Showing papers in "IEEE Signal Processing Magazine in 2021"
TL;DR: In this paper, an emerging technique called algorithm unrolling, or unfolding, offers promise in eliminating these issues by providing a concrete and systematic connection between iterative algorithms that are widely used in signal processing and deep neural networks.
Abstract: Deep neural networks provide unprecedented performance gains in many real-world problems in signal and image processing. Despite these gains, the future development and practical deployment of deep networks are hindered by their black-box nature, i.e., a lack of interpretability and the need for very large training sets. An emerging technique called algorithm unrolling, or unfolding, offers promise in eliminating these issues by providing a concrete and systematic connection between iterative algorithms that are widely used in signal processing and deep neural networks. Unrolling methods were first proposed to develop fast neural network approximations for sparse coding. More recently, this direction has attracted enormous attention, and it is rapidly growing in both theoretic investigations and practical applications. The increasing popularity of unrolled deep networks is due, in part, to their potential in developing efficient, high-performance (yet interpretable) network architectures from reasonably sized training sets.
377 citations
TL;DR: In this paper, the authors review recent advances in Snapshot Compressive Imaging (SCI) hardware, theory, and algorithms, including both optimization-based and deep learning-based algorithms.
Abstract: Capturing high-dimensional (HD) data is a long-term challenge in signal processing and related fields. Snapshot compressive imaging (SCI) uses a 2D detector to capture HD (g3D) data in a snapshot measurement. Via novel optical designs, the 2D detector samples the HD data in a compressive manner; following this, algorithms are employed to reconstruct the desired HD data cube. SCI has been used in hyperspectral imaging, video, holography, tomography, focal depth imaging, polarization imaging, microscopy, and so on. Although the hardware has been investigated for more than a decade, the theoretical guarantees have only recently been derived. Inspired by deep learning, various deep neural networks have also been developed to reconstruct the HD data cube in spectral SCI and video SCI. This article reviews recent advances in SCI hardware, theory, and algorithms, including both optimizationbased and deep learning-based algorithms. Diverse applications and the outlook for SCI are also discussed.
104 citations
TL;DR: A review of 3D point cloud processing and learning for autonomous driving can be found in this article, which summarizes the recent progress in this research area and summarizes what has been tried and what is needed for practical and safe AVs.
Abstract: We present a review of 3D point cloud processing and learning for autonomous driving. As one of the most important sensors in autonomous vehicles (AVs), lidar sensors collect 3D point clouds that precisely record the external surfaces of objects and scenes. The tools for 3D point cloud processing and learning are critical to the map creation, localization, and perception modules in an AV. Although much attention has been paid to data collected from cameras, such as images and videos, an increasing number of researchers have recognized the importance and significance of lidar in autonomous driving and have proposed processing and learning algorithms that exploit 3D point clouds. We review the recent progress in this research area and summarize what has been tried and what is needed for practical and safe AVs. We also offer perspectives on open issues that are needed to be solved in the future.
89 citations
TL;DR: The Bussgang decomposition as mentioned in this paper provides an exact probabilistic relationship between the output and the input of a nonlinearity: the output is equal to a scaled version of the input plus uncorrelated distortion.
Abstract: Many of the systems in various signal processing applications are nonlinear due to, for example, hardware impairments, such as nonlinear amplifiers and finite-resolution quantization. The Bussgang decomposition is a popular tool used when analyzing the performance of systems that involve such nonlinear components. In a nutshell, the decomposition provides an exact probabilistic relationship between the output and the input of a nonlinearity: the output is equal to a scaled version of the input plus uncorrelated distortion. The decomposition can be used to compute either exact performance results or lower bounds, where the uncorrelated distortion is treated as independent noise. This lecture note explains the basic theory, provides key examples, extends the theory to complex-valued vector signals, and clarifies some potential misconceptions.
68 citations
TL;DR: In this paper, the authors use seismic inversion to reconstruct large-scale subsurface Earth models for hydrocarbon exploration, mining, earthquake analysis, shallow hazard assessment, and other geophysical tasks.
