Frontiers in signal processing最新文献

{"title":"A mini-review of signal processing techniques for RIS-assisted near field THz communication","authors":"Vaishali Sharma, Navneet Garg, S. Sharma, V. Bhatia","doi":"10.3389/frsip.2023.1297945","DOIUrl":"https://doi.org/10.3389/frsip.2023.1297945","url":null,"abstract":"Reflecting Intelligent Surfaces (RISs) are reshaping the landscape of wireless communications, particularly in the terahertz (THz) frequency bands, offering promising solutions to inherent challenges in the bands. THz communication boasts bandwidths exceeding 100 GHz, leading to data rates potentially in the terabits per second (Tbps) range, thereby making it an attractive proposition for wireless communications, imaging, and sensing. However, benefits come with challenges, including significant molecular absorption, scattering, diffraction, and hardware limitations. Moreover, as bandwidth in the THz range increases, so does the difficulty of signal processing at Nyquist rate. RIS emerges as a game-changer for 6G and beyond by providing programmable reflecting elements that can adaptively modify the phases and amplitudes of incident signals, enabling precision in directing THz waves and enhancing received signal strength. Such capabilities can significantly mitigate path loss and atmospheric absorption challenges. Furthermore, inherent pencil beamforming capabilities of RIS lead to optimized energy utilization. Major challenge in THz communications is the pressing needs for efficient algorithms for robust THz transceivers and optimizing RIS elements. This review describes the integration of RIS and near-field THz communications, highlighting their future potential and challenges for the next-generation wireless networks. In this article, a comprehensive understanding of the complexities and nuances of near-field propagation in 6G networks, especially as the technology shifts towards extremely large-scale antenna arrays (ELAA). Additionally, it will introduce the transformative potential of sub-Nyquist rate signal processing and artificial intelligence (AI) offering innovative solutions to address the inherent challenges of 6G communication, especially in channel estimation and beamforming strategies.","PeriodicalId":93557,"journal":{"name":"Frontiers in signal processing","volume":"49 17","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139448063","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Editorial: Signal processing in computational video and video streaming","authors":"Anil Kokaram, Anil Anthony Bharath, Feng Yang","doi":"10.3389/frsip.2023.1357892","DOIUrl":"https://doi.org/10.3389/frsip.2023.1357892","url":null,"abstract":"","PeriodicalId":93557,"journal":{"name":"Frontiers in signal processing","volume":"16 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139381896","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Editorial: Editor’s challenge—image processing","authors":"Frédéric Dufaux","doi":"10.3389/frsip.2023.1338890","DOIUrl":"https://doi.org/10.3389/frsip.2023.1338890","url":null,"abstract":"","PeriodicalId":93557,"journal":{"name":"Frontiers in signal processing","volume":"24 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138591679","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improved circuitry and post-processing for interleaved fast-scan cyclic voltammetry and electrophysiology measurements","authors":"Ashwin K. Avula, Abhinav Goyal, Aaron E. Rusheen, Jason Yuen, Warren O. Dennis, Diane R. Eaker, Joshua B. Boesche, C. Blaha, K. Bennet, Kendall H. Lee, Hojin Shin, Yoonbae Oh","doi":"10.3389/frsip.2023.1195800","DOIUrl":"https://doi.org/10.3389/frsip.2023.1195800","url":null,"abstract":"The combination of electrophysiology and electrochemistry acquisition methods using a single carbon fiber microelectrode (CFM) in the brain has enabled more extensive analysis of neurochemical release, neural activity, and animal behavior. Predominantly, analog CMOS (Complementary Metal Oxide Semiconductor) switches are used for these interleaved applications to alternate the CFM output between electrophysiology and electrochemistry acquisition circuitry. However, one underlying issue with analog CMOS switches is the introduction of transient voltage artifacts in recorded electrophysiology signals resulting from CMOS charge injection. These injected artifacts attenuate electrophysiology data and delay reliable signal observation after every switch actuation from electrochemistry acquisition. Previously published attempts at interleaved electrophysiology and electrochemistry were able to recover reliable electrophysiology data within approximately 10–50 ms after switch actuation by employing various high-pass filtering methods to mitigate the observed voltage artifacts. However, high-pass filtering of this nature also attenuates valuable portions of the local-field potential (LFP) frequency range, thus limiting the extent of network-level insights that can be derived