Electronics, Volume 13, Issue 11 (June-1 2024) – 186 articles

Ocean observation is the first step in the development of the ocean, whose abundant resources and strategic significance are attracting increasing attention. Observation methods based on visual sensor networks have received great attention from researchers due to their visualization capability and high information capacity. However, below the sea surface, objective factors such as blurriness, turbulence, and underwater color casting can cause image distortion and affect the acquisition of images. In this paper, the enhancement of underwater images is tackled using an adversarial learning-based approach. First, pre-processing is applied to address the significant color casting in the dataset, thus enhancing feature learning for subsequent style transfer. Then, corresponding improvements are made to a generative adversarial network’s structure and loss functions to better restore the features of the network output. Finally, evaluations and comparisons are performed using underwater image quality assessment metrics and several public datasets. Through multidimensional experiments, the proposed algorithm is shown to exhibit excellent performance in both subjective and objective evaluation metrics compared to state-of-the-art algorithms, as well as in practical visual applications. Full article

Concept drift describes unforeseeable changes in the underlying distribution of streaming data over time. Concept drift is a phenomenon in which the statistical properties of a target domain change over time in an arbitrary way. These changes might be caused by changes in hidden variables that cannot be measured directly. With the onset of the big data era, domains such as social networks, meteorology, and finance are generating copious amounts of streaming data. Embedded within these data, the issue of concept drift can affect the attributes of streaming data in various ways, leading to a decline in the accuracy and performance of models. There is a pressing need for new models to re-adapt to the changes in streaming data. Traditional concept drift detection algorithms struggle to effectively capture and utilize the key feature points of concept drift within complex time series, thereby failing to maintain the accuracy and efficiency of the models. In light of these challenges, this study introduces a novel concept drift detection method that incorporates a temporal attention mechanism within a prototypical network. By integrating a temporal attention mechanism during the feature extraction process, our approach enhances the capability to process complex time series data, preserves temporal locality, strengthens the learning of key features, and reduces the amount of labeled data required. This method significantly improves the detection accuracy and efficiency of small sample streaming data by better capturing the local features of the data. Experiments conducted across multiple datasets demonstrate that this method exhibits comprehensive leading performance in terms of accuracy and F1-score, with excellent recall and precision, thereby validating its effectiveness in enhancing concept drift detection in streaming data. Full article

Currently, weed control robots that can accurately identify weeds and carry out removal work are gradually replacing traditional chemical weed control techniques. However, the computational and storage resources of the core processing equipment of weeding robots are limited. Aiming at the current problems of high computation and the high number of model parameters in weeding robots, this paper proposes a lightweight weed target detection model based on the improved YOLOv8 (You Only Look Once Version 8), called RVDR-YOLOv8 (Reversible Column Dilation-wise Residual). First, the backbone network is reconstructed based on RevCol (Reversible Column Networks). The unique reversible columnar structure of the new backbone network not only reduces the computational volume but also improves the model generalisation ability. Second, the C2fDWR module is designed using Dilation-wise Residual and integrated with the reconstructed backbone network, which improves the adaptive ability of the new backbone network RVDR and enhances the model’s recognition accuracy for occluded targets. Again, GSConv is introduced at the neck end instead of traditional convolution to reduce the complexity of computation and network structure while ensuring the model recognition accuracy. Finally, InnerMPDIoU is designed by combining MPDIoU with InnerIoU to improve the prediction accuracy of the model. The experimental results show that the computational complexity of the new model is reduced by 35.8%, the number of parameters is reduced by 35.4% and the model size is reduced by 30.2%, while the mAP₅₀ and mAP_50-95 values are improved by 1.7% and 1.1%, respectively, compared to YOLOv8. The overall performance of the new model is improved compared to models such as Faster R-CNN, SSD and RetinaNet. The new model proposed in this paper can achieve the accurate identification of weeds in farmland under the condition of limited hardware resources, which provides theoretical and technical support for the effective control of weeds in farmland. Full article