Abstract: Seismic inversion is a fundamental tool in geophysical analysis, providing a window into Earth. In particular, it enables the reconstruction of large-scale subsurface Earth models for hydrocarbon exploration, mining, earthquake analysis, shallow hazard assessment, and other geophysical tasks.
64 citations
TL;DR: The recognition of all these sounds and interpretation of the perceived scene as a city street soundscape comes naturally to humans as mentioned in this paper, however, the result of years of "training": encountering and learning associations among the vast varieties of sounds in everyday life, the sources producing these sounds, and the names given to them.
Abstract: Imagine standing on a street corner in the city. With your eyes closed you can hear and recognize a succession of sounds: cars passing by, people speaking, their footsteps when they walk by, and the continuous falling of rain. The recognition of all these sounds and interpretation of the perceived scene as a city street soundscape comes naturally to humans. It is, however, the result of years of "training": encountering and learning associations among the vast varieties of sounds in everyday life, the sources producing these sounds, and the names given to them.
58 citations
TL;DR: In this paper, the authors proposed a reconfigurable intelligent surface (RIS)-empowered communications toward 6G wireless networks, which can manipulate their impinging signals in an effective way to boost certain key performance indicators.
Abstract: The signal processing and communication communities have witnessed the rise of many exciting communication technologies in recent years. Notable examples include alternative waveforms, massive multiple-input, multiple-output (MIMO) signaling, nonorthogonal multiple access (NOMA), joint communications and sensing, sparse vector coding, index modulation, and so on. It is inevitable that 6G wireless networks will require a rethinking of wireless communication systems and technologies, particularly at the physical layer (PHY), considering the fact that the cellular industry reached another important milestone with the development of 5G wireless networks with diverse applications [1] . Within this perspective, this article aims to shed light on the rising concept of reconfigurable intelligent surface (RIS)-empowered communications toward 6G wireless networks [2] , [3] . Software-defined RISs can manipulate their impinging signals in an effective way to boost certain key performance indicators. We discuss the recent developments in the field and put forward promising candidates for future research and development. Specifically, we put our emphasis on active, transmitter-type, transmissive-reflective, and stand-alone RISs, by discussing their advantages and disadvantages compared to reflective RIS designs. Finally, we also envision an ultimate RIS architecture, which is able to adjust its operation modes dynamically, and introduce the new concept of PHY slicing over RISs toward 6G wireless networks.
45 citations
TL;DR: A broad review and a statistically grounded comparative study of EEG-based AAD algorithms are provided in this paper, where the authors address the main signal processing challenges in this field and provide a statistical analysis of the AAD decoding algorithms.
Abstract: People suffering from hearing impairment often have difficulties participating in conversations in so-called cocktail party scenarios where multiple individuals are simultaneously talking. Although advanced algorithms exist to suppress background noise in these situations, a hearing device also needs information about which speaker a user actually aims to attend to. The voice of the correct (attended) speaker can then be enhanced through this information, and all other speakers can be treated as background noise. Recent neuroscientific advances have shown that it is possible to determine the focus of auditory attention through noninvasive neurorecording techniques, such as electroencephalography (EEG). Based on these insights, a multitude of auditory attention decoding (AAD) algorithms has been proposed, which could, combined with appropriate speaker separation algorithms and miniaturized EEG sensors, lead to so-called neurosteered hearing devices. In this article, we provide a broad review and a statistically grounded comparative study of EEG-based AAD algorithms and address the main signal processing challenges in this field.
37 citations
TL;DR: In this article, the authors discuss several key aspects of multimodal emotion recognition (MER) and present a tutorial on how to recognize, interpret, process, and simulate emotions.
Abstract: Humans are emotional creatures. Multiple modalities are often involved when we express emotions, whether we do so explicitly (such as through facial expression and speech) or implicitly (e.g., via text or images). Enabling machines to have emotional intelligence, i.e., recognizing, interpreting, processing, and simulating emotions, is becoming increasingly important. In this tutorial, we discuss several key aspects of multimodal emotion recognition (MER).
