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This paper proposes a new reduced switch-count power cell for a regenerative Cascaded H-Bridge (CHB) motor drive that reduces the number of switching devices per power cell from ten switches to six switches. The proposed power cell employs a Four-Switch Three Phase Inverter (FSTPI) at the front-end and a half-bridge inverter at the output of the power cell. The new configuration introduces four challenges: cell input current unbalance, input low order harmonics, DC-link voltage capacitor balancing, and switching harmonics. This paper analyzes and addresses these issues to ensure proper operation, control, and compliance with grid connection standards. The solutions are verified through simulation studies of a 3kV CHB motor drive and experimental validation with a single power cell setup. The results show the possibility of replacing the traditional power cell with a proposed cell, effectively reducing the required number of switches from ten to six.

The idea of the IoT began back in 1982 when a vending machine was connected to the internet, then to the concept of Mark Weiser in 1992, then RFID, and so on. A detailed evolution of the IoT and how it was transforming is given. Due to the wide embracement of IoT by industries and home users, it has become the cornerstone of the Information and Communication Technology (ICT) market. The market value of the IoT was worth 1.90 billion in 2018; 25 billion in 2020; 925.2 billion as of 2023; while it is forecasted to hit 6 trillion in 2025 at 15.12% growth rate. This work presented some major technologies integrated with the IoT to achieve a certain goal and improve an existing system. IoT’s capability to create smart applications is demonstrated and some major distinctions between IoT & the Internet were highlighted.

Mineral (MO) oil has long been utilized as a dielectric and cooling medium in high-voltage power transformers due to its low cost and availability. Recently, there has been a great trend toward the use of gas-to-liquid (GTL) oil due to its purity, chemical consistency, and better performance than conventional oils. This study aims to investigate the normal and abnormal thermal impacts on GTL and MO oil types. In this regard, this paper presents two parts. Firstly, it studies a comprehensive evaluation of the aging characteristics under normal conditions of GTL oil in comparison to conventional MO. Aged oil samples were obtained using a laboratory-based accelerated aging test, for simulating the thermal stress during the transformer’s operation. Hence, the two oil types are subjected to different aging durations (i.e., 3, 6, and 10 days). These aged and fresh samples were tested for AC breakdown voltage, dielectric dissipation factor, permittivity, and resistivity as electrical testing. Additionally, as chemical and physical testing, moisture and acidity are measured for fresh and aged samples of two of these oil types. Besides, optical spectroscopy measurements have been used for insulating oil assessment; these kinds of measurements point out the existence of overall aging byproducts, including sludge formation, which can negatively impact the oil properties including physio-chemical and electrical properties. The used optical techniques include Ultraviolet-visible absorption and photoluminescence tests. Secondly, thermal faults impact as abnormal conditions is generally evaluated through dissolved gas analysis as a chemical test, where it is utilized to measure the gas concentrations for the two oils under abnormal conditions test after the thermal heating test. This study concluded that the electrical and physio-chemical properties of GTL oil with aging have superior results compared to MO oil.

The cascaded H-bridge (CHB) converters with diode-front-end (DFE) structure have been widely adopted in non-regenerative high-power medium-voltage motor drives. In order to allow regenerative capability, the diode-front end (DFE) in each power cell can be replaced by an IGBT-based active-front-end (AFE) operating as a PWM rectifier. Due to the inherent instantaneous power unbalance in the AFE-based CHB power cells, the DC-bus capacitance needs to be increased to reduce DC-bus voltage ripples. To reduce the DC-bus capacitance, control degrees of freedom provided by the PWM rectifiers can be exploited to mitigate the DC-bus voltage ripples. This paper proposes a new controller for regenerative CHB converters to perform DC-bus voltage control and ripple mitigation while maintaining high dynamic performance and canceling low order harmonics at the grid side to meet the grid standards. The effectiveness of the proposed control scheme is validated experimentally on a seven-level regenerative CHB drive.

Gradient coils are essential for the performance of magnetic resonance imaging systems. Usually, coils are designed assuming thin wire tracks. Here, we design an MR gradient coil set using a more general approach considering the exact track width using the discrete wire approach. The effect of track width on the DC current density distribution and resultant magnetic fields using both loop and Golay coils are first demonstrated. Both, self-shielded X and Z gradient coils of definite width/thickness are designed and optimized. The resistance and inductance of the coils are calculated using the stream functions approach. Track current distribution was used to compute the magnetic fields over the desired volume, and at the cryostat. The linearity of the magnetic field over the volume, the figure of power, and the shielding ratio of the coil are used as parameters in the optimization process. The DC characteristics of the designed coils with definite (small) track width and thickness were compared for verification to that of the corresponding thin wire design where they were found to have approximately similar characteristics. Using our design methodology, the coils’ frequency-dependent resistances and inductances were directly/efficiently calculated. The harmonic and transient eddy current interactions between the longitudinal and transverse gradient coils were computed where track slitting was employed to reduce such interactions. This work stresses the importance of considering coil track width in the design process particularly for wide tracks as well as computing the coil’s figure of merit, harmonic and transient coil characteristics/interactions.

