4,041 research outputs found
Systems approach to evaluating Hydrogenomonas cultures
Hydrogenomonas cultures investigated for metabolic waste utilization in aerospace life support system
Neuromorphic Hardware In The Loop: Training a Deep Spiking Network on the BrainScaleS Wafer-Scale System
Emulating spiking neural networks on analog neuromorphic hardware offers
several advantages over simulating them on conventional computers, particularly
in terms of speed and energy consumption. However, this usually comes at the
cost of reduced control over the dynamics of the emulated networks. In this
paper, we demonstrate how iterative training of a hardware-emulated network can
compensate for anomalies induced by the analog substrate. We first convert a
deep neural network trained in software to a spiking network on the BrainScaleS
wafer-scale neuromorphic system, thereby enabling an acceleration factor of 10
000 compared to the biological time domain. This mapping is followed by the
in-the-loop training, where in each training step, the network activity is
first recorded in hardware and then used to compute the parameter updates in
software via backpropagation. An essential finding is that the parameter
updates do not have to be precise, but only need to approximately follow the
correct gradient, which simplifies the computation of updates. Using this
approach, after only several tens of iterations, the spiking network shows an
accuracy close to the ideal software-emulated prototype. The presented
techniques show that deep spiking networks emulated on analog neuromorphic
devices can attain good computational performance despite the inherent
variations of the analog substrate.Comment: 8 pages, 10 figures, submitted to IJCNN 201
Observability-aware Self-Calibration of Visual and Inertial Sensors for Ego-Motion Estimation
External effects such as shocks and temperature variations affect the
calibration of visual-inertial sensor systems and thus they cannot fully rely
on factory calibrations. Re-calibrations performed on short user-collected
datasets might yield poor performance since the observability of certain
parameters is highly dependent on the motion. Additionally, on
resource-constrained systems (e.g mobile phones), full-batch approaches over
longer sessions quickly become prohibitively expensive.
In this paper, we approach the self-calibration problem by introducing
information theoretic metrics to assess the information content of trajectory
segments, thus allowing to select the most informative parts from a dataset for
calibration purposes. With this approach, we are able to build compact
calibration datasets either: (a) by selecting segments from a long session with
limited exciting motion or (b) from multiple short sessions where a single
sessions does not necessarily excite all modes sufficiently. Real-world
experiments in four different environments show that the proposed method
achieves comparable performance to a batch calibration approach, yet, at a
constant computational complexity which is independent of the duration of the
session
Improved Algorithms for Online Rent Minimization Problem Under Unit-Size Jobs
We consider the Online Rent Minimization problem, where online jobs with release times, deadlines, and processing times must be scheduled on machines that can be rented for a fixed length period of T. The objective is to minimize the number of machine rents. This problem generalizes the Online Machine Minimization problem where machines can be rented for an infinite period, and both problems have an asymptotically optimal competitive ratio of O(log(p_max/p_min)) for general processing times, where p_max and p_min are the maximum and minimum processing times respectively. However, for small values of p_max/p_min, a better competitive ratio can be achieved by assuming unit-size jobs. Under this assumption, Devanur et al. (2014) gave an optimal e-competitive algorithm for Online Machine Minimization, and Chen and Zhang (2022) gave a (3e+7) ? 15.16-competitive algorithm for Online Rent Minimization. In this paper, we significantly improve the competitive ratio of the Online Rent Minimization problem under unit size to 6, by using a clean oracle-based online algorithm framework
Mathematical Model of \u3cem\u3eChlorella minutissima\u3c/em\u3e UTEX2341 Growth and Lipid Production Under Photoheterotrophic Fermentation Conditions
To reduce the cost of algal biomass production, mathematical model was developed for the first time to describe microalgae growth, lipid production and glycerin consumption under photoheterotrophic conditions based on logistic, Luedeking–Piret and Luedeking–Piret-like equations. All experiments were conducted in a 2 L batch reactor without considering CO2 effect on algae’s growth and lipid production. Biomass and lipid production increased with glycerin as carbon source and were well described by the logistic and Luedeking–Piret equations respectively. Model predictions were in satisfactory agreement with measured data and the mode of lipid production was growth-associated. Sensitivity analysis was applied to examine the effects of certain important parameters on model performance. Results showed that S0, the initial concentration of glycerin, was the most significant factor for algae growth and lipid production. This model is applicable for prediction of other single cell algal species but model testing is recommended before scaling up the fermentation of process
The Oxford Multimotion Dataset: Multiple SE(3) Motions with Ground Truth
Datasets advance research by posing challenging new problems and providing
standardized methods of algorithm comparison. High-quality datasets exist for
many important problems in robotics and computer vision, including egomotion
estimation and motion/scene segmentation, but not for techniques that estimate
every motion in a scene. Metric evaluation of these multimotion estimation
techniques requires datasets consisting of multiple, complex motions that also
contain ground truth for every moving body.
The Oxford Multimotion Dataset provides a number of multimotion estimation
problems of varying complexity. It includes both complex problems that
challenge existing algorithms as well as a number of simpler problems to
support development. These include observations from both static and dynamic
sensors, a varying number of moving bodies, and a variety of different 3D
motions. It also provides a number of experiments designed to isolate specific
challenges of the multimotion problem, including rotation about the optical
axis and occlusion.
In total, the Oxford Multimotion Dataset contains over 110 minutes of
multimotion data consisting of stereo and RGB-D camera images, IMU data, and
Vicon ground-truth trajectories. The dataset culminates in a complex toy car
segment representative of many challenging real-world scenarios. This paper
describes each experiment with a focus on its relevance to the multimotion
estimation problem.Comment: 8 Pages. 8 Figures. Video available at
https://www.youtube.com/watch?v=zXaHEdiKxdA. Dataset available at
https://robotic-esp.com/datasets
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