Flexible, Fully Printable, and Inexpensive Paper-Based Chipless Arabic Alphabet-Based RFID Tags

This work presents the design and analysis of newly developed reconfigurable, flexible, inexpensive, optically-controlled, and fully printable chipless Arabic alphabet-based radio frequency identification (RFID) tags. The etching of the metallic copper tag strip is performed on a flexible simple thin paper substrate (ϵr = 2.31) backed by a metallic ground plane. The analysis of investigated tags is performed in CST MWS in the frequency range of 1–12 GHz for the determination of the unique signature resonance characteristics of each tag in terms of its back-scattered horizontal and vertical mono-static radar cross section (RCS). The analysis reflects that each tag has its own unique electromagnetic signature (EMS) due to the changing current distribution of metallic resonator. This EMS of each tag could be used for the robust detection and recognition of all realized 28 Arabic alphabet tags. The study also discusses, for the first time, the effect of the change in font type and size of realized tags on their EMS. The robustness and reliability of the obtained EMS of letter tags is confirmed by comparing the RCS results for selective letter tags using FDTD and MoM numerical methods, which shows very good agreement. The proposed tags could be used for smart internet of things (IoT) and product marketing applications.

Passive Sensing of Electromagnetic Signature of Roadway Material for Lateral Positioning of Vehicle

Autonomous vehicles (AV) and advanced driver-assistance systems (ADAS) offer multiple safety benefits for drivers and road agencies. However, maintaining the lateral position of an AV or a vehicle with ADAS within a lane is a challenge, especially in adverse weather conditions when lane markings are occluded. For significant penetration of AV without compromising safety, vehicle-to-infrastructure sensing capabilities are necessary, especially during severe weather conditions. This research proposes a method to create a continuous electromagnetic (EM) signature on the roadway, using materials compatible with existing paving materials and construction methods. Laboratory testing of the proposed concept was performed on notched concrete-slab specimens and concrete prisms containing EM materials. An induction-based eddy-current sensor and magnetometers were implemented to detect the EM signature. The detected signals were compared to evaluate the effects of sensor height above the concrete surface, type of EM materials, EM-material volume, material shape, and volume of EM concrete prisms. A layer of up to 2 in. (5.1 cm) of water, ice, snow, or sand was placed between the sensor and the concrete slab to represent adverse weather conditions. Results showed that factors such as sensor height, EM-material volume, EM dosage, types of the EM material, and shape of the EM material in the prism were significant attenuators of the EM signal and must be engineered properly. Presence of adverse surface conditions had a negligible effect, as compared to normal conditions, indicating robustness of the presented method. This study proposes a promising method to complement existing sensors’ limitations in AVs and ADAS for effective lane-keeping during normal and adverse weather conditions with the help of vehicle-to-pavement interaction.

A MULTIFUNCTIONAL AND RECONFIGURABLE MICROVASCULAR COMPOSITE

Fiber-reinforced polymer (FRP) composites, consisting of stiff/strong fibers embedded within a continuous matrix, are a lightweight structural platform supporting an array of modern applications. Bioinspired vascularization of fiber-composites can augment existing performance with dynamic functionalities via liquid infiltration of the internal micro-fluidic network. Some vascular-enabled capabilities include self-healing to repair delamination damage and active-cooling to prevent thermal degradation. While such attributes have been demonstrated in separate platforms, research investigations that combine functionalities within a single composite have been limited. Here we provide a recent study that highlights a promising pathway for achieving both multifunctional, and reconfigurable behavior in microvascular FRP composites. Specifically, we detail the ability to regulate temperature and modulate electromagnetic signature via fluid substitution within the same serpentine vasculature. Varying microchannel density alters both active-cooling efficiency by water circulation and polarized radio-frequency wave reflection by liquid metal infiltration. We control these bulk property pluralities by widespread vascularization, while minimizing impact on structural performance, and decode the effects of micro-vascular topology on macromechanical behavior. Our in-depth experimental and computational investigation provides a new benchmark for future design optimization and real-world translation of multifunctional and adaptive microvascular composites.

The Model-less Neuromimetic Chip and its Normalization of Neuroscience and Artificial Intelligence

<p></p><p>The conceptual basis of a novel neuromimetic chip is described. Based on an existing computational bioelectrodynamics study and adaptive brain signaling biophysics knowledge from neuroscience, the chip is, in effect, a form of inorganic artificial brain tissue. This ‘physics replication’ approach involves no abstract models of brain tissue physics or function. Instead of the physics of a general-purpose computer or the physics of abstract models of the brain on the chip (analogue or digital), this neuromimetic chip has an inorganic version of natural adaptive brain signaling physics. As a result of using the native brain physics, the chip has functionally relevant endogenous quasistatic electric and magnetic field systems of the form known to be expressed by excitable cell tissue. Fully developed at macroscopic scales it can be expected to produce an EEG/MEG-like electromagnetic signature. This article does an extended analysis to understand an observed generalized lack of the physics-replication approach and its implications for the neuroscience of natural and artificial intelligence. This is achieved through a technical comparison with the neuromimetic chip’s closest relative, the neuromorphic chip (of the class of general-purpose computers). The results indicate that the physics-replication approach is a possible but neglected option. It also reveals that the neuromimetic chip contributes empirical science, in contrast to the theoretical science conducted using general-purpose computers. Because of the chip’s novelty and proximity to foundational issues, the article contributes necessary background information in anticipation of the arrival of the first prototyping results over the coming years.<b></b></p><br><p></p>

The Model-less Neuromimetic Chip and its Normalization of Neuroscience and Artificial Intelligence