Tensor-based multi-view spectral clustering methods are promising in practical clustering applications. However, most of the existing methods adopt the ${\ell }_{2,1}$ norm to depict the sparsity of the error matrix, and they usually ignore the global structure embedded in each single view, compromising the clustering performance. Here, we design a robust tensor learning method for multi-view spectral clustering (RTL-MSC), which employs the weighted tensor nuclear norm to regularize the essential tensor for exploiting the high-order correlations underlying multiple views and adopts the nuclear norm to constrain each frontal slice of the essential tensor as the block diagonal matrix. Simultaneously, a novel column-wise sparse norm, namely, ${\ell }_{2,p}$, is defined in RTL-MSC to measure the error tensor, making it sparser than the one derived by the ${\ell }_{2,1}$ norm. We design an effective optimization algorithm to solve the proposed model. Experiments on three widely used datasets demonstrate the superiority of our method. Full article

Early detection and prevention of stress is crucial because stress affects our vital signs like heart rate, blood pressure, skin temperature, respiratory rate, and heart rate variability. There are different ways to determine stress using different devices, such as the electrocardiogram (ECG), electrodermal activity (EDA), the electroencephalogram (EEG), photoplethysmography (PPG), or a questionnaire-based method of stress assessment. In this study, we proposed a camera-based real-time stress detection system using remote photoplethysmography (rPPG). We trained different machine learning models using three datasets: the SWELL dataset, the PPG sensor dataset, and the last ECG and EEG-based stress dataset. The models with the highest predictive accuracy were used to classify stress based on HR and HRV features obtained from the face using a camera. HR and HRV estimations from the face were validated on the PURE public dataset and the custom dataset. In this study, it was observed that the random forest algorithm performs significantly better than other models, achieving an impressive 99% predictive accuracy in the SWELL dataset. In the second dataset, the logistic regression technique shows the best result, achieving an accuracy rate of 84.24%. In the last dataset, the ensemble model achieved an accuracy rate of 67%. We also checked the proposed algorithm in the process of public speaking to estimate stress in a real-time situation. Full article

Massive multiple input multiple output (MIMO) enabled grant-free random access (mGFRA) stands out as a promising random access (RA) solution, thus effectively addressing the need for massive access in massive machine-type communications (mMTCs) while ensuring high spectral efficiency and minimizing signaling overhead. However, the bottleneck of mGFRA is mainly dominated by the orthogonal preamble collisions, since the orthogonal preamble pool is small and of a fixed-sized. In this paper, we explore the potential of non-orthogonal preambles to overcome limitations and enhance the success probability of mGFRA without extending the length of the preamble. Given the RA procedure of mGFRA, we analyze the factors influencing the success rate of mGFRA with non-orthogonal preamble and propose to use two types of sequences, namely Gold sequence and Gaussian distribution sequence, as the preambles for mGFRA. Simulation results demonstrate the effectiveness of these two types pf non-orthogonal preambles in improving the success probability of mGFRA. Moreover, the system parameters’ impact on the performance of mGFRA with non-orthogonal preambles is examined and deliberated. Full article

To address the critical issue of polarization during lithium-ion battery charging and its adverse impact on battery capacity and lifespan, this research employs a comprehensive strategy that considers the charging duration, efficiency, and temperature increase. Central to this approach is the proposal of a novel negative pulsed charging technique optimized using the Non-Dominated Sorting Genetic Algorithm II (NSGA-II). This study initiates the creation of an intricate electrothermal coupling model, which simulates variations in internal battery parameters throughout the charging cycle. Subsequently, NSGA-II is implemented in MATLAB to fine-tune pulsed charging and discharging profiles, generating a Pareto front showcasing an array of optimal solutions tailored to a spectrum of goals. Leveraging the capabilities of the COMSOL Multiphysics software 6.2 platform, a high-fidelity simulation environment for lithium-ion battery charging is established that incorporates three charging strategies: constant-current (CC) charging, a multi-stage constant-current (MS-CC) charging protocol, and a pulsed-current (PC) charging strategy. This setup works as a powerful instrument for assessing the individual effects of these strategies on battery characteristics. The simulation results strongly support the superiority of the proposed pulsed-current charging strategy, which excels in increasing the battery temperature and amplifying battery charge capacity. This dual achievement not only bolsters charging efficiency significantly but also underscores the strategy’s potential to augment both the practical utility and long-term viability of lithium-ion batteries, thereby contributing to the advancement of sustainable energy storage solutions. Full article

