Wind turbine is a complex machine with its rotating and non-rotating equipment being sensitive to faults. Due to increased wear and tear, the maintenance aspect of a wind turbine is of critical importance. Unexpected failure of wind turbine components can lead to increased O&M costs which ultimately reduces effective power capture of a wind farm. Fault detection in wind turbines is often supplemented with SCADA data available from wind farm operators in the form of time-series format with a 10-minute sample interval. Moreover, time-series analysis and data representation has become a powerful tool to get a deeper understating of the dynamic processes in complex machinery like wind turbine. Wind turbine SCADA data is usually available in form of a multivariate time-series with variables like gearbox oil temperature, gearbox bearing temperature, nacelle temperature, rotor speed and active power produced. In this preprint, we discuss the concept of a digital twin for time to failure forecasting of the wind turbine gearbox where a predictive module continuously gets updated with real-time SCADA data and generates meaningful insights for the wind farm operator.

Image registration is a commonly required task in computer assisted surgical procedures. Existing registration methods in laparoscopic navigation systems suffer from several constraints, such as lack of deformation compensation. The proposed algorithm aims to provide the surgeons with updated navigational information about the deep-seated anatomy, which considers the continuous deformations in the operating environment.We extended an initial rigid registration to a shape-preserving deformable registration pathway by incorporating user interaction and an iterative mesh editing scheme which preserves local details. The proposed deformable registration workflow was tested with phantom and animal trial datasets. A qualitative evaluation based on expert feedback demonstrated satisfactory outcome, and an commensurate execution efficiency was achieved. The improvements offered by the method, couples with its relatively easy implementation, makes it an attractive method for adoption in future pre-clinical and clinical applications of augmented reality assisted surgeries.

Measuring data utility and privacy risk embedded in synthetic or other de-identified datasets is an increasingly important research area. Existing measures in the data privacy literature however are one-sided in that they either measure utility or privacy risk only. In this paper we propose a new measure that can evaluate both data utility and privacy, a well-known trade-off relationship in data synthesis. The proposed measure employs the notion of relative distance between the synthetic and original datasets at the dataset level, and can identify the optimally balanced position of the synthetic data in terms of both utility and privacy. In addition, we devise a graphical tool that visually reveals the current utility-privacy trade-off position of the synthetic data. Numerical studies show our new measure consistently performs better and offers richer interpretations than other existing global data utility measures, for both simulated and real datasets, confirming its distinctive advantages.

This paper develops a Finite Element Model (FEM) for a 138 kV High Temperature Vulcanized (HTV) Silicone Rubber (SiR) transmission insulator in which, in a first step, leakage current is calculated for different non-uniform continuous and discontinuous pollution deposits and then features based on the phase shift between the leakage current and the applied voltage are extracted for each kind of pollution layer to identify the existence of dry bands on the insulator surface. A phase shift of 84.75° lag between the leakage current and applied voltage was found for a pollution layer with dry bands inserted. This results evidence that the phase angle can be used to detect the dry band formation by measuring the leakage current and its phase angle.

Multimodal Land Cover Classification (MLCC) using the optical and Synthetic Aperture Radar (SAR) modalities has resulted in outstanding performances over using only unimodal data duo to their complementary information on land properties. Previous multimodal deep learning (MDL) methods have relied on handcrafted multi-branch convolutional neural networks (CNN) to extract the features of different modalities and merged them for land cover classification. However, natural images-oriented handcrafted CNN models may not the optimal strategies to handle Remote Sensing (RS) image interpretation problems, duo to the huge difference in terms of imaging angles and imaging ways. Furthermore, few MDL methods have analyzed optimal combinations of hierarchical features from different modalities. In this article, we propose an efficient multimodal architecture search framework, namely Multimodal Semantic Consistency-Based Fusion Architecture Search (M2SC-FAS) in continuous search space with the gradient-based optimization method, which can not only discover optimal optical- and SAR-specific architectures according to the different characteristics of the optical and SAR images, respectively, but also realizes the search of optimal multimodal dense fusion architecture. Specifically, the semantic-consistency constraint is introduced to guarantee dense fusion between hierarchical optical and SAR features with high semantic consistency and then capture the complementary performance on land properties. Finally, the basis of curriculum learning strategy is adopted on the M2SC-FAS. Extensive experiments show superior performances of our work on three broad co-registered optical and SAR datasets.

