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检索条件"主题词=Detection and Localization"

共 52 条记录，以下是41-50 订阅

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Time Delay Estimation of Ultra High Frequency Signals Radiated from Partial Discharge Sources

Time Delay Estimation of Ultra High Frequency Signals Radiat...

引用

IEEE Applied Signal Processing Conference (ASPCON)

作者： Anitha, Bhukya Koley, Chiranjib Natl Inst Technol Durgapur Dept Elect Engn Durgapur W Bengal India

ISBN: (纸本)9781538666869

Supply of uninterrupted power is of present need in progressing India. The most of these interruptions occur mainly due to failures in insulations of electrical apparatus. Therefore, the present work aimed at developing an early warning system for electrical distribution substations by predicting insulation breakdown. This paper, focused on time delay estimation in between the UHF signals received at multiple sensors placed around the substation. The delay estimation is most critical and essential part in detection and localization of electrical component having weakness or some defects in insulation and act as PD source. Unfortunately, the radiated UHF signals are highly contaminated with noise, form mobile, TV, and Radio broadcastings. Previous works on time delay estimation in the area mainly depends on time domain based estimation procedures, such as first peak (FP), cumulative energy (CE), and cross correlation (CC). However, recent study reveals that, each PD sources having less variant and almost unique frequency domain based signature. Therefore, in the work, Generalized Cross Correlation (GCC) which is a frequency domain based correlation technique for estimation of time delay has been investigated in details. In the work different weighing functions for the GCC, such as Smoothed Coherence Transform (SCOT), the Phase Transform (PHAT), the Eckart Filter (EF) and the Maximum Likelihood (ML) estimator. The study reveals that the GCC with ML function provide most accurate and precise estimation of time delay.

关键词： detection and localization Distribution Substation Generalized Cross Corelation,. Partial Discharge Time Delay Estimation Ultra High Frequency

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Discovering Connected Objects in the Criminal Investigations

Discovering Connected Objects in the Criminal Investigations

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2nd IEEE International Conference on Omni-Iayer Intelligent Systems (IEEE COINS)

作者： Bouchaud, Franois Vantroys, Thomas Grimaud, Gilles Buret, Pierrick Gendarmerie Natl IRCGN Forens Sci Lab Paris France Univ Lille CNRS Cent Lille UMR CRIStAL 9189 F-59000 Lille France Gendarmerie Natl C3N Natl Cyber Crime Unit Paris France

ISBN: (纸本)9781728163710

More and more things around us are becoming digital and connected to the Internet. This market ranges from smart devices, wellness and health control to smart cities. This development offers to malicious parties the possibility of carrying out attacks, directly impacting the consumers of these new services. Thus, the connected objects are actors or witnesses of events that have occurred. This opens up a challenge for security and forensic investigations on the Internet of Things (IoT). In this article, we present the problem of finding connected objects on an offence scene. In the absence of a technical solution, the investigators limit themselves to a manual search. Hidden objects are often neither detected nor found. Thus, we aim to give a clear and precise image of the current devices. We also want to determine their position. This work focuses on the study of the digital signature of the scene and the radio frequency characteristics of the objects. To understand the electromagnetic environment, we use a software defined radio (SDR) and we develop several tools: a sensor for a single protocol and a mesh network of sensors. The SDR returns the used frequencies. The single receiver offers a global mapping of the environment on a given protocol. The multi-sensor mesh network gives a precise and targeted vision of the infrastructure connected to several protocols and frequencies. We propose to assess the relevance of the measurement methods in relation to operational needs, on the basis of a use case and feedbacks.

