Research on the Application of Digital Signal Processing

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
AI ai	3.1	7.2	2020	17.6 Days	CHF 1600
Applied Sciences applsci	2.5	5.3	2011	17.8 Days	CHF 2400
Big Data and Cognitive Computing BDCC	3.7	7.1	2017	18 Days	CHF 1800
Digital digital	-	3.1	2021	23.6 Days	CHF 1000
Healthcare healthcare	2.4	3.5	2013	20.5 Days	CHF 2700
Journal of Imaging jimaging	2.7	5.9	2015	20.9 Days	CHF 1800
Signals signals	-	3.2	2020	26.1 Days	CHF 1000

16 pages, 1800 KiB

Open AccessReview

Source Camera Identification Techniques: A Survey

by Chijioke Emeka Nwokeji, Akbar Sheikh-Akbari, Anatoliy Gorbenko and Iosif Mporas

J. Imaging 2024, 10(2), 31; https://doi.org/10.3390/jimaging10020031 - 25 Jan 2024

Cited by 1 | Viewed by 3034

The successful investigation and prosecution of significant crimes, including child pornography, insurance fraud, movie piracy, traffic monitoring, and scientific fraud, hinge largely on the availability of solid evidence to establish the case beyond any reasonable doubt. When dealing with digital images/videos as evidence [...] Read more.

The successful investigation and prosecution of significant crimes, including child pornography, insurance fraud, movie piracy, traffic monitoring, and scientific fraud, hinge largely on the availability of solid evidence to establish the case beyond any reasonable doubt. When dealing with digital images/videos as evidence in such investigations, there is a critical need to conclusively prove the source camera/device of the questioned image. Extensive research has been conducted in the past decade to address this requirement, resulting in various methods categorized into brand, model, or individual image source camera identification techniques. This paper presents a survey of all those existing methods found in the literature. It thoroughly examines the efficacy of these existing techniques for identifying the source camera of images, utilizing both intrinsic hardware artifacts such as sensor pattern noise and lens optical distortion, and software artifacts like color filter array and auto white balancing. The investigation aims to discern the strengths and weaknesses of these techniques. The paper provides publicly available benchmark image datasets and assessment criteria used to measure the performance of those different methods, facilitating a comprehensive comparison of existing approaches. In conclusion, the paper outlines directions for future research in the field of source camera identification. Full article

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

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10 pages, 4781 KiB

Open AccessArticle

Defect Isolation from Whole to Local Field Separation in Complex Interferometry Fringe Patterns through Development of Weighted Least-Squares Algorithm

by Zhenkai Chen, Wenjing Zhou, Yingjie Yu, Vivi Tornari and Gilberto Artioli

Digital 2024, 4(1), 104-113; https://doi.org/10.3390/digital4010004 - 29 Dec 2023

Viewed by 974

Abstract

In this paper, based on Gaussian 1σ-criterion and histogram segmentation, a weighted least-squares algorithm is applied and validated on digital holographic speckle pattern interferometric data to perform phase separation on the complex interference fields. The direct structural diagnosis tool is used to investigate [...] Read more.

In this paper, based on Gaussian 1σ-criterion and histogram segmentation, a weighted least-squares algorithm is applied and validated on digital holographic speckle pattern interferometric data to perform phase separation on the complex interference fields. The direct structural diagnosis tool is used to investigate defects and their impact on a complex antique wall painting of Giotto. The interferometry data is acquired with a portable off-axis interferometer set-up with a phase-shifted reference beam coupled with the object beam in front of the digital photosensitive medium. A digital holographic speckle pattern interferometry (DHSPI) system is used to register digital recordings of interferogram sequences over time. The surface is monitored for as long as it deforms prior to returning to its initial reference equilibrium state prior to excitation. The attempt to separate the whole vs. local defect complex amplitudes from the interferometric data is presented. The main aim is to achieve isolation and visualization of each defect’s impact amplitude in order to obtain detailed documentation of each defect and its structural impact on the surface for structural diagnosis purposes. Full article

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

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16 pages, 9223 KiB

Open AccessArticle

Simulation-Assisted Augmentation of Missing Wedge and Region-of-Interest Computed Tomography Data

by Vladimir O. Alekseychuk, Andreas Kupsch, David Plotzki, Carsten Bellon and Giovanni Bruno

J. Imaging 2024, 10(1), 11; https://doi.org/10.3390/jimaging10010011 - 29 Dec 2023

Viewed by 1752

Abstract

This study reports a strategy to use sophisticated, realistic X-ray Computed Tomography (CT) simulations to reduce Missing Wedge (MW) and Region-of-Interest (RoI) artifacts in FBP (Filtered Back-Projection) reconstructions. A 3D model of the object is used to simulate the projections that include the [...] Read more.

