of ECAPs, which can be easily combined with the alternating-polarity (AP) stimulation method to reject stimulus artifacts overlapped with ECAP responses. Design techniques including the configurable folded infinite-impulse response (IIR) filter and division-free averaging are also presented for efficient hardware implementation. Implemented in 180-nm CMOS process, the proposed DSP architecture consumes 10.03-mm2 area and 2.35-mW post-layout simulated power. The efficacy of the DSP architecture in recovering ECAPs from recorded neural data contaminated by overlapped stimulus artifacts and periodic noises is validated in in-vivo electrical nerve stimulations. Experiment results show that compared with the previous coherent averaging technique, the proposed DSP architecture improves the signal-to-noise ratio (SNR) of ECAP responses by 11 dB and achieves a 3.1% waveform distortion that is 17.1× lower.

LoRD-Net: Unfolded Deep Detection Network With Low-Resolution Receivers

The need to recover high-dimensional signals from their noisy low-resolution quantized measurements is widely encountered in communications and sensing. In this paper, we focus on the extreme case of one-bit quantizers, and propose a deep detector entitled LoRD-Net for recovering information symbols from one-bit measurements. Our method is a model-aware data-driven architecture based on deep unfolding of first-order optimization iterations. LoRD-Net has a task-based architecture dedicated to recovering the underlying signal of interest from the one-bit noisy measurements without requiring prior knowledge of the channel matrix through which the one-bit measurements are obtained. The proposed deep detector has much fewer parameters compared to black-box deep networks due to the incorporation of domain-knowledge in the design of its architecture, allowing it to operate in a data-driven fashion while benefiting from the flexibility, versatility, and reliability of model-based optimization methods. LoRD-Net operates in a blind fashion, which requires addressing both the non-linear nature of the data-acquisition system as well as identifying a proper optimization objective for signal recovery. Accordingly, we propose a two-stage training method for LoRD-Net, in which the first stage is dedicated to identifying the proper form of the optimization process to unfold, while the latter trains the resulting model in an end-to-end manner. We numerically evaluate the proposed receiver architecture for one-bit signal recovery in wireless communications and demonstrate that the proposed hybrid methodology outperforms both data-driven and model-based state-of-the-art methods, while utilizing small datasets, on the order of merely $\sim 500$ samples, for training.

Multi-agent architecture for fault recovery in self-healing systems

Self-healing, a prominent property of self-adaptiveness provides reliability, availability, maintainability, and survivability to a software system. These qualitative factors are very salient to modern distributed systems in which components and their collaboration often vary. Survivability of such systems can be best addressed from an architectural viewpoint. When it comes to maintainability and reliability, architectural level adaptation is not often supported during the design phase. Adaptation to fault tolerance into the design phase of the system development process can increase the scope of software availability and thereby attaining self-healing. In distributed systems, most of the existing architectures are often associated with communication and correspondence as primary criteria. On the other hand, a multi-agent mechanism helps in schematic control of functionality, communication by emphasizing scalability. In this paper, a novel architecture was proposed that could support agent-based distributed systems to address fault recovery aspects for achieving self-adaptiveness. Unlike traditional multi-agent architecture, task-oriented functional multi-agent communication is incorporated for various activities during design phase designated to perform self-healing criteria. An adaptation of agent communication control flow is proposed using three novel mechanism such as planning, functioning and enacting as agents’ critical responsibility. The paper also validates the proposed architecture for resource and availability based faults related to crash and resource unavailability using performance-based evaluation metrics. A case-based application with single thread connectivity is used to reflect the architecture during application design phase and is tested for success using mean response time as evaluation metric.

