Qualcomm Incorporated

Boe Technology Group Co., Ltd.

International Business Machines Corporation

Chengdu Boe Optoelectronics Technology Co., Ltd.

Apple Inc.

Tsinghua University

Intel Corporation

Huawei Technologies Co., Limited

Chinese Academy of Sciences

Other

General Electric Company

Beijing Xiaomi Mobile Software Co., Ltd.

Globalfoundries Inc.

Xilinx, Inc.

Alibaba Group Holding Limited

Yu Zhang

Wanshi Chen

Chao Wei

Chenxi Hao

Liangming Wu

Peter S Gaal

Qiaoyu Li

Alexandros Manolakos

Ye Liu

Hao Xu

Joseph Binamira Soriaga

Tingfang Ji

Min Huang

Wei Zeng

Haitong Sun

Kun Wang

Dawei Zhang

Yuchul Kim

Jilei Hou

Wei Zhang

Yi Huang

Tao Luo

Peng Cheng

June Namgoong

Juan Montojo

Jing Jiang

Haipeng Lei

Weiyun Huang

Fan Yang

Yang Li

Jian Wang

Alexei Yurievitch Gorokhov

Alexander Golitschek Edler Von Elbwart

Bo Gao

Renqiu Wang

Bilal Sadiq

Jay Kumar Sundararajan

Xin Peng Liu

Zhu Ji

Zhiliang Jiang

Navid Abedini

Seyedkianoush Hosseini

Yue Wang

Seyed Ali Akbar Fakoorian

Muhammad Nazmul Islam

Taesang Yoo

Muhammad Sayed Khairy Abdelghaffar

Gabi Sarkis

Jing Wang

Sundar Subramanian

Ashwin Sampath

Gang Wang

Li Wang

Lin Sun

Liang Zhang

Jing Li

Zheng Wang

Xiaoxia Zhang

Jing X Sun

Sony Akkarakaran

Alberto Rico Alvarino

Gavin Bernard Horn

Naga Bhushan

Krishna Kiran Mukkavilli

Durga Prasad Malladi

Huilin Xu

Wei Wang

Lei Zhang

Ozcan Ozturk

Wei Liu

Linhai He

Makesh Pravin John Wilson

Yi Zhang

Wei Li

Hua Wang

Xipeng Zhu

Xin Li

Peter Kenneth Malkin

Ying Wang

Siddhartha Mallik

Ming Wang

Johnson O Sebeni

Bo Zhang

Xin Zhang

Changlong Xu

Qiang Wang

Weimin Duan

Jian Li

Dong Wang

Benlian Wang

Jiantao Liu

Xiaodong Yang

Kai Chen

Hari Sankar

Anbang Yao

Chong Li

Tao Li

Amir Aminzadeh Gohari

Techniques related to poly-scale kernel-wise convolutional neural network layers are discussed. A poly-scale kernel-wise convolutional neural network layer is applied to an input volume to generate an output volume and include filters each having a number of filter kernels with the same sample rate and differing dilation rates optionally in a repeating pattern of dilation rate groups within each of filters with the pattern of dilation rate groups offset between the filters the poly-scale kernel-wise convolutional neural network layer.

Poly-scale kernel-wise convolution for high-performance visual recognition applications

Embodiments of the present invention provide a control method for an image acquisition device, a control device therefor, and a storage medium, relate to the field of the image acquisition device, and aim to realize synchronization of an operation timing signal of the image acquisition device and an external trigger signal. The method comprises: receiving image data acquired by the image acquisition device; analyzing the image data, obtaining an image timing according to an analysis result, and determining a frame synchronization signal of the image data output by the image acquisition device according to the image timing; determining a phase offset between the frame synchronization signal and a preset trigger signal; and adjusting a phase of a control signal based on the phase offset, wherein the control signal is configured for adjusting an operation timing of the image acquisition device.

Phase and exposure synchronization control of image acquisition devices

Embodiments of the present disclosure relate to a method for task scheduling and an electronic device. The method comprises: based on determining there is a task to be scheduled in an online task set of a waiting queue, an electronic device schedules the task based on a preset online scheduling mode which may be a sequential mode, a throughput mode, or a round-robin mode. Accordingly, at least one processing engine of the electronic device can process the task.

Task scheduling based on scheduling mode

A connection method for a wireless smart wearable device is performed by a terminal device, and includes: receiving a connection switching instruction of the wireless smart wearable device from a preset control interface of the terminal device; determining a target device according to the connection switching instruction; and instructing, according to a preset parameter of the wireless smart wearable device, the target device to be connected with the wireless smart wearable device according to the preset parameter.

Connection method and apparatus for wireless smart wearable device and storage medium

The present disclosure provides a display module and a display apparatus. The display module includes a display panel, a backlight assembly, an intermediate frame and a backplane; the backplane includes a backplane body on a side of the backlight assembly away from the display panel; the backplane body includes a contact structure recessed in a direction from a side away from the light guide assembly toward a side close to the light guide assembly. The contact structure is in contact with the light guide assembly; a receiving space is between the backplane body and the light guide assembly and on a side of the contact structure close to a lateral backlight source; an elastic contact member is in a receiving space and is in contact with the backplane body and is adjacent to the light guide assembly; the intermediate frame includes an intermediate frame body surrounding the backlight assembly.

Display module and display apparatus

A system includes a high bandwidth memory (HBM) and a convolutional neural network (CNN) engine. The HBM includes a virtual bank portion and a system memory portion. The virtual bank portion is configured to store a feature map data and the system memory portion is configured to support data exchanges with a host. The CNN engine includes a convolutional unit configured to execute convolutional layer instructions, a depthwise convolutional unit configured to execute depthwise layer instructions, and a first on-chip buffer. The first on-chip buffer is configured to receive and store the feature map data from the virtual bank portion or receive and store data results from the convolutional unit. The first on-chip buffer is further configured to send the feature map data or the data results from the convolutional unit to the depthwise convolutional unit for processing.

