3-D Microwave Imaging for Breast Cancer

We introduce a novel microwave imaging technique for breast cancer detection. Our approach provides a one-pass inverse image solution, which is completely new and unprecedented, unrelated to tomography or radar-based algorithms, and unburdened by the optimization toil which lies at the heart of numerical schemes. It operates effectively at a single frequency, without requiring the bandwidth of radar techniques. Underlying this new method is our unique Field Mapping Algorithm (FMA), which transforms electromagnetic fields acquired upon one surface, be it through outright measurement or some auxiliary computation, into those upon another in an exact sense

AI for public health: Self-screening for eye diseases

A software-based visual-field testing (perimetry) system is described which incorporates several AI components, including machine learning, an intelligent user interface and pattern discovery. This system has been successfully used for self-screening in several different public environment

Managing the noisy glaucomatous test data by self organising maps

One of the main difficulties in obtaining reliable data from patients in glaucomatous tests is the measurement noise caused by the learning effect, inattention, failure of fixation, fatigue, etc. Using Kohonen's self-organising feature maps, we have developed a computational method to distinguish between the noise and true measurement. This method has been shown to provide a satisfactory way of locating and rejecting noise in the test data, an improvement over conventional statistical method

Recognition of Microseismic and Blasting Signals in Mines Based on Convolutional Neural Network and Stockwell Transform

The microseismic monitoring signals which need to be determined in mines include those caused by both rock bursts and by blasting. The blasting signals must be separated from the microseismic signals in order to extract the information needed for the correct location of the source and for determining the blast mechanism. The use of a convolutional neural network (CNN) is a viable approach to extract these blast characteristic parameters automatically and to achieve the accuracy needed in the signal recognition. The Stockwell Transform (or S-Transform) has excellent two-dimensional time-frequency characteristics and thus to obtain the microseismic signal and blasting vibration signal separately, the microseismic signal has been converted in this work into a two-dimensional image format by use of the S-Transform, following which it is recognized by using the CNN. The sample data given in this paper are used for model training, where the training sample is an image containing three RGB color channels. The training time can be decreased by means of reducing the picture size and thus reducing the number of training steps used. The optimal combination of parameters can then be obtained after continuously updating the training parameters. When the image size is 180 × 140 pixels, it has been shown that the test accuracy can reach 96.15% and that it is feasible to classify separately the blasting signal and the microseismic signal based on using the S-Transform and the CNN model architecture, where the training parameters were designed by synthesizing LeNet-5 and AlexNet

Solar flare hard X-ray spikes observed by RHESSI: a case study

In this paper, we analyze hard X-ray spikes observed by RHESSI to understand their temporal, spectral, and spatial properties. A recently developed demodulation code was applied to hard X-ray light curves in several energy bands observed by RHESSI. Hard X-ray spikes were selected from the demodulated flare light curves. We measured the spike duration, the energy-dependent time delay, and count spectral index of these spikes. We also located the hard X-ray source emitting these spikes from RHESSI mapping that was coordinated with imaging observations in visible and UV wavelengths. We identify quickly varying structures of <1 s during the rise of hard X-rays in five flares. These hard X-ray spikes can be observed at photon energies over 100 keV. They exhibit sharp rise and decay with a duration (FWHM) of less than 1 s. Energy-dependent time lags are present in some spikes. It is seen that the spikes exhibit harder spectra than underlying components, typically by 0.5 in the spectral index when they are fitted to power-law distributions. RHESSI clean maps at 25-100 keV with an integration of 2 s centered on the peak of the spikes suggest that hard X-ray spikes are primarily emitted by double foot-point sources in magnetic fields of opposite polarities. With the RHESSI mapping resolution of ~ 4 arsec, the hard X-ray spike maps do not exhibit detectable difference in the spatial structure from sources emitting underlying components. Coordinated high-resolution imaging UV and infrared observations confirm that hard X-ray spikes are produced in magnetic structures embedded in the same magnetic environment of the underlying components. The coordinated high-cadence TRACE UV observations of one event possibly reveal new structures on spatial scales <1-2 arsec at the time of the spike superposed on the underlying component. They are probably sources of hard X-ray spikes.Comment: 20 pages, 11 figure

Physical modelling of amorphous thermoplastic polymer and numerical simulation of micro hot embossing process

Micro hot embossing process is considered as one of the most promising micro replication processes for manufacturing of polymeric components, especially for the high aspect ratio components and large surface structural components. A large number of hot embossing experimental results have been published, the material modelling and processes simulation to improve the quality of micro replication by hot embossing process are still lacking. This paper consists to 3D modelling of micro hot embossing process with amorphous thermoplastic polymers, including the mechanical characterisation of polymers properties, identification of the viscoelastic behaviour law of the polymers, numerical simulation and experimental investigation of micro hot embossing process. Static compression creep tests have been carried out to investigate the selected polymers’ viscoelastic properties. The Generalized Maxwell model has been proposed to describe the relaxation modulus of the polymers and good agreement has been observed. The numerical simulation of the hot embossing process in 3D has been achieved by taking into account the viscoelastic behaviour of the polymers. The microfluidic devices with the thickness of 2 mm have been elaborated by hot embossing process. The hot embossing process has been carried out using horizontal injection/compression moulding equipment, especially developed for this study. A complete compression mould tool, equipped with the heating system, the cooling system, the ejection system and the vacuum system, has been designed and elaborated in our research. Polymer-based microfluidic devices have been successfully replicated by the hot embossing process using the compression system developed. Proper agreement between the numerical simulation and the experimental elaboration has been observed. It shows strong possibility for the development of the 3D numerical model to optimize the micro hot embossing process in the future