Automatic Structure Generation and Parameter Optimization for CMOS Voltage Reference Circuit

There has been considerable research done on multi-chamber mufflers used in the elimination of industrial venting noise. However, most research has been restricted to lower frequencies using the plane wave theory. This has led to underestimating acoustical performances at higher frequencies. Additionally, because of the space-constrained problem in most plants, the need for optimization of a compact muffler seems obvious. Therefore, a muffler composed of multiple rectangular fin-shaped chambers is proposed. Based on the eigenfunction theory, a four-pole matrix used to evaluate the acoustic performance of mufflers will be deduced. A numerical case for eliminating pure tones using a three-fin-chamber muffler will also be examined. To delineate the best acoustical performance of a space-constrained muffler, a numerical assessment using the Differential Evolution (DE) method is adopted. Before the DE operation for pure tone elimination can be carried out, the accuracy of the mathematical model must be checked using experimental data. The results reveal that the broadband noise has been efficiently reduced using the three-fin-chamber muffler. Consequently, a successful approach in eliminating a pure tone using optimally shaped three-fin-chamber mufflers and a differential evolution method within a constrained space has been demonstrated.

Ultra-low-power (ULP) sensor technologies for the future internet of things have presented challenges in ULP implementation of reference circuits while keeping traditional requirements of stable performance. For voltage reference circuits, as an essential block in SoCs to generate various internal supply voltages, the bandgap voltage-reference (BGVR) scheme has been widely used since it provides a well-defined value with strong immunity to process/voltage/temperature variations. Nanowatt-consuming BGVR circuits have been recently proposed using a capacitor network [4] and a leakage-based proportional-to-absolute-temperature (PTAT) circuit [5]. On the other hand, the current reference circuit that is required to set internal bias current still presents difficulties in achieving both stable performance and ULP consumption. The general approach to building a current reference is to use a BGVR with additional resistors for V-to-I conversion. Though it can provide a well-defined stable current reference, it also requires excessively large resistance for ULP consumption. Another approach is a CMOS-based current reference circuit that tries to make the exponential term in the subthreshold current equation constant or temperature-independent, hence reducing process and temperature dependencies. While CMOS reference circuits have achieved ULP implementations, the current is still determined by a number of process and design parameters, resulting in large sensitivity to process variations. This paper presents a sub-10nW bandgap-reference (BGR) circuit that implements both voltage and current references in one circuit. The BGR circuit is implemented with a 0.18µm CMOS process and generates voltage and a current references of 1.238V and 6.64nA while consuming 9.3nW. The voltage and current references show standard deviations of 0.43% and 1.19% with temperature coefficients of 26ppm/°C and 283ppm/°C, respectively.

This paper focuses on the real-time kinematics solution of an aerial manipulator mounted on an aerial vehicle, the vehicle’s motion isn’t considered in this study. Robot kinematics using Denavit-Hartenberg model was presented. The fundamental scope of this paper is to obtain a global online solution of design configurations with a weighted specific objective function and imposed constraints are fulfilled. Acknowledging the forward kinematics equations of the manipulator; the trajectory planning issue is consequently assigned to on an optimization issue. Several types of computing methods are documented in the literature and are well-known for solving complicated nonlinear functions. Accordingly, this study suggests two kinds of artificial intelligent techniques which are regarded as search methods; they are differential evolution (DE) method and modified shuffled frog-leaping algorithm (MSFLA). These algorithms are constrained metaheuristic and population-based approaches. moreover, they are able to solve the inverse kinematics problem taking into account the mobile platform additionally avoiding singularities since it doesn’t demand the inversion of a Jacobian matrix. Simulation results are carried out for trajectory planning of 6 degree-of-freedom (DOF) kinematically aerial manipulator and confirmed the feasibility and effectiveness of the supposed methods.

