This paper presents a spiking neural network (SNN) implementation which employs unsupervised feature extraction using spike timing dependent plasticity (STDP) to classify 8 different radioisotopes. With the implementation, the accuracy could reach 80% during training and overall testing accuracy of 72%. The whole network was implemented on SpiNNaker, a spiking neural network emulation platform. This work shows that unsupervised STDP, an SNN native training method, can be applied to the classification task of RIID to provide event-based training as well as inference.
Event driven cameras have the potential to revolutionise the real-time visual sensory processing paradigm. These asynchronous sensors detect change in the environment with low latency and high dynamic range, allowing for orders of magnitude faster systems than the state of the art using intensity cameras. On the other hand, deep artificial neural networks have refashioned machine vision in the last decade, greatly expanding the reach of viable tasks, supported by the creation of many large scale image datasets. In this work, we present a modality to leverage these large scale datasets for the purpose of training off-the-shelf deep learning architectures and re-appropriating them for event-based tasks. To this end, we describe an event representation, EROS, and a method to convert images to an EROS-like representation such that image datasets can train neural networks for event driven applications.
This paper is focused on the interpolation of a signal modeled by a random process from a set of discrete-time measurements. The process of signal sampling is studied as a conditioning of a random process at instants of its discrete-time observations. The analysis shows that even if an input is modeled by a stationary Gaussian process, the conditional random process with a set of observations at sampling instants, is still Gaussian but non-stationary. By the adoption of standard properties of the multivariate normal distribution, we derive the mean of the conditional process, which is at the same time the minimum mean-square error (MMSE) predictor for signal reconstruction based on the given information represented by the observations. It is shown that for Gaussian signals, the MMSE predictor is a linear function of the observed data. For bandlimited signals, the conditional MMSE predictor coincides to the well-known MMSE reconstruction derived by Yen based on the deterministic approach. Although the approach covers any measurement scheme, possibly non-uniform in time, the study is narrowed down to the interpolation of the signal from its level-crossing samples. The accuracy of the MMSE reconstruction has been verified by simulations.
Event cameras are promising sensors for on-line and real-time vision tasks due to their high temporal resolution, low latency, and redundant static data elimination. Many vision algorithms use some form of spatial convolution (i.e. spatial pattern detection) as a fundamental component. However, additional consideration must be taken for event cameras, as the visual signal is asynchronous and sparse. While elegant methods have been proposed for event-based convolutions, they are unsuitable for real scenarios due to their inefficient processing pipeline and subsequent low event-throughput. This paper presents an efficient implementation based on decoupling the event-based computations from the computationally heavy convolutions, increasing the maximum event processing rate by 15. 92 × to over 10 million events/second, while still maintaining the event-based paradigm of asynchronous input and output. Results on public datasets with modern 640 × 480 event-camera recordings show that the proposed implementation achieves real-time processing with minimal impact on the convolution result, while the prior state-of-the-art results in a latency of over 1 second.
Nowadays, controlled systems with constant time sampling are widely spread. Conversely, the event-triggered paradigm is highly appealing since it promises to reduce computation and data flow when the dynamics of the system requires little or no control update, while allowing more intense refresh during transition phases. The objective here is to extend this opportunity and to design a fully event-based control architecture by considering not only the controller but the whole sensorimotor chain from perception to action, with the hope of even better gains in resource utilization. The targeted application is the visual servoing of a ball-on-beam system. The proposal includes a visual sensor, perception and control algorithms, as well as an actuator, all of which being event driven. Moreover, the proposed strategy is implemented on an embedded CPU-based platform. Beyond the innovative design, experimental results highlight savings in computational cost and bandwidth, with even slightly better performances.
