Assessment of total transfer capability (TTC) is vital for determining the permissible power transfer between two areas of an interconnected power system. In the context of heightened volatility and time-variability in power system operating states after integrating high proportions of renewable energy, data-driven inferential assessment methods emerge as promising alternatives, offering faster assessment capabilities compared to knowledge-based iterative methods. However, data-driven methods typically struggle to establish reliable connections between assessment outcomes and security standards, hindering the guarantee of conservatism. A hybrid algorithm, combining knowledge-based and data-driven techniques, is proposed to accurately and efficiently assess TTC while strictly complying with pre-established security and stability constraints. Data-driven inference accelerates knowledge-based iterative processes by rapidly identifying reasonable initial values and providing adaptive step sizes, while knowledge-based analysis guides data-driven methods through offering stability margin information. This mechanism leverages the speed of data-driven methods while maintaining conservatism through knowledge-based approaches. The effectiveness of the proposed method is verified on benchmarks, including the IEEE 30-bus system and a real-world power system, which also exhibits conservatism and robustness in the face of increasing renewable energy penetration.

This narrative review explores the revolutionary impact of smartphone-based artificial intelligence (AI) in diabetic retinopathy (DR) diagnosis in third-world countries. Leveraging the widespread availability of smartphones and advanced AI algorithms, this technology offers promising solutions to overcome challenges faced by resource-limited healthcare systems. We discuss the benefits of smartphone-based AI, such as increased access to retinal screening, cost-effectiveness, timely detection, and enhanced patient engagement. Addressing challenges like image quality standardization, validation, ethical considerations, and expertise is essential for successful implementation. Smartphone-based AI has implications for healthcare delivery, including strengthened primary care, patient-centric care, and improved public health strategies. Future opportunities lie in advancements in AI algorithms, integration with wearable devices, collaborations with healthcare systems and NGOs, AI-powered disease monitoring, longitudinal data analysis, and research and development collaborations. By embracing innovation and overcoming barriers, smartphone-based AI can pave the way for a brighter future in diabetic retinopathy management and eye care delivery for all.Â

This study demonstrates the utility of combining different methods of Multicriteria Decision Analysis (MCDA) for selecting Unmanned Aircraft (UA) in Electronic Warfare (EW) applications. We begin by emphasizing the significance of EW in military operations and the critical importance of choosing the most suitable UAs for this dynamic arena. Our primary goal is to identify the optimal UA among various options, meticulously considering distinct EW criteria. We implement three MCDA techniques: the Analytic Hierarchy Process (AHP), AHP-Gaussian, and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Notably, these methods consistently converge in favor of Alternative 4, substantiating the robustness and minimizing subjective bias. These findings hold profound implications for strategic decision-making within EW operations and offer a sturdy framework for UA selection. This study not only advances the understanding of UA selection within EW but also underscores MCDA’s enduring value as a potent tool for guiding strategic choices. We anticipate that this work will inspire future research in UA selection, including the exploration of more intricate aircraft and the optimization of EW capabilities through sensor allocation and radiation-absorbing materials. Effective UA integration into EW is pivotal for modern military success, and our research provides a comprehensive roadmap to achieve this objective.

Recent advancements in ultrasound technologies, such as ultrasound localization microscopy and ultrafast ultrasound Doppler, have enabled high-definition imaging of microvasculature. However, detecting weak microbubble or blood flow signals amid strong background noise remains a challenge, particularly in deep tissues. This study aims to enhance the signal contrast of microbubble and blood flow by leveraging their distinct spatial-temporal coherence in comparison to undesired noise for robust microbubble detection and microvascular imaging. We propose to quantify the signal coherence based on similarity analysis of beamformed ultrasound microbubble/blood flow data within the plane wave imaging framework. A spatial pixel is considered more likely to be a true microbubble/blood flow signal with a higher level of similarity, which can be measured by either of the following methods: 1) spatially block-wise normalized cross-correlation between two compounded frames; 2) temporally normalized autocorrelation across multiple compounded frames; 3) normalized cross-correlation between two subsets of post-compounded frames; 4) normalized autocorrelation of the pre-compounded data across angular direction. The original microbubble/blood flow signal is then weighted by the similarity measurement on a pixel-by-pixel basis to generate images with an improved signal contrast. The robustness of the proposed methods was first demonstrated in both phantom experiments and in vivo microbubble data from kidney transplant. We further validated their feasibility in blood flow imaging without the use of microbubbles based on in vivo data of human liver and kidney. Significant contrast improvement was observed, facilitating better visualization and detection of both microbubble and noncontrast microflow signals, which indicates a great potential of the methods for improved microvascular imaging and widespread clinical translation.

