900 resultados para Detection system
Resumo:
The PhD research activity has taken place in the space debris field. In detail, it is focused on the possibility of detecting space debris from the space based platform. The research is focused at the same time on the software and the hardware of this detection system. For the software, a program has been developed for being able to detect an object in space and locate it in the sky solving the star field. For the hardware, the possibility of adapting a ground telescope for space activity has been considered and it has been tested on a possible electronic board.
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AIMS: Bacillus anthracis strains of various origins were analysed with the view to describe intrinsic and persistent structural components of the Bacillus collagen-like protein of anthracis glycoprotein associated anthrose containing tetrasaccharide in the exosporium. METHODS AND RESULTS: The tetrasaccharide consists of three rhamnose residues and an unique monosaccharide--anthrose. As anthrose was not found in spores of related strains of bacteria, we envisioned the detection of B. anthracis spores based on antibodies against anthrose-containing polysaccharides. Carbohydrate-protein conjugates containing the synthetic tetrasaccharide, an anthrose-rhamnose disaccharide or anthrose alone were employed to immunize mice. All three formulations were immunogenic and elicited IgG responses with different fine specificities. All sera and monoclonal antibodies derived from tetrasaccharide immunized mice cross-reacted not only with spore lysates of a panel of virulent B. anthracis strains, but also with some of the B. cereus strains tested. CONCLUSIONS: Our results demonstrate that antibodies to synthetic carbohydrates are useful tools for epitope analyses of complex carbohydrate antigens and for the detection of particular target structures in biological specimens. SIGNIFICANCE AND IMPACT OF THE STUDY: Although not strictly specific for B. anthracis spores, antibodies against the tetrasaccharide may have potential as immuno-capturing components for a highly sensitive spore detection system.
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Methods of heat detection were compared in the Mid- Crest Area Cattle Evaluation Program (MACEP) heifer development program in the 1998-breeding season. A total of 189 heifers from thirteen consignors entered the program on November 10, 1997. These heifers were condition scored, hip height measured, weighed, disposition scored, booster vaccinated, and treated for parasites at the time of arrival. Determination of the heifer’s mature weight was made and a target of 65% of their mature weight at breeding was established. The ration was designed to meet this goal. The heifers were kept in a dry lot until all heifers were AI bred once. The heifers were periodically weighed and condition scored to monitor their gains and the ration was adjusted as needed. The estrus synchronization program consisted of an oral progesterone analog for 14 days; 17 days after completion of the progesterone analog treatment a single injection of prostaglandin was given and the heifers were then estrus detected. Two concurrent methods of estrus detection were utilized: 1) Ovatec â electronic breeding probe (probe), 2) HeatWatchâ estrus detection system (HW), and 3) a combination of probe and HW. Probe readings were obtained each 12 hours and the heifers were continuously monitored for estrus activity using the HW system. The probe was used as the primary estrus detection method and the HW system was used as a back-up system. Those heifers that did not demonstrate any estrus signs prior to 96 hours post prostaglandin treatment were mass inseminated at 96 hours. Post AI breeding, 151 of the heifers were placed on pasture and ran with clean-up bulls for 60 days. The remaining heifers left the program after the AI breeding was completed. Pregnancy to the AI breeding was determined by ultrasound on June 29, 1998. Results from using both probe and HW were 60% pregnant by AI, probe alone was 32% pregnant by AI, and HW alone was 27% pregnant by AI. The result of mass insemination was 20% pregnant by AI.