The temperature coefficient (TC) of n-type polycrystalline silicon thin-film transistors (poly-Si TFTs) is investigated in this paper. The relationship between the TC and the activation energy is observed and explained. From the experimental results, it is also found that TC is not sensitive to the deviation of the laser crystallization energy. On the contrary, channel width can effectively modulate the TC of TFTs. By using the diode-connected poly-Si TFTs with different channel widths, the first voltage reference circuit with temperature compensation for precise analog circuit design on glass substrate is proposed and realized. From the experimental results in a low-temperature poly-Si process, the output voltage of voltage reference circuit with temperature compensation exhibits a very low TC of 195 ppm/degC , between 25degC and 125degC. The proposed voltage reference circuit with temperature compensation can be applied to design precise analog circuits for system-on-panel or system-on-glass applications, which enables the analog circuits to be integrated in the active-matrix liquid crystal display panels.

To overcome this noise, traditional reactive as well as dissipative mufflers have been used. However, the allowable backpressure and the extra load acting on the cooling tower are limited. So, in order to efficiently reduce the broad noise under a low-pressure drop and a small dead weight, a straight multi-array resonator muffler without sound absorbing wool is required. Multi-array Helmholtz mufflers with complicated Helmholtz resonators were found to be too expensive in manufacturing costs and can be easily accumulated with duct and water. Therefore, a straight multi-array side-branched muffler with an empty chamber is proposed. Here, to efficiently reduce the broadband noise level, eight kinds of mufflers (muffler A–H) with 1–8 raw chambers internally partitioned with concentric perforated plate will also be introduced. To delineate the best acoustical performance of a space-constrained muffler, a numerical assessment using a four-pole matrix method in conjunction with a differential evolution method is adopted. To verify the availability of the differential evolution optimization, a numerical optimization of mufflers A–H at a pure tone (400 Hz) is exemplified. Before the differential evolution operation in broadband noise elimination can be carried out, the accuracy of the mathematical model has been checked using the experimental data. Consequently, a successful approach in eliminating a broadband noise with a low-pressure drop and a dead load using optimally shaped straight multi-array side-branched mufflers and a differential evolution method within a constrained space has been demonstrated.

Based on the negative temperature characteristics of threshold voltage and positive temperature characteristics of a multiple of thermal voltage, adding them with proper weight coefficient A voltage reference circuit was proposed with a zero temperature coefficient (TC). The device consists of pure MOSFET operated in subthreshold region and uses no resistors and bipolar transistors. The triple-branch current reference structure is adopted for independence of supply voltage instead of cascade structure and embedded operational amplifier structure with the merit of chip area and power consumption. Simulation results showed that based on standard CMOS 0.18 um process, the circuit can operate at 0.75 V supply voltage with the output voltage only 563 mV. The TC of the voltage was 17.5 ppm/℃ in a range from −40 ℃–125 ℃. The line sensitivity was 569.5 ppm/V in a supply voltage range of 1.2 V–1.8 V, and the power supply rejection ratio (PSRR) was 66.5 dB at 100 Hz. The power dissipation was only 187.4 nW.

Two sub-1-V MOSFETs only voltage reference designs, working in subthreshold region are proposed. They consume ultra-low power, exhibit high temperature and supply voltage independence, operate at sub-1-V supply and are suitable for fabrication using N-well CMOS technology. A voltage reference circuit (circuit-1) with dual-threshold voltage MOSFETs, which can provide a nominal output voltage of 554 mV exhibiting high temperature-compensation is proposed. Simulation results show that it has a temperature coeffcient of $$13\,\hbox {ppm}/^{\circ }\hbox {C}$$ and a power consumption of 2.1 nW. Another reference circuit (circuit-2) using only single threshold voltage MOSFETs with a high nominal output of 773 mV is also proposed. Simulation results show that it has a line sensitivity of 0.073%/V and a power consumption of 2.4 nW. Statistical analysis based on Monte-Carlo simulation show that the performance of both the circuits will not be significantly affected by process variations.

