Design Principals of Social Navigation

8th Delos Workshop on "User Interfaces for Digital Libraries" (on 21 October it will be held in conjuction with the 4th ERCIM Workshop on "User Interfaces for All"), SICS, Kista, Sweden, 21-23 October 1998PERSON

LIDAR-Camera Fusion for Road Detection Using Fully Convolutional Neural Networks

In this work, a deep learning approach has been developed to carry out road detection by fusing LIDAR point clouds and camera images. An unstructured and sparse point cloud is first projected onto the camera image plane and then upsampled to obtain a set of dense 2D images encoding spatial information. Several fully convolutional neural networks (FCNs) are then trained to carry out road detection, either by using data from a single sensor, or by using three fusion strategies: early, late, and the newly proposed cross fusion. Whereas in the former two fusion approaches, the integration of multimodal information is carried out at a predefined depth level, the cross fusion FCN is designed to directly learn from data where to integrate information; this is accomplished by using trainable cross connections between the LIDAR and the camera processing branches. To further highlight the benefits of using a multimodal system for road detection, a data set consisting of visually challenging scenes was extracted from driving sequences of the KITTI raw data set. It was then demonstrated that, as expected, a purely camera-based FCN severely underperforms on this data set. A multimodal system, on the other hand, is still able to provide high accuracy. Finally, the proposed cross fusion FCN was evaluated on the KITTI road benchmark where it achieved excellent performance, with a MaxF score of 96.03%, ranking it among the top-performing approaches

LIDAR-based Driving Path Generation Using Fully Convolutional Neural Networks

In this work, a novel learning-based approach has been developed to generate driving paths by integrating LIDAR point clouds, GPS-IMU information, and Google driving directions. The system is based on a fully convolutional neural network that jointly learns to carry out perception and path generation from real-world driving sequences and that is trained using automatically generated training examples. Several combinations of input data were tested in order to assess the performance gain provided by specific information modalities. The fully convolutional neural network trained using all the available sensors together with driving directions achieved the best MaxF score of 88.13% when considering a region of interest of 60x60 meters. By considering a smaller region of interest, the agreement between predicted paths and ground-truth increased to 92.60%. The positive results obtained in this work indicate that the proposed system may help fill the gap between low-level scene parsing and behavior-reflex approaches by generating outputs that are close to vehicle control and at the same time human-interpretable.Comment: Changed title, formerly "Simultaneous Perception and Path Generation Using Fully Convolutional Neural Networks

Fast LIDAR-based Road Detection Using Fully Convolutional Neural Networks

In this work, a deep learning approach has been developed to carry out road detection using only LIDAR data. Starting from an unstructured point cloud, top-view images encoding several basic statistics such as mean elevation and density are generated. By considering a top-view representation, road detection is reduced to a single-scale problem that can be addressed with a simple and fast fully convolutional neural network (FCN). The FCN is specifically designed for the task of pixel-wise semantic segmentation by combining a large receptive field with high-resolution feature maps. The proposed system achieved excellent performance and it is among the top-performing algorithms on the KITTI road benchmark. Its fast inference makes it particularly suitable for real-time applications

Automated quantification tool to monitor plate waste in school canteens

Automated tools for waste quantification hold promise in providing preciser understanding of food waste. This study evaluated a tool to quantify plate waste in primary school canteens. It encompassed data from 421,015 instances of food wastage. The evaluation revealed high accuracy, with the tool’s plate waste detection falling within ±10% of manual recordings. However, the tool estimated 40% fewer individual guests compared to manual entry due to not all students wasting food. As a result, the automatically collected data indicated a 35% higher waste-to-guest ratio. The findings showed that a minority of students (20%) accounted for a majority (60%) of plate waste. Halving the waste generated by this group would reduce overall plate waste by 31%, emphasizing the importance of tailored interventions for high-profile wasters rather than applying general measures to all students. Targeting areas with the greatest potential can contribute to a more sustainable food system with reduced waste

