Reconfiguration Capabilities Research Articles

Bilayer Intelligent Omni-Surface-Assisted Full-Duplex Systems: Simultaneous Self-Interference Cancellation and Sum Rate Maximization.

Recently, reconfigurable intelligent surfaces (RISs) have attracted increasing attentions in the design of full-duplex (FD) systems due to their novel capability of propagation environment reconfiguration. However, in conventional RIS-assisted FD systems, the beamforming for self-interference cancellation (SIC) and sum rate maximization (SRM) are highly coupled during RIS optimization, which significantly degrades the system performance. To tackle this issue, we exploit a novel bilayer intelligent omni-surface (BIOS) structure in FD systems. Compared with the conventional RIS designs, the BIOS provides independent beams on both sides, thus enabling more flexible achievement of SRM and SIC. For the BIOS-assisted FD system, we first formulate an optimization problem to achieve SRM and efficient SIC simultaneously. Then, we exploit the relationship between the SRM and mean square error (MSE), and propose a weighted MSE minimization with SIC algorithm to solve the problem. Specifically, we jointly design the beamforming at the base station and the BIOS with manifold optimization while guaranteeing an SIC constraint. Furthermore, we theoretically derive a lower band for the BIOS size required for efficient SIC in FD systems. Simulation results indicate that the BIOS outperforms the conventional RIS designs in FD systems, and verify the accuracy of the derived lower bound for the BIOS size.

A Hierarchical Priority Based Multi-Bit Error Tolerant Design for FPGA Configuration Memory

The reconfigurability of a field-programmable gate array (FPGA), which is utilized in many applications, permits changes to be made to an existing design. In hostile environments such as space, where radiation and ionizing particles increase the risk for failure and human maintenance is either impossible or time-consuming, this reconfigurable capability becomes even more important. Single event upsets (SEUs) and multi-bit upsets (MBUs) on configuration memory can cause soft errors and circuit malfunctions in SRAM-based field programmable gate arrays (FPGAs). The ever-increasing quantity of configuration bits in contemporary FPGAs makes it more difficult for traditional scrubbing to identify faults in a timely manner, leading to a mismatch between scrubbing performance and MBU sensitivity.A hierarchical multi-bit error correcting method fully utilizes the MBU sensitivity at various configuration frame locations. It employs distinct techniques for varying priorities and differentiates across configuration frames with multiple priorities. They divided code words into several sections and used distinct error-correction techniques in each section. In high radiation conditions, the suggested integrated error correction technique enhances FPGA configuration memory protection by substituting memory scrubbing. The suggested approach lowers total TTD and enables priority-based asynchronous MBU detection and correction. Index Terms—FPGA,MBU,Sensitivity,Configuration memory

Electromechanically Reconfigurable Terahertz Stereo Metasurfaces.

Dynamic terahertz devices are vital for the next generation of wireless communication, sensing, and non-destructive imaging technologies. Metasurfaces have emerged as a paradigm-shifting platform, offering varied functionalities, miniaturization, and simplified fabrication compared to their 3D counterparts. However, the presence of in-plane mirror symmetry and reduced degree of freedom impose fundamental limitations on achieving advanced chiral response, beamforming, and reconfiguration capabilities. In this work, a platform composed of electrically actuated resonators that can be colossally reconfigured between planar and 3D geometries is demonstrated. To illustrate the platform, metadevices with 3D Split Ring Resonators are fabricated, wherein two counteracting driving forces are combined: i) folding induced by stress mismatch, which enables non-volatile state design and ii) unfolding triggered by the strain associated with insulator-to-metal transition in VO2, which facilitates volatile structural reconfiguration. This large structural reconfiguration space allows for resonance mode switching, widely tunable magnetic and electric polarizabilities, and increased frequency agility. Moreover, the unique properties of VO2, such as the hysteretic nature of its phase transition is harnessed to demonstrate a multi-state memory. Therefore, these VO2 integrated metadevices are highly attractive for the realization of 6G communication devices such as reconfigurable intelligent surfaces, holographic beam formers, and spatial light modulators.