Blockchains currently exist in silos, competing when they could be cooperating. Interoperability is essential to allow for communication between them and motivate mass adoption. In permissioned blockchains, interoperability is harder given their opaqueness. The solutions proposed so far to address interoperability require a trusted private third party, which may be insecure and is not ideal. We propose T-ODAP, a secure multi-layered protocol that enables a trustless solution for permissioned blockchain interoperability, eliminating the need for trust in the protocol’s participants. It provides a Decentralized View Storage, a connector that connects permissioned blockchains to the latter, and a trustless version of the ODAP protocol. T-ODAP models the participants as rational agents using game theory techniques and is implemented using \textit{Polkadot} and \textit{Hyperledger Cactus}. We tested the implemented solution, evaluated the system’s robustness in face of attacks, and concluded that the system is \textit{(k,t)-weak-robust}.

The ongoing coronavirus pandemic requires more effective disinfection methods. The disinfection using ultraviolet light (UV), especially longer UVCs such as 254 nm, 270/280 nm have been proved to have virucidal properties, but its adverse effects on human skin and eyes limit its use to enclosed unoccupied spaces. Several studies conducted the in past have shown the effectiveness of blue light (405nm) against bacteria and fungi, but the virucidal property of 405nm has largely been unexplored. Based on previous studies, visible light mediates inactivation by absorbing the porphyrins and reacting with oxygen to produce reactive oxygen species (ROS). This causes oxidative damage to biomolecules such as protein, lipids, and nucleic acids, essential constituents of any virus. The virucidal potential of visible light has been speculated because the virus lacks porphyrins. This study demonstrated porphyrin independent viral inactivation and a comparative analysis of the effectiveness of 405nm against other UVC wavelengths. The beta coronavirus 1 (strain OC43) was treated against 405nm, 270/280nm, 254nm, and 222nm, and its efficacy was determined using median tissue culture infectious dose, i.e., TCID50. The results support the disinfection potential of visible light technology by providing a quantitative effect that can serve as a basic groundwork for future visible light inactivation technologies. In the future, blue light technology usage can be widened to hospitals, public places, aircraft cabins, and/or infectious laboratories to inactivate SARS-CoV-2.

In the early stages of a novel technology development, it is difficult to provide a comprehensive assessment of its potential capabilities and impact. Nevertheless, some preliminary estimates can be drawn and are certainly of great interest and in this paper we follow this line of reasoning within the framework of the Spin Wave (SW) based computing paradigm. In particular, we are interested in assessing the technological development horizon that needs to be reached in order to unleash the full SW paradigm potential such that SW circuits can outperform CMOS counterparts in terms of energy consumption. In view of the zero power SWs propagation through ferromagnetic waveguides, the overall SW circuit power consumption is determined by the one associated to SWs generation and sensing by means of transducers. While current antenna based transducers are clearly power hungry recent developments indicate that magneto-electric (ME) cells have a great potential for ultra-low power SW generation and sensing. Given that MEs have been only proposed at the conceptual level and no actual experimental demonstration has been reported we cannot evaluate the impact of their utilization on the SW circuit energy consumption. However, we can perform a reverse engineering alike analysis to determine ME delay and power consumption upper bounds that can place SW circuits in the leading position. To this end, we utilize a 32-bit Brent-Kung Adder (BKA) as discussion vehicle and compute the maximum ME delay and power consumption that could potentially enable a SW implementation able to outperform its 7nm CMOS counterpart. We evaluate different BKA SW implementations that rely on conversion- or normalization-based gate cascading and consider continuous or pulsed SW generation scenarios. Our evaluations indicate that 31nW is the maximum transducer power consumption for which a 32-bit Brent-Kung SW implementation can outperform its 7nm CMOS counterpart in terms of energy consumption.

