Circuit Structure Research Articles

Arithmetic circuit tensor networks, multivariable function representation, and high-dimensional integration

Many computational problems can be formulated in terms of high-dimensional functions. Simple representations of such functions and resulting computations with them typically suffer from the ``curse of dimensionality,'' an exponential cost dependence on dimension. Tensor networks provide a way to represent certain classes of high-dimensional functions with polynomial memory. This results in computations where the exponential cost is ameliorated or, in some cases, removed, if the tensor network representation can be obtained. Here, we introduce a direct mapping from the arithmetic circuit of a function to arithmetic circuit tensor networks, avoiding the need to perform any optimization or functional fit. We demonstrate the power of the circuit construction in examples of multivariable integration on the unit hypercube in up to 50 dimensions, where the complexity of integration can be understood from the circuit structure. We find very favorable cost scaling compared with quasi--Monte Carlo integration for these cases and further give an example where efficient quasi--Monte Carlo integration cannot be performed without knowledge of the underlying tensor network circuit structure.

Study of Nonlinear Excitation Circuits for Fluxgate Magnetometer.

This paper presents the common methods and corresponding drawbacks concerning nonlinear analysis of fluxgate excitation circuits and emphasizes the importance of nonlinear analysis for these circuits. With regard to the nonlinearity of the excitation circuit, this paper proposes the use of the core-measured hysteresis curve for mathematical analysis and the use of a nonlinear model that considers the coupling effect of the core and winding and influence of the historical magnetic field on the core for simulation analysis. The feasibility of mathematical calculations and simulation for the nonlinear study of fluxgate excitation circuit is verified via experiments. The results demonstrate that, in this regard, the simulation is four times better than a mathematical calculation. The simulation and experimental results of the excitation current and voltage waveforms under different excitation circuit parameters and structures are essentially consistent, with a difference in current of no more than 1 mA, thereby verifying the effectiveness of the nonlinear excitation analysis method.

Intelligent management of industrial building energy saving based on artificial intelligence

In order to improve the efficiency of energy-saving management of industrial buildings, this paper combines artificial intelligence technology to analyze the intelligent energy-saving of industrial buildings, and reduces the energy consumption of industrial buildings through intelligent energy-saving management. Moreover, this paper analyzes the circuit structure design, work flow, and functional description of industrial energy-saving buildings to realize the control interface of electrical equipment, and explains the specific hardware design of energy consumption acquisition module and intelligent gateway. In addition, this paper uses the data collected by the hardware as the input to establish the impulse prediction model. Finally, this paper constructs an intelligent management system for industrial building energy conservation based on constraints and optimization conditions. The data analysis results show that the intelligent energy-saving management system of industrial buildings based on artificial intelligence constructed in this paper has good effects and can play an important role in the energy-saving management of industrial buildings.

Quasi-synchronous control of uncertain multiple electrohydraulic systems with prescribed performance constraint and input saturation

This article focuses on the high accuracy quasi-synchronous control issue of multiple electrohydraulic systems (MEHS). In order to overcome the negative effects of parameter uncertainty and external load interference of MEHS, a kind of finite-time disturbance observer (FTDO) via terminal sliding mode method is constructed based on the MEHS model to achieve fast and accuracy estimation and compensation ability. To avoid the differential explosion in backstepping iteration, the dynamic surface control is used in this paper to guarantee the follower electrohydraulic nodes synchronize to the leader motion with a better performance. Furthermore, a time-varying barrier Lyapunov function (tvBLF) is adopted during the controller design process to constraint the output tracking error of MEHS in a prescribed performance with time-varying exponential function. As the initial state condition is relax by tvBLF, the input saturation law is also adopted during the controller design process in this paper to restrain the surges of input signals, which can avoid the circuit and mechanical structure damage caused by the volatile input signal. An MEHS experimental bench is constructed to verify the effectiveness of the theoretical conclusions proposed in this paper and the advantages of the proposed conclusions in this paper are illustrated by a series of contradistinctive experimental results.