On reconfiguring heterogeneous parallel island models

This work introduces a new Parallel Island Model (PIM) that encompasses the benefits of heterogeneity and algorithmic reconfigurability. The former feature, heterogeneity, means that different islands may execute different evolutionary algorithms. The latter, reconfigurability, means that each island can change the algorithm being executed during the evolutionary process. Allowing such features increases the usual diversity obtained by the communication topologies and migration policies by homogeneous PIMs (HoPIMs). Previous (non-reconfigurable) heterogeneous PIMs (HePIMs) were able to provide competitive solutions regarding the HoPIMs. By adding the reconfiguration capability, PIMs can change dynamically from executing one evolutionary algorithm to another. In this manner, the required diversity and flexibility to outperform HoPIMs and HePIMs is achieved. This paper discusses policies to profit from the feature of reconfigurability on HePIM models and provides an innovative and successful stagnation-based reconfiguration policy. The benefits of the new reconfigurable model are verified using the unsigned reversal distance optimization problem as a case study.

On Lyapunov stability of artificial potential function-based low-thrust constellation reconfiguration control

The configuration control of constellations which consist of numerous satellites presents significant challenges. Such space systems demand the capability for autonomous reconfiguration to accomplish specific missions. Individual satellites within constellations are primarily equipped with low-thrust systems, rendering traditional impulse control methods for satellite formation and mission planning ineffective. To address this issue, this paper proposes a low-thrust constellations reconfiguration control method utilizing an artificial potential function. Relying exclusively on neighboring state information, satellites can dynamically modulate their control efforts in real-time. The stability of the proposed reconfiguration control method is established under modelable, bounded perturbation using Lyapunov theory. Furthermore, a repulsive potential function is formulated to ensure collision avoidance among satellites. Numerical results indicate that the proposed method is stable, computationally efficient, and guarantees the safe distance during maneuvers. Consequently, this method is ideally suited for reconfiguration tasks across a spectrum of large space systems, including mega-constellations.

High-Temperature Tensile Characteristics of an Al-Zn-Mg-Cu Alloy: Fracture Characteristics and a Physical Mechanism Constitutive Model.

High-temperature tensile tests were developed to explore the flow features of an Al-Zn-Mg-Cu alloy. The fracture characteristics and microstructural evolution mechanisms were thoroughly revealed. The results demonstrated that both intergranular fractures and ductile fractures occurred, which affected the hot tensile fracture mechanism. During high-temperature tensile, the second phase (Al2CuMg) at the grain boundaries (GBs) promoted the formation and accumulation of dimples. With the continual progression of high-temperature tensile, the aggregation/coarsening of dimples along GBs appear, aggravating the intergranular fracture. The coalescence and coarsen of dimples are reinforced at higher tensile temperatures or lower strain rates. Considering the impact of microstructural evolution and dimple formation/coarsening on tensile stresses, a physical mechanism constitutive (PMC) equation is herein proposed. According to the validation and analysis, the predictive results were in preferable accordance with the testing data, showing the outstanding reconfiguration capability of the PMC model for high-temperature tensile features in Al-Zn-Mg-Cu alloys.

Do green dynamic capabilities and absorptive capacity affect green innovation performance? A study on companies in Indonesia

