scholarly journals Acoustic Scene Classification Using Efficient Summary Statistics and Multiple Spectro-Temporal Descriptor Fusion

2018 ◽  
Vol 8 (8) ◽  
pp. 1363 ◽  
Author(s):  
Jiaxing Ye ◽  
Takumi Kobayashi ◽  
Nobuyuki Toyama ◽  
Hiroshi Tsuda ◽  
Masahiro Murakawa
Keyword(s):  
Extreme Values ◽  
Computing Time ◽  
Critical Issue ◽  
Time Frame ◽  
Scene Classification ◽  
Acoustic Feature ◽  
Computational Framework ◽  
Time Frequency ◽  
Novel Approach ◽  
Descriptor Fusion

This paper presents a novel approach for acoustic scene classification based on efficient acoustic feature extraction using spectro-temporal descriptors fusion. Grounded on the finding in neuroscience—“auditory system summarizes the temporal details of sounds using time-averaged statistics to understand acoustic scenes”, we devise an efficient computational framework for sound scene classification by using multipe time-frequency descriptors fusion with discriminant information enhancement. To characterize rich information of sound, i.e., local structures on the time-frequency plane, we adopt 2-dimensional local descriptors. A more critical issue raised in how to logically ‘summarize’ those local details into a compact feature vector for scene classification. Although ‘time-averaged statistics’ is suggested by the psychological investigation, directly computing time average of local acoustic features is not a logical way, since arithmetic mean is vulnerable to extreme values which are anticipated to be generated by interference sounds which are irrelevant to the scene category. To tackle this problem, we develop time-frame weighting approach to enhance sound textures as well as to suppress scene-irrelevant events. Subsequently, robust acoustic feature for scene classification can be efficiently characterized. The proposed method had been validated by using Rouen dataset which consists of 19 acoustic scene categories with 3029 real samples. Extensive results demonstrated the effectiveness of the proposed scheme.

Fuzzified time-frequency method for identification and localization of power system faults

2021 ◽  
pp. 1-13
Author(s):  
Pullabhatla Srikanth ◽  
Chiranjib Koley
Keyword(s):  
Power System ◽  
High Accuracy ◽  
Fuzzy Decision ◽  
Frequency Method ◽  
Time Frequency ◽  
Novel Approach ◽  
S Transform ◽  
Different Types ◽  
Power System Faults ◽  
Frequency Magnitude

In this work, different types of power system faults at various distances have been identified using a novel approach based on Discrete S-Transform clubbed with a Fuzzy decision box. The area under the maximum values of the dilated Gaussian windows in the time-frequency domain has been used as the critical input values to the fuzzy machine. In this work, IEEE-9 and IEEE-14 bus systems have been considered as the test systems for validating the proposed methodology for identification and localization of Power System Faults. The proposed algorithm can identify different power system faults like Asymmetrical Phase Faults, Asymmetrical Ground Faults, and Symmetrical Phase faults, occurring at 20% to 80% of the transmission line. The study reveals that the variation in distance and type of fault creates a change in time-frequency magnitude in a unique pattern. The method can identify and locate the faulted bus with high accuracy in comparison to SVM.

scholarly journals Practical example for use of the supervised vicarious calibration (SVC) method on multisource hyperspectral imagery data – ValCalHyp airborne hyperspectral campaign under the EUFAR framework

2014 ◽  
Vol XL-7 ◽  
pp. 51-53
Author(s):  
A. Brook ◽  
E. Ben Dor
Keyword(s):  
Atmospheric Correction ◽  
Ground Truth ◽  
Critical Issue ◽  
Radiometric Calibration ◽  
Vicarious Calibration ◽  
Flight Direction ◽  
Novel Approach ◽  
Novel Method ◽  
General Goal ◽  
Cross Calibration

