Nanoflowers Versus Magnetosomes: Comparison Between Two Promising Candidates for Magnetic Hyperthermia Therapy
Magnetic Fluid Hyperthermia mediated by iron oxide nanoparticles is one of the most promising therapies for cancer treatment. Among the different candidates, magnetite and maghemite nanoparticles have revealed to be some of the most promising candidates due to both their performance and their biocompatibility. Nonetheless, up to date, the literature comparing the heating efficiency of magnetite and maghemite nanoparticles of similar size is scarce. To fill this gap, here we provide a comparison between commercial Synomag Nanoflowers (pure maghemite) and bacterial magnetosomes (pure magnetite) synthesized by the magnetotactic bacterium Magnetospirillum gryphiswaldense of ⟨D⟩≈ 40 –45 nm. Both types of nanoparticles exhibit a high degree of crystallinity and an excellent degree of chemical purity and stability. The structural and magnetic properties in both nanoparticle ensembles have been studied by means of X–Ray Diffraction, Transmission Electron Microscopy, X–Ray Absorption Spectroscopy, and SQUID magnetometry. The heating efficiency has been analyzed in both systems using AC magnetometry at several field amplitudes (0–88 mT) and frequencies (130, 300, and 530 kHz).
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Published in the IEEE Magnetics Society Section.
An Experimental-Based Review of Image Enhancement and Image Restoration Methods for Underwater Imaging
Underwater images play a key role in ocean exploration but often suffer from severe quality degradation due to light absorption and scattering in water medium. Although major breakthroughs have been made recently in the general area of image enhancement and restoration, the applicability of new methods for improving the quality of underwater images has not specifically been captured. In this paper, we review the image enhancement and restoration methods that tackle typical underwater image impairments, including some extreme degradations and distortions. First, we introduce the key causes of quality reduction in underwater images, in terms of the underwater image formation model (IFM). Then, we review underwater restoration methods, considering both the IFM-free and the IFM-based approaches. Next, we present an experimental-based comparative evaluation of the state-of-the-art IFM-free and IFM-based methods, considering also the prior-based parameter estimation algorithms of the IFM-based methods, using both subjective and objective analyses (the used code is freely available at https://github.com/wangyanckxx/Single–Underwater–Image–Enhancement–and–Color–Restoration). Starting from this paper, we pinpoint the key shortcomings of existing methods, drawing recommendations for future research in this area. Our review of underwater image enhancement and restoration provides researchers with the necessary background to appreciate challenges and opportunities in this important field.
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Phantom Malware: Conceal Malicious Actions From Malware Detection Techniques by Imitating User Activity
State of the art malware detection techniques only consider the interaction of programs with the operating system’s API (system calls) for malware classification. This paper demonstrates that techniques like these are insufficient. A point that is overlooked by the currently existing techniques is presented in this paper: Malware is able to interact with windows providing the corresponding functionality in order to execute the desired action by mimicking user activity. In other words, harmful actions will be masked as simulated user actions. To start with, the article introduces User Imitating techniques for concealing malicious commands of the malware as impersonated user activity. Thereafter, the concept of Phantom Malware will be presented: This malware is constantly applying User Imitating to execute each of its malicious actions. A Phantom Ransomware (ransomware employs the User Imitating for every of its malicious actions) is implemented in C++ for testing anti-virus programs in Windows 10. Software of various manufacturers are applied for testing purposes. All of them failed without exception. This paper analyzes the reasons why these products failed and further, presents measures that have been developed against Phantom Malware based on the test results.
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Security Hardening of Intelligent Reflecting Surfaces Against Adversarial Machine Learning Attacks
Next-generation communication networks, also known as NextG or 5G and beyond, are the future data transmission systems that aim to connect a large amount of Internet of Things (IoT) devices, systems, applications, and consumers at high-speed data transmission and low latency. Fortunately, NextG networks can achieve these goals with advanced telecommunication, computing, and Artificial Intelligence (AI) technologies in the last decades and support a wide range of new applications. Among advanced technologies, AI has a significant and unique contribution to achieving these goals for beamforming, channel estimation, and Intelligent Reflecting Surfaces (IRS) applications of 5G and beyond networks. However, the security threats and mitigation for AI-powered applications in NextG networks have not been investigated deeply in academia and industry due to being new and more complicated. This paper focuses on an AI-powered IRS implementation in NextG networks along with its vulnerability against adversarial machine learning attacks. This paper also proposes the defensive distillation mitigation method to defend and improve the robustness of the AI-powered IRS model, i.e., reduce the vulnerability. The results indicate that the defensive distillation mitigation method can significantly improve the robustness of AI-powered models and their performance under an adversarial attack.
