Automatic citrus canker detection from leaf images captured in field

Citrus canker, a bacterial disease of citrus tree leaves, causes significant damage to citrus production worldwide. Effective and fast disease detection methods must be undertaken to minimize the losses of citrus canker infection. In this paper, we present a new approach based on global features and zone-based local features to detect citrus canker from leaf images collected in field which is more difficult than the leaf images captured in labs. Firstly, an improved AdaBoost algorithm is used to select the most significant features of citrus lesions for the segmentation of the lesions from their background. Then a canker lesion descriptor is proposed which combines both color and local texture distribution of canker lesion zones suggested by plant phytopathologists. A two-level hierarchical detection structure is developed to identify canker lesions. Thirdly, we evaluate the proposed method and its comparison with other approaches, and the experimental results show that the proposed approach achieves similar classification accuracy as human experts

Increasing allocated tasks with a time minimization algorithm for a search and rescue scenario

Rescue missions require both speed to meet strict time constraints and maximum use of resources. This study presents a Task Swap Allocation (TSA) algorithm that increases vehicle allocation with respect to the state-of-the-art consensus-based bundle algorithm and one of its extensions, while meeting time constraints. The novel idea is to enable an online reconfiguration of task allocation among distributed and networked vehicles. The proposed strategy reallocates tasks among vehicles to create feasible spaces for unallocated tasks, thereby optimizing the total number of allocated tasks. The algorithm is shown to be efficient with respect to previous methods because changes are made to a task list only once a suitable space in a schedule has been identified. Furthermore, the proposed TSA can be employed as an extension for other distributed task allocation algorithms with similar constraints to improve performance by escaping local optima and by reacting to dynamic environments

Classification of human hand movements using surface EMG for myoelectric control

© Springer International Publishing AG 2017.Surface electromyogram (sEMG) is a bioelectric signal that can be captured non-invasively by placing electrodes on the human skin. The sEMG is capable of representing the action intent of nearby muscles. The research of myoelectric control using sEMG has been primarily driven by the potential to create humanmachine interfaces which respond to users intentions intuitively. However, it is one of the major gaps between research and commercial applications that there are rarely robust simultaneous control schemes. This paper proposes one classification method and a potential real-time control scheme. Four machine learning classifiers have been tested and compared to find the best configuration for different potential applications, and non-negative matrix factorisation has been used as a pre-processing tool for performance improvement. This control scheme achieves its highest accuracy when it is adapted to a single user at a time. It can identify intact subjects hand movements with above 98% precision and 91% upwards for amputees but takes double the amount of time for decision-making

Cognitive and neuromuscular robotic rehabilitation framework

© Springer International Publishing AG 2017.This paper presents a cognitive and neuromuscular robotic rehabilitation framework to support enhanced control of arm movement for humans with muscular control impairment, typically with some level of memory deficiency due to, for example, suffering from a stroke. It describes the design, development and integration of the framework architecture as well as a Baxter robot based demonstration platform. Three key elements of the proposed framework (rehabilitation module, workspace and rehabilitation scenarios) have been described in detail. In the rehabilitation sessions, the users and the robot are asked to work together to place cubes so as to form a predefined shape. The robot and the user hold the same object in order to move it to a particular destination according to a rehabilitation scenario. If the robot detects a force from the user directed in the wrong direction during the navigation then it resists and corrects the movement in order to assist the user towards the right direction. The assistive support scenarios were designed to evaluate the achieved enhancement of precision, efficiency and dexterity of arm movements. The proposed rehabilitation framework provides a modular, automated and open-source platform for researchers and practitioners in neuromuscular rehabilitation applications

A novel approach for pilot error detection using Dynamic Bayesian Networks

In the last decade Dynamic Bayesian Networks (DBNs) have become one type of the most attractive probabilistic modelling framework extensions of Bayesian Networks (BNs) for working under uncertainties from a temporal perspective. Despite this popularity not many researchers have attempted to study the use of these networks in anomaly detection or the implications of data anomalies on the outcome of such models. An abnormal change in the modelled environment's data at a given time, will cause a trailing chain effect on data of all related environment variables in current and consecutive time slices. Albeit this effect fades with time, it still can have an ill effect on the outcome of such models. In this paper we propose an algorithm for pilot error detection, using DBNs as the modelling framework for learning and detecting anomalous data. We base our experiments on the actions of an aircraft pilot, and a flight simulator is created for running the experiments. The proposed anomaly detection algorithm has achieved good results in detecting pilot errors and effects on the whole system. © Springer Science+Business Media 2014

A heuristic distributed task allocation method for multivehicle multitask problems and its application to search and rescue scenario