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MAX dimerization protein 1 (MAD1) is a basic-helix-loop-helix transcription factors that recruits transcription repressor such as HDAC to suppress target genes transcription. It antagonizes to MYC because the promoter binding sites for MYC are usually also serve as the binding sites for MAD1 so they compete for it. However, the mechanism of the switch between MYC and MAD1 in turning on and off of genes' transcription is obscure. In this study, we demonstrated that AKT-mediated MAD1 phosphorylation inhibits MAD1 transcription repression function. The association between MAD1 and its target genes' promoter is reduced after been phosphorylated by AKT; therefore, consequently, allows MYC to occupy the binding site and activates transcription. Mutation of such phosphorylation site abrogates the inhibition from AKT. In addition, functional assays demonstrated that AKT suppressed MAD1-mediated transcription repression of its target genes hTERT and ODC. Cell cycle and cell growth were also been released from inhibition by MAD1 in the presents of AKT. Taken together, our study suggests that MAD1 is a novel substrate of AKT and AKT-mediated MAD1 phosphorylation inhibits MAD1function; therefore, activates MAD1 target genes expression. ^ Furthermore, analysis of protein-protein interaction is indispensable for current molecular biology research, but multiplex protein dynamics in cells is too complicated to be analyzed by using existing biochemical methods. To overcome the disadvantage, we have developed a single molecule level detection system with nanofluidic chip. Single molecule was analyzed based on their fluorescent profile and their profiles were plotted into 2 dimensional time co-incident photon burst diagram (2DTP). From this 2DTP, protein complexes were characterized. These results demonstrate that the nanochannel protein detection system is a promising tool for future molecular biology. ^
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A small Positron Emission Tomography demonstrator based on LYSO slabs and Silicon Photomultiplier matrices is under construction at the University and INFN of Pisa. In this paper we present the characterization results of the read-out electronics and of the detection system. Two SiPM matrices, composed by 8 × 8 SiPM pixels, 1.5 mm pitch, have been coupled one to one to a LYSO crystals array. Custom Front-End ASICs were used to read the 64 channels of each matrix. Data from each Front-End were multiplexed and sent to a DAQ board for the digital conversion; a motherboard collects the data and communicates with a host computer through a USB port. Specific tests were carried out on the system in order to assess its performance. Futhermore we have measured some of the most important parameters of the system for PET application.
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This paper describes a stress detection system based on fuzzy logic and two physiological signals: Galvanic Skin Response and Heart Rate. Instead of providing a global stress classification, this approach creates an individual stress templates, gathering the behaviour of individuals under situations with different degrees of stress. The proposed method is able to detect stress properly with a rate of 99.5%, being evaluated with a database of 80 individuals. This result improves former approaches in the literature and well-known machine learning techniques like SVM, k-NN, GMM and Linear Discriminant Analysis. Finally, the proposed method is highly suitable for real-time applications
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This paper proposes a stress detection system based on fuzzy logic and the physiological signals heart rate and galvanic skin response. The main contribution of this method relies on the creation of a stress template, collecting the behaviour of previous signals under situations with a different level of stress in each individual. The creation of this template provides an accuracy of 99.5% in stress detection, improving the results obtained by current pattern recognition techniques like GMM, k-NN, SVM or Fisher Linear Discriminant. In addition, this system can be embedded in security systems to detect critical situations in accesses as cross-border control. Furthermore, its applications can be extended to other fields as vehicle driver state-of-mind management, medicine or sport training.
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Actualmente la detección del rostro humano es un tema difícil debido a varios parámetros implicados. Llega a ser de interés cada vez mayor en diversos campos de aplicaciones como en la identificación personal, la interface hombre-máquina, etc. La mayoría de las imágenes del rostro contienen un fondo que se debe eliminar/discriminar para poder así detectar el rostro humano. Así, este proyecto trata el diseño y la implementación de un sistema de detección facial humana, como el primer paso en el proceso, dejando abierto el camino, para en un posible futuro, ampliar este proyecto al siguiente paso, que sería, el Reconocimiento Facial, tema que no trataremos aquí. En la literatura científica, uno de los trabajos más importantes de detección de rostros en tiempo real es el algoritmo de Viola and Jones, que ha sido tras su uso y con las librerías de Open CV, el algoritmo elegido para el desarrollo de este proyecto. A continuación explicaré un breve resumen sobre el funcionamiento de mi aplicación. Mi aplicación puede capturar video en tiempo real y reconocer el rostro que la Webcam captura frente al resto de objetos que se pueden visualizar a través de ella. Para saber que el rostro es detectado, éste es recuadrado en su totalidad y seguido si este mueve. A su vez, si el usuario lo desea, puede guardar la imagen que la cámara esté mostrando, pudiéndola almacenar en cualquier directorio del PC. Además, incluí la opción de poder detectar el rostro humano sobre una imagen fija, cualquiera que tengamos guardada en nuestro PC, siendo mostradas el número de caras detectadas y pudiendo visualizarlas sucesivamente cuantas veces queramos. Para todo ello