Medical imaging analysis with artificial neural networks

Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging

Reconfigurable Intelligent Surfaces in Challenging Environments: Underwater, Underground, Industrial and Disaster

Reconfigurable intelligent surfaces (RISs) have been introduced to improve the signal propagation characteristics by focusing the signal power in the preferred direction, thus making the communication environment "smart". The typical use cases and applications for the "smart" environment include beyond 5G communication networks, smart cities, etc. The main advantage of employing RISs in such networks is a more efficient exploitation of spatial degrees of freedom. This advantage manifests in better interference mitigation as well as increased spectral and energy efficiency due to passive beam steering. Challenging environments comprise a range of scenarios, which share the fact that it is extremely difficult to establish a communication link using conventional technology due to many impairments typically associated with the propagation medium and increased signal scattering. Although the challenges for the design of communication networks, and specifically the Internet of Things (IoT), in such environments are known, there is no common enabler or solution for all these applications. Interestingly, the use of RISs in such scenarios can become such an enabler and a game changer technology. Surprisingly, the benefits of RIS for wireless networking in underwater and underground medium as well as in industrial and disaster environments have not been addressed yet. In this paper, we aim at filling this gap by discussing potential use cases, deployment strategies and design aspects for RIS devices in underwater IoT, underground IoT as well as Industry 4.0 and emergency networks. In addition, novel research challenges to be addressed in this context are described.Comment: 16 pages, 13 figures, submitted for publication in IEEE journa

Enabling Hardware Green Internet of Things: A review of Substantial Issues

Between now and the near future, the Internet of Things (IoT) will redesign the socio-ecological morphology of the human terrain. The IoT ecosystem deploys diverse sensor platforms connecting millions of heterogeneous objects through the Internet. Irrespective of sensor functionality, most sensors are low energy consumption devices and are designed to transmit sporadically or continuously. However, when we consider the millions of connected sensors powering various user applications, their energy efficiency (EE) becomes a critical issue. Therefore, the importance of EE in IoT technology, as well as the development of EE solutions for sustainable IoT technology, cannot be overemphasised. Propelled by this need, EE proposals are expected to address the EE issues in the IoT context. Consequently, many developments continue to emerge, and the need to highlight them to provide clear insights to researchers on eco-sustainable and green IoT technologies becomes a crucial task. To pursue a clear vision of green IoT, this study aims to present the current state-of-the art insights into energy saving practices and strategies on green IoT. The major contribution of this study includes reviews and discussions of substantial issues in the enabling of hardware green IoT, such as green machine to machine, green wireless sensor networks, green radio frequency identification, green microcontroller units, integrated circuits and processors. This review will contribute significantly towards the future implementation of green and eco-sustainable IoT

Wireless Power Transfer Machine Learning Assisted Characteristics Prediction for Effective Wireless Power Transfer Systems

One of the main challenges in wireless power transfer (WPT) devices is performance degradation when the receiver's position and characteristics vary. The variations in the system parameters such as load impedance and coupling strength in WPT devices affect performance characteristics such as output voltage and power. When the system parameters are different from the optimal operating conditions, the performances are degraded. Therefore, the load impedance and coupling strength must be monitored to do the necessary optimization and control. However, such control approaches require additional sensing circuits and a data communication link between transmitter- and receiver-sides. This study proposes a new machine learning (ML) assisted the WPT system that predicts the power delivered to the receiver by only using measurements at the transmitter-side. In addition, a method is also proposed to estimate load impedance and coupling coefficient using a machine learning approach. We study what parameters measurable at the transmitter-side can be used to predict the output power delivered to receivers at variable load impedance and coupling strengths. In the proposed method, the output power of an inductor-capacitor-capacitor (LCC)-Series tuned WPT system is successfully predicted only using the measured root-mean-square (RMS) of the input current. The random forest algorithm has shown the best accuracy to estimate the output power based on transmitter-side parameters only. The proposed approach is experimentally validated using a laboratory prototype. Harmonic components of the input current are used to assess the load impedance and coupling coefficient successfully. Multi-output regression has the highest accuracy for estimating the load impedance and coupling coefficient. The proposed ML algorithm is also used to classify the turn-on and -off regimes to ensure high-efficient operation