Purpose: This study aims to empirically analyze the effect of absorptive capacity, green dynamic capability, and green manufacturing practices (GMP) on green innovation performance (GIP).Design/methodology/approach: A quantitative approach was employed in this study, with data collected through a questionnaire and analyzed for hypothesis using the partial least squares structural equation model. In order to enhance the depth of the analysis, in-depth interviews were conducted with experienced practitioners from three selected companies implementing green innovations.Findings: Empirical findings showed that reconfiguring capabilities positively affects GIP. Realized absorptive capacity positively affects GMP but does not affect GIP. Then, realized absorptive capacity positively impacts sensing and reconfiguration capabilities. GMP mediates the relationship between realized absorptive capacity and GIP. This study reveals the two pathways through which green dynamic capabilities affect realized absorptive capacity and GIP.Research limitations/implications: This study has yet to consider potential variations in company ownership, applied technology, and industry type that could lead to differences in green innovation characteristics. This study employed a small sample size and a Likert-scale questionnaire, possibly leading to respondent perception bias (although this is unlikely).Practical implications: The results of this study offered valuable insights into how companies developed dynamic capabilities to address essential green requirements in their product and processes. Thus, companies can prepare all available resources to adapt to various environment-related changes.Social implications: Companies with high green innovation performance will produce environmentally friendly products, conduct industrial activities without damaging the environment, and ultimately create a sustainable industry.Originality/value: What distinguishes this study is the division of green dynamic capabilities into three independent capabilities: SenCap, SeiCap, and Reconf, which had been described as a single capacity in previous studies. Furthermore, this study emphasizes GMP's position between absorptive capacity and green dynamic capability with GIP.

Day-ahead resilience-economic energy management and feeder reconfiguration of a CCHP-based microgrid, considering flexibility of supply

Many challenges have emerged due to the intense integration of renewables in the distribution system and the associated uncertainties in power generation. Consequently, local management strategies are developed at the distribution level, leading to the emergence of concepts such as microgrids. Microgrids include a variety of heating, cooling, and electrical resources and loads, and the operators' aim is to minimize operation and outage costs. Since significant distribution system outages are typically caused by events such as earthquakes, floods, and hurricanes, microgrid operators are compelled to improve resilience to ensure uninterrupted service during such conditions. A mixed-integer linear programming model is designed in this paper to optimize the energy management and structural configuration of microgrids. This optimization aims to enhance resilience cost, minimizing operation and capital costs as well as power loss and pollution. To achieve these goals, several tools are implemented including reconfiguration, storages, combined cooling, heat and power units, wind turbines, photovoltaic panels, as well as capacitors. Four case studies are defined to prove the developed model efficiency. The first case study focuses on energy management in the microgrid for operation cost minimization. The second case study emphasizes the improvement of resilience alongside energy management, aiming at minimizing costs and enhance resilience. In the third case, the microgrid's reconfiguration capability is also added to the second case. Therefore, this case aims to optimize both energy and structural management within the microgrid to simultaneously enhance resilience and minimize operational costs. Finally, in the fourth case, the problem is studied in a multi-objective approach. By comparing the results, the resilience impact on the operation of microgrids is elucidated. By considering the resilience concept in microgrid operation and based on the results of case 2, it is found that the operating costs are increased by an average of 10.38 %. However, because of reducing resilience costs by an average of 13.91 %, the total cost is reduced by an average of 5.93 % in case 2 compared to case 1. Furthermore, when comparing cases 2 and 3, the reconfiguration effect can be determined. It can be observed that the operating costs are decreased by an average of 4.5 %. Moreover, the resilience cost is decreased by an average of 1.61 %, resulting in an overall reduction of the total objective function by an average of 2.43 % in case 3 compared to case 2.

Reconfigurable stochastic neurons based on strain engineered low barrier nanomagnets

