Generalised balance equations for charged particle transport via localised and delocalised states: Mobility, generalised Einstein relations and fractional transport

A generalised phase-space kinetic Boltzmann equation for highly non-equilibrium charged particle transport via localised and delocalised states is used to develop continuity, momentum and energy balance equations, accounting explicitly for scattering, trapping/detrapping and recombination loss processes. Analytic expressions detail the effect of these microscopic processes on the mobility and diffusivity. Generalised Einstein relations (GER) are developed that enable the anisotropic nature of diffusion to be determined in terms of the measured field-dependence of the mobility. Interesting phenomena such as negative differential conductivity and recombination heating/cooling are shown to arise from recombination loss processes and the localised and delocalised nature of transport. Fractional transport emerges naturally within this framework through the appropriate choice of divergent mean waiting time distributions for localised states, and fractional generalisations of the GER and mobility are presented. Signature impacts on time-of-flight current transients of recombination loss processes via both localised and delocalised states are presented.Comment: 21 pages, 4 figure

Efficient numerical solution of the time fractional diffusion equation by mapping from its Brownian counterpart

The solution of a Caputo time fractional diffusion equation of order $0<\alpha<1$ is expressed in terms of the solution of a corresponding integer order diffusion equation. We demonstrate a linear time mapping between these solutions that allows for accelerated computation of the solution of the fractional order problem. In the context of an $N$-point finite difference time discretisation, the mapping allows for an improvement in time computational complexity from $O\left(N^2\right)$ to $O\left(N^\alpha\right)$, given a precomputation of $O\left(N^{1+\alpha}\ln N\right)$. The mapping is applied successfully to the least-squares fitting of a fractional advection diffusion model for the current in a time-of-flight experiment, resulting in a computational speed up in the range of one to three orders of magnitude for realistic problem sizes.Comment: 9 pages, 5 figures; added references for section

Generalized phase-space kinetic and diffusion equations for classical and dispersive transport

We formulate and solve a physically-based, phase space kinetic equation for transport in the presence of trapping. Trapping is incorporated through a waiting time distribution function. From the phase-space analysis, we obtain a generalized diffusion equation in configuration space. We analyse the impact of the waiting time distribution, and give examples that lead to dispersive or non-dispersive transport. With an appropriate choice of the waiting time distribution, our model is related to fractional diffusion in the sense that fractional equations can be obtained in the limit of long times. Finally, we demonstrate the application of this theory to disordered semiconductors

Third-order transport coefficients for localised and delocalised charged-particle transport

We derive third order transport coefficients of skewness for a phase-space kinetic model that considers the processes of scattering collisions, trapping, detrapping and recombination losses. The resulting expression for the skewness tensor provides an extension to Fick's law which is in turn applied to yield a corresponding generalised advection-diffusion-skewness equation. A physical interpretation of trap-induced skewness is presented and used to describe an observed negative skewness due to traps. A relationship between skewness, diffusion, mobility and temperature is formed by analogy with Einstein's relation. Fractional transport is explored and its effects on the flux transport coefficients are also outlined.Comment: 11 pages, 3 figure

A Survey of Asynchronous Programming Using Coroutines in the Internet of Things and Embedded Systems

Many Internet of Things and embedded projects are event-driven, and therefore require asynchronous and concurrent programming. Current proposals for C++20 suggest that coroutines will have native language support. It is timely to survey the current use of coroutines in embedded systems development. This paper investigates existing research which uses or describes coroutines on resource-constrained platforms. The existing research is analysed with regard to: software platform, hardware platform and capacity; use cases and intended benefits; and the application programming interface design used for coroutines. A systematic mapping study was performed, to select studies published between 2007 and 2018 which contained original research into the application of coroutines on resource-constrained platforms. An initial set of 566 candidate papers were reduced to only 35 after filters were applied, revealing the following taxonomy. The C & C++ programming languages were used by 22 studies out of 35. As regards hardware, 16 studies used 8- or 16-bit processors while 13 used 32-bit processors. The four most common use cases were concurrency (17 papers), network communication (15), sensor readings (9) and data flow (7). The leading intended benefits were code style and simplicity (12 papers), scheduling (9) and efficiency (8). A wide variety of techniques have been used to implement coroutines, including native macros, additional tool chain steps, new language features and non-portable assembly language. We conclude that there is widespread demand for coroutines on resource-constrained devices. Our findings suggest that there is significant demand for a formalised, stable, well-supported implementation of coroutines in C++, designed with consideration of the special needs of resource-constrained devices, and further that such an implementation would bring benefits specific to such devices.Comment: 22 pages, 8 figures, to be published in ACM Transactions on Embedded Computing Systems (TECS

Plasticiser leaching from polyvinyl chloride microplastics and the implications for environmental risk assessment

Microplastics in aquatic environments is a growing concern, particularly due to the leaching of chemical additives such as plasticisers. To develop comprehensive environmental risk assessments (ERAs) of high-concern polymers and plasticisers, an understanding of their leachability is required. This work investigated diethylhexyl phthalate (DEHP) and bisphenol A (BPA) leaching from polyvinyl chloride (PVC) microplastics (average diameter = 191 μm) under simulated marine conditions. Leaching behaviours were quantified using gel permeation chromatography (GPC) and thermal gravimetric analysis (TGA), and the polymer's physiochemical properties analysed using differential scanning calorimetry (DSC), Fourier Transform-Infrared Spectroscopy (FT-IR) and optical microscopy. Experimental data were fitted to a diffusion and boundary layer model, which found that BPA leaching was temperature-dependent (diffusion-limited), whereas DEHP leaching was controlled by surface rinsing. Model predictions also highlighted the importance of microplastic size on leaching dynamics. These data contribute towards greater accuracy in ERAs of microplastics, with implications for water quality and waste management, including decommissioning of plastic infrastructure

