A Linear Approach to Absolute Pose Estimation for Light Fields

This paper presents the first absolute pose estimation approach tailored to Light Field cameras. It builds on the observation that the ratio between the disparity arising in different sub-aperture images and their corresponding baseline is constant. Hence, we augment the 2D pixel coordinates with the corresponding normalised disparity to obtain the Light Field feature. This new representation reduces the effect of noise by aggregating multiple projections and allows for linear estimation of the absolute pose of a Light Field camera using the well-known Direct Linear Transformation algorithm. We evaluate the resulting absolute pose estimates with extensive simulations and experiments involving real Light Field datasets, demonstrating the competitive performance of our linear approach. Furthermore, we integrate our approach in a state-of-the-art Light Field Structure from Motion pipeline and demonstrate accurate multi-view 3D reconstruction

Corner-Based Geometric Calibration of Multi-focus Plenoptic Cameras

We propose a method for geometric calibration of multi-focus plenoptic cameras using raw images. Multi-focus plenoptic cameras feature several types of micro-lenses spatially aligned in front of the camera sensor to generate micro-images at different magnifications. This multi-lens arrangement provides computational-photography benefits but complicates calibration. Our methodology achieves the detection of the type of micro-lenses, the retrieval of their spatial arrangement, and the estimation of intrinsic and extrinsic camera parameters therefore fully characterising this specialised camera class. Motivated from classic pinhole camera calibration, our algorithm operates on a checker-board's corners, retrieved by a custom micro-image corner detector. This approach enables the introduction of a reprojection error that is used in a minimisation framework. Our algorithm compares favourably to the state-of-the-art, as demonstrated by controlled and freehand experiments, making it a first step towards accurate 3D reconstruction and Structure-from-Motion

Linkage mapping, comparative genome analysis, and QTL detection for growth in a non-model teleost, the meagre Argyrosomus regius, using ddRAD sequencing

Meagre (Argyrosomus regius), is a benthopelagic species rapidly emerging in aquaculture, due to its low food to biomass conversion rate, good fillet yield and ease of production. Tracing a species genomic background along with describing the genetic basis of important traits can greatly influence both conservation strategies and production perspectives. In this study, we employed ddRAD sequencing of 266 fish from six F1 meagre families, to construct a high-density genetic map comprising 4529 polymorphic SNP markers. The QTL mapping analysis provided a genomic appreciation for the weight trait identifying a statistically significant QTL on linkage group 15 (LG15). The comparative genomics analysis with six teleost species revealed an evolutionarily conserved karyotype structure. The synteny observed, verified the already well-known fusion events of the three-spine stickleback genome, reinforced the evidence of reduced evolutionary distance of Sciaenids with the Sparidae family, reflected the evolutionary proximity with Dicentrarchus labrax, traced several putative chromosomal rearrangements and a prominent putative fusion event in meagre’s LG17. This study presents novel elements concerning the genome evolutionary history of a non-model teleost species recently adopted in aquaculture, starts to unravel the genetic basis of the species growth-related traits, and provides a high-density genetic map as a tool that can help to further establish meagre as a valuable resource for research and production.info:eu-repo/semantics/publishedVersio

Substance deposition assessment in obstructed pulmonary system through numerical characterization of airflow and inhaled particles attributes

