IEEE Signal Processing Magazine最新文献

{"title":"From Discovery to Deployment: Building Bridges Across the Signal Processing Community [From the Editor]","authors":"Tülay Adali","doi":"10.1109/MSP.2026.3716790","DOIUrl":"https://doi.org/10.1109/MSP.2026.3716790","url":null,"abstract":"","PeriodicalId":13246,"journal":{"name":"IEEE Signal Processing Magazine","volume":"43 4","pages":"3-4"},"PeriodicalIF":10.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11655378","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148719027","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"IEEE Conversation","authors":"","doi":"10.1109/MSP.2026.3719393","DOIUrl":"https://doi.org/10.1109/MSP.2026.3719393","url":null,"abstract":"","PeriodicalId":13246,"journal":{"name":"IEEE Signal Processing Magazine","volume":"43 4","pages":"7-7"},"PeriodicalIF":10.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11655386","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148718999","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adding Reconfiguration Capabilities to the Cascaded Integrator-Comb (CIC) Decimation Architecture [Tips & Tricks]","authors":"David Ernesto Troncoso Romero;Miriam Guadalupe Cruz Jiménez;Juan Irving Vasquez-Gomez;Massimiliano Laddomada;Uwe Meyer-Baese","doi":"10.1109/MSP.2026.3669139","DOIUrl":"https://doi.org/10.1109/MSP.2026.3669139","url":null,"abstract":"Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.","PeriodicalId":13246,"journal":{"name":"IEEE Signal Processing Magazine","volume":"43 4","pages":"123-128"},"PeriodicalIF":10.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11655407","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148719030","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bio-Inspired Artificial Neural Networks Based on Predictive Coding [Lecture Notes]","authors":"Davide Casnici;Charlotte Frenkel;Justin Dauwels","doi":"10.1109/MSP.2026.3666766","DOIUrl":"https://doi.org/10.1109/MSP.2026.3666766","url":null,"abstract":"Backpropagation (BP) of errors is currently the backbone training algorithm for artificial neural networks (ANNs). It works by updating the network weights through gradient descent to minimize the value of a <italic>loss function</i>, which represents the mismatch between the network’s prediction and the desired output. BP relies on the chain rule from calculus to propagate the loss gradient backward through the network’s hierarchy, allowing each weight to be efficiently and precisely updated based on its contribution to the output error. However, this process constrains the weight updates at every layer to rely on a <italic>global</i> error signal generated at the extreme of the hierarchy. By contrast, the Hebbian model of synaptic plasticity in the brain states that weight updates should be <italic>local</i>, determined only by the activity of presynaptic and postsynaptic neurons. According to Hebb’s model, it is therefore unlikely that biological brains directly implement BP. Recently, an alternative algorithm for training ANNs called <italic>predictive coding</i> (<italic>PC</i>) is gaining interest, appearing as a more biologically plausible alternative that updates the network weights using only local information. Originating from Elias’s 1950s work on signal compression [1], PC was later proposed in neuroscience as a model of the visual cortex by Rao and Ballard [2]. Successively, Friston formalized it under the free energy principle (FEP) [3], [4], grounding PC within the frameworks of Bayesian inference and dynamical systems. PC weight updates rely only on presynaptic and postsynaptic information, eliminating BP’s dependence on a global error signal. Moreover, it theoretically provides features beyond those of standard BP, such as the ability to automatically scale each gradient based on the associated uncertainty. Despite these advantages, PC still faces several open challenges: iterative error minimization can slow training compared to BP, and scaling to very deep architectures remains difficult [5]. At the same time, its local learning rules and modular structure offer promising opportunities, including highly parallelizable hardware implementations, biologically plausible learning in ANNs, and new connections between artificial intelligence (AI) and neuroscience. These aspects make PC an active area of research, with both theoretical and practical questions yet to be explored. This “Lecture Notes” column offers a novel, tutorial-style introduction to PC, focusing on its formulation, derivation, and connections to well-established optimization and signal processing algorithms, such as BP and the Kalman filter (KF). It aims to provide accessible support to the existing literature, guiding readers from the mathematical foundations underlying PC to its practical implementation, including computational examples in Python using the PyTorch framework.","PeriodicalId":13246,"journal":{"name":"IEEE Signal Processing Magazine","volume":"43 4","pages":"86-99"},"PeriodicalIF":10.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11655381","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148719095","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ILN - Transformer Architectures for Multimodal Signal Processing & Decision Making","authors":"","doi":"10.1109/MSP.2026.3719406","DOIUrl":"https://doi.org/10.1109/MSP.2026.3719406","url":null,"abstract":"","PeriodicalId":13246,"journal":{"name":"IEEE Signal Processing Magazine","volume":"43 4","pages":"75-75"},"PeriodicalIF":10.