Real-time Reconfiguration Research Articles

Real-Time Reconfigurable Radio Frequency Arbitrary-Waveform Generation via Temporal Pulse Shaping with a DPMZM and Multi-Tone Inputs

Benefitting from a large bandwidth and compact configuration, a time-domain pulse-shaping (TPS) system provides possibilities for generating broadband radio frequency (RF) arbitrary waveforms based on the Fourier transform relationship between the input–output waveform pair. However, limited by the relatively low sampling rate and bit resolution of an electronic arbitrary-waveform generator (EAWG), the diversity and fidelity of the output waveform as well as its reconfiguration rate are constrained. To remove the EAWG’s limitation and realize dynamic real-time reconfiguration of RF waveforms, we propose and demonstrate a novel approach of RF arbitrary-waveform generation based on an improved TPS system with an integrated dual parallel Mach–Zehnder modulator (DPMZM) and multi-tone inputs. By appropriately adjusting the DC bias voltages of DPMZM and the power values, as well as the center frequencies of the multi-tone inputs, any desired RF arbitrary waveform can be generated and reconfigured in real time. Proof-of-concept experiments on the generation of different user-defined waveforms with a sampling rate up to 27 GSa/s have been successfully carried out. Furthermore, the impact of modulation modes and higher-order dispersion on waveform fidelity is also discussed in detail.

A multifunctional smart field-programmable radio frequency surface

Antennas that can operate across multiple communication standards have remained a challenge. To address these limitations, we propose a Field-Programmable Radio Frequency Surface (FPRFS), which is based on manipulating current flow on its surface to achieve desirable RF characteristics. In this work, we demonstrate that substantial enhancements in radiation efficiency can be achieved while preserving the high reconfigurability of antenna structures implemented on the FPRFS. This is accomplished by utilizing an asymmetric excitation, directing the excitation to the low-loss contiguous surface, and dynamically manipulating the imaged return current on a segmented ground plane by switches. This important insight allows for adaptable antenna performance that weakly depends on the number of RF switches or their loss. We experimentally validate that FPRFS antennas can achieve efficiencies comparable to traditionally implemented antenna counterparts. This permits the FPRFS to be effectively utilized as a productive antenna and impedance-matching network with real-time reconfigurability.

Machine learning-enhanced optical tweezers for defect-free rearrangement

Optical tweezers constitute pivotal tools in Atomic, Molecular, and Optical (AMO) physics, facilitating precise trapping and manipulation of individual atoms and molecules. This process affords the capability to generate desired geometries in both one-dimensional and two-dimensional spaces, while also enabling real-time reconfiguration of atoms. Due to stochastic defects in these tweezers, which cause catastrophic performance degradation especially in quantum computations, it is essential to rearrange the tweezers quickly and accurately. Our study introduces a machine learning approach that uses the Proximal Policy Optimization model to optimize this rearrangement process. This method focuses on efficiently solving the shortest path problem, ensuring the formation of defect-free tweezer arrays. By implementing machine learning, we can calculate optimal motion paths under various conditions, resulting in promising results in model learning. This advancement presents new opportunities in tweezer array rearrangement, potentially boosting the efficiency and precision of quantum computing research.

Granular biopolitics: Facial recognition, pandemics and the securitization of circulation

The COVID-19 pandemic has provided opportunities for facial recognition technology and other forms of biometric monitoring to expand into new markets. One anticipated result is the wholesale reconfiguration of shared and public space enabled by the automated identification and tracking of individuals in real time. Drawing on data from several industry trade shows, this article considers the forms of ‘environmental’ governance envisioned by those developing and deploying the technology for the purposes of security, risk management, and profit. We argue that the ‘contactless culture’ that emerged during the COVID-19 pandemic anticipates the normalization of a form of mass-customized biopolitics: the ability to operate on the population and the individual simultaneously through automated forms of passive identification. This form of governance relies not just on machinic recognition, but on the real-time reconfiguration of physical space through automated access controls and the channelling of both people and information.

Multifunctional droplet handling on surface-charge-graphic-decorated porous papers.

