Dynamic Bias Research Articles

Field-Free Magnetization Switching in a Ferromagnetic Single Layer through Multiple Inversion Asymmetry Engineering.

A simple, reliable, and self-switchable spin-orbit torque (SOT)-induced magnetization switching in a ferromagnetic single layer is needed for the development of next generation fully electrical controllable spintronic devices. In this work, field-free SOT-induced magnetization switching in a CoPt single layer is realized by broken multiple inversion symmetry through simultaneously introducing both oblique sputtering and a vertical composition gradient. A quantitative analysis indicates that multiple inversion asymmetries can produce dynamical bias fields along both z- and x-axes, leading to the observed field-free deterministic magnetization switching. Our study provides a method to accomplish fully electrical manipulation of magnetization in a ferromagnetic single layer without the external magnetic field and auxiliary heavy metal layer, enabling flexible design for future spin-orbit torque-based memory and logic devices.

High signal-noise ratio avalanche photodiodes with dynamic biasing technology for laser radar applications

In this work, a high signal-noise ratio (SNR) dynamic biasing InGaAs/InAlAs avalanche photodiode (APD) is demonstrated experimentally and first applied in a laser radar system. Combining with the dynamic biasing technology, the APDs are operated in an unexploited voltage range between linear mode and Geiger mode, which, in this work, is defined as a transition zone. Surprisingly, it is found that the excess noise of dynamic biasing APDs decreases with the gain in this transition zone. As expected, the maximum useful gain is as high as 620 in the dynamic biasing mode, which shows a greater promotion than that of the DC biasing mode (17.5). Compared with the traditional DC biasing mode, the optimal SNR for dynamic biasing mode is improved by 14 dB without the degradation of response time as the peak optical power is 525 nW. Moreover, when SNR = 10, the peak optical power for the dynamic biasing mode is 43.4 nW, which shows a 57.5-fold (17.6 dB) reduction in comparison with the DC biasing mode (2495 nW). Therefore, we believe this new optical receiver will pave a new way in high-sensitivity and high-speed light detection.

Metamagnetic anomalies in the kinetic Blume–Capel model with arbitrary spin

Using the effective-field theory, we have investigated the dynamic behavior of a kinetic spin-S Blume Capel model which is an extension of the conventional kinetic Ising model with S≥1. For integer and half-integer spins, we have evaluated the dynamic phase diagrams. By introducing a constant bias field hb, we have focused on the emergence of metamagnetic anomalies in dynamic susceptibility versus bias field curves which arise for a narrow range of the bias field. For long periods, a close examination of susceptibility versus bias field data leads to a linear variation in 1/(h0−|hbpeak|)−logP curves where P is the field period. This result confirms that |hbpeak| asymptotically approaches the oscillating field amplitude h0 as 1/logP in the slow critical dynamics regime. Our calculations indicate that recent findings regarding the DPT in kinetic Ising model also extents to the kinetic Blume–Capel model with arbitrary spin.

Encoding–decoding strategy based resilient state estimation for bias-corrupted stochastic nonlinear systems

This paper is concerned with the resilient state estimation problem for a type of stochastic nonlinear systems, in which the possible dynamical bias is considered that is depicted by a dynamical equation. In pursuit of enhancing the robustness of the propagated data, a binary encoding strategy (BES) is exploited in the binary symmetric channel (BSC). While the random bit errors caused by the channel noise may take place during the propagation of the binary bit string via the memoryless BSC. To characterize the occurrence of the bit errors, a series of Bernoulli distributed random variables is adopted. More specifically, in order to deal with the possible gain fluctuation of the estimator in the execution process, a resilient state estimator is employed. This paper intends to put forward a novel resilient estimation scheme under the BES, which can assure that the estimation error dynamics is exponentially ultimately bounded in mean square. A sufficient criterion is first acquired for the existence of the expected resilient estimator and the estimator parameter is achieved by solving a convex optimization problem. Finally, an illustrative simulation example is provided to verify the validity of the theoretical results.

The Dynamic Relationship Between Social Capital and Suicide Rates.

