Electric vehicles (EVs) are playing a pivotal role in transportation systems to conform to the rising exigencies for enhanced performance with safety and hindered environmental impact. Thus, to improve the efficiency and downsize the maintenance cost of EVs, an early fault diagnosis (FD) framework is crucial. Bearing and stator winding faults, which account for approximately 78% of induction machine (IM) incipient faults in EVs, remain rather elusive for conventional sensors to accurately classify them and their incipient values. To this end, this paper presents a novel Attention-enhanced Autoencoder-Gated Recurrent Unit (AAGRU) model that improves the accuracy and efficiency of fault analysis in IMs. Moreover, since bearing faults are usually captured through vibration sensors, which are expensive and require direct coupling with the IM, a hybrid signal re-constructor is devised based on merely stator current signals. The proposed model leverages Empirical Mode Decomposition (EMD), Fast Fourier Transform (FFT), and Discrete Wavelet Decomposition (DWT) to process the electric current data, which are then used by the AAGRU model to identify, detect, and classify the fault patterns. Experimental results demonstrate that the proposed model offers a 6%-20% improvement in fault detection and an 8%-28% improvement in inter-turn shortcircuit fault severity classification relative to different shallow and deep-based benchmark models. The proposed model was also tested on different load conditions to ensure its applicability in practice.

We present a wideband rectifying metasurface (RMS) with enhanced system efficiency for wireless power transmission and energy harvesting. The RMS consists of periodic arrays with integrated diodes, with a high input impedance matched with the diodes, thus eliminating the matching network between metasurface (MS) and rectifier. Besides, a unique harmonic feedback network is embedded in each unit cell, rectifying the high-order harmonic generated by the diode repeatedly, improving the total efficiency over a wide bandwidth. The converted DC power is channeled to one single load through the DC-paths formed by the branches of MS and a series of inductors, avoiding additional DC-combination network. The MS can effectively capture EM energy at an operating band of 2-3.5 GHz, and the simulated radiation-AC efficiency is up to 99% at 2.8 GHz. A finite 8 × 8 array is fabricated and measured. The total conversion efficiency reaches a peak value of 60.5% at 2.7 GHz, and is greater than 40% from 2.3 GHz to 2.9 GHz, exhibiting a wide bandwidth of 23.1% compared with existing state-of-the-art studies. The integrated MS-based RF energy harvester with advantages of simple structure is of great significance for wireless power transfer and energy harvesting applications.

The authors of Ref. [1] replied to my comments [2]. Their reply is briefly commented in this Addendum.

This research introduces a hybrid feature extraction approach that combines Linear Discriminant Analysis (LDA) and Multilayer Perceptron (MLP) methods to address the challenges of reducing feature vector dimensionality and accurately classifying smartphone-based human activities. Moreover, to refine activity classification accuracy, Support Vector Machine (SVM) optimization with Stochastic Gradient Descent (SGD) is employed. LDA, a statistical tool, is leveraged to derive a new feature space for data projection, enhancing class separation and test feature label prediction. The proposed approach, named LMSS, was evaluated using the UCI-HAR dataset and compared with state-of-the-art models. The results demonstrate that the proposed approach outperformed the best-performing method over this dataset. It achieved an accuracy rate of 99.52%, precision of 99.55%, recall of 99.53%, and an F1-score of 99.54%, highlighting the effectiveness of the proposed method in accurately classifying the data.

This study presents a novel approach to monitoring respiration rate using frequency modulated continuous wave (FMCW) mm-wave radar, aiming to overcome the limitations of traditional non-contact method. 14 participants were involved in an experiment involving alternating periods of stillness and left-right continuous body shaking. Data analysis involved non-negative matrix factorization (NMF) and template matching techniques. Respiration measured by radar were compared with ground truth measurements. Results demonstrated accurate respiration detection even during continuous body movement, with significant correlation observed between radar-based and ground truth measurements. This study pioneers radar-based respiration monitoring under continuous body movement conditions, offering promising implications for practical applications.

