Application Constraints Research Articles

Engineering tunability through electro-optic effects to manifest a multifunctional metadevice.

Ultrafast modulation of the refractive index exhibits either linear or nonlinear electro-optic (EO) effect, which is extensively utilized in tunable photonic circuits. Silicon, a mature material for on-chip devices, lacks a strong electro-optic (EO) Pockels effect. Utilization of the Pockels effect alters the intrinsic property (refractive index) of the material that manifests tunability and offers expanded functionalities. Driven by the limited space constraints in data storage, sensing, and imaging applications, we propose an electrically tunable meta-device whose resonance wavelength and focal length can be tuned by varying applied electric fields in the visible range. The fundamental unit of a metalens is the barium titanate (BTO) diffractive optical element placed on indium titanium oxide (which serves as an electrode) with SiO2 as the substrate. The metalens' tunability is characterized by point spread function (PSF), full-width half-maximum (FWHM), and imaging bandwidth that demonstrates the tuning of resonance wavelength and focal length. Moreover, polarization-insensitive meta-holograms are realized at a wavelength of 633 nm without utilizing propagation and Pancharatnam–Berry (PB) phase. The proposed study can find exciting applications in machine vision, broadband microscopy, and spectroscopy.

Deployment and Migration of Virtualized Services with Joint Optimization of Backhaul Bandwidth and Load Balancing in Mobile Edge-Cloud Environments

Mobile edge-cloud computing environments appear as a novel computing paradigm to offer effective processing and storage solutions for delay sensitive applications. Besides, the container based virtualization technology becomes solicited due to its natural lightweight and portability as well as its small migration overhead that leads to seamless service migration and load balancing. However, with the mobility property, the users’ demands in terms of the backhaul bandwidth is a critical parameter that influences the delay constraints of the running applications. Accordingly, a Binary Integer Programming (BIP) optimization problem is formulated. It minimizes the users’ perceived backhaul delays and enhances the load-balancing degree in order to offer more chance to accept new requests along the network. Also, by introducing bandwidth constraints, the available user backhaul bandwidth after the placement are enhanced. Then, the adopted methodology to design two heuristic algorithms based on Ant Colony System (ACS) and Simulated Annealing (SA) is presented. The proposed schemes are compared using different metrics,and the benefits of the ACS-based solution compared to the SA-based as well as a genetic algorithm (GA) based solutions are demonstrated. Indeed, the normalized cost and the total backhaul costs are given by more optimal values using the ACS algorithm compared to the other solutions.

A Reconfigurable Graphene-Based Spiking Neural Network Architecture

In the paper we propose a reconfigurable graphene-based Spiking Neural Network (SNN) architecture and a training methodology for initial synaptic weight values determination. The proposed graphene-based platform is flexible, comprising a programmable synaptic array which can be configured for different initial synaptic weights and plasticity functionalities and a spiking neuronal array, onto which various neural network structures can be mapped according to the application requirements and constraints. To demonstrate the validity of the synaptic weights training methodology and the suitability of the proposed SNN architecture for practical utilization, we consider character recognition and edge detection applications. In each case, the graphene-based platform is configured as per the application tailored SNN topology and initial state and SPICE simulated to evaluate its reaction to the applied input stimuli. For the first application, a 2-layer SNN is used to perform character recognition for 5 vowels. Our simulation indicates that the graphene-based SNN can achieve comparable recognition accuracy with the one delivered by a functionally equivalent Artificial Neural Network. Further, we reconfigure the architecture for a 3-layer SNN to perform edge detection on 2 grayscale images. SPICE simulation results indicate that the edge extraction results are close agreement with the one produced by classical edge detection operators. Our results suggest the feasibility and flexibility of the proposed approach for various application purposes. Moreover, the utilized graphene-based synapses and neurons operate at low supply voltage, consume low energy per spike, and exhibit small footprints, which are desired properties for largescale energy-efficient implementations.

