Circuit Geometry Research Articles

Propofol disrupts the functional core-matrix architecture of the thalamus in humans

Research into the role of thalamocortical circuits in anesthesia-induced unconsciousness is difficult due to anatomical and functional complexity. Prior neuroimaging studies have examined either the thalamus as a whole or focused on specific subregions, overlooking the distinct neuronal subtypes like core and matrix cells. We conducted a study of heathy volunteers and functional magnetic resonance imaging during conscious baseline, deep sedation, and recovery. We advanced the functional gradient mapping technique to delineate the functional geometry of thalamocortical circuits, within a framework of the unimodal-transmodal functional axis of the cortex. Here we show a significant shift in this geometry during deep sedation, marked by a transmodal-deficient geometry. This alteration is closely linked to the spatial variations in the matrix cell composition within the thalamus. This research bridges cellular and systems-level understanding, highlighting the crucial role of thalamic core–matrix functional architecture in understanding the neural mechanisms of states of consciousness.

Some Measures to Train the Capacity of Teaching Circuit Geometry Content in Primary School for Students in Primary Education, Tan Trao University

Some Measures to Train the Capacity of Teaching Circuit Geometry Content in Primary School for Students in Primary Education, Tan Trao University

Improved design-oriented analytical modelling of flux switching PM machines

Purpose This paper aims at the analytical formulation of the electromagnetic features of flux switching permanent magnet (PM) machines with emphasis on the PM air gap flux density and armature magnetic reaction. Design/methodology/approach The PM air gap flux density is formulated considering three different analytical models. These differ by the incorporation of the air gap magnetic saliency level from the stator side. In addition, the armature magnetic reaction is investigated based on a simplified magnetic reluctance circuit that considers the flux switching permanent magnet machines magnetic circuit geometry specification. Then, the no- and on-load torque is predicted based on the two air gap flux densities. Findings It has been found that the PM air gap flux density considering the stator saliencies with trapezoidal magnetomotive force waveform presents the highest accuracy. Despite the simplicity of the magnetic equivalent circuit-based approach, the predicted air gap armature magnetic reaction is in good agreement with the finite element analysis (FEA) one. These lead to the analytical predictions of the no- and on-load torque which is characterized by an acceptable accuracy. Research limitations/implications This work should be extended to experimental validation of the FEA results regarding the torque production generation. Originality/value The paper proposes an improved design-oriented analytical approach with emphasis on the PM air gap flux density and the armature magnetic reaction of flux switching PM machines.

Modular wind generators for autonomous power supply to remote mining areas mineral resources

The use of renewable energy sources to supply power to energy-isolated areas is an urgent topic. The simplicity of the design and high reliability of inductor generators create good prospects for their use in wind power plants as an alternative to synchronous and asynchronous electric machines that are currently in use. An obstacle to such an application is the specifics of the design, construction, and execution of modular machines with magnetic switching, concentrated windings, and a distributed magnetic system integrated into the wind turbine design. The purpose of the study is to consider cause-effect relationships and establish relationships between magnetic flux, EMF, the geometry of electric and magnetic circuits, the structural arrangement of electric windings, and circuit solutions of an automatic electromagnetic excitation system for the design of a new type of generators. The current issues of the effective use of an exciting magnetic flux for voltage regulation in power supply systems powered by power plants, the main generators of which contain a distributed magnetic system with concentrated electric windings, are considered. The optimal structural compositions and geometry of the ratios of electric and magnetic circuits are determined from the machine's basic calculation equation, which is obtained under the condition of maximum magnetic flux. The reasons for the induction of variable EMF in the DC excitation windings, which prevent the effective magnetization of the machine, have been established. The location of the electric windings is proposed, which makes it possible to weaken or completely compensate for the pulsations of the magnetic flux coupled with the exciting circuits. It is established that in order to eliminate parasitic EMF in the exciting windings, it is necessary to use two-circuit circuits for alternating switching of the magnetic flux in the circuits, with sequential activation of the sections of the windings of the exciting circuits. To completely eliminate these EMFs, performing two-circuit magnetic flux switching circuits with a common exciting winding is advisable. The methods and features of the circuit implementation of the conditions of electromagnetic excitation and maintenance of voltage constancy at the output of generators of a new type are considered.