Smart grids are a cornerstone of the transition to a decentralised, low-carbon energy system, which offer significant benefits, including increased reliability, improved energy efficiency, and seamless integration of renewable energy sources. However, ensuring the security and resilience of smart grids is paramount. Cyber attacks, physical disruptions, and other unforeseen threats pose a significant risk to the stability and functionality of the grid. This paper identifies the research gaps and technical hurdles that hinder the development of a robust and secure smart grid infrastructure. This paper addresses the critical gaps in smart grid security research, outlining the technical challenges and promising avenues for exploration by both the industry and academia. A novel framework designed to enhance the reliability and security of smart grids was proposed against cyber attacks, considering the interconnectedness of the physical and cyber components. The paper further explores future research trends and identifies the key open issues in the ongoing effort to strengthen the security and resilience of smart grids. Full article

The anti-electromagnetic interference capability of partial discharge (PD) acoustic signal conversion and collection circuits severely restrict the sensitivity of PD detection. The data acquisition (DAQ) systems available in the current market are costly and have limited functionality, making it difficult to satisfy the acquisition requirements for PD detection. This paper proposes a medium to high-speed fiber optic signal acquisition board with an adjustably controlled sampling rate and filter cutoff frequency. The circuit achieves a higher signal-to-noise (SNR) ratio by distributing the noise in each part of the signal acquisition chain reasonably. The temperature characteristics of the acquisition module are improved by utilizing the programmable T-type structure for transimpedance amplification of photocurrent. The DAQ card performs data acquisition and processing using STM32H743 internal ADC and caches data in bulk with an SRAM and SD card. A data uploading method based on time reference has been proposed, which enables full, effective information signal upload through a low-cost transmission interface. The research ultimately achieves a stable sampling of three channels at 1 MSps, SNR of 63 dB, and programmable gain amplification of the photocurrent with 0–60 dB. Finally, the system is used for PD acoustic signal acquisition in the frequency range of 20 Hz to 100 kHz. Full article

In mobile social networks, users can easily communicate with others through smart devices. Therefore, the protection of user privacy in social networks is becoming a significant subject. To solve this problem, this paper proposes a fine-grained data access control scheme that uses attributes to match friends. In our scheme, the friend-making parties generate friend preference and self-description lists, respectively, realizing attribute hiding by converting friendship preference into ciphertext access control policies and self-description into attribute keys. The social platform matches user profiles to quickly eliminate unmatched users and avoids invalid decryption. In order to reduce the computational burden and communication cost of mobile devices, we adopt an algorithm mechanism for outsourcing decryption. When the user meets the matching conditions, the algorithm outsources the bilinear pair operation with large computation to the friend server. After that, the user finally decrypts the ciphertext. Security analysis shows that our scheme is safe and effective. In addition, performance evaluation shows that the proposed scheme is efficient and practical. Full article

The motor imagery brain-computer interface (MI-BCI) has the ability to use electroencephalogram (EEG) signals to control and communicate with external devices. By leveraging the unique characteristics of task-related brain signals, this system facilitates enhanced communication with these devices. Such capabilities hold significant potential for advancing rehabilitation and the development of assistive technologies. In recent years, deep learning has received considerable attention in the MI-BCI field due to its powerful feature extraction and classification capabilities. However, two factors significantly impact the performance of deep-learning models. The size of the EEG datasets influences how effectively these models can learn. Similarly, the ability of classification models to extract features directly affects their accuracy in recognizing patterns. In this paper, we propose a Multi-Scale Spatio-Temporal and Dynamic Graph Convolution Fusion Network (MST-DGCN) to address these issues. In the data-preprocessing stage, we employ two strategies, data augmentation and transfer learning, to alleviate the problem of an insufficient data volume in deep learning. By using multi-scale convolution, spatial attention mechanisms, and dynamic graph neural networks, our model effectively extracts discriminative features. The MST-DGCN mainly consists of three parts: the multi-scale spatio-temporal module, which extracts multi-scale information and refines spatial attention; the dynamic graph convolution module, which extracts key connectivity information; and the classification module. We conduct experiments on real EEG datasets and achieve an accuracy of 77.89% and a Kappa value of 0.7052, demonstrating the effectiveness of the MST-DGCN in MI-BCI tasks. Our research provides new ideas and methods for the further development of MI-BCI systems. Full article

