Showing papers by "Uwe Meyer-Baese published in 2022"

Dracon: An Open-Hardware Based Platform for Single-Chip Low-Cost Reconfigurable IoT Devices

Abstract: The development of devices for the Internet of Things (IoT) requires the rapid prototyping of different hardware configurations. In this paper, a modular hardware platform allowing to prototype, test and even implement IoT appliances on low-cost reconfigurable devices is presented. The proposed platform, named Dracon, includes a Z80-clone microprocessor, up to 64 KB of RAM, and 256 inputs/outputs (I/Os). These I/Os can be used to connect additional co-processors within the same FPGA, external co-processors, communications modules, sensors and actuators. Dracon also includes as default peripherals a UART for programming and accessing the microprocessor, a Real Time Clock, and an Interrupt Timer. The use of an 8-bit microprocessor allows the use of the internal memory of the reconfigurable device as program memory, thereby, enabling the implementation of a complete IoT device within a single low-cost chip. Indeed, results using a Spartan 7 FPGA show that it is possible to implement Dracon with only 1515 6-input LUTs while operating at a maximum frequency of 80 MHz, which results in a better trade-off in terms of area and performance than other less powerful and less versatile alternatives in the literature. Moreover, the presented platform allows the development of embedded software applications independently of the selected FPGA device, enabling rapid prototyping and implementations on devices from different manufacturers.

Graph signal processing to identify biomarkers in brain networks in dementia

Abstract: Normal and aberrant cognitive functions are the result of the dynamic interplay between large-scale neural circuits. Describing the nature of these interactions has been a challenging task yet important for neurodegenerative disease evolution. Graph theory has been the standard tool to provide biomarkers in imaging connectomics showing the Alzheimer’s disease (AD). We propose a novel concept - graph signal processing - to analyze the evolution of disease graphs leading from mild cognitive impairment (MCI) to AD and derive frequency-based biomarkers representative for this disease. We show that high oscillations derived from the graph Fourier decomposition can provide important discriminatory information. To quantify the qualitative intuition of high oscillations, we use two concepts from signal theory: (1) zero crossings and (2) total variations. We apply these concepts on functional and structural brain connectivity networks for control (CN), mild cognitive impairment (MCI) and Alzheimer’s disease (AD) subjects. Our results applied to functional brain networks suggest that graph signal processing can accurately describe the frequencies of brain networks, and explain how AD is associated with low frequency and localized averaging confirmed by clinical results.