Transformer oil insulation condition may be deteriorated due to electrical and thermal faults, which may lead to transformer failure and system outage. In this regard, the first part of this paper presents comprehensive maintenance for power transformers aiming to diagnose transformer faults more accurately. Specifically, it aims to identify incipient faults in power transformers using what is known as dissolved gas analysis (DGA) with a new proposed integrated method. This proposed method for DGA is implemented based on the integration among the results of five different DGA techniques; 1) conditional probability, 2) clustering, 3) Duval triangle, 4) Roger’s four ratios refined, and 5) artificial neural network. Accordingly, this proposed integrated DGA method could improve the overall accuracy by 93.6% compared to the existing DGA techniques. In addition, the second part used for predictive maintenance is based on determining the health index for five new transformers and an aged power transformer (66/11 kV, 40 MVA) filled with NYTRO 10XN oil by evaluating the breakdown voltage, DGA, moisture content, and acidity for the oil. In the breakdown voltage test, two practical types of transformer oil; NYTRO 10XN and HyVolt III alongside their mixtures are estimated and compared. In addition, aged oil samples extracted from a real case study in-service transformer during operation with different aged durations; 9, 10, 11, 12, and 13 years, are tested for breakdown voltage, and then compared with fresh oil samples. For DGA, a temperature rise test is performed on the five new transformers with a comparison between dissolved gases before and after the temperature rise. In addition, winding resistance is measured after the temperature rise. Also, acidity and moisture are measured for oils extracted from the new five transformers and from the 13-year in-service transformer for studying their health index. The health index of the transformer insulation system is examined using only DGA and DGA plus breakdown voltage (BDV), moisture, and acidity. The results show that by using DGA plus BDV, moisture, and acidity, the health index provides reliable estimation results compared to using only DGA.

Cascaded H-Bridge (CHB) topology is one of the attractive topologies in high-power medium-voltage motor drive applications due to its modularity and scalability. Research in high power regenerative motor drives has gained significant attention with the increasing demand for efficient energy use. In a cascaded H-Bridge (CHB) converter, the regenerative capability can be introduced by replacing diode front end (DFE) with active front end (AFE) topologies. However, this results in a huge increase in the number of power semiconductors, gate drivers, and heat sink size and thus increases the overall size and cost of the regenerative CHB motor drives. To overcome the aforementioned challenges, different power cell designs have been introduced to reduce the switch count, allowing the design of more suitable-sized and more economical drives. This paper comprehensively reviews the reduced switch-count power cell designs, including single-phase and three-phase grid connections. Each reduced switch-count cell design is analyzed, and its advantages and disadvantages are studied in detail. The challenges that arise with each design and the method to address the challenges are discussed.

Gradient coils are essential for MRI where fast and large electrical current pulses are typically applied to conventional, single-channel gradient coils, particularly for high-performance gradient applications. However, these pulses result in significant power losses and heating of the coil. We investigate the design of power-efficient multi-channel Z-gradient coils operating in the conventional mode comparing them to conventional single-channel coils designed using similar dimensions and alike DC performance characteristics. The power-efficiencies of thirteen different two-channel configurations having various section lengths for two different dimensions are analyzed. The current density of each section is approximated by Fourier series expansion where a linear equation relating the desired target field and current density is formulated and then solved. A stream function is derived from the obtained current density and then used to extract the final winding patterns of each section using a particular track width and a specific number of turns. The design process involves optimizing the current driving each channel, the distribution of coil windings, and the section size. Similarly, the performance of three-channel coils is also investigated. Results show that a power dissipation reduction of 17-28% and ~23% can be achieved using two- and three-channel coils, respectively. Moreover, we showed that multi-channel coils may have a slightly better shielding efficiency compared to conventional coils. A new methodology for designing two- and three-channel coils is presented where an advantage in terms of power efficiency can be gained depending on design parameters, coil’s dimensions, number of turns, and other metrics.