34 citations
TL;DR: In this paper, the authors provide an encompassing review summarizing the state-of-the-art works combining AI and the Internet of Things (IoT) to help the elderly live easier and better.
Abstract: An aging population is increasingly prevalent in both developed and developing countries, raising a series of social challenges and economic burdens. In particular, more elderly people are staying alone at home than are living with people who can take care of them. Therefore, assisted living (AL) and health-care monitoring (HM) can be critical issues in this era of human-centered artificial intelligence (AI). In this context, we aim to provide an encompassing review summarizing the state-of-the-art works combining AI and the Internet of Things (IoT) to help the elderly live easier and better. We systematically and comprehensively compare paradigms in terms of methodologies and application scenarios. The pros and cons among these technologies are discussed in detail. Then, we summarize current achievements and indicate their limitations. Finally, perspectives on highly promising future work are presented.
32 citations
TL;DR: The importance of accurate behavior modeling in autonomous driving is summarized and the key approaches and major progress that researchers have made are analyzed, focusing on the potential of deep IRL (D-IRL) to overcome the limitations of previous techniques.
Abstract: Accurate behavior anticipation is essential for autonomous vehicles when navigating in close proximity to other vehicles, pedestrians, and cyclists. Thanks to the recent advances in deep learning and inverse reinforcement learning (IRL), we observe a tremendous opportunity to address this need, which was once believed impossible given the complex nature of human decision making. In this article, we summarize the importance of accurate behavior modeling in autonomous driving and analyze the key approaches and major progress that researchers have made, focusing on the potential of deep IRL (D-IRL) to overcome the limitations of previous techniques. We provide quantitative and qualitative evaluations substantiating these observations. Although the field of D-IRL has seen recent successes, its application to model behavior in autonomous driving is largely unexplored. As such, we conclude this article by summarizing the exciting pathways for future breakthroughs.
TL;DR: In this paper, the authors present a brief overview of neural interfaces and discuss the properties of multichannel sEMG in comparison to other CNS and PNS recording modalities, with a focus on recent breakthroughs in convolutive blind source separation (BSS) methods and deep learning techniques.
Abstract: Neural interfacing is essential for advancing our fundamental understanding of movement neurophysiology and for developing human-machine interaction systems. This can be achieved at different levels of the central nervous system (CNS) and peripheral nervous system (PNS); however, direct neural interfaces with brain and nerve tissues face important challenges and are currently limited to clinical cases of severe motor impairment. Recent advances in electronics and signal processing for recording and analyzing surface electromyographic (sEMG) signals allow for a radically new way of establishing human interfaces by reverse engineering the neural information embedded in the electrical activity of skeletal muscles. This approach provides a window into the spiking activity of motor neurons in the spinal cord. In this article, we present a brief overview of neural interfaces and discuss the properties of multichannel sEMG in comparison to other CNS and PNS recording modalities. We then describe signal processing approaches for neural interfacing from sEMG, with a focus on recent breakthroughs in convolutive blind source separation (BSS) methods and deep learning techniques. When combined, these approaches establish unique noninvasive human-machine interfaces for neurotechnologies, with applications in medical devices and large-scale consumer electronics.
TL;DR: In this paper, the vulnerability of CNNs used for semantic segmentation with respect to adversarial attacks is discussed, and insights into some of the existing adversarial defense strategies are provided.
Abstract: Enabling autonomous driving (AD) can be considered one of the biggest challenges in today?s technology. AD is a complex task accomplished by several functionalities, with environment perception being one of its core functions. Environment perception is usually performed by combining the semantic information captured by several sensors, i.e., lidar or camera. The semantic information from the respective sensor can be extracted by using convolutional neural networks (CNNs) for dense prediction. In the past, CNNs constantly showed stateof-the-art performance on several vision-related tasks, such as semantic segmentation of traffic scenes using nothing but the red-green-blue (RGB) images provided by a camera. Although CNNs obtain state-of-the-art performance on clean images, almost imperceptible changes to the input, referred to as adversarial perturbations, may lead to fatal deception. The goal of this article is to illuminate the vulnerability aspects of CNNs used for semantic segmentation with respect to adversarial attacks, and share insights into some of the existing known adversarial defense strategies. We aim to clarify the advantages and disadvantages associated with applying CNNs for environment perception in AD to serve as a motivation for future research in this field.
