Digital approaches in Electronic biochips

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We present Epidemic Learning ( EL ), a simple yet powerful decentralized learning (DL) algorithm that leverages changing communication topologies to achieve faster model convergence compared to conventional DL approaches. At each round of EL, each node sen ...

Event-based sensors have the potential to optimize energy consumption at every stage in the signal processing pipeline, including data acquisition, transmission, processing, and storage. However, almost all state-of-the-art systems are still built upon th ...

Higher-order asymptotics provide accurate approximations for use in parametric statistical modelling. In this thesis, we investigate using higher-order approximations in two-specific settings, with a particular emphasis on the tangent exponential model. ...

Semiconductor materials have given rise to today's digital technology and consumer electronics. Widespread adoption is closely linked to the ability to process and integrate them in devices at scale. Where flexibility and large surfaces are required, such ...

Two-dimensional (2D) transition metal carbides, and/or nitrides, so-called MXenes, have triggered intensive research interests in applications ranging from electrochemical energy storage to electronics devices. Producing these functional devices by printin ...

Monolithic pixel sensors integrate the sensor matrix and readout in the same silicon die, and therefore present several advantages over the more largely used hybrid detectors in high-energy physics. They offer an easier detector assembly, lower cost, lower ...

The evolution of electronics has largely relied on downscaling to meet the continuous needs for faster and highly integrated devices(1). As the channel length is reduced, however, classic electronic devices face fundamental issues that hinder exploiting ma ...

Organic electrochemical transistors (OECTs) have gained enormous attention due to their potential for bioelectronics and neuromorphic computing. However, their implementation into real-world applications is still impeded by a lack of understanding of the c ...

Bioaerosols are emitted from various sources into the indoor environment and can positively and negatively impact human health. Humans are the major source of bioaerosol emissions indoors, specifically for bacteria. However, efficient sampling to guarantee ...

We present Diffusion in Style, a simple method to adapt Stable Diffusion to any desired style, using only a small set of target images. It is based on the key observation that the style of the images generated by Stable Diffusion is tied to the initial lat ...

Magnetic resonance spectroscopy is a powerful, non-invasive, quantitative imaging technique that allows for the measurement of brain metabolites that has demonstrated utility in diagnosing and characterizing a broad range of neurological diseases. Its impa ...

Digital approaches in Electronic biochips

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Epidemic Learning: Boosting Decentralized Learning with Randomized Communication

An Error-Based Approximation Sensing Circuit for Event-Triggered Low-Power Wearable Sensors

Higher Order Asymptotics: Applications to Satellite Conjunction and Boundary Problems

Semiconductor-based device architectures in multimaterial fibers

Stable MXene Dough with Ultrahigh Solid Fraction and Excellent Redispersibility toward Efficient Solution Processing and Industrialization

Front-End Circuits for Radiation-Hard Monolithic CMOS Sensors targeting High-Energy Physics Applications

Electronic metadevices for terahertz applications

Unraveling the Electrochemical Electrode Coupling in Integrated Organic Electrochemical Transistors

Comparison of two methods for bioaerosol sampling and characterization in a low-biomass chamber environment

Diffusion in Style

Harmonization of multi-scanner in vivo magnetic resonance spectroscopy: ENIGMA consortium task group considerations