A gate sequence of single qubit transformations may be condensed into a single microwave pulse that maps a qubit from an initialized state directly into the desired state of the composite transformation. Here, machine learning is used to learn the parameterized values for a single driving pulse associated with a  transformation of three sequential gate operations on a qubit. This implies that future quantum circuits may contain roughly a third of the number of single qubit operations performed, greatly reducing the problems of noise and decoherence. There is a potential for even greater condensation and efficiency using the methods of quantum machine learning.

This paper proposes a multiple targets localization scheme using a clustered wireless sensor network (WSN) for terrestrial and underwater environments. In the considered system, sensors measure the total energy emitted by the targets and transmit quantized versions of their measurements to a data central device (DCD) with the assistance of intermediate cluster-heads (CHDs), which employ decode-and-forward relaying (DFR). Upon data collection from the sensors, the DCD performs the localization process, which involves estimating the number and positions of the targets. The data transmission from the sensors to the CHDs takes place through an imperfect medium, which is characterized by a Rician fading model. The penalized maximum likelihood estimator (PMLE), also known as regularized maximum likelihood estimation (MLE), is applied at the DCD to provide optimal estimates for the number and locations of targets. Furthermore, a suboptimal estimator is derived from PMLE, which can offer comparable performance under certain operating conditions with significantly reduced computational complexity. Cramer-Rao lower bound (CRLB) is derived to serve as an asymptotic benchmark for the root mean square error (RMSE) of the estimators in addition to the centroid-based localization benchmark. Monte Carlo simulation is used to evaluate the performance of the proposed estimation techniques under various system conditions. The results show that PMLE is an effective tool for estimating the number and locations of the targets. Additionally, it is demonstrated that the RMSE of the proposed estimation approaches the CRLB for a large number of sensors and high signal-to-noise ratio.

For the Bluetooth Low Energy (BLE) connection events, a 2-dimensional Markov chain-based model is proposed in this letter, to compute and analyze the duration, duty cycle, power consumption and the maximum throughputs. A general application of peer-to-peer bidirectional data transmission is described in this model, by taking the influence of data transmission states of both the central and peripheral devices on connection event state into account. The impacts of  connection parameters, closing rules of connection events and channel quality on the performance are also analyzed. Experimental results show that the proposed model can accurately calculate the terms of average connection event duration, average energy consumption and the maximum throughputs of both the central and peripheral devices.

Attacks and anomalies are quiet common in todayâ\euro™s technological world. However it is important to identify these anomalies as soon as possible since these anomalies can cause various problem. Therefore anomaly detection plays a vital role in detecting these outliers. Anomaly detection detects suspicious activity which fundamentally falls outside of the respected pattern of behaviour. Anomaly detection aims at identifying the unpredicted and unexpected events in data streams which are commonly referred to as anomalous events. Therefore proposed modified linearity based grey swarm algorithm and hyperparameter optimization techniques for improving the performance of the proposed model. Hyperparameter optimization techniques implemented in the proposed model are genetic algorithm (GA), Grid Search and Random Search. Feature selection is implemented in the proposed model is modified linearity based grey swarm algorithm since it has the ability to reduce the convergence. In addition to modified linearity based grey swarm algorithm, modified objective function is employed in the proposed model as it has the capability to find solutions to problems which are difficult to solve. Finally the performance of the proposed model is evaluated and assessed using various performance metrics such as accuracy, recall, precision and F1 score. At last, proposed model is compared with the existing models and found that proposed model outperformed other existing models.

This paper presents a comprehensive study of the single-stage capacitor microphone, an electromechanical device. It integrates temporal modeling and an efficient linear-quadratic regulator control system. Simulations are used to verify the reliability and performance of the single-stage capacitor microphone and the linear-quadratic regulator control system, demonstrating zero steady-state error and rapid convergence.

Coded aperture snapshot spectral imager (CASSI) can recover three-dimensional hyperspectral images (HSIs) from two-dimensional compressive measurements. Recently, deep unfolding approaches were shown impressive reconstruction performance among various algorithms. Existing deep unfolding methods usually employ linear projection methods to guide the iterative learning process. However, the linear projections do not include trainable parameters and ignore the essential characteristics of HSI. This paper proposes a novel learning-based deep estimation-enhancement unfolding (DEEU) framework to improve the HSI reconstruction. The deep estimation-enhancement (DEE) module is used to guide the iterative learning process of the network based on the prior information of the CASSI system, and then exploits the intrinsic features of the reconstructed HSI in both spectral and spatial dimensions. In addition, a multi-prior ensemble learning module is proposed to further improve the reconstruction performance without increasing runtime. As with most of deep unfolding methods, we plug a convolutional neural network as a denoiser in each stage of the DEEU framework, which finally forms the proposed DEEU-Net. Comprehensive experiments on both simulation and real datasets demonstrate that the effectiveness of our DEEU framework, and our DEEU-Net can achieve both high reconstruction quality and speed, outperforming the state-of-the-art methods.

