Serwis Infona wykorzystuje pliki cookies (ciasteczka). Są to wartości tekstowe, zapamiętywane przez przeglądarkę na urządzeniu użytkownika. Nasz serwis ma dostęp do tych wartości oraz wykorzystuje je do zapamiętania danych dotyczących użytkownika, takich jak np. ustawienia (typu widok ekranu, wybór języka interfejsu), zapamiętanie zalogowania. Korzystanie z serwisu Infona oznacza zgodę na zapis informacji i ich wykorzystanie dla celów korzytania z serwisu. Więcej informacji można znaleźć w Polityce prywatności oraz Regulaminie serwisu. Zamknięcie tego okienka potwierdza zapoznanie się z informacją o plikach cookies, akceptację polityki prywatności i regulaminu oraz sposobu wykorzystywania plików cookies w serwisie. Możesz zmienić ustawienia obsługi cookies w swojej przeglądarce.
In this paper, we consider optimization of number of secondary users (M) by maximizing average channel throughput of the cognitive radio (CR) network for a given fusion rule (k-out-of-M) at the fusion center (FC) over erroneous control channel. For a given arbitrary positive integer value n, we obtain the mathematical expressions for optimal M for k = 1 + n and k = M − n rules at FC. Considering energy...
In this paper, we will study and discussabout Edge Detection of Digital Images. For the purpose we will compare twodifferent Edge Detecting Algorithm: a) Bacterial Foraging Algorithm commonlyknown as BFA and b) Canny Algorithmwith the help of Edge DetectionofImages. Comparison will be shown bythe help of images of Detected Edges. Bacterial Foraging works on swarmintelligence whereas Canny depends...
Spectrum sensing presents a substantial challenge in the novel emerging telecommunications technologies, and a crucial step that should be performed by cognitive user to identify the unoccupied frequency bands, in order to enhance the spectral efficiency and the QoS to both licensed and unlicensed users. In this paper, we propose a novel sensing approach based on the cross-correlated moments' selection...
Road scenes can be naturally interpreted in terms of a hierarchical structure consisting of parts and sub-parts, which captures different degrees of abstraction at different levels of the hierarchy. We introduce Latent Hierarchical Part based Models (LHPMs), which provide a promising framework for interpreting an image using a tree structure, in the case when the root filter for non-leaf nodes may...
In this paper, we characterize and analyze the set of strategic stealthy false-data injection attacks on discrete-time linear systems. In particular, the threat scenarios tackled in the paper consider adversaries that aim at deteriorating the system's performance by maximizing the corresponding quadratic cost function, while remaining stealthy with respect to anomaly detectors. As opposed to other...
We examine the potential of sparse time-frequency representations for gravitational wave data analysis, as regards: i) characterizing the fine structure of the nonstationary disturbances (glitches) of instrumental/environmental origin affecting the interferometric detectors, and ii) improving the network detection of unmodeled and nonstationary gravitational wave signals coexisting with glitches....
Extracting knowledge from environments with a continuous flow of data (data streams) is progressively receiving more attention. In such environments, the data distribution usually changes over time, which is known as concept drift. This paper presents a genetic algorithm aimed at adjusting the parameters of concept drift detection methods to improve their accuracies. Experiments were performed with...
The paper proposes an innovative supervised learning method for human behavioral recognition in which the behavioral patterns are classified according to the classes importance. A detector classifier is trained to recognize the human behavioral patterns belonging to the most important class. The optimization is performed by fixing the classifier operating point to provide the appropriate performance...
This paper proposes a fast approach for traffic sign detection from video. First, we modify the image-based detector HHVCas to improve its accuracy and speed, then apply it to video-based detection with further acceleration by tracking. For the image-based detector, by optimizing the parameters in the cascade using an unsupervised approach, we achieve performance comparable to the state-of-the-art...
This paper presents a novel boundary current mode boost-type PFC converter for driving the light emitting diode lightings. In the PFC converter, a novel method for zero-crossing point of inductor current detecting is proposed. In conventional boundary current mode PFC converter, the zero-crossing point is detected by observing the inductor voltage. The detection of zero-crossing point is delayed by...
Pedestrian detection is a challenging task for video surveillance. The problem becomes more difficult when occlusion is prevalent. In this paper, we extend a deformable part-based pedestrian detector to pedestrian detection in crowded scenes by considering both body part detection responses and detections' mutual spatial relationship. Specifically, we first decompose the full body detector into several...
The design and performance of the ATLAS Inner Detector trigger algorithms running online on the High Level Trigger processor farm with the early LHC Run 2 data are discussed. The redesign of the ID trigger, which took place during the 2013–15 long shutdown, in order to satisfy the demands of the higher energy LHC Run 2 operation is described. The Inner Detector High Level Trigger algorithms are essential...
In this work, we investigate PET-image reconstruction by using optimization-based algorithms. The work is motivated by the observation that advanced algorithms may be exploited potentially for improving PET-reconstruction quality in current applications and for enabling innovative, advanced PET-scan configurations. may be used for enabling the design of innovative PET systems. Specifically, we investigate...
An automated diagnosis methodology is necessary for the maintenance of superannuated social infrastructures. In this context, the hammering test is an efficient inspection method, and it has been widely used because of the resulting accuracy and efficiency of operation. While robotic automation of the hammering inspection method is highly desirable, the development of an automatic diagnostic algorithm...
Energy efficiency (EE) of wireless communications has attracted growing attention in recent years. In this paper, we focus on the EE optimization in cognitive radio networks (CRN) from a perspective of designing MAC frame structure, namely scheduling the sensing-then-transmission time slots. Considering secondary users adopting channel handoff to avoid collisions with primary users, the EE of CRN...
This paper investigates the optimization of the generalized likelihood ratio test (GLRT) eigenvalue-based spectrum sensing detector in terms of decision thresholds and sensing time. In order to guarantee the interests of primary and secondary users simultaneously, the sensing performance is assessed using the total error rate, i.e., the summation of probabilities of false alarm and missed detection...
Distributed detection with dependent observations is always a challenging problem. The problemof detectionwith shared information has many applications when sensors have overlapped measurements, e.g., when distributed detection is performed in a security system where sensors have overlapped coverages. For this shared information scenario, we investigate the distributed detection problem in parallel...
Sparse representation has achieved great success in the hyperspectral image processing field. However, with regard to target detection, the state-of-the-art sparsity-based algorithms are ad hoc and no different to a classifier. In this paper, a novel target detection algorithm is proposed, combining an elaborately designed sparsity model and the binary hypothesis statistics. With the strong similarity...
Distributed estimation of a deterministic scalar parameter by using quantized data in the presence of spoofing attacks, which modify the statistical model of the physical phenomenon, is considered. The paper develops an efficient heuristic approach to jointly detect attacks and estimate under spoofing attacks that are undetectable by a traditional approach that relies on noticing the data is not consistent...
Representation learning has emerged recently as a useful tool in the extraction of features from data. In a range of applications, features learned from data have been shown superior to their hand-crafted counterpart. Many deep learning approaches have taken advantage of such feature extraction. However, further research is needed on how such features can be evaluated for re-use in related applications,...
Podaj zakres dat dla filtrowania wyświetlonych wyników. Możesz podać datę początkową, końcową lub obie daty. Daty możesz wpisać ręcznie lub wybrać za pomocą kalendarza.