In this study, a novel composite solder, Sn-3.5Ag-10.0Co, was tailored for transient liquid phase (TLP) bonding in electric vehicle power module integration. Employing a meticulous two-step joining process, the solder joint was transformed into a robust microstructure characterized by two high-melting point intermetallic compounds, Ni₃Sn₄ and (Co,Ni)Sn₂. After 1 h of TLP bonding, the Sn-3.5Ag-10.0Co paste transformed into the IMCs, but voids persisted between them, particularly between (Co,Ni)Sn₂ and Ni₃Sn₄. Voids significantly reduced after 2 h of bonding, with full coalescence of the joint microstructure observed. The joint continued to be densified after 3 h of TLP bonding, but voids tended to accumulate at the joint center. Failure analysis revealed crack propagation through Ni₃Sn₄/(Co,Ni)Sn₂ interfaces and internal voids. The engineered Sn-Ag-Co TLP joint exhibited superior shear strength retention even at an elevated temperature of 200 °C, contrasting with the significant reduction observed in the Sn-3.5Ag control specimen due to remaining Sn. Full article

Several attacks have been proposed against autonomous vehicles and their subsystems that are powered by machine learning (ML). Road sign recognition models are especially heavily tested under various adversarial ML attack settings, and they have proven to be vulnerable. Despite the increasing research on adversarial ML attacks against road sign recognition models, there is little to no focus on defending against these attacks. In this paper, we propose the first defense method specifically designed for autonomous vehicles to detect adversarial ML attacks targeting road sign recognition models, which is called ViLAS (Vision-Language Model for Adversarial Traffic Sign Detection). The proposed defense method is based on a custom, fast, lightweight, and salable vision-language model (VLM) and is compatible with any existing traffic sign recognition system. Thanks to the orthogonal information coming from the class label text data through the language model, ViLAS leverages image context in addition to visual data for highly effective attack detection performance. In our extensive experiments, we show that our method consistently detects various attacks against different target models with high true positive rates while satisfying very low false positive rates. When tested against four state-of-the-art attacks targeting four popular action recognition models, our proposed detector achieves an average AUC of 0.94. This result achieves a 25.3% improvement over a state-of-the-art defense method proposed for generic image attack detection, which attains an average AUC of 0.75. We also show that our custom VLM is more suitable for an autonomous vehicle compared to the popular off-the-shelf VLM and CLIP in terms of speed (4.4 vs. 9.3 milliseconds), space complexity (0.36 vs. 1.6 GB), and performance (0.94 vs. 0.43 average AUC). Full article

In the field of underwater acoustics, the signal-to-noise ratio (SNR) is generally low, and the underwater environment is complex and variable, making target azimuth estimation highly challenging. Traditional model-based subspace methods exhibit significant performance degradation when dealing with coherent sources, low SNR, and small snapshot data. To overcome these limitations, an improved model based on SubspaceNet, called PConv-GAM Residual SubspaceNet (PGR-SubspaceNet), is proposed. This model embeds the global attention mechanism (GAM) into residual blocks that fuse PConv convolution, making it possible to capture richer cross-channel and positional information. This enhancement helps the model learn signal features in complex underwater conditions. Simulation results demonstrate that the underwater target azimuth estimation method based on PGR-SubspaceNet exhibits lower root mean square periodic error (RMSPE) values when handling different numbers of narrowband coherent sources. Under low SNR and limited snapshot conditions, its RMSPE values are significantly better than those of traditional methods and SubspaceNet-based enhanced subspace methods. PGR-SubspaceNet extracts more features, further improving the accuracy of direction-of-arrival estimation. Preliminary experiments in a pool validate the effectiveness and feasibility of the underwater target azimuth estimation method based on PGR-SubspaceNet. Full article

