Research on the Application of Digital Signal Processing

Topic Information

Dear Colleagues,

Research on digital signal processing offers a variety of applications that range from the entertainment (music) industry to banking (economy). The next entertainment era is expected to have fully automated tools for music composition, where audio/signal processing is crucial. Regarding financing, most deals are agreed upon over telephone conversations/calls—and healthcare is no exception. According to a recent study by Reaction Data, 62% of healthcare providers surveyed said they are currently using speech recognition technologies for their records, 4% of healthcare providers stated that they are currently implementing medical speech recognition in electronic health records, and 11% of clinicians in the survey said they plan to adopt speech recognition in the next two years.

In this Topic, we invite papers on high-end machine learning models (deep learning) that can analyze big data (e.g., crowd-sourced, and fully labeled lab data) for multiple purposes.

Category 1: Entertainment industry (e.g., music separation and composition)
Category 2: Banking and marketing
Category 3: Healthcare (e.g., understanding emotions using speech data)
Category 4: Language learning (e.g., language recognition in multilingual scenarios)

Therefore, we aim to gather problem-driven research works on the following topics: signal processing, pattern recognition, anomaly detection, computer vision, machine learning, and deep learning. Original research works such as insightful research and practice notes, case studies, and surveys are invited. Submissions from academia, government, and industry are encouraged.

Dr. KC Santosh
Prof. Dr. Alejandro Rodríguez-Gon
Topic Editors

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
AI ai	-	-	2020	21.8 Days	CHF 1200	Submit
Applied Sciences applsci	2.7	4.5	2011	15.8 Days	CHF 2300	Submit
Big Data and Cognitive Computing BDCC	3.7	4.9	2017	16.4 Days	CHF 1600	Submit
Digital digital	-	-	2021	24.1 Days	CHF 1000	Submit
Healthcare healthcare	2.8	2.7	2013	21.7 Days	CHF 2700	Submit
Journal of Imaging jimaging	3.2	4.4	2015	21.9 Days	CHF 1600	Submit
Signals signals	-	-	2020	43.6 Days	CHF 1000	Submit

Published Papers (7 papers)

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Journals

All AI Applied Sciences BDCC Digital Healthcare J. Imaging Signals

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Open AccessArticle

FPGA Implementation of a Higher SFDR Upper DDFS Based on Non-Uniform Piecewise Linear Approximation

by

Xuan Liao

,

Longlong Zhang

,

Xiang Hu

,

Yuanxi Peng

and

Tong Zhou

Appl. Sci. 2023, 13(19), 10819; https://doi.org/10.3390/app131910819 - 29 Sep 2023

Viewed by 120

Abstract

We propose a direct digital frequency synthesizer (DDFS) by using an error-controlled piecewise linear (PWL) approximation method. For a given function and a preset max absolute error(MAE), this method iterates continuously from right to left within the input interval, dividing the entire interval [...] Read more.

We propose a direct digital frequency synthesizer (DDFS) by using an error-controlled piecewise linear (PWL) approximation method. For a given function and a preset max absolute error(MAE), this method iterates continuously from right to left within the input interval, dividing the entire interval into multiple segments. Within each segment, the least squares method is used to approximate the objective function, ensuring that each segment meets the error requirements. Based on this method, We first implemented a set of DDFS under different MAE to study the relationship between SFDR and MAE, and then evaluated its hardware overhead. In order to increase the frequency of the output signal, we implement a multi-core DDFS using time interleaving scheme. The experimental results show that our DDFS has significant advantages in SFDR, using fewer hardware resources to achieve high SFDR. Specifically, the SFDR of proposed DDFS can reach 114 dB using 399 LUTs, 66 flip flops and 3 DSPs. More importantly, we demonstrate through experiments that proposed DDFS breaks the SFDR theoretical upper bound of DDFS based on piecewise linear approximation methods. Full article

(This article belongs to the Topic Research on the Application of Digital Signal Processing)

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Open AccessArticle

Beyond Staircasing Effect: Robust Image Smoothing via ℓ₀ Gradient Minimization and Novel Gradient Constraints

by

Ryo Matsuoka

and

Masahiro Okuda

Signals 2023, 4(4), 669-686; https://doi.org/10.3390/signals4040037 - 26 Sep 2023

Viewed by 158

Abstract

In this paper, we propose robust image-smoothing methods based on ${\mathit{\ell }}_0$ gradient minimization with novel gradient constraints to effectively suppress pseudo-edges. Simultaneously minimizing the ${\mathit{\ell }}_0$ gradient, i.e., the number of nonzero gradients in an image, and the ${\mathit{\ell }}_2$ data fidelity [...] Read more.