TL;DR: In this article, music emotion recognition (MER) is a computational task that attempts to automatically recognize either the emotional content in music or the emotions induced by music to the listener, to do so, emotionally relevant features are extracted from music.
Abstract: Emotion is one of the main reasons why people engage and interact with music [1] . Songs can express our inner feelings, produce goosebumps, bring us to tears, share an emotional state with a composer or performer, or trigger specific memories. Interest in a deeper understanding of the relationship between music and emotion has motivated researchers from various areas of knowledge for decades [2] , including computational researchers. Imagine an algorithm capable of predicting the emotions that a listener perceives in a musical piece, or one that dynamically generates music that adapts to the mood of a conversation in a film—a particularly fascinating and provocative idea. These algorithms typify music emotion recognition (MER), a computational task that attempts to automatically recognize either the emotional content in music or the emotions induced by music to the listener [3] . To do so, emotionally relevant features are extracted from music. The features are processed, evaluated, and then associated with certain emotions. MER is one of the most challenging high-level music description problems in music information retrieval (MIR), an interdisciplinary research field that focuses on the development of computational systems to help humans better understand music collections. MIR integrates concepts and methodologies from several disciplines, including music theory, music psychology, neuroscience, signal processing, and machine learning.
TL;DR: In this paper, an overview of the HMIs commonly used for upper-limb prosthesis control and inspection of the collected signals and their processing methods is presented, as well as a review of the most commonly used interfaces.
Abstract: Prostheses provide a means for individuals with amputations to regain some of the lost functions of their amputated limb. Human-machine interfaces (HMIs), used for controlling prosthetic devices, play a critical role in users' experiences with prostheses. This review article provides an overview of the HMIs commonly adopted for upper-limb prosthesis control and inspects collected signals and their processing methods.
TL;DR: In this article, the authors review current progress on intelligent signal processing approaches for photoplethysmography (PPG) measurement, including earlier works on unsupervised approaches and recently proposed supervised models, benchmark data sets and performance evaluation.
Abstract: Monitoring physiological changes [e.g., heart rate (HR), respiration, and HR variability (HRV)] is important for measuring human emotions. Physiological responses are more reliable and harder to alter compared to explicit behaviors (such as facial expressions and speech), but they require special contact sensors to obtain. Research in the last decade has shown that photoplethysmography (PPG) signals can be remotely measured (rPPG) from facial videos under ambient light, from which physiological changes can be extracted. This promising finding has attracted much interest from researchers, and the field of rPPG measurement has been growing fast. In this article, we review current progress on intelligent signal processing approaches for rPPG measurement, including earlier works on unsupervised approaches and recently proposed supervised models, benchmark data sets, and performance evaluation. We also review studies on rPPG-based affective applications and compare them with other affective computing modalities. We conclude this article by emphasizing the current main challenges and highlighting future directions.
TL;DR: In this article, the authors used deep learning (DL) models to assist in the development of robust radiomics solutions for the diagnosis/prognosis, severity assessment, treatment response, and monitoring of COVID-19 patients.
Abstract: The novel coronavirus disease, COVID-19, has rapidly and abruptly changed the world as we knew it in 2020. It has become the most unprecedented challenge to analytic epidemiology (AE) in general and signal processing (SP) theories specifically. In this regard, medical imaging plays an important role for the management of COVID-19. SP and deep learning (DL) models can assist in the development of robust radiomics solutions for the diagnosis/prognosis, severity assessment, treatment response, and monitoring of COVID-19 patients.
TL;DR: In this paper, the authors focus on the trends, opportunities, and challenges of novel arithmetic for deep neural network (DNN) signal processing, with particular reference to assisted-and autonomous driving applications.