Biogas production from crop residues on a farm-scale level: Scale, choice of substrate and utilisation rate most important parameters for financial feasibility

Anaerobic digestion would enable the energy potential of agricultural crop residues such as ley crops, sugar beet tops and straw to be harnessed in Sweden. These residues are so spread out that full utilisation of the potential by centralised slurry-based technology is difficult, its appearing that simple but effective high-solids reactor systems have a better chance of being economically viable on a farm-scale level (30-300 kW). In the present study, the financial prospects of single-stage fed-batch high-solids digestion on three different scales, 51, 67 and 201 kW, were calculated, on the basis of experimental results and observations on a laboratory- and pilot-scale. The biogas was disposed as heat, combined heat and power or as vehicle fuel. The results indicate the importance of choosing substrates with a high methane yield and a high nitrogen content, and the necessity of fully utilising both the capacity of the equipment installed and the energy carriers produced

Paint it Black – To Protect the Qubits

This thesis deals with reducing quasiparticle generation in superconducting circuits caused by stray photons by utilizing electromagnetic absorbers. This thesis deals with reducing quasiparticle generation in superconducting circuits caused by stray photons by utilizing electromagnetic absorbers. Quasiparticle generation is a known problem in several superconducting circuit applications such as quantum computing research. In order to quantify successful radiation reduction, a superconducting resonator was used as a sensor and a cylindrical shield was designed for testing the absorbers. Four different absorbers were tested, three of them are commercially available microwave absorbers and one was designed by our group. Internal differences were observed between the absorbers, however, compared to reference measurements, no improvements were observed. The results also indicates that the resonators properties changed in-between measurements and more tests should be performed in order to draw final conclusions

Design Implications for Personal Information Management: A Theoretical Evaluation of a Prototype Interface

Personal Information management (PIM) is a research area that receives interest from a variety of disciplines including human-computer interaction, information retrieval, information systems research and psychology to mention but a few. The diversity in approaches and the cross-disciplinary nature of PIM have resulted in a fragmented picture of the problems and challenges designers of PIM tools are facing. In this paper we present a PIM evaluation framework based on a broad literature study of the known challenges within PIM. Focusing in particular on information fragmentation and the re-finding of information, we built and evaluated a PIM prototype using our framework. We found that zooming, separation between logical and physical structures, and showing search results in context seem like useful future design ideas

Enhancing performance in anaerobic high-solids stratified bed digesters by straw bed implementation

Anaerobic high-solids single-stage stratified bed digesters have been found to be simple and flexible design candidates for small-scale reactors located in medium- to low-technology environments. In the present study, wheat straw was used as the starter material for the stratified bed. Upon green mass feeding, the anaerobically stabilised straw bed functioned both as a biofilm support and as a particulate filter. It enabled a direct onset of 7 kg VSm(-3) batch loads, added twice a week, and permitted a low but consistent bed permeability during feeding at an average superficial flow velocity of 1 m d(-1) to be achieved. Fed-batch tests with sugar beet tops in pilot- and laboratory-scale setups at an average loading rate of 2 kg VSm(-3) d(-1) resulted in average biogas production rates of 1.2-1.4 m3 m(-3) d(-1) and methane yields of 0.31-0.36 m3 kg(-1) VS(added). At the end of the laboratory-scale feeding trial, the 200 day old straw bed had compacted to 50% of its initial volume, without any negative effects on performance being detectable

Modulatory effects on dendritic cells by human herpesvirus 6

Human herpesvirus 6A and 6B are β-herpesviruses approaching 100% seroprevalance worldwide. These viruses are involved in several clinical syndromes and have important immunomodulatory effects. Dendritic cells (DC) are key players in innate and adaptive immunity. Accordingly, DC are implicated in the pathogenesis of many human diseases, including infections. In this review the effects of HHV-6 infection on DC will be discussed