Developing a fluidic platform that combines high-throughput with reconfigurability is essential for a wide range of cutting-edge applications, but achieving both capabilities simultaneously remains a significant challenge. Herein, we propose a novel and unique method for droplet manipulation via drawing surface charge graphics on electrode-free papers in a contactless way. We find that opposite charge graphics can be written and retained on the surface layer of porous insulating paper by a controlled charge depositing method. The retained charge graphics result in high-resolution patterning of electrostatic potential wells (EPWs) on the hydrophobic porous surface, allowing for digital and high-throughput droplet handling. Since the charge graphics can be written/projected dynamically and simultaneously in large areas, allowing for on-demand and real-time reconfiguration of EPWs, we are able to develop a charge-graphic fluidic platform with both high reconfigurability and high throughput. The advantages and application potential of the platform have been demonstrated in chemical detection and dynamically controllable fluidic networks.

Advances in nanofluidic field-effect transistors: external voltage-controlled solid-state nanochannels for stimulus-responsive ion transport and beyond.

Ion channels, intricate protein structures facilitating precise ion passage across cell membranes, are pivotal for vital cellular functions. Inspired by the remarkable capabilities of biological ion channels, the scientific community has ventured into replicating these principles in fully abiotic solid-state nanochannels (SSNs). Since the gating mechanisms of SSNs rely on variations in the physicochemical properties of the channel surface, the modification of their internal architecture and chemistry constitutes a powerful strategy to control the transport properties and, consequently, render specific functionalities. In this framework, both the design of the nanofluidic platform and the subsequent selection and attachment of different building blocks gain special attention. Similar to biological ion channels, functional SSNs offer the potential to finely modulate ion transport in response to various stimuli, leading to innovations in a variety of fields. This comprehensive review delves into the intricate world of ion transport across stimuli-responsive SSNs, focusing on the development of external voltage-controlled nanofluidic devices. This kind of field-effect nanofluidic technology has attracted special interest due to the possibility of real-time reconfiguration of the ion transport with a non-invasive strategy. These properties have found interesting applications in drug delivery, biosensing, and nanoelectronics. This document will address the fundamental principles of ion transport through SSNs and the construction, modification, and applications of external voltage-controlled SSNs. It will also address future challenges and prospects, offering a comprehensive perspective on this evolving field.

Adaptive real-time reconfiguration gate scheduling scheme using time perceptive stream

An adaptive real-time gate scheduling scheme for time perceptive stream or packet flow is proposed to improve the standards of Ultra Low Latency during data transmission. For highly dynamic network conditions, the conventional configuration scheme is not suitable and therefore an adaptive real-time gate scheduling method is proposed for time perceptive streams. This dynamicity reconfiguration is difficult and the scheduling problem is formulated using Field Programmable Gate Array – Boolean Satisfiability Problem (FPGA-BSP) solver. This proposed scheme highly helps in network dynamicity conditions with good bandwidth utilization and high flexibility. End-to-end latency is required to be on sub-milliseconds order deal with the applications such as Industrial Internet of Things, 5G and 6G mobile, tactile internet and so on. Simulation analysis is carried out to prove the efficiency of the proposed model.

Integrating Intelligent Reflecting Surface Into Base Station: Architecture, Channel Model, and Passive Reflection Design

Intelligent reflecting surface (IRS) has emerged as a cost-efficient technique to improve the wireless network’s capacity and performance. Existing works on IRS have mainly considered IRS being deployed in the environment to dynamically control the wireless channels between the base station (BS) and its served users in favor of their communications. In contrast, we propose in this paper a new integrated IRS-BS architecture by deploying IRSs inside the BS’s antenna radome to directly reconfigure the signal radiation to/from the BS’s antennas. In other words, the IRSs can be considered as auxiliary passive arrays with real-time reconfigurability equipped at the BS to enhance its communication performance cost-effectively. Since the distance between the integrated IRSs and BS’s antenna array is practically small (in the order of several to tens of wavelengths), the path loss among them is significantly reduced as compared to conventional IRS deployed much farther away from the BS, while the real-time control of the IRS’s reflection by the BS becomes easier to implement. However, the resultant near-field channel model also becomes drastically different from its far-field counterpart for conventional far-away IRSs in the literature. Thus, we propose an element-wise channel model for IRS to characterize the channel vector between each single-antenna user and the antenna array of the BS, which includes the direct (without any IRS’s reflection) as well as the single and double IRS-reflection channel components. Based on this channel model, we formulate a problem to optimize the reflection coefficients of all IRS reflecting elements for maximizing the uplink sum-rate of the users. By considering two typical cases with/without perfect channel state information (CSI) at the BS, the formulated problem is solved efficiently by adopting the successive refinement method and iterative random phase algorithm (IRPA), respectively. Numerical results validate the substantial capacity gain of the integrated IRS-BS architecture over the conventional multi-antenna BS without integrated IRS. Moreover, the proposed algorithms significantly outperform other benchmark schemes in terms of sum-rate, and the IRPA without CSI can approach the performance upper bound with perfect CSI as the training overhead increases.

