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This index covers all technical items - papers, correspondence, reviews, etc. - that appeared in this periodical during the year, and items from previous years that were commented upon or corrected in this year. Departments and other items may also be covered if they have been judged to have archival value. The Author Index contains the primary entry for each item, listed under the first author's name. The primary entry includes the coauthors' names, the title of the paper or other item, and its location, specified by the publication abbreviation, year, month, and inclusive pagination. The Subject Index contains entries describing the item under all appropriate subject headings, plus the first author's name, the publication abbreviation, month, and year, and inclusive pages. Note that the item title is found only under the primary entry in the Author Index.
This index covers all technical items - papers, correspondence, reviews, etc. - that appeared in this periodical during the year, and items from previous years that were commented upon or corrected in this year. Departments and other items may also be covered if they have been judged to have archival value. The Author Index contains the primary entry for each item, listed under the first author's name. The primary entry includes the co-authors' names, the title of the paper or other item, and its location, specified by the publication abbreviation, year, month, and inclusive pagination. The Subject Index contains entries describing the item under all appropriate subject headings, plus the first author's name, the publication abbreviation, month, and year, and inclusive pages. Note that the item title is found only under the primary entry in the Author Index.
Previous article Next article Error-Free Recovery of Signals from Irregularly Spaced SamplesFrederick J. BeutlerFrederick J. Beutlerhttps://doi.org/10.1137/1008065PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAbout[1] H. S. Black, Modulation Theory, Van Nostrand, New York, 1953 0089.20302 Google Scholar[2] J. L. Yen, On nonuniform sampling of bandwidth-limited signals, IRE Trans. Circuit Theory, CT-3 (1956), 251–257 CrossrefGoogle Scholar[3] Harold S. Shapiro and , Richard A. Silverman, Alias-free sampling of random noise, J. Soc. Indust. Appl. Math., 8 (1960), 225–248 10.1137/0108013 MR0121948 0121.14204 LinkISIGoogle Scholar[4] Frederick J. Beutler, Sampling theorems and bases in a Hilbert space, Information and Control, 4 (1961), 97–117 10.1016/S0019-9958(61)80001-6 MR0147315 0143.41704 CrossrefGoogle Scholar[5] H. P. Kramer, A generalized sampling theorem, Bull. Amer. Math. Soc., 63 (1957), 117–, Abstract Google Scholar[6] Norman Levinson, Gap and Density Theorems, American Mathematical Society Colloquium Publications, v. 26, American Mathematical Society, New York, 1940viii+246 MR0003208 0145.08003 CrossrefGoogle Scholar[7] Philip J. Davis, Interpolation and approximation, Blaisdell Publishing Co. Ginn and Co. New York-Toronto-London, 1963xiv+393 MR0157156 0111.06003 Google Scholar[8] Frigyes Riesz and , Béla Sz.-Nagy, Functional analysis, Frederick Ungar Publishing Co., New York, 1955xii+468 MR0071727 0070.10902 Google Scholar[9] J. L. Doob, Stochastic processes, John Wiley & Sons Inc., New York, 1953viii+654 MR0058896 0053.26802 Google Scholar[10] Frederick J. Beutler and , Oscar A. Z. Leneman, The theory of stationary point processes, Acta Math., 116 (1966), 159–197 MR0199895 0147.16003 CrossrefISIGoogle Scholar Previous article Next article FiguresRelatedReferencesCited ByDetails Event-Driven Circuits and Systems: A Promising Low Power Technique for Intelligent Sensors in AIoT EraIEEE Transactions on Circuits and Systems II: Express Briefs, Vol. 69, No. 7 | 1 Jul 2022 Cross Ref Random sampling for effective spectrum sensing in cognitive radio time slotted environmentPhysical Communication, Vol. 49 | 1 Dec 2021 Cross Ref Applications of the Hilbert Transform – A Nonparametric Method for Interpolation/Extrapolation of DataModern Characterization of Electromagnetic Systems and Its Associated Metrology | 13 August 2021 Cross Ref DASP Implementation of Continuous-Time, Finite-Impulse-Response Systems2020 28th European Signal Processing Conference (EUSIPCO) | 24 Jan 2021 Cross Ref Compressed encoding of vibration signals using extremum samplingSN Applied Sciences, Vol. 