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作者： Engelhardt, Erik Zaman, Adrian Elzenheimer, Eric Frey, Norbert Schmidt, Gerhard Digital Signal Processing and System Theory Institute of Electrical Engineering and Information Technology Faculty of Engineering Kiel University Kaiserstr. 2 Kiel24143 Germany Medical Department Iii Specialised in Cardiology and Angiology University Medical Center Schleswig-Holstein Arnold-Heller-Str. 3/House K3 Kiel24105 Germany Internal Medicine III: Heart Vascular and Lung University Medical Center Heidelberg Im Neuenheimer Feld 410 Heidelberg69120 Germany

While metrics such as SNR (signal-to-noise ratio) and ASC (application-specific capacity) can be used to describe signal quality quantitatively, they do not allow qualitative assessment. If only the SNR of a signal is given, it is not clear for which applications it is suitable. The subjective evaluation of signals by application experts, on the other hand, can provide such a qualitative assessment, but it is not reproducible. In this study, we investigate the relationship between these easily computable metrics such as SNR and ASC and the subjective evaluation of cardiologists using MCG signals as an example. To do this, we define four quality classes and generate a collection of noisy prototype signals. The cardiologists' assessments are then compared with the SNR and ASC of each signal. Due to the small amount of data, we only use a linear regression to predict quality classes based on common quantitative metrics. The achieved coefficients of determination are RSNR 2 = 0.612 and RASC 2 = 0.593. In perspective, it is therefore plausible to analytically calculate the quality class of a MCG sensor system from the system characteristics with the support of a larger cohort of cardiologists. This will allow sensor system manufacturers to optimize their systems more efficiently for specific applications, enabling faster innovation loops at lower cost. For users of sensor systems, it will simplify the selection of the most suitable system for their application. © 2022 The Author(s), published by De Gruyter.

关键词： signal to noise ratio

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Semantic segmentation and object detection towards instance segmentation: Breast tumor identification

arXiv

引用

arXiv 2021年

作者： Mejri, Mohamed Mejri, Aymen Mejri, Oumayma Fekih, Chiraz School of Electrical and Computer Engineering Georgia Institute of Technology United States Department of Digital Signal Processing Telecom Paris Paris France Department of Gynecology and Obstetrics Medicine School of Tunis Tunis Tunisia

Breast cancer is one of the factors that cause the increase of mortality of women. The most widely used method for diagnosing this geological disease i.e. breast cancer is the ultrasound scan. Several key features such as the smoothness and the texture of the tumor captured through ultrasound scans encode the abnormality of the breast tumors (malignant from benign). However, ultrasound scans are often noisy and include irrelevant parts of the breast that may bias the segmentation of eventual tumors. In this paper, we are going to extract the region of interest (i.e, bounding boxes of the tumors) and feed-forward them to one semantic segmentation encoder-decoder structure based on its classification (i.e, malignant or benign). the whole process aims to build an instance-based segmenter from a semantic segmenter and an object detector. © 2021, CC BY.

关键词： Semantic Segmentation

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Analysis of Clipping a digital Modulated signal Affected by AWGN and Impulse Noise 12

Analysis of Clipping a Digital Modulated Signal Affected by ...

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12th International Symposium on Communication Systems, Networks and digital signal processing, CSNDSP 2020

作者： Shongwe, Thokozani Han Vinck, A. J Department of Electrical and Electronic Engineering Technology University of Johannesburg P.O. Box 17011 Doornfontein Johannesburg2028 South Africa University of Duisburg-Essen Institute of Digital Signal Processing Bismarckstrasse 81 Duisburg47057 Germany

ISBN: (纸本)9781728167435

This paper presents a discussion and results of clipped digital modulated waveform signal that is affected by Additive White Gaussian Noise (AWGN) and Impulse Noise (IN). It is shown that clipping the digital modulated waveform affected by AWGN results in the tails of the distribution being approximated by Gaussian distribution tails. Antipodal and non-coherent FSK modulation schemes are used to demonstrate the effect of clipping a digital modulated waveform signal, and in both schemes, the Bit Error Rate (BER) performance is signficantly improved by clipping, when both AWGN and IN are present. © 2020 IEEE.