A 275 pW, 0.5 V supply insensitive gate-leakage based current/voltage reference circuit for a wide temperature range of −55 to 100 °C without using amplifiers and resistors

An improved differential evolution (DE) method based on the dynamic search strategy (IDEBDSS) is proposed to solve dynamic economic dispatch problem with valve-point effects in this paper. The proposed method combines the DE algorithm with the dynamic search strategy, which improves the performance of the algorithm. DE is the main optimizer in the method proposed. While chaotic sequences are applied to obtain the dynamic parameter settings in DE, dynamic search strategy which consists of two steps, global search strategy and local search strategy, is used to improve algorithm efficiency. To accelerate convergence, a new infeasible solution handing method is adopted in the local search strategy; meanwhile, an orthogonal crossover (OX) operator is added to the global search strategy to enhance the optimization search ability. Finally, the feasibility and effectiveness of the proposed methods are demonstrated by three test systems, and the simulation results reveal that the IDEBDSS method can obtain better solutions with higher efficiency than the standard DE and other methods reported in the recent literature.

Wireless power transfer (WPT) is a biocompatible and flexible, convenient and stable energy transmission technology, has a huge application market in consumer electronics and implantable medical aspects. With regard to WPT, improving the transmission efficiency has always been a great challenge. In this paper, a novel four coil wireless energy transmission system based on magnetic resonances analyzed, the formula of quality factor, transmission distance and transmission efficiency is derived. A new coil structure parameter design is proposed. Through the model simulation of the number of turns, the number of strands and the pitch of the turns, the quality factor and inductance of the coil are analyzed by the electromagnetic simulation software HFSS, and the optimal structural parameter values are obtained. The coil with the optimal structural parameters can reach 102 at 13.56 MHz frequency. These structural parameters provide effective reference and guidance for obtaining high quality factor coils and efficient wireless energy systems.

Due to the Covid-19 virus millions of people lost their lives and livelihood all the over the world, so quick and accurate diagnosis ofcovid-19 patients is the emergent need of today. Deep learning (DL) techniques, nowadays widely gained much popularity as compared to machine learning methods. Furthermore, DL is well-known in the field of image processing, whereas X-ray pictures are frequently used to diagnose Covid-19 patients. In this article, HyperCNN approach introduced through which higher accuracy can be achieved as compared to other traditional Convolutional Neural Network (CNN). Normally, the tuning of hyper parameters is done manually, which are both costly and time consuming in order to identify the optimum model with the highest accuracy. As per the experiment done, the traditional CNN model provides the accuracy 96.6% whereas the proposed model which is tuned using Bayesian optimization achieves accuracy 98.33%. Hence, it is observed that there is an improvement of 2% in the performance of the proposed model as compared to the traditional CNN model. With this experiment, we claim that automatic hyper parameter optimization is a powerful method for improving the accuracy in transfer learning.KeywordsHyper parameterOptimizationConvolutional neural networkDeep learningCovid-19 diagnosis

With the rapid advancements in positioning technologies, the volume of spatio-temporal data has grown significantly. Analyzing the spatial and temporal characteristics of these data is imperative for uncovering underlying associations and deriving insights into natural and societal mechanisms. Clustering is a widely utilized technique for data analysis, which groups data with similar characteristics for further investigation. However, current clustering methodologies usually inadequately address temporal properties that are vital in numerous scenarios. Additionally, traditional spatio-temporal clustering approaches are constrained to standalone environments, which struggle to handle large-scale spatio-temporal datasets. To this end, we introduce DiST, the first distributed spatio-temporal clustering method, which simultaneously considers both temporal and spatial proximity. DiST comprises data partition, local clustering, and global merging stages, along with an auto-tuning framework for parameter optimization. DiST addresses key challenges, including the integration of temporal and spatial attributes, managing data duplication across distributed nodes, and selecting appropriate parameters for diverse data characteristics. Comparative experiments on two real-world datasets validate the performance and scalability of DiST, demonstrating its effectiveness in spatio-temporal data analysis.

