Multilevel Inverters: Topologies, Modulation and Control Strategies
Multilevel inverters have been experienced, in terms of research and applications, a continuous and increasing growth during the last three decades. Distinctive features of multilevel converters are their capability to overcome the voltage limits imposed by the adopted power devices and to reduce voltage and current harmonics content, thus reducing power losses, heat, noise and increasing efficiency and reliability. The effort of the researchers and industry has led to a rapid development of different multilevel inverter topologies, modulation techniques and control strategies. In this presentation, the speech will focus on three different related subjects to multilevel inverters; Topologies, Optimization Methods, and Control Strategies. At the first part, the speech will try to introduce some sub-modules to rebuild the presented topologies at the literature and also presenting new topologies of multilevel inverters. Another important point about multilevel inverters is that in most cases for a given topology, it is possible to use different configuration to generate the same number of output levels but with different number of components. So, it is strongly required to find a solution to optimize the topology in a way that by using minimum number of components to generate more number of levels. This subject will be the topic of second part. The next important point about multilevel inverters is their control methods. There are different control methods for multilevel inverters. Among them, the Selective Harmonic Modulation techniques (SHE) usually operate at fundamental frequency and are capable to cancel or mitigate one or more frequencies from the outputs. In addition, in symmetric multilevel inverters, balancing the provided energy by different DC voltage sources is other concern in controlling the multilevel inverters. After a theoretical discussion on the fundamentals of modulation algorithms with analytical methods, the speech will introduce and discuss in detail the selective harmonic modulation and charge balance control methods for modulation of cascaded H-Bridge multilevel converters.
Smart Sensing and AI for Physical Therapy in IoT Era
The convergence of healthcare, instrumentation and measurement technologies will transform healthcare as we know it, improving quality of healthcare services, reducing inefficiencies, curbing costs and improving quality of life. Smart sensors, wearable devices, Internet of Things (IoT) platforms, and big data offer new and exciting possibilities for more robust, reliable, flexible and low-cost healthcare systems and patient care strategies. These may provide value-added information and functionalities for patients, particularly for those with neuro-motor impairments. It has great importance in developed countries in the context of population ageing. In this invited talk the focus will be on: hardware and software infrastructure for neuro-motor rehabilitation; highlighting the developed solutions for motor rehabilitation based on virtual reality and serious games. As part of these interactive environments, 3D image sensors for natural user interaction with rehabilitation scenarios and remote sensing of user movement, as well as thermographic camera for remote evaluation of muscle activity will be presented. Additionally AI solutions applied for physical therapy data provided by the smart sensors embedded in prototypes such as smart walkers, crutches or wearable physical training monitors are considered. Example of applied AI algorithms including deep-learning and the importance on diagnosis and evaluation of physical therapy outcome for different type of physical therapy platform including serious game will be discussed.
The Least Mean Fourth algorithm: A Myth or Reality?
Adaptive filtering is a topic of immense practical and theoretical value, having applications in areas ranging from digital and wireless communications to biomedical systems. Adaptive filters are dynamic system whose parameters are adapted according to some criterion to meet certain requirements. Among the popular adaptive algorithm are The Least Mean Square (LMS) and The Least Mean Fourth (LMF). The LMF algorithm is known to perform better than the LMS algorithm in the case of non-Gaussian noise. Thus, it is more suitable in most of real life applications as the practicing environment is found to be non-Gaussian in most of the cases. Moreover, it has been shown that the LMF algorithm can outperform the LMS algorithm even in Gaussian environments when initialized far from the Wiener solution. However, there are some challenges with the implementation of the LMF algorithm such as stability, optimality between the convergence time and the final steady-state error, and its implementation for sparse system. For this reason, there exist many variants of the LMF algorithm in the literature.
The main aims of this talk are: Provide an overview of the LMF algorithm, Present different variants of the LMF algorithm, Show how various challenges are dealt in these LMF variants, Introduce other potentially related LMF-based algorithms for timely applications, such as distributed signal processing, communication, IoT, 5G, and beyond, and finally use of machine learning, neural networks and artificial intelligence in specifying the statistics of the input signal and the noise to come up with the right algorithm.
