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This paper presents the circuit of a low-noise amplifier (LNA) using active inductor (AI) input matching with common gate (CG) current-reused technique. This configuration is implemented in 90 nm CMOS and enables to achieve high power-gain (S21) with ultra-wideband (UWB) input matching at low power levels. Utilization of modified high-Q AI at the input side of the proposed LNA reduces the number of inductors and achieves UWB from only two inductors. Proposed LNA dissipates 10.4 mW from 1.0 V supply and exhibits an S21 response of 18.0 ± 0.8 dB for 3.1–10.6 GHz with a maximum and average S21 of 18.8 dB and 18.22 dB, respectively. The proposed LNA has noise-figure (NF) equal to 3.36–4.68 dB, with input (S11) and output (S22) reflection coefficients of less than −9.3 dB and −11.35 dB, respectively across the entire UWB range. [ABSTRACT FROM AUTHOR]

Lithium-ion batteries (LIBs) are widely used in electric vehicles (EVs) due to their superior power performance over other batteries. However, when connected in series, overcharged cells of LIBs face the risk of explosion, and undercharged cells decrease the life cycle of the battery. Eventually, the inconsistency phenomenon between cells resulting from manufacturing tolerance and usage process reduces the overall charging capacity of the battery and increases the risk of explosion after long-time use. Research has focused on synthesizing active material to achieve higher energy density and extended life cycle for LIBs while neglecting a comparative analysis of equalization technology on the performance of battery packs. In this paper, a nondissipative equalization structure is proposed to reconcile the inconsistency of series-connected LIB cells. In this structure, a circuit uses high-level equalization units to enable direct energy transfer between any two individual cells, and dual interleaved inductors in each equalization unit increase the equalization speed of a single cell in one equalization cycle by a factor of two. The circuit is compared with the classical inductor equalization circuit (CIEC), dual interleaved equalization circuit (DIEC), and parallel architecture equalization circuit (PAEC) in the states of standing, charging, and discharging, respectively, to validate the advantages of the proposed scheme. Considering the diversity of imbalance states, the state of charge (SOC) and terminal voltage are both chosen as the equalization criterion. The second-order RC model of the LIB and the adaptive unscented Kalman filter (AUKF) algorithm are employed for SOC estimation. For effective equalization, the adaptive fuzzy neural network (AFNN) is utilized to further reduce energy consumption and equalization time. The experiment results show that the AFNN algorithm reduces the total equalization time by approximately 37.4% and improves equalization efficiency by about 4.89% in contrast with the conventional mean-difference algorithm. Particularly, the experiment results of the equalization circuit verification certify that the proposed equalization structure can greatly accelerate the equalization progress and reduce the equalization loss compared to the other three equalization circuits. [ABSTRACT FROM AUTHOR]

In this study, one of deep learning methods, which has been very popular in recent years, is employed for the detection and classification of circuit components in hand-drawn circuit images. Each circuit component located in different positions on the scanned images of hand-drawn circuits, which are frequently used in electrical and electronics engineering, is considered as a separate object. In order to detect the components on the circuit image, Faster Region Based Convolutional Neural Network (R-CNN) method is used instead of conventional methods. With the Faster R-CNN method, which has been developed in recent years to detect and classify objects, preprocessing on image data is minimized, and the feature extraction phase is done automatically. In the study, it is aimed to detect and classify four different circuit components in the scanned images of hand-drawn circuits with high accuracy by using the Python programming language on the Google Colab platform. The circuit components to be detected on the hand-drawn circuits are specified as resistor, inductor, capacitor, and voltage source. For the training of the model used, a data set was created by collecting 800 circuit images consisting of hand drawings of different people. For the detection of the components, the pretrained Faster R-CNN Inception V2 model was used after fine tuning and arrangements depending on the process requirements. The model was trained in 50000 epochs, and the success of the trained model has been tested on the circuits drawn in different styles on the paper. The trained model was able to detect circuit components quickly and with a high rate of performance. In addition, the loss graphics of the model were examined. The proposed method shows its efficiency by quickly detecting each of the 4 different circuit components on the image and classifying them with high performance. [ABSTRACT FROM AUTHOR]

This paper presents a new modified voltage differencing voltage transconductance amplifier (MVDVTA) based meminductor emulator circuit. The proposed emulator circuit is memristor-less, uses only a single active building block (ABB) and has simple circuitry. The MVDVTA based emulator design consists of only two capacitors and a single resistor. The performance of the proposed design has been verified over a wide frequency span. For simulation purpose LTSpice tool is used with 0.18µm CMOS technology. The proposed emulator has also been employed in chaotic oscillator and adaptive learning application circuit to verify its workability. The proposed design gives satisfactory performance for both the applications, hence confirming its functionality in practical environment. [ABSTRACT FROM AUTHOR]

Nitrogen fixation, production of NO and NO2 from N2 and O2 in air, has been investigated with transient spark self-pulsing DC discharges. NO production is boosted by the addition of capacitors and an inductor to the electrical circuit which drives the discharge. The quantity of NO produced per joule of electrical input energy is doubled, though the quantity of NO2 produced drops. The yield of NO is also increased because the modified circuit enables higher discharge currents to be used. NO concentrations as high as 2000 ppm were obtained with input energy densities of around 300 J per liter of input gas, whilst NO2 concentrations were around 150 ppm. This simple modification of the driving circuit may have potential for optimizing the plasma chemistry with other input gas mixtures and for scaling up nitrogen fixation from air. [ABSTRACT FROM AUTHOR]

In this study, a new simulated grounded inductor (SGI) is developed. It has a simple structure because it employs two-terminal active devices (TTADs). Moreover, it has a minimum number of passive elements, considering the TTAD-based configurations. However, there is a single passive element-matching constraint for two resistors. Derived from the developed SGI, a second-order voltage-mode universal filter application is given. With proper connection of inputs, it yields band-pass (BP), high-pass (HP), low-pass (LP), all-pass (AP), and notch filter (NF) responses. The performance of the developed circuits is verified through the SPICE simulation program. Additionally, an experimental test result is given for the developed SGI. [ABSTRACT FROM AUTHOR]

Memristors are passive non‐linear circuit components with memory characteristics, and have been recognized as the fourth basic circuit component, along with resistors, capacitors, and inductors. It has been nearly half a century since the conceptualisation of the memristor, and related research has mainly focussed on the two aspects of binary and continuous memristors. However, compared with these two types of memristors, tri‐state and multi‐state memristors have greater data density per device, with rich dynamics and great potential in logic and chaotic circuit applications. Moreover, previous studies show that the series‐parallel connection of memristor generates more diverse circuit behaviours and increased capacity over a single memristor. However, most of this research is based on mathematical analysis, and lack behavioural circuit simulations or experimental validation. Here, the tri‐state memristor is proposed and the mathematic and equivalent Spice models of the tri‐state memristor is shown. Furthermore, the circuit characteristics are studied with a complete characterisation of its series‐parallel behaviours of the tri‐state memristor. Simulations are performed with LTSpice, and the results verify the theoretical analysis, which provides a strong experimental basis for the study of combinational memristive circuits. [ABSTRACT FROM AUTHOR]