关键词： Internet of Things IoT Investigations detection and localization

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A Practical Approach to detection and Analysis of Time-varying Waveform

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3rd International Conference on Measuring Technology and Mechatronics Automation (ICMTMA 2011)

作者： Liu Jingbo Zhao Guifen Hebei Univ Engn Sch Sci Handan 056038 Peoples R China Hebei Univ Engn Handan 056038 Peoples R China

ISBN: (纸本)9783037850190

The power quality has attracted considerable attention from both utilities and users due to the development of sensitive electronic equipment in power system network. A novel method for detecting and localizing power quality disturbance based on wavelet neural network is presented. The wavelet transform is established on decomposition of a signal according to time-scale using basis functions with adaptable scaling properties known as multiresolution analysis. The wavelet transform expands a signal not in terms of a trigonometric polynomial but by wavelets, generated using transition and dilation of a fixed wavelet function. The feature obtained from wavelet transform coefficients are inputted into the neural network for power quality disturbance pattern recognition. The improved network algorithm is utilized to complete the network structure initialization. The fundamental component of the signal is estimated to extract the mixed information using wavelet network, and then the disturbance is acquired by subtracting the fundamental component. The simulation results show that the proposed method is more sensitive to signal singularity, and achieves better performance over traditional method.

关键词： Power quality disturbance detection and localization wavelet neural network signal decomposition network structure fundamental component

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Identification of low-level point radiation sources using a sensor network

Identification of low-level point radiation sources using a ...

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7th International Symposium on Information Processing Sensor Networks

作者： Rao, Nageswara S. V. Shankar, Mallikarjun Chin, Jren-Chit Yau, David K. Y. Srivathsan, Srinivasagopalan Iyengar, S. Sitharama Yang, Yong Hou, Jennifer C. Oak Ridge Natl Lab Oak Ridge TN 37831 USA Purdue Univ W Lafayette IN 47907 USA Louisiana State Univ Baton Rouge LA 70803 USA Univ Illinois Chicago IL 60680 USA

ISBN: (纸本)9780769531571

Identification of a low-level point radiation source amidst background radiation is achieved by a network of radiation sensors using a two-step approach. Based on measurements from three sensors, the geometric difference triangulation method is used to estimate the location and strength of the source. Then a sequential probability ratio test based on current measurements and estimated parameters is employed to finally decide: (1) the presence of a source with the estimated parameters, or (2) the absence of the source, or (3) the insufficiency of measurements to make a decision. This method achieves specified levels of false alarm and missed detection probabilities, while ensuring a close-to-minimal number of measurements for reaching a decision. This method minimizes the ghost-source problem of current estimation methods, and achieves a lower false alarm rate compared with current detection methods. This method is tested and demonstrated using: (1) simulations, and (2) a test-bed that utilizes the scaling properties of point radiation sources to emulate high intensity ones that cannot be easily and safely handled in laboratory experiments.

关键词： point radiation source detection and localization sequential probability ratio test

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Radar Image Reconstruction from Raw ADC Data using Parametric Variational Autoencoder with Domain Adaptation 25

Radar Image Reconstruction from Raw ADC Data using Parametri...

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25th International Conference on Pattern Recognition (ICPR)

作者： Stephan, Michael Stadelmayer, Thomas Santra, Avik Fischer, Georg Weigel, Robert Lurz, Fabian Infineon Technol AG Neubiberg Germany Friedrich Alexander Univ Erlangen Nuremberg Erlangen Germany

ISBN: (纸本)9781728188089

This paper presents a parametric variational autoencoder-based human target detection and localization framework working directly with the raw analog-to-digital converter data from the frequency modulated continuous wave radar. We propose a parametrically constrained variational autoencoder, with residual and skip connections, capable of generating the clustered and localized target detections on the range-angle image. Furthermore, to circumvent the problem of training the proposed neural network on all possible scenarios using real radar data, we propose domain adaptation strategies whereby we first train the neural network using ray tracing based model data and then adapt the network to work on real sensor data. This strategy ensures better generalization and scalability of the proposed neural network even though it is trained with limited radar data. We demonstrate the superior detection and localization performance of our proposed solution compared to the conventional signal processing pipeline and earlier state-of-art deep U-Net architecture with range-doppler images as inputs.

关键词： detection and localization Parametric Deep Neural Network Variational Autoencoder Domain Adaptation

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MULTISENSORY SURVEILLANCE DRONE FOR SURVIVOR detection AND GEOlocalization IN COMPLEX POST-DISASTER ENVIRONMENT 39

MULTISENSORY SURVEILLANCE DRONE FOR SURVIVOR DETECTION AND G...