como bien he mencionado antes, el algoritmo usado para la detección facial es el de Viola and Jones. Este algoritmo se basa en el escaneo de toda la superficie de la imagen en busca del rostro humano, para ello, primero la imagen se transforma a escala de grises y luego se analiza dicha imagen, mostrando como resultado el rostro encuadrado. ABSTRACT Currently the detection of human face is a difficult issue due to various parameters involved. Becomes of increasing interest in various fields of applications such as personal identification, the man-machine interface, etc. Most of the face images contain a fund to be removed / discriminate in order to detect the human face. Thus, this project is the design and implementation of a human face detection system, as the first step in the process, leaving the way open for a possible future, extend this project to the next step would be, Facial Recognition , a topic not covered here. In the literature, one of the most important face detection in real time is the algorithm of Viola and Jones, who has been after use with Open CV libraries, the algorithm chosen for the development of this project. I will explain a brief summary of the performance of my application. My application can capture video in real time and recognize the face that the Webcam Capture compared to other objects that can be viewed through it. To know that the face is detected, it is fully boxed and followed if this move. In turn, if the user may want to save the image that the camera is showing, could store in any directory on your PC. I also included the option to detect the human face on a still image, whatever we have stored in your PC, being shown the number of faces detected and can view them on more times. For all as well I mentioned before, the algorithm used for face detection is that of Viola and Jones. This algorithm is based on scanning the entire surface of the image for the human face, for this, first the image is converted to gray-scale and then analyzed the image, showing results in the face framed.
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We are investigating the performances of a data acquisition system for Time of Flight PET, based on LYSO crystal slabs and 64 channels Silicon Photomultipliers matrices (1.2 cm2 of active area each). Measurements have been performed to test the timing capability of the detection system (SiPM matices coupled to a LYSO slab and the read-out electronics) with both test signal and radioactive source.
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The explosive growth of the traffic in computer systems has made it clear that traditional control techniques are not adequate to provide the system users fast access to network resources and prevent unfair uses. In this paper, we present a reconfigurable digital hardware implementation of a specific neural model for intrusion detection. It uses a specific vector of characterization of the network packages (intrusion vector) which is starting from information obtained during the access intent. This vector will be treated by the system. Our approach is adaptative and to detecting these intrusions by using a complex artificial intelligence method known as multilayer perceptron. The implementation have been developed and tested into a reconfigurable hardware (FPGA) for embedded systems. Finally, the Intrusion detection system was tested in a real-world simulation to gauge its effectiveness and real-time response.
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Detection of point mutations or single nucleotide polymorphisms (SNPs) is important in relation to disease susceptibility or detection in pathogens of mutations determining drug resistance or host range. There is an emergent need for rapid detection methods amenable to point-of-care applications. The purpose of this study was to reduce to practice a novel method for SNP detection and to demonstrate that this technology can be used downstream of nucleic acid amplification. The authors used a model system to develop an oligonucleotide-based SNP detection system on nitrocellulose lateral flow strips. To optimize the assay they used cloned sequences of the herpes simplex virus-1 (HSV-1) DNA polymerase gene into which they introduced a point mutation. The assay system uses chimeric polymerase chain reaction (PCR) primers that incorporate hexameric repeat tags ("hexapet tags"). The chimeric sequences allow capture of amplified products to predefined positions on a lateral flow strip. These "hexapet" sequences have minimal cross-reactivity and allow specific hybridization-based capture of the PCR products at room temperature onto lateral flow strips that have been striped with complementary hexapet tags. The allele-specific amplification was carried out with both mutant and wild-type primer sets present in the PCR mix ("competitive" format). The resulting PCR products carried a hexapet tag that corresponded with either a wild-type or mutant sequence. The lateral flow strips are dropped into the PCR reaction tube, and mutant sequence and wild-type sequences diffuse along the strip and are captured at the corresponding position on the strip. A red line indicative of a positive reaction is visible after 1 minute. Unlike other systems that require separate reactions and strips for each target sequence, this system allows multiplex PCR reactions and multiplex detection on a single strip or other suitable substrates. Unambiguous visual discrimination of a point mutation under room temperature hybridization conditions was achieved with this model system in 10 minutes after PCR. The authors have developed a capture-based hybridization method for the detection and discrimination of HSV-1 DNA polymerase genes that contain a single nucleotide change. It has been demonstrated that the hexapet oligonucleotides can be adapted for hybridization on the lateral flow strip platform for discrimination of SNPs. This is the first step in demonstrating SNP detection on lateral flow using the hexapet oligonucleotide capture system. It is anticipated that this novel system can be widely used in point-of-care settings.