In this paper, we propose robust image-smoothing methods based on ${\mathit{\ell }}_0$ gradient minimization with novel gradient constraints to effectively suppress pseudo-edges. Simultaneously minimizing the ${\mathit{\ell }}_0$ gradient, i.e., the number of nonzero gradients in an image, and the ${\mathit{\ell }}_2$ data fidelity results in a smooth image. However, this optimization often leads to undesirable artifacts, such as pseudo-edges, known as the “staircasing effect”, and halos, which become more visible in image enhancement tasks, like detail enhancement and tone mapping. To address these issues, we introduce two types of gradient constraints: box and ball. These constraints are applied using a reference image (e.g., the input image is used as a reference for image smoothing) to suppress pseudo-edges in homogeneous regions and the blurring effect around strong edges. We also present an ${\mathit{\ell }}_0$ gradient minimization problem based on the box-/ball-type gradient constraints using an alternating direction method of multipliers (ADMM). Experimental results on important applications of ${\mathit{\ell }}_0$ gradient minimization demonstrate the advantages of our proposed methods compared to existing ${\mathit{\ell }}_0$ gradient-based approaches. Full article

(This article belongs to the Topic Research on the Application of Digital Signal Processing)

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Open AccessArticle

Application of Wavelet Transform for the Detection of Cetacean Acoustic Signals

by

Ruilin He

,

Yang Dai

,

Siyi Liu

,

Yuhao Yang

,

Yingdong Wang

,

Wei Fan

and

Shengmao Zhang

Appl. Sci. 2023, 13(7), 4521; https://doi.org/10.3390/app13074521 - 02 Apr 2023

Viewed by 1114

Abstract

Cetaceans are an important part of the ocean ecosystem and are widely distributed in seas across the world. Cetaceans are heavily reliant on acoustic signals for communication. Some Odontoceti can perceive their environments using their sonar system, including the detection, localization, discrimination, and [...] Read more.

Cetaceans are an important part of the ocean ecosystem and are widely distributed in seas across the world. Cetaceans are heavily reliant on acoustic signals for communication. Some Odontoceti can perceive their environments using their sonar system, including the detection, localization, discrimination, and recognition of objects. Acoustic signals are one of the most commonly used types of data for Cetacean research, and it is necessary to develop Cetacean acoustic signal detection methods. This study compared the performance of a manual method, short-time Fourier transform (STFT), and wavelet transform (WT) in Cetacean acoustic signal detection. The results showed that WT performs better in click detection. According to this research, we propose using STFT for whistle and burst-pulse marking and WT for click marking in dataset building. This research will be helpful in facilitating research on the habits and behaviors of groups and individuals, thus providing information to develop methods for protecting species and developing biological resources. Full article

(This article belongs to the Topic Research on the Application of Digital Signal Processing)

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Open AccessArticle

ReUse: REgressive Unet for Carbon Storage and Above-Ground Biomass Estimation

by

Antonio Elia Pascarella

,

Giovanni Giacco

,

Mattia Rigiroli

,

Stefano Marrone

and

Carlo Sansone

J. Imaging 2023, 9(3), 61; https://doi.org/10.3390/jimaging9030061 - 07 Mar 2023

Cited by 1 | Viewed by 1659

Abstract

The United Nations Framework Convention on Climate Change (UNFCCC) has recently established the Reducing Emissions from Deforestation and forest Degradation (REDD+) program, which requires countries to report their carbon emissions and sink estimates through national greenhouse gas inventories (NGHGI). Thus, developing automatic systems [...] Read more.

The United Nations Framework Convention on Climate Change (UNFCCC) has recently established the Reducing Emissions from Deforestation and forest Degradation (REDD+) program, which requires countries to report their carbon emissions and sink estimates through national greenhouse gas inventories (NGHGI). Thus, developing automatic systems capable of estimating the carbon absorbed by forests without in situ observation becomes essential. To support this critical need, in this work, we introduce ReUse, a simple but effective deep learning approach to estimate the carbon absorbed by forest areas based on remote sensing. The proposed method’s novelty is in using the public above-ground biomass (AGB) data from the European Space Agency’s Climate Change Initiative Biomass project as ground truth to estimate the carbon sequestration capacity of any portion of land on Earth using Sentinel-2 images and a pixel-wise regressive UNet. The approach has been compared with two literature proposals using a private dataset and human-engineered features. The results show a more remarkable generalization ability of the proposed approach, with a decrease in Mean Absolute Error and Root Mean Square Error over the runner-up of 16.9 and 14.3 in the area of Vietnam, 4.7 and 5.1 in the area of Myanmar, 8.0 and 1.4 in the area of Central Europe, respectively. As a case study, we also report an analysis made for the Astroni area, a World Wildlife Fund (WWF) natural reserve struck by a large fire, producing predictions consistent with values found by experts in the field after in situ investigations. These results further support the use of such an approach for the early detection of AGB variations in urban and rural areas. Full article

(This article belongs to the Topic Research on the Application of Digital Signal Processing)

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Open AccessArticle

A Denoising Method for Seismic Data Based on SVD and Deep Learning

by

Guoli Ji

and

Chao Wang

Appl. Sci. 2022, 12(24), 12840; https://doi.org/10.3390/app122412840 - 14 Dec 2022

Cited by 3 | Viewed by 1162

Abstract

When reconstructing seismic data, the traditional singular value decomposition (SVD) denoising method has the challenge of difficult rank selection. Therefore, we propose a seismic data denoising method that combines SVD and deep learning. In this method, seismic data with different signal-to-noise ratios (SNRs) [...] Read more.

