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1. High Signal‐to‐Noise Ratio Event‐Driven MEMS Motion Sensing

   https://doi.org/10.1002/smll.202304591  

   Mousavi, Mohammad; Alzgool, Mohammad; Davaji, Benyamin; Towfighian, Shahrzad (March 2024, Small)

   Two solutions for improving MEMS triboelectric vibration sensors performance in contact‐separation mode are reported experimentally and analytically. Triboelectric sensors have mostly been studied in the mesoscale. The gap variation between the electrodes induces a potential difference that represents the external vibration. Miniaturizing the device limits the sensor output because of the limited gap. This work offers a warped MEMS diaphragm constrained on its edges. The dome‐shaped structure provides one order of magnitude larger displacement after contact‐separation than standard designs resulting in one order of magnitude greater voltage and signal‐to‐noise‐ratio. Second, micro triboelectric sensors do not operate unless the external vibration is sufficiently forceful to initiate contact between layers. The proposed constraints on the edge of the diaphragm provide friction during periodic motion and generate charges. The combination of the warped diaphragm and boundary constraints instead of serpentine springs increases the charge density and voltage generation. The mechanical properties and electrical output are thoroughly investigated including nonlinearity, sensitivity, and signal‐to‐noise ratio. A sensitivity of 250 mV/g and signal‐to‐noise‐ratio of 32 dB is provided by the presented device at resonance, which is very promising for event‐driven motion sensors because it does not require signal conditioning and therefore simplifies the sensing circuitry. 

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2. Exposure-Referred Signal-to-Noise Ratio for Digital Image Sensors

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   Gnanasambandam, Abhiram; Chan, Stanley H. (January 2022, IEEE Transactions on Computational Imaging)
3. Exceptional-point-based accelerometers with enhanced signal-to-noise ratio

   https://doi.org/10.1038/s41586-022-04904-w  

   Kononchuk, Rodion; Cai, Jizhe; Ellis, Fred; Thevamaran, Ramathasan; Kottos, Tsampikos (July 2022, Nature)
4. Signal-to-Noise Ratio Aware Minimaxity and Higher-Order Asymptotics

   https://doi.org/10.1109/TIT.2023.3347493  

   Guo, Yilin; Weng, Haolei; Maleki, Arian (May 2024, IEEE Transactions on Information Theory)

   Since its development, the minimax framework has been one of the cornerstones of theoretical statistics, and has contributed to the popularity of many well-known estimators, such as the regularized M-estimators for high-dimensional problems. In this paper, we will first show through the example of sparse Gaussian sequence model, that the theoretical results under the classical minimax framework are insufficient for explaining empirical observations. In particular, both hard and soft thresholding estimators are (asymptotically) minimax, however, in practice they often exhibit sub-optimal performances at various signal-to-noise ratio (SNR) levels. The first contribution of this paper is to demonstrate that this issue can be resolved if the signal-to-noise ratio is taken into account in the construction of the parameter space. We call the resulting minimax framework the signal-to-noise ratio aware minimaxity. The second contribution of this paper is to showcase how one can use higher-order asymptotics to obtain accurate approximations of the SNR-aware minimax risk and discover minimax estimators. The theoretical findings obtained from this refined minimax framework provide new insights and practical guidance for the estimation of sparse signals. 

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5. Evaluating an Elevated Signal-to-Noise Ratio in EEG Emotion Recognition

   https://doi.org/10.4018/IJSI.333161  

   Estreito, Zachary; Le, Vinh; Harris_Jr, Frederick C; Dascalu, Sergiu M (January 2024, International Journal of Software Innovation)

   Predicting valence and arousal values from EEG signals has been a steadfast research topic within the field of affective computing or emotional AI. Although numerous valid techniques to predict valence and arousal values from EEG signals have been established and verified, the EEG data collection process itself is relatively undocumented. This creates an artificial learning curve for new researchers seeking to incorporate EEGs within their research workflow. In this article, a study is presented that illustrates the importance of a strict EEG data collection process for EEG affective computing studies. The work was evaluated by first validating the effectiveness of a machine learning prediction model on the DREAMER dataset, then showcasing the lack of effectiveness of the same machine learning prediction model on cursorily obtained EEG data. 

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