关键词： Impulse noise

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Speech Stuttering Detection and Removal Using Deep Neural Networks

Speech Stuttering Detection and Removal Using Deep Neural Ne...

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作者： Shaswat Rajput Ruban Nersisson Alex Noel Joseph Raj A.Mary Mekala Olga Frolova Elena Lyakso School of Electrical Engineering Vellore Institute of Technology Key Laboratory of Digital Signal and Image Processing of Guangdong Province Department of Electronic Engineering College of Engineering Shantou University School of Information Technology and Engineering Vellore Institute of Technology St.Petersburg State University

There are more than 70 million people worldwide who suffer from stuttering problems. This will affect the confidence of public speaking in people who suffer from this issue. To solve this problem many people take therapy sessions but the therapy sessions are a temporary solution, as soon as they leave therapy sessions this problem might arise again. This work aims to use state of the art machine learning algorithms that have improved over the past few years to solve this problem. We have used the dataset from UCLASS archives which provide the data for stuttered speech *** format with time-aligned transcriptions. We have tried different algorithms and optimized our model by hyper parameter tuning to maximize the model's accuracy. The algorithm is tested on random speech data with low to heavy stuttering from the same dataset, and it is observed that there is significant reduction in the Word Error Rate(WER) for most of the test cases.

关键词： Speech stuttering Classification Deep learning Word Error Rate Filtering

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Towards A Conceptually Simple Defensive Approach for Few-shot classifiers against Adversarial Support Samples

arXiv

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arXiv 2021年

作者： Tan, Yi Xiang Marcus Chong, Penny Sun, Jiamei Cheung, Ngai-Man Elovici, Yuval Binder, Alexander Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore487372 Singapore Department of Software and Information Systems Engineerng Ben-Gurion University of the Negev Beer-Sheva653 Israel Department of Informatics Digital Signal Processing and Image Analysis group The University of Oslo Oslo0373 Norway

Few-shot classifiers have been shown to exhibit promising results in use cases where user-provided labels are scarce. These models are able to learn to predict novel classes simply by training on a non-overlapping set of classes. This can be largely attributed to the differences in their mechanisms as compared to conventional deep networks. However, this also offers new opportunities for novel attackers to induce integrity attacks against such models, which are not present in other machine learning setups. In this work, we aim to close this gap by studying a conceptually simple approach to defend few-shot classifiers against adversarial attacks. More specifically, we propose a simple attack-agnostic detection method, using the concept of self-similarity and filtering, to flag out adversarial support sets which destroy the understanding of a victim classifier for a certain class. Our extended evaluation on the miniImagenet (MI) and CUB datasets exhibit good attack detection performance, across three different few-shot classifiers and across different attack strengths, beating baselines. Our observed results allow our approach to establishing itself as a strong detection method for support set poisoning attacks. We also show that our approach constitutes a generalizable concept, as it can be paired with other filtering functions. Finally, we provide an analysis of our results when we vary two components found in our detection approach. Copyright © 2021, The Authors. All rights reserved.

关键词： Machine learning

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Terahertz Communications and Sensing for 6G and Beyond: A Comprehensive View

TechRxiv

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TechRxiv 2023年

作者： Jiang, Wei Zhou, Qiuheng He, Jiguang Habibi, Mohammad Asif Melnyk, Sergiy El-Absi, Mohammed Han, Bin Di Renzo, Marco Schotten, Hans Dieter Luo, Fa-Long El-Bawab, Tarek S. Juntti, Markku Debbah, Mérouane Leung, Victor C.M. Kaiserslautern Germany The Technology Innovation Institute Abu Dhabi United Arab Emirates Centre for Wireless Communications University of Oulu Oulu90014 Finland The Institue of Digital Signal Processing University of Duisburg-Essen Duisburg Germany Germany Université Paris-Saclay CNRS CentraleSupélec Laboratoire des Signaux et Systèmes 3 Rue Joliot-Curie Gif-sur-Yvette91192 France The Electrical and Computer Engineering Department University of Washington SeattleWA98195 United States Nigeria The University of Oulu Finland Khalifa University of Science and Technology P O Box 127788 Abu Dhabi United Arab Emirates The College of Computer Science and Software Engineering Shenzhen University China Canada Germany

