International Business Machines Corporation

Ya Dong Li

Zhong Fang Yuan

Jing Wen Xu

Jian Zhang

Tong Liu

He Li

De Shuo Kong

Hai B Zou

Zi Jian Ji

Si Tong Zhao

Rui W W Wang

Yun He Gao

Peng Yun Sun

Xuan Yin Xia

In an approach for road section recognition using multi-modal cognitive mechanism, a processor receives an audio signal from a road test. A processor processes the audio signal to generate an acoustic spectrum density distribution map to identify a respective at least one road section switching point in a first mode. A processor processes a spectrogram of the audio signal to identify the respective at least one road section switching point in a second mode. A processor uses a machine learning model to predict an expected sound at each frame of the audio signal, to calculate a similarity between the expected sound and an actual sound, and to identify the respective at least one road switching point when the similarity is lower than a pre-set similarity threshold in a third mode. A processor combines results of the three modes to obtain a final set of road section switching points.

Multi-modal cognitive mechanism for road section recognition

A first set of features associated with a neural network are parameterized. A decision tree is generated from the first set of features. One or more adjustments for the neural network are received at the decision tree. A second set of features associated with the adjustments at the decision tree are parameterized. The parameterized first and second set of features are combined into a plurality of parameters. From the plurality, an adjusted neural network is generated.

Decision tree interface for neural networks

Wifi extender

A method, system, and computer program product for detecting and monitoring a signal is provided. The method includes detecting an alignment point for a periodic signal segment of a periodic signal generated by an apparatus being monitored for standard functionality In response, the apparatus is activated from a period prior to the alignment point to an end point of the periodic signal segment and a first point of the periodic signal segment is located. Likewise, a second point of an additional periodic signal segment of the periodic signal is located. The periodic signal is normalized based on results of locating the first point and the second point.

Signal detection and monitoring

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Beijing, China

Ya Dong Li