Abstract

This paper presents the design, fabrication, and characterization of a thin-film Cr-Al-Cr (300 A°-1500 A°-300 A°) metal stack heater specially designed for chemical reactions which occur at a uniform temperature in the lab on a chip platform such as polymerase chain reaction (PCR) applications. The heater has been designed using COMSOL Multiphysics. The simulated design has been fabricated using lithography and patterning on a 5 × 5 cm2 glass substrate. For the validation of the proposed design thermal study of the fabricated heater has also been done using a FLIR IR camera. A very good agreement between the thermal image of modeled and fabricated heater has been achieved. This demonstrates the suitability of the proposed heater for PCR reaction applications.

Abstract

Electrocardiography (ECG) is a well-known noninvasive technique in medical science that provides information about the heart’s rhythm and current conditions. Automatic ECG arrhythmia diagnosis relieves doctors’ workload and improves diagnosis effectiveness and efficiency. This study proposes an automatic end-to-end 2D CNN (two-dimensional convolution neural networks) deep learning method with an effective DenseNet model for addressing arrhythmias recognition. To begin, the proposed model is trained and evaluated on the 97720 and 141404 beat images extracted from the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia and St. Petersburg Institute of Cardiological Technics (INCART) datasets (both are imbalanced class datasets) using a stratified 5-fold evaluation strategy. The data is classified into four groups: N (normal), V (ventricular ectopic), S (supraventricular ectopic), and F (fusion), based on the Association for the Advancement of Medical Instrumentation® (AAMI). The experimental results show that the proposed model outperforms state-of-the-art models for recognizing arrhythmias, with the accuracy of 99.80% and 99.63%, precision of 98.34% and 98.94%, and F1-score of 98.91% and 98.91% on the MIT-BIH arrhythmia and INCART datasets, respectively. Using a transfer learning mechanism, the proposed model is also evaluated with only five individuals of supraventricular MIT-BIH arrhythmia and five individuals of European ST-T datasets (both of which are also class imbalanced) and achieved satisfactory results. So, the proposed model is more generalized and could be a prosperous …

Abstract

The present description relates to a method based on artifi cial intelligence to implement a wide range of microelec tronic circuits that can adapt by themselves to the usage conditions ( e.g. loading changes ) , manufacturing variances or defects ( e.g. process variations , device parameter mis matches , device model inaccuracies or changes , etc. ) , as well as environmental conditions ( e.g. voltage , temperature , interference ) in order to negate all or part of their effects on the circuit performance characteristics and achieve a very tight set of specifications over the wide range of conditions . Each microelectronic circuit is represented by a neural network model whose behavior is a function of the actual input signals , the usage and environmental conditions . An attached AI engine will infer from the model , the input signals , the usage conditions and the environmental condi tions and create the adaptive changes required to modify the microelectronic circuit's behavior to negate all or part of their effects on the circuit performance characteristics and to achieve a very tight set of specifications .

Abstract

The paper presents a sub-nW gate-leakage based voltage and current reference in a single circuit whose reference values are scalable and doesn’t incorporate start-up circuits or resistors in the architecture. The power consumption of the proposed circuit increases by only 2.1x in the temperature range of -55°C to 100°C, unlike conventional voltage/current references where the power consumption increases exponentially w.r.t temperature. Implemented in 90nm technology, the proposed voltage reference (current reference) achieves post-trim typical accuracy of 22ppm/°C(58ppm/°C) and worst-case accuracy of 71ppm/°C(78ppm/°C). Excellent line sensitivities of 0.029%/V and 0.059%/V are observed for voltage and current reference respectively, in a supply range of 1V - 3V. Without any start-up circuit, the observed 99% settling times for voltage and current reference are 1.92ms and 2.526ms respectively. The area occupied by the total circuit is 0.0015mm 2 , while the power consumption is 156pW at typical corner, 27°C and 1V supply.

Abstract

Microelectromechanical systems (MEMS) are small-scale devices capable of producing motion using electrical energy as input. In this work, we present the determination of thermal and mechanical properties of SU-8, which is an emerging structural material for MEMS applications. We have experimentally calculated the coefficient of thermal expansion (CTE), thermal conductivity, and specific heat capacity of SU-8 as 29.68 ppm °C−1, 0.17 , and 1.6 , respectively. We observe a non-linear relationship between the input power with the displacement achieved by an SU-8-based thermal actuator because of the hyperelastic behavior of the material. The non-linearity in the experimental results has been explained using the two-parameter Mooney–Rivlin model. With our experimental data, we found the parameters for the uniaxial tensile stress, as Pa and Pa