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Structural Health Monitoring (SHM) ensures the structural health and safety of critical structures covering a wide range of application areas. This thesis presents novel, low-cost and good-performance fibre Bragg grating (FBG) based systems for detection of Acoustic Emission (AE) in aircraft structures, which is a part of SHM. Importantly a key aim, during the design of these systems, was to produce systems that were sufficiently small to install in an aircraft for lifetime monitoring. Two important techniques for monitoring high frequency AE that were developed as a part of this research were, Quadrature recombination technique and Active tracking technique. Active tracking technique was used extensively and was further developed to overcome the limitations that were observed while testing it at several test facilities and with different optical fibre sensors. This system was able to eliminate any low frequency spectrum shift due to environmental perturbation and keeps the sensor always working at optimum operation point. This is highly desirable in harsh industrial and operationally active environments. Experimental work carried out in the laboratory has proved that such systems can be used for high frequency detection and have capability to detect up to 600 kHz. However, the range of frequency depends upon the requirement and design of the interrogation system as the system can be altered accordingly for different applications. Several optical fibre configurations for wavelength detection were designed during the course of this work along with industrial partners. Fibre Bragg grating Fabry-Perot (FBG-FP) sensors have shown higher sensitivity and usability than the uniform FBGs to be used with such system. This was shown experimentally. The author is certain that further research will lead to development of a commercially marketable product and the use of active tracking systems can be extended in areas of healthcare, civil infrastructure monitoring etc. where it can be deployed. Finally, the AE detection system has been developed to aerospace requirements and was tested at NDT & Testing Technology test facility based at Airbus, Filton, UK on A350 testing panels.
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As microblog services such as Twitter become a fast and convenient communication approach, identification of trendy topics in microblog services has great academic and business value. However detecting trendy topics is very challenging due to huge number of users and short-text posts in microblog diffusion networks. In this paper we introduce a trendy topics detection system under computation and communication resource constraints. In stark contrast to retrieving and processing the whole microblog contents, we develop an idea of selecting a small set of microblog users and processing their posts to achieve an overall acceptable trendy topic coverage, without exceeding resource budget for detection. We formulate the selection operation of these subset users as mixed-integer optimization problems, and develop heuristic algorithms to compute their approximate solutions. The proposed system is evaluated with real-time test data retrieved from Sina Weibo, the dominant microblog service provider in China. It's shown that by monitoring 500 out of 1.6 million microblog users and tracking their microposts (about 15,000 daily) with our system, nearly 65% trendy topics can be detected, while on average 5 hours earlier before they appear in Sina Weibo official trends.
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A long-period grating (LPG) sensor is used to detect small variations in the concentration of an organic aromatic compound (xylene) in a paraffin (heptane) solution. A new design procedure is adopted and demonstrated to maximize the sensitivity of LPG (wavelength shift for a change in the surrounding refractive index, (dλ/dn3)) for a given application. The detection method adopted is comparable to the standard technique used in industry (high performance liquid chromatograph and UV spectroscopy) which has a relative accuracy between ∼±0.5% and 5%. The minimum detectable change in volumetric concentration is 0.04% in a binary fluid with the detection system presented. This change of concentration relates to a change in refractive index of Δn ∼ 6 × 10-5. © 2001 Elsevier Science B.V.
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It is proposed an agent approach for creation of intelligent intrusion detection system. The system allows detecting known type of attacks and anomalies in user activity and computer system behavior. The system includes different types of intelligent agents. The most important one is user agent based on neural network model of user behavior. Proposed approach is verified by experiments in real Intranet of Institute of Physics and Technologies of National Technical University of Ukraine "Kiev Polytechnic Institute”.