引用

IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Rohman, Budiman P. A. Andra, M. B. Putra, H. F. Fandiantoro, D. H. Nishimoto, Masahiko Kumamoto Univ Grad Sch Sci & Technol Kumamoto Japan Indonesian Inst Sci Jakarta Indonesia

ISBN: (纸本)9781538691540

The large scale natural disasters often result in a large death victim number and severe damage to buildings. Although some of the impacts can be reduced by the foreseeable future, they cannot be fully eliminated. In order to minimize the victim number, we propose a multisensory surveillance drone system employed an autonomous and robust unmanned aerial vehicle equipped with multiple sensors for specifically enhancing the search and rescue mission to detect the survivors in the post-disaster area. As the main sensors, we employ an ultra-wideband radar;microphone array, camera, and an RFID reader. For making the geolocalization of the detected survivors, we also employ laser range finder and LIDAR. This paper will discuss briefly the system design and the development progress of the project. Besides, the next step of the development is also presented.

关键词： disaster survivor detection and localization ultra-wideband radar microphone array computer vision radio frequency identification

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Evaluation of a Charged Coupled Device Camera for the detection of Ultraviolet Emissions by Corona Discharges 21st

Evaluation of a Charged Coupled Device Camera for the Detect...

引用

21st International Symposium on High Voltage Engineering (ISH)

作者： Raulf, Tobias Claudi, A. Zander, Rolf Fuchs, Christopher Univ Kassel Kassel Germany Westnetz GmbH Dortmund Germany

ISBN: (纸本)9783030316808;9783030316792

The current state of the art is UV cameras that use photomultiplier tube technology to make the comparatively weak UV emissions visible. These camera systems have a fixed lens with a fixed focal length, making it impossible to zoom or vary the focal length. In the application scenario of an overhead line inspection flight by helicopter, the cameras, which are available on the market, are approaching their limits. The inspector normally uses the available camera(s) during an inspection flight in parallel to the visual inspection, but this is often not possible due to the fixed focal length and resolution of the camera system. Hence, an adopted flight route would be advantageous for such UV cameras. As a result separate flights are needed for a visual inspection and corona detection. In this paper a compact camera with a Charged Coupled Device Sensor (short: CCD sensor), which covers a spectral working range of 190 nm-1100 nm was investigated within the scope of a research project. The camera is able to detect the UV emissions of corona discharges in the range of 250 nm to 400 nm. Another advantage of the front side illuminated CCD sensor is the high resolution of 1.4 megapixels and the dynamic range of 14-bit. The camera core offers the possibility to mount any lens with the appropriate focal length for the respective application. This circumstance allows a flexible use in helicopters for an overhead inspection flight. Laboratory performance tests of the camera system are promising and prove the feasibility. The use of the camera requires a preselection of the spectral range in order to take full advantage of the dynamics of the camera and to avoid overloading the sensor. For this purpose, filters with different bandpass characteristics were investigated. Finally the UV sensitivity of the camera at different intensities of corona surface discharges and in tip-plate arrangements was determined.

关键词： UV Discharge Corona detection and localization Camera

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Collimator Optimization in SPECT Based on an LROC Ideal Observer

Collimator Optimization in SPECT Based on an LROC Ideal Obse...

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IEEE Nuclear Science Symposium/Medical Imaging Conference

作者： Zhou, Lili Gindi, Gene SUNY Stony Brook Dept Radiol Stony Brook NY 11790 USA SUNY Stony Brook Dept Elect & Comp Engn Stony Brook NY 11790 USA

ISBN: (纸本)9781424427147

In SPECT the collimator is a crucial element of the imaging chain and controls the noise-resolution tradeoff of the collected data. Optimizing collimator design has been a long studied topic, with many different criteria used to evaluate the design. One class of criteria is tasked-based, in which the collimator is designed to optimize detection of a signal (lesion). Here we consider a new, more realistic, task, the joint detection and localization of a signal. Furthermore, we use an ideal observer - one that attains a theoretically maximum task performance - to optimize collimator design. The ideal observer operates on the sinogram data. We consider a family of parallel-hole low-energy collimators of varying resolution and efficiency and optimize over this set. We observe that for a 2-D object characterized by noise due to background variability and a sinogram with photon noise, the optimal collimator tends to of lower resolution and higher efficiency than equivalent commercial collimators. Furthermore, this optimal design is insensitive to the tolerance radius within which the signal must be localized. So for this scenario, the addition of a localization task does not change the optimal collimator. Optimal collimator resolution gets worse as signal size grows, and improves as the level of background variability noise increases. These latter two trends are also observed when the detection task is signal-known-exactly and background variable.