Background: Suicide is an important public health concern worldwide. Among various factors, social capital has been suggested to be an effective factor to prevent and reduce suicide. Aims: The purpose of this study was to investigate the association between social capital and suicide rates in Seoul, South Korea, using panel data from 2005 to 2018 at the administrative-district level. Methods: Data for the current study were obtained from Seoul Statistics. The within estimator and the system generalized methods of moments estimator were used. Results: The results showed that there was an inverse association between community facility and suicide rates. This result remained the same even after considering the dynamic relationship between social capital and suicide rates (B = -0.57, 95% CI: -1.10, -0.04) while adjusting for dynamic panel bias. A 10% increase in community facility per 1,000 population was associated with 5.2% reduction in age-standardized suicide rates per 100,000 population. Limitations: In this study, only a structural dimension of social capital was utilized due to the lack of available data. Conclusion: The results indicate that facilitating opportunities for social interactions and community lives has a potential to prevent and reduce suicide.

Innovative Techniques in Comparator Designs

This paper presents four innovative designs of comparators proposed these years. A latch-type dynamic bias adds a tail capacitor to prevent fully discharging at the pre-amplifier output nodes to reduce energy consumption. The comparator is analysed and then compared with floating inverter amplifier (FIA) type. The pre-amplifier of the FIA type adopts an inverter-based input pair by a floating reservoir capacitor, greatly boosting gm/Id and improving the energy efficiency. The edge-pursuit comparator (EPC) provides a new perspective when designing comparators. According to the input difference, it will automatically adjust the comparison energy, avoiding wasting unnecessary energy spent on coarse comparison. Finally, for the edge-race comparator (ERC), it further improves the performance of saving energy and addressing the relatively long comparison time in the EPC.

A comparative research of innovative comparators

This paper studies four novel design comparators and gives a detailed analysis and summary of them. edge-pursuit comparator (EPC) improved energy efficiency and noise over conventional comparators by a circuit loop consisting of numbers of delay units. The triple-tail fully dynamic comparator minimizes the comparator’s total delay time and enhances the sample rate. The dynamic bias architecture of the double-tail latch-type comparator can provide a relatively high voltage gain while ensuring a low power consumption by stabilizing the static operating point. It also has advantages over conventional comparators in noise and delay. A triple-latch feedforward (TLFF) comparator improves on the triple-tail fully dynamic comparator. The triple-latch feedforward (TLFF) dynamic comparator consists of three-stage latches and a parallel feedforward path. It has a smaller delay time than other circuit designs, especially for large differential input signals.

An Enhanced Memetic Algorithm using SKB tree representation for fixed-outline and temperature driven non-slicing floorplanning

Floorplanning is an ever-emerging field in the Very Large Scale Integration (VLSI) circuit design automation since it deals with essential design metrics of a floorplan, such as chip area, total wirelength, and temperature. This paper aims to optimize these design metrics to obtain an optimal floorplan with reduced computation time. A Skewed B* (SKB) tree based Enhanced Memetic Algorithm (EMA) is proposed for temperature-driven fixed-outline non-sliceable floorplanning. The proposed EMA has two search phases: a global search based on Genetic Algorithm (GA), and a local search based on the Adaptive Fast Simulated Annealing (AFSA) algorithm. A novel dynamic threshold bias search strategy is implemented to balance the search phases, thus increasing the convergence speed to obtain an optimal solution. The performance of the proposed algorithm is tested on MCNC (Microelectronic Centre for North Carolina), and GSRC (Gigascale System Research Centre) state-of-the-art benchmark circuits. Experimental results show that the EMA produces highly optimal design metrics on all tested benchmark circuits.

Minimum-Variance State and Fault Estimation for Multirate Systems With Dynamical Bias

This brief is concerned with the joint state and fault estimation problem for a class of multi-rate systems with dynamical bias. To reflect real practice, the multi-rate sampling is considered which allows the sensor sampling rate and the state update rate to be different. The sensor is subject to the sensor fault that changes according to a dynamic equation. Instead of applying the traditional lifting technique, we introduce a time-varying delay into the measurement equation so as to transform the multi-rate systems into single-rate ones. The aim of this brief is to develop a joint state and fault estimation algorithm with minimized estimation error covariance. The recursion of the estimation error covariance is first derived, and appropriate estimator gains are then characterized that minimizes the estimation error covariance. A simulation example on the DC servo system is given to confirm the usefulness of the developed recursive state and fault estimation algorithm.