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Design, Fabrication and Test of a High- Temperature Superconducting Linear Synchronous Motor Mover Magnet Prototype for High-Speed Maglev
High-temperature superconducting linear synchronous motors (HTS-LSMs) have many advantages, such as high thrust density, high efficiency, large electromagnetic gap, and liquid-helium-free refrigeration, because of the high operating temperature and good mechanical tolerance of high-temperature superconductors. Therefore, HTS-LSMs have broad application prospects in the field of high-speed maglev propulsion system. To study the dynamic stability of an HTS-LSM, this work aims at designing, fabricating and testing an HTS magnet as the mover magnet of an HTS-LSM. The HTS mover magnet is a monopole HTS magnet, and it is designed according to electromagnetic, structural, and thermal properties and the measurement system. A thermal model and structural dynamics model were constructed to analyze the dynamic refrigeration performance and structural dynamics characteristics of the HTS magnet. The validation of these models was verified by experimental results. The HTS coils in the HTS mover magnet were fabricated using epoxy impregnation with primary and secondary curing processes. Static tests and dynamic tests were performed to comprehensively study the characteristics of the HTS magnet. The magnet could be cooled to below 20 K and could be excited to 246 A with a certain temperature margin. An electromagnetic simulator was designed and manufactured to realize the off-line simulation of the actual operation of the HTS-LSM. The dynamic experimental results show that the HTS magnet could withstand a vibration environment of up to 18 gRMS without quenching and structural damage. This study provides useful information for the design and application of an HTS-LSM
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Power Electronics Based on Wide-Bandgap Semiconductors: Opportunities and Challenges
The expansion of the electric vehicle market is driving the request for efficient and reliable power electronic systems for electric energy conversion and processing. The efficiency, size, and cost of a power system is strongly related to the performance of power semiconductor devices, where massive industrial investments and intense research efforts are being devoted to new wide bandgap (WBG) semiconductors, such as silicon carbide (SiC) and gallium nitride (GaN). The electrical and thermal properties of SiC and GaN enable the fabrication of semiconductor power devices with performance well beyond the limits of silicon. However, a massive migration of the power electronics industry towards WBG materials can be obtained only once the corresponding fabrication technology reaches a sufficient maturity and a competitive cost. In this paper, we present a perspective of power electronics based on WBG semiconductors, from fundamental material characteristics of SiC and GaN to their potential impacts on the power semiconductor device market. Some application cases are also presented, with specific benchmarks against a corresponding implementation realized with silicon devices, focusing on both achievable performance and system cost.
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Segmental Degradation RUL Prediction of IGBT Based on Combinatorial Prediction Algorithms
Aiming at the segmentation nonlinear degradation characteristics of IGBT, the traditional single remaining useful lifetime (RUL) method has low accuracy. This paper proposes a method combining gray prediction and particle filter algorithm. The gray prediction model is used for slow degradation trends prediction in the early stage. When the health precusor parameters reach the fault warning line, the improved particle filter algorithm is used for this stage’s prediction with the characteristics of fast nonlinear degradation. The comparison analysis result shows that the combinatorial prediction algorithms used in this paper can be better for tracking the degradation trends of IGBT, and the prediction accuracy is higher than either of the two single prediction methods.
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Filterless and Compact ANy-WDM Transmission System Based on Cascaded Ring Modulators
To cope with the exponential increase in internet services and corresponding data traffic, especially data centers and access networks require new high data rate transmission methods with low cost, very small package and low energy consumption. In this paper, we demonstrate a filterless, agnostic Nyquist wavelength division multiplexing (ANy-WDM) transmission system based on cascaded ring modulators and a comb source. The single ring modulator acts as a filter, filtering one of the
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Output Power Prediction of a Photovoltaic Module Through Artificial Neural Network
With the increase in energy demand, renewable energy has become a need of almost every country. Solar Energy is an important constituent of it and contributes a large portion in it. Forecasting the output power of a Photovoltaic (PV) system has always been a challenging problem in the power sector from the last few decades. The output power of a PV system depends upon several environmental factors such as irradiance (G), temperature (T), humidity (H), wind speed (W), provided the tilt angle is kept constant, among which the vital role is played by irradiance. Researchers have utilized several techniques to accurately predict the output power of PV module but every method has various pros and cons. In this paper, an experimental measurement dataset of 28296 samples with all the environmental parameters mentioned above are taken as the inputs and power as its output, of a Poly-Silicon (Poly-Si) PV module, is trained through Artificial Neural Network (ANN), to predict the output power accurately. The proposed ANN contains a layer size of 15 and training algorithm used is Levenberg-Marquardt. A detailed analysis and preprocessing of the data is carried out through Pearson’s correlation method prior to training. The hyperparameters of Neural Network tuning are selected through heuristic method. The data division is done randomly with 70% dataset used for training, 15% dataset used for each validation and testing. The statistical results show that ANN accurately predicted the power output of PV module. The regression analysis values acquired are 98% and the MSE of all the three phases is 0.0604.
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A Novel Optimization Framework for High Dynamics Point-to-Point Direct Drive Motion Control System With a New Type of Surrogate Model
A new optimization framework for a high-dynamic point-to-point direct drive motion control system (HDPDMS) is proposed. The conventional system optimization approach considers all design parameters simultaneously, resulting in a high-dimensional search space and extensive computation. In contrast, the proposed framework uses a new DDM surrogate model that establishes a correlation between the key DDM characteristic parameters to decouple the whole optimization process. It begins with a system-level optimization to identify suitable driver types, motion profile design parameters, and characteristic parameters of the direct drive motors (DDMs) by the new surrogate model. Bayesian optimization then determines the DDM design parameters corresponding to the identified characteristic parameters. Once the DDM surrogate model is built, the proposed framework achieved the desired HDPDMS design in just 1 hour, saving 98.6% of computation time compared to the traditional approach. Additionally, multi-objective optimization and Gaussian process regression prediction intervals were employed to obtain a suitable training dataset and input range for the surrogate model, resulting in a 99.8% reduction in computation resources compared to the traditional DDM surrogate model. Through completing three unique motion task optimizations and creating a prototype, the optimization framework was proven effective, demonstrating the potential of this novel method.
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