Low-complexity IRS beamforming based on sphere decoding and tabu search

The real-time reconfiguration of intelligent reflecting surface (IRS) is one of the crucial, yet challenging tasks required to exploit the benefits of IRS. In this paper, we study an IRS-aided multiple-input single-output (MISO) wireless network, where a coarsely quantized (few resolution bits) IRS is reconfigured to help the point-to-point communication between a multi-antenna access point (AP) and a single-antenna user. Specifically, the transmit precoders at the AP and the reflecting discrete phase shifters at the IRS are jointly optimized to maximize the signal- to-noise ratio (SNR) at the user. The optimization is studied in two practical wireless propagation environments. First, we consider an environment where the direct AP-user link is in deep fade, which enables the relaxation of the optimization problem such that it can be optimally solved by the proposed sphere-decoder (SD) algorithm. Second, a normal propagation environment for all links is considered, wherein we propose the tabu search (TS) algorithm to solve the joint active and passive beamforming problem with low complexity. The proposed TS algorithm is shown to work well, even in deep-fade AP-user link scenarios. The presented numerical results confirm the validity of our analysis and demonstrate the effectiveness of the proposed schemes over the benchmark schemes on different system setups and propagation environments.

Optimal reconfiguration of distribution network based on deep fuzzy c-means clustering algorithm

This study examines the best distribution network reconfiguration to enhance distribution network safety and reduce active power loss. Distribution networks can be reconfigured using a deep fuzzy C-means (FCM) clustering method with the objective of the least amount of active power loss. By creating a novel framework, the size of the neural network is lowered by modifying the number of neurons in input layer. With the simulations tested on IEEE 33-bus distribution network, the outcomes of traditional approaches, such as the Branch-and-Cut and the switching algorithms, are contrasted with the simulation results. The comparative results clearly show advantages to employing the suggested framework for distribution network reconfiguration, such as a quick turnaround time far quicker than the alternatives. These characteristics demonstrate how well the suggested paradigm may be applied to distribution network real-time reconfiguration.

Efficient and High-Purity Sound Frequency Conversion with a Passive Linear Metasurface.

Despite the significance for wave physics and potential applications, high-efficiency frequency conversion of low-frequency waves cannot be achieved with conventional nonlinearity-based mechanisms with poor mode purity, conversion efficiency, and real-time reconfigurability of the generated harmonic waves in both optics and acoustics. Rotational Doppler effect provides an intuitive paradigm to shifting the frequency in a linear system which, however, needs a spiral-phase change upon the wave propagation. Here a rotating passive linear vortex metasurface is numerically and experimentally presented with close-to-unity mode purity (>93%) and high conversion efficiency (>65%) in audible sound frequency as low as 3000Hz. The topological charge of the transmitted sound is almost immune from the rotational speed and transmissivity, demonstrating the mechanical robustness and stability in adjusting the high-performance frequency conversion in situ. These features enable the researchers to cascade multiple vortex metasurfaces to further enlarge and diversify the extent of sound frequency conversion, which are experimentally verified. This strategy takes a step further toward the freewheeling sound manipulation at acoustic frequency domain, and may have far-researching impacts in various acoustic communications, signal processing, and contactless detection.

