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Microfluidics and lab-on-a-chip technologies have made it possible to manipulate small volume liquids with unprecedented resolution, automation and integration. However, most current microfluidic systems still rely on bulky off-chip…
This survey reviews several approaches of data mining (DM) in healthindustry from many research groups world wide. The focus is on modern multi-core processors built into today's commodity computers, which are typically found at university…
Deep learning (DL) is becoming the cornerstone of numerous applications both in datacenters and at the edge. Specialized hardware is often necessary to meet the performance requirements of state-of-the-art DL models, but the rapid pace of…
As the Covid-19 pandemic grips the world, healthcare systems are being reshaped, where the e-health concepts become more likely to be accepted. Wearable devices often carry sensitive information from users which are exposed to security and…
In this work, we introduce CHIPS-FF (Computational High-Performance Infrastructure for Predictive Simulation-based Force Fields), a universal, open-source benchmarking platform for machine learning force fields (MLFFs). This platform…
Controlling optical fields on the subwavelength scale is at the core of any nanophotonic device. Of particular interest are nanophotonic particle accelerators that promise a compact alternative to conventional radiofrequency-based…
Lab-on-a-Chip (LoC) devices combining microfluidic analyte provision with integrated optical analysis are highly desirable for several applications in biological oder medical sciences. While the microfluidic approach is already broadly…
With the emergence of the Internet-of-Things (IoT), there is a growing need for access control and data protection on low-power, pervasive devices. Biometric-based authentication is promising for IoT due to its convenient nature and lower…
In this paper, we describe the algorithms we implemented in FDPS to make efficient use of accelerator hardware such as GPGPUs. We have developed FDPS to make it possible for many researchers to develop their own high-performance parallel…
Applying deep neural networks (DNNs) in mobile and safety-critical systems, such as autonomous vehicles, demands a reliable and efficient execution on hardware. Optimized dedicated hardware accelerators are being developed to achieve this.…
Flexible electronics offer unique advantages for conformable, lightweight, and disposable healthcare wearables. However, their limited gate count, large feature sizes, and high static power consumption make on-body machine learning…
A low-power wearable wireless sensor measuring both molecular biomarkers and physiological signals is proposed, where the former are measured by a microfluidic biosensing system while the latter are measured electrically. The low-power…
Variational hybrid quantum-classical algorithms are some of the most promising workloads for near-term quantum computers without error correction. The aim of these variational algorithms is to guide the quantum system to a target state that…
Design For Manufacturing (DFM) approaches aim to integrate manufacturability aspects during the design stage. Most of DFM approaches usually consider only one manufacturing process, but products competitiveness may be improved by designing…
Photoacoustic microscopy holds the potential to measure biomarkers' structural and functional status without labels, which significantly aids in comprehending pathophysiological conditions in biomedical research. However, conventional…
Conventional dual-frequency fringe projection algorithm often suffers from phase unwrapping failure when the frequency ratio between the high frequency and the low one is too large. Zhang et.al. proposed an enhanced two-frequency…
Magnetic Resonance Fingerprinting (MRF) is an emerging technology with the potential to revolutionize radiology and medical diagnostics. In comparison to traditional magnetic resonance imaging (MRI), MRF enables the rapid, simultaneous,…
Atomic force microscopy (AFM) is an essential nanoinstrument technique for several applications such as cell biology and nanoelectronics metrology and inspection. The need for statistically significant sample sizes means that data…
Density-potential functional theory (DPFT) is an alternative formulation of orbital-free density functional theory that may be suitable for modeling the electronic structure of large systems. To date, DPFT has been applied mainly to quantum…
With many advancements in in silico biology in recent years, the paramount challenge is to translate the accumulated knowledge into exciting industry partnerships and clinical applications. Achieving models that characterize the link of…