Gate driver circuits to ensure proper turn-on and turn-off for power switches are essential parts of a power converter design. They become even more important for multilevel converters where multiple switches are operated at active voltage domains. Recent favorable use of Gallium-Nitride (GaN) devices for power switches makes gate driving even more challenging as the switch performance and reliability are more sensitive to variations of the gate driving signals and power compared with traditional power MOSFETs. This paper discusses gate driving methods using a multi-level multi-inductor hybrid (MIH) converter as the demonstration prototype to address two key challenges in designing gate drivers: 1) providing level-shifted PWM signals to active voltage domains and 2) powering schemes for gate driver circuits. To solve the first challenge, an optimal use of available half-bridge drivers is devised to eliminate the need for separate signal isolator chips. This method was implemented and verified in a MIH converter prototype for 48-V Point-of-Load (PoL) applications using three different powering schemes for gate drivers, including isolated power modules, regulated supplies from switch blocking voltages, and cascaded bootstrap power rails with regulations. The gate driver techniques and powering schemes are compared experimentally in terms of performance to illustrate their benefits and trade-offs.

This paper investigates the origin of the flying capacitor voltage imbalance in hybrid converters. By observation and logical deduction, an intuitive voltage-charge relationship is established which can give a general explanation of the flying capacitor voltage balance in hybrid converters. This relationship can establish a relatively simple and intuitive method to identify the difference of balance performance in hybrid converters for Vout<(Vin /N) cases. Converter with even number of inductor charging intervals, are shown to be susceptible to flying capacitor voltage imbalance, while flying capacitors in hybrid converters with inductors having odd charging intervals have inherently balanced operations. As a direct result of the analysis, a new symmetric operation of FCML converters is introduced to achieve an inherent balance of flying capacitor voltages. Hardware implementations and experiments have been carried out for verifications of the analytical analysis and the new symmetric operation.

The COVID-19 pandemic has emerged as the world’s most serious health crisis, affecting millions of people all over the world. The majority of nations have imposed nationwide curfews and reduced economic activity to combat the spread of this infectious disease. Governments are monitoring the situation and making critical decisions based on the daily number of new cases and deaths reported. Therefore, this study aims to predict the daily new deaths using four tree-based ensemble models i.e., Gradient Tree Boosting (GB), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Voting Regressor (VR) for the three most affected countries, which are the United States, Brazil, and India. The results showed that VR outperformed other models in predicting daily new deaths for all three countries. The predictions of daily new deaths made using VR for Brazil and India are very close to the actual new deaths, whereas the prediction of daily new deaths for the United States still needs to be improved.

A reliable robotic localization method is required for comparing three-dimensional pipe maps obtained via laser scans at various times for accurately monitoring the evolution of internal pipe surface defects. Existing robotic localization methods have limitations when visual features vanish due to changes in the pipe environment or when encoder data becomes highly uncertain due to long-distance robotic traverses. To address this issue, we leverage battery-free ultra-high frequency radio frequency identification (UHF-RFID) sensors for transmitting wireless signals to a two-antenna reader integrated mobile robotic system. Although there are literature on the investigation of UHFRFID behaviors and their applications in indoor environments, analysis of the same for in-pipe scenarios was not well studied. In this paper, we evaluate the UHF-RFID sensor signals inside a field extracted pipeline. Firstly, we examine the UHF-RFID sensor signal patterns through repeated robotic scans. Secondly, we examine how the placement of UHF-RFID reader antennas affects the transmission of UHF-RFID sensor signals, as well as we study the effects of robotic traverse direction and speed on the UHF-RFID wireless signals. Finally, we examine whether identical UHF-RFID sensors generate the same pattern when placed in a pipeline. Overall, the experimental evaluation demonstrates that the use of two-antenna UHF-RFID readers can ameliorate the capabilities of robotic localization in the pipeline.

