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The exceptional properties of skyrmion devices, including their miniature size, topologically protected nature, and low current requirements, render them highly promising for energy-efficient neuromorphic computing applications. Examining the creation, stability, and dynamics of magnetic skyrmions in thin-film systems is imperative to realize these skyrmion-based neuromorphic devices. Herein, we report the creation, stability, and tunability of magnetic skyrmions in the Ta/IrMn/CoFeB/MgO thin-film system. We use polar magneto-optic Kerr effect (MOKE) microscopy and micromagnetic simulations to investigate the magnetic-field dependence of skyrmion density and size. The topological charge evolution with time under a magnetic field is studied, and the transformation dynamics are explained. Furthermore, we demonstrate skyrmion size and density tunability as parameters controlled by voltage, current, and magnetic field via Voltage-Controlled Magnetoresistance (VCMA) and Dzyaloshinskii-Moriya Interaction (DMI). We propose a skyrmion-based synaptic device for neuromorphic computing applications. The device exhibits spin-orbit torque-controlled discrete topological resistance states with high linearity and uniformity, allowing for the realization of the hardware implementation of weight quantization in a Quantized Convolutional Neural Network (QCNN). Our experimental results demonstrate that the devices can be trained and tested on the CIFAR-10 dataset, achieving a recognition accuracy of ~87%. The findings open new avenues for developing neuromorphic computing devices based on tunable skyrmion systems.

Abnormal fluid buildup in the lungs, termed pulmonary edema (PE), is a result of congestive heart failure. It is a life-threatening condition, and early detection and prompt treatment can help save lives. In this article, we demonstrate the feasibility of using a microwave sensor to monitor changes in lung water content and hence detect PE. The research paper utilizes a combination of the Debye and Maxwell models, along with the Cole–Cole equation, to evaluate alterations in the dielectric properties and conductivity of lung tissue. By incorporating elements such as air and water found within the tissue, this dielectric model has been employed to foresee how lung tissues behave when subjected to different levels of hydration and inflation. A printed antenna resonating at 2.4 GHz was designed to work as a sensor. The static dielectric parameters of lung tissue at various water volume fractions were calculated at 2.4 GHz using the Debye–Maxwell model. These parameters were substituted in the Cole–Cole equation to calculate the dielectric constant of lung tissue for different levels of water in the lungs. These values were then substituted in the simulation environment, where the sensor is placed on blocks modeling the human thorax. This work is a first of its kind where the dielectric parameters at different levels of hydration have been previously estimated using mathematical models and substituted accordingly in the modeling environment to test the possibility of detection of PE with high precision. It was observed that the magnitude of the reflection coefficient values changes with increasing water volume fraction, making the microwave method of detection of PE feasible and a reliable technique.

Access to reliable electricity is crucial for rural development and improving the quality of life in remote areas. Standalone photovoltaic PV systems and hybrid power systems HPS are promising solutions for rural electrification. However, frequency stabilization is a critical challenge in such systems, and conventional control techniques are inadequate. This work proposes a novel approach to optimize the PID controller gains for the standalone PV-based isolated HPS (IHPS). The student psychology-based optimization algorithm (SPBOA) and quasi-oppositional-based whale optimization algorithm (QOWOA) are employed to enhance the performance of the IHPS models separately. The objective function considered is the integral of time absolute error (ITAE), and the frequency response profile is studied in the presence of the proposed SPBOA and QOWOA-based PID controllers. The results show that the proposed SPBOA outperforms the QOWOA in terms of reducing the ITAE by nearly 5% and minimizing the peak and settling time of the frequency response by 5%-7% in different scenarios. The transient responses of the systems to different input conditions are also analyzed, indicating that both the IHPS-I and IHPS-II models are feasible for remote rural electrification. Overall, the proposed approach offers an efficient solution to optimize the PID controller gains for frequency stabilization in standalone PV and HPS. The results demonstrate the benefits of using the SPBOA and QOWOA algorithms to enhance the performance of the systems, making them suitable for remote rural electrification.