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Previous article Next article The Pseudoinverse of a Rectangular or Singular Matrix and Its Application to the Solution of Systems of Linear EquationsT. N. E. GrevilleT. N. E. Grevillehttps://doi.org/10.1137/1001003PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAbout[1] R. Penrose, A generalized inverse for matrices, Proc. Cambridge Philos. Soc., 51 (1955), 406–413 MR0069793 0065.24603 CrossrefGoogle Scholar[2] R. Penrose, On best approximation solutions of linear matrix equations, Proc. Cambridge Philos. Soc., 52 (1956), 17–19 MR0074092 0070.12501 CrossrefGoogle Scholar[3] Arne Bjerhammar, Application of calculus of matrices to method of least squares with special reference to geodetic calculations, Trans. Roy. Inst. Tech. Stockholm, 1951 (1951), 86 pp. (2 plates) MR0055793 Google Scholar[4] Arne Bjerhammar, Rectangular reciprocal matrices, with special reference to geodetic calculations, Bull. Géodésique, 1951 (1951), 188–220 MR0043758 CrossrefGoogle Scholar Previous article Next article FiguresRelatedReferencesCited ByDetails H∞ -PID distributed control for output leader-tracking and containment of heterogeneous MASs with external disturbancesJournal of Control and Decision, Vol. 9, No. 4 | 24 December 2021 Cross Ref Improving MRAC Transient Response Using Generalized Dynamic Inversion and Nullspace ParametrizationIFAC-PapersOnLine, Vol. 55, No. 12 | 1 Jan 2022 Cross Ref Successive Generalized Dynamic Inversion Control of Lateral Fighter Aircraft Dynamics*2021 9th International Conference on Systems and Control (ICSC) | 24 Nov 2021 Cross Ref Experimental noise source identification in a fuselage test environment based on nearfield acoustical holographyCEAS Aeronautical Journal, Vol. 12, No. 4 | 18 August 2021 Cross Ref A Model-free Deep Reinforcement Learning Approach for Robotic Manipulators Path Planning2021 21st International Conference on Control, Automation and Systems (ICCAS) | 12 Oct 2021 Cross Ref Some additive results on M-P inverse of operator productAsian-European Journal of Mathematics, Vol. 14, No. 01 | 18 October 2019 Cross Ref An optimal distributed PID-like control for the output containment and leader-following of heterogeneous high-order multi-agent systemsInformation Sciences, Vol. 541 | 1 Dec 2020 Cross Ref Mutual Direct State Controllability Analysis of Multivariable Underactuated LTI Systems2020 Australian and New Zealand Control Conference (ANZCC) | 26 Nov 2020 Cross Ref Using prior parameter knowledge in model‐based design of experiments for pharmaceutical productionAIChE Journal, Vol. 66, No. 11 | 26 August 2020 Cross Ref Mutual Direct Controllability of MIMO Systems and Application to Longitudinal Aircraft Control2020 24th International Conference on System Theory, Control and Computing (ICSTCC) | 8 Oct 2020 Cross Ref Neural Network-based Hybrid Position/Force Tracking Control for Flexible Joint Robot2020 IEEE International Systems Conference (SysCon) | 24 Aug 2020 Cross Ref Femtosecond-resolved observation of the fragmentation of buckminsterfullerene following X-ray multiphoton ionizationNature Physics, Vol. 15, No. 12 | 23 September 2019 Cross Ref Canonical Kane's Equations of Motion for Discrete Dynamical SystemsAIAA Journal, Vol. 57, No. 10 | 1 Oct 2019 Cross Ref Sequential model-based A- and V-optimal design of experiments for building fundamental models of pharmaceutical production processesComputers & Chemical Engineering, Vol. 129 | 1 Oct 2019 Cross Ref Quadrotor Control Via Robust Generalized Dynamic Inversion and Adaptive Non‐Singular Terminal Sliding ModeAsian Journal of Control, Vol. 21, No. 3 | 30 April 2018 Cross Ref Development and experimental investigation of a Quadrotor's robust generalized dynamic inversion control systemNonlinear Dynamics, Vol. 96, No. 2 | 8 March 2019 Cross Ref Sequential Model-Based A-Optimal Design of Experiments When the Fisher Information Matrix Is NoninvertibleIndustrial & Engineering Chemistry Research, Vol. 58, No. 3 | 20 December 2018 Cross Ref Passing Through Jacobian Singularities in Motion Path Control of Redundant Robot ArmsAdvances in Service and Industrial Robotics | 29 September 2018 Cross Ref Optimization of Model Operator for Service Robot, Intended to Service Persons with DisabilityIFAC-PapersOnLine, Vol. 52, No. 25 | 1 Jan 2019 Cross Ref Positional control of rotary servo cart system using generalized dynamic inversionJournal of Vibroengineering, Vol. 20, No. 6 | 30 September 2018 Cross Ref Canonical Generalized Inversion Form of Kane's Equations of Motion for Constrained Mechanical SystemsNonlinear Systems - 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This paper provides an overview of NVIDIA NeMo's neural machine translation systems for the constrained data track of the WMT21 News and Biomedical Shared Translation Tasks. Our news task submissions for English-German (En-De) and English-Russian (En-Ru) are built on top of a baseline