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Because the protection of perimeters in national, agricultural, airport, prison, military sites, and residential areas against dangerous approaching human and vehicles when using humans to provide security is expensive or unsafe, acoustic/vibration signature identification of approaching human and vehicles threats has attracted increased attention. This paper addresses the development and deployment...
In this paper we propose using a new approach, a nonlinear Hebbian learning, to implement acoustic signature recognition of running vehicles. The proposed learning rule processes both time and frequency components of input data. The spectral analysis is realized using auditory gammatone filterbanks. The gammatone-filtered feature vectors are then assembled over multiple temporal frames to establish...
We propose using a neurobiology-motivated prototype to detect approaching vehicles and to identify the type of vehicles for perimeter security. Acoustic sound of running vehicles is analyzed. Motivated by mammalian auditory cortex studies, the proposed detector processes both spectral and temporal components of input data. With the exact acoustic signature being unknown, a nonlinear Hebbian learning...
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