Cell-free (CF) massive multiple-input multiple-output (MIMO), as a promising network architecture for beyond the fifth generation (5G), has a great potential to support grant-free (GF) transmission for machine-type communication (MTC). To shed light on this subject, this work aims to model and evaluate the performance of GF transmission in CF massive MIMO under a realistic network deployment scenario, where the spatial locations of both access points (APs) and devices are assumed to be random in nature. In particular, by capitalizing on the distinctive CF network architecture and features, we design a new two-disk based geometric model for GF transmission, which facilitates analysis and understanding in CF massive MIMO. Based on the proposed two-disk model, we derive an approximated closed-form expression for the access success probability by leveraging on techniques from stochastic geometry, and investigate the impact of different key system parameters on the network performance. To highlight the performance superiority of CF massive MIMO, we further provide a comparative analysis by using an analogous single-disk model in an equivalent co-located massive MIMO network. Simulation results verify our analysis and demonstrate that CF massive MIMO is able to significantly outperform its co-located counterpart in terms of access success probability and provide robust performance against increased access density, which well suits to crowd scenarios.

Optimal sensor placement is an important problem to look at. This problem becomes all the more relevant nowadays due to advancements in infrastructure monitoring robotic technologies including underground sensing. While there are multiple ways to solve optimal sensor placement problems, one of the most generic methods available is Bayesian Optimization and its variants. In this paper, we present a simple benchmark- like formulation for exploiting Gaussian Process uncertainty for sensor placement to measure a scalar field.

Improving power delivery and management plays a key role in minimizing the cost of building and operating future green data centers to meet the fast growth of high-performance computing. Toward this important goal, this paper presents a new complete power delivery architecture to bridge AC grid voltages to core levels for computing loads using only 2 conversion stages with new converter topologies. The first stage converts a commercial AC line voltage of 90V-110V to a 48-60V intermediate bus with power factor correction (PFC). The second stage converts the bus voltage to core voltages of ~1 V with high current density and simple duty cycle control. Individually, the first stage was measured at 96.1% peak efficiency for output currents ranging in 0-4.5 A, while the second stage achieved 90.7% peak efficiency with a load range of 0-220 A at 1 V. Measured peak power densities are 73 W/in3 for the first stage and 2020 W/in3 for the second stage. In combination, the direct conversion from a line AC voltage of ~110 VAC to 1 VDC achieves a peak efficiency of 84.1% while providing output currents up to 160A.

One of the most prominent machine learning advantages in the medical industry is the early detection of disease. Automatic kidney detection is of great importance for rapid diagnosis and treatment, where related diseases occupy over 73,750 new cases in the US in 2020 [1]. Today, the performance of diagnosis has been by highly trained radiologists. However, the complex structures contribute to speckle noise and inhomogeneous intensity profiles. Thus, there is a necessity to automate segmentation on kidney ultrasounds using U-Net Deep Learning architectures - an innovative solution for Medical Imaging Analysis. In this research, our focus is on the comparison of Attention U-Net in the context of different backbones such as VGG19, ResNet152V2, and EfficientNetB7. By providing this comparison, we will accomplish a survey for future researchers to more effectively decide on which Attention U-Net architecture to utilize for their segmentation projects.

Alternating current optimal power flow (ACOPF) problems are nonconvex and nonlinear optimization problems. Utilities and independent service operators (ISO) require ACOPF to be solved in almost real time. Interior point methods (IPMs) are one of the powerful methods for solving large-scale nonlinear optimization problems and are a suitable approach for solving ACOPF with large-scale real-world transmission networks. Moreover, the choice of the formulation is as important as choosing the algorithm for solving an ACOPF problem. In this paper, different ACOPF formulations with various linear solvers and the impact of employing box constraints are evaluated for computational viability and best performance when using IPMs. Different optimization structures are used in these formulations to model the ACOPF problem representing a range of sparsity. The numerical experiments suggest that the least sparse ACOPF formulations with polar voltages yield the best computational results. Additionally, nodal injected models and current-based branch flow models are improved by enforcing box constraints. A wide range of test cases, ranging from 500-bus systems to 9591-bus systems, are used to verify the test results.

