Three-phase unfolding based converters offer a radically different approach to two-stage dc to three-phase ac conversion, with potential benefits for electric vehicle (EV) powertrains. However, research on non-isolated three-phase un- folding particularly for EV powertrains is currently lacking. This paper presents a novel non-isolated bidirectional buck-boost three-phase unfolding system for EV powertrains. The proposed converter incorporates a non-inverting buck-boost converter and a voltage balancer circuit in the dc-dc stage. Unlike existing unfolding systems in the literature that require two dc-dc converters capable of handling full system power, the balancer in the proposed system only handles a fraction of the total power, resulting in reduced size and improved efficiency of the converter. The paper provides detailed design guidelines and presents a straightforward control strategy for the proposed converter. To evaluate its performance, a 5 kW prototype is constructed and tested with an induction motor. The experimental results demonstrate peak efficiencies exceeding 98%.

Breast cancer, cancer with the highest incidence rate worldwide, is also the most common cause of cancer death for women in all countries. Among them, brain metastasis (BrM) is a characteristic of a severely poor prognosis for breast cancer. Due to the unique anatomy of brain metastases, the understanding of the tumor immune microenvironment (TME) of BrM from breast cancer is limited, which hinders our diagnosis and treatment of breast cancer patients. In this study, we used single-cell RNA sequencing (scRNA-seq) to characterize immune cells in the cerebrospinal fluid (CSF) of BrM patients to describe the immune landscape of BrM. We identified 9 cell subtypes: T cells, Tregs, MonoMacs, Macs, TAMs, Myeloid cells, DCs, Endo_Fibs and Glioblastals from CSF samples and analyzed their functions in the TME. In addition, we classified T cell, macrophage and DC subclusters by UMAP analysis and compared the differential genes and biological processes of each subcluster with changes in prognosis. Finally, we also analyzed cell-cell interactions among cell subclusters and their differences. As our research focuses on the tumor immune microenvironment of brain metastasis, we believe that our research will be attractive not only to cancer immunologists but also to the interdisciplinary readership of Cancer Science.

Sixth generation (6G) networks must adopt spectral-efficient communication techniques to ensure massive connectivity for coexisting humans and machines. However, the impact of various practical issues must be analyzed and addressed, including imperfect channel state information (CSI), stemming by the channel estimation error (CEE) and feedback delay (F-D) with time-variant channels. This paper focuses on these issues in the context of uplink networks, relying on power-domain non-orthogonal multiple access (NOMA). Moreover, the degrading effect of imperfect successive interference cancellation (SIC), when randomly deployed multiple mobile terminals communicate with a single base station (BS) is considered. The system performance is measured by means of outage probability, error probability, ergodic rate, throughput, energy efficiency, and spectral efficiency. Analytical, asymptotic, and computer simulation results demonstrate that CEE causes system coding gain losses for low signal-to-noise ratio (SNR) while the disruptive effects of CEE become negligible in the high SNR. Results also show that F-D does not degrade the system performance in the low SNR but it causes system coding gain losses for high SNR. Also, imperfect SIC does not have any detrimental effect on the system performance for low SNR but results in reduced coding gain for high SNR.Â

This paper proposes a dynamic event-triggered control strategy for the leader-following multi-agent control under directed topology.  A synthesis approach combining distributed controllers and observers design is developed under a dynamic sampling scheme, and only local information is required for each agent to implement the proposed method. The control protocol incorporates model-based estimation and clock-like auxiliary dynamic variables to prolong the inter-event time as long as possible.  Sufficient conditions for leader-following consensus control are established by linear matrix inequalities, and an explicit inter-event time is given to enable flexible tuning. Due to the carefully selected Lyapunov function, the proposed method exhibits significant advantages over the dynamic event-triggered control methods described in the existing literature. Compared to the existing static event-triggered strategy, the proposed approach significantly reduces the utilization of communication resources while preserving asymptotic convergence to the state consensus.  The validity and effectiveness of the proposed theoretical results are demonstrated by comparative simulations.