In this paper, we propose a miniaturized series L-C loaded monopole antenna for catheter application in microwave ablation systems. Initially, a quarter wavelength long monopole antenna (Design1), having length 30 mm ( $\approx 0.24\lambda _{0}$ where $\lambda _{0}$ represents the free-space wavelength at 2.4 GHz operating frequency) using series LC loading concept is designed. The initial design is further extended to a miniaturized version (Design2) having length 5 mm ( $\approx 0.04\lambda _{0}$ ), showing nearly 83% size reduction compared to its former.Both the antennas are designed and simulated by immersing inside high permittivity egg white phantom ( $\epsilon _{r}$ = 63.84). Antenna Design 1 and 2 exhibit good impedance matching ( ${S_{11} < -12}$ dB) at and around the operating frequency with uniform monopolar radiation pattern having co-to-cross isolation of nearly 30–35 dB. Further, the in-phantom Specific Absorption Rate (SAR) values for both antennas are evaluated using simulation and verified with the measured SAR values such as 22 W/kg and 19.6 W/kg for Design1 and 2 respectively, using an in-house experimental setup. Moreover, the microwave ablation property of the proposed antennas is studied using simulated transient thermal gradient which translates to ablation zone formation. Here, Design 2 shows the advantage of having a low invasive diameter of 1.5 mm and near unity aspect ratio (AR) for the ablation zone as compared to Design1.

Magnetic racetrack memory has frequently been complicated by the pinning of domain wall bits on the one hand and the need to engineer precise synchronization and inter-track repulsion between skyrmionic bits on the other. Such proposals, however, do not capitalize on the complex 2-D motion of skyrmions, such as transverse Magnus force that tends to deviate the skyrmion trajectory from rectilinear motion along the current drive. The transverse deviation associated with such a skyrmion Hall effect is normally considered a liability for skyrmions, and efforts have focused on eliminating rather than utilizing it for proposed device applications. We propose a simple single skyrmion-based circuit macro with elementary and higher-order logic gates that utilize Magnus force and propose reconfigurable logic built on these gates. We demonstrate the reliability of the proposed approach with micromagnetics simulation. The energy consumption in this circuit lies mainly in the overhead, with the racetrack consuming a small fraction. The energy–delay product (EDP) is correspondingly low and can be improved by boosting the skyrmion speed.

The smart transformer (ST) offers several features in a hybrid microgrid due to the presence of both ac and dc links while providing various power flow paths. For ST based meshed microgrid, continuous operation is highly important and one of the effective ways to achieve it is by minimizing line losses. This paper proposes a power loss minimization strategy which is specific for an ST based islanded meshed hybrid microgrid. In such an islanded system, a battery energy storage system (BESS) is used for maintaining the low voltage (LV) dc bus voltage of ST. The hybrid microgrid consists of renewable energy sources, electric vehicles (EV) charging stations, storage, etc. The minimization of total distribution line power loss is chosen as an objective function for optimization problem. The active power references of distributed generation converters and charge or discharge schedules of EVs are optimally controlled using genetic algorithm while satisfying the various constraints of the system. Further, a smart LV load shedding is also incorporated to the power management. This is activated on crossing the lower critical state of charge of the BESS. It is used as a final measure to increase BESS discharge time resulting in prolonged operation of the islanded system. Simulation and experimental results verify the performance of the proposed scheme. For the various cases considered, the proposed system is compared with conventional power management strategies. It was observed that while in a particular case, the conventional method incurred 1.337 kW losses, the proposed method was able to reduce the line losses to 1.0487 kW. This shows a 22% reduction in line losses with proposed method as compared to conventional method.

This paper presents a novel Transformer-based facial landmark localization network named Localization Transformer (LOTR). The proposed framework is a direct coordinate regression approach leveraging a Transformer network to better utilize the spatial information in a feature map. An LOTR model consists of three main modules: 1) a visual backbone that converts an input image into a feature map, 2) a Transformer module that improves the feature representation from the visual backbone, and 3) a landmark prediction head that directly predicts landmark coordinates from the Transformer’s representation. Given cropped-and-aligned face images, the proposed LOTR can be trained end-to-end without requiring any post-processing steps. This paper also introduces a loss function named smooth-Wing loss, which addresses the gradient discontinuity of the Wing loss, leading to better convergence than standard loss functions such as L1, L2, and Wing loss. Experimental results on the JD landmark dataset provided by the First Grand Challenge of 106-Point Facial Landmark Localization indicate the superiority of LOTR over the existing methods on the leaderboard and two recent heatmap-based approaches. On the WFLW dataset, the proposed LOTR framework demonstrates promising results compared with several state-of-the-art methods. Additionally, we report an improvement in the performance of state-of-the-art face recognition systems when using our proposed LOTRs for face alignment.

We present a low energy-barrier magnet based compact hardware unit for analog stochastic neurons (ASNs) and demonstrate its use as a building-block for neuromorphic hardware. Networks assembled from these units are particularly suited for temporal inferencing and pattern recognition. We demonstrate example applications of these ASNs including multi-layer perceptrons, convolutional neurons, and reservoir computers showing tasks such as temporal sequence learning, processing, and prediction tasks which prove that these units can be used to build efficient, scalable, and adaptive neural network based signal-processors. We also provide an illustrative comparison with digital CMOS based circuits that implement similar functionality with networks built using the presented units, demonstrating a possible two orders of magnitude reduction in component-count and concomitant increase in energy efficiency. Efficient non von-Neumann hardware implementation of such signal-processors can open up a pathway for integration of hardware based cognition in a wide variety of emerging systems such as IoT, industrial controls, bio- and photo-sensors, self-driving automotives, and unmanned aerial vehicles.