This study reports a strategy to use sophisticated, realistic X-ray Computed Tomography (CT) simulations to reduce Missing Wedge (MW) and Region-of-Interest (RoI) artifacts in FBP (Filtered Back-Projection) reconstructions. A 3D model of the object is used to simulate the projections that include the missing information inside the MW and outside the RoI. Such information augments the experimental projections, thereby drastically improving the reconstruction results. An X-ray CT dataset of a selected object is modified to mimic various degrees of RoI and MW problems. The results are evaluated in comparison to a standard FBP reconstruction of the complete dataset. In all cases, the reconstruction quality is significantly improved. Small inclusions present in the scanned object are better localized and quantified. The proposed method has the potential to improve the results of any CT reconstruction algorithm. Full article

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

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16 pages, 3538 KiB

Open AccessArticle

Nearly Linear-Phase 2-D Recursive Digital Filters Design Using Balanced Realization Model Reduction

by Abdussalam Omar, Dale Shpak and Panajotis Agathoklis

Signals 2023, 4(4), 800-815; https://doi.org/10.3390/signals4040044 - 27 Nov 2023

Viewed by 1109

Abstract

This paper presents a new method for the design of separable-denominator 2-D IIR filters with nearly linear phase in the passband. The design method is based on a balanced realization model reduction technique. The nearly linear-phase 2-D IIR filter is designed using 2-D [...] Read more.

This paper presents a new method for the design of separable-denominator 2-D IIR filters with nearly linear phase in the passband. The design method is based on a balanced realization model reduction technique. The nearly linear-phase 2-D IIR filter is designed using 2-D model reduction from a linear-phase 2-D FIR filter, which serves as the initial filter. The structured controllability and observability Gramians

P^{s}

and

Q^{s}

serve as the foundation for this technique. onal positive-definite matrices that satisfy 2-D Lyapunov equations. An efficient method is used to compute these Gramians by minimizing the traces of

P^{s}

and

Q^{s}

under linear matrix inequality (LMI) constraints. The use of these Gramians ensures that the resulting 2-D IIR filter preserves stability and can be implemented using a separable-denominator 2-D filter with fewer coefficients than the original 2-D FIR filter. Numerical examples show that the proposed method compares favorably with existing techniques. Full article

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

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20 pages, 686 KiB

Open AccessArticle

High-Quality and Reproducible Automatic Drum Transcription from Crowdsourced Data

by Mickaël Zehren, Marco Alunno and Paolo Bientinesi

Signals 2023, 4(4), 768-787; https://doi.org/10.3390/signals4040042 - 10 Nov 2023

Cited by 1 | Viewed by 1763

Abstract

Within the broad problem known as automatic music transcription, we considered the specific task of automatic drum transcription (ADT). This is a complex task that has recently shown significant advances thanks to deep learning (DL) techniques. Most notably, massive amounts of labeled data [...] Read more.

Within the broad problem known as automatic music transcription, we considered the specific task of automatic drum transcription (ADT). This is a complex task that has recently shown significant advances thanks to deep learning (DL) techniques. Most notably, massive amounts of labeled data obtained from crowds of annotators have made it possible to implement large-scale supervised learning architectures for ADT. In this study, we explored the untapped potential of these new datasets by addressing three key points: First, we reviewed recent trends in DL architectures and focused on two techniques, self-attention mechanisms and tatum-synchronous convolutions. Then, to mitigate the noise and bias that are inherent in crowdsourced data, we extended the training data with additional annotations. Finally, to quantify the potential of the data, we compared many training scenarios by combining up to six different datasets, including zero-shot evaluations. Our findings revealed that crowdsourced datasets outperform previously utilized datasets, and regardless of the DL architecture employed, they are sufficient in size and quality to train accurate models. By fully exploiting this data source, our models produced high-quality drum transcriptions, achieving state-of-the-art results. Thanks to this accuracy, our work can be more successfully used by musicians (e.g., to learn new musical pieces by reading, or to convert their performances to MIDI) and researchers in music information retrieval (e.g., to retrieve information from the notes instead of audio, such as the rhythm or structure of a piece). Full article

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

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14 pages, 1125 KiB

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

Cited by 1 | Viewed by 1037

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 [...] 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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18 pages, 8959 KiB

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

Cited by 1 | Viewed by 1300

Abstract

In this paper, we propose robust image-smoothing methods based on

ℓ_{0}

gradient minimization with novel gradient constraints to effectively suppress pseudo-edges. Simultaneously minimizing the

ℓ_{0}

gradient, i.e., the number of nonzero gradients in an image, and the

ℓ_{2}

data fidelity [...] Read more.

In this paper, we propose robust image-smoothing methods based on

ℓ_{0}

gradient minimization with novel gradient constraints to effectively suppress pseudo-edges. Simultaneously minimizing the

ℓ_{0}

gradient, i.e., the number of nonzero gradients in an image, and the

ℓ_{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

ℓ_{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

ℓ_{0}

gradient minimization demonstrate the advantages of our proposed methods compared to existing

ℓ_{0}

gradient-based approaches. Full article

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

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14 pages, 2625 KiB

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 - 2 Apr 2023

Cited by 3 | Viewed by 2205

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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13 pages, 5323 KiB

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 - 7 Mar 2023

Cited by 6 | Viewed by 3824

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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21 pages, 5435 KiB

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 8 | Viewed by 2840

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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14 pages, 404 KiB

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

Cited by 1 | Viewed by 1739

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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9 pages, 1986 KiB

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 7 | Viewed by 2033

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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