Quantum computers pose a significant threat to blockchain technology's security, which heavily relies on public-key cryptography and hash functions. The cryptographic algorithms used in blockchains, based on large odd prime numbers and discrete logarithms, can be easily compromised by quantum computing algorithms like Shor's algorithm and its future qubit variations. This survey paper comprehensively examines the impact of quantum computers on blockchain security and explores potential mitigation strategies. We begin by surveying the existing literature on blockchains and quantum computing, providing insights into the current state of research. We then present an overview of blockchain, highlighting its key components and functionalities. We delve into the preliminaries and key definitions of quantum computing, establishing a foundation for understanding the implications on blockchain security. The application of blockchains in cybersecurity is explored, considering their strengths and vulnerabilities in light of evolving quantum computing capabilities. The survey focuses on the quantum security of blockchain's fundamental building blocks, including digital signatures, hash functions, consensus algorithms, and smart contracts. We analyze the vulnerabilities introduced by quantum computers and discuss potential countermeasures and enhancements to ensure the integrity and confidentiality of blockchain systems. Furthermore, we investigate the quantum attack surface of blockchains, identifying potential avenues for exploiting quantum computing to strengthen existing attacks. We emphasize the need for developing quantum-resistant defenses and explore solutions for mitigating the threat of quantum computers to blockchains, including the adoption of quantum and post-quantum blockchain architectures. By examining vulnerabilities and discussing mitigation strategies, we aim to guide researchers, practitioners, and policymakers in developing robust and secure blockchain systems capable of withstanding advancements in quantum computing technology.

We study the synchronization behavior of a class of identical FitzHugh-Nagumo-type oscillators under adaptive coupling. We describe the oscillators by a circuit model and we provide a sufficient synchronization condition that relies on the shape of the nonlinear conductance’s $(i,u)$-curve and the connectivity of the adaptive coupling network. The coupling network is allowed to be time-variant, state-dependent and locally adaptive, where we treat memristive coupling elements as a special case. We provide a physical interpretation of synchronization in terms of power dissipation and investigate the sharpness of our condition.

This work is part of a research project carried out under the R&D program regulated by the National Electric Energy Agency (ANEEL) and which has developed two smart greenhouses for protected cultivation of banana and orchid seedlings. Smart greenhouses feature control and automation systems, architecture and bioclimatic strategies, and energy systems (artificial lighting system, artificial heating system, evaporative cooling system and photovoltaic system). A sensor network monitors indoor environmental variables and an automatic weather station monitors outdoor environmental variables. A computer system based on artificial intelligence is responsible for the autonomous control of the greenhouses. This paper presents the study of power quality indicators of agrivoltaic systems applied to smart greenhouses. For relative powers greater than 20%, the total harmonic distortion of current (THDA) is less than 4% and is less than 2% for relative powers greater than 60%. At nominal power, THDA is approximately 1%. The main current harmonic components are of order 2, 3, 5, 7 and 9, however, with lower values and in accordance with the limits established in technical standards. From August 2022 to July 2023, the yield final of the agrivoltaic systems of the smart greenhouses of Santa Rosa do Sul/SC and Alpestre/RS was, respectively, 1418.91 kWh/kWP and 1490.68 kWh/kWP.

Electroencephalography (EEG)-based motor imagery (MI) decoding has established a novel experimental paradigm in brain-computer interface (BCI) applications that offer effective treatment for stroke paralyzed patients. However, existing MI-EEG-based BCI systems introduce deployment issues because of nonstationary EEG signals, suboptimal features, and limited multi-class scalability. To tackle these issues, we propose an enhanced sparse swarm decomposition method (ESSDM) based on selfish-herd optimization and sparse spectrum to solve the issue of choice of uniform decomposition and hyper-parameters in swarm decomposition and applied to enhance MI-EEG classification. ESSDM adopts improved swarm filtering to automatically deliver optimal frequency bands in sparse spectrums with optimized hyper-parameters to extract dominant oscillatory components (OCs) that significantly enhance MI activation-related sub-bands. In addition, new fitness criteria is designed based on the Kullback–Leibler divergence distance from spectral kurtosis of obtained modes to select hyper-parameters that optimize decomposition effect, avoid excessive iterations, and provide fast convergence with optimal modes. Further, fused time-frequency graph (FTFG) features were derived from computed time-frequency representation to find cross-channel mutual spectral information. The experimental results on the 2-class BCI III-4a and 4-class BCI IV-2a datasets reveal that the proposed FTFG feature with CapsNet classifier framework (ESSDM-FTFG-CapsNet) outperformed existing methods in specific-subject and cross-subject scenarios.