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11655389","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148719096","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"IEEE Connecting","authors":"","doi":"10.1109/MSP.2026.3719392","DOIUrl":"https://doi.org/10.1109/MSP.2026.3719392","url":null,"abstract":"","PeriodicalId":13246,"journal":{"name":"IEEE Signal Processing Magazine","volume":"43 4","pages":"4-4"},"PeriodicalIF":10.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11655409","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148718951","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quadratic Fractional Window for Spectral Resolution [SP Applications]","authors":"Raghavendra G. Kulkarni","doi":"10.1109/MSP.2026.3668149","DOIUrl":"https://doi.org/10.1109/MSP.2026.3668149","url":null,"abstract":"In this paper, we examine the windows available in the literature, that can be employed for spectral resolution, and see whether one of those windows can be modified to get enhanced features suitable for good spectral resolution. In the next section, a brief review of windows suitable for spectral resolution is given.","PeriodicalId":13246,"journal":{"name":"IEEE Signal Processing Magazine","volume":"43 4","pages":"69-74"},"PeriodicalIF":10.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148719012","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Missing Data in Signal Processing and Machine Learning: Models, methods, and modern approaches","authors":"Alexandre Hippert-Ferrer;Aude Sportisse;Amirhossein Javaheri;Mohammed Nabil El Korso;Daniel P. Palomar","doi":"10.1109/MSP.2026.3682182","DOIUrl":"https://doi.org/10.1109/MSP.2026.3682182","url":null,"abstract":"The goal of this paper is to provide an overview of recent methods for handling missing data in signal processing methods, from their origins to the challenges ahead. Missing data approaches are grouped by three main categories: i) missing-data imputation, ii) estimation with missing values and iii) prediction with missing values. We focus on methodological and experimental results through specific case studies on real-world applications. Promising and future research directions, including a better integration of informative missingness, are also discussed. We believe that the proposed conceptual framework and the presentation of the main problems related to missing data will encourage researchers of the signal processing community to develop original methods for handling missing values and to deal with new applications involving missing data in an adequate manner.","PeriodicalId":13246,"journal":{"name":"IEEE Signal Processing Magazine","volume":"43 4","pages":"14-35"},"PeriodicalIF":10.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148719018","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The IEEE Signal Processing Society’s Leading Role in Developing Standards for Computational Imaging and Sensing: Part II [SP Applications]","authors":"Andreas Bathelt;Benjamin J. B. Deutschmann;Hyeon Seok Rou;Kuranage Roche Rayan Ranasinghe;Giuseppe Thadeu Freitas de Abreu;Peter Vouras","doi":"10.1109/MSP.2026.3680073","DOIUrl":"https://doi.org/10.1109/MSP.2026.3680073","url":null,"abstract":"In every imaging or sensing application, the physical hardware creates constraints that must be overcome or they will limit system performance. Techniques that leverage additional degrees of freedom can effectively extend performance beyond the inherent physical capabilities of the hardware. An example includes synchronizing distributed sensors so as to synthesize a larger aperture for remote sensing applications. An additional example is integrating the communication and sensing functions in a wireless system through the clever design of waveforms and via optimized resource management. As these technologies mature beyond the conceptual and prototype phase they will ultimately transition to the commercial market. Here, standards play a critical role in ensuring success. Standards facilitate interoperability between systems manufactured by different vendors and define industry best practices for vendors and customers alike. The Signal Processing Society (SPS) of the Institute for Electrical and Electronics Engineers (IEEE) plays a leading role in developing high-quality standards for computational sensing technologies through the working groups of the Synthetic Aperture Standards Committee (SASC). In this column we highlight the standards activities of the P3383 Performance Metrics for Integrated Sensing and Communication (ISAC) Systems Working Group and the P3343 Spatiotemporal Synchronization of a Synthetic Aperture of Distributed Sensors Working Group.","PeriodicalId":13246,"journal":{"name":"IEEE Signal Processing Magazine","volume":"43 4","pages":"52-68"},"PeriodicalIF":10.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11655383","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148719073","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SS-OCT Image Analysis: Highlights from the IEEE VIP Cup 2024 Student Competition [Competitions]","authors":"Farnaz Sedighin;Azhar Zam;Mahnoosh Tajmirriahi;Arsham Hamidi;Mohammadreza Ommani;Parisa Ghaderi Daneshmand;Angshul Majumdar;Alireza Dehghani;Mansooreh Ezhei;Hossein Rabbani","doi":"10.1109/MSP.2025.3650343","DOIUrl":"https://doi.org/10.1109/MSP.2025.3650343","url":null,"abstract":"Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.","PeriodicalId":13246,"journal":{"name":"IEEE Signal Processing Magazine","volume":"43 4","pages":"111-117"},"PeriodicalIF":10.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11655414","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148718972","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}