The history of electronics is studied from physical and evolutionary viewpoints, identifying a crisis of “space overexploitation.” This space-central practice is signified by Moore’s Law, the 1.0 version. Electronics is also examined in philosophical standing, leading to an awareness that a paradigm was formed around the late 1940s. It is recognized that this paradigm is of reductionist nature and

By “social learning,” in this article we refer to mechanisms for opinion formation and decision making over graphs and the study of how agents’ decisions evolve dynamically through interactions with neighbors and the environment. The study of social learning strategies is critical for at least two reasons. On one hand, it allows for a deeper understanding of the fundamental cognitive mechanisms that

The flexibility and dexterity of human limbs rely on the processing of a vast quantity of signals within the sensory-motor networks in the brain and spinal cord, distilled into stimuli that govern the commands and movements. Hence, the use of assistive devices, such as robotic limbs or exoskeletons, is critically dependent on the processing of a large number of heterogeneous signals to mimic natural

Deep learning, in general, focuses on training a neural network from large labeled datasets. Yet, in many cases, there is value in training a network just from the input at hand. This is particularly relevant in many signal and image processing problems where training data are scarce and diversity is large on the one hand, and on the other, there is a lot of structure in the data that can be exploited

We show how to design a cheap system for detecting when music is present in audio recordings. We make use of a small neural network consisting of a simple multilayer perceptron (MLP) along with compact features derived from the mel spectrogram by means of temporal integration.

Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.

Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.

Recounts the career and contributions of Prof. Sophocles J. Orfanidis.

In the first issue of 2024, we introduced the new lead editorial team of IEEE Signal Processing Magazine ( SPM ), composed of our four area editors. Their terms started with mine this January, and they oversee the Society e-newsletter and the three main components of our magazine: feature articles, special issues, and columns and forum articles. As a team, we have undertaken a complete revision of

When we set out to host ICASSP 2024, in Seoul, South Korea, we had three goals in mind: organize an outstanding technical program, provide an excellent and engaging venue to foster meetings to exchange ideas, and deliver the most welcoming experience to our attendees. With the hard work and commitment from the outstanding organizing committee (OC), we were able to achieve these goals. The culturally

Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.

Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.

The digital twin of the ocean (DTO) is a groundbreaking concept that uses interactive simulations to improve decision-making and promote sustainability in earth science. The DTO effectively combines ocean observations, artificial intelligence (AI), advanced modeling, and high-performance computing to unite digital replicas, forecasting, and what-if scenario simulations of the ocean systems. However

Learning machines for vector sensor data are naturally developed in the quaternion domain and are underpinned by quaternion statistics. To this end, we revisit the “augmented” representation basis for discrete quaternion random variables (RVs) ${\bf{q}}^{a}[n]$ , i.e., ${[}{\bf{q}}{[}{n}{]}\;{\bf{q}}^{\imath}{[}{n}{]}\;{\bf{q}}^{\jmath}{[}{n}{]}{\bf{q}}^{\kappa}{[}{n}{]]}$ , and demonstrate its pivotal

Hypercomplex signal and image processing extends upon conventional methods by using hypercomplex numbers in a unified framework for algebra and geometry. The special issue is divided into two parts and is focused on current advances and applications in computational signal and image processing in the hypercomplex domain. The first part offered well-rounded coverage of the field, with seven articles

Hypercomplex numbers, such as quaternions and octonions, have recently gained attention because of their advantageous properties over real numbers, e.g., in the development of parameter-efficient neural networks. For instance, the 16-component sedenion has the capacity to reduce the number of network parameters by a factor of 16. Moreover, hypercomplex neural networks offer advantages in the processing

Hypercomplex signal processing (HSP) provides state-of-the-art tools to handle multidimensional signals by harnessing the intrinsic correlation of the signal dimensions through Clifford algebra. Recently, the hypercomplex representation of the phase retrieval (PR) problem, wherein a complex-valued signal is estimated through its intensity-only projections, has attracted significant interest. The hypercomplex

Hypercomplex algebras have recently been gaining prominence in the field of deep learning owing to the advantages of their division algebras over real vector spaces and their superior results when dealing with multidimensional signals in real-world 3D and 4D paradigms. This article provides a foundational framework that serves as a road map for understanding why hypercomplex deep learning methods are