<p></p><p>The conceptual basis of a novel neuromimetic chip is described. Based on an existing computational bioelectrodynamics study and adaptive brain signaling biophysics knowledge from neuroscience, the chip is, in effect, a form of inorganic artificial brain tissue. This ‘physics replication’ approach involves no abstract models of brain tissue physics or function. Instead of the physics of a general-purpose computer or the physics of abstract models of the brain on the chip (analogue or digital), this neuromimetic chip has an inorganic version of natural adaptive brain signaling physics. As a result of using the native brain physics, the chip has functionally relevant endogenous quasistatic electric and magnetic field systems of the form known to be expressed by excitable cell tissue. Fully developed at macroscopic scales it can be expected to produce an EEG/MEG-like electromagnetic signature. This article does an extended analysis to understand an observed generalized lack of the physics-replication approach and its implications for the neuroscience of natural and artificial intelligence. This is achieved through a technical comparison with the neuromimetic chip’s closest relative, the neuromorphic chip (of the class of general-purpose computers). The results indicate that the physics-replication approach is a possible but neglected option. It also reveals that the neuromimetic chip contributes empirical science, in contrast to the theoretical science conducted using general-purpose computers. Because of the chip’s novelty and proximity to foundational issues, the article contributes necessary background information in anticipation of the arrival of the first prototyping results over the coming years.<b></b></p><br><p></p>

The Model-less Neuromimetic Chip and its Normalization of Neuroscience and Artificial Intelligence

<p></p><p>The conceptual basis of a novel neuromimetic chip is described. Based on an existing computational bioelectrodynamics study and adaptive brain signaling biophysics knowledge from neuroscience, the chip is, in effect, a form of inorganic artificial brain tissue. This ‘physics replication’ approach involves no abstract models of brain tissue physics or function. Instead of the physics of a general-purpose computer or the physics of abstract models of the brain on the chip (analogue or digital), this neuromimetic chip has an inorganic version of natural adaptive brain signaling physics. As a result of using the native brain physics, the chip has functionally relevant endogenous quasistatic electric and magnetic field systems of the form known to be expressed by excitable cell tissue. Fully developed at macroscopic scales it can be expected to produce an EEG/MEG-like electromagnetic signature. This article does an extended analysis to understand an observed generalized lack of the physics-replication approach and its implications for the neuroscience of natural and artificial intelligence. This is achieved through a technical comparison with the neuromimetic chip’s closest relative, the neuromorphic chip (of the class of general-purpose computers). The results indicate that the physics-replication approach is a possible but neglected option. It also reveals that the neuromimetic chip contributes empirical science, in contrast to the theoretical science conducted using general-purpose computers. Because of the chip’s novelty and proximity to foundational issues, the article contributes necessary background information in anticipation of the arrival of the first prototyping results over the coming years.<b></b></p><br><p></p>

The Model-less Neuromimetic Chip and its Normalization of Neuroscience and Artificial Intelligence

<p>The conceptual basis of a novel neuromimetic chip is described. Based on an existing computational bioelectrodynamics study and adaptive brain signaling biophysics knowledge from neuroscience, the chip is, in effect, a form of inorganic artificial brain tissue. This ‘physics replication’ approach involves no abstract models of brain tissue physics or function. Instead of the physics of a general-purpose computer or the physics of abstract models on the chip (analogue or digital), this neuromimetic chip has an inorganic version of natural adaptive brain signaling physics. As a result of using the native brain physics, the chip has a functionally relevant endogenous quasistatic electric and magnetic field system of the form known to be expressed by excitable cell tissue. Fully developed at macroscopic scales it can be expected to produce an EEG/MEG-like electromagnetic signature. This article does an extended analysis to understand an observed generalized lack of the physics replication approach and its implications for the neuroscience of natural and artificial intelligence. This is achieved through a technical comparison with the neuromimetic chip’s closest relative, the neuromorphic chip (of the class of general-purpose computers). The results indicate that the physics-replication approach is a possible but neglected option. It also reveals that the neuromimetic chip contributes empirical science, in contrast to the theoretical science conducted using general-purpose computers. Because of the chip’s novelty and proximity to foundational issues, the article contributes the necessary background information in anticipation of the arrival of the first prototyping results over the coming years.<b></b></p><br>

An Ultra-Wide Band Antenna System for Pulsed Sources Direction Finding

Electric discharges in high-voltage power distribution systems can be localized through their electromagnetic signature in the radio-frequency range. Since discharges produce series of short pulses, the corresponding spectrum usually covers wide frequency ranges, typically up to 1 GHz. In this paper, we propose an ultra-wide band (UWB) antenna system and a direction-finding (DF) approach based on using energy-based descriptors, instead of classical frequency-domain parameters. As an antenna system, we propose a dual-padlock configuration with a suitable pulse-matched response, featuring two unbalanced outputs. The proposed antenna system was successfully validated, both by simulations and measurements.

Detecting gravitational self-lensing from stellar-mass binaries composed of black holes or neutron stars

ABSTRACT We explore a unique electromagnetic signature of stellar-mass compact-object binaries long before they are detectable in gravitational waves. We show that gravitational lensing of light emitting components of a compact-object binary, by the other binary component, could be detectable in the nearby Universe. This periodic lensing signature could be detected from present and future X-ray observations, identifying the progenitors of binaries that merge in the LIGO band, and also unveiling populations that do not merge, thus providing a tracer of the compact-object binary population in an enigmatic portion of its life. We argue that periodically repeating lensing flares could be observed for ≲100 ks orbital-period binaries with the future Lynx X-ray mission, possibly concurrent with gravitational wave emission in the LISA band. Binaries with longer orbital periods could be more common and be detectable as single lensing flares, though with reliance on a model for the flare that can be tested by observations of succeeding flares. Non-detection of such events, even with existing X-ray observations, will help to constrain the population of EM bright compact-object binaries.