Parallelized and adaptive square-root unscented Kalman filter for carrier recovery in satellite-to-ground coherent optical communications

An adaptive square-root unscented Kalman filter with a parallelized architecture for carrier recovery is proposed in QPSK based satellite-to-ground coherent optical communication systems. A new observation model is raised in the proposed scheme to make the state estimation more accurate. The process noise covariance (Q) and the measurement noise covariance (R) are adaptively estimated to make them consistent with their theoretical covariance. For verification of the feasibility of the proposed scheme, corresponding simulations of carrier recovery in satellite-to-ground laser links are carried out and compared with the scheme that is based on linear Kalman filter (LKF). The simulation results demonstrate that, compared with parallelized LKF, the proposed scheme has a wider range of block size and laser linewidth options, and it can relax the required transmit power by up to 0.5-3 dB under different zenith angle at the same BER. Moreover, the proposed scheme can achieve high estimation accuracy and fast tracking capability simultaneously in the case of dynamic frequency offset with better tolerance against the initial errors in Q and R.

Joint failure recovery, fault prevention, and energy-efficient resource management for real-time SFC in fog-supported SDN

Middleboxes have become a vital part of modern networks by providing services such as load balancing, optimization of network traffic, and content filtering. A sequence of middleboxes comprising a logical service is called a Service Function Chain (SFC). In this context, the main issues are to maintain an acceptable level of network path survivability and a fair allocation of the resource between different demands in the event of faults or failures. In this paper, we focus on the problems of traffic engineering, failure recovery, fault prevention, and SFC with reliability and energy consumption constraints in Software Defined Networks (SDN). These types of deployments use Fog computing as an emerging paradigm to manage the distributed small-size traffic flows passing through the SDN-enabled switches (possibly Fog Nodes). The main aim of this integration is to support service delivery in real-time, failure recovery, and fault-awareness in an SFC context. Firstly, we present an architecture for Failure Recovery and Fault Prevention called FRFP; this is a multi-tier structure in which the real-time traffic flows pass through SDN-enabled switches to jointly decrease the network side-effects of flow rerouting and energy consumption of the Fog Nodes. We then mathematically formulate an optimization problem called the Optimal Fog-Supported Energy-Aware SFC rerouting algorithm (OFES) and propose a near-optimal heuristic called Heuristic OFES (HFES) to solve the corresponding problem in polynomial time. In this way, the energy consumption and the reliability of the selected paths are optimized, while the Quality of Service (QoS) constraints are met and the network congestion is minimized. In a reliability context, the focus of this work is on fault prevention; however, since we use a reallocation technique, the proposed scheme can be used as a failure recovery scheme. We compare the performance of HFES and OFES in terms of energy consumption, average path length, fault probability, network side-effects, link utilization, and Fog Node utilization. Additionally, we analyze the computational complexity of HFES. We use a real-world network topology to evaluate our algorithm. The simulation results show that the heuristic algorithm is applicable to large-scale networks.

Embedded conformal deep low-rank auto-encoder network for matrix recovery

We present a novel embedded conformal deep low-rank auto-encoder (ECLAE) neural network architecture for matrix recovery and it can be utilized for image restoration and clustering. Traditionally, robust principal component analysis based methods attempt to decompose the raw matrix into two components: low-rank part and sparse part. For image data as an example, the primary information of the raw data is gathered in the low-rank component and other information, such as noise, exists in the sparse part. The principal components of a data matrix can be recovered even though a positive fraction of its elements are arbitrarily corrupted. However, these methods neglect the non-linear structure information of the original data. Many recent researches pay more attention to the structure of deep learning to extract the nonlinear relationship of data. The deep auto-encoder as a classical deep structure has performed many splendid results. Hence, we propose ECLAE to integrate the advantage of auto-encoder and low-rank representation. And the conformal local structure of data is perfectly embedded into the novel deep frame. Our key idea includes two folds. The first fold is to adaptively obtain latent layer learning the neighbor structure of data with the conformal constraint. The other is to embed the global information into the network by appending the low-rank constraint over the network outputs. To verify the ability of matrix decompose, our method is used for image restoration. And to evaluate the structure of low-dimensional space, the latent representation is exploited for cluster. Extensive experimental results illustrate the efficiency of the proposed algorithm.

Dual-Function Electrocatalytic and Macroporous Hollow-Fiber Cathode for Converting Waste Streams to Valuable Resources Using Microbial Electrochemical Systems.

