An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

This paper studies a number of quantized feedback design problems for linear systems. We consider the case where quantizers are static (memoryless). The common aim of these design problems is to stabilize the given system or to achieve certain performance with the coarsest quantization density. Our main discovery is that the classical sector bound approach is nonconservative for studying these design problems. Consequently, we are able to convert many quantized feedback design problems to well-known robust control problems with sector bound uncertainties. In particular, we derive the coarsest quantization densities for stabilization for multiple-input-multiple-output systems in both state feedback and output feedback cases; and we also derive conditions for quantized feedback control for quadratic cost and H/sub /spl infin// performances.

The sector bound approach to quantized feedback control | NOVA. The University of Newcastle's Digital Repository

What has happened in machine learning lately, and what does it mean for the future of medical image analysis? Machine learning has witnessed a tremendous amount of attention over the last few years. The current boom started around 2009 when so-called deep artificial neural networks began outperforming other established models on a number of important benchmarks. Deep neural networks are now the state-of-the-art machine learning models across a variety of areas, from image analysis to natural language processing, and widely deployed in academia and industry. These developments have a huge potential for medical imaging technology, medical data analysis, medical diagnostics and healthcare in general, slowly being realized. We provide a short overview of recent advances and some associated challenges in machine learning applied to medical image processing and image analysis. As this has become a very broad and fast expanding field we will not survey the entire landscape of applications, but put particular focus on deep learning in MRI. 
Our aim is threefold: (i) give a brief introduction to deep learning with pointers to core references; (ii) indicate how deep learning has been applied to the entire MRI processing chain, from acquisition to image retrieval, from segmentation to disease prediction; (iii) provide a starting point for people interested in experimenting and perhaps contributing to the field of machine learning for medical imaging by pointing out good educational resources, state-of-the-art open-source code, and interesting sources of data and problems related medical imaging.

An overview of deep learning in medical imaging focusing on MRI

Learning and then recognizing a route, whether travelled during the day or at night, in clear or inclement weather, and in summer or winter is a challenging task for state of the art algorithms in computer vision and robotics. In this paper, we present a new approach to visual navigation under changing conditions dubbed SeqSLAM. Instead of calculating the single location most likely given a current image, our approach calculates the best candidate matching location within every local navigation sequence. Localization is then achieved by recognizing coherent sequences of these “local best matches”. This approach removes the need for global matching performance by the vision front-end - instead it must only pick the best match within any short sequence of images. The approach is applicable over environment changes that render traditional feature-based techniques ineffective. Using two car-mounted camera datasets we demonstrate the effectiveness of the algorithm and compare it to one of the most successful feature-based SLAM algorithms, FAB-MAP. The perceptual change in the datasets is extreme; repeated traverses through environments during the day and then in the middle of the night, at times separated by months or years and in opposite seasons, and in clear weather and extremely heavy rain. While the feature-based method fails, the sequence-based algorithm is able to match trajectory segments at 100% precision with recall rates of up to 60%.

SeqSLAM : visual route-based navigation for sunny summer days and stormy winter nights

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Guidance And Control Of Ocean Vehicles

Robot 3D three-dimension path planning targets for finding an optimal and collision-free path in a 3D workspace while taking into account kinematic constraints including geometric, physical, and temporal constraints. The purpose of path planning, unlike motion planning which must be taken into consideration of dynamics, is to find a kinematically optimal path with the least time as well as model the environment completely. We discuss the fundamentals of these most successful robot 3D path planning algorithms which have been developed in recent years and concentrate on universally applicable algorithms which can be implemented in aerial robots, ground robots, and underwater robots. This paper classifies all the methods into five categories based on their exploring mechanisms and proposes a category, called multifusion based algorithms. For all these algorithms, they are analyzed from a time efficiency and implementable area perspective. Furthermore a comprehensive applicable analysis for each kind of method is presented after considering their merits and weaknesses.

/pdf/survey-of-robot-3d-path-planning-algorithms-1a99fixnk9.pdf

Survey of Robot 3D Path Planning Algorithms

A state-space electromyography (EMG) model is developed for continuous motion estimation of human limb in this paper While the general Hill-based muscle model (HMM) estimates only joint torque from EMG signals in an “open-loop” form, we integrate the forward dynamics of human joint movement into the HMM, and such an extended HMM can be used to estimate the joint motion states directly EMG features are developed to construct measurement equations for the extended HMM to form a state-space model With the state-space HMM, a normal closed-loop prediction–correction approach such as the Kalman-type algorithm can be used to estimate the continuous joint movement from EMG signals, where the measurement equation is used to reject model uncertainties and external disturbances Moreover, we propose a new normalization approach for EMG signals for the purpose of rejecting the dependence of the motion estimation on varying external loads Comprehensive experiments are conducted on the human elbow joint, and the improvements of the proposed methods are verified by the comparison of the EMG-based estimation and the inertial measurement unit measurements

http://www.sooxma.com/abstracts/A%20state-space%20EMG%20model%20for%20the%20estimation%20of%20continuous%20joint%20movements.pdf

A State-Space EMG Model for the Estimation of Continuous Joint Movements

Active vibration control of a flexible beam using a non-collocated acceleration sensor and piezoelectric patch actuator

Rapid search and rescue responses after earthquakes or in postseismic evaluation tend to be extremely difficult. To solve this problem, we summarized the requirements of search and rescue rotary-wing unmanned aerial vehicle SR-RUAV systems according to related works, manual earthquake search and rescue, and our knowledge to guide our research works. Based on these requirements, a series of research and technical works have been conducted to present an efficient SR-RUAV system. To help rescue teams locate interested areas quickly, a collapsed-building detecting approach that integrates low-altitude statistical image processing methods was proposed, which can increase survival rates by detecting collapsed buildings in a timely manner. The entire SR-RUAV system was illustrated by simulated earthquake response experiments in the China National Training Base for Search and Rescue CNTBSR from 2008 to 2010. On April 20, 2013, Lushan China experienced a disastrous earthquake magnitude 7.0. Because of the distribution of buildings in the rural areas, it was impossible to implement a rapid search and postseismic evaluation via ground searching. We provided our SR-RUAV to the Chinese International Search and Rescue Team CISAR and accurately detected collapsed buildings for ground rescue guidance at low altitudes. This system was significantly improved with respect to its searching/planning strategy and vision-based evaluation in different environments based on the lessons learned from actual missions after the earthquake. The SR-RUAV has proved to be applicable and time saving. The physical structure, searching and planning strategy, image-processing algorithm, and improvements in real missions are described in detail in this study.

Jianda Han

Papers

Survey of Robot 3D Path Planning Algorithms

A State-Space EMG Model for the Estimation of Continuous Joint Movements

Active vibration control of a flexible beam using a non-collocated acceleration sensor and piezoelectric patch actuator

Search and Rescue Rotary-Wing UAV and Its Application to the Lushan Ms 7.0 Earthquake

Control techniques of tilt rotor unmanned aerial vehicle systems: A review