Towards a cyber physical system for personalised and automatic OSA treatment

Obstructive sleep apnea (OSA) is a breathing disorder that takes place in the course of the sleep and is produced by a complete or a partial obstruction of the upper airway that manifests itself as frequent breathing stops and starts during the sleep. The real-time evaluation of whether or not a patient is undergoing OSA episode is a very important task in medicine in many scenarios, as for example for making instantaneous pressure adjustments that should take place when Automatic Positive Airway Pressure (APAP) devices are used during the treatment of OSA. In this paper the design of a possible Cyber Physical System (CPS) suited to real-time monitoring of OSA is described, and its software architecture and possible hardware sensing components are detailed. It should be emphasized here that this paper does not deal with a full CPS, rather with a software part of it under a set of assumptions on the environment. The paper also reports some preliminary experiments about the cognitive and learning capabilities of the designed CPS involving its use on a publicly available sleep apnea database

Advanced computational intelligence strategies for mental task classification using electroencephalography signals

University of Technology, Sydney. Faculty of Engineering and Information Technology.Brain-computer interface (BCI) has been known as a cutting-edge technology in the current research. It is able to measure the brain activity directly instead of using the natural peripheral nerves and muscles and translates the user’s intent brain activity into useful control signals. There is still a need for a technology for severely disabled individuals who suffer from locked-in syndromes, such as amyotrophic lateral sclerosis (ALS), cervical spinal cord injury (SCI) or tetraplegia and brain stem stroke. A brain-computer interface (BCI) could be used here as an alternative solution for control and communication. The main aim of this research is to develop a BCI system to assist mobility as hand-free technology for people with severe disability, with improved accuracy, which provides effective classification accuracy for wheelchair control. Electroencephalography (EEG) is the chosen BCI technology because it is non-invasive, portable and inexpensive. Currently, BCI using EEG can be divided into two strategies; selective attention and spontaneous mental signal. For the selective attention strategy, BCI relies on external stimuli which might be uncomfortable for severely disabled individuals who need to focus on external stimuli and the environment simultaneously. This is not the case for BCIs which rely on spontaneous mental signals initiated by the users themselves. BCI that uses sensorimotor rhythm (SMR) is one of the examples of the spontaneous mental strategy. There have been many reports in research using SMR-based BCI; however, there are still some people who are unable to use this. As a result, in this thesis, mental task-based EEG is used as an alternative. This thesis presents the embedded EEG system for mental task classification. A prototype wireless embedded EEG system for mental task BCI classification is developed. The prototype includes a wireless EEG as head gear and an embedded system with a wireless receiver. The developed wireless EEG provides a good common mode rejection ratio (CMRR) performance and a compact size with a low current consumption coin cell battery for power. Mental tasks data are collected using the prototype system from six healthy participants which include arithmetic, figure rotation, letter composing and counting task with additional eyes closed task. The developed prototype BCI system is able to detect the dominant alpha wave between 8-13Hz during eyes closed. Using the FFT as the features extractor and artificial neural network (ANN) as the classifier, the developed prototype EEG system provides high accuracy for the eyes closed and eyes open tasks. The classification of the three mental task combinations achieve an overall accuracy of around 70%. Also, an optimized BCI system for mental task classification using the Hilbert-Huang transform (HHT) feature extractor and the genetic algorithm optimization of the artificial neural network (GA-ANN) classifier is presented. Non motor imagery mental tasks are employed, including: arithmetic, letter composing, Rubik’s cube rolling, visual counting, ringtone, spatial navigation and eyes closed task. When more mental tasks are used, users are able to choose the most effective of tasks suitable for their circumstance. The result of classification for the three user chosen mental tasks achieves accuracy between 76% and 85% using eight EEG channels with GA-ANN (classifier) and FFT (feature extractor). In a two EEG channels classification using FFT as the features extractor, the accuracy is reduced between 65% and 79%. However, the HHT features extractor provides improved accuracy between 70% and 84%. Further, an advanced BCI system using the ANN with fuzzy particle swarm optimization using cross-mutated operation (FPSOCM-ANN) for mental task classification is presented. This experiment involves five able-bodied subjects and also five patients with tetraplegia as the target group of the BCI system. The three relevant mental tasks used for the BCI concentrates on mental letter composing, mental arithmetic and mental Rubik’s cube rolling forward. Although the patients group has lower classification accuracy, this is improved by increasing the time-window of data with the best at 7s. The results classification for 7s time-window show the best classifier is using the FPSOCM-ANN (84.4% using FPSOCM-ANN, 77.4% using GA-ANN, 77.0% using SVM, 72.1% using LDA, and 71.0% using linear perceptron). For practical use of a BCI, the two channels EEG is also presented using this advanced BCI classification method (FPSOCM-ANN). For overall, O1 and C4 are the best two channels at 80.5% of accuracy, followed by the second best at P3 and O2 at 76.4% of accuracy, and the third best at C3 and O2 channels at 75.4% of accuracy