Integration of Mechatronic Functions on Additively Manufactured Components via Laser‐Assisted Selective Metal Deposition

AbstractThe current industrial revolution derives much of its momentum from value creation based on interconnected products and related data based services. Such products must fulfill both mechanical and electrical requirements, making them mechatronic systems. The production of such systems via additive manufacturing (AM) processes offers advantages in achievable complexity, reduction of the amount of individual components, and cost‐effective as well as sustaina ble production of small quantities. In this work, a process chain is presented that allows for refining additively manufactured 3D structures made from industry‐standard materials into mechatronic components by creating electrically conductive structures directly on their surfaces. The process chain is based on masking the component's surface and selectively removing the masking according to the circuit geometry using laser radiation. In a wet–chemical bath process, the surface is then exposed to palladium nuclei, the masking is fully removed and metal layers (copper/nickel/gold) are deposited by electroless plating. The procedure is developed using stereolithography as a model process for AM and transferred to four additional AM methods. In all cases, despite markedly different surface properties, good selectivity of metal deposition is observed as well as adhesion strength and conductivity comparable to industrially common injection‐molded laser direct structured mechatronic interconnect devices.

A Tandem Approach to Fabricate a Hybrid, Organic‐Add‐Drop Filter Using Single‐Crystal Disk‐Resonators and Pseudo‐Plastic Crystal Waveguides

AbstractThe progress of photonic integrated circuits (PICs) and related nanophotonic technologies is greatly influenced by precise and complex circuit fabrication know‐how. The integration of chemically different, multiple optical components for constructing hybrid PICs is a tedious and costly affair. Herein, a novel approach to construct a key optical circuit component is reported, namely a hybrid organic add‐drop filter (HOADF) by tandem use of two different techniques, namely, mechanophotonics (micromechanical manipulation) and crystal photonics foundry (focused ion beam, FIB‐assisted crystal milling). The successful integration of FIB‐milled, yellow‐emissive, perylene disk resonators (DRs) to two chemically different pseudo‐plastic microcrystal waveguides, namely yellow–orange‐emitting (Z)−2‐(3,5‐bis(trifluoromethyl)phenyl)−3‐(7‐methoxybenzo[c][1,2,5]thiadiazol‐4‐yl)acrylonitrile (BTD2CF3) and green‐emitting (E)−1‐(((5‐bromopyridin‐2‐yl)imino)methyl)naphthalen‐2‐ol (BPyIN), provides a three‐component HOADF. The fabricated HOADF produce, split, route light signals of different wavelengths in a controlled fashion as anticipated from the circuit geometry. This presented innovative technique has the potential to enable bulk‐scale manufacturing of industrial standard organic PICs.

Unit Cell Design for Aperiodic Metasurfaces

A technique is presented for the design of printed unit cells in aperiodic metasurface environments. The method begins with a solved matrix equation governing electromagnetic scattering from a homogenized metasurface design. The matrix equation is used to find the local, inhomogeneous electric field exciting a printed-circuit unit cell geometry. The local field is then impressed onto the printed circuit geometry and the induced surface current numerically computed. The computed surface current is sampled at the matrix equation discretization. The matrix equation is then used to compute the electric field scattered by the printed-circuit unit cell onto its neighbors using the sampled current in place of the current of the original homogenized unit cell. The printed circuit geometry is optimized to scatter the same field as the homogenized unit cell when excited with the local electric field computed. Two design examples are provided. Both a finite-sized, wide-angle reflecting metasurface, and a metasurface reflectarray designed to scan and collimate an incident cylindrical wave, are realized with printed-circuit unit cells using the proposed approach. It is shown that the local periodicity approximation cannot be used to accurately design the unit cells of either finite-sized metasurface.

Temporospatial Nestedness in Consciousness: An Updated Perspective on the Temporospatial Theory of Consciousness.

Time and space are fundamental elements that permeate the fabric of nature, and their significance in relation to neural activity and consciousness remains a compelling yet unexplored area of research. The Temporospatial Theory of Consciousness (TTC) provides a framework that links time, space, neural activity, and consciousness, shedding light on the intricate relationships among these dimensions. In this review, I revisit the fundamental concepts and mechanisms proposed by the TTC, with a particular focus on the central concept of temporospatial nestedness. I propose an extension of temporospatial nestedness by incorporating the nested relationship between the temporal circuit and functional geometry of the brain. To further unravel the complexities of temporospatial nestedness, future research directions should emphasize the characterization of functional geometry and the temporal circuit across multiple spatial and temporal scales. Investigating the links between these scales will yield a more comprehensive understanding of how spatial organization and temporal dynamics contribute to conscious states. This integrative approach holds the potential to uncover novel insights into the neural basis of consciousness and reshape our understanding of the world-brain dynamic.