Remote electrical tilt (RET) optimization is a critical aspect of wireless communication networks to achieve efficient coverage and capacity. This paper presents a comprehensive comparison of reinforcement learning (RL)-based agents for RET optimization, exploring various flavors of agents and combinations thereof. We investigate single-step learning and multi-step training approaches, employing either deep Q-network (DQN) or proximal policy optimization (PPO) algorithms, with the agents themselves that either are able to change network state by dictating a single action at a single antenna or dictate actions for multiple cells simultaneously. Additionally, we evaluate the performance of the single-agent and collaborative multi-agent architectures in the context of RET optimization. In other words, we assess learnings of a single agent that learns from 'input-action-reward' cycles for all of the antennas, in comparison to the myopic yet collaborative learnings of its antenna-specific counterparts, which acquire knowledge (i. e., input to action mappings) based on the 'input-action-reward' cycles specific to the respective antennas. To this end, we train the agents through either a series of single-step episodes or the multi-step episodes, shaping their reward function in accordance. Through extensive experimentation and evaluation, we analyze key metrics such as coverage, capacity, and interference reduction to assess the effectiveness of different agent configurations. The findings provide valuable insights into the strengths and weaknesses of each approach, aiding in the selection and design of RL-based agents for optimizing RET in wireless communication networks.

The management of cellular networks and services has evolved due to the rapidly changing demands and complexity of service modeling and management. This paper uses intent-based networking (IBN) as a solution and couples it with contextual information from knowledge graphs (KGs) of network and service components to achieve the objective of service orchestration in cellular networks. This fusion of IBN with KGs facilitates an intelligent, flexible, and resilient service orchestration process. It can anticipate and mitigate issues that impact network performance or service delivery. We propose an intent completion approach using knowledge graph learning and a mapping model capable of inferring and validating the service intents in the network. Subsequently, these service intents are deployed using available network resources in a simulated fifth generation (5G) non-standalone (NSA). The compliance of the deployed intents is monitored, and mutual optimization against their required service key performance indicators is performed using Simultaneous Perturbation Stochastic Approximation (SPSA) and Multiple Gradient Descent Algorithm (MGDA). The simulation results cover various scenarios to discover the performance of the proposed intent processing pipeline for different service requirements and compliance states. The numerical results show that the knowledge graph with Gaussian embedding (KG2E) model outperforms other distance-based embedding models for the proposed service KG. Moreover, the optimal deployment and compliance of mission-critical (MC) intents is ensured for greater than 90% of the independent simulation runs with varying intent arrival times.

Quantum approximate optimization algorithm (QAOA) with layer depth p is promising near-optimum performance and low complexity for NP-hard maximum-likelihood (ML) detection in n × n multi-input multi-output (MIMO) systems. Experimental challenges for ML detection on Noisy Intermediate-Scale Quantum (NISQ) computers arise from accumulated errors with large p and n. Recursive QAOA (RQAOA) is promising with small p by reducing complexity over n steps. In this article, we modify RQAOA for p n with cost sorting and post-selection in m n steps, and then integrate it with majority voting (MV) and successive interference cancellation (SIC) into the QAOA-MVSIC algorithm to tackle experimental challenges. We truncate QAOA circuits to further improve experimental feasibility. Simulations with n = 24 and 12 for BPSK and QPSK modulations, respectively, show near-optimum bit-error rate (BER) with p = 1 and m ≤ 4. Truncated version requires O(m n p) quantum and O(m n2 ) classical operations with low complexity. We experimentally implement QAOA combined with MV (QAOA-MV) for n ∈ [17, 64] in IBM Eagle processor by observing superior performance of QAOA-MV over QAOA and reducing problem dimensions by at least n/4. We generalize QAOA as cost-restricted uniform sampling (CRUS) oracle and approximately simulate for n ≤ 128 to obtain comparison benchmark for future QAOA experiments.

