2019 IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS)最新文献_第6页

An Enhanced MUSIC DoA Scanning Scheme for Array Radar Sensing in Autonomous Movers 一种用于自主机器人阵列雷达传感的增强MUSIC DoA扫描方案

2019 IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS) Pub Date : 2019-03-01 DOI: 10.1109/AICAS.2019.8771584

Kuan-Ying Chang, Kuan-Ting Chen, W. Ma, Y. Hwang

引用次数: 5

Fast Detection of Objects Using a YOLOv3 Network for a Vending Machine 基于YOLOv3网络的自动售货机对象快速检测

2019 IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS) Pub Date : 2019-03-01 DOI: 10.1109/AICAS.2019.8771517

Youhak Lee, Chulhee Lee, Hyuk-Jae Lee, Jin-Sung Kim

引用次数: 12

SIFT Features and SVM Learning based Sclera Recognition Method with Efficient Sclera Segmentation for Identity Identification 基于SIFT特征和SVM学习的有效巩膜分割巩膜识别方法用于身份识别

2019 IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS) Pub Date : 2019-03-01 DOI: 10.1109/AICAS.2019.8771626

Sheng-Yu He, Chih-Peng Fan

引用次数: 4

AI-Based Edge-Intelligent Hypoglycemia Prediction System Using Alternate Learning and Inference Method for Blood Glucose Level Data with Low-periodicity 基于交替学习和推理方法的低周期血糖水平数据人工智能边缘智能低血糖预测系统

2019 IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS) Pub Date : 2019-03-01 DOI: 10.1109/AICAS.2019.8771604

Tran Minh Quan, Takuyoshi Doike, C. D. Bui, K. Hayashi, S. Arata, A. Kobayashi, Md. Zahidul Islam, K. Niitsu

引用次数: 10

Context-Preserving Filter Reorganization for VDSR-Based Super-resolution 基于vdsr的超分辨率上下文保留滤波器重组

2019 IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS) Pub Date : 2019-03-01 DOI: 10.1109/AICAS.2019.8771601

Donghyeon Lee, Sangheon Lee, H. Lee, Hyuk-Jae Lee, Kyujoong Lee

{"title":"Context-Preserving Filter Reorganization for VDSR-Based Super-resolution","authors":"Donghyeon Lee, Sangheon Lee, H. Lee, Hyuk-Jae Lee, Kyujoong Lee","doi":"10.1109/AICAS.2019.8771601","DOIUrl":"https://doi.org/10.1109/AICAS.2019.8771601","url":null,"abstract":"This paper presents a hardware design to process a CNN for single image super-resolution (SISR). Very deep convolutional network for image super-resolution (VDSR) is a promising algorithm for SISR but it is too complex to be implemented in hardware for commercial products. The proposed design aims to implement VDSR with relatively small hardware resources while minimizing a degradation of image quality. To this end, 1D reorganization of a convolution filter is proposed to reduce the number of multipliers. In addition, the 1D vertical filter is changed to reduce the internal SRAM to store the input feature map. For the implementation with a reasonable hardware cost, the numbers of layers and channels per layer, as well as the parameter resolution, are decreased without a significant reduction of image quality which is observed from simulation results. The 1D reorganization reduces the number of multiplies to 55.6% whereas the size reduction of 1D vertical filter halves the buffer size. As a result, the proposed design processes a full-HD video in real time with 8,143.5k gates and 333.1kB SRAM while the image quality is degraded by 1.06dB when compared with VDSR.","PeriodicalId":273095,"journal":{"name":"2019 IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS)","volume":"144 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124600991","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

AICAS 2019 Cover Page AICAS 2019封面

2019 IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS) Pub Date : 2019-03-01 DOI: 10.1109/aicas.2019.8771608

引用次数: 0

AMSNet: Analog Memristive System Architecture for Mean-Pooling with Dropout Convolutional Neural Network 基于Dropout卷积神经网络的均值池模拟记忆系统架构