Optimal Solutions for Real-Time Scheduling of Reconfigurable Embedded Systems Based on Neural Networks with Minimization of Power Consumption

In this study, Artificial Neural Networks (ANN) were used to model parameters of scheduling of reconfigurable embedded systems containing resource constraints for applications running in real-time. The main goal is to implement a neural network based approach for real-time scheduling in order to handle real-time constraints in execution scenarios. Many techniques have been proposed for both the planning of tasks and reducing energy consumption.This paper presents a new hybrid contribution that handles the real-time scheduling of embedded systems by keeping energy consumption at a low power depending on the combination of Dynamic Voltage Scaling (DVS) and the energy Priority Earlier Deadline First (PEDF) algorithm. Indeed, in our original proposed approach, an other combination of DVS and time feedback can be used to scale the frequency by dynamically adjusting the operating voltage.The originality of our algorithm appears by allowing medium priority tasks to be executed more quickly before their deadline while decreasing their ability to be send again to the waiting list in order to ensure the execution of a task with the lowest voltage possible.

The application of the rights and principles of the Charter of Fundamental Rights

The Charter of Fundamental Rights codifies and reaffirms the rights and principles recognised in European Union law. These rights and principles result from the constitutional traditions common to the Member States, the European Convention for the Protection of Human Rights and Fundamental Freedoms, the case law of the Court of Justice of the European Union and that of the European Court of Human Rights. The Charter was meant to strengthen the protection of fundamental rights and principles by making them more visible. This study explores the restrictions on the applicability of the rights and principles enshrined in the Charter. Given that the most problematic questions are those regarding the application of the Charter’s rights and principles by the Member States, it focuses on these questions and points out, when necessary, some other problems such as the distinction between rights and principles and their application in horizontal relations. The study also analyses the different modes and constraints of application of the Charter and interpretation of it by Member State courts. Finally, it examines whether the Charter, despite its limited scope of application, can provide national courts with new competences to apply the fundamental rights.

Reliability Analysis of 0.5μm CMOS Operational Amplifiers under TID Effects

Analog integrated circuits operating in radiation environments. In previous irradiation experiments performed on a switched-capacitor filter, implemented in a programmable analog array, it was observed a sudden recovery of the device performance during the irradiation, while increasing the accumulated dose. In some cases the considered performance parameters (such as the total harmonic distortion) may even be enhanced if compared to the pre-irradiation measurements, in specific accumulated dose intervals. This behavior is associated to partial inactivity windows in the internal components of the device. Spice simulations considering two complementary architectures of a simple CMOS Operational Amplifier (OpAmp) are performed, aiming to understand the origins of this effect. Results indicate that shifts on the operating point of the amplifier building blocks are responsible for the degradation and recovery of the OpAmp performance. Results also show that specific architectures, as well as, application constraints (such as the external feedback and the input signal amplitude and frequency) may result in different robustness levels related to the linear applications of the OpAmps in radiation environments.

Review on Fault Detection and Diagnosis Feature Engineering in Building Heating, Ventilation, Air Conditioning and Refrigeration Systems

Faults are inevitable in building energy systems, such as heating, ventilation, air conditioning and refrigeration systems. With the increasing utilization of these systems, their fault detection and diagnosis has become more significant for maintaining their operation and performance. The rapid development of data analytics has led to fault detection and diagnosis using data-based algorithms considering building intelligence. Feature engineering, which focuses on generating near minimum and optimal model inputs from the original dataset, is a fundamental technique to balance efficiency and accuracy of fault detection and diagnosis models. The generated inputs are fault features which ensures a better description of fault operations owing to their stronger fault indicative capability than original data. However, since most feature engineering research has been implemented alongside the development of data-based algorithms, most studies fail to effectively distinguish and understand their roles in fault detection and diagnosis models. To fill this research gap, this study presented the problems and mechanisms associated with feature engineering, comprehensively reviewed about 200 papers on the topic of feature engineering for heating, ventilation, air conditioning and refrigeration system fault detection and diagnosis tasks over the past 15 years. Firstly, feature engineering studies are divided according to their feature-generation technique, into manual and automated techniques. Next, feature engineering studies are analysed in terms of data type, system type, fault type, operation statue, methods and fault features. The desirable characteristics of feature engineering works are then discussed in terms of their capability of rapidly generating indicative, decoupled, interpretable and new fault features to enhance fault detection and diagnosis accuracy; the efficiency, reliability and robustness of feature engineering process; the mutual adaptability between feature engineering and fault detection and diagnosis; and their widespread applicability for more fault types and system operations. The challenges affecting feature engineering studies are discussed in terms of data volume, data diversity and quality, along with the performance evaluation of feature engineering with respect to feature engineering algorithms, fault detection and diagnosis models, fault features and online application constraints. Future work should aim to design a hybrid, heuristic, robust and automated feature engineering strategy under the guidance of expert knowledge and practical application constraints that consider the relationships between fault-related features and real-time fault impacts with energy consumption, occupants' thermal comfort, etc.