transformer-based sequence-to-sequence model. Specifically, we use a combination of 1) checkpoint averaging 2) model scaling 3) data augmentation with backtranslation and knowledge distillation from right-to-left factorized models 4) finetuning on test sets from previous years 5) model ensembling 6) shallow fusion decoding with transformer language models and 7) noisy channel re-ranking. Additionally, our biomedical task submission for English-Russian uses a biomedically biased vocabulary and is trained from scratch on news task data, medically relevant text curated from the news task dataset, and biomedical data provided by the shared task. Our news system achieves a sacreBLEU score of 39.5 on the WMT'20 En-De test set outperforming the best submission from last year's task of 38.8. Our biomedical task Ru-En and En-Ru systems reach BLEU scores of 43.8 and 40.3 respectively on the WMT'20 Biomedical Ta
With the emergence of communication services with stringent requirements such as autonomous driving or on-flight Internet, the sixth-generation (6G) wireless network is envisaged to become an enabling technology for future transportation systems. In this paper, two ways of interactions between 6G networks and transportation are extensively investigated. On one hand, the new usage scenarios and capabilities of 6G over existing cellular networks are firstly highlighted. Then, its potential in seamless and ubiquitous connectivity across the heterogeneous space-air-ground transportation systems is demonstrated, where railways, airplanes, high-altitude platforms and satellites are investigated. On the other hand, we reveal that the introduction of 6G guarantees a more intelligent, efficient and secure transportation system. Specifically, technical analysis on how 6G can empower future transportation is provided, based on the latest research and standardization progresses in localization, integrated sensing and communications, and security. The technical challenges and insights for a road ahead are also summarized for possible inspirations on 6G enabled advanced transportation.
Memristive neuromorphic systems are designed to emulate human perception and cognition, where the memristor states represent essential historical information to perform both low-level and high-level tasks. However, current systems face challenges with the separation of state modulation and acquisition, leading to undesired time delays that impact real-time performance. To overcome this issue, we introduce a dual-function circuit that concurrently modulates and acquires memristor state information. This is achieved through two key features: 1) a feedback operational amplifier (op-amp) based circuit that ensures precise voltage application on the memristor while converting the passing current into a voltage signal; 2) a division calculation circuit that acquires state information from the modulation voltage and the converted voltage, improving stability by leveraging the intrinsic threshold characteristics of memristors. This circuit has been evaluated in a memristor-based nociceptor and a memristor crossbar, demonstrating exceptional performance. For instance, it achieves mean absolute acquisition errors below 1 Ω during the modulation process in the nociceptor application. These re
Computer-assisted diagnostic and prognostic systems of the future should be capable of simultaneously processing multimodal data. Multimodal deep learning (MDL), which involves the integration of multiple sources of data, such as images and text, has the potential to revolutionize the analysis and interpretation of biomedical data. However, it only caught researchers' attention recently. To this end, there is a critical need to conduct a systematic review on this topic, identify the limitations of current work, and explore future directions. In this scoping review, we aim to provide a comprehensive overview of the current state of the field and identify key concepts, types of studies, and research gaps with a focus on biomedical images and texts joint learning, mainly because these two were the most commonly available data types in MDL research. This study reviewed the current uses of multimodal deep learning on five tasks: (1) Report generation, (2) Visual question answering, (3) Cross-modal retrieval, (4) Computer-aided diagnosis, and (5) Semantic segmentation. Our results highlight the diverse applications and potential of MDL and suggest directions for future research in the fi