A wheeled-leg pipeline robot suitable for operation in small pipes is proposed to address the challenges of detecting the condition of pipelines, such as solution corrosion and crack defects, which cannot be conducted externally due to the pre-buried pipe system embedded in other structures. Inspired by existing pipeline robots, the proposed robot employs a mechanical structure with six wheeled legs arranged in an alternating pattern. To analyze the motion state of the pipeline robot turning in curved pipes, kinematic analysis based on geometry is conducted to figure out the kinematic characteristics of the robot navigating in curved pipes. The relationship between the motion trajectories of each contact wheel and the posture angle of the robot in the pipeline is the focal point. Additionally, a turning method preventing wheel slippage is proposed specifically for this type of robot. Finally, an experiment with the pipeline robot navigating in the curved pipeline is implemented and demonstrates successful passing through curved pipes with an inner diameter of 120 mm as well as a turning radius of 240 mm, with the effectiveness of the kinematic analysis validated. Full article

In this article, the electromagnetic shielding properties of carbon nanotube–polymer nanocomposites are presented. The composite fabrication technique is spray-drying, the usage of which leads to a uniform dispersion of carbon nanotubes (CNTs) in a polymer matrix. Obtaining good filler dispersion is necessary to form a continuous, electrically conductive network of CNTs inside the polymer matrix. In the described nanocomposites, the network of conductive filler particles acts as an electromagnetic radiation barrier. For this reason, developing a highly effective fabrication method is very important. Also, the method should be simple enough to be easily adopted in an industrial environment. The authors shows in this text that both goals mentioned are achieved. The obtained nanocomposite material not only has electrostatic shielding capabilities but comprises electromagnetic shielding properties, which fulfills the main goal of the presented work. It is also worth mentioning that the developed manufacturing method allows for the usage of different fillers and polymers and thus the fabrication of materials capable of meeting a wide range of requirements. Full article

Traditional container orchestration platforms often suffer from resource wastage in educational settings, and stateless serverless services face challenges in maintaining container state persistence during the teaching process. To address these issues, we propose a stateful serverless mechanism based on Containerd and Kubernetes, focusing on optimizing the startup process for container groups. We first implement a checkpoint/restore framework for container states, providing fundamental support for managing stateful containers. Building on this foundation, we propose the concept of “container groups” to address the challenges in educational practice scenarios characterized by a large number of similar containers on the same node. We then propose the Rofuse optimization mechanism, which employs delayed loading and block-level deduplication techniques. This enables containers within the same group to reuse locally cached file system data at the block level, thus reducing container restart latency. Experimental results demonstrate that our stateful serverless mechanism can run smoothly in typical educational practice scenarios, and Rofuse reduces the container restart time by approximately 50% compared to existing solutions. This research provides valuable exploration for serverless practices in the education domain, contributing new perspectives and methods to improve resource utilization efficiency and flexibility in teaching environments. Full article

This study introduces an optimization approach for calculating the shortest path in mobile robot route planning. The proposed solution targets real-time processing requirements by offering a high-performance alternative. This is achieved by embedding in the dedicated hardware an architecture which emphasizes parallelism. Through improvements in parallel exploration techniques, our solution aims to present not only a boost in performance but also a dynamic adaptation to graph changes, accommodating randomly occurring edge insertions or deletions as environmental conditions fluctuate. We present the developed architecture alongside its results. Our method efficiently updates obstacle matrices, resulting in a remarkable 120-fold improvement for 1024-node graphs. When utilizing a cost-effective device like the Cyclone IV E, it achieves approximately 12 times the performance of software applications. Full article