Stochastic neurons are efficient hardware accelerators for solving a large variety of combinatorial optimization problems. ‘Binary’ stochastic neurons (BSN) are those whose states fluctuate randomly between two levels +1 and −1, with the probability of being in either level determined by an external bias. ‘Analog’ stochastic neurons (ASNs), in contrast, can assume any state between the two levels randomly (hence ‘analog’) and can perform analog signal processing. They may be leveraged for such tasks as temporal sequence learning, processing and prediction. Both BSNs and ASNs can be used to build efficient and scalable neural networks. Both can be implemented with low (potential energy) barrier nanomagnets (LBMs) whose random magnetization orientations encode the binary or analog state variables. The difference between them is that the potential energy barrier in a BSN LBM, albeit low, is much higher than that in an ASN LBM. As a result, a BSN LBM has a clear double well potential profile, which makes its magnetization orientation assume one of two orientations at any time, resulting in the binary behavior. ASN nanomagnets, on the other hand, hardly have any energy barrier at all and hence lack the double well feature. That makes their magnetizations fluctuate in an analog fashion. Hence, one can reconfigure an ASN to a BSN, and vice-versa, by simply raising and lowering the energy barrier. If the LBM is magnetostrictive, then this can be done with local (electrically generated) strain. Such a reconfiguration capability heralds a powerful field programmable architecture for a p-computer whereby hardware for very different functionalities such as combinatorial optimization and temporal sequence learning can be integrated in the same substrate in the same processing run. This is somewhat reminiscent of heterogeneous integration, except this is integration of functionalities or computational fabrics rather than components. The energy cost of reconfiguration is miniscule. There are also other applications of strain mediated barrier control that do not involve reconfiguring a BSN to an ASN or vice versa, e.g. adaptive annealing in energy minimization computing (Boltzmann or Ising machines), emulating memory hierarchy in a dynamically reconfigurable fashion, and control over belief uncertainty in analog stochastic neurons. Here, we present a study of strain engineered barrier control in unconventional computing.

Multifactorial evolutionary algorithm for optimal reconfiguration capability of distribution networks

Most existing studies on distribution network reconfiguration (DNRC) are predominantly based on a fixed initial topology and optimize switch operations to achieve various objectives, without considering the flexible allocation of backup lines. This paper proposes a novel problem of optimizing the reconfiguration capability of distribution networks (DNs) by considering the expansion of power lines under resource constraints. The reconfiguration capability reflects the ability of a DN to respond to uncertainties and disturbances by adjusting its topology. A novel metric based on the number of spanning trees is proposed to quantify the reconfiguration capability of a DN. Moreover, an optimization model is formulated to maximize the reconfiguration capability of a DN subject to resource constraints on line expansions. To solve this model efficiently, a multifactorial evolutionary algorithm (MFEA) is developed, which can optimize multiple tasks with different expansion line quantities simultaneously by exploiting knowledge transfer across tasks. The proposed metric, model, and algorithm are validated on two case studies using the IEEE 33-bus and 70-bus test systems, and the results show their effectiveness and superiority over existing methods.

Electrically empowered microcomb laser

Optical microcomb underpins a wide range of applications from communication, metrology, to sensing. Although extensively explored in recent years, challenges remain in key aspects of microcomb such as complex soliton initialization, low power efficiency, and limited comb reconfigurability. Here we present an on-chip microcomb laser to address these key challenges. Realized with integration between III and V gain chip and a thin-film lithium niobate (TFLN) photonic integrated circuit (PIC), the laser directly emits mode-locked microcomb on demand with robust turnkey operation inherently built in, with individual comb linewidth down to 600 Hz, whole-comb frequency tuning rate exceeding 2.4 × 1017 Hz/s, and 100% utilization of optical power fully contributing to comb generation. The demonstrated approach unifies architecture and operation simplicity, electro-optic reconfigurability, high-speed tunability, and multifunctional capability enabled by TFLN PIC, opening up a great avenue towards on-demand generation of mode-locked microcomb that is of great potential for broad applications.

A Novel Drone Design Based on a Reconfigurable Unmanned Aerial Vehicle for Wildfire Management

