Huawei Technologies Co., Limited

Microsoft Technology Licensing, LLC

Motorola Corporation

Chinese Academy of Sciences

Beijing University of Posts and Telecommunications

Chao Huang

Nan Li

Lixia Xiong

Hao Wang

Min Chu

Jeong J Ma

Mugen Peng

Zhongyuan Zhao

Yadong Wu

Shitou Wu

Yueheng Yang

Liewen Xie

Jingyun Chang

A packet processing method includes: a first device receives a packet from a second device; the first device determines a first queue buffer used to store the packet, and determines a first upper limit value of the first queue buffer based on an available value of a first port buffer and an available value of a global buffer, where the global buffer includes at least one port buffer, the first port buffer is one of the at least one port buffer, the first port buffer includes at least one queue buffer, and the first queue buffer is one of the at least one queue buffer. The first device processes the packet based on the first upper limit value of the first queue buffer, an occupation value of the first queue buffer, and a size of the packet.

Packet processing method and apparatus, communications device, and switching circuit

A packet processing method includes receiving, by a forwarding apparatus, a first packet, where the first packet belongs to a first packet flow, determining, by the forwarding apparatus, at least two types of information in the following four types of information a duration of staying in a first memory by the first packet flow, usage of the first memory, whether the first packet flow is a victim of a congestion control mechanism, and a drop priority of the first packet, and determining, by the forwarding apparatus based on the at least two types of information, whether explicit congestion notification marking needs to be performed on the first packet.

Packet processing method and apparatus

The present disclosure provides a method for simultaneously measuring the value of forsterite and trace elements in olivine, comprising the following steps: Step S1: selecting samples, wherein the samples are olivine samples; Step S2: placing the samples in a sample chamber of LA-ICP-MS, and adjusting the position of the samples in the optical axis direction so that the laser beam is well focused; Step S3: optimizing the instrument to make the signal-to-noise ratio ofFe be the best; Step S4: adopting LA-ICP-MS peak hopping mode and receiving all the mass peaks of the samples by single electron multiplier (SEM). The present disclosure overcomes the disadvantages of long test cycle and high test cost in the prior art.

Method for simultaneously measuring the value of forsterite and trace elements in olivine

A method and system for monitoring and processing a wireless signal are described. The method includes, if an authorized base station/Access Point (AP) receives a first abnormal signal from the terminal, which is identified as an unauthorized signal by a terminal, processing, by the authorized base station/AP, the first abnormal signal. If the authorized base station/Access Point (AP) receives a second abnormal signal from the terminal and the terminal cannot determine whether the second abnormal signal is the unauthorized signal or an authorized signal, identifying, by the authorized base station/AP, characteristics of the second signal and processing the second abnormal signal if the second abnormal signal is the unauthorized signal.

Method and system for monitoring and processing wireless signals

A phone set for use in speech processing such as speech recognition or text-to-speech conversion is used to model or form syllables of a tonal language having a plurality of different tones. Each syllable includes an initial part that can be glide dependent and a final part. The final part includes a plurality of phones. Each phones carries partial tonal information such that the phones taken together implicitly and jointly represent the different tones.

Segmental tonal modeling for tonal languages

The present invention is directed to a 3-stage adaptation framework based on speaker selection training. First a subset of cohort speakers is selected for a test speaker. Then cohort models are transformed to be closer to the test speaker. Finally the adapted model for the test speaker is obtained by combining these transformed cohort models. Combination weights as well as bias items can be adaptively learned from adaptation data.

Speaker selection training via a-posteriori Gaussian mixture model analysis, transformation, and combination of hidden Markov models

Housing for a headset

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Beijing, China

Chao Huang