2019 Proceeding
On behalf of the Committee responsible for the SPML 2019 and AITC 2019, I deeply appreciate your active participation in the conference. It was a great pleasure for me to meet you all. I have enjoyed sharing with you the new ideas on the current and emerging technologies and discussing exciting results with all the great scientists, undergraduate, postgraduate, PhD students, postdoctoral fellows, researchers, engineers, academicians as well as industrial professionals from all over the world with keen interest in Signal Processing, Machine Learning, Artificial Intelligence and their current applications.
Proceeding Downloads
Deep Neural Network-Based Scale Feature Model for BVI Detection and Principal Component Extraction
The blade-vortex interaction (BVI) is a typical helicopter noise, and has received significant attentions in the fields of structural stealth and acoustic detection. In this paper, a hybrid scheme combining aerodynamic and acoustic analysis based on the ...
Automated Detection of Sewer Pipe Defects Based on Cost-Sensitive Convolutional Neural Network
Regular inspection and repair of drainage pipes is an important part of urban construction. Currently, many classification methods have been used for defect diagnosis using images inside pipelines. However, most of these classification models train the ...
Maximum Bayes Boundary-Ness Training For Pattern Classification
The ultimate goal of pattern classifier parameter training is to achieve its optimal status (value) that produces Bayes error or a corresponding Bayes boundary. To realize this goal without unrealistically long training repetitions and strict parameter ...
A Small-Footprint End-to-End KWS System in Low Resources
In this paper, we propose an efficient end-to-end architecture, based on Connectionist Temporal Classification (CTC), for low-resource small-footprint keyword spotting (KWS) system. For a low-resource KWS system, it is difficult for the network to ...
Data Link Modeling and Simulation Based on DEVS
The Discrete Event System (DEVS)[1] Specification provides a reference standard for the model design and simulation development of complex discrete event state system. It designs a formal mechanism to describe discrete event state, which is composed of ...
Multi-scale Fusion and Channel Weighted CNN for Acoustic Scene Classification
Ensemble semantic features are useful for acoustic scene classification. In this paper, we proposed a multi-scale fusion and channel weighted CNN framework. The framework consists of two stages: the multi-scale feature fusion and channel weighting ...
Multi-source Radar Data Fusion via Support Vector Regression
Since the measurement error of surveillance sensors such as radar differs each other in the detection of the same target, it's necessary to fuse the multi-source radar data to estimate the true location of target and reduce the measurement error of ...
Discrete Sidelobe Clutter Determination Method Based on Filtering Response Loss
For air moving target detection with space-based radar (SBR), discrete sidelobe clutter is generally caused by strong scattering points at the sidelobe direction in the observation scene, which is difficult to discern from moving targets as a result of ...
Multi-Task Learning Based End-to-End Speaker Recognition
Recently, there has been an increasing interest in end-to-end speaker recognition that directly take raw speech waveform as input without any hand-crafted features such as FBANK and MFCC. SincNet is a recently developed novel convolutional neural ...
Minimum Classification Error Training with Speech Synthesis-Based Regularization for Speech Recognition
To increase the utility of Regularization, which is a common framework for avoiding the underestimation of ideal Bayes error, for speech recognizer training, we propose a new classifier training concept that incorporates a regularization term that ...
Multi-Scale Deep Convolutional Nets with Attention Model and Conditional Random Fields for Semantic Image Segmentation
Although Convolutional Neural Networks are effective visual models that generate hierarchies of features, there still exist some shortcomings in the application of Deep Convolutional Neural Networks to semantic image segmentation. In this work, our ...
An Attention-Enhanced Recurrent Graph Convolutional Network for Skeleton-Based Action Recognition
Dynamic movements of human skeleton have attracted more and more attention as a robust modality for action recognition. As not all temporal stages and skeleton joints are informative for action recognition, and the irrelevant information often brings ...
A Vision-based Human Action Recognition System for Moving Cameras Through Deep Learning
This study presents a vision-based human action recognition system using a deep learning technique. The system can recognize human actions successfully when the camera of a robot is moving toward the target person from various directions. Therefore, the ...
Method for Removing Motion Blur from Images of Harmful Biological Organisms in Power Places Based on Improved Cyclegan
Nowadays, the automatic detection of harmful organisms in power places has attracted attention due to the extensive unattended way of power places. However, surveillance pictures are prone to motion blurring and harmful organisms cannot be effectively ...
A Stereo Matching with Reconstruction Network for Low-light Stereo Vision
To solve the problem existing in the stereo matching of low-light images, this paper proposes a stereo matching with reconstruction network based on pyramid stereo matching network(PSMNet) and reconstruction module. In view of the characteristics of the ...
The Development and Trend of ECG Diagnosis Assisted by Artificial Intelligence
Due to the low accuracy and efficiency of traditional manual and existing automated interpretation of ECG, misdiagnosis and missed diagnosis are easy to occur. Studies have shown that, artificial intelligence technology is the direction of ECG diagnosis ...
Learning How to Avoiding Obstacles for End-to-End Driving with Conditional Imitation Learning
Obstacle avoiding is one of the most complex tasks for autonomous driving systems, which was also ignored by many cutting-edge end-to-end learning-based methods. The difficulties stem from the integrated process of detection and interpretation of ...
Implement AI Service into VR Training
In this paper, we described the implementation of using a collection of AI services in IBM Watson to facilitate user interaction in a virtual reality space for training simulations. The project aims to increase the efficiency of training employees in an ...
Entrepreneurship and Role of AI
The focus on promotion of entrepreneurial activities has been always crucial for economic development of the successful nation. Entrepreneurs are the leaders who innovate and invent ideas that give stimulus to economic growth activities. In the modern ...