Wideband electric field quantum sensing via motional Raman transitions
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Low-power Spiking Neural Network audio source localisation using a Hilbert Transform audio event encoding scheme
Sound source localisation is used in many consumer devices, to isolate audio from individual speakers and reject noise. Localization is frequently accomplished by “beamforming”, which combines phase-shifted audio streams to increase power from chosen source directions, under a known microphone array geometry. Dense band-pass filters are often needed to obtain narrowband signal components from wideband audio. These approaches achieve high accuracy, but narrowband beamforming is computationally demanding, and not ideal for low-power IoT devices. We introduce a method for sound source localisation on arbitrary microphone arrays, designed for efficient implementation in ultra-low-power spiking neural networks (SNNs). We use a Hilbert transform to avoid dense band-pass filters, and introduce an event-based encoding method that captures the phase of the complex analytic signal. Our approach achieves high accuracy for SNN methods, comparable with traditional non-SNN super-resolution beamforming. We deploy our method to low-power SNN inference hardware, with much lower power consumption than super-resolution methods. We demonstrate that signal processing approaches co-designed with spiking neural network implementations can achieve much improved power efficiency. Our Hilbert-transform-based method for beamforming can also improve the efficiency of traditional digital signal processing.
Nanoplasmonic SERS on fidget spinner for digital bacterial identification
Raman spectroscopy offers non-destructive and highly sensitive molecular insights into bacterial species, making it a valuable tool for detection, identification, and antibiotic susceptibility testing. However, achieving clinically relevant accuracy, quantitative data, and reproducibility remains challenging due to the dominance of bulk signals and the uncontrollable heterogeneity of analytes. In this study, we introduce an innovative diagnostic tool: a plasmonic fidget spinner (P-FS) incorporating a nitrocellulose membrane integrated with a metallic feature, referred to as a nanoplasmonic-enhanced matrix, designed for simultaneous bacterial filtration and detection. We developed a method to fabricate a plasmonic array patterned nitrocellulose membrane using photolithography, which is then integrated with a customized fidget spinner. Testing the P-FS device with various bacterial species (E. coli 25922, S. aureus 25923, E. coli MG1655, Lactobacillus brevis, and S. mutans 3065) demonstrated successful identification based on their unique Raman fingerprints. The bacterial interface with regions within the plasmonic array, where the electromagnetic field is most intensely concentrated—called nanoplasmonic hotspots—on the P-FS significantly enhances sensitivity, enabling more precise detection. SERS intensity mappings from the Raman spectrometer are transformed into digital signals using a threshold-based approach to identify and quantify bacterial distribution. Given the P-FS’s ability to enhance vibrational signatures and its scalable fabrication under routine conditions, we anticipate that nanoplasmonic-enhanced Raman spectroscopy—utilizing nanostructures made from metals (specifically gold and silver) deposited onto a nitrocellulose membrane to amplify Raman scattering signals—will become the preferred technology for reliable and ultrasensitive detection of various analytes, including those crucial to human health, with strong potential for transitioning from laboratory research to clinical applications.
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