970 resultados para detector
Resumo:
Recent decreases in costs, and improvements in performance, of silicon array detectors open a range of potential applications of relevance to plant physiologists, associated with spectral analysis in the visible and short-wave near infra-red (far-red) spectrum. The performance characteristics of three commercially available ‘miniature’ spectrometers based on silicon array detectors operating in the 650–1050-nm spectral region (MMS1 from Zeiss, S2000 from Ocean Optics, and FICS from Oriel, operated with a Larry detector) were compared with respect to the application of non-invasive prediction of sugar content of fruit using near infra-red spectroscopy (NIRS). The FICS–Larry gave the best wavelength resolution; however, the narrow slit and small pixel size of the charge-coupled device detector resulted in a very low sensitivity, and this instrumentation was not considered further. Wavelength resolution was poor with the MMS1 relative to the S2000 (e.g. full width at half maximum of the 912 nm Hg peak, 13 and 2 nm for the MMS1 and S2000, respectively), but the large pixel height of the array used in the MMS1 gave it sensitivity comparable to the S2000. The signal-to-signal standard error ratio of spectra was greater by an order of magnitude with the MMS1, relative to the S2000, at both near saturation and low light levels. Calibrations were developed using reflectance spectra of filter paper soaked in range of concentrations (0–20% w/v) of sucrose, using a modified partial least squares procedure. Calibrations developed with the MMS1 were superior to those developed using the S2000 (e.g. coefficient of correlation of 0.90 and 0.62, and standard error of cross-validation of 1.9 and 5.4%, respectively), indicating the importance of high signal to noise ratio over wavelength resolution to calibration accuracy. The design of a bench top assembly using the MMS1 for the non-invasive assessment of mesocarp sugar content of (intact) melon fruit is reported in terms of light source and angle between detector and light source, and optimisation of math treatment (derivative condition and smoothing function).
Resumo:
The soluble solids content of intact fruit can be measured non-invasively by near infrared spectroscopy, allowing “sweetness” grading of individual fruit. However, little information is available in the literature with respect to the robustness of such calibrations. We developed calibrations based on a restricted wavelength range (700–1100 nm), suitable for use with low-cost silicon detector systems, using a stepwise multiple linear regression routine. Calibrations for total soluble solids (°Brix) in intact pineapple fruit were not transferable between summer and winter growing seasons. A combined calibration (data of three harvest dates) validated reasonably well against a population set drawn from all harvest dates (r2 = 0.72, SEP = 1.84 °Brix). Calibrations for Brix in melon were transferable between two of the three varieties examined. However, a lack of robustness of calibration was indicated by poor validation within populations of fruit harvested at different times. Further work is planned to investigate the robustness of calibration across varieties, growing districts and seasons.
Fibre Transfer in Merino Ewes Mated with Damara, Merino or Dorper Rams in Central Western Queensland
Resumo:
Considerable concern has been expressed by the Australian wool industry regarding the contamination of the clip with coloured or kempy fibres from imported breeds of sheep. As part of the evaluation of imported sheep meat breeds in western Queensland, a study is examining fibre growth and transfer of fibres and the potential to cause physical contamination of Merino fleeces. The breeds of concern in this study are the Damara, a fat-tailed breed with a hairy, coloured fleece and the Dorper which has both pigmented fibres and a kempy fleece which is shed cyclically. Three groups of Merino 27 ewes were mated to Merino, Damara and Dorper rams respectively and fibre transfer to the Merino ewes during mating, from lambing to weaning and during grazing, assessed. Both a direct field method and a laboratory method (Hatcher 1995) are being used. Those measured by direct count were measured immediately after joining and 2, 4 and 8 weeks subsequently. and the other ewes were shorn and sampled and measured in the laboratory using the dark fibre detector. This paper presents preliminary findings of those ewes monitored by the direct field method. Animal production for a consuming world : proceedings of 9th Congress of the Asian-Australasian Association of Animal Production Societies [AAAP] and 23rd Biennial Conference of the Australian Society of Animal Production [ASAP] and 17th Annual Symposium of the University of Sydney, Dairy Research Foundation, [DRF]. 2-7 July 2000, Sydney, Australia.