Deterministic optimal quantum cloning via a quantum-optical neural network

Quantum machine learning represents a promising approach to disclose novel possibilities for quantum information protocols. Thus, recent efforts have been dedicated to determine how to merge, or extend, machine learning approaches in the quantum domain. A relevant example is provided by a quantum-optical neural network (QONN), a class of physical systems that aim to represent the quantum-optical version of classical neural networks. This architecture was first introduced to identify the structure of optical circuits in analogy to their classical counterpart, showing the possibility of tackling different quantum tasks. However, the full potential of such an approach still needs to be explored. In this work, we provide a step forward in this direction by employing it to tackle the fundamental task of optical quantum cloning. The employed architecture has been successfully trained to perform deterministic universal quantum cloning on a photonic platform, achieving results that are very close to that of an optimal universal quantum cloner. Furthermore, we have verified numerically the robustness of this approach with respect to experimental imperfections relevant for future implementations. This work thus shows the capabilities and flexibility of QONNs, which might prove important for designing even more complex optical circuits.

Brain-wide N2cG compensation permits glycoprotein-deleted rabies virus to trace neural circuits across multiple synapses

Rabies-viruses-based retrograde tracers can spread across multiple synapses in a retrograde direction in the nervous system of rodents and primates, making them powerful tools for determining the structure and function of the complicated neural circuits of the brain. However, they have some limitations, such as posing high risks to human health and the inability to retrograde trans-synaptic label inputs from genetically-defined starter neurons. Here, we established a new retrograde trans-multi-synaptic tracing method through brain-wide rabies virus glycoprotein (RVG) compensation, followed by glycoprotein-deleted rabies virus (RV-[Formula: see text]G) infection in specific brain regions. Furthermore, in combination with the avian tumor virus receptor A (TVA) controlled by a cell-type-specific promoter, we found that EnvA-pseudotyped RV-[Formula: see text]G can mediate efficient retrograde trans-multi-synaptic transduction from cell-type-specific starter neurons. This study provides new alternative methods for neuroscience researchers to analyze the input neural networks of rodents and nonhuman primates.

LEAP: Scaling Numerical Optimization Based Synthesis Using an Incremental Approach

While showing great promise, circuit synthesis techniques that combine numerical optimization with search over circuit structures face scalability challenges due to a large number of parameters, exponential search spaces, and complex objective functions. The LEAP algorithm improves scaling across these dimensions using iterative circuit synthesis, incremental reoptimization, dimensionality reduction, and improved numerical optimization. LEAP draws on the design of the optimal synthesis algorithm QSearch by extending it with an incremental approach to determine constant prefix solutions for a circuit. By narrowing the search space, LEAP improves scalability from four to six qubit circuits. LEAP was evaluated with known quantum circuits such as QFT and physical simulation circuits like the VQE, TFIM, and QITE. LEAP can compile four qubit unitaries up to 59× faster than QSearch and five and six qubit unitaries with up to 1.2× fewer CNOTs compared to the QFAST package. LEAP can reduce the CNOT count by up to 36×, or 7× on average, compared to the CQC Tket compiler. Despite its heuristics, LEAP has generated optimal circuits for many test cases with a priori known solutions. The techniques introduced by LEAP are applicable to other numerical optimization based synthesis approaches.

Development of a two-layer dense-pixel LYSO Compton camera prototype for prompt gamma imaging

