Suchergebnisse

The tunneling magnetoresistance micro-sensors (TMR) developed by magnetic multilayer material has many advantages, such as high sensitivity, high frequency response, and good reliability. It is widely used in military and civil fields. This work presents a high-performance interface circuit for TMR sensors. Because of the nonlinearity of signal conversion between sensitive structure and interface circuit in feedback loop and forward path, large harmonic distortion occurs in output signal spectrum, which greatly leads to the reduction of SNDR (signal noise distortion rate). In this paper, we analyzed the main source of harmonic distortion in closed-loop detection circuit and establish an accurate harmonic distortion model in TMR micro-sensors system. Some factors are considered, including non-linear gain of operational amplifier unit, effective gain bandwidth, conversion speed, nonlinearity of analog transmission gate, and nonlinearity of polycrystalline capacitance in high-order sigma-delta system. We optimized the CMOS switch and first-stage integrator in the switched-capacitor circuit. The harmonic distortion parameter is optimally designed in the TMR sensors system, aiming at the mismatch of misalignment of front-end system, non-linearity of quantizer, non-linearity of capacitor, and non-linearity of analog switch. The digital output is attained by the interface circuit based on a low-noise front-end interface circuit and a third-order sigma-delta modulator. The digital interface circuit is implemented by 0.35μm CMOS (complementary metal oxide semiconductor) technology. The high-performance digital TMR sensors system is implemented by double chip integration and the active interface circuit area is about 3.2 × 2 mm. The TMR sensors system consumes 20 mW at a single 5 V supply voltage. The TMR sensors system can achieve a linearity of 0.3% at full scale range (±10(5) nT) and a resolution of 0.25 nT/Hz(1/2)(@1Hz).

Purpose
The purpose of the current study is to identify variables that, when integrated into the random effects parametric survival model, could be used to forecast the failure rate of small banks in the USA. A bank's income production, efficiency and costs were taken into consideration when choosing the internal components. The breakout of the financial crisis, bank regulations that affect how the banking sector operates and the federal funds rate are the primary external variables.


Design/methodology/approach
This study uses the random effects parametric survival model to investigate the causes of small bank failures in the USA from 1996 to 2019. The study identifies several characteristics that failed banks frequently display. The main indications that may help to identify the elevated risk of small bank failures include the ROA, the cost of funds, the ratio of noninterest income to assets, the ratio of loan and lease losses to assets, noninterest expenses and core capital (leverage) ratio to assets. Economic disruptions, financial market distress and industry-based regulatory redress by the government exacerbate the financial distress borne by small banks.


Findings
The study revealed that a failed bank typically demonstrates a certain number of characteristics. The key factors that might assist identify which bank would be most likely to collapse include the cost of funding earning assets, the yield on earning assets, core Capital (leverage) ratio to assets, loan and lease loss provision to assets, noninterest expense and noninterest income to assets. Additionally, when a financial crisis occurs or the government changes regulations that could raise the cost of compliance for small banks, the likelihood that a bank will fail increases.


Originality/value
Models based on survival theories are more suitable when the authors examine bank failure as a unique event that happens gradually. The authors use a random effects parametric survival model to investigate the internal and external factors that may influence prospective small bank failure. This model has been developed and used in the medicinal research field. The authors do not choose the Cox proportional hazards model because it does not work well with panel data.