This study explores the challenges of frequency instability in isolated island power systems predominantly powered by fluctuating renewable sources like solar panels and wind turbines. These non-interconnected islands with a high reliance on renewables are prone to significant frequency fluctuations due to the variable nature of renewable energy sources and their inherently low inertia. The academic community has identified fast-reacting energy storage systems, such as batteries and pump storage, as potential solutions to offset these frequency variations through rapid frequency response services during power imbalances. However, simulations conducted in this paper, suggest that storage systems might struggle to effectively counter severe frequency excursions. This limitation stems from inherent delays in measurement, computation, and communication within their control systems. To improve the stability of these renewable-heavy, non-interconnected islands, we propose a viable approach: integrating renewables behind the energy storage systems. This method totally decouples renewables from the grid, ensuring that the network frequency and the rotational speed of thermal generators remain unaffected by abrupt changes in renewable power output. Adopting this strategy could facilitate the integration of additional renewable energy sources in non-interconnected islands without compromising the stability of their power systems.

In recent years, the proliferation of short video streaming platforms such as TikTok, Instagram Reels, and YouTube Shorts has not only captured global attention but also significantly altered the landscape of media consumption. These platforms, leveraging advanced technological frameworks and sophisticated algorithms, have created a new paradigm in which content is not only consumed but also produced and shared by users from diverse backgrounds. This paper aims to dissect the multitude of research trends encompassing the technological advancements, user engagement strategies, sociocultural impacts, and regulatory challenges associated with short video streaming. It highlights how these platforms harness powerful video compression technologies and artificial intelligence to deliver personalized content to users, thereby enhancing user experience and engagement. Additionally, the paper explores the sociocultural ramifications of these platforms, examining their role in global cultural diffusion and the shaping of social behaviors. It also addresses the pressing ethical and regulatory landscapes that govern content dissemination and privacy. By integrating various research findings, this paper presents a comprehensive analysis of the current state and potential future developments in short video streaming, providing insights into its continued influence on global digital culture and media consumption trends.

A perturbation method is used to obtain accurate analytic formulas for both gain (or loss) in transverse magnetic and transverse electric modes for symmetric and asymmetric three-layer waveguides. These analytic formulas show excellent agreement with results obtained from an accurate numerical approach. A universal definition of the modal gain confinement factor, applicable to both TE and TM modes is derived. For TE modes, the modal gain confinement factor is always < 1. The modal gain of a waveguide can be larger than the gain of a plane wave traveling in a medium with the same gain if the gain is only in the middle layer (for both TE and TM modes), and in some TM cases, with gain only in an evanescent layer.

A document by Neha Sharma. Click on the document to view its contents.

This paper proposes a novel coreless multiphase magnetic resonant motor (MMRM) with a high torque per unit volume density (77.6 Nm/m 3) based on the properties of magnetic resonance coupling. In the proposed model, the conventional magnetic circuit in electric motors is replaced with that of a resonant circuit. Resonant capacitors are connected across the separate multiphase windings to produce the required resonance behavior in the machine. Analytical models of the machine's characteristics, such as inductance, generated torque, and capacitance estimation are derived and verified with an equivalent simulation model through finite element methods. The key is to efficiently integrate electric machine design concepts with inductive magnetic resonance coupling technology to enhance the output torque in an air-cored machine. The motor consists of a two-phase multiphase stator and rotor windings arranged radially on non-magnetic cores made from reinforced plastic fiber. Finally, a prototype is manufactured, and the effectiveness of the MMRM is validated by experimental results. To evaluate the significance of the proposed design, a case-control experiment without resonant capacitors is set up. The proposed MMRM achieved improved performance compared to related resonant induction machines.