Graphene antennas operating at mm-wave and THz frequencies enable miniaturization by being smaller than its metallic counterparts, while additionally providing frequency tunability capabilities. These characteristics are interesting for 6G short-range communication applications, where the antenna size is still one of the largest and, consequently, costly components inside a chip. In this work, a tunable planar antenna for operation at terahertz frequencies based on a graphene stack is proposed and analyzed. The proposed antenna consists of a stack of two graphene patches separated by a thin dielectric to provide higher efficiency and gain. This concept can broadly be applied to future THz communication systems because it can directly be integrated into transceiver units by being compatible with CMOS processes and manufacturing techniques. We use a full-wave electromagnetic solver to evaluate and compare the performance of this antenna to a simple graphene patch antenna in the range of 220 - 325 GHz. The results show that an increase in efficiency and gain can be accomplished. We also demonstrate the frequency tuning possibility of the antenna, which can be achieved by the electric field effect in the graphene stack by applying a bias voltage between the two graphene sheets.

This research paper considers a dataset of students of a particular class and uses different regression algorithms to predict an overall rating for the students. These regression algorithms are then compared with each other and further analyzed according to their performances.

RAIN attenuation is a major concern in satellite links operating in Ka-band, and fade mitigation comes at the expense of higher satellite resource consumption, such as bandwidth and power. An accurate estimation of these resources is essential for the satellite service providers’ overall service pricing. However, the spatial correlation of rain fade introduces a high level of model complexity, such that its impact on resource consumption is currently unknown. This paper proposes a satellite resource dimensioning process that accounts for such a correlation. Firstly, broadband satellite networks and service level agreements are introduced along with existing dimensioning techniques. Then, a resource demand model is presented, from a single terminal to a system-wide point of view. Subsequently, the satellite resource dimensioning problem is formulated as a quantile estimation problem. A Monte Carlo process is proposed to solve the problem for a given relative confidence interval using spatially correlated rain fade sample generators. Finally, residential and enterprise broadband satellite simulation scenarios are thoroughly presented and numerical results are provided. Comparing these results against optimistic (independent) and pessimistic (fully correlated) rain fade assumptions found in existing dimensioning techniques, the proposed method shows that these techniques can underestimate by up to 70% or overestimate by up to 60% the satellite resource consumption.

Due to the capability to perform participation factor analysis and oscillation origin location, the state-space model (SSM)-based eigenvalue method has been widely used for stability assessment of inverter-penetrated power systems. However, possible internal confidentiality of inverters impedes the derivation of their SSMs. In addition, conventional derivation procedure of system SSM can be tedious when complicated transmission network topology and various transmission cables are involved, which may result in a high-order system SSM. To this end, this article presents a black box-based incremental reduced-order modeling framework. The reduced-order SSMs of the inverters and transmission cables are extracted from their dq-domain admittance frequency responses and abc-domain impedance frequency responses, respectively, by the matrix fitting algorithm. Then, the SSM operators proposed in this article recursively assemble the fitted components’ SSMs in the similar manner as the impedance model operator-based recursive components’ impedance aggregation, while preserving the dynamics of individual components. Simulation results show that the presented state-space modeling framework can properly identify the state-space models of black-box devices at component modeling stage, simplify assembling procedure at subsystems/components integration stage, and release computational burden at system participation factor analysis stage.

Passage of vehicles across the links between IoT-devices severely hampers low-power communication activities. In an urban area, deployment of an IoT-edge system, hence, gets heavily affected because of the traffic-congested roads. In order to achieve reliable delivery despite the presence of such vehicular obstructions, the IoT-devices need to make multiple repeated transmissions at the link layer or even frequently change the communication path in the network layer. However, such actions significantly drain the energy in the IoT-devices and shorten their life-span. This serious issue, although has been pointed out in several works, has not been yet addressed seriously from the point of view of the mitigation of the problem. In this work we first provide a detailed study of the dynamics of the traffic affected links. Next, we propose a lightweight sensor-assisted data-collection strategy SenseCollect, which first classifies the time periods as bad and good based on the presence and absence, respectively, of the vehicular congestion across the communication links. Furthermore, the strategy takes a delay tolerant approach which exploits the good period, i.e., when the vehicular congestion is comparatively less, for low-power communication more compared to the other (bad) periods. In addition, in order to manage emergency situations we also accommodate scopes for faster and more reliable delivery of the data which are marked as urgent by the corresponding source node. We implement the complete system with the said objectives in the real devices. Evaluation studies depict that SenseCollect achieves data collection with reliability above 99% while saving upto 60% of radio duty-cycle compared to the state-of-the-art strategies when communication links are seriously affected by vehicular traffic.

