Detection of Unwanted Odors using Unmasking Odor Algorithm (UOA)

A new approach to detection of the existence of unwanted odors after spraying the smart home and vehicular environment with perfumes is considered in the work. The approach is based on registering the response curve of an array of sensors to perfumes and to odors such as herbs, then using the proposed intersection algorithm to uncover the ability of the perfume to mask specific odors. Three odors (herbs) and three perfumes are tried and resulted in the ability of perfumes to mask two of the herbs, one deeper than the other. The response curve intersection technique (RCIT) provides the ability to unmask unwanted odor existence, thus forms the heart of the unmasking odor algorithms (UOA). Mathematical equations are used to prove the concept with digital logic is further used to support the presented algorithm. The research found that using the proposed technique, an odor masked by spraying of perfumes can be unmasked using the RCIT as the case in herb 3 presented in the work. The work also showed the unique curve shape for both perfumes and herbs and the fact that some herbs can be easily masked and hidden within the response of perfumes. In addition, it is shown that the perfumes response is much more complex compared to herbs

A Novel Joint TDOA/FDOA Passive Localization Scheme Using Interval Intersection Algorithm

Due to the large measurement error in the practical non-cooperative scene, the passive localization algorithms based on traditional numerical calculation using time difference of arrival (TDOA) and frequency difference of arrival (FDOA) often have no solution, i.e., the estimated result cannot meet the localization background knowledge. In this context, this paper intends to introduce interval analysis theory into joint FDOA/TDOA-based localization algorithm. The proposed algorithm uses the dichotomy algorithm to fuse the interval measurement of TDOA and FDOA for estimating the velocity and position of a moving target. The estimation results are given in the form of an interval. The estimated interval must contain the true values of the position and velocity of the radiation target, and the size of the interval reflects the confidence of the estimation. The point estimation of the position and the velocity of the target is given by the midpoint of the estimation interval. Simulation analysis shows the efficacy of the algorithm.

Mathematical Analysis of Line Intersection and Shortest Distance Algorithms

The time of arrival (TOA) trilateration is one of the representative location detection technologies (LDT) that determines the true location of a mobile station (MS) using a unique intersection point of three circles based on three radii corresponding to distances between MS and base stations (BSs) and center coordinates of BSs. Since the distance between MS and BS is estimated by using the number of time delays, three circles based on the estimated radii are generally increased and they may not meet at a single point, resulting in the location estimation error. In order to compensate this estimation error and to improve estimation performance, we present two advanced TOA trilateration localization algorithms with detail mathematical expressions. The considered algorithms are the shortest distance algorithm, which calculates an average of three interior intersection points among an entire six intersection points from three intersecting circles, and the line intersection algorithm, which calculates an intersection point of three lines connecting two intersection points of two circles among the three circles, as the estimated location of the MS. In this paper, we present both algorithms with detailed mathematical expressions. The computer simulation results are provided to compare the location estimation performance of both algorithms. In addition, in this paper, mathematical analysis is provided to indicate the relation between the line intersection algorithm and the shortest distance algorithm. In this analysis, we verify that line equations based on the intersection points obtained from the shortest distance algorithm are identical to those obtained from the line intersection algorithm.

Mapping Transcription Factor Networks By Comparing Tf Binding Locations To Tf Perturbation Responses

ABSTRACTBackgroundA transcription-factor (TF) network map indicates the direct, functional targets of each TF -- the genes it regulates by binding to their cis-regulatory DNA. Data on the genomic binding locations of each TF and the transcriptional responses to perturbations of its activity, such as overexpressing it, could support TF network mapping. Systematic data sets of both types exist for yeast and for human K562 and HEK293 cells.ResultsIn previous data, most TF binding sites appear to be non-functional, so one cannot take the genes in whose promoters a TF binds as its direct, functional (DF) targets. Taking the genes that are both bound by a TF and responsive to a perturbation of it as its DF targets (intersection algorithm) is also not safe, as we show by deriving a new lower bound on the expected false discovery rate of the intersection algorithm. When there are many non-functional binding sites and many indirect targets, non-functional sites are expected to occur in the cis-regulatory DNA of indirect targets by chance. Dual threshold optimization, a new method for setting significance thresholds on binding and response data, improves the intersection algorithm, as does post-processing perturbation-response data with NetProphet 2.0. A comprehensive new data set measuring the transcriptional response shortly after inducing overexpression of a TF also helps, as does transposon calling cards, a new method for identifying TF binding locations.ConclusionsThe combination of dual threshold optimization and NetProphet greatly expands the high-confidence TF network map in both yeast and human. In yeast, measuring the response shortly after inducing TF overexpression and measuring binding locations by using transposon calling cards improve the network synergistically.