Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Using Granule to Search Privacy Preserving Voice in Home IoT Systems

The Home IoT Voice System (HIVS) such as Amazon Alexa or Apple Siri can provide voice-based interfaces for people to conduct the search tasks using their voice. However, how to protect privacy is a big challenge. This paper proposes a novel personalized search scheme of encrypting voice with privacy-preserving by the granule computing technique. Firstly, Mel-Frequency Cepstrum Coefficients (MFCC) are used to extract voice features. These features are obfuscated by obfuscation function to protect them from being disclosed the server. Secondly, a series of definitions are presented, including fuzzy granule, fuzzy granule vector, ciphertext granule, operators and metrics. Thirdly, the AES method is used to encrypt voices. A scheme of searchable encrypted voice is designed by creating the fuzzy granule of obfuscation features of voices and the ciphertext granule of the voice. The experiments are conducted on corpus including English, Chinese and Arabic. The results show the feasibility and good performance of the proposed scheme

Evolutionary design for intelligent design and manufacture - a Computer-Automated Design (ACutoD) in the digital age

Design and manufacture are essential to the UK economy and reflect one of the most innovative and creative activities of UK business. A rising demand of design has been seen in the requirement of increased efficiency, reduced costs, and manufacturing flexibility. The design and manufacture are driven by technological innovation and intellectual ingenuity. For this, Computer-Automated Design (CAutoD) promises many benefits, including higher flexibility, improved efficiency (in design, manufacture and energy use), and improved design quality. However, there are many challenges to be overcome before concepts and creativity can become commercial designs for manufacture

Indoor Relocalization in Challenging Environments With Dual-Stream Convolutional Neural Networks

This paper presents an indoor relocalization system using a dual-stream convolutional neural network (CNN) with both color images and depth images as the network inputs. Aiming at the pose regression problem, a deep neural network architecture for RGB-D images is introduced, a training method by stages for the dual-stream CNN is presented, different depth image encoding methods are discussed, and a novel encoding method is proposed. By introducing the range information into the network through a dual-stream architecture, we not only improved the relocalization accuracy by about 20% compared with the state-of-the-art deep learning method for pose regression, but also greatly enhanced the system robustness in challenging scenes such as large-scale, dynamic, fast movement, and night-time environments. To the best of our knowledge, this is the first work to solve the indoor relocalization problems based on deep CNNs with RGB-D camera. The method is first evaluated on the Microsoft 7-Scenes data set to show its advantage in accuracy compared with other CNNs. Large-scale indoor relocalization is further presented using our method. The experimental results show that 0.3 m in position and 4° in orientation accuracy could be obtained. Finally, this method is evaluated on challenging indoor data sets collected from motion capture system. The results show that the relocalization performance is hardly affected by dynamic objects, motion blur, or night-time environments

ROS Based Multi-sensor Navigation of Intelligent Wheelchair

Our society is moving towards an ageing society and the number of population with physical impairments and disabilities will increase dramatically. It is necessary to provide mobility support to these people so that they can live independently at home and integrated into the society. This paper presents a ROS (Robot Operating System) based multi-sensor navigation for an intelligent wheelchair that can help the elderly and disabled people. ROS provides an easy to use framework for rapid system development at a reduced cost. Some experimental results are given in the paper to demonstrate the feasibility and performance of the developed system

Computational Intelligence Approaches to Robotics, Automation, and Control

No abstract available

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Stability analysis of token-based wireless networked control systems under deception attacks

Currently, cyber-security has attracted a lot of attention, in particular in wireless industrial control networks (WICNs). In this paper, the stability of wireless networked control systems (WNCSs) under deception, attacks is studied with a token-based protocol applied to the data link layer (DLL) of WICNS. Since deception attacks cause the stability problem of WNCSs by changing the data transmitted over a wireless network, it is important to detect deception attacks, discard the injected false data and compensate for the missing data (i.e., the discarded original data with the injected false data). The main contributions of this paper are: 1) With respect to the character of the token-based protocol, a switched system model is developed. Different from the traditional switched system where the number of subsystems is fixed, in our new model this number will be changed under deception attacks. 2) For this model, a new Kalman filter (KF) is developed for the purpose of attack detection and the missing data reconstruction. 3) For the given linear feedback WNCSs, when the noise level is below a threshold derived in this paper, the maximum allowable duration of deception attacks is obtained to maintain the exponential stability of the system. Finally, a numerical example based on a linearized model of an inverted pendulum is provided to demonstrate the proposed design

Multi-layered map based navigation and interaction for an intelligent wheelchair

Intelligent wheelchair is a paradigm of assisted living applications for elderly and disabled people. Its autonomous navigation and human-robot interaction is the major challenge. The previous intelligent wheelchair research has been mainly focused on geometric map based navigation, which is computational expensive in a large scale environment. This paper proposes the use of multi-layered maps for navigation and interaction of an intelligent wheelchair. The semantic information can improve the efficiency of path planning and navigation as well as extend the capability of task planning for the wheelchair. Some experimental results are given to demonstrate the feasibility and performance of the proposed approach

Using Unsupervised Deep Learning Technique for Monocular Visual Odometry

Deep learning technique-based visual odometry systems have recently shown promising results compared to feature matching-based methods. However, deep learning-based systems still require the ground truth poses for training and the additional knowledge to obtain absolute scale from monocular images for reconstruction. To address these issues, this paper presents a novel visual odometry system based on a recurrent convolutional neural network. The system employs an unsupervised end-to-end training approach. The depth information of scenes is used alongside monocular images to train the network in order to inject scale. Poses are inferred only from monocular images, thus making the proposed visual odometry system a monocular one. The experiments are conducted and the results show that the proposed method performs better than other monocular visual odometry systems. This paper has made two main contributions: 1) the creation of the unsupervised training framework in which the camera ground truth poses are only deployed for system performance evaluation rather than for training and 2) the absolute scale could be recovered without the post-processing of poses