L'Università dell'innovazione. L'ufficio di trasferimento tecnonolgico dell'Ateneo di Cagliari "Unica Laison Office"

Obiettivi ed organizzazzione dell'ufficio per il trasferimento tecnologico

Radar Evidence of Subglacial Liquid Water on Mars

Strong radar echoes from the bottom of the martian southern polar deposits are interpreted as being due to the presence of liquid water under 1.5 km of ice

Exploiting All Programmable SoCs in Neural Signal Analysis: A Closed-Loop Control for Large-Scale CMOS Multielectrode Arrays

Microelectrode array (MEA) systems with up to several thousands of recording electrodes and electrical or optical stimulation capabilities are commercially available or described in the literature. By exploiting their submillisecond and micrometric temporal and spatial resolutions to record bioelectrical signals, such emerging MEA systems are increasingly used in neuroscience to study the complex dynamics of neuronal networks and brain circuits. However, they typically lack the capability of implementing real-time feedback between the detection of neuronal spiking events and stimulation, thus restricting large-scale neural interfacing to open-loop conditions. In order to exploit the potential of such large-scale recording systems and stimulation, we designed and validated a fully reconfigurable FPGA-based processing system for closed-loop multichannel control. By adopting a Xilinx Zynq - all-programmable system on chip that integrates reconfigurable logic and a dual-core ARM-based processor on the same device, the proposed platform permits low-latency preprocessing (filtering and detection) of spikes acquired simultaneously from several thousands of electrode sites. To demonstrate the proposed platform, we tested its performances through ex vivo experiments on the mice retina using a state-of-the-art planar high-density MEA that samples 4096 electrodes at 18 kHz and record light-evoked spikes from several thousands of retinal ganglion cells simultaneously. Results demonstrate that the platform is able to provide a total latency from whole-array data acquisition to stimulus generation below 2 ms. This opens the opportunity to design closed-loop experiments on neural systems and biomedical applications using emerging generations of planar or implantable large-scale MEA systems

On-the-fly adaptivity for process networks over shared-memory platforms

Modern MPSoC architectures incorporate tens of processing elements on a single die. This trend poses the need of expressing the parallelism of the applications in order to effectively exploit the available resources. Several models of computation have been proposed, that specify an application as a network of independent computational elements. Such models represent a suitable solution for systematic mapping of parallel applications onto multiprocessor architectures. However, the workload of a given application can abruptly vary, as well as the amount of computing resources available, depending on the overall workload of the system and on the input data dependency. Traditional worst-case designs may overestimate workloads, leading to resource wasting and unnecessary power consumption. To overcome such limitation, in this work we devise a fast, run-time and automatic approach able to quickly re-configure the core-to-task mapping and the degree of parallelism of the application when the available resources or the application workload change, targeting shared-memory platforms. Experiments, carried out using an FPGA implementation, demonstrate the effectiveness of the proposed approach, in terms of achievable speed-up, power saving and introduced overhead

On-the-fly adaptivity for process networks over shared-memory platforms

Modern MPSoC architectures incorporate tens of processing elements on a single die. This trend poses the need of expressing the parallelism of the applications in order to effectively exploit the available resources. Several models of computation have been proposed, that specify an application as a network of independent computational elements. Such models represent a suitable solution for systematic mapping of parallel applications onto multiprocessor architectures. However, the workload of a given application can abruptly vary, as well as the amount of computing resources available, depending on the overall workload of the system and on the input data dependency. Traditional worst-case designs may overestimate workloads, leading to resource wasting and unnecessary power consumption. To overcome such limitation, in this work we devise a fast, run-time and automatic approach able to quickly re-configure the core-to-task mapping and the degree of parallelism of the application when the available resources or the application workload change, targeting shared-memory platforms. Experiments, carried out using an FPGA implementation, demonstrate the effectiveness of the proposed approach, in terms of achievable speed-up, power saving and introduced overhead