Abstract: This article focuses on the trends, opportunities, and challenges of novel arithmetic for deep neural network (DNN) signal processing, with particular reference to assisted- and autonomous driving applications. Due to strict constraints in terms of the latency, dependability, and security of autonomous driving, machine perception (i.e., detection and decision tasks) based on DNNs cannot be implemented by relying on remote cloud access. These tasks must be performed in real time in embedded systems on board the vehicle, particularly for the inference phase (considering the use of DNNs pretrained during an offline step). When developing a DNN computing platform, the choice of the computing arithmetic matters. Moreover, functional safe applications, such as autonomous driving, impose severe constraints on the effect that signal processing accuracy has on the final rate of wrong detection/decisions. Hence, after reviewing the different choices and tradeoffs concerning arithmetic, both in academia and industry, we highlight the issues in implementing DNN accelerators to achieve accurate and lowcomplexity processing of automotive sensor signals (the latter coming from diverse sources, such as cameras, radar, lidar, and ultrasonics). The focus is on both general-purpose operations massively used in DNNs, such as multiplying, accumulating, and comparing, and on specific functions, including, for example, sigmoid or hyperbolic tangents used for neuron activation.
TL;DR: In this article, the authors describe the importance of signal processing and information engineering, particularly its role in integrating different scientific disciplines through the use of a common set of tools and underlying mathematics, and the unifying idea is to apply similar mathematical methods for data processing in completely diverse areas.
Abstract: Rapid progress in the development of technological and computational tools has motivated substantial changes in the educational approach to the different disciplines of signal, image, and video processing. Moreover, the parallel evolution of sensor systems, data acquisition methods, and computational intelligence has emphasized the importance of signal processing and information engineering, particularly its role in integrating different scientific disciplines through the use of a common set of tools and underlying mathematics. Modern educational courses follow these trends and generally combine the teaching of fundamental computational methods of signal and system modeling with applications to selected case studies. The unifying idea is to apply similar mathematical methods for data processing in completely diverse areas. Emerging methods used in education contribute to this progress, and they provide opportunities to bring together specialists from different disciplines. New technologies facilitate real or virtual activities through excursions to remote laboratories, allowing the demonstration of robotic and speech recognition systems, for example. Participation in seminars, videoconferences, and discussions during colloquia meetings, when included in educational courses, can form further progressive and attractive teaching methods for the rapidly developing interdisciplinary area of signal processing.
TL;DR: In this paper, the authors investigated two different types of methods for the perception of the ego-vehicle environment: traditional analytical methods generally rely on handcrafted designs and features while on the other hand, learning methods aim at designing their own appropriate representation of the observed scene.
Abstract: The interest in autonomous driving has continuously increased in the last two decades. However, to be adopted, such critical systems need to be safe. Concerning the perception of the ego-vehicle environment, the literature has investigated two different types of methods. On the one hand, traditional analytical methods generally rely on handcrafted designs and features while on the other hand, learning methods aim at designing their own appropriate representation of the observed scene.
TL;DR: In this paper, the authors investigate the challenges of AI/ML-based personalized education and discuss potential solutions, including compensating for the adverse effects of the absence of peers, creating and maintaining motivations for learning, increasing diversity, removing the biases induced by data and algorithms, and so on.
Abstract: The objective of personalized learning is to design an effective knowledge acquisition track that matches the learner's strengths and bypasses his/her weaknesses to ultimately meet his/her desired goal. This concept emerged several years ago and is being adopted by a rapidly growing number of educational institutions around the globe. In recent years, the rise of artificial intelligence (AI) and machine learning (ML), together with advances in big data analysis, has introduced novel perspectives that enhance personalized education in numerous ways. By taking advantage of AI/ML methods, the educational platform precisely acquires the student?s characteristics. This is done, in part, by observing past experiences as well as analyzing the available big data through exploring the learners' features and similarities. It can, for example, recommend the most appropriate content among numerous accessible ones, advise a well-designed long-term curriculum, and connect appropriate learners by suggestion, accurate performance evaluation, and so forth. Still, several aspects of AI-based personalized education remain unexplored. These include, among others, compensating for the adverse effects of the absence of peers, creating and maintaining motivations for learning, increasing the diversity, removing the biases induced by data and algorithms, and so on. In this article, while providing a brief review of state-of-the-art research, we investigate the challenges of AI/ML-based personalized education and discuss potential solutions.