WeedCLR: Weed Contrastive Learning through Visual Representations with Class-Optimized Loss in Long-Tailed Datasets

Image classification is a crucial task in modern weed management and crop intervention technologies. However, the limited size, diversity, and balance of existing weed datasets hinder the development of deep learning models for generalizable weed identification. In addition, the expensive labelling requirements of mainstream fully-supervised weed classifiers make them cost- and time-prohibitive to deploy widely, for new weed species, and in site-specific weed management. This paper proposes a novel method for Weed Contrastive Learning through visual Representations (WeedCLR), that uses class-optimized loss with Von Neumann Entropy of deep representation for weed classification in long-tailed datasets. WeedCLR leverages self-supervised learning to learn rich and robust visual features without any labels and applies a class-optimized loss function to address the class imbalance problem in long-tailed datasets. WeedCLR is evaluated on two public weed datasets: CottonWeedID15, containing 15 weed species, and DeepWeeds, containing 8 weed species. WeedCLR achieves an average accuracy improvement of 4.3\% on CottonWeedID15 and 5.6\% on DeepWeeds over previous methods. It also demonstrates better generalization ability and robustness to different environmental conditions than existing methods without the need for expensive and time-consuming human annotations. These significant improvements make WeedCLR an effective tool for weed classification in long-tailed datasets and allows for more rapid and widespread deployment of site-specific weed management and crop intervention technologies.Comment: 24 pages, 10 figures, 8 tables. Submitted to the Computers and Electronics in Agriculture journa

Comparing inertial measurement units and marker-based biomechanical models during dynamic rotation of the torso

Inertial measurement units (IMUs) enable human movements to be captured in the field and are being used increasingly in high performance sport. One key metric that can be derived from IMUs are relative angles of body segments which are important for monitoring form in many sports. The purpose of this study was to a) examine the validity of relative angles derived from IMUs placed on the torso and pelvis; and b) determine optimal positioning for torso mounted sensors such that the IMU relative angles match closely with gold standard torso-pelvis and thorax-pelvis relative angle data derived from an optoelectronic camera system. Seventeen adult participants undertook a variety of motion tasks. Four IMUs were positioned on the torso and one was positioned on the pelvis between the posterior superior iliac spines. Reflective markers were positioned around each IMU and over torso and pelvis landmarks. Results showed that the IMUs are valid with the root mean square errors expressed as a percentage of the angle range (RMSE%) ranging between 1% and 7%. Comparison between the IMU relative angles and the torso-pelvis and thorax-pelvis relative angles showed there were moderate to large differences with RMSE% values ranging between 4% and 57%. IMUs are highly accurate at measuring orientation data; however, further work is needed to optimize positioning and modelling approaches so IMU relative angles align more closely with relative angles derived using traditional motion capture methods

DeepWeeds: a multiclass weed species image dataset for deep learning

Robotic weed control has seen increased research of late with its potential for boosting productivity in agriculture. Majority of works focus on developing robotics for croplands, ignoring the weed management problems facing rangeland stock farmers. perhaps the greatest obstacle to widespread uptake of robotic weed control is the robust classification of weed species in their natural environment. the unparalleled successes of deep learning make it an ideal candidate for recognising various weed species in the complex rangeland environment. This work contributes the first large, public, multiclass image dataset of weed species from the Australian rangelands; allowing for the development of robust classification methods to make robotic weed control viable. The DeepWeeds dataset consists of 17,509 labelled images of eight nationally significant weed species native to eight locations across northern Australia. This paper presents a baseline for classification performance on the dataset using the benchmark deep learning models, Inception-v3 and ResNet-50. These models achieved an average classification accuracy of 95.1% and 95.7%, respectively. We also demonstrate real time performance of the ResNet-50 architecture, with an average inference time of 53.4 ms per image. These strong results bode well for future field implementation of robotic weed control methods in the Australian rangelands

Charge transport in organic solar cells

The process of charge transport is fundamental to the operation of all electronic devices. In organic photovoltaics, high efficiencies can only be achieved if charge transport is able to extract charge carriers from the active layer with minimal recombination losses. This work presents new insights into the measurement of charge transport, the underlying physics, as well as new approaches for modelling. Numeric simulation software using a drift-diffusion-recombination model is developed and applied to organic photovoltaic devices. Specifically, this model is used to design and interpret charge transport experiments that are applicable to operational organic solar cells.\ud \ud Charge carrier mobility is studied using photogenerated charge extraction by linearly increasing voltage (photo-CELIV) and the novel technique of resistance-dependent photovoltage (RPV). These experiments demonstrate the absence of "hot carrier" relaxation effects on the timescales of charge transport in several organic photovoltaic polymer:fullerene blends. This is surprising because it has previously been argued that such relaxation is the cause of the deterimental dispersive transport that affects many organic semiconductor devices. It is argued instead that dispersive transport arises from the loss of carriers to trap states. Next, the techniques are extended to recombination measurements, where the recombination coefficient in a benchmark polymer:fullerene system is found to depend upon the polymer's molecular weight.\ud \ud Modelling of the steady-state photocurrent produced by a solar cell demonstrates the conditions under which non-geminate recombination may be avoided, and presents a design rule for avoiding non-geminate recombination. Experimental measurements on devices of varying thickness support the conclusion that the space-charge limited current is a fundamental threshold for high-efficiency photocurrent extraction.\ud \ud Finally, fractional kinetics and generalised diffusion equations are explored. We show that the Poisson summation theorem permits the analytic solution of a fractional diffusion equation to be collapsed into closed form. Subsequently, these techniques are applied to a new type of kinetic model that is capable of unifying normal and dispersive transport within a single framework