Resumo:
Predictive models based on near infra-red spectroscopy for the assessment of fruit internal quality attributes must exhibit a degree of robustness across the parameters of variety, district and time to be of practical use in fruit grading. At the time this thesis was initiated, while there were a number of published reports on the development of near infra-red based calibration models for the assessment of internal quality attributes of intact fruit, there were no reports of the reliability ("robustness") of such models across time, cultivars or growing regions. As existing published reports varied in instrumentation employed, a re-analysis of existing data was not possible. An instrument platform, based on partial transmittance optics, a halogen light source and (Zeiss MMS 1) detector operating in the short wavelength near infra-red region was developed for use in the assessment of intact fruit. This platform was used to assess populations of macadamia kernels, melons and mandarin fruit for total soluble solids, dry matter and oil concentration. Calibration procedures were optimised and robustness assessed across growing areas, time of harvest, season and variety. In general, global modified partial least squares regression (MPLS) calibration models based on derivatised absorbance data were better than either multiple linear regression or `local' MPLS models in the prediction of independent validation populations . Robustness was most affected by growing season, relative to the growing district or variety . Various calibration updating procedures were evaluated in terms of calibration robustness. Random selection of samples from the validation population for addition to the calibration population was equivalent to or better than other methods of sample addition (methods based on the Mahalanobis distance of samples from either the centroid of the population or neighbourhood samples). In these exercises the global Mahalanobis distance (GH) was calculated using the scores and loadings from the calibration population on the independent validation population. In practice, it is recommended that model predictive performance be monitored in terms of predicted sample GH, with model updating using as few as 10 samples from the new population undertaken when the average GH value exceeds 1 .0 .
Resumo:
Fatigue of the steel in rails continues to be of major concern to heavy haul track owners despite careful selection and maintenance of rails. The persistence of fatigue is due in part to the erroneous assumption that the maximum loads on, and stresses in, the rails are predictable. Recent analysis of extensive wheel impact detector data from a number of heavy haul tracks has shown that the most damaging forces are in fact randomly distributed with time and location and can be much greater than generally expected. Large- scale Monte-Carlo simulations have been used to identify rail stresses caused by actual, measured distributions of wheel-rail forces on heavy haul tracks. The simulations show that fatigue failure of the rail foot can occur in situations which would be overlooked by traditional analyses. The most serious of these situations are those where track is accessed by multiple operators and in situations where there is a mix of heavy haul, general freight and/or passenger traffic. The least serious are those where the track is carrying single-operator-owned heavy haul unit trains. The paper shows how using the nominal maximum axle load of passing traffic, which is the key issue in traditional analyses, is insufficient and must be augmented with consideration of important operational factors. Ignoring such factors can be costly.
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Polarization properties of Gaussian laser beams are analyzed in a manner consistent with the Maxwell equations, and expressions are developed for all components of the electric and magnetic field vectors in the beam. It is shown that the transverse nature of the free electromagnetic field demands a nonzero transverse cross-polarization component in addition to the well-known component of the field vectors along the beam axis. The strength of these components in relation to the strength of the principal polarization component is established. It is further shown that the integrated strengths of these components over a transverse plane are invariants of the propagation process. It is suggested that cross- polarization measurement using a null detector can serve as a new method for accurate determination of the center of Gaussian laser beams.
Resumo:
Drug Analysis without Primary Reference Standards: Application of LC-TOFMS and LC-CLND to Biofluids and Seized Material Primary reference standards for new drugs, metabolites, designer drugs or rare substances may not be obtainable within a reasonable period of time or their availability may also be hindered by extensive administrative requirements. Standards are usually costly and may have a limited shelf life. Finally, many compounds are not available commercially and sometimes not at all. A new approach within forensic and clinical drug analysis involves substance identification based on accurate mass measurement by liquid chromatography coupled with time-of-flight mass spectrometry (LC-TOFMS) and quantification by LC coupled with chemiluminescence nitrogen detection (LC-CLND) possessing equimolar response to nitrogen. Formula-based identification relies on the fact that the accurate mass of an ion from a chemical compound corresponds to the elemental composition of that compound. Single-calibrant nitrogen based quantification is feasible with a nitrogen-specific detector since approximately 90% of drugs contain nitrogen. A method was developed for toxicological drug screening in 1 ml urine samples by LC-TOFMS. A large target database of exact monoisotopic masses was constructed, representing the elemental formulae of reference drugs and their metabolites. Identification was based on matching the sample component s measured parameters with those in the database, including accurate mass and retention time, if available. In addition, an algorithm for isotopic pattern match (SigmaFit) was applied. Differences in ion abundance in urine extracts did not affect the mass accuracy or the SigmaFit values. For routine screening practice, a mass tolerance of 10 ppm and a SigmaFit tolerance of 0.03 were established. Seized street drug samples were analysed instantly by LC-TOFMS and LC-CLND, using a dilute and shoot approach. In the quantitative analysis of amphetamine, heroin and cocaine findings, the mean relative difference between the results of LC-CLND and the reference methods was only 11%. In blood specimens, liquid-liquid extraction recoveries for basic lipophilic drugs were first established and the validity of the generic extraction recovery-corrected single-calibrant LC-CLND was then verified with proficiency test samples. The mean accuracy was 24% and 17% for plasma and whole blood samples, respectively, all results falling within the confidence range of the reference concentrations. Further, metabolic ratios for the opioid drug tramadol were determined in a pharmacogenetic study setting. Extraction recovery estimation, based on model compounds with similar physicochemical characteristics, produced clinically feasible results without reference standards.
