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Highly performance flexible strain sensor is a crucial component for wearable devices, human‐machine interfaces, and e‐skins. However, the sensitivity of the strain sensor is highly limited by the strain range for large destruction of the conductive network. Here the quasi‐1D conductive network (QCN) is proposed for the design of an ultra‐sensitive strain sensor. The orientation of the conductive particles can effectively reduce the number of redundant percolative pathways in the conductive composites. The maximum sensitivity will reach the upper limit when the whole composite remains only "one" percolation pathway. Besides, the QCN structure can also confine the tunnel electron spread through the rigid inclusions which significantly enlarges the strain‐resistance effect along the tensile direction. The strain sensor exhibits state‐of‐art performance including large gauge factor (862227), fast response time (24 ms), good durability (cycled 1000 times), and multi‐mechanical sensing ability (compression, bending, shearing, air flow vibration, etc.). Finally, the QCN sensor can be exploited to realize the human‐machine interface (HMI) application of acoustic signal recognition (instrument calibration) and spectrum restoration (voice parsing). [ABSTRACT FROM AUTHOR]

The multifaceted ability to produce, transmit, receive, and respond to acoustic signals is widespread in animals and forms the basis of the interdisciplinary science of bioacoustics. Bioacoustics research methods, including sound recording and playback experiments, are applicable in cognitive research that centers around the processing of information from the acoustic environment. We provide an overview of bioacoustics techniques in the context of cognitive studies and make the case for the importance of bioacoustics in the study of cognition by outlining some of the major cognitive processes in which acoustic signals are involved. We also describe key considerations associated with the recording of sound and its use in cognitive applications. Based on these considerations, we provide a set of recommendations for best practices in the recording and use of acoustic signals in cognitive studies. Our aim is to demonstrate that acoustic recordings and stimuli are valuable tools for cognitive researchers when used appropriately. In doing so, we hope to stimulate opportunities for innovative cognitive research that incorporates robust recording protocols. This article is categorized under:Neuroscience > CognitionPsychology > Theory and MethodsNeuroscience > BehaviorNeuroscience > Cognition [ABSTRACT FROM AUTHOR]