Fast unsupervised learning for visual pattern recognition using spike timing dependent plasticity

Real-time learning needs algorithms operating in a fast speed comparable to human or animal, however this is a huge challenge in processing visual inputs. Research shows a biological brain can process complicated real-life recognition scenarios at milliseconds scale. Inspired by biological system, in this paper, we proposed a novel real-time learning method by combing the spike timing-based feed-forward spiking neural network (SNN) and the fast unsupervised spike timing dependent plasticity learning method with dynamic post-synaptic thresholds. Fast cross-validated experiments using MNIST database showed the high e�ciency of the proposed method at an acceptable accuracy

Multihop Rendezvous Algorithm for Frequency Hopping Cognitive Radio Networks

Cognitive radios allow the possibility of increasing utilization of the wireless spectrum, but because of their dynamic access nature require new techniques for establishing and joining networks, these are known as rendezvous. Existing rendezvous algorithms assume that rendezvous can be completed in a single round or hop of time. However, cognitive radio networks utilizing frequency hopping that is too fast for synchronization packets to be exchanged in a single hop require a rendezvous algorithm that supports multiple hop rendezvous. We propose the Multiple Hop (MH) rendezvous algorithm based on a pre-shared sequence of random numbers, bounded timing differences, and similar channel lists to successfully match a percentage of hops. It is tested in simulation against other well known rendezvous algorithms and implemented in GNU Radio for the HackRF One. We found from the results of our simulation testing that at 100 hops per second the MH algorithm is faster than other tested algorithms at 50 or more channels with timing ±50 milliseconds, at 250 or more channels with timing ±500 milliseconds, and at 2000 channels with timing ±5000 milliseconds. In an asymmetric environment with 100 hops per second, a 500 millisecond timing difference, and 1000 channels the MH algorithm was faster than other tested algorithms as long as the channel overlap was 35% or higher for a 50% required packet success to complete rendezvous. We recommend the Multihop algorithm for use cases with a fast frequency hop rate and a slow data transmission rate requiring multiple hops to rendezvous or use cases where the channel count equals or exceeds 250 channels, as long as timing data is available and all of the radios to be connected to the network can be pre-loaded with a shared seed

Algorithm for Optimal Mode Scheduling in Switched Systems

This paper considers the problem of computing the schedule of modes in a switched dynamical system, that minimizes a cost functional defined on the trajectory of the system's continuous state variable. A recent approach to such optimal control problems consists of algorithms that alternate between computing the optimal switching times between modes in a given sequence, and updating the mode-sequence by inserting to it a finite number of new modes. These algorithms have an inherent inefficiency due to their sparse update of the mode-sequences, while spending most of the computing times on optimizing with respect to the switching times for a given mode-sequence. This paper proposes an algorithm that operates directly in the schedule space without resorting to the timing optimization problem. It is based on the Armijo step size along certain Gateaux derivatives of the performance functional, thereby avoiding some of the computational difficulties associated with discrete scheduling parameters. Its convergence to local minima as well as its rate of convergence are proved, and a simulation example on a nonlinear system exhibits quite a fast convergence

The ATLAS tau trigger

The ATLAS tau trigger consists of three level trigger systems: the first one (L1) is hardware based and uses FPGAs, while the second (L2) and third levels (EF -Event Filter-) are software based and use commodity computers (2 x Intel Harpertown quad-core 2.5 GHz), running scientific linux 5. In this contribution, we discuss both the physics characteristics of tau leptons and the technical solutions to quick data access and fast algorithms. We show that L1 selects narrow jets in the calorimeter with an overall rejection against QCD jets of 300, whilst L2 and EF (referred together as High Level Trigger -HLT-) use all the detectors with full granularity and apply a typical rejection of 15 within the stringent timing requirements of the LHC. In the HLT there are two complementary approaches: specialized, fast algorithms are used at L2, while more refined and sophisticated algorithms, imported from the offline, are utilized in the EF

On Time Optimization of Centroidal Momentum Dynamics

Recently, the centroidal momentum dynamics has received substantial attention to plan dynamically consistent motions for robots with arms and legs in multi-contact scenarios. However, it is also non convex which renders any optimization approach difficult and timing is usually kept fixed in most trajectory optimization techniques to not introduce additional non convexities to the problem. But this can limit the versatility of the algorithms. In our previous work, we proposed a convex relaxation of the problem that allowed to efficiently compute momentum trajectories and contact forces. However, our approach could not minimize a desired angular momentum objective which seriously limited its applicability. Noticing that the non-convexity introduced by the time variables is of similar nature as the centroidal dynamics one, we propose two convex relaxations to the problem based on trust regions and soft constraints. The resulting approaches can compute time-optimized dynamically consistent trajectories sufficiently fast to make the approach realtime capable. The performance of the algorithm is demonstrated in several multi-contact scenarios for a humanoid robot. In particular, we show that the proposed convex relaxation of the original problem finds solutions that are consistent with the original non-convex problem and illustrate how timing optimization allows to find motion plans that would be difficult to plan with fixed timing.Comment: 7 pages, 4 figures, ICRA 201