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An electrocardiogram (ECG) records the electrical signal from the heart to check for different heart conditions, but it is susceptible to noises. ECG signal denoising is a major pre-processing step which attenuates the noises and accentuates the typical waves in ECG signals. Researchers over time have proposed numerous methods to correctly detect morphological anomalies. This study discusses the workflow, and design principles followed by these methods, and classify the state-of-the-art methods into different categories for mutual comparison, and development of modern methods to denoise ECG. The performance of these methods is analysed on some benchmark metrics, viz., root-mean-square error, percentage-root-mean-square difference, and signal-to-noise ratio improvement, thus comparing various ECG denoising techniques on MIT-BIH databases, PTB, QT, and other databases. It is observed that Wavelet-VBE, EMD-MAF, GAN2, GSSSA, new MP-EKF, DLSR, and AKF are most suitable for additive white Gaussian noise removal. For muscle artefacts removal, GAN1, new MP-EKF, DLSR, and AKF perform comparatively well. For base-line wander, and electrode motion artefacts removal, GAN1 is the best denoising option. For power-line interference removal, DLSR and EWT perform well. Finally, FCN-based DAE, DWT (Sym6) soft, MABWT (soft), CPSD sparsity, and UWT are promising ECG denoising methods for composite noise removal.
This edited book brings together research from laboratories across the world, in order to offer a global perspective on advances in prosthetic hand control. State-of-the-art control of prosthetics in the laboratory and clinical spaces are presented and the challenges discussed, and the effect of user training on control of prosthetics to evaluate the translational efficacy and value for the end-user is highlighted. The book begins with a chapter introducing the fundamental principles, engineering challenges and control solutions for prosthetic hands. Further chapters address methods to design bespoke sockets, magnetomyography, implantable technologies for closed-loop control of prostheses, direct neural control of prostheses via nerve implants as well as user-prosthesis co-adaptation, and two chapters on prosthetics for children. The book concludes with a chapter by Dr Nazarpour on the future of myoelectric prosthetics control, with particular focus on the successful translation of research advances into real clinical gains. The book is essential reading for anyone involved in research or undertaking advanced courses in prosthetic design and control. It provides an in-depth exploration of this rewarding topic, by exploring technologies with the potential to improve the quality of life of upper-limb prosthetic users.
Electroactive polymer (EAP) is a kind of smart material, which can change its shape under the stimulation of electric field. Dielectric elastomer (DE) is an important member of the EAP. DE has the characteristics of excellent performance, such as light weight, low noise, low cost, and so on, which guarantee its wide applications in the fields of actuators, generators, sensors. In this review, the principles of energy conversion, the research status and latest development of new technologies for DEs, and the performance characteristics of DEs are summarised. Simultaneously, it points out the development problems and feasible countermeasures. At last, the application prospects of DE are discussed, combined with the research direction of the international frontier.
Functional imaging has successfully been applied to capture functional changes in the pathological tissues of a body in recent years. Nuclear medicine functional imaging has been used to acquire information about areas of concerns (e.g. lesions and organs) in a non-invasive manner, enabling semi-automated or automated decision-making for disease diagnosis, treatment, evaluation, and prediction. Focusing on functional nuclear medicine images, in this study, the authors review existing work on the classification of single-photon emission computed tomography, positron emission tomography, and their hybrid modalities with computed tomography and magnetic resonance imaging images by using convolutional neural network (CNN) techniques. Specifically, they first present an overview of nuclear imaging and the CNN technique, such as nuclear imaging modalities, nuclear image data format, CNN architecture, and the main CNN classification models. According to the diseases of concern, they then classify the existing CNN-based work on the classification of functional nuclear images into three different categories. For the typical work in each of these categories, they present details about their research objectives, adopted CNN models, and achieved main results. Finally, they discuss research challenges and directions for developing technological solutions to classify nuclear medicine images based on the CNN technique.
In integrated optics, optical waveguides are fundamental elements for the development of integrated devices, including light sources, modulators, detectors, optical pathways, receivers and switches. For the fabrication of optical integrated waveguide components, grating couplers or other complex integrated structures realized on planar optical waveguides, materials with high photorefractivity are surely of great interest since they allow applying photorefractive direct-laser-writing techniques which are of lower cost and less time-consuming than conventional photolithography and etching processes. This chapter provides an overview of classes of photorefractive materials such as photorefractive glasses, glass-ceramics, crystals and polymers, which are promising candidates for integrated optics. Different aspects, including material preparation, photorefractivity investigation and the fabrication of photorefractive direct-laser-written micro/nano-optical structures such as channel waveguides and gratings in each type of photorefractive materials, are also discussed.
