Using Machine Vision to Estimate Fish Length from Images using Regional Convolutional Neural Networks

An image can encode date, time, location and camera information as metadata and implicitly encodes species information and data on human activity, for example the size distribution of fish removals. Accurate length estimates can be made from images using a fiducial marker; however, their manual extraction is time-consuming and estimates are inaccurate without control over the imaging system. This article presents a methodology which uses machine vision to estimate the total length (TL) of a fusiform fish (European sea bass). Three regional convolutional neural networks (R-CNN) were trained from public images. Images of European sea bass were captured with a fiducial marker with three non-specialist cameras. Images were undistorted using the intrinsic lens properties calculated for the camera in OpenCV; then TL was estimated using machine vision (MV) to detect both marker and subject. MV performance was evaluated for the three R-CNNs under downsampling and rotation of the captured images. Each R-CNN accurately predicted the location of fish in test images (mean intersection over union, 93%) and estimates of TL were accurate, with percent mean bias error (%MBE [95% CIs]) = 2.2% [2.0, 2.4]). Detections were robust to horizontal flipping and downsampling. TL estimates at absolute image rotations >20° became increasingly inaccurate but %MBE [95% CIs] was reduced to −0.1% [−0.2, 0.1] using machine learning to remove outliers and model bias. Machine vision can classify and derive measurements of species from images without specialist equipment. It is anticipated that ecological researchers and managers will make increasing use of MV where image data are collected (e.g. in remote electronic monitoring, virtual observations, wildlife surveys and morphometrics) and MV will be of particular utility where large volumes of image data are gathered

Accurate estimation of fish length in single camera photogrammetry with a fiducial marker

Videogrammetry and photogrammetry are increasingly being used in marine science for unsupervised data collection. The camera systems employed are complex, in contrast to "consumer"digital cameras and smartphones carried by potential citizen scientists. However, using consumer cameras in photogrammetry will introduce unknown length estimation errors through both the image acquisition process and lens distortion. This study presents a methodology to achieve accurate 2-dimensional (2-D) total length (TL) estimates of fish without specialist equipment or proprietary software. Photographs of fish were captured with an action camera using a background fiducial marker, a foreground fiducial marker and a laser marker. The geometric properties of the lens were modelled with OpenCV to correct image distortion. TL estimates were corrected for parallax effects using an algorithm requiring only the initial length estimate and known fish morphometric relationships. Correcting image distortion decreased RMSE by 96% and the percentage mean bias error (%MBE) by 50%. Correcting for parallax effects achieved a %MBE of -0.6%. This study demonstrates that the morphometric measurement of different species can be accurately estimated without the need for complex camera equipment, making it particularly suitable for deployment in citizen science and other volunteer-based data collection endeavours

Automatic sound synthesis using the fly algorithm

Our study is demonstrated a new type of evolutionary sound synthesis method. This work based on the fly algorithm, a cooperative co-evolution algorithm; it is derived from the Parisian evolution approach. The algorithm has relatively amended the position of individuals (the Flies) represented by 3-D points. The fly algorithm has successfully investigated in different applications, starting with a real-time stereo vision for robotics. Also, the algorithm shows promising results in tomography to reconstruct 3-D images. The final application of the fly algorithm was generating artistic images, such as digital mosaics. In all these applications, the flies’representation started for simple, 3-D points, to complex one, the structure of 9-elements. Our method follows evolutionary digital art with the fly algorithm in representing the pattern of the flies. They represented in a way of having their structure. This structure includes position, colour, rotation angle, and size. Our algorithm has the benefit of graphics processing units (GPUs) to generate the sound waveform using the modern OpenGL shading language

Anomalous polarization conversion in arrays of ultrathin ferromagnetic nanowires

We study optical properties of arrays of ultrathin nanowires by means of the Brillouin scattering of light on magnons. We employ the Stokes/anti-Stokes scattering asymmetry to probe the circular polarization of a local electric field induced inside nanowires by linearly polarized light waves. We observe the anomalous polarization conversion of the opposite sign than that in a bulk medium or thick nanowires with a great enhancement of the degree of circular polarization attributed to an unconventional refraction in the nanowire medium.Comment: 5 pages, 4 figure

PET reconstruction using a cooperative coevolution strategy in LOR space

Abstract—This paper presents preliminary results of a novel method that takes advantage of artificial evolution for positron emission tomography (PET) reconstruction. Fully 3D tomo-graphic reconstruction in PET requires high computing power and leads to many challenges. To date, the use of such methods is still restricted due to the heavy computing power needed. Evolutionary algorithms have proven to be efficient optimisation techniques in various domains. However the use of evolutionary computation in tomographic reconstruction has been largely overlooked. We propose a computer-based algorithm for fully 3D reconstruction in PET based on artificial evolution and evaluate its relevance. Index Terms—Positron emission tomography, genetic algo-rithms, optimization methods. I. INTRODUCTION AND MOTIVATION

Mechanisms and dynamics of cortical motor inhibition in the stop-signal paradigm: a TMS study