This study explores the efficacy of integrating stroboscopic glasses with smartphone-based applications to evaluate postural control, offering a cost-effective alternative to traditional forceplate technology. Athletes, particularly those with visual and visuo-oculomotor enhancements due to sports, often suffer from injuries that necessitate reliance on visual inputs for balance—conditions that can be simulated and studied using visual perturbation methods such as stroboscopic glasses. These glasses intermittently occlude vision, mimicking visual impairments that are crucial in assessing dependency on visual information for postural stability. Participants performed these tasks under three visual conditions: full vision, partial vision occlusion via stroboscopic glasses, and no vision (eyes closed), on foam surfaces to induce postural instability. The use of a smartphone app to measure postural sway was validated against traditional force plate measurements, providing a comparative analysis of both tools under varied sensory conditions. We investigated postural parameters like anterior–posterior and medial–lateral sway ranges, root mean square values, 95% confidence ellipse area, and sway velocity and median dominant sway frequency from both the smartphone and the force plates. Our findings indicate that force plates exhibit high sensitivity to various visual conditions, as evidenced by significant differences observed in certain postural parameters, which were not detected by smartphone-based measurements. Overall, our findings indicate that smartphones show promise as a cost-effective alternative to force plate measurements for routine monitoring of postural control in sports, although they may not achieve the same level of accuracy as force plates. The integration of stroboscopic glasses further refined the assessment by effectively simulating visual impairments, thereby allowing precise evaluation of an individual’s ability to maintain balance under visually perturbed conditions. Full article

This paper proposes a new static output feedback control method to address the congestion control problem in transmission control protocol networks using active queue management routers. Based on linear matrix inequality optimization, this method determines a static output feedback control law to minimize the norm of the transfer function between the controlled queue length of the buffer and the exogenous disturbance affecting the available link bandwidth. A linear matrix inequality formulation is presented as a sufficient condition to guarantee the closed-loop system’s asymptotic stability while maintaining disturbance rejection within a specified level, regardless of round-trip time delays. The proposed robust static output feedback control eliminates the need to measure or estimate all system states, thus simplifying practical implementation. The effectiveness of the proposed design method is demonstrated by applying it in a practical process, as illustrated through a numerical example. Full article

In the field of satellite imaging, effectively managing the enormous volumes of data from remotely sensed hyperspectral images presents significant challenges due to the limited bandwidth and power available in spaceborne systems. In this paper, we describe the hardware acceleration of a highly efficient lossless compression algorithm, specifically designed for real-time hyperspectral image processing on FPGA platforms. The algorithm utilizes an innovative seed generation method for square root calculations to significantly boost data throughput and reduce energy consumption, both of which represent key factors in satellite operations. When implemented on the Cyclone V FPGA, our method achieves a notable operational throughput of 1598.67 Mega Samples per second (MSps) and maintains a power requirement of under 1 Watt, leading to an efficiency rate of 1829.1 MSps/Watt. A comparative analysis with existing and related state-of-the-art implementations confirms that our system surpasses conventional performance standards, thus facilitating the efficient processing of large-scale hyperspectral datasets, especially in environments where throughput and low energy consumption are prioritized. Full article

Given their intricate nature and inherent ambiguity, sarcastic texts often mask deeper emotions, making it challenging to discern the genuine feelings behind the words. The proposal of the sarcasm detection task is to assist us with more accurately understanding the true intention of the speaker. Advanced methods, such as deep learning and neural networks, are widely used in the field of sarcasm detection. However, most research mainly focuses on sarcastic texts in English, as other languages lack corpora and annotated datasets. To address the challenge of low-resource languages in sarcasm detection tasks, a zero-shot cross-lingual transfer learning method is proposed in this paper. The proposed approach is based on prompt learning and aims to assist the model with understanding downstream tasks through prompts. Specifically, the model uses prompt templates to construct training data into cloze-style questions and then trains them using a pre-trained cross-lingual language model. Combining data augmentation and contrastive learning can further improve the capacity of the model for cross-lingual transfer learning. To evaluate the performance of the proposed model, we utilize a publicly accessible sarcasm dataset in English as training data in a zero-shot cross-lingual setting. When tested with Chinese as the target language for transfer, our model achieves F1-scores of 72.14% and 76.7% on two test datasets, outperforming the strong baselines by significant margins. Full article