Background Chronic obstructive pulmonary disease (COPD) and asthma are considered as the two most widespread obstructive lung diseases, whereas they affect more than 500 million people worldwide. Unfortunately, the requirement for detailed geometric models of the lungs in combination with the increased computational resources needed for the simulation of the breathing did not allow great progress to be made in the past for the better understanding of inflammatory diseases of the airways through detailed modelling approaches. In this context, computational fluid dynamics (CFD) simulations accompanied by fluid particle tracing (FPT) analysis of the inhaled ambient particles are deemed critical for lung function assessment. Also they enable the understanding of particle depositions on the airways of patients, since these accumulations may affect or lead to inflammations. In this direction, the current study conducts an initial investigation for the better comprehension of particle deposition within the lungs. More specifically, accurate models of the airways obstructions that relate to pulmonary disease are developed and a thorough assessment of the airflow behavior together with identification of the effects of inhaled particle properties, such as size and density, is conducted. Our approach presents a first step towards an effective personalization of pulmonary treatment in regards to the geometric characteristics of the lungs and the in depth understanding of airflows within the airways. Methods A geometry processing technique involving contraction algorithms is established and used to employ the different respiratory arrangements associated with lung related diseases that exhibit airways obstructions. Apart from the normal lung case, two categories of obstructed cases are examined, i.e. models with obstructions in both lungs and models with narrowings in the right lung only. Precise assumptions regarding airflow and deposition fraction (DF) over various sections of the lungs are drawn by simulating these distinct incidents through the finite volume method (FVM) and particularly the CFD and FPT algorithms. Moreover, a detailed parametric analysis clarifies the effects of the particles size and density in terms of regional deposition upon several parts of the pulmonary system. In this manner, the deposition pattern of various substances can be assessed. Results For the specific case of the unobstructed lung model most particles are detected on the right lung (48.56% of total, when the air flowrate is 12.6 L/min), a fact that is also true when obstructions arise symmetrically in both lungs (51.45% of total, when the air flowrate is 6.06 L/min and obstructions occur after the second generation). In contrast, when narrowings are developed on the right lung only, most particles are pushed on the left section (68.22% of total, when the air flowrate is 11.2 L/min) indicating that inhaled medication is generally deposited away from the areas of inflammation. This observation is useful when designing medical treatment of lung diseases. Furthermore, particles with diameters from 1 μm to 10 μm are shown to be mainly deposited on the lower airways, whereas particles with diameters of 20 μm and 30 μm are mostly accumulated in the upper airways. As a result, the current analysis indicates increased DF levels in the upper airways when the particle diameter is enlarged. Additionally, when the particles density increases from 1000 Kg/m3 to 2000 Kg/m3, the DF is enhanced on every generation and for all cases investigated herein. The results obtained by our simulations provide an accurate and quantitative estimation of all important parameters involved in lung modeling. Conclusions The treatment of respiratory diseases with inhaled medical substances can be advanced by the clinical use of accurate CFD and FPT simulations and specifically by evaluating the deposition of inhaled particles in a regional oriented perspective in regards to different particle sizes and particle densities. Since a drug with specific characteristics (i.e. particle size and density) exhibits maximum deposition on particular lung areas, the current study provides initial indications to a qualified physician for proper selection of medication

Accelerating Deep Neural Networks for Efficient Scene Understanding in Multi-Modal Automotive Applications

Environment perception constitutes one of the most critical operations performed by semi- and fully- autonomous vehicles. In recent years, Deep Neural Networks (DNNs) have become the standard tool for perception solutions owing to their impressive capabilities in analyzing and modelling complex and dynamic scenes, from (often multi-modal) sensory inputs. However, the well-established performance of DNNs comes at the cost of increased time and storage complexity, which may become problematic in automotive perception systems due to the requirement for a short prediction horizon (as in many cases inference must be performed in real-time) and the limited computational, storage, and energy resources of mobile systems. A common way of addressing this problem is to transform the original large pre-trained networks into new smaller models, by utilizing Model Compression and Acceleration (MCA) techniques, improving both their storage and execution efficiency. Within the MCA framework, in this paper, we investigate the application of two state-of-the-art weight-sharing MCA techniques, namely a Vector Quantization (VQ) and a Dictionary Learning (DL) one, as well as two novel extensions, towards the acceleration and compression of widely used DNNs for 2D and 3D object-detection in automotive applications. Apart from the individual (uni-modal) networks, we also present and evaluate a multi-modal late-fusion algorithm for combining the detection results of the 2D and 3D detectors. Our evaluation studies are carried out on the KITTI Dataset. The obtained results lend themselves to a twofold interpretation. On the one hand, they showcase the significant acceleration and compression gains that can be achieved via the application of weight sharing on the selected DNN detectors, with limited accuracy loss, as well as highlight the performance differences between the two utilized weight-sharing approaches. On the other, they demonstrate the substantial boost in detection performance obtained by combining the outcome of the two unimodal individual detectors, using the proposed late-fusion-based multi-modal approach. Indeed, as our experiments have shown, pairing the high-performance DL-based MCA technique with the loss-mitigating effect of the multi-modal fusion approach, leads to highly accelerated models (up to approximately $2.5 \times$ and $6\times$ for the 2D and 3D detectors, respectively) with the performance loss of the fused results ranging in most cases within single-digits figures (as low as around 1% for the class “cars”)