A Study on the Effects of Vertical Mass Irregularity on Seismic Behavior of BRBFs and CBFs

Today, architectural application and economic constraints require that vertical-irregular structures be constructed in urban areas. Proposing methods to minimize damage to these structures during earthquakes is therefore crucial. Strict regulations have been enforced for the design and analysis of irregular structures given their higher vulnerability to damage compared to that of regular structures. The present study aimed to evaluate eight regular and irregular 10-story and 15-story steel structures with buckling-restrained braces frames (BRBFs) and concentric braced frames (CBFs) in terms of their responses to twelve far-field earthquakes. According to the obtained results, the mean value of maximum drift, top floor displacement and floor acceleration were higher in both regular and irregular structures with BRBFs than in those with CBFs.

Rethinking safe consistency in distributed object-oriented programming

Large scale distributed systems require to embrace the trade off between consistency and availability, accepting lower levels of consistency to guarantee higher availability. Existing programming languages are, however, agnostic to this compromise, resulting in consistency guarantees that are the same for the whole application and are implicitly adopted from the middleware or hardcoded in configuration files. In this paper, we propose to integrate availability in the design of an object-oriented language, allowing developers to specify different consistency and isolation constraints in the same application at the granularity of single objects. We investigate how availability levels interact with object structure and define a type system that preserves correct program behavior. Our evaluation shows that our solution performs efficiently and improves the design of distributed applications.

How Can Blockchain Technology Accelerate Energy Efficiency Interventions? A Use Case Comparison

This paper qualitatively evaluates the application of blockchain technology for three energy efficiency use cases. To achieve the Sustainable Development Agenda, energy efficiency improvements have to double by 2030. However, the adoption of energy efficiency interventions is slow due to several market barriers. Blockchain technology is a nascent technology with the potential to address these barriers or even fundamentally change energy system designs, by enabling transparent, decentralised, and tamper-resilient systems. Nevertheless, a blockchain application comes with trade-offs and needs to be considered on a case by case basis. In this paper, we examine the benefits and constraints of a blockchain application for three different approaches to achieving energy efficiency: (i) peer-to-peer (P2P) energy trading; (ii) White Certificate Scheme (WCS); and (iii) Energy Service Companies (ESCOs). For each of these cases, we apply a decision framework to assess blockchain feasibility and outline a potential blockchain-based design. The analysis shows that blockchain functions are case dependent and that an application creates different governance and system designs due to varying case characteristics. We discuss how the identified blockchain adoption barriers can be overcome and stress the need for policy action to advance the development of pilot studies. By decentralising system governance, blockchain enables innovative designs that can accelerate the implementation of energy efficiency interventions.

Parametric Study of Damping Characteristics of Magneto-Rheological Damper: Mathematical and Experimental Approach

The present research work is a part of a project was a semi-active structural control technique using magneto-rheological damper has to be performed. Magneto-rheological dampers are an innovative class of semi-active devices that mesh well with the demands and constraints of seismic applications; this includes having very low power requirements and adaptability. A small stroke magneto-rheological damper was mathematically simulated and experimentally tested. The damper was subjected to periodic excitations of different amplitudes and frequencies at varying voltage. The damper was mathematically modeled using parametric Modified Bouc-Wen model of magneto-rheological damper in MATLAB/SIMULINK and the parameters of the model were set as per the prototype available. The variation of mechanical properties of magneto-rheological damper like damping coefficient and damping force with a change in amplitude, frequency and voltage were experimentally verified on INSTRON 8800 testing machine. It was observed that damping force produced by the damper depended on the frequency as well, in addition to the input voltage and amplitude of the excitation. While the damping coefficient (c) is independent of the frequency of excitation it varies with the amplitude of excitation and input voltage. The variation of the damping coefficient with amplitude and input voltage is linear and quadratic respectively. More ever the mathematical model simulated in MATLAB was in agreement with the experimental results obtained.