This work develops a novel observer-based finite-time methodology to make disturbed wheeled mobile robots (WMRs)  follow a desired reference signal. With the kinematic and reference models, two decoupled systems are computed. Then, each subsystem’s control signal is split into three terms. The first part estimates the disturbances affecting the system, transforming the closed-loop system into two new second-order systems with uniformly bounded and convergent disturbance terms. Then, based on a new sliding surface and a design function, the second and third controller’s parts are employed to accomplish the trajectory tracking task despite disturbances. The proposed controller is compared against a finite-time and a feedback controller. Then, detailed numerical simulations show the outstanding performance of the novel proposal.

There is unrealized potential in using automation to alleviate the visual inspection associated with non-destructive testing in manufacturing facilities. The identification of defects during the production can help avoid substantial manufacturing errors by indicating that preventative maintenance should be introduced. The use of an autoencoder for this application reduces the need to generate datasets for various defect types, instead only one training dataset would be needed. To address this, this paper proposes a Convolution Neural Network (CNN) autoencoder approach to detect surface defects on cast components during the production. The proposed method categorizes the data into damaged and undamaged components by clustering based on the loss associated with the reconstructed image. The average F1-score and accuracy from retraining the model 10 times was 89.14% and 88.52% respectively. Although previous studies have obtained higher metrics, they have focused their efforts on supervised training techniques where as this research proposes an unsupervised training method with results comparable to the previous studies.

This survey paper compiles and benchmarks the performance of integrated magnetic sensors, in terms of their noise and full-scale magnetic field. In total, we collected performance metrics of 31 sensors realized in various technologies. For each sensor, we derived a noise model to estimate the noise under the same conditions. We obtained a comprehensive benchmark with a focus on integrated devices. We also measured the noise spectrum of 11 of those sensors to confirm our noise model. Given that for many emerging applications, such as tactile, current, torque and biomagnetic sensing, the signal of interest is the field gradient to reject magnetic stray fields, we extended the benchmark to gradiometers. We developed a low-noise measurement setup and a low-field gradient (full scale: 10 μT/mm) generation setup to characterize magnetic gradiometers in this regime. The paper discusses the key sensor trade-off between precision and range, and summarizes the technology trends graphically on a trade-off curve, providing broad insight. This paper has been ACCEPTED for publication in IEEE Access.

This paper presents a novel feature-line points tracking algorithm designed to be highly efficient and accurate for real-time monocular line-based SfM applications in man-made environments. The proposed algorithm exploits feature-line points proprieties to detect and extract long line segments and ensure their temporal evolution in the subsequent frames while handling line segment detection and tracking issues such as noisy images, over-segmentation, edge thresholding, false detections, occlusions, and ambiguous matches. To fulfill the real-time and embeddability constraints of our proposed method, we adopt the hardware/software co-design to achieve a suitable implementation with a scalable FPGA-based embedded heterogeneous architecture. Based on this, experimental results show the portability of the line segment detector block of this proposed algorithm on scalable FPGA-based embedded heterogeneous architectures, improving the acceleration of the sequential execution by about 98\%, whereas a 53\% improvement with GPU-based heterogeneous architectures. For qualitative and quantitative assessment, we exhibit extensive benchmarking with other state-of-the-art algorithms regarding feature-line segment extraction and tracking, showing the efficiency of the proposed algorithm and ensuring very hopeful real-time performance using two common datasets.

With the increasing connectivity of critical infrastructures through wireless technologies, the importance of edge computing is increasing many-folds. Edge computing has become an important phenomenon combining the strengths of distributed computing technologies with those of telecommunication technologies. Therefore, new technological breakthroughs are happening in the realm of edge computing for critical environments, such as the next generation underground and open-pit mining. Since the mining technologies are moving at faster pace towards digitization leveraging on connectivity technologies, edge computing plays a crucial role in bringing computing and connectivity into mines to provide latency- and security-critical services on site. In this article, we study how edge computing fulfills the needs of critical environments, focusing on mining environments, and provide important insight into future research directions.