A closed-loop system for neural networks analysis through high density MEAs

In this work we present a FPGA-based system for real-time processing of neural signals acquired by commercial high-density microelectrode array (HDMEA). The considered MEA features 4096 electrodes with 18kHz sampling frequency and 12-bit resolution, thus produces nearly 1 Gbps of data. Within the implementation, we considered low-latency as a main objective, to allow for closed-loop acquisition-stimulation experiments, that represent a novel promising frontier in neuro-physiology and in the development of brain-machine interfaces. The developed platform is implemented on a low-to-mid Zynq all-programmable SoC, and is able to perform all the required computation (from signal acquisition to response generation) with less than 2ms latency, enabling closed-loop applications in a wide range of experiments

On-FPGA real-time processing of biological signals from high-density MEAs: A design space exploration

High-density microelectrode arrays (HDMEAs) are promising tools to tackle fundamental questions in neuroscience and brain diseases with unprecedented experimental capabilities. The acquisition of the biological signals sampled by such MEAs, that usually involves filtering, preliminary processing and finally data storage, is an intrinsically parallel and computation-intensive activity, particularly in systems targeting thousands of recording channels acquired with sub-millisecond time resolution. Within several applications, these operations need to be performed in real-time. A promising solution offering an adequate performance level relies on parallel hardware structures, making FPGA devices the perfect target technology.\\In this paper, we present an evaluation of an acquisition and processing system, to be implemented on an FPGA device, which is conceived to be connected to multi-channel CMOS-MEAs and is specifically designed for in-vitro and in-vivo recordings of neural activity. The template, implemented on reconfigurable logic, performs the first steps of the computing chain: filtering and adaptive detection of neural spikes. The filtered samples together with information regarding the presence of spikes are stored in an external DDR memory, for further elaboration and communication with the external environment. We performed a design space exploration measuring resource utilization and precision of the detection algorithm for different use-cases, corresponding to different state-of-the-art HDMEAs, and for different application parameters, such as the filtering scheme, number of parallel input channels, and sampling frequency. A prototype instance of the proposed platform, implemented on a low-end Xilinx Zynq SoC, allows to process more than 1 Gbps of data coming from up to 4096 18-kHz channels, within a time latency of 1.8 ms

Does better job accessibility help people gain employment? The role of public transport in Great Britain

The combined decentralisation of many firms and services and the increasing concentration of traditional public transport services in the main corridors of urban centres have made it more difficult for people to access jobs, in particular when residing outside these prime accessibility areas. This is the first national study within the context of Great Britain to examine whether better public transport job accessibility, modelled at the micro level of individuals, improves employment probabilities for people living in Great Britain. While previous studies have typically concentrated on US metropolitan areas, our study uses British national employment micro datasets to assess which urban and rural areas and population groups would benefit from better public transport services. In an important departure from most standard accessibility methodologies, we computed a public transport job accessibility measure applied nationwide and combined this with individual-level employment probability models for Great Britain. The models were corrected for endogeneity by applying an instrumental variable approach. The study finds that better public transport job accessibility improves individual employment probabilities, in particular in metropolitan areas and smaller cities and towns with lower car ownership rates and in low-income neighbourhoods. It further shows that mainly lower educated groups and young people would benefit from better public transport job accessibility. The findings in this study are important for policymakers in that they imply that, in particular, job seekers who rely on public transport services may benefit from more targeted public policies to improve their accessibility to employment and thereby their social mobility

Radar evidence of subglacial liquid water on Mars.

The presence of liquid water at the base of the martian polar caps has long been suspected but not observed. We surveyed the Planum Australe region using the MARSIS (Mars Advanced Radar for Subsurface and Ionosphere Sounding) instrument, a low-frequency radar on the Mars Express spacecraft. Radar profiles collected between May 2012 and December 2015 contain evidence of liquid water trapped below the ice of the South Polar Layered Deposits. Anomalously bright subsurface reflections are evident within a well-defined, 20-kilometer-wide zone centered at 193°E, 81°S, which is surrounded by much less reflective areas. Quantitative analysis of the radar signals shows that this bright feature has high relative dielectric permittivity (>15), matching that of water-bearing materials. We interpret this feature as a stable body of liquid water on Mars