The promise of deep learning in multimodality medical image analysis
This talk presents the fundamental principles and major applications of artificial intelligence (AI), in particular deep learning approaches, in multimodality medical image analysis research. To this end, the applications of deep learning in five generic fields of multimodality medical imaging, including imaging instrumentation design, image denoising (low-dose imaging), image reconstruction quantification and segmentation, radiation dosimetry and computer-aided diagnosis and outcome prediction are discussed. Deep learning algorithms have been widely utilized in various medical image analysis problems owing to the promising results achieved in image reconstruction, segmentation, regression, denoising (low-dose scanning) and radiomics analysis. This talk reflects the tremendous increase in interest in quantitative molecular imaging using deep learning techniques in the past decade to improve image quality and to obtain quantitatively accurate data from dedicated combined PET/CT and PET/MR systems including algorithms used to correct for physical degrading factors and to quantify tracer uptake and volume for radiation therapy treatment planning. The majority of AI-related works in the literature report on single-institution efforts under controlled conditions (e.g. diversity of patient population or image quality). The challenge of performance/bias assessment of AI approaches under realistically diverse conditions (e.g. multi-centre studies) warrants further investigation. The performance of AI algorithms depends largely on the training data used for model development. As such, the analysis of risks associated with the deployment of AI-based methods when exposed to a different test dataset to ensure that the developed model has sufficient generalizability is an important part of quality control measures that need to be implemented prior to their use in the clinic. Novel deep learning techniques are revolutionizing clinical practice and are now offering unique capabilities to the clinical molecular imaging community and biomedical researchers at large. Future opportunities and the challenges facing the adoption of deep learning approaches and their role in molecular imaging research are also addressed.
Upper Extremity Rehabilitation Robot
Stroke affects each year more than 15 million people worldwide1. In the US alone, more than 795,000 US people suffer a stroke each year that results in significant deficits in upper/lower Extremity functions and the performance of everyday tasks for those affected2. The problem is further compounded by the constantly growing number of such cases1,2. It is estimated that about two-thirds of stroke survivors incur acute arm impairment3. Therefore, one of the challenging aspects of stroke rehabilitation is upper/lower extremity intervention. The conventional therapeutic approach requires a long time commitment and dedication by both patient and therapist and/or caregiver. There is a pressing need to develop improve treatment/therapeutic approaches to decrease the disability period due to stroke. Citing the constant growth of upper/lower extremity dysfunctions (ULED) and the required prolonged rehabilitation, robot-assisted therapy has already been contributing to upper/lower extremity rehabilitation. Although extensive research has been conducted on rehabilitation robotics, a few robotic therapeutic devices are currently commercially available to provide upper extremity (UE) rehabilitation but are limited to use in a clinical setting. The regulatory approval process4 for medical/therapeutic devices is usually long, as these devices closely work with the human subject.
To provide upper limb rehabilitation therapy, we have developed a 7DOF exoskeleton type therapeutic robot named Smart Robotic Exoskeleton (SREx). The SREx comprises a shoulder motion support part, an elbow and forearm motion support part, and a wrist motion support part. It is designed to be worn on the upper limb’s lateral side to provide (i) shoulder joint vertical and horizontal flexion/extension and internal/external rotation, (ii) elbow flexion/extension motion, (iii) forearm pronation/supination, and (iv) wrist joint radial/ulnar deviation and flexion/extension motion. The exoskeleton was developed based on the upper-limb biomechanics and was designed for use by typical adults. The SREx’s kinematic model was developed based on modified Denavit-Hartenberg notations, and Newton-Euler formulation was used in dynamic modeling. Nonlinear control techniques, including model-based approaches such as sliding mode control5 and adaptive controller6, were used to maneuver the robots to provide active and passive arm movement therapy. The control architecture was executed on a field-programmable gate array (FPGA) in conjunction with a real-time PC. Experiments were carried out with healthy adults where typical rehabilitation exercises for single and multi-joint movements were performed.
The 4th industrial revolution and its most likely future impacts
During the last years, two main factors have led to an inflection point in the global economy of the whole word. The first factor is related to the explosion of data, mainly the part of data that is being stored in the cloud due to the accessibility of internet and the emergence of few large storage hubs like Google, Facebook and Amazon, etc. The second factor is related to the important advances in the silicon interconnect technologies, which allow a huge and very wide bandwidth for processor – memory communications. These two factors were behind the appearance of the Artificial Intelligence technology to the forefront. This technology coupled with other technologies, such as 3D printing, IoT, 5G, have given rise to the 4th industrial revolution.