The ubiquitous diffusion of connected devices in every context of the daily life of citizens, public bodies, and companies is stimulating the creation of new applications that require very high wireless communication performances. To fulfill this need, the sixth generation of communication standards (6G) is planned to roll out by 2030. While structuring this new standard, it is crucial to take into account the security aspects given the impact of the technologies that will rely on its reliability and resiliency. In this paper, we provide an overview of the technologies that will be used in 6G to achieve the required functional goals for the development of key applications. Then, we proceed to discuss the threats and the solutions to make the communications infrastructure secure and reliable, and finally, we elaborate on the concept of how to achieve trust in this scenario. Full article

The article presents the results of a study on modeling cloud systems. In this research, the authors developed both analytical and simulation models. System analysis was conducted at the level of virtual machine support, corresponding to Infrastructure as a Service (IaaS). The models assumed that virtual machines of different sizes are offered as part of IaaS, reflecting the heterogeneous nature of modern systems. Additionally, it was assumed that due to limitations in access to physical server resources, only a portion of these resources could be used to create virtual machines. The model is based on Markov chain analysis for state-dependent systems. The system was divided into an external structure, represented by a collection of physical machines, and an internal structure, represented by a single physical machine. The authors developed a novel approach to determine the equivalent traffic, approximating the real traffic appearing at the input of a single physical machine under the assumptions of request distribution. As a result, it was possible to determine the actual request loss probability in the entire system. The results obtained from both models (simulation and analytical) were summarized in common graphs. The studies were related to the actual parameters of commercially offered physical and virtual machines. The conducted research confirmed the high accuracy of the analytical model and its independence from the number of different instances of virtual machines and the number of physical machines. Thus, the model can be used to dimension cloud systems. Full article

Reduced-order dynamic models of wireless power transfer (WPT) systems are desired to simplify the analysis and design of power control, phase synchronization, and maximum efficiency tracking. The reduced-order dynamic phasor model is a good choice because of its straightforward physical meaning and concise mathematical formula. However, the model relies on the assumption of loose coupling and loses accuracy when the coupling becomes stronger. In this paper, a model reduction method with split frequency matching is proposed to improve model accuracy under relatively strong coupling conditions, which is suitable for most short-distance WPT applications, such as wireless electrical vehicle charging. Split frequency matching is achieved through a pair of conjugate equivalent mutual inductances, which are derived from the asymmetry characteristics of the full-order dynamic phasor model in the positive and negative frequency domains. The proposed model retains the advantages of the existing model while significantly improving the accuracy under strong coupling conditions. Its characteristics are verified by comparing the experimental results and model predictions under both large step changes and small-signal perturbations. Full article

The neuroTac, a neuromorphic visuo-tactile sensor that leverages the high temporal resolution of event-based cameras, is ideally suited to applications in robotic manipulators and prosthetic devices. In this paper, we pair the neuroTac with Spiking Neural Networks (SNNs) to achieve a movement-invariant neuromorphic tactile sensing method for robust texture classification. Alongside this, we demonstrate the ability of this approach to extract movement profiles from purely tactile data. Our systems achieve accuracies of 95% and 83% across their respective tasks (texture and movement classification). We then seek to reduce the size and spiking activity of our networks with the aim of deployment to edge neuromorphic hardware. This multi-objective optimisation investigation using Pareto frontiers highlights several design trade-offs, where high activity and large network sizes can both be reduced by up to 68% and 94% at the cost of slight decreases in accuracy (8%). Full article

This study introduces a novel Quantum Wasserstein Generative Adversarial Network approach with a Gradient Penalty (QWGAN-GP) model that leverages a quantum generator alongside a classical discriminator to synthetically generate time series data. This approach aims to accurately replicate the statistical properties of the S&P 500 index. The synthetic data generated by this model were compared to the original series using various metrics, including Wasserstein distance, Dynamic Time Warping (DTW) distance, and entropy measures, among others. The outcomes demonstrate the model’s robustness, with the generated data exhibiting a high degree of fidelity to the statistical characteristics of the original data. Additionally, this study explores the applicability of the synthetic time series in enhancing prediction models. An LSTM (Long-Short Term Memory)-based model was developed to evaluate the impact of incorporating synthetic data on forecasting accuracy, particularly focusing on general trends and extreme market events. The findings reveal that models trained on a mix of synthetic and real data significantly outperform those trained solely on historical data, improving predictive performance. Full article