Objective. Lutetium–yttrium orthosilicate (LYSO)-based Compton camera (CC) has been proposed for prompt gamma imaging due to its high detection efficiency and position resolution. However, very few LYSO CC prototypes have been built and used for practical evaluation. In this study, we built a lightweight dense-pixel silicon photomultiplier-based two-layer LYSO CC prototype for future prompt gamma imaging. Approach. We attempt the first-ever effort to use the double-encoding with the thick light guide and coding circuit structure for 46 × 46 dense-pixel LYSO detectors construction and use pixel segmentation based on centroid mapping to obtain 4232 spectral calibrations. We also present a framework for list-mode projection data acquisition based on the decoding of the time series data obtained by data acquisition card in this study. Finally, the standard source calibration, ring-like 22Na source with non-uniform intensity, and mixed point-like source with a wide energy spectrum experiments were implemented to evaluate the resolution metrics and imaging performance of the prototype. Main results. The lateral position resolution of the prototype was 1 mm, and the maximum measurement deviation is 2.5 mm and 5 mm in the depth direction for the scatterer and absorber, respectively. In the experiments, the measured energy resolution was 9.63% @ 1.33 MeV for the scatterer and 10.8% @ 1.33 MeV for the absorber. And the detection efficiency of the prototype for a spherical 60Co source with a diameter of 2.8 mm at 10 cm far was 5.7 × 10−3 @ 1.33 MeV and the full width at half maximum of the reconstruction was 5.5 mm. Besides, the spatial position offset within 2 mm of the radioactive source at 10 cm can be distinguished. Signification. The developed two-layer dense-pixel LYSO CC contributes to incorporating Compton imaging techniques for prompt gamma detection and multiple energy sources into nuclear medical imaging.

Research on direct-drive motor for wave energy conversion based on Halbach array

In this paper, an improved Halbach array structure of a direct-drive linear motor for wave energy conversion is proposed, and its magnetic circuit structure is modeled and the induced electromotive force (EMF) generated by vertical wave energy conversion is deduced. Then, using the internal and external parameters of the wave energy conversion direct-drive motor, that is, four internal parameters of the magnetic circuit topology and two external parameters of the mover motion, the influence of these parameters on the induced EMF of the direct-drive motor in the wave energy conversion is analyzed. Since the theoretical analysis results of the model are consistent with the results of the two kinds of permanent magnet materials analyzed by the finite element method (FEM), the model and analysis method of the direct drive motor are correct. The investigation show that the modeling method proposed in this paper can be used to optimize the energy conversion of the direct-drive motor from two aspects: the internal parameters of the magnetic circuit topology of the direct-drive motor and the external parameters of the mover motion.

Design and dynamic performance research of MR hydro-pneumatic spring based on multi-physics coupling model

Aiming at the problem that the damping coefficient of the traditional hydro-pneumatic spring cannot be adjusted in real-time, the magnetorheological (MR) damping technology was introduced into the traditional hydro-pneumatic spring with single gas chamber. A new shear-valve mode MR hydro-pneumatic spring was proposed. And its dynamic performance was analyzed based on multi-physical coupling simulation and mechanical property test. Firstly, a structural scheme of MR hydro-pneumatic suspension was proposed to ensure the original height adjustment function based on the working principle of traditional hydro-pneumatic suspension with single gas chamber. Secondly, based on the design requirements, the parameter of MR hydro-pneumatic spring damping structure was designed by using MR damper design method. Thirdly, the multi-physical coupling dynamic performance of the MR hydro-pneumatic spring damping structure was analyzed based on the electromagnetic field analysis theory, flow field analysis theory and thermal field analysis theory. The analysis results showed that the designed MR hydro-pneumatic spring has reasonable magnetic circuit structure and excellent working performance. Then, the mechanical properties of MR hydro-pneumatic spring were tested. The results showed that the maximum damping force can reach 20 kN, and the dynamic adjustable multiple can reach 6.4 times. It has good controllability and meets the design requirements. Finally, a nonlinear model of MR hydro-pneumatic spring was established based on the elastic force calculation model of the gas and the Bouc–Wen model. The simulation results of the established model agree well with the experimental results, which can accurately describe the dynamic properties of the hydro-pneumatic spring. The proposed design and modeling method of the MR hydro-pneumatic spring can provide a theoretical basis for the related vibration damping devices.

Novel Structure of Single-Shunt Rectifier Circuit with Impedance Matching at Output Filter

This study proposes a new structure of a single-shunt rectifier circuit that can reduce circuit loss and improve efficiency over the conventional structure. The proposed structure can provide impedance matching to the measurement system (or receiving antenna) without the use of conventional matching circuits, such as stubs and tapers. The proposed structure can simultaneously perform full-wave rectification and impedance matching by placing a feeding point on the output filter's λ/4 transmission line. We use circuit simulation to compare the RF-DC conversion efficiency and circuit loss of the conventional and proposed structures. The simulation results show that the proposed structure has lower circuit loss and higher RF-DC conversion efficiency than the conventional structure. We fabricate the proposed rectifier circuit using a GaAs Schottky barrier diode. The simulation and measurement results show that the single-shunt rectifier circuit's proposed structure is capable of rectification and impedance matching. The fabricated rectifier circuit's RF-DC conversion efficiency reaches a maximum of 91.0%. This RF-DC conversion efficiency is a world record for 920-MHz band rectifier circuits.