The next-generation wireless technologies, commonly referred to as the sixth generation (6G), are envisioned to support extreme communications capacity and in particular disruption in the network sensing capabilities. The terahertz (THz) band is one potential enabler for those due to the enormous unused frequency bands and the high spatial resolution enabled by both short wavelengths and bandwidths. Different from earlier surveys, this paper presents a comprehensive treatment and technology survey on THz communications and sensing in terms of the advantages, applications, propagation characterization, channel modeling, measurement campaigns, antennas, transceiver devices, beamforming, networking, the integration of communications and sensing, and experimental testbeds. Starting from the motivation and use cases, we survey the development and historical perspective of THz communications and sensing with the anticipated 6G requirements. We explore the radio propagation, channel modeling, and measurements for THz band. The transceiver requirements, architectures, technological challenges, and approaches together with means to compensate for the high propagation losses by appropriate antenna and beamforming solutions. We survey also several system technologies required by or beneficial for THz systems. The synergistic design of sensing and communications is explored with depth. Practical trials, demonstrations, and experiments are also summarized. The paper gives a holistic view of the current state of the art and highlights the issues and challenges that are open for further © 2023, CC BY-NC-SA.

关键词： Beamforming

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Coding for Write -step-up Memories

Coding for Write -step-up Memories

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2019 IEEE International Symposium on Information Theory, ISIT 2019

作者： Chee, Yeow Meng Kiah, Han Mao Vinck, A. J. Han Vu, Van Khu Yaakobi, Eitan Department of Industrial Systems Engineering and Management National University of Singapore Singapore Singapore School of Physical and Mathematical Sciences Nanyang Technological University Singapore Singapore Institute of Digital Signal Processing University of Duisburg-Essen Duisburg47057 Germany Computer Science Department Technion - Israel Institute of Technology Haifa Israel

ISBN: (纸本)9781538692912

In this work, we propose and study a new class of non-binary rewriting codes, called write -step-up memories (WM) codes. From an information-theoretic point of view, this coding scheme is a generalization of non-binary write-once memories (WOM) codes. From a practical point of view, this coding scheme can be used not only to increase the lifetime of flash memories but also mitigate their over-shooting problem. We first provide an exact formula for the capacity region and the maximum sum-rate of WM codes. Lastly, we present several explicit constructions of high-rate WM codes with efficient encoding/decoding algorithms. © 2019 IEEE.

关键词： Flash memory

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Facial emotion detection using thermal and visual images based on deep learning techniques

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The Journal of Photographic Science 2024年第2期72卷

作者： Richa Rashmi U. Snekhalatha Anela L. Salvador Alex Noel Joseph Raj a Department of Biomedical Engineering College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur 603203 Tamil Nadu India a Department of Biomedical Engineering College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur 603203 Tamil Nadu Indiab College of Engineering Architecture and Fine Arts Batangas State University The National Engineering University Alangilan Campus Batangas City Batangas Philippines 4200 b College of Engineering Architecture and Fine Arts Batangas State University The National Engineering University Alangilan Campus Batangas City Batangas Philippines 4200 c Key laboratory of Digital Signal and Image Processing of Guangdong Province Department of Electronic Engineering College of Engineering Shantou University People’s Republic of China

ABSTRACTThe main objectives of this study are (i) to determine the different emotions, such as happy, anger, neutral, and sad, based on the visual and thermal images for facial expression recognition of healthy individuals; (ii) to train the modified pre-trained models, such as DenseNet-121, ResNet-50, and VGG-19 based on transfer learning technique, using the visual and thermal image dataset, and (iii) to compare the three different customize model with that of three pre-trained models in terms of accuracy. The customized CNN models were proposed to classify the four classes of emotions with high efficiency. Among thermal and visual image dataset, the thermal images have produced highest classification accuracy of 95.8% using the Customized Net-3 model and have outperformed the three modified pre-trained models while classifying the four facial expressions. Thus, the proposed customized CNN model is proven effective for facial emotion recognition based on thermal imaging using deep learning techniques.