Mirror symmetry in the geometry of nonlocal two-qubit gates and universal two-qubit quantum circuits

Mirror symmetry in the geometry of nonlocal two-qubit gates and universal two-qubit quantum circuits

Stepwise Fabrication and Optimization of Coplanar Waveguide Resonator Hybrid Devices

From the background of microwave‐optomechanical experiments involving carbon nanotubes, the optimization of superconducting coplanar waveguide resonator devices is discussed. Two devices, one with unmodified geometry compared to previous work and one integrating several improvements, are lithographically built up step‐by‐step. After each step, the low‐temperature GHz transmission properties are retested. This allows to identify the impact of the fabrication and the geometry modification on the device properties. In addition, simplified circuit geometries are modeled numerically, confirming the experimental results and providing further insights for optimization.

Miniaturized metallic symmetrical resonator backed CPW inspired dual band filter using Meander line for 5G mm‐wave applications

AbstractAn ultra‐miniaturized symmetrical resonator backed coplanar waveguide based dual band pass filter using Meander line for 5G mm‐wave applications is presented. Meander line CPW structure with ultra miniaturized size was designed, investigated and its equivalent circuit was extracted using theoretical and experimental results to explore basic RF characteristics. The resonator at the back side of the substrate is symmetrical in both horizontal and vertical plane. This resonator is electrically coupled with the Meander line structure in the conducting plane. Symmetrical resonator structure coupled with open stubs on both sides on ground plane and Meander line structure with uniform spacing implemented at the conducting layer constitutes the metallic backed Co‐Planar waveguide based dual band filter. By using Meander line, the overall size of the communication devices has been reduced in large scale. The combination of symmetrical resonator and Meander line structure produces the overall bandwidth from (1 to 30) GHz. 5G mm‐wave filter operates in two bands; Band I operates from (1 to 14.2) GHz with insertion loss −1.8 dB and return loss −34 dB with fractional bandwidth of 73.6%; Band II operates from (16.2 to 30) GHz with insertion loss −1.2 dB, return loss −30 dB and fractional bandwidth of 59.7%. The overall filter circuit geometry is (5 × 5 × 1.6) mm and in terms of wavelength: (0.52 × 0.52) λg.

A novel strategy for fabrication, activation and cleaning of fully 3D printed flexible planar electrochemical platforms

AbstractIn recent years, 3D printing of carbon‐based conductive filaments has received growing attention for assembling electrodes to be used in a wide variety of electroanalytical devices and applications. Despite the large amount of work present in literature concerning the development of three‐dimensional (3D) conductive structures, its potential as dry deposition method for assembling two‐dimensional (2D) electrodes to be used in planar configuration is still largely unexplored. In fact, the possibility to rapidly change the geometry of the electrochemical circuits, associated with the reduction of waste and the absence of solvents, which are instead important components of ink and paste formulations, makes this strategy a valid green and efficient alternative to other deposition approaches such as screen‐printing technology. We report here a rapid and solvent‐free method for assembling fully 3D printed flexible planar electroanalytical platforms (3DEPs) to be used with microliters of liquid. At the same time, a novel protocol for the surface pre‐treatment of 3D printed electrodes based on ultrasonication in aqueous NaOH solution followed by electrochemical activation using the same medium, is presented. In addition, the same procedure has proved to be efficient for cleaning the electrode surface after electrochemical passivation, thus confirming the validity of both time‐efficient and environmentally‐friendly assembling and activation/cleaning procedures developed which allow efficient and reusable electrodes to be produced. Finally, 3DEPs were tested by a proof‐of‐concept quantification of a commonly used food dye (Brilliant Blue, E‐133) in commercial solutions used for homemade food coloring.