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Various intrusion detection systems (IDSs) reported in the literature have shown distinct preferences for detecting a certain class of attack with improved accuracy, while performing moderately on the other classes. In view of the enormous computing power available in the present-day processors, deploying multiple IDSs in the same network to obtain best-of-breed solutions has been attempted earlier. The paper presented here addresses the problem of optimizing the performance of IDSs using sensor fusion with multiple sensors. The trade-off between the detection rate and false alarms with multiple sensors is highlighted. It is illustrated that the performance of the detector is better when the fusion threshold is determined according to the Chebyshev inequality. In the proposed data-dependent decision ( DD) fusion method, the performance optimization of ndividual IDSs is first addressed. A neural network supervised learner has been designed to determine the weights of individual IDSs depending on their reliability in detecting a certain attack. The final stage of this DD fusion architecture is a sensor fusion unit which does the weighted aggregation in order to make an appropriate decision. This paper theoretically models the fusion of IDSs for the purpose of demonstrating the improvement in performance, supplemented with the empirical evaluation.
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ALICE (A Large Ion Collider Experiment) is an experiment at CERN (European Organization for Nuclear Research), where a heavy-ion detector is dedicated to exploit the unique physics potential of nucleus-nucleus interactions at LHC (Large Hadron Collider) energies. In a part of that project, 716 so-called type V4 modules were assembles in Detector Laboratory of Helsinki Institute of Physics during the years 2004 - 2006. Altogether over a million detector strips has made this project the most massive particle detector project in the science history of Finland. One ALICE SSD module consists of a double-sided silicon sensor, two hybrids containing 12 HAL25 front end readout chips and some passive components, such has resistors and capacitors. The components are connected together by TAB (Tape Automated Bonding) microcables. The components of the modules were tested in every assembly phase with comparable electrical tests to ensure the reliable functioning of the detectors and to plot the possible problems. The components were accepted or rejected by the limits confirmed by ALICE collaboration. This study is concentrating on the test results of framed chips, hybrids and modules. The total yield of the framed chips is 90.8%, hybrids 96.1% and modules 86.2%. The individual test results have been investigated in the light of the known error sources that appeared during the project. After solving the problems appearing during the learning-curve of the project, the material problems, such as defected chip cables and sensors, seemed to induce the most of the assembly rejections. The problems were typically seen in tests as too many individual channel failures. Instead, the bonding failures rarely caused the rejections of any component. One sensor type among three different sensor manufacturers has proven to have lower quality than the others. The sensors of this manufacturer are very noisy and their depletion voltage are usually outside of the specification given to the manufacturers. Reaching 95% assembling yield during the module production demonstrates that the assembly process has been highly successful.
Resumo:
Early detection of (pre-)signs of ulceration on a diabetic foot is valuable for clinical practice. Hyperspectral imaging is a promising technique for detection and classification of such (pre-)signs. However, the number of the spectral bands should be limited to avoid overfitting, which is critical for pixel classification with hyperspectral image data. The goal was to design a detector/classifier based on spectral imaging (SI) with a small number of optical bandpass filters. The performance and stability of the design were also investigated. The selection of the bandpass filters boils down to a feature selection problem. A dataset was built, containing reflectance spectra of 227 skin spots from 64 patients, measured with a spectrometer. Each skin spot was annotated manually by clinicians as "healthy" or a specific (pre-)sign of ulceration. Statistical analysis on the data set showed the number of required filters is between 3 and 7, depending on additional constraints on the filter set. The stability analysis revealed that shot noise was the most critical factor affecting the classification performance. It indicated that this impact could be avoided in future SI systems with a camera sensor whose saturation level is higher than 106, or by postimage processing.
