International Journal of Numerical Modelling-Electronic Networks Devices and Fields最新文献

This article focuses on dynamic parameter identification for industrial robots and proposes a parameter identification method based on an improved excitation trajectory. First, a complex nonlinear friction model is adopted and modified according to joint friction characteristics, with a genetic algorithm utilized to determine its six parameters. Second, a weighted optimal excitation trajectory is designed to address nonlinear friction requirements and smooth operation constraints. Then, a global parameter optimization algorithm based on the least squares method and the modified sparrow search algorithm is proposed. Finally, the proposed method is validated on a self-developed six-axis industrial robot. Experimental results demonstrate that the proposed method achieves higher identification accuracy compared with two representative identification approaches.

Bias temperature instability (BTI) is a time-based degradation mechanism that causes serious damage to the performance of analog and digital integrated circuits. The increasingly probabilistic nature of this phenomenon renders machine learning-based modeling approaches more advantageous, as they can deliver more accurate results in that context compared to analytical methods. In this paper, the Long Short-Term Memory (LSTM) method, a time-series approach, has been adopted to model BTI in 40 nm CMOS p-type metal-oxide-semiconductor field-effect transistors (MOSFETs). The aging model has been established by training the experimental data collected from a dedicated test chip. A bi-directional LSTM structure has been employed in model generation. Mean-square error (MSE) results indicate that the model can be effectively utilized in interpolation exercises where the test data falls within the same interval as the training data, with great accuracy. Moreover, the model has yielded promising outcomes in extrapolation exercises where the test data lies outside the defined training range. This property potentially qualifies the proposed approach for time-to-market and cost-reduction efforts.

In this paper, a gap coupled ring-shape multi-band antenna of quad-band characteristics has been designed. The geometry of the proposed gap coupled ring-shape antenna is obtained by loading two horizontal and six vertical strips of the same width and the same gap. The quad band characteristic of the proposed antenna is obtained at frequencies 2.48, 3.85, 5.14, and 5.61 GHz. The quad band lies within frequency ranges 2.34–2.59 GHz (first band), 3.56–4.06 GHz (second band), 5.09–5.21 GHz (third band) and 5.47–5.96 GHz (fourth band). The second and fourth bands of the antenna arise due to outer vertical strips, while the first and third bands of the antenna are due to middle and inner vertical strips, respectively. The return loss of −17.31, −29.18, −18.40, and − 19.19 dB is obtained at frequencies 2.48, 3.85, 5.14, and 5.61 GHz, respectively. Additionally, a parametric analysis is also conducted to enhance the performance of the proposed gap coupled antenna. To validate the geometry of the proposed antenna, experimental measurement is performed with the prototype antenna. The first band (2.34–2.59 GHz) of the antenna covers WLAN, ISM band, and WiMAX; the second band (3.56–4.06 GHz) of the antenna covers 5G; the third band (5.09–5.21 GHz) of the antenna covers WLAN; and the fourth band (5.47–5.96 GHz) of the antenna covers WiMAX and WLAN applications. The proposed quad band antenna is a cost-effective and efficient solution for multi-band communication devices. It eliminates the need for multiple antennas and lowers hardware costs.

This paper presents a novel wearable antenna fabricated using denim material, designed for flexible electronics and medical monitoring applications. The proposed antenna leverages common jean fabric as the substrate material, offering a cost-effective and readily available solution while combining esthetic appeal with practical functionality through its unique configuration. Operating across 2.269–19.42 GHz with a maximum gain of 6.75 dB, the antenna achieves an enhanced bandwidth of 158.15%. Notably, the design measured as 0.488λ_o × 0.488λ_o × 0.008λ_o exhibits a low specific absorption rate (SAR) compared to FCC standards that is 1.6 W/kg averaged over 1 g of tissue, making it particularly suitable for medical monitoring applications. We obtained a maximum SAR value for the antenna as 1.61, 1.01 W/kg for 1 and 10 g at 2 mm from the body phantom, 0.488 and 0.769 W/kg for 1 and 10 g when placed at 5 mm from the human phantom, and 1.02, 0.73 W/kg for 1 and 10 g at on-body placement of the antenna. Experimental results demonstrate the antenna's effectiveness for vital signs surveillance while maintaining wearer safety and comfort. The use of denim as the substrate material not only ensures flexibility and durability but also provides an eco-friendly approach by utilizing common textile materials. The high-fidelity factor and wideband characteristics ensure reliable data transmission, making this design a promising solution for next-generation wearable healthcare devices.

