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Signals
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Advanced Signal Processing Technologies: Integrating AI, Future Communications, and Innovative...
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Advanced Signal Processing Technologies: Integrating AI, Future Communications, and Innovative Applications

A special issue of Signals (ISSN 2624-6120).

Deadline for manuscript submissions: 30 November 2026 | Viewed by 6302

Special Issue Editors

Dr. Xiao-Rong Ding

E-Mail Website
Guest Editor
School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: wearable health monitoring; biomedical signal processing; smart healthcare
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Ying-Ren Chien

E-Mail Website
Guest Editor
Adaptive and Autonomous Communication Lab, Department of Electronic Engineering, National Taipei University of Technology (NTUT), Taipei 10608, Taiwan
Interests: adaptive signal processing; IoT; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Pavel Loskot

E-Mail Website
Guest Editor
University of Illinois at Urbana-Champaign Institute, Zhejiang University, Haining 314400, China
Interests: statistical and digital signal processing; computational biology; network models and protocols
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to announce the upcoming Special Issue "Advanced Signal Processing Technologies: Integrating AI, Future Communications, and Innovative Applications". This Special Issue aims to bring together cutting-edge research and innovative contributions from scholars, researchers, and practitioners in the field of signal processing.

The Issue will focus on recent advancements, emerging trends, and novel methodologies in signal processing, including but not limited to, the following topics:

  1. Core and fundamental signal processing theories

- Blind signal processing;

- Array signal processing;

- Theory and applications of frequency diverse array (a specific type of array processing);

- Super-resolution theory, methods, and applications.

  1. Application-specific signal processing

- Image and video processing;

- Audio and acoustic signal processing;

- Biomedical and healthcare signal processing;

- Radar, sensing, and localization.

  1. Communication systems signal processing

- Communication signal processing;

- New waveforms for 6G communications, sensing, and localization;

- Intelligence of system communication, control and reliability.

  1. AI and machine learning for signal processing

- Machine learning for signal processing;

- AI-aided signal processing;

- Large language models and signal processing;

- Intelligent information processing and its marine applications.

We invite submissions of original research articles and comprehensive reviews that demonstrate significant theoretical and practical contributions to the field. This Special Issue will provide a platform for disseminating high-quality research and fostering collaborations among academics and industry experts.

We look forward to your valuable contributions.

Dr. Xiao-Rong Ding
Prof. Dr. Ying-Ren Chien
Dr. Pavel Loskot
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Signals is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • core and fundamental signal processing theories
  • application-specific signal processing
  • communication systems signal processing
  • AI and machine learning for signal processing

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (6 papers)

