892 resultados para Detection System
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We describe a rational approach to simultaneously test Escherichia coli strains for the presence of known virulence genes in a reverse dot blot procedure. Specific segments of virulence genes of E. coli designed to have similar hybridization parameters were subcloned on plasmids and subsequently amplified by PCR as unlabeled probes in amounts sufficient to be bound to nylon membranes. Various pathogenic isolates and laboratory strains of E. coli were probed for the presence of virulence genes by labeling the genomic DNA of these strains with digoxigenin and then hybridizing them to the prepared nylon membranes. These hybridization results demonstrated that besides the E. coli K-12 safety strain derivatives, E. coli B and C strains are also devoid of genes encoding any of the investigated virulence factors. In contrast, pathogenic E. coli control strains, used to evaluate the method, showed typical hybridization patterns. The described probes and their easy application on a single filter were shown to provide a useful tool for the safety assessment of E. coli strains to be used as hosts in biotechnological processes. This approach might also be used for the identification and characterization of clinically significant E. coli isolates from human and animal species.
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A stress-detection system is proposed based on physiological signals. Concretely, galvanic skin response (GSR) and heart rate (HR) are proposed to provide information on the state of mind of an individual, due to their nonintrusiveness and noninvasiveness. Furthermore, specific psychological experiments were designed to induce properly stress on individuals in order to acquire a database for training, validating, and testing the proposed system. Such system is based on fuzzy logic, and it described the behavior of an individual under stressing stimuli in terms of HR and GSR. The stress-detection accuracy obtained is 99.5% by acquiring HR and GSR during a period of 10 s, and what is more, rates over 90% of success are achieved by decreasing that acquisition period to 3-5 s. Finally, this paper comes up with a proposal that an accurate stress detection only requires two physiological signals, namely, HR and GSR, and the fact that the proposed stress-detection system is suitable for real-time applications.
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Sensing systems in living bodies offer a large variety of possible different configurations and philosophies able to be emulated in artificial sensing systems. Motion detection is one of the areas where different animals adopt different solutions and, in most of the cases, these solutions reflect a very sophisticated form. One of them, the mammalian visual system, presents several advantages with respect to the artificial ones. The main objective of this paper is to present a system, based on this biological structure, able to detect motion, its sense and its characteristics. The configuration adopted responds to the internal structure of the mammalian retina, where just five types of cells arranged in five layers are able to differentiate a large number of characteristics of the image impinging onto it. Its main advantage is that the detection of these properties is based purely on its hardware. A simple unit, based in a previous optical logic cell employed in optical computing, is the basis for emulating the different behaviors of the biological neurons. No software is present and, in this way, no possible interference from outside affects to the final behavior. This type of structure is able to work, once the internal configuration is implemented, without any further attention. Different possibilities are present in the architecture to be presented: detection of motion, of its direction and intensity. Moreover, some other characteristics, as symmetry may be obtained.
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Federal Highway Administration, Washington, D.C.
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Cover title.
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"14 February 1972."
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Includes index.
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"19 May 1983."
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This paper presents an innovative approach for signature verification and forgery detection based on fuzzy modeling. The signature image is binarized and resized to a fixed size window and is then thinned. The thinned image is then partitioned into a fixed number of eight sub-images called boxes. This partition is done using the horizontal density approximation approach. Each sub-image is then further resized and again partitioned into twelve further sub-images using the uniform partitioning approach. The features of consideration are normalized vector angle (α) from each box. Each feature extracted from sample signatures gives rise to a fuzzy set. Since the choice of a proper fuzzification function is crucial for verification, we have devised a new fuzzification function with structural parameters, which is able to adapt to the variations in fuzzy sets. This function is employed to develop a complete forgery detection and verification system.