In this work we propose an autoencoder based framework for simultaneous reconstruction and classification of biomedical signals. Previously these two tasks, reconstruction and classification were treated as separate problems. This is the first work to propose a combined framework to address the issue in a holistic fashion. Reconstruction techniques for biomedical signals for tele-monitoring are largely based on compressed sensing (CS) based method, these are designed techniques where the reconstruction formulation is based on some assumption regarding the signal. In this work, we propose a new paradigm for reconstruction we learn to reconstruct. An autoencoder can be trained for the same. But since the final goal is to analyze classify the signal we learn a linear classification map inside the autoencoder. The ensuing optimization problem is solved using the Split Bregman technique. Experiments have been carried out on reconstruction and classification of ECG arrhythmia classification and EEG seizure classification signals. Our proposed tool is capable of operating in a semi-supervised fashion. We show that our proposed method is better and more than an order magnitude faster in re
Humanoid robots have potential applications across diverse sectors, including education, healthcare, and customer service. This paper presents a project on designing and building a low-cost humanoid robot equipped with a flex sensor- based movement mechanism, highlighting its compatibility with Raspberry Pi and microcontrollers such as Arduino Uno and Nano. The project aims to investigate the robot's relevance and effectiveness within educational settings to showcase how a low- cost humanoid robot can potentially support the United Nations' fourth Sustainable Development Goal (UN SDG4) by improving access to quality education through innovative robotics solutions. The robot was tested in a cycle two school (covering Grades 5 to 8 (ages 10 to 13)) in Dubai, United Arab Emirates. It was integrated into math, science, and design technology classes to assess its functionality and efficiency. Surveys conducted among students and teachers showed a high level of acceptance towards the robot, with over 85% of respondents expressing positive attitudes about its presence and interaction in the classroom. However, teachers and students provided feedback concerning the robot's shape, capabilities, and movement mechanism. Teachers also appreciated the robot's alignment with the UN SDG4, stating its capability to support students learning and engagement. The authors highlighted the robot's potential to assist students with sensory challenges, such as hearing and vision impairments, and learning difficulties like dyslexia while emphasizing their commitment to enhancing its accessibility features for a more inclusive learning environment.
This paper addresses the fundamental problem of non-uniform area coverage in multi-agent systems, where different regions require varying levels of attention due to mission-dependent priorities. Existing uniform coverage strategies are insufficient for realistic applications, and many non-uniform approaches either lack optimality guarantees or fail to incorporate crucial real-world constraints such as agent dynamics, limited operation time, the number of agents, and decentralized execution. To resolve these limitations, we propose a novel framework called Density-Driven Optimal Control (D2OC). The central idea of D2OC is the integration of optimal transport theory with multi-agent coverage control, enabling each agent to continuously adjust its trajectory to match a mission-specific reference density map. The proposed formulation establishes optimality by solving a constrained optimization problem that explicitly incorporates physical and operational constraints. The resulting control input is analytically derived from the Lagrangian of the objective function, yielding closed-form optimal solutions for linear systems and a generalizable structure for nonlinear systems. Furthermore,
Industrial demand response (IDR) plays an important role in promoting the utilization of renewable energy (RE) in power systems. However, it will lead to power adjustments on the supply side, which is also a non-negligible factor in affecting RE utilization. To comprehensively analyze this impact while enhancing RE utilization, this paper proposes a power demand-supply cooperative response (PDSCR) strategy based on both day-ahead and intraday time scales. The day-ahead PDSCR determines a long-term scheme for responding to the predictable trends in RE supply. However, this long-term scheme may not be suitable when uncertain RE fluctuations occur on an intraday basis. Regarding intraday PDSCR, we formulate a profit-driven cooperation approach to address the issue of RE fluctuations. In this context, unreasonable profit distributions on the demand-supply side would lead to the conflict of interests and diminish the effectiveness of cooperative responses. To mitigate this issue, we derive multi-individual profit distribution marginal solutions (MIPDMSs) based on satisfactory profit distributions, which can also maximize cooperative profits. Case studies are conducted on an modified IEE