This article presents an improved antenna array pattern synthesis method, which can be effectively applied to synthesize of the radiation pattern of arbitrary linear and planar arrays with equal space between the array elements. In mathematics theory, method of the least squares is a routine way to discover the best fit curve to the specified data. Least square method (LSM) with Richardson technique will be a more direct technique to synthesize the radiation pattern of any linear and planar, equally spaced antenna arrays. The goal of them is to find the amplitude of each element of the under-studying array. Simulation results demonstrate that pattern synthesis of linear antenna arrays using the proposed method, provides considerable enhancements compared to the other ones.

Litz wire planar coils are widely used in inductive power transfer systems due to the low profile and high efficiency in medium frequency operation. In high frequency region, the ac resistance of litz wire planar coils increases significantly, which leads to efficiency-drop and temperature-rise problems. Meanwhile, misalignment occurs during the daily operation and causes unstable power supply. In this work, we propose a novel winding pattern for reducing the ac resistance, thermal stress and misalignment sensitivity. From experimental result, the proposed winding pattern reduces the ac resistance maximally by 78\% at 1 MHz, and 263\% at 20 MHz compared with the conventional winding pattern under the same geometry and wire length. The total efficiency is improved by 2\% and misalignment tolerance is maximally improved by 46\% in terms of the output power, and 5\% in terms of the efficiency with the proposed winding pattern.

We use the following anomaly detection benchmark data sets: KDD is an intrusion detection benchmark data set containing simulated military-like traffics data, but reported to have duplicated samples [38].NSL is a revisited version of the KDD data set [38].IDS is a simulated data set containing normal traffics and several types of attacks from a complex network [36].KITSUNE is an attack data set captured from either an IP-based surveillance system or a network of IoT devices, containing different types of attacks [21].CICIOT is a real-time data containing 33 attacks that are executed in an IoT network of 105 devices [23].CIFAR is a data set is designed for image classification tasks [15]. To make it an AD data set, we consider the whole CIFAR-10 as the in-distribution data set and test on CIFAR-100, which contains different classes.

This paper introduces Continual Learning for Multilingual ASR (CL-MASR), a benchmark for continual learning applied to multilingual ASR. CL-MASR offers a curated selection of medium/low-resource languages, a modular and flexible platform for executing and evaluating various CL methods on top of existing large-scale pretrained multilingual ASR models such as Whisper and AWavLM, and a standardized set of evaluation metrics.

Identifying cells in microscopic images is a crucial step toward studying image-based cell biology research. Cell instance segmentation provides an opportunity to study the shape, structure, form, and size of cells. Deep learning approaches for cell instance segmentation rely on the instance segmentation mask for each cell, which is a labor-intensive and expensive task. An ample amount of unlabeled microscopic data is available in the cell biology domain, but due to the tedious and exorbitant nature of the annotations needed for the cell instance segmentation approaches, the full potential of the data is not explored. This paper presents a weakly supervised approach, which can perform cell instance segmentation by using only point and bounding box-based annotation. This enormously reduces the annotation efforts. The proposed approach is evaluated on a benchmark dataset i.e., LIVECell, whereby only using a bounding box and randomly generated points on each cell, it achieved the mean average precision score of 43.53% which is as good as the full supervised segmentation method trained with complete segmentation mask. In addition, it is 3.71 times faster to annotate with a bounding box and point in comparison to full mask annotation.

This paper is a comment to the article “A Novel Designed Sparse Array for Noncircular Sources with High Degree of Freedom” published by Mathematical Problems in Engineering Journal of Hindawi publishers. This comment identifies a few errors made by the authors of the above paper and provides suitable corrective measures.

Visual grounding of remote sensing (RSVG) aims to detect the specific objects associated with query expressions in remote sensing (RS) images.

In this work a methodology aimed at land cover mapping over geographically wide regions, leveraging multitemporal Sentinel-1 SAR data, is presented. The paper describes an effective way to process SAR multitemporal data in order to obtain a set of spatio-temporal features, which well-summarize the temporal patterns of different land cover classes. Moreover, in this paper an innovative approach to smartly select training points from an existing Medium Resolution Land Cover (MRLC) map is presented. Both qualitative and quantitative results over four regions of interest, with the geographical extension of 100x100 square kilometre, confirm the validity of the proposed procedure and the potential of SAR data for land cover mapping purposes. These regions, located in Siberia, Italy, Brazil and Africa, were selected to test the methodology in completely different climate environments. The experimental results show that the proposed approach allows to increase the overall accuracy by 16%, on average, with respect to existing global products.