In this paper, we will focus on investigations on steady-state behaviour in the spectral domain.
Edited by two recognised experts, this book in two volumes provides a comprehensive overview of integrated optics, from modelling to fabrication, materials to integration platforms, and characterization techniques to applications. The technology is explored in detail, and set in a broad context that addresses a range of current and potential future research and development trends. Volume 1 begins with introductory chapters on the history of integrated optics technology, design tools, and modelling techniques. The next section of the book goes on to discuss the range of materials used for integrated optics, their deposition techniques, and their specific applications, including glasses, plasmonic nanostructures, SOI and SOS, and III-V and II-VI semiconductors. Volume 2 addresses characterization techniques, integrated optical waveguides and devices. A range of applications are also discussed, including devices for sensing, telecommunications, optical amplifiers and lasers, and quantum computing. The introductory chapters are intended to be of use to newcomers to the field, but its depth and breadth of coverage means that this book is also appropriate reading for early-career and senior researchers wishing to refresh their knowledge or keep up to date with recent developments in integrated optics.
Edited by two recognised experts, this book in two volumes provides a comprehensive overview of integrated optics, from modelling to fabrication, materials to integration platforms, and characterization techniques to applications. The technology is explored in detail, and set in a broad context that addresses a range of current and potential future research and development trends. Volume 1 begins with introductory chapters on the history of integrated optics technology, design tools, and modelling techniques. The next section of the book goes on to discuss the range of materials used for integrated optics, their deposition techniques, and their specific applications, including glasses, plasmonic nanostructures, SOI and SOS, and III-V and II-VI semiconductors. Volume 2 addresses characterization techniques, integrated optical waveguides and devices. A range of applications are also discussed, including devices for sensing, telecommunications, optical amplifiers and lasers, and quantum computing. The introductory chapters are intended to be of use to newcomers to the field, but its depth and breadth of coverage means that this book is also appropriate reading for early-career and senior researchers wishing to refresh their knowledge or keep up to date with recent developments in integrated optics.
This chapter provides an introduction on the benefits of artificial intelligence (Al) techniques for the field of affective computing, through a case study about emotion recognition via brain (electroencephalography EEG) signals. Readers are first pro-vided with a general description of the field, followed by the main models of human affect, with special emphasis to Russell's circumplex model and the pleasur-arousal-dominance (PAD) model. Finally, an AI-based method for the detection of affect elicited via multimedia stimuli is presented. The method combines both connectivity-and channel-based EEG features with a selection method that considerably reduces the dimensionality of the data and allows for efficient classification. In particular, the relative energy (RE) and its logarithm in the spatial domain, as well as the spectral power (SP) in the frequency domain are computed for the four typically used EEG frequency bands (a, 0, y and 0) and complemented with the mutual information measured over all EEG channel pairs. The resulting features are then reduced by using a hybrid method that combines supervised and unsupervised feature selection. Detection results are compared to state-of-the-art methods on the DEAP benchmark-ing data set for emotion analysis, which is composed of labelled EEG recordings from 32 individuals, acquired while watching 40 music videos. The acquired results demonstrate the potential of AI-based methods for emotion recognition, an applica-tion that can significantly benefit the fields of human-computer interaction (HCI) and of quality-of-experience (QoE).
Connected health is continuously developing, particularly with the advent of the Internet of Things (IoT) interconnecting various sensing nodes capable of measuring a person's vital signs such as electrocardiogram (ECG). In the years to come, the current forecasts indicate a significant increase in demand of such devices, especially among a currently underserved but significant population. Most of the existing devices performing measurement and data transmission require significant effort to integrate more intelligent processing or even decision-making, at least for data reduction and more autonomy. In this chapter, we propose to combine a simple compressed sensing (CS) measurement technique with a machine learning classification, both for data reduction and low power consumption. The classification is performed on compressed data, whereas the transmission is achieved only for warnings, by sending classification information in the case of a probable pathology detection, and if neces-sary the compressed data for further analysis. For data acquisition, we utilize a simple deterministic measurement matrix that facilitates the hardware implementation. The performance of the proposed approach is demonstrated using ECG recordings from three PhysioNet databases: MIT-BIH Arrhythmia Database, MIT-BIH Normal Sinus Rhythm Database and The BIDMC Congestive Heart Failure Database.