from in vivo measurements. This paper proposes a solution to overcome the limitation of charge injection artifacts that affect electrophysiological data while preserving important lower-frequency LFP bands. A voltage follower operational amplifier was integrated before the CMOS switch to increase current flow to the switch and dissipate any injected charge. This hardware addition resulted in a 16.98% decrease in electrophysiology acquisition delay compared to circuitry without a voltage follower. Additionally, single-term exponential modeling was implemented in post-processing to characterize and subtract remaining transient voltage artifacts in recorded electrophysiology data. As a result, electrophysiology data was reliably recovered 3.26 ± 0.22 ms after the beginning of the acquisition period (a 60% decrease from previous studies), while also minimizing LFP attenuation. Through these advancements, coupled electrophysiology and electrochemistry measurements can be conducted at higher scan rates while retaining data integrity for a more comprehensive analysis of neural activity and neurochemical release.","PeriodicalId":93557,"journal":{"name":"Frontiers in signal processing","volume":"27 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139206183","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bounds for Haralick features in synthetic images with sinusoidal gradients","authors":"A. Oprisan, S. Oprisan","doi":"10.3389/frsip.2023.1271769","DOIUrl":"https://doi.org/10.3389/frsip.2023.1271769","url":null,"abstract":"Introduction: The gray-level co-occurrence matrix (GLCM) reduces the dimension of an image to a square matrix determined by the number of gray-level intensities present in that image. Since GLCM only measures the co-occurrence frequency of pairs of gray levels at a given distance from each other, it also stores information regarding the gradients of gray-level intensities in the original image.Methods: The GLCM is a second-order statical method of encoding image information and dimensionality reduction. Image features are scalars that reduce GLCM dimensionality and allow fast texture classification. We used Haralick features to extract information regarding image gradients based on the GLCM.Results: We demonstrate that a gradient of k gray levels per pixel in an image generates GLCM entries on the kth parallel line to the main diagonal. We find that, for synthetic sinusoidal periodic gradients with different wavelengths, the number of gray levels due to intensity quantization follows a power law that also transpires in some Haralick features. We estimate bounds for four of the most often used Haralick features: energy, contrast, correlation, and entropy. We find good agreement between our analytically predicted values of Haralick features and the numerical results from synthetic images of sinusoidal periodic gradients.Discussion: This study opens the possibility of deriving bounds for Haralick features for targeted textures and provides a better selection mechanism for optimal features in texture analysis applications.","PeriodicalId":93557,"journal":{"name":"Frontiers in signal processing","volume":"532 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139244760","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Editorial: Feature extraction and deep learning for digital pathology images","authors":"M. Jagannath","doi":"10.3389/frsip.2023.1296745","DOIUrl":"https://doi.org/10.3389/frsip.2023.1296745","url":null,"abstract":"","PeriodicalId":93557,"journal":{"name":"Frontiers in signal processing","volume":"225 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139323275","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multilingual video dubbing—a technology review and current challenges","authors":"Dan Bigioi, Peter Corcoran","doi":"10.3389/frsip.2023.1230755","DOIUrl":"https://doi.org/10.3389/frsip.2023.1230755","url":null,"abstract":"The proliferation of multi-lingual content on today’s streaming services has created a need for automated multi-lingual dubbing tools. In this article, current state-of-the-art approaches are discussed with reference to recent works in automatic dubbing and the closely related field of talking head generation. A taxonomy of papers within both fields is presented, and the main challenges of both speech-driven automatic dubbing, and talking head generation are discussed and outlined, together with proposals for future research to tackle these issues.","PeriodicalId":93557,"journal":{"name":"Frontiers in signal processing","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135817736","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automated segmentation and labeling of subcutaneous mouse implants at 14.1T","authors":"Julien Adda, G. Bioley, D. Van de Ville, C. Cudalbu, M. G. Preti, N. Gninenko","doi":"10.3389/frsip.2023.1155618","DOIUrl":"https://doi.org/10.3389/frsip.2023.1155618","url":null,"abstract":"Magnetic resonance imaging (MRI) is