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SPM Article

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.

The IEEE Signal Processing Society Sensor Array and Multichannel Technical Committee (SAM TC) promotes activities within the technical areas of sensor array processing and multichannel statistical signal processing, including:

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 Internet of Things (IoT ) refers to the wireless connection of ordinary objects, such as vehicles, cash machines, door locks, cameras, industrial controls, and municipal traffic systems, to the Internet. Research firm BI Intelligence predicts that 22.5 billion devices will be connected to the IoT in 2021, compared to 6.6 billion in 2016.

I started drafting this editorial on July 4th while sitting in my hotel room in Versailles, France. Both the date and location have great significance in our modern history, which motivated my choice for the theme of the article.

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.
In many signal processing applications, filtering is accomplished through linear time-invariant (LTI) systems described by linear constant-coefficient differential and difference equations since they are conveniently implemented using either analog or digital hardware [1]. An LTI system can be completely characterized in the time domain by its impulse response or in the frequency domain by its frequency response, which is the Fourier transform of the system’s impulse response.

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).

Low-rank modeling plays a pivotal role in signal processing and machine learning, with applications ranging from collaborative filtering, video surveillance, and medical imaging to dimensionality reduction and adaptive filtering. Many modern high-dimensional data and interactions thereof can be modeled as lying approximately in a low-dimensional subspace or manifold, possibly with additional structures, and its proper exploitations lead to significant cost reduction in sensing, computation, and storage.

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