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Research

22 pages, 421 KB  
Article
Frame-Level Audio Forgery Localization Using Handcrafted and Neural Features
by Mostafa Moallim, Taqwa A. Alhaj, Fatin A. Elhaj, Inshirah Idris and Tasneem Darwish
Signals 2026, 7(3), 42; https://doi.org/10.3390/signals7030042 - 7 May 2026
Viewed by 388
Abstract
Audio forgery has emerged as a significant security and forensic challenge, driven by rapid advances in generative artificial intelligence and the widespread availability of audio editing tools, which enable the creation of highly realistic manipulated speech with minimal technical expertise. Existing approaches predominantly [...] Read more.
Audio forgery has emerged as a significant security and forensic challenge, driven by rapid advances in generative artificial intelligence and the widespread availability of audio editing tools, which enable the creation of highly realistic manipulated speech with minimal technical expertise. Existing approaches predominantly operate at the file level, providing only coarse binary decisions without identifying when or where manipulation occurs. This study addresses fine-grained temporal localization through a unified frame-level localization framework. We introduce a controlled forgery generation framework derived from the TIMIT speech corpus, applying atomic, localized manipulations under strict temporal constraints and producing precise frame-level annotations across diverse manipulation types. Building on this dataset, we then propose a transform-agnostic localization-driven detection approach using temporal inconsistency modeling, enabling unified analysis across heterogeneous manipulations at frame-level resolution. To analyze forensic evidence, we present an evidence-stratified modeling paradigm comparing three complementary strategies: a handcrafted anomaly-based method, a deep localization model leveraging pretrained wav2vec 2.0 representations, and a hybrid approach combining both through confidence-aware fusion and temporal consistency reinforcement. A systematic experimental analysis evaluates the effects of representation adaptation, hybrid fusion, and manipulation type on detection and localization performance. Results show that handcrafted features are insufficient for reliable frame-level localization, while task-adapted wav2vec 2.0 achieves strong and consistent performance. The hybrid approach does not consistently improve frame-level accuracy but yields substantial gains in segment-level localization by enforcing temporal coherence. Per-transform analysis confirms robust performance across most manipulations, with deletion-based operations remaining the most challenging. Full article
(This article belongs to the Special Issue Advanced Signal Processing Technologies: Integrating AI, Future Communications, and Innovative Applications)
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23 pages, 42794 KB  
Article
Crypto-Agile FPGA Architecture with Single-Cycle Switching for OFDM-Based Vehicular Networks
by Mahmoud Elomda, Ahmed A. Ibrahim and Mahmoud Abdelaziz
Signals 2026, 7(2), 38; https://doi.org/10.3390/signals7020038 - 16 Apr 2026
Viewed by 645
Abstract
This paper presents a hardware-accelerated signal processing architecture for OFDM-based vehicular networks that integrates crypto-agile adaptive encryption on a Xilinx Kintex-7 FPGA. The encryption layer is tightly coupled to the OFDM modulation/demodulation pipeline, enabling secure real-time signal processing for V2X communications without disrupting [...] Read more.
This paper presents a hardware-accelerated signal processing architecture for OFDM-based vehicular networks that integrates crypto-agile adaptive encryption on a Xilinx Kintex-7 FPGA. The encryption layer is tightly coupled to the OFDM modulation/demodulation pipeline, enabling secure real-time signal processing for V2X communications without disrupting the baseband chain. A context-aware pre-selection unit dynamically selects among hardware cipher primitives based on latency constraints, security requirements, and channel conditions. The current prototype implements and synthesizes AES-128 as the primary block cipher, while ASCON (NIST lightweight AEAD) and Keccak (SHA-3 foundation) are validated through RTL simulation and architectural integration, demonstrating crypto-agility across block, AEAD, and sponge-based primitives. DES is retained solely as a legacy reference for backward-compatibility evaluation and is not recommended for secure V2X deployment. The design adopts a modular decoupling strategy in which cryptographic engines interface with a unified buffering and interleaving subsystem, enabling hardware-based single-cycle cipher switching without partial reconfiguration. FPGA results demonstrate sub-microsecond cryptographic processing latencies with moderate resource utilization, preserving the timing budget of latency-sensitive vehicular services. AES-128 provides standard-strength encryption, while ASCON and Keccak offer lightweight and sponge-based alternatives suited to constrained IoV platforms. Specifically, the implemented AES-128 core achieves a throughput of 1.02 Gbps with a switching latency of 86 ns, verified across 10 randomized transitions with a 99.99% success rate and zero data corruption. The ASCON and Keccak cores attain throughput-to-area efficiencies of 2.01 and 1.47 Mbps/LUT, respectively, at a unified clock frequency of 50 MHz. All acronyms are defined at first use and a complete list of abbreviations is provided prior to the reference section. Full article
