use of fisheye parrot bebop 2 images for 3d modelling using commercial photogrammetric software

Fisheye camera installed on-board mass market UAS are becoming very popular and it is more and more frequent the use of such platforms for photogrammetric purposes. The interest of wide-angles images for 3D modelling is confirmed by the introduction of fisheye models in several commercial software packages. The paper exploits the different mathematical models implemented in the most famous commercial photogrammetric software packages, highlighting the different processing pipelines and analysing the achievable results in terms of checkpoint residuals, as well as the quality of the delivered 3D point clouds. A two-step approach based on the creation of undistorted images has been tested too. An experimental test has been carried out using a Parrot Bebop 2 UAS by performing a flight over an historical complex located near Piacenza (Northern Italy), which is characterized by the simultaneous presence of horizontal, vertical and oblique surfaces. Different flight configurations have been tested to evaluate the potentiality and possible drawbacks of the previously mentioned UAS platform. Results confirmed that the fisheye images acquired with the Parrot Bebop 2 are suitable for 3D modelling, ensuring accuracies of the photogrammetric blocks of the order of the GSD (about 0.05 m normal to the optic axis in case of a flight height equal to 35 m). The generated point clouds have been compared to a reference scan, acquired by means of a MS60 MultiStation, resulting in differences below 0.05 in all directions

Unusual polymerization in the Li4C60 fulleride

Li4C60, one of the best representatives of lithium intercalated fullerides, features a novel type of 2D polymerization. Extensive investigations, including laboratory x-ray and synchrotron radiation diffraction, 13C NMR, MAS and Raman spectroscopy, show a monoclinic I2/m structure, characterized by chains of [2+2]-cycloaddicted fullerenes, sideways connected by single C-C bonds. This leads to the formation of polymeric layers, whose insulating nature, deduced from the NMR and Raman spectra, denotes the complete localization of the electrons involved in the covalent bonds.Comment: 7 pages, 6 figures, RevTex4, submitted to Phys. Rev.

Mobile health for cancer in low to middle income countries: priorities for research and development.

The is the accepted manuscript of an article published in the European Journal of Cancer Care (Holeman, I., Evans, J., Kane, D., Grant, L., Pagliari, C. and Weller, D. (2014), Mobile health for cancer in low to middle income countries: priorities for research and development. European Journal of Cancer Care, 23: 750–756. doi: 10.1111/ecc.12250)Many current global health opportunities have less to do with new biomedical knowledge than with the coordination and delivery of care. While basic research remains vital, the growing cancer epidemic in countries of low and middle income warrants urgent action - focusing on both research and service delivery innovation. Mobile technology can reduce costs, improve access to health services, and strengthen health systems to meet the interrelated challenges of cancer and other noncommunicable diseases. Experience has shown that even very poor and remote communities that only have basic primary health care can benefit from mobile health (or 'mHealth') interventions. We argue that cancer researchers and practitioners have an opportunity to leverage mHealth technologies that have successfully targeted other health conditions, rather than reinventing these tools. We call for particular attention to human centred design approaches for adapting existing technologies to suit distinctive aspects of cancer care and to align delivery with local context - and we make a number of recommendations for integrating mHealth delivery research with the work of designers, engineers and implementers in large-scale delivery programmes

A Semi-Empirical Model of PV Modules Including Manufacturing I-V Mismatch

This paper presents an analysis of the impact of manufacturing variability in PV modules when interconnected into a large PV panel. The key enabling technology is a compact semiempirical model, that is built solely from information derived from datasheets, without requiring extraction of electrical parameters or measurements. The model explicits the dependency of output power on those quantities that are heavily affected by variability, like short circuit current and open circuit voltage. In this way, variability can be included with Monte Carlo techniques and tuned to the desired distributions and tolerance. In the experimental results, we prove the effectiveness of the model in the analysis of the optimal interconnection of PV modules, with the goal of reducing the impact of variability

C-NMT: A Collaborative Inference Framework for Neural Machine Translation

Collaborative Inference (CI) optimizes the latency and energy consumption of deep learning inference through the inter-operation of edge and cloud devices. Albeit beneficial for other tasks, CI has never been applied to the sequence-to-sequence mapping problem at the heart of Neural Machine Translation (NMT). In this work, we address the specific issues of collaborative NMT, such as estimating the latency required to generate the (unknown) output sequence, and show how existing CI methods can be adapted to these applications. Our experiments show that CI can reduce the latency of NMT by up to 44% compared to a non-collaborative approach

