Towards insect inspired visual sensors for robots

Flying insects display a repertoire of complex behaviours that are facilitated by their non-standard visual system that if understood would offer solutions for weight- and power- constrained robotic platforms such as micro unmanned aerial vehicles (MUAVs). Crucial to this goal is revealing the specific features of insect eyes that engineered solutions would benefit from possessing, however progress in exploration of the design space has been limited by challenges in accurately replicating insect vision. Here we propose that emerging ray-tracing technologies are ideally placed to realise the high-fidelity replication of the insect visual perspective in a rapid, modular and adaptive framework allowing development of technical specifications for a new class of bio-inspired sensor. A proof-of-principle insect eye renderer is shown and insights into research directions it affords discussed

L*a*b*fruits : a rapid and robust outdoor fruit detection system combining bio-inspired features with one-stage deep learning networks

Automation of agricultural processes requires systems that can accurately detect and classify produce in real industrial environments that include variation in fruit appearance due to illumination, occlusion, seasons, weather conditions, etc. In this paper we combine a visual processing approach inspired by colour-opponent theory in humans with recent advancements in one-stage deep learning networks to accurately, rapidly and robustly detect ripe soft fruits (strawberries) in real industrial settings and using standard (RGB) camera input. The resultant system was tested on an existent data-set captured in controlled conditions as well our new real-world data-set captured on a real strawberry farm over two months. We utilise F1 score, the harmonic mean of precision and recall, to show our system matches the state-of-the-art detection accuracy ( F1 : 0.793 vs. 0.799) in controlled conditions; has greater generalisation and robustness to variation of spatial parameters (camera viewpoint) in the real-world data-set ( F1 : 0.744); and at a fraction of the computational cost allowing classification at almost 30fps. We propose that the L*a*b*Fruits system addresses some of the most pressing limitations of current fruit detection systems and is well-suited to application in areas such as yield forecasting and harvesting. Beyond the target application in agriculture this work also provides a proof-of-principle whereby increased performance is achieved through analysis of the domain data, capturing features at the input level rather than simply increasing model complexity

Improved large-mode area endlessly single-mode photonic crystal fibers

We numerically study the possibilities for improved large-mode area endlessly single mode photonic crystal fibers for use in high-power delivery applications. By carefully choosing the optimal hole diameter we find that a triangular core formed by three missing neighboring air holes considerably improves the mode area and loss properties compared to the case with a core formed by one missing air hole. In a realized fiber we demonstrate an enhancement of the mode area by ~30 % without a corresponding increase in the attenuation.Comment: 3 pages including 3 eps-figures. Accepted for Optics Letter

CONTEST : a Controllable Test Matrix Toolbox for MATLAB

Large, sparse networks that describe complex interactions are a common feature across a number of disciplines, giving rise to many challenging matrix computational tasks. Several random graph models have been proposed that capture key properties of real-life networks. These models provide realistic, parametrized matrices for testing linear system and eigenvalue solvers. CONTEST (CONtrollable TEST matrices) is a random network toolbox for MATLAB that implements nine models. The models produce unweighted directed or undirected graphs; that is, symmetric or unsymmetric matrices with elements equal to zero or one. They have one or more parameters that affect features such as sparsity and characteristic pathlength and all can be of arbitrary dimension. Utility functions are supplied for rewiring, adding extra shortcuts and subsampling in order to create further classes of networks. Other utilities convert the adjacency matrices into real-valued coefficient matrices for naturally arising computational tasks that reduce to sparse linear system and eigenvalue problems

Visual tracking of small animals in cluttered natural environments using a freely moving camera

Image-based tracking of animals in their natural habitats can provide rich behavioural data, but is very challenging due to complex and dynamic background and target appearances. We present an effective method to recover the positions of terrestrial animals in cluttered environments from video sequences filmed using a freely moving monocular camera. The method uses residual motion cues to detect the targets and is thus robust to different lighting conditions and requires no a-priori appearance model of the animal or environment. The detection is globally optimised based on an inference problem formulation using factor graphs. This handles ambiguities such as occlusions and intersections and provides automatic initialisation. Furthermore, this formulation allows a seamless integration of occasional user input for the most difficult situations, so that the effect of a few manual position estimates are smoothly distributed over long sequences. Testing our system against a benchmark dataset featuring small targets in natural scenes, we obtain 96% accuracy for fully automated tracking. We also demonstrate reliable tracking in a new data set that includes different targets (insects, vertebrates or artificial objects) in a variety of environments (desert, jungle, meadows, urban) using different imaging devices (day / night vision cameras, smart phones) and modalities (stationary, hand-held, drone operated)

Validation of a nutria (\u3ci\u3eMyocastor coypus\u3c/i\u3e) environmental DNA assay highlights considerations for sampling methodology

Nutria (Myocastor coypus) is a semiaquatic rodent species that is invasive across multiple regions within the United States. Here, we evaluated a qPCR assay previously described for use in Japan for application across invasive populations in the United States. We also compared two environmental DNA sampling methodologies for this assay: field filtration of large volumes of water passed through filters versus direct sampling of small volumes of water. We validated assay specificity, generality, and sensitivity, compared assay performance between two independent laboratories, and successfully tested the assay in situ on a known wild population. The filtration method required fewer samples for environmental DNA detection than direct sampling, but the choice of methods should be assessed based on specific field conditions and time and budget considerations. Our extensive assay validation and comparison across laboratories suggest that the assay is ready to be applied in environmental DNA monitoring of nutria throughout the United States

Immigration Statistics on the Web

Report on immigration statistics from the internet, including selected websites, federal government sources, labor and business data, and more