The Co-Design of Broadband Diplexer and Quasi-Reflectionless Lowpass/Highpass TFIPD Filter Chip

A new ultra-miniaturized 5G N78 and N79 diplexer based on GaAs Thin Film Integrated Passive Device (TFIPD) technology is proposed in this brief. By a NiCr thin film resistor, the quasi-reflectionless lowpass/highpass filter can also be achieved on the same circuit structure without any increase of size. Circuit schematic is composed of serial-parallel inductances and capacitances, which generates out-of-band transmission zeros. The proposed design provides <2 dB insertion loss and >15 dB out-of-band rejection. Therefore, the GaAs-based TFIPD technology can support low-loss, high-precision, and miniaturized design desires of 5G radio-frequency (RF) front-end devices. Chip model is demonstrated through EM simulation, wafer fabrication, and measurement.

CurIAs: Current-Based IC Authentication by Exploiting Supply Current Variations

Physical unclonable functions (PUFs) have emerged as one of the most notable hardware primitives to mitigate the ever-growing global issue of counterfeiting and cloning of integrated circuits (ICs) in recent times. PUFs exploit the intrinsic manufacturing process-induced parametric variations for generating unique chip identifiers. However, most of the existing PUF implementations require complex structures or the inclusion of additional components, which incur performance and area overheads. In this work, we introduce <i>CurIAs</i> , a supply current-based novel PUF implementation to authenticate ICs and to protect them from counterfeiting attacks. It exploits the dynamic current stemming from temporal switching activities in existing on-chip structures as an entropy source to generate high-quality IC-specific digital signatures. First, we investigate the source of the entropy of this PUF, <i>i.e.</i> , the dynamic current variations in different circuit structures, with transistor-level Monte-Carlo simulations in HSPICE. Next, to evaluate its effectiveness in Silicon, we apply this approach to map LFSR (Linear Feedback Shift Register) designs into 20 FPGA chips (fabricated in TSMC 55nm process node), perform practical measurements, and generate digital signatures. These signatures show high uniqueness, robustness, uniformity, and randomness features, and the overall implementation requires modest hardware overhead ( <inline-formula><tex-math notation="LaTeX">$&lt;$</tex-math></inline-formula> 1%). We assess and substantiate the robustness of this approach at eight different operating points by varying supply voltage and temperature. Furthermore, we upscale the design to more extended LFSR sizes, and it exhibits a constant trend of performance improvement over the operating points. Through a judicious selection of challenge vectors, <i>CurIAs</i> demonstrates a high resilience against model learning attacks, with an average prediction accuracy of 50%. These intrinsic variations in supply current across ICs for varying workloads entail unique chip-specific signatures, which are extremely difficult to clone, and can be deployed effectively against IC counterfeiting issues.

Three-dimensional highly porous hydrogel scaffold for neural circuit dissection and modulation.

