Shenzhen Microbt Electronics Technology Co., Ltd.

University of California

Applied Materials, Inc.

Guo Ai

Zuoxing Yang

Zhihong Xiang

Ruming Fang

Gao Liu

Hui Zhao

Sergey D Lopatin

Haifeng Guo

Ajey M Joshi

Donghai Wang

Eric H Liu

Zhihui Wang

Sibin Gao

Zhongping Wang

Ju He

Disclosed include a data collection device, a data collection system, and an electronic image stabilization device. The data collection device includes a logic circuit. The logic circuit includes: a timer, configured to generate timestamp data; a control module, configured to, in response to receiving a pre-set input signal, generate a data obtaining command and obtain the timestamp data from the timer; a communication module, configured to, in response to the data obtaining command, obtain MEMS (MEMS) data from a MEMS sensor; and a packing module, configured to pack the MEMS data and the timestamp data into a data packet.

Data collection device, data collection system, and electronic image stabilization device

Embodiments of the present disclosure provide a voice wake-up method, apparatus, electronic device, and readable storage medium. The method includes: acquiring a deep feature encoded by a voice activity detection (VAD) encoding layer through a VAD decoding layer, the deep feature being extracted from a digital audio signal used for VAD encoding and voice wake-up encoding; determining whether each audio segment in the digital audio signal is a voice according to a probability that each audio frame in the digital audio signal is a voice frame, each audio segment including a plurality of audio frames; and when determining that the audio segment is the voice, indicating, by the VAD decoding layer, to input a deep feature of the audio segment into a voice wake-up decoding layer to perform voice wake-up. The embodiments of the present disclosure improve the accuracy of voice wake-up.

Voice wake-up method and apparatus, electronic device, and readable storage medium

A memory management method for a device, and a memory management device and a computing system. The method comprises: determining a plurality of application scenarios of the device; and according to the number of physical memory blocks required by each hardware unit in each application scenario and according to an established mapping relationship between system memory blocks in system memory and physical memory blocks in physical memory, determining system memory blocks allocated to corresponding hardware units, the system memory comprising the physical memory and expanded memory corresponding to the physical memory; wherein the system memory blocks allocated to all the hardware units running in a same application scenario are respectively mapped to different physical memory blocks, or in the system memory blocks allocated to at least two hardware units respectively running in different application scenarios, the system memory block of at least one of the different application scenarios and the system memory block of the other application scenario are mapped to the same physical memory block.

Memory management method for device, memory management device, and computing system

The present disclosure relates to a training method for a parameter configuration model, a parameter configuration method, and a parameter configuration device. The training method includes: obtaining a training sample set of a data processing device, wherein the training sample set includes operation parameters and benefit parameters in one-to-one correspondence with the operation parameters, the operation parameter includes at least one of a global operation parameter or a local operation parameter associated with position distribution, the benefit parameter is configured to reflect a ratio of power consumption to computing power of the data processing device; training the parameter configuration model based on the training sample set, wherein the operation parameter is configured as an input of the parameter configuration model, and the benefit parameter is configured as an output of the parameter configuration model; and in a case that a training accuracy of the parameter configuration model is greater than or equal to a first preset accuracy, ending the training and obtaining a trained parameter configuration model.

Training method for parameter configuration model, parameter configuration method, and parameter configuration device

A simple solution processing method is developed to achieve uniform and scalable stabilized lithium metal powder coating on Li-ion negative electrode. A solvent and binder system for stabilized lithium metal powder coating is developed, including the selection of solvent, polymer binder and enhancement of polymer concentration. The enhanced binder solution is 1% concentration of polymer binder in xylene, and the polymer binder is chosen as the mixture of poly(styrene-co-butadiene) rubber (SBR) and polystyrene (PS). Long-sustained, uniformly dispersed stabilized lithium metal powder suspension can be achieved with the enhanced binder solution. A uniform stabilized lithium metal powder coating can be achieved with simple doctor blade coating method and the resulting stabilized lithium metal powder coating can firmly glued on the anode surface. With the prelithiation of negative electrode by stabilized lithium metal powder, improvements in electrochemical performances are demonstrated in both graphite/NMC and SiO/NMC full-cell.

Anode structure with binders for silicon and stabilized lithium metal powder

The present invention provides for a composition of matter comprising: poly(9,9-dioctylfluorene-co-fluorenone-co-methylbenzoic ester)(PFM), carbon nanotubes (CNT), and sulfur particles nanocomposite, wherein the nanocomposite is porous. The present invention also provides for an electrode comprising: poly(9,9-dioctylfluorene-co-fluorenone-co-methylbenzoic ester)(PFM), carbon nanotubes (CNT), and sulfur particles nanocomposite, wherein the nanocomposite is porous. The present invention also provides for a lithium sulfur (Li&#x2014;S) battery comprising: an electrode comprising poly(9,9-dioctylfluorene-co-fluorenone-co-methylbenzoic ester)(PFM), carbon nanotubes (CNT), and sulfur particles nanocomposite, wherein the nanocomposite is porous.

Poly(9,9-dioctylfluorene-co-fluorenone-co-methylbenzoic ester), carbon nanotubes, and sulfur nanocomposite, electrode and lithium-fulfur battery including the same

Growing community of inventors

Guangdong, China

Guo Ai