The 2x-thru calibration often faces the problem that at the highest measurement frequency fmax, the calibrated results exhibit spurious ripples or spikes that are clearly unphysical such as violating passivity. Though being a common phenomenon, it has attracted little attention in the literature. In this paper, the cause of this problem is identified, and a solution is proposed which is to pre-extrapolate the S parameters before carrying out 2x-thru algorithm. Meanwhile, a novel extrapolation method, called the Sun-Earth-Moon (SEM) fitting, is proposed, which is shown to be able to provide smooth extension of S parameters for several GHz beyond fmax. The effectiveness of SEM extrapolation on reducing the spurious response at fmax is demonstrated by measurement examples. The results also indicate that with extrapolation, the calibration accuracy can be slightly improved even at lower frequencies. Comparison with three commercial 2x-thru tools reveals that this near-fmax problem shows up also in the industry-standard tools, indicating that the proposed method has the potential of advancing the state-of-the-art.Â

For wideband transceivers operating at millimeter wave and terahertz frequencies, the implementation of conventional digital predistortion for nonlinearity mitigation faces significant challenges due to the limited availability and/or complexity of high-speed digital signal processing. In this paper, a simple received signal model is proposed for wideband system with nonlinearity and other practical impairments, such as transmitter (Tx) and receiver (Rx) I/Q imbalances (IQIs), carrier frequency offset (CFO), and phase noise, to enable low-complexity impairment mitigation. An expanded memory polynomial (EMP) model is firstly proposed to capture Tx IQI and the nonlinearity over the entire transceiver chain. Exploiting the CFO and a novel transmission protocol, a blind Rx IQI estimation is also proposed. The noise enhancement after Rx IQI and CFO compensation is then evaluated as a noise factor related to the mean-square-error of the Rx IQI estimation. As a result, the received signal of the wideband system is finally modelled as an EMP plus additive noises followed by a band-limited noisy receiver filter. Simulation results using a millimeter wave system with 2.5 GHz bandwidth and 73.5 GHz carrier frequency are presented to verify the accuracy of the EMP modelling and validate the theoretical analyses.

Induction machines (IMs) are widely used in many applications, e.g., electric vehicles or industrial automation, which motivates efficiency-optimal operation for the sake of energy and cost savings. However, a loss-minimal IM control leads to reduced flux operation at partial load, i.e., achievable torque dynamics after load steps are physically limited due to the IMâ\euro™s large rotor time constant. Hence, designing the IM torque control for minimal settling time is of particular importance to allow both an efficient and sufficiently dynamic drive operation. Against this background, a model predictive control (MPC) framework is proposed, which utilizes a precise model of the IM covering magnetic saturation, iron losses, skin effect and thermal influences. As this model is highly nonlinear, it is iteratively linearized along the predicted control trajectory so that a computationally efficient quadratic program can be defined for the MPC problem. To enable sufficiently long prediction horizons, a hierarchical control structure with the stator currents as the actuation variables of the model predictive torque control is utilized. Thanks to this scalable control approach, the proposed framework can be easily extended to multi-machine drive systems covering higher-dimensional problem spaces. Empirical tests validate the feasibility of the proposed approach both for single as well as multi-machine drive applications.

A document by Anton Krynkin . Click on the document to view its contents.

Wideband millimeter wave and terahertz systems face severe nonlinearity and other practical impairments such as transmitter and receiver I/Q imbalances (IQIs), carrier frequency offset, and phase noise. Based on a simplified yet effective received signal model, this paper proposes a low-complexity digital post-cancellation (DPC) framework for transmitter IQI and overall system nonlinearity mitigation. The nonlinearity parameters with reduced nonlinearity order are firstly estimated with low-complexity using a novel transmission protocol incorporating both frame rotation and preamble power scaling. Through widely linear system equalization and interference cancellation, the signal distortion caused by frequency-dependent IQI and nonlinearity are then mitigated with significant performance improvement. The mean-squared-error measurement of the EMP-modelled signals also provides a practical means for the nonlinear system identification and characterization. Both simulation and experiment results obtained from a millimeter wave system with 2.5 GHz bandwidth and 73.5 GHz carrier frequency are presented to verify the theoretical analyses and demonstrate the effectiveness of the DPC technology.

