Detection Performance (detection + performance)

Distribution by Scientific Domains

Selected Abstracts

A low-leakage sample plug injection scheme for crossform microfluidic capillary electrophoresis devices incorporating a restricted cross-channel intersection

Chin-Lung Chang
Abstract This study develops a crossform CE microfluidic device in which a single-circular barrier or a double-circular barrier is introduced at the cross-channel intersection. Utilizing a conventional crossform injection scheme, it is shown that these barriers reduce sample leakage and deliver a compact sample band into the separation channel, thereby ensuring an enhanced detection performance. A series of numerical and experimental investigations are performed to investigate the effects of the barrier type and the barrier ratio on the flow streamlines within the microchannel and to clarify their respective effects on the sample leakage ratio and sample plug variance during the injection process. The results indicate that a single-circular barrier injector with a barrier ratio greater than 20% and a double-circular barrier injector with a barrier ratio greater than 40% minimize the sample leakage ratio and produce a compact sample plug. As a result, both injectors have an excellent potential for use in high-quality, high-throughput chemical analysis procedures and in many other applications throughout the micro-total analysis systems field. [source]

Detecting and classifying delay Data Exceptions on communication networks using rule based algorithms

Tammam Benmusa
Abstract Network performance monitoring is essential for managing a network efficiently and for ensuring reliable operation of the network. Monitored network performance changes reflect events in the network, such as faults, significant changes in usage patterns or planned alterations. Network managers are interested in how and when the performance of a network changes; however it is inefficient to analyse all the data resulting from the monitoring operation manually. In this paper a rule based algorithm to automate detection of the changes in one of the network performance parameters, namely delay, is presented and described in detail. The nature of the delay pattern in a commercial communication network was the key issue in developing this algorithm. The approach was tested with monitored delay data generated from three different networks and showed good results. Also, the algorithm was tested with sets of delay data which have been already input to a previously developed detector based on a different approach, and the results between the two detectors are compared. In addition to a noticeable improvement in detection performance, the new approach provides more generality and independency of the source of the delay data, making the approach generally applicable to other networks. Copyright © 2004 John Wiley & Sons, Ltd. [source]

LIDAR and vision-based pedestrian detection system

Cristiano Premebida
A perception system for pedestrian detection in urban scenarios using information from a LIDAR and a single camera is presented. Two sensor fusion architectures are described, a centralized and a decentralized one. In the former, the fusion process occurs at the feature level, i.e., features from LIDAR and vision spaces are combined in a single vector for posterior classification using a single classifier. In the latter, two classifiers are employed, one per sensor-feature space, which were offline selected based on information theory and fused by a trainable fusion method applied over the likelihoods provided by the component classifiers. The proposed schemes for sensor combination, and more specifically the trainable fusion method, lead to enhanced detection performance and, in addition, maintenance of false-alarms under tolerable values in comparison with single-based classifiers. Experimental results highlight the performance and effectiveness of the proposed pedestrian detection system and the related sensor data combination strategies. © 2009 Wiley Periodicals, Inc. [source]

The contrast sensitivity function for detection and resolution of blue-on-yellow gratings in foveal and peripheral vision

R. S. Anderson
Abstract Previous studies using polychromatic gratings have shown that the peripheral grating contrast sensitivity function is significantly different when the task is resolution rather than detection. Specifically, in the middle frequency range, while resolution acuity drops suddenly to zero, detection performance continues up to much higher frequencies, accompanied by observations of aliasing. We wanted to determine if the same holds true for blue-cone isolating gratings in either foveal or peripheral vision. Contrast sensitivity function (CSFs) were measured at the fovea and 20 degrees eccentricity in the temporal retina under conditions of short-wavelength-sensitive (SWS)-cone pathway isolation using a two-alternative forced choice paradigm. The detection and resolution CSF were identical at the low frequency end but at higher frequencies resolution sensitivity falls abruptly while contrast detection remained possible till higher frequencies [cut-off frequencies: fovea detection 6.0 cycles (degree),1, resolution 4.6 cycles (degree),1; periphery detection 1.6 cycles (degree),1, resolution 1.05 cycles (degree),1]. Aliasing was observable when spatial frequency exceeded the resolution limit. Medium/high contrast blue-cone-mediated resolution acuity is sampling limited in both the fovea and periphery. Previous studies of blue-cone contrast sensitivity which employed a detection task do not reflect the true resolution limit. [source]

Attack,norm separation for detecting attack-induced quality problems on computers and networks

Nong Ye
Abstract Cyber attacks on computer and network systems induce system quality and reliability problems, and present a significant threat to the computer and network systems that we are heavily dependent on. Cyber attack detection involves monitoring system data and detecting the attack-induced quality and reliability problems of computer and network systems caused by cyber attacks. Usually there are ongoing normal user activities on computer and network systems when an attack occurs. As a result, the observed system data may be a mixture of attack data and normal use data (norm data). We have established a novel attack,norm separation approach to cyber attack detection that includes norm data cancelation to improve the data quality as an important part of this approach. Aiming at demonstrating the importance of norm data cancelation, this paper presents a set of data modeling and analysis techniques developed to perform norm data cancelation before applying an existing technique of anomaly detection, the chi-square distance monitoring (CSDM), to residual data obtained after norm data cancelation for cyber attack detection. Specifically, a Markov chain model of norm data and an artificial neural network (ANN) of norm data cancelation are developed and tested. This set of techniques is compared with using CSDM alone for cyber attack detection. The results show a significant improvement of detection performance by CSDM with norm data cancelation over CSDM alone. Copyright © 2006 John Wiley & Sons, Ltd. [source]

Using Profile Monitoring Techniques for a Data-rich Environment with Huge Sample Size

Kaibo Wang
Abstract In-process sensors with huge sample size are becoming popular in the modern manufacturing industry, due to the increasing complexity of processes and products and the availability of advanced sensing technology. Under such a data-rich environment, a sample with huge size usually violates the assumption of homogeneity and degrades the detection performance of a conventional control chart. Instead of charting summary statistics such as the mean and standard deviation of observations that assume homogeneity within a sample, this paper proposes charting schemes based on the quantile,quantile (Q,Q) plot and profile monitoring techniques to improve the performance. Different monitoring schemes are studied based on various shift patterns in a huge sample and compared via simulation. Guidelines are provided for applying the proposed schemes to similar industrial applications in a data-rich environment. Copyright © 2005 John Wiley & Sons, Ltd. [source]

Lymphocyte volume and conductivity indices of the haematology analyser Coulter® GEN.STM in lymphoproliferative disorders and viral diseases

Summary The haematology analyser Coulter® GEN.STM gives a set of data ,,positional parameters', defining white blood cell (WBC) populations by mean of index values (mean and standard deviation of volume, conductivity and scatter, used to identify the WBC populations). The volume and conductivity parameters related to the lymphocytes were analysed at diagnosis in patients suffering from chronic B-lymphocytic leukaemia (B-CLL), other non-CLL lymphoproliferative disorders (OLPD) and viral diseases. The standard deviation of volume index (SDVI) is significantly higher in the three groups, whereas the mean volume index (MVI) is significantly lower in B-CLL, and increased in OLPD and viral diseases. These two groups could be distinguished by their mean conductivity index (MCI), which is significantly lower in viral disease group. Cut-offs were calculated for each parameter by the mean of Receiver Operating Characteristic (ROC) analysis. The study of the detection performances showed that the combination of lymphocyte count with SDVI, MVI and MCI could be used with a good sensitivity and specificity to discriminate between the most frequent lymphocyte pathologies, even in patients with normal lymphocyte count. [source]