Our study introduces a new approach, leveraging robotics technology and remote sensing for multifaceted applications in forest and wildfire management. Presented in this paper is PULSAR, an innovative UAV with reconfigurable capabilities, able of operating as a quadcopter, a co-axial quadcopter, and a standalone octocopter. Tailored to diverse operational requirements, PULSAR accommodates multiple payloads, showcasing its adaptability and versatility. This paper meticulously details material selection and design methods, encompassing both initial and detailed design, while the electronics design section seamlessly integrates essential avionic components. The 3D drone layout design, accomplished using SOLIDWORKS, enhances understanding by showcasing all three different configurations of PULSAR’s structure. Serving a dual purpose, this study highlights UAV applications in forest and wildfire management, particularly in detailed forest mapping, edge computing, and cartographic product generation, as well as detection and tracking of elements, illustrating how a UAV can be a valuable tool. Following the analysis of applications, this paper presents the selection and integration of payloads onto the UAV. Simultaneously, each of the three distinct UAV configurations is matched with a specific forest application, ensuring optimal performance and efficiency. Lastly, computational validation of the UAV’s main components’ structural integrity is achieved through finite element analysis (FEA), affirming the absence of issues regarding stress and displacement. In conclusion, this research underscores the efficacy of PULSAR, marking a significant leap forward in applying robotics technology for wildfire science.

A compound reconfigurable electronically switched parasitic monopole antenna for sub 6 GHz wireless and vehicular applications

In this paper, a compound reconfigurable electronically switched parasitic monopole antenna is reported for sub-6 GHz (n77/n78 bands) wireless standards and vehicular communication. The antenna comprises a half-hexagonal-shaped radiating element and two inverted L-shaped parasitic stubs. These stubs are connected through two PIN diodes, allowing for control over frequency. An additional pair of PIN diodes incorporates analogous parasitic stubs into the ground plane to facilitate pattern tilting and polarization. By switching these PIN diodes across seven operating states (OS), the antenna structure could resonate in a single band, spanning 4.5–5.8 GHz (sub-6 GHz higher band), with an omnidirectional pattern specific to OS1. In OS2 and OS3, the antenna resonates in a single band within the range of 4.1–5.7 GHz, featuring beam tilting with linear polarization. In OS4 and OS5, the structure has two bands: one covers 3.3–4.2 GHz (5G-n77 band) with linear polarization, and the other covers 5.1–6.1 GHz (5G WLAN band) with right-hand circular polarization (RHCP) in OS5 and left-hand circular polarization (LHCP) in OS4 with an axial ratio bandwidth of 14.5 %, including pattern tilting within the range of ± 340. Finally, in the OS6 and OS7 structure introduce dual bands of 3.3–3.8 GHz (5G–n78 band) and 4.8–5.8 GHz (sub-6 GHz higher band). Both bands feature LHCP/RHCP with an axial ratio bandwidth of 8.57 % for the lower resonant band and 7.4 % for the higher resonant band. Additionally, it incorporates radiation pattern reconfiguration capabilities. The structure is printed on an FR-4 substrate and measures various associated parameters. The experimental results closely match the simulated results.

The role of supply chain analytics on supply chain responsiveness, resilience, and restoration (3Rs) capabilities in the United Arab Emirates

AbstractThis paper responds to calls for research and conceptualizes resilience, responsiveness and reconfigurable (3Rs) capabilities required for handling disruptions in pre‐disruption, during disruption and post‐disruption stages. This paper examined the impact of analytics on 3Rs capabilities in the supply chain context building on the tenets of contingency theory. Drawing on survey data obtained from individuals with managerial positions in firms operating in the United Arab Emirates (n = 151), this study investigates how supply chain analytics foster 3Rs capacities in the face of a pandemic using canonical correlation and linear regression analyses. Supply chain analytics is a relevant predictor for supply chain (i) resilience, (ii) responsiveness, and (iii) restoration/reconfiguration. Inevitable supply chain disruptions call for investment and integration of digital tools like supply chain analytics because they offer timely information for informed decision‐making. This work provides theoretical contributions to the domain of interest by exploring the role of supply chain analytics on the 3Rs capabilities.

