An approach to sign language translation using the Intel Realsense camera

An Intel RealSense camera is used for translating static manual American Sign Language gestures into text. The system uses palm orientation and finger joint data as inputs for either a support vector machine or a neural network whose architecture has been optimized by a genetic algorithm. A data set consisting of 100 samples of 26 gestures (the letters of the alphabet) is extracted from 10 participants. When comparing the different learners in combination with different standard preprocessing techniques, the highest accuracy of 95% is achieved by a support vector machine with a scaling method, as well as principal component analysis, used for preprocessing. The highest performing neural network system reaches 92.1% but produces predictions much faster. We also present a simple software solution that uses the trained classifiers to enable user-friendly sign language translation

Exploring memory through the essay film To Remember: An exercise into the decolonisation of the filmmaker’s unconscious

This research started as an effort to recover lost childhood memories. I (Emilio Bassail) used the film-making apparatus as a device that allowed me to excavate, elaborate and produce representations based on the small fragments of memory I had left. After creating an archive of reconstructed memories, I started questioning the images I had unearthed. This position allowed me to interrogate and challenge the discourses behind the images. What I discovered is that forgetfulness was in fact an effect of the suppression of potentially subversive discourses. I had not really forgotten, but rather I had chosen not to remember (since the hidden childhood memories defied the internalised discourses of power and structure). To be able to remember and therefore to create, first I had to debilitate the discourses of the power structures that prevented me from going forward in my research. Following Suely Rolnik’s (2019) proposal for a decolonisation of the unconscious, and expanding on my own work, this research is an exploration of film-making as a device to remember and to produce desiring/creative subjectivities

A micro-mechanically based continuum model for strain-induced crystallization in natural rubber

AbstractRecent experimental results show that strain-induced crystallization can substantially improve the crack growth resistance of natural rubber. While this might suggest superior designs of tires or other industrial applications where elastomers are used, a more thorough understanding of the underlying physics of strain-induced crystallization in natural rubber has to be developed before any design process can be started. The objective of this work is to develop a computationally-accessible micro-mechanically based continuum model, which is able to predict the macroscopic behavior of strain crystallizing natural rubber. While several researchers have developed micro-mechanical models of partially crystallized polymer chains, their results mainly give qualitative agreement with experimental data due to a lack of good micro–macro transition theories or the lack of computational power. However, recent developments in multiscale modeling in polymers give us new tools to continue this early work. To begin with, a micro-mechanical model of a constrained partially crystallized polymer chain with an extend-chain crystal is derived and connected to the macroscopic level using the non-affine micro-sphere model. Subsequently, a description of the crystallization kinetics is introduced using an evolution law based on the gradient of the macroscopic free energy function (chemical potential) and a simple threshold function. Finally a numerical implementation of the model is proposed and its predictive performance assessed using published data

Experiments and Simulations of short-pulse laser-pumped extreme ultraviolet lasers

Recent experimental work on the development of extreme ultraviolet lasers undertaken using as the pumping source the VULCAN laser at the Rutherford Appleton Laboratory is compared to detailed simulations. It is shown that short duration (similar topicosecond) pumping can produce X-ray laser pulses of a few picosecond duration and that measurement of the emission from the plasma can give an estimate of the duration of the gain coefficient. The Ehybrid fluid and atomic physics code developed at the University of York is used to simulate X-ray laser gain and plasma emission. Two postprocessors to the Ehybrid code are utilized: 1) to raytrace the X-ray laser beam amplification and refraction and 2) to calculate the radiation emission in the kiloelectronvolt photon energy range. The raytracing and spectral simulations are compared, respectively, to measured X-ray laser output and the output of two diagnostics recording transverse X-ray emission. The pumping laser energy absorbed in the plasma is examined by comparing the simulations to experimental results. It is shown that at high pumping irradiance (>10(15) Wcm(-2)), fast electrons are produced by parametric processes in the preformed long scale-length plasmas. These fast electrons do not pump the population inversion and so pumping efficiency is reduced at high irradiance

Modelling and flow conditioning to manage discolouration in trunk mains

This paper presents predictive discolouration modelling and subsequent field trial results for a cast iron trunk main network. This enabled a UK water company to propose an ‘operational flow conditioning’ maintenance plan that reduces discolouration risk, improves network resilience and asset condition and yet does not require the trunk main to be decommissioned for invasive cleaning. This represents substantial time and cost benefits. Pre-and-post trial turbidity monitoring data is also presented which identified a daily flux of material, a factor in the regeneration of material layers that have been shown to cause discolouration when mobilised. Additional data detecting the occurrence of pressure transients is also presented, a possible cause of contaminant ingress and asset failure