关键词： Facial expression emotion recognition thermal imaging deep neural network convolution neural network Facial thermal image Visual image Pre-trained models

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Difficulty adjustable and scalable constrained multiobjective test problem toolkit

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Evolutionary Computation 2019年第3期28卷 339-378页

作者： Fan, Zhun Li, Wenji Cai, Xinye Li, Hui Wei, Caimin Zhang, Qingfu Deb, Kalyanmoy Goodman, Erik Department of Electronic Engineering Shantou University Guangdong515063 China Key Lab of Digital Signal and Image Processing of Guangdong Province Guangdong China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Jiangsu210016 China School of Mathematics and Statistics Xi’an Jiaotong University Shan’xi710049 China Department of Computer Science City University of Hong Kong Hong Kong BEACON Center for the Study of Evolution in Action Michigan State University East LansingMI United States

Multiobjective evolutionary algorithms (MOEAs) have progressed significantly in recent decades, butmost of them are designed to solve unconstrained multiobjective optimization problems. In fact, many real-world multiobjective problems contain a number of constraints. To promote research on constrained multiobjective optimization, we first propose a problem classification scheme with three primary types of difficulty, which reflect various types of challenges presented by real-world optimization problems, in order to characterize the constraint functions in constrained multiobjective optimization problems (CMOPs). These are feasibility-hardness, convergence-hardness, and diversity-hardness. We then develop a general toolkit to construct difficulty adjustable and scalable CMOPs (DAS-CMOPs, or DAS-CMaOPs when the number of objectives is greater than three) with three types of parameterized constraint functions developed to capture the three proposed types of difficulty. In fact, the combination of the three primary constraint functions with different parameters allows the construction of a large variety of CMOPs, with difficulty that can be defined by a triplet, with each of its parameters specifying the level of one of the types of primary difficulty. Furthermore, the number of objectives in this toolkit can be scaled beyond three. Based on this toolkit, we suggest nine difficulty adjustable and scalable CMOPs and nine CMaOPs, to be called DAS-CMOP1-9 and DAS-CMaOP1-9, respectively. To evaluate the proposed test problems, two popular CMOEAs—MOEA/D-CDP (MOEA/D with constraint dominance principle) and NSGA-II-CDP (NSGA-II with constraint dominance principle) and two popular constrainedmany-objective evolutionary algorithms (CMaOEAs)—C-MOEA/DD and C-NSGA-III—are used to compare performance on DAS-CMOP1-9 and DAS-CMaOP1-9 with a variety of difficulty triplets, respectively. The experimental results reveal that mechanisms in MOEA/D-CDP may be more effective in solving conv

关键词： Hardness

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Harmony-Search and Otsu based System for Coronavirus Disease (COVID-19) Detection using Lung CT Scan Images

arXiv

引用

arXiv 2020年

作者： Rajinikanth, V. Dey, Nilanjan Raj, Alex Noel Joseph Hassanien, Aboul Ella Santosh, K.C. Raja, N. Sri Madhava Department of Electronics and Instrumentation Engineering St. Joseph’s College of Engineering Chennai600119 India Department of Information Technology Techno India College of Technology KolkataWest Bengal700156 India Key Laboratory of Digital Signal and Image Processing of Guangdong Province Department of Electronic Engineering College of Engineering Shantou University Shantou Guangdong China Faculty of Computers and Artificial Intelligence Cairo University Egypt Department of Computer Science University of South Dakota 414 E Clark St VermillionSD57069 United States

Pneumonia is one of the foremost lung diseases and untreated pneumonia will lead to serious threats for all age groups. The proposed work aims to extract and evaluate the Coronavirus disease (COVID-19) caused pneumonia infection in lung using CT scans. We propose an image-assisted system to extract COVID-19 infected sections from lung CT scans (coronal view). It includes following steps: (i) Threshold filter to extract the lung region by eliminating possible artifacts;(ii) Image enhancement using Harmony-Search-Optimization and Otsu thresholding;(iii) Image segmentation to extract infected region(s);and (iv) Region-of-interest (ROI) extraction (features) from binary image to compute level of severity. The features that are extracted from ROI are then employed to identify the pixel ratio between the lung and infection sections to identify infection level of severity. The primary objective of the tool is to assist the pulmonologist not only to detect but also to help plan treatment process. As a consequence, for mass screening processing, it will help prevent diagnostic burden. Copyright © 2020, The Authors. All rights reserved.

关键词： Coronavirus

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