Non Line of Sight (NLOS) channels are one of the major drawbacks for accurate ranging and localization with Ultra-Wideband (UWB) technology. Whereas several proposals exist to detect these situations, a comprehensive ...
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Non Line of Sight (NLOS) channels are one of the major drawbacks for accurate ranging and localization with Ultra-Wideband (UWB) technology. Whereas several proposals exist to detect these situations, a comprehensive overview, investigation and testing of these methods has to the authors' knowledge not yet been prepared. This paper tries to fill this gap with a classification of the algorithms proposed of the UWB and mobile phone community. In addition, one novel method based on the signal power variation is suggested. Afterwards, the methods are evaluated regarding their practicability for real UWB localization systems, which excludes some of them from further investigation. For the remaining algorithms thresholds are proposed, which are strived to be as independent of the system and environment as possible. Finally, the NLOS detection algorithms are tested and compared with both an UWB simulation environment and an UWB localization test bed.
Three detection algorithms used in implantable cardioverter defibrillators (ICDs) are described and compared. Synthetic and prerecorded human tachyarrhythmias were applied to algorithm simulations. As rhythm variabili...
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Three detection algorithms used in implantable cardioverter defibrillators (ICDs) are described and compared. Synthetic and prerecorded human tachyarrhythmias were applied to algorithm simulations. As rhythm variability increased, the consistency of the result near detection zone boundaries fell and had different trends for each algorithm. For 456 prerecorded rhythms and fixed zones, VT-VF sensitivities were 96-97% and specificities were 83-85%.< >
Performance measures to compare QRS detection algorithms are suggested and evaluated. The measures are used to compare various linear and nonlinear filters and to determine optimal threshold levels. In low error cases...
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Performance measures to compare QRS detection algorithms are suggested and evaluated. The measures are used to compare various linear and nonlinear filters and to determine optimal threshold levels. In low error cases the separation divergence is suggested as a performance measure. For single lead systems this measure becomes an F-ratio measure. A database of more than 2700 complexes is used. With this database several types of linear filters and nonlinear operators are compared and optimal threshold levels are determined.< >
Nowadays, social, natural, technological and information systems can be exhibited by complex networks having millions of nodes interconnected to each other. The extraction of comprehensive information from these massi...
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Nowadays, social, natural, technological and information systems can be exhibited by complex networks having millions of nodes interconnected to each other. The extraction of comprehensive information from these massive networks call for computationally efficient methods. A promising approach to accomplish this task is to disintegrate the network into sub-units or communities and then using these identified communities to uncover relevant information. Thus, identifying communities in large scale networks plays a pivotal role in several scientific domains. In this paper, we extensively evaluate the functioning of two known algorithms and propose an improvement over one of them, in order to overcome its shortcomings to some extent, for optimal identification of community structure. We also present experimental results and evidences indicating that both the established algorithms, as well as our suggested approach, when applied to large social network datasets yields different results in terms of goodness and performance.
Many approaches in computer vision are based on point detection algorithms. In the literature, a wide variety of such algorithms are available. Therefore, it is an important task to evaluate existing and newly develop...
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Many approaches in computer vision are based on point detection algorithms. In the literature, a wide variety of such algorithms are available. Therefore, it is an important task to evaluate existing and newly developed point detection algorithms. Up to now, this process was done by means of several methods. In this paper, we recall current point detection evaluation techniques, and motivated by their insufficiency for certain applications, we present a new application-oriented method which is based on distances between sets of points.
The problem of detecting distributed denial of service (DDoS) attacks, and particularly SYN flood attacks, has received much attention in current literature. A variety of algorithms for detecting such attacks have bee...
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The problem of detecting distributed denial of service (DDoS) attacks, and particularly SYN flood attacks, has received much attention in current literature. A variety of algorithms for detecting such attacks have been published. Researchers have tested their own algorithms using traces containing real or synthetic attacks, and have reported good results based on those tests. However, the traces used and parameters of the attacks seen or generated vary greatly between published works. This paper compares three published SYN flood detection algorithms using traces collected from the UCLA Computer Science Department network and synthetic attacks in an Emulab network. The algorithms vary significantly in the speed at which they detect the start and end of attacks, their false positive and false negative rates, the types of non- DDoS activity they detect, and other properties. Their qualitative strengths and weaknesses are discussed, and suggestions are made for enhancements.
We have employed principles from change detection algorithms and statistical process control (SPC) to improve the estimation of the average of the round-trip time (RTT). Two mechanisms are proposed. The first is based...
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We have employed principles from change detection algorithms and statistical process control (SPC) to improve the estimation of the average of the round-trip time (RTT). Two mechanisms are proposed. The first is based on SPC principles. The other is based on an intuition derived from the log-likelihood ratio concept. Data employed to test the mechanisms are obtained by pinging several nodes on a campus network. Both methods improved the ability to track the change in the mean and take appropriate action
Developing systems for accurate apple detection plays a significant role in precision agriculture tasks and orchard management practices, hence having a direct impact on the economics of orchard fields. Thus, this res...
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ISBN:
(数字)9798350355444
ISBN:
(纸本)9798350355451
Developing systems for accurate apple detection plays a significant role in precision agriculture tasks and orchard management practices, hence having a direct impact on the economics of orchard fields. Thus, this research compares between different detection algorithms as it plays the baseline for subsequent tasks of counting, apple grasping and growth monitoring. In this work, four different state-of-the-art (SOTA) object detection models have been compared for apple detection: Faster R-CNN, YOLOv5, YOLOv8 and RT-DETR. The models were examined for their accuracy in apple detection and their invariance to the background apples. As Faster R-CNN and YOLOv5 are commonly used algorithms in this domain, they were chosen alongside YOLOv8. Furthermore, as the transformer-based detectors like RT-DETR outperform YOLO on large datasets, their comparison for apple detection is also warranted. The models are evaluated on the MinneApple dataset and a manually collected dataset. In addition, the effect of attention mechanisms in YOLOv8 architecture, hyperparameter optimization and data augmentation were explored. The evaluation showed that RT-DETR outperformed all models for $\mathbf{mAP}_{\mathbf{0.50}:\mathbf{0.95}}$ on the MinneApple and custom dataset for the trained image size. Furthermore, given the smaller background interference at a larger mAP, the RT-DETR model not only outperformed YOLO and Faster R-CNN models, but was also more background invariant.
In this paper, we propose the combination of different mass detection algorithms to increase overall mass detection sensitivity for various types of breast masses on mammograms. In particular, supervised and unsupervi...
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In this paper, we propose the combination of different mass detection algorithms to increase overall mass detection sensitivity for various types of breast masses on mammograms. In particular, supervised and unsupervised mass detection algorithms are effectively combined to maximize complementary effects of both approaches. By combining the aforementioned mass detection algorithms, we can arrive at a combined mass detection approach that makes stronger and accurate detection results. Comparative experiments have been conducted on public mammogram data set. Our results show that the proposed detection system can considerably improve the mass detection sensitivity with relatively small number of false positives, compared to the implementation of using only a single detection solution.
detection and tracking of chemical vapors at kilometer distances constitute an important component in early warning for the US military. The adaptive infrared imaging spectroradiometer (AIRIS) passively interrogates c...
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ISBN:
(纸本)9781424435906
detection and tracking of chemical vapors at kilometer distances constitute an important component in early warning for the US military. The adaptive infrared imaging spectroradiometer (AIRIS) passively interrogates chemical vapor infrared emission spectra.
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