Source Camera Identification Based on Sensor Readout Noise

2012 ◽  
pp. 21-34
Author(s):  
H. R. Chennamma ◽  
Lalitha Rangarajan
Keyword(s):  
Computer Software ◽  
Sensor Data ◽  
Processing Parameter ◽  
Source Camera Identification ◽  
Camera Identification ◽  
Novel Approach ◽  
Deviation Plot ◽  
Readout Noise ◽  
The One ◽  
Imaging Sensor

A digitally developed image is a viewable image (TIFF/JPG) produced by a camera’s sensor data (raw image) using computer software tools. Such images might use different colour space, demosaicing algorithms or by different post processing parameter settings which are not the one coded in the source camera. In this regard, the most reliable method of source camera identification is linking the given image with the sensor of camera. In this paper, the authors propose a novel approach for camera identification based on sensor’s readout noise. Readout noise is an important intrinsic characteristic of a digital imaging sensor (CCD or CMOS) and it cannot be removed. This paper quantitatively measures readout noise of the sensor from an image using the mean-standard deviation plot, while in order to evaluate the performance of the proposed approach, the authors tested against the images captured at two different exposure levels. Results show datasets containing 1200 images acquired from six different cameras of three different brands. The success of proposed method is corroborated through experiments.

Source Camera Identification Based on Sensor Readout Noise

2010 ◽  
Vol 2 (3) ◽  
pp. 28-42 ◽  
Author(s):  
H. R. Chennamma ◽  
Lalitha Rangarajan
Keyword(s):  
Computer Software ◽  
Sensor Data ◽  
Processing Parameter ◽  
Source Camera Identification ◽  
Camera Identification ◽  
Novel Approach ◽  
Deviation Plot ◽  
Readout Noise ◽  
The One ◽  
Imaging Sensor

A digitally developed image is a viewable image (TIFF/JPG) produced by a camera’s sensor data (raw image) using computer software tools. Such images might use different colour space, demosaicing algorithms or by different post processing parameter settings which are not the one coded in the source camera. In this regard, the most reliable method of source camera identification is linking the given image with the sensor of camera. In this paper, the authors propose a novel approach for camera identification based on sensor’s readout noise. Readout noise is an important intrinsic characteristic of a digital imaging sensor (CCD or CMOS) and it cannot be removed. This paper quantitatively measures readout noise of the sensor from an image using the mean-standard deviation plot, while in order to evaluate the performance of the proposed approach, the authors tested against the images captured at two different exposure levels. Results show datasets containing 1200 images acquired from six different cameras of three different brands. The success of proposed method is corroborated through experiments.

Collecting Words: A Clinical Example of a Morphology-Focused Orthographic Intervention

2020 ◽  
Vol 51 (3) ◽  
pp. 544-560 ◽  
Author(s):  
Kimberly A. Murphy ◽  
Emily A. Diehm
Keyword(s):  
Written Language ◽  
Morphological Knowledge ◽  
English Orthography ◽  
Word Level ◽  
Novel Approach ◽  
Language Pathology ◽  
The One ◽  
Critical Intervention ◽  
Reading And Spelling ◽  
Reading And Spelling Difficulties

Purpose Morphological interventions promote gains in morphological knowledge and in other oral and written language skills (e.g., phonological awareness, vocabulary, reading, and spelling), yet we have a limited understanding of critical intervention features. In this clinical focus article, we describe a relatively novel approach to teaching morphology that considers its role as the key organizing principle of English orthography. We also present a clinical example of such an intervention delivered during a summer camp at a university speech and hearing clinic. Method Graduate speech-language pathology students provided a 6-week morphology-focused orthographic intervention to children in first through fourth grade ( n = 10) who demonstrated word-level reading and spelling difficulties. The intervention focused children's attention on morphological families, teaching how morphology is interrelated with phonology and etymology in English orthography. Results Comparing pre- and posttest scores, children demonstrated improvement in reading and/or spelling abilities, with the largest gains observed in spelling affixes within polymorphemic words. Children and their caregivers reacted positively to the intervention. Therefore, data from the camp offer preliminary support for teaching morphology within the context of written words, and the intervention appears to be a feasible approach for simultaneously increasing morphological knowledge, reading, and spelling. Conclusion Children with word-level reading and spelling difficulties may benefit from a morphology-focused orthographic intervention, such as the one described here. Research on the approach is warranted, and clinicians are encouraged to explore its possible effectiveness in their practice. Supplemental Material https://doi.org/10.23641/asha.12290687

scholarly journals A novel approach to the computation of one-loop three- and four-point functions: I. The real mass case

2019 ◽  
Vol 2019 (11) ◽  
Author(s):  
J Ph Guillet ◽  
E Pilon ◽  
Y Shimizu ◽  
M S Zidi
Keyword(s):  
Building Blocks ◽  
The Real ◽  
Alternative Method ◽  
Novel Approach ◽  
Reduction Methods ◽  
Real Mass ◽  
Novel Method ◽  
Algebraic Reduction ◽  
The One ◽  
Mass Case

Abstract This article is the first of a series of three presenting an alternative method of computing the one-loop scalar integrals. This novel method enjoys a couple of interesting features as compared with the method closely following ’t Hooft and Veltman adopted previously. It directly proceeds in terms of the quantities driving algebraic reduction methods. It applies to the three-point functions and, in a similar way, to the four-point functions. It also extends to complex masses without much complication. Lastly, it extends to kinematics more general than that of the physical, e.g., collider processes relevant at one loop. This last feature may be useful when considering the application of this method beyond one loop using generalized one-loop integrals as building blocks.