A novel approach for radiometric calibration and atmospheric correction of airborne hyperspectral (HRS) data, termed supervised vicarious calibration (SVC) was proposed by Brook and Ben-Dor in 2010. The present study was aimed at validating this SVC approach by simultaneously using several different airborne HSR sensors that acquired HSR data over several selected sites at the same time. The general goal of this study was to apply a cross-calibration approach to examine the capability and stability of the SVC method and to examine its validity. This paper reports the result of the multi sensors campaign took place over Salon de Provenance, France on behalf of the ValCalHyp project took place in 2011. The SVC method enabled the rectification of the radiometric drift of each sensor and improves their performance significantly. The flight direction of the SVC targets was found to be a critical issue for such correction and recommendations have been set for future utilization of this novel method. The results of the SVC method were examined by comparing ground-truth spectra of several selected validation targets with the image spectra as well as by comparing the classified water quality images generated from all sensors over selected water bodies.

scholarly journals On adaptive propensity score truncation in causal inference

2018 ◽  
Vol 28 (6) ◽  
pp. 1741-1760 ◽  
Author(s):  
Cheng Ju ◽  
Joshua Schwab ◽  
Mark J van der Laan
Keyword(s):  
Propensity Score ◽  
Causal Inference ◽  
Extreme Values ◽  
Likelihood Estimation ◽  
Experimental Treatment ◽  
Point Estimation ◽  
Finite Sample ◽  
Novel Approach ◽  
Confidence Interval Coverage ◽  
Interval Coverage

The positivity assumption, or the experimental treatment assignment (ETA) assumption, is important for identifiability in causal inference. Even if the positivity assumption holds, practical violations of this assumption may jeopardize the finite sample performance of the causal estimator. One of the consequences of practical violations of the positivity assumption is extreme values in the estimated propensity score (PS). A common practice to address this issue is truncating the PS estimate when constructing PS-based estimators. In this study, we propose a novel adaptive truncation method, Positivity-C-TMLE, based on the collaborative targeted maximum likelihood estimation (C-TMLE) methodology. We demonstrate the outstanding performance of our novel approach in a variety of simulations by comparing it with other commonly studied estimators. Results show that by adaptively truncating the estimated PS with a more targeted objective function, the Positivity-C-TMLE estimator achieves the best performance for both point estimation and confidence interval coverage among all estimators considered.

LASRE: A Novel Approach to Large area Accelerated Segmentation for Reverse Engineering on SEM images

2020 ◽  
Author(s):  
Ronald Wilson ◽  
Domenic Forte ◽  
Navid Asadizanjani ◽  
Damon L. Woodard
Keyword(s):  
Integrated Circuits ◽  
Reverse Engineering ◽  
Imaging Modality ◽  
Primary Source ◽  
Time Frame ◽  
Large Area ◽  
Sem Images ◽  
Model Free ◽  
Novel Approach ◽  
Daunting Task

Abstract In the hardware assurance community, Reverse Engineering (RE) is considered a key tool and asset in ensuring the security and reliability of Integrated Circuits (IC). However, with the introduction of advanced node technologies, the application of RE to ICs is turning into a daunting task. This is amplified by the challenges introduced by the imaging modalities such as the Scanning Electron Microscope (SEM) used in acquiring images of ICs. One such challenge is the lack of understanding of the influence of noise in the imaging modality along with its detrimental effect on the quality of images and the overall time frame required for imaging the IC. In this paper, we characterize some aspects of the noise in the image along with its primary source. Furthermore, we use this understanding to propose a novel texture-based segmentation algorithm for SEM images called LASRE. The proposed approach is unsupervised, model-free, robust to the presence of noise and can be applied to all layers of the IC with consistent results. Finally, the results from a comparison study is reported, and the issues associated with the approach are discussed in detail. The approach consistently achieved over 86% accuracy in segmenting various layers in the IC.

scholarly journals A Novel Approach for Developing High-Resolution Sub-Fossil Peat Chronologies with 14C Dating

Radiocarbon ◽  
2004 ◽  
Vol 46 (1) ◽  
pp. 455-463 ◽  
Author(s):  
T H Donders ◽  
F Wagner ◽  
K van der Borg ◽  
A F M de Jong ◽  
H Visscher
Keyword(s):  
Mass Spectrometry ◽  
High Resolution ◽  
20Th Century ◽  
Accelerator Mass Spectrometry ◽  
Meteorological Data ◽  
Time Frame ◽  
14C Dating ◽  
Novel Approach

Sub-fossil sections from a Florida wetland were accelerator mass spectrometry (AMS) dated and the sedimentological conditions were determined. 14C data were calibrated using a combined wiggle-match and 14C bomb-pulse approach. Repeatable results were obtained providing accurate peat chronologies for the last 130 calendar yr. Assessment of the different errors involved led to age models with 3–5 yr precision. This allows direct calibration of paleoenvironmental proxies with meteorological data. The time frame in which 14C dating is commonly applied can possibly be extended to include the 20th century.

