Proposal for the Realization of a Single-Detector Acoustic Camera Using a Space-Coiling Anisotropic Metamaterial

Jiang, Tianxi; He, Qingbo; Peng, Zhike

doi:10.1103/physrevapplied.11.034013

Cited by 21 publications

(12 citation statements)

References 40 publications

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“…2(m) at disordered bulk collections of resonators, sometimes referred to as "resonant metalenses." [57][58][59] This technique has been mainly explored in acoustics to place the receiver inside such a frequency-diverse system for multi-speaker listening, 57,60 or to place the system very close to the scene to couple evanescent waves to the far field. 58 The concept has also been extended to elastic vibrations.…”

Section: B Implementationsmentioning

confidence: 99%

Intelligent meta-imagers: From compressed to learned sensing

Saigre-Tardif

Faqiri

Zhao

et al. 2022

Applied Physics Reviews

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Computational meta-imagers synergize metamaterial hardware with advanced signal processing approaches such as compressed sensing. Recent advances in artificial intelligence (AI) are gradually reshaping the landscape of meta-imaging. Most recent works use AI for data analysis, but some also use it to program the physical meta-hardware. The role of “intelligence” in the measurement process and its implications for critical metrics like latency are often not immediately clear. Here, we comprehensively review the evolution of computational meta-imaging from the earliest frequency-diverse compressive systems to modern programmable intelligent meta-imagers. We introduce a clear taxonomy in terms of the flow of task-relevant information that has direct links to information theory: compressive meta-imagers indiscriminately acquire all scene information in a task-agnostic measurement process that aims at a near-isometric embedding; intelligent meta-imagers highlight task-relevant information in a task-aware measurement process that is purposefully non-isometric. The measurement process of intelligent meta-imagers is, thus, simultaneously an analog wave processor that implements a first task-specific inference step “over-the-air.” We provide explicit design tutorials for the integration of programmable meta-atoms as trainable physical weights into an intelligent end-to-end sensing pipeline. This merging of the physical world of metamaterial engineering and the digital world of AI enables the remarkable latency gains of intelligent meta-imagers. We further outline emerging opportunities for cognitive meta-imagers with reverberation-enhanced resolution, and we point out how the meta-imaging community can reap recent advances in the vibrant field of metamaterial wave processors to reach the holy grail of low-energy ultra-fast all-analog intelligent meta-sensors.

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Section: B Implementationsmentioning

confidence: 99%

Intelligent meta-imagers: From compressed to learned sensing

Saigre-Tardif

Faqiri

Zhao

et al. 2022

Applied Physics Reviews

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“…According to the application and functionality scenarios, electronic machines can be sorted into, including but not limited to, manufacturing machinery, living appliances, working appliances, medical devices, entertainment equipment, etc. From the interactive approach, the HMI can be sorted into mechanical (touch, press, stretch) [ 3 , 9 ], acoustic (sound) [ 10 , 11 ], optics (light) [ 12 , 13 ], thermal (infrared or bionic) [ 14 , 15 , 16 ], and so on. Along with the advent of machines, people have began to interact with these machines, but the human-machine interface (HMI) concept was firstly proposed and systematically investigated in the 1980s [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

Metamaterials-Enabled Sensing for Human-Machine Interfacing

2020

Sensors

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Our modern lives have been radically revolutionized by mechanical or electric machines that redefine and recreate the way we work, communicate, entertain, and travel. Whether being perceived or not, human-machine interfacing (HMI) technologies have been extensively employed in our daily lives, and only when the machines can sense the ambient through various signals, they can respond to human commands for finishing desired tasks. Metamaterials have offered a great platform to develop the sensing materials and devices from different disciplines with very high accuracy, thus enabling the great potential for HMI applications. For this regard, significant progresses have been achieved in the recent decade, but haven’t been reviewed systematically yet. In the Review, we introduce the working principle, state-of-the-art sensing metamaterials, and the corresponding enabled HMI applications. For practical HMI applications, four kinds of signals are usually used, i.e., light, heat, sound, and force, and therefore the progresses in these four aspects are discussed in particular. Finally, the future directions for the metamaterials-based HMI applications are outlined and discussed.

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“…Researchers have attempted to replicate these bionic features in artificial listening systems in an effort to achieve monaural sound localization and separation . To design such a system, one has to deal with two important issues.…”

Section: Introductionmentioning

confidence: 99%

“…According to bionics, people attempt to demonstrate these features on the artificial listening systems to realize monaural localization and separation. Previous works have sought to address these challenges by mimicking the listening capability of human auditory systems 14,[18][19][20][21] . To design such a system, one has to deal with two important issues.…”

Section: Introductionmentioning

confidence: 99%

“…Acoustic metamaterials have been proved effective in modulating both the phase distribution and the amplitude distribution of the acoustic signals [25][26][27][28][29][30] . Combining acoustic metamaterials and compressive sensing, a Helmholtz-based device was used to localize known noise sources 18 , and a spacecoiling anisotropic metamaterial was designed to demonstrate a single-detector acoustic camera 20 . However, both these researches primarily focused on the twodimensional(2D) space.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Sound Localization and Separation in 3D Space Using a Single Microphone with a Metamaterial Enclosure

et al. 2019

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Conventional approaches to sound localization and separation are based on microphone arrays in artificial systems. Inspired by the selective perception of the human auditory system, a multisource listening system which can separate simultaneous overlapping sounds and localize the sound sources in 3D space, using only a single microphone with a metamaterial enclosure is designed. The enclosure modifies the frequency response of the microphone in a direction‐dependent manner by giving each direction a characteristic signature. Thus, the information about the location and the audio content of sound sources can be experimentally reconstructed from the modulated mixed signals using a compressive sensing algorithm. Due to the low computational complexity of the proposed reconstruction algorithm, the designed system can also be applied in source identification and tracking. The effectiveness of the system in multiple real‐life scenarios is evaluated through multiple random listening tests. The proposed metamaterial‐based single‐sensor listening system opens a new way of sound localization and separation, which can be applied to intelligent scene monitoring and robot audition.

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Proposal for the Realization of a Single-Detector Acoustic Camera Using a Space-Coiling Anisotropic Metamaterial

Cited by 21 publications

References 40 publications

Intelligent meta-imagers: From compressed to learned sensing

Intelligent meta-imagers: From compressed to learned sensing

Metamaterials-Enabled Sensing for Human-Machine Interfacing

Sound Localization and Separation in 3D Space Using a Single Microphone with a Metamaterial Enclosure

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