The application of machine learning to signal processing for detection and identification of signals of interest and anomalies

Quality assessment of audiovisual (AV) signals is important from the perspective of system design, optimization, and management of a modern multimedia communication system. However, automatic prediction of AV quality via the use of computational models remains challenging. In this context, machine learning (ML) appears to be an attractive alternative to the traditional approaches. This is especially when such assessment needs to be made in no-reference (i.e., the original signal is unavailable) fashion. While development of ML-based quality predictors is desirable, we argue that proper assessment and validation of such predictors is also crucial before they can be deployed in practice. To this end, we raise some fundamental questions about the current approach of ML-based model development for AV quality assessment and signal processing for multimedia communication in general. We also identify specific limitations associated with the current validation strategy which have implications on analysis and comparison of ML-based quality predictors. These include a lack of consideration of: (a) data uncertainty, (b) domain knowledge, (c) explicit learning ability of the trained model, and (d) interpretability of the resultant model. Therefore, the primary goal of this article is to shed some light into mentioned factors. Our analysis and proposed recommendations are of particular importance in the light of significant interests in ML methods for multimedia signal processing (specifically in cases where human-labeled data is used), and a lack of discussion of mentioned issues in existing literature.

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An Overview of Machine Learning-Based Techniques for Solving Optimization Problems in Communications and Signal Processing

IEEE Access ◽

10.1109/access.2021.3079639 ◽

2021 ◽

pp. 1-1

Author(s):

Hayssam Dahrouj ◽

Rawan Alghamdi ◽

Hibatallah Alwazani ◽

Sarah Bahanshal ◽

Alaa Alameer Ahmad ◽

...

Keyword(s):

Machine Learning ◽

Signal Processing ◽

Optimization Problems

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Early Detection of Exposure to Toxic Chemicals Using Continuously Recorded Multi-Sensor Physiology

Sensors ◽

10.3390/s21113616 ◽

2021 ◽

Vol 21 (11) ◽

pp. 3616

Author(s):

Jan Ubbo van Baardewijk ◽

Sarthak Agarwal ◽

Alex S. Cornelissen ◽

Marloes J. A. Joosen ◽

Jiska Kentrop ◽

...

Keyword(s):

Machine Learning ◽

Early Detection ◽

Physiological Signals ◽

Machine Learning Techniques ◽

Detection Accuracy ◽

Chemical Agent ◽

Counter Measures ◽

Detection And Identification ◽

Life Saving ◽

Future Work

Early detection of exposure to a toxic chemical, e.g., in a military context, can be life-saving. We propose to use machine learning techniques and multiple continuously measured physiological signals to detect exposure, and to identify the chemical agent. Such detection and identification could be used to alert individuals to take appropriate medical counter measures in time. As a first step, we evaluated whether exposure to an opioid (fentanyl) or a nerve agent (VX) could be detected in freely moving guinea pigs using features from respiration, electrocardiography (ECG) and electroencephalography (EEG), where machine learning models were trained and tested on different sets (across subject classification). Results showed this to be possible with close to perfect accuracy, where respiratory features were most relevant. Exposure detection accuracy rose steeply to over 95% correct during the first five minutes after exposure. Additional models were trained to correctly classify an exposed state as being induced either by fentanyl or VX. This was possible with an accuracy of almost 95%, where EEG features proved to be most relevant. Exposure detection models that were trained on subsets of animals generalized to subsets of animals that were exposed to other dosages of different chemicals. While future work is required to validate the principle in other species and to assess the robustness of the approach under different, realistic circumstances, our results indicate that utilizing different continuously measured physiological signals for early detection and identification of toxic agents is promising.

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Divide-and-conquer signal processing, feature extraction, and machine learning for big data

Neurocomputing ◽

10.1016/j.neucom.2015.08.052 ◽

2016 ◽

Vol 174 ◽

pp. 383 ◽

Cited By ~ 1

Author(s):

Chen Bo-Wei ◽

Wen Ji ◽

Seungmin Rho

Keyword(s):

Machine Learning ◽

Signal Processing ◽

Feature Extraction ◽

Big Data ◽

Divide And Conquer

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Bayesian Machine Learning: EEG\/MEG signal processing measurements

IEEE Signal Processing Magazine ◽

10.1109/msp.2015.2481559 ◽

2016 ◽

Vol 33 (1) ◽

pp. 14-36 ◽

Cited By ~ 55

Author(s):

Wei Wu ◽

Srikantan Nagarajan ◽

Zhe Chen

Keyword(s):

Machine Learning ◽

Signal Processing ◽

Bayesian Machine Learning

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Rapid bacteria identification using structured illumination microscopy and machine learning

Journal of Innovative Optical Health Sciences ◽

10.1142/s1793545818500074 ◽

2017 ◽

Vol 11 (01) ◽

pp. 1850007 ◽

Cited By ~ 3

Author(s):

Yingchuan He ◽

Weize Xu ◽

Yao Zhi ◽

Rohit Tyagi ◽

Zhe Hu ◽

...

Keyword(s):

Machine Learning ◽

Optical Microscopy ◽

Pathogenic Bacteria ◽

Morphological Features ◽

Structured Illumination ◽

Structured Illumination Microscopy ◽

Standard Pattern ◽

Bacteria Identification ◽

Detection And Identification ◽

Combined Use

Traditionally, optical microscopy is used to visualize the morphological features of pathogenic bacteria, of which the features are further used for the detection and identification of the bacteria. However, due to the resolution limitation of conventional optical microscopy as well as the lack of standard pattern library for bacteria identification, the effectiveness of this optical microscopy-based method is limited. Here, we reported a pilot study on a combined use of Structured Illumination Microscopy (SIM) with machine learning for rapid bacteria identification. After applying machine learning to the SIM image datasets from three model bacteria (including Escherichia coli, Mycobacterium smegmatis, and Pseudomonas aeruginosa), we obtained a classification accuracy of up to 98%. This study points out a promising possibility for rapid bacterial identification by morphological features.

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