Using distributed task allocation methods for cooperating multivehicle systems is becoming increasingly attractive. However, most effort is placed on various specific experimental work and little has been done to systematically analyze the problem of interest and the existing methods. In this paper, a general scenario description and a system configuration are first presented according to search and rescue scenario. The objective of the problem is then analyzed together with its mathematical formulation extracted from the scenario. Considering the requirement of distributed computing, this paper then proposes a novel heuristic distributed task allocation method for multivehicle multitask assignment problems. The proposed method is simple and effective. It directly aims at optimizing the mathematical objective defined for the problem. A new concept of significance is defined for every task and is measured by the contribution to the local cost generated by a vehicle, which underlies the key idea of the algorithm. The whole algorithm iterates between a task inclusion phase, and a consensus and task removal phase, running concurrently on all the vehicles where local communication exists between them. The former phase is used to include tasks into a vehicle’s task list for optimizing the overall objective, while the latter is to reach consensus on the significance value of tasks for each vehicle and to remove the tasks that have been assigned to other vehicles. Numerical simulations demonstrate that the proposed method is able to provide a conflict-free solution and can achieve outstanding performance in comparison with the consensus-based bundle algorithm

Robot competence development by constructive learning

This paper presents a constructive learning approach for developing sensor-motor mapping in autonomous systems. The system's adaptation to environment changes is discussed and three methods are proposed to deal with long term and short term changes. The proposed constructive learning allows autonomous systems to develop network topology and adjust network parameters. The approach is supported by findings from psychology and neuroscience especially during infants cognitive development at early stages. A growing radial basis function network is introduced as a computational substrate for sensory-motor mapping learning. Experiments are conducted on a robot eye/hand coordination testbed and results show the incremental development of sensory-motor mapping and its adaptation to changes such as in tool-use

S.A.R.A.H.: The bipedal robot with machine learning step decision making

Herein, we describe a custom-made bipedal robot that uses electromagnets for performing movements as opposed to conventional DC motors. The robot uses machine learning to stabilize its self by taking steps. The results of several machine learning techniques for step decision are described. The robot does not use electric motors as actuators. As a result, it makes imprecise movements and is inherently unstable. To maintain stability, it must take steps. Classifiers are required to learn from users about when and which leg to move to maintain stability and locomotion. Classifiers such as Decision tree, Linear/Quadratic Discriminant, Support Vector Machine, K-Nearest Neighbor, and Neural Networks are trained and compared. Their performance/accuracy is noted

Effectiveness of surface electromyography in pattern classification for upper limb amputees

This study was undertaken to explore 18 time domain (TD) and time-frequency domain (TFD) feature configurations to determine the most discriminative feature sets for classification. Features were extracted from the surface electromyography (sEMG) signal of 17 hand and wrist movements and used to perform a series of classification trials with the random forest classifier. Movement datasets for 11 intact subjects and 9 amputees from the NinaPro online database repository were used. The aim was to identify any optimum configurations that combined features from both domains and whether there was consistency across subject type for any standout features. This work built on our previous research to incorporate the TFD, using a Discrete Wavelet Transform with a Daubechies wavelet. Findings report configurations containing the same features combined from both domains perform best across subject type (TD: root mean square (RMS), waveform length, and slope sign changes; TFD: RMS, standard deviation, and energy). These mixed-domain configurations can yield optimal performance (intact subjects: 90.98%; amputee subjects: 75.16%), but with only limited improvement on single-domain configurations. This suggests there is limited scope in attempting to build a single absolute feature configuration and more focus should be put on enhancing the classification methodology for adaptivity and robustness under actual operating conditions

Fast consensus for fully distributed multi-agent task allocation

In distributed multi-agent task allocation problems, the time to find a solution and a guarantee of reaching a solution, i.e. an execution plan, is critical to ensure a fast response. The problem is made more difficult by time constraints on tasks and on agents, which may prevent some tasks from being executed. This paper proposes a new distributed consensus-based task allocation algorithm that reduces convergence time with respect to previous methods, i.e. the time required for the network of agents to agree on a task allocation, while maximising the number of allocated tasks. The novel idea is to reduce the time to reach consensus among agents by using a hierarchy or rank-based conflict resolution among agents. Unlike other existing algorithms, this method enables different agents to construct their task schedules using any insertion heuristic, and still guarantee convergence. Simulation results demonstrate that the proposed approach can allocate a greater number of tasks in a shorter time than an established baseline method. Additionally, the analysis delineates dependencies between optimal insertion strategies and number of tasks per agent, providing insights for further optimisation strategies