Graph‐based subterranean exploration path planning using aerial and legged robots

AbstractAutonomous exploration of subterranean environments remains a major challenge for robotic systems. In response, this paper contributes a novel graph‐based subterranean exploration path planning method that is attuned to key topological properties of subterranean settings, such as large‐scale tunnel‐like networks and complex multibranched topologies. Designed both for aerial and legged robots, the proposed method is structured around a bifurcated local‐ and global‐planner architecture. The local planner utilizes a rapidly exploring random graph to reliably and efficiently identify paths that optimize an exploration gain within a local subspace, while simultaneously avoiding obstacles, respecting applicable traversability constraints and honoring dynamic limitations of the robots. Reflecting the fact that multibranched and tunnel‐like networks of underground environments can often lead to dead‐ends and accounting for the robot endurance, the global planning layer works in conjunction with the local planner to incrementally build a sparse global graph and is engaged when the system must be repositioned to a previously identified frontier of the exploration space, or commanded to return‐to‐home. The designed planner is detailed with respect to its computational complexity and compared against state‐of‐the‐art approaches. Emphasizing field experimentation, the method is evaluated within multiple real‐life deployments using aerial robots and the ANYmal legged system inside both long‐wall and room‐and‐pillar underground mines in the United States and in Switzerland, as well as inside an underground bunker. The presented results further include missions conducted within the Defense Advanced Research Projects Agency (DARPA) Subterranean Challenge, a relevant competition on underground exploration.

Collaborative Data Scheduling for Vehicular Edge Computing via Deep Reinforcement Learning

With the development of autonomous driving, the surging demand for data communications as well as computation offloading from connected and automated vehicles can be expected in the foreseeable future. With the limited capacity of both communication and computing, how to efficiently schedule the usage of resources in the network toward best utilization represents a fundamental research issue. In this article, we address the issue by jointly considering the communication and computation resources for data scheduling. Specifically, we investigate on the vehicular edge computing (VEC) in which edge computing-enabled roadside unit (RSU) is deployed along the road to provide data bandwidth and computation offloading to vehicles. In addition, vehicles can collaborate among each other with data relays and collaborative computing via vehicle-to-vehicle (V2V) communications. A unified framework with communication, computation, caching, and collaborative computing is then formulated, and a collaborative data scheduling scheme to minimize the system-wide data processing cost with ensured delay constraints of applications is developed. To derive the optimal strategy for data scheduling, we further model the data scheduling as a deep reinforcement learning problem which is solved by an enhanced deep $Q$ -network (DQN) algorithm with a separate target $Q$ -network. Using extensive simulations, we validate the effectiveness of the proposal.

I-SEE: Intelligent, Secure, and Energy-Efficient Techniques for Medical Data Transmission Using Deep Reinforcement Learning

The rapid evolution of remote health monitoring applications is foreseen to be a crucial solution for facing an unpredictable health crisis and improving the quality of life. However, such applications come with many challenges, including: the transmission of a large amount of private medical data and the limited power budget for battery-operated devices. Thus, this article proposes an intelligent, secure, and energy-efficient (I-SEE) framework for secure and energy-efficient medical data transmission, leveraging the potential of physical-layer security. In particular, we incorporate a practical secrecy metric, namely, the secrecy outage probability (SOP), along with the adaptive compression at the edge for providing a secure solution for health monitoring applications. In the proposed framework, we first formulate an optimization problem that maximizes the energy efficiency, while maintaining quality-of-service constraints of the health application. Second, we propose a deep reinforcement learning process that obtains the optimal strategy for secure data transmission. Specifically, a multiobjective reward function is defined to optimize energy efficiency and distortion, resulting from the compression scheme. Then, a deep deterministic policy gradients (DDPGs) algorithm, named Static-DDPG is proposed to solve our problem efficiently. Third, the problem is extended to consider the battery lifetime maximization with varying channel conditions. Indeed, a Dynamic-DDPG algorithm is proposed in order to allow the edge to adapt to the environment dynamics while maximizing its battery lifetime. The conducted simulations validate the efficiency of the proposed algorithms in terms of finding the optimal policy that addresses the tradeoff between the considered conflicting objectives, along with the battery lifetime maximization