Neural network architecture with high bandwidth memory (HBM)

A method of wireless communication by a user equipment (UE), comprises inputting sensor data, captured at the UE, to a local machine learning model. The method also includes extracting features from the sensor data, with the local machine learning model, while anonymizing the sensor data. The method further includes transmitting the features to a base station, and receiving a handover decision from the base station based on the features. A method of wireless communication by abase station inputs anonymized sensor feature data, received from a user equipment (UE), into a network machine learning model. The base station also inputs handover decision information into the network machine learning model. The method further includes inferring a handover decision based on the handover decision information and the anonymized sensor feature data, and transmitting the handover decision to the UE.

Machine learning handover prediction based on sensor data from wireless device

Embodiments of the present disclosure relate to methods and apparatuses for determining a HARQ-ACK codebook for multi-slot PDSCH transmission. According to some embodiments of the disclosure, a method for wireless communications performed by a UE may include: receiving a first plurality of downlink control information (DCI) formats for scheduling a second plurality of physical downlink shared channel (PDSCH) transmissions on at least one serving cell of the UE; receiving the second plurality of PDSCH transmissions based on the first plurality of DCI formats; generating a HARQ-ACK codebook based on the indicators in the first plurality of DCI formats; and transmitting the HARQ-ACK codebook.

Method and apparatus for determining hybrid automatic repeat request acknowledgement (HARQ-ACK) codebook for multi-slot physical downlink shared channel (PDSCH) transmission

The present application relates to a method and an apparatus for multiple uplink transmissions. The method includes: receiving a first control information for a first uplink transmission; receiving a second control information for a second uplink transmission; and determining a beam for transmitting the first uplink transmission, wherein the beam is indicated in the first control information or indicated in the second control information.

Method and apparatus for multiple uplink transmissions

The disclosure relates to a method, apparatus, computer-readable medium and electronic device for program loading. The method includes: loading the bin file at a program loading speed that is a first speed, adjusting the program loading speed to a second speed based on the configuration parameter in the bin file, the second speed being faster than the first speed, and then loading a FSBL at the program loading speed that is the second speed. By executing the configuration parameter in the bin file to adjust the speed of the Bootrom loading the FSBL from flash memory, the time for the Bootrom to load FSBL is shortened, and thus embedded systems are enabled to be compatible with various flash memories while also being applicable in occasions sensitive to startup time.

Method, apparatus and computer-readable medium for program loading

Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment may determine an automatic gain control parameter, for a group of downlink symbols associated with a base station, based at least in part on an automatic gain control resource included in the group of downlink symbols. The user equipment may perform, based at least in part on the automatic gain control parameter, automatic gain control for one or more data symbols included in the group of downlink symbols. Numerous other aspects are provided.

Receiver automatic gain control

Methods, systems, and devices for wireless communications are described. In some examples, a UE may receive an indication of a set of sounding reference signal (SRS) parameters corresponding to a set of SRS resources for transmitting SRSs, where the set of SRS resources may refer to frequency, or time, or both. In some examples, the SRS parameters may include, among other examples, a frequency allocation, a frequency overlap parameter, a number of allocated symbols, a comb value, a cyclic shift, a comb offset, a frequency hopping parameter, a repetition parameter, a symbol grouping parameter, or any combination thereof. The UE may determine an SRS configuration for the one or more SRSs based on the set of SRS parameters and transmit the one or more SRSs based on the configuration. In some examples, the SRS configuration includes a configuration for SRS frequency hopping, or SRS repetition, or both.

Enhanced sounding reference signal resource configurations

Provided is a support bracket for supporting a diffuser, including: a support post and a mounting part arranged in a first direction. The mounting part includes: an elastic connection part and a base arranged opposite to each other along the first direction, the elastic connection part is connected to the base, the support post extends in the first direction and an end of the support post close to the mounting part is fixed to the elastic connection part, and the elastic connection part is configured to be elastically deformable in the first direction to change a distance between the end of the support post close to the mounting part and the base in the first direction. Further provided are a sub-backlight module, a display device and a splicing display module.

Support bracket, sub-backlight module, display device and splicing display module

Apparatus, methods, processing systems, and computer readable mediums for beam management in multi-TRP scenarios are disclosed. An exemplary method generally includes receiving a channel state information reference signal (CSI-RS) resource configuration from at least one base station (BS) for one or more transmission hypotheses, wherein the CSI-RS resource configuration includes CSI-RS resources corresponding to at least a first downlink (DL) demodulation reference signal (DMRS)-port-group and a second DL DMRS-port-group, performing one or more CSI-RS measurements for the one or more transmission hypotheses based, at least in part on the CSI-RS resource configuration, and determining, for each of the one or more transmission hypotheses, a preferred reception beam for communicating with the at least one BS based, at least in part, on the one or more CSI-RS measurements.

Beam management enhancements for multi-TRP scenarios

A power control method of a lower RF power supply of semiconductor processing equipment includes, when a processing chamber starts semiconductor processing, with a predetermined power compensation equation and according to a pre-obtained first RF circuit parameter set of a reference chamber and a second RF circuit parameter set of a present processing chamber performing the semiconductor processing, obtaining a power compensation coefficient of the present processing chamber relative to the reference chamber, according to the power compensation coefficient and a power setting value of the lower RF power supply of the present processing chamber, calculating a power compensation value of the present processing chamber relative to the reference chamber, and controlling the lower RF power supply to output the power compensation value.

Power control method and device of lower radio frequency power supply and semiconductor processing equipment

Growing community of inventors

Beijing, China

Yu Zhang