Biomimetic brain structures and artificial neural networks have provided a simplified strategy for quantitatively investigating the complex structural and functional characteristics of highly interconnected neural networks. To achieve this, three-dimensional (3D) cell culture approaches have attracted much attention, which can mimic cell-cell interactions at the organism level and help better understand the function of specific neurons and neuronal networks than traditional two-dimensional cell culture methods. However, 3D scaffolds similar to the natural extracellular matrix to support the culturing, recording, and manipulation of neurons have long been an unresolved challenge. To resolve this, 3D hydrogel scaffolds can be fabricated via an innovative thermal treatment followed by an esterification process. A highly porous microstructure was formed within the bulk hydrogel scaffold, which showed a high porosity of 91% and a low Young's modulus of 6.11 kPa. Due to the merits of the fabricated hydrogel scaffolds, we constructed 3D neural networks and detected spontaneous action potentials in vitro. We successfully induced seizure-like waveforms in 3D cultured neurons and suppressed hyperactivated discharges by selectively activating γ-aminobutyric acid-ergic (GABAergic) interneurons. These results prove the advantages of our hydrogel scaffolds and demonstrate their application potential in the accurate dissection of neural circuits, which may help develop effective treatments for various neurological disorders. STATEMENT OF SIGNIFICANCE: While 3D cell culture approaches have attracted much attention and offer more advantages than two-dimensional cell culture methods, 3D scaffolds similar to the natural extracellular matrix to support the culturing, recording, and manipulation of neurons have long been an unresolved challenge. Herein, we developed a simplified and low-cost strategy for fabricating highly porous and cytocompatible hydrogel scaffolds for the construction of three-dimensional (3D) neural networks in vitro. The cultured 3D neural networks can mimic the in vivo connection among different neuron subgroups and help accurately dissect and manipulate the structure and function of specific neural circuits.

Coupled Inductor Based Soft Switched High Gain Bidirectional DC-DC Converter With Reduced Input Current Ripple

Bidirectional dc–dc converter (BDC) is an integral part of energy storage interface, where high efficiency, high voltage transfer ratio and small input ripple current are essential for application, such as dc microgrid and electric vehicle. In this article, a new approach is proposed to achieve reduced input ripple current at the low voltage side, which potentially replaces the necessity of using interleaved circuit structure. Parallel coupled inductor and capacitor circuit are utilized for charging and discharging the two parallel paths in a complementary fashion using a <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mosfet</small> . The proposed solution helps to increase the voltage conversion ratio unlike interleaved structure. Additionally, in proposed BDC, zero voltage switching turn <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">on</small> operation of all active switches are possible utilizing synchronous rectification principle in both the operating modes. Parallel path structure not only reduce the input current ripple, but also helps to share the input current, thereby reducing the coil size and associated losses. A 250 W BDC is designed to validate performance in 1kW dc microgrid system

다기능 투과배열 안테나 단위셀 소형화 설계를 위한 360° 반사형 축소형 아날로그 위상 천이기 설계

This study compares the designs of one-stage and two-stage reflection-type phase shifters for miniaturized S-band multi-functional transmit-array antennas. Both phase shifters have areas smaller than 0.2 λ0×0.4 λ0, which is less than half of the conventional unit cell size of 0.5λ λ0×0.5 λ0, and both phase shifters have a phase shifting range of more than 360°. The one-stage phase shifter requires a miniaturized hybrid coupler, 1/4 wavelength transmission line, and additional elements to implement a wide phase shifting range; hence, the phase shifter has a non-uniform insertion loss distribution and a peak insertion loss of 4.3 dB. The two-stage phase shifter outperformed the one-stage phase shifter; it had a relatively uniform insertion loss distribution of 2.5 dB because of its simple circuit structure.

Scale-free behavioral dynamics directly linked with scale-free cortical dynamics.

Naturally occurring body movements and collective neural activity both exhibit complex dynamics, often with scale-free, fractal spatiotemporal structure. Scale-free dynamics of both brain and behavior are important because each is associated with functional benefits to the organism. Despite their similarities, scale-free brain activity and scale-free behavior have been studied separately, without a unified explanation. Here, we show that scale-free dynamics of mouse behavior and neurons in the visual cortex are strongly related. Surprisingly, the scale-free neural activity is limited to specific subsets of neurons, and these scale-free subsets exhibit stochastic winner-take-all competition with other neural subsets. This observation is inconsistent with prevailing theories of scale-free dynamics in neural systems, which stem from the criticality hypothesis. We develop a computational model which incorporates known cell-type-specific circuit structure, explaining our findings with a new type of critical dynamics. Our results establish neural underpinnings of scale-free behavior and clear behavioral relevance of scale-free neural activity.

Fast all-fiber ultraviolet photodetector based on an Ag-decorated ZnO micro-pillar.