2019 IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS) Pub Date : 2019-03-01 DOI: 10.1109/AICAS.2019.8771611

O. Krestinskaya, A. Bakambekova, A. P. James

引用次数: 2

Epilepsy Identification System with Neural Network Hardware Implementation 神经网络癫痫识别系统的硬件实现

2019 IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS) Pub Date : 2019-03-01 DOI: 10.1109/AICAS.2019.8771529

Chieh Tsou, Chi-Chung Liao, Shuenn-Yuh Lee

{"title":"Epilepsy Identification System with Neural Network Hardware Implementation","authors":"Chieh Tsou, Chi-Chung Liao, Shuenn-Yuh Lee","doi":"10.1109/AICAS.2019.8771529","DOIUrl":"https://doi.org/10.1109/AICAS.2019.8771529","url":null,"abstract":"This paper presents a real-time identification system for epilepsy detection with a neural network (NN) classifier. The identification flow of the proposed system in animal testing is described as follows: 1. Two channel signals are collected from mouse brain. 2. Original signals are filtered in the appropriate bandwidth. 3. Six feature values are calculated. 4. Normal and epilepsy are classified by the classifier. The electroencephalography signal is measured from C57BL/6 mice in animal testing with a sampling rate of 400 Hz. The proposed system is verified on software design and hardware implementation. The software is designed in Matlab, and the hardware is implemented by the field programmable gate array (FPGA) platform. The chip is fabricated with TSMC 0.18 μm CMOS technology. The feature extraction function is realized in FPGA, and the NN architecture is implemented with a chip. The chosen feature sets from the previous measured animal testing data are amplitude, frequency bins, approximate entropy, and standard deviation. The accuracies of the proposed system are approximately 98.76% and 89.88% on software verification and hardware implementation, respectively. Results reveal that the proposed architecture is effective for epilepsy recognition.","PeriodicalId":273095,"journal":{"name":"2019 IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS)","volume":"439 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125763058","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9

Hyperdimensional Computing-based Multimodality Emotion Recognition with Physiological Signals 基于生理信号的多维计算多模态情绪识别

2019 IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS) Pub Date : 2019-03-01 DOI: 10.1109/AICAS.2019.8771622

En-Jui Chang, Abbas Rahimi, L. Benini, A. Wu

{"title":"Hyperdimensional Computing-based Multimodality Emotion Recognition with Physiological Signals","authors":"En-Jui Chang, Abbas Rahimi, L. Benini, A. Wu","doi":"10.1109/AICAS.2019.8771622","DOIUrl":"https://doi.org/10.1109/AICAS.2019.8771622","url":null,"abstract":"To interact naturally and achieve mutual sympathy between humans and machines, emotion recognition is one of the most important function to realize advanced human-computer interaction devices. Due to the high correlation between emotion and involuntary physiological changes, physiological signals are a prime candidate for emotion analysis. However, due to the need of a huge amount of training data for a high-quality machine learning model, computational complexity becomes a major bottleneck. To overcome this issue, brain-inspired hyperdimensional (HD) computing, an energy-efficient and fast learning computational paradigm, has a high potential to achieve a balance between accuracy and the amount of necessary training data. We propose an HD Computing-based Multimodality Emotion Recognition (HDC-MER). HDCMER maps real-valued features to binary HD vectors using a random nonlinear function, and further encodes them over time, and fuses across different modalities including GSR, ECG, and EEG. The experimental results show that, compared to the best method using the full training data, HDC-MER achieves higher classification accuracy for both valence (83.2% vs. 80.1%) and arousal (70.1% vs. 68.4%) using only 1/4 training data. HDC-MER also achieves at least 5% higher averaged accuracy compared to all the other methods in any point along the learning curve.","PeriodicalId":273095,"journal":{"name":"2019 IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126009224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 48

Multi-task ADAS system on FPGA 基于FPGA的多任务ADAS系统

2019 IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS) Pub Date : 2019-03-01 DOI: 10.1109/AICAS.2019.8771615

Jinzhan Peng, Lu Tian, Xijie Jia, Haotian Guo, Yongsheng Xu, Dongliang Xie, Hong Luo, Yi Shan, Yu Wang

引用次数: 9