Deep learning models such as long short-term memory (LSTM) are valuable classifiers for time series data like hourly clinical statistics. However, access to health data is challenging due to privacy and legal issues. Homomorphic encryption (HE) offers a potential solution by encrypting confidential data. However, the computational complexity of encrypted operations makes it infeasible for training with gradient descent. Prior works only deal with encrypted procedures during the inference phase of the network. To this end, we design Collective Learning protocol, a secure protocol for sharing classified time-series data within entities to train parameters of the binary classifier model partially. The protocol encrypts each data’s feature activations using HE and trains the last layers using encrypted logistic regression. We evaluate our protocol on a benchmark LSTM network trained on the Medical Information Mart for Intensive Care (MIMIC-III) dataset. We selected the Cheon-Kim-Kim-Song (CKKS) encryption scheme because of its ability to work with real numbers using approximate arithmetic. Our protocol improved the area under the precision-recall curve (AUPRC) score of the in-hospital mortality prediction model. The same protocol was reimplemented using secure multi-party computation (MPC) and compared.

Cryptocurrencies have been a target for cybercriminal activities because of the pseudo-anonymity and privacy they offer. Researchers have been actively working on analyzing and developing innovative defensive mechanisms to prevent these activities. A significant challenge facing researchers is collecting datasets to train defensive systems to detect and analyze these cyberattacks. Our aims in this systematic review are to explore and aggregate the state of the art threats that have emerged with cryptocurrencies and the defensive mechanisms that have been proposed. We also discuss the threats type, scale, and how efficient the defensive mechanisms are in providing early detection and prevention. We also list out the resources that have been used to collect datasets, and we identify the publicly available ones. In this study, we extracted 1,221 articles from four top scientific and engineering databases and libraries in Computer Science: IEEE Xplore, ACM Digital Library, Elsevier's Scopus, and Crarivate's Web of Science. We defined inclusion, exclusion, and quality of assessment criteria, and after a detailed review process, 66 publications were included in the final review. Our analysis revealed that the literature contains a significant amount of research to detect and analyze several attack types, such as the high yield investment programs and pump and dump. These attacks have been used to steal millions of USD, abuse millions of connected devices, and have created even more significant loss in denial of services and productivity losses. We have found that the researchers use various sources to collect training datasets. Many authors have made their dataset publicly available. We have created a list of these datasets, which we have made available along with other supplementary websites, tools, and libraries that can be used in the data collection and analysis process.

We present an efficient and non-interactive method for Text Classification while preserving the privacy of the content using Fully Homomorphic Encryption (FHE). Our solution (named Private Fast Text (PrivFT)) provides two services: 1) making inference of encrypted user inputs using a plaintext model and 2) training an effective model using an encrypted dataset. For inference, we use a pre-trained plaintext model and outline a system for homomorphic inference on encrypted user inputs with zero loss to prediction accuracy compared to the non-encrypted version. In the second part, we show how to train a supervised model using fully encrypted data to generate an encrypted model. For improved performance, we provide a GPU implementation of the Cheon-Kim-Kim-Song (CKKS) FHE scheme that shows 1 to 2 orders of magnitude speedup against existing implementations. We build PrivFT on top of our FHE engine in GPUs to achieve a run time per inference of 0.17 seconds for various Natural Language Processing (NLP) public datasets. Training on a relatively large encrypted dataset is more computationally intensive requiring 5.04 days.

To ensure maximum comfort for the wearer, electronic components that include energy harvesters need to be mechanically conformable. In this context, we demonstrate a versatile, cost-effective and efficient method for fabricating graphene supercapacitor electrodes using Laser Induced Graphene (LIG). A CO2 laser beam instantly transforms the irradiated polyethersulfone polymer (PES) into a highly porous carbon structure. The LIG method was used to deposit graphene layers on graphite sheets to produce the supercapacitor electrodes. Graphene formation and morphology were examined and confirmed using several techniques including Scanning Electron Microscopy (SEM), Energy Dispersive X-ray (EDX) spectroscopy, Raman Spectroscopy and Fourier transform infrared spectroscopy (FTIR). Moreover, the electrochemical characterization was performed in different electrolytes (NaOH and KOH). At 5 mV s-1, the LIG electrode achieved 165 mF cm-2 and 250 mF cm-2 in NaOH and KOH electrolytes, respectively. Consequently, we show that a wearable symmetric supercapacitor device with LIG electrodes achieved 98.5 mF cm-2 at 5 mV s-1 in KOH electrolyte. The device demonstrated an energy density of 11.3 μWh.cm-2 with power density of 0.33 mWcm-2 at 0.5 mA cm-2. The retention of capacitance was 75% after 2000 cycles, with outstanding performance for the comparable graphene-based electrodes. These results further validate the use of LIG for developing flexible energy harvesters for wearable applications.