In today's era, internet-connected things provide immense opportunities to the world for enhancing the quality of lives through better data processing and intelligent decision making. Since the last decade, IoT brought numerous changes in people's personal as well as professional lives. With the enhancement in quality of lives, IoT also comes up with challenges such as security and privacy of data and devices. Every day, the attacker generates new zero-day attacks for IoT devices and data, and it's important to detect and protect the IoT eco-system from this type of attacks. Numerous researchers have proposed security schemes and methods to protect the IoT eco-system through either cryptography way or learning technique based way. AI and ML learning techniques have got immense popularity in handling the IoT security challenges as they are automatic in nature and can outperform provided the sufficient quality and quantity of data. Moreover, the AI techniques, including ML, DL and FL helps in intelligent decision-making and can also generate knowledge through its learning techniques. AI needs data to process, and IoT supplies the necessary data to process. In this paper, we provide a state-of-the-art survey for IoT security solutions proposed based on learning techniques. We provide an in-depth review of available learning techniques to solve critical security challenges such as IoT authentication, access control, anomaly detection and malware analysis. At the end, we also highlighted various futuristic technologies that can invigorate IoT research and help in the design of full proof IoT eco-system.

Automatic segmentation of multi-modal Cardiac Magnetic Resonance Imaging (CMRI) scans is challenging due to the variant intensity distribution and unclear boundaries between the neighbouring tissues and other organs. The deep convolutional neural networks have shown great potential in medical image segmentation tasks. In this paper, we present a deep convolutional neural network model named Multi-Modal Cardiac Network (MMC-Net) for segmenting three cardiac structures namely right ventricle (RV), left ventricle (LV), and left ventricular myocardium (LVM) from multi-modal CMRI’s. The proposed MMC-Net is designed using a densely connected backbone enabling feature reuse, an atrous convolution module for fusing multi-scale features, and a pixel-classification module for generating the segmentation result. This model was evaluated on a publicly available MS-CMRSeg-2019 challenge dataset in segmentation of RV, LV, and LVM from CMRI scans. The segmentation results from extensive experiments demonstrate our MMC-Net can achieve better segmentation performance compared to other state-of-the-art models, and the existing approaches. Additionally, the generalization ability of the proposed MMC-Net is validated on another publicly available ACDC dataset without fine-tuning. The results demonstrate that the proposed MMC-Net shows a powerful generalisation ability of segmenting RV, LV, and LVM with higher performance.

The Department for Business Innovation and Skills (BIS) has recognized ensuring a good supply of talented Power Electronics engineers as a challenge. Inability to recruit high-quality engineers would drive companies out of the UK. The use of outdated or inappropriate teaching materials at universities has been identified as a gap to address this challenge. Some academic institutions have well-recognised power electronics, machines and drives (PEMD) programmes where their undergraduate courses are also linked to their research interests. However, other academic institutions do not provide that depth of knowledge required by PEMD industry, considering it as optional knowledge and do not have suitable training materials. This project responds to contribute to filling the gaps in skills, talent and training for the PEMD industry by developing a guidance on a developed framework for Power Electronics curriculum, which is supported by industrial-oriented knowledge and inputs. The developed framework has been designed to fit other disciplines also and a sample curriculum has been provided.

This paper presents the comprehensive numerical analysis of four, second order sliding manifold control algorithms to estimate rotor angle/position, θe, sensorlessly in brushless DC synchronous machine, namely, twisting, super twisting, convergence law and quasi-continuous sliding controller. The Lyapunov stability and convergence along with trajectories in the discrete time is also analyzed for each of these algorithms for both continuous and discontinuous controls.

Digital Delta Sigma modulators (DDSM) are typically used in phase locked loops for fractional frequency synthesis. Different types and configurations of DDSMs have been presented in the past which have distinctive characteristics in terms of spectral shaping of the quantization errors, spur immunity and implementation cost. In this paper we present a family of DDSMs that have high spur immunity and low folded noise when used in fractional-N frequency synthesizers.

Novel hardware design aimed at capturing data packets under high inteferance.

