We review techniques for sensor fusion in robot navigation, emphasizing algorithms for self-location. These find use when the sensor suite of a mobile robot comprises several different sensors, some complementary and some redundant. Integrating the sensor readings, the robot seeks to accomplish tasks such as constructing a map of its environment, locating itself in that map, and recognizing objects that should be avoided or sought. Our review describes integration techniques in two categories: low-level fusion is used for direct integration of sensory data, resulting in parameter and state estimates; high-level fusion is used for indirect integration of sensory data in hierarchical architectures, through command arbitration and integration of control signals suggested by different modules. The review provides an arsenal of tools for addressing this (rather ill-posed) problem in machine intelligence, including Kalman filtering, rule-based techniques, behavior based algorithms, and approaches that borrow from information theory, Dempster-Shafer reasoning, fuzzy logic and neural networks. It points to several further-research needs, including: robustness of decision rules; simultaneous consideration of self-location, motion planning, motion control and vehicle dynamics; the effect of sensor placement and attention focusing on sensor fusion; and adaptation of techniques from biological sensor fusion.
Abstract-Active authentication is the problem of continuously verifying the identity of a person based on behavioral aspects of their interaction with a computing device. In this study, we collect and analyze behavioral biometrics data from 200 subjects, each using their personal Android mobile device for a period of at least 30 days. This dataset is novel in the context of active authentication due to its size, duration, number of modalities, and absence of restrictions on tracked activity. The geographical colocation of the subjects in the study is representative of a large closed-world environment such as an organization where the unauthorized user of a device is likely to be an insider threat: coming from within the organization. We consider four biometric modalities: (1) text entered via soft keyboard, (2) applications used, (3) websites visited, and (4) physical location of the device as determined from GPS (when outdoors) or WiFi (when indoors). We implement and test a classifier for each modality and organize the classifiers as a parallel binary decision fusion architecture. We are able to characterize the performance of the system with respect to intruder detection time and to quantify the contribution of each modality to the overall performance.
We present a fully automated multi-sperm tracking algorithm. It has the demonstrated capability to detect and track simultaneously hundreds of sperm cells in recorded videos while accurately measuring motility parameters over time and with minimal operator intervention. Algorithms of this kind may help in associating dynamic swimming parameters of human sperm cells with fertility and fertilization rates. Specifically, we offer an image processing method, based on radar tracking algorithms, that detects and tracks automatically the swimming paths of human sperm cells in timelapse microscopy image sequences of the kind that is analyzed by fertility clinics. Adapting the well-known joint probabilistic data association filter (JPDAF), we automatically tracked hundreds of human sperm simultaneously and measured their dynamic swimming parameters over time. Unlike existing CASA instruments, our algorithm has the capability to track sperm swimming in close proximity to each other and during apparent cell-to-cell collisions. Collecting continuously parameters for each sperm tracked without sample dilution (currently impossible using standard CASA systems) provides an opportunity to compare such data with standard fertility rates. The use of our algorithm thus has the potential to free the clinician from having to rely on elaborate motility measurements obtained manually by technicians, speed up semen processing, and provide medical practitioners and researchers with more useful data than are currently available.
We report on the first controlled study comparing the abilities of forensic document examiners (FDEs) and laypersons in the area of signature examination. Layersons and professional FDEs were given the same signature-authentication/simulation-detection task. They compared six known signatures generated by the same person with six unknown signatures. No a priori knowledge of the distribution of genuine and nongenuine signatures in the unknown signature set was available to test-takers. Three different monetary incentive schemes were implemented to motivate the laypersons. We provide two major findings: (i) the data provided by FDEs and by laypersons in our tests were significantly different (namely, the hypothesis that there is no difference between the assessments provided by FDEs and laypersons about genuineness and nongenuineness of signatures was rejected); and (ii) the error rates exhibited by the FDEs were much smaller than those of the laypersons. In addition, we found no statistically significant differences between the data sets obtained from laypersons who received different monetary incentives. The most pronounced differences in error rates appeared when nongenuine signatures were declared authentic (Type I error) and when authentic signatures were declared nongenuine (Type II error). Type I error was made by FDEs in 0.49% of the cases, but laypersons made it in 6.47% of the cases. Type II error was made by FDEs in 7.05% of the cases, but laypersons made it in 26.1% of the cases.
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