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  1. Free, publicly-accessible full text available November 1, 2024
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  3. The cold sintering process (CSP) is a low-temperature consolidation method used to fabricate materials and their composites by applying transient solvents and external pressure. In this mechano-chemical process, the local dissolution, solvent evaporation, and supersaturation of the solute lead to “solution-precipitation” for consolidating various materials to nearly full densification, mimicking the natural pressure solution creep. Because of the low processing temperature (<300°C), it can bridge the temperature gap between ceramics, metals, and polymers for co-sintering composites. Therefore, CSP provides a promising strategy of interface engineering to readily integrate high-processing temperature ceramic materials (e.g., active electrode materials, ceramic solid-state electrolytes) as “grains” and low-melting-point additives (e.g., polymer binders, lithium salts, or solid-state polymer electrolytes) as “grain boundaries.” In this minireview, the mechanisms of geomimetics CSP and energy dissipations are discussed and compared to other sintering technologies. Specifically, the sintering dynamics and various sintering aids/conditions methods are reviewed to assist the low energy consumption processes. We also discuss the CSP-enabled consolidation and interface engineering for composite electrodes, composite solid-state electrolytes, and multi-component laminated structure battery devices for high-performance solid-state batteries. We then conclude the present review with a perspective on future opportunities and challenges. 
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  4. Indoor localization has played a significant role in facilitating a collection of emerging applications in the past decade. This paper presents a novel indoor localization solution via inaudible acoustic sensing, called EchoSpot, which relies on only one speaker and one microphone that are readily available on audio devices at households. We program the speaker to periodically send FMCW chirps at 18kHz-23kHz and leverage the co-located microphone to capture the reflected signals from the body and the wall for analysis. By applying the normalized cross-correlation on the transmitted and received signals, we can estimate and profile their time-of-flights (ToFs). We then eliminate the interference from device imperfection and environmental static objects, able to identify the ToFs corresponding to the direct reflection from human body. In addition, a new solution to estimate the ToF from wall reflection is designed, assisting us in spotting a human location in the two-dimensional space. We implement EchoSpot on three different types of speakers, e.g., Amazon Echo, Edifier R1280DB, and Logitech z200, and deploy them in real home environments for evaluation. Experimental results exhibit that EchoSpot achieves the mean localization errors of 4.1cm, 9.2cm, 13.1cm, 17.9cm, 22.2cm, respectively, at 1m, 2m, 3m, 4m, and 5m, comparable to results from the state-of-the-arts while maintaining favorable advantages. 
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  5. The fall detection system is of critical importance in protecting elders through promptly discovering fall accidents to provide immediate medical assistance, potentially saving elders' lives. This paper aims to develop a novel and lightweight fall detection system by relying solely on a home audio device via inaudible acoustic sensing, to recognize fall occurrences for wide home deployment. In particular, we program the audio device to let its speaker emit 20kHz continuous wave, while utilizing a microphone to record reflected signals for capturing the Doppler shift caused by the fall. Considering interferences from different factors, we first develop a set of solutions for their removal to get clean spectrograms and then apply the power burst curve to locate the time points at which human motions happen. A set of effective features is then extracted from the spectrograms for representing the fall patterns, distinguishable from normal activities. We further apply the Singular Value Decomposition (SVD) and K-mean algorithms to reduce the data feature dimensions and to cluster the data, respectively, before input them to a Hidden Markov Model for training and classification. In the end, our system is implemented and deployed in various environments for evaluation. The experimental results demonstrate that our system can achieve superior performance for detecting fall accidents and is robust to environment changes, i.e., transferable to other environments after training in one environment. 
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