Large-scale modular and uniformly thick origami-inspired adaptable and load-carrying structures

Existing Civil Engineering structures have limited capability to adapt their configurations for new functions, non-stationary environments, or future reuse. Although origami principles provide capabilities of dense packaging and reconfiguration, existing origami systems have not achieved deployable metre-scale structures that can support large loads. Here, we established modular and uniformly thick origami-inspired structures that can deploy into metre-scale structures, adapt into different shapes, and carry remarkably large loads. This work first derives general conditions for degree-N origami vertices to be flat foldable, developable, and uniformly thick, and uses these conditions to create the proposed origami-inspired structures. We then show that these origami-inspired structures can utilize high modularity for rapid repair and adaptability of shapes and functions; can harness multi-path folding motions to reconfigure between storage and structural states; and can exploit uniform thickness to carry large loads. We believe concepts of modular and uniformly thick origami-inspired structures will challenge traditional practice in Civil Engineering by enabling large-scale, adaptable, deployable, and load-carrying structures, and offer broader applications in aerospace systems, space habitats, robotics, and more.

Tunable hyperbolic polaritons with plasmonic phase-change material In3SbTe2

Abstract Hyperbolic polaritons that originate from the extreme optical anisotropy in van der Waals (vdW) crystals have gained much attention for their potential in controlling nanolight. For practical use, there has been a strong interest to develop various manipulation strategies to customize the propagation of hyperbolic polaritons on a deeply sub-diffractional scale. In this regard, phase-change materials (PCMs) that possess two phases with different refractive indices offer suitably a tunable dielectric environment. Here, we report on the tuning of hyperbolic phonon polaritons in natural vdW crystals, hexagonal boron nitride (hBN), and alpha-phase molybdenum trioxide (α-MoO3), using the plasmonic phase-change material In3SbTe2 (IST). Unlike conventional PCMs whose both phases are dielectric, IST features a metallic crystalline phase that is stable at room temperature. The coupling between polaritons with their mirror charges in the underneath crystalline IST triggers an even stronger field confinement for polaritons. Moreover, benefited from the metallicity of laser-writable crystalline IST, we show an all-optical material platform in which crystalline IST boundaries efficiently excite and focus hyperbolic phonon polaritons in α-MoO3. Our experiments highlight the possibility to obtain new degrees of freedom in polariton engineering with plasmonic PCMs, thereby expanding the toolkit of tunable nanophotonics with flexible, on-demand fabrication and reconfiguration capabilities.

Design of a binary programmable transmitarray based on phase change material for beam steering applications in D-band

This paper introduces the design of a reconfigurable transmitarray operating within the D-band frequency range (110–170 GHz). The transmitarray unit cell is composed of three metal layers and two quartz dielectric substrates. It achieves a 1-bit phase shift resolution through the alternating states of two innovative switches integrated into the active transmitting patch of the unit cell. To address the challenge of miniaturization in the D-band, compact switches compatible with the proposed unit cell dimensions are introduced. These switches are constructed using phase change materials (PCM) that change between amorphous and crystalline states when exposed to heat. The paper includes a full-wave simulation of the unit cell, demonstrating an insertion loss below 1.5 dB across a wide frequency band of 27%. Additionally, a 10 ×\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes $$\\end{document} 10 elements transmitarray is synthesized using a numerical tool and its theoretical results are compared to full-wave electromagnetic simulations for validation purposes. The results indicate that by incorporating the proposed switches into the unit cell, the transmitarray achieves promising reconfiguration capabilities within the D-band. Moreover, the paper presents the architecture of a command line designed to bias the PCM switches. Notably, this command line represents a novel approach, as it enables individual biasing of each PCM switch using direct current (DC). The influence of these command lines on the transmitarray’s performance is thoroughly investigated. Although there is a compromise in the 1-dB gain bandwidth, the overall behavior of the transmitarray remains encouraging.

Reconfigurable UAV-aided 3D sustainable surveillance in classified air-spaces

The recent advancement of smart UAVs (Unmanned Aerial Vehicles) allows its applicability to wide-range fields covering time-critical reports, emergent item delivery, virtual emotion services and so on. In particular, the surveillance can be supported by a fleet of smart UAVs in cooperation with various system components including sensors, mobile robots, autonomous vehicles and so on. Additionally, it is crucial to consider the reconfigurable capabilities of smart UAVs leveraging for rapid movement in disregarded airspace. In this paper, we introduce a reconfigurable UAV-enabled 3D sustainable surveillance in classified air-spaces to provide energy-efficient monitoring and security surveillance enforcement. We formally define a problem to maximize the reconfigurable UAV-aided sustainable surveillance efficiency with optimal height in 3D classified air-spaces avoiding excessive resource consumption and waste. To solve this issue, two different schemes of Lined-Up and Zig-Zag are proposed. Furthermore, we analyze their performances based on the gained numerical results through expanded simulations with detailed discussions.