Research on Four Different Designs of Comparator

Abstract Comparators contribute a significant role to analogue to digital converters (ADC). This paper describes and evaluates four excellent comparator optimisation schemes in recent years and analyses their advantages and disadvantages, providing ideas for the following comparator research direction. In addition, this paper introduces the design steps of each comparator optimisation scheme. It shows how the designer completes the final optimisation scheme step by step from the practical problems, which provides a specific reference for the comparator designers in the future. A double-tail latch-type comparator with dynamic bias adds a tail capacitor to the pre-amplifier to achieve low noise and high gain. Furthermore, a triple-tail dynamic comparator addresses a cascoded integrator. The new stage defines and attenuates the noise to achieve high speed and low noise. Compared to the triple-tail comparator, another design of a three-stage comparator is through adding a feedforward path between the first amplifier/latch and third latch to construct a triple-latch feedforward dynamic comparator. It is aimed to reduce delay and get low consumption in the region of the high voltage signal. Moreover, Edge-Pursuit Comparator (EPC) uses NAND gates and inverter delay cells to generate the comparison result between two input signals. Its circuit structure allows input noise tunability, automatic energy scaling, and low voltage tolerance.

The Feminisation U, cultural norms, and the plough

The Feminisation U describes the tendency of female labour force participation (FLFP) to first decline and then rise in the process of economic development. While the Feminisation U is often presented as a ‘stylised fact’ of development, empirical support for it is mixed. Here, we show that cultural norms inherited from ancestral plough use exert a moderating influence on the shape of the Feminisation U. Specifically, we find a significantly U-shaped path of FLFP only in countries whose ancestors employed a plough-based agricultural technology. The shape of the U-curve becomes progressively more muted as the share of a country's ancestors that practiced plough agriculture decreases. In countries with little or no legacy of historical plough use, the time path of FLFP is effectively flat. This pattern of results is robust to correcting for dynamic panel bias, instrumenting for per-capita income, and controlling for other potential effect modifiers. Our findings are compatible with a nuanced reading of the main theoretical models proposed in the literature to explain the Feminisation U.

An Ultra-Low Quiescent Current Tri-Mode DC-DC Buck Converter With 92.1% Peak Efficiency for IoT Applications

An ultra-low quiescent current tri-mode DC-DC buck converter is presented in this paper, which is able to handle a 100,000X load range. In pulse width modulation (PWM) and pulse frequency modulation (PFM) mode, adaptive on-time (AOT) V² control is utilized to achieve seamless mode transition and constant switching frequency in PWM mode. A deep green mode (DGM) is proposed for light load, where both the switching loss and the power of control circuitry are minimized. By reducing the comparator current, a delay-based hysteresis window adaptive to load current is generated, reducing the switching frequency and the loss. Meanwhile, the average current of zero current detector (ZCD) and AOT controller are reduced significantly by dynamic biasing. As a result, the efficiency of the converter is improved while maintaining a reasonable output ripple. The proposed converter is implemented in a 0.18μ m BCD technology. Experimental results show that the converter can provide a 1.6 V output from a 2.7 to 4.7V input for 1μ A to 100 mA load, while consuming only 490nA quiescent current. The proposed converter achieves a 92.1% peak efficiency and efficiency can be maintained above 80% in a load range of 20μ A to 60 mA.

A 919 MHz—923 MHz, 21 dBm CMOS Power Amplifier With Bias Modulation Linearization Technique Achieving PAE of 29% for LoRa Application

This paper presents a bias modulation linearization technique for a 919-923MHz CMOS power amplifier which employs driver voltage modulation and main amplifier split bias. Through the proposed linearization technique, it is observed that the peak third-order intercept point (OIP3) across the output power is shifting according to the bias conditions of the split-bias power amplifier (SBPA). The third-order transconductance (gm₃) terms are suppressed at the output by phase cancellation achieved by optimization of the bias voltages of the PA. A high dynamic range bias circuit is integrated at the driver and split main to enhance the linearity of the CMOS PA, eradicating the need for pre-distortion linearizers. The two-stage SBPA is designed and fabricated in a 180 nm CMOS process with six-metal layers and a chip size of 1.820 x 1.771 mm² to operate at the supply voltage of 3.3 V. The bias voltages of both driver and split main stages are varied from 0 V to 2.0 V with a linear step size of 0.2 V. The proposed SBPA delivers a saturated output power (P_out) of 27 dBm with maximum power-added efficiency (PAE) of 44.4 % and peak OIP3 of 39 dBm. A maximum linear P_out of 21 dBm with 29 % PAE is achieved at an adjacent channel leakage ratio (ACLR) of -30 dBc and 4 % error vector magnitude (EVM), satisfying the LoRa specifications.