This book covers the use of SAR for maritime surveillance applications. It provides a comprehensive source of material on the subject, divided into two parts. The first part deals with models and techniques, while the second part is devoted to maritime surveillance applications. Each chapter covers the basic principles, a critical review of the current technology, techniques and applications, and the latest developments in the field. The book begins with an introduction to the topic written by the editors. The following topics are then addressed by an international team of expert authors: scattering models; acquisition modes; SAR polarimetry; ambiguity problems and their mitigation; ship detection; monitoring of intertidal areas and coastal habitats; sea ice and icebergs; oil spill imaging; joint use of SAR and collaborative signals; and finally sea state and wind speed. This book, with its comprehensive coverage of SAR for maritime surveillance applications, will be a valuable resource for SAR system engineers, private and public corporations, oceanographers, and remote-sensing researchers and end-users.
Deep learning (DL), especially Convolutional neural networks (CNN), has gained wide popularity in various image processing tasks. With the significant achievements obtained in DL, it has provided many successful solutions for real-world applications as well as in medical domain. Automated retinal images analysis has been widely applied to screening Diabetic retinopathy (DR), which can greatly help preventing the occurrence of complete blindness when used in the early screening. In this paper, we mainly focus on DL, and we will give an overview of the deep learning-based methods for DR screening. Finally, we will discuss the main issues encountered in the DR screening systems.
Internet of Things (IoT) enabled technology is evolving healthcare from conventional hubbased systems to more personalized eHealth systems, enabling faster and safer preventive care, lower overall cost, improved patient-centric practice and enhanced sustainability. Efficient IoT-enabled eHealth systems can be realized by providing highly customized access to rich medical information and efficient clinical decisions to each individual with unobtrusive monitoring. Wireless medical sensor networks (WMSNs) are at the heart of this concept, and their development is a key issue if such a concept is to achieve its potential. This book addresses the major challenges in realizing WMSNs in forthcoming IoT-based eHealth systems. Challenges vary from cost and energy efficiency to security and service quality, and to tackle such challenges WMSNs must meet certain expectations and requirements such as size constraints, manufacturing costs and resistance to environmental factors existing at deployment locations. Reflecting this the book focuses on both design and implementation aspects Topics covered include the impact of medical sensor networks in smart-cities; an evaluation of mobile patient monitoring technologies; overview of wireless sensor devices in medical applications; cyber security issues in WMSNs and eHealth; smart hospital rooms and automated systems; medical sensor capabilities in smart cloud networks; swarm intelligence based medical diagnosis systems; and smart systems and device for the blind.
The need for a deep decarbonization of the energy sector and the associated opportunities are now increasingly recognized, with fossil fuel projects simultaneously becoming more risky business propositions. With large-scale wind and solar generation now being the cheapest options in many parts of the world, a deeply renewable electricity sector is predestined to become the main driver of this transition. Yet, misconceptions abound. In part, this can be traced back to the complexity of the electricity sector and the processes involved in its transformation, located at the intersection between grid design and operation, markets and regulations. This book is intended to provide some clarity in this matter, by taking the reader from the conceptual foundations of a deeply decarbonized electricity sector in part 1 to new strategies for the renewable energy transition in part 3. Insights into essential building blocks are provided in part 2 where the role of transmission, distributed generation, smart grids, demand response, storage, and forecasting are covered in some detail. The synthesis part 3 explores the connections between the mobility and electricity sectors, the design of renewable economies, and possible roadmaps for a world-wide transition to a deeply decarbonized economy. While striving to be technically rigorous, this book is also meant as an entertaining and inspiring read for researchers and advanced students, experts with the electric power industry, and decision makers in politics, industry and finance.
Polycrystalline thin-film solar cells have reached a levelized cost of energy that is competitive with all other sources of electricity. The technology has significantly improved in recent years, with laboratory cell efficiencies for cadmium telluride (CdTe), perovskites, and copper indium gallium diselenide (CIGS) each exceeding 22 percent. Both CdTe and CIGS solar panels are now produced at the gigawatt scale. However, there are ongoing challenges, including the continued need to improve performance and stability while reducing cost. Advancing polycrystalline solar cell technology demands an in-depth understanding of efficiency, scaling, and degradation mechanisms, which requires sophisticated characterization methods. These methods will enable reseachers and manufacturers to improve future solar modules and systems. This work provides researchers with a concise overview of the status of thin-film solar cell technology and characterization. Chapters describe material systems and their properties and then provide an in-depth look at relevant characterization methods and the learning facilitated by each of these. Following an introductory chapter, the book provides systematic and thorough coverage of the following topics: trends to improve CdTe solar cell performance; Cu(In,Ga)Se2 and related materials; perovskite solar cells; photovoltaic device modelling; luminescence and thermal imaging of thin-film photovoltaic materials, devices, and modules; application of spatially resolved spectroscopy characterization techniques on Cu2ZnSnSe4 solar cells; time-resolved photoluminescence characterization of polycrystalline thin-film solar cells; fundamentals of electrical material and device spectroscopies applied to thin-film polycrystalline chalcogenide solar cells; nanometer-scale characterization of thin-film solar cells by atomic force microscopy-based electrical probes; scanning transmission electron microscopy characterization of solar cells; photoelectron spectroscopy methods in solar cell research; time-of-flight secondary-ion mass spectrometry and atom probe tomography; and solid-state nuclear magnetic resonance characterization for photovoltaic applications. The final chapter provides an overview and describes future prospects.