The use of the digital twin has been quickly adopted in industry in recent years and continues to gain momentum. The recent redefinition of the digital twin from the digital replica of a physical asset to the replica of a living or nonliving entity has increased its potential. The digital twin not only disrupts industrial processes, but also expands the domain of health and well-being towards fostering smart healthcare services in smart cities. In this paper, we propose an ISO/IEEE 11073 standardized digital twin framework architecture for health and well-being. This framework encompasses the process of data collection from personal health devices, the analysis of this data, and conveying the feedback to the user in a loop cycle. The framework proposes a solution to include not only X73 compliant devices, but also noncompliant health devices, by interfacing them with an X73 wrapper module as we explain in this paper. Besides, we propose a configurable X73 mobile application, designed to work with any X73 compliant device. We designed and implemented the proposed framework, and the X73 mobile app, and conducted an experiment as a proof of concept of the digital twin in the domain of health and well-being in smart cities. The experiment shows promising results and the potential of benefiting from the proposed framework, by gaining insights on the health and well-being of individuals, and providing feedback to the individual and caregiver.

Since the introduction of the ISO/IEEE 11073 personal health device (X73-PHD) standards, as part of ISO/IEEE 11073 family of standards, it has been applied to many health systems developed for personal use. In this systematic literature review, we review existing literature collected using three databases: Scopus, Pub Med, and Web of Science. We propose a classification for personal health systems based on the location in which they are used, the technology used to develop them, and the purpose which is determined by the targeted users. We found 51% of the devices used in such systems are standardized while approximately 40% are not and five systems did not specify the device status (9%). Various adaption techniques were used for standardization. Besides, the pulse oximeter is the most used device in such systems since it was used in 43% of them. In addition, we present the role of the X73-PHD standards in the Internet of Things (IoT) and tele-healthcare systems, discuss the challenges of utilizing this set of standards in health monitoring systems and converting the non-standardized devices into standardized ones. Finally, we propose the requirements of personal health systems based on our review of the literature.

Chemical and infra-red sensors generate distinct responses under similar conditions because of sensor drift, noise or resolution errors. In this paper, we develop novel machine learning methods for detecting and identifying VOC and Ammonia vapor from time-series data obtained by uncalibrated chemical and infrared sensors. We process time-series sensor signals using deep neural networks (DNN). Three neural network algorithms are utilized for this purpose. Additive neural networks (termed AddNet) are based on a multiplication-devoid operator and consequently exhibit energy efficiency compared to regular neural networks. The second algorithm uses generative adversarial neural networks so as to expose the classifying neural network to more realistic data points in order to help the classifier network to deliver improved generalization. Finally, we use conventional convolutional neural networks as a baseline method. Our findings indicate that using raw time-series data obtained from uncalibrated sensors and processing them using deep-learning-based methods yield better results than using hand-crafted feature parameters.

This work explores the performance of a distinct power saving and relatively simple design visible light communication system based on pulse position modulation (PPM) modulation scheme in a real life (i.e. diffuse) scenario. The mathematical background for both communication and illumination performance is developed for the Sub-Carrier Pulse Position Modulation (SC-LPPM) Scheme in non-line-of-sight (NLOS) environment. An evaluative study is then carried out to provide an integrated picture for the SC-LPPM performance under several system/environmental parameters. Adding the NLOS components increases the total received power, but still, the LOS component dominates. At the corners of a typical room, the received optical power enhances by 120% when including NLOS components, while at room center the optical power enhances only by 12%. For illumination, the NLOS components improve the system's lighting performance and ensure meeting illumination standards. It provides an extra illumination of about 260 lux at room corner and 175 lux at room center over LOS scenario. Wall reflectivity (ρ), subcarrier modulation factor (SCMF), and the number of time slots (L) are environmental/system parameters that can significantly optimize the overall performance. For diffuse scenario, a BER of 10-5 to 10-7 with L = 4, normal wall reflectivity (plaster walls ρ = 0.8) and 100% SCMF (i.e. 20 mW/LED) can be achieved easily at bit rates of 1 to 2 Mbps. These BER performance levels can be enhanced by selecting wall painting/ materials with high reflectivity. As a power saving scheme, increasing SCMF with low power/LED level can greatly enhance the BER performance in the diffuse scenario. A remarkable BER performance of 10-9 can be reached when increasing SCMF by 25% (i.e. to reach 25 mW/LED) under the same conditions (L = 4, ρ = 0.8 and 1-2 Mbps). At higher data rates the performance is getting worse but still can be optimized, controlled, and enhanced through using a careful choice of environmental and system parameters that can be selected from this work.