The gallery as therapeutic venue: exploring visitor perceptions in a contemporary space

This prospective qualitative study explored the social and psychological impact of an exhibition at a contemporary art gallery. Three focus groups, including 8 people aged 45-69, were held following a guided tour of an exhibition at Nottingham Contemporary gallery. The exhibition Uneven Geographies featured the works of a variety of international artists focussing upon the politics of globalisation through different media including film, installation and photography. Focus group transcripts were analysed using Braun and Clarke’s 6-phase model of thematic analysis. Three themes were identified: 1) ’It’s almost like a piece of a jigsaw puzzle’, 2) Interpreting the Message and 3) Dissolving Social Barriers. Results indicated that the exhibition helped participants make sense of a variety of experiences, art interpretation was a key educational skill, and attending the gallery promoted social inclusion. These findings build on previous studies that indicate the potential for cultural institutions, such as art galleries and museums, to promote education, health and wellbeing at individual and societal levels

The Importance of Antioxidant Micronutrients in Pregnancy

Pregnancy places increased demands on the mother to provide adequate nutrition to the growing conceptus. A number of micronutrients function as essential cofactors for or themselves acting as antioxidants. Oxidative stress is generated during normal placental development; however, when supply of antioxidant micronutrients is limited, exaggerated oxidative stress within both the placenta and maternal circulation occurs, resulting in adverse pregnancy outcomes. The present paper summarises the current understanding of selected micronutrient antioxidants selenium, copper, zinc, manganese, and vitamins C and E in pregnancy. To summarise antioxidant activity of selenium is via its incorporation into the glutathione peroxidase enzymes, levels of which have been shown to be reduced in miscarriage and preeclampsia. Copper, zinc, and manganese are all essential cofactors for superoxide dismutases, which has reduced activity in pathological pregnancy. Larger intervention trials are required to reinforce or refute a beneficial role of micronutrient supplementation in disorders of pregnancies

Absorption of gamma-emitting fission products and activation products by rice under flooded and unflooded conditions from two tropical soils

The absorption of gamma-emitting fission products 106Ru,125Sb,137Cs and144Ce and activation products59Fe,58Co.54Mn and65Zn by rice plants grown on two contrasting tropical soils, namely, a blak soil (pellustert) and a laterite (oxisol), and the effects of flooding were studied under controlled conditions. Results indicated greater uptake of 106Ru and 125Sb from the black soil than from the laterite. In contrast, the uptake of 144Ce and 137Cs was greater in the laterite than in the black soil. Flooding treatment enhanced the uptake of all these fission products by rice plants in the laterite soil whereas this effect was observed only for 125Sb and 137Cs in the black soil. The plant uptake of activation products from the two soil types showed maximum accumulation of 65Zn followed by 54Mn,59Fe and 58Co in both soil types. Besides, uptake of these nuclides was greater from the laterite soil than from the black soil. Flooding treatment for rice while showing a reduction of 59Fe uptake, showed an increase in plant uptake of 58Co,54Mn and 65Zn in both soil types

Documentation technique : équipement et réparation des livres

For robots of increasing complexity such as humanoid robots, conventional identification of rigid body dynamics models based on CAD data and actuator models becomes difficult and inaccurate due to the large number of additional nonlinear effects in these systems, e.g., stemming from stiff wires, hydraulic hoses, protective shells, skin, etc. Data driven parameter estimation offers an alternative model identification method, but it is often burdened by various other problems, such as significant noise in all measured or inferred variables of the robot. The danger of physically inconsistent results also exists due to unmodeled nonlinearities or insufficiently rich data. In this paper, we address all these problems by developing a Bayesian parameter identification method that can automatically detect noise in both input and output data for the regression algorithm that performs system identification. A post-processing step ensures physically consistent rigid body parameters by nonlinearly projecting the result of the Bayesian estimation onto constraints given by positive definite inertia matrices and the parallel axis theorem. We demonstrate on synthetic and actual robot data that our technique performs parameter identification with 10 to 30% higher accuracy than traditional methods. Due to the resulting physically consistent parameters, our algorithm enables us to apply advanced control methods that algebraically require physical consistency on robotic platforms