TL;DR: In this article, the authors briefly introduce the basic principles associated with mobile device-based mental health analysis, review the main system components, and highlight the conventional technologies involved, and describe several major challenges and various DL technologies that have potential for strongly contributing to dealing with these issues, and discuss other problems to be addressed via research collaboration across multiple disciplines.
Abstract: Mental health plays a key role in everyone’s day-to-day lives, impacting our thoughts, behaviors, and emotions. Also, over the past years, given their ubiquitous and affordable characteristics, the use of smartphones and wearable devices has grown rapidly and provided support within all aspects of mental health research and care—from screening and diagnosis to treatment and monitoring—and attained significant progress in improving remote mental health interventions. While there are still many challenges to be tackled in this emerging cross-disciplinary research field, such as data scarcity, lack of personalization, and privacy concerns, it is of primary importance that innovative signal processing and deep learning (DL) techniques are exploited. In particular, recent advances in DL can help provide a key enabling technology for the development of next-generation user-centric mobile mental health applications. In this article, we briefly introduce the basic principles associated with mobile device-based mental health analysis, review the main system components, and highlight the conventional technologies involved. We also describe several major challenges and various DL technologies that have potential for strongly contributing to dealing with these issues, and we discuss other problems to be addressed via research collaboration across multiple disciplines.
TL;DR: The human hand can perform many precise functions and is relied upon for countless aspects of daily life as discussed by the authors, and when upper limb amputation is necessitated, an affected individual's sense of independence is understandably impacted.
Abstract: The human hand can perform many precise functions and is relied upon for countless aspects of daily life. When upperlimb amputation is necessitated, an affected individual's sense of independence is understandably impacted.
TL;DR: In this article, the authors review the problem of free energy minimization as a unified framework underlying the definition of maximum entropy modeling, generalized Bayesian inference, learning with latent variables, the statistical learning analysis of generalization, and local optimization.
Abstract: The goal of this lecture note is to review the problem of free energy minimization as a unified framework underlying the definition of maximum entropy modeling, generalized Bayesian inference, learning with latent variables, the statistical learning analysis of generalization, and local optimization. Free energy minimization is first introduced, here and historically, as a thermodynamic principle. Then, it is described mathematically in the context of Fenchel duality. Finally, the applications to modeling, inference, learning, and optimization are covered, starting from basic principles.
TL;DR: In this paper, the authors review some popular categories of IoT-based applications for health care along with their devices and discuss how research can properly address the open issues and improve the already-existing implementations in health care.
Abstract: Medical conditions and cases are growing at a rapid pace, and physical space is starting to be constrained. Hospitals and clinics no longer have the ability to accommodate large numbers of incoming patients. It is clear that the health industry needs to improve the current state of its valuable and limited resources. The evolution of Internet-of-Things (IoT) devices along with assistive technologies can alleviate the problem in health care by providing a convenient and easy means of accessing health-care services wirelessly. There is a plethora of IoT devices and potential applications that can take advantage of the unique characteristics that these technologies can offer. However, at the same time, these services pose novel challenges that need to be properly addressed. In this article, we review some popular categories of IoT-based applications for health care along with their devices. We then describe the challenges and discuss how research can properly address the open issues and improve the already-existing implementations in health care. Further possible solutions, including machine learning (ML) techniques, are also discussed to show their potential as viable solutions for future health-care applications.
TL;DR: In this paper, the authors illustrate how music may serve as a vehicle to support education in signal processing, using Fourier analysis as a concrete example, and demonstrate how the music domain provides motivating and tangible applications that make learning signal processing an interactive pursuit.
