Abstract: Location estimation for a first target object assisted by a second target object, which have co-located signalling devices is provided. A positioning engine employs a data model of a location-dependent physical quantity and determines location probability distributions for the target objects. One signalling device sends positioning-assisting signals to the other which makes observations from it. The positioning engine uses observations on the physical quantity and the positioning-assisting signals to make a quantity observation set, and determines location probability distributions for the target objects. The positioning engine determines an updated first location probability distribution based on the location probability distributions and the positioning-assisting observation set.

Abstract: A wireless signal transceiver receiving and storing predefined wireless network identifiers and a scan mode program for enabling either an active or passive scan mode. In the active scan mode, the transceiver repeatedly transmits probe request messages until the transceiver receives a probe response message from the network (successful active scan) or until a predetermined criterion terminates the active scan (failed active scan). In the passive scan mode, the transceiver does not send anything but waits for a received network identifier from one or more wireless networks. The transceiver enables the active scan mode in response to a successful active scan or to a reception of a wireless network identifier that matches at least one of the predefined identifiers stored in memory. The transceiver enables the passive scan mode in response to a failed active scan.

Abstract: Location estimation for a first target object assisted by a second target object, which have co-located signalling devices is provided. A positioning engine employs a data model of a location-dependent physical quantity and determines location probability distributions for the target objects. One signalling device sends positioning-assisting signals to the other which makes observations from it. The positioning engineuses observations on the physical quantity and the positioning-assisting signals to make a quantity observation set, and determines location probability distributions for the target objects. The positioning engine determines an updated first location probability distribution based on the location probability distributions and the positioning-assisting observation set.

Abstract: A method for gene mapping from genotype and phenotype data utilizes linkage disequilibrium between genetic markers mi, which are polymorphic nucleic acid or protein sequences or strings of single-nucleotide polymorphisms deriving from a chromosomal region. All marker patterns P that satisfy a certain pattern evaluation function e(P) are searched from the data, each marker mi of the data is scored by a marker score and the location of the gene is predicted as a function of the scores s(mi) of all the markers mi in the data.

Abstract: The present invention relates to a method for gene mapping from chromosome and phenotype data, which utilizes linkage disequilibrium between genetic markers mi, which are polymorphic nucleic acid or protein sequences or strings of single-nucleotide polymorphisms deriving from a chromosomal region. All marker patterns P that satisfy a certain pattern evaluation function e(P) are searched from the data, each marker mi of the data is scored by a marker score and the location of the gene is predicted as a function of the scores s(mi) of all the markers mi in the data.

Abstract: The present invention relates to a method for gene mapping from chromosome and phenotype data, which utilizes linkage disequilibrium between genetic markers mi, which are polymorphic nucleic acid or protein sequences or strings of single-nucleotide polymorphisms deriving from a chromosomal region. All marker patterns P that satisfy a certain pattern evaluation function e(P) are searched from the data, each marker mi of the data is scored by a marker score and the location of the gene is predicted as a function of the scores s(mi) of all the markers mi in the data.