Advances in the Theory and Modeling of Linear Beam VE Amplifiers

We present an overview of the progress made by our Naval Research Laboratory (NRL)-based group over the last 25 years in the development of computational tools used in the large-signal modeling and design of linear-beam vacuum electronic (VE) amplifiers, klystrons, and traveling-wave tubes (TWTs). These tools belong to a class of codes that we call parametric beam–wave interaction (PBWI) codes, which includes family of the codes CHRISTINE and TESLA. Notable progress includes the formulation and application of an impedance matrix description of a general class of slow wave circuits, the tracking of slow or reversed particles in klystrons, parallel modeling of multiple-beam devices, linear stability analysis based on Nyquist theory, and geometrical and electrical tolerance analysis and optimization based on solutions to the adjoint equations that govern the beam–wave interaction. The impedance matrix description, in particular, has allowed the detailed effects of circuit geometry, including complex terminations, matching sections, and severs, to be included in PBWI simulations for the first time.

Analytical Design and Simulation of a 9.3-GW Tesla Transformer for HPM Sources

This article presents a step-by-step design and modeling of a high-voltage pulse generator (PG) based on a Tesla transformer (TT) with an open-magnetic core to drive a low-impedance high-power microwave (HPM) source. Our design uses the OFF-resonance mode of operation for higher voltage transformation ratios. The pulse-forming line of the transformer is filled with deionized water for a higher energy density of the line and an increased output pulse duration. Maxwell equations are used to establish the transformer parameters such as the magnetizing and leakage inductances, and subsequently, the geometry of the TT for optimum power transfer between the resonant circuits. The required circuit parameters and geometry of the generator producing up to 250 kV and peak power of 9.3 GW when matched to a 6.7- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Omega $ </tex-math></inline-formula> load were determined through analytical design and validated through simulations.

Natural parametrized quantum circuit

Noisy intermediate-scale quantum computers are useful for various tasks such as state preparation and variational quantum algorithms. However, the non-Euclidean quantum geometry of parameterized quantum circuits is detrimental for these applications. Here we introduce the natural parameterized quantum circuit (NPQC) that can be initialized with a Euclidean quantum geometry. The initial training of variational quantum algorithms is substantially sped up as the gradient is equivalent to the quantum natural gradient. Further, we show how to estimate the parameters of the NPQC by sampling the circuit, which could be used for benchmarking or calibrating NISQ hardware. For a general class of quantum circuits, the NPQC has the minimal quantum Cram\'er-Rao bound, which highlights its potential for quantum metrology. Finally, we show how to generate arbitrary superpositions of two states with the NPQCs for state preparation tasks. Our results can be used to enhance currently available quantum processors.

Ansatz-Independent Variational Quantum Classifiers and the Price of Ansatz

The paradigm of variational quantum classifiers (VQCs) encodes classical information as quantum states, followed by quantum processing and then measurements to generate classical predictions. VQCs are promising candidates for efficient utilizations of noisy intermediate scale quantum (NISQ) devices: classifiers involving M-dimensional datasets can be implemented with only lceil log _2 M rceil qubits by using an amplitude encoding. A general framework for designing and training VQCs, however, is lacking. An encouraging specific embodiment of VQCs, quantum circuit learning (QCL), utilizes an ansatz: a circuit with a predetermined circuit geometry and parametrized gates expressing a time-evolution unitary operator; training involves learning the gate parameters through a gradient-descent algorithm where the gradients themselves can be efficiently estimated by the quantum circuit. The representational power of QCL, however, depends strongly on the choice of the ansatz, as it limits the range of possible unitary operators that a VQC can search over. Equally importantly, the landscape of the optimization problem may have challenging properties such as barren plateaus and the associated gradient-descent algorithm may not find good local minima. Thus, it is critically important to estimate (i) the price of ansatz; that is, the gap between the performance of QCL and the performance of ansatz-independent VQCs, and (ii) the price of using quantum circuits as classical classifiers: that is, the performance gap between VQCs and equivalent classical classifiers. This paper develops a computational framework to address both these open problems. First, it shows that VQCs, including QCL, fit inside the well-known kernel method. Next it introduces a framework for efficiently designing ansatz-independent VQCs, which we call the unitary kernel method (UKM). The UKM framework enables one to estimate the first known computationally-determined bounds on both the price of ansatz and the price of any speedup advantages of VQCs: numerical results with datatsets of various dimensions, ranging from 4 to 256, show that the ansatz-induced gap can vary between 10 and 20%, while the VQC-induced gap (between VQC and kernel method) can vary between 10 and 16%. To further understand the role of ansatz in VQCs, we also propose a method of decomposing a given unitary operator into a quantum circuit, which we call the variational circuit realization (VCR): given any parameterized circuit block (as for example, used in QCL), it finds optimal parameters and the number of layers of the circuit block required to approximate any target unitary operator with a given precision.