Uncertainty Management for Wearable IoT Wristband Sensors Using Laplacian-Based Matrix Completion

Contemporary sensing devices provide reliable mechanisms for continuous process monitoring, accommodating use cases related to mHealth and smart mobility, by generating real-time data streams of numerous physiological and vital parameters. Such data streams can be later utilized by machine learning algorithms and decision support systems to predict critical clinical states and motivate users to adopt behaviours that improve the quality of their life and the society as a whole. However, in many cases, even when deployed over highly sophisticated, cutting-edge network infrastructure and deployment paradigms, data may exhibit missing values and non-uniformities due to various reasons, including device malfunction, deliberate data reduction for efficient processing, or data loss due to sensing and communication failures. This work proposes a novel approach to deal with missing entries in heart rate measurements. Benefiting from the low-rank property of the generated data matrices and the proximity of neighbouring measurements, we provide a novel method that combines classical matrix completion approaches with weighted Laplacian interpolation offering high reconstruction accuracy at fast execution times. Extensive evaluation studies carried out with real measurements show that the proposed methods could be effectively deployed by modern wristband-cloud computing systems increasing the robustness, the reliability and the energy efficiency of these systems

Enhancing an eco-driving gamification platform through wearable and vehicle sensor data integration

As road transportation has been identified as a major contributor of environmental pollution, motivating individuals to adopt a more eco-friendly driving style could have a substantial ecological as well as financial benefit. With gamification being an effective tool towards guiding targeted behavioural changes, the development of realistic frameworks delivering a high end user experience, becomes a topic of active research. This paper presents a series of enhancements introduced to an eco-driving gamification platform by the integration of additional wearable and vehicle-oriented sensing data sources, leading to a much more realistic evaluation of the context of a driving session

Coping with missing data in an unobtrusive monitoring system for office workers

Current trend of population ageing at global level is accompanied by increased prevalence of chronic diseases and higher rates of early retirement and labor market exit. In particular, the lifestyle of office workers is characterized by prolonged sitting and overall sedentary life, which alone is a high risk factor for cardiometabolic diseases, obesity and other related chronic diseases. The SmartWork unobtrusive monitoring system allows for continuous monitoring of various lifestyle, health, behavioural and work related parameters of office workers targeting to empower work ability sustainability. The large amounts of collected data in such systems are often characterized by the presence of missing entries. This work is an exploratory study on the potential of a Laplacian matrix completion variant for data imputation on the multi-channel time-series data collected with wearable or work devices in the SmartWork system

Exploiting Gamification to Improve Eco-driving Behaviour: The GamECAR Approach

GamECAR aims to develop a highly innovative and interactive Serious Games platform that will empower and guide users to adopt an eco-friendly driving style. This will be achieved, without distracting them from safe driving, through a multidisciplinary approach aiming at the development of a user friendly, unobtrusive multi-player gaming environment, where the users will not only play collaboratively/competitively using their mobile device but also use the car itself and their own bodies, thus turning eco-driving into an immersive and highly motivating experience. The sensing infrastructure of GamECAR will acquire data related to driving from an OBD sensor that will capture a complex set of parameters related to eco-driving, and will also sense environmental and physiological parameters of the driver, so as to better position the state of the system (car) in context (environment, user). The GamECAR system will be quantified and evaluated in test campaigns with drivers in three different sites that will serve system development and will also demonstrate usefulness and exploitation potential