With the ratification of the IEEE 802.15.3d amendment to the 802.15.3, a first step has been made to standardize consumer wireless communications in the sub-THz frequency band. The IEEE 802.15.3d offers switched point-to-point connectivity with the data rates of 100\,Gbit/s and higher at distances ranging from tens of centimeters up to a few hundred meters. In this article, we provide a detailed introduction to the IEEE 802.15.3d and the key design principles beyond the developed standard. We particularly describe the target applications and usage scenarios, as well as the specifics of the IEEE 802.15.3d physical and medium access layers. Later, we present the results of the initial performance evaluation of IEEE 802.15.3d wireless communications. The obtained first-order performance predictions show non-incremental benefits compared to the characteristics of the fifth-generation wireless systems, thus paving the way towards the six-generation (6G) THz networks. We conclude the article by outlining the further standardization and regulatory activities on wireless networking in the THz frequency band.
The integration of AI-assisted biomedical image analysis into clinical practice demands AI-generated findings that are not only accurate but also interpretable to clinicians. However, existing biomedical AI models generally lack the ability to simultaneously generate diagnostic findings and localize corresponding biomedical objects. This limitation makes it challenging for clinicians to correlate AI-generated findings with visual evidence (e.g., tiny lesions) in images and interpret the results of AI models. To address this challenge, we introduce UniBiomed, the first universal foundation model for grounded biomedical image interpretation, which is capable of generating accurate diagnostic findings and simultaneously segmenting the corresponding biomedical targets. UniBiomed is based on a novel integration of Multi-modal Large Language Model and Segment Anything Model, which can effectively unify diverse biomedical tasks in universal training for advancing grounded interpretation. To develop UniBiomed, we curate a large-scale dataset comprising over 27 million triplets of images, region annotations, and text descriptions across ten biomedical imaging modalities. Extensive validatio
BACKGROUND: Conventional ways of making bio-electrodes are generally complicated, expensive and unconformable. Here we describe for the first time the method of applying Ga-based liquid metal ink as drawable electrocardiogram (ECG) electrodes. Such material owns unique merits in both liquid phase conformability and high electrical conductivity, which provides flexible ways for making electrical circuits on skin surface and a prospective substitution of conventional rigid printed circuit boards (PCBs). METHODS: Fundamental measurements of impedance and polarization voltage of the liquid metal ink were carried out to evaluate its basic electrical properties. Conceptual experiments were performed to draw the alloy as bio-electrodes to acquire ECG signals from both rabbit and human via a wireless module developed on the mobile phone. Further, a typical electrical circuit was drawn in the palm with the ink to demonstrate its potential of implementing more sophisticated skin circuits. RESULTS: With an oxide concentration of 0.34%, the resistivity of the liquid metal ink was measured as 44.1 µΩ·cm with quite low reactance in the form of straight line. Its peak polarization voltage with the physiological saline was detected as -0.73 V. The quality of ECG wave detected from the liquid metal electrodes was found as good as that of conventional electrodes, from both rabbit and human experiments. In addition, the circuit drawn with the liquid metal ink in the palm also runs efficiently. When the loop was switched on, all the light emitting diodes (LEDs) were lit and emitted colorful lights. CONCLUSIONS: The liquid metal ink promises unique printable electrical properties as both bio-electrodes and electrical wires. The implemented ECG measurement on biological surface and the successfully run skin circuit demonstrated the conformability and attachment of the liquid metal. The present method is expected to innovate future physiological measurement and biological circuit manufacturing technique in a large extent.