scholarly journals Tenebrio molitor in the circular economy: a novel approach for plastic valorisation and PHA biological recovery

2021 ◽  
Author(s):  
Paola Sangiorgio ◽  
Alessandra Verardi ◽  
Salvatore Dimatteo ◽  
Anna Spagnoletta ◽  
Stefania Moliterni ◽  
...  
Keyword(s):  
Circular Economy ◽  
Raw Materials ◽  
Economic Value ◽  
Tenebrio Molitor ◽  
World Population ◽  
Plastic Pollution ◽  
Biological Recovery ◽  
Great Opportunity ◽  
Novel Approach ◽  
The One

AbstractThe increase in the world population leads to rising demand and consumption of plastic raw materials; only a small percentage of plastics is recovered and recycled, increasing the quantity of waste released into the environment and losing its economic value. The plastics represent a great opportunity in the circular perspective of their reuse and recycling. Research is moving, on the one hand, to implement sustainable systems for plastic waste management and on the other to find new non-fossil-based plastics such as polyhydroxyalkanoates (PHAs). In this review, we focus our attention on Tenebrio molitor (TM) as a valuable solution for plastic biodegradation and biological recovery of new biopolymers (e.g. PHA) from plastic-producing microorganisms, exploiting its highly diversified gut microbiota. TM’s use for plastic pollution management is controversial. However, TM microbiota is recognised as a source of plastic-degrading microorganisms. TM-based plastic degradation is improved by co-feeding with food loss and waste as a dietary energy source, thus valorising these low-value substrates in a circular economy perspective. TM as a bioreactor is a valid alternative to traditional PHA recovery systems with the advantage of obtaining, in addition to highly pure PHA, protein biomass and rearing waste from which to produce fertilisers, chitin/chitosan, biochar and biodiesel. Finally, we describe the critical aspects of these TM-based approaches, mainly related to TM mass production, eventual food safety problems, possible release of microplastics and lack of dedicated legislation.

scholarly journals Machine Learning Solutions for Bridge Scour Forecast Based on Monitoring Data

2021 ◽  
pp. 036119812110126
Author(s):  
Negin Yousefpour ◽  
Steve Downie ◽  
Steve Walker ◽  
Nathan Perkins ◽  
Hristo Dikanski
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Monitoring Data ◽  
Sensor Data ◽  
Monitoring Systems ◽  
Bridge Scour ◽  
Scour Monitoring ◽  
Novel Approach ◽  
Bayesian Inference Method ◽  
Water Level Variations

Bridge scour is a challenge throughout the U.S.A. and other countries. Despite the scale of the issue, there is still a substantial lack of robust methods for scour prediction to support reliable, risk-based management and decision making. Throughout the past decade, the use of real-time scour monitoring systems has gained increasing interest among state departments of transportation across the U.S.A. This paper introduces three distinct methodologies for scour prediction using advanced artificial intelligence (AI)/machine learning (ML) techniques based on real-time scour monitoring data. Scour monitoring data included the riverbed and river stage elevation time series at bridge piers gathered from various sources. Deep learning algorithms showed promising in prediction of bed elevation and water level variations as early as a week in advance. Ensemble neural networks proved successful in the predicting the maximum upcoming scour depth, using the observed sensor data at the onset of a scour episode, and based on bridge pier, flow and riverbed characteristics. In addition, two of the common empirical scour models were calibrated based on the observed sensor data using the Bayesian inference method, showing significant improvement in prediction accuracy. Overall, this paper introduces a novel approach for scour risk management by integrating emerging AI/ML algorithms with real-time monitoring systems for early scour forecast.

scholarly journals Gas Detection and Identification Using Multimodal Artificial Intelligence Based Sensor Fusion

2021 ◽  
Vol 4 (1) ◽  
pp. 3
Author(s):  
Parag Narkhede ◽  
Rahee Walambe ◽  
Shruti Mandaokar ◽  
Pulkit Chandel ◽  
Ketan Kotecha ◽  
...  
Keyword(s):  
Sensor Fusion ◽  
Network Architecture ◽  
Cost Effective ◽  
Sensor Data ◽  
Dense Layer ◽  
Gaseous Emissions ◽  
Multiple Sensors ◽  
Single Output ◽  
Novel Approach ◽  
Single Sensor

With the rapid industrialization and technological advancements, innovative engineering technologies which are cost effective, faster and easier to implement are essential. One such area of concern is the rising number of accidents happening due to gas leaks at coal mines, chemical industries, home appliances etc. In this paper we propose a novel approach to detect and identify the gaseous emissions using the multimodal AI fusion techniques. Most of the gases and their fumes are colorless, odorless, and tasteless, thereby challenging our normal human senses. Sensing based on a single sensor may not be accurate, and sensor fusion is essential for robust and reliable detection in several real-world applications. We manually collected 6400 gas samples (1600 samples per class for four classes) using two specific sensors: the 7-semiconductor gas sensors array, and a thermal camera. The early fusion method of multimodal AI, is applied The network architecture consists of a feature extraction module for individual modality, which is then fused using a merged layer followed by a dense layer, which provides a single output for identifying the gas. We obtained the testing accuracy of 96% (for fused model) as opposed to individual model accuracies of 82% (based on Gas Sensor data using LSTM) and 93% (based on thermal images data using CNN model). Results demonstrate that the fusion of multiple sensors and modalities outperforms the outcome of a single sensor.

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