There are urgent demands of ultraviolet (UV) photodetectors with high sensitivity and fast response due to the wide application of ultraviolet light in the fields of medical treatment, space exploration, optical communication and semiconductor industry. The response speed of traditional ZnO-based UV photodetectors is always limited by the carrier mobility and electrical resistance caused by the external circuits. Utilizing the all-optical detection method may replace the complex circuit structure and effectively improve the response speed of photodetectors. Here, a fast-response fiber-optic UV photodetector is proposed, where a ZnO micro-pillar is fixed on the end face of a fiber-tip and acts as a Fabry-Pérot interferometer (FPI). Under the irradiation of UV light, the photo-generated carriers change the refractive index of the ZnO micro-pillar, leading to a redshift of the interference wavelengths of the ZnO FPI. To enhance this effect, a discontinuous Ag film with an island-like structure is coated on the surface of ZnO micro-pillars through magnetron sputtering, and therefore the sensitivity of the proposed device achieves to 1.13 nm/(W·cm-2), which is 3.9 times higher than that of without Ag-decoration, due to the intensification of photo-carrier change with the help of the Schottky junction formed between Ag film and ZnO micro-pillar. Meanwhile, since the response speed of the proposed device is mainly determined by the temporal RI change of ZnO micro-pillar, the fiber-optic UV photodetector also shows very fast response with a rise time of 35 ns and a decay time of 40 µs. The demonstrated structure takes full advantage of optical fiber devices, exhibiting compactness, flexibility, fast response and immune to electromagnetic interference, which paves a new way for the next generation of photodetection devices.

Transmission Lines in Capacitance Measurement Systems: An Investigation of Receiver Structures.

Dielectric sensing based on capacitive measurement technology is a favourable measurement approach in many industries and fields of application. From an electrical point of view, a coupling capacitance must be measured in the presence of stray capacitances. Different receiver circuit structures have been proposed for the underlying displacement current measurement. Ideally, the sensor assembly is directly connected to the sensor circuitry to minimize the influence with respect to these parasitic capacitances. However, under harsh operating conditions, e.g., at high temperatures, the sensor and the receiver circuit must be separated in order to protect the electronics. Consequently, the receiver circuit and the sensor have to be connected by cables, e.g., coaxial cables. The measurement setup differs significantly from the ideal design with a direct connection. In this paper, we investigate the behaviour of three common measurement circuits for capacitive measurements in instrumentations with cables. We study the interaction between the sensor and the electronics and analyse the operating behaviour of the circuit, as well as the operating states of the amplifiers used. We also address cross-sensitivities in the sensor design due to stray capacitances. The analyses are carried out for different cable lengths and measuring frequencies, and conditions for the usability of the circuit are deduced. In addition to the operational behaviour, we also evaluate the circuits by means of a noise analyses. Based on this analysis, we show a direct comparison of the circuits. The analysis is based on simulation studies, as well as collaborative measurements on test circuits where all circuit parameters are provided. The test circuits are realized with dedicated state-of-the-art circuit elements and, together with the analysis approach and the results, thus provide a basis for future developments.

3D printing of graphene oxide/carbon nanotubes hydrogel circuits for multifunctional fire alarm and protection

Hydrogel circuit materials have a wide range of applications, especially in the field of flexible electronics. However, developing smart hydrogel circuit materials with remarkable properties remains a difficult problem. Based on the flexible design of circuit structure and direct inkjet 3D printing technology, we designed a printable conductive sodium alginate/graphene/carbon nanotube (SGC) ink and achieved three-dimensional graphene-based flexible circuits with high resolution and multifunction. Compared with previous work, the addition of GO, SWCNT and Ca2+ make SGC have excellent conductivity and flame retardancy. The conductivity of SGC reaches 24.11S/m, which is 3 times higher than SA hydrogel. And the LOI is above 99.7%, which is far higher than most conductive polymer. We have conducted preliminary explorations and provided theoretical basis for the 3D printing of multifunctional SGC hydrogels with complex structures, which are expected to be applied in the field of flexible circuits, fire protection and artificial electronic skin.