Uav Photogrammetry: Block Triangulation Comparisons

UAVs systems represent a flexible technology able to collect a big amount of high resolution information, both for metric and interpretation uses. In the frame of experimental tests carried out at Dept. ICA of Politecnico di Milano to validate vector-sensor systems and to assess metric accuracies of images acquired by UAVs, a block of photos taken by a fixed wing system is triangulated with several software. The test field is a rural area included in an Italian Park ("Parco Adda Nord"), useful to study flight and imagery performances on buildings, roads, cultivated and uncultivated vegetation. The UAV SenseFly, equipped with a camera Canon Ixus 220HS, flew autonomously over the area at a height of 130 m yielding a block of 49 images divided in 5 strips. Sixteen pre-signalized Ground Control Points, surveyed in the area through GPS (NRTK survey), allowed the referencing of the block and accuracy analyses. Approximate values for exterior orientation parameters (positions and attitudes) were recorded by the flight control system. The block was processed with several software: Erdas-LPS, EyeDEA (Univ. of Parma), Agisoft Photoscan, Pix4UAV, in assisted or automatic way. Results comparisons are given in terms of differences among digital surface models, differences in orientation parameters and accuracies, when available. Moreover, image and ground point coordinates obtained by the various software were independently used as initial values in a comparative adjustment made by scientific in-house software, which can apply constraints to evaluate the effectiveness of different methods of point extraction and accuracies on ground check points

A comparison analysis of ble-based algorithms for localization in industrial environments

Proximity beacons are small, low-power devices capable of transmitting information at a limited distance via Bluetooth low energy protocol. These beacons are typically used to broadcast small amounts of location-dependent data (e.g., advertisements) or to detect nearby objects. However, researchers have shown that beacons can also be used for indoor localization converting the received signal strength indication (RSSI) to distance information. In this work, we study the effectiveness of proximity beacons for accurately locating objects within a manufacturing plant by performing extensive experiments in a real industrial environment. To this purpose, we compare localization algorithms based either on trilateration or environment fingerprinting combined with a machine-learning based regressor (k-nearest neighbors, support-vector machines, or multi-layer perceptron). Each algorithm is analyzed in two different types of industrial environments. For each environment, various configurations are explored, where a configuration is characterized by the number of beacons per square meter and the density of fingerprint points. In addition, the fingerprinting approach is based on a preliminary site characterization; it may lead to location errors in the presence of environment variations (e.g., movements of large objects). For this reason, the robustness of fingerprinting algorithms against such variations is also assessed. Our results show that fingerprint solutions outperform trilateration, showing also a good resilience to environmental variations. Given the similar error obtained by all three fingerprint approaches, we conclude that k-NN is the preferable algorithm due to its simple deployment and low number of hyper-parameters

Channel-wise Mixed-precision Assignment for DNN Inference on Constrained Edge Nodes

Quantization is widely employed in both cloud and edge systems to reduce the memory occupation, latency, and energy consumption of deep neural networks. In particular, mixed-precision quantization, i.e., the use of different bit-widths for different portions of the network, has been shown to provide excellent efficiency gains with limited accuracy drops, especially with optimized bit-width assignments determined by automated Neural Architecture Search (NAS) tools. State-of-The-Art mixed-precision works layer-wise, i.e., it uses different bit-widths for the weights and activations tensors of each network layer. In this work, we widen the search space, proposing a novel NAS that selects the bit-width of each weight tensor channel independently. This gives the tool the additional flexibility of assigning a higher precision only to the weights associated with the most informative features. Testing on the MLPerf Tiny benchmark suite, we obtain a rich collection of Pareto-optimal models in the accuracy vs model size and accuracy vs energy spaces. When deployed on the MPIC RISC-V edge processor, our networks reduce the memory and energy for inference by up to 63% and 27% respectively compared to a layer-wise approach, for the same accuracy

Predicting Hard Disk Failures in Data Centers Using Temporal Convolutional Neural Networks

In modern data centers, storage system failures are major contributors to downtimes and maintenance costs. Predicting these failures by collecting measurements from disks and analyzing them with machine learning techniques can effectively reduce their impact, enabling timely maintenance. While there is a vast literature on this subject, most approaches attempt to predict hard disk failures using either classic machine learning solutions, such as Random Forests (RFs) or deep Recurrent Neural Networks (RNNs). In this work, we address hard disk failure prediction using Temporal Convolutional Networks (TCNs), a novel type of deep neural network for time series analysis. Using a real-world dataset, we show that TCNs outperform both RFs and RNNs. Specifically, we can improve the Fault Detection Rate (FDR) of ≈ 7.5% (FDR = 89.1%) compared to the state-of-the-art, while simultaneously reducing the False Alarm Rate (FAR = 0.052%). Moreover, we explore the network architecture design space showing that TCNs are consistently superior to RNNs for a given model size and complexity and that even relatively small TCNs can reach satisfactory performance. All the codes to reproduce the results presented in this paper are available at https://github.com/ABurrello/tcn-hard-disk-failure-prediction