(This article belongs to the Special Issue Advanced Signal Processing Technologies: Integrating AI, Future Communications, and Innovative Applications)
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30 pages, 1323 KB  
Article
Circular Polarization-Based Quantum Encoding for Image Transmission over Error-Prone Channels
by Udara Jayasinghe and Anil Fernando
Signals 2026, 7(2), 37; https://doi.org/10.3390/signals7020037 - 8 Apr 2026
Viewed by 591
Abstract
Quantum image transmission over noisy communication channels remains a challenge due to the fragility of quantum states and their susceptibility to channel impairments. Existing quantum encoding schemes often exhibit limited noise resilience, while advanced approaches introduce computational and implementation complexity. To address these [...] Read more.
Quantum image transmission over noisy communication channels remains a challenge due to the fragility of quantum states and their susceptibility to channel impairments. Existing quantum encoding schemes often exhibit limited noise resilience, while advanced approaches introduce computational and implementation complexity. To address these limitations, this paper proposes a circular polarization-based quantum encoding framework for image transmission over error-prone channels. In the proposed approach, source images are compressed and source-encoded using standard image coding formats, including the joint photographic experts group (JPEG) standard and the high-efficiency image file format (HEIF), and converted into classical bitstreams. The resulting bitstreams are protected using channel coding and mapped onto quantum states via circular polarization representations, where left- and right-hand circularly polarized states encode binary information. The encoded quantum states are transmitted over noisy quantum channels to model channel impairments. At the receiver, appropriate quantum decoding and channel decoding operations are applied to recover the classical bitstream, followed by source decoding to reconstruct the image. The performance of the proposed framework is evaluated using image quality metrics, including peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and universal quality index (UQI). Simulation results demonstrate that the proposed circular polarization-based encoding scheme outperforms existing quantum image encoding techniques, achieving channel SNR gains of 4 dB over state-of-the-art Hadamard-based encoding and 3 dB over frequency-domain quantum encoding methods under severe noise conditions. These results indicate that circular polarization-based quantum encoding provides improved noise robustness and reconstruction fidelity for practical quantum image transmission systems. Full article
(This article belongs to the Special Issue Advanced Signal Processing Technologies: Integrating AI, Future Communications, and Innovative Applications)
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20 pages, 1932 KB  
Article
Non-Contact Heart Rate Estimation via Higher Harmonic Analysis Using 24-GHz Doppler Radar: Validation in Humans and Anesthetized Cat
by Huu-Son Nguyen, Masaki Kurosawa, Koichiro Ishibashi, Ryou Tanaka, Cong-Kha Pham and Guanghao Sun
Signals 2026, 7(2), 24; https://doi.org/10.3390/signals7020024 - 4 Mar 2026
Viewed by 1011
Abstract
This study presents a harmonic-based method for non-contact heart rate (HR) estimation from continuous-wave (CW) Doppler radar signals, validated across multiple species including humans and small animals (cat). Traditional frequency-domain methods struggle when the HR fundamental frequency is weak or overlaps with respiratory [...] Read more.
This study presents a harmonic-based method for non-contact heart rate (HR) estimation from continuous-wave (CW) Doppler radar signals, validated across multiple species including humans and small animals (cat). Traditional frequency-domain methods struggle when the HR fundamental frequency is weak or overlaps with respiratory components. The proposed approach addresses this by identifying three higher-order HR harmonics (2nd, 3rd, and 4th) then reconstructing the HR fundamental frequency from their integer ratios (3/2, 4/3, 2/1). The algorithm processes 20-s sliding windows (1-s overlap) using bandpass filtering to remove respiratory components and HR fundamental while preserving higher harmonics, followed by Power Spectral Density (PSD) analysis. When a complete harmonic set cannot be found, the proposed algorithm switches to harmonic pair detection, enhancing robustness when one harmonic is absent or attenuated. Besides, an adaptive tolerance mechanism enables detection under non-ideal conditions. The method was validated using a public human dataset and an experimental cat dataset with varied positions (supine/prone) and anesthesia levels (1–3% isoflurane). For humans, the algorithm achieved HR Accuracy consistently above 98% with an average RMSE of 1.33 bpm (MAPE: 1.29%, MAE: 0.86 bpm) and Bland-Altman bias below 0.9 bpm. For the cat dataset, performance was even better with HR Accuracy remaining above 99%, an average RMSE of 0.39 bpm (MAPE: 0.22%, MAE: 0.30 bpm), and bias below 0.14 bpm. Full article