The aim of this review paper is to summarize recent developments in the field of wearable sensors and systems that are relevant to the field of rehabilitation. The growing body of work focused on the application of wearable technology to monitor older adults and subjects with chronic conditions in the home and community settings justifies the emphasis of this review paper on summarizing clinical applications of wearable technology currently undergoing assessment rather than describing the development of new wearable sensors and systems. A short description of key enabling technologies (i.e. sensor technology, communication technology, and data analysis techniques) that have allowed researchers to implement wearable systems is followed by a detailed description of major areas of application of wearable technology. Applications described in this review paper include those that focus on health and wellness, safety, home rehabilitation, assessment of treatment efficacy, and early detection of disorders. The integration of wearable and ambient sensors is discussed in the context of achieving home monitoring of older adults and subjects with chronic conditions. Future work required to advance the field toward clinical deployment of wearable sensors and systems is discussed.
Under transactive (market-based) coordination, a population of distributed energy resources (DERs), such as thermostatically controlled loads (TCLs) and storage devices, bid into an energy market. Consequently, a certain level of demand will be cleared based on the operating conditions of the grid. This paper analyzes the influence of various factors, such as price signals, feeder limits, and user-defined bid functions and preferences, on the aggregate energy usage of DERs. We identify cases that can lead to load synchronization, undesirable power oscillations and highly volatile prices. To address these issues, the paper develops an aggregate model of DERs under transactive coordination. A set of Markov transition equations have been developed over discrete ranges (referred to as "bins") of price levels and their associated DER operating states. A detailed investigation of the performance of this aggregate model is presented. With reformulation of the transition equations, the bin model has been incorporated into a model predictive control setting using both mixed integer programming and quadratic programming. A case study shows that a population of TCLs can be managed economicall
Accurate histopathologic interpretation is key for clinical decision-making; however, current deep learning models for digital pathology are often overconfident and poorly calibrated in out-of-distribution (OOD) settings, which limit trust and clinical adoption. Safety-critical medical imaging workflows benefit from intrinsic uncertainty-aware properties that can accurately reject OOD input. We implement the Spectral-normalized Neural Gaussian Process (SNGP), a set of lightweight modifications that apply spectral normalization and replace the final dense layer with a Gaussian process layer to improve single-model uncertainty estimation and OOD detection. We evaluate SNGP vs. deterministic and MonteCarlo dropout on six datasets across three biomedical classification tasks: white blood cells, amyloid plaques, and colorectal histopathology. SNGP has comparable in-distribution performance while significantly improving uncertainty estimation and OOD detection. Thus, SNGP or related models offer a useful framework for uncertainty-aware classification in digital pathology, supporting safe deployment and building trust with pathologists.
Artificial intelligence (AI) enabled products and services are becoming a staple of everyday life. While governments and businesses are eager to enjoy the benefits of AI innovations, the mixed impact of these autonomous and intelligent systems on human well-being has become a pressing issue. This article introduces one of the first international standards focused on the social and ethical implications of AI: The Institute of Electrical and Electronics Engineering (IEEE) Standard (Std) 7010-2020 Recommended Practice for Assessing the Impact of Autonomous and Intelligent Systems on Human Well-being. Incorporating well-being factors throughout the lifecycle of AI is both challenging and urgent and IEEE 7010 provides key guidance for those who design, deploy, and procure these technologies. We begin by articulating the benefits of an approach for AI centered around well-being and the measurement of well-being data. Next, we provide an overview of IEEE 7010, including its key principles and how the standard relates to approaches and perspectives in place in the AI community. Finally, we indicate where future efforts are needed.
Within clinical, biomedical, and translational science, an increasing number of projects are adopting graphs for knowledge representation. Graph-based data models elucidate the interconnectedness between core biomedical concepts, enable data structures to be easily updated, and support intuitive queries, visualizations, and inference algorithms. However, knowledge discovery across these "knowledge graphs" (KGs) has remained difficult. Data set heterogeneity and complexity; the proliferation of ad hoc data formats; poor compliance with guidelines on findability, accessibility, interoperability, and reusability; and, in particular, the lack of a universally-accepted, open-access model for standardization across biomedical KGs has left the task of reconciling data sources to downstream consumers. Biolink Model is an open source data model that can be used to formalize the relationships between data structures in translational science. It incorporates object-oriented classification and graph-oriented features. The core of the model is a set of hierarchical, interconnected classes (or categories) and relationships between them (or predicates), representing biomedical entities such as ge