A two-coil wearable system is proposed for wireless communication and powering between a transmitter coil in a necklace and a receiver coil in a smart contact lens, where the necklace is invisible in contrast to coils embedded in wearables like spectacles or headbands. Magneto-quasistatic(MQS) field coupling facilitates communication between the transmitter in the necklace and the contact lens receiver, enabling AR/VR and health monitoring. As long as the receiver coil remains within the magnetic field generated by the transmitter, continuous communication is sustained through MQS field coupling despite the misalignments present. Resonant frequency tuning enhances system efficiency. The system's performance was tested for coil misalignments, showing a maximum path loss variation within 10dB across scenarios, indicating robustness. Finite Element Method(FEM) analysis has been used to study the system for efficient wireless data transfer and powering. A communication channel capacity is 4.5M bps over a 1M Hz bandwidth. Simulations show negligible path loss differences with or without human tissues, as magnetic coupling remains unaffected at MQS frequencies below 30M Hz due to similar magnetic permeability of tissues and air. Therefore, the possibility of efficient communication and powering of smart contact lenses through a necklace is shown for the first time using resonant MQS coupling at an axial distance of 15cm and lateral distance of over 9cm to enable AR/VR and health monitoring on the contact lens.

In this work, spatially-discrete traveling-wave modulation (SDTWM) is applied to an electrically small antenna (ESA) to increase its efficiency-bandwidth product. The antenna is formed of closely coupled sectors and is rotationally symmetric. It supports a fundamental radiating mode as well as non-radiating modes that are analogous to discrete orbital angular momentum modes. Time-modulated capacitors, placed in each sector, couple these modes using SDTWM. The modulation scheme is similar to that of a classic negative-resistance parametric amplifier, but instead of using the modulation to obtain gain from the system, the modulation parameters are chosen to enhance the matching bandwidth of the antenna. We show how this can be done by choosing an appropriate combination of the modulation depth and modulation frequency. After extending the definition of radiation efficiency and mismatch loss to the case of a parametrically time-varying antenna, the extended definitions are used to characterize the simulated performance of the time-varying antenna under two different SDTWM modulation schemes. The first modulation scheme couples the radiative mode to a single non-radiative mode, while the second modulation scheme couples the radiative mode to two different non-radiative modes. Both schemes yield efficiency-bandwidth products that are 6.4-7.6 dB larger than that of the time-invariant version of the antenna.

Birch and Swinnerton-Dyer Conjecture can be understood with uniqueness provided by continued fractions.

Although several machine learning driven solutions are deemed to be effective at detecting data breaches, the recent proliferation in data breach incidents resulting from cyber attacks demands an updated, thorough analysis of machine learning (ML) based data breach countermeasures to identify research gaps and guide future studies. In view of this, this study employs a systematic approach and draws insight from 81 research articles to classify machine learning based data breach countermeasures using six criteria namely learning tasks, learning classifiers, proactive learning strategies, feature engineering methods and multimodal approaches. In classifying the studies, we: (a) propose a taxonomy of feature extraction and representation to classify studies using ten sub-criteria, (b) identify proactive learning techniques to categorise studies using four sub-criteria including self labelling, data augmentation, automated feature extraction and re-training, (c) classify multimodal machine learning approaches used in the studies into three fusion sub-criteria: namely early fusion, intermediate fusion and late fusion. To aid the literature identification, we analyse forty recent incidents and obtain prevalent cyber attack vectors of data breaches, which we present as the general workflow for data breaches due to cyber attacks. Finally, we highlight the research issues associated with existing ML-based data breach countermeasures and recommend future research directions.

Transformer architectures have revolutionized natural language processing through their ability to handle longrange dependencies and generate contextually coherent text. Despite their impressive capabilities, these models frequently produce outputs that are not directly grounded in the input data, a phenomenon known as hallucination. The paper posits that hallucination is not merely a flaw but a mathematical necessity for enabling improvisation within transformer-based large language models. Through rigorous theoretical analysis, the relationship between hallucination and improvisation is explored, demonstrating that the probabilistic nature of language generation inherently leads to outputs extending beyond the training data. The proofs and lemmas presented substantiate the claim that hallucinations are essential for the model's ability to generate novel and creative responses. The research further delves into the implications of this necessity, suggesting a reevaluation of performance metrics and training strategies to balance accuracy with creativity. The findings highlight the intricate dynamics between hallucination and improvisation, providing foundational insights into the design and evaluation of transformer-based models.