Modular rewritable Petri nets: An efficient model for dynamic distributed systems

Modern distributed systems are becoming pervasive and increasingly provided with adaptation, (self-)reconfiguration and mobility capability. On one side, to face the challenges of the highly dynamic environments where they are deployed. On the other side, to keep production/maintenance costs down. Therein lies the increasing demand for formal models encompassing all of these aspects (besides concurrency). Hardly any of the classical formalisms like Petri Nets, Automata, and Process Algebra, even though powerful, permits designers to easily specify dynamic structural changes to systems and evaluate their impact on system behaviour. That has led to several extensions of classical formal models (e.g., the Pi calculus or the Nets-within-Nets paradigm) rarely accompanied by suitable analysis techniques. A recent formalization of a class of Rewritable Place-Transition Nets (RwPT) in Maude has proved potentially convenient to specify dynamically reconfigurable systems. Concerning analogous proposals, the RwPT formalism provides more abstraction/flexibility in modelling and efficiency in analysis. Nevertheless, its ability to scale the size of distributed systems built of several similar (nested) components is limited.This paper presents a compositional approach to define large RwPT models in a typical algebraic way and to exploit the modular structure of models during the analysis: Symmetries are implicitly captured by composite node-labelling (reflecting the model's hierarchical structure) that is preserved by net rewrites. A distributed, gracefully degrading production system is used as a case study. Experimental evidence points out the dramatic impact of the approach against a non-modular one and the advantages over alternative techniques. Even if the emphasis is on state-space-based verification, the paper shows the convenience of combining it with structural analysis, which is typical of Petri nets as well. For that purpose, rewrite-rule abstractions are given in the form of guidelines.

Multi-criteria Optimization of Real-time DAGs on Heterogeneous Platforms under P-EDF

This article tackles the problem of optimal placement of complex real-time embedded applications on heterogeneous platforms. Applications are composed of directed acyclic graphs of tasks, with each directed-acyclic-graph (DAG) having a minimum inter-arrival period for its activation requests and an end-to-end deadline within which all of the computations need to terminate since each activation. The platforms of interest are heterogeneous power-aware multi-core platforms with Dynamic Voltage and Frequency Scaling (DVFS) capabilities, including big.LITTLE Arm architectures and platforms with GPU or FPGA hardware accelerators with Dynamic Partial Reconfiguration capabilities. Tasks can be deployed on CPUs using partitioned EDF-based scheduling. Additionally, some of the tasks may have an alternate implementation available for one of the accelerators on the target platform, which are assumed to serve requests in non-preemptive FIFO order. The system can be optimized by minimizing power consumption, respecting precise timing constraints, maximizing the applications’ slack, respecting given power consumption constraints, or even a combination of these, in a multi-objective formulation. We propose an off-line optimization of the mentioned problem based on mixed-integer quadratic constraint programming (MIQCP). The optimization provides the DVFS configuration of all the CPUs (or accelerators) capable of frequency switching and the placement to be followed by each task in the DAGs, including the software-vs.-hardware implementation choice for tasks that can be hardware accelerated. For relatively big problems, we developed heuristic solvers capable of providing suboptimal solutions in a significantly reduced time compared to the MIQCP strategy, thus widening the applicability of the proposed framework. We validate the approach by running a set of randomly generated DAGs on Linux under SCHED_DEADLINE, deployed onto two real boards, one with Arm big.LITTLE architecture, the other with FPGA acceleration, verifying that the experimental runs meet the theoretical expectations in terms of timing and power optimization goals.