The material is dedicated to various aspects of graphene metasurface implementation of the Reconfigurable Intelligent Surface (RIS) in the framework of the incoherent paradigm for NOMA-RIS-MIMO transmission design. Digital coded model of the metasurface is considered in the framework of the general representation of the graphene metasurface as a planar antenna array (PA). The simplification of the PA in the form of the non-uniform ULA (n-ULA) array and uniform linear array (ULA) is explained as well. In this regard array orthogonalization was applied in the form of the universal Prolate Spheroidal Wave Functions (PSWF) eigenbasis and it was used for the formulation of the RIS controller algorithm.

Learning in sequential decision making problems can be significantly affected by the choice of reward function. Although crucial for learning success, reward function design remains a complex task, requiring expertise and not always aligned with human preferences. Large Language Models (LLMs) offer a promising avenue for reward design through textual prompts that leverage the prior knowledge and contextual reasoning of LLMs. Despite their potential, LLM responses lack guarantees and their reasoning abilities are poorly understood. To mitigate potential LLM errors, we introduce an alternative approach: learning a new reward function utilizing LLM outputs as auxiliary rewards. This problem is tackled through a bi-level optimization framework, showcasing the method’s proficiency in both optimal reward acquisition and adaptive reward shaping. The proposed approach demonstrates robustness and effectiveness, offering a novel strategy for enhancing reinforcement learning outcomes.     Â

We explore essential factors pertaining to the spatial directivity of quantum radiating source systems (QSSs), encompassing quantum antennas and quantum sensors. Our primary focus is on their capacity to control the emission of photons in specific spatial directions. We present a comprehensive definition of quantum directivity, drawing inspiration from Glauberâ\euro™s photon detection theory. This definition closely parallels the framework of analogous concepts in classical antenna theory. By conducting thorough conceptual and mathematical analysis, we address the challenge of characterizing the directive properties of a general QSS. Essentially, our approach presents a computational model that relies solely on the radiation field operatorâ\euro™s density as input.

Edge/Fog computing recently emerged as a novel distributed virtualized computing paradigm, where cloud services are extended to the edge of the network, thereby increasing network capacity and reducing latencies. In fog computing, applications are composed of {\it microservices} that are mapped to edge computing and communication devices ({\it fog nodes}). A crucial component in fog computing is placement algorithms that assign microservices to fog nodes since they determine the overall system performance in terms of energy consumption, communication costs, load balancing, and others. Placement strategies devised for cloud computing are generally centralized and, therefore, not well suited for decentralized fog systems. In this paper, we consider the joint optimization of two conflicting objectives, energy consumption at fog nodes and communication costs of applications, as a game between fog nodes and applications where each agent is modeled to control one objective. We follow a Markov approximation method for the design of a fully distributed autonomic service placement strategy without central coordination or global state information. Evaluation results show that the new approach provides a more optimal solution as compared to previous autonomic placement algorithms.

This work addresses symbol detection in timevarying Massive Multiple-Input Multiple-Output (M-MIMO) systems. While conventional symbol detection techniques often exhibit subpar performance or impose significant computational burdens in such systems, learning-based methods have shown potential in stationary scenarios but often struggle to adapt to nonstationary conditions. To address these challenges, we introduce a hierarchy of extensions to the Learned Conjugate Gradient Network (LcgNet) M-MIMO detector. Firstly, we present Preconditioned LcgNet (PrLcgNet), which incorporates a preconditioner during training to enhance the uplink M-MIMO detectorâ\euro™s filter matrix. This enhancement enables the detector to achieve faster convergence with fewer layers compared to the original, nonpreconditioned approach. Secondly, we introduce an extension of PrLcgNet, known as the Dynamic Conjugate Gradient Network (DyCoGNet), specifically designed for time-varying environments. DyCoGNet leverages self-supervised learning with forward error correction, enabling autonomous adaptation without the need for explicit labeled data during training. It also employs metalearning, facilitating rapid adaptation to unforeseen channel conditions. Our simulation results demonstrate that PrLcgNet achieves faster convergence, lower residual error, and comparable symbol error rate (SER) performance to LcgNet in stationary scenarios. Furthermore, in the time-varying context, DyCoGNet exhibits swift and efficient adaptation, achieving significant SER performance gains compared to baseline cases without metalearning and online self-supervised learning.