Due to its simple gain selection and implementation procedures high-gain sliding mode control idea was introduced. Apart from achieving robustness features, the aim was to reduce product-design cycle time. To overcome its basic chattering issues, it was replaced by complex super twisting control (STC). Subsequently, the research in this field has been extensive, intense and persistent. The idea has been validated in several applications generating great hope in minds of industry experts. Its real success, however, would depend on its adoption and diffusion into industry domain, where an analysis of validating STC based products is due. In STC, selection of gains is based on worst-case values of disturbance and/or its derivative. Can such procedure ensure controller’s reliable functioning? This article proposes that such simplistic procedure of gain selection may not work for power electronics controllers where, particularly, the actuation or controlled power transfer to load is through magnetically-coupled system. Using practical approach, this article elaborates that optimally designed transformer, driven by high-gain super twisting controller, invites problem of core saturation leading to higher switching losses, poor operating duty cycle of the system and there could be problem of reliability. It further details an alternate high-gain controller that generates superior efficacy.

The safety and reliability of autonomous driving pivots on the accuracy of perception and motion prediction pipelines, which in turn reckons primarily on the sensors deployed onboard. Slight confusion in perception and motion prediction can result in catastrophic consequences due to misinterpretation in later pipelines. Therefore, researchers have recently devoted considerable effort towards developing accurate perception and motion prediction models. To that end, we propose LIDAR Camera network (LiCaNet) that leverages multi-modal fusion to further enhance the joint perception and motion prediction performance accomplished in our earlier work. LiCaNet expands on our previous fusion network by adding a camera image to the fusion of RV image with historical BEV data sourced from a LIDAR sensor. We present a comprehensive evaluation to validate the outstanding performance of LiCaNet compared to the state-of-the-art. Experiments reveal that utilizing a camera sensor results in a substantial perception gain over our previous fusion network and a steep reduction in displacement errors. Moreover, the majority of the achieved improvement falls within camera range, with the highest registered for small and distant objects, confirming the significance of incorporating a camera sensor into a fusion network.

This paper describes how it is possible to apply a recursion to the original formula of Erlang B., to solve the problem of evaluating large numbers in that formula, using mathematical assistants, in applications designed to determine the grade of service in telephone exchanges.

With the development of DNA synthesis and sequencing technologies, DNA becomes a promising medium forlong-term data storage. Three types of errors may occur in the DNA strand, insertions, deletions and substitutions,which we collectively call edit errors. It is still challenging to design a code that can correct multiple edit errors onnon-binary alphabets. In this paper, we propose a new coding schema for correcting multiple edit errors on DNAstrands by splitting the whole strand into consecutive blocks with appropriate length and correcting a single editerror in each block. Our method, called theDNA-LMcode, could be considered a generalization of the Levenshteincode combined with the marker code. We provide a linear encoding and decoding algorithm for ourDNA-LMcode.Compared to other encoding methods for DNA strands of several hundred base-pairs, ourDNA-LMcode achievedsimilar code rates and a much lower average nucleotide error rate in decoding.

Convolutional Neural Networks have been considered the go-to option for object recognition in computer vision for the last couple of years. However, their invariance to object’s translations is still deemed as a weak point and remains limited to small translations only via their max-pooling layers. One bio-inspired approach considers the What/Where pathway separation in Mammals to overcome this limitation. This approach works as a nature-inspired attention mechanism, another classical approach of which is Spatial Transformers. These allow an adaptive endto-end learning of different classes of spatial transformations throughout training. In this work, we overview Spatial Transformers as an attention-only mechanism and compare them with the What/Where model. We show that the use of attention restricted or “Foveated” Spatial Transformer Networks, coupled alongside a curriculum learning training scheme and an efficient log-polar visual space entry, provides better performance when compared to the What/Where model, all this without the need for any extra supervision whatsoever.