(This article belongs to the Special Issue Advanced Signal Processing Technologies: Integrating AI, Future Communications, and Innovative Applications)
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22 pages, 1119 KB  
Article
Robust SNR Estimation Based on Time–Frequency Analysis and Residual Blocks
by Longqing Li, Wenjun Xie, Deming Hu, Jingke Nie, Fei Xie, Zhiping Huang and Yongjie Zhao
Signals 2026, 7(2), 23; https://doi.org/10.3390/signals7020023 - 4 Mar 2026
Viewed by 907
Abstract
Signal-to-noise ratio (SNR) estimation plays a crucial role in communication systems, directly impacting the quality and reliability of signal transmission. This paper proposes a novel deep learning framework aimed at enhancing the accuracy and robustness of SNR estimation. The framework converts received signals [...] Read more.
Signal-to-noise ratio (SNR) estimation plays a crucial role in communication systems, directly impacting the quality and reliability of signal transmission. This paper proposes a novel deep learning framework aimed at enhancing the accuracy and robustness of SNR estimation. The framework converts received signals into time–frequency matrices as feature inputs, effectively capturing both temporal and spectral characteristics through time–frequency analysis. Extensive experimental results across an SNR range of −5 dB to 15 dB demonstrate that our method achieves a mean squared error (MSE) that closely approaches the theoretical Cramér–Rao bound (CRB), comparable to data-aided (DA) maximum likelihood methods. A quantitative analysis reveals that, even under challenging conditions, such as a low SNR of −5 dB, the model maintains superior accuracy with a mean absolute error (MAE) as low as 0.352, significantly outperforming traditional M2M4 and NDA estimators. The model’s performance was systematically evaluated in a wide range of scenarios, encompassing various signal modulation formats, upsampling factors, multipath fading channels, frequency offsets, phase shifts, and roll-off factors. The evaluation highlights its exceptional generalization capability and robustness, with high performance and stability maintained even in challenging and dynamic environments. Full article
(This article belongs to the Special Issue Advanced Signal Processing Technologies: Integrating AI, Future Communications, and Innovative Applications)
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26 pages, 29009 KB  
Article
Quantifying the Relationship Between Speech Quality Metrics and Biometric Speaker Recognition Performance Under Acoustic Degradation
by Ajan Ahmed and Masudul H. Imtiaz
Signals 2026, 7(1), 7; https://doi.org/10.3390/signals7010007 - 12 Jan 2026
Cited by 1 | Viewed by 1853
Abstract
Self-supervised learning (SSL) models have achieved remarkable success in speaker verification tasks, yet their robustness to real-world audio degradation remains insufficiently characterized. This study presents a comprehensive analysis of how audio quality degradation affects three prominent SSL-based speaker verification systems (WavLM, Wav2Vec2, and [...] Read more.
Self-supervised learning (SSL) models have achieved remarkable success in speaker verification tasks, yet their robustness to real-world audio degradation remains insufficiently characterized. This study presents a comprehensive analysis of how audio quality degradation affects three prominent SSL-based speaker verification systems (WavLM, Wav2Vec2, and HuBERT) across three diverse datasets: TIMIT, CHiME-6, and Common Voice. We systematically applied 21 degradation conditions spanning noise contamination (SNR levels from 0 to 20 dB), reverberation (RT60 from 0.3 to 1.0 s), and codec compression (various bit rates), then measured both objective audio quality metrics (PESQ, STOI, SNR, SegSNR, fwSNRseg, jitter, shimmer, HNR) and speaker verification performance metrics (EER, AUC-ROC, d-prime, minDCF). At the condition level, multiple regression with all eight quality metrics explained up to 80% of the variance in minDCF for HuBERT and 78% for WavLM, but only 35% for Wav2Vec2; EER predictability was lower (69%, 67%, and 28%, respectively). PESQ was the strongest single predictor for WavLM and HuBERT, while Shimmer showed the highest single-metric correlation for Wav2Vec2; fwSNRseg yielded the top single-metric R2 for WavLM, and PESQ for HuBERT and Wav2Vec2 (with much smaller gains for Wav2Vec2). WavLM and HuBERT exhibited more predictable quality-performance relationships compared to Wav2Vec2. These findings establish quantitative relationships between measurable audio quality and speaker verification accuracy at the condition level, though substantial within-condition variability limits utterance-level prediction accuracy. Full article
(This article belongs to the Special Issue Advanced Signal Processing Technologies: Integrating AI, Future Communications, and Innovative Applications)
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