A High-Sensitivity Wide Input-Power-Range Ultra-Low-Power RF Energy Harvester for IoT Applications

Radio frequency energy harvesting (RFEH) is very attractive for the Internet of things (IoT) and self-powered micro-systems such as wearable biomedical devices and wireless sensor networks. This paper proposes, analyzes, and implements a new RF-DC converter in standard 130 nm CMOS technology. The developed converter is designed and optimized for ultra-low-power IoT and wearable biomedical applications using the 900 MHz ISM band. The proposed 10-stage cross-connected rectifier compensates the transistors threshold voltage by using both dynamic and static bias compensation techniques. An analytical model of the rectifier based on the MOSFET transistor equations is presented, allowing optimization of the rectifier as a function of the number of stages and transistors sizing, improving the sensitivity and the input power range of the converter. The measurement results demonstrate a sensitivity of &#x2212;25.5 dBm for 1 V output across a 5-<inline-formula> <tex-math notation="LaTeX">$\text{M}\Omega $ </tex-math></inline-formula> resistive load and &#x2212;29 dBm for a 100 <inline-formula> <tex-math notation="LaTeX">$\text{M}\Omega $ </tex-math></inline-formula> load, which is better than the best previously reported results. The measured peak end-to-end efficiency of the proposed harvester is 42.4&#x0025; at &#x2212;16 dBm input power, delivering 2.19 V to a 450 <inline-formula> <tex-math notation="LaTeX">$\text{k}\Omega $ </tex-math></inline-formula> load.

A tutorial on Bollen and Brand's approach to modeling dynamics while attending to dynamic panel bias.

Recent studies demonstrate that when researchers are interested in dynamics they are better off using a statistical model described in Bollen and Brand (2010) rather than the often employed random-coefficient or multi-level model (Moral-Benito et al., 2019; Xu et al., 2020). Their Monte Carlo studies, however, were methodologically advanced papers. Here, we present a beginner, hands-on tutorial describing the technique. We provide code in snippet form that any researcher can apply to his or her longitudinal data, introduce fundamentals of dynamic modeling, and generalize the basic model in Bollen and Brand (2010) to situations that cover broader inferences than those discussed in the simulation articles. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Fluorine NMR study of proline-rich sequences using fluoroprolines.

Proline homopolymer motifs are found in many proteins; their peculiar conformational and dynamic properties are often directly involved in those proteins' functions. However, the dynamics of proline homopolymers is hard to study by NMR due to a lack of amide protons and small chemical shift dispersion. Exploiting the spectroscopic properties of fluorinated prolines opens interesting perspectives to address these issues. Fluorinated prolines are already widely used in protein structure engineering - they introduce conformational and dynamical biases - but their use as F NMR reporters of proline conformation has not yet been explored. In this work, we look at model peptides where C-fluorinated prolines with opposite configurations of the chiral C centre have been introduced at two positions in distinct polyproline segments. By looking at the effects of swapping these (4)-fluoroproline and (4)-fluoroproline within the polyproline segments, we were able to separate the intrinsic conformational properties of the polyproline sequence from the conformational alterations instilled by fluorination. We assess the fluoroproline F relaxation properties, and we exploit the latter in elucidating binding kinetics to the SH3 (Src homology 3) domain.

Human Activity Recognition: A Dynamic Inductive Bias Selection Perspective.

