Audio- and image-based soft failure detection methods are developed, which can detect both severe failures (such as system hang) and subtle ones (such as glitch or a momentary disturbance on display). Incorporating th...
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Audio- and image-based soft failure detection methods are developed, which can detect both severe failures (such as system hang) and subtle ones (such as glitch or a momentary disturbance on display). Incorporating the developed detection methods with a robotic ESD (electrostatic discharge) tester, we developed a fully automated soft failure investigation tool. Using this fully automated tool, we obtained failure-specific susceptibility maps for a camera (our target device). These susceptibility maps not only illustrated the sensitive locations of the device, they also showed what type of soft failure is correlated with which locations.
Because of the large forces they exert, large amplitude, highly nonlinear internal waves (NLIWs) represent a potential danger for offshore structures. This threat has prompted expensive operational observations to gua...
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Because of the large forces they exert, large amplitude, highly nonlinear internal waves (NLIWs) represent a potential danger for offshore structures. This threat has prompted expensive operational observations to guarantee the safety of drilling platforms. The potential for automated detection using seafloor pressure measurements is investigated here, using data from New Jersey's continental shelf. The detection algorithm is first tested using the complete time series. detection in the pressure record is verified by comparison to coincident velocity measurements permitting identification of false positives and false negatives. The detection algorithm achieves 100% success for NLIWs with pressure amplitude >250 Pa, roughly equivalent to vertical velocity 0.1 m s(-1), or a moderately energetic NLIW on New Jersey's continental shelf. Executed in pseudo-realtime mode, to simulate an automated detection scheme, waves >250 Pa are accurately detected with 1 h delay. (C) 2011 Elsevier Ltd. All rights reserved.
Relevant physiological studies have revealed that the response of the classical receptive field (CRF) to visual stimuli could be suppressed by non-CRF (nCRF) inhibition of the kernel in the primary visual cortex (V1)....
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Relevant physiological studies have revealed that the response of the classical receptive field (CRF) to visual stimuli could be suppressed by non-CRF (nCRF) inhibition of the kernel in the primary visual cortex (V1). Based on this mechanism, many bio-inspired contour detection models have been proposed, which are mainly achieved through CRF responses and nCRF surround inhibition calculation. In fact, the dynamic characteristics of neurons play an important role in contour detection in biological vision. Inspired by these visual mechanisms, the authors propose a contour detection model that emulates these dynamic characteristics. By introducing a multi-bandwidth Gabor filter, according to the target image, they can effectively adjust the weight ratios of the filter to protect the contours and filter the background textures in the calculation of CRF responses. Additionally, they logarithmically modulate the nCRF inhibition kernel to make texture suppression more flexible and effective, thus improving the accuracy of detection algorithm as a whole. Compared with existing bio-inspired contour detection models, the proposed model is more effective at contour detection, which will aid engineering applications that utilise pattern recognition in machine vision.
This paper addresses the issue of transmitting and reconstructing vector quantization (VQ) coded images over a noisy channel. It presents a novel approach which exploits the spatial contiguity and interpixel correlati...
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This paper addresses the issue of transmitting and reconstructing vector quantization (VQ) coded images over a noisy channel. It presents a novel approach which exploits the spatial contiguity and interpixel correlation of image data sequences, Specifically, a boundary-matching-based detection algorithm (BMDA) is proposed along with a principal-component-splitting-based indexing (PCSBI) scheme which organizes the codebook in a way similar to the GLA-splitting-rule-based indexing scheme in [9] so that some bits of an index are more important than others. Simulation results show that the proposed scheme yields significantly better visual quality and higher signal-to-noise ratio (SNR) than a maximum a posteriori (MAP) detection-based scheme [12], and has robust performance. It is also shown that incorporation of PCSBI with BMDA greatly reduces the detection complexity, thus facilitating the implementation of BMDA.
The present work shows the application of a change point model (CPM) algorithm, based on non parametric tests, to turbulent structures detection in an airflow. It seeks to detect the vortices generated in the wake of ...
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The present work shows the application of a change point model (CPM) algorithm, based on non parametric tests, to turbulent structures detection in an airflow. It seeks to detect the vortices generated in the wake of an airfoil, equipped with a passive flow control device (Gurney mini flap) in its trailing edge. By applying CPM models to the sample data, this paper seeks to determine the possible changes to the velocity fluctuations and compare the model's effectiveness to traditional methods. The main objective of this study is to detect the characteristic frequencies of the turbulent structures immersed in the airflow. The results show that the CPM methodology, based on the Cramer-von Mises (CPM-CvM) test, produces results that coincide with values predicted by traditional methods (less than 9.5% of mismatch), validating its use as a real time alternative tool for the analysis of these types of flows. Finally, this work shows a new application of CPM for detecting changes in a time-dependent random signal, which has an a priori unknown distribution. (C) 2016 CIMNE (Universitat Politecnica de Catalunya). Published by Elsevier Espana, S.L.U.