A NOVEL APPROACH OSA DETECTION USING SINGLE-LEAD ECG SCALOGRAM BASED ON DEEP NEURAL NETWORK

2019 ◽  
Vol 19 (04) ◽  
pp. 1950026 ◽  
Author(s):  
SINAM AJITKUMAR SINGH ◽  
SWANIRBHAR MAJUMDER
Keyword(s):  
Neural Network ◽  
Sleep Apnea ◽  
Learning Algorithm ◽  
Low Cost ◽  
Performance Outcomes ◽  
Classification Systems ◽  
Analysis Model ◽  
Time Frequency ◽  
Novel Approach ◽  
Obstructive Sleep

Obstructive sleep apnea (OSA) is the most common and severe breathing dysfunction which frequently freezes the breathing for longer than 10[Formula: see text]s while sleeping. Polysomnography (PSG) is the conventional approach concerning the treatment of OSA detection. But, this approach is a costly and cumbersome process. To overcome the above complication, a satisfactory and novel technique for interpretation of sleep apnea using ECG were recording is under development. The methods for OSA analysis based on ECG were analyzed for numerous years. Early work concentrated on extracting features, which depend entirely on the experience of human specialists. A novel approach for the prediction of sleep apnea disorder based on the convolutional neural network (CNN) using a pre-trained (AlexNet) model is analyzed in this study. After filtering per-minute segment of the single-lead ECG recording accompanied by continuous wavelet transform (CWT), the 2D scalogram images are generated. Finally, CNN based on deep learning algorithm is adopted to enhance the classification performance. The efficiency of the proposed model is compared with the previous methods that used the same datasets. Proposed method based on CNN is able to achieve the accuracy of 86.22% with 90% sensitivity in per-minute segment OSA classification. Based on per-recording OSA diagnosis, our works correctly classify all the abnormal apneic recording with 100% accuracy. Our OSA analysis model using time-frequency scalogram generates excellent independent validation performance with different state-of-the-art OSA classification systems. Experimental results proved that the proposed method produces excellent performance outcomes with low cost and less complexity.

scholarly journals Robust Visual Tracking Based on Fusional Multi-Correlation-Filters with a High-Confidence Judgement Mechanism

2020 ◽  
Vol 10 (6) ◽  
pp. 2151
Author(s):  
Wenbin Wang ◽  
Chao Liu ◽  
Bo Xu ◽  
Long Li ◽  
Wei Chen ◽  
...  
Keyword(s):  
Time Frame ◽  
Spatial Layout ◽  
Motion Blur ◽  
Frame Rate ◽  
Visual Object ◽  
Correlation Filters ◽  
High Confidence ◽  
Novel Approach ◽  
Highly Sensitive ◽  
Fast Motion

Visual object trackers based on correlation filters have recently demonstrated substantial robustness to challenging conditions with variations in illumination and motion blur. Nonetheless, the models depend strongly on the spatial layout and are highly sensitive to deformation, scale, and occlusion. As presented and discussed in this paper, the colour attributes are combined due to their complementary characteristics to handle variations in shape well. In addition, a novel approach for robust scale estimation is proposed for mitigatinge the problems caused by fast motion and scale variations. Moreover, feedback from high-confidence tracking results was also utilized to prevent model corruption. The evaluation results for our tracker demonstrate that it performed outstandingly in terms of both precision and accuracy with enhancements of approximately 25% and 49%, respectively, in authoritative benchmarks compared to those for other popular correlation- filter-based trackers. Finally, the proposed tracker has demonstrated strong robustness, which has enabled online object tracking under various scenarios at a real-time frame rate of approximately 65 frames per second (FPS).

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