Search results for "Detection"
showing 10 items of 2543 documents
D Sensor-Based Obstacle Detection Comparing Octrees and Point clouds Using CUDA
2012
This paper presents adaptable methods for achieving fast collision detection using the GPU and Nvidia CUDA together with Octrees. Earlier related work have focused on serial methods, while this paper presents a parallel solution which shows that there is a great increase in time if the number of operations is large. Two dierent models of the environment and the industrial robot are presented, the rst is Octrees at dierent resolutions, the second is a point cloud representation. The relative merits of the two dierent world model representations are shown. In particular, the experimental results show the potential of adapting the resolution of the robot and environment models to the task at h…
Automatic object detection in point clouds based on knowledge guided algorithms
2013
The modeling of real-world scenarios through capturing 3D digital data has been proven applicable in a variety of industrial applications, ranging from security, to robotics and to fields in the medical sciences. These different scenarios, along with variable conditions, present a challenge in discovering flexible appropriate solutions. In this paper, we present a novel approach based on a human cognition model to guide processing. Our method turns traditional data-driven processing into a new strategy based on a semantic knowledge system. Robust and adaptive methods for object extraction and identification are modeled in a knowledge domain, which has been created by purely numerical strate…
Probabilistic Transition-Based Approach for Detecting Application-Layer DDoS Attacks in Encrypted Software-Defined Networks
2017
With the emergence of cloud computing, many attacks, including Distributed Denial-of-Service (DDoS) attacks, have changed their direction towards cloud environment. In particular, DDoS attacks have changed in scale, methods, and targets and become more complex by using advantages provided by cloud computing. Modern cloud computing environments can benefit from moving towards Software-Defined Networking (SDN) technology, which allows network engineers and administrators to respond quickly to the changing business requirements. In this paper, we propose an approach for detecting application-layer DDoS attacks in cloud environment with SDN. The algorithm is applied to statistics extracted from…
Locality-sensitive hashing enables signal classification in high-throughput mass spectrometry raw data at scale
2021
Mass spectrometry is an important experimental technique in the field of proteomics. However, analysis of certain mass spectrometry data faces a combination of two challenges: First, even a single experiment produces a large amount of multi-dimensional raw data and, second, signals of interest are not single peaks but patterns of peaks that span along the different dimensions. The rapidly growing amount of mass spectrometry data increases the demand for scalable solutions. Existing approaches for signal detection are usually not well suited for processing large amounts of data in parallel or rely on strong assumptions concerning the signals properties. In this study, it is shown that locali…
Learning from Errors: Detecting ZigBee Interference in WiFi Networks
2014
In this work we show how to detect ZigBee interference on commodity WiFi cards by monitoring the reception errors, such as synchronization errors, invalid header formats, too long frames, etc., caused by ZigBee transmissions. Indeed, in presence of non-WiFi modulated signals, the occurrence of these types of errors follows statistics that can be easily recognized. Moreover, the duration of the error bursts depends on the transmission interval of the interference source, while the error spacing depends on the receiver implementation. On the basis of these considerations, we propose the adoption of hidden Markov chains for characterizing the behavior of WiFi receivers in presence of controlle…
Location-Aware Mobile Intrusion Detection with Enhanced Privacy in a 5G Context
2010
Published version of an article from the journal: Wireless Personal Communications. The original publication is available at Spingerlink. http://dx.doi.org/10.1007/s11277-010-0069-6 The paper proposes a location-aware mobile Intrusion Prevention System (mIPS) architecture with enhanced privacy that is integrated in Managed Security Service (MSS). The solution is envisaged in a future fifth generation telecommunications (5G) context with increased but varying bandwidth, a virtualised execution environment and infrastructure that allows threads, processes, virtual machines and storage to be migrated to cloud computing services on demand, to dynamically scale performance and save power. 5G mob…
A novel method for network intrusion detection based on nonlinear SNE and SVM
2017
In the case of network intrusion detection data, pre-processing techniques have been extensively used to enhance the accuracy of the model. An ideal intrusion detection system (IDS) is one that has appreciable detection capability overall the group of attacks. An open research problem of this area is the lower detection rate for less frequent attacks, which result from the curse of dimensionality and imbalanced class distribution of the benchmark datasets. This work attempts to minimise the effects of imbalanced class distribution by applying random under-sampling of the majority classes and SMOTE-based oversampling of minority classes. In order to alleviate the issue arising from the curse…
The iterative object symmetry transform
2005
This paper introduces a new operator named the Iterated Object Transform that is computed by combining the Object Symmetry Transform with the morphological operator erosion. This new operator has been applied on both binary and gray levels images showing the ability to grasp the internal structure of a digital object. We present some experiments on real images in face analysis.
Update on biomarkers for the detection of lung cancer
2017
Patients at risk for lung cancer may have subclinical disease for years before presentation. The diagnosis of this disease is primarily based on symptoms, and detection often occurs after curative intervention is no longer possible. At present, no lung cancer early-detection biomarker is clinically available. This study reviews the most recent advances in early detection and molecular diagnostic biomarkers for the detection of lung cancer. This review includes an overview of the various biological specimens and matrices in which these biomarkers could be analyzed, as well as the diverse strategies and approaches for identifying new biomarkers that are currently being explored. Several novel…
“The risk of age”? Early detection test, prostate cancer and practices of self
2010
Abstract Drawing on Rose and Novas’s concept of “biological citizenship” and Michel Foucault's "practices of self", this paper reflects on how men become agents of their own therapeutic regimens, and yet internalise messages of risk and practices of self within early detection of prostate cancer discourses in the late 20th century. In doing so, it traces the ways in which concepts of age, gender and risk converge at the problematic site of prostate cancer and preventative health strategies, both of the state and the medical profession. Analysing how insecurities have simultaneously resulted in over-promoting and over-diagnosing risk, thereby blurring the lines between normal and pathologica…