Social media are an important medium for exchanging information acting a vital role in today’s living edge. Social media are computer-mediated communication (CMC) technologies that facilitate the creation and sharing of  information, ideas, career interests and other forms of  expression dedicated to community-based input, interaction, content-sharing and collaboration via online communities and networks. In Italy more than 25 million of the population use social media among different generation for various purposes. Social media also claimed to have negative impacts on personality and physiological aspects of human beings, yet these statistics variables have not much been emphasized in previous studies. The objective of this paper is to compare and discuss the use of different social network platforms among Italians, specific purpose and to identify duration of usage. These finding will give us knowledge of the popular social media platform and usage among different age groups and to determine the usefulness and draw backs spending inordinate amount of time in social media usage among Italian population living in Italy.

Theory of characteristic modes (TCM) is applied to the design of a programmable flexible metasurface (PFMS) to achieve its multistate tunable microwave absorption. The designed metasurface consists of two layers. The first layer is a very thin polyimide (PI), on which unit cells consisting of two trapezoidal patches symmetrical along the central bias microstrip are printed. The second layer is grounded polyvinyl chloride (PVC) substrate to maintain the flexibility of the designed structure. The TCM is used to analyze and understand the modal behavior of the unit cell, which provides clear underlying electromagnetic (EM) physics to guide us how to load positive-intrinsic-negative (PIN) diode as a tunable resistance to achieve variable absorption states. The designed PFMS absorber have been fabricated and experimentally verified on the flat and cylindrical foams, respectively. Good agreements can be found for both planar and cylindrical metasurface structures between full-wave simulations and measurements. Less than -10 dB reflectivity is achieved from 3.6 to 10.7 GHz (99%). The mimicry of time-varying scattering cross section (SCS) is further realized through real-time coding PFMS absorber, which can be precisely tuned from -30 dB to 0 dB with a step of 1.5 dB at 5.5 GHz and 10.4 GHz, respectively.

Deep Learning, especially Convolutional Neural Networks (CNN) have been performing very well for the last decade in medical image classification. CNN has already shown a great prospect in detecting COVID-19 from chest X-ray images. However, due to its three dimensional data, chest CT scan images can provide better understanding of the affected area through segmentation in comparison to the chest X-ray images. But the chest CT scan images have not been explored enough to achieve sufficiently good results in comparison to the X-ray images. However, with proper image pre-processing, fine tuning and optimization of the models better results can be achieved. This work aims in contributing to filling this void of the literature. On this aspect, this work explores and design both custom CNN model and three other models based on transfer learning: InceptionV3, ResNet50 and VGG19. The best performing model is VGG19 with an accuracy of 98.39% and F-1 score of 98.52%. The main contribution of this work includes: (i) modeling a custom CNN model and three pre-trained models based on InceptionV3, ResNet50, and VGG19 (ii) training and validating the models with a comparatively larger dataset of 1252 COVID19 and 1230 non-COVID CT images (iii) fine tune and optimize the designed models based on the parameters like number of dense layers, optimizer, learning rate, batch size, decay rate, and activation functions to achieve better results than the most of the state-of-the-art literature (iv) the designed models are made public in [1] for reproducibility by the research community for further developments and improvements.

In this paper we report on our investigations on the use of a Deep Learning based brain-computer interface in the context of a continuous control application. A continuous control application is an application where a Deep neural network (DNN) is supposed to recognize defined motor-imagery related neural states from continuously measured EEG data stream and to initiate corresponding actions. Decisive for the quality of such an application is the achieved response time of the system, which means the time it takes to detect a certain motor-imagery induced neural state, as well as the state detection time, which means the time duration for which this neural state is recognized. Our investigations show that the neural patterns in the 0-8 Hz low-frequency band are essential for a short response time and that the patterns valid in the 8-30 Hz frequency band should be used to achieve a good state detection time. We show that both parameters, response time and state detection time, can be significantly improved when so-called cropped training method is used to train the deep neural net. Reaching a short response time and a good state detection time is significant for most continuous control applications. To the best of our knowledge, this is the first time the use of cropped training to optimize a continuous control application has been investigated.