Knowingly or unknowingly, digital-data is an integral part of our day-to-day lives. Realistically, there is probably not a single day when we do not encounter some form of digital-data. Typically, data originates from diverse sources in various formats out of which time-series is a special kind of data that captures the information about the time-evolution of a system under observation. How- ever, capturing the temporal-information in the context of data-analysis is a highly non-trivial challenge. Discrete Fourier-Transform is one of the most widely used methods that capture the very essence of time-series data. While this nearly 200-year-old mathematical transform, survived the test of time, however, the nature of real-world data sources violates some of the intrinsic properties presumed to be present to be able to be processed by DFT. Adhoc noise and outliers fundamentally alter the true signature of the frequency domain behavior of the signal of interest and as a result, the frequency-domain representation gets corrupted as well. We demonstrate that the application of traditional digital filters as is, may not often reveal an accurate description of the pristine time-series characteristics of the system under study. In this work, we analyze the issues of DFT with real-world data as well as propose a method to address it by taking advantage of insights from modern data-science techniques and particularly our previous work SOCKS. Our results reveal that a dramatic, never-before-seen improvement is possible by re-imagining DFT in the context of real-world data with appropriate curation protocols. We argue that our proposed transformation DFT21 would revolutionize the digital world in terms of accuracy, reliability, and information retrievability from raw-data.

The magnetic force acts exclusively perpendicular to the direction of motion of a test charge, whereas the electric force does not depend on the velocity of the charge. This article provides experimental evidence that, in addition to these two forces, there is a third electromagnetic force that (i) is proportional to the velocity of the test charge and (ii) acts parallel to the direction of motion rather than perpendicular. This force cannot be explained by the Maxwell equations and the Lorentz force, since it is mathematically incompatible with this framework. However, this force is compatible with Weber electrodynamics and Ampère’s original force law, as this older form of electrodynamics not only predicts the existence of such a force but also makes it possible to accurately calculate the strength of this force.

In low-power wide-area networks (LPWANs), various trade-offs among the bandwidth, data rates, and energy per bit have different effects on the quality of service under different propagation conditions (e.g. various types of fading), interference scenarios, multi-user requirements, and design constraints. Such compromises, and the manner in which they are implemented, fur- ther affect other technical aspects, such as system’s computational complexity and power efficiency. At the same time, this difference in trade-offs also adds to the technical flexibility in addressing a broader range of the Internet of Things (IoT) applications. This paper addresses a physical layer LPWAN approach based on the Aggregate Spread Pulse Modulation (ASPM) and provides a brief assessment of its properties in an additive white Gaussian noise (AWGN) channel. In the binary ASPM, the control of the quality of service is performed through the change in the spectral efficiency, i.e., the data rate at a given bandwidth. Implementing M-ary encoding in ASPM further enables controlling service quality through changing the energy per bit (in about an order of magnitude range) as an additional trade-off parameter. Such encoding is especially useful for improving the ASPM’s energy per bit performance, thus increasing its range and overall energy efficiency, and making it more attractive for use in LPWANs.

The fifth-generation (5G) mobile system is now being deployed across the world and the scale of 5G subscribers is growing quickly in many countries. The attention of academia and industry is increasingly shifting towards the sixth generation (6G) and many pioneering works are being kicked off, indicating an important milestone in the history of 6G. At this juncture, an overview of the current state of the art of 6G research and a vision of future communications are of great interest. This paper thus investigates up-to-date 6G research programs, ambitions, and main viewpoints of representative countries, institutions and companies worldwide. Then, the key technologies are outlined and a vision on “What 6G may look like?” is provided. This paper aims to serve as an enlightening guideline for interested researchers to quickly get an overview when kicking off their 6G research.