Towards tactile sensing active capsule endoscopy

Examination of the gastrointestinal(GI) tract has traditionally been performed using tethered endoscopy tools with limited reach and more recently with passive untethered capsule endoscopy with limited capability. Inspection of small intestines is only possible using the latter capsule endoscopy with on board camera system. Limited to visual means it cannot detect features beneath the lumen wall if they have not affected the lumen structure or colour. This work presents an improved capsule endoscopy system with locomotion for active exploration of the small intestines and tactile sensing to detect deformation of the capsule outer surface when it follows the intestinal wall. In laboratory conditions this system is capable of identifying sub-lumen features such as submucosal tumours.Through an extensive literary review the current state of GI tract inspection in particular using remote operated miniature robotics, was investigated, concluding no solution currently exists that utilises tactile sensing with a capsule endoscopy. In order to achieve such a platform, further investigation was made in to tactile sensing technologies, methods of locomotion through the gut, and methods to support an increased power requirement for additional electronics and actuation. A set of detailed criteria were compiled for a soft formed sensor and flexible bodied locomotion system. The sensing system is built on the biomimetic tactile sensing device, Tactip, \cite{Chorley2008, Chorley2010, Winstone2012, Winstone2013} which has been redesigned to fit the form of a capsule endoscopy. These modifications have required a $360^{o}$ cylindrical sensing surface with $360^{o}$ panoramic optical system. Multi-material 3D printing has been used to build an almost complete sensor assembly with a combination of hard and soft materials, presenting a soft compliant tactile sensing system that mimics the tactile sensing methods of the human finger. The cylindrical Tactip has been validated using artificial submucosal tumours in laboratory conditions. The first experiment has explored the new form factor and measured the device's ability to detect surface deformation when travelling through a pipe like structure with varying lump obstructions. Sensor data was analysed and used to reconstruct the test environment as a 3D rendered structure. A second tactile sensing experiment has explored the use of classifier algorithms to successfully discriminate between three tumour characteristics; shape, size and material hardness. Locomotion of the capsule endoscopy has explored further bio-inspiration from earthworm's peristaltic locomotion, which share operating environment similarities. A soft bodied peristaltic worm robot has been developed that uses a tuned planetary gearbox mechanism to displace tendons that contract each worm segment. Methods have been identified to optimise the gearbox parameter to a pipe like structure of a given diameter. The locomotion system has been tested within a laboratory constructed pipe environment, showing that using only one actuator, three independent worm segments can be controlled. This configuration achieves comparable locomotion capabilities to that of an identical robot with an actuator dedicated to each individual worm segment. This system can be miniaturised more easily due to reduced parts and number of actuators, and so is more suitable for capsule endoscopy. Finally, these two developments have been integrated to demonstrate successful simultaneous locomotion and sensing to detect an artificial submucosal tumour embedded within the test environment. The addition of both tactile sensing and locomotion have created a need for additional power beyond what is available from current battery technology. Early stage work has reviewed wireless power transfer (WPT) as a potential solution to this problem. Methods for optimisation and miniaturisation to implement WPT on a capsule endoscopy have been identified with a laboratory built system that validates the methods found. Future work would see this combined with a miniaturised development of the robot presented. This thesis has developed a novel method for sub-lumen examination. With further efforts to miniaturise the robot it could provide a comfortable and non-invasive procedure to GI tract inspection reducing the need for surgical procedures and accessibility for earlier stage of examination. Furthermore, these developments have applicability in other domains such as veterinary medicine, industrial pipe inspection and exploration of hazardous environments

A Critical Analysis of a Wireless Power Transmission (WPT) with an Improvement Method for a Non-Radiative WPT

This paper describes the analysis of the wireless power transmission including recent progresses in non-radiative wireless power transmission (WPT) and the improvement methods. Generally, WPT transmitter side consists of a DC supply voltage source, inverter/power amplifier, transmitter impedance matching device (IMD), source resonator and primary coil. The WPT receiver meanwhile consists of the secondary coil, device resonator, receiver IMD, rectifier and load. In order to achieve an efficient WPT, the WPT transmitter must transmit energy with minimum loss at the receiver side.  This setup can be achieved by employing the power amplifier (PA). In this paper, the power amplifier in wireless power transmission for portable devices was designed. A Class E power amplifier was proposed and designed to improve the WPT transmitter side. The effects of zero voltage and zero derivative voltage switching on PA with optimization method was also discussed