Dual-function electrocatalytic and macroporous hollow-fiber cathodes are recently proposed as promising advanced material for maximizing the conversion of waste streams such as wastewater and waste CO2 to valuable resources (e.g., clean freshwater, energy, value-added chemicals) in microbial electrochemical systems. The first part of this progress report reviews recent developments in this type of cathode architecture for the simultaneous recovery of clean freshwater and energy from wastewater. Critical insights are provided on suitable materials for fabricating these cathodes, as well as addressing some challenges in the fabrication process with proposed strategies to overcome them. The second and complementary part of the progress report highlights how the unique features of this cathode architecture can solve one of the intrinsic bottlenecks (gas-liquid mass transfer limitation) in the application of microbial electrochemical systems for CO2 reduction to value-added products. Strategies to further improve the availability of CO2 to microbial catalysts on the cathode are proposed. The importance of understanding microbe-cathode interactions, as well as electron transfer mechanisms at the cathode-cell and cell-cell interface to better design dual-function macroporous hollow-fiber cathodes, is critically discussed with insights on how the choice of material is important in facilitating direct electron transfer versus mediated electron transfer.

A learning-based variable size part extraction architecture for 6D object pose recovery in depth images

State-of-the-art techniques for 6D object pose recovery depend on occlusion-free point clouds to accurately register objects in 3D space. To deal with this shortcoming, we introduce a novel architecture called Iterative Hough Forest with Histogram of Control Points that is capable of estimating the 6D pose of an occluded and cluttered object, given a candidate 2D bounding box. Our Iterative Hough Forest (IHF) is learnt using parts extracted only from the positive samples. These parts are represented with Histogram of Control Points (HoCP), a “scale-variant” implicit volumetric description, which we derive from recently introduced Implicit B-Splines (IBS). The rich discriminative information provided by the scale-variant HoCP features is leveraged during inference. An automatic variable size part extraction framework iteratively refines the object's roughly aligned initial pose due to the extraction of coarsest parts, the ones occupying the largest area in image pixels. The iterative refinement is accomplished based on finer (smaller) parts, which are represented with more discriminative control point descriptors by using our Iterative Hough Forest. Experiments conducted on a publicly available dataset report that our approach shows better registration performance than the state-of-the-art methods.

An efficient architecture for energy recovery in hydraulic elevators

This study introduces an efficient architecture for hydraulically counter-weighting an elevator system while controlling for cab speed. A physical model of the architecture is developed and posed as a single-input single-output system in which the ratio of two hydraulic pump/motor swash plate angles serve as the control input for regulating the output cab speed. Heuristic control rules based on efficiency considerations and elevator operation are posed for the swash plate angles. A high-fidelity simulation tool is then employed to assess the new architecture and control approach. Simulations demonstrate the effectiveness of the devised control strategy and the overall satisfactory operation of the elevator system. Simulations also provide comparisons of the new architecture’s efficiency vs. an electrohydraulic elevator architecture employing a motor/generator for energy capture and return. It’s shown that the introduced architecture yields up to a 13% increase in actuation efficiency over the electrohydra...

Brain-computer interface with somatosensory feedback improves functional recovery from severe hemiplegia due to chronic stroke.

Recent studies have shown that scalp electroencephalogram (EEG) based brain-computer interface (BCI) has a great potential for motor rehabilitation in stroke patients with severe hemiplegia. However, key elements in BCI architecture for functional recovery has yet to be clear. We in this study focused on the type of feedback to the patients, which is given contingently to their motor-related EEG in a BCI context. The efficacy of visual and somatosensory feedbacks was compared by a two-group study with the chronic stroke patients who are suffering with severe motor hemiplegia. Twelve patients were asked an attempt of finger opening in the affected side repeatedly, and the event-related desynchronization (ERD) in EEG of alpha and beta rhythms was monitored over bilateral parietal regions. Six patients were received a simple visual feedback in which the hand open/grasp picture on screen was animated at eye level, following significant ERD. Six patients were received a somatosensory feedback in which the motor-driven orthosis was triggered to extend the paralyzed fingers from 90 to 50°. All the participants received 1-h BCI treatment with 12–20 training days. After the training period, while no changes in clinical scores and electromyographic (EMG) activity were observed in visual feedback group after training, voluntary EMG activity was newly observed in the affected finger extensors in four cases and the clinical score of upper limb function in the affected side was also improved in three participants in somatosensory feedback group. Although the present study was conducted with a limited number of patients, these results imply that BCI training with somatosensory feedback could be more effective for rehabilitation than with visual feedback. This pilot trial positively encouraged further clinical BCI research using a controlled design.