Deep Neural Networks for Rapid Simulation of Planar Microwave Circuits Based on Their Layouts

This article demonstrates a deep learning (DL)-based methodology for the rapid simulation of planar microwave circuits based on their layouts. We train convolutional neural networks (CNNs) to compute the scattering parameters of general, two-port circuits consisting of a metallization layer printed on a grounded dielectric substrate, by processing the metallization pattern along with the thickness and dielectric permittivity of the substrate. This approach harnesses the efficiency of CNNs with pattern recognition tasks and extends previous efforts to employ neural networks for the simulation of parameterized circuit geometries. Furthermore, we integrate this CNN in a hybrid network with a long-short term memory (LSTM) module that uses coarse mesh finite-difference time-domain (FDTD) simulation data as an additional input. We show that this hybrid network is computationally efficient and generalizable, accurately modeling geometries well beyond those that the network has previously seen.

Effectively Optimized Dual-band Frequency Selective Surface Design for GSM Shielding Applications

Nowadays, global system for mobile communication (GSM) systems are widely used. Therefore, the impact of mobile GSM signals on human health is one of the current research topics. Furthermore, electromagnetic interference caused by GSM signals can be a significant threat to electronic devices today. To overcome these problems, the surfaces of electronic equipment and exterior or interior walls of buildings can be covered with frequency selective surface (FSS) that stops unwanted GSM signals and passing the other signal. This work presents a novel dual-band FSS that attenuates the 900 MHz and 1800–2170 MHz GSM bands by at least 10 dB in the transmission direction. At the design stage, nested geometries are used to achieve multi-stop band frequency response. The proposed design is effectively optimized by utilizing the equivalent circuit geometries and current and electric field distribution diagrams. So, minimum of 10 dB attenuation is achieved in targeted GSM bands for all polarizations and up to 60 degrees (except TM 60 degrees) of incidence angles.

Adjoint Equations for Beam-Wave Interaction and Optimization of TWT Design

By formulating and solving the adjoint equations governing the beam–wave interaction in a traveling-wave tube (TWT), we show the partial derivatives with respect to design parameters of various TWT figures of merit (FoMs) may be efficiently calculated. FoMs include average gain, gain flatness, and gain–bandwidth product, and design parameters include beam voltage and circuit geometry. We use these derivatives in an optimization algorithm that finds parameter values that minimize or maximize the desired FoM. We show that a 1-D large-signal simulation code, such as CHRISTINE, may be easily modified to compute the adjoint solutions. We further show that only three runs of the modified code suffice to compute the partial derivatives of the output power and phase at a specified frequency with respect to an arbitrary number of parameters, resulting in potentially large savings in computing time compared with direct, finite difference calculation of the partial derivatives. We illustrate the method by optimizing the beam voltage and gap spacing of a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$W$ </tex-math></inline-formula> -band folded-waveguide (FWG) TWT. The formulation given here applies only to TWT slow wave structures, such as coupled-cavity and FWGs, and to klystrons, composed of discrete gaps followed by drift spaces; it does not apply to helix structures, which may be the subject of a future paper.

Connecting geometry and performance of two-qubit parameterized quantum circuits

Parameterized quantum circuits (PQCs) are a central component of many variational quantum algorithms, yet there is a lack of understanding of how their parameterization impacts algorithm performance. We initiate this discussion by using principal bundles to geometrically characterize two-qubit PQCs. On the base manifold, we use the Mannoury-Fubini-Study metric to find a simple equation relating the Ricci scalar (geometry) and concurrence (entanglement). By calculating the Ricci scalar during a variational quantum eigensolver (VQE) optimization process, this offers us a new perspective to how and why Quantum Natural Gradient outperforms the standard gradient descent. We argue that the key to the Quantum Natural Gradient&amp;apos;s superior performance is its ability to find regions of high negative curvature early in the optimization process. These regions of high negative curvature appear to be important in accelerating the optimization process.