Why quaternions in neural networks (NNs)? Are there quaternions in the human brain? “No” may be an ordinary answer. However, quaternion NNs (QNNs) are a powerful framework that strongly connects artificial intelligence (AI) and the real world. In this article, we deal with NNs based on quaternions and describe their basics and features. We also detail the underlying ideas in their engineering applications

Despite the many successful applications of deep learning models for multidimensional signal and image processing, most traditional neural networks process data represented by (multidimensional) arrays of real numbers. The intercorrelation between feature channels is usually expected to be learned from the training data, requiring numerous parameters and careful training. In contrast, vector-valued

This article introduces a simple formula that provides the exact frequency of a pure sinusoid from just two samples of its discrete-time Fourier transform (DTFT). Even when the signal is not a pure sinusoid, this formula still works in a very good approximation (optimally after a single refinement), paving the way for the high-resolution frequency tracking of quickly varying signals or simply improving

My three years of service as the editor-in-chief (EIC) of Signal Processing Magazine (SPM) are now coming to a close. During the past three years, many of us were deeply affected by serious political, social, and environmental events such as the war in Ukraine; protests for freedom in Iran; coups d’état in Africa; the COVID-19 pandemic; seisms in Turkey, Syria, and Morocco; huge floods in Libya and

My end of term as IEEE Signal Processing Society (SPS) president is fast approaching. It has been an incredible experience that has provided me with so many opportunities to engage with our members around the globe, forge relationships with other IEEE Societies, and meet a diverse range of people that I hope will become active members of our Society in the future. It has been a great privilege to be

Joseph Fourier’s methods (and their variants) are omnipresent in audio signal processing. However, it turns out that the underlying ideas took some time to penetrate the field of sound analysis and that different paths were first followed in the period immediately following Fourier’s pioneering work, with or without reference to him. This illustrates the interplay between mathematics and physics as

This article is devoted to the polynomial eigenvalue decomposition (PEVD) and its applications in broadband multichannel signal processing, motivated by the optimum solutions provided by the EVD for the narrowband case [1], [2]. In general, we would like to extend the utility of the EVD to also address broadband problems. Multichannel broadband signals arise at the core of many essential commercial

Encoding-decoding convolutional neural networks (CNNs) play a central role in data-driven noise reduction and can be found within numerous deep learning algorithms. However, the development of these CNN architectures is often done in an ad hoc fashion and theoretical underpinnings for important design choices are generally lacking. Up to now, there have been different existing relevant works that have

This study evaluates the capability of a single inertial sensor based joint angles estimation during four different walking patterns in an outdoor setting. The sensor was placed on the upper part of the tibia, which was chosen due to its large range of motion and minimal foot-ground impact. A Bi-LSTM (bidirectional long short-term memory) data-driven approach was used for joint angle estimation. The

Moving average filters output the average of N samples, and it is easy to see (and to prove) that they are low-pass filters.

This study evaluates the capability of a single inertial sensor based joint angles estimation during four different walking patterns in an outdoor setting. The sensor was placed on the upper part of the tibia, which was chosen due to its large range of motion and minimal foot-ground impact. A Bi-LSTM (bidirectional long short-term memory) data-driven approach was used for joint angle estimation. The

In the last decade, the signal processing (SP) community has witnessed a paradigm shift from model-based to data-driven methods. Machine learning (ML)—more specifically, deep learning—methodologies are nowadays widely used in all SP fields, e.g., audio, speech, image, video, multimedia, and multimodal/multisensor processing, to name a few. Many data-driven methods also incorporate domain knowledge

Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.

In this “Lecture Notes” column, we show that it is possible to use deterministic arguments to gain some intuition into why using periodograms without averaging does not work well and why they “fail” in the way they do. We then explain how the probabilistic case can be seen as an extension of the deterministic case. Next, we give a brief description of the method of averaged periodograms and explain

Nikola Tesla said: “If you want to find the secrets of the universe, think in terms of energy, frequency and vibration.” Unfortunately, this is a hieroglyph, and we are still looking for its Rosetta Stone.