In this plenary session, we will cover the history of industrial revolutions and we will focus in particular on the 4th one and try to list few of its most likely future impacts. We will also cover in our talk main support of these revolutions which is the parallel path of the important evolution of the electronics industries.
Game dynamics on graphs
Directed or undirected graphs are the most natural way of a mathematical description of interacting agents. The vertices of the graph can hold information about the agent, while the edges represent their spatial structure. Such a model is particularly useful if the agents are described by a game-theoretical framework. Then the vertices represent the strategies which each agent employs, while the edges serve as the spatial interaction. Such a description also integrates dynamics as the agent’s strategies as well as their interaction may change over time. In the talk recent results on game dynamics on graphs are presented and possible field of application for systems and automation are discussed.
The output regulation problem: classical results and new trends
In this talk, we will review the problem of tracking a reference signal, while maintaining closed-loop stability, even in the presence of disturbances, both signals, the reference and disturbances generated by an exogenous system, called the exosystem. We will formulate the classical state-feedback/output feedback regulation problem for linear systems, and then recall the conditions for the solvability of both problems. The case of robust regulation in the presence of parameter variations will also be reviewed, and the results will be stated in terms of the so-called immersion dynamics. Also, the nonlinear regulation systems will be then presented, and a review of some important results both for continuous and discrete systems will be given.
To conclude, we will present new trends in the problem, and give a few open problems in the matter.
Grid-Connected Renewable Energy Sources-Challenges and Trends
The world is moving towards decentralized power station, formally called distributed power generation. The main sources of distributed power generations are Solar Power and Wind Power systems. Among solar Power sources, solar photovoltaic has received much attention.
Wind energy is also one of the most important and promising sources of renewable energy, mainly because it is considered to be non-polluting and economically viable. At the same time, there has been a rapid development of related wind turbine technology. The current surge in wind energy development is driven by multiple forces in favor of wind power. These include tremendous environmental, social and economical benefits, technological maturity, deregulation of electricity markets throughout the world, public support, and governmental incentives. Recent developments in wind power generation have provided an economically competitive and technically sound solution to reduce greenhouse gas emissions. The conversion efficiency of the wind power plant is much higher compared to the solar PV system and this can be further enhanced by employing appropriate maximum power point tracking strategy. According to Betz limit, the conversion efficiency of a wind power plant is as high as 59.3%.
Grid-connected photovoltaic (PV) systems are one of the fastest growing renewable energy conversion systems in the world. In fact it has increased more than 7 times in the in recent past (from 5.4GW to 40GW of installed capacity). The main reason for this remarkable development is the cost reduction of PV modules and the introduction of economic incentives or subsidies due to growing environmental concerns. This has made PV generated electrical energy cost-effective and competitive in some regions of the world with good sun radiation conditions. Projections show that PV technology costs will continue declining in the next decade, making large-scale PV systems more and more attractive.
Power electronics, used as an interface between the distributed generation sources and the electricity grid play an important role in facilitating an efficient and optimal energy transfer, as well as increasing system reliability and utilizing an economically viable solution. However, despite the clear trend in the increase of power rating of PV plants, power converter interfaces for large-scale PV systems have not evolved much and are concentrated in a couple of system configurations and converter topologies.
The talk will focus on the energy scenario, existing international standards in relation to the grid-connected distributed energy source, existing inverter topologies, their classifications and operations. The control of grid connected inverters and synchronization requirements. The present challenges and requirements related to grid-connected inverters is presented. The issue related with Partial shading and MPPT requirement is discussed in the talk. The new class of PV inverters namely Z-Source, quasi Z Source and their cascaded structure is elaborated. The future directions of research is also given.
SMART GRID AS THE NEXT ENERGY PARADIGM
The smart grid has been called “electricity with a brain”, the “energy Internet” and the “Electronet”. Basically, the smart grid integrates electricity and information and communication infrastructures to produce electricity more efficiently and reliably, as well as cleanly and safely for the environment. The smart grid is the new energy paradigm that is characterized by a bidirectional flow of electricity and information and the integration of huge amount of distributed energy resources.
The integration of renewable energy resources and energy storage into the smart grid is associated with power electronics converters and involves many aspects, such as: efficiency, reliability and energy conversion cost, forecasting of energy production, safe connection to the electric grid and the capability to work in islanded mode. Advanced control and data utilization are essential for the success of this energy paradigm.
The talk will discuss the possibility and challenges of creating the smart grid paradigm and will highlight its enabling technologies, current status, and future prospective.