Effects of flow cell design on charge percolation and storage in the carbon slurry electrodes of electrochemical flow capacitors

The electrochemical flow capacitor (EFC) is an electrical energy storage concept recently introduced for grid-scale energy storage applications. The EFC utilizes flowable carbon-based electrodes as the active material in a flow battery type architecture for capacitive storage and recovery of energy. Charged slurry can be stored in external reservoirs until it is needed, enabling scalable energy storage to satisfy a variety of large-scale applications. Here, the capacitance and conductivity of EFC slurry electrodes were measured as a function of flow rate (from 0 to 10 ml min−1) and flow cell channel depth (electrode ‘thickness’, ranging from 0.5 to 3 mm). The effect of salt concentration in the electrolyte was also explored. The interfacial resistance associated with the current collector|slurry interface was found to constitute a large portion of the total cell resistance. Bulk slurry conductivity was found to vary significantly with changes in electrolyte concentration, flow rate and channel depth. Very respectable capacitance values of up to ∼30 F ml−1 (150 F g−1) were obtained during intermittent flow operation. However, significant underutilization of the slurry due to increased ohmic losses at larger channel depths was observed, as evidenced by a rapid decay in capacitance with increasing channel depth.

A Hierarchical Self-Repairing Architecture for Fast Fault Recovery of Digital Systems Inspired From Paralogous Gene Regulatory Circuits

Self-repairing digital systems have received increasing attention as modern systems are getting more complex and fast. Currently available self-repairing architectures have, however, some limitations such as storage overhead required to prepare all possible rewiring strategies and temporal incorrectness caused by elongated repairing time. In this paper, we propose a novel self-repairing architecture for fast fault recovery with an efficient use of limited resources, which can be easily applied to real complex digital systems. The proposed architecture consists of three layers: a working layer, a control layer, and an interface layer. The working layer employs a hybrid scheme of using both redundant and empty cells with a newly devised self-test. This relieves the overhead of redundant cells required to be prepared in advance by considering every possible fault situation. In the control layer, an ordered assignment control is proposed. The order of working-priority of each processor that controls a normal cell in the working layer is predetermined. A faulty processor is detected by a majority decision among neighboring control processors and corrected by rearranging the order of working-priority. The interface layer connects an external PC for reprogramming. Through this fault recovery mechanism, the system can keep normal functioning under noisy environments. We implemented the proposed self-repairing architecture using an field-programmable gate array board with an application of a dot-matrix LED display and verified its robust operation. The proposed architecture can be widely used as a new platform for self-repairing systems.

Optimizing decomposition of software architecture for local recovery

The increasing size and complexity of software systems has led to an amplified number of potential failures and as such makes it harder to ensure software reliability. Since it is usually hard to prevent all the failures, fault tolerance techniques have become more important. An essential element of fault tolerance is the recovery from failures. Local recovery is an effective approach whereby only the erroneous parts of the system are recovered while the other parts remain available. For achieving local recovery, the architecture needs to be decomposed into separate units that can be recovered in isolation. Usually, there are many different alternative ways to decompose the system into recoverable units. It appears that each of these decomposition alternatives performs differently with respect to availability and performance metrics. We propose a systematic approach dedicated to optimizing the decomposition of software architecture for local recovery. The approach provides systematic guidelines to depict the design space of the possible decomposition alternatives, to reduce the design space with respect to domain and stakeholder constraints and to balance the feasible alternatives with respect to availability and performance. The approach is supported by an integrated set of tools and illustrated for the open-source MPlayer software.