Abstract: In this artaicle, we illustrate how music may serve as a vehicle to support education in signal processing. Using Fourier analysis as a concrete example, we demonstrate how the music domain provides motivating and tangible applications that make learning signal processing an interactive pursuit. Furthermore, we indicate how software tools, originally developed for music analysis, provide students multiple entry points to delve deeper into classical signal processing techniques while bridging the gap between education and cutting-edge research.
TL;DR: Speech emotion recognition (SER) is an important research area, with direct impacts in applications of our daily lives, spanning education, health care, security and defense, entertainment, and human-computer interaction as discussed by the authors.
Abstract: Speech emotion recognition (SER) is an important research area, with direct impacts in applications of our daily lives, spanning education, health care, security and defense, entertainment, and human–computer interaction. The advances in many other speech signal modeling tasks, such as automatic speech recognition, text-to-speech synthesis, and speaker identification, have led to the current proliferation of speech-based technology. Incorporating SER solutions into existing and future systems can take these voice-based solutions to the next level. Speech is a highly nonstationary signal, with dynamically evolving spatial-temporal patterns. It often requires a sophisticated representation modeling framework to develop algorithms capable of handling real-life complexities.
TL;DR: The authors provide an overview of the various definitions of bias and measures of fairness within the field of facial affective signal processing and categorize the algorithms and techniques that can be used to investigate and mitigate bias.
Abstract: Given the increasing prevalence of facial analysis technology, the problem of bias in the tools is now becoming an even greater source of concern. Several studies have highlighted the pervasiveness of such discrimination, and many have sought to address the problem by proposing solutions to mitigate it. Despite this effort, to date, understanding, investigating, and mitigating bias for facial affect analysis remain an understudied problem. In this work we aim to provide a guide by 1) providing an overview of the various definitions of bias and measures of fairness within the field of facial affective signal processing and 2) categorizing the algorithms and techniques that can be used to investigate and mitigate bias in facial affective signal processing. We present the opportunities and limitations within the current body of work, discuss the gathered findings, and propose areas that call for further research.
TL;DR: In this paper, a methodology that incorporates liberal arts concepts into the teaching of signal processing techniques was proposed to increase comprehension and memorization of abstract concepts by stimulating students' creativity and curiosity.
Abstract: Generally, the curriculum design for undergraduate students enrolled in digital signal processing (DSP)-related engineering programs covers hard topics from specific disciplines, namely, mathematics, digital electronics, or programming. Typically, these topics are very demanding from the point of view of both students and teachers due to the inherent complexity of the mathematical formulations. However, improvements to the effectiveness of teaching can be achieved through a multisensorial approach supported by the liberal arts. By including the development of art and literacy skills in the curriculum design, the fundamentals of DSP topics may be taught from a qualitative perspective, compared to the solely analytical standpoint taken by traditional curricula. We postulate that this approach increases both the comprehension and memorization of abstract concepts by stimulating students' creativity and curiosity. In this article, we elaborate upon a methodology that incorporates liberal arts concepts into the teaching of signal processing techniques. We also illustrate the application of this methodology through specific classroom activities related to the digital processing of multimedia contents in undergraduate academic programmes. With this proposal, we also aim to lessen the perceived difficulty of the topic, stimulate critical thinking, and establish a framework within which nonengineering departments may contribute to the teaching of engineering subjects.
TL;DR: Two of the most popular denoising algorithms are l2 and l1 trend filtering as discussed by the authors, which are used in science, engineering, and statistical signal and image processing, respectively.
Abstract: Two of the most popular denoising algorithms are l2 and l1 trend filtering, which are used in science, engineering, and statistical signal and image processing. They are typically treated as separate entities, with the former as a linear time-invariant (LTI) filter, which is commonly used for smoothing the noisy data and detrending the time-series signals, while the latter is a nonlinear filtering method suited for the estimation of piecewise-polynomial signals (e.g., piecewise constant, piecewise linear, piecewise quadratic, and so on) observed in additive white Gaussian noise.