Time optimal control of triple integrator with input saturation and full state constraints

Multiple integrator systems with input saturation and state constraints ubiquitously exist in practical problems such as trajectory planning in CNC (computer numerical control) systems, robots, automotive systems and industrial processes. Without state constraints, the optimal control of the multiple integrator with only input saturation is well solved in classic optimal control theory. However, if state constraints are considered, it is still challenging to obtain a global time optimal analytical solution for multiple integrator systems with even higher than second order, e.g., a double integrator plant. In this paper, the global time optimal control law for triple integrator with input saturation and full state constraints is considered. The system has a serial structure of three integral elements, while the control input and system states are within box constraints. A bang-singular-bang time optimal control law is synthesized according to Pontryagin minimum principle. Then, costates and jump conditions are analyzed in detail. Based on Bellman’s principle of optimality, switching surfaces and curves in phase space are obtained through dynamic programming method. The time optimal control law is then obtained in analytical state feedback form, and its global convergence property is proved. Through the comparison with numerical solutions, the effectiveness of the proposed control strategy is verified. The proposed scheme will be useful for trajectory planning under input saturation and full state constraints in practical applications.

A frame-level MLP-based bit-rate controller for real-time video transmission using VVC standard

Real-time video transmission is one of the most popular applications that are included in the versatile video coding (VVC) standard. However, real-time applications are encountered with practical limitations, including the buffer size and available bandwidth. In these applications, the buffer overflow and underflow should be strictly prevented and also the bit-rate fluctuation should be suppressed. In this paper, a video bit-rate controller is proposed that completely conforms with the constraints of real-time applications. The proposed controller is based on a multi-layer perceptron (MLP) neural network which estimates the proper quantization parameter (QP) modification at the frame level. The buffer occupancy is directly included in the QP derivation process for robust buffer control. Experimental results show that the proposed bit-rate controller fulfils the buffering constraints and controls the bit-rate accurately. The average bit-rate error of the proposed method is 0.29% while providing a low initial buffering delay of about 0.21 s. Also, the rate-distortion analysis shows that the performance of the proposed method is close to those of the conventional algorithms.

Demonstration of engineering current density exceeding 1 kA mm−2 in ultra-thin no-insulation, soldered coil windings using NbTi/Cu wires with CuNi cladding

The no-insulation, or more precisely, controlled-resistance coil winding method, nowadays being exclusively used for high-temperature superconducting solenoids, has proven its effectiveness for improving quench protection. When considering low-temperature superconductor magnet technology, which is mostly focused on stability and training issues, controlled-resistance insulation windings are directly addressing these aspects as well. Fully soldered coil windings of non-insulated turns can also show superior mechanical properties and feature simplified manufacturing when compared to epoxy impregnated coil windings and are of high practical interest for quasi-stationary magnets provided the related charging time constant can be controlled and kept low enough. For demonstrating the principle feasibility two demonstrator coils were developed using NbTi/Cu wire with CuNi cladding of 1 mm diameter. The wire performance is reported including critical current and n-values at 4.2 K and background magnetic fields from 0 to 9 T, as well as effective transverse resistivity at room temperature and 77 K. Two solenoids with fully soldered windings comprising one layer on a 50 mm bore and three layers on a 100 mm bore, respectively, were manufactured and tested in liquid helium. Their performance is directly compared to data obtained on short wire samples. The drastically enhanced stability of the coils against thermal disturbances allows to avoid any training and enables to operate the coils up, or even slightly beyond, the short-sample critical current, resulting in generated magnetic fields of 2.2 and 3.8 T and time constants of 5 and 55 s, respectively. When initiating a quench deliberately by excessive heating or spontaneously at their limiting currents, the coils entirely switch to the normal state almost instantly, thus requiring no quench protection system. Design, manufacturing and test experiences with the two super stable coils are reported and their use and design constraints for certain applications discussed.