This research presents an effective spectral collocation scheme based on orthogonalized Bernoulli polynomials for solving the time-fractional diffusion equation (TFDE). To provide a numerical method, we consider the Bernoulli polynomials and estimate the derivatives as well as the Caputo fractional derivative by operational matrices. By collocating the discretized equations, we obtain a system of algebraic equations. By solving this system, we obtain the approximate solution. The advantages of the suggested method are its low computational cost and exponential convergence. Also, the convergence analysis of the presented method is discussed. Finally, we present several test problems to demonstrate the capability of the proposed method. The obtained results are compared with the existing methods in the literature.

In the present study, we introduce an iterative technique grounded in shifted Vieta–Lucas polynomials for the numerical solution of nonlinear stochastic Volterra integral equations. Notably, our iterative approach is fast and provides solutions without solving algebraic equations. This method addresses nonlinear problems with high accuracy, making it very useful. We present an error estimation for the suggested approach, theoretically confirming its accuracy. Several numerical examples illustrate the practicality and efficacy of our technique. Furthermore, we compare the numerical outcomes of our method with those reported in existing literature and, whenever available, with exact solutions. This comparative analysis affirms the practicality and high precision of the suggested approach.

High data rate applications are becoming more popular every day. A small die area and low power consumption filters are essential for RF front-end modules in order to achieve effective integration. This paper presents two small, on-chip bandpass filters (BPFs) for 5G mm-wave communication systems based on the second-generation current-controlled current conveyor (CCCII) using 32 nm carbon nanotube field-effect transistors (CNTFET). The whale optimization algorithm is used for optimizing the current cut-off frequency and port X parasitic resistance (R_X) of the designed CCCII. The first filter is designed for the 5G n258 band, with a center frequency of 24.25–27.5 GHz, and the second one is designed for the 5G n260 band, with a center frequency of 37–40 GHz. The novelty of these CNTFET-based filters is that they are the first active filters, operating at the Ka-band frequency with low power consumption and a tiny size.

In this paper, a simple and novel residual parameter extraction technique is provided for impedance substrate calibration standards. It uses the measured scattering parameters of four calibration standards, that is the THRU, SHORT, OPEN, and LOAD standards, with only the DC resistance known in advance. Based on the electric structures and the frequency range of interest, the equivalent circuit of each standard is provided. The residual parameters in the equivalent circuits might show frequency dependence or frequency non-dependence, which are both considered in the following analysis. In the parameter extraction algorithm, no other calibration is needed. Instead, only the recorded raw data of the four standards are used, by assuming that the two ports of the SHORT, OPEN, and LOAD standards are symmetric and identical. A series of validation experiments are performed on a commercial calibration substrate, within the broad frequency range from 200 MHz to 110 GHz. The results have shown that the extracted residual parameters by using the proposed method are in very good consistency with the values provided by the manufacturer. In addition, the extracted parameters are further used for SOLT calibration, by measuring another group of calibration standards on the commercial calibration substrate. The calibration accuracy and reliability are further verified by using another open structure, a transmission line, and a mismatched load.

This paper investigates two conventional and four proposed structures designed by patterning the SiO₂ film over the (0°, 138.5°, 26.6°) cut langasite (LGS) substrate. The results are compared with those obtained with a langasite resonator designed without the SiO₂ film. All langasite structures are investigated at elevated temperatures up to 600°C using a 3-D finite element modeling method. The proposed structures are optimized for the lowest temperature coefficient of frequency (TCF) and high coupling factor (k²) as a function of SiO₂ film thickness for the operating temperature range. The optimized structure reduces the TCF to 2.52 ppm/°C at room temperature and for high temperatures to as low as 13.78 ppm/°C at 600°C. An enhanced coupling factor of 0.05% is obtained for the optimized structure at room temperature compared to the conventional structures. Thus, the systematically optimized structure may be selected to realize a temperature sensor that can perform at elevated temperatures.