Real-Time Reconfiguration Planning for the Dynamic Control of Reconfigurable Cable-Driven Parallel Robots

Abstract The movable anchor points make reconfigurable cable-driven parallel robots (RCDPRs) advantageous over conventional cable-driven parallel robots with fixed anchor points, but the movable anchor points also introduce an inherent problem—reconfiguration planning. Scholars have proposed reconfiguration planning approaches for RCDPRs, taking into account the statics and kinematics of RCDPRs. However, a real-time reconfiguration planning approach that considers the dynamics of an RCDPR and is computationally efficient enough to be integrated into the RCDPR's dynamic controller is still not available in the literature yet. This paper develops a real-time reconfiguration planning approach for RCDPRs. A novel reconfiguration value function is defined to reflect the “value” of an RCDPR configuration and provide a reference index for the reconfiguration planning of an RCDPR. And then, the developed approach conducts reconfiguration planning based on the value of RCDPR configurations. The developed approach is computationally efficient, reducing the reconfiguration planning time by more than 93%, compared to single iteration of a box-constrained optimization-based reconfiguration planning approach. Such a high efficiency allows the developed approach to be integrated into an RCDPR's dynamic controller that usually runs with a high frequency. Integrating reconfiguration planning and dynamic control enhances the control performance of the RCDPR. To verify the effectiveness of the developed approach and the integration of reconfiguration planning and dynamic control for RCDPRs, a case study of an RCDPR with seven cables and four movable anchor points is conducted.

Learning to Cooperate: A Hierarchical Cooperative Dual Robot Arm Approach for Underactuated Pick-and-Placing

Cooperative multiagent manipulation systems allow to extend on the manipulative limitations of individual agents, increasing the complexity of the manipulation tasks the ensemble can handle. Controlling such a system requires meticulous planning of subsequent subtasks, queried to the individual agents, in order to execute the master task successfully. Real-time (re)planning is essential to ensure the task can still be achieved when subtasks execution suffers from uncertainty or when the master task changes intermittently requiring real-time reconfiguration of the plan. In this article, we develop a supervisory control architecture tailored to the cooperation of two robotic manipulators equipped with standard pick-and-place facilities in the plane. We control the planar position and orientation of an object using two underactuated manipulators so that only the position of the object can be controlled directly. The desired orientation follows from the accumulation of alternating relative angles. A time-invariant policy function is trained using deep reinforcement learning, which can determine a finite sequence of pick-and-place maneuvers to manipulate the object to a desired configuration. Two policy architectures are compared. The first uses the kinematic model to determine the final step, whilst the second policy makes this decision itself. The more information is given to the policy, the easier it trains. In return, it becomes less adaptable and loses some of its generalizability.

Real-time [formula omitted] norm optimization of the linear time-invariant system

H∞ norm is widely adopted in control system design and its model-based computation is well-established. In these applications, the optimization of the H∞ norm follows an offline manner. However, the real-time optimization of the H∞ norm of a (stable) linear time-invariant system remains open, which is of considerable interest in the domains of performance optimization, fault-tolerant control, etc. Driven by this desire, as an alternative to the model-based offline solution of the H∞ control/optimization problem, this paper proposes a real-time H∞ norm optimization approach. It would be beneficial to the real-time reconfiguration of the controller/observer under the H∞ optimization framework.

An integrated acoustic/LoRa system for transmission of multimedia sensor data over an Internet of Underwater Things

In the last years, underwater research has been gaining momentum due to the plethora of emerging application scenarios that are appearing, typically associated to marine wildlife protection, national borders monitoring or underwater historical sites preservation. However, research in this area is still at infancy, and implementation of the Internet of Underwater Things vision is not yet at its adulthood. One of the most critical issues is indeed associated to the need for developing a network of devices which can connect and remotely deliver data to a front-end elaboration center. To this purpose, the need for increasing network lifetime and improving the quality of the monitored parameters, while guaranteeing real time control of the network, calls for the design of tools for remotisation, actuation and control. In this paper we report about the design and development of an integrated acoustic/LoRa system for transmission of multimedia sensor data over the Internet of Underwater Things. We detail the blocks composing the system and show results obtained through field tests that assess the possibility to transmit multimedia data, as well as control data, for real time reconfiguration of the system parameters through properly designed Android or Web applications.