A Multiscale Algorithm for Joint Forecasting–Scheduling to Solve the Massive Access Problem of IoT

The massive access problem of the Internet of Things (IoT) is the problem of enabling the wireless access of a massive number of IoT devices to the wired infrastructure. In this article, we describe a multiscale algorithm (MSA) for joint forecasting-scheduling at a dedicated IoT gateway to solve the massive access problem at the medium access control (MAC) layer. Our algorithm operates at multiple time scales that are determined by the delay constraints of IoT applications as well as the minimum traffic generation periods of IoT devices. In contrast with the current approaches to the massive access problem that assume random arrivals for IoT data, our algorithm forecasts the upcoming traffic of IoT devices using a multilayer perceptron architecture and preallocates the uplink wireless channel based on these forecasts. The multiscale nature of our algorithm ensures scalable time and space complexity to support up to 6650 IoT devices in our simulations. We compare the throughput and energy consumption of MSA with those of reservation-based access barring (RAB), priority based on average load (PAL), and enhanced predictive version burst-oriented (E-PRV-BO) protocols, and show that MSA significantly outperforms these beyond 3000 devices. Furthermore, we show that the percentage control overhead of MSA remains less than 1.5%. Our results pave the way to building scalable joint forecasting-scheduling engines to handle a massive number of IoT devices at IoT gateways.

Novel QoS-Guaranteed Orchestration Scheme for Energy-Efficient Mobile Augmented Reality Applications in Multi-Access Edge Computing

In this study, we focus on improving the energy efficiency of multiple mobile augmented reality (MAR) devices (MDs) with different MAR applications, which are connected to a single multi-access edge computing (MEC) server, through the centralized orchestration of the MEC server. To achieve this, a trade-off between the accuracy, latency, and energy consumption of each MD is considered as intertwined costs. Accordingly, we minimize a sum of intertwined costs including the energy consumption, latency and accuracy loss of multiple MDs, while satisfying the maximum latency constraint and the minimum accuracy constraint of each MAR application. With rigorous analysis, we design a theoretical framework for the optimization of MD performance by jointly managing the frame resolution of the image to offload to the MEC server from MDs, and the computation capacity distribution over MDs in the case of the limited computation capacity of the MEC server. Our numerical analysis validates that the proposed scheme significantly reduces the number of service-level agreement violations, such as exceeding the maximum latency constraint, compared with the existed algorithm. Specifically, the proposed work significantly improve costs of service latency, accuracy loss, and energy consumption of MDs for MEC-assisted MAR services compared with baseline schemes.

A nonlinear inverse model for airborne wind energy system analysis, control, and design optimization

AbstractThis paper describes a nonlinear model inversion for performance analysis and system optimization for airborne wind energy (AWE) systems. Airborne wind energy systems are lighter and potentially lower in cost than comparable conventional wind turbines due to using a tether and bridle rather than blades and a tower which must support high bending and compressive loads. They are also easier to install due to using a tension anchor rather than a foundation. There are many AWE systems in various states of R&D, but no commercial AWE wind farms currently exist. Because AWE systems are novel and significantly more complex to design, analyze, and test than conventional wind turbines, better analysis tools are important for the technology to mature.AWE system design involves many interdependent design trade‐offs, which are difficult to analyze with traditional tools; using simulations to iterate through parameters for design optimization is undesirable. A nonlinear model inversion based on appropriate simplifying assumptions is better suited to this task. The inverse model uses a trajectory input then calculates attitudes, forces, and control values required to follow that trajectory as well as power produced. Applicable constraints ensure that the trajectory is realizable. A validation is presented, using the output of the inverse model as a feed‐forward controller for a high fidelity simulation. The analysis shows good agreement with the simulation, despite simplifications such as a straight rigid tether, constant lift and drag coefficients, and a simplified rotor model.