Efficient classification of billions of points into complex geographic regions using hierarchical triangular mesh

We present a case study about the spatial indexing and regional classification of billions of geographic coordinates from geo-tagged social network data using Hierarchical Triangular Mesh (HTM) implemented for Microsoft SQL Server. Due to the lack of certain features of the HTM library, we use it in conjunction with the GIS functions of SQL Server to significantly increase the efficiency of pre-filtering of spatial filter and join queries. For example, we implemented a new algorithm to compute the HTM tessellation of complex geographic regions and precomputed the intersections of HTM triangles and geographic regions for faster false-positive filtering. With full control over the index structure, HTM-based pre-filtering of simple containment searches outperforms SQL Server spatial indices by a factor of ten and HTM-based spatial joins run about a hundred times faster.Comment: appears in Proceedings of the 26th International Conference on Scientific and Statistical Database Management (2014

The effect of arsenic (As) contamination on domestic vegetables

Elemental arsenic and arsenic compounds are classified as "toxic" and "dangerous for the environment" in the European Union under directive 67/548/EEC. The International Agency for Research on Cancer (IARC) recognizes arsenic and arsenic compounds as group 1 carcinogens, and the EU lists arsenic trioxide, arsenic pentoxide and arsenate salts as category 1 carcinogens. Arsenic is easily absorbed by vegetables from irrigation water. The accumulation of arsenic in vegetables could pose a serious risk on the quality of vegetables and human health. The two forms of inorganic arsenic, arsenate/As(V) and arsenite/As(III), are easily taken up by plant root cells (e.g. carrot, parsley, kohlrabi). Once in the cell, As(V) can be readily converted to As(III), the more toxic form of arsenic. In the present research we have determined the level of arsenic contamination in two of the economically most important vegetables grown in Hungary (sweet pepper; tomato) and the irrigation water in an arsenic contaminated area. In order to eliminate arsenic, decontamination was achieved by use of a Japanese-developed special cerium filter

The effect of arsenic (As) contamination on domestic vegetables

Elemental arsenic and arsenic compounds are classified as "toxic" and "dangerous for the environment" in the European Union under directive 67/548/EEC. The International Agency for Research on Cancer (IARC) recognizes arsenic and arsenic compounds as group 1 carcinogens, and the EU lists arsenic trioxide, arsenic pentoxide and arsenate salts as category 1 carcinogens. Arsenic is easily absorbed by vegetables from irrigation water. The accumulation of arsenic in vegetables could pose a serious risk on the quality of vegetables and human health. The two forms of inorganic arsenic, arsenate/As(V) and arsenite/As(III), are easily taken up by plant root cells (e.g. carrot, parsley, kohlrabi). Once in the cell, As(V) can be readily converted to As(III), the more toxic form of arsenic. In the present research we have determined the level of arsenic contamination in two of the economically most important vegetables grown in Hungary (sweet pepper; tomato) and the irrigation water in an arsenic contaminated area. In order to eliminate arsenic, decontamination was achieved by use of a Japanese-developed special cerium filter

Towards Reliable Multisensory Perception and Its Automotive Applications

Autonomous driving poses numerous challenging problems, one of which is perceiving and understanding the environment. Since self-driving is safety critical and many actions taken during driving rely on the outcome of various perception algorithms (for instance all traffic participants and infrastructural objects in the vehicle's surroundings must reliably be recognized and localized), thus the perception might be considered as one of the most critical subsystems in an autonomous vehicle. Although the perception itself might further be decomposed into various sub-problems, such as object detection, lane detection, traffic sign detection, environment modeling, etc. In this paper the focus is on fusion models in general (giving support for multisensory data processing) and some related automotive applications such as object detection, traffic sign recognition, end-to-end driving models and an example of taking decisions in multi-criterial traffic situations that are complex for both human drivers and for the self-driving vehicles as well