Real-Time Vision-Based Aircraft Vertical Tail Damage Detection and Parameter Estimation

The need for reliable, fast, and accurate detection of fault is the key to fault-tolerant control reconfiguration of any flight control system. It is known that faults such as structural damage greatly modify the aerodynamic characteristics, often represented by stability and control derivatives. They also result in abrupt deterioration of flight performance and handling quality, which impairs safety. Since no detection technique is as reliable and fast as the Human Visual System (HVS), this paper demonstrates how popular vision algorithms such as edge detection and structural similarity index can be used for real-time detection of vertical tail damage of a fighter aircraft. A novel geometric method is proposed for fault identification using traditional mathematical geometry. Empirical techniques are used for the estimation of stability and control derivatives to generate a mathematical model of damaged aircraft instead of relying on expensive wind tunnel data. Flight mechanics analysis of the data generated confirms the effects of vertical damage in terms of reduction of static and dynamic directional stability and its control effectiveness. The parametrized state matrix thus obtained for the given flight condition thus leads to efficient real-time control reconfiguration of a damaged aircraft for safe recovery. The importance of this paper lies in using the established techniques of image processing for fault detection of damaged aircraft and parametrization for subsequent control reconfiguration, which is a novel application.

An SDN-based traffic handover control procedure and SGD management logic for EHF satellite networks

Smart Gateway Diversity (SGD) is a fundamental concept introduced to maintain high service availability in High Throughput Satellites (HTS) working at frequency bands above Ka-band. This paper proposes a novel approach for the SGD management logic that takes into account the “graceful degradation” of the link for triggering a handover of traffic among gateways (GWs.) Moreover, the paper proposes a Software Defined Network (SDN) implementation of the handover control procedure and an algorithm for building the flows matrices of the SDN switches that guarantees a fair balancing of traffic among the gateways, thus allowing a better exploitation of resources and maximization of the overall operational capacity of the HTS system. The SDN paradigm, by allowing efficient network programmability and real time re-configuration of the flow tables, enables the possibility to implement dynamic and granular resource allocation; this is a fundamental requirement for the proposed novel SGD network management approach, where re-configuration is triggered by smaller and more frequent signal-to-noise ratio (SNR) fluctuations. The performance of the proposed handover control procedures have been evaluated over real Q/V band link by using the experimental data collected through the TDP5 “Aldo Paraboni” payload embarked on the Alphasat satellite. Performance results show a gain of 40% in terms of aggregated capacity of the system with respect to the case of using an on–off SGD management logic.

Time-modulated linear array synthesis with optimal time schemes for the simultaneous suppression of sidelobe and sidebands

AbstractAn efficient analysis of time-modulated array (TMA) toward realizing less-attenuating radiation patterns with simultaneously suppressed sidelobe and sidebands is presented in this paper. In this framework, an optimal outer element-controlled time sequence is derived. The proposed time scheme, along with optimized array excitations, is profitably applied for the desired solution. TMAs are considered unconventional alternatives to the phased arrays. The desired array radiation features can be attained by periodically enabling and disabling the array elements through high-speed switches. Despite the advantages of architectural simplicity and real-time reconfigurability of periodic time sequences, time-domain antenna arrays inherently generate unavoidable sideband radiations (SRs). The undesired SRs obtained at multiple harmonics around the carrier frequency of the array resembles power loss in unintended directions. This paper aims to minimize the SRs as well as the sidelobe level (SLL) for an efficient analysis of time-modulated linear array (TMLA) with high-directive radiation patterns. The starting instants and the period of on-times are optimized to generate a unique shifted time scheme for the edge elements of the TMLA to reduce the sideband levels (SBLs). The array excitations and the uniform spacing between the elements are also optimized together with the shifted time scheme for the coveted solution. Other methods of suppressing SLLs and SBLs with shifted pulse schemes and sub-sectioned pulse schemes are also presented for a fair comparison. Modified versions of the particle swarm optimization algorithm (PSO) are applied for the desired solutions. The optimal results attained by wavelet mutation-based novel PSO is compared with the conventional PSO and the modified novel PSO-based results. The representative results are reported, and the superior performance abilities of the proposed method compared to other published studies are assessed.

Optimization algorithm of communication systems with intelligent reflecting surface for internet of things

With the development of 5th-generation(5G) wireless systems and internet of things(IoT), high speed wireless communications are facing severe challenges due to numerous connections of communication terminals and limited frequency resources. Intelligent reflecting surface(IRS), a newly appeared wireless communication technology, has attracts people's attentions worldwide by its low power consumptions and costs. In order to improve the communication rates of users, this paper proposes a novel optimization algorithm by implementing the deep learning to IRS for establishing the mapping from the channel state information to the optimal reflecting coefficient matrix of IRS. The present algorithm can perform real-time reconfiguration of IRS while protecting the privacy of IoT users.

Physical Internet: First results and next challenges

Physical Internet: First results and next challenges