A Computationally Light Pruning Strategy for Single Layer Neural Networks based on Threshold Function

Embedded machine learning relies on inference functions that can fit resource-constrained, low-power computing devices. The literature proves that single layer neural networks using threshold functions can provide a suitable trade off between classification accuracy and computational cost. In this regard, the number of neurons directly impacts both on computational complexity and on resources allocation. Thus, the present research aims at designing an efficient pruning technique that can take into account the peculiarities of the threshold function. The paper shows that feature selection criteria based on filter models can effectively be applied to neuron selection. In particular, valuable outcomes can be obtained by designing ad-hoc objective functions for the selection process. An extensive experimental campaign confirms that the proposed objective function compares favourably with state-of-the-art pruning techniques

A survey on deep learning in image polarity detection: Balancing generalization performances and computational costs

Deep convolutional neural networks (CNNs) provide an effective tool to extract complex information from images. In the area of image polarity detection, CNNs are customarily utilized in combination with transfer learning techniques to tackle a major problem: the unavailability of large sets of labeled data. Thus, polarity predictors in general exploit a pre-trained CNN as the feature extractor that in turn feeds a classification unit. While the latter unit is trained from scratch, the pre-trained CNN is subject to fine-tuning. As a result, the specific CNN architecture employed as the feature extractor strongly affects the overall performance of the model. This paper analyses state-of-the-art literature on image polarity detection and identifies the most reliable CNN architectures. Moreover, the paper provides an experimental protocol that should allow assessing the role played by the baseline architecture in the polarity detection task. Performance is evaluated in terms of both generalization abilities and computational complexity. The latter attribute becomes critical as polarity predictors, in the era of social networks, might need to be updated within hours or even minutes. In this regard, the paper gives practical hints on the advantages and disadvantages of the examined architectures both in terms of generalization and computational cost

MRI/TRUS data fusion for brachytherapy

BACKGROUND: Prostate brachytherapy consists in placing radioactive seeds for tumour destruction under transrectal ultrasound imaging (TRUS) control. It requires prostate delineation from the images for dose planning. Because ultrasound imaging is patient- and operator-dependent, we have proposed to fuse MRI data to TRUS data to make image processing more reliable. The technical accuracy of this approach has already been evaluated. METHODS: We present work in progress concerning the evaluation of the approach from the dosimetry viewpoint. The objective is to determine what impact this system may have on the treatment of the patient. Dose planning is performed from initial TRUS prostate contours and evaluated on contours modified by data fusion. RESULTS: For the eight patients included, we demonstrate that TRUS prostate volume is most often underestimated and that dose is overestimated in a correlated way. However, dose constraints are still verified for those eight patients. CONCLUSIONS: This confirms our initial hypothesis

First Calorimetric Measurement of OI-line in the Electron Capture Spectrum of 163^{163}Ho

The isotope $^{163}$Ho undergoes an electron capture process with a recommended value for the energy available to the decay, $Q_{\rm EC}$, of about 2.5 keV. According to the present knowledge, this is the lowest $Q_{\rm EC}$ value for electron capture processes. Because of that, $^{163}$Ho is the best candidate to perform experiments to investigate the value of the electron neutrino mass based on the analysis of the calorimetrically measured spectrum. We present for the first time the calorimetric measurement of the atomic de-excitation of the $^{163}$Dy daughter atom upon the capture of an electron from the 5s shell in $^{163}$Ho, OI-line. The measured peak energy is 48 eV. This measurement was performed using low temperature metallic magnetic calorimeters with the $^{163}$Ho ion implanted in the absorber. We demonstrate that the calorimetric spectrum of $^{163}$Ho can be measured with high precision and that the parameters describing the spectrum can be learned from the analysis of the data. Finally, we discuss the implications of this result for the Electron Capture $^{163}$Ho experiment, ECHo, aiming to reach sub-eV sensitivity on the electron neutrino mass by a high precision and high statistics calorimetric measurement of the $^{163}$Ho spectrum.Comment: 5 pages, 3 figure

On the keV sterile neutrino search in electron capture

A joint effort of cryogenic microcalorimetry (CM) and high-precision Penning-trap mass spectrometry (PT-MS) in investigating atomic orbital electron capture (EC) can shed light on the possible existence of heavy sterile neutrinos with masses from 0.5 to 100 keV. Sterile neutrinos are expected to perturb the shape of the atomic de-excitation spectrum measured by CM after a capture of the atomic orbital electrons by a nucleus. This effect should be observable in the ratios of the capture probabilities from different orbits. The sensitivity of the ratio values to the contribution of sterile neutrinos strongly depends on how accurately the mass difference between the parent and the daughter nuclides of EC-transitions can be measured by, e.g., PT-MS. A comparison of such probability ratios in different isotopes of a certain chemical element allows one to exclude many systematic uncertainties and thus could make feasible a determination of the contribution of sterile neutrinos on a level below 1%. Several electron capture transitions suitable for such measurements are discussed.Comment: 16 pages, 9 figures, 2 table

CONTAINER LOCALISATION AND MASS ESTIMATION WITH AN RGB-D CAMERA

In the research area of human-robot interactions, the automatic estimation of the mass of a container manipulated by a person leveraging only visual information is a challenging task. The main challenges consist of occlusions, different filling materials and lighting conditions. The mass of an object constitutes key information for the robot to correctly regulate the force required to grasp the container. We propose a single RGB-D camera-based method to locate a manipulated container and estimate its empty mass i.e., independently of the presence of the content. The method first automatically selects a number of candidate containers based on the distance with the fixed frontal view, then averages the mass predictions of a lightweight model to provide the final estimation. Results on the CORSMAL Containers Manipulation dataset show that the proposed method estimates empty container mass obtaining a score of 71.08% under different lighting or filling conditions

Prospects for measuring the 229Th isomer energy using a metallic magnetic microcalorimeter

The Thorium-229 isotope features a nuclear isomer state with an extremely low energy. The currently most accepted energy value, 7.8 +- 0.5 eV, was obtained from an indirect measurement using a NASA x-ray microcalorimeter with an instrumental resolution 26 eV. We study, how state-of-the-art magnetic metallic microcalorimeters with an energy resolution down to a few eV can be used to measure the isomer energy. In particular, resolving the 29.18 keV doublet in the \gamma-spectrum following the \alpha-decay of Uranium-233, corresponding to the decay into the ground and isomer state, allows to measure the isomer transition energy without additional theoretical input parameters, and increase the energy accuracy. We study the possibility of resolving the 29.18 keV line as a doublet and the dependence of the attainable precision of the energy measurement on the signal and background count rates and the instrumental resolution.Comment: 32 pages, 8 figures, eq. (3) correcte

Towards Smart Sensing Systems: A New Approach to Environmental Monitoring Systems by Using LoRaWAN

The proliferation of monitoring in unpredictable environments has aided the world in solving challenges that were previously thought to be insurmountable. Drastic advancement has been pinpointed in the way we live, work, and play; however, the data odyssey has yet started. From sensing to monitoring, the endless possibility enabled by LoRa, the long-range low power solution has made its mark on the technological world. With the adoption of the LoRaWAN, the long-range low power wide area network has appeared in existence to cope with the constraints associated with the Internet of Things (IoT) infrastructure. This paper presents a practical experiment for sensing the environmental condition using the LoRaWAN solution. The proposed work allows the users to check the environmental effects (temperature, and humidity) online. Furthermore, the signal behavior has been recorded and cross-verified by using MATLAB software implementation