HPC (high-performance computing) plays a more important role now because of AI training needs for big models. The latest HPC requirement is to scale up to fifty thousand processors (or servers). HPC is based on the direct memory exchange between processors that is typically called â\euroœinterconnectâ\euro?. Historically, it was a big scientific challenge to define a topology for interconnect due to the low scalability of switching elements. Initially, on-board switching ASICs supported below 10 directions/buses which creates a challenge to scale even to hundreds of processors. Different topologies invented at that time (like Torus) have many hops for the traffic that greatly increase cost and latency but decrease reliability. Moreover, many compromises resulted in the requirement for applications to be engineered in such a way as to distribute traffic unequally inside and between local groups. Later, Infiniband external switches greatly improved the scalability to dozens of interfaces permitting to creation of much more scalable topologies (like Dragonfly) with less number of hops. Infiniband considerably improved latency and cost. Yet, the scale of Inifiband still pushes for restrictions (like performance) that are far from optimal. The latest generation of Ethernet switches achieved the scale (hundreds of interfaces) that is enough to create topologies with minimal compromise or even without any compromise at all: the minimal number of hops that results in minimal cost and latency, maximum reliability, wire-speed or close to it (regulated at design time), no need for network load distribution at the application layer. The scale without any compromise could satisfy the biggest AI training requirements. The scale with minimal compromises could be 8 times high then needed now.

This paper proposes a look-ahead, fault-tolerant and congestion-aware routing algorithm for Networks-on-Chip (NoCs). It helps in reducing the throughput degradation and increase in average delay, by properly selecting the ports at each node of the network. To avoid dead lock and live lock situations, necessary turn restrictions have been adopted, while selecting the output ports of the nodes. Performance comparison of the proposed routing algorithm has been made for the parameters like throughput degradation, average delay increase, and area. For fault free scenarios, the proposed algorithm performs at par with the state-of-the-art routing algorithms in terms of average packet latency and throughput. For faulty scenarios, with 20\% fault rate in an 8$\times$8 mesh NoC, the proposed routing algorithm improves the throughput degradation by at least 5.85$\times$, compared to benchmark routing algorithms, such as DyAD. Compared to many state-of-the-art fault-tolerant and congestion-aware routing algorithms, the proposed approach consumes less area.

Monkeypox has recently emerged as a public health emergency with rising cases worldwide. Early clinical diagnosis is challenging due to symptom overlap with other diseases, but characteristic skin lesions provide distinguishing visual cues. This work proposes a deep convolutional neural network (CNN) tailored for automated monkeypox screening from lesion images. A dataset of over 3000 dermatological images was compiled, with data augmentation to enhance diversity. The CNN architecture comprised convolutional blocks for feature extraction and dense layers for classification. Rigorous training and cross-validation were conducted over 100 epochs to optimize model performance. On an unseen test set, the model achieved 86.87\% accuracy in classifying monkeypox lesions, with 94\% precision, 79\% recall and 86\% F1-score. These metrics were better than baseline models, indicating reliable screening potential. Though the model overlooked some atypical presentations, successes showcase utility for mass case-finding. As monkeypox monitoring intensifies, robust computer vision approaches can assist clinicians through explainable, real-time forecasts. Prospective validation across demographics and integration with clinical workflows is warranted before full-scale deployment. Overall, the study demonstrates deep learning’s promise in tackling the monkeypox outbreak through enhanced diagnosis.

A modular voltage equalizer can be used in a wide variety of applications without changing the module design. However, the modularization of an equalizer topology often involves slow inter-module equalization, additional energy loss, and cost. A study on available modularization techniques shows that the transformer-based equalizers offer simple and low-cost modularization methods. Conversely, the transformer-less equalizers can offer more compact design, but their modularization methods suffer from either high complexity and loss, or slow inter-module equalization speed. In this work, one two-winding transformer is introduced into a transformer-less equalizer module to leverage the modularization benefits of transformer isolation while still achieving a more compact design than transformer-based equalizers. The proposed direct modularization method achieves fast inter-module equalization and fixed design for a variety of applications with a small increase in cost and size. The proposed method is implemented on a transformer-less equalizer and its effectiveness is validated experimentally with a three-module prototype.