Secure Network Recovery from Base Station Failure of Surveillance WSN in Hostile Environment

Securing surveillance wireless sensor networks (WSNs) in hostile environments such as borders, perimeters and battlefields during Base Station (BS) failure is challenging. Surveillance WSNs are highly vulnerable to BS failure. The attackers can render the network useless by only destroying the BS as the needed efforts to destroy the BS is much less than that is needed to destroy the network. This attack scenario will give the attackers the best chance to compromise many legitimate nodes. Previous works have tackled BS failure by deploying a mobile BS or by using multiple BSs. Despite the best electronic countermeasures, intrusion tolerance and anti-traffic analysis strategies to protect the BSs, an adversary still can destroy them. This paper proposes a novel security architecture called Surveillance Security (SurvSec) for secure and reliable network recovery from single BS failure of surveillance WSN with single BS. SurvSec relies on a set of sensor nodes serve as Security Managers for management and storage of the security related data of all sensor nodes. SurvSec security architecture provides methodology for choosing and changing the security managers of the surveillance WSN. SurvSec has four components: (1) Sensor nodes serve as Security Managers, (2) Data Storage System, (3) Data Recovery System, (4) Security for the Data Storage System. Furthermore, both the frame format of the stored data is carefully built and the security threats are encoded to allow minimum overheads for SurvSec security architecture. In this paper, we provide detailed specifications of SurvSec security architecture along with its security system for secure and reliable network recovery from single BS failure. We evaluate our designed security architecture for reliable network recovery from BS failure. Our evaluation shows that the proposed new security architecture can meet all the desiredspecifications and our analysis shows that the provided Security Managers are capable of network recovery from BS failure.

SurvSec: A New Security Architecture for Reliable Network Recovery from Base Station Failure of Surveillance WSN

Abstract Securing surveillance wireless sensor networks (WSNs) in hostile environments such as borders, perimeters and battlefields during Base Station (BS) failure is challenging. Surveillance WSNs are highly vulnerable to BS failure. The attackers can render the network useless by only destroying the BS as the needed efforts to destroy the BS is much less than that is needed to destroy the network. This attack scenario will give the attackers the best chance to compromise many legitimate nodes. Previous works have tackled BS failure by deploying a mobile BS or by using multiple BSs. Despite the best electronic countermeasures, intrusion tolerance and anti-traffic analysis strategies to protect the BSs, an adversary still can destroy them. This paper proposes a novel security architecture called Surveillance Security (SurvSec) for reliable network recovery from single BS failure of surveillance WSN with single BS. SurvSec relies on a set of sensor nodes serve as Security Managers for management and storage of the security related data of all sensor nodes. SurvSec security architecture provides methodologies for choosing and changing the security managers of the surveillance WSN. SurvSec has three components: (1) Sensor nodes serve as Security Managers, (2) Data Storage System, (3) Data Recovery System. Furthermore, both the frame format of the stored data is carefully built and the security threats are encoded to allow minimum overheads for SurvSec security architecture. In this paper, we provide detailed specifications of SurvSec security architecture. We evaluate our designed security architecture for reliable network recovery from BS failure. Our evaluation shows that the proposed new security architecture can meet all the desired specifications and our analysis shows that the provided Security Managers are capable of network recovery from BS failure.

FLORA: a framework for decomposing software architecture to introduce local recovery

AbstractThe decomposition of software architecture into modular units is usually driven by the required quality concerns. In this paper we focus on the impact of local recovery concern on the decomposition of the software system. For achieving local recovery, the system needs to be decomposed into separate units that can be recovered in isolation. However, it appears that this required decomposition for recovery is usually not aligned with the decomposition based on functional concerns. Moreover, introducing local recovery to a software system, while preserving the existing decomposition, is not trivial and requires substantial development and maintenance effort. To reduce this effort we propose a framework that supports the decomposition and implementation of software architecture for local recovery. The framework provides reusable abstractions for defining recoverable units and the necessary coordination and communication protocols for recovery. We discuss our experiences in the application and evaluation of the framework for introducing local recovery to the open‐source media player called MPlayer. Copyright © 2009 John Wiley & Sons, Ltd.