When reconstructing seismic data, the traditional singular value decomposition (SVD) denoising method has the challenge of difficult rank selection. Therefore, we propose a seismic data denoising method that combines SVD and deep learning. In this method, seismic data with different signal-to-noise ratios (SNRs) are processed by SVD. Data sets are created from the decomposed right singular vectors and data sets divided into two categories: effective signal and noise. The lightweight MobileNetV2 network was chosen for training because of its quick response speed and great accuracy. We forecasted and categorized the right singular vectors by SVD using the trained MobileNetV2 network. The right singular vector (RSV) corresponding to the noise in the seismic data was removed during reconstruction, but the effective signal was kept. The effective signal was projected to smooth the RSV. Finally, the goal of low SNR denoising of two-dimensional seismic data was accomplished. This approach addresses issues with deep learning in seismic data processing, including the challenge of gathering sample data and the weak generalizability of the training model. Compared with the traditional denoising method, the improved denoising method performs well at removing Gaussian and irregular noise with strong amplitudes. Full article

(This article belongs to the Topic Research on the Application of Digital Signal Processing)

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Open AccessArticle

Finding Subsampling Index Sets for Kronecker Product of Unitary Matrices for Incoherent Tight Frames

by

Jooeun Kwon

and

Nam Yul Yu

Appl. Sci. 2022, 12(21), 11055; https://doi.org/10.3390/app122111055 - 31 Oct 2022

Viewed by 1123

Abstract

Frames are recognized for their importance in many fields of communications, signal processing, quantum physics, and so on. In this paper, we design an incoherent tight frame by selecting some rows of a matrix that is the Kronecker product of Fourier and unitary [...] Read more.

Frames are recognized for their importance in many fields of communications, signal processing, quantum physics, and so on. In this paper, we design an incoherent tight frame by selecting some rows of a matrix that is the Kronecker product of Fourier and unitary matrices. The Kronecker-product-based frame allows its elements to have a small number of phases, regardless of the frame length, which is suitable for low-cost implementation. To obtain the Kronecker-product-based frame with low mutual coherence, we first derive an objective function by transforming the Gram matrix expression to compute the coherence. If the Hadamard matrix is employed as a unitary matrix, the objective function can be computed efficiently with low complexity. Then, we find a subsampling index set for the Kronecker-product-based frame by minimizing the objective function. In simulations, we show that the Kronecker-product-based frames can achieve similar mutual coherence to optimized harmonic frames of a large number of phases. We apply the frames to compressed sensing (CS) as the measurement matrices, where the Kronecker-product-based frames demonstrate reliable performance of sparse signal recovery. Full article

(This article belongs to the Topic Research on the Application of Digital Signal Processing)

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Open AccessArticle

Absolute Distance Measurement Based on Self-Mixing Interferometry Using Compressed Sensing

by

Li Li

,

Yue Zhang

,

Ye Zhu

,

Ya Dai

,

Xuan Zhang

and

Xuwen Liang

Appl. Sci. 2022, 12(17), 8635; https://doi.org/10.3390/app12178635 - 29 Aug 2022

Cited by 3 | Viewed by 1094

Abstract

An absolute distance measurement sensor based on self-mixing interferometry (SMI) is suitable for application in aerospace due to its small size and light weight. However, an SMI signal with a high sampling rate places a burden on sampling devices and other onboard sources. [...] Read more.

An absolute distance measurement sensor based on self-mixing interferometry (SMI) is suitable for application in aerospace due to its small size and light weight. However, an SMI signal with a high sampling rate places a burden on sampling devices and other onboard sources. SMI distance measurement using compressed sensing (CS) is proposed in this work to relieve this burden. The SMI signal was sampled via a measurement matrix at a sampling rate lower than Nyquist’s law and then recovered by the greedy pursuit algorithm. The recovery algorithm was improved to increase its robustness and iteration speed. On a distance measuring system with a measurement error of 60 µm, the difference between raw data with 1800 points and CS recovered data with 300 points was within 0.15 µm, demonstrating the feasibility of SMI distance measurement using CS. Full article

(This article belongs to the Topic Research on the Application of Digital Signal Processing)

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