Abstract ■ The ability to stop ongoing motor responses in a splitsecond is a vital element of human cognitive control and flexibility that relies in large part on prefrontal cortex. We used the stop-signal paradigm to elucidate the engagement of primary motor cortex (M1) in inhibiting an ongoing voluntary motor response. The stop-signal paradigm taps the ability to flexibly countermand ongoing voluntary behavior upon presentation of a stop signal. We applied single-pulse TMS to M1 at several intervals following the stop signal to track the time course of excitability of the motor system related to generating and stopping a manual response. Electromyography recorded from the flexor pollicis brevis allowed quantification of the excitability of the corticospinal tract and the involvement of intracortical GABA B ergic circuits within M1, indexed respectively by the amplitude of the motor-evoked potential and the duration of the late part of the cortical silent period (SP). The results extend our knowledge of the neural basis of inhibitory control in three ways. First, the results revealed a dynamic interplay between response activation and stopping processes at M1 level during stop-signal inhibition of an ongoing response. Second, increased excitability of inhibitory interneurons that drives SP prolongation was evident as early as 134 msec following the instruction to stop. Third, this pattern was followed by a stoprelated reduction of corticospinal excitability implemented around 180 after the stop signal. These findings point to the recruitment of GABA B ergic intracortical inhibitory circuits within M1 in stop-signal inhibition and support the notion of stopping as an active act of control.

Visualization for epidemiological modelling: challenges, solutions, reflections and recommendations.

From Europe PMC via Jisc Publications RouterHistory: epub 2022-08-15, ppub 2022-10-01Publication status: PublishedFunder: UK Research and Innovation; Grant(s): ST/V006126/1, EP/V054236/1, EP/V033670/1We report on an ongoing collaboration between epidemiological modellers and visualization researchers by documenting and reflecting upon knowledge constructs-a series of ideas, approaches and methods taken from existing visualization research and practice-deployed and developed to support modelling of the COVID-19 pandemic. Structured independent commentary on these efforts is synthesized through iterative reflection to develop: evidence of the effectiveness and value of visualization in this context; open problems upon which the research communities may focus; guidance for future activity of this type and recommendations to safeguard the achievements and promote, advance, secure and prepare for future collaborations of this kind. In describing and comparing a series of related projects that were undertaken in unprecedented conditions, our hope is that this unique report, and its rich interactive supplementary materials, will guide the scientific community in embracing visualization in its observation, analysis and modelling of data as well as in disseminating findings. Equally we hope to encourage the visualization community to engage with impactful science in addressing its emerging data challenges. If we are successful, this showcase of activity may stimulate mutually beneficial engagement between communities with complementary expertise to address problems of significance in epidemiology and beyond. See https://ramp-vis.github.io/RAMPVIS-PhilTransA-Supplement/. This article is part of the theme issue 'Technical challenges of modelling real-life epidemics and examples of overcoming these'

A chemical survey of exoplanets with ARIEL

Thousands of exoplanets have now been discovered with a huge range of masses, sizes and orbits: from rocky Earth-like planets to large gas giants grazing the surface of their host star. However, the essential nature of these exoplanets remains largely mysterious: there is no known, discernible pattern linking the presence, size, or orbital parameters of a planet to the nature of its parent star. We have little idea whether the chemistry of a planet is linked to its formation environment, or whether the type of host star drives the physics and chemistry of the planet’s birth, and evolution. ARIEL was conceived to observe a large number (~1000) of transiting planets for statistical understanding, including gas giants, Neptunes, super-Earths and Earth-size planets around a range of host star types using transit spectroscopy in the 1.25–7.8 μm spectral range and multiple narrow-band photometry in the optical. ARIEL will focus on warm and hot planets to take advantage of their well-mixed atmospheres which should show minimal condensation and sequestration of high-Z materials compared to their colder Solar System siblings. Said warm and hot atmospheres are expected to be more representative of the planetary bulk composition. Observations of these warm/hot exoplanets, and in particular of their elemental composition (especially C, O, N, S, Si), will allow the understanding of the early stages of planetary and atmospheric formation during the nebular phase and the following few million years. ARIEL will thus provide a representative picture of the chemical nature of the exoplanets and relate this directly to the type and chemical environment of the host star. ARIEL is designed as a dedicated survey mission for combined-light spectroscopy, capable of observing a large and well-defined planet sample within its 4-year mission lifetime. Transit, eclipse and phase-curve spectroscopy methods, whereby the signal from the star and planet are differentiated using knowledge of the planetary ephemerides, allow us to measure atmospheric signals from the planet at levels of 10–100 part per million (ppm) relative to the star and, given the bright nature of targets, also allows more sophisticated techniques, such as eclipse mapping, to give a deeper insight into the nature of the atmosphere. These types of observations require a stable payload and satellite platform with broad, instantaneous wavelength coverage to detect many molecular species, probe the thermal structure, identify clouds and monitor the stellar activity. The wavelength range proposed covers all the expected major atmospheric gases from e.g. H2O, CO2, CH4 NH3, HCN, H2S through to the more exotic metallic compounds, such as TiO, VO, and condensed species. Simulations of ARIEL performance in conducting exoplanet surveys have been performed – using conservative estimates of mission performance and a full model of all significant noise sources in the measurement – using a list of potential ARIEL targets that incorporates the latest available exoplanet statistics. The conclusion at the end of the Phase A study, is that ARIEL – in line with the stated mission objectives – will be able to observe about 1000 exoplanets depending on the details of the adopted survey strategy, thus confirming the feasibility of the main science objectives.Peer reviewedFinal Published versio