Search | Engineering

Subscribe Submit

Home Journals Focus Achievement Fronts About Us 中文版

Resource Type

Journal Article 5

Year

2023 1

2018 1

2017 1

2016 1

2007 1

Keywords

AAC 1

Answer selection 1

Attention 1

Attention mechanism 1

Deep learning 1

Encoder– 1

FFC 1

Fully optional polynomials 1

Low resource consumption 1

Multi-parameter 1

Neural network 1

Pipelined structure 1

Question answering 1

Reconfigurable Viterbi decoder 1

Reed-Solomon codes 1

Robot grasp detection 1

SoC 1

Standard convolutional symbols generator (SCSG) 1

Step-by-step algorithm 1

Ultra-wideband 1

open ︾

Search scope:

排序： Display mode:

A multistandard and resource-efficient Viterbi decoder for a multimode communication system None

Yi-qi XIE, Zhi-guo YU, Yang FENG, Lin-na ZHAO, Xiao-feng GU

Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 4, Pages 536-543 doi: 10.1631/FITEE.1601596

Abstract: standard convolutional symbols generator (SCSG) block for a multi-parameter reconfigurable Viterbi decoderThe architecture of the Viterbi decoder based on the SCSG reduces resource consumption for recalculatingThe proposed Viterbi decoder has been implemented on the Xilinx XC7VX485T device with a high throughput

Keywords： Reconfigurable Viterbi decoder Multi-parameter Low resource consumption Standard convolutional symbols

HTML PDF Collect

Optimization Strategy of MPEG-4 AAC Decoder on a Low-cost SoC

Gao Gugang,Shi Longxing,Pu Hanlai,Zhou Fan

Strategic Study of CAE 2007, Volume 9, Issue 10, Pages 60-64

Abstract:

This paper proposes software optimization strategies using a low-cost SoC which include float-point to fix-point conversion scheme based on statistical analysis and performance oriented customizing scheme for on-chip memory's capacity, and presents optimization methodology based on these strategies for computation intensive applications. the MPEG-4 AAC decoding in real-time is implemented as a case study to illustrate the efficiency of the proposed optimization strategy in both performance and cost. The strategy and methodology also can be used to optimize other DSP applications.

Keywords： software optimization SoC FFC on-chip memory AAC

HTML PDF Collect

Attention-based encoder-decoder model for answer selection in question answering Article

Yuan-ping NIE, Yi HAN, Jiu-ming HUANG, Bo JIAO, Ai-ping LI

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 4, Pages 535-544 doi: 10.1631/FITEE.1601232

Abstract: The proposed model employs a bidirectional long short-term memory (LSTM) encoder-decoder, which has been

Keywords： Question answering Answer selection Attention Deep learning

HTML PDF Collect

ApipelinedReed-Solomon decoder based on a modified step-by-step algorithm Article

Xing-ru PENG,Wei ZHANG,Yan-yan LIU

Frontiers of Information Technology & Electronic Engineering 2016, Volume 17, Issue 9, Pages 954-961 doi: 10.1631/FITEE.1500303

Abstract: We propose a pipelined Reed-Solomon (RS) decoder for an ultra-wideband system using a modified stepby-stepThe pipelined structure allows the decoder to work at high rates with minimum delay.The area of the proposed decoder is 11.3% less than that of the previous step-by-step decoder with a

Keywords： Reed-Solomon codes Step-by-step algorithm Ultra-wideband Pipelined structure

HTML PDF Collect

Attention-based efficient robot grasp detection network Research Article

Xiaofei QIN, Wenkai HU, Chen XIAO, Changxiang HE, Songwen PEI, Xuedian ZHANG,xiaofei.qin@usst.edu.cn,obmmd_zxd@163.com

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 10, Pages 1430-1444 doi: 10.1631/FITEE.2200502

Abstract: To balance the inference speed and detection accuracy of a grasp detection algorithm, which are both important for robot grasping tasks, we propose an ; structured pixel-level grasp detection named the attention-based efficient network (AE-GDN). Three spatial attention modules are introduced in the encoder stages to enhance the detailed information, and three channel attention modules are introduced in the stages to extract more semantic information. Several lightweight and efficient DenseBlocks are used to connect the encoder and paths to improve the feature modeling capability of AE-GDN. A high intersection over union (IoU) value between the predicted grasp rectangle and the ground truth does not necessarily mean a high-quality grasp configuration, but might cause a collision. This is because traditional IoU loss calculation methods treat the center part of the predicted rectangle as having the same importance as the area around the grippers. We design a new IoU loss calculation method based on an hourglass box matching mechanism, which will create good correspondence between high IoUs and high-quality grasp configurations. AE-GDN achieves the accuracy of 98.9% and 96.6% on the Cornell and Jacquard datasets, respectively. The inference speed reaches 43.5 frames per second with only about 1.2×10 parameters. The proposed AE-GDN has also been deployed on a practical robotic arm grasping system and performs grasping well. Codes are available at https://github.com/robvincen/robot_gradethttps://github.com/robvincen/robot_gradet.

Keywords： Robot grasp detection Attention mechanism Encoder– decoder Neural network