Assessing biomechanics of upper limb movement is essential for guiding targeted therapy to treat conditions such as spasticity and dystonia. Targeted therapy, including injections of medications into specific muscles (e.g., lidocaine, botulinum toxin type A), requires accurate identification (activity) and contribution of as many muscles as possible. Currently, this is achieved by visual clinical assessment or using surface electromyography (sEMG). Although sEMG could provide a reasonable estimate of muscle activity for certain superficial muscles after an intense filtering process, they are unable to provide separated activity and contribution for every superficial and deep muscle. Other proposed musculoskeletal and machine learning models similarly do not provide a detailed and accurate activity of every muscle. The objective of the study is to design a subject-specific musculoskeletal model to predict the activity and contribution of each muscle pertaining to any upper limb movement with improved detail and accuracy over existing methodologies. Performance metrics were calculated for validation by comparing the predicted muscle activity with the normalized sEMG data computed from 8 superficial muscles, while the deeper muscles were not included in the validation as the sEMG is unable to provide a separated activity for those muscles. The results show that the proposed model has a mean R2 value of 0.8190 and also indicated a statistically significant correlation ( $P < 0.0001$ ) between the calculated (normalized sEMG data) and predicted activity value. Additionally, and significantly, compared to earlier studies, the proposed model predicts the individual muscle activity and contribution of deeper muscles.

The poor interface quality of the Silicon Carbide/oxide (SiC/SiO2) interface severely degrades the electron surface channel mobility in SiC-based power devices. Based on transfer characteristic simulations (with a deck calibrated to experimental data), this work predicts improved mobility with stress engineering, a well-established technique for performance enhancement in low power silicon (Si) transistor technology. Process simulation of Si and SiC-based devices with Silicon Nitride (Si3N4) stressor layer has been carried out to estimate the stress generated in the channel. SiC D-MOSFET we have also computed the effect of varying stress magnitude, direction, and position in the device.

A smart transformer (ST) based meshed hybrid distribution network is realized by extending ST low voltage dc (LVDC) link to form a LVDC line which connects dc buses of existing distributed generation (DG) converters. This paper proposes a method for optimal operation in such ST based meshed hybrid distribution network. A CIGRE LV residential distribution network with DG sources is connected at ST low voltage ac (LVAC) and LVDC terminals. All the DG sources are connected to LVDC line. The power management scheme and method of determining power flow solution for the considered distribution network are proposed. An optimal problem is formulated for determining the active and reactive power references of DG converters while maintaining the LVAC load bus voltages within the grid limits and considering the DG converters constraints. The minimization of energy drawn from ST medium voltage (STMV) grid is considered as objective function and solved using genetic algorithm. To know the impact of proposed optimal control of DG converters, various performance indicators i.e., energy loss, operating energy costs, voltage profile and sizing of ST converters are considered and compared with existing literature.

A methodology has been proposed to accurately model the gate stack of Gallium Nitride (GaN) based metal-insulator-semiconductor high electron mobility transistors (MIS-HEMTs). Small-signal analysis has been performed for the device biased in the spill-over region, where electrons accumulate at the insulator/III-Nitride 'critical' interface. Accounting for the barrier layer resistance with an accurate model yields a near but inexact match between simulation and measurement. It is found that the border trap admittance and the energy distribution of the interface trap capture cross section (σ) need to be included in order to achieve a very close one. It is concluded that the interface trap density, extracted using conventional small-signal admittance methods, can be significantly off if these non-ideal effects are not incorporated within the equivalent circuit models of GaN-based MIS-HEMTs.

Race logic is a relative timing code that represents information in a wavefront of digital edges on a set of wires in order to accelerate dynamic programming and machine learning algorithms. Skyrmions, bubbles, and domain walls are mobile magnetic configurations (solitons) with applications for Boolean data storage. We propose to use current-induced displacement of these solitons on magnetic racetracks as a native temporal memory for race logic computing. Locally synchronized racetracks can spatially store relative timings of digital edges and provide nondestructive readout. The linear kinematics of skyrmion motion, the tunability and low-voltage asynchronous operation of the proposed device, and the elimination of any need for constant skyrmion nucleation and annihilation make these magnetic racetracks a natural memory for low-power, high-throughput race logic applications.