This article presents a coil couple-based transcutaneous energy transmission system (TETS) for wirelessly powering implanted artificial hearts. In the TETS, the performance of the system is commonly affected by the change in the position of the coupling coils, which are placed inside and outside the skin. However, to some extent, the influence of coupling efficiency caused by misalignment can be reduced by optimizing the coil. Thus, different types of coils are designed in this paper for comparison. It has been found that the curved coil better fits the surface of the skin and provides better performance for the TETS. Various types of curved coils have been designed in response to observed bending deformations, dislocations, and other coupling variations in the curved coil couple. The numerical model of the TETS is established to analyze the effects of the different types of coils. Subsequently, a series of experiments are designed to evaluate the resilience to misalignment and to verify the heating of the coil under conditions of severe coupling misalignment. The results indicated that, in the case of misalignment of the coils used in artificial hearts, the curved transmission coil demonstrated superior efficiency and lower temperature rise compared to the planar coil. Full article

Due to huge investments by both the public and private sectors, artificial intelligence (AI) has made tremendous progress in solving multiple real-world problems such as disease diagnosis, chatbot misbehavior, and crime control. However, the large-scale development and widespread adoption of AI have been hindered by the model-centric mindset that only focuses on improving the code/architecture of AI models (e.g., tweaking the network architecture, shrinking model size, tuning hyper-parameters, etc.). Generally, AI encompasses a model (or code) that solves a given problem by extracting salient features from underlying data. However, when the AI model yields a low performance, developers iteratively improve the code/algorithm without paying due attention to other aspects such as data. This model-centric AI (MC-AI) approach is limited to only those few businesses/applications (language models, text analysis, etc.) where big data readily exists, and it cannot offer a feasible solution when good data are not available. However, in many real-world cases, giant datasets either do not exist or cannot be curated. Therefore, the AI community is searching for appropriate solutions to compensate for the lack of giant datasets without compromising model performance. In this context, we need a data-centric AI (DC-AI) approach in order to solve the problems faced by the conventional MC-AI approach, and to enhance the applicability of AI technology to domains where data are limited. From this perspective, we analyze and compare MC-AI and DC-AI, and highlight their working mechanisms. Then, we describe the crucial problems (social, performance, drift, affordance, etc.) of the conventional MC-AI approach, and identify opportunities to solve those crucial problems with DC-AI. We also provide details concerning the development of the DC-AI approach, and discuss many techniques that are vital in bringing DC-AI from theory to practice. Finally, we highlight enabling technologies that can contribute to realizing DC-AI, and discuss various noteworthy use cases where DC-AI is more suitable than MC-AI. Through this analysis, we intend to open up a new direction in AI technology to solve global problems (e.g., climate change, supply chain disruption) that are threatening human well-being around the globe. Full article

Renewable energy resources are playing a crucial role in minimizing fossil fuel emissions. Integrating machine learning techniques with tidal power forecasting could greatly enhance the accuracy and reliability of predictions, which is crucial for efficient energy production and management. A hybrid approach combining different methods often yields better results than relying on individual techniques. The accuracy of tidal current power is very important, especially for smart grid applications. This work proposes hybrid adaptive neuro-fuzzy inference system (ANFIS) with the Kalman filter (KF) and a neuro-wavelet (WNN) for tidal current speed, direction, and power forecasting. The turbine used in this study is driven by a direct drive permanent magnet synchronous generator (DDPMSG). The predictions of individual and hybrid models including the ANFIS, the Kalman filter, and the WNN for tidal current speed and the power it generates are compared with another dataset as a way of validation which is the tidal currents direction. Also, other published work results in the literature are compared to the proposed work. Different hybrid models are proposed for smart grid integration. The results of this work indicate that the hybrid model of the WNN and the ANFIS for tidal current power or speed forecasting has the highest performance compared to all other models. Full article