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In this paper, we present a low-complexity algorithm for detection in high-rate, non-orthogonal space-time block coded (STBC) large-multiple-input multiple-output (MIMO) systems that achieve high spectral efficiencies of the order of tens of bps/Hz. We also present a training-based iterative detection/channel estimation scheme for such large STBC MIMO systems. Our simulation results show that excellent bit error rate and nearness-to-capacity performance are achieved by the proposed multistage likelihood ascent search (M-LAS) detector in conjunction with the proposed iterative detection/channel estimation scheme at low complexities. The fact that we could show such good results for large STBCs like 16 X 16 and 32 X 32 STBCs from Cyclic Division Algebras (CDA) operating at spectral efficiencies in excess of 20 bps/Hz (even after accounting for the overheads meant for pilot based training for channel estimation and turbo coding) establishes the effectiveness of the proposed detector and channel estimator. We decode perfect codes of large dimensions using the proposed detector. With the feasibility of such a low-complexity detection/channel estimation scheme, large-MIMO systems with tens of antennas operating at several tens of bps/Hz spectral efficiencies can become practical, enabling interesting high data rate wireless applications.
Resumo:
In this paper, we present a low-complexity, near maximum-likelihood (ML) performance achieving detector for large MIMO systems having tens of transmit and receive antennas. Such large MIMO systems are of interest because of the high spectral efficiencies possible in such systems. The proposed detection algorithm, termed as multistage likelihood-ascent search (M-LAS) algorithm, is rooted in Hopfield neural networks, and is shown to possess excellent performance as well as complexity attributes. In terms of performance, in a 64 x 64 V-BLAST system with 4-QAM, the proposed algorithm achieves an uncoded BER of 10(-3) at an SNR of just about 1 dB away from AWGN-only SISO performance given by Q(root SNR). In terms of coded BER, with a rate-3/4 turbo code at a spectral efficiency of 96 bps/Hz the algorithm performs close to within about 4.5 dB from theoretical capacity, which is remarkable in terms of both high spectral efficiency as well as nearness to theoretical capacity. Our simulation results show that the above performance is achieved with a complexity of just O(NtNt) per symbol, where N-t and N-tau denote the number of transmit and receive antennas.
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According to a press release dated 9 March 2009, the two experiments CDF (Collider Detector at Fermilab) and DZero have announced the discovery of ‘single top quark’ events, which represent a spectacular discovery and confirmation of the standard model of elementary particle physics. The results of their findings are now available as preprints which have been submitted for publication in Physical Review Letters1,2.
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Multicode operation in space-time block coded (STBC) multiple input multiple output (MIMO) systems can provide additional degrees of freedom in code domain to achieve high data rates. In such multicode STBC systems, the receiver experiences code domain interference (CDI) in frequency selective fading. In this paper, we propose a linear parallel interference cancellation (LPIC) approach to cancel the CDI in multicode STBC in frequency selective fading. The proposed detector first performs LPIC followed by STBC decoding. We evaluate the bit error performance of the detector and show that it effectively cancels the CDI and achieves improved error performance. Our results further illustrate how the combined effect of interference cancellation, transmit diversity, and RAKE diversity affect the bit error performance of the system.
Resumo:
An imaging technique is developed for the controlled generation of multiple excitation nano-spots for far-field microscopy. The system point spread function (PSF) is obtained by interfering two counter-propagating extended depth-of-focus PSF (DoF-PSF), resulting in highly localized multiple excitation spots along the optical axis. The technique permits (1) simultaneous excitation of multiple planes in the specimen; (2) control of the number of spots by confocal detection; and (3) overcoming the point-by-point based excitation. Fluorescence detection from the excitation spots can be efficiently achieved by Z-scanning the detector/pinhole assembly. The technique complements most of the bioimaging techniques and may find potential application in high resolution fluorescence microscopy and nanoscale imaging.