关键词： ideal observer LROC SPECT collimator optimization detection and localization

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Characterization of dynamic phenomena based on the signal analysis in phase diagram representation domain

Characterization of dynamic phenomena based on the signal an...

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作者： Angela Digulescu University Of Grenoble

学位级别：博士

Signals' deformation along their propagation path is among the most important aspect which has to be taken into account at reception. These effects are caused by phenomena like attenuation, reflection, dispersion and noise. Whereas the first two are rather easy to monitor, because they affect the amplitude, respectively the delay, the latter two are more difficult to control, because they change signals' parameters (amplitude, frequency and phase) in an environment-dependent manner. In this thesis, the main objective is to contribute to the analysis of signals related to different physical phenomena, aiming to better understand them as well as to estimate their parameters that are interesting from application point of view. Different applicative contexts have been investigated in active and passive sensing configurations. For the active part, we mention the monitoring of cavitation phenomena and its characterization for hydraulic system surveillance. The second application of the active sensing is the underwater object detection and tracking without synchronization between sensors. For the passive configuration, we focus on the pressure transient analysis in water pipes investigation with a non- intrusive method and on the surveillance of electrical power systems in the presence of transient phenomena such as electrical arcs. Despite the differences between the physical considerations, we propose a unique mathematical model of the signals issued from the active/passive sensing system used to analyze the considered phenomena. This model is based on the Recurrence Plot Analysis (RPA) and Recurrence Quantification Analysis (RQA) methods. With this concept, we propose the phase-space based waveform design. This waveform design technique presents the interest to conduct to a high speed sensing methods, very useful to monitor dynamic phenomena. Moreover, we propose new tools for the investigation of the signals characteristics. The first one is the TDR* measure (Time Dist

关键词： Signal deformation Phase diagram Recurrence Plot Analysis Phase-space based adaptive waveform detection and localization Signal characterization

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Improving the Performance and Evaluation of Computer-Assisted Semen Analysis

Improving the Performance and Evaluation of Computer-Assiste...

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作者： Choi, Ji Won New Jersey Institute of Technology

学位级别：Ph.D., Doctor of Philosophy

Semen analysis is performed routinely in fertility clinics to analyze the quality of semen and sperm cells of male patients. The analysis is typically performed by trained technicians or by Computer-Assisted Semen Analysis (CASA) systems. Manual semen analysis performed by technicians is subjective, time-consuming, and laborious, and yet most fertility clinics perform semen analysis in this manner. CASA systems, which are designed to perform the same tasks automatically, have a considerable market share, yet many studies still express concerns about their accuracy and consistency. In this dissertation, the focus is on detection, tracking, and classification of sperm cells in semen images, key elements of CASA systems. The objective is to improve existing CASA algorithms and systems by applying validated computer vision, tracking, and computational intelligence algorithms. The first step of the study is the development of simulation models for generating synthetic images of semen samples. The images enable the assessment of CASA systems and their algorithms. Specifically, the simulation models generate time-lapse images of semen samples for various sperm image categories and include ground truth labels. The models exploit standard image processing operations such as point spread functions and 2D convolutions, as well as new models of sperm cell swimming, developed for this study. They embody multiple studies of sperm motility in the form of parameterized motion equations. Use cases are presented to use the swimming models and the simulated images to assess and compare algorithms for sperm cell segmentation, localization, and tracking. Second, a digital washing algorithm is presented for unwashed semen samples. Digital washing has the potential to replace the chemical washing techniques used by fertility clinics at present, which are costly, time-consuming, and unfriendly to the environment. The digital washing algorithm extracts features from moving sperm cells in an

关键词： Computational Intelligence Computer-assisted semen analysis detection and localization Image processing Pattern recognition Tracking

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