Abstract

Viscosity measurement has wide ranging applications from oil industry to pharmaceutical industry. However, measuring viscosity in real-time is not a facile process. This paper provides an elaborate mathematical model and study of viscosity measurement in real-time using pressure sensors. For a given flowrate, a change in liquid viscosity gives rise to a change in pressure difference across a particular section of the pipe. Hence, by recording the pressure change, viscosity can be calculated dynamically. A mathematical model as well as a finite element analysis model has been presented to determine viscosity from flowrate and pressure difference. A set of pressure sensors can be placed at a fixed distance from each other to get the real- time pressure change, while flowrate can be obtained using a flowmeter. For the finite element analysis, the pressure sensors were placed 60 mm away from each other. The radius of the pipe was 19 mm. A mixture of water and glycerol, with different ratios, was used to provide variable viscosity. Index Terms—Viscosity, Flowrate, Microfluidic channel, Pressure sensor, Real-time measurement

Abstract

Structural engineering plays an essential role in designing, improving, and optimizing an electromechanical system, instinctively affecting its performance. In this study, design optimization, finite element analysis, and experimental evaluation of capacitive pressure sensors were conducted. The air pressure sensing application was demonstrated to characterize different sensors, which include a combination of multiple rectangular cantilevers and diaphragms (square and circular-shaped). After the design improvement, we found that the square and circular diaphragms each with two trapezoidal cantilevers exhibited highest sensitivity to air pressure monitoring among the different investigated designs which combine the square and circular diaphragms with cantilevers. These designs were then selected for further analysis for acoustic pressure monitoring. The sensors were fabricated using the doit-yourself technique with household materials such as post-it paper, posted tape, and foil. Our approach offers an alternative to the conventional cleanroom fabrication technique and uses easily available materials to fabricate affordable sensors. Therefore, this is the first step toward the development of democratized and sustainable electronic devices that are affordable and available to everyone on the internet. Index Terms—Capacitive sensing, air pressure sensing, acoustic pressure sensing, paper electronics, democratized electronics, affordable electronics, structural engineering.

Abstract

Decentralized Finance (DeFi) promises to transform the traditional financial systems into fair and transparent protocols that do not require trusted third parties. To circumvent the high volatility of crypto-assets, DeFi protocols advocate collateralizing their assets against conventional financial instruments. To do so, these protocols require access to external or off-chain data, such as asset exchange rates. DeFi protocols rely on oracles to access such information. Importing external data onto the chain via oracles consists of multiple data processing and aggregation stages. Thus, it is critical to minimize errors or deviations while the ground truth data moves through these stages. In this paper, we investigate the degree of price deviations at different levels between the data source and the final output rendered to an on-chain requester. In particular, we focus on Chainlink’s oracle network for ETH-USD pricing. Our results …

Abstract

Electric vehicles (EVs) represent the long-term green substitute for traditional fuel-based vehicles. To encourage EV adoption, the trust of the end-users must be assured. In this work, we focus on a recently emerging privacy threat of profiling and identifying EVs via the analog electrical data exchanged during the EV charging process. The core focus of our work is to investigate the feasibility of such a threat at scale. To this end, we first propose an improved EV profiling approach that outperforms the state-of-the-art EV profiling techniques. Next, we exhaustively evaluate the performance of our improved approach to profile EVs in real-world settings. In our evaluations, we conduct a series of experiments including 25032 charging sessions from 530 real EVs, sub-sampled datasets with different data distributions, etc. Our results show that even with our improved approach, profiling and individually identifying the growing number of EVs appear extremely difficult in practice; at least with the analog charging data utilized throughout the literature. We believe that our findings from this work will further foster the trust of potential users in the EV ecosystem, and consequently, encourage EV adoption.

Abstract

urpose: Melanin is a major contributor to the intensity and width of the retinal pigment epithelium (RPE) on optical coherence tomography (OCT). It is hypothesized, high melanin concentrations cause scattering making the RPE appear as a thick band and obscures adjacent layers. A split band appearance of the outermost hyperreflective band has been demonstrated in Albinism, meaning the RPE and Bruch’s Membrane appear as two separate bands. We performed a cross-sectional observational study to test the hypothesis that a reduction in melanin causes an increased ability to differentiate the hyperreflective bands seen on OCT images of the outer retina. Methods: Using the UK Biobank dataset, we generated a randomised sample of 300 participants with specific inclusion and exclusion criteria. Inclusion criteria was an age between 40 to 65, visual acuity better than 0.60 logMAR, no ocular pathology, and