a valuable tool for studying subcutaneous implants in rodents, providing non-invasive insight into biomaterial conformability and longitudinal characterization. However, considerable variability in existing image analysis techniques, manual segmentation and labeling, as well as the lack of reference atlases as opposed to brain imaging, all render the manual implant segmentation task tedious and extremely time-consuming. To this end, the development of automated and robust segmentation pipelines is a necessary addition to the tools available in rodent imaging research. In this work, we presented and compared commonly used image processing contrast-based segmentation approaches—namely, Canny edge detection, Otsu’s single and multi-threshold methods, and a combination of the latter with morphological operators—with more recently introduced convolutional neural network (CNN-) based models, such as the U-Net and nnU-Net (“no-new-net”). These fully automated end-to-end state-of-the-art neural architectures have shown great promise in online segmentation challenges. We adapted them to the implant segmentation task in mice MRI, with both 2D and 3D implementations. Our results demonstrated the superiority of the 3D nnU-Net model, which is able to robustly segment the implants with an average Dice accuracy of 0.915, and an acceptable absolute volume prediction error of 5.74%. Additionally, we provide researchers in the field with an automated segmentation pipeline in Python, leveraging these CNN-based implementations, and allowing to drastically reduce the manual labeling time from approximately 90 min to less than 5 min (292.959 s ± 6.49 s, N = 30 predictions). The latter addresses the bottleneck of constrained animal experimental time in pre-clinical rodent research.","PeriodicalId":93557,"journal":{"name":"Frontiers in signal processing","volume":"18 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91099212","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Detection of OFDM modulations based on the characterization in the phase diagram domain","authors":"A. Digulescu, A. Sârbu, Denis Stanescu, D. Nastasiu, Cristina Despina-Stoian, C. Ioana, A. Mansour","doi":"10.3389/frsip.2023.1197590","DOIUrl":"https://doi.org/10.3389/frsip.2023.1197590","url":null,"abstract":"Signal modulation identification is of high interest for applications in military communications, but is not limited only to this specific field. Some possible applications are related to spectrum surveillance, electronic warfare, quality services, and cognitive radio. Distinguishing between multi-carrier signals, such as orthogonal frequency division multiplexing (OFDM) signals, and single-carrier signals is very important in several applications. Conventional methods face a stalemate in which the classification accuracy process is limited, and, therefore, new descriptors are needed to complement the existing methods. Another drawback is that some features cannot be extracted using conventional feature extraction techniques in practical OFDM systems. This paper introduces a new signal detection algorithm based on the phase diagram characterization. First, the proposed algorithm is described and implemented for simulated signals in MATLAB. Second, the algorithm performance is verified in an experimental scenario by using long-term evolution OFDM signals over a software-defined radio (SDR) frequency testbed. Our findings suggest that the algorithm provides good detection performance in realistic noisy environments.","PeriodicalId":93557,"journal":{"name":"Frontiers in signal processing","volume":"41 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81315891","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Phase-only array transmit beamforming without iterative/numerical optimization methods","authors":"D. Orlando, Alfonso Farina","doi":"10.3389/frsip.2023.1244530","DOIUrl":"https://doi.org/10.3389/frsip.2023.1244530","url":null,"abstract":"In this letter, we address the problem of phase-only transmit beamforming to generate a wide beam with an almost flat mainlobe for phased arrays. Instead of resorting to time-demanding optimization procedures, the proposed method is grounded on the Fourier analysis and exploits the fact that radiation pattern can be written as the Fourier transform of the aperture illumination function. In this context, we consider a complex linear frequency modulated illumination function and derive the equations allowing for a control of the beam width. The related computational complexity is linear in the number of the array elements. The numerical examples show the effectiveness of the proposed method in forcing the desired beam shape with good sidelobes’ properties and also in comparison with an iterative competitor.","PeriodicalId":93557,"journal":{"name":"Frontiers in signal processing","volume":"23 2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77507356","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}