Metamaterials that derive their properties from the subwavelength structure of meta-atoms offer unprecedented flexibility for manipulating light-matter interaction. To promote the development of practical metadevices, considerable efforts have been made to achieve photonic metamaterials that exhibit an active response under external stimuli. The primary strategy is to incorporate active materials that possess variable refractive index into the design of metamaterials. Among the various active materials, phase-change materials have recently attracted particular attention due to their variation of material properties in a broad frequency band. On the other hand, as structures and geometric arrangement of building blocks (i.e., artificial atoms or meta-atoms) determine the properties of metamaterials, reconfigurable metamaterials based on mechanical tuning have also been intensively studied. Unlike most reported mechanically tunable metamaterials based on elastic and electromagnetic forces, directed self-assembly (DSA) of nanoparticles opens a new path for low-cost, large-area reconfigurable photonic systems. In this chapter, we present our findings on active photonics metadevices based on phase transition of vanadium dioxide (VO2) and reconfigurable nanowire assemblies. Our work shows the potential of the proposed metamaterial systems for applications ranging from electro-optical information process, storage, and display to energy-efficient smart windows.
As the field of subwavelength optics continues to boom and novel nanophotonic device designs emerge (as other chapters in this book highlight), existing nanofabrication methods will continue to evolve and new technologies will surface to keep pace with the increasingly sophisticated demands. We foresee that the crossroads of ingenious nanophotonic designs and nanofabrication techniques will thrive as a leading source of inspiration for researchers aspiring to explore the field. In this spirit, it is the authors' hope that this chapter can serve as a convenient solution guide to both theorists and experimentalists who are exploring unconventional subwavelength structures. Finally, we hope that the discussions here can further inspire design and process innovations in the dynamic field of subwavelength optics, where 'there is plenty of room at the bottom' for both dancing angels and optical engineers alike.
This chapter discussed an accurate and powerful semiclassical multiphysics approach to numerical modeling of several critical nonlinear phenomena employing carrier kinetics. We conclude that coupling a system of multilevel rate equations with Maxwell's equations provides a versatile full-wave framework with significantly enhanced predictive power that is capable of simulating sophisticated nanostructured photonic and optoelectronic devices with embedded nonlinearities and vectorial input beams of arbitrary spatiotemporal complexity.
Cancer is a leading cause of death worldwide. Despite the great advancement in understanding the pharmacology and biology of cancer, it still signifies one of the most serious human-health related problems. The current treatments for cancer may include surgery, radiotherapy, and chemotherapy, but these procedures have several limitations. Current studies have shown that nanoparticles (NPs) can be used as a novel strategy for cancer treatment. Developing nanosystems that allow lower doses of therapeutic agents, as well as their selective release in tumour cells, may resolve the challenges of targeted cancer therapy. In this review, the authors discuss the role of the size, shape, and surface modifications of NPs in cancer treatment. They also address the challenges associated with cancer therapies based on NPs. The overall purpose of this review is to summarise the recent developments in designing different hybrid NPs with promising therapeutic properties for different types of cancer.
Drug delivery is one of the major challenges in the treatment of central nervous system disorders. The brain needs to be protected from harmful agents, which are done by the capillary network, the so-called blood–brain barrier (BBB). This protective guard also prevents the delivery of therapeutic agents to the brain and limits the effectiveness of treatment. For this reason, various strategies have been explored by scientists for overcoming the BBB from disruption of the BBB to targeted delivery of nanoparticles (NPs) and cells and immunotherapy. In this review, different promising brain drug delivery strategies including disruption of tight junctions in the BBB, enhanced transcellular transport by peptide-based delivery, local delivery strategies, NP delivery, and cell-based delivery have been fully discussed.