In this article, we study activity recognition in the context of sensor-rich environments. In these environments, many different constraints arise at various levels during the data generation process, such as the intrinsic characteristics of the sensing devices, their energy and computational constraints, and their collective (collaborative) dimension. These constraints have a fundamental impact on the final activity recognition models as the quality of the data, its availability, and its reliability, among other things, are not ensured during model deployment in real-world configurations. Current approaches for activity recognition rely on the activity recognition chain which defines several steps that the sensed data undergo: This is an inductive process that involves exploring a hypothesis space to find a theory able to explain the observations. For activity recognition to be effective and robust, this inductive process must consider the constraints at all levels and model them explicitly. Whether it is a bias related to sensor measurement, transmission protocol, sensor deployment topology, heterogeneity, dynamicity, or stochastic effects, it is essential to understand their substantial impact on the quality of the data and ultimately on activity recognition models. This study highlights the need to exhibit the different types of biases arising in real situations so that machine learning models, e.g., can adapt to the dynamicity of these environments, resist sensor failures, and follow the evolution of the sensors’ topology. We propose a metamodeling approach in which these biases are specified as hyperparameters that can control the structure of the activity recognition models. Via these hyperparameters, it becomes easier to optimize the inductive processes, reason about them, and incorporate additional knowledge. It also provides a principled strategy to adapt the models to the evolutions of the environment. We illustrate our approach on the SHL dataset, which features motion sensor data for a set of human activities collected in real conditions. The obtained results make a case for the proposed metamodeling approach; noticeably, the robustness gains achieved when the deployed models are confronted with the evolution of the initial sensing configurations. The trade-offs exhibited and the broader implications of the proposed approach are discussed with alternative techniques to encode and incorporate knowledge into activity recognition models.

Development of a More Accurate Dynamic Bias Error Model for Two-Phase Flow Measurements Performed with Radiation Transmission

Application of radiation transmission measurement of two-phase flow phenomena has surged in the past decades due to the advancements in radiation detection along with its non-intrusive nature in comparison with other advanced two-phase flow measurements instrumentation such as wire mesh sensors and needle probes. However, radiation transmission measurement entails various levels of complexity when measuring temporally varying signals, which is the case for two-phase flow phenomena. The time-integrated radiation transmission acquisition yields a biased result, known as dynamic bias. In the present study, a more accurate method to estimate the dynamic bias is formulated. In addition, the behavior of the dynamic bias in two-phase flow measurements is investigated and characterized as a function of the void-fraction's temporal phase distribution, the gas phase magnitude distribution, and the contrast attenuation between the liquid and gas phases. Both numerical simulations and actual measurements of two-phase flows obtained with wire-mesh sensors, which includes several flow patterns, are used in the study. The dynamic bias was simulated for a contrast attenuation factor, λ, ranging from 0.01 to 5; this is a dimensionless factor that considers the geometry, elemental composition of the phases, radiation type and energy. The RMSE error was estimated for the proposed model and the reference model derived by Harms; the RMSE percentage decrease for the proposed model relative to Harm's for the range of λ improves by 50% to 58% for the bubbly regime, 40% to 59% for the bubbly to churn transitional regime, 36% to 56% for the churn regime, and 15% to 56% for the wispy annular.

A Short Study on Bias Present in Classical Random Processes

Sometimes, outcomes of random processes don’t seem to follow the theoretical probabilities due to the presence of bias and even when the probabilities are followed in a large number of trials, dynamic bias is still evident in many of these processes. This paper provides a short study on the bias using examples and defines what kind of processes could be biased. It also demonstrates the two types of bias which are dynamic and fixed. This study could be used to analyze the bias in various random processes and get a better understanding of the outcomes. Dynamic bias has further been explained with the help of 52 cards. The study helps in providing a better understanding of randomness and further helps in designing experiments.

Exploring the utility of radar and satellite-sensed precipitation and their dynamic bias correction for integrated prediction of flood and landslide hazards

It is important to develop the integrated flood and landslide modeling system driven by radar and satellite to predict these hazards to mitigate their damages. In this study, we investigated the utility of the C-band, one-polarization radar quantitative precipitation estimation (QPE) and the Global Satellite Mapping of Precipitation (GSMaP) satellite QPE for the integrated prediction of floods and landslides in two hilly basins of southern Shaanxi Province of China. We further developed a dynamic bias correction to reduce uncertainty in radar and satellite QPEs using gauge observations and explored the impacts of gauge density and spatial resolution of QPE on the effectiveness of bias correction. Our results show that the radar and GSMaP QPEs have respective large negative and positive biases. The bias-correction method has significantly improved the quality of both radar and GSMaP QPEs and the associated accuracies in the simulated hydrological processes and slope stability. The bias-correction method with a correction time interval of 24 h can achieve the optimal results for both radar and GSMaP QPEs. Although gauge density and spatial resolution impact the accuracy of the bias-corrected methods for both radar and GSMaP, inclusion of the observations from even a small number of rain gauges will be helpful for reducing the uncertainty in the radar and satellite QPEs.