A long-term prediction approach based on long short-term memory neural networks with automatic parameter optimization by Tree-structured Parzen Estimator and applied to time-series data of NPP steam generators

Despite the significant impact of network hyperparameters on deep learning car-following models, there has been relatively little research on network hyperparameters of deep learning car-following models. Therefore, this study proposes a car-following model that combines particle swarm optimization (PSO) and gated recurrent unit (GRU) networks. The PSO-GRU car-following model is trained and tested using data from the natural driving database. The results demonstrate that compared to the intelligent driver model (IDM) and the GRU car-following model, the PSO-GRU car-following model reduces the mean squared error (MSE) for the speed simulation of following vehicles by 88.36% and 72.92%, respectively, and reduces the mean absolute percentage error (MAPE) by 64.81% and 50.14%, respectively, indicating a higher prediction accuracy. Dataset 3 from the drone video trajectory database of Southeast University and NGSIM’s I-80 dataset are used to study the car-following model’s cross-dataset adaptability, that is, to verify its transferability. Compared to the GRU car-following model, the PSO-GRU car-following model reduces the standard deviation of the test results by 60.64% and 32.89%, highlighting its more robust prediction stability and better transferability. Verifying the ability of the car-following model to produce the stop-and-go phenomenon can evaluate its transferability more comprehensively. The PSO-GRU car-following model outperforms the GRU car-following model in creating stop-and-go sensations through platoon simulation tests, demonstrating its superior transferability. Therefore, the proposed PSO-GRU car-following model has higher prediction accuracy and cross-dataset adaptability compared to other car-following models.

Fuel cells are complex multiphysical systems characterized by a stratified geometry consisting of thin layers of different materials. Their full characterization involves a number of physical parameters, which are important for quantifying and comparing the performance of the materials. The physical parameters are also used in fuel cell models, which allow the fast exploration of different operating scenarios and can be implemented in optimization procedures aimed at achieving the best structural and operating conditions. The system of equations involved in these models, e.g. the Nernst equation, the Butler-Volmer equation and the diffusion equation, are strongly non-linear; thus, the model itself is extremely sensitive to parameter variations. Careful ex-situ measurements of such parameters can be performed by means of a number of diagnostic techniques, whose results are however not completely consistent with fuel cell operation due to several effects, e.g. variable interactions and boundary effects in the thin layers of the fuel cell. Conversely, “in situ” measurements can provide meaningful operational values, but a very few techniques are available to determine a limited number of parameters.A solution can consist in multiple parameter identification from multiple fundamental measurements, such as polarization curve and electrochemical impedance spectroscopy, performed in different conditions, e.g. at different temperature, pressure, concentration, and humidification.If there is only one unknown parameter, the solution is easy, requiring just a statistical interpolating technique. In the case of multiple unknown parameters, which is the most frequent condition, the problem becomes increasingly difficult as the number of parameters increases as duplicity problem emerges, i.e., several groups of parameters may lead to the same performance (e.g. polarization curve).Recently, a number of numerical tools are under development to face this kind of problems, some based on stochastic mathematical models, each presenting specific features in terms of accuracy, convergence, stability, robustness, and calculation speed [1]. Among the most promising methods, some of which have already been applied to multiple fuel cell parameter identification, are the Ishibashi–Kimura method [2], the Lambert W-function method [3], the Levenberg-Marquardt algorithm (LMA), the Gauss-Newton algorithm (GNA), the hybrid artificial bee colony algorithm [4], the trust region method, the current switching method [5], the simulated annealing method [6], and the particle swarm optimization (PSO) [7], and the differential evolution method [8].The presentation will focus on the application of a particular class of stochastic optimization methods to the problem of multiple parameters identification. These methods have already been widely tested and applied successfully to the simultaneous search of sets of optimal parameters with different physical dimensions, making them ideal tools to cope with the present problem. Their recent relevant literature is vast. They are especially suited for multimodal and noisy functions over large search spaces, which makes them particularly appealing for the problem at hand. The main drawback of such methods is the high number of objective function evaluations needed for convergence; however, thanks to a particular formulation of the PEMFC model, such drawback can be of minor impact, i.e. the overall computational times are on the order of a few tens of seconds.We are going to present the application of two of the most well-known metaheuristic methods, namely Particle Swarm Optimization (PSO) and Differential Evolution (DE), developed in previous work and suitably adapted. It should be noted that PSO has already been applied to parameter identification for FCs, while the application of DE is novel in this context. This is somewhat surprising since, as results will show, DE provides better results and offers higher robustness. We will present the implementation details regarding PSO and DE. The optimization algorithms have not been tuned to the specific problem and algorithm control parameters have been chosen from reasonable values suggested in literature.