With the rising surge of online toxicity, automating the identification of abusive language becomes crucial for improving online discourse. This study proposes a deep learning system that efficiently uses multiple labels to classify harmful comments using bi-directional Long Short-Term Memory (LSTM) networks. By leveraging contextual information, the bi-LSTM model achieves state-of-the-art performance in classifying subtle forms of toxicity such as threats, insults, identity hate, and obscenity. The model achieves above 95\% accuracy on benchmark datasets with rigorous data processing, optimized neural architecture, and the utilization of FastText embeddings to handle words that are not in the vocabulary. This technique can automatically filter different levels of toxicity, promoting positive online interactions when integrated into online platforms. The proposed study outlines an end-to-end pipeline incorporating recent NLP advancements and deep contextualized language models to address contemporary challenges in AI-enabled content moderation.

In this paper, we develop an economic mechanism for the DSO to exploit the flexibility of the LEM and integrate it into the techno-economic framework for coordinated procurement of DF supported by the novel active network technologies, in this case SOPs. The LEM flexibility mechanism independently clears the LEM, while respecting the network constraints and optimizing network operation cost by leveraging it with the social welfare of the LEM participants. The coordinated procurement framework is based on the bi-level programming approach, where the lower level ensures unconditional clearing of the LEM bids and offers, while the upper level minimizes network operational cost, subject to network constraints and DF originating from all three types of flexibility (i.e., network, contracted, and LEM). Both, the mechanism and the framework can be applied by the DSO to ensure secure and economic network operation while facilitating LEM clearing in the independent manner. In the numerical study, the framework has been applied to IEEE 33-bus benchmark system, where we investigate the performance of the coordination method proposed, evaluate the value of the LEM flexibility mechanism to the DSO, and the value of the novel active network technology (i.e., SOP). Using realistic flexibility demand and supply data, the numerical study showed that the optimal utilization of network and LEM flexibilities can lead to the DSO contracted flexibility service to become unnecessary. While the estimated value of coordinated procurement of the LEM and DSO contracted flexibilities accounts for 26.6% of the network operation cost, technology supported procurement of the LEM flexibility only can reduce the original network operation cost almost twice (by 48.9%).

Ransomware, a formidable cybersecurity challenge, has escalated in prevalence and sophistication, impacting individuals, organizations, and infrastructures globally. This article delves into the operational mechanisms, progression, and tactics of ransomware, particularly focusing on locker, cryptoransomware, and data theft ransomware. It highlights the significant effects of these assaults across various sectors and emphasizes the pivotal role of knowledge dissemination and educational efforts in ransomware defense. The article encompasses an examination of preventative measures, appraisal of awareness campaigns, and educational initiatives, along with a critical assessment of challenges and future directions in ransomware mitigation. It argues that public awareness and educational programs are not merely supplementary but fundamental aspects of a comprehensive cybersecurity strategy. This piece contributes to the ongoing conversation in cybersecurity, providing insights and recommendations for policymakers, professionals, educators, and the public, and emphasizes the necessity of a well-informed and proactive approach in combating ransomware threats.

The incidence angle dependence of synthetic aperture radar (SAR) backscatter from sea ice has been observed to differ between ice types. This is indicative of its complex dielectric and backscattering properties and cannot be analytically determined or easily modelled. Without assumptions this dependence can only be measured from SAR through multiple observations of the same ice. Due to the sea ice drift, doing so at high resolution is time inefficient and to some extent only feasible for land fast ice. Thus, the incidence angle dependence can currently not be easily related to ground measurements or be fully exploited for classification tasks. By reformulating the problem of incidence angle dependence to a domain transfer task and making use of known physical relations, we can train a Generative Adversarial Network (GAN) to predict the incidence angle dependence from a single Sentinel-1 satellite scene patch-wise using no only local data. The network’s predictions are validated for multiyear and first-year ice backscatter using coincident ICESat-2 altimeter measurements. The network suggests a diverse incidence angle dependence in the HV channel, that so far have been observed only scarsely. The results of this study are a novelty in sea ice remote sensing insofar that artificial intelligence manages to solve a task that would be nearly impossible for a human observer.