China
Africa
Explore chapters and articles related to this topic
Design of a new in-flight entertainment terminal
Published in Jimmy C.M. Kao, Wen-Pei Sung, Civil, Architecture and Environmental Engineering, 2017
Hairong Xu, Hong Zhou, Hui Yang
The CPU subsystem is the core of the entire hardware system. It is made up of application processor, power management chip, and memory chip. Qualcomm Snapdragon 410 processor APQ8016 is used as the multimedia processor. APQ8016 has quad 64-bit ARM Cortex-A53 MPcore Harvard Superscalar core, LP-DDR2 / LP-DDR3 SDRAM interface, Hexagon QDSP6, 13.5 MP camera support, 400 MHz Adreno 306 GPU, 1080p video encode/decode, gpsOneGen 8 A with GLONASS, Bluetooth 4.0, OpenGL ES 3.0, DirectX, OpenCL, Renderscript Compute, and FlexRender support. APQ8016 can support a variety of multimedia applications and game applications.
Neural network pruning based on channel attention mechanism
Published in Connection Science, 2022
Jianqiang Hu, Yang Liu, Keshou Wu
Furthermore, we deploy CNNs on resource-constrained devices with CUP (Qualcomm Snapdragon 865), GUP (Adreno 650), Memory (12GB) and 256GB storage capacity. Specifically, it includes the following steps: Converting the compressed YOLOv5s Pytorch model into an ONNX model.Converting the ONNX model into an NCNN model.Building an Android application with the NCNN model.The Android project contained NCNN model is packaged into an APK and transplanted to the mobile device for installation and operation.
Fast and Energy-Efficient Block Ciphers Implementations in ARM Processors and Mali GPU
Published in IETE Journal of Research, 2022
W. K. Lee, Raphael C.-W. Phan, B. M. Goi
As embedded GPUs are becoming a common part in many mobile computing platforms [20], they are being used in many areas including wearables [21], communications and video surveillance [22]. Major embedded GPU manufacturers had started to support OpenCL (e.g. Mali from ARM, Adreno from Qualcomm and PowerVR from Imagination Technologies) since 2012. Recently, Android announced another GPGPU framework, Render Script Android 4.2 or higher. NVIDIA's CUDA is also supported in embedded GPU, Tegra X1. It is clear that GPGPU will soon become main stream for embedded computing. Hence we are motivated to utilize this device for generic computing to achieve better computational performance. This powerful platform can also be used to design IoT (internet of things) gateway as well as sophisticated IoT sensor nodes used to perform sensing, data fusion and aggregation.
CityGML goes mobile: application of large 3D CityGML models on smartphones
Published in International Journal of Digital Earth, 2019
Christoph Blut, Timothy Blut, Jörg Blankenbach
We used a Google\LG Nexus 5™ smartphone with the Android™ 6 (Marshmallow) OS. The hardware specifications are: Qualcomm Snapdragon 800 2.26 GHz processorAdreno-330 450 MHz GPU2 GB of RAM32 GB internal memory