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The safety and success of autonomous vehicles (AVs) depend on their ability to accurately map and respond to their surroundings in real time. One of the most promising recent technologies for depth mapping is single-photon lidar (SPL), which measures the time of flight of individual photons. The long-range capabilities (kilometers), excellent depth resolution (centimeters), and use of low-power (eye-safe) laser sources renders this modality a strong candidate for use in AVs. 

The current big data era routinely requires the processing of large-scale data on massive distributed computing clusters. In these applications, data sets are often so large that they cannot be housed in the memory and/or the disk of any one computer. Thus, the data and the processing are typically distributed across multiple nodes.

The success of artificial neural networks (ANNs) in carrying out various specialized cognitive tasks has brought renewed efforts to apply machine learning (ML) tools for economic, commercial, and societal aims, while also raising expectations regarding the advent of an artificial “general intelligence” [1][2][3]. Recent highly publicized examples of ML breakthroughs include the ANN-based algorithm AlphaGo...

In today's era of the Internet of Things (IoT), the amalgamation of information and communication technologies with actuating devices has reached all corners of the modern world. In the context of critical infrastructures, such as the power grid, this cyberphysical transformation has permeated all system levels as evident in devices ranging from crucial operational components (e.g., generators) and advanced sensors...

In the era of big data, analysts usually explore various statistical models or machine-learning methods for observed data to facilitate scientific discoveries or gain predictive power. Whatever data and fitting procedures are employed, a crucial step is to select the most appropriate model or method from a set of candidates. 

The knowledge of spatial distributions of physical quantities, such as radio-frequency (RF) interference, pollution, geomagnetic field magnitude, temperature, humidity, audio, and light intensity, will foster the development of new context-aware applications. For example, knowing the distribution of RF interference might significantly improve cognitive radio systems [1], [2].

Backscatter presents an emerging ultralow-power wireless communication paradigm. The ability to offer submilliwatt power consumption makes it a competitive core technology for Internet of Things (IoT) applications. In this article, we provide a tutorial of backscatter communication from the signal processing perspective as well as a survey of the recent research activities in this domain, primarily focusing on bistatic backscatter systems.

Advances in engineering and health science have brought a significant improvement in health care and increased life expectancy. As a result, there has been a substantial growth in the number of older adults around the globe, and that number is rising. According to a United Nations report, between 2015 and 2030, the number of adults over the age of 60 is projected to grow by 56%, with the total reaching nearly 2.1 billion by the year 2050 [1].

The rapid advancement and proliferation of information and communication technologies in the past two decades significantly impacted how we do research. The research process has been digitalized and is increasingly relying on growing computing power and storage capacity to gather and process a constant production of data—our observations of systems and phenomena we would like to understand, control, and improve.

Principal component analysis (PCA) is one of the most widely used dimension reduction techniques. A related easier problem is termed subspace learning or subspace estimation. Given relatively clean data, both are easily solved via singular value decomposition (SVD). The problem of subspace learning or PCA in the presence of outliers is called robust subspace learning (RSL) or robust PCA (RPCA).

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