A detection algorithm, aimed at separating the foveal avascular zone from the eye background in a numerical angiogram image, is developed. This method is based on the use of a test derived from signal statistical theo...
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A detection algorithm, aimed at separating the foveal avascular zone from the eye background in a numerical angiogram image, is developed. This method is based on the use of a test derived from signal statistical theory. Results show that our approach could be considered as a good computer-assisted tool for location and follow-up of retinal pathologies.
Indoor positioning and life detection using radio frequency has been widely researched;however, to achieve both indoor positioning and life detection has been a very challenging task until now. By careful design of th...
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Indoor positioning and life detection using radio frequency has been widely researched;however, to achieve both indoor positioning and life detection has been a very challenging task until now. By careful design of the waveform and a novel detection algorithm, asynchronous multiple frequency shift keying (A-MFSK) is proposed to solve this task for the first time, which can operate between an MFSK mode and a single tone continuous wave mode, providing a possibility of A-MFSK for this task. Detailed explanation about the detection algorithm is given. Simulations and measurements results of both modes demonstrate that A-MFSK has the capability of indoor positioning and life detection.
Pulse Arrival Time (PAT) derived from Electrocardiogram (ECG) and Photoplethysmogram (PPG) for cuff-less Blood Pressure (BP) measurement has been a contemporary and widely accepted technique. However, the features ext...
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Pulse Arrival Time (PAT) derived from Electrocardiogram (ECG) and Photoplethysmogram (PPG) for cuff-less Blood Pressure (BP) measurement has been a contemporary and widely accepted technique. However, the features extracted for it are conventionally from an isolated pulse of ECG and PPG signals. As a result, the estimated BP is intermittent. Objective: This paper presents feature extraction from each beat of ECG and PPG signals to make BP measurements uninterrupted. These features are extracted by employing Haar transformation to adaptively attenuate measurement noise and improve the fiducial point detection precision. Method: the use of only PAT feature as an independent variable leads to an inaccurate estimation of either Systolic Blood Pressure (SBP) or Diastolic Blood Pressure (DBP) or both. We propose the extraction of supplementary features that are highly correlated to physiological parameters. Concurrent data was collected as per the Association for the Advancement of Medical Instrumentation (AAMI) guidelines from 171 human subjects belonging to diverse age groups. An Adaptive Window Wavelet Transformation (AWWT) technique based on Haar wavelet transformation has been introduced to segregate pulses. Further, an algorithm based on log-linear regression analysis is developed to process extracted features from each beat to calculate BP. Results: The mean error of 0.43 and 0.20 mmHg, mean absolute error of 4.6 and 2.3 mmHg, and Standard deviation of 6.13 and 3.06 mmHg is achieved for SBP and DBP respectively. Conclusions: The features extracted are highly precise and evaluated BP values are as per the AAMI standards. Clinical Impact: This continuous real-time BP monitoring technique can be useful in the treatment of hypertensive and potential-hypertensive subjects.
This paper presents an algorithm to detect very faint object streaks on CCD images acquired with an optical system. The proposed detection method uses image filters simulating the geometrical form and orientation of p...
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This paper presents an algorithm to detect very faint object streaks on CCD images acquired with an optical system. The proposed detection method uses image filters simulating the geometrical form and orientation of possible streaks on the CCD image. The method searches for a matching between streak and filter evaluating the convolutions of the image with all possible filters. Based on the statistics of the image background a threshold is applied in order to accept, respectively reject, the possible streak candidates. The detection probabilities and the effect of different parameter settings are estimated with tests on simulated images, while subframes of real images are used to evaluate the applicability of the algorithm to real cases. The detection probability of this method depends on the length and on the signal-to-noise ratio of the streak. For long streaks, a detection for signal-to-noise values around 0.5 is achieved. The further characterization of the detected streak (e.g. centroid and length), which is not performed in the current algorithm, and the reduction of the computation time, which is relatively high for full acquired frames, as well as possible improvements are briefly addressed. (C) 2019 COSPAR. Published by Elsevier Ltd. All rights reserved.
On the basis of YOLO deep network detection method, a new abnormal data detection method is proposed to meet the needs of gas boiler abnormal data detection. In the feature extraction layer, the SENet structure is emb...
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On the basis of YOLO deep network detection method, a new abnormal data detection method is proposed to meet the needs of gas boiler abnormal data detection. In the feature extraction layer, the SENet structure is embedded between DBL and Pooling. Through compression, excitation and recalibration, the feature extraction of data information is more accurate. After feature extraction layer, multi-scale pooling processing mechanism is introduced to improve the learning efficiency of YOLO network. The first group and the second group of experiments respectively proved that the introduction of SENet structure and multi-scale pooling mechanism improved the feature extraction accuracy of YOLO network and the convergence speed of iteration process. The third group of experimental results show that the detection accuracy of the detection method proposed in this paper is significantly higher than CNN method, RNN method and YOLO method, and it is more suitable for the detection of abnormal data of gas boilers. (c) 2023 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).
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